CCSP SAP 3.3 August 16, 2007
1 2 3 U.S. Climate Change Science Program 4 5 6 Synthesis and Assessment Product 3.3 7 8 Weather and Climate Extremes in 9 a Changing Climate 10 11 Regions of Focus: North America, Hawaii, 12 Caribbean, and U.S. Pacific Islands 13 14 15 16 17 Lead Agency: 18 National Oceanic and Atmospheric Administration 19 20 Contributing Agencies: 21 Department of Energy 22 National Aeronautics and Space Administration 23 U.S. Geological Survey 24
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25 Table of Contents
26 ABSTRACT ...... 5
27 PREFACE...... 7
28 EXECUTIVE SUMMARY ...... 11
29 ES.1 What Are Extremes and Why Do They Matter ...... 11
30 ES.2 Temperature–related Extremes...... 13 31 ES.2.1 Observed Changes ...... 13 32 ES.2.2 Attribution of Changes...... 13 33 ES.2.3 Projected Changes ...... 14
34 ES.3 Precipitation Extremes...... 15 35 ES.3.1 Observed Changes ...... 15 36 ES.3.2 Attribution of Changes...... 15 37 ES.3.3 Projected Changes ...... 15
38 ES.4 Drought...... 16 39 ES.4.1 Observed Changes ...... 16 40 ES.4.2 Attribution of Changes...... 16 41 ES.4.3 Projected Changes ...... 17
42 ES.5 Storms...... 17 43 ES.5.1 Hurricanes and Tropical Storms...... 17 44 ES.5.2 Other Storms...... 19
45 ES.6 Recommendations: What Measures Can be Taken to Improve the Understanding of Weather 46 and Climate Extremes?...... 21
47 CHAPTER 1 WHY WEATHER AND CLIMATE EXTREMES MATTER ...... 29
48 1.1 Extremes Matter Because They Impact People, Plants, and Animals ...... 30
49 1.2 Extremes Are Changing...... 34
50 1.3 Systems Are Sensitive to Changes in Extremes...... 38
51 1.4 Future Impacts of Changing Extremes Also Depend on System Vulnerability ...... 43
52 1.5 Systems are Adapted to Particular Morphologies of Extremes so Changes in Extremes Pose 53 Challenges ...... 49
54 1.6 Actions Can Increase or Decrease the Impact of Extremes...... 52
55 1.7 Assessing Impacts of Changes in Extremes Is Difficult...... 56
56 1.8 Summary and Conclusions ...... 58
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57 CHAPTER 2 OBSERVED CHANGES OF WEATHER AND CLIMATE 58 EXTREMES ...... 95
59 2.1 Background...... 98
60 2.2 Observed Changes and Variations in Weather and Climate Extremes...... 99 61 2.2.1 Temperature Extremes...... 99 62 2.2.2 Precipitation Extremes...... 107 63 2.2.3 Storm Extremes ...... 128
64 2.3 Key Uncertainties Related to Measuring Specific Variations and Change ...... 181 65 2.3.1 Methods Based on Counting Exceedances Over a High Threshold ...... 181 66 2.3.2 The GEV Approach...... 184
67 CHAPTER 3 HOW WELL DO WE UNDERSTAND THE CAUSES OF 68 OBSERVED CHANGES IN EXTREMES, AND WHAT ARE THE PROJECTED 69 FUTURE CHANGES? ...... 240
70 3.1 Introduction ...... 242
71 3.2 What Are the Physical Mechanisms of Observed Changes in Extremes?...... 243 72 3.2.1 Detection and Attribution of Anthropogenic Influences on Climate Extremes Over North 73 America ...... 243 74 3.2.2 Changes in Temperature Extremes...... 252 75 3.2.3 Changes in Precipitation Extremes ...... 254 76 3.2.4 Tropical Cyclones ...... 259 77 3.2.5 Extratropical Storms ...... 274 78 3.2.6 Convective Storms ...... 276
79 3.3 Projected Future Changes in Extremes, Their Causes, Mechanisms, and Uncertainties ...... 277 80 3.3.1 Temperature...... 277 81 3.3.2 Frost ...... 281 82 3.3.3 Growing Season Length ...... 283 83 3.3.4 Snow Cover and Sea Ice ...... 283 84 3.3.5 Precipitation...... 284 85 3.3.6 Flooding and Dry Days...... 286 86 3.3.7 Drought...... 288 87 3.3.8 Snowfall ...... 290 88 3.3.9 Tropical Storms ...... 291 89 3.3.10 Extratropical Storms ...... 308 90 3.3.11 Convective Storms ...... 310
91 CHAPTER 4 RECOMMENDATIONS FOR IMPROVING OUR 92 UNDERSTANDING ...... 351
93 4.1 The continued establishment and maintenance of high quality climate observing systems to 94 monitor climate variability and change should be of the highest priority...... 352
95 4.2 Efforts to digitize, homogenize and analyze long-term observations in the instrumental record 96 should be expanded...... 353
97 4.3 Current weather observing systems should adhere to standards of observation that are consistent 98 with the needs of both the climate and the weather forecasting communities...... 355
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99 4.4 Efforts to extend reanalysis products using surface observations should be pursued...... 356
100 4.5 Research is needed to create annually-resolved, regional-scale reconstructions of the climate for 101 the past 2,000 years...... 356
102 4.6 Research efforts to improve our understanding of the mechanisms that govern hurricane 103 intensity should be increased...... 358
104 4.7 Substantial increases in computational and human resources should be made available to fully 105 investigate the ability of climate models to recreate the recent past as well as make projections under 106 a variety of future forcing scenarios...... 359
107 4.8 Modeling groups should make available high temporal resolution data (daily, hourly) from 108 climate model simulations both of the past and for the future to allow the investigation of potential 109 changes in weather and climate extremes...... 361
110 4.9 Research needs to move beyond purely statistical analysis and focus more on linked physical 111 processes that produce extremes and their changes with climate...... 362
112 4.10 Communication between the science community and the user community should be enhanced in 113 both directions...... 364
114 4.11 Summary ...... 365
115 APPENDIX A STATISTICAL TREND ANALYSIS ...... 370 116
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117 Abstract
118 Convening Lead Authors: Thomas R. Karl, NOAA; Jerry Meehl, NCAR 119 120 Lead Authors: Thomas C. Peterson, NOAA; Kenneth Kunkel, Univ. Ill. Urbana- 121 Champaign, Ill. State Water Survey; William J. Gutowski, Iowa State Univ.; David R. 122 Easterling, NOAA 123 124 Science Editor: Susan J. Hassol, STG, Inc. 125
126 Changes in extreme weather and climate events are among the most serious challenges to
127 society in coping with global warming. While some extremes, such as snowstorms and
128 ice storms in the U.S., have not generally been observed to change, many others have,
129 and are already causing impacts. For example, most of North America is experiencing
130 more unusually hot days and nights and fewer unusually cold days and nights. There has
131 been a decrease in frost days, particularly in the western part of North America. Extreme
132 precipitation episodes (heavy downpours) have become more frequent and intense. All
133 these trends are expected to continue or accelerate.
134
135 There are recent regional tendencies toward more severe droughts in the southwestern
136 U.S., and parts of Canada, Alaska, and Mexico. In the future, droughts are likely to
137 become more frequent and severe in some regions, leading to an increased need to
138 respond to reduced water supplies, increased wildfires, and various ecological impacts.
139
140 Atlantic tropical storm and hurricane destructive potential has increased substantially
141 since about 1970. Although there have been fluctuations from decade to decade, the
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142 balance of evidence suggests that human activity has caused a discernible increase in
143 tropical storm/hurricane and major hurricane frequency in the North Atlantic over the
144 past century. U.S. land-falling hurricane frequency has not increased since the late 1800s.
145 Hurricane intensity in the eastern Pacific has decreased since 1980, but rainfall from
146 near-coastal hurricanes has increased since 1949. Hurricane rainfall rates, wind speeds,
147 and the potential for storm surge damage are projected to increase.
148
149 For cold-season storms, in the North Pacific, storm tracks are shifting northward and the
150 strongest storms are becoming even stronger, with increases in extreme wave heights
151 along the Pacific Northwest coast. In the future, there are likely to be more frequent
152 strong cold-season storms in both the Atlantic and Pacific basins, with stronger winds and
153 more extreme wave heights. Observations and models used to determine changes in
154 tornadoes and severe thunderstorms are inadequate to make definitive statements about
155 past and projected changes.
156
157 Current and future impacts resulting from these changes depend not only on the changes
158 in extremes, but also on responses by human and natural systems.
159
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160 Preface 161 162 Authors: Thomas R. Karl, NOAA; Jerry Meehl, NCAR; Christopher D. Miller, NOAA; 163 William L. Murray, NOAA 164
165 According to the National Research Council, “an essential component of any research
166 program is the periodic synthesis of cumulative knowledge and the evaluation of the
167 implications of that knowledge for scientific research and policy formulation.” The U.S.
168 Climate Change Science Program (CCSP) is helping to meet that fundamental need
169 through a series of 21 “synthesis and assessment products” (SAPs). A key component of
170 the CCSP Strategic Plan (released July 2003), the S&A products integrate research
171 results focused on important science issues and questions frequently raised by decision
172 makers.
173
174 The SAPs support informed discussion and decisions by policymakers, resource
175 managers, stakeholders, the media, and the general public. They are also used to help
176 define and set the future direction and priorities of the program. The products help meet
177 the requirements of the Global Change Research Act of 1990. The law directs agencies to
178 “produce information readily usable by policymakers attempting to formulate effective
179 strategies for preventing, mitigating, and adapting to the effects of global change” and to
180 undertake periodic scientific assessments. This SAP (3.3) provides an in-depth
181 assessment of the state of our knowledge about changes in weather and climate extremes
182 in North America (and U.S. territories), where we live, work, and grow much of our food.
183
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184 The impact of weather and climate extremes can be severe and wide-ranging although, in
185 some cases, the impact can also be beneficial. Weather and climate extremes affect all
186 sectors of the economy and the environment, including human health and well-being.
187 During the period 1980-2006, the U.S. experienced 70 weather-related disasters in which
188 overall damages exceeded $1 billion at the time of the event. Clearly, the direct impact of
189 extreme weather and climate events on the U.S. economy is substantial.
190
191 There is scientific evidence that a warming world will be accompanied by changes in the
192 intensity, duration, frequency, and spatial extent of weather and climate extremes. The
193 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report has
194 evaluated extreme weather and climate events on a global basis in the context of observed
195 and projected changes in climate. However, prior to SAP 3.3 there has not been a specific
196 assessment of observed and projected changes in weather and climate extremes across
197 North America (including the U.S. territories in the Caribbean Sea and the Pacific
198 Ocean), where observing systems are among the best in the world and the extremes of
199 weather and climate are some of the most notable occurring across the globe.
200
201 The term “weather extremes”, as used in SAP 3.3, signifies individual weather events that
202 are unusual in their occurrence (minimally, the event must lie in the upper or lower ten
203 percentile of the distribution) or have destructive potential, such as hurricanes and
204 tornadoes. The term “climate extremes” is used to represent the same type of event, but
205 viewed over seasons (e.g., droughts), or longer periods. In this assessment we are
206 particularly interested in whether climate extremes are changing in terms of a variety of
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207 characteristics, including intensity, duration, frequency, or spatial extent, and how they
208 are likely to evolve in the future although, due to data limitations and the scarcity of
209 published analyses, there is little that can be said about extreme events in Hawaii, the
210 Caribbean or the Pacific Islands outside of discussion of tropical cyclone intensity and
211 frequency. It is often very difficult to attribute a particular climate or weather extreme,
212 such as a single drought episode or a single severe hurricane, to a specific cause. It is
213 more feasible to attribute the changing “risk” of extreme events to specific causes. For
214 this reason, this assessment focuses on the possible changes of past and future statistics of
215 weather and climate extremes.
216
217 In doing any assessment, it is helpful to precisely convey the degree of certainty of
218 various findings and projections. For this reason, a lexicon expressing the likelihood of
219 each key finding is presented below and used throughout this report. There are numerous
220 choices for categories of likelihood and appropriate wording to define these categories.
221 CCSP SAP 5.2, currently under review, but scheduled for public release before SAP 3.3
222 is finalized, provides useful guidance. Additionally, the community of scientists and
223 policy-makers familiar with the IPCC assessments provide yet another consistent way to
224 express likelihood statements. The US National Assessment published in 2000 provides a
225 similar approach to likelihood statements. Figure Preface.1 provides the common terms
226 used in this report to express likelihood of occurrence based on experience with previous
227 likelihood statements. Because of the nature of the topic being considered, our likelihood
228 statements do not have discrete boundaries, unlike in IPCC, but characterize likelihood in
229 more general terms (Figure Preface.1). In some cases, there is sufficiently strong
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230 evidence to draw a conclusion, but not enough to allow a determination of 'likely'. In
231 these few cases, the term 'the balance of evidence' is used to express our assessment of
232 the state of the science. Statements made without likelihood qualifiers are intended to
233 indicate a high degree of certainty.
234
235 Dedication
236 This Climate Change and Synthesis Product is dedicated to the memory of our colleague,
237 friend and co-author Dr. Miguel Cortez whose untimely passing during the writing of the
238 report was a loss to us all.
239
240
241 242 Figure Preface.1 Language in this Synthesis and Assessment Product used to express the 243 team’s expert judgment of likelihood.
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244 Executive Summary 245 246 Convening Lead Authors: Thomas R. Karl, NOAA; Jerry Meehl, NCAR 247 248 Lead Authors: Thomas C. Peterson, NOAA; Kenneth Kunkel, Univ. Ill. Urbana- 249 Champaign, Ill. State Water Survey; William J. Gutowski, Iowa State Univ.; David R. 250 Easterling, NOAA 251 252 Editors: Susan J. Hassol, STG, Inc.; Christopher D. Miller, NOAA; William L. Murray, 253 NOAA; Anne M. Waple, STG, Inc. 254 255 ES.1 What Are Extremes and Why Do They Matter
256 Weather and climate extremes (Figure ES.1) have always posed serious challenges to
257 society. Changes in extremes are already observed to be having impacts on
258 socioeconomic and natural systems, and future changes associated with continued
259 warming will present additional challenges. Increased frequency of heat waves and
260 drought, for example, could seriously affect human health, agricultural production, water
261 resources, and water quality (chapter 1, section 1.1).
262
263 Extremes are a natural part of even a stable climate system and have associated costs
264 (Figure ES.2) and benefits. For example, extremes are essential in some systems to keep
265 insect pests under control. While hurricanes cause significant death, injury, damage, and
266 disruption, they also provide needed rainfall to certain areas, and some tropical plant
267 communities are dependent on hurricane winds toppling tall trees, allowing more sunlight
268 to rejuvenate low-growing trees. But on balance, because systems have adapted to their
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269 historical range of extremes, the majority of the impacts of events outside this range are
270 negative impacts (chapter 1, section 1.4 and 1.5).
271
272 The impacts of changes in extremes depend on both changes in climate and ecosystem
273 and societal vulnerability. Vulnerability is shaped by factors such as population dynamics
274 and economic status as well as developing and utilizing adaptation measures such as
275 appropriate building codes, disaster preparedness, and water use efficiency. Some actions
276 taken to lessen the risk from extreme events can lead to increases in vulnerability to even
277 larger extremes. For example, moderate flood control measures on a river can stimulate
278 development in a now “safe” floodplain, only to see those new structures damaged when
279 a very large flood occurs (chapter 1, section 1.6).
280
281 Human activities are known to affect climate averages. This is relevant to extremes
282 because small changes in the averages of many variables result in larger changes in their
283 extremes. Thus, within a changing climate system, what are now considered to be
284 extreme events will occur more frequently (Figures ES.3, ES.4). More frequent extreme
285 events occurring over a shorter period reduce the time available for recovery and
286 adaptation. In addition, extreme events often occur in clusters. The cumulative effect of
287 compound or back-to-back extremes has far larger impacts than the same events spread
288 out over a longer period of time. For example, heat waves, droughts, air stagnation, and
289 resulting wildfires often occur concurrently and have more severe impacts than any of
290 these alone (chapter 1, section 1.2).
291
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292 ES.2 Temperature–related Extremes
293 ES.2.1 Observed Changes
294 Since 1998, over half of the U.S. annual average temperatures have been extremely high,
295 including the hottest two years on record (1998 and 2006). Accompanying a general rise
296 in the average temperature, most of North America is experiencing more unusually hot
297 days and nights. The number of heat waves (extended periods of extremely hot weather)
298 also has been increasing since 1950. However, the heat waves of the 1930s remain the
299 most severe in the U.S. historical record (chapter 2, section 2.2.1).
300
301 There have been fewer unusually cold days during the last few decades. The last 10 years
302 have seen fewer severe cold waves than any other 10-year period in the historical record,
303 which dates back to 1895. There has been a decrease in frost days and a lengthening of
304 the frost-free season over the past century (chapter 2, section 2.2.1).
305
306 In summary, there is a shift towards a warmer climate with an increase in extreme high
307 temperatures and a reduction in extreme low temperatures. These changes have been
308 especially apparent in the western half of North America (chapter 2, section 2.2.1).
309
310 ES.2.2 Attribution of Changes
311 Human-induced warming has likely caused much of the average temperature increase in
312 North America over the past fifty years and, consequently, changes in temperature
313 extremes. The very hot year of 2006 has now been scientifically attributed primarily to
314 human influences (chapter 3, section 3.2.1 and 3.2.2).
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315 ES.2.3 Projected Changes
316 Future changes in extreme temperatures will generally follow changes in average
317 temperature. Abnormally hot days and nights and heat waves are very likely to become
318 more frequent. Cold days and cold nights are very likely to become much less frequent.
319 The number of days with frost is very likely to decrease (chapter 3, section 3.3.1 and
320 3.3.2).
321
322 Climate models indicate that currently rare extreme events will become more
323 commonplace. For example, for a mid-range emissions scenario, a day so hot that it is
324 currently experienced once every 20 years would occur every three years by the middle
325 of the century over much of the continental U.S. and every five years over most of
326 Canada. By the end of the century, it would occur every other year or more (chapter 3,
327 section 3.3.1).
328
329 Episodes of what are now considered to be unusually high sea-surface temperature are
330 very likely to become more frequent and widespread. Sustained periods (e.g., months) of
331 unusually high temperatures could lead, for example, to more coral bleaching and death
332 of the corals (chapter 3, section 3.3.1).
333
334 Sea ice extent is expected to continue to decrease and may even disappear entirely in the
335 Arctic Ocean in summer in the coming decades. This increases extreme coastal erosion in
336 Arctic Alaska and Canada due to the increased exposure of the coastline to strong wave
337 action (chapter 3, section 3.3.1 and 3.3.10).
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338 ES.3 Precipitation Extremes
339 ES.3.1 Observed Changes
340 Extreme precipitation episodes (heavy downpours) have become more frequent and more
341 intense in recent decades over most of North America and now account for a larger
342 percentage of total precipitation. For example, intense precipitation (the heaviest 1%) in
343 the continental U.S. increased by 20% over the past century while total precipitation
344 increased by 7% (chapter 2, section 2.2.2.2).
345
346 The monsoon season is beginning about 10 days later than usual in Mexico. In general,
347 for the summer monsoon in southwestern North America, there are fewer rain events, but
348 the events are more intense (chapter 2, section 2.2.2.3).
349
350 ES.3.2 Attribution of Changes
351 Heavy precipitation events averaged over North America have increased over the past 50
352 years, consistent with the increased water holding capacity of the atmosphere in a warmer
353 climate and the observed increase in water vapor over the oceans (chapter 3, section
354 3.2.3).
355
356 ES.3.3 Projected Changes
357 On average, precipitation is likely to be less frequent but more intense, and precipitation
358 extremes are very likely to increase. For example, for a mid-range emission scenario,
359 daily precipitation so heavy that it now occurs only once every 20 years is projected by
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360 climate models to occur every eight years or so by the end of this century over much of
361 Eastern North America (chapter 3, section 3.3.5).
362
363 ES.4 Drought
364 ES.4.1 Observed Changes
365 Drought can be defined in many ways, from acute short-term to chronic long-term
366 hydrological drought, agricultural drought, meteorological drought, and so on. The
367 assessment in this report focuses primarily on drought as measured by the Palmer
368 Drought Severity Index (PDSI), which best represents multi-seasonal aspects of drought.
369 Some other indices are included where and when available especially because of known
370 deficiencies in the PDSI (Box 2.1).
371
372 Averaged over the continental U.S. and southern Canada the most severe droughts
373 occurred in the 1930s and there is no indication of an overall trend in the observational
374 record, which dates back to 1895. In Mexico and the U.S. Southwest, the 1950s were the
375 driest period, though droughts in the past 10 years now rival the 1950s drought. There are
376 also recent regional tendencies toward more severe droughts in parts of Canada and
377 Alaska (chapter 2, section 2.2.2.1).
378
379 ES.4.2 Attribution of Changes
380 No formal attribution studies for greenhouse warming and changes in drought severity in
381 North America have been attempted. However, it is likely that the increasing
382 temperatures (and associated increasing evaporation potential over land) are already
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383 contributing to droughts that are longer and more intense. The location and severity of
384 droughts are also affected by the spatial pattern of sea surface temperatures, which appear
385 to have been a factor in the severe droughts of the 1930s and 1950s (chapter 3, section
386 3.2.3).
387
388 ES.4.3 Projected Changes
389 A contributing factor to droughts becoming more frequent and severe is higher air
390 temperatures increasing the potential for evaporation. It is likely that droughts will
391 become more severe in the southwestern U.S. and parts of Mexico. In other places where
392 precipitation increases cannot keep pace with increased evaporation, droughts are also
393 likely to become more severe (chapter 3, section 3.3.7).
394
395 It is likely that droughts will continue to be exacerbated by earlier and possibly lower
396 spring snowmelt run-off in the mountainous West, which results in less water available in
397 late summer (chapter 3, section 3.3.4 and 3.3.7).
398
399 In southwestern North America, the precipitation in the winter rainy season is projected
400 to continue to decrease, increasing the risk of drought (chapter 3, section 3.3.7).
401
402 ES.5 Storms
403 ES.5.1 Hurricanes and Tropical Storms
404 ES.5.1.1 Observed Changes
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405 Atlantic tropical storm and hurricane destructive potential as measured by the Power
406 Dissipation Index (which combines storm intensity, duration, and frequency) has
407 increased. This increase is substantial since about 1970, and is likely substantial since the
408 1950s and 60s, in association with warming Atlantic sea surface temperatures (Figure
409 ES.5) (chapter 2, section 2.2.3.1).
410
411 There have been fluctuations from decade to decade, and data uncertainty is larger in the
412 early part of the record compared to the satellite era beginning in 1965. Taking these into
413 account, it is likely that the annual numbers of tropical storms/hurricanes and major
414 hurricanes in the North Atlantic have increased over the past 100 years, a time in which
415 Atlantic sea surface temperatures also increased. The evidence is less compelling for
416 trends beginning in the late 1800s because uncertainties due to data issues preclude any
417 definitive conclusions prior to 1900. Despite the likely increase in basin-wide hurricane
418 counts over the past 100 years, there is no observational evidence for an increase in North
419 American mainland land-falling hurricanes since the late 1800s (Chapter 2, section
420 2.2.3.1).
421
422 The hurricane Power Dissipation Index in the eastern Pacific, affecting the Mexican west
423 coast and shipping lanes, has decreased since 1980. However, coastal station observations
424 show that rainfall from hurricanes has increased since 1949, in part due to slower moving
425 storms (chapter 2, section 2.2.3.1)
426
427
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428 ES.5.1.2 Attribution of Changes
429 It is likely that human activities have caused a discernible increase in sea surface
430 temperatures in the hurricane formation region of the tropical Atlantic Ocean over the
431 past 100 years. The balance of evidence suggests that human activity has caused a
432 discernable increase in tropical storm/hurricane and major hurricane frequency in the
433 North Atlantic (chapter 3, section 3.2.4.3).
434
435 ES.5.1.3 Projected Changes
436 For North Atlantic and North Pacific hurricanes (both basin-wide and land-falling) it is
437 likely that for each one degree Celsius increase in tropical sea surface temperatures core
438 rainfall rates will increase by 6 to 18% and the surface wind speeds of the strongest
439 hurricanes will increase by about 2 to 10% (chapter 3, section 3.3.9.2 and 3.3.9.4).
440
441 ES.5.2 Other Storms
442 ES.5.2.1 Observed Changes
443 It is very likely that there has been a northward shift in the tracks of strong low-pressure
444 systems (storms) in both the N. Atlantic and N. Pacific over the past fifty years. In the
445 North Pacific, the strongest storms are becoming even stronger. Evidence in the Atlantic
446 is insufficient to draw a conclusion about changes in storm strength (Chapter 2, section
447 2.2.3.2).
448
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449 Increases in extreme wave heights have been observed along the Pacific Northwest coast
450 of North America based on three decades of buoy data, and are likely a reflection of
451 changes in cold season storm tracks (chapter 2, section 2.2.3.3).
452
453 While snow cover extent has decreased over North America, overall trends in
454 snowstorms and episodes of freezing rain have not been observed over the past century
455 (chapter 2, section 2.2.3.4).
456
457 The data used to examine changes in the frequency and severity of tornadoes and severe
458 thunderstorms are inadequate to make definitive statements about actual changes (chapter
459 2, section 2.2.3.5).
460
461 ES.5.2.2 Attribution of Changes
462 Human influences on changes in sea-level pressure patterns have been detected over the
463 Northern Hemisphere and this affects the location and intensity of storms (chapter 3,
464 section 3.2.5).
465
466 ES.5.2.3 Projected Changes
467 There are likely to be more frequent deep low-pressure systems (strong storms) outside
468 the tropics, with stronger winds and more extreme wave heights (Figure ES.6) (chapter 3,
469 section 3.3.10).
470
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471 ES.6 Recommendations: What Measures Can be Taken to Improve the
472 Understanding of Weather and Climate Extremes?
473 Drawing on the material presented in this report, recommendations are described in detail
474 in Chapter 4. Briefly summarized here, they emphasize the highest priority areas for rapid
475 and substantial progress in improving understanding of weather and climate extremes.
476
477 4.1. The continued establishment and maintenance of high quality climate observing
478 systems to monitor climate variability and change should be of the highest priority
479
480 4.2 Efforts to digitize, homogenize, and analyze long-term observations in the
481 instrumental record should be expanded.
482
483 4.3 Current weather observing systems should adhere to standards of observation that
484 are consistent with the needs of both the climate and the weather forecasting
485 communities.
486
487 4.4 Efforts to extend reanalysis products using surface observations should be pursued.
488
489 4.5 Research is needed to create annually-resolved, regional-scale reconstructions of the
490 climate for the past 2,000 years.
491
492 4.6 Research efforts to improve our understanding of the mechanisms that govern
493 hurricane intensity should be increased.
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494
495 4.7 Substantial increases in computational and human resources should be made
496 available to fully investigate the ability of climate models to recreate the recent past as
497 well as make projections under a variety of future forcing scenarios.
498
499 4.8 Modeling groups should make available high temporal resolution data (daily, hourly)
500 from climate model simulations both of the past and for the future to allow the
501 investigation of potential changes in weather and climate extremes.
502
503 4.9 Research needs to move beyond purely statistical analysis and focus more on linked
504 physical processes that produce extremes and their changes with climate.
505
506 4.10 Communication between the science community and the user community should be
507 enhanced in both directions.
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508
509 510 511 Figure ES.1 Most measurements of temperature (top) will tend to fall within a range 512 close to average, so their probability of occurrence is high. A very few measurements will 513 be considered extreme and these occur very infrequently. Similarly, for rainfall (bottom), 514 there tend to be more days with relatively light precipitation and only very infrequently 515 are there extremely heavy precipitation events, meaning their probability of occurrence is 516 low. The exact threshold for what is classified as an extreme varies from one analysis to 517 another, but would normally be as rare as, or rarer than, the top or bottom 10% of all 518 occurrences. For the purposes of this report, all tornadoes and hurricanes are considered 519 extreme.
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520 521 522 Figure ES.2 The blue bars show the number of events per year that exceed a cost of 1 523 billion dollars (these are scaled to the left side of the graph). The blue line (actual costs at 524 the time of the event) and the red line (costs adjusted for wealth/inflation) are scaled to 525 the right side of the graph, and depict the annual damage amounts in billions of dollars.
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526 527 528 Figure ES.3 Increase in the percentage of days in a year in which the daily low 529 temperature is unusually warm (falling in the top 10% of annual daily lows, using 1961 to 530 1990 as a baseline). Under the lower emissions scenario∗, the percentage of very warm 531 nights increases about 20% by 2100 whereas under the higher emissions scenarios, it 532 increases by about 40%.
∗3 future emission scenarios from the IPCC Special Report on Emissions Scenarios: B1 blue line: emissions increase very slowly for a few more decades, then level off and decline A2 black line: emissions continue to increase rapidly and steadily throughout this century A1B red line: emissions increase rapidly until 2050 and then decline. There are more details on these emission scenarios in the glossary.
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533 534 535 Figure ES.4 Increase in the amount of precipitation that falls in heavy events (the top 536 5% of all precipitation events in a year) compared to the 1961-1990 average Various 537 emission scenarios are used for future projections*
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538 539 540 Figure ES.5 Sea surface temperatures (blue) are correlated with the Power Dissipation 541 Index for North Atlantic hurricanes (Emanuel, 2007)
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542 543 544 Figure ES.6 The projected change in intense low pressure systems (strong storms) during the 545 cold season for the Northern Hemisphere for various emission scenarios* (adapted from Lambert 546 and Fyfe; 2006).
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547 Chapter 1 Why Weather and Climate Extremes Matter 548 549 Convening Lead Author: Thomas C. Peterson, NOAA 550 551 Lead Authors: David Anderson, NOAA; Stewart J. Cohen, Environment Canada and 552 Univ. of British Columbia; Miguel Cortez, National Meteorological Service of Mexico; 553 Richard Murnane, Bermuda Inst. of Ocean Sciences; Camille Parmesan, Univ. of Tex. at 554 Austin; David Phillips, Environment Canada; Roger Pulwarty, NOAA; John Stone, 555 Carleton Univ. 556 557 Contributing Authors: Tamara G. Houston, NOAA; Susan L. Cutter, Univ. of S.C. 558 559 KEY FINDINGS
560 • Climate extremes expose existing human and natural system vulnerabilities.
561 • Changes in extreme events are one of the most significant ways socio-economic and
562 natural systems are likely to experience climate change.
563 – Systems have adapted to their historical range of extreme events.
564 – The impacts of extremes in the future, some of which are expected to be outside
565 the historical range of experience, will depend on both climate change and future
566 vulnerability. The latter is shaped by factors such as population dynamics and
567 poverty as well as by development and utilization of climate change adaptation
568 measures such as appropriate building codes, disaster preparedness, and water use
569 efficiency.
570 • Changes in extreme events are already observed to be having impacts on socio-
571 economic and natural systems.
572 – Two or more extreme events that occur over a short period reduce the time
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573 available for recovery.
574 – The cumulative effect of back-to-back extremes is greater than if the same
575 events are spread over a longer period.
576 • Extremes can have positive or negative effects. However, on balance, because
577 systems have adapted to their historical range of extremes, the majority of the impacts
578 of events outside this range are expected to be negative.
579 • Actions that lessen the risk from small or moderate events in the short-term can lead
580 to increases in vulnerability to larger extremes in the long-term.
581
582 1.1 Extremes Matter Because They Impact People, Plants, and Animals
583 Observed and projected warming of North America has direct implications for the
584 occurrence of extreme weather and climate events. It is very unlikely that the climate
585 could change without extremes changing as well. Extreme events drive natural systems
586 much more than average climate (Parmesan et al., 2000). Extreme events cause property
587 damage, injury, loss of life and threaten the existence of some species. Society recognizes
588 the need to plan for the protection of communities from extreme events of various kinds.
589 Structural measures (such as engineering works), governance measures (such as zoning
590 and building codes), financial instruments (such as insurance and contingency funds) and
591 emergency measures practices have all been used to lessen the impacts of historical
592 extremes. To the extent that changes in extremes can be reliably forecast, society can
593 engage in practices that would mitigate future impacts.
594
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595 Global and regional climate patterns have changed throughout the history of our planet.
596 Prior to the Industrial Revolution, these changes occurred due to natural causes, including
597 variations in the Earth’s orbital parameters, volcanic eruptions, and fluctuations in solar
598 output. Since the late nineteenth century, atmospheric concentrations of carbon dioxide
599 and other trace greenhouse gases (GHG) have been increasing due to human activity,
600 such as fossil-fuel combustion and land-use change. On average, the world has warmed
601 by 0.74°C over the last century with most of that coming in the last three decades, as
602 documented by instrumental observations of air temperature over land and ocean surface
603 temperature (IPCC, 2007a; Arguez, 2007; Lanzante et al., 2006). These observations are
604 corroborated by, among many examples, the shrinking of mountain glaciers (Barry,
605 2006), later lake and river freeze dates and earlier thaw dates (Magnuson et al., 2000),
606 earlier blooming of flowering plants (Cayan et al., 2001), earlier spring bird migrations
607 (Sokolov, 2006), thawing permafrost and associated shift in ecosystem functioning,
608 shrinking sea ice (Arctic Climate Impact Assessment, 2004), earlier spring events and
609 shifts of plant and animal ranges both poleward and up mountainsides both within the
610 U.S. (Parmesan and Galbraith, 2004) and globally (Walther et al., 2002; Parmesan and
611 Yohe, 2003; Root et al., 2003; Parmesan 2006). Most of the recent warming observed
612 around the world has very likely been due to observed changes in GHG concentrations
613 (IPCC, 2007a). The continuing increase in GHG concentration is projected to result in
614 additional warming of the global climate by 1.1 to 6.4°C by the end of this Century
615 (IPCC, 2007a).
616
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617 Extremes are already having significant impacts on North America. As examination of
618 Figure 1.1 reveals, it is a rare year when the United States doesn’t have any billion dollar
619 weather and climate-related disasters. Furthermore, the costs of weather and climate-
620 related disasters in the U.S. have been increasing faster than non-weather related disaster
621 costs (Hazards and Vulnerability Research Institute, 2007). For the world as a whole,
622 “weather-related [insured] losses in recent years have been trending upward much faster
623 than population, inflation, or insurance penetration, and faster than non-weather-related
624 events” (Mills, 2005a). Numerous studies indicate that both the climate and the socio-
625 economic vulnerability to weather and climate extremes are changing, although their
626 relative contributions to observed increases in disaster costs are subject to debate. For
627 example the extent to which increases in coastal building damage is due to population
628 growth1 in vulnerable coastal locations versus increase in storm intensity is not easily
629 quantified. Though the causes of the current damage increases are difficult to
630 quantitatively assess, it is clear that any change in extremes will have a significant
631 impact.
632
633 Hurricanes and tropical storms are the leading cause of billion dollar weather and climate
634 events followed by floods, droughts and heat waves.. It should be noted that partitioning
635 losses into the different categories is often not clear cut. For example, tropical storms also
636 contribute to damages that were categorized as flooding and coastal. The annual mean
637 loss of life from weather extremes in the U.S. exceeds 1,500 per year (Kunkel et al.,
638 1999) without including such factors as fog-related traffic fatalities. Approximately half
1 Since 1980, the U.S. coastal population growth has generally reflected the same rate of growth as the entire nation (Crossett et al., 2004).
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639 of these deaths are related to hypothermia due to extreme cold, with extreme heat
640 responsible for another one-fourth of the fatalities. There appears to be no trend in the
641 number of these deaths (Goklany and Straja, 2000). However, it should be noted that
642 these statistics were compiled before the 1,400 hurricane-related fatalities in 2004-2005
643 (Chowdhury and Leatherman, 2007).
644
645 Natural systems display complex vulnerabilities to climate change that sometimes are not
646 evident until after the event. According to van Vliet and Leemans (2006), “the
647 unexpected rapid appearance of ecological responses throughout the world” can be
648 explained largely by the observed changes in extremes over the last few decades. Insects
649 in particular have the ability to respond quickly to climate amelioration by increasing in
650 abundances and/or increasing numbers of generations per year, which has resulted in
651 widespread mortality of previously healthy trees (Logan et al., 2003). The observed
652 warming-related biological changes may have direct adverse effects on biodiversity,
653 which in turn may impact ecosystem stability, resilience, and ability to provide societal
654 goods and services (Parmesan and Galbraith, 2004; Arctic Climate Impact Assessment,
655 2004). The greater the change in global mean temperature, the greater will be the change
656 in extremes and their consequent impacts on species and systems.
657
658 This introductory chapter addresses various questions that are relevant to the points raised
659 above. Section 1.2 focuses on defining characteristics of extremes. Section 1.3 discusses
660 the sensitivities of socio-economic and natural systems to changes in extremes. Factors
661 that influence the vulnerability of systems to changes in extremes are described in section
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662 1.4. As systems are already adapted to particular morphologies of extremes, section 1.5
663 explains why changes in extremes usually pose challenges. Section 1.6 describes how
664 actions taken in response to those challenges can either increase or decrease future
665 impacts of extremes. Lastly, in section 1.7, the difficulties in assessing extremes are
666 discussed. The chapter also includes several text boxes, which highlight a number of
667 topics related to particular extremes and their impacts, as well as analysis tools for
668 assessing impacts.
669
670 1.2 Extremes Are Changing
671 When most people think of extreme weather or climate events, they focus on short-term
672 intense episodes. However, this perspective ignores longer-term, more cumulative events,
673 such as droughts. Thus, rather than defining extreme events solely in terms of temporal
674 considerations, it is useful to look at them from a statistical point of view. If one plots all
675 values of a particular variable, such as temperature, the values most likely will fall within
676 a typical bell-curve with many values near average and fewer occurrences of values far
677 away from the average. Extreme temperatures are in the tails of such distributions, as
678 shown in the top panel of Figure 1.2.
679
680 According to the Glossary of the Intergovernmental Panel on Climate Change (IPCC)
681 Fourth Assessment Report (IPCC, 2007a), “an extreme weather event is an event that is
682 rare at a particular place and time of year. Definitions of rare vary, but an extreme
683 weather event would normally be as rare as or rarer than the 10th or 90th percentile2 of
2 On average, one in every ten temperature values is cold enough to be at or below the 10th percentile just as one in every ten temperature values is hot enough to be at or above the 90th percentile.
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684 the observed probability density function3. By definition, the characteristics of what is
685 called extreme weather may vary from place to place in an absolute sense. When a
686 pattern of extreme weather persists for some time, such as a season, it may be classed as
687 an extreme climate event, especially if it yields an average or total that is itself extreme
688 (e.g., drought or heavy rainfall over a season).” Extreme climate events such as drought
689 can often be viewed similarly to the tails on the temperature distribution.
690
691 Daily precipitation, however, has a distribution which is very different than the
692 temperature distribution. Over most of North America, the majority of days have no
693 precipitation at all. Of the days where some rain or snow does fall, many have very light
694 precipitation while only a few have heavy precipitation, as illustrated by the bottom panel
695 of Figure 1.2. Extreme value theory is a branch of statistics that fits a probability
696 distribution to historical observations. The tail of the distribution can be used to estimate
697 the probability of very rare events. This is the way the 100-year flood level can be
698 estimated using 50 years of data. One problem with relying on historical data is that some
699 extremes are far outside the observational record. For example, the heat wave that struck
700 Europe in 2003 was so far outside natural variability that public health services were
701 unprepared for the excess mortality (see Figure 1.3). Climate change is likely to increase
702 the severity and frequency of extreme events for both statistical and physical reasons.
703
704 Wind is one parameter where statistics derived from all observations are not generally
705 used to define what is an extreme. This is because most extreme wind events are
3 A probability density function is the distribution of the probabilities of all different possible weather or climate events which is depicted by the heavy black lines in Figure 1.2.
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706 generated by special meteorological conditions that are well known. For purposes of this
707 report, all tornadoes and hurricanes are considered extreme. Extreme wind events
708 associated with other phenomena, such as blizzards or nor’easters, tend to be defined by
709 thresholds based on impacts rather than statistics or are just one aspect of the measure of
710 intensity of these storms.
711
712 Most considerations of extreme weather and climate events are limited to discrete
713 occurrences. However, in some cases, events that occur repeatedly can have impacts
714 greater than the simple sum of each individual event. For example, the ice storm that
715 occurred in eastern Ontario and southern Quebec in 1998 was the most destructive and
716 disruptive storm in Canada in recent memory. The storm featured record amounts of
717 freezing rain and sleet in a series of storms over a record number of hours. Further, the
718 storm brutalized an area extending nearly 1000 km which included one of the largest
719 urban areas of Canada, leaving more than 4 million people freezing in the dark for hours,
720 if not days. The conditions were so severe that no clean-up action could be taken between
721 storms and the ice built up, stranding even more people at airports, bringing down high-
722 tension transmission towers, and straining food supplies. Such cumulative events need
723 special consideration.
724
725 Also, compound extremes are conditions that depend on two or more parameters. For
726 example, heat waves have greater impacts on human health when they are accompanied
727 by high humidity. Additionally, problems with one extreme, such as a windstorm, may
728 only be present if it is preceded by a different extreme, such as drought, which would, in
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729 this example, result in far more wind-blown dust than the storm would generate without
730 the drought.
731
732 As the global climate continues to adjust to changes in radiative forcing brought on by
733 increasing concentrations of GHG in the atmosphere, many different aspects of extremes
734 have the potential to change as well (Easterling et al., 2000a,b). The most commonly
735 considered parameter is frequency. Is the extreme occurring more frequently? Will
736 currently rare events become commonplace in 50 years? Changes in intensity are as
737 important as changes in frequency. Are, for example, hurricanes becoming more intense?
738 This is important because, as explained in the box on hurricanes, damage increases
739 exponentially with the speed of the wind so a more intense hurricane causes much more
740 destruction than a weak hurricane.
741
742 Frequency and intensity are only two parts of the puzzle. There are also temporal
743 considerations, such as time of occurrence and duration. For example, the timing of peak
744 snow melt in the western mountains has become earlier (Johnson et al., 1999; Cayan et
745 al. 2001). Earlier snowmelt in the Sierra Nevada Mountains means a longer dry season
746 with far-reaching impacts on the ecologies of plant and animal communities, fire threat
747 and human water resources. Indeed, in the American West, wildfires are strongly
748 associated with increased spring and summer temperatures and correspondingly earlier
749 spring snowmelt in the mountains (Westerling et al., 2006). In Canada, human-induced
750 warming of summer temperatures has a detectable influence on the increased area burned
751 by forest fires in recent decades (Gillett et al., 2004). Changing the timing and/or number
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752 of wildfires might pose threats to certain species by overlapping with their active seasons
753 (causing increased deaths) rather than occurring during a species’ dormant phase (when
754 they are less vulnerable). Further, early snowmelt reduces summer water resources,
755 particularly in California where summer rains are rare. The duration of events (such as
756 heat waves, flood-inducing rains, and droughts), is also potentially subject to change.
757 Spatial characteristics also need to be considered. Is the size of the impact area changing?
758 In addition to the size of the individual events, the location is subject to change. For
759 example, is the region susceptible to freezing rain moving farther north?
760
761 Therefore, the focus of this assessment is not only the meteorology of extreme events, but
762 how climate change might alter the characteristics of extremes. Figure 1.4 illustrates how
763 the tails of the distribution of temperature and precipitation are anticipated to change in a
764 warming world. For temperature both the mean and the tails of the distributions are
765 expected to warm. While the change in the number of average days may be small, the
766 percentage change in the number of very warm and very cold days can be quite large. For
767 precipitation, model and observational evidence indicates an increase in the number of
768 heavy rain events which are balanced by a proportionate decrease in the number of light
769 precipitation events.
770
771 1.3 Systems Are Sensitive to Changes in Extremes
772 Climate sensitivity is defined as the degree to which a system is affected by climate-
773 related stimuli. The effect may be direct, such as changing crop yield due to variations in
774 temperature or precipitation, or indirect, such as the decision to build a house in a
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775 location based on insurance rates, which can change due to flood risk caused by sea level
776 rise (IPCC, 2007b). Indicators of climate sensitivity can include changes in, timing of life
777 events, or distributions of individual species, or alteration of whole ecosystem
778 functioning (Parmesan and Yohe, 2003; Parmesan and Galbraith, 2004).
779
780 Climate sensitivity directly impacts the vulnerability of a system or place. As a result,
781 managed systems, both rural and urban, are constantly adjusting to changing perceptions
782 of risks and opportunities. For example, hurricane destruction can lead to the adoption of
783 new building codes (or enforcement of existing codes) and the implementation of new
784 construction technology, which alter the future climate sensitivity of the community.
785 Further, artificial selection and genetic engineering of crop plants can adjust agricultural
786 varieties to changing temperature and drought conditions. Warrick (1980) suggested that
787 the impacts of extreme events would gradually decline because of improved planning and
788 early warning systems. Ausubel (1991) went further, suggesting that irrigation, air
789 conditioning, artificial snow making, and other technological improvements, were
790 enabling society to become more climate-proof. While North American society is not as
791 sensitive to extremes as it was 400 years ago ― for example, a megadrought in Mexico
792 mid to late 1500s contributed to conditions that caused tremendous population declines as
793 illustrated by Figure 1.5 ― socio-economic systems are still far from being climate-
794 proof.
795
796 Society is clearly altering relationships between climate and society, and thereby
797 sensitivities to climate. However, this is not a unidirectional change. Societies make
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798 decisions that alter regional-scale landscapes (urban expansion, pollution, land-use
799 change, water withdrawals) which can increase or decrease both societal and ecosystem
800 sensitivities (e.g., Mileti, 1999; Glantz, 2003). Contrary to an anticipated gradual decline
801 in impacts, recent droughts have resulted in increased economic losses and conflicts
802 (Riebsame et al., 1991; Wilhite, 2005). The increased concern about El Niño’s impacts
803 reflect a heightened awareness of its effects on extreme events worldwide, and growing
804 concerns about the gap between scientific information and adaptive responses by
805 communities and governments (Glantz, 1996). In the U.S. Disaster Mitigation Act of
806 2000, Congress specifically wrote that a “greater emphasis needs to be placed on . . .
807 implementing adequate measures to reduces losses from natural disasters.”
808
809 Many biological processes undergo sudden shifts at particular thresholds of temperature
810 or precipitation (Precht et al., 1973; Weiser, 1973; Hoffman and Parsons, 1997). The
811 adult male/female sex ratios of certain reptile species such as turtles and snakes are
812 determined by the extreme maximum temperature experienced by the growing embryo
813 (Bull, 1980; Bull and Vogt, 1979; Janzen, 1994). A single drought year has been shown
814 to affect population dynamics of many insects, causing drastic crashes in some species
815 (Singer and Ehrlich, 1979; Ehrlich et al., 1980; Hawkins and Holyoak, 1998) and
816 population booms in others (Mattson and Haack, 1987). The nine-banded armadillo
817 (Dasypus novemcinctus) cannot tolerate more than nine consecutive days below freezing
818 (Taulman and Robbins, 1996). The high sea surface temperature (SST) event associated
819 with El Niño in 1997-98 ultimately resulted in the death of 16% of the world’s corals
820 (Hoegh-Guldberg 1999, 2005; Wilkinson 2000); see the box on coral bleaching for more
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821 information. Further, ecosystem structure and function are impacted by major disturbance
822 events, such as tornadoes, floods, and hurricanes (Pickett and White, 1985; Walker,
823 1999). Warming winters, with a sparse snow cover at lower elevations, have led to false
824 springs and subsequent population declines and extirpation in certain butterfly species
825 (Parmesan, 1996, 2005).
826
827 By far, most of the documented impacts on natural systems have been ecological in
828 nature. Observed ecological responses to local, regional and continental warming include
829 changes in species’ distributions, changes in species’ phenologies (the timing of the
830 different phases of life events) and alterations of ecosystem functioning (Walther et al.,
831 2002; Parmesan and Yohe, 2003; Root et al., 2003; Parmesan and Galbraith, 2004;
832 Parmesan, 2006; IPCC 2007b). Changes in species’ distributions include a northward and
833 upward shift in the mean location of populations of the Edith’s checkerspot butterfly in
834 western North America of a magnitude approximately equal to the degree expected from
835 the observed shift in thermal isotherms from 0.7 C warming – about 100 km northward
836 and 100 m upward (Parmesan, 1996; Karl et al., 1996). Phenological (e.g., timing)
837 changes includes lilac blooming 1.5 days earlier per decade and honeysuckle blooming
838 3.5 days earlier per decade since the 1960s in the western U.S. (Cayan et al., 2001). In
839 another example, tree swallows across the U.S. and southern Canada bred about 9 days
840 earlier from 1959 to 1991, mirroring a gradual increase in mean May temperatures (Dunn
841 and Winkler, 1999). One of the clearest examples of the impacts of warming on whole
842 ecosystem functioning comes from the Arctic tundra, where warming trends have been
843 considerably stronger than in the contiguous U.S. Melting and drying of the permafrost
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844 layer has caused an increase in decomposition rates of dead organic matter during winter,
845 which ultimately in some areas has already resulted in a shift from the tundra being a
846 carbon sink to being a carbon source (Oechel et a., 1993; Oechel et al., 2000).
847
848 Very few behavioral changes have been observed, but there is some evidence that
849 individuals of the sooty shearwater have shifted their migration pathway from the coastal
850 California current to a more central Pacific pathway, apparently in response to a
851 warming-induced shift in regions of high productivity during their summer flight (Spear
852 and Ainley, 1999; Oedekoven et al., 2001). Evolutionary studies of climate change
853 impacts are also few (largely due to dearth of data), but it is clear that genetic responses
854 have already occurred (Parmesan, 2006). Genetic changes in local populations have taken
855 place resulting in much higher frequencies of individuals who are warm-adapted (e.g., for
856 fruit flies; Rodriguez-Trelles and Rodriguez, 1998; Levitan, 2003; Balanya et al., 2006),
857 or can disperse better (e.g., for the bush cricket; Thomas et al., 2001). For species-level
858 evolution to occur, either appropriate novel mutations or novel genetic architecture (i.e.,
859 new gene complexes) would have to emerge to allow a response to selection for increased
860 tolerance to more extreme climate than the species is currently adapted to (Parmesan et
861 al., 2000; Parmesan et al., 2005). However, so far there is no evidence for change in the
862 absolute climate tolerances of a species, and hence no indication that evolution at the
863 species level is occurring, nor that it might occur in the near future (Parmesan, 2006).
864
865 Ecological impacts of climate change on natural systems are beginning to have carry-over
866 impacts on human health (Parmesan and Martens, 2007). The best example comes from
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867 the bacteria which causes human cholera, Vibrio cholerae, which lives in brackish rivers
868 and sea water and uses a diversity of marine life as reservoirs, including many shellfish,
869 some fish, and even water hyacinth. Two-hundred years of observational records strong
870 repeated patterns in which extreme warm water temperatures cause algae blooms which
871 then promote rapid increases in zooplankton abundances, and hence also in their
872 associated V. cholerae bacteria (Colwell, 1996). Analyses of long-term data sets from
873 Peru and Bangladesch (from 18 years up to 70 years) show that cholera has recently
874 become associated with El Niño events, suggesting a threshold for high transmission as
875 only recently been commonly surpassed as El Niño events have become stronger and
876 more frequent in the past three decades (Pascual et al., 2000: Rodó et al., 2002). Even
877 when known epidemiological dynamics are taken into account (such as cycling of
878 immunity in human populations), a strong El Niño signal in cholera dynamics is
879 maintained (Koelle et al., 2005). In summary, there is compelling evidence for links
880 between climate variability, climate change (via increases in strength of El Niño), native
881 plankton dynamics, bacterial dynamics in the wild, and cholera disease epidemics.
882
883 1.4 Future Impacts of Changing Extremes Also Depend on System Vulnerability
884 Climate change presents a significant risk management challenge, and dealing with
885 weather and climate extremes is one of its more demanding aspects. In human terms,
886 extreme events are important precisely because they expose the vulnerabilities of
887 communities and the infrastructure on which they rely. Extreme weather and climate
888 events are not simply hydrometeorological occurrences. They impact socio-economic
889 systems and are often exacerbated by other stresses, such as social inequalities, disease,
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890 and conflict. Extreme events can threaten our very well-being. Understanding
891 vulnerabilities from weather and climate extremes is a key first step in managing the risks
892 of climate change.
893
894 According to IPCC (2007b), “vulnerability to climate change is the degree to which
895 systems are susceptible to, and unable to cope with, adverse impacts.” Vulnerability is a
896 function of the character, magnitude, and rate of climate variation to which a system is
897 exposed, its sensitivity, and its adaptive capacity. A system can be sensitive to change but
898 not vulnerable, such as agriculture in North America; or relatively insensitive but highly
899 vulnerable. An example of the latter is incidence of diarrhea (caused by a variety of
900 water-borne organisms) in less developed countries. Diarrhea, which is not correlated
901 with temperatures in the U.S. because of highly-developed sanitation facilities, shows a
902 strong correlation with high temperatures in Lima, Peru (Checkley et al., 2000; WHO,
903 2003, 2004). Thus, vulnerability is highly dependent on robust societal infrastructures,
904 which have been shown to break down under flood events even in the U.S. (Curreriero et
905 al., 2001). Systems that normally survive are those well adapted to the more frequent
906 forms of low-damage events. On the other hand, the less frequent high-damage events
907 can overwhelm the ability of any system to quickly recover.
908
909 The adaptive capacity of socio-economic systems is determined largely by their
910 characteristics such as poverty and resource availability, which often can be managed.
911 Communities with little adaptive capacities are those with limited economic resources,
912 low levels of technology, weak information systems, poor infrastructure, unstable or
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913 weak institutions, and uneven access to resources. Enhancement of social capacity,
914 effectively addressing some of the exacerbating stresses, represents a practical means of
915 coping with changes and uncertainties in climate. However, despite advances in
916 knowledge and technologies, costs appear to be a major factor in limiting the adoption of
917 adaptation measures (White et al., 2001).
918
919 Communities can often achieve significant reductions in losses from natural disasters by
920 adopting land-use plans that avoid the hazards, e.g., by not allowing building in a
921 floodplain. Building codes are also effective for reducing disaster losses but they need to
922 be enforced. For example, more than 25% of the damage from Hurricane Andrew could
923 have been prevented if the existing building codes had been enforced (Board on Natural
924 Disasters, 1999). The first major industry sector to pay attention to the threats posed by
925 climate change was insurance, which recognized the steady increase in claims paralleling
926 an increase in the number and severity of extreme weather and climate events – a trend
927 that is expected to continue. The insurance industry in fact has an array of
928 instruments/levers that can stimulate policy-holders to take actions to adapt to future
929 extremes. These possibilities are increasingly being recognized by governments. When
930 such measures take effect, the same magnitude event can have less impact, as illustrated
931 by the top panel of Figure 1.6.
932
933 Extreme events themselves can alter vulnerability and expose underlying stresses. There
934 are obvious response times for recovery from the effects of any extreme weather or
935 climate event – ranging from several decades in cases of significant loss of life, to years
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936 for the salinization of agricultural land following a tropical storm, to several months for
937 stores to restock after a hurricane. A series of extreme events that occurs in a shorter
938 period than the time for recovery can exacerbate the impacts as illustrated in the bottom
939 panel of Figure 1.6. For example, in 2005 there was a series of hurricanes that made
940 landfall in Florida; these occurred close enough in time and space that it often proved
941 impossible to recover from one hurricane before the next arrived. Hardware stores and
942 lumberyards were not able to restock quickly enough. A multitude or sequence of
943 extreme events can also strain the abilities of insurance and re-insurance companies to
944 compensate victims. Extremes can also initiate adaptive responses. For example,
945 droughts in the 1930s triggered waves of human migration that altered the demographics
946 of the United States. After the 1998 eastern Canadian ice storm the design criteria for
947 freezing rain on high-voltage power and transmission lines were changed to
948 accommodate radial ice accretion of 25 mm in the Great Lakes region to 50 mm for
949 Newfoundland and Labrador (Canadian Standards Association, 2001).
950
951 Factors such as societal exposure, vulnerability, and sensitivity to weather and climate
952 can play a significant role in determining whether a weather or climate event is
953 considered extreme. In fact, an extreme weather or climate event, defined solely using
954 statistical properties, may not be perceived to be an extreme if it affects an exposure unit4
955 that is designed to withstand that extreme. Conversely, a weather or climate event that is
956 not extreme in a statistical sense might still be considered an extreme event because of
957 the resultant impacts. Case in point, faced with an extended dry spell, consider the
958 different effects and responses in a city with a well-developed water supply infrastructure
4 An exposure unit can be a person, home, city, or animal or plant community.
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959 and a village in an underdeveloped region with no access to reservoirs. These differences
960 also highlight the role of adaptive capacity in a society’s response to an extreme event.
961 Wealthy societies will be able to devote the resources needed to construct a water supply
962 system that can withstand an extended drought.
963
964 Given the relationship between extreme events and their resultant socio-economic
965 impacts, it would seem that the impacts alone would provide a good way to assess
966 changes in extremes. Unfortunately, attempts to quantify trends in the impacts caused by
967 extreme events are hindered by the difficulty in obtaining loss-damage records. As a
968 result, there have been many calls for improvements in how socio-economic data are
969 collected (Changnon, 2003; Cutter and Emrich, 2005; National Research Council, 1999).
970 However, there is no government-level coordinated mechanism for collecting data on all
971 losses or damage caused by extreme events. A potentially valuable effort, led by the
972 Hazards Research Lab at the University of South Carolina, is the assembly of the Spatial
973 Hazard Events and Losses Database for the United States (Cutter et al., 2007). If
974 successful, this effort could provide standardized guidelines for loss estimation, data
975 compilation, and metadata standards. Without these types of guidelines, a homogeneous
976 national loss inventory will remain a vision and it will not be possible to precisely and
977 accurately detect and assess trends in losses and quantify the value of mitigation.
978
979 To date most efforts at quantifying trends in losses caused by impacts are based on
980 insured loss data or on total loss (insured plus non-insured losses) estimates developed by
981 insurers. Unfortunately, the details behind most of the insured loss data are proprietary
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982 and only aggregated loss data are available. The relationship between insured losses and
983 total losses will likely vary as a function of extreme event and societal factors such as
984 building codes, the extent of insurance penetration, and more complex societal factors.
985 The National Hurricane Center generally assumes that for the United States, total losses
986 are twice insured loss estimates. However, this relationship will not hold for other
987 countries or other weather phenomena.
988
989 Regardless of the uncertainties in estimating insured and total losses, it is clear that the
990 absolute dollar value of losses from extreme events has increased over the past few
991 decades, even after accounting for the effects of inflation (see Figure 1.1). However,
992 much of the increasing trend in losses, particularly from tropical cyclones, appears to be
993 related to an increase in population and wealth (Pielke et al., 2003; Pielke, 2005; Pielke
994 and Landsea, 1998). The counter argument is that there is a climate change signal in
995 recent damage trends. Similarly, those damage trends have increased significantly despite
996 ongoing adaptation efforts that have been taking place (Mills, 2005b; Stott et al., 2004;
997 Kunkel et al., 1999). A number of other complicating factors also play a role in
998 computing actual losses. For example, all other things being equal, the losses from
999 Hurricane Katrina would have been dramatically lower if the dikes had not failed. In
1000 addition, the potential for an increase in storm intensity (e.g., tropical cyclone wind
1001 speeds and precipitation) (Knutson and Tuleya, 2003) and the intensity of the
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1002 hydrological cycle5 (Trenberth et al., 2003) raises the possibility that changes in climate
1003 extremes will contribute to an increase in loss.
1004
1005 Another confounding factor in assessing extremes through their impacts is that an
1006 extreme event that lasts for a few days or even less can have impacts that persist for
1007 decades. For example, it will take years for Honduras and Guatemala to recover from the
1008 damage caused by Hurricane Mitch in 1998 and it seems likely that New Orleans will
1009 need years to recover from Hurricane Katrina. Furthermore, extreme events not only
1010 produce “losers” but “winners” too. Examples of two extreme-event winners are the
1011 construction industry in response to rebuilding efforts and the tourism industry at
1012 locations that receive an unexpected influx of tourists who changed plans because their
1013 first-choice destination experienced an extreme event that crippled the local tourism
1014 facilities. Even in a natural ecosystem there are winners and losers. For example, the
1015 mountain pine beetle infestation in British Columbia has been warmly greeted as a dinner
1016 bell by woodpeckers.
1017
1018 1.5 Systems are Adapted to Particular Morphologies of Extremes so Changes in
1019 Extremes Pose Challenges
1020 Over time, socio-economic and natural systems adapt to their climate, including
1021 extremes. Snowstorms that bring traffic to a standstill in Atlanta are shrugged off in
1022 Minneapolis. Hurricane-force winds that topple tall non-indigenous Florida trees like the
1023 Australian pine (Casuarina equisetifolia) may only break a few small branches from the
5 The hydrologic cycle is the continuous movement of water on, above and below the surface of the Earth where it evaporates from the surface, condenses in clouds, falls to Earth as rain or snow, flows downhill in streams and rivers and then evaporates again.
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1024 native live oak (Quercus virginiana) or gumbo-limbo (Bursera simaruba) trees that
1025 evolved in areas frequented by strong winds. Some species even depend on major
1026 extremes happening. For example, the jack pine (Pinus banksiana) produces very durable
1027 resin-filled cones that remain dormant until wildfire flames melt the resin. Then the cones
1028 pop open and spread their seeds (Herring, 1999).
1029
1030 Therefore, it is less a question of whether extremes are good or bad, but rather, what will
1031 be the impact of their changing characteristics? For certain species and biological
1032 systems, various processes may undergo sudden shifts at specific thresholds of
1033 temperature or precipitation (Precht et al., 1973; Weiser, 1973; Hoffman and Parsons,
1034 1997), as discussed in section 1.3. Generally, managed systems are more buffered against
1035 extreme events than natural systems, but certainly are not immune to them. The heat
1036 waves of 1995 in Chicago and 2003 in Europe caused considerable loss of life in large
1037 part because building architecture and city design were adapted for more temperate
1038 climates and not adapted for dealing with such extreme and enduring heat (Patz et al.,
1039 2005). On balance, because systems have adapted to their historical range of extremes,
1040 the majority of the impacts of events outside this range are negative (IPCC, 2007b).
1041
1042 When considering how the statistics of extreme events have changed, and may change in
1043 the future, it is important to recognize how such changes may affect efforts to adapt to
1044 them. Adaptation is important because it can reduce the extent of damage caused by
1045 extremes (e.g., Mileti, 1999; Wilhite, 2005). Currently, long-term planning uses, where
1046 possible, the longest historical time series, including consideration of extreme events. The
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1047 combined probabilities of various parameters that can occur at any given location can be
1048 considered the cumulative hazard of a place. Past observations lead to expectations of
1049 their recurrence, and these form the basis of building codes, infrastructure design and
1050 operation, land-use zoning and planning, insurance rates, and emergency response plans.
1051
1052 However, what would happen if statistical attributes of extreme events were to change as
1053 the climate changes? Individuals, groups, and societies would seek to adjust to changing
1054 exposure. Yet the climate may be changing in ways that pose difficulties to the historical
1055 decision-making approaches (Burton et al., 1993). The solution is not just a matter of
1056 utilizing global climate model projections. It is also involves translating the projected
1057 changes in extremes into changes in risk.
1058
1059 Smit et al. (2000) outline an “anatomy” of adaptation to climate change and variability,
1060 consisting of four elements: a) adapt to what, b) who or what adapts, c) how does
1061 adaptation occur, and d) how good is the adaptation. Changing extreme statistics will
1062 influence the adaptation. As noted earlier, a change in the frequency of extreme events
1063 may be relatively large, even though the change in mean is small. Increased frequencies
1064 of extreme events could lead to reduced time available for recovery, altering the
1065 feasibility and effectiveness of adaptation measures. Changes to the timing and duration
1066 of extremes, as well as the occurrence of new extreme thresholds (e.g., greater
1067 precipitation intensity, stronger wind speeds), would be a challenge to both managed and
1068 unmanaged systems.
1069
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1070 Trends in losses or productivity of climate-sensitive goods exhibit the influences of both
1071 climate variability/change and ongoing behavioral adjustments. For example, U.S. crop
1072 yields have generally increased with the introduction of new technologies. As illustrated
1073 by Figure 1.7, climatic variability still causes short-term fluctuations in crop production,
1074 but a poor year in the 1990s tends to have better yields than a poor year (and sometimes
1075 even a good year) in the 1960s. Across the world, property losses show a substantial
1076 increase in the last 50 years, but this trend is being influenced by both increasing property
1077 development and offsetting adaptive behavior. For example, economic growth has
1078 spurred additional construction in vulnerable areas but the new construction is often
1079 better able to withstand extremes than older construction. Future changes in extreme
1080 event will be accompanied by both autonomous and planned adaptation, which will
1081 further complicate calculating losses due to extremes.
1082
1083 1.6 Actions Can Increase or Decrease the Impact of Extremes
1084 It is important to note that most people do not use climate and weather data, and forecasts
1085 directly. People who make decisions based on meteorological information typically base
1086 their decisions on the output of an intermediate model that translates the data into a form
1087 that is more relevant for their decision process (Figure 1.8). For example, a farmer will
1088 not use weather forecasts or climate data directly when making a decision on when to
1089 fertilize a crop or on how much pesticide to apply. Instead, the forecast is filtered through
1090 a model or mental construct that uses such information as one part of the decision process
1091 and includes other inputs such as crop type, previous pesticide application history,
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1092 government regulations, market conditions, producer recommendations, and the
1093 prevalence and type of pest.
1094
1095 One useful decision tool is a plant hardiness zone map (Cathey, 1990). Plant hardiness
1096 zones are primarily dependent on extreme cold temperatures. Already due to changing
1097 locations of plant hardiness zones, people are planting fruit trees such as cherries farther
1098 north than they did 30 years ago as the probability of winterkill has diminished. This type
1099 of adaptation is common among farmers who continually strive to plant crop species and
1100 varieties well suited to their current local climate.
1101
1102 To a large extent, individual losses for hazard victims have been reduced as the larger
1103 society absorbs a portion of their losses through disaster relief and insurance. Clearly
1104 relevant for settings such as New Orleans is the so-called levee effect, first discussed by
1105 Burton (1962), in which construction of levees (dams, revetments, beach nourishment)
1106 induces additional development leading to much larger losses when the levee is
1107 eventually overtopped. A more general statement of this proposition is found in the safe
1108 development paradox in which increased safety (e.g., flood control) induces increased
1109 development (such as in areas considered safe due to the protection provided by levees or
1110 dams) leading to increased losses when a major event hits. The notion that cumulative
1111 reduction of smaller scale risks might increase vulnerability to large events has been
1112 referred to as the levee effect even when the concern has nothing to do with levees
1113 (Bowden et al., 1981).
1114
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1115 After particularly severe or visible catastrophes, policy windows have been identified as
1116 windows of opportunity for creating long-term risk reduction plans which can include
1117 adaptation for climate change. A policy window opens when the opportunity arises to
1118 change policy direction and is thus an important part of agenda setting (Kingdon, 1995).
1119 Policy windows can be created by triggering or focusing events, such as disasters, as well
1120 as by changes in government and shifts in public opinion. Immediately following a
1121 disaster, the social climate may be conducive to much needed legal, economic, and social
1122 change, which can begin to reduce structural vulnerabilities. Indeed, an extreme event
1123 that is far out of normal experience can wake society up to the realization that extremes
1124 are changing and that society must adapt to these changes.
1125
1126 The assumptions behind the utility of policy windows are that (1) new awareness of risks
1127 after a disaster leads to broad consensus, (2) agencies are reminded of disaster risks, and
1128 (3) enhanced community will and resources become available. However, during the post-
1129 recovery phase, reconstruction requires weighing, prioritizing, and sequencing of policy
1130 programming, and there are usually too many mainstreaming agendas for most decision
1131 makers and operational actors to digest with attendant requests for resources for various
1132 actions. Thus, there is pressure to quickly return to the “normal” conditions prior to the
1133 event, rather than incorporate longer-term development strategies (Berube and Katz,
1134 2005; Christoplos, 2006). In addition, while adaptive institutions clearly matter, they are
1135 often not there in the aftermath (or even before the occurrence) of a disaster.
1136
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1137 In contrast to the actual reconstruction plans, the de facto decisions and rebuilding
1138 undertaken ten months after Katrina clearly demonstrate the rush to rebuild the familiar,
1139 as found after other major disasters in other parts of the world (Kates et al., 2006). This
1140 perspective helps explain the evolution of vulnerability of settings such as New Orleans,
1141 where smaller events have been mitigated, but with attendant increases in long-term
1142 vulnerability. As in diverse contexts such as El Niño-Southern Oscillation (ENSO)
1143 related impacts in Latin America, induced development below dams or levees in the
1144 United States, and flooding in the United Kingdom, the result is that focusing only on
1145 short-term risk reduction can actually produce greater vulnerability to future events
1146 (Pulwarty et al., 2003). Thus, the evolution of responses in the short-term after each
1147 extreme event can appear logical, but might actually increase long-term risk to larger or
1148 more frequent events. Adaptation to climate change must be placed within the context of
1149 adaptation to climate across time scales (from extremes and interannual variability
1150 through long-term change) if it is to be embedded into effective response strategies.
1151
1152 According to the Stern Review on the economics of climate change (Stern, 2006), “many
1153 developing countries are already struggling to cope with their current climate. Both the
1154 economic costs of natural disasters and their frequency have increased dramatically in the
1155 recent past. Global losses from weather-related disasters amounted to a total of around
1156 $83 billion during the 1970s, increasing to a total of around $440 billion in the 1990s
1157 with the number of ‘great natural catastrophe’ events increasing from 29 to 74 between
1158 those decades. The financial costs of extreme weather events represent a greater
1159 proportion of GDP loss in developing countries, even if the absolute costs are more in
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1160 developed countries given the higher monetary value of infrastructure. And over 96% of
1161 all disaster-related deaths worldwide in recent years have occurred in developing
1162 countries. Climatic shocks can - and do - cause setbacks to economic and social
1163 development in developing countries. The IMF, for example, estimates costs of over 5%
1164 of GDP per large disaster on average in low-income countries between 1997 and 2001.”
1165 Given the high costs, wise adaptation has ample opportunity to save money in the long
1166 run.
1167
1168 1.7 Assessing Impacts of Changes in Extremes Is Difficult
1169 As has been mentioned, assessing consequences relevant to extreme weather and climate
1170 events is not simply a function of the hydrometeorological phenomena but depends
1171 critically on the vulnerability of the system being impacted. Thus, the context in which
1172 these extreme events take place is crucial. This means that while the changes in extreme
1173 events are consistent with a warming climate (IPCC, 2007a), any analysis of past events
1174 or projection of future events has to carefully weigh non-climatic factors. In particular,
1175 consideration must be given to changes in demographic distributions and wealth. It is
1176 likely that part of the increase in economic losses shown in Figure 1.1 has been due to
1177 increases in population in regions that are vulnerable such as coastal communities
1178 affected by hurricanes, sea-level rise, and storm surges. In addition, property values have
1179 risen. These factors increase the sensitivity of our infrastructure to extreme events.
1180 Together with the expected increase in the frequency and severity of extreme events
1181 (IPCC 2007a), our vulnerability to extreme events is very likely to increase.
1182 Unfortunately, because many extreme events occur at small temporal and spatial scales,
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1183 where model skill is currently limited and local conditions are highly variable,
1184 projections of future impacts cannot always be made with a high level of confidence.
1185
1186 While anthropogenic climate change is very likely to affect the distribution of extreme
1187 events, it can be misleading to attribute any particular event solely to human causes.
1188 Nevertheless, scientifically valid statements regarding the increased risk can sometimes
1189 be made. A case in point is the 2003 heat wave in Europe, where it it is very likely that
1190 human influence at least doubled the risk of such a heat wave occurring (Stott et al.,
1191 2004). Furthermore, over time, there is expected to be some autonomous adaptation to
1192 experienced climate variability and other stresses. Farmers, for example, have
1193 traditionally altered their agricultural practices, such as planting different crop varieties,
1194 based on experience and water engineers have built dams and reservoirs to better manage
1195 resources during recurring floods or droughts. Such adaptation needs to be considered
1196 when assessing the importance of future extreme events.
1197
1198 Assessing historical extreme weather and climate events is more complicated than just
1199 the statistical analysis of available data. Intense rain storms are often of short duration
1200 and not always captured in standard meteorological records; however, they can often do
1201 considerable damage to urban communities, especially if the infrastructure has not been
1202 enhanced as the communities have grown. Similarly, intense wind events (hurricanes are
1203 a particular example), may occur in sparsely populated areas or over the oceans, and it is
1204 only since the 1960s, with the advent of satellite observations, that a comprehensive
1205 picture can be put together. Therefore, it is important to continually update the data sets
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1206 and improve the analyses. For example, probabilistic estimates of rainfall intensities for a
1207 range of durations, from 5 minutes to 24 hours for return periods, or recurrence intervals
1208 of 20, 50, and 100 years, have long been employed by engineers when designing many
1209 types of infrastructure. In the United States, these probabilistic estimates of intense
1210 precipitation are in the process of being updated. Newer analysis based on up-to-date
1211 rainfall records often differ by more than 45% from analyses done in the 1970s (Bonnin
1212 et al., 2003).
1213
1214 1.8 Summary and Conclusions
1215 For good and for ill, weather and climate extremes have always been present. Both socio-
1216 economic and natural systems are adapted to historical extremes. Changes from this
1217 historical range matter because people, plants, and animals tend to be more impacted by
1218 changes in extremes compared to changes in average climate. Extremes are changing, and
1219 in some cases impacts on socio-economic and natural systems have been observed. The
1220 vulnerability of these systems is a function not only of the rate and magnitude of climate
1221 change but also depends on the sensitivity of the system, the extent to which it is
1222 exposed, and its adaptive capacity. Vulnerability can be exacerbated by other stresses
1223 such as social inequalities, disease, and conflict, and can be compounded by changes in
1224 other extremes events (e.g., drought and heat occurring together) and by rapidly-recurring
1225 events.
1226
1227 Despite the widespread evidence that humans have been impacted by extreme events in
1228 the past, predicting future risk to changing climate extremes is difficult. Extreme
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1229 phenomena are often more difficult to predict than changes in mean climate. In addition,
1230 systems are adapting and changing their vulnerability to risk in different ways. The
1231 ability to adapt differs among systems and changes through time. Decisions to adapt to or
1232 mitigate the effect of changing extremes will be based not only on our understanding of
1233 climate processes but also on our understanding of the vulnerability of socio-economic
1234 and natural systems.
1235
1236 BOX 1.1: Warm Temperature Extremes and Coral Bleaching
1237 Corals are marine animals that obtain much of their nutrients from symbiotic unicellular
1238 algae that live protected within the coral’s calcium carbonate skeleton. Elevated sea
1239 surface temperatures (SST), one degree C above long-term summer averages, lead to the
1240 loss of algal symbionts resulting in bleaching of tropical corals (Hoegh-Guldberg, 1999).
1241 While global SST has risen an average of 0.13°C per decade since 1950 (IPCC, 2007a), a
1242 more acute problem for coral reefs is the increase in episodic warming events such as El
1243 Niño. High SSTs associated with the strong El Niño event in 1997-98 caused bleaching
1244 in every ocean (up to 95% of corals bleached in the Indian Ocean), ultimately resulting in
1245 16% of corals dying globally (Hoegh-Guldberg, 1999, 2005; Wilkinson, 2000).
1246
1247 Recent evidence for genetic variation in temperature thresholds among the obligate algal
1248 symbionts suggests that some evolutionary response to higher water temperatures may be
1249 possible (Baker, 2001; Rowan, 2004). Changes in genotype frequencies toward increased
1250 frequency of high temperature-tolerant symbionts appear to have occurred within some
1251 coral populations between the mass bleaching events of 1997/1998 and 2000/2001 (Baker
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1252 et al., 2004). However, other studies indicate that many entire reefs are already at their
1253 thermal tolerance limits (Hoegh-Guldberg, 1999). Coupled with poor dispersal of
1254 symbionts between reefs, this has led several researchers to conclude that local
1255 evolutionary responses are unlikely to mitigate the negative impacts of future temperature
1256 rises (Donner et al., 2005; Hoegh-Guldberg et al., 2002). Interestingly, though, hurricane-
1257 induced ocean cooling can temporarily alleviate thermal stress on coral reefs (Manzello et
1258 al., 2007).
1259
1260 Examining coral bleaching in the Caribbean, Donner et al. (2007) concluded that “the
1261 observed warming trend in the region of the 2005 bleaching event is unlikely to be due to
1262 natural climate variability alone.” Indeed, “simulation of background climate variability
1263 suggests that anthropogenic warming may have increased the probability of occurrence of
1264 significant thermal stress events for corals in this region by an order of magnitude. Under
1265 scenarios of future greenhouse gas emissions, mass coral bleaching in the eastern
1266 Caribbean may become a biannual event in 20–30 years.” As coral reefs make significant
1267 contributions to attracting tourists to the Caribbean, coral bleaching has adverse socio-
1268 economic impacts.
1269
1270 BOX 1.2: Cold Temperature Extremes and Forest Beetles
1271 Forest beetles in western North America have been responding to climate change in ways
1272 that are destroying large areas of forests (see Figure 1.9). The area affected is 50 times
1273 larger than the area affected by forest fire with an economic impact nearly five times as
1274 great (Logan et al., 2003). Two separate responses are contributing to the problem. The
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1275 first is a response to warm summers, which enable the mountain pine beetle
1276 (Dendroctonus ponderosae), in the contiguous United States, to have two generations in a
1277 year, when previously it had only one (Logan et al., 2003). In south-central Alaska, the
1278 spruce beetle (Dendroctonus rufipennis) is maturing in one year, where previously it took
1279 two years (Berg et al., 2006).
1280
1281 The second response is to winter temperatures, specifically extremely cold winter
1282 temperatures, which strongly regulate over-winter survival of the spruce beetle in the
1283 Yukon (Berg et al., 2006) and the mountain pine beetle in British Columbia. The
1284 supercooling threshold, which is the temperature at which the insect freezes and dies, for
1285 spruce beetle larvae, is −41°C6 and for adults −37°C (Werner et al., 2006). Recent
1286 warming, limiting the frequency of sub−40°C occurrences, has reduced over-winter
1287 mortality of mountain pine beetle larvae in British Columbia. It has led to an explosion of
1288 the beetle population, with tree losses covering an area of 8.7 million hectares7 in 2005, a
1289 doubling since 2003, and a 50-fold increase since 1999 (British Columbia Ministry of
1290 Forests and Range, 2006a). It is estimated that at the current rate of spread, 80% of
1291 British Columbia’s mature lodgepole pine trees, the province’s most abundant
1292 commercial tree species, will be dead by 2013 (Natural Resources Canada, 2007).
1293 Similarly in Alaska, approximately 847,000 hectares of south-central Alaska spruce
1294 forests were infested by spruce beetles from 1920 to 1989 while from 1990 to 2000, an
1295 extensive outbreak of spruce beetles caused mortality of spruce across 1.19 million
6 The freezing point of water is 0°C or 32°F. The boiling point of water is 100 degrees higher in Celsius (100°C) and 180 degrees higher in Fahrenheit (212°F). Therefore, to convert from Celsius to Fahrenheit, multiply the Celsius temperature by 1.8 and then add 32. 7 One hectare is 10,000 square meters or the area in a square with sides of 100 meters and equals 2.5 acres.
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1296 hectares, approximately 40% more forest area than had infested the state the previous 70
1297 years (Werner et al., 2006). The economic loss goes well beyond the millions of board
1298 feet of dead trees as tourism revenue is highly dependent on having healthy, attractive
1299 forests. Hundreds of millions of dollars are being spent to mitigate the impacts of beetle
1300 infestation in British Columbia alone (British Columbia Ministry of Forests and Range,
1301 2006b).
1302
1303 The beetle-forest relationships are much more complex than just climate and beetle
1304 survival and life cycle. In the contiguous United States, increased beetle populations have
1305 increased incidences of a fungus they transmit (pine blister rust, Cronartium ribicola)
1306 (Logan et al., 2003). Further, in British Columbia and Alaska, long-term fire suppression
1307 activities have allowed the area of older forests to double. Older trees are more
1308 susceptible to beetle infestation. The increased forest litter from infected trees has, in
1309 turn, exacerbated the forest fire risks. Forest managers are struggling to keep up with
1310 changing conditions brought about by changing climate extremes.
1311
1312 BOX 1.3: Heavy Precipitation and Human Health
1313 Anthropogenic climate change is already affecting human health (WHO 2002, 2003,
1314 2004). For the year 2000, the World Health Organization estimated that 6% of malaria
1315 infections, 7% of dengue fever cases and 2.4% of diarrhea could be attributed to climate
1316 change (Campbell-Lendrum et al., 2003). Increases in these water borne diseases has
1317 been attributed to increases in intensity and frequency of flood events, which in turn has
1318 been linked to greenhouse-gas driven climate change (Easterling et al., 2000a,b; IPCC
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1319 2007a). Floods directly promote transmission of water-borne diseases by causing
1320 mingling of untreated or partially treated sewage with freshwater sources, as well as
1321 indirectly from the breakdown of normal infrastructure causing post-flood loss of
1322 sanitation and fresh water supplies (Atherholt et al., 1998; Rose et al., 2000; Curriero et
1323 al., 2001; Patz et al., 2003). Precipitation extremes also cause increases in malnutrition
1324 due to drought and flood-related crop failure. For all impacts combined, WHO estimated
1325 that for a single year, total deaths due to climate change of 150,000 people (WHO 2002).
1326
1327 There is general agreement that the health sectors are strongly buffered against responses
1328 to climate change, and that a suite of more traditional factors is often responsible for both
1329 chronic and epidemic health problems. These include quality and accessibility of health
1330 care, sanitation infrastructure and practices, land use change (particularly practices which
1331 alter timing and extent of standing water), pollution, population age structure, presence
1332 and effectiveness of vector control programs, and general socio-economic status (Patz et
1333 al., 2001; IPCC 2001b; Gubler et al., 2001; Campbell-Lendrum et al., 2003; Wilkinson et
1334 al., 2003; WHO 2004, IPCC 2007b).
1335
1336 It is generally assumed that diarrhea incidence in developed countries, which have much
1337 better sanitation infrastructure, has little or no association with climate (WHO 2003,
1338 2004). Studies for the U.S., however, indicate that the assumption that developed
1339 countries have low vulnerability may be premature, as independent studies have
1340 repeatedly concluded that water and food-borne pathogens (that cause diarrhea) will
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1341 likely increase with projected increases in regional flooding events, primarily by
1342 contamination of main waterways (Rose et al., 2000; Ebi et al., 2006).
1343
1344 A U.S. study documented that 51% of waterborne disease outbreaks were preceded by
1345 precipitation events above the 90th percentile, with 68% of outbreaks preceded by
1346 precipitation above the 80th percentile (Curriero et al., 2001). These outbreaks comprised
1347 mainly intestinal disorders due to contaminated well water or water treatment facilities
1348 that allowed microbial pathogens, such as E. coli, to enter drinking water. In 1993, 54
1349 people in Milwaukee, Wisconsin died in the largest reported flood-related disease
1350 outbreak (Curriero et al., 2001). The costs associated with this one outbreak were $31.7
1351 million in medical costs and $64.6 million in productivity losses (Corso et al., 2003).
1352
1353 Another heavy precipitation-human health link comes from the southwestern desert of the
1354 United States. This area experienced extreme rainfalls during the intense 1992/1993 El
1355 Niño. Excess precipitation promoted lush vegetative growth, which led to population
1356 booms of deer mice (Peromyscus maniculatus). This wild rodent carries the hantavirus
1357 which is transmissible to humans and causes a hemorrhagic fever that is frequently lethal.
1358 The virus is normally present at moderate levels in wild mouse populations. In most
1359 years, humans in nearby settlements experienced little exposure. However, in 1993, local
1360 overcrowding arising from the wet-year/population boom, caused greater spillover rodent
1361 activity. Subsequent increased human contact and higher transmission rates led to a major
1362 regional epidemic of the virus (Engelthaler et al., 1999; Glass et al., 2000). Similar
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1363 dynamics have been shown for plague in the western United States (Parmenter et al.,
1364 1999).
1365
1366 BOX 1.4: Drought
1367 Drought should not be viewed as merely a physical phenomenon. Its impacts on society
1368 result from the interplay between a physical event (less precipitation than expected) and
1369 the demand people place on water supply. Human beings often exacerbate the impact of
1370 drought. Recent droughts in both developing and developed countries and the resulting
1371 economic and environmental impacts and personal hardships have underscored the
1372 vulnerability of all societies to this natural hazard (National Drought Mitigation Center,
1373 2006).
1374
1375 Over the past century, the area affected by severe and extreme drought in the United
1376 States each year averages around 14% with the affected area as high as 65% in 1934. In
1377 recent years, the drought-affected area ranged between 35 and 40% as shown in Figure
1378 1.10. FEMA (1995) estimates that average annual drought-related losses at $6-8 billion
1379 (based on relief payments alone). Losses were as high as $40 billion in 1988 (Riebsame
1380 et al., 1991). Available economic estimates of the impacts of drought are difficult to
1381 reproduce. This problem has to do with the unique nature of drought relative to other
1382 extremes, such as hurricanes. The onset of drought is slow. Further, the secondary
1383 impacts may be larger than the immediately visible impacts and often occur past the
1384 lifetime of the event (Wilhite and Pulwarty, 2005).
1385
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1386 In recent years, the western United States has experienced considerable drought impacts,
1387 with 30% of the region under severe drought since 1995. Widespread declines in
1388 springtime snow water equivalent in the U.S. West have occurred over the period 1925–
1389 2000, especially since mid-century. While non-climatic factors, such as the growth of
1390 forest canopy, might be partly responsible, the primary cause is likely changing climate
1391 because the patterns of climatic trends are spatially consistent and the trends are
1392 dependent on elevation (Mote et al., 2005). Increased temperature appears to have led to
1393 increasing drought (Andreadis and Lettenmaier, 2006). In the Colorado River Basin, the
1394 2000-2004 period had an average flow of 9.9 million acre feet8 (maf) per year, lower than
1395 the driest period during the Dust Bowl years of (1931-35 with 11.4 maf), and the 1950s
1396 with (10.2 maf) (Pulwarty et al., 2005). For the winter of 2004-5, average precipitation in
1397 the Basin was around 100% of normal. However, the combination of low antecedent soil
1398 moisture (absorption into soil and depleted high mountain aquifers) and the warmest
1399 January-July period on record (driving evaporation) resulted in a reduced flow of 75% of
1400 average.
1401
1402 At the same time, states in the U.S. Southwest experienced some of the most rapid
1403 economic and population growth in the country, with attendant demands on water
1404 resources and associated conflicts. It is estimated that as a result of the 1999-2004
1405 drought and increased water resources extraction, Lake Mead and Lake Powell9 will take
1406 13 to 15 years of average flow conditions to refill. In the Colorado River Basin, high-
1407 elevation snow pack contributes approximately 70% of the annual runoff. Because the
8 One acre foot is equal to 325,853 U.S. gallons or 1233.5 cubic meters. 9 Lake Mead and Lake Powell are reservoirs on the Colorado River. Lake Mead is the largest man-made lake in the United States.
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1408 Colorado River Compact10 prioritizes the delivery of water to the Lower Basin states of
1409 Arizona, California, and Nevada, the largest impacts may be felt in the Upper Basin
1410 states of Wyoming, Utah, Colorado, and New Mexico. With increased global warming,
1411 the compact requirements may only be met 59% to 75% of the time (Christensen et al.,
1412 2004).
1413
1414 While there are multi-billion dollar estimates for annual agricultural losses (averaging
1415 about $4 billion a year over the last ten years), it is unclear whether these losses are
1416 directly related to crop production alone or other factors. Wildfire suppression costs to
1417 the United States Department of Agriculture (USDA) alone have surpassed $1 billion
1418 each of the last four years but it is unclear how much of this is attributable to dry
1419 conditions. Little or no official loss estimates exist for the energy, recreation/tourism,
1420 timber, livestock, or environmental sectors, although the drought impacts within these
1421 sectors in recent years is known to be large. Better methods to quantify the cumulative
1422 direct and indirect impacts associated with drought need to be developed. The recurrence
1423 of a drought today of equal or similar magnitude to major droughts experienced in the
1424 past will likely result in far greater economic, social, and environmental losses and
1425 conflicts between water users.
1426
1427 BOX 1.5: Hurricanes
1428 There are substantial vulnerabilities from hurricanes along the Atlantic seaboard of the
1429 United States. Four major concentrations of economic vulnerability (capital stock greater
10 The Colorado River Compact is a 1922 agreement among seven U.S. states in the basin of the Colorado River which governs the allocation of the river's water.
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1430 than $100 billion) are along the Miami coast, New Orleans, Houston, and Tampa. Three
1431 of these four areas have been hit by major storms in the last fifteen years (Nordhaus,
1432 2006). A simple extrapolation of the current trend of doubling losses every ten years
1433 suggests that a storm like the 1926 Great Miami Hurricane could result in perhaps $500
1434 billion in damages as early as the 2020s (Pielke et al., 2007; Collins and Lowe, 2001).
1435
1436 Property damages are well correlated to hurricane intensity. The formula for the kinetic
1437 energy of a moving object, be it a baseball or the wind, is one half the mass times the
1438 square of the speed. The mass of the wind in a hurricane does not change significantly.
1439 However because the kinetic energy increases with the square of the wind speed, faster
1440 winds have much more energy, dramatically increasing damages, as shown in Figure
1441 1.11. Only 21% of the hurricanes making landfall in the United States are in Saffir-
1442 Simpson categories 3, 4, or 5, yet they cause 83% of the damage (Pielke and Landsea,
1443 1998). Nordhaus (2006) argues that hurricane damage does not increase with the square
1444 of the wind speed as kinetic energy does, but rather, damage appears to rise with the
1445 eighth power of maximum wind speed. The 2005 total hurricane economic damage of
1446 $174 billion was primarily due to the cost of Katrina ($135 billion). As Nordhaus (2006)
1447 notes, 2005 was an economic outlier not because of extraordinarily strong storms but
1448 because the cost as a function of hurricane strength was high.
1449
1450 A fundamental problem within many economic impact studies lies in the unlikely
1451 assumption that there are no other influences on the macro-economy during the period
1452 analyzed for each disaster (Pulwarty et al., 2007). However, more is at work than
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1453 aggregate indicators of population and wealth. It has long been known that different
1454 social groups, even within the same community, can experience the same climate event
1455 quite differently. In addition, economic analysis of capital stocks and densities does not
1456 capture the fact that many cities, such as New Orleans, represent unique corners of
1457 American culture and history (Kates et al., 2006). Importantly, the implementation of
1458 past adaptations (such as levees) actually conditions the degree of present and future
1459 impacts (Pulwarty et al., 2003). At least since 1979, the reduction of mortality over time
1460 has been noted, including drought in the United States and Africa, tropical cyclones in
1461 Bangladesh, and floods and hurricanes in the United States. On the other hand, a
1462 reduction in property damage is less clear because aggregate property damages have risen
1463 along with increases in the population, material wealth, and development in hazardous
1464 areas.
1465
1466 BOX 1.6: Impacts Tools
1467 There are a variety of impact tools that help users translate climate information into an
1468 assessment of what the impacts will be and provide guidance on how to plan accordingly.
1469 These tools would be part of the filter/medium circle in Figure 1.8. However, as
1470 illustrated, using the example of a catastrophe risk model, the model has clear linkages to
1471 all the other boxes in Figure 1.8.
1472
1473 A catastrophe risk model can be divided into four main components, as shown in Figure
1474 1.12. The hazard component provides information on the characteristics of a hazard. For
1475 probabilistic calculations, this component would include a catalog with a large number of
Do Not Cite or Quote 69 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1476 simulated events with realistic characteristics and frequencies. Event information for each
1477 hazard would include the frequency, size, location, and other characteristics. The overall
1478 statistics should agree with an analysis of historical events.
1479
1480 The inventory component provides an inventory of structures that are exposed to a hazard
1481 and information on their construction. The vulnerability component simulates how
1482 structures respond to a hazard. This component requires detailed information on the
1483 statistical response of a structure to the forces produced by a hazard. This component
1484 would also account for secondary damage such as interior water damage after a
1485 structure's windows are breached. The fourth component in the risk model estimates
1486 losses produced by a hazard event and accounts for repair or replacement costs. In cases
1487 of insurance coverage, the loss component also accounts for business interruption costs
1488 and demand surge. If the model is used for emergency management purposes, the loss
1489 component also accounts for factors such as emergency supplies and shelters.
1490
1491 It should be noted, though, that how the loss component is treated impacts the
1492 vulnerability and inventory components, as indicated by the curved upward pointing
1493 arrows. Is a house destroyed in a flood rebuilt in the same location or on higher ground?
1494 Is a wind damaged building repaired using materials that meet higher standards? These
1495 actions have profound effects on future catastrophe risk models for the area.
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1496 Chapter 1 References 1497 1498 Acuna-Soto, R., D.W. Stahle, M.K. Cleaveland, and M.D. Therrell, 2002: Megadrought 1499 and megadeath in 16th century Mexico. Emerging Infectious Diseases, 8, 360-362. 1500 1501 Arguez, A., ed., 2007: State of the Climate in 2006. Bulletin of the American 1502 Meteorological Society, 88, s1–s135. 1503 1504 Andreadis, K.M. and D.P. Lettenmaier, 2006: Trends in 20th century drought over the 1505 continental United States. Geophysical Research Letters, 33, L10403, 1506 doi:10.1029/2006GL025711. 1507 1508 Arctic Climate Impact Assessment, 2004: Impacts of a Warming Arctic. Cambridge 1509 University Press, Cambridge, UK. 1510 1511 Atherholt,T.B., M.W. LeChevallier, W.D. Norton, J. S. Rosen, 1998: Effect of rainfall 1512 on giardia and crypto. Journal of the American Water Works Association, 90,66- 1513 80. 1514 1515 Ausubel, J.H., 1991: Does climate still matter? Nature, 350, 649-652. 1516 1517 Baker, A.C., 2001: Reef corals bleach to survive change. Nature, 411, 765-66. 1518 1519 Baker, A.C., C.J. Starger, T.R. McClanahan, and P.W. Glynn, 2004: Coral reefs: corals’ 1520 adaptive response to climate change. Nature, 430, 741. 1521 1522 Balanya, J., J.M. Oller, R.B. Huey, G.W. Gilchrist and L. Serra, 2006: Global genetic 1523 change tracks global climate warming in drosophila subobscura. Science, 313, 1524 1773-1775. 1525 1526 Barry, R.G., 2006: The status of research on glaciers and global glacier recession: a 1527 review. Progress in Physical Geography, 30, 285-306. 1528 1529 Bull, J.J., 1980: Sex determination in reptiles. Quarterly Review of Biology, 55, 3-21. 1530 1531 Bull, J.J. and R.C. Vogt, 1979: Temperature-dependent sex determination in turtles. 1532 Science, 206, 1186-1188. 1533 1534 Berg, E.E., J.D. Henry, C.L. Fastie, A.D. De Volder, and S.M. Matsuoka, 2006: Spruce 1535 beetle outbreaks on the Kenai Peninsula, Alaska, and Kluane National Park and 1536 Reserve, Yukon Territory: Relationship to summer temperatures and regional 1537 differences in disturbance regimes. Forest Ecology and Management, 227, 219- 1538 232. 1539 1540 Berube, A. and B. Katz, 2005: Katrina’s window: Confronting concentrated poverty 1541 across America. The Brookings Institution, 13 pp.
Do Not Cite or Quote 71 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1542 1543 Board on Natural Disasters, 1999: Mitigation emerges as a major strategy for reducing 1544 losses caused by natural disasters. Science, 284, 1943-1947. 1545 1546 Bonnin, G. M., B. Lin, and T. Parzybok, 2003: Updating NOAA/NWS Rainfall 1547 Frequency Atlases. Symposium on Observing and Understanding the Variability 1548 of Water in Weather and Climate, Long Beach, Calif., 9-13 February. 1549 1550 Bowden, M., R. Kates, P. Kay, W. Riebsame, H. Gould, D. Johnson, R. Warrick, and D. 1551 Weiner, 1981: The effects of climate fluctuations on human populations: two 1552 hypotheses, In: Climate and history: Studies in Past Climates and Their Impact 1553 on Man, [Wigley, T.M.L., M.J. Ingram, and G. Farmer (eds.)], Cambridge, 479- 1554 513. 1555 1556 British Columbia Ministry of Forests and Range, 2006a: Backgrounder: Mountain 1557 pine beetle district breakdown. FOR0011-000152, British Columbia Ministry of 1558 Forestry and Range, Victoria. 1 pp. 1559 1560 British Columbia Ministry of Forests and Range, 2006b: Beetle Plan Guides Forests, 1561 Communities into the Future, News Release. 1562 http://www2.news.gov.bc.ca/news_releases_2005-2009/2006FOR0112- 1563 001109.htm 1564 1565 Burton, I., R.W. Kates, and G.F. White, 1993: The Environment as Hazard, 2nd edition. 1566 The Guilford Press, New York, 290 pp. 1567 1568 Burton, I., 1962: Types of Agricultural Occupance of Flood Plains in the United States. 1569 University of Chicago Press, Chicago. 1570 1571 Campbell-Lendrum, D., A. Pruss-Ustun, and C. Corvalan, 2003: How much disease 1572 could climate change cause? ch. 7 (pp. 133-158) in: WHO 2003: Climate Change 1573 and Human Health. Risks and Responses, McMichael, A.J., D.H Campbell- 1574 Lendrum, C.F. Corvalán, K.L. Ebi, A.K. Githeko, J.D. Scheraga and A. 1575 Woodward (Eds), the World Health Organization, the World Meteorological 1576 Organization and the United Nations Environment Programme 1577 (WHO/WMO/UNEP). World Health Organization, Geneva, Switzerland, 333 pp. 1578 1579 Canadian Standards Association, 2001: Canadian Standard CSA-S37 Antennas, 1580 Towers, and Antenna Supporting Structures. Ice and Wind Loads Contributed by 1581 R. Morris, T. Yip, and H. Auld. Canadian Standards Association, Rexdale. 1582 1583 Cathey, H.M., 1990: USDA Plant Hardiness Zone Map, USDA Miscellaneous 1584 Publication No. 1475. United States Department of Agriculture, Washington, DC. 1585
Do Not Cite or Quote 72 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1586 Cayan, D.R., S.A. Kammerdiener, M.D. Dettinger, J.M. Capiro, and D.H. Peterson, 1587 2001: Changes in the onset of spring in the western United States. Bulletin of the 1588 American Meteorological Society, 82, 399-415. 1589 1590 Changnon, S.D. [sic], 2003: Measures of economic impacts of weather extremes. 1591 Getting better but far from what is needed - a call for action, Bulletin of the 1592 American Meteorological Society, 84, doi:10.1175/BAMS-1184-1179-1231, 1593 1231-1235. 1594 1595 Checkley, W., L.D. Epstein, R.H. Gliman, D. Figueroa, R.I. Cama, J.A. Patz, R.E. Black, 1596 2000: Effects of El Niño and ambient temperature on hospital admissions for 1597 diarrhoeal diseases in Peruvian children. Lancet, 355, 442–450. 1598 1599 Chowdhury, A.G. and S.P. Leatherman, 2007: Innovative testing facility to mitigate 1600 hurricane-induced losses. EOS, Transactions, American Geophysical Union, 88, 1601 p. 262. 1602 1603 Christensen, N.S., A.W.Wood, N. Voisin, D.P. Lettenmaier, and R.N. Palmer, 2004: The 1604 effects of climate change on the hydrology and water resources of the Colorado 1605 River Basin. Climatic Change, 62, 337-363. 1606 1607 Christoplos, I., 2006: The elusive ‘window of opportunity’ for risk reduction in post- 1608 disaster recovery Discussion Paper ProVention Consortium Forum 2006 1609 Strengthening global collaboration in disaster risk reduction, Bangkok, February 1610 2-3, 2006. 1611 1612 Collins, D. J. and S.P. Lowe, (2001): A macro validation dataset for U.S. hurricane 1613 models. Casualty Actuarial Society Forum, Casualty Actuarial Society, Arlington, 1614 Va., pp. 217-251, Available at 1615 http://www.casact.org/pubs/forum/01wforum/01wf217.pdf 1616 1617 Colwell, R.R., 1996: Global climate and infectious disease: The cholera paradigm. 1618 Science 274, 2025-2031. 1619 1620 Corso, P.S., M.H. Kramer, K.A. Blair, D.G. Addiss, J.P. Davis and A.C. Haddix, 2003: 1621 Cost of Illness in the 1993 Waterborne Cryptosporidium Outbreak, Milwaukee, 1622 Wisconsin. Emerging Infections Diseases, 9, 426-431. 1623 1624 Crossett, K.M., T.J. Culliton, P.C. Wiley, T.R. Goodspeed, 2004: Population Trends 1625 Along the Coastal United States: 1980-2008. National Oceanic and Atmospheric 1626 Administration, National Ocean Service, Washington, DC, 54 pp. 1627 1628 Curriero, F.C., J.A. Patz, J.B. Rose, and S. Lele, 2001: The association between extreme 1629 precipitations and waterborne disease outbreaks in the United States, 1948-1994. 1630 American Journal of Public Health, 91, 1194-1199. 1631
Do Not Cite or Quote 73 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1632 Cutter, S.L. and C. Emrich, 2005: Are natural hazards and disaster losses in the U.S. 1633 increasing? EOS, Transactions, American Geophysical Union, 86, 381. 1634 1635 Cutter, S.L., M. Gall and C.T. Emrich, 2007: Toward a comprehensive loss inventory of 1636 weather and climate hazards, In: Climate Extremes and Society, [Diaz H.L. and R. 1637 J. Murnane (eds.)], Cambridge University Press, Cambridge (in press). 1638 1639 Donner, S.D., W.J. Skirving, C.M. Little, M. Oppenheimer, and O. Hoegh-Guldberg, 1640 2005: Global assessment of coral bleaching and required rates of adaptation under 1641 climate change. Global Change Biology, 11, 2251-65. 1642 1643 Donner, S.D., T.R. Knutson and M. Oppenheimer, 2007: Model-based assessment of the 1644 role of human-induced climate change in the 2005 Caribbean coral bleaching 1645 event. Proceedings of the National Academy of Sciences, 104, 5483-5488. 1646 1647 Dunn P.O. and D.W. Winkler, 1999: Climate change has affected the breeding date of 1648 tree swallows throughout North America. Proc. R. Soc. London Ser. B., 266, 1649 2487-2490. 1650 1651 Easterling, D.R., J.L. Evans, P.Y. Groisman, T.R. Karl, K.E. Kunkel, and P. Ambenje, 1652 2000a: Observed variability and trends in extreme climate events: A brief review. 1653 Bulletin of the American Meteorological Society, 81, 417–425. 1654 1655 Easterling, D.R., S. Chagnon, T.R. Karl, J. Meehl, and C. Parmesan, 2000b: Climate 1656 extremes: observations, modeling, and impacts. Science, 289, 2068–2074. 1657 1658 Ebi, K.L., D.M. Mills, J.B. Smith and A. Grambsch, 2006: Climate change and human 1659 health impacts in the United States: An update on the results of the U.S. National 1660 Assessment. Environmental Health Perspectives, 114, 1318-1324. 1661 1662 Ehrlich, P.R., D.D. Murphy, M.C. Singer, C.B. Sherwood, R.R. White, and I.L. Brown, 1663 1980: Extinction, reduction, stability and increase: the responses of checkerspot 1664 butterfly (Euphydryas editha) populations to the California drought. Oecologia, 1665 46, 101-105. 1666 1667 Engelthaler, D.M., D.G. Mosley, J.E. Cheek, C.E. Levy, K.K. Komatsu, P. Ettestad, T. 1668 Davis, D.T. Tanda, L. Miller, J.W. Frampton, et al., 1999: Climatic and 1669 environmental patterns associated with hantavirus pulmonary syndrome, Four 1670 Corners region, United States. Emerging Infectious Diseases, 5, 87-94. 1671 1672 Federal Emergency Management Agency (FEMA), 1995: National Mitigation 1673 Strategy: Partnerships for Building Safer Communities, Federal Emergency 1674 Management Agency, Washington, D.C., 40 pp. 1675
Do Not Cite or Quote 74 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1676 Gillett, N.P., A.J. Weaver, F.W. Zwiers, and M.D. Flannigan, 2004: Detecting the effect 1677 of climate change on Canadian forest fires. Geophys. Res. Lett., 31, L18211, 1678 doi:10.1029/2004GL020876. 1679 1680 Glantz, M.A., 1996: Currents of change: El Niño’s impact on climate and society. 1681 Cambridge University Press, Cambridge, UK. 1682 1683 Glantz, M.A., 2003: Climate affairs: A primer. Island Press, Washington, D.C., 291 pp. 1684 1685 Glass, G.E., J.E. Cheek, J.A. Patz, T.M. Shields, T.J. Doyle, D.A. Thoroughman, D.K. 1686 Hunt, R.E. Enscore, K.L. Gage, C. Ireland, C.J. Peters, and R. Bryan, 2000: Using 1687 remotely sensed data to identify areas of risk for hantavirus pulmonary syndrome. 1688 Emerging Infectious Diseases, 6, 238-247. 1689 1690 Goklany, I.M. and S.R. Straja, 2000: U.S. trends in crude death rates due to extreme heat 1691 and cold ascribed to weather, 1979-97. Technology, 7, 165-173. 1692 1693 Gubler, D.J., P. Reiter, K.I. Ebi, W. Yap, R. Nasci, and J.A. Patz, 2001: Climate 1694 variability and change in the United States: Potential impacts on vector- and 1695 rodent-borne diseases. Environmental Health Perspectives, 109, (supplement 1696 2):223-233. 1697 1698 Hazards and Vulnerability Research Institute, 2007: The Spatial Hazard Events and 1699 Losses Database for the United States, Version 5.1 [Online Database]. Columbia, 1700 SC: University of South Carolina. Available from http://www.sheldus.org. 1701 Hawkins, B.A. and M. Holyoak, 1998: Transcontinental crashes of insect populations? 1702 American Naturalist, 152, 480-484. 1703 1704 Heinz, The H. John Heinz III Center for Science, Economics and the Environment, 2002: 1705 The State of the Nation’s Ecosystems: Measuring the Lands, Waters, and Living 1706 Resources of the United States. Cambridge University Press, Cambridge, 288 pp. 1707 Herring, D., 1999: Evolving in the presence of fire. Earth Observatory, NASA, October. 1708 1709 Hoegh-Guldberg, O., 1999: Climate change, coral bleaching and the future of the 1710 world’s coral reefs. Marine and Freshwater Research, 50, 839-66. 1711 1712 Hoegh-Guldberg, O., 2005: Marine ecosystems and climate change. In: Lovejoy, T.E. 1713 and L. Hannah (eds) Climate Change and Biodiversity. Yale University Press, 440 1714 pp, 256-271. 1715 1716 Hoegh-Guldberg, O., R.J. Jones, S. Ward, W.K. Loh, 2002: Is coral bleaching really 1717 adaptive? Nature, 415, 601-2. 1718 1719 Hoffman, A.A. and P.A. Parsons, 1997: Extreme Environmental Change and Evolution. 1720 Cambridge University Press, Cambridge, UK, 1997. 1721
Do Not Cite or Quote 75 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1722 Intergovernmental Panel on Climate Change (IPCC) 2007a: Climate Change 2007: 1723 The Physical Science Basis. Contribution of Working Group I to the Fourth 1724 Assessment Report (AR4), www.ipcc.ch, 987 pp. 1725 1726 Intergovernmental Panel on Climate Change (IPCC) 2007b: Climate Change 2007: 1727 Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the 1728 Fourth Assessment Report (AR4), www.ipcc.ch, Summary for Policy Makers, 23 1729 pp. 1730 1731 Intergovernmental Panel on Climate Change (IPCC), 1992: Climate Change 1992: 1732 The Supplementary Report to the IPCC Scientific Assessment. [Houghton, J.T., 1733 Callandar, B.A., and S.K. Varney, (eds.), Cambridge University Press, 198 pp. 1734 1735 Janzen, F.J., 1994: Climate change and temperature-dependent sex determination in 1736 reptiles. Proceedings of the National Academy of Sciences, USA, 91, 7487-7490. 1737 1738 Johnson, T., J. Dozier and J. Michaelsen, 1999: Climate change and Sierra Nevada 1739 snowpack. International Association of Hydrological Sciences Publication, 256, 1740 63–70. 1741 1742 Karl, T.R., R.W. Knight, D.R. Easterling, and R.G. Quayle, 1996: Indices of climate 1743 change for the United States. Bulletin of the American Meteorological Society, 77, 1744 279–292. 1745 1746 Kates, R.W., C.E. Colten, S. Laska, and S.P. Leatherman, 2006: Reconstruction of New 1747 Orleans after Hurricane Katrina: A research perspective. Proceedings of the 1748 National Academy of Sciences of the United States of America, Special Feature, 1749 103(40) (26 September): 14653-14660. 1750 1751 Kingdon, J.W., 1995: Agendas, Alternatives and Public Policies, 2nd ed., HarperCollins 1752 Publishers, New York, 254 pp. 1753 1754 Koelle, K., X. Rodó, M. Pascual, M. Yunus, G. Mostafa, 2005: Refractory periods and 1755 climate forcing in cholera dynamics. Nature, 436,696-700. 1756 1757 Kunkel, K.E., R.A. Pielke Jr., and S.A. Changnon, 1999: Temporal fluctuations in 1758 weather and climate extremes that cause economic and human health impacts: a 1759 review. Bulletin of the American Meteorological Society, 80, 1077-1098. 1760 1761 Knutson, T. R. and R. E. Tuleya, 2003: Increased hurricane intensities with CO2- 1762 induced warming as simulated using the GFDL hurricane prediction system. 1763 Climate Dynamics, 15, 503-519. 1764 1765 Lanzante, J.R., T.C. Peterson, F.J. Wentz, and K.Y. Vinnikov, 2006: What do 1766 observations indicate about the change of temperatures in the atmosphere and at 1767 the surface since the advent of measuring temperatures vertically? In:
Do Not Cite or Quote 76 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1768 Temperature Trends in the Lower Atmosphere: Steps for Understanding and 1769 Reconciling Differences. [T.R. Karl, S.J. Hassol, C.D. Miller, and W.L. Murray, 1770 (eds.)]. A Report by the Climate Change Science Program and the Subcommittee 1771 on Global Change Research, Washington, D.C. 1772 1773 Levitan, M., 2003: Climatic factors and increased frequencies of ‘southern’ chromosome 1774 forms in natural populations of Drosophila robusta. Evol. Ecol. Res., 5, 597-604. 1775 1776 Logan, J.A., J. Regniere, J.A. Powell, 2003: Assessing the impacts of global warming on 1777 forest pest dynamics. Frontiers in Ecology and the Environment, 1, 130-137. 1778 1779 Lott, N., and T. Ross, 2006: Tracking billion-dollar U.S. weather disasters. (Nowcast, 1780 news and notes--conference notebook). Bulletin of the American Meteorological 1781 Society, 87 (5), 557-559 (May 2006). 1782 ftp://ftp.ncdc.noaa.gov/pub/data/papers/2006nl557free.pdf 1783 1784 Lott, N., and T. Ross, 2006: Tracking and evaluating U.S. billion dollar weather 1785 disasters, 1980-2005, 86th AMS Annual Meeting, 29 January - 2 February 2006, 1786 Atlanta, Georgia, combined preprints [CD-ROM], American Meteorological 1787 Society, Boston, MA, 1.2, 7 p. (January 2006) 1788 ftp://ftp.ncdc.noaa.gov/pub/data/papers/200686ams1.2nlfree.pdf 1789 1790 Magnuson, J.J., D.M. Robertson, B.J. Benson, R.H. Wynne, D.M. Livingstone, T. Arai, 1791 R.A. Assel, R.G. Barry, V. Card, E. Kuusisto, N.G. Granin, T.D. Prowse, K.M 1792 Stewart, V.S. Vuglinski, 2000: Historical Trends in lake and river ice cover in the 1793 Northern Hemisphere. Science, 289, 1743- 1746. 1794 1795 Manzello, D.P., M. Brandt, T.B. Smith, D. Lirman, J.C. Hendee and R.S. Nemeth, 2007: 1796 Hurricanes benefit bleached corals. Proceedings National Academy of Sciences, 1797 104, 12035-12039. 1798 1799 Mattson, W.J. and R.A. Haack, 1987: The role of drought in outbreaks of plant-eating 1800 insects, Bioscience, 37, 110-118. 1801 1802 Meyer, P., M. Bisping, and M. Weber, 1997: Tropical Cyclones. Swiss Reinsurance 1803 Company, Zurich, 31 pp. 1804 1805 Mileti, D., 1999: Disasters by Design. Joseph Henry Press, Washington, 351 pp. 1806 1807 Mills, E., 2005a: Insurance in a climate of change. Science, 309. 1040-1044. 1808 1809 Mills, E., 2005b: Response to Pielke. Science, 310, 1616. 1810 1811 Mote, P., A. Hamlet, M. Clark, and D. Lettenmaier, 2005: Declining mountain snowpack 1812 in western North America. Bulletin of the American Meteorological Society, 86, 1813 39-49.
Do Not Cite or Quote 77 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1814 1815 National Drought Mitigation Center, 2006: Understanding and Defining Drought. 1816 Available from http://www.drought.unl.edu/whatis/concept.htm. 1817 1818 National Research Council, 1999: The Impacts of Natural Disasters: A Framework for 1819 Loss Estimation, National Academy Press, Washington, D.C., 80 pp. 1820 1821 Natural Resources Canada, 2007: Mountain Pine Beetle Program, 1822 http://mpb.cfs.nrcan.gc.ca/index_e.html. 1823 1824 Nordhaus, W.D., 2006: The Economics of Hurricanes in the United States. National 1825 Bureau of Economic Research (NBER) Working Paper, Cambridge, Mass., 1826 Available at http://www.nber.org/papers/w12813 1827 1828 Oechel, W.C., G.L. Vourlitis, S.J. Hastings, R.C. Zulueta, L. Hinzman, and D. Kane, 1829 2000: Acclimation of ecosystem CO2 exchange in the Alaska Arctic in response 1830 to decadal warming. Nature, 406, 978–981. 1831 1832 Oechel, W.C., S.J. Hastings, G. Vourlitis, and M. Jenkins, 1993: Recent change of arctic 1833 tundra ecosystems from net carbon dioxide sink to a source. Nature, 361, 520– 1834 523. 1835 1836 Oedekoven, C. S., D. G. Ainley and L. B. Spear, 2001: Variable responses of seabirds to 1837 changes in marine climate: California Current 1985-1994. Marine Ecology 1838 Progress Series, 212, 265-281. 1839 1840 Parmenter, R.R., E.P. Yadav, C.A. Parmenter, P. Ettestad, and K.L. Gage, 1999: 1841 Incidence of plague associated with increased winter-spring precipitation in New 1842 Mexico. American Journal of Tropical Medicine and Hygiene, 61, 814-821. 1843 1844 Parmesan, C., 2005: Case Study: Euphydryas editha. Special essay In: Lovejoy, T.E. and 1845 L. Hannah (eds) Climate Change and Biodiversity. Yale University Press, 440 pp. 1846 1847 Parmesan, C., 1996: Climate and species range. Nature, 382, 765-766. 1848 1849 Parmesan, C., 2006: Ecological and evolutionary responses to recent climate change. 1850 Annual Reviews of Ecology, Evolution and Systematics, 37, 637-669. 1851 1852 Parmesan, C., T.L. Root, and M.R. Willig, 2000: Impacts of extreme weather and 1853 climate on terrestrial biota. Bulletin of the American Meteorological Society, 81, 1854 443-50. 1855 1856 Parmesan C. and G. Yohe, 2003: A globally coherent fingerprint of climate change 1857 impacts across natural systems. Nature, 421, 37-42. 1858
Do Not Cite or Quote 78 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1859 Parmesan C., and H. Galbraith, 2004: Observed Ecological Impacts of Climate Change 1860 in North America. Pew Center for Global Climate Change, Arlington, Va. 1861 1862 Parmesan, C., S. Gaines, L. Gonzalez, D.M. Kaufman, J. Kingsolver, A.T. Peterson and 1863 R. Sagarin, 2005: Empirical perspectives on species’ borders: from traditional 1864 biogeography to global change. Oikos, 108, 58-75. 1865 1866 Parmesan, C. and P. Martens, 2007: Climate change, wildlife and human health. Chapter 1867 14 in: SCOPE Assessment: “Biodiversity, Global Change and Human Health”, O. 1868 Sala, C. Parmesan and L. Myers (Eds), Island Press, in press. 1869 1870 Pascual, M., X. Rodó, S.P. Ellner, R. Colwell and M.J. Bouma, 2000: Cholera dynamics 1871 and El Niño southern oscillation. Science, 289, 1766-1769. 1872 1873 Patz, J.A., M.A McGeehin, S.M. Bernard, K.L. Ebi, P.R. Epstein, A Grambsch, D.J. 1874 Gubler, P. Reiter, E. Romieu, J.B. Rose, J.M. Samet, J. Trtanj and T. F. Cecich, 1875 2001: Potential consequences of climate variability and change for human health 1876 in the United States. ch. 15 (pp. 437-458) in: The U.S. National Assessment on 1877 Potential Consequences of Climate Variability and Climate Change, Executive 1878 Office of the President of the United States of America, United States Global 1879 Change Research Program, National Assessment Synthesis Team, Cambridge 1880 University Press, Cambridge, UK. 1881 1882 Patz, J. A., A.K. Githeko, J.P. McCarty, S. Hussein, U. Confalonieri and N. de Wet, 1883 2003: Climate change and infectious diseases. Chapter 6 in: WHO 2003: Climate 1884 Change and Human Health. Risks and Responses, McMichael, A.J., D.H 1885 Campbell-Lendrum, C.F. Corvalán, K.L. Ebi, A.K. Githeko, J.D. Scheraga and A. 1886 Woodward (Eds), the World Health Organization, the World Meteorological 1887 Organization and the United Nations Environment Programme 1888 (WHO/WMO/UNEP). World Health Organization, Geneva, Switzerland, pp. 333. 1889 1890 Patz, J.A., D. Campbell-Lendrum, T. Holloway and J.A. Foley, 2005: Impact of regional 1891 climate change on human health. Nature, 438, 310-317. 1892 1893 Pielke, R.A., Jr., J. Rubiera, C. Landsea, M. Fernandez, and R.A. Klein, 2003: Hurricane 1894 vulnerability in Latin America and the Caribbean: normalized damage and loss 1895 potentials. Natural Hazards Review, 4, 101-114. 1896 1897 Pielke, R.A., Jr., 2005: Are there trends in hurricane destruction? Nature, 438, E11, 1898 doi:10.1038/nature04426. 1899 1900 Pielke, R.A., Jr., and C.W. Landsea, 1998: Normalized hurricane damages in the United 1901 States: 1925-1995. Weather Forecasting, 13, 621-631. 1902
Do Not Cite or Quote 79 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1903 Pielke, R, Jr., J. Gratz, W. Christopher, C. Landsea., D. Douglas Collins, M. Saunders, 1904 and R. Musulin, 2007: Normalized hurricane damages in the United States: 1900- 1905 2005 (in review). 1906 1907 Precht, H., J. Christophersen, H. Hensel, and W. Larcher, 1973: Temperature and Life, 1908 Springer-Verlag, New York, 779 pp. 1909 1910 Pickett, S.T.A. and P.S. White, (eds.), The Ecology of Natural Disturbance and Patch 1911 Dynamics. Academic Press, San Diego, 472 pp. 1912 1913 Pulwarty, R., K. Jacobs, R. Dole, 2005: The hardest working river: Drought and critical 1914 water problems on the Colorado. In: Drought and Water Crises: Science, 1915 Technology and Management, [D. Wilhite (ed.)]. Taylor and Francis Press, 249- 1916 285. 1917 1918 Pulwarty, R., U. Trotz, and L. Nurse, 2007: Risk and criticality-Caribbean Islands in a 1919 changing climate. In: Key Vulnerable Regions and Climate Change, [W. Hare and 1920 A. Battaglini (eds.)] (in press). 1921 1922 Pulwarty, R., K. Broad, T. Finan, 2003: ENSO forecasts and decision making in Brazil 1923 and Peru. 83-98. In Bankoff, G., G. Frerkes, and T. Hilhorst (eds.), Mapping 1924 Vulnerability: Disasters, Development and People. Earthscan, London, 236 pp. 1925 1926 Riebsame, W.E., S.A. Changnon Jr., and T.R. Karl, 1991: Drought and Natural 1927 Resource Management in the United States. Westview Press Inc., Boulder, 174 p. 1928 1929 Rodó, X., M. Pascual, G. Fuchs, and A.S.G. Faruque, 2002: ENSO and cholera: a 1930 nonstationary link related to climate change? PNAS, 20, 12901-12906. 1931 1932 Rodríguez-Trelles F, M.A. Rodriguez, 1998: Rapid micro-evolution and loss of 1933 chromosomal diversity in Drosophila in response to climate warming. Evol. Ecol., 1934 12, 829-838 1935 1936 Root, T.L., J.T. Price, K.R. Hall, S.H. Schneider, C. Rosenzweig and J.A. Pounds, 2003: 1937 Fingerprints of global warming on wild animals and plants. Nature, 421, 57–60. 1938 1939 Rose, J.B.S. et al., 2000: Climate and waterborne outbreaks in the US: a preliminary 1940 descriptive analysis. Journal of the American Water Works Association, 92, 77– 1941 86. 1942 1943 Rowan, R., 2004: Thermal adaptation in reef coral symbionts. Nature, 430, 742. 1944 1945 Schär, C., P.L. Vidale, D. Lüthi, C. Frei, C. Häberli, M.A. Liniger and C. Appenzeller, 1946 2004: The role of increasing temperature variability for European summer heat 1947 waves. Nature, 427, 332-336; doi:10.1038/nature02300. 1948
Do Not Cite or Quote 80 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1949 Singer, M.C. and P.R. Ehrlich, 1979: Population dynamics of the checkerspot butterfly 1950 Euphydryas editha. Fortschr. Zool., 25, 53-60. 1951 1952 Sokolov, L.V., 2006: The influence of global warming on timing of migration and 1953 breeding of passerine bird in the twentieth century. Zoologichesky Zhurnal, 85, 1954 317-341. 1955 1956 Smit, B., I. Burton, R.J.T. Klein, and J. Wandel, 2000: An anatomy of adaptation to 1957 climate change and variability. Climatic Change, 45, 223-251. 1958 Spear, L.B., D.G. Ainley, 1999: Migration routes of sooty shearwaters in the Pacific 1959 ocean. Condor, 101, 205-218 1960 1961 Stern, N., 2006: The Economics of Climate Change: The Stern Review, Cambridge 1962 University Press, 712 pp. 1963 1964 Stott, P.A., D.A. Stone, and M.R. Allen, 2004: Human contribution to the European heat 1965 wave of 2003. Nature, 432, 610-614. 1966 1967 Taulman, J.F., and L.W. Robbins, 1996: Recent range expansion and distributional 1968 limits of the nine-banded armadillo (Dasypus novemcinctus) in the United States. 1969 Journal of Biogeography, 23, 635-648. 1970 1971 Thomas, C.D., E.J. Bodsworth, R.J. Wilson, et al., 2001: Ecological and evolutionary 1972 processes at expanding range margins. Nature, 411, 577-581. 1973 1974 Trenberth, K. E., A. Dai, R.M. Rasmussen, and D.B. Parsons, 2003: The changing 1975 character of precipitation. Bulletin of the American Meteorological Society, 84, 1976 1205-1217. 1977 1978 van Vliet, A. and R. Leemans, 2006: Chapter 12: Rapid species’ responses to changes in 1979 climate require stringent climate protection targets. Avoiding Dangerous Climate 1980 Change, Cambridge University Press, Cambridge, 135-141. 1981 1982 Walker, L.R., ed., 1999: Ecology of Disturbed Ground. Elsevier, Amsterdam, 900 pp. 1983 1984 Walther, G.R., E. Post, P. Convery, A. Menzel, C. Parmesan, et al., 2002: Ecological 1985 responses to recent climate change. Nature, 416, 389-95. 1986 1987 Warrick, R.A., 1980: Drought in the Great Plains: A case study of research on climate 1988 and society in the USA. In Ausubel J. and A.K. Biswas (eds.), Climatic 1989 constraints and human activities. Pergamon, New York, 215 pp. 1990 Westerling, A. L., H. G. Hidalgo, D. R. Cayan and T. W. Swetnam, 2006: Warming and 1991 earlier spring increase in Western U.S. forest wildfire activity. Science, 313, 940- 1992 943. 1993
Do Not Cite or Quote 81 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
1994 Weiser, W., ed., 1973: Effects of Temperature on Ectothermic Organisms. Springer- 1995 Verlag, New York, 298 pp. 1996 1997 Werner, R.A., E.H. Holsten, S.M. Matsuoka, and R.E. Burnside, 2006: Spruce beetles 1998 and forest ecosystems in south-central Alaska: A review of 30 years of research. 1999 Forest Ecology and Management, 227, 195-206. 2000 2001 White, G.F., R.W. Kates, and I. Burton, 2001: Knowing Better and Losing Even More: 2002 The Use of Knowledge in Hazards Management. Environmental Hazards, 3, 81- 2003 92. 2004 2005 Wilkinson, C.R., ed., 2000: Global Coral Reef Monitoring Network: Status of Coral 2006 Reefs of the World in 2000. Australian Institute of Marine Science. Townsville, 2007 Queensland. 363 pp. 2008 2009 Wilkinson, P., D.H. Campbell-Lendrum and C.L. Bartlett, 2003: Monitoring the health 2010 effects of climate change. Chapter 10 in: Climate Change Impacts on the United 2011 States: the Potential Consequences of Climate Variability and Change. National 2012 Assessment Synthesis Team, United States Global Change Research Program, 2013 Washington, D.C. 2014 2015 Wilhite, D.A., ed., 2005: Drought and Water Crises: Science, Technology and 2016 Management Issues. Taylor and Francis Press, Boca Raton, 432 pp. 2017 2018 Wilhite, D. and R. Pulwarty, 2005: Drought, crises and water management. 289-298. In: 2019 Wilhite, D.A., ed.: Drought and Water Crises: Science, Technology and 2020 Management Issues. Taylor and Francis Press, Boca Raton, 432 pp. 2021 2022 World Health Organization, 2002: World Health Report 2002: Reducing Risks, 2023 Promoting Healthy Life. World Health Organization, Geneva, Switzerland, 230 2024 pp. 2025 2026 World Health Organization, 2003: Climate Change and Human Health: Risks and 2027 Responses. Project Team: A.J. McMichael, D.H. Campbell-Lendrum, C.F. 2028 Corvalán, K.L. Ebi, A.K. Githeko, J.D. Scheraga, A Woodward. World Health 2029 Organization, Geneva, Switzerland, pp. 333. 2030 2031 World Health Organization, 2004: Using climate to predict infectious disease 2032 outbreaks: a review. Project team: M Anker, C. Corvalán, K. Kuhn. D. Campbell- 2033 Lendrum, A. Haines, J. Cox. World Health Organization, Geneva, Switzerland, 55 2034 pp.
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2035
2036 2037 Figure 1.1 The blue bars show the number of events per year that exceed a cost of 1 2038 billion dollars (these are scaled to the left side of the graph). The blue line (actual costs at 2039 the time of the event) and the red line (costs adjusted for wealth/inflation) are scaled to 2040 the right side of the graph, and depict the annual damage amounts in billions of dollars. 2041 Over the last 27 years, the U.S. averaged between two and three weather and climate- 2042 related disasters a year that exceeded one billion dollars in cost. Data from NOAA’s 2043 National Climatic Data Center.
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2044
2045 2046 Figure 1.2 Probability distributions of daily temperature and precipitation. The higher 2047 the black line, the more often weather with those characteristics occurs.
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2048
2049 2050 Figure 1.3 Like the European summer temperature of 2003, some extremes that are 2051 more likely to be experienced in the future will be far outside the range of historical 2052 observations. Each vertical line represents the summer temperature for a single year with 2053 the extreme values from the years 1909, 1947 and 2003 identified. From Schär et al., 2054 2004.
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2055
2056 2057 Figure 1.4 Simplified depiction of the changes in temperature and precipitation in a 2058 warming world.
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2059
2060 2061 Figure 1.5 Megadrought and megadeath in 16th Century Mexico. Four hundred years 2062 ago the Mexican socio-economic and natural systems were so sensitive to extremes that a 2063 mega-drought in Mexico led to a massive population declines (Acuna-Soto et al., 2002). 2064 The 1545 Codex En Cruz depicts the effects of the cocoliztli epidemic which has 2065 symptoms similar to rodent-borne hantavirus hemorrhagic fever.
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2066
2067 2068 Figure 1.6 Extreme events such as hurricanes can have significant sudden impacts that 2069 take some time to recover from. Top: Two similar magnitude events take place but after 2070 the first one, new adaptation measures are undertaken, such as changes in building codes, 2071 so the second event doesn’t have as great an impact. Bottom: An extreme that occurs 2072 before an area has completely recovered from the previous extreme can have a total 2073 impact in excess of what would have occurred in isolation.
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2074
2075 2076 2077 Figure 1.7 Climate variability may reduce crop yield, but because of technological 2078 improvements, a poor yield in the 1990s can still be higher than a good yield in the 1950s 2079 indicating a changing relationship between climate and agricultural yield. Data are in 2080 units of cubic meters or metric tons per unit area with the yield in 1975 defined as 1. Data 2081 from USDA National Agricultural Statistics Service via update to Heinz Center (2002).
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2082
2083
2084 Figure 1.8 Illustration of how climate information is processed, filtered, and combined
2085 with other information in the decision process relevant to stakeholder interests.
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2086
2087 Beetle Damage to Pine Trees in Canada
2088
2089
2090 Figure 1.9 Photograph of a pine forest showing pine trees dying (red) from beetle
2091 infestation in the Quesnel-Prince George British Columbia area. Fewer instances of
2092 extreme cold winter temperatures that control beetle populations as well as hotter
2093 summers that increase populations are leading to a greater likelihood of beetle
2094 infestations. (Figure inclusion in Final Document subject to copyright permission).
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2095
2096
2097 Figure 1.10 Percent of area in the contiguous U.S. and western U.S. affected by severe 2098 and extreme drought as indicated by Palmer Drought Severity Index (PDSI) values of 2099 less than or equal to −3. Data from NOAA’s National Climatic Data Center.
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2100
2101
2102 Figure 1.11 More intense hurricanes cause much greater losses. Mean damage ratio is the 2103 average expected loss as a percent of the total insured value. Adapted from Meyer et al. 2104 (1997).
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2105
2106 2107 Figure 1.12 Schematic diagram of a typical risk model used by the insurance industry. 2108 The diagram highlights the three major components (hazard, damage, and loss) of a risk 2109 model. What happens to the loss component feedbacks to the vulnerability and inventory 2110 components.
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2111 Chapter 2 Observed Changes of Weather and Climate 2112 Extremes 2113
2114 Convening Lead Author: Kenneth Kunkel, Univ. Ill. Urbana-Champaign, Ill. State 2115 Water Survey 2116 2117 Lead Authors: Peter Bromirski, Scripps Inst. Oceanography, UCSD; Harold Brooks, 2118 NOAA; Tereza Cavazos, Centro de Investigación Científica y de Educación Superior de 2119 Ensenada, Mexico; Arthur Douglas, Creighton Univ.; David Easterling, NOAA; Kerry 2120 Emanuel, Mass. Inst. Tech.; Pavel Groisman, UCAR/NCDC; Greg Holland, NCAR; 2121 Thomas Knutson, NOAA; James Kossin, Univ. Wis.-Madison, CIMSS; Paul Komar, 2122 Oreg. State Univ.; David Levinson, NOAA; Richard Smith, Univ. N.C., Chapel Hill 2123 2124 Contributing Authors: Jonathan Allan, Oreg. Dept. Geology and Mineral Industries; 2125 Raymond Assel, NOAA; Stanley Changnon, Univ. Ill. Urbana-Champaign, Ill. State 2126 Water Survey; Jay Lawrimore, NOAA; Kam-biu Liu, La. State Univ., Baton Rouge; 2127 Thomas Peterson, NOAA 2128 2129 KEY FINDINGS 2130 2131 Observed Changes
2132 Upward trends in the frequency of unusually warm nights, extreme precipitation
2133 episodes, the frequency of North Atlantic tropical cyclones (hurricanes), the length of the
2134 frost-free season, and extreme wave heights along the west coast are notable changes in
2135 the North American climate record.
2136 • Most of North America is experiencing more unusually hot days. The number of
2137 warm spells has been increasing since 1950. However, the heat waves of the 1930s
2138 remain the most severe in the U.S. historical record back to 1895.
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2139 • There are fewer unusually cold days during the last few decades. The last 10 years
2140 have seen a lower number of severe cold waves than for any other 10-yr period in the
2141 historical record which dates back to 1895. There has been a decrease in the number
2142 of frost days and a lengthening of the frost-free season, particularly in the western
2143 part of North America.
2144 • Extreme precipitation episodes (heavy downpours) have become more frequent and
2145 more intense in recent decades than at any other time in the historical record dating
2146 back to the late 19th Century and account for a larger percentage of total precipitation.
2147 The most significant changes have occurred in most of the U.S., northern Mexico,
2148 southeastern, northern and western Canada, and southern Alaska.
2149 • There are recent regional tendencies toward more severe droughts in the southwestern
2150 U.S., parts of Canada and Alaska, and Mexico.
2151 • For the continental U.S. and southern Canada, the most severe droughts occurred in
2152 the 1930s and there is no indication of an overall trend since 1895; in Mexico, the
2153 1950s and 1994-present were the driest period.
2154 • Atlantic tropical cyclone (hurricane) activity, as measured by both frequency and the
2155 Power Dissipation Index (which combines storm intensity, duration and frequency)
2156 has increased.
2157 – The increases are substantial since about 1970, and are likely substantial since
2158 the 1950s and 60s, in association with warming Atlantic sea surface temperatures.
2159 There is less confidence in data prior to 1900.
2160 – It is likely that there has been an increase in tropical cyclone frequency in the
2161 North Atlantic over the past 100 years, which has closely followed warming
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2162 tropical Atlantic sea surface temperatures. There is increasing uncertainty in the
2163 data as one proceeds further back in time.
2164 – The frequency of major hurricanes has increased coincident with overall
2165 tropical cyclone numbers.
2166 • There is no observational evidence for an increase in North American mainland land-
2167 falling hurricanes since the late 1800s.
2168 • The hurricane Power Dissipation Index in the eastern Pacific, affecting the Mexican
2169 west coast and shipping lanes, has decreased since 1980, but rainfall from near-
2170 coastal hurricanes has increased since 1949.
2171 • The balance of evidence suggests that there has been a northward shift in the tracks of
2172 strong low pressure systems (storms) in both the N. Atlantic and N. Pacific basins.
2173 There is a trend toward stronger intense low pressure systems in the North Pacific.
2174 • Increases in extreme wave height characteristics have been observed along the
2175 Atlantic and Pacific coasts of North America during recent decades based on 3
2176 decades of buoy data.
2177 – Increases along the West coast have been greatest in the Pacific Northwest, and are
2178 likely a reflection of changes in storm tracks.
2179 – Increases along the U.S. east coast are evident during the hurricane season.
2180 • Although snow cover extent has decreased over North America, there is no indication
2181 of continental-scale trends in snowstorms and episodes of freezing rain during the
2182 20th Century.
2183 • There is no trend in the frequency of tornadoes and other severe convective storms
2184 when the data are adjusted for changes in observing practices.
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2185 2.1 Background
2186 Weather and climate extremes exhibit substantial spatial variability. It is not unusual for
2187 severe drought and flooding to occur simultaneously in different parts of North America
2188 (e.g. catastrophic flooding in the Mississippi River basin and severe drought in the
2189 southeast U.S. during summer 1993). These reflect temporary shifts in large-scale
2190 circulation patterns that are an integral part of the climate system (Chapter 2, Box 2.3).
2191 The central goal of this chapter is to identify long-term shifts/trends in extremes and to
2192 characterize the continental-scale patterns of such shifts. Such characterization requires
2193 data that is homogeneous, of adequate length, and with continental-scale coverage. Many
2194 datasets meet these requirements for limited periods only. For temperature and
2195 precipitation, rather high quality data are available for the conterminous U.S. back to the
2196 late 19th Century. However, shorter data records are available for parts of Canada,
2197 Alaska, Hawaii, Mexico, the Caribbean, and U.S. territories. In practice, this limits true
2198 continental-scale analyses of temperature and precipitation extremes to the middle part of
2199 the 20th Century onward. Other phenomena have similar limitations and continental-scale
2200 characterizations are generally limited to the last 50-60 years or less, or must confront
2201 data homogeneity issues which add uncertainty to the analysis. We consider all studies
2202 that are available, but in many cases these studies have to be interpreted carefully because
2203 of these limitations. A variety of statistical techniques are used in the studies cited here.
2204 General information about statistical methods along with several illustrative examples are
2205 given in the Appendix.
2206
2207
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2208 2.2 Observed Changes and Variations in Weather and Climate Extremes
2209 2.2.1 Temperature Extremes
2210 Extreme temperatures do not always correlate with average temperature, but they often
2211 change in tandem; thus, average temperature changes provide a context for discussion of
2212 extremes. In 2005, virtually all of North America was above to much above average11
2213 (Shein et al. 2006) and 2006 was one of the warmest years on record in the conterminous
2214 United States (Arguez et al., 2007). The areas experiencing the largest temperature
2215 anomalies included the higher latitudes of Canada and Alaska. Annual average
2216 temperature time series for Canada, Mexico and the United States all show substantial
2217 warming since the middle of the 20th century (Shein et al. 2006). Since 1998 over half of
2218 the U.S. annual average temperatures have been extremely high, including the hottest two
2219 years on record (1998 and 2006).
2220
2221 Since 1950, the annual percent of days exceeding the 90th, 95th, and 97.5th percentile
2222 thresholds12 for both maximum (daytime highs) and minimum (nighttime lows)
2223 temperature has increased when averaged over all the land area (Figure 2.1; Peterson et
2224 al. 2007). Although the changes are greatest in the 90th percentile (increasing from about
2225 10% of the days to about 13% for maximum and almost 15% for minimum) and decrease
2226 as the threshold temperatures increase indicating more rare events (the 97.5th percentage
2227 increases from about 3% of the days to 4% for maximum and 5% for minimum), the
11 NOAA’s National Climatic Data Center uses the following terminology for classifying its monthly/seasonal/annual U.S. temperature and precipitation rankings: “near-normal” is defined as within the mid-tercile, “above/below normal” is within the top-tercile, and “much-above/much-below normal” is within the top-decile of all such periods on record. 12 An advantage of the use of percentile, rather than absolute, thresholds is that they account for regional climate differences
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2228 relative changes are similar. There are important regional differences in the changes. For
2229 example, the largest increases in the 90th percentile threshold temperature occur in the
2230 western part of the continent from northern Mexico through the western U.S. and Canada
2231 and across Alaska, while some areas, such as eastern Canada, show declines of as many
2232 as 10 days per year from 1950 to 2004 (Fig. 2.2).
2233
2234 Other regional studies have shown similar patterns of change. For the U.S., the number of
2235 days exceeding the 90th, 95th and 99th percentile thresholds (defined monthly) have
2236 increased in recent years13, but are also dominated earlier in the 20th century by the
2237 extreme heat and drought of the 1930s14 (DeGaetano and Allen 2002). Changes in cold
2238 extremes (days falling below the 10th, 5th, and 1st percentile threshold temperatures) show
2239 decreases, particularly since 196015. For the 1900-1998 period in Canada, there are fewer
2240 cold extremes in winter, spring and summer in most of southern Canada and more high
2241 temperature extremes in winter and spring, but little change in warm extremes in
2242 summer16 (Bonsal et al. 2001). However, for the more recent (1950-1998) period there
2243 are significant increases in warm extremes over western Canada, but decreases in eastern
2244 Canada. Similar results averaged across all of Canada are found for the longer 1900-2003
2245 period, with 28 fewer cold nights, 10 fewer cold days, 21 more extreme warm nights and
2246 8 more warm days per year now than in 190017 (Vincent and Mekis 2006). For the U.S.
2247 and Canada, the largest increases in daily maximum and minimum temperature are
13 The number of stations with statistically significant positive trends for 1960-1996 passed tests for field significance based on resampling. 14 The number of stations with statistically significant negative trends for 1930-1996 was greater than the number with positive trends. 15 The number of stations with statistically significant downward trends for 1960-1996 passed tests for field significance based on resampling, but not for 1930-1996. 16 Statistical significance of trends was assessed using Kendall’s tau test 17 These trends were statistically significant at more than 20% of the stations based on Kendall’s tau test
Do Not Cite or Quote 100 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2248 occurring in the colder days of each month (Robeson 2004). For the Caribbean region,
2249 there is an 8% increase in the number of very warm nights and 6% increase in the number
2250 of very warm days for the 1958-1999 period. There also has been a corresponding
2251 decrease of 7% in the number of cold days and 4% in the number of cold nights (Peterson
2252 et al. 2002). The number of warm nights has increased by 10 or more per year for Hawaii
2253 and 15 or more per year for Puerto Rico from 1950 to 2004 (Fig. 2.2).
2254
2255 Analysis of multi-day very extreme heat and cold episodes18 in the U.S. were updated19
2256 from Kunkel et al. (1999) for the period 1895-2005. The most notable feature of the
2257 pattern of the annual number of the extreme heat waves (Fig. 2.3a) through time is the
2258 high frequency in the 1930s compared to the rest of the years in the 1895-2005 period.
2259 This was followed by a decrease to a minimum in the 1960s and 1970s and then an
2260 increasing trend since then. There is no trend over the entire period, but a highly
2261 statistically significant upward trend since 1960. The heat waves during the 1930s were
2262 characterized by extremely high daytime temperatures while nighttime temperatures were
2263 not as unusual (Fig. 2.3b,c). An extended multi-year period of intense drought
2264 undoubtedly played a large role in the extreme heat of this period, particularly the
2265 daytime temperatures, by depleting soil moisture and reducing the moderating effects of
2266 evaporation. By contrast, the recent period of increasing heat wave index is distinguished
2267 by the dominant contribution of a rise in extremely high nighttime temperatures (Fig.
2268 2.3c). Cold waves show a decline in the first half of the 20th century, then a large spike of
18 The threshold is approximately the 99.9 percentile. 19 The data were first transformed to create near-normal distributions using a log transformation for the heat wave index and a cube root transformation for the cold wave index. The transformed data were then subjected to least squares regression. Details are given in the Appendix, Example 2.
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2269 events during the mid-1980s, then a decline20. The last 10 years have seen a lower
2270 number of severe cold waves in the U.S. than in any other 10-yr period since 1895,
2271 consistent with observed impacts such as insect populations (Chapter 1, Box 1.2).
2272 Decreases in the frequency of extremely low nighttime temperatures have made a
2273 somewhat greater contribution than extremely low daytime temperatures to this recent
2274 low period of cold waves. Over the entire period there is a downward trend but it is not
2275 statistically significant at the p=0.05 level.
2276
2277 The annual number of warm spells21 averaged over North America has increased since
2278 1950 (Peterson et al. 2007). In the U.S. the annual number of warm spells22 has increased
2279 by about 1 ½ per year, and the duration has increased by about 1 day since 1950
2280 (Easterling et al. 2007a). Regionally the largest increases, up to about 2 ½ per year, were
2281 found in the western U.S., with many parts of the south and southeast showing little
2282 change. Seasonal results show the largest increases in the spring and winter, with little
2283 change in the number of events for the fall or summer. These results for warm spells are
2284 roughly consistent with those for the much more extreme heat waves illustrated in Fig.
2285 2.3a for the common period of analysis (1950-present); the warm spell analyses do not
2286 extend back to the 1930s when very extreme heat was frequent. The frequency and extent
2287 of hot summers23 was highest in the 1930s, 1950s, and 1995-2003; the geographic pattern
20 Details of this analysis are given in the Appendix, Example 1. 21 Defined as at least 3 consecutive days above the 90th percentile threshold done separately for maximum and minimum temperature. 22 Defined as at least 3 consecutive days with both the daily maximum and succeeding daily minimum temperature above the 80th percentile. 23 Based on percentage of North American grid points with summer temperatures above the 90th or below the 10th percentiles of the 1950-1999 summer climatology.
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2288 of hot summers during 1995-2003 was similar to that of the 1930s (Gershunov and
2289 Douville 2007).
2290
2291 The occurrence of temperatures below the biologically- and societally-important freezing
2292 threshold (0°C, 32°F) is an important aspect of the cold season climatology. Studies have
2293 typically characterized this either in terms of the number of frost days (days with the
2294 minimum temperature below freezing) or the length of the frost-free season24. The
2295 number of frost days decreased by 4 days per year in the U.S. during the 1948-1999
2296 period, with the largest decreases, as many as 13 days per year, occurring in the Western
2297 U.S.25 (Easterling 2002). In Canada, there have been significant decreases in frost day
2298 occurrence over the entire country from 1950 to 2003, with the largest decreases in
2299 extreme western Canada where there have been decreases of up to 40 or more frost days
2300 per year, and slightly smaller decreases in eastern Canada (Vincent and Mekis 2006). The
2301 start of the frost-free season in the Northeastern U.S. occurred 11 days earlier in the
2302 1990s than in the 1950s (Cooter and LeDuc 1995). For the entire U.S., the average length
2303 of the frost-free season over the 1895-2000 period for the U.S. increased by almost 2
2304 weeks26 (Figure 2.4; Kunkel et al. 2004). The change is characterized by 4 distinct
2305 regimes, with decreasing frost-free season length from 1895 to1910, an increase in length
2306 of about 1 week from 1910 to 1930, little change during1930-1980, and large increases
2307 since1980. The frost-free season length has increased more in the western U.S. than in
2308 the eastern U.S. (Easterling 2002; Kunkel et al. 2004), which is consistent with the
24 The difference between the date of the last spring frost and the first fall frost 25 Trends in the western half of the U.S. were statistically significant based on simple linear regression 26 Statistically significant based on least-squares linear regression
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2309 finding that the spring pulse of snow melt water in the Western U.S. now comes as much
2310 as 7-10 days earlier than in the late 1950s (Cayan et al. 2001).
2311
2312 Ice cover on lakes and the oceans is a direct reflection of the number and intensity of
2313 cold, below freezing days. Ice cover on the Laurentian Great Lakes of North American
2314 usually forms along the shore and in shallow areas in December and January, and in
2315 deeper mid-lake areas in February due to their large depth and heat storage capacity. Ice
2316 loss usually starts in early to-mid-March and lasts through mid-to-late April (Assel 2003).
2317
2318 Annual maximum ice cover on the Great Lakes has been monitored since 1963. The
2319 maximum extent of ice cover over the past 4 decades varied from less then 10% to over
2320 90%. The winters of 1977-1982 were characterized by a higher ice cover regime relative
2321 to the prior 14 winters (1963-1976) and the following 24 winters (1983-2006) (Assel et
2322 al. 2003, Assel 2005a, Assel personal communication for winter 2006). A majority of the
2323 mildest (lowest) seasonal average ice cover winters (Assel 2005b) over the past 4 decades
2324 occurred during the most recent 10-year period (1997-2006). Analysis of ice breakup
2325 dates on other smaller lakes in North America with at least 100 years of data (Magnuson
2326 et al. 2000) show a uniform trend toward earlier breakup dates (up to 13 days earlier per
2327 100 years)27.
2328
2329 Reductions in Arctic sea ice, especially near-shore sea ice, allow strong storm and wave
2330 activity to produce extensive coastal erosion resulting in extreme impacts. Observations
2331 from satellites starting in 1978 show that there has been a substantial decline in Arctic sea
27 Statistically significant trends were found for 16 of 24 lakes
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2332 ice, with a statistically significant decreasing trend in annual Arctic sea ice extent of -33
3 2 2333 ± 8.8 x 10 km per year (equivalent to approximately -2.7% ± 0.7% per decade).
2334 Seasonally the largest changes in Arctic sea ice have been observed in the ice that
2335 survives the summer, where the trend in the minimum Arctic sea ice extent, between
3 2 2336 1979 and 2005, was -60 ± 24 x 10 km per year (–7.4 ± 2.9% per decade) (Lemke et al.
2337 2007).
2338
2339 Rising sea surface temperatures have led to an increase in the frequency of extreme high
2340 SST events causing coral bleaching (see Box 1.1, Chapter 1). Mass bleaching events were
2341 not observed prior to 1980. However, since the 1970s, there have been 6 major global
2342 cycles of mass bleaching, with increasing frequency and intensity (Hoegh-Guldberg
2343 2005). Almost 30% of the world’s coral reefs have disappeared in that time.
2344
2345 Less scrutiny has been focused on Mexico temperature extremes, in part, because much
2346 of the country can be classified as a ‘tropical climate’ where temperature changes are
2347 presumed fairly small, or semi-arid to arid climate where moisture availability exerts a far
2348 greater influence on human activities than does temperature.
2349
2350 Most of the sites in Mexico’s oldest temperature observing network are located in major
2351 metropolitan areas and there is considerable evidence to indicate that trend behaviors at
2352 least partly reflect urbanization and urban heat island influences (Englehart and Douglas,
2353 2003). To avoid such issues in analysis, a monthly rural temperature dataset has recently
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2354 been developed28. Examined in broad terms as a national aggregate, a couple of basic
2355 behaviors emerge. First, long period temperature trends over Mexico are generally
2356 compatible with continental-scale trends which indicate a cooling trend over North
2357 America from about the mid-1940s to the mid-1970s, with a warming trend thereafter.
2358
2359 The rural gridded data set indicates that much of Mexico experienced decreases in both
2360 Tmax and Tmin during 1941-1970 (-0.27°C/decade for Tmax and -0.19°C/decade for Tmin)
2361 while the later period of 1971-2001 is dominated by positive trends that are most strongly
2362 evident in Tmax (0.35°C/decade for Tmax and 0.10°C/decade for Tmin). Based on these
2363 results it appears very likely that much of Mexico has experienced an increase in average
2364 temperature driven in large measure by increases in Tmax. The diurnal temperature range
2365 (Tmax minus Tmin) for the warm season (June-September) averaged over all of Mexico has
2366 increased by 0.26°C/decade since 1970 with particularly rapid rises since 1990 (Fig. 2.5)
2367 reflecting a comparatively rapid rise in Tmax with respect to Tmin (Englehart and Douglas
2368 2005)29. This behavior departs from the general picture for many regions of the world,
2369 where warming is attributable mainly to a faster rise in Tmin than in Tmax (e.g. Easterling
2370 et al., 1997).
2371
2372 Given Mexico’s largely tropical/sub-tropical climate and the influence of nearby oceans,
2373 a reasonable expectation would be that changes in the behavior of temperature extremes
28 It consists of monthly historical surface air temperature observations (1940-2001) compiled from stations (n=103) located in places with population <10,000 (2000 Census). To accommodate variable station record lengths and missing monthly observations, the dataset is formatted as a grid-type (2.5° x 2.5° lat.-long.) based on the climate anomaly method (Jones and Moberg, 2003) 29 Statistically significant trends were found in the northwest, central, and south, but not the northeast regions
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2374 could be small and difficult to detect as compared to at many mid-and high latitude
2375 locations. However, the cold surge30 phenomena – the equatorward penetration of
2376 modified cold air, known as nortes in Mexico – is an integral part of the country’s cool
2377 season climatology. The frequency of both cold surge days and cold surge events tends to
2378 vary depending in part on Pacific Decadal Oscillation (PDO) phase: under negative PDO
2379 phase cold surge activity tends to be more prevalent. However, the intensity of cold surge
2380 events as indicated by the maximum daily drop in Tmin tends to be greater under positive
2381 PDO phase. Analysis of linear trends indicates that from the early 1950s onward, it is
2382 very likely that southern Mexico has experienced a trend toward decreasing frequency of
2383 both cold surge days by 2.4 cold days/decade and cold surge events by 0.88
2384 events/decade (Englehart and Douglas 2007).
2385 2386 2.2.2 Precipitation Extremes
2387 2.2.2.1 Drought
2388 Droughts are one of the most costly natural disasters (Chapter 1, Box 1.4), with estimated 2389 annual U.S. losses of $6 – 8 billion (Federal Emergency Management Agency, 1995). An 2390 extended period of deficient precipitation is the root cause of a drought episode, but the 2391 intensity can be exacerbated by high evaporation rates arising from excessive 2392 temperatures, high winds, lack of cloudiness, and/or low humidity. Drought can be 2393 defined in many ways, from acute short-term to chronic long-term hydrological drought, 2394 agricultural drought, meteorological drought, and so on. The assessment in this report 2395 focuses mainly on meteorological droughts based on the Palmer (1965) Drought Severity 2396 Index (PDSI), though other indices are also documented in the report (Chapter 2, Box 2397 2.1).
30 Cold surges are defined for the period 1925-2002 based on daily station observations of Tmin from two locations – stations in south Texas and near coastal stations from the southern Mexican state of Veracruz. Cold surge days have Tmin below its climatological values by 1 standard deviation. Cold surge events are runs of 1 or more consecutive cold surge days.
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2398
2399 Individual droughts can occur on a range of spatial scales, but they often affect rather
2400 large areas and can persist for many months and even years. Thus, the aggregate impacts
2401 can be very large. For the U.S., the percentage area affected by severe to extreme drought
2402 (Fig. 2.6) highlights some major episodes of extended drought. The most widespread and
2403 severe drought conditions occurred in the 1930s and 1950s (Andreadis et al. 2005). The
2404 early 2000s were also characterized by severe droughts in some areas, notably in the
2405 western U.S. When averaged across the entire U.S. (Fig. 2.6), there is no clear tendency
2406 for a trend based on the PDSI. Similarly, long-term trends (1925-2003) of hydrologic
2407 droughts based on model derived soil moisture and runoff show that droughts have, for
2408 the most part, become shorter, less frequent, and cover a smaller portion of the U. S. over
2409 the last century (Andreadis and Lettenmaier, 2006). The main exception is the Southwest
2410 and parts of the interior of the West, where increased temperature has led to positive
2411 drought trends (Andreadis and Lettenmaier, 2006). The trends averaged over all of North
2412 America since 1950 (Fig. 2.6) are similar to U.S. trends for the same period, indicating no
2413 overall trend.
2414
2415 Since the contiguous United States has experienced an increase in both temperature and
2416 precipitation during the 20th century, one question is whether these increases are
2417 impacting the occurrence of drought. Easterling et al (2007b) examined this possibility by
2418 looking at drought, as defined by the PDSI, for the United States using detrended
2419 temperature and precipitation. Results indicate that without the upward trend in
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2420 precipitation the increase in temperatures would have lead to an increase in the area of
2421 the U.S. in severe-extreme drought of up to 30% in some months.
2422
2423 Summer conditions, which relate to fire danger, have trended toward lesser drought in the
2424 upper Mississippi, Midwest, and Northwest, but the fire danger has increased in the
2425 Southwest, in California in the spring season (not shown), and, surprisingly, over the
2426 Northeast, despite the fact that annual precipitation here has increased. A century-long
2427 warming in this region is quite significant in summer, which reverses the tendencies of
2428 the precipitation contribution to soil wetness (Groisman et al. 2004). Westerling et al.
2429 (2006) document that large wildfire activity in the Western U.S. increased suddenly and
2430 markedly in the mid-1980s, with higher large-wildfire frequency, longer wildfire
2431 durations, and longer wildfire seasons. The greatest increases occurred in mid-elevation,
2432 Northern Rockies forests, where land-use histories have relatively little effect on fire risks
2433 and are strongly associated with increased spring and summer temperatures and an earlier
2434 spring snowmelt.
2435
2436 For the entire North American continent, there is a north-south pattern in drought trends
2437 (Dai et al. 2004). Since 1950, there is a trend toward wetter conditions over much of the
2438 conterminous U.S., but a trend toward drier conditions over southern and western
2439 Canada, Alaska, and Mexico. The summer PDSI averaged for Canada indicates dry
2440 conditions during the 1940s and 1950s, generally wet conditions from the 1960s to 1995,
2441 but much drier after 1995 (Shabbar and Skinner, 2004). In Alaska and Canada, the
2442 upward trend in temperature, resulting in increased evaporation rates, has made a
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2443 substantial contribution to the upward trend in drought (Dai et al. 2004). In agreement
2444 with this drought index analysis, the area of forest fires in Canada has been quite high
2445 since 1980 compared to the previous 30 years and Alaska experienced a record high year
2446 for forest fires in 2004 followed by the third highest in 2005 (Soja et al. 2007). During
2447 the mid-1990s and early 2000s, central (Stahle et al. 2007) and western Mexico (Kim et
2448 al. 2002; Nicholas and Battisti, 2006; Hallack and Watkins, 2007) experienced
2449 continuous cool-season droughts having major impacts in agriculture, forestry, and
2450 ranching, especially during the warm summer season. In 1998, “El Niño” caused one of
2451 the most severe droughts in Mexico since the 1950s (Ropelewski, 1999), creating the
2452 most difficult wildfire season in Mexico’s history. Mexico had 14,445 wildfires affecting
2453 849,632 hectares - the largest area ever burned in Mexico in a single season
2454 (SEMARNAP, 2000).
2455
2456 Reconstructions of drought prior to the instrumental record based on tree-ring
2457 chronologies indicate that the 1930s may have been the worst drought since 1700 (Cook
2458 et al. 1999). There were three major multiyear droughts in the U.S. during the latter half
2459 of the 1800s: 1856-1865, 1870-1877 and 1890-1896 (Herweijer et al. 2006). Similar
2460 droughts have been reconstructed for northern Mexico (Therrell et al. 2002). There is
2461 evidence of earlier, even more intense drought episodes (Woodhouse and Overpeck
2462 1998). A period in the mid to late 1500s has been termed a “mega-drought” and was
2463 longer-lasting and more widespread than the 1930s Dust Bowl (Stahle et al. 2000).
2464 Several additional mega-droughts occurred during 1000-1470 (Herweijer et al. 2007).
2465 These droughts were about as severe as the 1930s Dust Bowl episode but much longer,
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2466 lasting 20-40 years. In the western U.S., the period of 900-1300 was characterized by
2467 widespread drought conditions (Fig. 2.7; Cook et al. 2004). In Mexico, reconstructions of
2468 seasonal precipitation (Stahle et al. 2000, Acuña-Soto et al. 2002, Cleaveland et al. 2004)
2469 indicate that there have been droughts more severe than the 1950s drought, e.g., the
2470 mega-drought in the mid- to late- 16th century, which appears as a continental-scale
2471 drought.
2472
2473 During the summer months, excessive heat and drought often occur simultaneously
2474 because the meteorological conditions typically causing drought are also conducive to
2475 high temperatures. The impacts of the Dust Bowl droughts and the 1988 drought were
2476 compounded by episodes of extremely high temperatures. The month of July 1936 in the
2477 central U.S. is a notable example. To illustrate, Lincoln, NE received only 0.05” of
2478 precipitation that month (after receiving less than 1 inch the previous month) while
2479 experiencing temperatures reaching or exceeding 110°F on 10 days, including 117°F on
2480 July 24. Although no studies of trends in such “compound” extreme events have been
2481 performed, they represent a significant societal risk.
2482
2483 BOX 2.1: Drought Indicators and Resources
2484 • Palmer Drought Severity Index (PDSI; Palmer, 1965) – meteorological drought.
2485 The PDSI is a commonly-used drought index that measures intensity, duration, and
2486 spatial extent of drought. It is derived from measurements of precipitation, air
2487 temperature, and local estimated soil moisture content. Categories range from less
2488 than -4 (extreme drought) to more than +4 (extreme wet conditions), and have been
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2489 standardized to facilitate comparisons from region to region. Alley (1984) identified
2490 some positive characteristics of the PDSI that contribute to its popularity: (1) it is an
2491 internationally recognized index; (2) it provides decision makers with a measurement
2492 of the abnormality of recent weather for a region; (3) it provides an opportunity to
2493 place current conditions in historical perspective; and (4) it provides spatial and
2494 temporal representations of historical droughts. However, the PDSI has some
2495 limitations (1) it may lag emerging droughts by several months; (2) it is less well
2496 suited for mountainous land or areas of frequent climatic extremes; (3) it does not
2497 take into account streamflow, lake and reservoir levels, and other long-term
2498 hydrologic impacts (Karl and Knight, 1985), such as snowfall and snow cover; (4) the
2499 use of temperature alone to estimate potential evapotranspiration (PET) can introduce
2500 biases in trend estimates because humidity, wind and radiation also affect PET and
2501 changes in these elements are not accounted for. In fact, Hobbins et al. (2007) show
2502 that the PDSI trends in Australia and New Zealand are exaggerated compared to
2503 trends using more realistic methods to estimate evapotranspiration. The use of
2504 temperature alone is a practical consideration since measurements of these other
2505 elements are often not available.
2506 • Crop Moisture Index (CMI; Palmer, 1968) – short-term meteorological drought.
2507 Whereas the PDSI monitors long-term meteorological wet and dry spells, the CMI
2508 was designed to evaluate short-term moisture conditions across major crop-producing
2509 regions. It is based on the mean temperature and total precipitation for each week, as
2510 well as the CMI value from the previous week. Categories range from less than -3
2511 (severely dry) to more than +3 (excessively wet). The CMI responds rapidly to
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2512 changing conditions, and it is weighted by location and time so that maps, which
2513 commonly display the weekly CMI across the United States, can be used to compare
2514 moisture conditions at different locations. Weekly maps of the CMI are available as
2515 part of the USDA/JAWF Weekly Weather and Crop Bulletin.
2516 • Standardized Precipitation Index (SPI; McKee et al., 1993) – precipitation-based
2517 drought. The SPI was developed to categorize rainfall as a standardized departure
2518 with respect to a rainfall probability distribution function; categories range from less
2519 than -3 (extremely dry) to more than +3 (extremely wet). The SPI is calculated on the
2520 basis of selected periods of time (typically from 1 to 48 months of total precipitation)
2521 and it indicates how the precipitation for a specific period compares with the long-
2522 term record at a given location (Edwards and McKee, 1997). The index correlates
2523 well with other drought indices. Sims et al. (2002) suggested that the SPI was more
2524 representative of short-term precipitation and a better indicator of soil wetness than
2525 the PDSI. The 9-month SPI corresponds closely to the PDSI (Heim 2002; Guttman
2526 1998).
2527 • Keetch-Byram Index (KBDI; Keetch and Byram, 1968) – meteorological drought
2528 and wildfire potential index. This was developed to characterize the level of potential
2529 fire danger. It uses daily temperature and precipitation information and estimates soil
2530 moisture deficiency. High values of KBDI are indicative of favorable conditions for
2531 wildfires. However, the index needs to be regionalized, as values are not comparable
2532 among regions Groisman et al. 2004, 2007a).
2533 • No-rain episodes – meteorological drought. Groisman and Knight (2007) proposed
2534 to directly monitor frequency and intensity of prolonged no-rain episodes (greater
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2535 than 20, 30, 60, etc. days) during the warm season, when evaporation and
2536 transpiration are highest and the absence of rain may affect natural ecosystems and
2537 agriculture. They found that during the past four decades the duration of prolonged
2538 dry episodes has significantly increased over the Eastern and Southwestern United
2539 States and adjacent areas of Northern Mexico and Southeastern Canada.
2540 • Soil Moisture and Runoff Index (SMRI; Andreadis and Lettenmaier, 2006) –
2541 hydrologic and agricultural droughts. The SMRI is based on model-derived soil
2542 moisture and runoff as drought indicators; it uses percentiles and the values are
2543 normalized from 0 (dry) to 1 (wet conditions). The limitation of this index is that it is
2544 based on land-surface model-derived soil moisture. However, long-term records of
2545 soil moisture – a key variable related to drought – are essentially non-existent
2546 (Andreadis and Lettenmaier, 2006). Thus, the advantage of the SMRI is that it is
2547 physically based and with the current sophisticated land-surface models it is easy to
2548 produce multimodel average climatologies and century-long reconstructions of land
2549 surface conditions, which could be compared under drought conditions.
2550 Resources: A list of these and other drought indicators, data availability, and current
2551 drought conditions based on observational data can be found at the National Climatic
2552 Data Center (NCDC, http://www.ncdc.noaa.gov). The North American Drought Monitor
2553 at NCDC monitors current drought conditions in Canada, the United States, and Mexico.
2554 Tree-ring reconstruction of PDSI across North America over the last 2000 years can be
2555 also found at NCDC
2556
2557
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2558 2.2.2.2 Short Duration Heavy Precipitation
2559 2.2.2.2.1 Data Considerations and Terms
2560 Intense precipitation often exhibits higher spatial variability than many other extreme
2561 phenomena. This poses challenges for the analysis of observed data since the heaviest
2562 area of precipitation in many events may fall between stations. This adds uncertainty to
2563 estimates of regional trends based on the climate network. The uncertainty issue is
2564 explicitly addressed in some recent studies.
2565
2566 Precipitation extremes are typically defined based on the frequency of occurrence [by
2567 percentile (e.g., upper 5%, 1%, 0.1%, etc) or by return period (e.g. an average occurrence
2568 of once every 5 years, once every 20 years, etc.)] of rain events and/or their absolute
2569 values (e.g., above 50 mm, 100 mm, 150 mm, or more). Values of percentile or return
2570 period thresholds vary considerably across North America. For example, in the U.S.,
2571 regional average values of the 99.9 percentile threshold for daily precipitation are lowest
2572 in the Northwest and Southwest (average of 55 mm) and highest in the South (average of
2573 130mm)31.
2574
2575 As noted above, spatial patterns of precipitation have smaller spatial correlation scales
2576 (for example, compared to temperature and atmospheric pressure) which means that a
2577 denser network is required in order to achieve a given uncertainty level. While monthly
2578 precipitation time series for flat terrain have typical radii of correlation32 (ρ) of ~300 km
31 The large magnitude of these differences is a major motivation for the use of regionally-varying thresholds based on percentiles. 32 Spatial correlation decay with distance, r, for many meteorological variables, X, can be approximated by
Do Not Cite or Quote 115 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2579 or even more, daily precipitation may have ρ less than 100 km with typical values for
2580 convective rainfall in isolated thunderstorms of ~15 to 30 km (Gandin and Kagan 1976).
2581 Values of ρ can be very small for extreme rainfall events and sparse networks may not be
2582 adequate to detect a desired minimum magnitude of change that can result in societally-
2583 important impacts and can indicate important changes in the climate system.
2584
2585 2.2.2.2.2 United States
2586 One of the clearest trends in the U.S. observational record is that of an increasing
2587 frequency and intensity of heavy precipitation events (Karl and Knight 1998; Groisman et
2588 al. 1999, 2001, 2004; Kunkel et al. 1999; Easterling et al. 2000; IPCC 2001; Semenov
2589 and Bengtsson 2002, Kunkel 2003). For example, the area of the United States affected
2590 by a much above normal contribution to total annual precipitation of daily precipitation
2591 events exceeding 50.8 mm (2 inches) increased by a statistically significant amount from
2592 about 9% in the 1910s to about 11% in the 1980s and 1990s (Karl and Knight 1998).
2593 Total precipitation also increased during this time, due in large part to increases in the
2594 intensity of heavy precipitation events (Karl and Knight 1998). In fact, there has been
2595 little change or decreases in the frequency of light and average precipitation days
2596 (Easterling et al. 2000; Groisman et al. 2004, 2005) during the last 30 years while heavy
2597 precipitation frequencies have increased (Sun and Groisman 2004). For example, the
2598 amount of precipitation falling in the heaviest 1% of rain events has increased by 20%
2599 during the 20th Century while total precipitation has increased by only 7% (Groisman et
2600 al. 2004). Although the exact character of those changes has been questioned (e.g.
an exponential function of distance: Corr (X(A), X(B)) ~ e - r/ρ where r is a distance between point A and B and ρ is a radius of correlation, which is a distance where the correlation between the points is reduced to 1/e compared to an initial “zero” distance.
Do Not Cite or Quote 116 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2601 Michaels et al. 2004), it is highly likely that in recent decades extreme precipitation
2602 events have increased more than light to medium events.
2603
2604 A statistically significant 50% increase during the 1900s in the frequency of days with
2605 precipitation exceeding 101.6 mm (4 inches) was found in the upper Midwest U.S.
2606 (Groisman et al. 2001). Upward trends in the amount of precipitation occurring in the
2607 upper 0.3% of daily precipitation events are statistically significant for the period of
2608 1908-2002 within three major regions (the South, Midwest, and Upper Mississippi; see
2609 Fig. 2.8) of the central United States (Groisman et al. 2004). The upward trends are
2610 primarily a warm season phenomenon when the most intense rainfall events typically
2611 occur. A time series of the frequency of events in the upper 0.3% averaged for these 3
2612 regions (Fig 2.8) shows a 20% increase over the period of 1893-2002 with all of this
2613 increase occurring over the last third of the 1900s (Groisman et al. 2005).
2614
2615 Examination of intense precipitation events defined by return period, covering the period
2616 of 1895-2000, indicates that the frequencies of extreme precipitation events before 1920
2617 were generally above the long-term averages for durations of 1 to 30 days and return
2618 periods 1 to 20 years and only slightly lower than values during the 1980s and 1990s
2619 (Kunkel et al. 2003). The highest values occur after about 1980, but the elevated levels
2620 prior to about 1920 are an interesting feature suggesting that there is considerable
2621 variability in the occurrence of extreme precipitation on multi-decadal time scales
2622
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2623 There is a seeming discrepancy between the results for the 99.7th percentile (which do not
2624 show high values early in the record in the analysis of Groisman et al. 2004) and for 1 to
2625 20-yr return periods (which do in the analysis of Kunkel et al. 2003). The number of
2626 stations with available data is only about half (about 400) in the late 1800s of what is
2627 available in most of the 1900s (800-900). Furthermore, the spatial distribution of stations
2628 throughout the record is not uniform; the density in the western U.S. is relatively lower
2629 than in the central and eastern U.S. It is possible that the resulting uncertainties in heavy
2630 precipitation estimates are too large to make unambiguous statements about the recent
2631 high frequencies.
2632
2633 Recently, this question was addressed (Kunkel et al. 2007a) by analyzing the modern
2634 dense network to determine how the density of stations affects the uncertainty and then to
2635 estimate the level of uncertainty in the estimates of frequencies in the actual (sparse)
2636 network used in the long-term studies. The results were unambiguous. For all
2637 combinations of three durations (1-day, 5-days and 10-days) and 3 return periods (1-yr,
2638 5-yr, and 20-yr), the frequencies for 1983-2004 were significantly higher than those for
2639 1895-1916 at a high level of confidence. In addition, the observed linear trends were all
2640 found to be upward, again with a high level of confidence. Based on these results, it is
2641 highly likely that the recent elevated frequencies in the U.S. are the highest since 1895.
2642
2643 2.2.2.2.3 Alaska and Canada
2644 The sparse network of long-term stations in Canada increases the uncertainty in estimates
2645 of extremes. Changes in the frequency of heavy events exhibit considerable multi-decadal
Do Not Cite or Quote 118 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2646 variability since 1900, but no long-term trend for the entire century (Zhang et al. 2001).
2647 However, according to Zhang et al. (2001), there are not sufficient instrumental data to
2648 discuss the nationwide trends in precipitation extremes over Canada prior to 1950.
2649 Nevertheless, there are changes that are noteworthy. For example, the frequency of
2650 99.7% events exhibits a statistically significant upward trend of 19%/50yr in British
2651 Columbia since 1910 (Fig. 2.8; Groisman et al. 2005). For Canada, increases in
2652 precipitation intensity during the second half of the 1900s are concentrated in heavy and
2653 intermediate events, with the largest changes occurring in Arctic areas (Stone et al. 2000).
2654 The tendency for increases in the frequency of intense precipitation while the frequency
2655 of days with average and light precipitation does not change or decreases has also been
2656 observed in Canada over the last 30 years (Stone et al. 2000), mirroring U.S. changes.
2657 Recently, Vincent and Mekis (2006) repeated analyses of precipitation extremes for the
2658 second half of 1900s (1950-2003 period). They reported a statistically significant increase
2659 of 1.8 days per 54 years in heavy precipitation days (defined as the days with
2660 precipitation above 10 mm) and statistically insignificant increases in the maximum 5-
2661 day precipitation (by ~5%) and in the number of “very wet days” defined as days with
2662 precipitation above the upper 5th percentiles of local daily precipitation (by 0.4 days).
2663
2664 There is an upward trend of 37%/50yr in southern Alaska since 1950 although this trend
2665 is not statistically significant (Fig. 2.8; Groisman et al. 2005).
2666
2667
2668
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2669 2.2.2.2.4 Mexico
2670 On an annual basis, the number of heavy precipitation (P > 10 mm) days has increased in
2671 northern Mexico and the Sierra Madre Occidental and decreased in the south-central part
2672 of the country (Alexander et al. 2006). The percent contribution to total precipitation
2673 from heavy precipitation events exceeding the 95th percentile threshold has increased in
2674 the monsoon region (Alexander et al., 2006) and along the southern Pacific coast
2675 (Aguilar et al. 2005), while some decreases are documented for south-central Mexico
2676 (Aguilar et al. 2005).
2677
2678 On a seasonal basis, the maximum precipitation reported in 5 consecutive days during
2679 winter and spring has increased in Northern Mexico and decreased in south-central
2680 Mexico (Alexander et al. 2006). Northern Baja California, the only region in Mexico
2681 characterized by a Mediterranean climate, has experienced an increasing trend in winter
2682 precipitation exceeding the 90th percentile, especially after 1977 (Cavazos and Rivas,
2683 2004). Heavy winter precipitation in this region is significantly correlated with El Niño
2684 events (Pavia and Badan, 1998; Cavazos and Rivas, 2004); similar results have been
2685 documented for California (e.g., Gershunov and Cayan, 2003). During the summer there
2686 has been a general increase of 2.5 mm in the maximum 5-consecutive-day precipitation in
2687 most of the country and an upward trend in the intensity of events exceeding the 99th and
2688 99.7th percentiles in the high plains of Northern Mexico during the summer season
2689 (Groisman et al. 2005).
2690
Do Not Cite or Quote 120 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2691 During the monsoon season (June-September) in northwestern Mexico, the intensity and
2692 seasonal contribution of rainfall events exceeding the 95th percentiles significantly
2693 increased (p<0.05) in the core monsoon region and at mountain sites (Fig. 2.8; Cavazos et
2694 al. 2007). The mean intensity of 95th percentile events in the monsoon region increased
2695 significantly by 0.6 mm dec-1 during 1950-2003. It went from 17.9 mm d-1 in the 1950-
2696 1976 period to 19.6 mm d-1 in 1977-2003 while at mountain sites the increase was from
2697 40.8 mm d-1 to 43.9 mm d-1, respectively. These increases are mainly due to an increase
2698 in tropical cyclone-derived rainfall after 1980. The frequency of heavy events does not
2699 show a significant trend (Englehart and Douglas 2001; Neelin et al. 2006; Cavazos et al.,
2700 2007). Similarly, Groisman et al. (2005) report that the frequency of very heavy summer
2701 precipitation events (above the 99th percentile) in the high plains of Northern Mexico
2702 (east of the core monsoon) has not increased, whereas their intensity has increased
2703 significantly.
2704
2705 The increase in the mean intensity of heavy summer precipitation events in the core
2706 monsoon region during the 1977-2003 period are significantly correlated with the
2707 Oceanic El Niño Index (ONI33) conditions during the cool season. El Niño SST
2708 anomalies antecedent to the monsoon season are associated with less frequent, but more
2709 intense, heavy precipitation events34 (exceeding the 95th percentile threshold), and vice
2710 versa.
33 ONI INDEX: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml Warm and cold episodes based on a threshold of +/- 0.5°C for the Oceanic Niño Index (ONI) [3 month running mean of ERSST.v2 SST anomalies in the Niño 3.4 region (5oN-5oS, 120o-170oW)], based on the 1971-2000 base period. 34 The correlation coefficient between ONI and heavy precipitation frequency (intensity) is -0.37 (+0.46)
Do Not Cite or Quote 121 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2711 There has been an insignificant decrease in the number of consecutive dry days in
2712 northern Mexico, while an increase is reported for south-central Mexico (Alexander et
2713 al., 2006), and the southern Pacific coast (Aguilar et al. 2005).
2714
2715 2.2.2.2.5 Summary
2716 All studies indicate that changes in heavy precipitation frequencies are always higher
2717 than changes in precipitation totals and, in some regions, an increase in heavy and/or
2718 very heavy precipitation occurred while no change or even a decrease in precipitation
2719 totals was observed (e.g., in the summer season in central Mexico). There are regional
2720 variations in where these changes are statistically significant (Fig. 2.8). The most
2721 significant changes occur in the central U.S., central Mexico, southeastern, northern and
2722 western Canada, and southern Alaska. These changes have resulted in a wide range of
2723 impacts, including human health impacts (Chapter 1, Box 1.3).
2724
2725 2.2.2.3 Monthly to Seasonal Heavy Precipitation
2726 On the main stems of large river basins, significant flooding will not occur from short
2727 duration extreme precipitation episodes alone. Rather, excessive precipitation must be
2728 sustained for weeks to months. The 1993 Mississippi River flood, which resulted in an
2729 estimated $17 billion in damages, was caused by several months of anomalously high
2730 precipitation (Kunkel et al. 1994).
2731
2732 A time series of the frequency of 90-day precipitation totals exceeding the 20-year return
2733 period (a simple extension of the approach of Kunkel et al. 2003) indicates a statistically
Do Not Cite or Quote 122 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2734 significant upward trend (Fig. 2.9). The frequency of such events during the last 25 years
2735 is 20% higher than during any earlier 25-year period. Even though the causes of multi-
2736 month excessive precipitation are not necessarily the same as for short duration extremes,
2737 both show moderately high frequencies in the early 20th Century, low values in the 1920s
2738 and 1930s, and the highest values in the past 2-3 decades. The trend35 over the entire
2739 period is highly statistically significant.
2740
2741 2.2.2.4 North American Monsoon
2742 Much of Mexico is dominated by a monsoon type climate with a pronounced peak in
2743 rainfall during the summer (June through September) when up to 60% to 80% of the
2744 annual rainfall is received (Douglas et al., 1993; Higgins et al., 1999 and Cavazos et al.,
2745 2002). Monsoon rainfall in southwest Mexico is often supplemented by tropical cyclones
2746 moving along the coast. Farther removed from the tracks of Pacific tropical cyclones,
2747 interior and northwest sections of Mexico receive less than 10% of the summer rainfall
2748 from passing tropical cyclones (Fig. 2.10; Englehart and Douglas 2001). The main
2749 influences on total monsoon rainfall in these regions rests in the behavior of the monsoon
2750 as defined by its start and end date, rainfall intensity and duration of wet and dry spells
2751 (Englehart and Douglas 2006). Extremes in any one of these parameters can have a
2752 strong effect on the total monsoon rainfall.
2753
2754 The monsoon in northwest Mexico has been studied in detail because of its singular
2755 importance to that region and because summer rainfall from this core monsoon region
35 The data were first subjected to a square root transformation to produce a data set with an approximate normal distribution; then least squares regression was applied. Details can be found in the Appendix, Example 4.
Do Not Cite or Quote 123 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2756 spills over into the U.S. Desert Southwest (Douglas et al., 1993; Higgins et al. 1999,
2757 Cavazos et al. 2002). Based on long term data from 8 stations in southern Sonora, the
2758 summer rains have become increasingly late in arriving (Englehart and Douglas 2006)
2759 and this has had strong hydrologic and ecologic repercussions for this northwest core
2760 region of the monsoon. Based on linear trend, the mean start date for the monsoon has
2761 been delayed almost 10 days (9.89 days with a significant trend of 1.57 days per decade)
2762 over the past 63 years (Figure 2.11a). Because extended periods of intense heat and
2763 desiccation typically precede the arrival of the monsoon, the trend toward later starts to
2764 the monsoon will place additional stress on the water resources and ecology of the region
2765 if continued into the future.
2766
2767 Accompanying the tendency for later monsoon starts, there also has been a notable
2768 change in the “consistency” of the monsoon as indicated by the average duration of wet
2769 spells in southern Sonora (Figure 2.11b). Based on a linear trend, the average wet spell36
2770 has decreased by almost one day (0.88 days with a significant trend of -0.14 days per
2771 decade) from nearly four days in the early 1940s to slightly more than three days in
2772 recent years. The decrease in wet spell length indicates a more erratic monsoon is now
2773 being observed. Extended periods of consecutive days with rainfall are now becoming
2774 less common during the monsoon. These changes can have profound influences on
2775 surface soil moisture levels which affect both plant growth and runoff in the region.
2776
36 For southern Sonora, Mexico, wet spells are defined as the mean number of consecutive days with mean regional precipitation >1mm.
Do Not Cite or Quote 124 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2777 A final measure of long term change in monsoon activity is associated with the change in
2778 rainfall intensity over the past 63 years (Figure 2.11c). Based on linear trend, rainfall
2779 intensity37 in the 1940s was roughly 5.6mm per rain day, but in recent years has risen to
2780 nearly 7.5mm per rain day38. Thus, while the summer monsoon has become increasingly
2781 late in arriving and wet spells have become shorter, the average rainfall during rain
2782 events has actually increased very significantly by 17% or 1.89mm over the 63 year
2783 period (0.3 mm per decade) as well as the intensity of heavy precipitation events (Fig.
2784 2.9). Taken together, these statistics indicate that the rainfall in the core region of the
2785 monsoon (i.e., northwest Mexico) has become more erratic with a tendency towards high
2786 intensity rainfall events countering the tendency towards a shorter monsoon with shorter
2787 wet spells.
2788
2789 Variability in Mexican monsoon rainfall shows modulation by large-scale climate modes.
2790 Englehart and Douglas (2002) demonstrate that a well developed inverse relationship
2791 exists between ENSO and total seasonal rainfall (June-September) over much of Mexico,
2792 but the relationship is only operable in the positive phase of the PDO. Evaluating
2793 monsoon rainfall behavior on intraseasonal time scales, Englehart and Douglas (2006)
2794 demonstrate that rainfall intensity (mm/rain day) in the core region of the monsoon is
2795 related to PDO phase with the positive (negative) phase favoring relatively high (low)
2796 intensity rainfall events. Analysis indicates that other rainfall characteristics of the
2797 monsoon respond to ENSO with warm events favoring later starts to the monsoon and
37 Daily rainfall intensity during the monsoon is defined as the regional average rainfall for all days with rainfall > 1mm. 38 The linear trend in this time series is significant at the p=0.01 level
Do Not Cite or Quote 125 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2798 shorter length wet spells (days) with cold events favoring opposite behavior (Englehart
2799 and Douglas 2006).
2800
2801 2.2.2.5 Tropical Storm Rainfall in Western Mexico
2802 Across southern Baja California and along the southwest coast of Mexico, 30% to 50% of
2803 warm season rainfall (May-November) is attributed to tropical cyclones (Fig. 2.10) and in
2804 years heavily affected by tropical cyclones (upper 95th percentile) 50% to 100% of the
2805 summer rainfall comes from tropical cyclones. In this region of Mexico, there is a long
2806 term, upward trend in tropical cyclone-derived rainfall at both Manzanillo
2807 (41.8mm/decade; Fig. 2.12a) and Cabo San Lucas (20.5mm/decade)39. This upward trend
2808 in tropical cyclone rainfall has led to an increase in the importance of tropical cyclone
2809 rainfall in the total warm season rainfall for southwest Mexico (Fig. 2.12b) and this has
2810 resulted in a higher ratio of tropical cyclone rainfall to total warm season rainfall. Since
2811 these two stations are separated by more than 700km, these significant trends in tropical
2812 cyclone rainfall imply large scale shifts in the summer climate of Mexico.
2813
2814 This recent shift in emphasis on tropical cyclone warm season rainfall in western Mexico
2815 has strong repercussions as rainfall becomes less reliable from the monsoon and becomes
2816 more dependent on heavy rainfall events associated with passing tropical cyclones. Based
2817 on the large scale and heavy rainfall characteristics associated with tropical cyclones,
2818 dams in the mountainous regions of western Mexico are often recharged by strong
39 The linear trends in tropical cyclone rainfall at these two stations are significant at the p=0.01 and p=0.05 level, respectively.
Do Not Cite or Quote 126 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2819 tropical cyclone events which therefore have positive benefits for Mexico despite any
2820 attendant damage due to high winds or flooding.
2821
2822 This trend in tropical cyclone-derived rainfall is consistent with a long term analysis of
2823 near-shore tropical storm tracks along the west coast of Mexico (storms passing within 5°
2824 of the coast) which indicates an upward trend in the number of near shore storms over the
2825 past 50 years (Fig. 2.12c). While the number of tropical cyclones occurring in the entire
2826 east Pacific Basin is uncertain prior to the advent of satellite tracking in about 1967, it
2827 should be noted that the long term data sets for near shore storm activity (within 5° of the
2828 coast) are considered to be much more reliable due to coastal observatories and heavy
2829 ship traffic to and from the Panama Canal to Pacific ports in Mexico and the United
2830 States. The number of near shore storm days (storms less than 550km from the station)
2831 has increased by 1.3 days/decade in Manzanillo and about 0.7days/decade in Cabo San
2832 Lucas (1949-2006)40. The long term correlation between tropical cyclone days at each
2833 station and total tropical cyclone rainfall is r = 0.61 for Manzanillo and r = 0.37 for Cabo
2834 San Lucas, illustrating the strong tie between passing tropical cyclones and the rain that
2835 they provide to coastal areas of Mexico.
2836
2837 Interestingly, the correlations between tropical cyclone days and total tropical cyclone
2838 rainfall actually drop slightly when based only on the satellite era, 1967-2006 ( r = 0.54
2839 for Manzanillo and r = 0.31 for Cabo San Lucas). The fact that the longer time series has
2840 the higher set of correlations shows no reason to suggest problems with near shore
40 The linear trends in near shore storm days are significant at the p=0.05 level and p=0.10 level, respectively.
Do Not Cite or Quote 127 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
2841 tropical cyclone tracking in the pre-satellite era. The lower correlations in the more recent
2842 period between tropical cyclone days and total tropical cyclone rainfall may be tied to
2843 tropical cyclone derived rainfall rising at a faster pace compared to the rise in tropical
2844 cyclone days. In other words, tropical cyclones are producing more rain per event than in
2845 the earlier 1949-1975 period when SSTs were colder.
2846
2847 2.2.2.6 Tropical Storm Rainfall in the Southeastern United States
2848 Tropical cyclone-derived rainfall along the southeastern coast of the United States on a
2849 century time scale has changed insignificantly in summer (when no century-long trends
2850 in precipitation was observed) as well as in autumn (when the total precipitation
2851 increased by more than 20% since the 1900s; Groisman et al. 2004).
2852
2853 2.2.3 Storm Extremes
2854 2.2.3.1 Tropical Cyclones
2855 2.2.3.1.1 Introduction
2856 Each year, about 90 tropical cyclones develop over the world's oceans, and some of these
2857 make landfall in populous regions, exacting heavy tolls in life and property. Their
2858 occurrence is often statistically modeled as a Poisson process. The global number has
2859 been quite stable since 1970, when global satellite coverage began in earnest, having a
2860 standard deviation of 10 and no evidence of any substantial trend (e.g. Webster et al
2861 1995). However,, there is some evidence for trends in storm intensity and/or duration
2862 (e.g. Holland and Webster 2007 and quoted references for the North Atlantic; Chan 2000
2863 for the Western North Pacific), and there is substantial variability in tropical cyclone
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2864 frequency within each of the ocean basins they affect. Regional variability occurs on all
2865 resolved time scales, and there is also some evidence of trends in certain measures of
2866 tropical cyclone energy, affecting many of these regions and perhaps the globe as well.
2867
2868 There are at least two reasons to be concerned with such variability. The first and most
2869 obvious is that tropical cyclones rank with flash floods as the most lethal and expensive
2870 natural catastrophes, greatly exceeding other phenomena such as earthquakes. In
2871 developed countries, such as the U.S., they are enormously costly: Hurricane Katrina is
2872 estimated to have caused in excess of $80 billion 2005 dollars in damage, and killed more
2873 than 1500 people. Death and injury from tropical cyclones is yet higher in developing
2874 nations; for example, Hurricane Mitch of 1998 took more than 11,000 lives in Central
2875 America. Any variation or trend in tropical cyclone activity is thus of concern to coastal
2876 residents in affected areas, compounding trends related to societal factors such as
2877 changing coastal population.
2878
2879 A second, less obvious and more debatable issue is the possible feedback of tropical
2880 cyclone activity on the climate system itself. The inner cores of tropical cyclones have
2881 the highest specific entropy content of any air at sea level, and for this reason such air
2882 penetrates higher into the stratosphere than is the case with other storm systems. Thus
2883 tropical cyclones may play a role in injecting water and trace gases and microscopic
2884 airborne particles into the upper troposphere and lower stratosphere, though this idea
2885 remains largely unexamined. There is also considerable evidence that tropical cyclones
2886 vigorously mix the upper ocean, affecting its circulation and biogeochemistry, perhaps to
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2887 the point of having a significant effect on the climate system. Since the current generation
2888 of coupled climate models greatly under-resolves tropical cyclones, such feedbacks are
2889 badly underrepresented, if they are represented at all.
2890 For these reasons, it is important to quantify, understand, and predict variations in
2891 tropical cyclone activity. The following sections review current knowledge of these
2892 variations on various time scales.
2893
2894 2.2.3.1.2 Data Issues
2895 Quantifying tropical cyclone variability is limited, sometimes seriously, by a large suite
2896 of problems with the historical record of tropical cyclone activity. In the North Atlantic
2897 and eastern North Pacific regions, responsibility for the tropical cyclone database rests
2898 with NOAA's National Hurricane Center (NHC), while in other regions, archives of
2899 hurricane activity are maintained by several organizations, including the U.S. Navy's
2900 Joint Typhoon Warning Center (JTWC), the Japan Meteorological Agency (JMA), the
2901 Hong Kong Observatory (HKO) and the Australian Bureau of Meteorology (BMRC).
2902 The data, known as ``best track'' data (Jarvinen et al. 1984; Chu et al. 2002), comprise a
2903 global historical record of tropical cyclone position and intensity, along with more recent
2904 structural information. Initially completed in real time, the best tracks are finalized by
2905 teams of forecasters update the best track data at the end of the hurricane season in each
2906 ocean basin using data collected during and after each hurricane's lifetime.
2907
2908 It should first be recognized that the primary motivation for collecting data on tropical
2909 cyclones was initially to support real-time forecasts and this remains the case in many
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2910 regions today. From the 1970s onwards increasing emphasis has been placed on
2911 improving the archive for climate purposes, and on extending the record back to include
2912 historical systems (e.g. Laurensz 1977; Neumann 1993; Landsea et al 2004).
2913 Unfortunately, improvements in measurement and estimation techniques have often been
2914 implemented with little or no effort to calibrate against existing techniques and with poor
2915 documentation where such calibrations were done. Thus the available tropical cyclone
2916 data contain an inhomogeneous mix of changes in quality of observing systems, reporting
2917 policies, and the methods utilized to analyze the data
2918
2919 It remains a scientific tragedy that insufficient effort is expended in re-examining and
2920 quality controlling the tropical cyclone record on a year to year basis, particularly outside
2921 the Atlantic and eastern North Pacific regions. Efforts are ongoing to reanalyze the
2922 historic best track data, but such a posteriori reanalyses are less than optimal because not
2923 all of the original data that the best track was based on are readily available.
2924
2925 Documentation of the occurrence of tropical cyclones is thought to be reliable back to
2926 about 1945 in the Atlantic and 1970 in the Eastern Pacific (e.g. Holland and Webster
2927 2007 and references therein), and back to about 1975 for the Western and Southern
2928 Pacific basins, thanks to earth-orbiting satellites (e.g. Holland 1981). Until the launch of
2929 MeteoSat-7 in 1998, the Indian Oceans were seen only obliquely, but storm counts may
2930 still be expected to be accurate after 1977. Before those periods, storms could and
2931 undoubtedly remain undetected, especially if they did not pass near ships at sea or land
2932 masses. For the North Atlantic it is likely that up to 3 storms per year were missing
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2933 before 1900 dropping to zero by the early 1960s (Holland and Webster 2007; Chang and
2934 Guo 2007). Estimates of the duration of storms are considered to be less reliable prior to
2935 the 1970’s due particularly to a lack of good information on their time of genesis. Since
2936 the 1970s storms were more accurately tracked throughout their lifetimes by
2937 geostationary satellites.
2938
2939 Estimates of storm intensity are far less reliable, and this remains true for large portions
2940 of the globe even today. Airborne hurricane reconnaissance flight became increasingly
2941 routine in the North Atlantic and western North Pacific regions after 1945, but was
2942 discontinued in the western North Pacific region in 1987. Some missions are today being
2943 conducted under the auspices of the government of Taiwan. However airborne
2944 reconnaissance only samples a small fraction of storms, and then only over a fraction of
2945 their lifetimes; moreover, good, quantitative estimates of wind speeds from aircraft did
2946 not become available until the late 1950s. Beginning in the mid 1970s, tropical cyclone
2947 intensity has been estimated from satellite imagery. Until relatively recently, techniques
2948 for doing so were largely subjective, and the known lack of homogeneity in both the data
2949 and techniques applied in the post-analyses has resulted in significant skepticism
2950 regarding the consistency of the intensity estimates in the data set. This lack of temporal
2951 consistency renders the data suspect for identifying trends, particularly in metrics related
2952 to intensity.
2953
2954 Recent studies have addressed these known data issues. Kossin et al. (2007a) constructed
2955 a more homogeneous record of hurricane activity and found remarkably good agreement
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2956 in both variability and trends between their new record and the best track data in the N.
2957 Atlantic and Eastern Pacific basins during the period 1983–2005. They concluded that the
2958 best track maintained by the NHC does not appear to suffer from data quality issues
2959 during this period. On the other hand, they were not able to corroborate the presence of
2960 upward intensity trends in any of the remaining tropical cyclone-prone ocean basins. This
2961 could be due to inaccuracies in the satellite best tracks, or could be due to the training of
2962 the Kossin et al technique on North Atlantic data. This is supported by Wu et al. (2006),
2963 who considered Western Pacific best track data constructed by other agencies (HKMO
2964 and JMA) who construct best track data for the western North Pacific. Harper and
2965 Callaghan (2006) report on reanalyzed data from the Southeastern Indian Ocean and
2966 showed some biases, but a remaining upward intensity trend. These studies underscores
2967 the need for improved care in analyzing tropical cyclones and in obtaining better
2968 understanding of the climatic controls of tropical cyclone activity beyond SST-based
2969 arguments alone.
2970
2971 The standard tropical cyclone databases do not usually contain information pertaining to
2972 the geometric size of tropical cyclones Exceptions include the Australian region and the
2973 enhanced database for the North Atlantic over the last few decades. A measure of size of
2974 a tropical cyclone is a crucial complement to estimates of intensity as it relates directly to
2975 storm surge and damage area associated with landfalling storms. Such size measures can
2976 be inferred from aircraft measurements and surface pressure distributions, and can now
2977 be estimated from satellite imagery (e.g. Mueller et al. 2006; Kossin et al. 2007b).
2978
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2979
2980 2.2.3.1.3 Low-frequency Variability and Trends of Tropical cyclone Activity Indices
2981 “Low frequency” variability is here defined as variations on time scales greater than
2982 those associated with ENSO (i.e. more than 3-4 years). Several papers in recent years
2983 have quantified interdecadal variability of tropical cyclones in the Atlantic (Goldenberg
2984 et al., 2001; Bell and Chelliah, 2006) and the western North Pacific (Chan and Shi, 1996),
2985 attributing most of the variability to natural interdecadal variability of regional climates
2986 in the Atlantic and Pacific, respectively. In the last few years, however, several papers
2987 have attributed both low frequency variability and trends in tropical cyclone activity to
2988 changing radiative forcing owing to anthropogenic sulfate aerosols and greenhouse gases.
2989 Emanuel (2005a) developed a “Power Dissipation Index” (PDI) of tropical cyclones,
2990 defined as the sum of the cubed estimated maximum sustained surface wind speeds at 6-
2991 hour intervals accumulated over each Atlantic tropical cyclone from the late 1940s to
2992 2003. Landsea (2005) commented on the quality of data comprising the index. An
2993 updated version of this analysis (Emanuel 2007), shown in Fig. 2.13, confirms that there
2994 has been a substantial increase in tropical cyclone activity since about 1970, and indicates
2995 that the low-frequency Atlantic PDI variations are strongly correlated with low-frequency
2996 variations in tropical Atlantic SSTs. Based on this analysis, it is likely that hurricane
2997 activity, as measured by the Power Dissipation Index (PDI), has increased substantially
2998 since the 1950s and 60s in association with warmer Atlantic SSTs. The magnitude of this
2999 increase depends on the adjustment to the wind speed data from the 1950s and 60s
3000 (Landsea 2005; Emanuel 2007). It is very likely that PDI has generally tracked SST
3001 variations on decadal time scales in the tropical Atlantic since 1950 and likely that it also
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3002 generally tracked the secular increase of SST. Confidence in these statistics prior to the
3003 late 1940s is low, due mainly to the decreasing confidence in hurricane duration and
3004 intensity observations. The PDI in the eastern Pacific has decreased since 1980 (Kossin et
3005 al. 2007).
3006
3007 The Power Dissipation Index for U.S. landfalling tropical cyclones has not increased
3008 since the late 1800s (Landsea 2005). Pielke (2005) noted that there are no evident trends
3009 in observed damage in the North Atlantic region, after accounting for population
3010 increases and coastal development. However, Emanuel (2005b) notes that a PDI series
3011 such as Landsea's (2005) based on only U.S. landfalling data, contains only about 1
3012 percent of the data that Emanuel's (2005a) basin-wide PDI contains, which is based on all
3013 storms over their entire lifetimes. Thus a trend in basin-wide PDI may not be detectable
3014 in U.S. landfalling PDI since the former index has a factor of 10 advantage in signal to
3015 noise ratio.
3016
3017 Figure 2.14 (from Holland and Webster 2007), indicates that there has been no distinct
3018 trend in the mean intensity of all Atlantic storms, hurricanes, and major hurricanes. A
3019 distinct increase in the most intense storms occurred around the time of onset of aircraft
3020 reconnaissance, but this is considered to be largely due to better observing methods.
3021 Holland and Webster also found that the overall proportion of hurricanes has remained
3022 remarkably constant during the 20th century at around 50%, and there has been a marked
3023 oscillation in major hurricane proportions, which has no observable trend.
3024 Webster et al. (2005) reported that the number of category 4 and 5 hurricanes has almost
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3025 doubled globally over the past three decades. The recent reanalysis of satellite data
3026 beginning in the early 1980s by Kossin et al. (2007a) support these results in the Atlantic
3027 although the results in the remaining basins were not corroborated.
3028
3029 The recent Emanuel and Webster et al. studies have generated much debate in the
3030 hurricane research community, particularly with regard to homogeneity of the tropical
3031 cyclone data over time and the required adjustments (e.g. Landsea 2005; Knaff and
3032 Sampson 2006; Chan 2006; Hoyos et al. 2006; Landsea et al. 2006; Sriver and Huber
3033 2006; Klotzbach 2006; Elsner et al. 2006; Maue and Hart 2007; Manning and Hart 2007;
3034 Holland and Webster 2007, Landsea 2007, Mann et al 2007, Holland 2007). Several of
3035 these studies argue that data problems preclude determination of significant trends in
3036 various tropical cyclone measures, while others provide further evidence in support of
3037 reported trends. In some cases, differences between existing historical data sets
3038 maintained by different nations can yield strongly contrasting results (e.g., Kamahori et
3039 al. 2006).
3040
3041 Several studies have examined past regional variability in tropical cyclone tracks (Wu et
3042 al. 2005; Xie et al. 2005; Vimont and Kossin 2007; Kossin and Vimont 2007). Thus far,
3043 no clear long-term trends in this metric have been reported, but there is evidence that
3044 Atlantic tropical cyclone formation regions have undergone systematic long-term shifts to
3045 more eastward developments (Holland 2007). These shifts affect track and duration,
3046 which subsequently affects intensity. The modulation of the Atlantic tropical cyclone
3047 genesis region occurs through systematic changes of the regional SST and circulation
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3048 patterns. Thus SST affects intensity not just through thermodynamic pathways that are
3049 local to the storms, but also through changes in basinwide circulation patterns (Kossin
3050 and Vimont 2007).
3051
3052 In summary, we conclude that Atlantic tropical storm and hurricane destructive potential
3053 as measured by the Power Dissipation Index (which combines storm intensity, duration,
3054 and frequency) has increased. This increase is substantial since about 1970, and is likely
3055 substantial since the 1950s and 60s, in association with warming Atlantic sea surface
3056 temperatures.
3057
3058 2.2.3.1.4 Low-frequency Variability and Trends of Tropical Cyclone Counts
3059 Mann and Emanuel (2006) reported that Atlantic tropical cyclone counts closely track
3060 low-frequency variations in tropical Atlantic SSTs, including a long-term increase since
3061 the late 1800s and early 1900s (see also Fig. 2.15 from Holland and Webster 2007).
3062 There is currently debate on the relative roles of internal climate variability (e.g.,
3063 Goldenberg et al. 2001) versus radiative forcing, including greenhouse gases, and sulfate
3064 aerosols (Mann and Emanuel 2006; Santer et al 2006) in producing the multi-decadal
3065 cooling of the tropical North Atlantic. This SST variation is correlated with reduced
3066 hurricane activity during the 1970s and 80s relative to the 1950s and 60s or to the period
3067 since 1995 (see also Zhang et al. 2007).
3068
3069 On a century time scale, time series of tropical storm frequency in the Atlantic (Fig. 2.15)
3070 show substantial interannual variability and a marked increase (of over 100%) since
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3071 about 1900. This increase occurred in two sharp jumps of around 50%, one in the 1930s
3072 and another that commenced in 1995 and has not yet stabilized. Holland and Webster
3073 (2007) have suggested that these sharp jumps are transition periods between relatively
3074 stable climatic periods of tropical cyclone frequency (Fig. 2.15). Figure 2.15 uses
3075 unadjusted storm—an issue which will be addressed further below.
3076
3077 For tropical cyclone frequency, the finding that the largest recorded increases over the
3078 past century have been in the eastern North Atlantic (e.g., see recent analysis in Vecchi
3079 and Knutson 2007; Holland 2007), which historically has been the least well observed,
3080 has led to questions of whether this may be due to data issues (Landsea et al. 2004;
3081 Landsea 2007). The major observing system change points over the past century have
3082 been:
3083 • The implementation of routine aircraft reconnaissance in 1944-45;
3084 • The use of satellite observations and related analysis procedures from the late
3085 1960s onwards; and,
3086 • A change in analysis practice by the National Hurricane Center from 1970 to
3087 include more mid-latitude systems.
3088 In addition, there have steady improvements in techniques and instrumentation, which
3089 may also introduce some spurious trends.
3090
3091 Landsea (2007) has used the fraction of storms striking land in the satellite and pre-
3092 satellite era to estimate the number of missing storms per year in the pre-satellite era
3093 (1900 to 1965) to be about 3.2 storms per year. This assumes that the fraction of all
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3094 storms that strike land in the real world has been relatively constant over time, which has
3095 been shown to be incorrect by Holland (2007). Holland also shows that the smaller
3096 fraction of storms that made landfall during the past fifty years (1956-2005) compared to
3097 the previous fifty years (1906-1955) is directly related to changes in the main formation
3098 location regions, with a decrease in western Caribbean and Gulf of Mexico developments
3099 and an increase in the eastern Atlantic.
3100
3101 Alternative approaches to estimating the earlier data deficiencies have been used by
3102 Chang and Guo (2007), Vecchi and Knutson (2007) and Mann et al (2007). The first two
3103 studies use historical ship tracks from the pre-satellite era, combined with storm track
3104 information from the satellite era, to infer an estimated adjustment for missing storms in
3105 the pre-satellite era (assumed as all years prior to 1965). Mann et al used statistical
3106 climate relationships to estimate potential errors. Vecchi and Knutson found 2.5 storms
3107 per year were missing prior to 1900, decreasing to zero by 1960. Chang and Guo found
3108 1.2 storms missing around 1910 also decreasing to zero by 1960. Mann et al, estimated a
3109 more modest undercount bias of 1 per year back to 1970. The adjusted time series by
3110 Vecchi and Knutson (Fig. 2.16) suggest a statistically significant (p=0.003 or less)
3111 positive linear trend in adjusted storm counts of 55%/century since 1900. However,
3112 beginning the trend from 1878, the trend through 2006 is smaller (+15%/century) and not
3113 statistically significant at the p=0.05 level (p-value of about 0.3)41. It is notable that the
3114 degree of increase over the past century depends on the analysis methodology. When
3115 using a linear trend, as above, the increase from 1900 to 2005 is around 55% in the
3116 adjusted storm counts. However, using the essentially non-linear approach by Holland
41 Details of the statistical analysis are given in the Appendix, Example 5.
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3117 and Webster (2007) of separate climatic regimes, the increase in adjusted storm counts
3118 from the 1900-1920 regime to the 1995-2006 regime is 85%. The trend from 1900 begins
3119 near a local minimum in the time series and ends with the recent high activity, perhaps
3120 exaggerating the significance of the trend due to multidecadal variability. On the other
3121 hand high levels of activity during the late 1800s, which lead to the insignificant trend
3122 result, are indirectly inferred in large part from lack of ship track data, and the uncertainty
3123 in the late 1800s storm counts is greater than that during the 1900s.
3124
3125 Hurricane frequency closely follows the tropical cyclone variability, with a stable 50% of
3126 all cyclones developing to hurricane strength over much of the past century (Holland and
3127 Webster 2007). However, there has been a concomitant increase in both overall storm
3128 frequency and the proportion of major hurricanes since 1995. Taken together, these result
3129 in a very sharp increase in major hurricane numbers, which can be associated with
3130 changes of SST (Holland and Webster 2007, Webster et al 2005). The PDI trend reported
3131 by Emanuel (2007) is largely due to this increase in major hurricane numbers.
3132
3133 Atlantic basin total hurricane counts, major hurricane counts, and U.S. landfalling
3134 hurricane counts as recorded in the HURDAT data base for the period 1851-2006 are
3135 shown in Fig. 2.17. These have not been adjusted for missing storms, as there was likely
3136 less of a tendency to miss both hurricanes and major hurricanes in earlier years compared
3137 to tropical storms, largely because of their intensity and damage potential. There is a
3138 slight negative trend in U.S. landfalling hurricane frequency. The basin-wide major
3139 hurricane counts increase over the long-term. For total hurricanes, trends to 2005
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3140 beginning in 1881 through 1921 are positive and statistically significant (p=0.05)
3141 whereas trends beginning in 1851 through 1871 are positive but not statistically
3142 significant, owing to the prolonged active period in the late 1800s. For major hurricanes,
3143 trends beginning in 1851 through 1911 were positive and statistically significant, whereas
3144 the trend beginning from 1921 was positive but not statistically significant42.
3145
3146 Regional storm track reconstructions for the basin (Vecchi and Knutson 2007; Holland
3147 and Webster 2007b(?)) indicate a decrease in tropical storm occurrence in the western
3148 part of the basin, consistent with the minimal change or slight decrease in U.S.
3149 landfalling tropical storm or hurricane counts. These analyses further suggest that—after
3150 adjustment for missing storms-- a century-scale increase in basin-wide Atlantic tropical
3151 storm occurrence has occurred, with increases mainly in the central and eastern parts of
3152 the basin (also consistent with Chang and Guo 2007). From a climate variability
3153 perspective, Kossin and Vimont (2007) have shown that a positive phases of the Atlantic
3154 Meridional Mode is correlated to an systematic eastward extension of the genesis region
3155 in the Atlantic. Elsner (1996) and Holland and Webster (2007) have shown that the
3156 increasing frequency over the past 30 years is associated with a changeover to equatorial
3157 developments and particularly to developments in the eastern equatorial region.
3158
3159 In summary, we conclude that there have been fluctuations from decade to decade in
3160 tropical cyclone numbers, and data uncertainty is larger in the earlier parts of the record,
3161 particularly prior to aircraft reconnaissance beginning in the mid-1940s. While there are
3162 undoubtedly data deficiencies and missing storms in the early record, they appear
42 Further details of the statistical analysis are given in the Appendix, Example 6.
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3163 insufficient to remove the observed positive trends in basin-wide tropical storm counts. It
3164 is likely that that the annual numbers of tropical storms/hurricanes and major hurricanes
3165 in the North Atlantic basin have increased significantly over the past 100 years in close
3166 relationship with warming equatorial Atlantic sea surface temperatures. The positive
3167 linear trend in all storm categories extends back to the 1800s, but is generally not
3168 significant prior to 1890. The increasingly decreased confidence in the data before 1900
3169 precludes any definitive conclusions from this era. The increases in basin-wide storm
3170 counts has occurred primarily from an eastward shift in the formation and occurrence
3171 regions and there has been a distinct decrease in western Caribbean and Gulf of Mexico
3172 developments. As a result, North American mainland land-falling hurricanes have
3173 remained quasi-static over the past century.
3174
3175 2.2.3.1.5 Paleoclimate Proxy Studies of Past Tropical Cyclone Activity
3176 Paleotempestology is an emerging field of science that attempts to reconstruct past
3177 tropical cyclone activity using geological proxy evidence or historical documents. This
3178 work attempts to expand knowledge about hurricane occurrence back in time beyond the
3179 limits of conventional instrumental records, which cover roughly the last 150 years. A
3180 broader goal of paleotempestology is to help researchers explore physically based
3181 linkages between prehistoric tropical cyclone activity and other aspects of past climate.
3182
3183 Among the geologically based proxies, overwash sand layers deposited in coastal lakes
3184 and marshes have proven to be quite useful (Liu and Fearn, 1993, 2000; Liu 2004;
3185 Donnelly and Webb 2004). Similar methods have been used to produce proxy records of
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3186 hurricane strikes from back-barrier marshes in Rhode Island and New Jersey extending
3187 back about 700 years (Donnelly et al. 2001a, 2001b; Donnelly et al. 2004; Donnelly and
3188 Webb 2004), and more recently in the Caribbean (Donnelly 2005). Stable isotope signals
3189 in tree rings (Miller et al. 2006), cave deposits (Frappier et al. 2007) and coral reef
3190 materials are also being actively explored for their utility in providing paleoclimate
3191 information on tropical cyclone activity. Historical documents apart from traditional
3192 weather service records (newspapers, plantation diaries, Spanish and British historical
3193 archives, etc.) can also be used to reconstruct some aspects of past tropical cyclone
3194 activity (Ludlam, 1963; Millas, 1968; Fernandez-Partagas and Diaz, 1996; Chenoweth,
3195 2003; Mock 2004; Garcia Herrera et al. 2004; 2005; Liu et al. 2001; Louie and Liu 2003;
3196 Louie and Liu 2004).
3197
3198 Donnelly and Woodruff's (2007) proxy reconstruction the past 5,000 years of intense
3199 hurricane activity in the western North Atlantic suggests that hurricane variability has
3200 been strongly modulated by El Nino during this time and that the past 250 years has been
3201 relatively active in the context of the past 5,000 years. Nyberg et al. (2007) suggest that
3202 major hurricane activity in the Atlantic was anomalously low in the 1970s and 1980s
3203 relative to the past 270 years. As with Donnelly and Woodruff, their proxy measures
3204 were located in the western part of the basin (near Puerto Rico), and in their study,
3205 hurricane activity was inferred indirectly through statistical associations with proxies for
3206 vertical wind shear and SSTs.
3207
3208
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3209
3210 2.2.3.2 Strong Extratropical Cyclones Overview
3211 Extra-tropical cyclone (ETC)43 is a generic term for any non-tropical, large-scale low
3212 pressure storm system that develops along a boundary between warm and cold air
3213 masses. These types of cyclonic44 disturbances are the dominant weather phenomenon
3214 occurring in the mid- and high-latitudes during the cold season because they are typically
3215 large and often have associated severe weather. The mid-latitude North Pacific and North
3216 Atlantic basins, between ~30oN-60 oN, are regions where large-numbers of ETC’s
3217 develop and propagate across the ocean basins each year. Over land or near populous
3218 coastlines, strong or extreme ETC events generate some of the most devastating impacts
3219 associated with extreme weather and climate, and have the potential to affect large areas
3220 and dense population centers. A notable example was the blizzard of 12-14 March 1993
3221 along the East Coast of the U.S. that is often referred to as the “super-storm” or “storm of
3222 the century”45 (e.g., Kocin et al.1995;). Over the ocean, strong ETCs generate high waves
3223 that can cause extensive coastal erosion when combined with storm surge as they reach
3224 the shore, resulting in significant economic impact. Rising sea level extends the zone of
3225 impact from storm surge and waves farther inland, and will likely result in increasingly
3226 greater coastal erosion and damage from storms of equal intensity.
3227
3228
43 The fundamental difference between the characteristics of extra-tropical and tropical cyclones is that ETC’s have a cold core and their energy is derived from baroclinic instability, while tropical cyclones have a warm core and derive their energy from barotropic instability (Holton 1979). 44 A term applied to systems rotating in the counter-clockwise direction in the Northern Hemisphere. 45 The phrase “Storm of the Century” is also frequently used to refer to the 1991 Halloween ETC along the Northeast US coast, immortalized in the movie The Perfect Storm
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3229
3230 Studies of changes in strong ETC’s and associated frontal systems have focused on
3231 locations where ETCs form and the resulting storm tracks, frequencies, and intensities46.
3232 The primary constraint on these studies has been the limited period of record available
3233 that has the best observation coverage for analysis and verification of results, with most
3234 research focused on the latter half of the 20th century. Model reanalysis data is used in the
3235 majority of studies, either NCEP-NCAR (Kalnay et al. 1996) or ERA-40 (Upalla et al.
3236 2005) datasets, although prior to 1965 data quality have been shown to be less reliable
3237
3238 It is important to stress that any observed changes in ETC storm tracks, frequencies or
3239 intensities are highly dependent on broad-scale atmospheric modes of variability, and the
3240 noise associated with this variability is large in relation to any observed linear trend.
3241 Therefore, detection and attribution of long-term (decadal- to century-scale) changes in
3242 ETC activity is extremely difficult.
3243
3244 2.2.3.2.1 Variability of Extra-Tropical Cyclone Activity
3245 Inter-annual and inter-decadal variability of ETC’s is primarily driven by the location and
3246 other characteristics associated with the Polar jet stream. The mean location of the Polar
3247 jet is often referred to as the “storm track”. The large-scale circulation is governed by the
3248 equator-to-pole temperature gradient, which is strongly modulated by SST’s over the
3249 oceans. The magnitude of the equator-to-pole temperature gradient is of utmost
46 These studies use in situ observations (both surface and upper-air), re-analysis fields, and Atmospheric- Ocean Global Climate Model (GCM) hind-casts
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3250 importance in determining the intensity of storms: the smaller (larger) the gradient in
3251 temperature, the weaker (stronger) the potential energy available for extra-tropical
3252 cyclone formation. The observed intensity of ETC’s at the surface is related to the
3253 amplitude of the large-scale circulation pattern, with high-amplitude, negatively tilted
3254 troughs favoring stronger development of ETC’s at the surface (Sanders and Gyakum
3255 1980).
3256
3257 From a seasonal perspective, the strongest ETC’s are temporally out of phase in the
3258 Pacific and Atlantic basins, since the baroclinic wave energy climatologically reaches a
3259 peak in late fall in the North Pacific and in January in the North Atlantic (Nakamura
3260 1992; Eichler and Higgins 2006). While it remains unclear what the physical basis is for
3261 the offset in peak storm activity between the two basins, Nakamura (1992) showed
3262 statistically that when the Pacific jet exceeds 45 m s-1 there is a suppression of baroclinic
3263 wave energy, even though the low-level regional baroclinicity and strength of the Pacific
3264 jet are at a maximum (this effect is not evident in the Atlantic basin, since the peak
3265 strength of the jet across the basin rarely exceeds 45 m s-1). Despite the observed
3266 seasonal difference in the peak of ETC activity, Chang and Fu (2002) found a strong
3267 positive correlation between the Pacific and Atlantic storm tracks using monthly mean
3268 reanalysis data covering 51 winters (1949 to 1999). They found the correlations between
3269 the two basins remained positive and robust over individual months during winter (DJF)
3270 or over the entire season (Chang and Fu 2002).
3271
3272 It has been widely documented that the track position, intensity and frequency of ETC’s
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3273 is strongly modulated on inter-annual time-scales by different modes of variability, such
3274 as the El Niño/Southern Oscillation (ENSO) phenomenon (Gershunov and Barnett 1998;
3275 An et al. 2007). In a recent study, Eichler and Higgins (2006) used both NCEP-NCAR
3276 and ERA-40 reanalysis data to diagnose the behavior of ETC activity during different
3277 ENSO phases. Their results showed that during El Niño events there is an equator-ward
3278 shift in storm tracks in the North Pacific basin, as well as an enhancement of the storm
3279 track along the U.S. East Coast. However, they found significant variability related to the
3280 magnitude of the El Niño event. During strong El Niños, ETC frequencies peak over the
3281 North Pacific and along the eastern U.S., from the southeast coast to the Maritime
3282 Provinces of Canada (Eichler and Higgins 2006), with a weaker track across the Midwest
3283 from the lee of the Rocky Mountains to the Great Lakes. During weak to moderate El
3284 Niños, the storm tracks are similar to the strong El Niños, except there is a slight increase
3285 in the number of ETC’s over the northern Plains and the frequency of ETC activity
3286 decreases over the mid-Atlantic region. Similar to other previous studies (e. g. Hirsch et
3287 al. 2001; Noel and Changnon 1998), an inverse relationship typically exists during La
3288 Niñas; as the strength of La Niña increases, the frequency maxima of East Coast storms
3289 shifts poleward, the North Pacific storm track extends eastward toward the Pacific
3290 Northwest, and the frequency of cyclones increases across the Great Lakes region
3291 (Eichler and Higgins (2006).
3292
3293 In addition to ENSO, studies have shown that the Arctic Oscillation (AO) can strongly
3294 influence the position of storm tracks and the intensity of ETC’s. Previous studies have
3295 shown that during positive AO conditions Northern Hemisphere cyclone activity shifts
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3296 poleward (Serreze et al. 1997; Clark et al. 1999). Inversely, during negative AO
3297 conditions the polar vortex is weaker and cyclone activity shifts southward. Since the
3298 North Atlantic Oscillation (NAO) represents the primary component of the AO, it has a
3299 similar affect on storm tracks position, especially over the eastern North Atlantic basin
3300 (McCabe et al. 2001). For futher information on the different atmospheric modes of
3301 variability (Chapter 2, Box 2.3).
3302
3303 2.2.3.2.2 Changes in Storm Tracks and Extra-Tropical Cyclone Characteristics
3304 Many studies have documented changes in storm track activity. Specifically, a significant
3305 pole-ward shift of the storm track in both the Pacific and Atlantic ocean basins has been
3306 verified by a number of recent studies that have shown a decrease in ETC frequency in
3307 mid-latitudes, and a corresponding increase in ETC activity in high-latitudes (Wang et al.
3308 2006a; Simmons and Keay 2002; Paciorek et al. 2002; Graham and Diaz 2001; Geng and
3309 Sugi 2001; McCabe et al. 2001; Key and Chan 1999; Serreze et al. 1997). Several of
3310 these studies have examined changes in storm tracks over the entire Northern Hemisphere
3311 (i.e. McCabe et al. 2001; Paciorek et al. 2002; Key and Chan 1999), while several others
3312 have focused on the storm track changes over the Pacific (i.e., Graham and Diaz 2001)
3313 and Atlantic basins (i.e., Geng and Sugi 2001), or both (i.e., Wang and Swail 2001). Most
3314 of these studies focused on changes in frequency and intensity observed during winter
3315 (DJF) or the entire cold season (Oct-Mar). However, for spring, summer and autumn,
3316 Key and Chan (1999) found opposite trends in 1000-hPa and 500-hPa cyclone
3317 frequencies for both the mid- and high latitudes of the Northern Hemisphere.
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3318 The standardized annual departures47 of ETC frequency for the entire Northern
3319 Hemisphere over the period 1959-1997 (Fig. 2.18a,b; McCabe et al. 2001) shows that
3320 cyclone frequency has decreased for the mid-latitudes (30o-60oN) and increased for the
3321 high latitudes (60o-90oN). For the 55-year period of 1948-2002, a metric called the
3322 Cyclone Activity Index (CAI)48 was developed by Zhang et al. (2004) to document the
3323 variability of Northern Hemisphere cyclone activity. The CAI has increased in the Arctic
3324 Ocean (70o-90oN) during the latter half of the 20th century, while it has decreased in mid-
3325 latitudes (30o-60oN) from 1960 to 1993, which is evidence of a pole-ward shift in the
3326 average storm track position. Interestingly, the number and intensity of cyclones entering
3327 the Arctic from the mid-latitudes has increased, particularly during summer (Zhang et al.
3328 2004). The increasing activity in the Arctic was more recently verified by Wang et al.
3329 (2006a), who analyzed ETC counts by applying two separate cyclone detection
3330 thresholds to ERA-40 reanalysis of mean sea level pressure data. Their results showed an
3331 increase in high latitude storm counts, and a decrease in ETC counts in the mid-latitudes
3332 during the latter half of the 20th century.
3333
3334 Northern Hemisphere ETC intensity has increased over the period 1959-1997 across both
3335 mid- and high-latitudes cyclone intensity (McCabe et al. 2001; Fig. 2.18c,d), with the
3336 upward trend more significant for the high latitudes (0.01 level) than for the mid-latitudes
47 Standardized departures (z scores) were computed for each 5o latitudinal band by subtracting the respective 1959-1997 mean from each value and dividing by the respective 1959-1997 standard deviation (McCabe et al. 2001). 48 The CAI integrates information on cyclone intensity, frequency, and duration into a comprehensive index of cyclone activity. The CAI is defined as the sum over all cyclone centers, at a 6-hourly resolution, of the differences between the cyclone central SLP and the climatological monthly mean SLP at corresponding grid points in a particular region during the month (Zhang et al. 2004).
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3337 (0.10 level). From an ocean basin perspective, the observed increase in intense ETC’s
3338 appears to be more robust across the Pacific than the Atlantic. Using reanalysis data
3339 covering the period 1949-1999, Paciorek et al. (2002) found that extreme wind speeds
3340 have increased significantly in both basins (Fig. 2.19a,d). Their results also showed that
3341 the observed upward trend in the frequency of intense cyclones has been more
3342 pronounced in the Pacific basin (Fig. 2.19c), although the inter-annual variability is much
3343 less in the Atlantic (Fig. 2.19f). Surprisingly, they found that the overall counts of ETC’s
3344 showed either no long-term change, or a decrease in the total number of cyclones (Fig.
3345 2.19b,e). However, this may be a result of the large latitudinal domain used in their study
3346 (20o-70oN), which included parts of the tropics, sub-tropics, mid- and high latitudes.
3347
3348 On a regional scale, ETC activity has increased in frequency, duration and intensity in the
3349 lower Canadian Arctic during 1953-2002 with the most statistically significant trends
3350 during winter49 (p=0.05 level; Wang et al. 2006b). In contrast to the Arctic region,
3351 cyclone activity was less frequent and weaker along the southeast and southwest coasts of
3352 Canada. Winter cyclone deepening rates (i.e. rates of intensification) have increased in
3353 the zone around 60oN, but decreased further south in the Great Lakes area and southern
3354 Prairies-British Columbia region of Canada. This is also indicative of a pole-ward shift in
3355 ETC activity, and corresponding weakening of ETC’s in the mid-latitudes and an
3356 increase in observed intensities in the high latitudes. For the period of 1949-1999, the
3357 intensity of Atlantic ETC’s increased from the 1960’s to the 1990’s during the winter
49 Results based on hourly average sea level pressure data observed at 83 stations
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3358 season50 (Harnik and Chang 2003). Their results showed no significant trend in the
3359 Pacific region but this is a limited finding because of a lack of upper-air (i.e. radiosonde)
3360 data over the central North Pacific51 in the region of the storm track peak (Harnik and
3361 Chang 2003).
3362
3363 There have been very few studies that have analyzed the climatological frequencies and
3364 intensities of ETC’s across the central U.S., specifically in the Great Lakes region (e.g.,
3365 Lewis 1987; Harmon et al. 1980; Garriott 1903). Over the period 1900 to 1990 the
3366 number of strong cyclones (≤992 mb) increased significantly across the Great Lakes
3367 (Angel and Isard 1998). This increasing trend was evident (at the p=0.05 level) both
3368 annually and during the cold season,. In fact, over the 91-yr period analyzed, they found
3369 that the number of strong cyclones per year more than doubled during both November
3370 and December.
3371
3372 In addition to studies using reanalysis data, which have limited record lengths, other
3373 longer-term studies of the variability of storminess typically use wave or water level
3374 measurements as proxies for storm frequency and intensity. Along the U.S. West Coast,
3375 one of the longest continuous climate-related instrumental time series in existence is the
3376 hourly tide gauge record at San Francisco that dates back to 1858. A derived metric
3377 called non-tide residuals (NTR)52, which are related to broad-scale atmospheric
50 Results based on gridded rawinsonde observations covering the Northern Hemisphere 51 Besides the few radiosonde sites located on islands (i.e., Midway or the Azores), most upper-air observations over the vast expanses of the North Pacific and Atlantic are from automated pilot reports (pireps) that measure temperature, wind speed, and barometric pressure onboard commercial aircraft traveling at or near jet stream level (between 200-300 hPa). 52 Non-tide residuals are obtained by first removing the known tidal component from the water level variations using a spectral method; then, variations longer than 30 days and shorter than 2.5 days are
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3378 circulation patterns across the eastern North Pacific that affect storm track location,
3379 provides a measure of storminess variability along the California coast (Bromirski et al.
3380 2003). Average monthly variations in NTR, which are associated with the numbers and
3381 intensities of all ETCs over the eastern North Pacific, did not change substantially over
3382 the period 1858-2000 or over the period covered by most ETC reanalysis studies, 1951-
3383 2000. However, the highest 2% of extreme winter NTR (Fig. 2.20), which are related to
3384 the intensity of the most extreme ETCs, had a significant upward trend since ~1950, with
3385 a pronounced quasi-periodic decadal-scale variability that is relatively consistent over the
3386 last 140 yr. Changes in storm intensity from the mid-1970s to early 1980s are also
3387 suggested by a substantial pressure decreases at an elevation above sea level of about
3388 3000 m over the eastern North Pacific and North America (Graham 1994), indicating that
3389 the pattern of variability of extreme storm conditions observed at San Francisco (as
3390 shown in Fig. 2.20) likely extends over much of the North Pacific basin and the U.S. The
3391 oscillatory pattern of variability is thought to be influenced by teleconnections from the
3392 tropics, predominately during ENSO events (Trenberth and Hurrell 1994), resulting in a
3393 deepened Aleutian low shifted to the east that causes both ETC intensification and a shift
3394 in storm track. It is interesting to note that peaks in the 5-year moving average in Fig.
3395 2.20 generally correspond to peaks in extreme rainfall in Fig. 2.10 suggesting that the
3396 influence of El Niño and broad-scale atmospheric circulation patterns across the Pacific
3397 that affect sea level variability along the West Coast are associated with storm systems
3398 that affect rainfall variability across the U.S..
3399
3400 The amplitude and distribution of ocean wave energy measured by ocean buoys is
removed with a bandpass filter.
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3401 determined by ETC intensity and track location. Changes in long period (>12 sec),
3402 intermediate period, and short period (<6 sec) components in the wave-energy spectra
3403 permit inferences regarding the changes over time of the paths of the storms, as well as
3404 their intensities and resulting wave energies (Bromirski et al. 2005). Analysis of the
3405 combination of observations from several buoys in the eastern North Pacific supports a
3406 progressive northward shift of the dominant Pacific storm tracks to the central latitudes
3407 (section 2.2.3.3).
3408
3409 2.2.3.2.3 Nor’easters
3410 Those ETCs that develop and propagate along the East Coast of the U.S. and southeast
3411 Canada are often termed colloquially as Nor’easters53. In terms of their climatology and
3412 any long-term changes associated with this subclass of ETCs, there are only a handful of
3413 studies in the scientific literature that have analyzed their climatological frequency and
3414 intensity (Jones and Davis 1995), likely due to a lack of any formal objective definition
3415 of this important atmospheric phenomenon (Hirsch et al. 2001).
3416
3417 Because waves generated by ETCs are a function of storm size and the duration and area
3418 over which high winds persist, changes in significant wave heights can also be used as a
3419 proxy for changes in Nor’easters. Using hindcast wave heights and assigning a minimum
3420 criterion of open ocean waves greater than 1.6 m in height (a commonly used threshold
3421 for storms that caused some degree of beach erosion along the mid-Atlantic coast) to
3422 qualify as a nor’easter, the frequency of nor’easters along the Atlantic coast peaked in the
53 According to the Glossary of Meteorology (Huschke 1959), a nor’easter is any cyclone forming within 167 km of the East Coast between 30o-40oN and tracking to the north-northeast
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3423 1950’s, declined to a minimum in the 1970’s, and then increased again to the mid-1980’s
3424 (Dolan et al. 1988; Davis et al. 1993).
3425
3426 An alternate approach utilized by Hirsch et al. (2001) uses pressure, direction of
3427 movement and wind speed to identify such systems and generically names them as East
3428 Coast Winter Storms (ECWS)54. Hirsch et al. (2001) defined an ECWS as “strong” if the
3429 maximum wind speed is greater than 23.2 m s-1 (45 kt). During the period of 1951-1997,
3430 their analysis showed that there were an average of 12 ECWS events occurring each
3431 winter (October-April), with a maximum in January, and an average of 3 strong events
3432 (Fig. 2.21a). They found a general tendency toward weaker systems over the past few
3433 decades, based on a marginally significant (at the 90% confidence level) increase in
3434 average storm minimum pressure (not shown). However, their analysis found no
3435 statistically significant trends in ECWS frequency for all nor’easters identified in their
3436 analysis, for those storms that occurred over the northern portion of the domain (>35oN),
3437 or those that traversed full coast (Fig. 2.21b,c) during the 46-year period of record used in
3438 this study.
3439
3440 Because strong storms over the open ocean generate high amplitude waves, buoy
3441 measurements of wave height and wave period can be used to infer characteristics of
3442 ETC variability. The wave power index (WPI) of strong storm-forced wave events
54 According to Hirsch et al. (2001), in order to be classified as an ECWS, an area of low pressure is required to (1) have a closed circulation; (2) be located along the east coast of the United States, within the quadrilateral bounded at 45oN by 65o and 70oW and at 30oN by 75o and 85oW; (3) show general movement from the south-southwest to the north-northeast; and (4) contain winds greater than 10.3 m s-1 (20 kt) during at least one 6-h period.
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3443 (significant wave heights > 3 m) measured at deep-water open-ocean NOAA buoys
3444 44004, 41001, 41002 along the U.S. Atlantic coast (see Figure 2.25 for locations) during
3445 winter months (October-March, excluding tropical cyclone wave events) shows a
3446 decreasing trend that is significant at the p=0.05 level amounting to a decrease in ETC-
3447 forced wave power of about 1%/yr (Bromirski 2007). Coupled with no statistically
3448 significant change in either mean wave height or the number of measurements exceeding
3449 3 m (implying no change in storm duration and/or the number of strong storms), the
3450 downward trend in the WPI suggests that winter ETC intensity has decreased since 1980,
3451 in general agreement with Hirsch et al. (2001).
3452
3453 BOX 2.2: Extreme Coastal Storm Impacts: “The Perfect Storm” as a True
3454 Nor’easter: From a coastal impacts perspective, damage is greatest when large storms
3455 are propagating towards the coast, which generally results in both a larger storm surge
3456 and more long period wave energy (resulting in greater run-up causing more
3457 beach/coastal erosion/damage). Storm intensity (winds) is usually greatest in the right-
3458 front quadrant of the storm (based on the cyclone’s forward movement), so the typical
3459 track of east coast winter storms propagating parallel to the coast leaves the most intense
3460 part of the storm out to sea. In contrast to storms propagating parallel to the coast,
3461 Nor’easters (such as “the Perfect Storm”) that propagate from east-to-west in a retrograde
3462 track at some point in their lifetime (Fig. 2.22) can generate much greater surge and
3463 greater long period wave energy, and also potentially have the most intense associated
3464 winds making landfall along the coast.
3465
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3466 2.2.3.3 Coastal Waves: Trends of Increasing Heights and Their Extremes
3467 The high wind speeds of hurricanes and extratropical cyclones over bodies of water cause
3468 extremes in the heights and energies of the waves they generate. Seasonal and long-term changes
3469 in storm intensities and their tracks produce corresponding variations in wave heights and
3470 periods along coasts, defining their wave climates. Waves generated by extratropical storms
3471 dominate the oceans at higher latitudes, including the Northeast Pacific along the shores of
3472 Canada and the west coast of the United States, and along the Atlantic coast of North America
3473 where they originate from destructive Nor’easters. Tropical cyclones dominate the wave climates
3474 at lower latitudes during the warm season (June-September), including the southeast Atlantic
3475 coast of the United States, Gulf of Mexico, and the Caribbean, while hurricanes in the East
3476 Pacific generate waves along the western shores of Mexico and Central America . The
3477 magnitude of associated damage from storm waves depends to a large extent on whether the
3478 storms make landfall, when storm surge, high winds, and heavy rainfall combined with high
3479 waves cause severe impacts. However, high waves from strong tropical cyclones that reach
3480 hurricane strength and then track northward along the East Coast as they weaken, can combine
3481 with extratropical systems, such as the 1991 Halloween Storm (Bromirski 2001; Chapter 2, Box
3482 2.2), and cause severe coastal erosion and have significant economic impacts (Davis et al. 1993;
3483 Dolan et al. 1988; Mather et al. 1967).
3484
3485 2.2.3.3.1 The Waves of Extratropical Storms and Hurricanes
3486 The heights and periods of waves generated by a storm depend on the speed of its winds, the area
3487 over which the winds blow (the storm’s fetch), and on the duration of the storm, factors that
3488 determine the amount of energy transferred to the waves. Wave climate variability has been
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3489 estimated from: (1) direct measurements by buoys; (2) visual observations from ships; (3) wave
3490 hindcast analyses where wave heights and periods are assessed using forecast models that are run
3491 retrospectively using observed meteorological data; and (4) in recent years from satellite
3492 altimetry. The reliability of the wave records ranges widely for these different sources, and
3493 changes in data-collection methodologies and processing techniques can affect the data
3494 consistency. However, long records from these sources make it possible to identify long-term
3495 trends, and to investigate underlying climate controls.
3496
3497 In the Northern Hemisphere the hurricane winds are strongest on the right-hand side of the storm
3498 relative to it track, where its cyclonic winds coincide with the direction of the storm’s
3499 propagation, in turn producing the highest waves on that side of the storm. They achieve their
3500 greatest heights in proximity to the wall of the storm’s eye where the winds reach their
3501 maximum, and systematically decrease outward as the wind speeds are reduced. Extreme heights
3502 are closely associated with the Saffir-Simpson hurricane classification system, where the central
3503 atmospheric pressures are lower and the associated wind speeds are higher for the higher
3504 hurricane categories. A correlation between the meausred wave heights and the central
3505 atmospheric pressure (Hsu et al. 2000) allows the magnitude of the significant wave height55,
56 3506 HS,to be related to the hurricane categories . Estimates of the maximum HS generated close to
3507 the wall of the hurricane’s eye on the storm’s leading right quadrant where the wind speeds are
3508 greatest, range from 6 to 7 m for Category 1 storms to about 20 m and greater for Category 5
3509 storms. The decrease in observed HS outward from the hurricane’s eye in response to the
55 The “significant wave height” is a commonly used statistical measure for the waves generated by a storm, defined as the average of the highest one-third of the measured wave heights 56 Hsu et al. (2000) have developed the empirical formula Hsmax=0.2(Pn-Pc) where Pc and Pn ~ 1013 mbar are respectively the atmospheric pressures at the center and edge of the tropical cyclone, and Hsmax is the maximum value of the significant wave height
Do Not Cite or Quote 157 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3510 outward decrease in wind speeds, demonstrates that HS is reduced by 50% at approximately a
3511 distance of 5 times the radius of the eye, typically occurring about 250 km outward from the
3512 storm’s center (Hsu, et al. 2000).
3513
3514 The impression has been, however, that such models under-predict the highest waves of
3515 Category 4 and 5 storms, and this has led to recent investigations that included the direct
3516 measurement of waves generated by hurricanes. For example, measurements obtained by six
3517 wave gauges deployed by the Naval Research Laboratory (NRL) at depths of 60 to 90 m in the
3518 Gulf of Mexico, when the Category 4 Hurricane Ivan passed directly over the array on 15
3519 September 2004, recorded significant wave heights ranging from 16.1 to 17.9 m; the largest
3520 individual wave height reached 27.7 m (Wang et al. 2005). The simple model of Hsu et al.
3521 (2000) yields a maximum significant wave height of 15.6 m for Ivan’s 935-mbar central
3522 pressure, seemingly in agreement with the 16-m measured waves. However, the NRL gauges
3523 were about 30 km outward from the zone of strongest winds and were positioned toward the
3524 forward face of Ivan rather than in its right-hand quadrant, so Wang et al. (2005) concluded it is
3525 likely that the maximum significant wave height was greater than 21 m, with the largest
3526 individual wave heights having been greater than 40 m, indicating that the Hsu et al. (2000)
3527 empirical formula somewhat under predicts the waves generated by high-category hurricanes. On
3528 the other hand, hurricane waves from more complex models that use spatially distributed surface
3529 wind measurements (Tolman et al. 2002) compare well with satellite and buoy observations both
3530 in deep water and in shallow water as hurricanes make landfall (Moon et al. 2003).
3531
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3532 Any trend over the years of increasing intensities of hurricanes or of extratropical storms should
3533 on average be reflected in similar upward trends in associated wave heights. Analyses of wave-
3534 buoy data along both the Atlantic and Pacific coasts of the United States document that wave-
3535 height increases have occurred at some locations since the late 1970s.
3536
3537 2.2.3.3.2 Atlantic Coast Waves
3538 Two analyses have recently been undertaken of the hourly measurements of the significant wave
3539 heights collected by the buoys of NOAA’s National Data Buoy Center (NDBC) along the U.S.
3540 Atlantic shore. These analyses, while differing in some important methodological aspects that
3541 affect some of the results, both show changes in waves generated by hurricanes while the ranges
3542 of wave heights created by extratropical storms appear to have undergone little change.
3543
3544 Komar and Allan (2007a) analyzed the data from three buoys located in deep water to the east of
3545 Cape May, New Jersey, Cape Hatteras, North Carolina, and offshore from Charleston, South
3546 Carolina. These buoys were selected due to their long record lengths and because the sites
3547 represent a range of latitudes where the wave climate is expected to be affected by both tropical
3548 hurricanes and extratropical storms (Nor’easters). Separate analyses were undertaken for the
3549 winter season dominated by extratropical storms and the summer season of hurricanes57. There
3550 was not a statistically significant change over the decades in the heights of waves generated by
3551 extratropical storms, but statistically significant increases have occurred for the hurricane-
3552 generated waves. The increases in annual-averaged significant wave heights measured by the
57 The hurricane waves were analyzed for the months of July through September, expected to be dominated by tropical cyclones, while the waves of extratropical storms were based on the records from November through March; transitional months such as October were not included, when both types of storms could be expected to be important in wave generation. Also, strict missing data criteria eliminated some years from the analysis.
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3553 three Atlantic buoys for the summer hurricane seasons are graphed in Figure 2.23. These annual
3554 averages have included only occurrences when the significant wave heights were greater than 3
3555 m, it having been found that those higher waves can be directly attributed to specific hurricanes,
3556 whereas the lower waves represent the calmer periods between storms. It is seen in Figure 2.23
3557 that there has been a dependence on the latitude, with the highest rate of increase having
3558 occurred in the south; 0.059 m/yr (1.8 m in 30 years) for the Charleston buoy, 0.024 m/yr for the
3559 Hatteras buoy, and 0.017 m/yr for Cape May58.
3560
3561 Figure 2.24 provides a comparison of histograms for the numbers of significant wave heights
3562 measured during the hurricane season by the Cape Hatteras buoy, one histogram for data from
3563 early in its record (1977-1990) and the second from 1996-2005, this comparison further
3564 documenting the decadal increase seen in Figure 2.23, especially of the more-extreme waves59.
3565 The histogram for the early decade in the wave record shows that the maximum significant wave
3566 height measured was 7.8 m, providing an approximate estimate for the height expected to have a
3567 10-year recurrence interval. From this, we could expect that the 100-year extreme (1%
3568 probability) would have been on the order of 9.5 m significant wave height. In contrast, during
3569 1996-2005 there has been a considerably larger number of occurrences having significant wave
3570 heights greater than 4 m, with the most extreme heights measured ranging up to 10.3 m. The
3571 100-year extreme is now on the order of 12 m, about 3 m higher than in the 1980s. Similar
58 The regressions in Figure 2.38 for the Charleston and Cape Hatteras buoy data are statistically significant at the p=0.05 level according to the Wilcoxon Test, whereas the value of the trend for the Cape May does not pass that test. 59 Traditionally a wave histogram is graphed as the percentages of occurrences, but here the actual numbers of occurrences for the range of wave heights have been plotted, using a log scale that emphasizes the most- extreme heights.
Do Not Cite or Quote 160 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3572 results have been found in analyses of the wave-height histograms for the Cape May and
3573 Charleston buoys (Komar and Allan, 2007a).
3574
3575 This analysis of the three U.S. East Coast buoys (Figures 2.23 and 2.24) demonstrate that there
3576 has been a 30-year increase in wave heights measured during the hurricane season. This increase
3577 could depend on several factors, including changes from year to year in the numbers and
3578 intensities of storms, their tracks that determine whether they traveled northward through the
3579 Atlantic where their generated waves could be recorded by these buoys, and how distant the
3580 hurricanes were from the buoys, whether they passed far offshore within the central Atlantic, or
3581 approached the east coast and possibly made landfall. Analyses by Komar and Allan (2007b)
3582 indicate that all of these factors have been important to the observed wave-height increases, but
3583 the increased hurricane intensities found by Emanuel (2005) based on the measured wind speeds
3584 provide the best explanation for the progressive increase in wave heights seen in Figure 2.23,
3585 since the numbers and tracks of the storms show considerable variability from year to year.
3586
60 3587 In the second study (Bromirski and Kossin, 2007) , extreme tropical cyclone-associated HS
3588 events (deep water HS exceeding 3 m) measured at buoys in both the Atlantic and Gulf regions
3589 (Figure 2.25a) show a general tendency for more significant tropical cyclone-associated wave
3590 events since 1995 (Figure 2.25b), consistent with increasing overall counts of named storms
3591 during recent years [Webster et al. 2005; Klotzbach 2006]. As would be expected, the intense
3592 2005 hurricane season had the highest incidence of significant HS events over the data record in
60 In this study, the entire hurricane season (June-November) was analyzed. Hurricane track data were used to restrict the analysis to time periods when hurricanes were likely the cause of extreme waves, the goal being to minimize the effects of ETCs during the transition months of October and November. Less stringent missing data were applied, resulting in the inclusion of more years than in Komar and Allan (2007).
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3593 the Gulf when Hurricanes Katrina, Rita, and Wilma occurred. Since 1978, there were
3594 substantially more significant HS events along the Atlantic coast than in the Gulf, with almost
3595 three times as many events during September (Figure 2.25c). The monthly distribution along
3596 both coasts peaks in September, with an equally likely chance of a significant tropical cyclone
3597 wave event occurring during October as during August over the 1978-2006 data record. About 3
3598 times as many extreme events occurred in September in the Atlantic compared with the Gulf
3599 from 1978-2006. However, inclusion of all tropical cyclone generated wave events (listed in
3600 http://www.nhc.noaa.gov/pastall.shtml) for the entire June though November hurricane season
3601 indicates that there is no significant trend in mean tropical cyclone associated HS at either the
3602 western North Atlantic or Gulf buoys (Bromirski and Kossin, 2007; Figure 2.25b).
3603
3604 A tropical cyclone wave power index, WPI61, shows an increase in the Atlantic during
3605 the mid-1990s (Bromirski and Kossin, 2007; Figure 2.26), associated with an increase in
3606 the number of significant tropical cyclone forced wave events, that is proportionally
3607 consistent with the increase observed for the tropical cyclone power dissipation index
3608 (PDI, Emanuel 2005]\). The Gulf WPI indicates that only the 2005 hurricane season was
3609 exceptional in the Gulf, but is highly correlated with the Atlantic multidecadal oscillation
3610 (AMO, Goldenberg et al. 2001) over the 1980-2006 period. In contrast, the Atlantic WPI
3611 is not well correlated with the AMO, suggesting that tropical sea surface temperature
3612 variability has a greater influence on the characteristics of tropical cyclones that reach the
3613 Gulf.
3614
61 The WPI for the Atlantic and Gulf regions is obtained as the average of the total wave power for all tropical cyclone associated wave events during the June – November hurricane season at the three southernmost Atlantic buoys and the three Gulf buoys in Figure 2.waves.1a (Bromirski, 2007).
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3615 To summarize, these 2 studies both detect changes in tropical cyclone-related waves, but
3616 in different aspects. Komar and Allan (2007a) show statistically significant increases in
3617 extreme wave heights during July-September, while Bromirski and Kossin (2007) do not
3618 find the trends over the entire hurricane season to be statistically significant. However,
3619 Bromirski and Kossin (2007) do find a statistically significant increase in tropical
3620 cyclone-caused wave power, a trend that is attributed to an increase in numbers of events
3621 rather than intensity.
3622
3623 In contrast to the changes in the hurricane waves, analyses of the winter wave heights
3624 generated by extratropical storms and recorded since the mid-1970s by the three buoys
3625 along the central U.S. Atlantic shore have shown little change (Komar and Allan, 2007a).
3626 The records from the Cape Hatteras and Charleston NDBC buoys yield regressions
3627 indicating that they have actually experienced a slight decrease over the decades (-0.005
3628 m/yr), while the Cape May buoy shows a lower rate of reduction (-0.001 m/yr). These
3629 trends are not statistically significant, but may a reflection in the changes in storm tracks
3630 over the decades, with the storms having shifted to the north.
3631
3632 Analyses of the winter waves generated by extratropical storms demonstrate that the highest
3633 measured occurrences are on the order of 10.5-m significant wave heights, with the extreme-
3634 value assessments placing the 100-year event at on the order of 11.5 m, effectively the same as
3635 seen in the histogram of Figure 2.24 for the summer hurricane waves recorded by the Hatteras
3636 buoy during the 1996-2005 decade, so the wave climates of the two seasons are now quite
3637 similar. However, thirty-years ago when these buoys first became operational, the significant
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3638 wave heights generated in the summer by hurricanes were much lower than those of the
3639 extratropical storms during the winter; while the heights of hurricane-generated waves have
3640 progressively increased since the 1970s, the wave heights due to extratropical storms have not.
3641
3642 Although minimal change in the heights of waves generated by extratropical storms have been
3643 measured by buoys along the U.S. shore in the Western Atlantic, progressive increases have
3644 occurred in the Northeast Atlantic extending back to at least the 1960s, documented by the Seven
3645 Stones ship-borne wave recorder located in deep water off the southwest coast of England
3646 (Carter and Draper, 1988; Bacon and Carter, 1991). Of interest, the rate of increase (0.022 m/yr)
3647 in the annual averages are closely similar to those measured by buoys along the northwest coast
3648 of the United States in the Pacific Ocean, discussed below.
3649
3650 The documentation by buoys of trends in wave heights in the North Atlantic are limited by
3651 their relatively short records, hindering a determination of the longevity of the identified trends
3652 and the possible presence of any decadal cycles in climate-determined variability. To
3653 supplement the buoy data, visual observations from ships in transit provide longer time series
3654 of estimated ocean wave-heights; although the quality of the data may be questionable, its
3655 availability extends back through the entire 20th century, and in general appears to yield
3656 reasonably consistent trends when compared with the buoy data and with wave hindcasts.
3657 Gulev and Grigorieva (2004) have undertaken detailed analyses of the visual assessments of
3658 wave heights from ships, covering the years 1895-2002 except for a gap in the data during
3659 World War II. The observations for the northeast Atlantic showed a distinct increase in wave
3660 heights after about 1955, corresponding to the wave-sensor measurements since the 1960s
Do Not Cite or Quote 164 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3661 collected southwest of England. Earlier in the 20th century, however, there were distinct cycles
3662 in the visual wave heights observed from ships, with years during which the average wave
3663 heights were some 0.2 m higher than at present. These cycles correlate with the North Atlantic
3664 Oscillation (NAO), with the higher wave heights having been associated with high NAO
3665 indices.
3666
3667 Hindcasts by Wang and Swail (2001) of the wave climates based on the meteorological records
3668 of extratropical storms have been analyzed with respect to changes in the 90th and 99th
3669 percentiles of the significant wave heights, thereby representing the trends for the more
3670 extreme wave conditions. The results indicate a lack of change along the east coast of North
3671 America, in agreement with the buoy data for waves generated by extratropical storms.
3672
3673 2.2.3.3.3 Pacific Coast Waves
3674 Analyses of the wave data from NDBC buoys have also been undertaken along the U.S. Pacific
3675 coast, similar to those discussed above for the Atlantic but with the focus having been on the
3676 waves generated by extratropical storms in the Northeast Pacific. The principal investigations of
3677 the trends of changing wave heights and their potential climate controls are those of Allan and
3678 Komar (2000, 2006), who analyzed the records from 6 buoys along the coast from Washington
3679 to south-central California (Point Conception). The analyses were limited to the “winter” waves,
3680 October though the following March, the season with the most intense storms and highest waves.
3681 Trends of increasing wave heights spanning the past 30 years were found, with the greatest rate
3682 of increase having occurred off the coast of Washington where the regression yielded an average
3683 rate of 0.032 m/yr for the winter, with a regular pattern of lesser rates of increase for the latitudes
Do Not Cite or Quote 165 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3684 to the south, such that off the coast of south-central California there has not been a statistically
3685 significant trend62.
3686
3687 Analyses of the more extreme wave heights measured off the Washington coast were undertaken
3688 due to their importance to coastal-erosion occurrences (Allan and Komar, 2006). Figure 2.27
3689 contains graphs of the annual averages of the winter wave heights, and the averages of the five
3690 largest significant wave heights measured each winter, the latter showing a higher rate of
3691 increase (0.095 m/yr, a 2.85-m increase in the significant wave heights in 30 years). The full
3692 series of analyses are listed in Table 2.1, demonstrating that there is an orderly progression with
3693 the more extreme the assessment the greater the rate of increase, up to a rate of 0.108 m/yr for
3694 the single highest measured significant wave height each year. While the data in Figure 2.27 for
3695 the averages of the largest five storm-wave occurrences each year are statistically significant at
3696 the p=0.05 level, the trends for the more extreme waves do not meet this criterion (Table 2.1).
3697 However, for applications to engineering design of coastal structures and in coastal management
3698 assessments of hazards, these extremes for the measured wave heights are of greatest relevance,
3699 and therefore are sometimes used in applications as is the trend for the assessment of the 100-
3700 year projected extreme, which has increased at a still greater rate over the decades, from about 11
3701 m in 1975 to 16 m at present. This use in applications is further supported by the fact that much
3702 of the scatter in the diagrams, as seen in Figure 2.27, can be accounted for by considering the
3703 range of climate events from El Niños to La Niñas (Allan and Komar, 2000, 2006).
3704
62 Where trends of increasing wave heights do exist, they have again been verified by application of the Wilcoxon test, a statistical analysis that basically compares the first half the record with the second half to establish that there has been a meaningful change.
Do Not Cite or Quote 166 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3705 The intensities of North Pacific extratropical storms and their associated tracks are strongly
3706 affected by the depth and position of the Aleutian Low, which tends to intensify and shift
3707 southward and eastward during strong El Nino events (Mo and Livezey, 1986). This southward
3708 shift results in increased occurrences of extreme waves throughout the eastern North Pacific,
3709 particularly along the south-central California coast (Seymour et al. 1984; Allan and Komar
3710 2000, 2006; Bromirski et al. 2005). Correlations between the measured wave heights and the
3711 Multivariate ENSO Index show that increased wave heights occur at all latitudes along the U.S.
3712 Pacific coast during major El Niños, but with the greatest increases along the shore of southern
3713 California (Allan and Komar, 2006). Along the coast of California where the trends of decadal
3714 increases are small to non-existent, it is this cycle between El Niños and La Niñas that exerts the
3715 primary climate control on the storm-wave heights and their extremes (and also on the monthly-
3716 mean winter water levels, which are elevated by 20 to 50 cm during a major El Niño above the
3717 long-term mean sea levels).
3718
3719 The documentation of increasing wave heights in the North Pacific is given limited by the
3720 relatively short records from buoys, extending back only to the 1970s. Similar to discussed for
3721 the Atlantic, visual observations from ships in transit provide longer time series of ocean wave
3722 height estimates, but of questionable quality. Gulev and Grigorieva (2004)examined this source
3723 of wave data for the North Pacific, finding that there has been a general increase in the
3724 significant wave heights throughout the 20th century, with a rapid increase from 1900 to about
3725 1925, and a leveling off from 1925 to about 1950-60 but with an apparent maximum during the
3726 1940s (there being a gap in the data during World War II). There was a renewed increase
3727 beginning in about 1960, corresponding to that documented by the wave buoy measurements
Do Not Cite or Quote 167 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3728 (Fig. 2.27). The wave hindcasts63 by Wang and Swail (2001), representing the more extreme
3729 significant wave-height occurrences (the 90th and 99th percentiles), largely also confirm the
3730 general increase in wave heights throughout the central to eastern North Pacific.
3731
3732 There is the potential for the use of proxy evidence to examine the changes in wave heights
3733 back beyond that provided by the wave data, the proxy having the clearest potential being
3734 measurements by seismometers installed to monitor earthquake activity. During the “quiet”
3735 intervals between earthquakes it has been noted that there is a consistent level of "noise" in the
3736 recorded ground motions, termed "microseisms". It has been shown that much of this energy is
3737 derived from surf on the coast, with the microseisms increasing at times of storms. Analyses
3738 have been undertaken by Bromirski et al. (1999) correlating buoy measurements of ocean
3739 waves along the coast of central California and the microseisms measured by the seismometer
3740 at the University of California, Berkeley. The results of that study yielded a calibration
3741 between the ocean wave heights and the microseism energy, demonstrating the potential use of
3742 the archived seismic data that dates back to 1930, to investigate changes in the U.S. West Coast
3743 wave climate.
3744
3745 2.2.3.4 Winter Storms
3746 2.2.3.4.1 Snowstorms
3747 The amount of snow that causes serious impacts varies depending on a given location’s
3748 usual snow conditions. A snowstorm is defined here as an event in which more than 15
3749 cm of snow falls in 24 hours or less at some location in the U.S. This is an amount
63 Hindcasts are model estimates of waves using forecast models that are run retrospectively using observed meteorological data
Do Not Cite or Quote 168 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3750 sufficient to cause societally-important impacts in most locations. During the 1901-2001
3751 period, 2,257 snowstorms occurred (Changnon et al. 2006). Temporal assessment of the
3752 snowstorm incidences during 1901-2000 revealed major regional differences.
3753 Comparison of the storm occurrences in 1901-1950 against those in 1951-2000 revealed
3754 that much of the eastern U.S. had more storms in the early half of the 20th Century,
3755 whereas in the West and New England, the last half of the century had more storms.
3756 Nationally, 53% of the weather stations had their peaks in 1901-1950 and 47% peaked in
3757 1951-2000.
3758
3759 The South and lower Midwest had distinct statistically significant downward trends in
3760 snowstorm frequency from 1901 to 2000. In direct contrast, the Northeast and upper
3761 Midwest had statistically significant upward linear trends. These contrasting regional
3762 trends suggest a northward shift in snowstorm occurrence. Nationally, the regionally
3763 varying up and down trends resulted in a national storm trend that was slightly upward
3764 for 1901-2000, but not statistically significant. The long-term increases in the upper
3765 Midwest and Northeast occurred where snowstorms are most frequent, and thus had an
3766 influence on the upward trend in national snowstorm activity. Research has shown that
3767 cyclonic activity was low during 1931-1950, a period of few snowstorms in the U.S.
3768
3769 Nationally, 39 of 231 stations with long-term records had their lowest frequencies of
3770 storms during 1931-1940, whereas 29 others had their peak of incidences then. The
3771 second ranked decade with numerous stations having low snowstorm frequencies was
3772 1981-1990. Very few low storm occurrences were found during 1911-1920 and in the
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3773 1961-1980 period, times when storms were quite frequent. The 1911-1920 decade had the
3774 greatest number of high station values with 38 stations. The fewest peak values occurred
3775 in the next decade, 1921-1930. Comparison of the decades of high and low frequencies of
3776 snowstorms reveals, as expected, an inverse relationship. That is, when many high storm
3777 values occurred, there are few low storm frequencies.
3778
3779 Generally, the decades with high snowstorm frequencies were characterized by cold
3780 winters. The three highest decades for snowstorms (1911-1920, 1961-1970, and 1971-
3781 1980) were ranked 1st, 4th, and 3rd coldest, respectively while the two lowest decades
3782 (1921-1930 and 1931-1940) were ranked as 3rd and 4th warmest. One exception to this
3783 general relationship is the warmest decade (1991-2000), which experienced a moderately
3784 high number of snowstorms.
3785
3786 Very snowy seasons (those with seasonal snowfall totals exceeding the 90th percentile
3787 threshold) were infrequent in the 1920s and 1930s and have also been rare since the mid-
3788 1980s (Kunkel et al. 2007b). There is a high correlation with average winter temperature.
3789 Warm winters tend to have few stations with high snowfall totals and most of the snowy
3790 seasons have also been cold.
3791
3792 Some of the snowiest regions in North America are the southern and eastern shores of the
3793 Great Lakes where cold northwesterly winds flowing over the warmer lakes pick up
3794 moisture and deposit on the shoreline areas. There is evidence of upward trends in
3795 snowfall since 1951 in these regions even while locations away from the snowy shoreline
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3796 areas have not experienced increases (Burnett et al. 2003). An analysis of historical heavy
3797 lake-effect snowstorms identified several weather conditions to be closely related to
3798 heavy lake-effect snowstorm occurrence including moderately high surface wind speed,
3799 wind direction promoting a long fetch over the lakes, surface air temperature in the range
3800 of -10 to 0°C, lake surface to air temperature difference of at least 7°C, and an unstable
3801 lower troposphere (Kunkel et al. 2002). It is also necessary that the lakes be mostly ice-
3802 free.
3803
3804 Snow cover extent for North America based on satellite data (Robinson et al. 1993)
3805 abruptly decreased in the mid-1980s and generally has remained low since then
3806 (http://climate.rutgers.edu/snowcover/chart_anom.php?ui_set=0&ui_region=nam&ui_mo
3807 nth=6).
3808
3809 2.2.3.4.2 Ice Storms
3810 Freezing rain is a phenomenon where even light amounts can have substantial impacts.
3811 All days with freezing rain (ZR) were determined during the 1948-2000 period based on
3812 data from 988 stations across the U.S. (Changnon and Karl 2003). The national frequency
3813 of freezing rain days (FZRA) exhibited a downward trend, being higher during 1948-
3814 1964 than in any subsequent period.
3815
3816 The temporal distributions of FZRA for three climate regions (Northeast, Southeast, and
3817 South) reveal substantial variability. They all were high in 1977-1980, low in 1985-1988,
3818 and lowest in 1973-1976. The 52-year linear trends for all three regions were downward
Do Not Cite or Quote 171 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3819 over time. The time distributions for the Central, West North Central, and East North
3820 Central regions are alike, all showing that high values occurred early, 1949-1956. All
3821 climate regions had their lowest FZRA during 1965-1976. The East north central,
3822 Central, Northwest, and Northeast regions, which embrace the northern half of the
3823 conterminous U.S., all had statistically significant downward linear trends. This is in
3824 contrast to trends in snowstorm incidences.
3825
3826 Both snowstorms and ice storms are often accompanied or followed by extreme cold
3827 because a strong ETC (which is the meteorological cause of the snow and ice) is one of
3828 the meteorological components of the flow of extreme cold air from the Arctic. This
3829 compounds the impacts of such events in a variety of ways, including increasing the risks
3830 to human health and adversely affecting the working environment for snow removal and
3831 repair activities. While there have been no systematic studies of trends in such compound
3832 events, observed variations in these events appear to be correlated. For example, the late
3833 1970s were characterized both by a high frequency of extreme cold (Kunkel et al. 1999)
3834 and a high frequency of high snowfall years (Kunkel et al. 2007b).
3835
3836 2.2.3.5 Convective Storms
3837 Thunderstorms in the United States are defined to be severe by the National Weather
3838 Service (NWS) if they produce hail of at least 1.9 cm (3/4 inch) in diameter, wind gusts
3839 of at least 25.5 m s-1 (50 kt) or a tornado. Currently, reports come from a variety of
3840 sources to the local NWS forecast offices that produce a final listing of events for their
3841 area. Over the years, procedures and efforts to produce that listing have changed. Official
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3842 data collection in near real-time began in 1953 for tornadoes and 1955 for hail and wind.
3843 Prior to 1973, tornado reports were verified by state climatologists (Changnon 1982). In
3844 addition, efforts to improve verification of severe thunderstorm and tornado warnings, the
3845 introduction of Doppler radars, changes in population, and increases in public awareness,
3846 have led to increases in reports over the years. Changes in reporting practices have also
3847 led to inconsistencies in many aspects of the records (e.g., Brooks 2004). Changnon and
3848 Changnon (2000) identified regional changes in hail frequency from reports made at
3849 official surface observing sites. With the change to automated surface observing sites in
3850 the 1990s, the number of hail reports at those locations dropped dramatically because of
3851 the loss of human observers at the sites. As a result, comparisons to the Changnon and
3852 Changnon work cannot be continued, although Changnon et al. (2001) have attempted to
3853 use insurance loss records as a proxy for hail occurrence.
3854
3855 The raw reports of annual tornado occurrences show an approximately doubling from
3856 1954-2003 (Brooks and Dotzek 2007), a reflection of the changes in observing and
3857 reporting. When detrended to remove this artificial trend, the data show large interannual
3858 variability, but a persistent minimum in the late 1980s (Fig. 2.28). There were changes in
3859 assigning intensity estimates in the mid-1970s that resulted in tornadoes prior to 1975
3860 being rated more strongly than those in the later part of the record (Verbout et al. 2006).
3861 More recently, there have been no tornadoes rated F5, the highest rating, since 3 May
3862 1999, the longest gap on record. Coupled with a large decrease in the number of F4
3863 tornadoes (McCarthy et al. 2006), it has been suggested that the strongest tornadoes are
3864 now being rated lower than practice prior to 2000.
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3865
3866 A dataset of F2 and stronger tornadoes extending back before the official record
3867 (Grazulis 1993) provides an opportunity to examine longer trends. This examination64 of
3868 the record from 1921-1995 indicates that the variability between periods was large,
3869 without significant long-term trends (Concannon et al. 2000).
3870
3871 The fraction of strong tornadoes (F2 and greater) that have been rated as violent (F4 and
3872 greater) has been relatively consistent in the US from the 1950s through the 1990s65
3873 (Brooks and Doswell 2001)66. There were no significant changes in the high-intensity
3874 end of these distributions from 1950s through the 1990s, although the distribution from
3875 2000 and later may differ.
3876
3877 Nontornadic reports have increased even more rapidly than tornadic reports (Doswell et
3878 al. 2005, 2006). Over the period 1955-2004, this increase was approximately exponential,
3879 resulting in an almost 20-fold increase over the period. The increase is mostly in
3880 marginally severe thunderstorm reports (Brooks 2007. An overall increase is seen, but the
3881 distribution by intensity is similar in the 1970s and post-2000 eras for the strongest 10%
3882 of reports of hail and wind. Thus, there is no evidence for a change in the severity of
3883 events, and the large changes in the overall number of reports make it impossible to
3884 detect if meteorological changes have occurred.
64 This analysis used the technique described in Brooks et al. (2003a) to estimate the spatial distribution over different periods 65 Note that consistent overrating will not change this ratio. 66 Feuerstein et al. (2005) showed that the distribution in the US and other countries could be fit to Weibull distributions with the parameters in the distribution converging as time goes along, which they associated with more complete reporting of events.
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3885
3886 Environmental conditions that are most likely associated with severe and tornadic
3887 thunderstorms have been derived from reanalysis data (Brooks et al. 2003b) and counts of
3888 the frequency of favorable environments for significant severe thunderstorms67 have been
3889 determined for the area east of the Rocky Mountains in the US for the period 1958-1999
3890 (Brooks and Dotzek 2007). The count of favorable environments decreased from the late
3891 1950s into the early 1970s and increased after that through the 1990s, so that the
3892 frequency was approximately the same at both ends of the analyzed period. Given the
3893 high values seen at the beginning of the reanalysis era, it is likely that the record is long
3894 enough to sample natural variability, so that it is possible that even though the 1973-1999
3895 increase is statistically significant, it does not represent a departure from natural
3896 variability. The time series of the count of reports of very large hail (7 cm diameter and
3897 larger) shows an inflection at about the same time as the inflection in the counts of
3898 favorable environments. A comparison of the rate of increase of the two series suggested
3899 that the change in environments could account for approximately 7% of the change in
3900 reports from the mid-1970s through 1999, with the rest coming from non-meteorological
3901 sources. Changes in tornado reports do not correspond to the changes in overall favorable
3902 severe thunderstorm environment, in part because the discrimination of tornadic
3903 environments in the reanalysis data is not as good as the discrimination of severe
3904 thunderstorm environments (Brooks et al. 2003a).
3905
3906
3907
67 Hail of at least 5 cm diameter, wind gusts of at least 33 m s-1, and/or a tornado of F2 or greater intensity
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3908 BOX 2.3: Changes in Modes of Variability
3909 The atmosphere-ocean system has a wide variety of circulation patterns, or modes, of
3910 climate variability that pulse on time scales ranging from days, to many decades, or
3911 longer. For example, the well-known winter weather pattern of a storm followed by clear
3912 skies and then another storm a week later is, part of an atmospheric wave (wind) pattern
3913 that circles the Earth. As these waves move over the ocean, heat from the ocean is given
3914 up to the air, which impacts both the intensity and the movement of the atmospheric
3915 waves (weather) as well as ocean circulations. Weather and climate extremes are often
3916 linked to one or more of these modes of climate variability, and following is a brief
3917 description of the most important circulation regimes. However, it is important to keep in
3918 mind that these modes of variability are not independent of each other.
3919
3920 El Niño-Southern Oscillation (ENSO)
3921 The ENSO phenomenon is the result of coupled ocean-atmosphere dynamics and is the
3922 largest source of interannual variability in global weather and climate. It is characterized
3923 by changes in eastern equatorial Pacific sea surface temperature (SST) and surface air
3924 pressure in the tropical Pacific region. Warm (cold) eastern Pacific SST anomalies are
3925 associated with El Niño (La Niña) events. El Niños occur at irregular intervals of
3926 approximately 2 to 7 years, and generally persists for 12 to 18 months. The Southern
3927 Oscillation component of ENSO is defined by air pressure differences between the
3928 eastern and western tropical Pacific (typically between Darwin and Tahiti) and is
3929 characterized by changes in tropical atmospheric flow patterns which are caused by and
3930 can enhance tropical Pacific SST variations. These tropical atmospheric circulation
Do Not Cite or Quote 176 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3931 changes can alter both the intensity and tracks of North American storms. For example,
3932 El Niño is often associated with heavy winter rains in southern California.
3933
3934 The nature of ENSO has varied considerably through time. Strong ENSO events occurred
3935 with regularity from the late 19th Century through 1925 and again after 1950. Between
3936 1976 and 1977 rapid warming occurred in the Tropical Pacific with concurrent cooling in
3937 the Central Pacific that has been termed the climate shift of 1976/1977 (Trenberth 1990,
3938 Miller et al. 1994). The shift has been associated with increased El Niño activity, changes
3939 in storm tracks, increased storm intensity and is at the start of the period of rapid
3940 warming in global temperatures, and the 1997-1998 El Niño was the strongest on record.
3941
3942 The North Atlantic Oscillation (NAO)
3943 The NAO is the most important mode of winter climate variability in the North Atlantic
3944 region and is measured by an index that is based on air pressure differences between
3945 Iceland/Greenland and the Azores in the north Atlantic. As Figure 2.29 illustrates, high
3946 values of the NAO index are associated with intensified westerly winds around the arctic.
3947 Changes in the strength and location of the westerlies produce characteristic shifts in
3948 temperature, rainfall, and winds. Low NAO values correspond with cold extremes in
3949 central North America and high NAO index values increase the chances of warm winter
3950 extremes. Proxy and instrumental data show evidence for intervals of decadal and longer
3951 positive and negative NAO index in the last few centuries (Cook et al., 2002; Jones et al.,
3952 2003). A reversal occurred from minimum winter index values in the late 1960s to
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3953 strongly positive NAO index values in the mid-1990s but since have declined to near the
3954 long-term mean.
3955
3956 Atlantic Multidecadal Oscillation (AMO)
3957 The Atlantic Ocean meridional overturning circulation carries warm salty surface waters
3958 into far-northern latitudes around Greenland where it cools, sinks, and returns cold deep
3959 waters southward across the equator. An oscillating pattern of SSTs in the northern
3960 Atlantic that is related to this overturning circulation, called the Atlantic Multidecadal
3961 Oscillation (AMO), has been identified by a number of researchers (Delworth and Mann,
3962 2000; Folland et al., 1986; Mann and Park, 1994). The AMO is commonly identified by
3963 subtracting a linear trend from a time series of the North Atlantic SST. This trend
3964 subtraction is intended to remove, or at least reduce, the influence of greenhouse-gas
3965 induced global warming from the AMO so that the bulk of the variability in the
3966 remainder is due to natural causes. The warm phase, the decades when the temperature is
3967 above the trend line, is associated with increased Atlantic hurricane activity, and the cool
3968 phase is associated with reduced Atlantic hurricane activity. Instrumental data has been
3969 used to identify warm phases roughly between 1860-1880, 1930-1960, and one beginning
3970 in the mid-1990s which continues to present. Cool phases were present during 1905-1925
3971 and 1970-1990 (Schlesinger and Ramankutty, 1994).
3972
3973 Some scientists, however, question the validity of subtracting a linear trend from a time
3974 series created by non-linear forcings and wonder if the AMO as commonly calculated is
3975 primarily an artifact of this creation process rather than a real change in the ocean
Do Not Cite or Quote 178 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007
3976 circulation. Some suggest that subtracting the global SST time series from the North
3977 Atlantic SST time series removes a global climate change signal better than subtracting a
3978 linear trend and produces a very different historical AMO record (Trenberth and Shea,
3979 2006). Proxy and modeling studies have identified an AMO-like signal and found that
3980 multidecadal eras in hurricane activity in the North Atlantic are correlated with the AMO
3981 (Bell and Chelliah, 2006). No matter how it is calculated, the AMO has such a long
3982 period that the observational SST data only records about 1.5 cycles which makes it
3983 difficult to determine whether the AMO is truly a natural oscillation or caused in whole
3984 or at least in part by greenhouse-gas induced climate change.
3985
3986 Pacific Decadal Oscillation (PDO)
3987 The Pacific Decadal Oscillation (PDO) is a multidecadal pattern of monthly SST
3988 anomalies in the North Pacific Ocean poleward of 20°N. Two full PDO cycles occurred
3989 through the twentieth century with each phase persisting for 20 to 30 years. The typical
3990 spatial pattern of the “warm” phase of the PDO has negative SST anomalies in the central
3991 and eastern North Pacific and positive SST anomalies along the coast of North America.
3992 Sea level pressure (SLP) anomalies during the warm phase tend to have a basin-scale low
3993 centered over the Aleutian Islands and high sea-level pressure over western North
3994 America. The cool phase of the PDO has SST and SLP patterns that are essentially the
3995 opposite of the warm phase. Because the PDO influences various weather systems it can
3996 affect the chances of, for example, winter temperatures cold enough to cause mountain
3997 pine beetle mortality in British Columbia (Stahl et al., 2006). When an El Niño event
3998 occurs during a warm phase of the PDO, the characteristic El Niño-related temperature
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3999 and precipitation anomalies in North America tend to be accentuated. The PDO had
4000 extended periods of negative values indicative of weakened circulation from 1900 to
4001 1924 and 1947 to 1976, and positive values indicative of strengthened circulation from
4002 1925 to 1946 and 1977 to 2005. The 1976-1977 climate shift in the Pacific described
4003 above was associated with a phase change in the PDO from negative to positive
4004 (Trenberth et al., 2002; Deser et al., 2004).
4005
4006 Pacific North American Pattern (PNA)
4007 The PNA can be defined as a secondary pattern in the variability of monthly atmospheric
4008 pressure anomalies for the latitude range 20-90°N. When the PNA is positive, the mid-
4009 tropospheric winds over North America and the North Pacific have a strong meridional
4010 (north-south) wave pattern while the negative PNA has more zonal (west to east) flow.
4011 Strong wave patterns tend to bring extreme weather; whether the extremes are warm,
4012 cold, wet or dry at a particular location depends on the shape of the wave. A positive
4013 PNA is associated with El Niños and negative PNA is associated with La Niña.
4014
4015 The Madden-Julian Oscillation (MJO)
4016 The atmospheric response to convection on the equator, which heats the atmosphere, is
4017 the creation of circulation cells, which then move eastward. These cells have a period of
4018 about 50 days and either enhance tropical convection or help suppress it. Referred to as
4019 the Madden-Julian Oscillation (MJO), after the two scientists who discovered it (Madden
4020 and Julian, 1971 and 1972), it is the dominant source of tropical atmospheric variability
4021 on intraseasonal time scales. The MJO is related to North American extremes through its
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4022 influence on the dynamics of tropical cyclone formation (Hartmann and Maloney, 2001;
4023 Maloney and Hartmann, 2000a; 2000b, 2001) as well as western North American winter
4024 rainfall variability. The MJO can enhance or suppress either depending on which part of
4025 the circulation cell is active in the region.
4026
4027 As the climate changes, some of the atmospheric circulation patterns or modes of
4028 atmospheric variability described above have changed as well. However, only one
4029 circulation pattern, the MJO, would not be expected to have long-term changes since it is
4030 a localized circulation response to convection on the equator.
4031
4032 2.3 Key Uncertainties Related to Measuring Specific Variations and Change
4033 In this section we review the statistical methods that have been used to assess
4034 uncertainties in studies of changing extremes. The focus of the discussion is on
4035 precipitation events, though similar methods have also been used for temperature.
4036
4037 2.3.1 Methods Based on Counting Exceedances Over a High Threshold
4038 Most existing methods follow some variant of the following procedure, given by Kunkel
4039 et al. (1999). First, daily data are collected, corrected for biases such as winter
4040 undercatchment. Only stations with nearly complete data are used (typically, “nearly
4041 complete” means no more than 5% missing values). Different event durations (for
4042 example, 1-day or 7-day) and different return periods (such as 1 year or 5 years) are
4043 considered. For each station, a threshold is determined according to the desired return
4044 value – for example, with 100 years of data and a 5-year return value, the threshold is the
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4045 20th largest event. The number of exceedances of the threshold is computed for each year,
4046 and then averaged either regionally or nationally. The averaging is a weighted average in
4047 which, first, simple averaging is used over climate divisions (typically there are about 7
4048 climate divisions in each state), and then, an area-weighted average is computed over
4049 climate divisions, either for one of the nine U.S. climate regions or the whole contiguous
4050 U.S. This averaging method ensures that parts of the country with relatively sparse data
4051 coverage are adequately represented in the final average. Sometimes (e.g. Groisman et al.
4052 2005, Kunkel et al. 2007a) the climate divisons are replaced by 1o by 1o grid cells. Two
4053 additional refinements used by Groisman et al. (2005) are (i) to replace the raw
4054 exceedance counts for each year by anomalies from a 30-year reference period, computed
4055 separately for each station, (ii) to assess the standard error of the regional average using
4056 spatial statistics techniques. This calculation is based on an exponentially decreasing
4057 spatial covariance function with a range of the order 100-500 km. and a nugget:sill ratio
4058 (the proportion of the variability that is not spatially correlated) between 0 and 85%,
4059 depending on the region, season and threshold.
4060
4061 Once these spatially averaged annual exceedance counts or anomalies are computed, the
4062 next step is to compute trends. In most studies, the emphasis is on linear trends computed
4063 either by least squares regression or by the Kendall slope method, in which the trend is
4064 estimated as the median of all possible slopes computed from pairs of data points. The
4065 standard errors of the trends should theoretically be corrected for autocorrelation, but in
4066 the case of extreme events the autocorrelation is usually negligible (Groisman et al.,
4067 2004).
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4068
4069 One of the concerns about this methodology is the effect of changing spatial coverage of
4070 the data set, especially for comparisons that go back to the late years of the 19th century.
4071 Kunkel et al. (2007a) generated simulations of the 1895-2004 data record by first
4072 randomly sampling complete years of data from a modern network of 6351 stations for
4073 1971-2000, projecting to a random subnetwork equivalent in size and spatial extent to the
4074 historical data network, then using repeat simulations to calculate means and 95%
4075 confidence intervals for five 22-year periods. The confidence intervals were then
4076 superimposed on the actual 22-year means calculated from the observational data record.
4077 The results for 1-year, 5-year and 20-year return values show clearly that the most recent
4078 period (1983-2004) has the highest return values of the five periods, but they also show
4079 the second highest return values in 1895-1916 with a sharp drop thereafter, implying a
4080 still not fully explained role due to natural variability.
4081
4082 Some issues that might justify further research include the following:
4083 1. Further exploration of why extreme precipitation apparently decreases
4084 after the 1895-1916 period before the recent (post-1983) rise when they exceeded
4085 that level. For example, if one breaks the data down into finer resolution spatially,
4086 does one still see the same effect?
4087 2. What about the effect of large-scale circulation effects such as ENSO
4088 events, AMO, PDO, etc? These could potentially be included as covariates in a
4089 time series regression analysis, thus allowing one to “correct” for circulation
4090 effects in measuring the trend.
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4091 3. The spatial analyses of Groisman et al. (2005) allow for spatial correlation
4092 in assessing the significance of trends, but they don’t do the logical next step,
4093 which is to use the covariance function to construct optimal interpolations (also
4094 known as kriging) and thereby produce more detailed spatial maps. This is
4095 something that might be explored in the future.
4096
4097 2.3.2 The GEV Approach
4098 An alternative approach to extreme value assessment is though the Generalized Extreme
4099 Value (GEV) distribution68, and its variants. The GEV combines together three “types”
4100 of extreme value distributions that in earlier treatments were often regarded as separate
4101 families (e.g. Gumbel 1958). The distribution is most frequently applied to the annual
4102 maxima of a meteorological or hydrological variable, though it can also be applied to
4103 maxima over other time periods (e.g. one month or one season). With minor changes in
4104 notation, the distributions are also applicable to minima rather than maxima. The
4105 parameters may be estimated by maximum likelihood, though there are also a number of
4106 more specialized techniques such as L-moments estimation. The methods have been
4107 applied in climate researchers by a number of authors including Kharin and Zwiers
4108 (2000), Wehner (2004,2005), Kharin et al. (2007).
4109
68 The basic GEV distribution is given by the formula (see, e.g. Zwiers and Kharin (1998)) F(x) = exp{-[1-k(x-ξ)/α]1/k} in which ξ plays the role of a centering or location constant, α determines the scale, and k is a key parameter that determines the shape of the distribution. (Other authors have used different notations, especially for the shape parameter.) The range of the distribution is x<ξ+α/k when k<0, x>ξ+α/k when k<0, -∞ Do Not Cite or Quote 184 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4110 The potential advantage of GEV methods over those based on counting threshold 4111 exceedances is that by fitting a probability distribution to the extremes, one obtains more 4112 information that is less sensitive to the choice of threshold, and can also derive other 4113 quantities such as the T-year return value XT, calculated by solving the equation F(XT)=1- 4114 1/T. Trends in the T-year return value (for typical values of T, e.g. 1, 10, 25 or 100 years) 4115 would be particularly valuable as indicators of changing extremes in the climate. 4116 Direct application of GEV methods is often inefficient because they only use very sparse 4117 summaries of the data (typically one value per year), and need reasonably long time 4118 series before they are applicable at all. Alternative methods are based on exceedances 4119 over thresholds, not just counting exceedances but also fitting a distribution to the excess 4120 over the threshold. The most common choice of distribution of excess is the Generalized 4121 Pareto distribution or GPD, which is closely related to the GEV (Pickands 1975, Davison 4122 and Smith 1990). Some recent overviews of extreme value distributions, threshold 4123 methods, and a variety of extensions are by Coles (2001) and Smith (2003). 4124 4125 Much of the recent research (e.g. Wehner 2005, Kharin et al. 2007) has used model 4126 output data, using the GEV to estimate for example a 20-year return value at each grid 4127 cell, then plotting spatial maps of the resulting estimates. Corresponding maps based on 4128 observational data must take into account the irregular spatial distribution of weather 4129 stations, but this is also possible using spatial statistics (or “kriging”) methodology. For 4130 example, Cooley et al. (2007) have applied a hierarchical modeling approach to 4131 precipitation data from the Front Range of Colorado, fitting a GPD to threshold 4132 exceedances at each station and combining results from different stations through a Do Not Cite or Quote 185 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4133 spatial model to compute a map of 25-year return values. Smith et al. (2007) applied 4134 similar methodology to data from the whole contiguous U.S., producing spatial maps of 4135 return values and also calculating changes in return values over the 1970-1999 period. Do Not Cite or Quote 186 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4136 Chapter 2 References 4137 4138 Acuña-Soto, R., D. W. Stahle, M. K. Cleaveland, and M. D. Therrel, 2002: Megadrought 4139 and megadeath in 16th century Mexico. Historical Review, 8 (4), 360-362. 4140 4141 Aguilar, E., et al. (2005), Changes in precipitation and temperature extremes in Central 4142 America and northern South America, 1961–2003, J. Geophys. Res., 110, 4143 D23107, doi:10.1029/2005JD006119. 4144 4145 Alexander, L. V., et al. (2006), Global observed changes in daily climate extremes of 4146 temperature and precipitation, J. Geophys. Res., 111, D05109, 4147 doi:10.1029/2005JD006290. 4148 4149 Allan, J. C., and P. D. Komar, 2000: Are ocean wave heights increasing in the eastern 4150 North Pacific? EOS, Transaction of the American Geophysical Union, 47, 561- 4151 567. 4152 4153 Allan, J. C. and P. D. Komer, 2006: Climate controls on US West Coast erosion 4154 processes. Journal of Coastal Research, 22, 511-529. 4155 4156 Alley, W.M. 1984. The Palmer Drought Severity Index: Limitations and assumptions. 4157 Journal of Climate and Applied Meteorology 23:1100–1109. 4158 4159 An, S.I., J.S. Kug, A. Timmermann, I.S. Kang, and O. Timm, 2007: The influence of 4160 ENSO on the generation of decadal variability in the North Pacific. J. Climate, 4161 20, 667–680. 4162 4163 Andreadis, K.M., E. A. Clark, A. W. Wood, A. F. Hamlet, and D. P. Lettenmaier, 2005: 4164 Twentieth-Century drought in the conterminous United States. Journal of 4165 Hydrometeorology, 6, 985–1001. 4166 4167 Andreadis, K. M., and D. P. Lettenmaier, 2006: Trends in 20th century drought over the 4168 continental United States. Geophysical Research Letters, 33, L10403, 4169 doi:10.1029/2006GL025711. 4170 4171 Angel, J.R., and S.A. Isard, 1998: The frequency and intensity of Great Lake cyclones. J. 4172 Climate, 11, 61–71. 4173 4174 Arguez, A.(ed.), 2007: State of the Climate in 2006. Bull. Amer. Meteor. Soc., 88, s1– 4175 s135. 4176 4177 Assel, R. A., K. Cronk, and D. C. Norton, 2003: Recent trends in Laurentian Great Lakes 4178 ice cover. Climatic Change, 57, 185-204. 4179 Do Not Cite or Quote 187 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4180 Assel, R. A., 2003: Great Lakes ice cover, first ice, last ice, and ice duration. NOAA 4181 Technical Memorandum GLERL-125. NOAA, Great Lakes Environmental 4182 Research Laboratory, Ann Arbor, MI, 49 pp. 4183 4184 Assel, R. A. Great Lakes ice cover climatology update: Winters 2003, 2004, and 2005. 4185 NOAA Technical Memorandum GLERL-135, 2005a: NOAA, Great Lakes 4186 Environmental Research Laboratory, Ann Arbor, MI, 21 pp. 4187 4188 Assel, R. A., 2005b: Classification of annual Great Lakes ice cycles: Winters of 1973- 4189 2002. Journal of Climate, 18, 4895-4905. 4190 4191 Bacon, S. and D.J.T. Carter, 1991: Wave climate changes in the North Atlantic and North 4192 Sea. International Journal of Climatology, 11, 545-558. 4193 4194 Bell, D. B. and M. Chelliah, 2006: Leading tropical modes associated with interannual 4195 and multidecadal fluctuations in North Atlantic hurricane activity. Journal of 4196 Climate, 17, 590-612. 4197 4198 Bonsal, B. R., X. Zhang, L. Vincent and W. Hogg, 2001: Characteristics of daily and 4199 extreme temperatures over Canada. Journal of Climate, 14, 1959-1976. 4200 4201 Bromirski, P.D., 2001, Vibrations from the “Perfect Storm”, Geochem. Geophys. 4202 Geosys., 2 (7), doi:10.1029/2000GC000119. 4203 4204 Bromirski, P. D., 2007: Extratropical cyclone-forced wave power variability along the 4205 U.S. Atlantic coast. Nature, submitted. 4206 4207 Bromirski, P. D. and J. Kossin, 2007: Hurricane waves along the U.S. east and Gulf 4208 coasts. Science, submitted. 4209 4210 Bromirski, P. D., R.E. Flick and N. Graham, 1999: Ocean wave height determined from 4211 inland seismometer data: Implications for investigating wave climate changes: 4212 Journal of Geophysical Research, 104, 20753-20766. 4213 4214 Bromirski, P.D., D.R. Cayan, and R.E. Flick, 2005: Wave spectral energy variability in 4215 the northeast Pacific. Journal of Geophysical Research, 110, C03005, 4216 doi:10.1029/2004JC002398. 4217 4218 Bromirski, P.D., R.E. Flick, and D.R. Cayan, 2003: Decadal storminess variability along 4219 the California coast: 1858 – 2000. Journal of Climate, 16, 982-993. 4220 4221 Brooks, H. E., 2004: On the relationship of tornado path length and width to intensity. 4222 Weather and Forecasting, 19, 310-319. 4223 Do Not Cite or Quote 188 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4224 Brooks, H. E., 2007: Development and use of climatologies of convective weather. 4225 Atmospheric Convection: Research and Operational Forecasting Aspects. 4226 Springer-Verlag, in press. 4227 4228 Brooks, H. E. and C. A. Doswell III, 2001: Some aspects of the international climatology 4229 of tornadoes by damage classification. Atmospheric Research, 56, 191-201. 4230 4231 Brooks, H. E., C. A. Doswell III and M. P. Kay, 2003a: Climatological estimates of local 4232 daily tornado probability. Weather and Forecasting, 18, 626-640. 4233 4234 Brooks, H. E., J. W. Lee and J. P. Craven, 2003b: The spatial distribution of severe 4235 thunderstorm and tornado environments from global reanalysis data. Atmospheric 4236 Research, 67-68, 73-94. 4237 4238 Brooks, H. E. and N. Dotzek, 2007: The spatial distribution of severe convective storms 4239 and an analysis of their secular changes. Climate Extremes and Society. H. F. 4240 Diaz and R. Murnane, Eds., Cambridge University Press, in press. 4241 4242 Burnett, A.W., M.E. Kirby, H.T. Mullins, and W.P. Patterson, 2003: Increasing Great 4243 Lake-effect snowfall during the Twentieth Century: A regional response to global 4244 warming? J. Climate, 16, 3535-3541. 4245 4246 Carter, D.J.T. and L. Draper, 1988: Has the north-east Atlantic become rougher? Nature, 4247 332, 494. 4248 4249 Cavazos, T., A. C. Comrie and D. M. Liverman, 2002: Intraseasonal variability 4250 associated with wet monsoons in southeast Arizona. J. Climate, 15, 2477-2490. 4251 4252 Cavazos, T. and D. Rivas, 2004: Variability of extreme precipitation events in Tijuana, 4253 Mexico. Climate Research, 25, 229-243. 4254 4255 Cavazos, T., C. Turrent., and D. P. Lettenmaier, 2007: Extreme precipitation variability 4256 in the core of the North American monsoon. Geophysical Research Letters, to be 4257 submitted. 4258 4259 Cayan, D. R. .A. Kammerdiener, M.D. Dettinger, J.M. Caprio, and D.H. Peterson, 2001: 4260 Changes in the onset of spring in the Western United States. Bulletin of the 4261 American Meteorological Society, 82, 399-415. 4262 4263 Chan, J. C. L., 2006: Comment on “Changes in tropical cyclone number, duration, and 4264 intensity in a warming environment. Science, 311, 1713. 4265 4266 Chan, J. C. L. and J.-E. Shi, 1996: Long-term trends and interannual variability in 4267 tropical cyclone activity over the western North Pacific. Geophysical Research 4268 Letters, 23, 2765-2767. 4269 Do Not Cite or Quote 189 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4270 Chang, E.K.M., and Y. Fu, 2002: Inter-decadal variations in Northern Hemisphere 4271 winter storm track intensity. J. Climate, 15, 642–658. 4272 4273 Chang, E. K., and Y. Guo, 2007: Is the number of North Atlantic tropical cyclones 4274 significantly underestimated prior to the availability of satellite observations? 4275 Geophys. Res. Lett., 34, L14801, doi:10.1029/2007GL030169. 4276 4277 Changnon, D., S.A. Changnon and S. Changnon, 2001: A method for estimating crop 4278 losses from hail in uninsured periods and regions. Journal of Applied 4279 Meteorology, 40, 84-91. 4280 4281 Changnon, S.A., 1982: Trends in tornado frequency: Fact or fallacy? Preprints, 12th 4282 conference on severe local storms, San Antonio, TX, American Meteorological 4283 Society, 42-44. 4284 4285 Changnon, S.A. and D. Changnon, 2000: Long-term fluctuations in hail incidences in 4286 the United States. Journal of Climate, 13, 658-664. 4287 4288 Changnon, S.A., D. Changnon, and T.R. Karl, 2006: Temporal and spatial characteristics 4289 of snowstorms in the contiguous United States. Journal of Applied Meteorology 4290 and Climatology, 45, 1141-1155. 4291 4292 Changnon, S. and T. Karl, 2003: Temporal and spatial variations in freezing rain in 4293 the contiguous U.S. Journal of Applied Meteorology, 42, 1302-1315. 4294 4295 Chenoweth, M., 2003: The 18th century climate of Jamaica. American Philosophical 4296 Society, 212-219. 4297 4298 Chu, J. –H., C. R. Sampson, A. S. Levine and E. Fukada, 2002: The joint typhoon 4299 warning center tropical cyclone best tracks, 1945-2000. Naval research 4300 Laboratory Reference Number NRL/MR/7540-02-16. 4301 4302 Clark, M. P., M. C. Serreze, and D. A. Robinson, 1999: Atmospheric controls on 4303 Eurasian snow cover extent. Int. J. Climatol., 19, 27-40. 4304 4305 Cleaveland, M. L., D. W. Stahle, M. D. Therrell, J. Villanueva-Diaz, and B. T. Burnes, 4306 2004: Tree-ring reconstructed winter precipitation and tropical teleconnections in 4307 Durango, Mexico. Climatic Change, 59(3), 369-388. 4308 4309 Coles, S.G., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer 4310 Verlag, New York. 4311 4312 Concannon, P. R., H. E. Brooks, and C. A. Doswell III, 2000: Climatological risk of 4313 strong and violent tornadoes in the United States. Preprints, 2nd Symposium on 4314 Environmental Applications, Long Beach, California, American Meteorological 4315 Society, 212-219. Do Not Cite or Quote 190 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4316 4317 Cook, E.R., D. M. Meko, D. W. Stahle, and M. K. Cleaveland, 1999: Drought 4318 reconstructions for the continental United States. Journal of Climate, 12, 1145– 4319 1162. 4320 4321 Cook, E.R., R.D. D'Arrigo, and M.E. Mann, 2002: A well-verified, multiproxy 4322 reconstruction of the winter North Atlantic Oscillation index since A.D. 1400. J. 4323 Climate, 15, 1754–1764. 4324 4325 Cook ER, Woodhouse CA, Eakin CM, et al., Long-term aridity changes in the western 4326 United States SCIENCE 306 (5698): 1015-1018 NOV 5 2004 4327 4328 Cooley, D., P. Naveau and D. Nychka, 2007: Bayesian spatial modeling of extreme 4329 precipitation return levels. Journal of the American Statistical Association, to 4330 appear. 4331 4332 Cooter, E. and S. LeDuc, 1995: Recent frost date trends in the northeastern United 4333 States. International Journal of Climatology, 15, 65-75. 4334 4335 Dai A.G., K.E. Trenberth, and T.T. Qian, 2004: A global dataset of Palmer Drought 4336 Severity Index for 1870-2002: Relationship with soil moisture and effects of 4337 surface warming. Journal of Hydrometeorology, 5, 1117-1130. 4338 4339 Davis, R. E., R. Dolan and G. Demme, 1993: Synoptic climatology of Atlantic coast 4340 north-easters, International Journal of Climatology, 13 (2), 171-189. 4341 4342 Davison, A.C. and R.L. Smith, 1990: Models for exceedances over high thresholds (with 4343 discussion). Journal of the Royal Statistical Society 52, 393-442. 4344 4345 DeGaetano, A. T. and R. J. Allen, 2002: Trends in the twentieth century temperature 4346 extremes across the United States. Journal of Climate, 15, 3188-3205. 4347 4348 Delworth, T.L. and M.E. Mann, 2000: Observed and simulated multidecadal variability 4349 in the Northern Hemisphere, Climate Dynamics, 16, 661-676. 4350 4351 Deser, C., A.S. Phillips, and J.W. Hurrell, 2004: Pacific interdecadal climate variability: 4352 Linkages between the tropics and the north Pacific during boreal winter since 4353 1900. J. Climate, 17, 3109–3124. 4354 4355 Dolan, R., H. Lins and B. Hayden, 1988: Mid-Atlantic coastal storms. Journal of Coastal 4356 Research, 4 (3), 417-433. 4357 4358 Donnelly, J.P., 2005: Evidence of past intense tropical cyclones from backbarrier salt 4359 pond sediments: a case study from Isla de Culebrita, Puerto Rico, U.S.A. Journal 4360 of Coastal Research, 42, 201-210. 4361 Do Not Cite or Quote 191 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4362 Donnelly, J.P., S. S. Bryant, J. Butler, J. Dowling, L. Fan, N. Hausmann, P. Newby, B. 4363 Shuman, J. Stern, K. Westover and T. Webb, III, 2001a: A 700 yr. sedimentary 4364 record of intense hurricane landfalls in southern New England. Geological Society 4365 of America Bulletin, 113, 714-727. 4366 4367 Donnelly, J.P., J. Butler, S. Roll, M. Wengren, and T. Webb, III, 2004: A backbarrier 4368 overwash record of intense storms from Brigantine, New Jersey. Marine Geology, 4369 210: 107-121. 4370 4371 Donnelly, J.P., S. Roll, M. Wengren, J. Butler, R. Lederer and T. Webb, III, 2001b: 4372 Sedimentary evidence of intense hurricane strikes from New Jersey. Geology, 29, 4373 615-618. 4374 4375 Donnelly, J.P. and T. Webb, III, 2004: Backbarrier sedimentary records of intense 4376 hurricane landfalls in the northeastern United States. In: Hurricanes and 4377 Typhoons: Past, Present, and Future (eds. R.J. Murnane, R.J. and K-b. Liu, 4378 Columbia University Press, pp. 58-95. 4379 4380 Donnelly, J. P., and J. D. Woodruff, 2007: Intense hurricane activity over the past 5,000 4381 years controlled by El Nino and the West African monsoon. Nature, 447, 465- 4382 468. 4383 4384 Doswell, C. A. III, H. E. Brooks and M. P. Kay, 2005: Climatological estimates of daily 4385 local nontornadic severe thunderstorm probability for the United States. Weather 4386 and Forecasting, 20, 577-595. 4387 4388 Doswell, C. A. III, R. Edwards, R. L. Thompson and K. C. Crosbie, 2006: A simple and 4389 flexible method for ranking severe weather events. Weather and Forecasting, 21, 4390 in press. 4391 4392 Douglas, M.W., R.A. Maddox, K. Howard, and S. Reyes, 1993: The Mexican monsoon. 4393 J. Climate, 6, 1665-1677. 4394 4395 Easterling, D.R., J.L. Evans, P.Ya. Groisman, T.R. Karl, K.E. Kunkel, and P. Ambenje, 4396 2000a: Observed variability and trends in extreme climate events: a brief review. 4397 Bulletin of the American Meteorological Society, 81, 417-425. 4398 4399 Easterling, D. R., 2002: Recent changes in frost days and the frost-free season in the 4400 United States. Bulletin of the American Meteorological Society, 83, 1327-1332. 4401 4402 Easterling, D.R., B. Horton, P.D. Jones, T.C. Peterson, T.R. Karl, D.E. Parker, M.J. 4403 Salinger, V. Razuvayev, N. Plummer, P. Jamison, and C.K. Folland, 1997: 4404 Maximum and minimum temperature trends for the globe. Science 277: 364-367. 4405 Do Not Cite or Quote 192 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4406 Easterling, D.R., T. Wallis, J. Lawrimore, and R. Heim, 2007b: The effects of 4407 temperature and precipitation trends on U.S. drought. Geophyical. Research 4408 Letters, submitted. 4409 4410 Easterling, D. R. B. Gleason, K. E. Kunkel and R, J. Stouffer, 2007a: A comparison 4411 between observed and model produced warm spells for the United States, to be 4412 submitted. 4413 4414 Edwards, D. C. and T. B. McKee, 1997: Characteristics of 20th Century Drought in the 4415 United States at Multiple Time Scales. Fort Collins, Colorado, Department of 4416 Atmospheric Science, Colorado State University. 4417 4418 Eichler, T., and W. Higgins, 2006: Climatology and ENSO-related variability of North 4419 American extra-tropical cyclone activity. J. Climate, 19, 2076–2093. 4420 4421 Elsner, J. B., A. A. Tsonis and T. H. Jagger, 2006: High-frequency variability in 4422 hurricane power dissipation and its relationship to global temperature. Bulletin of 4423 the American Meteorological Society, 87, 763-768. 4424 4425 Emanuel, K. A., 2005a: Increasing destructiveness of tropical cyclones over the past 30 4426 years. Nature, 436, 686-688. 4427 4428 Emanuel, K. A., 2005b: Emanuel replies. Nature, 438, doi:10.1038/nature04427. 4429 4430 Emanuel, K. A., 2007: Environmental factors affecting tropical cyclone power 4431 dissipation. J. Climate, accepted for publication. 4432 4433 Englehart, P.J. and A.V. Douglas, 2001: The Role of Eastern North Pacific Tropical 4434 Storms in the Rainfall Climatology of Western Mexico. Int. J. Climatology, 21, 4435 1357-1370. 4436 4437 Englehart, P.J., and A.V. Douglas, 2002: Mexico’s summer rainfall patterns: an analysis 4438 of regional modes and changes in their teleconnectivity. Atmósfera, 15, pp. 147- 4439 164. 4440 4441 Englehart, P.J. and A.V. Douglas, 2003: Urbanization and seasonal temperature trends: 4442 observational evidence from a data sparse part of North America. Int. J. Climatol 4443 23: 1253-1263. 4444 4445 Englehart P.J. and A.V. Douglas, 2005: Changing behavior in the diurnal range of 4446 surface air temperatures over Mexico. Geophys. Res. Ltrs. 32 No. 1: L01701 4447 10.1029/2004GL021139. 4448 4449 Englehart, P.J. and A.V. Douglas, 2006: Defining intraseasonal variability within the 4450 North American monsoon. J. Climate, 19, 4243-4253. 4451 Do Not Cite or Quote 193 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4452 Englehart, P. J., M. D. Lewis, and A. V. Douglas, 2007: Defining the frequency of near 4453 shore tropical cyclone activity in the eastern North Pacific from historical surface 4454 observations 1921-2005. Submitted to Geophys. Res. Lett.. 4455 4456 Federal Emergency management Agency, 1995: National mitigation strategy: 4457 Partnerships for building safer communities. Report, 40 pp, Washington, D. C. 4458 4459 Fernandez-Partagas, J. and H. F. Diaz, 1996: Atlantic hurricanes in the second half of 4460 the nineteenth century. Bulletin of the American Meteorological Society, 77, 4461 2899-2906. 4462 4463 Feuerstein, B., N. Dotzek and J. Grieser, 2005: Assessing a tornado climatology from 4464 global tornado intensity distributions, Journal of Climate, 18, 585-596. 4465 4466 Folland, C.K., et al., 1986: Sahel rainfall and worldwide sea temperatures, Nature, 320, 4467 686-688. 4468 4469 Frappier, A., Sahagian, D., Carpeter, S.J., Gonzalez, L.A., Frappier, B., 2007: A 4470 stalagmite record of recent tropical cyclones. Geology, 7,, 111114; doi: 4471 10.1130/G23145A. 4472 4473 Gandin L. S., and R. L. Kagan, 1976: Statistical Methods of Interpretation of 4474 Meteorological Data. (in Russian). Gidrometeoizdat, 359 pp 4475 4476 Garcia Herrera, R., L. Gimeno, P. Ribera and E. Hernandez, 2005: New records of 4477 Atlantic hurricanes from Spanish documentary sources, Journal of Geophysical 4478 Research, 110:D03109. 4479 4480 Garcia Herrera, R., F. Rubio, D. Wheeler, E. Hernandez, M. R. Prieto and L. Gimero, 4481 2004: The use of Spanish and British documentary sources in the investigation of 4482 Atlantic hurricane incidence in historical times. In: Hurricanes and Typhoons: 4483 Past, Present, and Future (eds. R.J. Murnane, R. J. and K-b. Liu), p. 149-176. 4484 Columbia University Press. 4485 4486 Garriott, E.B., 1903: Storms of the Great Lakes. U.S. Department of Agriculture, 4487 Weather Bureau, Bulletin K, 486 pp. 4488 4489 Geng, Q., and M. Sugi, 2001: Variability of the North Atlantic cyclone activity in winter 4490 analyzed from NCEP–NCAR reanalysis data. J. Climate, 14, 3863–3873. 4491 4492 Gershunov, A., and T.P. Barnett, 1998: Inter-decadal modulation of ENSO 4493 teleconnections. Bull. Amer. Meteor. Soc., 79, 2715–2725. 4494 4495 Gershunov, A. and D. R. Cayan, 2003: Heavy daily precipitation frequency over the 4496 contiguous United States: sources of climate variability and seasonal Do Not Cite or Quote 194 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4497 predictability. J. Climate, 16, 2752-2765. 4498 4499 Gershunov, A. and H. Douville, 2007: Extensive summer hot and cold extremes under 4500 current and possible future climatic conditions: Europe and North America. In 4501 Diaz, H.F. and Murnane, R.J. eds., Climate Extremes and Society, Cambridge: 4502 Cambridge University Press (in press). 4503 4504 Goldenberg, S. B., C. W. Landsea, A.M. Mesta-Nuñez and W. M. Gray, 2001: The 4505 recent increase in Atlantic hurricane activity: Causes and implications. Science, 4506 293, 474-479. 4507 4508 4509 Graham, N.E., 1994: Decadal-scale climate variability in the tropical and North Pacific 4510 during the 1970s and 1980s: observations and model results, Climate Dynamics, 4511 10, 135-162. 4512 4513 Graham, N. E. and H. F. Diaz, 2001: Evidence for intensification of North Pacific winter 4514 cyclones since 1948, Bulletin of the American Meteorological Society, 82, 1869- 4515 1893. 4516 4517 Grazulis, T. P., 1993: Significant Tornadoes, 1680-1991. Environmental Films, St. 4518 Johnsbury, VT, 1326 pp. 4519 4520 Groisman, P.Ya., T.R. Karl, D.R. Easterling, R.W. Knight, P.B. Jamason, K.J. 4521 Hennessy, R. Suppiah, Ch.M. Page, J. Wibig, K. Fortuniak, V.N. Razuvaev, A. 4522 Douglas, E. Førland, and P.-M. Zhai, 1999: Changes in the probability of heavy 4523 precipitation: Important indicators of climatic change. Climatic Change, 42, 243- 4524 283. 4525 4526 Groisman, P.Ya. R.W. Knight , and T.R. Karl, 2001: Heavy precipitation and high 4527 streamflow in the contiguous United States: Trends in the 20th century. Bull. 4528 Amer. Meteorol. Soc., 82, 219-246. 4529 4530 Groisman, P. Ya., R. W. Knight, and T. R. Karl, D. R. Easterling, B. Sun and J. 4531 H. Lawrimore, 2004: Contemporary changes of the hydrological cycle over the 4532 contiguous United States: Trends derived from in situ observations, Journal of 4533 Hydrometeorology, 5, 64–85. 4534 4535 Groisman, P.Ya, R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl and V. 4536 N. Razuvaev, 2005: Trends in intense precipitation in the climate record. Journal 4537 of Climate, 18, 1326–1350. 4538 4539 Groisman, P.Ya. and R.W. Knight, 2007: Prolonged dry episodes over the conterminous 4540 United States: New tendencies emerged during the last 40 years. J. Climate, 4541 submitted. 4542 Do Not Cite or Quote 195 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4543 Groisman, P.Ya., B.G. Sherstyukov, V.N. Razuvaev, R.W. Knight, J.G. Enloe, N.S. 4544 Stroumentova, P. H. Whitfield, E. Førland, I. Hannsen-Bauer, H. Tuomenvirta, H. 4545 Aleksandersson, A. V. Mescherskaya, and T.R. Karl, 2007a: Potential forest fire 4546 danger over Northern Eurasia: Changes during the 20th century. Global and 4547 Planetary Change, 56, issue 3-4, 371-386. 4548 4549 Groisman, P. Ya., R. W. Knight, R. W. Reynolds, A. B. Smith, T.R. Karl, and O. N. 4550 Bulygina, 2007b: Tropical Cyclone Related Precipitation over the Southeastern 4551 United States. (to be submitted) 4552 4553 Gulev S. K. and V. Grigorieva, 2004: Last century changes in ocean wind wave height 4554 from global visual wave data. Geophys. Res. Lett., 31, L24302, 4555 doi:10.1029/2004GL021040. 4556 4557 Gumbel, E.J., 1958: Statistics of Extremes. Columbia University Press. 4558 4559 Guttman, N.B., 1998: Comparing the Palmer Drought Index and the Standardized 4560 Precipitation Index. Journal of the American Water Resources Association 34, 4561 113–121, doi:10.1111/j.1752-1688.1998.tb05964. 4562 4563 Hallack-Alegria, M., and D. W. Watkins Jr., 2007: Annual and warm season drought 4564 intensity-duration-frequency analysis for Sonora, Mexico. Journal of Climate, 20, 4565 1897-1909. 4566 4567 Harman, J.R., R. Rosen, and W. Corcoran, 1980: Winter cyclones and circulation 4568 patterns on the western Great Lakes. Phys. Geogr., 1, 28–41. 4569 4570 Harnik N. and E.K.M. Chang, 2003: Storm track variations as seen in radiosonde 4571 observations and reanalysis data. Journal of Climate, 16, 480-495. 4572 4573 Harper B. A. and J. Callaghan, 2006: On the importance of reviewing historical tropical 4574 cyclone intensities. American Meteorological Society, 27th Conference on 4575 Hurricanes and Tropical Meteorology, 2C.1, Monterey, Apr. 4576 4577 Hartmann, D.L. and E.D. Maloney, 2001: The Madden-Julian Oscillation, barotropic 4578 dynamics, and North Pacific tropical cyclone formation. Part II: Stochastic 4579 barotropic modeling, Journal of Atmospheric Sciences, 58, 2559-2570. 4580 4581 Heim Jr., R.R., 2002: A review of Twentieth-Century drought indices used in the United 4582 States. Bulletin of the American Meteorological Society, 83, 1149–1165. 4583 4584 Herweijer, C., R. Seager and E.R. Cook, 2006: North American droughts of the mid-to- 4585 late nineteenth century: a history, simulation and implication for mediaeval 4586 drought. The Holocene, 16, 159-171. 4587 Do Not Cite or Quote 196 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4588 Herweijer, C., R. Seager, E.R. Cook and J. Emile-Geay, 2007: North American droughts 4589 of the last millennium from a gridded network of tree-ring data. Journal of 4590 Climate, in press. 4591 4592 Higgins, R.W., Y. Chen and A.V. Douglas, 1999: Interannual Variability of the North 4593 American Warm Season Precipitation Regime. J. Climate, 12, 653-680. 4594 4595 4596 Hirsch, M. E., A. T. DeGaetano and S. J. Colucci, 2001: An East Coast winter storm 4597 climatology. Journal of Climate, 14 (5), 882-899. 4598 4599 Hobbins, M.T., A. Dai, M. L. Roderick, and G. D. Farquhar, 2007: Back to basics: 4600 Revisiting potential evapotranspiration as a driver of water balance trends. 4601 Geophysical Research Letters, submitted. 4602 4603 Hoegh-Guldberg, O., 2005: Low coral cover in a high-CO2 world. Journal of 4604 Geophysical Research, 110, C09S06, doi:10.1029/2004JC002528. 4605 4606 Holland, G.J., 2007: Misuse of landfall as a proxy for Atlantic tropical cyclone activity. 4607 EOS (in press). 4608 4609 Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the North 4610 Atlantic: natural variability or climate trend? Phil. Trans. R. Soc. A, 4611 doi:10.1098/rsta.2007.2083. 4612 4613 Hoyos, C. D., P. A. Agudelo, P. J. Webster and J. A. Curry, 2006: Deconvolution of the 4614 factors contributing to the increase in global hurricane intensity. Science, 312: 94- 4615 97. 4616 4617 Hsu, S.A., Martin, M.F., and Blanchard, B.W., 2000: An evaluation of the USACE’s 4618 deepwater wave prediction techniques under hurricane conditions during Georges in 4619 1998. Journal of Coastal Research, 16, 823-829. 4620 4621 Huschke, R. E. (editor) (1959) Glossary of Meteorology, American Meteorological Society, 4622 Boston, Massachusetts, USA, pgs. 106 and 419. 4623 4624 IPCC, 2001: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to 4625 the Third Assessment Report of the Intergovernmental Panel on Climate Change 4626 [Houghton, J.T.,Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. 4627 Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, United 4628 Kingdom and New York, NY, USA, 881pp. 4629 4630 Jarvinen, B. R., C. J. Neumann and M. A. S. Davis, 1984: A tropical cyclone data tape 4631 for the North Atlantic Basin, 1886-1983. Contents, limitations and uses. Tech. 4632 Memo. NWS NHC-22, 21pp. NOAA, Washington DC. 4633 Do Not Cite or Quote 197 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4634 Jones, G.V., and R.E. Davis, 1995: Climatology of Nor’easters and the 30 kPa jet. J. 4635 Coastal Res., 11 (3), 1210-1220. 4636 4637 Jones P.D. and A. Moberg, 2003: Hemispheric and large-scale surface air temperature 4638 variations: an extensive revision and an update to 2001. J Climate 16: 206-223. 4639 4640 Jones, P.D., T.J. Osborn, and K.R. Briffa, 2003: Pressure-based measures of the North 4641 Atlantic Oscillation (NAO): A comparison and an assessment of changes in the 4642 strength of the NAO and in its influence on surface climate parameters. In: The 4643 North Atlantic Oscillation: Climatic Significance and Environmental Impact 4644 [Hurrell, J.W., et al. (eds.)]. 4645 4646 Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. 4647 Saha, G. White, J. Woollen, Y. Zhu, A. Leetmaa, B. Reynolds, M. Chelliah, W. 4648 Ebisuzaki, W. Higgins, J. Janowiak, K. Mo, C. Ropelewski, J. Wang, R. Jenne, 4649 and D. Joseph, 1996: The NCEP/NCAR 40-Year reanalysis project. Bull. Amer. 4650 Meteor. Soc., 77, 437–471. 4651 4652 Kamahori, H. N. Yamazaki, N. Mannoji, and K. Takahashi, 2006: Variability in intense 4653 tropical cyclone days in the western North Pacific. SOLA, 2, 104-107, 4654 doi:10.2151/sola.2006-027. 4655 4656 Karl, T.R.; and R.W. Knight. 1985. Atlas of Monthly Palmer Hydrological Drought 4657 Indices (1931–1983) for the Contiguous United States. Historical Climatology 4658 Series 3–7, National Climatic Data Center, Asheville, North Carolina. 4659 4660 Karl, T.R. and R. W. Knight, 1998: Secular Trends of Precipitation Amount, Frequency, 4661 and Intensity in the United States. Bulletin of the American Meteorological 4662 Society, 79, 231–241 4663 4664 Keetch, J.J. and G.M. Byram, 1968: A drought index for forest fire control. U.S.D.A. 4665 Forest Service Research Paper SE-38. 35 pp. [Available from: 4666 http://www.srs.fs.fed.us/pubs/] 4667 4668 Key, J. R. and A. C. K. Chan, 1999: Multidecadal global and regional trends in 1000 mb 4669 and 500 mb cyclone frequencies. Geophysical Research Letters, 26 2035-2056. 4670 4671 Kharin, V.V., and F.W. Zwiers, 2000: Changes in the extremes in an ensemble of 4672 transient climate simulation with a coupled atmosphere-ocean GCM. Journal of 4673 Climate, 13, 3760-3788. 4674 4675 Kharin, V. V., F. W. Zwiers, X. Zhang, and G. C. Hegerl, 2007: Changes in temperature 4676 and precipitation extremes in the IPCC ensemble of global coupled model 4677 simulations. Journal of Climate, accepted. 4678 Do Not Cite or Quote 198 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4679 Kim, T-W., J. B. Valdes, and J. Aparicio, 2002: Frequency and spatial characteristics of 4680 droughts in the Conchos river basin, Mexico. International Water Resources, 4681 27(3), 420-430. 4682 4683 Klotzbach, P. J., 2006: Trends in global tropical cyclone activity over the past twenty 4684 years (1986-2005). Geophysical Research Letters, 33, L10805, 4685 doi:10.1029/2006GL025881. 4686 4687 Knaff, J. A and C. R. Sampson, 2006: Reanalysis of West Pacific tropical cyclone th 4688 intensity 1966-1987. Proceedings of 27 AMS Conference on Hurricanes and 4689 Tropical Meteorology, #5B.5. Available online at: 4690 http://ams.confex.com/ams/pdfpapers/108298.pdf 4691 4692 Kocin, P. J., P. N. Schnumacher, R. F. Morales Jr. and L.W.Uccellini, 1995: Overview of 4693 the 12-14 March 1993 Superstorm. Bulletin of the American Meteorological 4694 Society, 76 (2), 165-182. 4695 4696 Komar, P.D. and J.C. Allan, 2007a: Higher waves along U.S. East Coast linked to 4697 hurricanes. EOS, Transactions, American Geophysical Union, 88, 301. 4698 4699 Komar, P.D., and J.C. Allan, 2007b: Increasing wave heights along the U.S. Atlantic 4700 coast due to the intensification of hurricanes. Journal of Coastal Research, in 4701 press. 4702 4703 Kossin, J. P., K. R. Knapp, D. J. Vimont, R. J. Murnane, and B. A. Harper, 2007a: A 4704 globally consistent reanalysis of hurricane variability and trends. Geophys. Res. 4705 Lett., 34, L04815, doi:10.1029/2006GL028836. 4706 4707 Kossin, J. P., J. A. Knaff, H. I. Berger, D. C. Herndon, T. A. Cram, C. S. Velden, R. J. 4708 Murnane, and J. D. Hawkins, 2007b: Estimating hurricane wind structure in the 4709 absence of aircraft reconnaissance. Wea. Forecasting, 22, 89-101 4710 4711 Kossin, J. P., and D. J. Vimont, 2007: A more general framework for understanding 4712 Atlantic hurricane variability and trends. Bull. Amer. Meteor. Soc., in press. 4713 4714 Kunkel, K.E., 2003: North American trends in extreme precipitation. Natural Hazards, 4715 29, 291-305. 4716 4717 Kunkel, K.E., S.A. Changnon, and J.R. Angel, 1994: Climatic aspects of the 1993 Upper 4718 Mississippi River basin flood. Bull. Amer. Meteor. Soc., 75, 811-822. 4719 4720 Kunkel, K. E., K. Andsager and D.R. Easterling, 1999: Long-term trends in extreme 4721 precipitation events over the conterminous United States and Canada. Journal of 4722 Climate, 12, 2515-2527. 4723 Do Not Cite or Quote 199 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4724 Kunkel, K.E., N.E. Westcott, and D.A.R. Kristovich, 2002: Assessment of potential 4725 effects of climate change on heavy lake-effect snowstorms near Lake Erie. J. 4726 Great Lakes Res., 28, 521-536. 4727 4728 Kunkel, K. E., D. R. Easterling, K. Redmond and K. Hubbard, 2003: Temporal 4729 variations of extreme precipitation events in the United States: 1895-2000. 4730 Geophysical Research Letters, 30, 1900, 10.1029/2003GL018052. 4731 4732 Kunkel, K.E., D.R. Easterling, K. Redmond, and K. Hubbard, 2004: Temporal variations 4733 in frost-free season in the United States: 1895–2000, Geophys. Res. Lett., 31, 4734 L03201, doi:10.1029/2003GL018624. 4735 4736 Kunkel, K.E., T.R. Karl, and D.R. Easterling, 2007a: A Monte Carlo assessment of 4737 uncertainties in heavy precipitation frequency variations. J. Hydrometeor., in 4738 press. 4739 4740 Kunkel, K. E., R. A. Pielke, Jr. and S. A. Changnon, 1999: Temporal fluctuations in 4741 weather and climate extremes that cause economic and human health impacts: A 4742 review. Bulletin of the American Meteorological Society, 80, 1077-1098. 4743 4744 Kunkel, K.E., K.E., M. Palecki, L. Ensor, D. Robinson, K.Hubbard, D. Easterling, and 4745 K. Redmond, 2007b: Trends in 20th Century U.S. snowfall using a quality- 4746 controlled data set. Proceedings, 75th Annual Meeting, Western Snow Conference, 4747 in press. 4748 4749 Landsea, C. W., et al, 2004: The Atlantic hurricane database re-analysis project: 4750 Documentation for the 1851-1910 alterations and additions to the HURDAT 4751 database. In Hurricanes and Typhoons: Past, Present and Future, R. J. Murnane 4752 and K.-B. Liu, Eds., Columbia University Press. 4753 4754 Landsea, C. W., 2005: Hurricanes and global warming. Nature, 438, 4755 doi:10.1038/nature04477. 4756 4757 Landsea, C. (2007), Counting Atlantic tropical cylones back in time. EOS, 88(18), 197- 4758 203. 4759 4760 Landsea, C.W., B.A. Harper, K. Hoarau and J.A. Knaff, 2006: Can we detect trends in 4761 extreme tropical cyclones? Science. 313, 452-454. 4762 4763 Lemke, P., et al. 2007: Observations: Changes in Snow, Ice and Frozen Ground., Chapter 4764 4, Report of Working Group I, Intergovernmental Panel on Climate Change, 4765 Fourth Assessment Report, in press. 4766 4767 Lewis, P.J., 1987: Severe storms over the Great Lakes: a catalogue summary for the 4768 period 1957–1985. Canadian Climate Center Report No. 87–13, Atmospheric 4769 Environment Service, Downsview, ON, Canada, 342 pp. Do Not Cite or Quote 200 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4770 4771 Liu, K-b., 2004: Paleotempestology: Principles, methods, and examples from Gulf Coast 4772 lake sediments. In: Hurricanes and Typhoons: Past, Present, and Future, R.J. 4773 Murnane, R. J. and K-b. Liu, Eds., Columbia University Press, pp. 13-57. 4774 4775 Liu, K-b. and M. L. Fearn, 1993: Lake-sediment record of late holocene hurricane 4776 activities from coastal Alabama. Geology, 21, 793-796. 4777 4778 Liu, K-b. and M. L. Fearn, 2000: Reconstruction of prehistoric landfall frequencies of 4779 catastrophic hurricanes in northwestern Florida from lake sediment records. 4780 Quaternary Research, 54, 238-245. 4781 4782 Liu, K-b., C. Shen, C. and K. S. Louie, 2001: A 1,000-year history of typhoon landfalls 4783 in Guangdong, southern China, reconstructed from Chinese historical 4784 documentary records. Annals of the Association of American Geographers, 91, 4785 453-464. 4786 4787 Louie, K.S. and K.-b. Liu, 2003: Earliest historical records of typhoons in China. Journal 4788 of Historical Geography, 29, 299-316. 4789 4790 Louie, K.S. and K.-b. Liu, 2004: Ancient records of typhoons in Chinese historical 4791 documents. In: Hurricanes and Typhoons: Past, Present, and Future , R.J. 4792 Murnane, R. J. and K-b. Liu, Eds., Columbia University Press, pp. 222-248. 4793 4794 Ludlam, D.M., 1963: Early American hurricanes, 1492-1870. American Meteorological 4795 Society. 4796 4797 Madden, R. and P. Julian, 1971: Detection of a 40-50 day oscillation in the zonal wind in 4798 the tropical Pacific, Journal of the Atmospheric Sciences, 28, 702-708. 4799 4800 Madden, R. and P. Julian, 1972: Description of global-scale circulation cells in the 4801 tropics with a 40-50 day period, Journal of the Atmospheric Sciences, 29, 1109- 4802 1123. 4803 4804 Magnuson, J. J., D.M. Robertson, B. J. Benson, R. H. Wynne, D.M. Livingston, T. Arai, 4805 R. A. Assel, R. G. Barry, V. Card, E. Kuusisto, N.G. Granin, T. D. Prowse, K. M. 4806 Stewart, and V. S. Vuglinski, 2000: Historical trends in lake and river ice cover in 4807 the Northern Hemisphere. Science, 289, 1743–1746. 4808 4809 Maloney, E. D. and D.L. Hartmann, 2000a: Modulation of eastern North Pacific 4810 hurricanes by the Madden-Julian Oscillation, Journal of Climate, 13, 1451-1460. 4811 4812 Maloney, E. D. and D.L. Hartmann, 2000b: Modulation of hurricane activity in the Gulf 4813 of Mexico by the Madden-Julian Oscillation, Science, 287, 2002-2004. 4814 Do Not Cite or Quote 201 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4815 Maloney, E. D. and D.L. Hartmann, 2001: The Madden-Julian Oscillation, barotropic 4816 dynamics, and North Pacific tropical cyclone formation. Part I: Observations, 4817 Journal of the Atmospheric Sciences, 58, 2545-2558. 4818 4819 Mann, M. E. and K. Emanuel, 2006: Atlantic hurricane trends linked to climate change. 4820 EOS, 87, 233-241. 4821 4822 Mann, M.E. and J. Park, 1994: Global-scale modes of surface temperature variability on 4823 interannual to century timescales, Journal of Geophysical Research, 99, 25819- 4824 25833. 4825 4826 Mann, M.E., K.A. Emanuel, G.J. Holland and P.J. Webster, 2007: Atlantic tropical 4827 cyclones revisited. EOS (in press). 4828 4829 Mann, M.E., T.A. Sabbatelli and U. Neu, 2007: Evidence for a modest undercount bias 4830 in early historical Atlantic tropical cyclone counts. Nature (submitted). 4831 4832 Manning, D. M., and R. E. Hart, 2007: Evolution of North Atlantic ERA40 tropical 4833 cyclone representation. Geophys. Res. Lett., 34, L05705. 4834 doi:10.1029/2006GL028266. 4835 4836 Mather, J.R., R.T. Field, and G.A. Yoskioka (1967), Storm hazard damage along the 4837 East Coast of the U.S., J. Appl. Meteor., 6, 20-30. 4838 4839 Maue, R. N., and R. E. Hart, 2007: Comment on “Low frequency variability in globally 4840 integrated tropical cyclone power dissipation” by Ryan Sriver and Matthew 4841 Huber. Geophys. Res. Lett., 34, L05705. doi:10.1029/2006GL028266. 4842 4843 McCabe, G. J., M. P. Clark and M. C. Serreze, 2001: Trends in Northern Hemisphere 4844 surface cyclone frequency and intensity. Journal of Climate, 14, 2763-2768. 4845 4846 McCarthy, D. W., J. T. Schaefer and R. Edwards, 2006: What are we doing with (or to) 4847 the F-xcale? Preprints, 23rd Conference on Severe Local Storms, St. Louis, 4848 Missouri, American Meteorological Society, Conference CD. (Available online at 4849 http://ams.confex.com/ams/pdfpapers/115260.pdf.) 4850 4851 McKee, T. B., N. J. Doesken and J. Kleist, 1993: Drought monitoring with multiple 4852 timescales. Preprints of the Eight Conference on Applied Climatology, Anaheim, 4853 California. 4854 4855 Michaels, P.J., P.C. Knappenberger, O.W. Frauenfeld, and R.E. Davis, 2004: Trends in 4856 precipitation on the wettest days of the year across the contiguous USA. Int. J. 4857 Climatol. 24: 1873–1882. 4858 4859 4860 Do Not Cite or Quote 202 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4861 Millas, J.C., 1968: Hurricanes of the Caribbean and adjacent regions, 1492-1800. 4862 Academy of the Arts and Sciences of the Americas. 4863 4864 Miller, A.J., Cayan, D., T. Barnett, N. Graham, and J. Oberhuber, 1994: The 1976-77 4865 climate shift of the Pacific Ocean, OCEANOGRAPHY, 1, 21-26. 4866 4867 Miller, D. L., Mora, C. I., Grissino-Mayer, H. D., Uhle, M. E. and Sharp, Z., 2006: Tree- 4868 ring isotope records of tropical cyclone activity. Proc. Nat. Acad. Sci., 103, 4869 14294-14297. 4870 4871 Mo, K.C. and R.E. Livezey, 1986: Tropical-extratropical geopotential height 4872 teleconnections during the northern hemisphere winter, Mon. Wea. Rev., 114, 4873 2488-2515, 1986. 4874 4875 Mock, C.J., 2004: Tropical cyclone reconstructions from documentary records: examples 4876 for South Carolina, United States. In: Hurricanes and Typhoons: Past, Present, 4877 and Future, R.J. Murnane, R. J. and K-b. Liu, Eds., Columbia University Press, p. 4878 121-148. 4879 4880 Moon, I.J., I. Ginis, T. Hara, H.L. Tolman, C.W. Wright, and E.J. Walsh (2003), 4881 Numerical simulation of sea surface directional wave spectra under hurricane 4882 wind forcing, J. Phys. Ocean., 33, 1680-1706. 4883 4884 Mueller K.J., M. DeMaria, J. Knaff, et al., 2006: Objective estimation of tropical cyclone 4885 wind structure from infrared satellite data. Weather and Forecasting, 21, 990- 4886 1005. 4887 4888 Nakamura, H., 1992: Midwinter suppression of baroclinic wave activity in the Pacific. J. 4889 Atmos. Sci., 49, 1629–1642. 4890 4891 Neelin J.D., M. Munnich, H. Su, et al., 2006: Tropical drying trends in global warming 4892 models and observations. Proceedings of the National Academy of Sciences of the 4893 United States of America, 103, 6110-6115. 4894 4895 Nicholas, R. and D. S. Battisti, 2006. Drought recurrence and seasonal rainfall prediction 4896 in the Yaqui basin, Mexico. Journal Applied Meteorology and 4897 Climatology,accepted. 4898 4899 Noel, J., and D. Changnon, 1998: A Pilot Study Examining U.S. Winter Cyclone 4900 Frequency Patterns Associated with Three ENSO Parameters. J. Climate, 11, 4901 2152–2159. 4902 4903 Nyberg, J., B. A. Malmgren, A. Winter, M. R. Jury, K. Halimeda Kilbourne, and T. M. 4904 Quinn, 2007: Low Atlantic hurricane activity in the 1970s and 1980s compared to 4905 the past 270 years. Nature, 447, 698-702. 4906 Do Not Cite or Quote 203 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4907 Paciorek, C.J., J.S. Risbey, V. Ventura, and R.D. Rosen, 2002: Multiple indices of 4908 Northern Hemisphere cyclone activity, Winters 1949–99. J. Climate, 15, 1573– 4909 1590. 4910 4911 Palmer, W. C. 1965. Meteorological drought. Research Paper No. 45, U.S. Department 4912 of Commerce Weather Bureau, Washington, D.C 4913 4914 Palmer, W.C. 1968. Keeping track of crop moisture conditions, nationwide: The new 4915 Crop Moisture Index. Weatherwise 21:156–161. 4916 4917 Pavia E.G. and A. Badan, 1998: ENSO modulates rainfall in the Mediterranean 4918 Californias. GEOPHYSICAL RESEARCH LETTERS, 25, 3855-3858 4919 4920 Peterson, T. C. et al. 2002: Recent changes in the climate extemes in the Caribbean 4921 region. Journal of Geophysical Research, 107, NO.D21, 4601, 4922 doi:10.1029/2002JD002251,2002. 4923 4924 Peterson, T. C., X. Zhang, M. Brunet India, J. L. Vázquez Aguirre, 2007: Changes in 4925 North American extremes derived from daily weather data. Proceedings of the 4926 National Academy of Science, submitted. 4927 4928 Pickands, J. (1975), Statistical inference using extreme order statistics. Annals of 4929 Statistics 3, 119-131. 4930 4931 Pielke, R. A., Jr., 2005: Are there trends in hurricane destruction? Nature, 438, E11. 4932 4933 Robeson, S. M., 2004: Trends in time-varying percentiles of daily minimum and 4934 maximum temperature over North America," Geophysical Research Letters, 31, 4935 L04203, doi:10.1029/2003GL019019. 4936 4937 Ropelewski, C., 1999: The great El Niño of 1997-1998: Impacts on precipitation and 4938 temperature. Consequences, 5 (2). 4939 4940 Robinson, D.A., K.F. Dewey, and R.R. Heim Jr., 1993: Global snow cover monitoring: 4941 an update. Bull. Am. Meteorol. Soc., 74, 1689–1696. 4942 4943 Sanders, F., and J.R. Gyakum, 1980: Synoptic-dynamic climatology of the “bomb”. 4944 Mon. Wea. Rev., 108, 1589–1606. 4945 4946 Schlesinger, M.E. and N. Ramankutty, 1994: An oscillation in the global climate system of 4947 period 65–70 years. Nature, 367, 723–726. 4948 4949 SEMARNAP, 2000: Programa nacional contra incendios forestales. Resultados 1995- 4950 2000. Mexico, 263 pp. 4951 Do Not Cite or Quote 204 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4952 Semenov VA, Bengtsson L, 2002: Secular trends in daily precipitation characteristics: 4953 greenhouse gas simulation with a coupled AOGCM. CLIMATE DYNAMICS, 19, 4954 123-140 4955 4956 Serreze, M. C., F. Arse, R. G. Barry and J. C. Rogers, 1997: Iclandic low cyclone 4957 activity. Climatological features, linkages with the NAO, and relationships with 4958 recent changes in the Northern Hemisphere circulation. Journal of Climate, 10 4959 (3), 453-464. 4960 4961 Seymour, R. J., R. R. Strange, D. R. Cayan and R. A. Nathan, 1984: Influence of El 4962 Niños on California’s wave climate. Proceedings 19th International Conference 4963 on Coastal Engineering, Amer. Soc. Civil Engrs., 577-592. 4964 4965 Shabbar, A. and W. Skinner, 2004: Summer drought patterns in Canada and the 4966 relationship to global sea surface temperatures. J. Climate, 17, 2866–2880. 4967 4968 Shein, K. A., ed., 2006: State of the climate in 2005. Bulletin of the American 4969 Meteorological Society, 87, 1-S102. 4970 4971 Simmonds, I., and K. Keay, 2000: Variability of Southern Hemisphere extra-tropical 4972 cyclone behavior, 1958–97. J. Climate, 13, 550–561. 4973 4974 Simmonds, I. and K. Keay, 2002: Surface fluxes of momentum and mechanical energy 4975 over the North Pacific and North Atlantic Oceans. Meteorology and Atmospheric 4976 Physics, 80, 1-18. 4977 4978 Sims, A. P., D. D. S. Niyogi and S. Raman, 2002: Adopting drought indices for 4979 estimating soil moisture: A North Carolina case study. Geophysical Research 4980 Letters, 29, 1183. 4981 4982 Smith, R.L., 2003: Statistics of extremes, with applications in environment, insurance 4983 and finance. Chapter 1 of Extreme Values in Finance, Telecommunications and 4984 the Environment, edited by B. Finkenstadt and H. Rootzen, Chapman and 4985 Hall/CRC Press, London, pp. 1-78. 4986 4987 Smith, R.L., C. Tebaldi, D. Nychka and L.O. Mearns, 2007: Bayesian modeling of 4988 uncertainty in ensembles of climate models. Journal of the American Statistical 4989 Association, under revision. 4990 4991 Soja, A.J., N. M. Tchebakova, N. H.F. French, M. D. Flannigan, H. H. Shugart, B. J. 4992 Stocks, A. I. Sukhinin, E.I. Parfenova, F. S. Chapin III and Jr., and P. W. 4993 Stackhouse, 2007: Climate-induced boreal forest change: Predictions versus 4994 current observations. Global and Planetary Change, in press. 4995 4996 4997 Do Not Cite or Quote 205 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 4998 Sriver, R. and M. Huber, 2006: Low frequency variability in globally integrated tropical 4999 cyclone power dissipation. Geophysical Research Letters, 33, L11705, 5000 doi:10.1029/2006GL026167. 5001 5002 Stahl, K., R.D. Moore, I.G. McKendry, 2006: Climatology of winter cold spells in 5003 relation to mountain pine beetle mortality in British Columbia, Canada. Climate 5004 Research, 32, 13-23. 5005 5006 Stahle, D. W., E. R. Cook, M. K. Cleaveland, M. D. Therrell, D. M. Meko, H. D. 5007 Grissino-Mayer, E. Watson, and B. H. Luckman, 2000: Tree-ring data document 5008 16th century megadrought over North America, Eos Trans. AGU, 81, 121. 5009 5010 Stone, D.A., A.J. Weaver and F.W. Zwiers, 2000: Trends in Canadian precipitation 5011 intensity. Atmosphere-Ocean, 38, 321-347 5012 5013 Sun B.M. and P.Y. Groisman, 2004: Variations in low cloud cover over the United States 5014 during the second half of the twentieth century. JOURNAL OF CLIMATE, 17, 5015 1883-1888. 5016 5017 Therrell, M. D., D. W. Stahle, M. K. Cleaveland, and J. Villanueva-Diaz, 2002: Warm 5018 season tree growth and precipitation in Mexico. Journal of Geophysical Research, 5019 107 (D14), 4205. 5020 5021 Tolman, H.L., B. Balasubramaniyan, L.D. Burroughs, D. Chalikov, Y.Y. Chao, H.S. 5022 Chen, and V.M. Gerald (2002), Development and implementation of wind- 5023 generated ocean surface wave models at NCEP, Wea. Forecasting, 17, 311-333. 5024 5025 Trenberth, K.E., 1990: Recent observed interdecadal climate changes in the Northern 5026 Hemisphere. Bull. Amer. Meteor. Soc., 71, 988–993. 5027 5028 Trenberth, K.E. and J.W. Hurrell, 1994: Decadal atmospheric-ocean variations in the 5029 Pacific. Climate Dynamics, 9, 303-319 5030 5031 Trenberth, K.E. and D.J. Shea, 2006: Atlantic hurricanes and natural variability in 2005. 5032 Geophysical Research Letters, 33, doi:10.1029/2006GL026894. 5033 5034 Trenberth, K.E., et al., 2002: The evolution of ENSO and global atmospheric 5035 temperatures. J. Geophys. Res., 107, 4065, doi:10.1029/2000JD000298. 5036 5037 Uppala, S.M., Kållberg, P.W., Simmons, A.J., Andrae, U., da Costa Bechtold, V., 5038 Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, 5039 K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, 5040 M.A., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Caires, S., 5041 Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, 5042 S., Hólm, E., Hoskins, B.J., Isaksen, L., Janssen, P.A.E.M., Jenne, R., McNally, 5043 A.P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N.A., Saunders, R.W., Simon, P., Do Not Cite or Quote 206 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5044 Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J. 5045 2005: The ERA-40 re-analysis. Quart. J. R. Meteorol. Soc., 131, 2961-3012 5046 (doi:10.1256/qj.04.176). 5047 5048 Vecchi, G. A., and T. R. Knutson, 2007: On estimates of historical North Atlantic 5049 tropical cyclone activity. J. Climate, submitted. 5050 5051 Verbout, S. M., H.E. Brooks, L. M. Leslie,and D. M. Schultz, 2006: Evolution of the US 5052 tornado database: 1954-2003. Weather and Forecasting, 21, 86-93. 5053 5054 Vimont, D. J., and J. P. Kossin, 2007: The Atlantic meridional mode and hurricane 5055 activity. Geophysical Research Letters, in press. 5056 5057 Vincent, L. A. and E. Mekis, 2006: Changes in daily and extreme temperature and 5058 precipitation indices for Canada over the 20th century. Atmoshere-Ocean, 44, 177- 5059 193. 5060 5061 Wang, D. W., D. A. Mitchell, W. J. Teague, E. Jarosz and M.S. Hulbet, 2005: Extreme 5062 waves under Hurricane Ivan. Science, 309, 896. 5063 5064 Wang, W. L. and V. R. Swail, 2001: Changes of extreme wave heights in Northern 5065 Hemisphere oceans and related atmospheric circulation regimes. Journal of 5066 Climate, 14, 2201-2204. 5067 5068 Wang, X. L., V. R. Swail and F. W. Zwiers, 2006a: Climatology and changes of 5069 extratropical storm tracks and cyclone activity. Comparison of ERA-40 with 5070 NCEP/NCAR reanalysis for 1958-2001. Journal of Climate, 19, 3145-3166. 5071 5072 Wang, X. L., H. Wan and V. R. Swail, 2006b: Observed changes in cyclone activity in 5073 Canada and their relationships to major circulation regimes. Journal of Climate, 5074 19, 895-916. 5075 5076 Webster, P. J., G. J. Holland, J. A. Curry and H.-R. Chang, 2005: Changes in tropical 5077 cyclone number, duration, and intensity in a warming environment. Science, 309, 5078 1844-1846. 5079 5080 Wehner, M.F., Predicted 21st century changes in seasonal extreme precipitation events in 5081 the Parallel Climate Model, J. Climate 17 (2004) 4281-4290 5082 5083 Wehner, M., Changes in daily precipitation and surface air temperature extremes in the 5084 IPCC AR4 models. US CLIVAR Variations, 3, (2005) pp 5-9. LBNL-61594 5085 5086 Westerling, A.L., H.G. Hidalgo, and D.R. Cayan, 2006. Warming and earlier spring 5087 increases in western U.S. forest wildfire activity. Science 313:940-943. 5088 Do Not Cite or Quote 207 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5089 Woodhouse, C.A. and J. T. Overpeck, 1998: 2000 Years of Drought Variability in the 5090 Central United States. Bulletin of the American Meteorological Society, 79, 2693– 5091 2714. 5092 5093 Wu, L., B. Wang, and S. Geng, 2005: Growing typhoon influence on east Asia, 5094 Geophysical Research Letters, 32, L18703, doi:10.1029/2005GL022937. 5095 5096 Wu, C.C., K.H. Chou, Y.Q. Wang, et al., 2006: Tropical cyclone initialization and 5097 prediction based on four-dimensional variational data assimilation. JOURNAL OF 5098 THE ATMOSPHERIC SCIENCES, 63, 2383-2395. 5099 5100 Xie, L., L. J. Pietrafesa, J. M. Morrison, and T. Karl, 2005: Climatological and 5101 interannual variability of North Atlantic hurricane tracks. Journal of Climate, 18, 5102 5370-5381. 5103 5104 Zhang, R., T. L. Delworth, and I. M. Held, 2007: Can the Atlantic Ocean drive the 5105 observed multidecadal variability in Northern Hemisphere mean temperature? 5106 Geophysical Research Letters, 34, L02709, doi:10.1029/2006GL028683. 5107 5108 Zhang, X., W. D. Hogg, and E. Mekis, 2001: Spatial and temporal characteristics of 5109 heavy precipitation events over Canada. Journal of Climate, 14, 19231936. 5110 5111 Zhang, X., J. E. Walsh, J. Zhang, U. S. Bhatt and M. Ikeda, 2004: Climatology and inter- 5112 annual variability of Arctic cyclone activity. 1948-2002. Journal of Climate, 17, 5113 2300-2317. 5114 5115 Zwiers, F. W., and V. V. Kharin, 1998: Changes in the extremes of the climate simulated 5116 by CCC GCM2 under CO2 doubling. Journal of Climate, 11, 2200-2222 Do Not Cite or Quote 208 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5117 Table 2.1 Regressions for the decadal trends of increasing wave heights measured off the 5118 Washington coast (NDBC buoy #46005). [after Allan and Komar (2006)] 5119 —————————————————————————————— 5120 Wave Heights Rate Ratio of Rate to Statistical 5121 (m/yr) Annual Average Significance* 5122 —————————————————————————————— 5123 Annual Average 0.024 1.0 SS 5124 Winter Average 0.032 1.3 SS 5125 Five Largest 0.095 4.0 SS 5126 Three Largest 0.103 4.3 NSS 5127 Maximum 0.108 4.5 NSS 5128 100-yr Projection ≈0.13 ≈5.5 estimate 5129 —————————————————————————————— 5130 SS = statistically significant at the 0.05 level; NSS = not statistically significant. Do Not Cite or Quote 209 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5131 5132 5133 5134 Figure 2.1 Changes in the percent of days in a year above three thresholds for North 5135 America for daily high temperature (top) and daily low temperature (bottom) from 5136 Peterson et al. (2007). Do Not Cite or Quote 210 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5137 Trends in Number of Days With Unusually Warm Daily Low 5138 Temperature 5139 5140 5141 Figure 2.2 Trends in the number of days in a year when the daily low is unusually warm 5142 (ie. In the top 10% of warm nights for the 1950-2004 period). Grid boxes with green 5143 squares are statistically significant at the p=0.05 level, (from Peterson et al. 2007). A 5144 trend of 1.8 days/decade translates to a trend of 9.9 days over the entire 55-year (1950- 5145 2004) period, meaning that 10 days more a year will have unusually warm nights. Do Not Cite or Quote 211 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5146 5147 5148 5149 Figure 2.3 Time series of (a) annual values of a U.S. national average “heat wave” 5150 index. Heat waves are defined as warm spells of 4 days in duration with mean 5151 temperature exceeding the threshold for a 1 in 10 year event. (updated from Kunkel et al. 5152 1999); (b)Area of the U.S. (in percent) with much above normal daily high temperatures 5153 in summer; (c) Area of the U.S. (in percent) with much above normal daily low 5154 temperatures in summer. Blue vertical bars give values for individual seasons while red 5155 lines are smoothed (9-yr running) averages. Do Not Cite or Quote 212 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 U.S. Frost-Free Season Length 10 ) 5 0 -5 Deviation from average (days average from Deviation -10 1895 1910 1925 1940 1955 1970 1985 2000 Year 5156 5157 5158 Figure 2.4 Change in the length of the frost free season averaged over the U.S. (from 5159 Kunkel et al. 2003). The frost-free season is at least 10 days longer on average than the 5160 long-term average. Do Not Cite or Quote 213 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5161 Changes in the Daily Range of Temperature for Mexico 5162 5163 5164 5165 5166 1.25 5167 1.00 5168 5169 0.75 5170 5171 0.50 5172 0.25 5173 5174 0.00 5175 -0.25 5176 5177 DTR Anomaly (°C) -0.50 5178 -0.75 5179 5180 -1.00 5181 5182 40 45 50 55 60 65 70 75 80 85 90 95 00 05 19 19 19 19 19 19 19 19 19 19 19 19 20 20 5183 5184 5185 5186 Figure 2.5 Change in the daily range of temperature (difference between the daily low 5187 and the daily high temperature) during the warm Season (June-Sept) for Mexico. This 5188 difference is known as a Diurnal Temperature Range (DTR). The recent rise in the daily 5189 temperature range reflects hotter daily summer highs. The time series represents the 5190 average DTR taken over the four temperature regions of Mexico as defined in Englehart 5191 and Douglas, 2004. Trend line (red) based on LOWESS smoothing (n=30). Do Not Cite or Quote 214 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5192 5193 5194 Figure 2.6 the area (in percent) of area in severe to extreme drought as measured by the 5195 Palmer Drought Severity Index for the U.S. (red) from 1900 to present and for North 5196 America (blue) from 1950 to present. Do Not Cite or Quote 215 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5197 Western U.S. Drought Area for the last 1200 years 5198 5199 60 5200 5201 . 5202 50 5203 5204 5205 5206 40 5207 5208 5209 30 5210 5211 5212 20 Percent drought area 5213 5214 5215 10 5216 800 1000 1200 1400 1600 1800 2000 5217 5218 YEAR 5219 5220 5221 Figure 2.7 Area of drought in the western U.S. as reconstructed from tree rings (Cook et 5222 al. 2004). Do Not Cite or Quote 216 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5223 5224 5225 Figure 2.8 Regions where disproportionate increases in heavy and very heavy 5226 precipitation during the past decades were documented compared to the change in the 5227 annual and/or seasonal precipitation. Because these results come from different studies, 5228 the definitions of extreme precipitation vary. (a) annual anomalies (% departures) of 5229 heavy precipitation for northern Canada. (b) as (a), but for southeastern Canada. (c) the 5230 top 0.3% of daily rain events over the central United States and the trend (22%/113 yrs) 5231 (updated from Groisman et al. 2005). (d) as for (c), but for southern Mexico. (e) change 5232 of intensity of the upper 5% of daily rain events in the core monsoon region of Mexico, 5233 relative to the 1961-1990 base period. (Cavazos et al., 2007) (f) upper 5%, top points, and 5234 upper 0.3%, bottom points, of daily precipitation events and linear trends for British 5235 Columbia south of 55°N. (g) upper 5% of daily precipitation events and linear trend for 5236 Alaska south of 62°N. Do Not Cite or Quote 217 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5237 5238 5239 Figure 2.9 Frequency (expressed as a percentage anomaly from the period of record 5240 average) of excessive precipitation periods of 90 day duration exceeding a 1-in-20-year 5241 event threshold for the U.S. Annual values have been smoothed with a 9-yr running 5242 average filter. The black line shows the trend (a linear fit) for the annual values. Do Not Cite or Quote 218 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5243 5244 5245 5246 Figure 2.10 Average (median) percentage of warm season rainfall (May-November) 5247 from Hurricanes and tropical storms affecting Mexico and the Gulf Coast of the United 5248 States. Figure updated from Englehart and Douglas 2001. Do Not Cite or Quote 219 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5249 5250 5251 Figure 2.11 Variations and linear trend in various characteristics of the summer 5252 monsoon in southern Sonora, Mexico, including (a) the mean start date June 1 = Day 1 5253 on the graph; (b) the mean wet spell length defined as the mean number of consecutive 5254 days with mean regional precipitation >1mm; and (c) the mean daily rainfall intensity for 5255 wet days defined as the regional average rainfall for all days with rainfall > 1mm. Do Not Cite or Quote 220 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5256 5257 5258 Figure 2.12 Trends in hurricane/tropical storm rainfall statistics at Manzanillo, Mexico, 5259 including (a) the total warm season rainfall from hurricanes/tropical storms; (b) the ratio 5260 of hurricane/tropical storm rainfall to total summer rainfall; and (c) the number of days 5261 each summer with a hurricane or tropical storm within 550km of the stations Do Not Cite or Quote 221 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5262 5263 5264 Figure 2.13 Sea surface temperatures (blue) correlated with the Power Dissipation Index 5265 for North Atlantic hurricanes (Emanuel, 2007). Sea Surface Temperature is from the 5266 Hadley Centre dataset and is for the Main Development Region for tropical cyclones in 5267 the Atlantic, defined as 6-18°N, 20-60°W. The time series have been smoothed using a 1- 5268 3-4-3-1 filter to reduce the effect of interannual variability and highlight fluctuations on 5269 time scales of 3 years and longer. Do Not Cite or Quote 222 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5270 5271 5272 Figure 2.14 Century changes in the intensity of North Atlantic tropical cyclones, 5273 hurricanes and major hurricanes. Also shown are all individual tropical cyclone 5274 intensities. (From Holland and Webster 2007). Do Not Cite or Quote 223 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5275 5276 5277 Figure 2.15 Combined annual numbers of hurricanes and tropical storms for the North 5278 Atlantic (black dots), together with a 9-year running mean filter (black line) and the 9- 5279 year smoothed sea surface temperature in the eastern North Atlantic (red line). Adapted 5280 from Holland and Webster (2007). Do Not Cite or Quote 224 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5281 5282 5283 Figure 2.16 Atlantic hurricanes and tropical storms for 1878-2006, using the adjustment 5284 method A for missing storms described in the text. Black curve is the adjusted annual 5285 storm count, red is the 5-yr running mean, and solid blue curve is a normalized (same 5286 mean and variance) 5-yr running mean sea surface temperature index for the Main 5287 Development Region of the tropical Atlantic (HadISST, 80-20W, 10-20N; Aug.-Oct.). 5288 Green curves show the adjustment that has been added for missing storms to obtain the 5289 black curve, assuming two simulated ship-storm “encounters” are required for a modern- 5290 day storm to be “detected” by a historical ship traffic for a given year. Dashed green 5291 curve is an alternative adjustment sensitivity test requiring just one ship-storm simulated 5292 encounter for detection. Straight lines are least squares trend lines for the adjusted storm 5293 counts. (Adapted from Vecchi and Knutson, 2007). Do Not Cite or Quote 225 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5294 5295 5296 Figure 2.17 Counts of total North Atlantic basin hurricanes (black), major hurricanes 5297 (red) and U.S. landfalling hurricanes (blue) based on annual data from 1851 to 2006 and 5298 smoothed (using a 5-year running mean). Asterisks on the time series indicate years 5299 where trends beginning in that year and extending through 2005 are statistically 5300 significant (p=0.05) based on annual data; circles indicate non-significant trend results. Do Not Cite or Quote 226 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5301 5302 5303 Figure 2.18 Changes from average (1959-1997) in the number of winter (Nov-Mar) 5304 storms each year in the Northern Hemisphere for (a) high latitudes (60o-90oN), and (b) 5305 mid-latitudes (30o-60oN), and the change from average of winter storm intensity in the 5306 Northern Hemisphere each year for (c) high latitudes (60o-90oN), and (d) mid-latitudes 5307 (30o-60oN). [Adapted from McCabe et al. 2001]. Do Not Cite or Quote 227 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5308 5309 5310 Fig. 2.19 Extreme wind speed (meters per second), number of winter storms, and number 5311 of intense (≤980 hPa) winter storms for the Pacific region (20o-70oN, 130oE-112.5oW; 5312 panels a-b-c) and the Atlantic region (20o-70oN, 7.5oE-110oW; panels d-e-f):. The thick 5313 smooth lines are the trends determined using a Bayesian spline model, and the thin 5314 dashed lines denote the 95% confidence intervals. [Adapted from Paciorek et al. 2002]. Do Not Cite or Quote 228 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5315 5316 5317 Figure 2.20 Cumulative extreme Non-Tide Residuals (NTR) (water level) exceeding the 5318 98th percentile level of hourly NTR levels at San Francisco, during winter months (Dec- 5319 Mar), with the 5-yr running mean (red line). Least squares trend estimates for the entire 5320 winter record (light dashed line) and since 1948 (bold dashed line), the period covered by 5321 NCEP reanalysis and ERA-40 data used in most ETC studies. [Adapted from Bromirski 5322 et al. 2003]. Do Not Cite or Quote 229 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5323 5324 5325 Figure 2.21 Seasonal totals (gray line) covering the period of 1951-1997 for (a) all East 5326 Coast Winter Storms (ECWS; top curve) and strong ECWS (bottom curve), (b) northern 5327 ECWS (>35oN), and (c) those ECWS tracking along the full coast. Data points along the 5328 5-yr moving average (black) correspond to the middle year. [Adapted from Hirsch et al. 5329 2001]. Do Not Cite or Quote 230 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5330 5331 5332 Figure 2.22 Track of the October 1991 “Perfect Storm” (PS) center showing the east-to- 5333 west retrograde propagation of a non-typical Nor’easter. The massive ETC was 5334 reenergized as it moved southward by absorbing northward propagating remnants of 5335 Hurricane Grace, becoming unnamed Hurricane #8 and giving rise to the name “Perfect 5336 Storm” for this composite storm. Storm center locations with date/hr time stamps at 6-hr 5337 intervals are indicated by stars. Also shown are locations of open ocean NOAA buoys 5338 that measured the extreme waves generated by these storms. [Adapted from Bromirski 5339 2001]. Do Not Cite or Quote 231 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5340 5341 5342 Figure 2.23 Increases in the summer, hurricane-generated wave heights of 3 meters and 5343 higher significant wave heights (from Komar and Allan 2007, and in review). Do Not Cite or Quote 232 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5344 5345 5346 Figure 2.24 Number of significant wave heights measured by the Cape Hatteras buoy 5347 during the July-September season, early in its record 1976-1991 and during the recent 5348 decade,1996-2005 (from Komar and Allan 2007a,b). Do Not Cite or Quote 233 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5349 5350 5351 Figure 2.25 (a) Location of the NOAA Atlantic and Gulf buoys discussed. Bathymetric 5352 contours identify the continental shelf boundary. (b) Total number of significant wave 5353 events per hurricane season. (c) Total number of wave events identified during each 5354 month of the June-November hurricane season for all buoy data available from NOAA’s 5355 National Ocean Data Center (NODC) from 1978-2006. Panels (b) and (c) show the Do Not Cite or Quote 234 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5356 number of wave events associated with hurricanes/tropical storms with wave heights that 5357 exceeded 3 m at a minimum of one of the buoys in each group. Each event was counted 5358 only once, even if observed at multiple buoys in a group. No data were available from 5359 NODC for any of the Atlantic buoys during the 1979 hurricane season. [Adapted from 5360 Bromirski, 2007b] Do Not Cite or Quote 235 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5361 5362 5363 Figure 2.26 A measure of the total annual tropical cyclone wave power in the western 5364 North Atlantic and Gulf regions obtained as the mean of the available annual deep water 5365 wave power (the wave power index, WPI). Longer period variability is emphasized by 5366 lowpass filtering the annual data with three iterations of a 1-2-1 smoothing operator, 5367 giving the Atlantic and Gulf region WPI (thick lines). [Adapted from Bromirski, 2007b) Do Not Cite or Quote 236 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5368 5369 5370 Figure 2.27 The trends of increasing wave heights measured by NOAA’s National Data 5371 Buoy Center (NDBC) buoy #46005 off the coast of Washington [after Allan and Komar 5372 (2006)] Do Not Cite or Quote 237 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5373 5374 5375 Figure 2.28 Tornado reports in official database in USA from 1954-2004. Open circles 5376 are raw reports, solid line (linear regression) is the trend for raw reports, solid circles are 5377 reports adjusted to 2002 reporting system. The adjusted data show little or no trend in 5378 reported tornadoes. The trend in raw reports reflects an increasing density of population 5379 in tornado-prone areas, and therefore more opportunity for sightings, rather than a real 5380 increase in the occurrences of tornadoes. Do Not Cite or Quote 238 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5381 5382 5383 Figure 2.29 Schematic of the North Atlantic Oscillation (NAO) showing its effect on 5384 extremes. Illustrations by Fritz Heidi and Jack Cook, Woods Hole Oceanographic 5385 Institution. Do Not Cite or Quote 239 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5386 Chapter 3 How Well Do We Understand the Causes of 5387 Observed Changes in Extremes, and What Are the 5388 Projected Future Changes? 5389 5390 Convening Lead Author: William J. Gutowski, Jr., Iowa State Univ. 5391 5392 Lead Authors: Gabriele C. Hegerl, Duke Univ.; Greg J. Holland, NCAR; Thomas R. 5393 Knutson, NOAA; Linda O. Mearns, NCAR; Ronald J. Stouffer, NOAA; Peter J. Webster, 5394 Ga. Inst. Tech.; Michael F. Wehner, DOE LBNL; Francis W. Zwiers, Environment 5395 Canada 5396 5397 Contributing Authors: Harold E. Brooks, NOAA; Kerry A. Emanuel, Mass. Inst. 5398 Tech.; Paul D. Komar, Oreg. State Univ.; James P. Kossin, Univ. Wisc., Madison; 5399 Kenneth E. Kunkel, Univ. Ill. Urbana-Champaign, Ill. State Water Survey; Christopher 5400 W. Landsea, NOAA; Ruth McDonald, Met Office, United Kingdom; Gerald A. Meehl, 5401 NCAR; Robert J. Trapp, Purdue Univ. 5402 5403 KEY FINDINGS 5404 Observed Changes 5405 Changes in some weather and climate extremes are attributable to human-induced 5406 changes in greenhouse gases. 5407 • Human-induced warming has likely caused much of the average temperature increase 5408 in North America over the past 50 years. This affects changes in temperature 5409 extremes. 5410 • Heavy precipitation events averaged over North America have increased over the past 5411 50 years, consistent with the increased water holding capacity of the atmosphere in a 5412 warmer climate and observed increases in water vapor over the oceans. Do Not Cite or Quote 240 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5413 • It is likely that human activities have caused a discernable increase in sea surface 5414 temperatures in the hurricane formation region of the tropical Atlantic Ocean over the 5415 past 100 years. The balance of evidence suggests that human activity has caused a 5416 discernable increase in tropical storm/hurricane and major hurricane frequency in the 5417 North Atlantic. 5418 5419 Projected Changes 5420 • Future changes in extreme temperatures will generally follow changes in average 5421 temperature: 5422 – Abnormally hot days and nights and heat waves are very likely to become more 5423 frequent. 5424 – Cold days and cold nights are very likely to become much less frequent. 5425 – The number of days with frost is very likely to decrease. 5426 • Droughts are likely to become more frequent and severe in some regions as higher air 5427 temperatures increase the potential for evaporation. 5428 • Over most regions, precipitation is likely to be less frequent but more intense, and 5429 precipitation extremes are very likely to increase. 5430 • According to theory and models for North Atlantic and North Pacific hurricanes and 5431 typhoons (both basin-wide and land-falling): 5432 – Hurricane/typhoon core rainfall rates will likely increase by about 6 to 18% per 5433 degree Celsius tropical sea surface warming. 5434 – It is likely that surface wind speeds of the strongest hurricanes/typhoons will 5435 increase by about 2 to 10% per degree Celsius tropical sea surface warming. Do Not Cite or Quote 241 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5436 – Frequency changes are too uncertain for confident projections. 5437 – The spatial distribution of hurricanes/typhoons will likely change. 5438 – Due to projected sea level rise, the potential for storm surge damage will very 5439 likely increase. 5440 • There are likely to be more frequent deep low-pressure systems (strong storms) 5441 outside the tropics, with stronger winds and more extreme wave heights. 5442 5443 3.1 Introduction 5444 Understanding physical mechanisms of extremes involves processes governing the timing 5445 and location of extreme behavior, such as ENSO cycles, as well as the mechanisms of 5446 extremes themselves (e.g., processes producing heavy precipitation). In other words, 5447 processes creating an environment conducive to extreme behavior and processes of the 5448 extreme behavior itself. This includes not only the extreme events but also the factors 5449 governing their timing and location. 5450 5451 A deeper understanding of physical mechanisms is of course important for understanding 5452 why extremes have occurred in the past and for predicting their occurrence in the future. 5453 Understanding physical mechanisms serves a further purpose for projected climate 5454 changes. Because the verification time for climate-change projections can be many 5455 decades into the future, strict verification of projections is not always possible. Other 5456 means of attaining confidence in projections are therefore needed. Confidence in 5457 projected changes in extremes increases when the physical mechanisms producing 5458 extremes in models are consistent with observed behavior. This requires careful analysis Do Not Cite or Quote 242 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5459 of the observed record as well as model output. Assessment of physical mechanisms is 5460 also necessary to determine the physical realism of changes in extremes. While physical 5461 consistency of simulations with observed behavior is not sufficient evidence for accurate 5462 projection, it is necessary. 5463 5464 3.2 What Are the Physical Mechanisms of Observed Changes in Extremes? 5465 3.2.1 Detection and Attribution of Anthropogenic Influences on Climate Extremes 5466 Over North America 5467 Climate change detection, as discussed in this chapter, is distinct from the concept that is 5468 used in Chapter 2. In that chapter, detection refers to the identification of change in a 5469 climate record that is statistically distinguishable from the record’s previous 5470 characteristics. A typical example is the detection of a statistically significant trend in a 5471 temperature record. Here, detection and attribution involves the assessment of observed 5472 changes in relation to those that are expected to have occurred in response to external 5473 forcing. Detection of climatic changes in extremes involves demonstrating statistically 5474 significant changes in properties of extremes over time. Attribution further links those 5475 changes with variations in climate forcings, such as changes in greenhouse gases, solar 5476 radiation or frequency of volcanoes. Attribution is a necessary step toward identifying the 5477 physical causes of changes in extremes. Attribution often uses quantitative comparison 5478 between climate-model simulations and observations, comparing expected changes due to 5479 physical understanding integrated in the models with those that have been observed. By 5480 comparing observed changes with those anticipated to result from external forcing, 5481 detection and attribution studies also provide an assessment of the performance of climate Do Not Cite or Quote 243 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5482 models in simulating climate change. The relationships between observed and simulated 5483 climate change that are diagnosed in these studies also provide an important means of 5484 constraining projections of future change made with those models. 5485 5486 3.2.1.1 Detection and Attribution of Anthropogenic Changes in Mean Climate That 5487 Affect Climate Extremes 5488 This section discusses the present understanding on the causes of large-scale changes in 5489 the climatic state over North America. Simple statistical reasoning indicates that 5490 substantial changes in the frequency and intensity of extreme events can result from a 5491 relatively small shift in the average of a distribution of temperatures, precipitation or 5492 other climate variables (Katz and Brown 1992). Expected changes in temperature 5493 extremes are largely but not entirely due to changes in seasonal mean temperatures. Some 5494 differences between the two arise because moderate changes are expected in the shape of 5495 the temperature distribution affecting climate extremes, for example, due to changes in 5496 snow cover, soil moisture, and cloudiness (e.g., Hegerl et al., 2004; Kharin et al., 2007). 5497 In contrast, increases in mean precipitation are expected to increase the precipitation 5498 variance , thus increasing precipitation extremes, but decreases in mean precipitation do 5499 not necessarily imply that precipitation extremes will decrease, because of the different 5500 physical mechanisms that control mean and extreme precipitation (e.g., Allen and 5501 Ingram, 2002; Kharin et al., 2007). Therefore, changes in the precipitation background 5502 state are also interesting for interpreting changes in extremes, although more difficult to 5503 interpret (Groisman et al., 1999). Relevant information about mean temperature changes Do Not Cite or Quote 244 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5504 appeared in Chapter 2. More detailed discussion of historical mean changes appears in 5505 CCSP Synthesis and Assessment Products 1-1, 1-2 and 1-3. 5506 5507 Global-scale analyses using space-time detection techniques have robustly identified the 5508 influence of anthropogenic forcing on the 20th century near-surface temperature changes. 5509 This result is robust to applying a variety of statistical techniques and using many 5510 different climate simulations (Hegerl et al., 2007). Detection and attribution analyses also 5511 indicate that over the past century there has likely been a cooling influence from aerosols 5512 and natural forcings counteracting some of the warming influence of the increasing 5513 concentrations of greenhouse gases. Spatial information is required in addition to 5514 temporal information to reliably detect the influence of aerosols and distinguish them 5515 from the influence of increased greenhouse gases. 5516 5517 A number of studies also consider sub-global scales. Studies examining North America 5518 find a detectable human influence on 20th century temperature changes, either by 5519 considering the 100-year period from 1900 (Stott 2003) or the 50-year period from 1950 5520 (Zwiers and Zhang 2003, Zhang et al. 2006). Based on such studies, a substantial part of 5521 the warming over North America has been attributed to human influence (Hegerl et al., 5522 2007). 5523 5524 Further analysis has compared simulations using changes in both anthropogenic 5525 (greenhouse gas and aerosol) and natural (solar flux and volcano eruption) forcings with 5526 others that neglect anthropogenic changes. There is a clear separation in North American Do Not Cite or Quote 245 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5527 temperature changes of ensembles of simulations including just natural forcings from 5528 ensembles of simulations containing both anthropogenic and natural forcings (Karoly et 5529 al. 2003, IDAG 2005, Karoly and Wu 2005, Wang et al. 2006, Knutson et al. 2006, 5530 Hegerl et al. 2007), especially for the last quarter of the 20th century, indicating that the 5531 warming in recent decades is inconsistent with natural forcing alone. 5532 5533 Attribution of observed changes on regional (subcontinental) scales has generally not yet 5534 been accomplished. One reason is that as spatial scales considered become smaller, the 5535 uncertainty becomes larger (Stott and Tett 1998, Zhang et al., 2006) because internal 5536 climate variability is typically larger than the expected responses to forcing on these 5537 scales. Also, small-scale forcings and model uncertainty make attribution on these scales 5538 more difficult. Therefore, interpreting changes on sub-continental scales is difficult (see 5539 discussion in Hegerl et al., 2007). For example, in Alaska warming has been large but 5540 high levels of internal variability lead to an overlap of naturally forced and all-forcing 5541 simulations even at the end of the 20th century (Wang et al. 2007). In central North 5542 America, there is a relatively small warming over the 20th century compared to other 5543 regions around the world (Hegerl et al. 2007) and the observed changes lie (just) within 5544 the envelop of changes simulated by models using natural forcing alone. In this context, 5545 analysis of a multi-model ensemble by Kunkel et al. (2006) for a central U.S. region 5546 suggests that the region’s warming from 1901to 1940 and cooling from 1940 to 1979 5547 may have been a consequence on unforced internal variability. 5548 Do Not Cite or Quote 246 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5549 Burkholder and Karoly (2007) detected an anthropogenic signal in multidecadal trends of 5550 a U.S. climate extremes index. The observed increase is largely due to an increase in the 5551 number of months with monthly mean daily maximum and daily minimum temperatures 5552 that are much above normal and an increase in the area of the US that experienced a 5553 greater than normal proportion of their precipitation from extreme one-day events. 5554 Twentieth century simulations from coupled climate models show a similar, significant 5555 increase in the same US climate extremes index for the late twentieth century. There is 5556 some evidence of an anthropogenic signal in regions a few hundred kilometers across 5557 (Karoly and Wu 2005, Knutson et al. 2006, Zhang et al. 2006, Burkholder and Karoly 5558 2007), suggesting the potential for progress in regional attribution if careful attention is 5559 given to the choice of appropriate time scales, region sizes and fields analyzed and if all 5560 relevant forcings are considered. 5561 5562 Warming from greenhouse gas increases is expected to increase the moisture content of 5563 the atmosphere and lead to a small increase in global mean precipitation. More important, 5564 the increase in water holding capacity of the atmosphere is expected to affect more 5565 strongly changes in heavy precipitation, for which the Clausius-Clapeyron relation 5566 provides an approximate physical constraint (e.g., Allen and Ingram, 2002). Observed 5567 changes in moisture content and mean and extreme precipitation are generally consistent 5568 with these expectations (Chapter 2 of this document, Trenberth et al. 2007). In addition, 5569 greenhouse gas increases are also expected to cause enhanced horizontal transport of 5570 water vapor that is expected to lead to a drying of the subtropics and parts of the tropics Do Not Cite or Quote 247 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5571 (Kumar et al., 2004; Neelin et al., 2006), and a further increase in precipitation in the 5572 equatorial region and at high latitudes (Emori and Brown, 2005; Held and Soden, 2006). 5573 5574 Several studies have demonstrated that simulated global land mean precipitation in 5575 climate model integrations including both natural and anthropogenic forcings is 5576 significantly correlated with that observed (Allen and Ingram, 2002; Gillett et al., 2004b; 5577 Lambert et al., 2004), thereby detecting external influence in observations of 5578 precipitation. This external influence on global land mean precipitation during the 20th 5579 century is dominated by volcanic forcing. Anthropogenic influence on the spatial 5580 distribution of global land precipitation, as represented by zonal-average precipitation 5581 changes, has also been detected (Zhang et al. 2007). Both changes are significantly larger 5582 in observations than simulated in climate models, raising questions about whether models 5583 underestimate the response to external forcing in precipitation changes (see also Wentz et 5584 al 2007). Changes in North American continental-mean rainfall have not yet been 5585 attributed to anthropogenic influences. A large part of North America falls within the 5586 latitude band identified by Zhang et al. (2007) where the model simulated response to 5587 forcing is not in accord with the observed response. However, both models and 5588 observations show a pattern of wetting north of 50N and drying between 0-30N, and this 5589 together with agreement on moistening south of the equator provides support for the 5590 detection of a global anthropogenic influence. 5591 Do Not Cite or Quote 248 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5592 3.2.1.2 Detection and Attribution of Other Modes of Climate-system Behavior 5593 Affecting Climate Extremes 5594 North American extreme climate is also substantially affected by changes in atmospheric 5595 circulation (e.g., Thompson and Wallace 2001). Natural low frequency variability of the 5596 climate system is dominated by a small number of large-scale circulation patterns such as 5597 the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the 5598 Northern Annular Mode (NAM). The impact of these modes on terrestrial climate on 5599 annual to decadal time scales can be profound. In particular, there is considerable 5600 evidence that the state of these modes affects substantially the risk of extreme 5601 temperature (Thompson and Wallace 2002, Kenyon and Hegerl 2007), droughts 5602 (Hoerling and Kumar 2003), and short-term precipitation extremes (e.g., Gershunov and 5603 Cayan 2003, Eichler and Higgins 2006) over North America. 5604 5605 Some evidence of anthropogenic influence on these modes appears in surface-pressure 5606 analyses. Gillett et al. (2003, 2005, 2006) and Wang et al. (2007) diagnosed 5607 anthropogenic influence on Northern Hemisphere sea level pressure change, although the 5608 model-simulated change is not as large as has been observed. Model-simulated changes 5609 in extremes related to circulation changes may therefore be affected. The change in sea 5610 level pressure largely manifests itself through an intensification of the Northern and 5611 Southern Annular Modes with reduced pressure above both poles and equatorward 5612 displacement of mass. However, apart from these modes, the extent to which modes of 5613 variability are excited or altered by external forcing remains uncertain. While some 5614 modes might be expected to change as a result of anthropogenic effects such as the Do Not Cite or Quote 249 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5615 enhanced greenhouse effect, there is little a priori expectation about the direction or 5616 magnitude of such changes. In addition, models may not simulate well the behavior of 5617 these modes in some regions and seasons. 5618 5619 ENSO is the leading mode of variability in the tropical Pacific, and it has impacts on 5620 climate around the globe (Trenberth et al. 2007, see also Chapter 1 of this report). There 5621 have been multi-decadal oscillations in the ENSO index throughout the 20th century, with 5622 more intense El Niño events since the late 1970s, which may reflect in part a mean 5623 warming of the eastern equatorial Pacific (Mendelssohn et al., 2005). There is presently 5624 no clear consensus on the possible impact of anthropogenic forcing on observed ENSO 5625 variability (Merryfield 2006, Meehl et al. 2007). 5626 5627 Decadal variability in the North Pacific is characterised by variations in the strength of 5628 the Aleutian Low coupled to changes in North Pacific SST. The leading mode of decadal 5629 variability in the North Pacific is usually termed the Pacific Decadal Oscillation (PDO) 5630 and has a spatial structure in the atmosphere and upper North Pacific Ocean similar to the 5631 pattern that is associated with ENSO. Pacific Decadal variability can also be 5632 characterized by changes in sea level pressure in the North Pacific, termed the North 5633 Pacific Index (Deser et al., 2004). One recent study showed a consistent tendency 5634 towards the positive phase of the PDO in observations and model simulations that 5635 included anthropogenic forcing (Shiogama et al., 2005), though differences between the 5636 observed and simulated PDO patterns, and the lack of additional studies, limit confidence 5637 in these findings. Do Not Cite or Quote 250 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5638 5639 ENSO and Pacific decadal variability affect the mean North American climate and its 5640 extremes (e.g., Kenyon and Hegerl, 2007), particularly when both are in phase, at which 5641 time considerable energy is propagated from tropical and northern Pacific sources 5642 towards the North American land mass (Yu et al. 2007, Zwiers and Yu 2007). 5643 5644 The Northern Annular Mode (NAM) is an approximately zonally symmetric mode of 5645 variability in the Northern Hemisphere (Thompson and Wallace, 1998, Chapter 1 of this 5646 report), and the North Atlantic Oscillation (NAO) (Hurrell, 1996) may be viewed as its 5647 Atlantic counterpart. The NAM index exhibited a pronounced trend towards its positive 5648 phase between the 1960s and the 1990s, corresponding to a decrease in surface pressure 5649 over the Arctic and an increase over the subtropical North Atlantic (e.g., Hurrell, 1996; 5650 Thompson et al., 2000; Gillett et al., 2003a). Several studies have shown this trend to be 5651 inconsistent with simulated internal variability (Osborn et al., 1999; Gillett et al., 2000; 5652 Gillett et al., 2002b; Osborn, 2004; Gillett, 2005) and similar to, although larger than, 5653 simulated changes in coupled climate models in response to 20th century forcing, 5654 particularly, greenhouse gas forcing and ozone depletion (Gillett et al., 2002b, Osborn, 5655 2004, Gillet 2005, Hegerl et al. 2007). The mechanisms underlying Northern Hemisphere 5656 circulation changes also remain open to debate (see e.g., Hoerling et al., 2005; Hurrell et 5657 al., 2005, Scaife et al. 2005). 5658 5659 Over the period 1968–1997, the trend in the NAM was associated with approximately 5660 50% of the winter surface warming in Eurasia, a decrease in winter precipitation over Do Not Cite or Quote 251 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5661 Southern Europe and an increase over Northern Europe, due the northward displacement 5662 of the storm track (Thompson et al., 2000). Such a change would have substantial 5663 influence on North America, too, reducing the probability of cold extremes in winter 5664 even over large areas (for example, Thompson and Wallace, 2001; Kenyon and Hegerl, 5665 2007), although part of the northeastern U.S. tends to show a tendency for more cold 5666 extremes with the NAO trend (Wettstein and Mearns, 2002). 5667 5668 3.2.2 Changes in Temperature Extremes 5669 As discussed in Chapter 2, observed changes in temperature extremes are consistent with 5670 the observed warming of the climate (Alexander et al., 2006). Globally, there has been a 5671 widespread reduction in the number of frost days in mid-latitude regions in recent 5672 decades, an increase in the number of warm extremes, particularly warm nights, and a 5673 reduction in the number of cold extremes, such as cold nights. 5674 5675 There is now evidence that anthropogenic forcing has likely affected extreme 5676 temperatures. Christidis et al. (2005) analyzed a new dataset of gridded daily 5677 temperatures (Caesar et al., 2006) using the indices shown by Hegerl et al. (2004) to have 5678 potential for attribution, namely the average temperature of the most extreme 1, 5, 10 and 5679 30 days of the year. Christidis et al. (2005) detected robust anthropogenic changes in a 5680 global analysis of indices of extremely warm nights using fingerprints from the HadCM3 5681 model, with some indications that the model over-estimates the observed warming of 5682 warm nights. Human influence on cold days and nights was also detected, but in this case 5683 the model underestimated the observed changes, significantly so in the case of the coldest Do Not Cite or Quote 252 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5684 day of the year. Anthropogenic influence was not detected in observed changes in 5685 extremely warm days. Tebaldi et al. (2006) find that changes simulated by an ensemble 5686 of eight global models that include anthropogenic and natural forcing changes agrees well 5687 with observed global trends in heat waves, warm nights and frost days over the last four 5688 decades. 5689 5690 North American observations also show a general increase in the number of warm nights, 5691 but with a decrease in the center of the continent that models generally do not reproduce 5692 (e.g., Christidis et al 2005). However, analysis for North America of models (Table 3.1) 5693 used by Tebaldi et al. (2006) shows reasonable agreement between observed and 5694 simulated changes in the frequency of warm nights, number of frost days and growing 5695 season length over the latter half of the 20th century when averaged over the continent 5696 (Fig. 3.1a,b,c). There is also good agreement between the observed and ensemble mean 5697 simulated spatial pattern of change in frost days (Fig.3.2a,b) over the latter half of the 5698 20th century. Note that the observational estimate has a much greater degree of temporal 5699 (Fig. 3.1) and spatial (Fig. 3.2) variability than the model result. The model result is 5700 derived from an ensemble of simulations produced by many models, some of which 5701 contributed multiple realizations. Averaging over many simulations reduces much of the 5702 spatial and temporal variability that arises from internal climate variability. The 5703 variability of individual model realizations is comparable to the single set of 5704 observations, which is well bounded by the two standard deviation confidence interval 5705 about the model ensemble average. Furthermore, Meehl et al. (2007b) demonstrate that 5706 ensemble simulations using two coupled climate models driven with human and natural Do Not Cite or Quote 253 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5707 forcings approximate well the observed changes, but when driven with natural forcings 5708 only cannot reproduce the observed changes, indicating a human contribution to observed 5709 changes in heat waves, frost days and warm nights. Output from one of these ensembles, 5710 produced by the Parallel Climate Model, also shows significant trends in the Karl-Knight 5711 heat-wave index (Karl and Knight, 1997) in the eastern half of the U.S. for 1961-1990 5712 that are similar to observed trends (Fig. 3.3). 5713 5714 There have also been some methodological advances whereby it is now possible to 5715 estimate the impact of external forcing on the risk of a particular extreme event. For 5716 example, Stott et al (2004), assuming a model-based estimate of temperature variability, 5717 estimate that past human influence may have more than doubled the risk of European 5718 mean summer temperatures as high as those recorded in 2003. Such a methodology has 5719 not yet been applied to North American extremes, though Hoerling et al. (2007) have 5720 used the method to conclude that the very hot 2006 in the United States was primarily 5721 due to human influences. 5722 5723 3.2.3 Changes in Precipitation Extremes 5724 3.2.3.1 Heavy Precipitation 5725 Allen and Ingram (2002) suggest that while global annual mean precipitation is 5726 constrained by the energy budget of the troposphere, extreme precipitation is constrained 5727 by the atmospheric moisture content, as governed by the Clausius-Clapeyron equation, 5728 though this constraint may be most robust in extratropical regions and seasons where the 5729 circulation’s fundamental dynamics are not driven by latent heat release (Pall et al. 2007). 5730 For a given change in temperature the constraint predicts a larger change in extreme Do Not Cite or Quote 254 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5731 precipitation than in mean precipitation, which is consistent with changes in precipitation 5732 extremes simulated by the ensemble of GCMs available for the IPCC Fourth Assessment 5733 Report (Kharin et al, 2007). Emori and Brown (2005) discuss physical mechanisms 5734 governing changes in the dynamic and thermodynamic components of mean and extreme 5735 precipitation and conclude that changes related to the dynamic component (i.e., that due 5736 to circulation change) are secondary factors in explaining the larger increase in extreme 5737 precipitation than mean precipitation seen in models. On the other hand, Meehl et al. 5738 (2005) demonstrate that while tropical precipitation intensity increases are related to 5739 water vapour increases, mid-latitude intensity increases are related to circulation changes 5740 that affect the distribution of increased water vapor. 5741 5742 Climatological data show that the most intense precipitation occurs in warm regions 5743 (Easterling et al., 2000) and diagnostic analyses have shown that even without any 5744 change in total precipitation, higher temperatures lead to a greater proportion of total 5745 precipitation in heavy and very heavy precipitation events (Karl and Trenberth, 2003). In 5746 addition, Groisman et al. (1999) have demonstrated empirically, and Katz (1999) 5747 theoretically, that as total precipitation increases a greater proportion falls in heavy and 5748 very heavy events if the frequency of raindays remains constant. Trenberth et al. (2005) 5749 point out that a consequence of a global increase in precipitation intensity should be an 5750 offsetting global decrease in the duration or frequency of precipitation events, though 5751 some regions could have differing behavior, such as reduced total precipitation or 5752 increased frequency of precipitation. 5753 Do Not Cite or Quote 255 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5754 Simulated changes in globally averaged annual mean and extreme precipitation appear to 5755 be quite consistent between models. The greater and spatially more uniform increases in 5756 heavy precipiation as compared to mean precipitation may allow extreme precipitation 5757 change to be more robustly detectable (Hegerl et al., 2004). 5758 5759 Evidence for changes in observations of short-duration precipitation extremes varies with 5760 the region considered (Alexander et al., 2006) and the analysis method that is employed 5761 (e.g., Trenberth et al., 2007). Significant increases in observed extreme precipitation have 5762 been reported over the United States, where the increase is qualitatively similar to 5763 changes expected under greenhouse warming (e.g., Karl and Knight, 1998; Semenov and 5764 Bengtsson, 2002; Groisman et al., 2005). However, a quantitative comparison between 5765 area-based extreme events simulated in models and station data remains difficult because 5766 of the different scales involved (Osborn and Hulme, 1997, Kharin et al. 2005) and the 5767 pattern of changes does not match observed changes. Part of this difference is expected 5768 since most current GCMs do not simulate small-scale (< 100 km) variations in 5769 precipitation intensity, as occurs with convective storms. Nevertheless, when compared 5770 with a gridded reanalysis product (ERA40), the ensemble of currently available 5771 AOGCMs reproduces observed precipitation extremes reasonably well over North 5772 America (Kharin et al., 2007). An attempt to detect anthropogenic influence on 5773 precipitation extremes using global data based on the Frich et al. (2002) indices used 5774 fingerprints from atmospheric model simulations with prescribed sea surface temperature 5775 (Kiktev et al., 2003). This study found little similarity between patterns of simulated and 5776 observed rainfall extremes. This is in contrast to the qualitative similarity found in other Do Not Cite or Quote 256 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5777 studies (Semenov and Bengtsson, 2002; Groisman et al., 2005; Fig. 3.4). Tebaldi et al. 5778 (2006) reported that an ensemble of eight global climate models simulating the 20th 5779 century showed a general tendency toward more frequent heavy-precipitation events over 5780 the past four decades, most coherently in the high latitudes of the Northern Hemisphere, 5781 broadly consistent with observed changes (Groisman et al., 2005). This is also seen when 5782 analyzing these models for North America (Fig. 3.1d). The pattern similarity of change in 5783 precipitation extremes over this period is more difficult to assess, particularly on 5784 continental and smaller scales. 5785 5786 3.2.3.2 Runoff and Drought 5787 Changes in runoff have been observed in many parts of the world, with increases or 5788 decreases corresponding to changes in precipitation. Climate models suggest that runoff 5789 will increase in regions where precipitation increases faster than evaporation, such as at 5790 high Northern latitudes (Milly et al., 2005; Wu et al., 2005). Gedney et al. (2006a) 5791 attributed increased continental runoff in the latter decades of the 20th century in part to 5792 suppression of transpiration due to CO2-induced stomatal closure. However, their result is 5793 subject to considerable uncertainty in the runoff data (Peel and McMahon, 2006; Gedney 5794 et al. 2006b). Qian et al. (2006) simulate observed runoff changes in response to observed 5795 temperature and precipitation alone, and Milly et al. (2005) demonstrate that 20th century 5796 runoff trends simulated by several global climate models are significantly correlated with 5797 observed runoff trends. Wu et al. (2005) find that observed increases in Arctic river 5798 discharge are simulated in a global climate model with anthropogenic and natural forcing, 5799 but not in the same model with natural forcings only. Anthropogenic changes in runoff Do Not Cite or Quote 257 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5800 may be emerging, but attribution studies specifically on North American runoff are not 5801 available. 5802 5803 Mid-latitude summer drying is another anticipated response to greenhouse gas forcing 5804 (Meehl et al., 2006) and drying trends have been observed in the both the Northern and 5805 Southern hemispheres since the 1950’s (Trenberth et al., 2006). Burke et al. (2006), using 5806 the HadCM3 model with all natural and anthropogenic external forcings and a global 5807 Palmer Drought Severity Index (PDSI) dataset compiled from observations by Dai et al. 5808 (2004), detect the influence of anthropogenic forcing in the observed global trend 5809 towards increased drought in the second half of the 20th century, although the model trend 5810 was weaker than observed and the relative contributions of natural external forcings and 5811 anthropogenic forcings was not assessed. Nevertheless, this supports the conclusion that 5812 anthropogenic forcing has influenced the global occurrence of drought. However, the 5813 spatial pattern of observed PDSI change over North America is dissimilar to that in the 5814 coupled model, so no anthropogenic influence has been detected for North America 5815 alone. 5816 5817 Nevertheless, the long term trends in the precipitation patterns over North America are 5818 well reproduced in atmospheric models driven with observed changes in sea-surface 5819 temperatures (Schubert et al., 2003; Seager et al., 2005), indicating the importance of sea- 5820 surface temperatures in determining North American drought (see also, for example, 5821 Hoerling and Kumar, 2003). Specifically, Schubert et al. (2003) and Seager et al. (2005), 5822 using AGCMs forced with observed SSTs, show that some SST anomaly patterns, Do Not Cite or Quote 258 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5823 particularly in the tropical Pacific, can produce drought over North America. Using the 5824 observed SST anomalies, both studies successfully reproduce many aspects of the 1930’s 5825 drought. Only the Seager et al. (2005) model simulates the 1950’s drought over North 5826 America, indicating that more modelling studies of this kind are needed. 5827 5828 3.2.4 Tropical Cyclones 5829 Long-term (multidecadal to century) scale observational records of tropical cyclone 5830 activity (frequency, intensity, power dissipation, etc.) were described in Chapter 2. Here 5831 discussion focuses on whether the any changes can be attributed to particular causes, 5832 including anthropogenic forcings. Tropical cyclones respond to their environment in 5833 quite different manners for initial development, intensification, determination of overall 5834 size, and motion. Therefore this section begins with a brief summary of the major 5835 physical mechanisms and understanding. 5836 5837 3.2.4.1 Development Criteria and Mechanisms 5838 Gray (1968) drew on a global analysis of tropical cyclones and a large body of earlier 5839 work to arrive at a set of criteria for tropical cyclone development, which he called 5840 Seasonal Genesis Parameters: 5841 • Sufficient available oceanic energy for the cyclone to develop, usually defined as 5842 a requirement for ocean temperatures > 26 oC down to a depth of 60 m; 5843 • Sufficient cyclonic (counterclockwise in Northern Hemisphere, clockwise in 5844 Southern Hemisphere) rotation to enhance the capacity for convective heating to 5845 accelerate the vertical winds, Do Not Cite or Quote 259 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5846 • A small change in horizontal wind with height (weak shear) so that the upper 5847 warming can become established over the lower vortex. 5848 • A degree of atmospheric moist instability to enable convective clouds to develop; 5849 • A moist mid-level atmosphere to inhibit the debilitating effects of cool 5850 downdrafts; and 5851 • Some form of pre-existing disturbance, such as an easterly wave, capable of 5852 development into a tropical cyclone. 5853 5854 A more recent study by Camargo et al. (2007) has developed a new genesis index, which 5855 is based on monthly mean values of 850 hPa relative vorticity, 700 hPa humidity, 850- 5856 250 hPa wind shear, and Potential Intensity (Bister and Emanuel, 1998). Some skill has 5857 been demonstrated in applying it to re-analysis data and global climate models to 5858 estimate the frequency and location of storms. 5859 5860 In the North Atlantic, the bulk of tropical cyclone developments arise from easterly 5861 waves, though such development is a relatively rare event, with only around 10-20% of 5862 waves typically developing into a tropical cyclone (Dunn 1940, Frank and Clarke 1980, 5863 Pasch et al 1998, Thorncroft and Hodges 2001). Thus, any large-scale mechanism that 5864 can help produce more vigorous easterly waves leaving Africa or provide an environment 5865 to enhance their development is of importance. ENSO is a major influence; during El 5866 Nino years, tropical cyclone development is suppressed by a combination of associated 5867 increased vertical wind shear, general drying of the mid-levels and oceanic cooling (e.g., 5868 Gray 1984). The Madden-Julian Oscillation (MJO) influences cyclogenesis in the Gulf of Do Not Cite or Quote 260 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5869 Mexico region on 1-2 month time scales (Maloney and Hartmann 2000). Approximately 5870 half of the North Atlantic tropical cyclone developments are associated with upper-level 5871 troughs migrating into the tropics (e.g. Pasch et al 1998; Davis and Bosart, 2001; 2006). 5872 The large scale zonal wind flow may also modulate development of easterly wave 5873 troughs into tropical cyclones (Holland 1995, Webster and Chang 1988). The easterly 5874 wave development process is particularly enhanced in the wet, westerly phase of the 5875 MJO. 5876 5877 The eastern and central North Pacific experience very little subtropical interaction and 5878 appear to be dominated by easterly wave development (e.g. Frank and Clarke 1980). The 5879 two major environmental influences are the ENSO and MJO, associated with the same 5880 effects as described for the North Atlantic. The MJO is a particularly large influence, 5881 being associated with a more than 2:1 variation in tropical cyclone frequency between the 5882 westerly-easterly phases (Liebmann et al 1994, Molinari and Vollaro 2000). 5883 5884 Suitable conditions in the western Pacific development region are present throughout the 5885 year. Developments in this region are associated with a variety of influences, including 5886 easterly waves, monsoon development and mid-latitude troughs (e.g. Ritchie and Holland 5887 1999). The dominant circulation is the Asiatic monsoon, and tropical cyclones typically 5888 form towards the eastern periphery of the main monsoonal trough, or further eastwards 5889 (Holland, 1995), though development can occur almost anywhere (e.g. Lander 1994). 5890 ENSO has a major impact, but it is opposite to that in the eastern Pacific and Atlantic, Do Not Cite or Quote 261 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5891 with western Pacific tropical cyclone development being enhanced during the El Nino 5892 phase (Chan 1985, Lander 1994, Wang and Chan 2002). 5893 5894 Mesoscale influences include those that occur on scales similar to, or smaller than the 5895 tropical cyclone circulation and seem to be operative in some form or other to all ocean 5896 basins. These influences include interactions amongst the vorticity fields generated by 5897 Mesoscale Convective Complexes (MCCs), which may enhance cyclogenesis under 5898 suitable atmospheric conditions, but also may introduce a stochastic element in which the 5899 interactions may also inhibit short-term development (Houze 1977; Zipser 1977; Ritchie 5900 and Holland 1997; Simpson et al. 1997; Ritchie 2003; Bister and Emanuel 1997; 5901 Hendricks et al. 2004; Montgomery et al. 2006) and inherent barotropic instability (e.g. 5902 Schubert et al. 1991; Ferreira and Schubert 1997). 5903 5904 3.2.4.1.1 Factors Influencing Intensity and Duration 5905 Once a cyclone develops it proceeds through several stages of intensification. The 5906 maximum achievable intensity of a tropical cyclone appears to be limited by the available 5907 energy in the ocean and atmosphere. This has led to various thermodynamic assessments 5908 of the Potential Intensity (PI) that can be achieved by a cyclone for a given 5909 atmospheric/oceanic thermodynamic state (Emanuel 1987, 1995, 2000, Holland 1997, 5910 Tonkin et al 1999, Rotunno and Emanuel 1987). The basis for these assessments is 5911 characteristically the sea surface temperature and the thermodynamic structure of the 5912 near-cyclone atmospheric environment, with particular emphasis on the temperature at 5913 the outflow level of air ascending in the storm core. Do Not Cite or Quote 262 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5914 5915 In most cases tropical cyclones do not reach this thermodynamic limit, due to a number 5916 of processes that have a substantial negative influence on intensification. Major negative 5917 impacts may include: vertical shear of the horizontal wind (Frank and Ritchie 1999, 5918 DeMaria 1995), oceanic cooling by cyclone-induced mixing of cool water from below 5919 the mixed layer to the surface (Price 1981, Bender and Ginis 2000, Schade and Emanuel 5920 1999); potential impacts of sea spray on the surface exchange process (Wang et al. 2001, 5921 Andreas and Emanuel 2001); processes that force the cyclone into an asymmetric 5922 structure (Wang 2002, Corbosiero and Molinari 2003); ingestion of dry air, perhaps also 5923 with suspended dust (Neal and Holland 1976, Dunion and Velden 2004); and internal 5924 processes. Since many of these factors tend to be transitory in nature, the longer a 5925 cyclone can spend in a region with plentiful thermodynamic energy, the better its chances 5926 of approaching the PI. This is reflected in, for example, the observation that over 80% of 5927 major hurricanes in the North Atlantic occur in systems that formed at low latitudes in the 5928 eastern region, the so-called Cape Verde storms. 5929 5930 A weakening tropical cyclone may merge with an extratropical system, or it may 5931 redevelop into a baroclinic system (Jones et al. 2003). Since the system carries some of 5932 its tropical vorticity and moisture, it can produce extreme rains and major flooding. The 5933 transition is also often accompanied by a rapid acceleration in translation speed, which 5934 leads to an asymmetric wind field with sustained winds that may be of hurricane force on 5935 the right (left) side of the storm track in the northern (southern) hemisphere, despite the 5936 overall weakening of the cyclone circulation. Do Not Cite or Quote 263 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5937 5938 3.2.4.1.2 Movement Mechanisms 5939 Tropical cyclones are steered by the mean flow in which they are embedded, but they 5940 also propagate relative to this mean flow due to dynamical effects (Holland 1984, Fiorino 5941 and Elsberry 1989). This combination leads to the familiar hyperbolic (recurving) track 5942 of tropical cyclones as storms initially move westward, embedded in the low-latitude 5943 easterly flow, then more poleward and eventually eastward as they encounter the mid- 5944 latitude westerlies. 5945 5946 An important result of this pattern of movement is that storms affecting the Caribbean, 5947 Mexico, Gulf States, Lower Eastern Seaboard and Pacific Trust Territories have mostly 5948 developed in low-latitudes (which also comprise the most intense systems). Eastern 5949 Pacific cyclones tend to move away from land, and those that recurve are normally 5950 suffering from combined negative effects of cold water and vertical shear. Upper Eastern 5951 US Seaboard and Atlantic Canada cyclones are typically recurving and undergoing 5952 various stages of extratropical transition. 5953 5954 3.2.4.2 Attribution Preamble 5955 Determining the causal influences on the observed changes in tropical cyclone 5956 characteristics is currently subject to vigorous community debate. Chief amongst the 5957 more contentious topics are data deficiencies in early years, natural variability on decadal 5958 time scales, and trends associated with greenhouse warming. A summary of the published 5959 contributions to this debate at the end of 2006 is contained in a report and accompanying Do Not Cite or Quote 264 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5960 statement that was put together by the attendees at a World Meteorological Society 5961 Workshop on Tropical Cyclones held in November 2006 (WMO 2006, Knutson et al 5962 2006b). Of direct relevance in the WMO statement are the following: 5963 • Though there is evidence both for and against the existence of a detectable 5964 anthropogenic signal in the tropical cyclone climate record to date, no firm 5965 conclusion can be made on this point. 5966 • It is likely that some increase in tropical cyclone intensity and rainfall will occur 5967 if the climate continues to warm. Model studies and theory project a 3-5% 5968 increase in wind speed per degree increase of tropical sea surface temperatures. 5969 • No individual tropical cyclone can be directly attributed to climate change. 5970 • Some studies of the observational record conclude that the reported global 5971 increase in tropical cyclone activity is questionable owing to data problems, since 5972 tropical cyclone monitoring has improved continuously. 5973 • There is an observed multi-decadal variability of Atlantic hurricanes whose 5974 causes, whether natural, anthropogenic or a combination, are currently being 5975 debated. This variability makes detecting any long-term trends in tropical cyclone 5976 activity difficult. 5977 • Current theories and computer models predict an increase in wind speed and 5978 rainfall of tropical cyclones in a warmer climate. 5979 • Recent climate model simulations project a decrease or no change in global 5980 tropical cyclone numbers in a warmer climate, although there is low confidence in 5981 this projection. 5982 Do Not Cite or Quote 265 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 5983 We note that these were consensus views, and no attempt was made to assign likely 5984 probabilities to the possible outcomes. The International Panel for Climate Change 5985 (IPCC) arrived at similar findings, but also focused on the observed changes in the North 5986 Atlantic (IPCC 2007): 5987 5988 • There is observational evidence for an increase of intense tropical cyclone 5989 activity in the North Atlantic since about 1970, correlated with increases of 5990 tropical sea surface temperatures. 5991 • There are also suggestions of increased intense tropical cyclone activity in 5992 some other regions where concerns over data quality are greater. 5993 • Multi-decadal variability and the quality of the tropical cyclone records prior 5994 to routine satellite observations in about 1970 complicate the detection of 5995 long-term trends in tropical cyclone activity. 5996 • There is no clear trend in the annual global numbers of tropical cyclones. 5997 5998 The IPCC also made the following probability assessments on intense tropical cyclone 5999 activity: 6000 • Likely that increases have occurred in some regions since 1970; 6001 • More likely than not a human contribution to the observed trend; 6002 • Likely that there will be future trends in tropical cyclone intensity and heavy 6003 precipitation associated with ongoing increases of tropical SSTs; 6004 • Less confidence in projections of a global decrease in the numbers of tropical 6005 cyclones. Do Not Cite or Quote 266 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6006 6007 Emanuel (2005) and Webster et al. (2005) showed a clear increase in the more intense 6008 Northwest Pacific cyclones (as shown by category 4 and 5 frequency or PDI) since the 6009 commencement of the satellite era. These increases have been closely related to 6010 concomitant changes in SSTs in this region. On the other hand there are also concerns 6011 about the quality of the data (WMO 2006) and there has been little focused research on 6012 attributing the changes in this region. For these reasons this report accepts the overall 6013 findings of WMO (2006) and IPCC (2007) as they relate to the North Pacific. 6014 6015 One area where there is consensus is on tropical cyclone rainfall. WMO (2007) and IPCC 6016 (2007) concur on there being a likely increase in heavy rainfall associated with tropical 6017 cyclones, though the actual level of increase is not clear. 6018 6019 The remainder of the attribution section on tropical cyclones concentrates on attribution 6020 in the North Atlantic, where the available data and published work enables more detailed 6021 attribution analysis compared to other basins. 6022 6023 3.2.4.3 Attribution of North Atlantic Changes 6024 Chapter 2 provides an overall summary of the observed variations and trends in storm 6025 frequency, section 3.3.9.6 considers future scenarios, and Holland and Webster (2007) 6026 present a detailed analysis of the changes in North Atlantic tropical cyclones, hurricanes 6027 and major hurricanes over the past century, together with a critique of the potential Do Not Cite or Quote 267 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6028 attribution mechanisms. Here we examine these changes in terms of the potential 6029 causative mechanisms. 6030 6031 3.2.4.3.1 Storm Intensity 6032 There has been no distinct trend in the mean intensity of all storms, hurricanes, or major 6033 hurricanes (Chapter 2). Holland and Webster (2007) also found that there has been a 6034 marked oscillation in major hurricane proportions, which has no observable trend. The 6035 attribution of this oscillation has not been adequately defined, but it is known that it is 6036 associated with a similar oscillation in the proportion of hurricanes that develop in low 6037 latitudes and thus experience environmental conditions that are more conducive to 6038 development into an intense system than those at more poleward locations. The lack of a 6039 mean intensity trend or a trend in major hurricane proportions is in agreement with 6040 modeling and theoretical studies that predict a relatively small increase of around 1 to 7% 6041 for the observed 0.5 to 0.7oC trend in tropical North Atlantic SSTs (Henderson-Sellers et 6042 al 1998, Knutson et al 1998; 2001; Knutson and Tuleya 2004; 2007). 6043 6044 Multidecadal increases of maximum intensity due to multidecadal increases of SST may 6045 play a relatively small role in increases of overall hurricane activity, and increases in 6046 frequency (discussed in the next section), for which variations in duration due to large- 6047 scale circulation changes may be the dominant factors. The relationship between SST, 6048 circulation patterns, and hurricane activity variability is not as well understood as the 6049 thermodynamic relationships that constrain maximum intensity. 6050 Do Not Cite or Quote 268 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6051 3.2.4.3.2 Storm Frequency and Integrated Activity Measures 6052 Emanuel (2005a; 2007a) examined a Power Dissipation Index (PDI), which combines the 6053 frequency, lifetime and intensity, and is related to the cube of the maximum winds 6054 summed over the lifetime of the storm. In Chapter 2, it was concluded that there has been 6055 a substantial increase in tropical cyclone activity, as measured by the Power Dissipation 6056 Index (PDI), since about 1970, strongly correlated with low-frequency variations in 6057 tropical Atlantic SSTs. It is likely that hurricane activity (PDI) has increased substantially 6058 since the 1950s and 60s in association with warmer Atlantic SSTs. It is also likely that 6059 PDI has generally tracked SST variations on multidecadal time scales in the tropical 6060 Atlantic since 1950. Holland and Webster (2007) have shown that the PDI changes have 6061 arisen from a combination of increasing frequency of tropical cyclones of all categories: 6062 tropical storms, hurricanes and major hurricanes; and a multi-decadal oscillation in the 6063 proportion of major hurricanes. They found no evidence of a trend in the major hurricane 6064 proportions or in overall intensity, but a marked trend in frequency. 6065 6066 While there is a close statistical relationship between low frequency variations of tropical 6067 cyclone activity (e.g., the PDI and storm frequency) and SSTs (Ch. 2), this almost 6068 certainly arises from a combination of factors, including joint relationships to other 6069 atmospheric process that effect cyclone development, such as vertical windshear (Shapiro 6070 1982, Kossin and Vitmer 2007, Goldenberg et al 2001, Shapiro and Goldenberg 1998). It 6071 is also notable that the recent SST increases have been associated with a concomitant 6072 shift towards increased developments in low latitudes and the eastern Atlantic, regions Do Not Cite or Quote 269 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6073 where the conditions are normally more conducive to cyclogenesis and intensification 6074 (Holland and Webster 2007, Ch. 2). 6075 6076 Low-frequency variations in Atlantic tropical cyclone activity have previously been 6077 attributed to a natural variability in Atlantic SSTs associated with the Atlantic Multi- 6078 decadal Oscillation (Bell and Chelliah 2006, Goldenberg et al. 2001). However, these 6079 studies either did not consider the trends over the 20th century in SST (Goldenberg et al. 6080 2001) or did not cover a long enough period to confidently distinguish between 6081 oscillatory (internal climate variability) behavior and radiatively forced variations or 6082 trends. For example, the multi-decadal AMM2 mode in Bell and Chelliah (2006) first 6083 obtains substantial amplitude around 1970. Their circulation-based indices are of 6084 insufficient length to determine whether they have a cyclical or trend-like character, or 6085 some combination thereof. 6086 6087 While there is undoubtedly a natural variability component to the observed tropical 6088 Atlantic SSTs, it is also likely that a discernable warming trend, due to greenhouse gases, 6089 has occurred, especially over the past 30-40 years. For example, Santer et al. (2006; see 6090 also Gillett et al. 2007) have shown that the observed trends in Atlantic tropical SSTs are 6091 unlikely to be caused entirely by internal climate variability, and that the pronounced 6092 Atlantic warming since around 1970 that is reproduced in their model is predominantly 6093 due to increased greenhouse gases. These conclusions are supported by several other 6094 studies that use different methodologies (e.g., Knutson et al. 2006; Trenberth and Shea 6095 2006; Mann and Emanuel 2006; Karoly and Wu 2005). There is also evidence for a Do Not Cite or Quote 270 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6096 detectable greenhouse gas-induced SST increase in the NW Pacific tropical cyclogenesis 6097 region (Santer et al. 2006, Gillett et al. 2007; see also Knutson et al. 2006 and Karoly and 6098 Wu 2005). 6099 6100 We conclude that there has been an observed SST increase of 0.5-0.7oC over the past 6101 century in the main development region for tropical cyclones in the Atlantic. Based on 6102 comparison of observed SST trends and corresponding trends in climate models with and 6103 without external forcing, it is likely that increased greenhouse gases have caused a 6104 discernible increase in SSTs both the North Atlantic and the NW Pacific tropical storm 6105 basins over the past 100 yrs and also for the period since about 1950. 6106 6107 Chapter 2 also concludes that it is likely that there has been an increase in tropical 6108 storm/hurricane and major hurricane frequency in the North Atlantic over the past 6109 century or so, a time during which tropical Atlantic SSTs also increased. Ongoing efforts 6110 to reconstruct a complete record of Atlantic tropical cyclone counts back to 1900 or the 6111 late 1800s find evidence (in several analyses) for a statistically significant increasing 6112 trend since 1900. The evidence is much less compelling for significant positive trends 6113 beginning in the late 1800s, although there is increasing uncertainty in the data as one 6114 proceeds further back in time. There has not been a significant trend in U.S. landfalling 6115 activity since the late 1880s as the overall impacts of the increasing trend in North 6116 Atlantic tropical cyclones appear to have been to some extent ameliorated by concomitant 6117 shifts into eastern North Atlantic developments, which are less likely to directly affect US 6118 coastal regions. Do Not Cite or Quote 271 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6119 6120 Attribution of these past changes in tropical storm/hurricane activity (e.g., PDI) and 6121 frequency to various climate forcings is hampered by the lack of adequate model 6122 simulations of tropical cyclone climatologies. In the case of global scale temperature 6123 increase formal detection-attribution studies have detected strong evidence for the 6124 presence of the space-time pattern of warming expected due to greenhouse gas increases. 6125 These studies find that other plausible explanations, such as solar and volcanic forcing 6126 together with climate variability alone, fail to explain the observed changes sufficiently. 6127 The relatively good agreement between observed and simulated trends based on climate 6128 model experiments with estimated past forcings lends substantial confidence to 6129 attribution statements for SST. However, since adequate model-based reconstructions of 6130 historical tropical cyclone variations are not currently available, we do not have estimates 6131 of expected changes in tropical cyclone variations due to a complete representation of the 6132 changes in the physical system that would have been caused by greenhouse gas increases 6133 and other forcing changes. We therefore must rely on statistical analyses and expert 6134 judgement to make attribution assessments. Further discussion of these issues is 6135 contained in section 3.3.9.6 (Reconciliation of Future Projections and Past Variations). 6136 6137 The strong relationship with SST—and particularly the large increase in both tropical 6138 cyclone activity (PDI ) and SST since 1970, along with the observed increases in tropical 6139 storm/hurricane and major hurricane frequency and SSTs over the past century or so— 6140 provides evidence in support of a discernible impact of anthropogenic forcing on Atlantic 6141 tropical cyclone activity. Although there is evidence both for and against this Do Not Cite or Quote 272 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6142 interpretation (e.g., lack of trend in U.S. landfalling hurricanes), the balance of evidence 6143 now suggests that human activity has caused a discernible increase in tropical storm, 6144 hurricane and major hurricane frequency. It is more difficult to judge whether 6145 anthropogenic forcing will cause further increases in activity as the climate continues to 6146 warm, since the precise physical reasons for the relationship have not been fully 6147 elucidated. It is noted that relevant anthropogenic forcing includes increasing greenhouse 6148 gases, as well as changes in aerosol forcing, and possibly decreasing stratospheric ozone 6149 and other factors associated with cooling upper atmospheric (~100mb) temperatures in 6150 recent decades (Emanuel 2007a). 6151 6152 This assessment is consistent with the IPCC (2007) conclusion that it is more likely than 6153 not that there has been a human contribution to the observed increase in intense tropical 6154 cyclone activity. It is further supported by several recent related studies, including 6155 Trenberth and Shea (2006), Mann and Emanuel (2006), Santer et al (2006), Elsner 6156 (2006), Emanuel (2007a), Gillett et al. (2007), Kossin and Vitmer (2007), Vitmer and 6157 Kossin (2007), Vecchi and Knutson (2007), and Holland and Webster (2007a). 6158 6159 3.2.4.3.3 Storm lifetime, Track and Extratropical Transition 6160 There has been insufficient work done on the changes, or otherwise, in these important 6161 aspects of tropical cyclones to arrive at any firm conclusions. 6162 Do Not Cite or Quote 273 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6163 3.2.5 Extratropical Storms 6164 Chapter 2 documents changes in strong extratropical storms during the twentieth century, 6165 especially for oceanic storm track bordering North America. Changes include altered 6166 intensity and tracks of intense storms (Wang et al. 2006, Caires and Sterl 2005). Analysis 6167 of physical mechanisms is lacking. Natural cycles of large-scale circulation affect 6168 variability, through the North Atlantic Oscillation (e.g., Lozano and Swail, 2002, Caires 6169 and Sterl 2005) or the related Northern Annular Mode (Hurrell 1995, Ostermeier and 6170 Wallace 2003). Changes in sea-surface temperature (Graham and Diaz 2001) and 6171 baroclinicity (Fyfe 2003) may also play a role. Analysis of a multi-century GCM 6172 simulation by Fischer-Bruns et al. (2005) suggests that changes in solar activity and 6173 volcanic activity have negligible influence on strong-storm activity. However, it is likely 6174 that anthropogenic influence has contributed to extratropical circulation change during 6175 the latter half of the 20th century (Hegerl et al, 2007; see also Gillett et al., 2003, 2005, 6176 2006; Wang et al 2007), which would have influenced storm activity. There is also some 6177 evidence that anthropogenic forcing has affected related variables such as geostrophic 6178 wind energy and significant wave height (Wang et al 2007) during the latter half of the 6179 20th century, although as with sea-level pressure change, the model simulated response to 6180 forcing is not as large as observed. On the other hand, the WASA Group (1998), using 6181 long records of station data, suggest that observed changes in storminess in Northern 6182 Europe over the latter part of the 20th century are not inconsistent with natural internal 6183 low-frequency variability. However, analyses based on direct observations suffer from 6184 incomplete spatial and temporal coverage, especially in storm-track regions over adjacent 6185 oceans, and generally cover regions that may be too small to allow detection of externally Do Not Cite or Quote 274 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6186 forced signals (Hegerl et al., 2007). Studies of global reanalysis products generally cover 6187 less than 50 years. While 50-year records are generally considered adequate for detection 6188 and attribution research (Hegerl et al, 2007), a difficulty with reanalysis products is that 6189 they are affected by inhomogeneities resulting from changes over time in the type and 6190 quantity of data that is available for assimilation (e.g., Trenberth et al. 2005). 6191 6192 A number of investigations have considered the climate controls on the storm intensities 6193 or on the decadal trends of wave heights generated by those storms. Most of this attention 6194 has been on the North Atlantic, and as noted above the important role of the North 6195 Atlantic Oscillation has been recognized (e.g., Neu, 1984; WASA, 1998; Gulev and 6196 Grignorieva, 2004). Fewer investigations have examined the climate controls on the 6197 storms and waves in the North Pacific, and with less positive conclusions (Graham and 6198 Diaz, 2001; Gulev and Grignorieva, 2004). In particular, definite conclusions have not 6199 been reached concerning the climate factor producing the progressive increase seen in 6200 wave heights, apparently extending at least back to the 1960s. However, Wang et al. 6201 (2007) indicate that anthropogenically forced circulation change may have been an 6202 important factor in changes of significant wave heights. 6203 6204 A definite control on the wave conditions experienced along the west coast of North 6205 America is occurrences of major El Niños such as those in 1982-83 and 1997-98. Both of 6206 these events in particular brought extreme wave conditions to south-central California, 6207 attributed primarily to the more southerly tracks of the storms compared with non-El 6208 Niño years. Allan and Komar (2006) found a correlation between the winter-averaged Do Not Cite or Quote 275 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6209 wave heights measured along the west coast and the multivariate ENSO index (MEI), 6210 showing that while the greatest increase during El Niños takes place at the latitudes of 6211 south-central California, some increase occurs along the entire west coast, evidence that 6212 the storms are stronger as well as having followed more southerly tracks. The wave 6213 climates of the west coast therefore have been determined by the decadal increase found 6214 by Allan and Komar (2000, 2006), but further enhanced during occurrences of major El 6215 Niños. 6216 6217 3.2.6 Convective Storms 6218 Trenberth et al. (2005) point out that since the amount of moisture in the atmosphere is 6219 likely to rise much faster as a consequence of rising temperatures than the total 6220 precipitation, this should lead to an increase in the intensity of storms, offset by decreases 6221 in duration or frequency of events. Environmental conditions that are most likely 6222 associated with severe and tornadic thunderstorms have been derived from reanalysis 6223 data (Brooks et al. 2003b). Brooks and Dotzek (2007) applied those relationships to count 6224 the frequency of favorable environments for significant severe thunderstorms (hail of at 6225 least 5 cm diameter, wind gusts of at least 33 m s-1, and/or a tornado of F2 or greater 6226 intensity) for the area east of the Rocky Mountains in the US for the period 1958-1999. 6227 The count of favorable environments decreased by slightly more than 1% per year from 6228 1958 until the early-to-mid 1970s, and increased by approximately 0.8% per year from 6229 then until 1999, so that the frequency was approximately the same at both ends of the 6230 analyzed period. They went on to show that the time series of the count of reports of very 6231 large hail (7 cm diameter and larger) shows an inflection at about the same time as the Do Not Cite or Quote 276 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6232 inflection in the counts of favorable environments. A comparison of the rate of increase 6233 of the two series suggested that the change in environments could account for 6234 approximately 7% of the change in reports from the mid-1970s through 1999, with the 6235 rest coming from non-meteorological sources. 6236 6237 3.3 Projected Future Changes in Extremes, Their Causes, Mechanisms, and 6238 Uncertainties 6239 Projections of future changes of extremes are relying on an increasingly sophisticated set 6240 of models and statistical techniques. Studies assessed in this section rely on multi- 6241 member ensembles (3 to 5 members) from single models, analyses of multi-model 6242 ensembles ranging from 8 to 15 or more AOGCMs, and a perturbed physics ensemble 6243 with a single mixed layer model with over 50 members. The discussion here is intended 6244 to identify the characteristics of changes of extremes in North America and set in the 6245 broader global context. 6246 6247 3.3.1 Temperature 6248 The IPCC Third Assessment Report concluded there was a very likely risk of increased 6249 high temperature extremes (and reduced risk of low temperature extremes), with more 6250 extreme heat episodes in a future climate. This latter result has been confirmed in 6251 subsequent studies (e.g., Yonetani and Gordon, 2001). An ensemble of more recent 6252 global simulations projects marked increase in the frequency of very warm daily- 6253 temperature minima (Fig. 3.1a). Kharin and Zwiers (2005) show in a single model that 6254 future increases in temperature extremes follow increases in mean temperature over most Do Not Cite or Quote 277 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6255 of the world including North America. They show a large reduction in the wintertime 6256 cold temperature extremes in regions where snow and sea ice decrease due to changes in 6257 the effective heat capacity and albedo of the surface. They also show that summertime 6258 warm temperature extremes increase in regions where the soil dries due to a smaller 6259 fraction of surface energy used for evaporation. Furthermore, that study showed that in 6260 most instances warm-extreme changes are similar in magnitude to the increases in daily 6261 maximum temperature, but cold extremes shift to warmer temperatures faster than daily 6262 minimum temperatures, though this result is less consistent when model parameters are 6263 varied in a perturbed physics ensemble where there are increased daily temperature 6264 maxima for nearly the whole land surface. However, the range in magnitude of increases 6265 was substantial indicating a sensitivity to model formulations (Clark et al., 2006). 6266 6267 Events that are rare could become more commonplace. Recent studies using both 6268 individual models (Kharin and Zwiers, 2005) and an ensemble of models (Wehner 2006, 6269 Kharin, et al 2007) show that events that currently reoccurr on average once every 20 6270 years (i.e., have a 5% chance of occurring in a given year) will become signficantly more 6271 frequent over North America. For example, by the middle of the 21st century, in 6272 simulations of the SRES A1B scenario, the recurrence period (or expected average 6273 waiting time) for the current 20-year extreme in daily average surface-air temperature 6274 reduces to three years over most of the continental United States and five years over most 6275 of Canada (Kharin, et al 2007). By the end of the century (Fig. 3.5a), the average 6276 reoccurrence time may further reduce to every other year or less (Wehner, 2006). 6277 Do Not Cite or Quote 278 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6278 Similar behavior occurs for seasonal average temperatures. For example, Weisheimer and 6279 Palmer (2005) examined changes in extreme seasonal (DJF and JJA) temperatures in 14 6280 models for 3 scenarios. They showed that by the end of 21st century, the probability of 6281 such extreme warm seasons is projected to rise in many areas including North America. 6282 Over the North American region, an extreme seasonal temperature event that occurs 1 out 6283 of 20 years in the present climate becomes a 1 in 3 year event in the A2 scenario by the 6284 end of this century. This result is consistent with that from the perturbed physics 6285 ensemble of Clark et al. (2006) where, for nearly all land areas, extreme JJA temperatures 6286 were at least 20 times and in some areas 100 times more frequent compared to the control 6287 ensemble mean, making these changes greater than the ensemble spread. 6288 6289 Others have examined possible future cold-air outbreaks. Vavrus et al. (2006) analysed 7 6290 AOGCMs run with the A1B scenario, and defined a cold air outbreak as 2 or more 6291 consecutive days when the daily temperatures were at least 2 standard deviations below 6292 the present-day winter-time mean. For a future warmer climate, they documented a 6293 decline in frequency of 50 to 100% in NH winter in most areas compared to present-day, 6294 with some of the smallest reductions occurring in western North America due to 6295 atmospheric circulation changes (blocking and ridging on West Coast) associated with 6296 the increase of GHGs. 6297 6298 Several recent studies have addressed explicitly possible future changes in heat waves 6299 (very high temperatures over a sustained period of days), and found that in a future 6300 climate there is an increased risk of more intense, longer-lasting and more frequent heat Do Not Cite or Quote 279 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6301 waves (Meehl and Tebaldi, 2004; Schär et al., 2004; Clark et al., 2006). Meehl and 6302 Tebaldi (2004) related summertime heat waves to circulation patterns in the models and 6303 observations. They found that the more intense and frequent summertime heat waves 6304 over the southeast and western U.S. were related in part to base state circulation changes 6305 due to the increase in GHGs. An additional factor for extreme heat is drier soils in a 6306 future warmer climate (Brabson et al., 2005; Clark et al., 2006). The “Heat Index”, a 6307 measure of the apparent temperature felt by humans that includes moisture influences, 6308 was projected in a GFDL model study to increase substantially more than the air 6309 temperature in a warming climate in many regions (Delworth et al. 1999). The regions 6310 most prone to this effect included humid regions of the tropics and summer hemisphere 6311 extratropics, including the Southeast U.S. and Caribbean. A multi-model ensemble 6312 showed that simulated heat waves increase during the latter part of the 20th century, and 6313 are projected to increase globally and over most regions including North America 6314 (Tebaldi et al., 2006), though different model parameters can influence the range in the 6315 magnitude of this response (Clark et al., 2006). 6316 6317 Warm episodes in ocean temperatures can stress marine ecosystems, causing impacts 6318 such as coral bleaching (e.g., Liu et al. 2006). Key factors appear to be clear skies, low 6319 winds and neap tides occurring near annual maximum temperatures since they promote 6320 heating with little vertical mixing of warm waters with cooler, deeper layers (Strong et al. 6321 2006). At present, widespread bleaching episodes do not appear to be related to 6322 variability such as ENSO cycles (Arzayus and Skirving 2004) or Pacific Decadal 6323 Oscillation (Strong et al. 2006). The 2005 Caribbean coral bleaching event has been Do Not Cite or Quote 280 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6324 linked to warm ocean temperatures that appear to have been partially due to long-term 6325 warming associated with anthropogenic forcing and not a manifestation of unforced 6326 climate variability alone (Donner et al. 2007). Warming trends in the ocean increase the 6327 potential for temperatures to exceed thresholds for mass coral bleaching, and thus may 6328 greatly increase the frequency of bleaching events in the future, depending on the ability 6329 of corals and their symbionts to adapt to increasing water temperatures (see Donner et al. 6330 2007 and references therein). 6331 6332 A decrease in diurnal temperature range in most regions in a future warmer climate was 6333 reported in Cubasch et al. (2001) and is substantiated by more recent studies (e.g., Stone 6334 and Weaver, 2002), which are assessed in the 2007 IPCC report (Meehl et al. 2007a, 6335 Christensen et al. 2007). However, noteworthy departures from this tendency have been 6336 found in the western portion of the US (particularly the Southwest), where increased 6337 diurnal temperature ranges occur in several regional (e.g., Bell et al. 2004, Leung et al. 6338 2004) and global (Christensen et al., 2007) climate-change simulations. Increased diurnal 6339 temperature range often occurs in areas that experience drying in the summer. 6340 6341 3.3.2 Frost 6342 As the mean climate warms, the number of frost days are expected to decrease (Cubasch 6343 et al. 2001). Meehl et al (2004a) have shown that there would indeed be decreases in frost 6344 days in a future warmer climate in the extratropics, particularly along the northwest coast 6345 of North America, with the pattern of the decreases dictated by the changes in 6346 atmospheric circulation from the increase in GHGs. Results from a multi-model ensemble Do Not Cite or Quote 281 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6347 show simulated and observed decreases in frost days for the 20th century continuing into 6348 the 21st century over North America and most other regions (Meehl et al. 2007a, Fig. 6349 3.1b). By then end of the 21st century, the number of frost days averaged over North 6350 America has decreased by about 1 month in the 3 future scenarios considered here. 6351 6352 In both the models and the observations, the number of frost days is decreasing over the 6353 20th century (Fig. 3.1b). This decrease is generally related to warming climate, although 6354 the pattern of the warming and pattern of the frost-days changes (Fig. 3.2) are not well 6355 correlated. The decrease in the number of frost days per year is biggest in the Rockies 6356 and along the west coast of North America. The 21st century frost day pattern of change 6357 is similar to the 20th century pattern, just much larger in magnitude. In some places by 6358 2100, the number of frost days decrease by more than 2 months. 6359 6360 These changes would have a large impact on biological activity both positive and 6361 negative (See chapter 1 for more discussion). An example of a positive change is that 6362 there would be increase in growing season length directly related to the decrease in frost 6363 days per year. A negative example is fruit trees, which need a certain number of frost 6364 periods per winter season to set their buds. In places, this threshold would no longer be 6365 exceeded. Note also that changes in wetness and CO2 content of the air would also impact 6366 the biological changes. 6367 6368 6369 Do Not Cite or Quote 282 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6370 3.3.3 Growing Season Length 6371 A quantity related to frost days in many mid and high latitude areas, particularly in the 6372 Northern Hemisphere, is growing season length as defined by Frich et al. (2002), and this 6373 has been projected to increase in future climate in most areas (Tebaldi et al., 2006). This 6374 result is also shown in a multi-model ensemble where the simulated increase in growing 6375 season length in the 20th century continues into the 21st century over North America and 6376 most other regions (Meehl et al. 2007a, Fig. 3.1c). The growing season length has 6377 increased by about 1 week over the 20th century when averaged over all of North 6378 America in the models and observations. By the end of the 21st century, the growing 6379 season is on average more than 2 weeks longer than present day. (For more discussion on 6380 the reasons these changes are important, see chapter 1) 6381 6382 3.3.4 Snow Cover and Sea Ice 6383 Warming generally leads to reduced snow and ice cover (Meehl et al. 2007a). Reduction 6384 in perennial sea ice may be large enough to yield a summertime, ice-free Arctic Ocean by 6385 the end of the 21st century (Arzel et al. 2006; Zhang and Walsh 2006). Summer Arctic 6386 Ocean ice also may undergo substantial, decadal-scale abrupt changes rather than smooth 6387 retreat (Holland et al. 2006). The warming may also produce substantial reduction in the 6388 duration of seasonal ice in lakes across Canada and the U.S. (Hodgkins et al. 2002, Gao 6389 and Stefan 2004, Williams et al. 2004, Morris et al. 2005) and in rivers (Hodgkins et al. 6390 2003, Huntington et al. 2003). Reduced sea ice in particular, may produce more strong 6391 storms over the ocean (Section 3.3.10). Reduced lake ice may alter the occurrence of 6392 heavy lake-effect snowfall (Section 3.3.8). The annual cycle of snow cover and river Do Not Cite or Quote 283 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6393 runoff may be substantially altered in western U.S. basins (Miller et al. 2003, Leung et al. 6394 2004), affecting water-resource management and potentially exacerbating the impacts of 6395 droughts. 6396 6397 3.3.5 Precipitation 6398 Climate models continue to confirm the earlier results that in a future climate warmed by 6399 increasing GHGs, precipitation intensity (i.e., precipitation amount per event) is projected 6400 to increase over most regions (Wilby and Wigley, 2002; Kharin and Zwiers, 2005; Meehl 6401 et al., 2005a; Barnett et al., 2006), and the increase of precipitation extremes is greater 6402 than changes in mean precipitation (Kharin and Zwiers, 2005). Rare events precipitation 6403 events could become more commonplace in North America (Wehner, 2006, Kharin et al. 6404 2007). For example, by the middle of the 21st century, in simulations of the SRES A1B 6405 scenario, the recurrence period (or expected average waiting time) for the current 20-year 6406 extreme in daily total precipitation reduces to between 12 and 15 years over much of 6407 North America (Kharin, et al 2007). By the end of the century (Fig. 3.5b), the expected 6408 average reoccurrence time may further reduce to every six to eight years (Wehner, 2006, 6409 Kharin, et al 2007). Note the area of little change in expected average reoccurrence time 6410 in the central United States in Fig. 3.5b. 6411 6412 As discussed in section 3.2.3 of this chapter and in Hegerl et al. (2007), the substantial 6413 increase in precipitation extremes is related to the fact that the energy budget of the 6414 atmosphere constrains increases of large-scale mean precipitation, but extreme 6415 precipitation responds to increases in moisture content and thus the nonlinearities Do Not Cite or Quote 284 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6416 involved with the Clausius-Clapeyron relationship. This behavior means that for a given 6417 increase in temperature, increases in extreme precipitation can be relatively larger than 6418 the mean precipitation increase (e.g., Allen and Ingram, 2002), so long as the character of 6419 the regional circulation does not change substantially (Pall et al., 2007). Additionally, 6420 timescale can play a role whereby increases in the frequency of seasonal mean rainfall 6421 extremes can be greater than the increases in the frequency of daily extremes (Barnett et 6422 al., 2006). The increase of mean and extreme precipitation in various regions has been 6423 attributed to contributions from both dynamic (circulation) and thermodynamic (moisture 6424 content of the air) processes associated with global warming (Emori and Brown, 2005) 6425 although the precipitation mean and variability changes are largely due to the 6426 thermodynamic changes over most of North America. Changes in circulation also 6427 contribute to the pattern of precipitation intensity changes over northwest and northeast 6428 North America (Meehl et al., 2005a). Kharin and Zwiers (2005) showed that changes to 6429 both the location and scale of the extreme value distribution produced increases of 6430 precipitation extremes substantially greater than increases of annual mean precipitation. 6431 An increase in the scale parameter from the gamma distribution represents an increase in 6432 precipitation intensity, and various regions such as the Northern Hemisphere land areas in 6433 winter showed particularly high values of increased scale parameter (Semenov and 6434 Bengtsson, 2002; Watterson and Dix, 2003). Time slice simulations with a higher 6435 resolution model (~1°) show similar results using changes in the gamma distribution, 6436 namely increased extremes of the hydrological cycle (Voss et al., 2002). 6437 6438 Do Not Cite or Quote 285 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6439 3.3.6 Flooding and Dry Days 6440 Changes in the precipitation extremes have a large impact on both flooding and the 6441 number of precipitation free days. The discussion of both is combined because their 6442 changes are related, in spite of the apparent contradiction. 6443 6444 A number of studies have noted that increased rainfall intensity may imply increased 6445 flooding. McCabe et al. (2001) and Watterson (2005) showed there was an increase in 6446 extreme rainfall intensity in extratropical surface lows, particularly over Northern 6447 Hemisphere land. However, analyses of climate changes from increased greenhouse 6448 gases gives mixed results, with increased or decreased risk of flooding depending on the 6449 model analyzed (Arora and Boer 2001,Milly et al. 2002, Voss et al. 2002). 6450 6451 Global and North American averaged time series of the Frich et al. (2002) indices in the 6452 multi-model analysis of Tebaldi et al. (2006) show simulated increases in heavy 6453 precipitation during the 20th century continuing through the 21st century (Meehl et al. 6454 2007a, Fig. 3.1d), along with a somewhat weaker and less consistent trend for increasing 6455 dry periods between rainfall events for all scenarios (Meehl et al. 2007a). Part of the 6456 reason for these results is that precipitation intensity increases almost everywhere, but 6457 particularly at mid and high latitudes, where mean precipitation increases (Meehl et al., 6458 2005a). 6459 6460 There are regions of increased runs of dry days between precipitation events in the 6461 subtropics and lower midlatitudes, but a decreased number of consectutive dry days at Do Not Cite or Quote 286 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6462 higher midlatitudes and high latitudes where mean precipitation increases. Since there are 6463 areas of both increases and decreases of consecutive dry days between precipitation 6464 events in the multi-model average, the global mean trends are smaller and less consistent 6465 across models. Consistency of response in a perturbed physics ensemble with one model 6466 shows only limited areas of increased frequency of wet days in July, and a larger range of 6467 changes of precipitation extremes relative to the control ensemble mean in contrast to the 6468 more consistent response of temperature extremes (discussed above), indicating a less 6469 consistent response for preciptitation extremes in general compared to temperature 6470 extremes (Barnett et al., 2006). 6471 6472 Associated with the risk of drying is a projected increase in chance of intense 6473 precipitation and flooding. Though somewhat counter-intuitive, this is because 6474 precipitation is projected to be concentrated into more intense events, with longer periods 6475 of little precipitation in between. Therefore, intense and heavy episodic rainfall events 6476 with high runoff amounts are interspersed with longer relatively dry periods with 6477 increased evapotranspiration, particularly in the subtropics (Frei et al., 1998; Allen and 6478 Ingram, 2002; Palmer and Räisänen, 2002; Christensen and Christensen, 2003; Beniston, 6479 2004; Christensen and Christensen, 2004; Pal et al., 2004; Meehl et al., 2005a). However, 6480 increases in the frequency of dry days do not necessarily mean a decrease in the 6481 frequency of extreme high rainfall events depending on the threshold used to define such 6482 events (Barnett et al., 2006). Another aspect of these changes has been related to the 6483 mean changes of precipitation, with wet extremes becoming more severe in many areas 6484 where mean precipitation increases, and dry extremes becoming more severe where the Do Not Cite or Quote 287 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6485 mean precipitation decreases (Kharin and Zwiers, 2005; Meehl et al., 2005a; Räisänen, 6486 2005a; Barnett et al., 2006). However, analysis of a 53-member perturbed-physics 6487 ensemble indicates that the change in the frequency of extreme precipitation at an 6488 individual location can be difficult to estimate definitively due to model parameterization 6489 uncertainty (Barnett et al., 2006). 6490 6491 3.3.7 Drought 6492 A long-standing result from global coupled models noted in Cubasch et al. (2001) has 6493 been a projected increase of summer drying in the midlatitudes in a future warmer 6494 climate, with an associated increased risk of drought. The more recent generation of 6495 models continues to show this behavior (Burke et al., 2006; Meehl et al., 2006b, 2007a; 6496 Rowell and Jones, 2006). For example, Wang (2005) analyzed 15 recent AOGCMs to 6497 show that in a future warmer climate, the models simulate summer dryness in most parts 6498 of northern subtropics and midlatitudes, but there is a large range in the amplitude of 6499 summer dryness across models. Hayhoe et al. (2007) found in an ensemble of AOGCMs 6500 an increased frequency of droughts lasting a month or longer in the northeastern U.S. 6501 Droughts associated with summer drying could result in regional vegetation die-offs 6502 (Breshears et al., 2005) and contribute to an increase in the percentage of land area 6503 experiencing drought at any one time. For example, extreme drought increases from 1% 6504 of present day land area (by definition) to 30% by the end of the century in the Hadley 6505 Centre AOGCM’s A2 scenario (Burke et al., 2006). Drier soil conditions can also 6506 contribute to more severe heat waves as discussed above (Brabson et al., 2005). 6507 Do Not Cite or Quote 288 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6508 A recent analysis of Milly et al. (2005) shows that several AOGCMs project greatly 6509 reduced annual water availability over the southwest US and northern Mexico in the 6510 future (Fig. 3.6). In the historical context, this area is subject to very severe and long 6511 lasting droughts (Cook et al. 2004). The tree-ring record indicates that the late 20th 6512 century was a time of greater than normal water availability. However, the consensus of 6513 most climate models is for a reduction of cool season precipitation across the Southwest 6514 and northwest Mexico (Christensen et al., 2007). This is consistent with a recent 10-year 6515 shift to shorter and weaker winter rainy seasons and an observed northward shift in 6516 northwest Pacific winter storm tracks (Yin, 2005). Reduced cool season precipitation 6517 promotes drier summer conditions by reducing the amount of soil water available for 6518 evapotranspiration in summer. 6519 6520 The model projections of reduced water availability over the southwest US and Mexico 6521 in the future needs further study. The uncertainty associated with these projections is 6522 related to the ability of models to simulate the precipitation distribution and variability in 6523 the present climate and to correctly predict the response to future changes. For example, 6524 the uncertainty associated with the ENSO response to climate change (Zelle et al. 2005, 6525 Meehl et al. 2007a) also impacts the projections of future water availability in southwest 6526 US and northern Mexico (e.g., Meehl and Tebaldi 2007). See Chapter 1 for more 6527 discussion on the importance of drought. 6528 6529 6530 Do Not Cite or Quote 289 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6531 3.3.8 Snowfall 6532 Extreme snowfall events could change as a result of both precipitation and temperature 6533 change. Although reductions in North American snow depth and areal coverage have 6534 been projected (Frei and Gong, 2005; Bell and Sloan, 2006; Déry and Wood, 2006), there 6535 appears to be little analysis of changes in extreme snowfall. An assessment of possible 6536 future changes in heavy lake-effect snowstorms (Kunkel et al. 2002) from the Laurentian 6537 Great Lakes found that surface air temperature increases are likely to be the dominant 6538 factor. They examined simulations from 2 different climate models and found that 6539 changes in the other factors favorable for heavy snow events were relatively small. In the 6540 snowbelts south of Lakes Ontario, Erie and Michigan, warming decreases the frequency 6541 of temperatures in the range of -10 ˚C to 0 ˚C that is favorable for heavy lake-effect 6542 snowfall. Thus, decreases in event frequency are likely in these areas. However, in the 6543 northern, colder snowbelts of the Great Lakes, such as the Upper Peninsula of Michigan, 6544 moderate increases in temperature have minor impacts on the frequency of favorable 6545 temperatures because in the present climate temperatures are often too cold for very 6546 heavy snow; warming makes these days more favorable, balancing the loss of other days 6547 that become too warm. Thus, the future frequency of heavy events may change little in 6548 the northern snowbelts. 6549 6550 Increased temperature suggests that heavy snow events downwind of the Great Lakes will 6551 begin later in the season, and on most lakes end earlier. Also, increased temperature with 6552 concomitant increased atmospheric moisture implies that in central and northern Canada, Do Not Cite or Quote 290 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6553 Alaska, and other places cold enough to snow (e.g., high mountains) the intensity of 6554 heavy snow events may increase. 6555 6556 3.3.9 Tropical Storms 6557 3.3.9.1 Introduction 6558 In response to future anthropogenic climate warming (IPCC 2001) tropical cyclones 6559 could potentially change in a number of important ways, including frequency, intensity, 6560 size, duration, tracks, area of genesis or occurrence, precipitation, and storm surge 6561 characteristics. 6562 6563 Overarching sources of uncertainty in future projections of hurricanes include 6564 uncertainties in future emission scenarios for climatically important radiative forcings, 6565 global-scale climate sensitivity to these forcings and the limited capacity of climate 6566 models to adequately simulate intense tropical cyclones. The vulnerability to storm surge 6567 flooding from future hurricanes will very likely be enhanced to some degree due to 6568 continuing global sea level rise associated with anthropogenic warming, modulated by 6569 local sea level changes due to other factors such as local land elevation changes and 6570 regionally varying sea level rise patterns. These related topics are covered in more detail 6571 in other CCSP Synthesis and Assessment Products 2-1, 3-2, and 4-1, or IPCC Fourth 6572 Assessment Report chapters on climate sensitivity, future emission scenarios, and sea 6573 level rise. An assessment of the state of understading of tropical cyclones and climate 6574 change as of 2006 has been prepared by the tropical cyclone community (IWTC VI, 6575 2006; section 3.2.4 of this document). Although not published in the literature as yet, the Do Not Cite or Quote 291 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6576 full summary statement and condensed summary are available online at 6577 http://www.wmo.ch/web/arep/arep-home.html. 6578 6579 Future projections of hurricanes will depend upon not only on global mean climate 6580 considerations, but also on regional-scale projections of a number of aspects of climate 6581 that can potentially affect tropical cyclone behavior. These include: 6582 • The local potential intensity (Emanuel 2005a; 2006a, Holland 1997), which 6583 depends on sea surface temperatures, atmospheric temperature and moisture 6584 profiles, and near-surface ocean temperature stratification; 6585 • Influences of vertical wind shear, large-scale vorticity, and other circulation 6586 features (Gray 1968; 1984; Goldenberg et al. 2001; Bell and Chelliah 2006); and, 6587 • The characteristics of precursor disturbances such as easterly waves and their 6588 interaction with the environment (Dunn 1940, Frank and Clarke 1980, Pasch et al 6589 1998, Thorncroft and Hodges 2001). 6590 Details of future projections in regions remote from the tropical storm basin in question 6591 may also be important. For example, El Nino fluctuations in the Pacific influence 6592 Atlantic basin hurricane activity (Chapter 2, Section 3.2 of this chapter). West African 6593 monsoon activity has been correlated with Atlantic hurricane activity (Gray 1990), as 6594 have African dust outbreaks (Evans et al. 2006). Zhang and Delworth (2006) show how a 6595 warming of the northern tropical Atlantic SST relative to the southern tropical Atlantic 6596 produces atmospheric circulation features, such as reduced vertical wind shear of the 6597 mean wind field, that are correlated with low-frequency variations in major hurricane 6598 activity (Goldenberg et al. 2001). Do Not Cite or Quote 292 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6599 6600 The high sensitivity of tropical storm and hurricane activity in the Atlantic basin to 6601 modest environmental variations suggests the possibility of strong sensitivity of hurricane 6602 activity to anthropogenic climate change, though the nature of such changes remains to 6603 be determined. Confidence in any future projections of anthropogenic influence on 6604 Atlantic hurricanes will depend on the reliability of future projections of the local 6605 thermodynamic state (e.g., potential intensity) as well as circulation changes driven by 6606 both local and remote influences, as described above. Projected effects of global warming 6607 on El Niño remain uncertain (Timmermann, 1999; Zelle et al., 2005; Meehl et al. 2007a). 6608 There is climate model-based evidence that the time-mean climate late in the 21st century 6609 will be characterized by higher tropical-cyclone potential intensity in most tropical- 6610 cyclone regions, and also tend toward having a decreased east-west overturning 6611 circulation in the Pacific sector in the 21st century, with likely consequences for vertical 6612 wind shear and other characteristics in the tropical Atlantic (Vecchi and Soden 2007). 6613 6614 Even assuming that the climate factors discussed above can be projected accurately, 6615 additional uncertainties in hurricane future projections arise from uncertainties in 6616 understanding and modeling the response of hurricanes to changing environmental 6617 conditions. This is exacerbated by projections that the large-scale conditions for some 6618 factors, such as decadal means and seasonal extremes of SSTs, will be well outside the 6619 range of historically experienced values. This raises questions of the validity of statistical 6620 models trained in the present day climate (Ryan et al. 1992; Royer et al. 1998), thus the 6621 emphasis here is placed on physical models and inferences as opposed to statistical Do Not Cite or Quote 293 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6622 methods and extrapolation. Thus, we consider projections based on global and regional 6623 nested modeling frameworks as well as more idealized modeling or theoretical 6624 frameworks developed specifically for hurricanes. The idealized approaches include 6625 potential intensity theories as well as empirical indices which attempt to relate tropical 6626 cyclone frequency to large-scale environmental conditions. Global and regional nested 6627 models simulate the development and life cycle of tropical storm-like phenomena that are 6628 typically much weaker and with a larger spatial scale than observed tropical cyclones. 6629 These model storms are identified and tracked using automated storm tracking 6630 algorithms, which typically differ in detail between studies but include both intensity and 6631 “warm-core” criteria which must be satisfied. Models used for existing studies vary in 6632 horizontal resolution, with the low-resolution models having a grid spacing of about 300 6633 km, medium resolution with grid spacing of about 120 km, and high resolution with grid 6634 spacing of 20-50 km. 6635 6636 3.3.9.2 Tropical Cyclone Intensity 6637 Henderson-Sellers et al. (1998), in an assessment of tropical cyclones and climate 6638 change, concluded that the warming resulting from a doubling of CO2 would cause the 6639 potential intensity of tropical cyclones to remain the same or increase by 10 to 20%. 6640 (Their estimate was given in terms of central pressure fall; all other references to intensity 6641 in this section will refer to maximum surface winds, except where specifically noted 6642 otherwise.) They also noted limitations of the potential intensity theories, such as sea 6643 spray influences and ocean interactions. Further studies using a high resolution hurricane 6644 prediction model for case studies or idealized experiments under boundary conditions Do Not Cite or Quote 294 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6645 provided from high CO2 conditions (Knutson et al. 1998; Knutson and Tuleya 1999; 6646 2004; 2007) have provided additional model-based evidence to support these theoretical 6647 assessments. For a CO2-induced tropical SST warming of 1.75C, they found a 14% 6648 increase in central pressure fall (Fig. 3.7) and a 6% increase in maximum surface wind or 6649 a maximum wind speed sensitivity of about 4% per degree Celsius (Knutson and Tuleya 6650 2007). In a related study, Knutson et al. (2001) demonstrated that inclusion of an 6651 interactive ocean in their idealized hurricane model did not significantly affect the 6652 percentage increase in hurricane intensity associated with CO2-induced large-scale SST 6653 warming. Caveats to these idealized studies are the simplified climate forcing (CO2 only 6654 versus a mixture of forcings in the real world) and neglect of potentially important factors 6655 such as vertical wind shear and changes in tropical cyclone distribution. 6656 6657 Global climate model experiments have historically been performed at resolutions which 6658 precluded the simulation of realistic hurricane intensities (e.g., major hurricanes). To 6659 date, the highest resolution tropical cyclone/climate change experiment published is that 6660 of Oouchi et al. (2006). Under present climate conditions, they simulated tropical 6661 cyclones with central pressures as low as about 935 hPa and surface wind speeds as high 6662 as about 53 m/sec. Oouchi et al. report a 14% increase in the annual maximum tropical 6663 cyclone intensity globally and a 20% increase in the Atlantic, both in response to a 6664 greenhouse-warming experiment with global SSTs increasing by about 2.5˚C. A notable 6665 aspect of their results is the finding that the occurrence rate of the most intense storms 6666 increased despite a large reduction in the global frequency of tropical cyclones. 6667 Statistically significant intensity increases in their study were limited to two of six basins Do Not Cite or Quote 295 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6668 (North Atlantic and South Indian Ocean). Bengtsson et al. (2007) also find a slightly 6669 reduced tropical storm frequency in the Atlantic coupled with an increase in the 6670 intensities (measured in terms of relative vorticity) of the most intense storms. The latter 6671 finding only became apparent at relative high model resolution (~30-40 km grid). 6672 6673 Other studies using comparatively lower resolution models have reported tropical- 6674 cyclone intensity results. However, the simulated response of intensity to changes in 6675 climate in lower resolution models may not be reliable as they have not been able to 6676 simulate the marked difference in achievable tropical-cyclone intensities for different 6677 SST levels (e.g., Yoshimura et al. 2006) as documented for observed tropical cyclones 6678 (DeMaria and Kaplan 1994; Whitney and Hobgood 1997; Baik and Paek 1998). Given 6679 this important caveat, the lower resolution model results for intensity are mixed: Tsutsui 6680 (2002) and McDonald et al. (2005) report intensity increases under warmer climate 6681 conditions, while Sugi et al. (2002), Bengtsson et al. (2006), and Hasegawa and Emori 6682 (2005; western North Pacific only) , and Chauvin et al. (2006; North Atlantic only) found 6683 either no increase or a decrease of intensity. 6684 6685 Vecchi and Soden (2007) present maps of projected late 21st century changes in 6686 Emanuel’s potential intensity, vertical wind shear, vorticity, and mid-tropospheric 6687 relative humidity as obtained from the latest (IPCC AR4, 2007) climate models (Fig. 6688 3.8). While their results indicate an increase in potential intensity in most tropical cyclone 6689 regions, the Atlantic basin in particular displays a mixture with about two-thirds of the 6690 area showing increases and about one-third slight decreases. In some regions, they also Do Not Cite or Quote 296 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6691 found a clear tendency for increased vertical wind shear and reduced mid-tropospheric 6692 relative humidity – factors that are detrimental for tropical storm development. In the 6693 Gulf of Mexico and closer to the U.S. and Mexican coasts the potential intensity 6694 generally increases. The net effect of these composite changes remains to be modelled in 6695 detail, although existing global modelling studies (Oouchi et al. 2006; Bengtsson et al. 6696 2007) suggest increases in the intensities and frequencies of the strongest storms. In the 6697 Eastern Pacific, the potential intensity is predicted to increase across the entire basin, 6698 although the vertical wind shear increases may counteract this to some extent. 6699 6700 A more recent idealized calculation by Emanuel et al. (2006) finds that artificially 6701 increasing the modelled potential intensity by 10% leads to a marked increase in the 6702 occurrence rate of relatively intense hurricanes (Fig. 3.9a), and to a 65% increase in the 6703 PDI. Increasing vertical wind shear by 10% leads to a much smaller decrease in the 6704 occurrence rate of relatively intense hurricanes (Fig. 3.9b) and a 12% reduction in the 6705 PDI. This suggests that increased potential intensity in a CO2-warmed climate implies a 6706 much larger percentage change in potential destructiveness of storms from wind damage 6707 than the percentage change in wind speed itself. 6708 6709 In summary, theory and high-resolution idealized models indicate increasing intensity 6710 and frequency of the strongest hurricanes/typhoons in a CO2-warmed climate. Parts of the 6711 Atlantic basin may have small decreases in the upper limit intensity, according to one 6712 multi-model study of theoretical potential intensity. Expected changes in tropical cyclone 6713 intensity and their confidence is therefore assessed as follows: in the Atlantic and North Do Not Cite or Quote 297 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6714 Pacific basins, some increase of maximum surface wind speeds of the strongest 6715 hurricanes and typhoons is likely. We estimate the likely range for the intensity change 6716 (in terms of maximum surface winds) to be +2% to +10% per degree Celsius tropical sea 6717 surface warming over most tropical storm regions. This range is based on our subjective 6718 judgement that the likely range is from about half to twice the sensitivity found in current 6719 hurricane models and theory. Furthermore, the balance of evidence suggests that 6720 maximum intensities may decrease in some regions, particularly in parts of the Atlantic 6721 basin, even though sea surfaces are expected to warm in all regions. 6722 6723 This assessment assumes that there is no change in geographical distribution of the 6724 storms (i.e. the storms move over the same locations, but with a generally warmer 6725 climate). On the other hand, there is evidence (Holland and Webster 2007a) that changes 6726 in distribution (e.g. tropical-cyclone development occurring more equatorward, or 6727 poleward of present day) have historically been associated with large changes in the 6728 proportion of major hurricanes. It is uncertain how such distributions will change in the 6729 future (see below), but such changes potentially could strongly modify the projections 6730 reported here. 6731 6732 3.3.9.3 Tropical Cyclone Frequency and Area of Genesis 6733 In contrast to the case for tropical-cyclone intensity, the existing theoretical frameworks 6734 for relating tropical-cyclone frequency to global climate change are relatively less well- 6735 developed. Gray (1979) developed empirical relationships that model the geographical 6736 variation of tropical-cyclone genesis in the present climate relatively well, but several Do Not Cite or Quote 298 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6737 investigators have cautioned against the use of these relationships in a climate change 6738 context (Ryan et al. 1992, Royer et al. 1998). Royer et al. proposed a modified form of 6739 the Gray relationships based on a measure of convective rainfall as opposed to SST or 6740 oceanic heat content, but this alternative has not been widely tested. They showed that 6741 tropical-cyclone frequency results for a future climate scenario depended strongly on 6742 whether the modified or unmodified genesis parameter approach was used. More 6743 recently, Emanuel and Nolan (2004) and Nolan et al. (2006) have developed a new 6744 empirical scheme designed to be more appropriate for climate change application (see 6745 also Camargo et al. 2006), but tropical-cyclone frequency/climate change scenarios with 6746 this framework have not been published to date. 6747 6748 Vecchi and Soden (2007) have assessed the different components of the Emanuel and 6749 Nolan (2004) scheme using outputs from the IPCC AR4 models. Their results suggest 6750 that a decrease in tropical cyclone frequency may occur over some parts of the Atlantic 6751 basin associated with a SW-NE oriented band of less favorable conditions for tropical 6752 cyclogenesis and intensification, including enhanced vertical wind shear, reduced mid- 6753 tropospheric relative humidity, and slight decrease in potential intensity. The enhanced 6754 vertical shear feature (present in about 14 of 18 models in the Caribbean region) also 6755 extends into the main cyclogenesis region of the Eastern Pacific basin. Physically, this 6756 projection is related to the weakening of the east-west oriented Walker Circulation in the 6757 Pacific region, similar to that occurring during El Nino events. During El Nino conditions 6758 in the present-day climate, hurricane activity is reduced, as occurred for example in the 6759 latter part of the 2006 season. While this projection may appear at odds with Do Not Cite or Quote 299 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6760 observational evidence for an increase in Atlantic tropical storm counts during the past 6761 century (Holland and Webster 2007a; Vecchi and Knutson 2007), there is evidence that 6762 this has occurred in conjunction with a regional decreasing trend in storm occurrence and 6763 formation rates in the western part of the Caribbean and Gulf of Mexico (Vecchi and 6764 Knutson 2007; Holland 2007). Earlier, Knutson and Tuleya (2004) had examined the 6765 vertical wind shear of the zonal wind component for a key region of the tropical Atlantic 6766 basin using nine different coupled models from the CMIP2+ project. Their analysis 6767 showed a slight preference for increased vertical shear under high CO2 conditions if all 6768 of the models are considered, and a somewhat greater preference for increased shear if 6769 only the six models with the most realistic present-day simulation of shear in the basin 6770 are considered. Note that these studies are based on different sets of models, and that a 6771 more idealized future forcing scenario was used in the earlier Knutson and Tuleya study. 6772 6773 Alternative approaches to the empirical analysis of large-scale fields are the global and 6774 regional climate simulations, in which the occurrence of model tropical cyclones can be 6775 tracked. Beginning with the early studies of Broccoli and Manabe (1990), Haarsma et al. 6776 (1993), and Bengtsson et al. (1996), a number of investigators have shown that global 6777 models can generate tropical storm-like disturbances in roughly the correct geographical 6778 locations with roughly the correct seasonal timing. The annual occurrence rate of these 6779 systems can be quite model dependent (Camargo et al. 2005) and is apparently sensitive 6780 to various aspects of model physics (e.g., Vitart et al. 2001). 6781 Do Not Cite or Quote 300 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6782 The notion of using global models to simulate the climate change response of tropical 6783 cyclone counts is given some support by several studies showing that such models can 6784 successfully simulate certain aspects of interannual to interdecadal variability of tropical- 6785 cyclone occurrence seen in the real world (Vitart et al. 1997; Carmargo et al. 2005; Vitart 6786 and Anderson 2001). A recent regional model dynamical downscaling study (Knutson et 6787 al. 2007) with an18 km grid model, and a more idealized modelling approach (Emanuel 6788 et al. 2007) both indicate that the increase in hurricane activity in the Atlantic from 1980- 6789 2005 can be reproduced in a model using specified SSTs and large-scale historical 6790 atmospheric information from reanalyses. 6791 6792 Since tropical storms are relatively rare events and can exhibit large interannual to 6793 interdecadal variability, large samples sizes (i.e. many seasons) are typically required to 6794 test the significance of any changes in a model simulation against the model’s “natural 6795 variability”. 6796 6797 The most recent future projection results obtained from medium and high resolution (120 6798 km-20 km) GCMs are summarized in Table 3.2. Among these models, the higher 6799 resolution ones indicate a consistent signal of fewer tropical cyclones globally in a 6800 warmer climate, while two lower resolution models find essentially no change. There are, 6801 however, regional variations in the sign of the changes, and these vary substantially 6802 between models (Table 3.2). For the North Atlantic in particular, more tropical storms are 6803 projected in some models, despite a large reduction globally (Sugi et al. 2002; Oouchi et 6804 al. 2006), while fewer Atlantic tropical cyclones are projected by other models (e.g., Do Not Cite or Quote 301 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6805 McDonald et al. 2005; Bengtsson et al. 2007). It is not clear at present how the Sugi et al. 6806 (2002) and Oouchi et al. (2006) results for the Atlantic reconciles with the tendency for 6807 increased vertical wind shear projected for parts of that basin by most recent models 6808 (Vecchi and Soden 2007). For example, Oouchi et al. (2006) did not analyze how 6809 Atlantic vertical wind shear changed in their warming experiment. However, their results 6810 suggest that a future increase in tropical cyclone frequency in the Atlantic is at least 6811 plausible, based on current models. Chauvin et al. (2006) and Emanuel et al. (2007) find, 6812 in multi-model experiments, that the sign of the changes in tropical cyclone frequency in 6813 the north Atlantic basin depends on the climate model used . All of these results cited 6814 here should be treated with some caution, as it is not always clear that these changes are 6815 greater than the model’s natural variability, or that the natural variability or the tropical- 6816 cyclone genesis process are being properly simulated in the models. 6817 6818 From the above summarized results, it is not clear that current models provide a confident 6819 assessment of even the sign of change of tropical storm frequency in the Atlantic, East 6820 Pacific, or Northwest Pacific basins. From an observational perspective, recent studies 6821 (Chapter 2) report that there has been a long term increase in Atlantic tropical-cyclone 6822 counts since the late 1800s, although the magnitude and in some cases statistical 6823 significance of the trend depends on adjustments for missing storms early in the record. 6824 6825 Based on the above available information, we assess that it is unknown how late 21st 6826 century tropical cyclone frequency in the Atlantic and North Pacific basins will change, 6827 compared to the historical period (~1950-2006). Do Not Cite or Quote 302 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6828 3.3.9.4 Tropical Cyclone Precipitation 6829 The notion the tropical cyclone precipitation rates could increase in a warmer climate is 6830 based on the hypothesis that moisture convergence into tropical cyclones will be 6831 enhanced by the increased column integrated water vapor – with the increased water 6832 vapor being extremely like to accompany a warming of tropical SSTs. The increased 6833 moisture convergence would then be expected to lead to enhanced precipitation rates. 6834 This mechanism has been discussed in the context of extreme precipitation in general by 6835 Trenberth (1999), Allen and Ingram (2002), and Emori and Brown (2005). In contrast to 6836 the near-storm or storm core precipitation rate, accumulated rainfall at a locality along the 6837 storm’s path is strongly dependent upon the speed of the storm, and there is little 6838 guidance at present on whether any change in this factor is likely in a future warmed 6839 climate. 6840 6841 An enhanced near-storm tropical rainfall rate for high CO2 conditions has been 6842 simulated, for example, by Knutson and Tuleya (2004, 2007) based on an idealized 6843 version of the GFDL hurricane model. The latter study reported an increase of 21.6% for 6844 a 1.75˚C tropical SST warming (Fig. 3.10), or about 12% per degree Celsius SST 6845 increase. Using a global model, Hasegawa and Emori (2005) found an increase in 6846 tropical-cyclone-related precipitation in a warmer climate in the western North Pacific 6847 basin, despite a decrease in tropical-cyclone intensity there in their model. Chauvin et al 6848 (2006) found a similar result in the North Atlantic in their model, and Yoshimura et al. 6849 (2006) found a similar result on a global domain. There are issues with all of these 6850 modelling studies as they are of course resolution and thus generally depend on Do Not Cite or Quote 303 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6851 parameterization of much of the rainfall within the grid box. Further there is a tendency 6852 towards tropical cyclone rainfall simulations that have a high bias in core rainfall rates 6853 (e.g. Marchok et al. 2007). Nevertheless, the consistent result of an increased rainfall with 6854 greenhouse warming over a number of models, together with the theoretical expectations 6855 that this will occur lends credibility to there being a real trend. 6856 6857 Based on the modeling studies to date, the relatively straightforward proposed physical 6858 mechanism, and the observed increases in extremely heavy rainfall in the U.S. (although 6859 not established observationally for hurricane-related rainfall (Groismann et al. 2004)) we 6860 assess the projections that hurricane related rainfall (per storm) will increase in the 21st 6861 century as likely. Note that if the frequency of tropical cyclones decreases, the total 6862 rainfall from tropical cyclones may decrease. The expected general magnitude of the 6863 change for storm core rainfall rates is about +6% to +18% per degree Celsius increase in 6864 tropical sea surface temperature. 6865 6866 3.3.9.5 Tropical Cyclone Size, Duration, Track, Storm Surge, and Regions of 6867 Occurrence 6868 In this section, other possible impacts of greenhouse gas induced climate warming on 6869 tropical cyclones are briefly assessed. The assessment is highly preliminary and the 6870 discussion for these relatively brief owing to the lack of detailed studies on these possible 6871 impacts at this time. 6872 Do Not Cite or Quote 304 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6873 Wu and Wang (2004) explored the issue of tropical cyclone track changes in a climate 6874 change context. Based on experiments derived from one climate model, they found some 6875 evidence for inferred track changes in the NW Pacific, although the pattern of changes 6876 was fairly complex. 6877 6878 Concerning storm duration, using an idealized hurricane simulation approach in which 6879 the potential intensity of a large sample of Atlantic basin storms with synthetically 6880 generated storm tracks was artificially increased by 10%, Emanuel (2006b) found that the 6881 average storm lifetime of all storms increased by only 3%, whereas the average duration 6882 at hurricane intensity for those storms that attained hurricane intensity increased by 15%. 6883 However, in the Atlantic and NE Pacific, future changes in duration are quite uncertain, 6884 owing to the uncertainties in formation locations and potential circulation changes 6885 mentioned previously. 6886 6887 Few studies have attempted to assess possible future changes in hurricane size. Knutson 6888 and Tuleya (1999) noted that the radius of hurricane-force winds increased a few percent 6889 in their experiments in which the intensities also increased a few percent. 6890 6891 An important question for regions along the periphery of tropical cyclone basins is 6892 whether regions with have never or only infrequently experienced tropical cyclones in 6893 recorded history may experience them more frequently in the future owing to climate 6894 change. Little guidance is available at present on this important question. 6895 Do Not Cite or Quote 305 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6896 Storm surge depends on many factors, including storm intensity, size and track, local 6897 bathymetry and the structure of coastal features such as wetlands and river inlets. 6898 Unknowns in storm frequency, tracks, size and future changes to coastal features lead to 6899 considerable uncertainty in assessing storm surge changes. However, the high confidence 6900 of there being future sea level rise as well as the likely increase of intensity of the 6901 strongest hurricanes, leads to an assessment that the potential for storm surge damage 6902 (per hurricane) is very likely to increase. 6903 6904 In summary, tropical cyclone size, duration, track and regions of occurrence are 6905 important questions that need to be addressed. However, based on available published 6906 work and previous assessments, it is unknown how these will change in the future 6907 (IWTC-VI 2006). Storm surge damage (per hurricane) is likely to rise. 6908 6909 3.3.9.6 Reconciliation of Future Projections and Past Variations 6910 In this section, we attempt to reconcile the future projections discussed above with the 6911 past observed variations in TC activity. The balance of evidence suggests that human 6912 activity has caused a discernible increase in tropical storm/hurricane and major hurricane 6913 frequency in the North Atlantic. U.S. landfalling hurricane frequency has not increased. 6914 However, it is more difficult to judge whether anthropogenic forcing will cause further 6915 increases in basin-wide activity as the climate continues to warm, since the precise 6916 physical reasons for the observed increases have not been fully elucidated. It is noted that 6917 relevant anthropogenic forcing includes increasing greenhouse gases, as well as changes 6918 in aerosol forcing, and possibly decreasing stratospheric ozone and other factors Do Not Cite or Quote 306 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6919 associated with cooling upper atmospheric (~100mb) temperatures in recent decades 6920 (Emanuel 2007a). A recent modeling study (Knutson et al. 2007) indicates that the 6921 increase in hurricane activity in the Atlantic from 1980-2005 can be reproduced using a 6922 high-resolution nested regional model downscaling approach. However the various 6923 changes in the large-scale atmospheric and SST forcings used to drive their regional 6924 model were prescribed from observations. 6925 6926 No published model study has directly simulated a substantial century-scale rise in 6927 Atlantic tropical cyclone counts similar to those reported for the observations (e.g., Ch. 6928 2). In fact the 20th century behavior in TC frequency has not yet been documented for 6929 existing models. One exception is Bengtsson et al. (2007) who simulate little change in 6930 tropical storm frequencies comparing the late 1800s and late 1900s. Given the future 6931 regional climate projections arising from the models, including the multi-model 6932 consensus increase of vertical wind shear in the IPCC AR4 models (Vecchi and Soden 6933 2007), the substantial variability among existing models of such projected characteristics 6934 as Atlantic vertical wind shear and the differing mixtures of climate forcings that may be 6935 relevant in the two periods, we anticipate that it would be difficult to confidently 6936 extrapolate the strong increasing trend in 20th century storm counts using future 6937 consensus projections available from existing models. Nonetheless, a significant trend (or 6938 anthropogenic signal, whether trend-like or not) detected in observed tropical cyclone 6939 activity and attributed to increasing greenhouse gases could imply that a future increase 6940 in tropical cyclone frequency in the Atlantic is much more likely than assessed here. 6941 Do Not Cite or Quote 307 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6942 3.3.10 Extratropical Storms 6943 Scientists have used a variety of methods for diagnosing extratropical storms in GCM 6944 projections of future climate. These include sea-level pressure (Lambert and Fyfe 2006), 6945 strong surface winds (Fischer-Bruns et al. 2005), lower atmosphere vorticity (Bengtsson 6946 et al. 2006) and significant wave heights (Wang et al. 2004; Caires et al. 2006). 6947 Consequently, there are no consistent definitions used to diagnose extreme extratropical 6948 storms. Some analyses do not, for example, determine events in extreme percentiles but 6949 rather consider storms that deepen below a threshold sea-level pressure (e.g., Lambert 6950 and Fyfe, 2006), though such thresholds may effectively select the most extreme 6951 percentiles. 6952 6953 Wave heights of course indicate strong storms only over oceans, but the strongest 6954 extratropical storms typically occur in ocean storm tracks, so all three methods focus on 6955 similar regions. Ocean storms in the North Atlantic and North Pacific are relevant for this 6956 study because they affect coastal areas and shipping to and from North America. GCMs 6957 projecting climate change can supply sea-level pressure and surface winds, but they 6958 typically do not compute significant wave heights. Rather, empirical relationships (Wang 6959 et al. 2004; Caires et al. 2006) using sea-level pressure anomalies and gradients provide 6960 estimates of significant wave heights. 6961 6962 Despite the variety of diagnoses, some consistent changes emerge in analyses of 6963 extratropical storms under anthropogenic greenhouse warming. Projections of future 6964 climate indicate strong storms will be more frequent (Fig. 3.11; Wang et al. 2004, Do Not Cite or Quote 308 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6965 Fischer-Bruns et al. 2005, Bengtsson et al. 2006, Caires et al. 2006, Lambert and Fyfe 6966 2006, Pinto et al. 2007), though the overall number of storms may decrease. These 6967 changes are consistent with observed trends over the last half of the twentieth century 6968 (Paciorek et al. 2002). More frequent strong storms may reduce the frequency of all 6969 extratropical storms by increasing the stability of the atmosphere (Lambert and Fyfe 6970 2006). Analyses of strong winds (Fischer-Bruns et al. 2005, Pinto et al. 2007), lower 6971 atmosphere vorticity (Bengtsson et al. 2006) and significant wave heights (Wang et al. 6972 2004; Caires et al. 2006) from single models suggest increased storm strength in the 6973 northeast Atlantic, but this increase is not apparent an analysis using output from multiple 6974 GCMs (Lambert and Fyfe 2006). Differences may be due to the focus on cold season 6975 behavior in the wind and wave analyses, whereas Lambert and Fyfe’s (2006) analysis 6976 includes the entire year. 6977 6978 The warming projected for the 21st century is largest in the high latitudes due to a 6979 poleward retreat of snow and ice resulting in enhanced warming (Meehl et al. 2007a). 6980 Projected seasonal changes in sea ice extent show summertime ice area declining much 6981 more rapidly than wintertime ice area and that sea ice thins largest where it is initially the 6982 thickest, which is consistent with observed sea ice thinning in the late 20th century (Meehl 6983 et al. 2007a). Increased storm strength the northeast Atlantic found by some may be 6984 linked to the poleward retreat of arctic ice (Fischer-Bruns et al. 2005) and a tendency 6985 toward less frequent blocking and more frequent positive phase of the Northern Annular 6986 mode (Pinto et al. 2007), though further analysis is needed to diagnose physical 6987 associations with ice line, atmospheric temperature and pressure structures and storm Do Not Cite or Quote 309 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 6988 behavior. Whether or not storm strength increases, the retreat of sea ice together with 6989 changing sea levels will likely increase the exposure of arctic coastlines to damaging 6990 waves and erosion produced by strong storms (Lynch et al. 2004, Brunner et al. 2004, 6991 Cassano et al. 2006), continuing an observed trend of increasing coastal erosion in arctic 6992 Alaska (Mars and Houseknecht, 2007). Rising sea levels, of course, may expose all 6993 coastlines to more extreme wave heights (e.g., Cayan et al., 2007). 6994 6995 3.3.11 Convective Storms 6996 Conclusions about possible changes in convective precipitating storms (CPSs) and 6997 associated severe-weather hazards under elevated greenhouse gas concentrations have 6998 remained elusive. Perhaps the most important reason for this is the mesoscale (10s of km) 6999 and smaller dynamics that control behavior of these storms, particularly the initiation of 7000 storms. Marsh et al. (2007) and Trapp et al. (2007) have evaluated changes in the 7001 frequency of environments that are favorable for severe thunderstorms in GCM 7002 simulations of greenhouse-enhanced climates. In both cases, increases in the frequency of 7003 environments favorable to severe thunderstorms are seen, but the absence of the 7004 mesoscale details in the models means that the results are preliminary. Nevertheless, the 7005 approach and the use of nested models within the GCMs show promise for yielding 7006 estimates of changes in extreme convective storms. Do Not Cite or Quote 310 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7007 Chapter 3 References 7008 7009 Allan, J. C., and Komar, P. D., 2000: Are ocean wave heights increasing in the eastern 7010 North Pacific? EOS, Transaction of the American Geophysical Union, 47, 561- 7011 567. 7012 7013 Allan, J. C., and Komar, P. D., 2006: Climate controls on US West Coast erosion 7014 processes: Journal of Coastal Research, 22, 511-529. 7015 7016 Allen, M. and W. Ingram, 2002: Constraints on future changes in climate and the 7017 hydrological cycle, Nature, 419, 224-232. 7018 7019 Arora, V.K., and G.J. Boer, 2001: Effects of simulated climate change on the hydrology 7020 of major river basins. J. Geophys. Res., 106, 3335-3348. 7021 7022 Arzayus, L. F., and W. J. Skirving, 2004: Correlations between ENSO and coral reef 7023 bleaching. Tenth International Coral Reef Symposium, Okinawa. 7024 7025 Arzel, O., T. Fichefet, H. Goosse, 2006: Sea ice evolution over the 20th and 21st 7026 centuries as simulated by current AOGCMs. Oc. Modelling, 12, 401-415. 7027 7028 Baik, J.-J., and J.-S. Paek, 1998: A climatology of sea surface temperature and the 7029 maximum intensity of western North Pacific tropical cyclones. J. Meteor. Soc. 7030 Japan, 76, 129-137. 7031 7032 Barnett, D.N., S.J. Brown, J.M. Murphy, D.M.H. Sexton, and M.J. Webb, 2006: 7033 Quantifying uncertainty in changes in extreme event frequency in response to 7034 doubled CO using a large ensemble of GCM simulations. Clim. Dyn., online 2 7035 1432-0894. 7036 7037 Barnett, T. P., D.W. Pierce, K. M. AchutaRao, P. J. Gleckler, B. D. Santer, J. M. 7038 Gregory, and W. M. Washington, 2006: Penetration of human-induced warming 7039 into the world’s oceans. Science, 309, 284-287. 7040 7041 Bell, G. D., and M. Chelliah, 2006: Leading tropical modes associated with interannual 7042 and multidecadal fluctuations in North Atlantic hurricane activity. J. Climate, 19, 7043 590-612. 7044 7045 Bell, J.L., and L.C. Sloan, 2006: CO2 Sensitivity of extreme climate events in the 7046 western United States. Earth Int., 10, Paper No. 15. 7047 7048 Bengtsson, L., M. Botzet, and M. Esch, 1995: Hurricane-type vortices in a general 7049 circulation model. Tellus 47A: 175-196 7050 Do Not Cite or Quote 311 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7051 Bengtsson, L., M. Botzet, and M. Esch, 1996: Will greenhouse gas-induced warming 7052 over the 50 years lead to a higher frequency and greater intensity of hurricanes? 7053 Tellus, 48A, 57-73. 7054 7055 Bengtsson, L., K. Hodges, and E. Roeckner, 2006: Storm tracks and climate change. J. 7056 Climate, 19, 3518-3543. 7057 7058 Bengtsson, L., K. I. Hodges, M. Esch, N. Keenlyside, L. Kornblueh, J.-J. Luo, and T. 7059 Yamagata, 2007: How may tropical cyclones change in a warmer climate. 7060 Tellus, 59A, 539-561. 7061 7062 Beniston, M., 2004: The 2003 heat wave in Europe: A shape of things to come? An 7063 analysis based on Swiss climatological data and model simulations. Geophys. Res. 7064 Lett., 31, L02202. 7065 7066 Boos, W. R., J. R. Scott and K. A. Emanuel, 2004: Transient diapycnal mixing and the 7067 meridional overturning circulation. J. Phys. Ocean., 34, 334-341 7068 7069 Brabson, B.B., D.H. Lister, P.D. Jones, and J.P. Palutikof, 2005: Soil moisture and 7070 predicted spells of extreme temperatures in Britain. J. Geophys. Res., 110, 7071 D05104. 7072 7073 Breshears, D.D., et al., 2005: Regional vegetation die-off in response to global-change- 7074 type drought. Proc. Natl. Acad. Sci. USA, 102, 15144-15148. 7075 7076 Broccoli, A. J., and S. Manabe, 1990: Can existing climate models be used to study 7077 anthropogenic changes in tropical cyclone climate? Geophys. Res. Lett., 17, 7078 1917-1920. 7079 7080 Brooks, H.E., J.W. Lee, and J.P. Craven, 2003: The spatial distribution of severe 7081 thunderstorm and tornado environments from global reanalysis data. Atmos. Res., 7082 67, 73-94. 7083 7084 Brooks, H. E., and N. Dotzek, 2007: The spatial distribution of severe convective storms 7085 and an analysis of their secular changes. Climate Extremes and Society. H. F. 7086 Diaz and R. Murnane, Eds., Cambridge University Press, in press. 7087 7088 Brunner, R.D., A.H. Lynch, J.C. Pardikes, E.N. Cassano, L.R. Lestak, J.M. Vogel., 7089 2004: An Arctic disaster and its policy implications. Arctic, 57, 336-346. 7090 7091 Burke, E.J., S.J. Brown, and N. Christidis, 2006: Modelling the recent evolution of 7092 global drought and projections for the 21st century with the Hadley Centre 7093 climate model. J. Hydrometeor., 7, 1113-1125. 7094 Do Not Cite or Quote 312 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7095 Burkholder, B.A., and D.J. Karoly, 2007: Assessment of US climate variations using the 7096 US Climate Extremes Index. Nineteenth Conference on Climate Variability and 7097 Change, paper 2B.9 7098 7099 Caires, S., and A. Sterl, 2005: 100-year return value estimates for wind speed and 7100 significant wave height from the ERA-40 data. Journal of Climate, 18, 1032– 7101 1048. 7102 7103 Caires, S., V.R. Swail, and X.L. Wang, 2006: Projection and analysis of extreme wave 7104 climate. Journal of Climate, 19, 5581-5605. 7105 7106 Camargo, S., A. G. Barnston, and S. E. Zebiak, 2005: A statistical assessment of 7107 tropical cyclone activity in atmospheric general circulation models. Tellus 57A: 7108 589-604, DOI: 10.1111/j.1600-0870.2005.00117. 7109 7110 Camargo, S., K. A. Emanuel, and A. H. Sobel, 2006: ENSO and genesis potential index 7111 in reanalysis and AGCMs. Proc. 27th AMS Conf. on Hurr. and Trop. Meteor., 7112 abstract #15C.2. http://ams.confex.com/ams/pdfpapers/108038.pdf 7113 7114 Camp, J. P., and M. T. Montgomery, 2001: Hurricane maximum intensity: past and 7115 present. Mon. Wea. Rev., 129, 1704-1717. 7116 7117 Cassano, E.N., A.H. Lynch, J.J. Cassano, and M.R. Koslow, 2006: Classification of 7118 synoptic patterns in the western Arctic associated with extreme events at Barrow, 7119 Alaska, USA. Clim. Res., 30, 83-97. 7120 7121 Cayan, D.R., P.D. Bromirski, K. Hayhoe, M. Tyree, M.D. Dettinger, and R.E. Flick, 7122 2007: Climate change projections of sea level extremes along the California 7123 coast. Climatic Change (accepted). 7124 7125 Chan, J.C. L., 1985: Tropical cyclone activity in the northwest Pacific in relation to the 7126 El Niño / Southern Oscillation phenomenon. Mon. Wea. Rev., 113: 599-606 7127 7128 Chan, J. C. L., 2006: Comment on “Changes in Tropical Cyclone Number, Duration, and 7129 Intensity in a Warming Environment, Science, v. 311, p. 1713. 7130 7131 Chan, J. C. L., and K. S. Liu, 2004: Global warming and Western North Pacific typhoon 7132 activity from an observational perspective. J. Climate, 17, 4590-4602. 7133 7134 Chan, S., and J. L. Evans, 2002: Comparison of the structure of the ITCZ in the West 7135 Pacific during the boreal summers of 1989-1993 using AMIP simulations and 7136 ECMWF reanalysis. J. Climate, 15, 3549-3568. 7137 7138 Chauvin, F., J.-F. Royer and M. Déqué, 2006: Response of hurricane-type vortices to 7139 global warming as simulated by ARPEGE-Climat at high resolution. Clim. Dyn., 7140 27, 377-399. Do Not Cite or Quote 313 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7141 7142 Christensen, J.H., and O.B. Christensen, 2003: Severe summertime flooding in Europe. 7143 Nature, 421, 805-806. 7144 7145 Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, W.-T. 7146 Kwon, R. Laprise, V. Magaña, L. Mearns, C. Menendez, J. Räisänen, A. Rinke, 7147 R. K. Kolli, A. Sarr, and P. Whetton, 2007: Regional Climate Projections. In: S. 7148 Solomon et al. (ed.) Climate Change 2007. The Fourth Scientific Assessment, 7149 Intergovernmental Panel on Climate Change (IPCC), in press. 7150 7151 Christensen, O.B., and J.H. Christensen, 2004: Intensification of extreme European 7152 summer precipitation in a warmer climate. Glob. Planet. Change, 44, 107-117. 7153 7154 Clark, R., S. Brown, and J. Murphy, 2006: Modelling northern hemisphere summer heat 7155 extreme changes and their uncertainties using a physics ensemble of climate 7156 sensitivity experiments. J. Climate, 19, 4418-4435. 7157 7158 Cubasch, U., G.A. Meehl, G.J. Boer, R.J. Stouffer, M. Dix, A. Noda, C.A. Senior, S. 7159 Raper, and K.S. Yap, 2001: Projections of future climate change. In: Climate 7160 Change 2001: The Scientific Basis. Contribution of Working Group I to the Third 7161 Assessment Report of the Intergovernmental Panel on Climate Change [J.T. 7162 Houghton, et al. (eds.)]. Cambridge University Press, Cambridge, pp. 525-582. 7163 7164 Delworth, T. L., J. D. Mahlman, and T. R. Knutson, 1999: Changes in heat index 7165 associated with CO2 -induced global warming. Climatic Change, 43(2), 369-386. 7166 7167 Delworth, T. L., and T. R. Knutson, 2000: Simulation of early 20th Century global 7168 warming. Science, 287(5461), 2246-2250. 7169 7170 Delworth, T. L., and M. E. Mann, 2000: Observed and simulated multidecadal variability 7171 in the Northern Hemisphere. Clim. Dyn., 16(9), 661-676. 7172 7173 DeMott, C. A., and D. A. Randall, 2004: Observed variations of tropical convective 7174 available potential energy. J. Geophys. Res., 109, D02102, 7175 doi:10.1029/2003JD003784. 7176 7177 Dery, S.J., and E.F. Wood, 2006: Analysis of snow in the 20th and 21st century 7178 Geophysical Fluid Dynamics Laboratory coupled climate model simulations. J. 7179 Geophys. Res., 111, Art. No. D19113, doi:10.1029/2005JD006920. 7180 7181 Deser, C., A.S. Phillips, and J.W. Hurrell, 2004: Pacific decadal interdecadal climate 7182 variability: Linkages between the tropics and the North Pacific during boreal 7183 winter since 1900. J. Clim., 17, 3109-3124. 7184 7185 Donnelly, J. P., S. S. Bryant, J. Butler, J. Dowling, L. Fan, N. Hausmann, P. Newby, B. 7186 Shuman, J. Stern, K. Westover, and T. Webb, III, 2001a: A 700 yr sedimentary Do Not Cite or Quote 314 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7187 record of intense hurricane landfalls in southern New England. Geol. Soc. Amer. 7188 Bull., 113, 714-727. 7189 7190 Donnelly, J. P., S. Roll, M. Wengren, J. Butler, R. Lederer, and T. Webb, III, 2001b: 7191 Sedimentary evidence of intense hurricane strikes from New Jersey. Geology, 29, 7192 615-618. 7193 7194 Donnelly, J. P. and T. Webb, III,. 2004: Backbarrier sedimentary records of intense 7195 hurricane landfalls in the northeastern United States. In: Hurricanes and 7196 Typhoons: Past, Present, and Future (eds. R.J. Murnane, R. J. and K-b. Liu), p. 7197 58-95. Columbia University Press. 7198 7199 Donnelly, J.P., J. Butler, S. Roll, M. Wengren, and T. Webb, III, 2004. A backbarrier 7200 overwash record of intense storms from Brigantine, New Jersey. Marine Geology, 7201 210, 107-121. 7202 7203 Donnelly, J P. 2005. Evidence of past intense tropical cyclones from backbarrier salt 7204 pond sediments: a case study from Isla de Culebrita, Puerto Rico, USA. J. Coastal 7205 Res., 42, 201-210. 7206 7207 Donner, S. D., T. R. Knutson, and M. Oppenheimer, 2007: Model-based assessment of 7208 the role of human-induced climate change in the 2005 Caribbean coral bleaching 7209 event. Proc. Nat. Acad. Sci., 104(13), 5483-5488. 7210 7211 Dutton, J. F. , C. J. Poulsen, and J. L. Evans, 2000: The effect of global climate change 7212 on the regions of tropical convection in CSM1. Geophysical Research Letters, 27, 7213 3049. 7214 7215 Eichler, T. and W. Higgins, 2006: Climatology and ENSO-related variability of North 7216 American extratropical cyclone activity. J. Climate, 10, 2076-2093. 7217 7218 Elsner, J. B., 2006: Evidence in support of the climate change-Atlantic hurricane 7219 hypothesis. Geophys. Res. Lett., 33, L16705, doi:10.1029/2006GL026869. 7220 7221 Elsner, J. B., K-b. Liu, and B. L. Kocher, 2000: Spatial variations in major U.S. 7222 hurricane activity: Statistics and a physical mechanism. J. Climate, 13, 2293- 7223 2305. 7224 7225 Elsner, J. B., R. J. Murnane, and T. H. Jagger, 2006: Forecasting U.S. hurricanes 6 7226 months in advance. Geophys. Res. Lett., accepted for publication. Available 7227 online at: http://garnet.acns.fsu.edu/~jelsner/PDF/Research/LongLead.pdf. 7228 7229 Elsner, J. B., A. A. Tsonis, and T. H. Jagger, 2006: High-frequency variability in 7230 hurricane power dissipation and its relationship to global temperature. Bull. 7231 Amer. Meteor. Soc., 87, 763-768. 7232 Do Not Cite or Quote 315 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7233 Emanuel, K.A., 1987: The dependence of hurricane intensity on climate. Nature, 326, 7234 483-485. 7235 7236 Emanuel, K. A., 1999: Thermodynamic control of hurricane intensity. Nature, 401, 7237 665-669. 7238 7239 Emanuel, K., 2000: A statistical analysis of tropical cyclone intensity. Mon. Wea. Rev., 7240 128, 1139-1152. 7241 7242 Emanuel, K.A., 2001: The contribution of tropical cyclones to the oceans’ meridional 7243 heat transport. J. Geophys. Res., 106, D14, 14,771-14,781. 7244 7245 Emanuel, K. A., 2005a: Increasing destructiveness of tropical cyclones over the past 30 7246 years. Nature, 436, 686-688. 7247 7248 Emanuel, K. A., 2005b: Emanuel replies. Nature, 438, doi:10.1038/nature04427. 7249 7250 Emanuel, K., 2006: Environmental influences on tropical cyclone variability and trends. 7251 Proc. 27th AMS Conf. on Hurr. and Trop. Meteor., #4.2. Available online at: 7252 http://ams.confex.com/ams/pdfpapers/107575.pdf. 7253 7254 Emanuel, K., S. Ravela, E. Vivant, and C. Risi. 2006: A statistical deterministic 7255 approach to hurricane risk assessment. Bull. Amer. Meteor. Soc.,: 87, 299–314. 7256 7257 Emanuel, K. and D. S. Nolan, 2004: Tropical cyclone activity and the global climate 7258 system. Proc. 26th AMS Conf. on Hurr. and Trop. Meteor., #10A.2, p. 240-241. 7259 7260 Emanuel, K., R. Sundararajan, and J. Williams, 2007: Hurricanes and global warming: 7261 results from downscaling IPCC AR4 simulations. Bull. Am. Meteor. Soc., 7262 submitted. 7263 7264 Emori, S., A. Hasegawa, T. Suzuki and K. Dairaku, 2005: Validation, parameterization 7265 dependence, and future projection of daily precipitation simulated with a high- 7266 resolution atmospheric GCM. Geophys. Res. Lett., 32, L06708, 7267 doi:10.1029/2004GL022306. 7268 7269 Emori, S. and S. J. Brown, 2005: Dynamic and thermodynamic changes in mean and 7270 extreme precipitation under changed climate. Geophys. Res. Lett., 32, L17706, 7271 doi:10.1029/2005GL023272. 7272 7273 Enfield, D.B., and A.M. Mestas-Nuñez, 2000: Global modes of ENSO and non-ENSO 7274 SST variability and their associations with climate. In: El Niño and the Southern 7275 Oscillation: Multiscale Variability and Global and Regional Impacts, H.F. Diaz 7276 and V. Markgraf, Eds., Cambridge University Press (ISBN 0521621380), p. 89- 7277 112. 7278 Do Not Cite or Quote 316 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7279 Fernandez-Partagas, J., and H. F. Diaz, 1996: Atlantic hurricanes in the second half of 7280 the nineteenth century. Bull. Amer. Meteor. Soc., 77, 2899-2906. 7281 7282 Fischer-Bruns, I., H. Von Storch, J.F. Gonzalez-Rouco, and E. Zorita, 2005: Modelling 7283 the variability of midlatitude storm activity on decadal to century time scales. 7284 Climate Dynamics, 25, 461-476. 7285 7286 Frappier, A.B., Sahagian, D., Carpenter, S.J., Gonzalez, L.A. and Frappier, B.R., 2006: 7287 A stalagmite stable isotope record of recent tropical cyclone events. Geology, (in 7288 review). 7289 7290 Free, M., M. Bister, and K. Emanuel, 2004: Potential intensity of tropical cyclones: 7291 comparison of results from radiosonde and reanalysis data. J. Climate, 17, 1722- 7292 1727. 7293 7294 Frei, C., C. Schär, D. Lüthi, and H.C. Davies, 1998: Heavy precipitation processes in a 7295 warmer climate. Geophys. Res. Lett., 25, 1431-1434. 7296 7297 Frei, A., and G. Gong, 2005: Decadal to century scale trends in North American snow 7298 extent in coupled atmosphere-ocean general circulation models. Geophys. Res. 7299 Lett., 32, L18502, doi:10.1029/2005GL023394. 7300 7301 Frich, P., L.V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A.M.G.K. Tank, 7302 T. Peterson, 2002: Observed coherent changes in climatic extremes during the 7303 second half of the twentieth century. Clim. Res., 19, 193-212. 7304 7305 Fyfe, J.C., G.J. Boer, and G.M. Flato, 1999: The Arctic and Antarctic Oscillations and 7306 their projected changes under global warming. Geophysical Research Letters, 26, 7307 1601-1604. 7308 7309 Fyfe, J.C., 2003: Extratropical southern hemisphere cyclones: Harbingers of climate 7310 change? J. Climate, 16, 2802-2805. 7311 7312 Gao, S.B., and H.G. Stefan, 2004: Potential climate change effects on ice covers of five 7313 freshwater lakes. J. Hydrol. Eng., 9, 226-234. 7314 7315 Garcia Herrera, R., L. Gimeno, P. Ribera, and E. Hernandez, 2005. New records of 7316 Atlantic hurricanes from Spanish documentary sources. J. Geophys. Res., 110: 7317 D03109. 7318 7319 Garcia Herrera, R., F. Rubio, D. Wheeler, E. Hernandez, M. R. Prieto, and L. Gimero, 7320 2004: The use of Spanish and British documentary sources in the investigation of 7321 Atlantic hurricane incidence in historical times. In: Hurricanes and Typhoons: 7322 Past, Present, and Future (eds. R.J. Murnane, R. J. and K-b. Liu), p. 149-176. 7323 Columbia University Press. 7324 Do Not Cite or Quote 317 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7325 Gedney, N., P.M. Cox, R.A. Betts, O. Boucher, C. Huntingford, and P.A. Stott, 2006a: 7326 Detection of a direct carbon dioxide effect in continental river runoff records. 7327 Nature, 439, 835-838. 7328 7329 Gedney, N., P.M. Cox, R.A. Betts, O. Boucher, C. Huntingford, and P.A. Stott, 2006b: 7330 Continental runoff - A quality-controlled global runoff data set - Reply. Nature, 7331 444, E14-15 (doi:10.1038/nature05481). 7332 7333 Gershunov, A., and D. R. Cayan, 2003: Heavy Daily Precipitation Frequency over the 7334 Contiguous United States: Sources of Climatic Variability and Seasonal 7335 Predictability. J. Climate, 16, 2752-2765. 7336 7337 Gettelman, A. D. J. Seidel, M. C. Wheeler, and R. J. Ross, 2002: Multidecadal trends in 7338 tropical convective available potential energy. J. Geophys. Res., 107, 4606, 7339 doi:10.1029/2001JD001082. 7340 7341 Gillett, N.P., G.C. Hegerl, M.R. Allen, and P.A. Stott, 2000: Implications of changes in 7342 the Northern Hemispheric circulation for the detection of anthropogenic climate 7343 change. Geophysical Research Letters, 27, 993-996. 7344 7345 Gillett, N.P., et al., 2002b: How linear is the Arctic Oscillation response to greenhouse 7346 gases? Journal of Geophysical Research, 107, doi: 10.1029/2001JD000589. 7347 7348 Gillett, N.P., H.F. Graf, and T.J. Osborn, 2003a: Climate change and the North Atlantic 7349 Oscillation. [Y.K. J. Hurrell, G. Ottersen and M. Visbeck (ed). American 7350 Geophysical Union, Washington, DC. 7351 7352 Gillett, N.P., A.J. Weaver, F.W. Zwiers, and M.F. Wehner, 2004b: Detection of volcanic 7353 influence on global precipitation. Geophysical Research Letters, 31(12), L12217, 7354 doi:10.1029/2004GL020044. 7355 7356 Gillett, N.P., 2005: Northern Hemisphere circulation. Nature, 437, 496 7357 7358 Gillett , N. P., P. A. Stott, and B. D. Santer, 2007: Attribution of Cyclonogenesis Region 7359 Sea Surface Temperature Change to Anthropogenic Influence. Geophysical 7360 Research Letters (submitted). 7361 7362 Giorgi, F., B. Hewitson, J. Christensen, M. Hulme, H. Von Storch, P. Whetton, R. Jones, 7363 L. Mearns, and C. Fu, 2001: Regional climate information - evaluation and 7364 projections. In Climate Change 2001: The Scientific Basis. Contribution of 7365 Working Group I to the Third Assessment Report of the Intergovernmental Panel 7366 on Climate Change. [Houghton JT, Ding Y, Griggs DJ, Noguer M, van der 7367 Linden PJ, Dai X, Maskell K and Johnson CA, eds.] 881 pp. Cambridge 7368 University Press, Cambridge, United Kingdom and New York, NY, USA. 7369 Do Not Cite or Quote 318 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7370 Goldenberg, S. B., C. W. Landsea, A.M. Mesta-Nuñez, and W. M. Gray, 2001: The 7371 recent increase in Atlantic hurricane activity: causes and implications. Science, 7372 293, 474-479. 7373 7374 Graham, N.E., and H. F. Diaz, 2001: Evidence for intensification of North Pacific 7375 winter cyclones since 1948. Bull. Amer. Meteor. Soc., 82, 1869-1893. 7376 7377 Gray, W. M., 1979: Hurricanes: their formation, structure, and likely role in the tropical 7378 circulation. In Meteorology over the tropical oceans. Ed., D. B. Shaw, Roy. 7379 Meteor. Soc., 155-218. 7380 7381 Gray, W. M., 1990: Strong association between West African rainfall and U.S. landfall 7382 of intense hurricanes. Science, 249, 1251-1256. 7383 7384 Groisman, P. Y., R. W. Knight, T. R. Karl, D. R. Easterling, B. Sun, and J. H. 7385 Lawrimore, 2004: Contemporary changes of the hydrological cycle over the 7386 contiguous United States: trends derived from in situ observations. J. 7387 Hydormeteor., 5, 64-85. 7388 7389 Gulev, S.K., and V. Grigorieva, 2004: Last century changes in ocean wind wave height 7390 from global visual wave data. Geophys. Res. Lett., 31, Art. No. L24302. 7391 7392 Hart, R. E., 2006: The winter impact of recurving tropical cyclones. 27th AMS 7393 Conference on Hurricanes and Tropical Meteorology, 24-28 April 2006, 7394 Monterey, CA. 7395 7396 Hart, R. E., L. F. Bosart, and C. Hosler, 2006: The possible hemispheric impacts of 7397 anomalous recurving tropical cyclone frequency. Submitted to Mon. Wea. Rev., 7398 August 2006. 7399 7400 Hasegawa, A. and S. Emori, 2005: Tropical cyclones and associated precipitation over 7401 the Western North Pacific: T106 atmospheric GCM simulation for present-day 7402 and doubled CO2 climates. SOLA, 1, 145-148, SOI:10.2151/sola.2005-038. 7403 7404 Hayhoe, K, C.P. Wake, T.G. Huntington, L.F. Luo, M.D. Schwartz, J. Sheffield, E. 7405 Wood, B. Anderson, J. Bradbury, A. DeGaetano, T.J. Troy, D. Wolfe, 2007: Past 7406 and future changes in climate and hydrological indicators in the US Northeast. 7407 Clim. Dyn., 28, 381-407. 7408 7409 Hayne, M. and J. Chappell. 2001. Cyclone frequency during the last 5,000 years from 7410 Curacoa Island, Queensland. Palaeogeography, Palaeoclimatology, 7411 Palaeoecology, 168, 201-219. 7412 7413 Hegerl, G.C., F.W. Zwiers, V.V. Kharin, and P.A. Stott, 2004: Detectability of 7414 anthropogenic changes in temperature and precipitation extremes. Journal of 7415 Climate, 17, 3683-3700. Do Not Cite or Quote 319 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7416 7417 Hegerl, G. C., F. W. Zwiers, P. Braconnot, N. P. Gillett, Y. Luo, J. Marengo, N. 7418 Nicholls, J. E. Penner and P. A, Stott: Understanding and Attributing Climate 7419 Change. In: S. Solomon et al. (ed.) Climate Change 2007. The Fourth Scientific 7420 Assessment, Intergovernmental Panel on Climate Change (IPCC), in press. 7421 7422 Held, I. M., T. L. Delworth, J. Lu, K. L. Findell, and T. R. Knutson, 2005: Simulation of 7423 Sahel drought in the 20th and 21st centuries. Proc. Nat. Acad. Sci., 102(50), 17891- 7424 17896. 7425 7426 Held, I.M., and B.J. Soden, 2006: Robust Responses of the Hydrological Cycle to Global 7427 Warming. Journal of Climate, in press. 7428 7429 Henderson-Sellers, A., H. Zhang, G. Berz, K. Emanuel, W. Gray, C. Landsea, 7430 G. Holland, J. Lighthill, S-L. Shieh, P. Webster, and K. McGuffie, 1998: Tropical 7431 cyclones and global climate change: a post-IPCC assessment. Bull. Amer. Meteor. 7432 Soc., 79, 19-38. 7433 7434 Hodgkins, G.A., I.C. James, and T.G. Huntington, 2002: Historical changes in lake ice- 7435 out dates as indicators of climate change in New England, 1850-2000. Int. J. 7436 Climatology, 22, 1819-1827. 7437 7438 Hodgkins, G.A., R.W. Dudley, and T. G. Huntington, 2003: Changes in the timing of 7439 high river flows in New England over the 20th century. J. Hydrololgy, 278, 244- 7440 252. 7441 7442 Hoerling, M. P., and A. Kumar, 2003: The perfect ocean for drought. Science, 299, 691– 7443 694. 7444 7445 Hoerling, M., J. Eischeid, X. Quan, T.Y. Xu, 2007: Explaining the Record US Warmth 7446 of 2006. Geophys. Res. Lett. (submitted). 7447 7448 Holland, J. Lighthill, S-L. Shieh, P. Webster, and K. McGuffie, 1998: Tropical cyclones 7449 and global climate change: a post-IPCC assessment. Bull. Amer. Meteor. Soc., 79, 7450 19-38. 7451 7452 Holland, G.J., 1997: The maximum potential intensity of tropical cyclones. J. Atmos. 7453 Sci., 54, 2519-2541. 7454 7455 Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the 7456 North Atlantic: natural variability or climate trend? Phil. Trans. R. Soc. A, 7457 doi:10.1098/rsta.2007.2083. 7458 7459 Holland, M.M., C.M. Bitz, B. Tremblay, 2006: Future abrupt reductions in the summer 7460 Arctic sea ice. Geophys. Res. Lett., 33, Art. No. L23503. 7461 Do Not Cite or Quote 320 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7462 Hoskins, B. J., and K. I. Hodges, 2005: A new perspective on southern hemisphere storm 7463 tracks. J. Climate, 18, 4108-4129. 7464 7465 Hoyos, C. D., P. A. Agudelo, P. J. Webster, and J. A. Curry, 2006: Deconvolution of the 7466 factors contributing to the increase in global hurricane intensity. Science, 312: 7467 94-97. 7468 7469 Huntington, T.G., G.A. Hodgkins, and R.W. Dudley, 2003: Historical trend in river ice 7470 thickness and coherence in hydroclimatological trends in Maine. Clim. Change, 7471 61, 217-236. 7472 7473 Hurrell, J.W., 1995: Decadal Trends in the North-Atlantic Oscillation - Regional 7474 Temperatures and Precipitation. Science, 269, 676-679. 7475 7476 Hurrell, J.W., 1996: Influence of variations in extratropical wintertime teleconnections 7477 on Northern Hemisphere temperature. Geophysical Research Letters, 23, 665- 7478 668. 7479 7480 IPCC, 2001. Climate Change 2001: The Scientific Basis. Contribution of Working 7481 Group I to the Third Assessment Report of the Intergovernmental Panel on 7482 Climate Change [Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der 7483 Linden, X. Dai, K. Maskell, and C. A. Johnson (eds.)]. Cambridge University 7484 Press, Cambridge, United Kingdom and New York, NY, USA, 881pp. 7485 7486 International Ad Hoc Detection and Attribution Group (IADAG), 2005: Detecting 7487 and attributing external influences on the climate system: a review of recent 7488 advances. J. Climate, 18, 1291-1314. 7489 7490 International Workshop on Tropical Cyclones VI (IWTC-VI), 2006: Statement on 7491 tropical cyclones and climate change. Available at: 7492 http://www.wmo.ch/web/arep/arep-home.html. 7493 7494 Kamahori, H. N. Yamazaki, N. Mannoji, and K. Takahashi, 2006: Variability in intense 7495 tropical cyclone days in the western North Pacific. SOLA, 2, 104-107, 7496 doi:10.2151/sola.2006-027. 7497 7498 Karl, T.R., and R.W. Knight, 1997: The 1995 Chicago heat wave: How likely is a 7499 recurrence? Bulletin of the Am. Meteor. Soc., 78, 1107-1119. 7500 7501 Karoly, D.J., et al., 2003: Detection of a human influence on North American climate. 7502 Science, 302, 1200-1203. 7503 7504 Karoly, D. J., and Q. Wu, 2005: Detection of regional surface temperature trends. J. 7505 Climate, 18, 4337-4343. 7506 Do Not Cite or Quote 321 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7507 Katz, R.W., and B.G. Brown, 1992: Extreme events in a changing climate: Variability is 7508 more important than averages. Climatic Change, 21, 289–302. 7509 7510 Kenyon, J., and G.C. Hegerl, 2007: Influence of modes of climate variability on global 7511 temperature extremes. J. Clim., submitted. 7512 7513 Kharin, V.V., and F.W. Zwiers, 2005: Estimating extremes in transient climate change 7514 simulations. J. Clim., 18, 1156-1173. 7515 7516 Kharin, V., F. W. Zwiers, X. Zhang and G. C. Hegerl, 2007: Changes in temperature and 7517 precipitation extremes in the IPCC ensemble of global coupled model 7518 simulations. J. Climate, in press. 7519 7520 Kiktev, D., D. Sexton, L. Alexander, C. Folland, 2003: Comparison of modelled and 7521 observed trends in indices of daily climate extremes. Journal of Climate, 16, 7522 3560-3571. 7523 7524 Klotzbach, P. J., 2006: Trends in global tropical cyclone activity over the past twenty 7525 years (1986-2005), Geophys. Res. Lett., 33, L10805, 7526 DOI:10.1029/2006GL025881. 7527 7528 Knaff, J. A, and C. R. Sampson, 2006: Reanalysis of West Pacific tropical cyclone 7529 intensity 1966-1987. Proceedings of 27th AMS Conference on Hurricanes and 7530 Tropical Meteorology, #5B.5. Available online at: 7531 http://ams.confex.com/ams/pdfpapers/108298.pdf. 7532 7533 Knight, J. R., R. J. Allan, C. K. Folland, M. Vellinga, and M. E. Mann, 2005: A 7534 signature of persistent natural thermohaline circulation cycles in observed climate, 7535 Geophys. Res. Lett., 32, L20708, doi:10.1029/2005GL024233. 7536 7537 Knight, J. R., C. K. Folland, and A. A. Scaife, 2006: Climate impacts of the Atlantic 7538 Multidecadal Oscillation. Geophys. Res. Lett., 33, 17, L17706 7539 http://dx.doi.org/10.1029/2006GL026242. 7540 7541 Knutson, T. R., T. L. Delworth, K. W. Dixon, I. M. Held, J. Lu, V. Ramaswamy, D. 7542 Schwarzkopf, G. Stenchikov, and R. J. Stouffer, 2006. Assessment of twentieth- 7543 century regional surface temperature trends using the GFDL CM2 coupled 7544 models. J. Climate, 19(9), 1624-1651. 7545 7546 Knutson, T. R., and R. E. Tuleya, 2004: Impact of CO2-induced warming on simulated 7547 hurricane intensity and precipitation: sensitivity to the choice of climate model 7548 and convective parameterization. J. Climate, 17, 3477-3495. 7549 7550 Knutson, T. R., and R. E. Tuleya, 2005: Reply. J. Climate, 18, 5183-5187. 7551 Do Not Cite or Quote 322 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7552 Knutson, T. R., and R. E. Tuleya, 2007: Tropical cyclones and climate change: 7553 Revisiting recent studies at GFDL. In Climate Extremes and Society, H. Diaz, 7554 and R. Murnane, eds., New York: Columbia Univ. Press, accepted for 7555 publication. 7556 7557 Knutson, T. R., R. E. Tuleya, W. Shen, and I. Ginis, 2001: Impact of CO2-induced 7558 warming on hurricane intensities as simulated in a hurricane model with ocean 7559 coupling. J. Climate, 14, 2458-2468. 7560 7561 Knutson, T. R., R. E. Tuleya, and Y. Kurihara, 1998: Simulated increase of hurricane 7562 intensities in a CO2-warmed climate. Science, 279(5353), 1018-1020. 7563 7564 Kumar, A., F. Yang, L. Goddard, and S. Schubert, 2004: Differing Trends in the 7565 Tropical Surface Temperatures and Precipitation over Land and Oceans. Journal 7566 of Climate, 17, 653-664. 7567 7568 Kunkel, K.E., N.E. Westcott, and D.A.R. Kristovich, 2002: Assessment of potential 7569 effects of climate change on heavy lake-effect snowstorms near Lake Erie. J. Gt. 7570 Lakes Res., 28, 521-536. 7571 7572 Kunkel, K.E., X.-Z. Liang, J. Zhu, and Y. Lin, 2006: Can CGCMS simulate the 7573 twentieth century “warming hole” in the central United States? Journal of 7574 Climate, in press. 7575 7576 Lambert, F.H., P.A. Stott, M.R. Allen, and M.A. Palmer, 2004: Detection and attribution 7577 of changes in 20th century land precipitation. Geophysical Research Letters, 7578 31(10), L10203. 7579 7580 Lambert, S.J., and J.C. Fyfe, 2006: Changes in winter cyclone frequencies and strengths 7581 simulated in enhanced greenhouse warming experiments: results from the models 7582 participating in the IPCC diagnostic exercise. Climate Dynamics, online: 7583 10.1007/s00382-006-0110-3. 7584 7585 Landman, W. A., A. Seth, and S. J. Camargo, 2005: The effect of regional climate 7586 model domain choice on the simulation of tropical cyclone-like vortices in the 7587 Southwestern Indian Ocean. J. Climate, 18, 1253-1274, DOI: 7588 10.1175/JCLI3324.1 7589 7590 Landsea, C. W., 1993: A climatology of intense (or major) Atlantic hurricanes. Mon. 7591 Wea. Rev., 121, 1703-1713. 7592 7593 Landsea, C. W., C. Anderson, N. Charles, G. Clark, J. Dunion, J. Fernandez-Partagas, P. 7594 Hungerford, C. Neumann, and M. Zimmer, 2004: The Atlantic hurricane database 7595 re-analysis project: Documentation for the 1851-1910 alterations and additions to 7596 the HURDAT database. In: Hurricanes and Typhoons: Past, Present and Future, 7597 R. J. Murname and K.-B. Liu, Eds., Columbia University Press, p. 177-221. Do Not Cite or Quote 323 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7598 7599 Landsea, C. W., 2005: Hurricanes and global warming. Nature, 438, 7600 doi:10.1038/nature04477. 7601 7602 Landsea,C.W., B.A.Harper, K.Hoarau, and J.A.Knaff, 2006: Can we detect trends in 7603 extreme tropical cyclones? Science. 313, 452-454. 7604 7605 Lawrence, J. R. and S. D. Gedzelman, 1996: Low stable isotope ratios of tropical 7606 cyclone rains. Geophys. Res. Lett., 23, 527-530. 7607 7608 Leung, L.R., Y. Qian, X.D. Bian, W.M. Washington, J.G. Han, and J.O. Roads, 2004: 7609 Mid-century ensemble regional climate change scenarios for the western United 7610 States. Climatic Change, 62, 75-113. 7611 7612 Liu, G., A.E. Strong, W. Skirving, L.F. Arzayus, 2006: Overview of NOAA Coral Reef 7613 Watch Program’s near-real-time satellite global coral bleaching monitoring 7614 activities. Proceedings of the 10th International Coral Reef Symposium. p. 1783- 7615 1793. 7616 7617 Liu, K-b., 2004: Paleotempestology: Principles, Methods, and Examples from Gulf Coast 7618 Lake Sediments. In: Hurricanes and Typhoons: Past, Present, and Future, R.J. 7619 Murnane, R. J. and K-b. Liu, Eds., Columbia University Press, p. 13-57. 7620 7621 Liu, K-b. and M. L. Fearn, 1993: Lake-sediment record of late Holocene hurricane 7622 activities from coastal Alabama. Geology, 21, 793-796. 7623 7624 Liu, K-b. and M. L. Fearn, 2000: Reconstruction of prehistoric landfall frequencies of 7625 catastrophic hurricanes in northwestern Florida from lake sediment records. 7626 Quaternary Research, 54, 238-245. 7627 7628 Liu, K-b., C. Shen, C, and K. S. Louie, 2001: A 1000-year history of typhoon landfalls 7629 in Guangdong, southern China, reconstructed from Chinese historical 7630 documentary records. Annals of the Association of American Geographers 91, 7631 453-464. 7632 7633 Louie, K.S. and K.-b. Liu, 2003: Earliest historical records of typhoons in China. J. 7634 Historical Geography, 29, 299-316. 7635 7636 Louie, K.S. and K.-b. Liu, 2004: Ancient records of typhoons in Chinese historical 7637 documents. In: Hurricanes and Typhoons: Past, Present, and Future , R.J. 7638 Murnane, R. J. and K-b. Liu, Eds., Columbia University Press, p. 222-248. 7639 7640 Lozano, I., and V. Swail, 2002: The link between wave height variability in the North 7641 Atlantic and the storm track activity in the last four decades. Atmos.-Oc., 40, 377- 7642 388. 7643 Do Not Cite or Quote 324 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7644 Lynch, A.H., J.A. Curry, R.D. Brunner, J.A. Maslanik, 2004: Toward an integrated 7645 assessment of the impacts of extreme wind events on Barrow, Alaska. Bull. 7646 Amer. Meteor. Soc., 85, 209-221. 7647 7648 Mann, M. and K. Emanuel, 2006: Atlantic hurricane trends linked to climate change. 7649 EOS, 87, 233-241. 7650 7651 Marchok, T., R. Rogers, and R. Tuleya, 2007: Validation schemes for tropical cyclone 7652 quantitative precipitation forecasts: Evaluation of operational models for U.S. 7653 landfalling cases. Wea. Forecast., 22, 726-746. 7654 7655 Mars, J.C., D.W. Houseknecht, 2007: Quantitative remote sensing study indicates 7656 doubling of coastal erosion rate in past 50 yr along a segment of the Arctic coast 7657 of Alaska. Geology, 35, 583-586. 7658 7659 Marsh, P. T., H. E. Brooks, and D. J. Karoly, 2007: Assessment of the severe weather 7660 environment in North America simulated by a global climate model. Atmos. Sci. 7661 Letters, in review. 7662 7663 Marshall, G. J., P. A. Stott, J. Turner, W. M. Connolley, J. C. King, T. A. Lachlan-Cope, 7664 2004: Causes of exceptional atmospheric circulation changes in the Southern 7665 Hemisphere. Geophys. Res. Lett., 31(14), L14205,doi:10.1029/2004GL019952. 7666 7667 McCabe, G.J., M.P. Clark, and M.C. Serreze, 2001: Trends in Northern Hemisphere 7668 surface cyclone frequency and intensity, J. Clim., 14, 2763-2768. 7669 7670 McDonald, R. E., D. G. Bleaken, D. R. Cresswell, V. D. Pope, and C. A. Senior, 2005: 7671 Tropical storms: representation and diagnosis in climate models and the impacts 7672 of climate change. Clim. Dyn., 25: 19-36, DOI: 10.1007/s00382-004-0491-0. 7673 7674 Mears, C. A., and F. J. Wentz, 2005: The effect of diurnal correction on satellite-derived 7675 lower tropospheric temperature. Science, 309, 1548-1551. 7676 7677 Meehl, G.A., and C. Tebaldi, 2004: More intense, more frequent, and longer lasting heat 7678 waves in the 21st century. Science, 305, 994-997. 7679 7680 Meehl, G.A., C. Tebaldi, and D. Nychka, 2004: Changes in frost days in simulations of 7681 twentyfirst century climate. Clim. Dyn., 23, 495-511. 7682 7683 Meehl, G. M., W. M. Washingon, C. M. Amman, J. M. Arblaster, T. M. L. Wigley, and 7684 C. Tebaldi, 2004: Combinations of natural and anthropogenic forcings in 7685 twentieth-century climate. J. Climate, 17, 3721-3727. 7686 7687 Meehl, G. M., and Coauthors. 2007a: Global Climate Projections. In: S. Solomon et al. 7688 (ed.) Climate Change 2007. The Fourth Scientific Assessment, Intergovernmental 7689 Panel on Climate Change (IPCC), in press. Do Not Cite or Quote 325 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7690 7691 Meehl, G.A., J.M. Arblaster, and C. Tebaldi, 2007b: Contributions of natural and 7692 anthropogenic forcing to changes in temperature extremes over the U.S. Geophys. 7693 Res. Lett. (submitted). 7694 7695 Meehl, G.A., and C. Tebaldi, 2007: Extremes and El Nino. Geophys. Res. Lett. 7696 (submitted). 7697 7698 Mendelssohn, R., S.J. Bograd, F.B. Schwing, and D.M. Palacios, 2005: Teaching old 7699 indices new tricks: A state-space analysis of El Niño related climate indices. 7700 Geophysical Research Letters, 32, L07709. 7701 7702 Merryfield, W.J., 2006: Changes to ENSO under CO2 doubling in the IPCC AR4 7703 coupled climate models. Journal of Climate, in press. 7704 7705 Michaels, P. J., P. C. Knappenberger, and C. Landsea, 2005: Comments on “Impacts of 7706 CO2-induced warming on simulated hurricane intensity and precipitation: 7707 Sensitivity to the choice of climate model and convective scheme.” J. Climate, 7708 18, 5179-5182. 7709 7710 Michaels, P. J., P. C. Knappenberger, and R. E. Davis, 2006: Sea-surface temperatures 7711 and troical cyclones in the Atlantic basin. Geophys. Res. Lett., 33, L09708, 7712 doi:10.1029/2006GL025757. 7713 7714 Miller, D. L., C. I. Mora, H. D. Grissino-Mayer, C. J. Mock, and M. E. Uhle, 2003: 7715 Tropical cyclone activity and climate fluctuations captured by oxygen isotopes in 7716 tree-ring cellulose from the Southeastern U.S. Abstracts, American Geophysical 7717 Union Fall 2003 Meeting. 7718 7719 Miller, N.L., K. E. Bashford, and E. Strem, 2003: Potential impacts of climate change on 7720 California hydrology. Journal of the American Water Resources Association, 39, 7721 771-784. 7722 7723 Milly, P.C.D., K.A. Dunne, and A.V. Vecchia, 2005: Global pattern of trends in 7724 streamflow and water availability in a changing climate. Nature, 438, 347-350. 7725 7726 Mock, C.J., 2004: Tropical cyclone reconstructions from documentary records: 7727 examples for South Carolina, United States. In: Hurricanes and Typhoons: Past, 7728 Present, and Future, R.J. Murnane, R. J. and K-b. Liu, Eds., Columbia University 7729 Press, p. 121-148. 7730 7731 Morris, K., M. Jeffries, and C. Duguay, 2005: Model simulation of the effects of climate 7732 variability and change on lake ice in central Alaska, USA. Ann. Glaciology, 40, 7733 113-118. 7734 Do Not Cite or Quote 326 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7735 Neelin, J.D., M. Münnich, H. Su, J.E. Meyerson, and C. Holloway, 2006: Tropical drying 7736 trends in global warming models and observations. Proceedings of the National 7737 Academy of Sciences, accepted. 7738 7739 Neu, H. J. A., 1984: Interannual variations and longer-term changes in the sea state of the 7740 North Atlantic from 1970 to 1982: Journal of Geophysical Research, 89: 6397- 7741 6402. 7742 7743 Nolan, D. S., E. D. Rappin, and K. A. Emanuel, 2006: Could hurricanes form from 7744 random convection in a warmer world? Preprints, 27th AMS Meeting on 7745 Hurricanes and Tropical Meteorology, Monterey, CA. Available online at: 7746 http://ams.confex.com/ams/pdfpapers/107936.pdf. 7747 7748 Nott, J. and M. Hayne. 2001. High frequency of “super-cyclones” along the Great Barrier 7749 Reef over the past 5,000 years. Nature, 413, 508-512. 7750 7751 Nguyen, K. C., and K. J. E. Walsh, 2001: Interannual, decadal, and transient greenhouse 7752 simulation of tropical cyclone-like vortices in a regional climate model of the 7753 South Pacific. J. Climate, 14, 3043-3054. 7754 7755 Osborn, T.J., K.R. Briffa, S.F.B. Tett, P.D. Jones, and R.M. Trigo, 1999: Evaluation of 7756 the North Atlantic Oscillation as simulated by a coupled climate model. Climate 7757 Dynamics, 15, 685-702. 7758 7759 Osborn, T.J., 2004: Simulating the winter North Atlantic Oscillation: the roles of internal 7760 variability and greenhouse gas forcing. Climate Dynamics, 22, 605-623. 7761 7762 Ostermeier, G.M., and J.M. Wallace, 2003: Trends in the North Atlantic Oscillation- 7763 Northern Hemisphere annular mode during the twentieth century. J. Climate, 16, 7764 336-341. 7765 7766 Oouchi, K., J.Yoshimura, H. Yoshimura, R. Mizuta, S. Kusunoki, and A. Noda, 2006: 7767 Tropical cyclone climatology in a global-warming climate as simulated in a 7768 20km-mesh global atmospheric model: frequency and wind intensity analysis. J. 7769 Meteorol. Soc. Japan, , 84, 259-276. 7770 7771 Paciorek, C.J., J.S. Risbey, V. Ventura, R.D. Rosen (2002). Multiple indices of Northern 7772 Hemisphere cyclone activity, winters 1949-99, J. Climate, 15, 1573-1590. 7773 7774 Pall, P., M. R. Allen, and D. A. Stone, 2007: Testing the Clausius–Clapeyron constraint 7775 on changes in extreme precipitation under CO2 warming . Clim. Dyn., 28, 351– 7776 363. 7777 7778 Peel, M.C., and T.A. McMahon, 2006: A quality-controlled global runoff data set. 7779 Nature, 444, E14 (doi:10.1038/nature05480). 7780 Do Not Cite or Quote 327 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7781 Peterson, T.C., X. Zhang, M.B. India, and J.L.V. Aguirre, 2007: Changes in North 7782 American extremes derived from daily weather data. Proc. Nat. Acad. Science 7783 (submitted). 7784 7785 Pezza, A. B., and I. Simmonds, 2005: The first South Atlantic hurricane: unprecedented 7786 blocking, low shear, and climate change. Geophys. Res. Lett., 32, L15712, 7787 doi:10.1029/2005GL023390. 7788 7789 Pielke, R. A., Jr., 2005: Are there trends in hurricane destruction? Nature, 438, E11. 7790 7791 Pinto, J.G.. T. Spangehl, U. Ulbrich and P. Speth, 2006: Assessment of winter cyclone 7792 activity in a transient ECHAM4-OPYC3 GHG experiment. Meteor. Ziet., 15, 279- 7793 291. 7794 7795 Pinto, J. G., U. Ulbrich, G. C. Leckebusch, T. Spangehl, M. Reyers, and S. Zacharias, 7796 2007: Changes in storm track and cyclone activity in three SRES ensemble 7797 experiments with the ECHAM5/MPI-OM1 GCM. Climate Dynamics, 29, 195– 7798 210 (DOI 10.1007/s00382-007-0230-4). 7799 7800 Rotstayn, L. and U. Lohmann, 2002: Tropical rainfall trends and the indirect aerosol 7801 effect. J. Climate, 15, 2103-2116. 7802 7803 Royer. J.-F., F. Chauvin, B. Timbal, P. Araspin, and D. Grimal, 1998: A GCM study of 7804 the impact of greenhouse gas increase on the frequency of occurrence of tropical 7805 cyclones. Clim. Change, 38, 307-343. 7806 7807 Ryan, B.F., I. G. Watterson, and J. L. Evans, 1992: Tropical cyclones frequencies 7808 inferred from Gray's yearly genesis parameter: validation of GCM tropical 7809 climate. Geophys. Res. Lett., 19, 1831-1834. 7810 7811 Santer, B. D., and Coauthors, 2005: Amplification of surface temperature trends and 7812 variability in the tropical atmosphere. Science, 309, 1551-1556. 7813 7814 Santer, B. D., and Coauthors, 2006: Forced and unforced ocean temperature changes in 7815 Atlantic and Pacific tropical cyclogenesis regions. Proc. Nat. Acad. Sci., 103, 7816 13905-13910, 10.1073/pnas.0602861103. 7817 7818 Saunders, M. A., and A. S. Lea, 2005: Seasonal prediction of hurricane activity 7819 reaching the coast of the United States. Nature, 434, 1005-1008. 7820 7821 Schär, C., P.L. Vidale, D. Lüthi, C. Frei, C. Häberli, M.A. Liniger, and C. Appenzeller, 7822 2004: The role of increasing temperature variability in European summer heat 7823 waves. Nature, 427, 332-336. 7824 7825 Schubert, S. D., M. J. Suarez, P. J. Region, R. D. Koster, and J. T. Bacmeister, 2004: 7826 Causes of long term drought in the U. S. Great Plains. J Climate, 17, 485-503. Do Not Cite or Quote 328 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7827 7828 Seager, R. Y. Kushnir, C. Herweijer, N. Naik, and J. Velez, 2005: Modeling of tropical 7829 forcing of persistent droughts and pluvials over western North America: 1856- 7830 2000. J Climate, 18, 4065-4088. 7831 7832 Selten, F.M., Branstator, G. W.Dijkstra, and M. Kliphuis, 2004: Tropical origins for 7833 recent and future Northern Hemisphere climate change. Geophysical Research 7834 Letters, 31, L21205. 7835 7836 Shen, W., R.E. Tuleya, and I. Ginis, 2000: A sensitivity study of the thermodynamic 7837 environment on GFDL model hurricane intensity: implications for global 7838 warming. J. Climate, 13, 109-121. 7839 7840 Sherwood, S. C., J. R. Lanzante, and C. L. Meyer, 2005: Radiosonde daytime biases and 7841 late-20th century warming. Science, 309, 1556-1559. 7842 7843 Shiogama, H., M. Watanabe, M. Kimoto, and T. Nozawa, 2005: Anthropogenic and 7844 natural forcing impacts on ENSO-like decadal variability during the second half 7845 of the 20th century. Geophysical Research Letters, 32, L21714. 7846 7847 Sriver, R. and M. Huber, 2006: Low frequency variability in globally integrated tropical 7848 cyclone power dissipation. Geophys. Res. Lett., 33, L11705, 7849 doi:10.1029/2006GL026167. 7850 7851 Stott, P.A., 2003: Attribution of regional-scale temperature changes to anthropogenic and 7852 natural causes. Geophysical Research Letters, 30, doi:10.1029/2003GL017324. 7853 7854 Stott, P.A., and S.F.B. Tett, 1998: Scale-dependent detection of climate change. Journal 7855 of Climate, 11, 3282-3294. 7856 7857 Stott, P. A., S. F. B. Tett, G. S. Jones, M. R. Allen, J. F. B. Mitchell, and G. J. Jenkins, 7858 2000: External control of 20th century temperature by natural and anthropogenic 7859 forcings. Science, 290, 2133-2137. 7860 7861 Strong, A.E., F. Arzayus, W. Skirving, and S.F. Heron, 2006: Identifying coral 7862 bleaching remotely via coral reef watch – improved integration and implications 7863 for changing climate, in Corals and Climate Change: Science and Management, 7864 Coastal and Estuarine Studies, 61, 163-180. 7865 7866 Sugi, M., A. Noda, and N. Sato, 2002: Influence of global warming on tropical cyclone 7867 climatology: an experiment with the JMA global model. J. Meteorol. Soc. Japan 7868 80: 249-272, DOI:10.2151/jmsj.80.249. 7869 7870 Sugi, M., and J. Yoshimura, 2004: A mechanism of tropical precipitation change due to 7871 CO2 increase. J. Climate, 17, 238-243. 7872 Do Not Cite or Quote 329 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7873 Tang, B. H., and J. D. Neelin, 2004: ENSO influence on Atlantic hurricanes via 7874 tropospheric warming. Geophys. Res. Lett., 31, L24204, 7875 doi:10.1029/2004GL021072. 7876 7877 Tebaldi, C., K. Hayhoe, J.M. Arblaster, and G.A. Meehl, 2006: Going to the extremes. 7878 Clim. Change, 79, 185-211. 7879 7880 Thompson, D.W.J., and J.M. Wallace, 1998: The Arctic Oscillation signature in the 7881 wintertime geopotential height and temperature fields. Geophysical Research 7882 Letters, 25, 1297-1300. 7883 7884 Thompson, D., J.M. Wallace, and G.C. Hegerl, 2000: Annular modes in the extratropical 7885 circulation: Part II, Trends. Journal of Climate, 13, 1018-1036. 7886 7887 Tonkin, H., G. J. Holland, N. Holbrook, and A. Henderson-Sellers, 2000: An evaluation 7888 of thermodynamic estimates of climatological maximum potential tropical 7889 cyclone intensity. Mon. Wea. Rev., 128, 746-762. 7890 7891 Tonkin, H., G. Holland, C. Landsea, and S. Li, 1997: Tropical cyclones and climate 7892 change: a preliminary assessment. In Assessing Climate Change: Results from 7893 the Model Evaluation and Consortium for Climate Assessments, W. Howe and A. 7894 Henderson-Sellers, eds., Taylor and Francis Group, plc., London, 327-360. 7895 7896 Trapp, R. J., N. S. Diffenbaugh, H. E. Brooks, M. E. Baldwin, E. D. Robinson, and J. S. 7897 Pal, 2007: Changes in severe thunderstorm frequency during the 21st century due 7898 to anthropogenically enhanced global radiative forcing. Proc. National Acad. Sci., 7899 in review. 7900 7901 Trenberth, K., 1999: Conceptual framework for changes of extremes of the hydrological 7902 cycle with climate change. Clim. Change, 42, 327-339. 7903 7904 Trenberth, K., 2005: Uncertainty in hurricanes and global warming. Science, 308, 7905 1753-1754. 7906 7907 Trenberth, K. E., J. Fasullo, and L. Smith, 2005: Trends and variability in column- 7908 integrated atmospheric water vapor. Clim. Dyn., 24: 741-758. 7909 7910 Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in 7911 2005. Geophys. Res. Lett., 33, L12704, doi:10.1029/2006GL026894. 7912 7913 Trenberth, K. E., and Coauthors, 2007: Chapter 3: Observations: Surface and 7914 Atmospheric Climate Chang. In: S. Solomon et al. (ed.) Climate Change 2007. 7915 The Fourth Scientific Assessment, Intergovernmental Panel on Climate Change 7916 (IPCC), in press. 7917 Do Not Cite or Quote 330 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7918 Tsutsui, J., 2002: Implications of anthropogenic climate change for tropical cyclone 7919 activity: a case study with the NCAR CCM2. J. Meteorol. Soc. Japan, 80, 45-65, 7920 DOI:10.2151/jmsj.80.45 7921 7922 Tsutsui, J., and A. Kasahara, 1996: Simulated tropical cyclones using the National 7923 Center for Atmospheric Research community climate model. J. Geophys. Res. 7924 101, 15013-15032. 7925 7926 Tuleya, R. E., M. DeMaria, and R. J. Kuligowski, 2007: Evaluation of GFDL and simple 7927 statistical model rainfall forecasts for U.S. landfalling tropical storms. Weather 7928 and Forecasting, 22, 56-70. 7929 7930 Uhlhorn, E. W, and P. G. Black, 2003: Verification of remotely sensed sea surface winds 7931 in hurricanes. J. Atmos. and Ocean. Tech., 20, 99-116. 7932 7933 Vavrus, S.J., J.E. Walsh, W.L. Chapman, and D. Portis, 2006: The behavior of extreme 7934 cold air outbreaks under greenhouse warming. Int. J. Clim., in press. 7935 7936 Vecchi, G. A., and B. J. Soden, 2007: Increased tropical Atlantic wind shear in model 7937 projections of global warming. Geophys. Res. Lett., 34, L08702, 7938 doi:10.1029/2006GL028905. 7939 7940 Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. 7941 Harrison, 2006: Weakening of the tropical Pacific atmospheric circulation due to 7942 anthropogenic forcing. Nature, 441, 73-76. doi:10.1038/nature/04744. 7943 7944 Vitart, F., J. L. Anderson, and W. F. Stern, 1997: Simulation of inter-annual variability 7945 of tropical storm frequency in an ensemble of GCM integrations. J. Climate, 10, 7946 745-760. 7947 7948 Vitart, F. and J. L. Anderson, 2001: Sensitivity of Atlantic tropical storm frequency to 7949 ENSO and interdecadal variability of SSTs in an ensemble of AGCM 7950 integrations. J. Climate, 14( 4), 533-545. 7951 7952 Vitart, F., J. L. Anderson, J. Sirutis, R. E. Tuleya, 2001: Sensitivity of tropical storms 7953 simulated by a general circulation model to changes in cumulus parametrization. 7954 Quart. J. Roy. Meteorol. Soc. 127, 25-51. 7955 7956 Walsh, K., 2004: Tropical cyclones and climate change: unresolved issues. Clim. Res., 7957 27, 77-83. 7958 7959 Walsh, K. J. E., K.-C. Nguyen, and J. L. McGregor, 2004: Fine-resolution regional 7960 climate model simulations of the impact of climate change on tropical cyclones 7961 near Australia. Clim. Dyn., 22, 47-56, DOI: 10.1007/s00382-003-0362-0. 7962 Do Not Cite or Quote 331 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 7963 Walsh, K. J. E. and B. F. Ryan, 2000: Tropical cyclone intensity increase near Australia 7964 as a result of climate change. J. Climate, 13, 3029-3036. 7965 7966 Walsh, K. J. E., M. Fiorino, C. W. Landsea, and K. McInnes, 2006: Objectively- 7967 determined resolution-dependent threshold criteria for the detection of tropical 7968 cyclones in climate models and reanalyses. J. Climate, (in press). 7969 7970 Wang, X.L., F.W. Zwiers, and V.R. Swail, 2004: North Atlantic ocean wave climate 7971 change scenarios for the twenty-first century. Journal of Climate, 17, 2368-2383. 7972 7973 Wang X.L., V.R. Swail, and F.W. Zwiers, 2006: Climatology and changes of extra- 7974 tropical storm tracks and cyclone activity: Comparison of ERA-40 with 7975 NCEP/NCAR Reanalysis for 1958–2001. J. Climate, 19, 3145-3166. 7976 7977 Wang, X.L., V.R. Swail, X. Zhang, F.W. Zwiers, and Y. Feng, 2007: Detection of human 7978 influence on trends of atmospheric storminess and northern oceans wave heights. 7979 Clim. Dyn. (in preparation). 7980 7981 WASA, 1998: Changing waves and storms in the northeast Atlantic? Bull. Amer. Meteor. 7982 Soc., 79, 741-760. 7983 7984 Watterson, I. G., J. L. Evans, and B. F. Ryan, 1995: Seasonal and inter-annual variability 7985 of tropical cyclogenesis: diagnostics from large-scale fields. J. Climate, 8, 3052- 7986 3066. 7987 7988 Webster, P. J., G. J. Holland, J. A. Curry, and H.-R. Chang, 2005: Changes in tropical 7989 cyclone number, duration, and intensity in a warming environment. Science, 309, 7990 1844-1846. 7991 7992 Webster, P. J., J. A. Curry, J. Liu, and G. J. Holland, 2006: Response to comment on 7993 “Changes in tropical cyclone number, duration, and intensity in a warming 7994 environment”. Science, 311, 1713c. 7995 7996 Wehner, M., 2005: Changes in daily precipitation and surface air temperature extremes 7997 in the IPCC AR4 models. US CLIVAR Variations, 3, 5-9. 7998 7999 Weisheimer, A., and T.N. Palmer, 2005: Changing frequency of occurrence of extreme 8000 seasonal-mean temperatures under global warming. Geophys. Res. Lett., 32, 8001 L20721. 8002 8003 Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will 8004 global warming bring? Science, 317, 233-235, doi: 10.1126/science.1140746. 8005 8006 Wettstein, J.J., and L.O. Mearns, 2002: The influence of the North Atlantic-Arctic 8007 Oscillation on mean, variance, and extremes of temperature in the northeastern 8008 United States and Canada. J. Climate, 15, 3586-3600. Do Not Cite or Quote 332 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8009 8010 Williams, G., K.L. Layman, H.G. Stefan, 2004: Dependence of lake ice covers on 8011 climatic, geographic and bathymetric variables. Cold Reg. Sci. Tech., 40, 145- 8012 164. 8013 8014 Wittenberg, A. T., A. Rosati, N.-C. Lau, and J. J. Ploshay, 2006: GFDL's CM2 global 8015 coupled climate models - Part 3: Tropical Pacific climate and ENSO. J. Climate, 8016 19(5), 698-722. 8017 8018 Wu, L., and B. Wang, 2004: Assessing impacts of global warming on tropical cyclone 8019 tracks. J. Climate, 17, 1686-1698. 8020 8021 Wu, L., B. Wang, and S. Geng, 2005: Growing typhoon influence on east Asia, Geophys. 8022 Res. Lett., 32, L18703, doi:10.1029/2005GL022937. 8023 8024 Wu, P.L., R. Wood, and P. Stott, 2005: Human influence on increasing Arctic river 8025 discharges. Geophys. Res. Lett., 32, Art. No. L02703. 8026 8027 Yin, J.H., 2005: A consistent poleward shift of the storm tracks in simulations of 21st 8028 century climate. Geophys. Res. Lett., 32, L18701. 8029 8030 Yonetani, T., and H.B. Gordon, 2001: Simulated changes in the frequency of extremes 8031 and regional features of seasonal/annual temperature and precipitation when 8032 atmospheric CO2 is doubled. J. Clim., 14, 1765-1779. 8033 8034 Yoshimura, J. and M. Sugi, 2005: Tropical cyclone climatology in a high-resolution 8035 AGCM - impacts of SST warming and CO2 increase. SOLA, 1, 133-136, DOI: 8036 10.2151/sola.2005-035. 8037 8038 Yoshimura, J., M. Sugi and A. Noda, 2006: Influence of greenhouse warming on 8039 tropical cyclone frequency. J. Meteor. Soc. Japan, 84, 405-428. 8040 8041 Zelle, H., G.J. Van Oldenborgh, G. Burgers, and H. Dijkstra, 2005: El Nino and 8042 greenhouse warming: Results from ensemble simulations with the NCAR CCSM. 8043 J. Climate, 18, 4669-4683. 8044 8045 Zeng, Z., Y. Wang, and C.-C. Wu, 2006: Environmental dynamical control of tropical 8046 cyclone intensity-An observational study. Mon. Wea. Rev., (in press). 8047 8048 Zhang, R., and T. L. Delworth, 2006: Impact of Atlantic multidecadal oscillations on 8049 India/Sahel rainfall and Atlantic hurricanes. Geophys. Res. Lett., 33, L17712, 8050 doi:10.1029/2006GL026267. 8051 8052 Zhang, X., F.W. Zwiers, and P.A. Stott, 2006: Multi-model multi-signal climate change 8053 detection at regional scale. Journal of Climate, in press. 8054 Do Not Cite or Quote 333 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8055 Zhang, X., F. W. Zwiers, G. C. Hegerl, N. Gillett, H. Lambert, and S. Solomon, 2007: 8056 Detection of human Influence on 20th century precipitation trends. Nature, in 8057 press. 8058 8059 Zhang, X.D., and J.E. Walsh, 2006: Toward a seasonally ice-covered Arctic Ocean: 8060 Scenarios from the IPCC AR4 model simulations. J. Climate, 19, 1730-1747. 8061 8062 Zwiers, F.W., and X. Zhang, 2003: Toward regional scale climate change detection. 8063 Journal of Climate, 16, 793-797. 8064 Do Not Cite or Quote 334 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8065 Table 3.1 Models and scenarios used for computing the Frich et al. (2002) indices 8066 for North America that appear in this document. Scenario Models SRES A1B ccsm3.0 cnrm gfdl2.0 gfdl2.1 inmcm3 ipsl miroc3_2_medres miroc3_2_hires mri_cgcm2_3_2a SRES A2 cnrm gfd2.0 gfdl2.1 inmcm3 ipsl miroc3_2_medre mri_cgcm2_3_2a SRES B1 ccsm3.0 cnrm gfdl2.0 gfdl2.1 inmcm3 ipsl miroc3_2_medres miroc3_2_hires 8067 Do Not Cite or Quote 335 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8068 Table 3.2 Summary of tropical storm frequency, expressed as a percent of present 8069 day levels, as simulated by several climate GCMs under global warming conditions. Reference Model Resolution Experiment N NW NE Global Atlantic Pacific Pacific Sugi et al. 2002 JMA T106 L21 10y 66 161 34 33 timeslice (~120km) 1xCO2, 2xCO2 Tsutsui 2002 NCAR T42 L18 10y 102 86 111 91 CCM2 1xCO2 2xCO2 from 115y CO2 1% pa McDonald et al. HadAM3 N144 L30 15y IS95a 94 75 70 180 2005 timeslice (~100km) 1979-1994 2082-2097 Hasegawa and CCSR/NIES T106 L56 5x20y at 1xCO2 96 Emori 2005 /FRCGC (~120km) 7x20y at 2xCO2 timeslice Yoshimura et al. JMA T106 L21 10y 85 2006 timeslice (~120km) 1xCO2, 2xCO2 Bengtsson et al. ECHAM5- T63 L31 A1B 3 members 94 2006 OM 1.5° L40 30y 20C and 21C Oouchi et al. MRI/JMA TL959 L60 10y A1B 70 134 62 66 2006 timeslice (~20km) 1982-1993 Do Not Cite or Quote 336 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 2080-2099 Chauvin et al. ARPEGE- Stretched 10y 2006 Climat time non-uniform CNRM SRES-B2: 118 slice grid (~50 Hadley SRES-A2: 75 km) Bengtsson et al. ECHAM5 up to T319 20yr, A1B scenario --- 87 72 107 2007 time slice (down to ~30-40 km grid) 8070 8071 Bold = significantly more tropical storms in the future simulation 8072 Italic = significantly fewer tropical storms in the future simulation 8073 Plain text = not significant or significance level not tested Do Not Cite or Quote 337 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8074 8075 8076 8077 Figure 3.1 Indices (Frich et al. ,2002) averaged over North America for model 8078 simulations and observations for the 20th and 21st centuries showing changes relative to 8079 1961-1990 in the a) percentage of days in a year for which daily low temperature is in the 8080 top 10% of warm nights for the period 1961-1990, b) number of frost days per year, c) 8081 growing season length (days) and d) sum of precipitation on days in the top 5% of heavy 8082 precipitation days for the period 1961-1990. In the 20th century, the 1-σ and 2-σ bounds 8083 are computed from the ensemble of 20th century simulations. In the future, the bounds are Do Not Cite or Quote 338 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8084 from an ensemble of simulations that used the A1B, A2 or B1 scenarios∗. The bounds are 8085 the max (or min) standard deviation plus (or minus) signal over all three scenarios. The 8086 model plots are obtained from the CMIP-3 multi-model data set at PCMDI and the 8087 observations are from Peterson et al. (2007). ∗3 future emission scenarios from the IPCC Special Report on Emissions Scenarios: B1 blue line: emissions increase very slowly for a few more decades, then level off and decline A2 black line: emissions continue to increase rapidly and steadily throughout this century A1B red line: emissions increase rapidly until 2050 and then decline. There are more details on these emission scenarios in the glossary. Do Not Cite or Quote 339 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8088 8089 Figure 3.2 Indices (Frich et al., 2002) for frost days over North America for model 8090 simulations and observations: a) 20th century trend for model ensemble, b) Observed 20th 8091 century trend and c) 21st century trend for emission scenario A2 from model ensemble. 8092 The model plots are obtained from the CMIP-3 multi-model data set at PCMDI and the 8093 observations are from Peterson et al. (2007). Do Not Cite or Quote 340 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8094 8095 8096 Figure 3.3 Trends in the Karl-Knight heat-wave index (Karl and Knight, 1997) for 1961- 8097 1990 in observations (top panel) and in an ensemble of climate simulations by the 8098 Parallel Climate Model (bottom panel). Dots mark trends that are significant at the 95% 8099 level. Do Not Cite or Quote 341 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8100 8101 8102 Figure 3.4 Comparison between regions with disproportionate trends in the number of 8103 exceedances of the heaviest rainfall events (99.7th percentile) in two climate models at the 8104 time of CO2 doubling. See figure 2.8 for areas of N. America which show observed 8105 increases in very heavy rainfall Model 1 is the CGCM2 and model 2 is the HadCM3. 8106 After Groisman et al. (2005). Do Not Cite or Quote 342 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8107 8108 Figure 3.5 Simulations for 2090-2099 indicating how currently rare extremes (a 1-in-20- 8109 year event) are projected to become more commonplace. a) Temperature - a day so hot 8110 that it is currently experienced once every 20 years would occur every other year or more 8111 by the end of the century, (b) daily total precipitation events that occur on average every 8112 20 years in the present climate would, for example, occur once in every 4-6 years for 8113 N.E. North America. These results are based on a multi-model ensemble of global 8114 climate models simulating the midrange A1B emission scenario*. (from Wehner 2005). 8115 [units: years]. Do Not Cite or Quote 343 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8116 8117 8118 Figure 3.6 Change in annual runoff (%) for the period 2090-2099, relative to 1980-1999. 8119 Values are obtained from the median in a multi-model dataset that used the A1B emission 8120 scenario. White areas are where less than 66% of the models agree in the sign of change 8121 and stippled areas are where more than 90% models agree in the sign of change. [Derived 8122 from the analysis of Milly et al. (2005)] Do Not Cite or Quote 344 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8123 8124 8125 Figure 3.7 Frequency histograms of hurricane intensities in terms of central pressure 8126 (mb) aggregated across all idealized hurricane experiments in the Knutson and Tuleya 8127 (2004) study. The light curve shows the histogram from the experiments with present-day 8128 conditions, while the dark curve is for high CO2 conditions (after an 80 yr warming trend 8129 in a +1%/yr CO2 experiment). The results indicate that hurricanes in a CO2-warmed 8130 climate will have significantly higher intensities (lower central pressures) than hurricanes 8131 in the present climate. 8132 Do Not Cite or Quote 345 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8133 8134 8135 Figure 3.8 Percent changes in June-November ensemble mean a) vertical wind shear, b) 8136 mid-tropospheric relative humidity, and c) maximum potential intensity of tropical 8137 cyclones for the period 2081-2100 minus the period 2001-2021 for an ensemble of 18 Do Not Cite or Quote 346 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8138 GCMs, available in the IPCC AR4 archive, using the A1B scenario. The percentage 8139 changes are normalized by the global surface air temperature increase projected by the 8140 models. From Vecchi and Soden (2007). Do Not Cite or Quote 347 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8141 8142 8143 8144 Figure 3.9 Number of events per 1000 yrs with peak wind speeds exceeding the value 8145 on the x-axis. Results obtained by running a simple coupled hurricane intensity prediction 8146 model over a set of 3000 synthetic storm tracks for the North Atlantic. The grey bars 8147 depict storms for present day climate conditions. The black bars depict storms for similar 8148 conditions except that the potential intensity (a) or vertical wind shear (b) of the 8149 environment is increased everywhere by 10%. From Emanuel (2006). Do Not Cite or Quote 348 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8150 8151 8152 8153 Figure 3.10 As in Fig. 3.7, but for near-hurricane precipitation, estimated as the average 8154 precipitation rate for the 102 model grid points (32,700 km2 area) with highest 8155 accumulated rainfall over the last 6 hours of the 5-day idealized hurricane experiments in 8156 Knutson and Tuleya (2004). The results indicate that hurricanes in a CO2-warmed 8157 climate will have substantially higher core rainfall rates than those in the present climate. 8158 (From Knutson and Tuleya, 2007). Do Not Cite or Quote 349 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8159 8160 8161 Figure 3.11 The projected change in intense low pressure systems (strong storms) during 8162 the cold season for the Northern Hemisphere for various emission scenarios* (adapted 8163 from Lambert and Fyfe; 2006). Storms counted have central pressures less than 970 mb 8164 and occur poleward of 30˚N during 120-day season starting November 15. Adapted from 8165 Lambert and Fyfe (2006). Do Not Cite or Quote 350 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8166 Chapter 4 Recommendations for Improving our 8167 Understanding 8168 8169 Convening Lead Author: David R. Easterling, NOAA 8170 8171 Lead Authors: David M. Anderson, NOAA; William J. Gutowski, Iowa State Univ.; 8172 Greg J. Holland, NCAR; Kenneth E. Kunkel, Univ. Ill. Urbana-Champaign, Ill. State 8173 Water Survey; Thomas C. Peterson, NOAA; Roger S. Pulwarty, NOAA; Michael F. 8174 Wehner, DOE LBNL 8175 8176 In this chapter we provide a set of key recommendations for improving our understanding 8177 that stem from the previous three chapters. Many of these findings and recommendations 8178 are consistent with previous reports, especially the CCSP 1.1 report on reconciling 8179 temperature trends between the surface and free atmosphere. 8180 8181 Many types of extremes, such as excessively hot and cold days, drought, and heavy 8182 precipitation show changes over North America consistent with observed warming of the 8183 climate. Regarding future changes, model projections show large changes in warm and 8184 cold days consistent with projected warming of the climate by the end of the 21st century. 8185 However, there remains uncertainty in both observed changes, due to the quality and 8186 homogeneity of the observations, and in model projection, due to constraints in model 8187 formulation, in a number of other types of climate extremes, including tropical cyclones, 8188 extratropical cyclones, tornadoes, and thunderstorms. 8189 Do Not Cite or Quote 351 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8190 4.1 The continued establishment and maintenance of high quality climate observing 8191 systems to monitor climate variability and change should be of the highest priority. 8192 Recently, more emphasis has been placed on the development of true climate observing 8193 networks that adhere to the Global Climate Observing System (GCOS) Climate 8194 Monitoring Principles. This is exemplified by the establishment in the U.S. of the Climate 8195 Reference Network, in Canada of the Reference Climate Network, and recent efforts in 8196 Mexico to establish a climate observing network. Stations in these networks are carefully 8197 sited and instrumented and are designed to be benchmark observing systems adequate to 8198 detect the true climate signal for the region being monitored. 8199 8200 Similar efforts to establish a high-quality, global upper-air reference network have been 8201 undertaken under the auspices of GCOS. However, this GCOS Reference Upper-air 8202 Network (GRUAN) is dependent on the use of current and proposed new observing 8203 stations, whose locations will be determined through observing system simulation 8204 experiments (OSSEs) that use both climate model simulations and observations to 8205 determine where best to locate new observing stations 8206 8207 However, at the present these efforts generally are restricted to a few countries and large 8208 areas of the world, even large parts of North America remain under observed. A 8209 commitment to developing climate observing networks, especially in areas that 8210 traditionally have not had long-term climate observations, is critical for monitoring and 8211 detecting future changes in climate, including extremes. Do Not Cite or Quote 352 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8212 4.2 Efforts to digitize, homogenize and analyze long-term observations in the 8213 instrumental record should be expanded. 8214 Research using homogeneity-adjusted observations will provide a better understanding of 8215 climate system variability in extremes. Observations of past climate have, by necessity, 8216 relied on observations from weather observing networks established for producing and 8217 verifying weather forecasts. In order to make use of these datasets in climate analyses, 8218 non-climatic changes in the data, such as changes due to station relocations, land use 8219 change, instrument changes, and observing practices must be accounted for through data 8220 adjustment schemes. 8221 8222 The intent of these data adjustments is to approximate homogeneous time series where 8223 the variations are only due to variations in climate and not due to the non-climatic 8224 changes discussed above. However, the use of these adjustment schemes introduces 8225 another layer of uncertainty into the results of analyses of climate variability and change. 8226 Thus, research into both the methods and quantifying uncertainties introduced through 8227 use of these methods is critical for understanding observed changes in climate. 8228 8229 Even with the recent efforts to develop true climate observing networks, an 8230 understanding of natural and anthropogenic effects on historical weather and climate 8231 extremes is best achieved through study of very long (century-scale) records because of 8232 the presence of multi-decadal modes of variability in the climate system. For many of the 8233 extremes discussed here, including temperature and precipitation extremes, storms, and Do Not Cite or Quote 353 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8234 drought, there are significant challenges in this regard because long-term, high quality, 8235 homogeneous records are not available. For example, recent efforts have been made in 8236 the U.S. to digitize surface climate data for the 19th Century; however, using these data 8237 poses several problems. The density of stations is considerably less than in the 20th 8238 Century. Equipment and observational procedures were quite variable and different than 8239 the standards established within the U.S. Cooperative Network (COOP). Thus, the raw 8240 data are not directly comparable to COOP data. However, initial efforts to homogenize 8241 these data have been completed and analysis shows interesting features, including high 8242 frequencies of extreme precipitation and low frequencies of heat waves for the 1850-1905 8243 period over the conterminous U.S. 8244 8245 In some cases, heterogeneous records of great length are available and useful information 8246 has been extracted. However, there are many opportunities where additional research 8247 may result in longer and better records to better characterize the historical variations. For 8248 example, the ongoing uncertainty and debate about tropical cyclone trends is rooted in the 8249 heterogeneous nature of the observations and different approaches toward approximating 8250 homogeneous time series. Therefore, efforts to resolve the existing uncertainties in 8251 tropical cyclone frequency and intensity should continue by re-examining the 8252 heterogeneous records, and paleotempestological studies should be pursued to better 8253 understand variations on multi-century time scales. 8254 Do Not Cite or Quote 354 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8255 4.3 Current weather observing systems should adhere to standards of observation 8256 that are consistent with the needs of both the climate and the weather forecasting 8257 communities. 8258 Smaller-scale storms, such as thunderstorms and tornadoes are particularly difficult to 8259 observe since historical observations have been highly dependent on population density. 8260 For example, the U.S. record of tornadoes shows a questionable upward trend that 8261 appears to be due mainly to increases in population density in tornado-prone regions. 8262 With more people in these regions, tornadoes that may have gone unobserved in earlier 8263 parts of the record are now being recorded, thus hampering any analysis of true climate 8264 trends of these storms. Since many of the observations of extreme events are collected in 8265 support of operational weather forecasting, changes in policies and procedures regarding 8266 those observations need to take climate change questions into account, in order to collect 8267 high-quality, consistently collected data over time and space. Therefore, consistent 8268 standards of collection of data about tornadoes and severe thunderstorms need to be 8269 developed and applied. Included in this process is a need for the collection of information 8270 about reports that allows users to know the confidence levels that can be applied to 8271 reports. 8272 8273 However, in the absence of homogeneous observations of extremes, such as 8274 thunderstorms and tornadoes, one promising method to infer changes is through the use 8275 of surrogate measures. For example, since the data available to study past trends in these 8276 kinds of storms suffer from the problems outlined above an innovative way to study past 8277 changes lies in techniques that relate environmental conditions to the occurrence of Do Not Cite or Quote 355 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8278 thunderstorms and tornadoes. Studies along these lines could then produce better 8279 relationships, than presently exist, between favorable environments and storms. Those 8280 relationships could then be applied to past historical environmental observations and 8281 reanalysis data to make improved estimates of long-term trends. 8282 8283 4.4 Efforts to extend reanalysis products using surface observations should be 8284 pursued. 8285 Studies of the temporal variations in the frequency of strong extratropical cyclones have 8286 typically examined the past 50 years and had to rely on reanalysis fields due to 8287 inconsistencies with the historical record. But a much longer period is desirable to gain a 8288 better understanding of possible multi-decadal variability in strong storms. There are 8289 surface pressure observations extending back to the 19th Century and, although the spatial 8290 density of stations decreases backwards in time, it may be possible to identify strong 8291 extratropical cyclones and make some deductions about long-term variations. 8292 Additionally, efforts to extend reanalysis products back to the early 20th Century using 8293 only surface observations have recently begun. These efforts should continue since they 8294 provide physically-consistent depictions of climate behavior and will contribute to an 8295 understanding of causes of observed changes in climate extremes. 8296 8297 4.5 Research is needed to create annually-resolved, regional-scale reconstructions of 8298 the climate for the past 2,000 years. 8299 The development of a wide-array of climate reconstructions for the last two millennia, 8300 such as temperature, precipitation, and drought will provide the longer baseline needed to Do Not Cite or Quote 356 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8301 analyze infrequent extreme events, such as those occurring once a century or less. This 8302 and other paleoclimatic research can also answer the question of how extremes change 8303 when the global climate was warmer and colder than today. 8304 8305 The instrumental record of climate is generally limited to the past 150 years or so. 8306 Although there are observations of temperature and precipitation as recorded by 8307 thermometers and rain gauges for some locations prior to the early to mid-1800s, they are 8308 few and contain problems due to inconsistent observing practices thus their utility is 8309 limited. However, the paleoclimate record covering the past 2,000 years and beyond 8310 reveals extremes of greater amplitude and longer duration compared to events observed 8311 in the instrumental record of the past 100 years (e.g. Woodhouse and Overpeck 1998). 8312 The paleoclimate record also reveals that some events occur so infrequently that they 8313 may be observed only once, or even not at all during the instrumental period. An 8314 improved array of paleo time series is essential to understanding the repeat frequency of 8315 rare events, for example events occurring only once a century. 8316 8317 The frequency of some extremes appears tied to the background climate state, according 8318 to some paleoclimate records. For example, century-scale changes in the position of the 8319 subtropical high may have affected hurricane tracks and the frequency of hurricanes in 8320 the Gulf of Mexico (Elsner, et al., 2000). Throughout the western United States, the area 8321 exposed to drought may have been elevated for four centuries from 900-1300 AD, 8322 according the Palmer Drought Severity Index reconstructed from tree rings (Cook, et al., 8323 2004). The period from 900-1300 AD was a period when the global mean temperature Do Not Cite or Quote 357 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8324 was above average (Mann et al. 1999), consistent with the possibility that changes in the 8325 background climate state can affect some extremes. The paleoclimatic record can be used 8326 to further understand the possible changes in extremes during warmer and colder climates 8327 of the past. 8328 8329 4.6 Research efforts to improve our understanding of the mechanisms that govern 8330 hurricane intensity should be increased. 8331 A major limitation of our current knowledge lies in the understanding of hurricane 8332 intensity together with surface wind structure and rainfall, and particularly how these 8333 relate to a combination of external forcing from the ocean and surrounding atmosphere, 8334 and potentially chaotic internal processes. This lack of understanding and related low 8335 predictive capacity has been recognized by several expert committees set up in the wake 8336 of the disastrous 2005 Atlantic hurricane season: 8337 8338 • The National Science Board recommended that the relevant Federal agencies 8339 commit to a major hurricane research program to reduce the impacts of hurricanes 8340 and encompassing all aspects of the problem: physical sciences, engineering, 8341 social, behavioral, economic and ecological (NSB 2006); 8342 • The NOAA Science Advisory Board established an expert Hurricane Intensity 8343 Research Working Group that recommended specific action on hurricane intensity 8344 and rainfall prediction (NOAA SAB 2006); Do Not Cite or Quote 358 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8345 • The American Geophysical Union convened a meeting of scientific experts to 8346 produce a white paper recommending action across all science-engineering and 8347 community levels (AGU 2006)]; and, 8348 • A group of leading hurricane experts convened several workshops to develop 8349 priorities and strategies for addressing the most critical hurricane issues (HiFi 8350 2006). 8351 8352 While much of the focus for these groups was on the short-range forecasting and impacts 8353 reduction aspects of hurricanes, the research recommendations also apply to longer term 8354 projections. Understanding the manner in which hurricanes respond to their immediate 8355 atmospheric and oceanic environment is critical to prediction on all scales. 8356 8357 A critical issue common to all of these expert findings is the need for understanding and 8358 parameterization of the complex interactions occurring at the high wind oceanic interface 8359 and for very high model resolution in order for forecast models to be able to capture the 8360 peak intensity and fluctuations in intensity of major hurricanes. Climate models are 8361 arriving at the capacity to resolve regional structures but not relevant details of the 8362 hurricane core region. As such, some form of statistical inference will be required to fully 8363 assess future intensity projections. 8364 8365 4.7 Substantial increases in computational and human resources should be made 8366 available to fully investigate the ability of climate models to recreate the recent past 8367 as well as make projections under a variety of future forcing scenarios. Do Not Cite or Quote 359 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8368 The continued development and improvement of numerical climate models, and 8369 observational networks for that matter, is highly related to funding levels of these 8370 activities. A key factor, which is often overlooked, is the recruitment and retention of 8371 people necessary to perform the analysis of models and observations. For the 8372 development and analysis of models, scientists are drawn to institutions with 8373 supercomputing resources which require large sources of funding to sustain them. For 8374 example, the high resolution global simulations of Oouchi, et al. (2006) to predict future 8375 hurricane activity are currently beyond the reach of US tropical cyclone research 8376 scientists. This limitation is also true for other smaller-scale storm systems, such as 8377 severe thunderstorms and tornadoes. Yet, to understand how these extreme events might 8378 change in the future it is critical that climate models are developed that can realistically 8379 resolve these types of weather systems. Given sufficient computing resources current 8380 U.S. climate models can achieve very high horizontal resolution. Current generation high 8381 performance computing (HPC) platforms are also sufficient provided that enough access 8382 to computational cycles is made available. Furthermore, many other aspects of the 8383 climate system relevant to extreme events, such as extra-tropical cyclones, would be 8384 much better simulated in such integrations than they are at typical global model 8385 resolutions of today. 8386 8387 Even atmospheric models at ~20 kilometer horizontal resolution are still not finely 8388 resolved enough to simulate the high wind speeds and low pressure centers of the most 8389 intense hurricanes (Category 5 on the Saffir-Simpson scale). Realistically capturing 8390 details of such intense hurricanes, such as the inner eye-wall structure, will require Do Not Cite or Quote 360 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8391 models up to one kilometer horizontal resolution. Such ultra-high resolution global 8392 models will require very high computational rates to be viable (Wehner, et al 2006). This 8393 is not beyond the reach of next generation HPC platforms but will need significant 8394 investments in both model development (human resources) as well as in dedicated 8395 computational infrastructure (Randall, 2005). 8396 8397 4.8 Modeling groups should make available high temporal resolution data (daily, 8398 hourly) from climate model simulations both of the past and for the future to allow 8399 the investigation of potential changes in weather and climate extremes. 8400 In order to achieve high levels of statistical confidence in analyses of climate extremes 8401 using methods such as those based on generalized extreme value theory, lengthy 8402 stationary datasets are required. Although climate model output is well suited to such 8403 analysis, the datasets are often unavailable to the research community at large. Many of 8404 the models utilized for the Intergovernmental Panel on Climate Change Fourth 8405 Assessment Report (IPCC AR4) were integrated as ensembles permitting more robust 8406 statistical analysis. The simulations were made available at the Program for Climate 8407 Model Diagnostics and Intercomparison (PCMDI) at Lawrence Livermore National 8408 Laboratory. However, the higher temporal resolution data necessary to analyze extreme 8409 events is quite incomplete in the PCMDI database with only four models represented in 8410 the daily averaged output sections with ensemble sizes greater than three realizations and 8411 many models not represented at all. Lastly, a critical component of this work is the 8412 development of enhanced data management and delivery capabilities such as those in the 8413 NOAA Operational Model Archive and Distribution System (NOMADS), not only for Do Not Cite or Quote 361 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8414 archive and delivery of model simulations, but for reanalysis and observational data sets 8415 as well (NRC 2006). 8416 8417 4.9 Research needs to move beyond purely statistical analysis and focus more on 8418 linked physical processes that produce extremes and their changes with climate. 8419 Analyses should include attribution of probability distribution changes to natural or 8420 anthropogenic influences, comparison of individual events in contemporary and projected 8421 climates and the synoptic climatology of extremes and its change in projected climates. 8422 The ultimate goal should be a deeper understanding of the physical basis for changes in 8423 extremes that improves modeling and thus lends confidence in projected changes. 8424 8425 Literature is lacking that analyzes the physical processes producing extremes and their 8426 changes as climate changes. One area that is particularly sparse is analysis of so-called 8427 “compound extremes”, events that contain more than one type of extreme such as drought 8428 and extremely high temperatures occurring simultaneously. 8429 8430 A substantial body of work has emerged on attribution of changes, with a growing subset 8431 dealing with attribution of changes in extremes. Such work shows associations between 8432 climate forcing mechanisms and changes in extremes, which is an important first step 8433 toward understanding what changes in extremes are attributable to climate change. 8434 However, such work typically does not examine the coordinated physical processes 8435 linking the extreme behavior to the climate in which it occurs. 8436 Do Not Cite or Quote 362 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8437 More effort should be dedicated to showing how the physical processes producing 8438 extremes are changing. Good examples are studies by Meehl and Tebaldi (2004) on 8439 severe heat waves, Meehl et al. (2004) on changes in frost days and Meehl and Hu (2006) 8440 on megadroughts. Each of these examples involves diagnosing a coherent set of climate- 8441 system processes that yield the extreme behavior. An important aspect of the work is 8442 demonstrating correspondence between observed and simulated physical processes that 8443 yield extremes and, in some of these cases, evaluation of changes in the physical 8444 processes in projected climates. 8445 8446 More broadly, the need is for greater analysis of the physical climatology of the climate 8447 system leading to extremes. Included in this are further studies of the relationship in 8448 projected future climates between slow oscillation modes, such as PDO and AO, and 8449 variation in extremes (e.g., Thompson and Wallace 2001). Methods of synoptic 8450 climatology (e.g., Cassano et al. 2006, Lynch et al. 2006) could also provide deeper 8451 physical insight into the processes producing extremes and their projected changes. Also 8452 the development and use of environmental proxies for smaller storm systems such as 8453 severe thunderstorms and tornadoes from regional and nested climate models is 8454 encouraged. Finally, more probability analysis of the type applied by Stott et al. (2004) to 8455 the 2003 European heat wave is needed to determine how much the likelihood of 8456 individual extreme events has been altered by human influences on climate. 8457 8458 Do Not Cite or Quote 363 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8459 4.10 Communication between the science community and the user community 8460 should be enhanced in both directions. 8461 Because extremes can have major impacts on socio-economical and natural systems, 8462 changes in climate extremes will affect the ability of states, provinces and local 8463 communities to cope with rare weather events. The process of adaptation to climate 8464 change begins with addressing existing vulnerabilities to current and near-term climatic 8465 extremes and is directly linked to disaster risk management. Research and experience 8466 have shown that mitigating the impacts of extremes and associated complex multiple- 8467 stress risks, involve improvements in early warning systems, information for better land- 8468 use planning and resource management, building codes, and, coordination of contingency 8469 planning for pre- and post-event mitigation and response. 8470 8471 Many adaptations can be implemented at low cost, but comprehensive estimates of 8472 adaptation costs and benefits are currently limited partly because detailed information 8473 about costs of extreme events are not adequately archived and made available to 8474 researchers. To address this problem, guidelines should be developed to improve the 8475 methods to collect, archive and quality control detailed information on impacts of 8476 extreme events and sequences of extremes, including costs, loss estimates, loss of life, 8477 and ecological damage as well as the effectiveness of post event responses. Additionally, 8478 networks of systematic observations of key elements of physical, biological, and socio- 8479 economical systems affected by climate extremes should be developed, particularly in 8480 regions where such networks are already known to be deficient. 8481 Do Not Cite or Quote 364 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8482 Because the links between impacts and changes in extremes can be complex, unexpected 8483 and highly nonlinear, especially when modified by human interventions over time, 8484 research into these linkages should be strengthened to better understand system 8485 vulnerabilities and capacity, to develop a portfolio of best practices, and to implement 8486 better response options. But best practices guidelines do not do any good unless they are 8487 adequately communicated to the relevant people. Therefore, mechanisms for 8488 collaboration and exchange of information among climate scientists, impacts researchers, 8489 decision makers (including resources managers, insurers, emergency officials and 8490 planners) and the public should be developed and supported. Such mechanisms would 8491 involve multi-way information exchange systems and pathways. Better communication 8492 between these groups would help communities and individuals make the most 8493 appropriate responses to changing extremes. As climate changes, making the 8494 complexities of climate risk management explicit can transform event to event response 8495 into a learning process for informed proactive management. In such learning-by-doing 8496 approaches, the base of knowledge is enhanced through the accumulation of practical 8497 experience for risk scenario development and disaster mitigation and preparedness. 8498 8499 4.11 Summary 8500 Figure 4.1 shows the complex interrelationships between the different sections and 8501 recommendations in this chapter. Enhanced observing systems and data sets allow better 8502 analyses of the observed climate record for patterns of observed variability and change. 8503 This provides information for the climate modeling community to verify that their models 8504 produce realistic simulations of the observed record, providing increased confidence in Do Not Cite or Quote 365 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8505 simulations of future climate. Both of these activities help improve our physical 8506 understanding of the climate which, linked with model simulations through observing 8507 system simulation experiments (OSSEs), helps understand where we need better 8508 observations, and leads to better formulation of model physics through process studies of 8509 observations. This link between observed and modeling patterns of climate change also 8510 provides the basis for establishing the cause and effect relationships critical for attribution 8511 of climate change to human activities. Since the ultimate goal of this assessment is to 8512 provide better information to policy and decision makers, a better understanding of the 8513 relationships between climate extremes and their impacts is critical information for 8514 reducing the vulnerability of societal and natural systems to climate extremes. Do Not Cite or Quote 366 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8515 Chapter 4 References 8516 8517 AGU, 2006: Hurricanes and the U.S. Gulf Coast: Science and Sustainable Rebuilding 8518 www.agu.org/report/hurricanes/ 8519 8520 Cassano, J.J., P. Uotila, and A. Lynch, 2006: Changes in synoptic weather patterns in the 8521 polar regions in the 20th and 21st centuries, Part 1: Arctic. Int. J. Clim., in press. 8522 8523 Cook, E., C.A. Woodhouse, C.M. Eakin, D.M. Meko, and D.W. Stahle, 2004: Long- 8524 Term Aridity Changes in the Western United States. Science, 306, 1015-1018. 8525 8526 Elsner, J.B., K-B. Liu and B. Kocher, 2000: Spatial variations in major U.S. hurricane 8527 activity: Statistics and a physical mechanism. Journal of Climate, 13, 2293-2305. 8528 8529 HiFi, 2006: HiFi Science Strategy 8530 http://www.nova.edu/ocean/hifi/hifi_science_strategy.pdf 8531 8532 Lynch, A., P. Uotila, and J.J. Cassano, 2006: Changes in synoptic weather patterns in the 8533 polar regions in the 20th and 21st centuries, Part 2: Antarctic. Int. J. Clim., in 8534 press. 8535 8536 Mann, M.E., R. Bradley, and M. Hughes, 1999: Northern Hemisphere temperatures 8537 during the past millennium: inferences, uncertainties, and limitations., Geophys. 8538 Res. Letts., 26, 759-762. 8539 8540 Meehl, G.A., and C. Tebaldi, 2004: More intense, more frequent, and longer lasting heat 8541 waves in the 21st century. Science, 305, 994-997. 8542 8543 Meehl, G.A., C. Tebaldi, and D. Nychka, 2004a: Changes in frost days in simulations of 8544 twenty-first century climate. Clim. Dyn., 23, 495-511. 8545 8546 Meehl, G. A., and A. Hu, 2006: Megadroughts in the Indian monsoon region and 8547 southwest North America and a mechanism for associated multi-decadal Pacific 8548 sea surface temperature anomalies. Journal of Climate, 19, 1605–1623. 8549 8550 National Research Council (NRC) of the National Academies, Board of Atmospheric 8551 Science and Climate, (2006), “Completing the Forecast: Characterizing and 8552 Communicating Uncertainty for Better Decisions Using Weather and Climate 8553 Forecasts”, Recommendation 3.4, pp76, October 2006 pre-publication, National 8554 Academies Press 8555 8556 NOAA SAB, 2006: HIRWG Final Report 8557 http://www.sab.noaa.gov/Reports/HIRWG_final73.pdf 8558 8559 NSB, 2006: Hurricane Warning: The Critical Need for a National Hurricane Research 8560 Initiative www.nsf.gov/nsb/committees/hurricane/pre_publication.pdf 8561 Do Not Cite or Quote 367 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8562 Oliker, L., J. Carter, M. Wehner, A. Canning, S. Ethier, B. Govindasamy, A. Mirin , D. 8563 Parks, P. Worley, S. Kitawaki, Y. Tsuda, 2005: Leading Computational Methods 8564 on Scalar and Vector HEC Platforms, 2005 SuperComputer Conference 8565 Proceedings LBNL-58053 8566 8567 Oouchi, K., Yoshimura, J., Yoshimura, H., Mizuta, R., Kusunoki, S., and Noda, A. 2006: 8568 Tropical cyclone climatology in a global–warming climate as simulated in a 8569 20km–mesh global atmospheric model: frequency and wind intensity analyses, J. 8570 Meteorol. Soc. Japan, 84, 259–276. 8571 8572 Randall, D., 2005: Counting the clouds., 2005 SciDAC Meeting Proceedings, San 8573 Francisco, CA, June 26-30, 2005. www.scidac.org 8574 8575 Stott, P.A., D.A. Stone, and M.R. Allen, 2004: Human contribution to the European 8576 heatwave of 2003. Nature, 432, 610-614 8577 8578 Thompson, D.W.J. and J.M. Wallace, 2001: Regional climate impacts of the Northern 8579 Hemisphere annular mode and associated climate trends, Science, 293, 85-89. 8580 8581 Wehner, M., L. Oliker, and J. Shalf, “Towards Ultra-High Resolution Models of Climate 8582 and Weather”, International Journal of High Performance Computing 8583 Applications., in press. 8584 8585 Woodhouse, C., and J. Overpeck, 1998: 2000 years of drought variability in the Central 8586 Unites States, Bull. Amer. Meteor. Soc., 79, 2693-2714. Do Not Cite or Quote 368 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8587 8588 8589 8590 Figure 4.1 Interrelationships between recommendations. Thick arrows indicate major 8591 linkages included in this assessment. Better observing systems result in improved 8592 analyses which helps improve modeling, physical understanding, and impacts through 8593 clearer documentation of observed patterns in climate. Similarly, improved modeling 8594 helps improve physical understanding and together can point to deficiencies in observing 8595 systems as well as helping to understand future impacts. Lastly, a better understanding of 8596 the relationships between climate extremes and impacts can help improve observations 8597 by identifying deficiencies in observations (e.g. under-observed areas), and improve 8598 modeling efforts by identifying specific needs from model simulations for use in impacts 8599 studies. Do Not Cite or Quote 369 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8600 Appendix A Statistical Trend Analysis 8601 8602 Author: Richard L. Smith, Univ. N.C., Chapel Hill 8603 8604 In many places in this report, but especially Chapter 2, trends have been calculated, either 8605 based directly on some climatic variable of interest (e.g. hurricane or cyclone counts) or 8606 from some index of extreme climate events. Statistical methods are used in determining 8607 the form of a trend, estimating the trend itself along with some measure of uncertainty 8608 (e.g. a standard error), and in determining the statistical significance of a trend. A broad- 8609 based introduction to these concepts has been given by Wigley (2006). The present 8610 review extends Wigley’s by introducing some of the more advanced statistical methods 8611 that involve time series analysis. 8612 8613 Some initial comments are appropriate about the purpose, and also the limitations, of 8614 statistical trend estimation. Real data rarely conform exactly to any statistical model, such 8615 as a normal distribution. Where there are trends, they may take many forms. For example, 8616 a trend may appear to follow a quadratic or exponential curve rather than a straight line, 8617 or it may appear to be superimposed on some cyclic behavior, or there may be sudden 8618 jumps (also called changepoints) as well or instead of a steadily increasing or decreasing 8619 trend. In these cases, assuming a simple linear trend (equation (1) below) may be 8620 misleading. However, the slope of a linear trend can still represent the most compact and 8621 convenient method of describing the overall change in some data over a given period of 8622 time. 8623 Do Not Cite or Quote 370 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8624 In this appendix, we first outline some of the modern methods of trend estimation that 8625 involve estimating a linear or non-linear trend in a correlated time series. Then, the 8626 methods are illustrated on a number of examples related to climate and weather extremes. 8627 8628 The basic statistical model for a linear trend can be represented by the equation 8629 8630 (1) yt = b0 + b1 t + ut 8631 8632 where t represents the year, yt is the data value of interest (e.g. temperature or some 8633 climate index in year t), b0 and b1 are the intercept and slope of the linear regression, and 8634 ut represents a random error component. The simplest case is when ut are uncorrelated 8635 error terms with mean 0 and a common variance, in which case we typically apply the 8636 standard ordinary least squares (OLS) formulas to estimate the intercept and slope, 8637 together with their standard errors. Usually the slope (b1) is interpreted as a trend so this 8638 is the primary quantity of interest. 8639 8640 The principal complication with this analysis in the case of climate data is usually that the 8641 data are autocorrelated, in other words, the terms cannot be taken as independent. This 8642 brings us within the field of statistics known as time series analysis, see e.g. the book by 8643 Brockwell and Davis (2002). One common way to deal with this is to assume the values 8644 form an autoregressive, moving average process (ARMA for short). The standard 8645 ARMA(p,q) process is of the form 8646 Do Not Cite or Quote 371 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8647 (2) ut -φ1ut -1-…-φp ut-p = εt+θ1εt-1+…+θqεt-q 8648 8649 where φ1…φp are the autoregressive coefficients, θ1…θq are the moving average 8650 coefficients and the εt terms are independent with mean 0 and common variance. The 8651 orders p and q are sometimes determined empirically or sometimes through more formal 8652 model-determination techniques such as the Akaike Information Criterion (AIC) or the 8653 Bias-Corrected Akaike Information Criterion (AICC). The autoregressive and moving 8654 average coefficients may be determined by one of several estimation algorithms 8655 (including maximum likelihood) and the regression coefficients b0 and b1 by the 8656 algorithm of generalized least squares or GLS. Typically, the GLS estimates are not very 8657 different from the OLS estimates that arise when autocorrelation is ignored, but the 8658 standard errors can be very different. It is quite common that a trend that appears to be 8659 statistically significant when estimated under OLS regression is not statistically 8660 significant under GLS regression, because of the larger standard error that is usually 8661 though not invariably associated with GLS. This is the main reason why it is important to 8662 take autocorrelation into account. 8663 8664 An alternative model which is an extension of (1) is 8665 8666 (3) yt = b0 + b1 xt1+…+ bk xtk + ut 8667 8668 where xt1…xtk are k regression variables (covariates) and b1…bk are the associated j 8669 coefficients. A simple example is polynomial regression, where xtj=t for j=1,…,k. Do Not Cite or Quote 372 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8670 However, a polynomial trend, when used to represent a non-linear trend in a climatic 8671 dataset, often has the disadvantage that it behaves unstably at the endpoints, so alternative 8672 representations such as cubic splines are usually preferred. These can also be represented 8673 in the form of (3) with suitable xt1…xtk. As with (1), the ut terms can be taken as 8674 uncorrelated with mean 0 and common variance, in which case OLS regression is again 8675 appropriate, but it is also common to consider the ut as autocorrelated. 8676 8677 There are, by now, several algorithms available that fit these models in a semi-automatic 8678 fashion. The book by Davis and Brockwell (2002) includes a CD containing a time series 8679 program, ITSM, that among many other features, will fit a model of the form (1) or (3) in 8680 which the ut terms follow an ARMA model as in (2). The orders p and q may be specified 8681 by the user or selected automatically via AICC. Alternatively, the statistical language R 8682 (R Development Core Team, 2007) contains a function “arima” which allows for fitting 8683 these models by exact maximum likelihood. The inputs to the arima function include the 8684 time series, the covariates, and the orders p and q. The program calculates maximum 8685 likelihood/GLS estimates of the ARMA and regression parameters, together with their 8686 standard errors, and various other statistics including AIC. Although R does not contain 8687 an automated model-selection procedure, it is straightforward to write a short subroutine 8688 that fits the time series model for various values of p and q (for example, all values of p 8689 and q between 0 and 10) and then identifies the model with minimum AIC. This method 8690 has been routinely used for several of the following analyses. 8691 Do Not Cite or Quote 373 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8692 However, it is not always necessary to search through a large set of ARMA models. In 8693 very many cases, the AR(1) model in which p=1, q=0, captures almost all of the 8694 autocorrelation, in which case this would be the preferred approach. 8695 8696 In other cases, it may be found that there is cyclic behavior in the data corresponding to 8697 large-scale circulation indexes such as the Southern Oscillation Index (SOI – often taken 8698 as an indicator of El Niño) or the Atlantic Multidecadal Oscillation (AMO) or the Pacific 8699 Decadal Oscillation (PDO). In such cases, an alternative to searching for a high-order 8700 ARMA model may be to include SOI, AMO or PDO directly as one of the covariates in 8701 (2). 8702 8703 Two other practical features should be noted before we discuss specific examples. First, 8704 the methodology we have discussed assumes the observations are normally distributed 8705 with constant variances (homoscedastic). Sometimes it is necessary to make some 8706 transformation to improve the fit of these assumptions. Common transformations include 8707 taking logarithms or square roots. With data in the form of counts (such as hurricanes) a 8708 square root transformation is often made, because count data are frequently represented 8709 by a Poisson distribution, and for that distribution, a square root transformation is a so- 8710 called variance-stabilizing transformation, making the data approximately homoscedastic. 8711 8712 The other practical feature that occurs quite frequently is that the same linear trend may 8713 not be apparent through all parts of the data. In that case, it is tempting to select the start 8714 and finish points of the time series and recalculate the trend just for that portion of the Do Not Cite or Quote 374 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8715 series. There is a danger in doing this, because in formally testing for the presence of a 8716 trend, the calculation of significance levels typically does not allow for the selection of a 8717 start and finish point. Thus, the procedure may end up selecting a spurious trend. On the 8718 other hand, it is sometimes possible to correct for this effect, for example using a 8719 Bonferroni correction procedure. An example of this is given in our analysis of the 8720 heatwave index dataset below. 8721 8722 Example 1: Cold Index Data (Section 2.2.1) 8723 The data consist of the “cold index”, 1895-2005. A density plot of the data shows that the 8724 original data are highly right-skewed, but a cube-root transformation leads to a much 8725 more symmetric distribution (Figure A.1). 8726 8727 We therefore proceed to look for trends in the cube root data. 8728 8729 A simple OLS linear regression yields a trend of -.00125 per year, standard error .00068, 8730 for which the 2-sided p-value is .067. Recomputing using the minimum-AIC ARMA 8731 model yields the optimal values p=q=3, trend -.00118, standard error .00064, p-value 8732 .066. In this case, fitting an ARMA model makes very little difference to the result, 8733 compared with OLS. By the usual criterion of a .05 significance level, this is not a 8734 statistically significant result, but it is close enough that we are justified in concluding 8735 there is still some evidence of a downward linear trend. Figure A.2 illustrates the fitted 8736 linear trend on the cube root data. 8737 Do Not Cite or Quote 375 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8738 Example 2: Heat Wave Index Data (Section 2.2.1 and Fig. 2.3(a)) 8739 This example is more complicated to analyze because of the presence of several outlying 8740 values in the 1930s which frustrate any attempt to fit a linear trend to the whole series. 8741 However, a density plot of the raw data show that they are very right-skewed, whereas 8742 taking natural logarithms makes the data look much more normal (Figure A.3). 8743 Therefore, for the rest of this analysis we work with the natural logarithms of the heat 8744 wave index. 8745 8746 In this case there is no obvious evidence of a linear trend, either upwards or downwards. 8747 However, nonlinear trend fits suggest an oscillating pattern up to about 1960, followed by 8748 a steadier upward drift in the last four decades. For example, the solid curve in Figure 8749 A.4, which is based on a cubic spline fit with 8 degrees of freedom, fitted by ordinary 8750 linear regression, is of this form. 8751 8752 Motivated by this, a linear trend has been fitted by time series regression to the data from 8753 1960-2005 (dashed straight line, Figure A.4). In this case, searching for the best ARMA 8754 model by the AIC criterion led to the ARMA(1,1) model being selected. Under this 8755 model, the fitted linear trend has a slope of 0.031 per year and a standard error of .0035. 8756 This is very highly statistically significant – assuming normally distributed errors, the 8757 probability that such a result could have been reached by chance, if there were no trend, 8758 is of the order 10-18. 8759 Do Not Cite or Quote 376 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8760 We should comment a little about the justification for choosing the endpoints of the linear 8761 trend (in this case, 1960 and 2005) in order to give the best fit to a straight line. The 8762 potential objection to this is that it creates a bias associated with multiple testing. 8763 Suppose, as an artificial example, we were to conduct 100 hypothesis tests based on some 8764 sample of data, with significance level .05. This means that if there were in fact no trend 8765 present at all, each of the tests would have a .05 probability of incorrectly concluding that 8766 there was a trend. In 100 such tests, we would typically expect about 5 of the tests to lead 8767 to the conclusion that there was a trend. 8768 8769 A standard way to deal with this issue is the Bonferroni correction. Suppose we still 8770 conducted 100 tests, but adjusted the significance level of each test to .05/100=.0005. 8771 Then even if no trend were present, the probability that at least one of the tests led to 8772 rejecting the null hypothesis would be no more than 100 times .0005, or .05. In other 8773 words, with the Bonferroni correction, .05 is still an upper bound on the overall 8774 probability that one of the tests falsely rejects the null hypothesis. 8775 8776 In the case under discussion, if we allow for all possible combinations of start and finish 8777 dates, given a 111-year series, that makes for 111x110/2=6105 tests. To apply the 8778 Bonferroni correction in this case, we should therefore adjust the significance level of the 8779 individual tests to .05/6105=.0000082. However, this is still very much larger than 10-18 8780 The conclusion is that the statistically significant result cannot be explained away as 8781 merely the result of selecting the endpoints of the trend. 8782 Do Not Cite or Quote 377 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8783 This application of the Bonferroni correction is somewhat unusual – it is rare for a trend 8784 to be so highly significant that selection effects can be explained away completely, as has 8785 been shown here. Usually, we have to make a somewhat more subjective judgment about 8786 what are suitable starting and finishing points of the analysis. 8787 8788 Example 3: 1-day Heavy Precipitation Frequencies (Section 2.1.2.2) 8789 In this example we considered the time series of 1-day heavy precipitation frequencies 8790 for a 20-year return value. In this case, the density plot for the raw data is not as badly 8791 skewed as in the earlier examples (Figure A.5, left plot), but is still improved by taking 8792 square roots (Figure A.5, right plot). Therefore, we take square roots in the subsequent 8793 analysis. 8794 8795 Looking for linear trends in the whole series from 1895-2005, the overall trend is positive 8796 but not statistically significant (Figure A.6). Based on simple linear regression, the 8797 estimated slope is .00023 with a standard error of .00012, which just fails to be 8798 significant at the 5% level. However, time series analysis identifies an ARMA (5, 3) 8799 model, when the estimated slope is still .00023, the standard error rises to .00014, which 8800 is again not statistically significant. 8801 8802 However, a similar exploratory analysis to that in Example 2 suggested that a better 8803 linear trend could be obtained starting around 1935. To be specific, we have considered 8804 the data from 1934-2005. Over this period, time series analysis identifies an ARMA(1,2) 8805 model, for which the estimated slope is .00067, standard error .00007, under which a Do Not Cite or Quote 378 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8806 formal test rejects the null hypothesis of no slope with a significance level of the order of 8807 10-20 under normal theory assumptions. As with Example 2, an argument based on the 8808 Bonferroni correction shows that this is a clearly significant result even allowing for the 8809 subjective selection of start and finish points of the trend. 8810 8811 Therefore, our conclusion in this case is that there is an overall positive but not 8812 statistically significant trend over the whole series, but the trend post-1934 is much 8813 steeper and clearly significant. 8814 8815 Example 4: 90-day Heavy Precipitation Frequencies (Section 2.1.2.3 and Fig. 2.9) 8816 This is a similar example based on the time series of 90-day heavy precipitation 8817 frequencies for a 20-year return value. Once again, density plots suggest a square root 8818 transformation (the plots look rather similar to Figure A.5 and are not shown here). 8819 8820 After taking square roots, simple linear regression leads to an estimated slope of .00044, 8821 standard error .00019, based on the whole data set. Fitting ARMA models with linear 8822 trend leads us to identify the ARMA(3,1) as the best model under AIC: in that case the 8823 estimated slope becomes .00046 and the standard error actually goes down, to .00009. 8824 Therefore, we conclude that the linear trend is highly significant in this case (Figure A.7). 8825 8826 Example 5: Tropical cyclones in the North Atlantic (Section 2.1.3.1) 8827 This analysis is based on historical reconstructions of tropical cyclone counts described in 8828 the recent paper of Vecchi and Knutson (2007). We consider two slightly different 8829 reconstructions of the data, the “one-encounter” reconstruction in which only one Do Not Cite or Quote 379 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8830 intersection of a ship and storm is required for a storm to be counted as seen, and the 8831 “two-encounter” reconstruction that requires two intersections before a storm is counted. 8832 We focus particularly on the contrast between trends over the 1878-2005 and 1900-2005 8833 time periods, since before the start of the present analysis, Vecchi and Knutson had 8834 identified these two periods as of particular interest. 8835 8836 For 1878-2005, using the one-encounter dataset, we find by ordinary least squares a 8837 linear trend of .017 (storms per year), standard error .009, which is not statistically 8838 significant. Selecting a time series model by AIC, we identify an ARMA(9,2) model as 8839 best (an unusually large order of a time series model in this kind of analysis), which leads 8840 to a linear trend estimate of .022, standard error .022, which is clearly not significant. 8841 8842 When the same analysis is repeated from 1900-2005, we find by linear regression a slope 8843 of .047, standard error .012, which is significant. Time series analysis now identifies the 8844 ARMA(5,3) model as optimal, with a slope of .048, standard error .015, very clearly 8845 significant. Thus, the evidence is that there is a statistically significant trend over 1900- 8846 2005, though not over 1878-2005. 8847 8848 A comment here is that if the density of the data is plotted as in several earlier examples, 8849 this suggests a square root transformation to remove skewness. Of course the numerical 8850 values of the slopes are quite different if a linear regression is fitted to square root 8851 cyclones counts instead of the raw values, but qualitatively, the results are quite similar to Do Not Cite or Quote 380 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8852 those just cited – significant for 1900-2005, not significant for 1878-2005, after fitting a 8853 time series model. We omit the details of this. 8854 8855 The second part of the analysis uses the “two-encounter” data set. In this case, fitting an 8856 ordinary least-squares linear trend to the data 1878-2005 yields an estimated slope .014 8857 storms per year, standard error .009, not significant. The time series model (again 8858 ARMA(9,2)) leads to estimated slope .018, standard error .021, not significant. 8859 8860 When repeated for 1900-2005, ordinary least-squares regression leads to a slope of .042, 8861 standard error .012. The same analysis based on a time series model (ARMA(9,2)) leads 8862 to a slope of .045 and a standard error of .021. Although the standard error is much bigger 8863 under the time series model, this is still significant with a p-value of about .03. 8864 8865 Example 6: U.S. Landfalling Hurricanes (Section 2.1.3.1) 8866 The final example is a time series of U.S. landfalling hurricanes for 1851-2006 taken 8867 from the website http://www.aoml.noaa.gov/hrd/hurdat/ushurrlist18512005-gt.txt. The 8868 data consist of annual counts and are all between 0 and 7. In such cases a square root 8869 transformation is often performed because this is a variance stabilizing transformation for 8870 the Poisson distribution. Therefore, square roots have been taken here. 8871 8872 A linear trend was fitted to the full series and also for the following subseries: 1861-2006, 8873 1871-2006 and so on up to 1921-2006. As in preceding examples, the model fitted was 8874 ARMA (p, q) with linear trend, with p and q identified by AIC. Do Not Cite or Quote 381 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8875 For 1871-2006, the optimal model was AR(4), for which the slope was -.00229, standard 8876 error .00089, significant at p=.01. 8877 8878 For 1881-2006, the optimal model was AR(4), for which the slope was -.00212, standard 8879 error .00100, significant at p=.03. 8880 8881 For all other cases, the estimated trend was negative but not statistically significant. 8882 8883 Appendix A References 8884 8885 Brockwell, P.J. and Davis, R.A. (2002), Introduction to Time Series and Forecasting 8886 (Second edition). Springer, New York. 8887 8888 R Development Core Team (2007), R: A language and environment for statistical 8889 computing. R Foundation for Statistical Computing, Vienna, Austria, 8890 http://www.R-project.org. 8891 8892 Vecchi, G.S. and Knutson, T.R. (2007), On estimates of historical North Atlantic tropical 8893 cyclone activity. Manuscript, submitted for publication. 8894 8895 Wigley, T.M.L. (2006), Statistical Issues Regarding Trends. Appendix A (pp. 129-139) 8896 of Temperature Trends in the Lower Atmosphere: Steps for Understanding and 8897 Reconciling Differences. Thomas R. Karl, Susan J. Hassol, Christopher D. Miller, 8898 and William L. Murray, editors, 2006. A Report by the Climate Change Science 8899 Program and the Subcommittee on Global Change Research, Washington, DC. 8900 Do Not Cite or Quote 382 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8901 8902 8903 Figure A.1 Density plot for the cold index data (left), and for the cube roots of the same 8904 data (right). Do Not Cite or Quote 383 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8905 8906 8907 8908 Figure A.2 Cube root of cold wave index with fitted linear trend. Do Not Cite or Quote 384 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8909 8910 8911 8912 Figure A.3 Density plot for the heat index data (left), and for the natural logarithms of 8913 the same data (right). Do Not Cite or Quote 385 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8914 8915 8916 Figure A.4 Trends fitted to natural logarithms of heat index. Solid curve: non-linear 8917 spline with 8 degrees of freedom fitted to the whole series. Dashed line: linear trend fitted 8918 to data from 1960-2005. Do Not Cite or Quote 386 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8919 8920 8921 Figure A.5 Density plot for 1-day heavy precipitation frequencies for a 20-year return 8922 value (left), and for square roots of the same data (right). Do Not Cite or Quote 387 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8923 8924 8925 Figure A.6 Trend analysis for the square roots of 1-day heavy precipitation frequencies 8926 for a 20-year return value, showing estimated linear trends over 1895-2005 and 1934- 8927 2005. Do Not Cite or Quote 388 of 389 Public Review Draft CCSP SAP 3.3 August 16, 2007 8928 8929 8930 Figure A.7 Trend analysis for the square roots of 90-day heavy precipitation 8931 frequencies. 8932 Do Not Cite or Quote 389 of 389 Public Review Draft