KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
1 Climate Model Simulations of Stratospheric Geoengineering in the Arctic Spring 2 3 4 Ben Kravitz, Alan Robock, and Allison Marquardt 5 6 7 Department of Environmental Sciences, Rutgers University, New Brunswick, New Jersey 8 9 10 11 12 13 14 15 16 17 18 19 Submitted to Journal of Geophysical Research - Atmospheres 20 21 September, 2010 22 23 24 25 26 27 28 29 30 31 Ben Kravitz, Department of Environmental Sciences, Rutgers University, 14 College Farm 32 Road, New Brunswick, NJ 08901, USA. ([email protected]) (Corresponding 33 Author) 34 35 Alan Robock, Department of Environmental Sciences, Rutgers University, 14 College Farm 36 Road, New Brunswick, NJ 08901, USA. ([email protected]) 37 38 Allison Marquardt, Department of Environmental Sciences, Rutgers University, 14 College Farm 39 Road, New Brunswick, NJ 08901, USA. ([email protected]) 40 41 42
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43 Abstract 44 We use a general circulation model of Earth’s climate to conduct simulations of
45 stratospheric sulfate aerosol geoengineering in the Arctic spring to determine whether
46 geoengineering during the months of maximum insolation is as effective as geoengineering year-
47 round. As control cases, we simulate a global warming ensemble and an ensemble of global
48 warming combined with daily stratospheric injections of SO2 at high latitude, totaling 3 Tg SO2
49 per year. We compare these to two ensembles, each with global warming forcing and high
50 latitude stratospheric injections of 0.75 Tg SO2 per year: daily injections throughout April, May,
51 and June; and daily injections throughout April. These spring injection experiments show
52 smaller aerosol optical depth than the year-round injections, especially in the winter, during
53 which all of the sulfate aerosols from the spring injection experiments are removed each year.
54 They also show summer cooling over the Northern Hemisphere continents, as is seen in large
55 volcanic eruptions, although not as much as in the year-round injections. No significant
56 monsoonal precipitation perturbation is detected, in contrast to previous simulations with this
57 same model. Year-round injection results in an increase in Arctic sea ice from a control
58 scenario, and the spring injection experiments show reduced sea ice loss from the global
59 warming simulations. Further simulations are required, but these results suggest that while SO2
60 injections only in the spring are not as effective as year-round injections, a strategy of injections
61 in spring and summer combined would maximize the cooling of the aerosol cloud, requiring
62 slightly smaller total annual injections than a year-round strategy.
63
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64 1. Introduction
65 Geoengineering with stratospheric sulfate aerosols has been proposed [e.g., Crutzen,
66 2006] as a cheap [e.g., Robock et al., 2009], effective [e.g., Rasch et al., 2008], and temporary
67 [e.g., Wigley, 2006] means of reducing global average surface air temperature to alleviate
68 negative climate impacts from increasing greenhouse gas concentrations. In an effort to tailor
69 geoengineering and reduce the degree to which humans directly interfere with and modify the
70 climate, some have suggested geoengineering only in the Arctic [Caldeira and Wood, 2008].
71 They propose that this would have the effect of cooling the Northern Hemisphere continents and
72 potentially “saving” the Arctic sea ice, as is seen temporarily in the case of large volcanic
73 eruptions, but would not impact temperatures in the tropics or the Southern Hemisphere. Due to
74 the reduced area needing to be shaded by sulfate aerosols, geoengineering in the Arctic would in
75 theory require smaller injections of SO2 than geoengineering in the tropics.
76 Robock et al. [2008] performed simulations of both tropical and Arctic geoengineering.
77 They found features similar to those of large volcanic eruptions: summer cooling over the
78 Northern Hemisphere continents and weakening of the Indian/African summer monsoon, which
79 was more pronounced for the case of the Arctic injection.
80 These simulations by Robock et al. involved year-round injections of SO2. However, in
81 the Arctic, year-round injections would not be necessary, as there is no sunlight for the aerosols
82 to backscatter during the winter. This motivated our study to investigate Arctic geoengineering
83 that is tailored to backscatter solar radiation only during the summer, which is the period of
84 maximum insolation.
85 In addition to replicating the Arctic injection experiment from Robock et al. [2008], we
86 designed two additional experiments of geoengineering only in the Arctic spring. Assuming the
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87 same daily rate of injection as the year-round experiment, this would reduce the amount of SO2
88 that is injected into the stratosphere, lowering the cost and the degree to which humans directly
89 interfere with the climate system. However, we hypothesize that since the aerosols would be
90 present during summer, the radiative effects of the aerosols would be similar to those of a year-
91 round injection. We also wished to investigate whether the Asian/African monsoon system is
92 negatively impacted under these scenarios and whether they can prevent the loss of Arctic sea
93 ice.
94 2. Experiment
95 We conducted simulations with the coupled atmosphere-ocean general circulation model
96 ModelE, which was developed by the National Aeronautics and Space Administration Goddard
97 Institute for Space Studies [Schmidt et al., 2006]. We used the stratospheric version with 4°
98 latitude by 5° longitude horizontal resolution and 23 vertical levels up to 80 km. It is fully
99 coupled to a 4° latitude by 5° longitude dynamic ocean with 13 vertical levels [Russell et al.,
100 1995].
101 The aerosol module [Koch et al., 2006] accounts for SO2 conversion to sulfate aerosols,
102 as well as transport and removal of the aerosols. The chemical model calculates the sulfur cycle
103 in the stratosphere, where the conversion rate of SO2 to sulfate is based on the respective
104 concentrations of SO2 and the hydroxyl radical, the latter of which is prescribed [Oman et al.,
105 2006a]. We specified the dry aerosol effective radius to be 0.25 µm, which is the value used for
106 simulation of past volcanic eruptions and geoengineering. The model hydrates the aerosols
107 based on ambient humidity values according to formulas prescribed by Tang [1996], resulting in
108 a distribution of hydrated aerosols with an effective radius of approximately 0.30-0.35 µm,
109 which is consistent with the findings of Stothers [1997]. Radiative forcing from the aerosols is
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110 fully interactive with the atmospheric circulation and in our paper is the conventional one as
111 defined by the Intergovernmental Panel on Climate Change [IPCC, 2001], also called “adjusted
112 forcing” (Fa) by Hansen et al. [2005]. For more details, we refer the reader to Kravitz et al.
113 [2010a], which used the same model version and setup.
114 This version of ModelE has been successfully used in the past to simulate both volcanic
115 eruptions and geoengineering. Simulations have been conducted for the eruptions of Laki in
116 1783-1784 [Oman et al., 2006a, 2006b], Katmai in 1912 [Oman et al., 2005], Pinatubo in 1991
117 [Robock et al., 2007], and the recent eruption of Kasatochi in 2008 [Kravitz et al., 2010a]. In all
118 of these cases, ModelE was shown to be reliable in recreating the climate impacts of the
119 eruption. Moreover, Robock et al. [2008] used this model to simulate geoengineering, and
120 results from this study agreed with similar experiments performed by the Hadley Centre [Jones
121 et al., 2010]. Therefore, we are confident in this model’s ability to simulate geoengineering to a
122 degree of accuracy that is scientifically useful.
123 We used the same version of ModelE that was used by Robock et al. [2008], using the
124 same specifications except for two tuning parameters and the atmospheric and oceanic initial
125 conditions. Robock et al. used atmospheric and oceanic conditions from the year 1999, whereas
126 we used those conditions from the year 2007. The current version of ModelE was tuned by
127 modifying two parameters that change planetary albedo, and hence, the net radiation of the
128 planet. This model was tuned because Robock et al. detected a significant temperature trend
129 during the period over which they conducted their simulations, due to insufficient time allowed
130 for model spin-up. These tuning parameters modify the critical humidities for ice cloud and
131 water cloud condensation. Specifically, Robock et al. used tuning parameters U00ice=0.590 and
132 U00wtrx=1.33. The version used for the simulations in our study used parameters U00ice=0.595
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133 and U00wtrx=1.40. Before simulations were performed with these new parameters, the model
134 was spun-up for an additional 100 years. This resulted in a much smaller trend over the model
135 period 2007-2026, and, after conducting further simulation with a control run, the temperature
136 trend under these new tuning parameters is largely negligible. In Section 5, we discuss the
137 effects this tuning had on our results.
138 We began with a 6-member control ensemble of 20-year runs (2007-2026), during which
139 global greenhouse gas concentrations, as well as aerosol concentrations, remained fixed at
140 constant 2007 conditions. We then simulated a 6-member ensemble of 20-year runs covering the
141 same period, in which global greenhouse gas concentrations increased according to the IPCC’s
142 A1B scenario [IPCC, 2007]. The greenhouse gas concentrations at the beginning of the
143 simulation were prescribed to be January 1, 2007 levels, and they increased to the A1B
144 scenario’s estimation of December 31, 2026 levels by the end of the simulation.
145 We conducted three 6-member ensembles of 20-year runs to simulate geoengineering, all
146 of which had the A1B greenhouse gas concentrations as a background. One ensemble involved
147 daily injections of 0.0082 Tg of SO2 into one grid box centered at 66°N, 122.5°E in the Arctic,
148 distributed equally in the three model layers that cover an altitude of 10-16 km. This
149 corresponds to an annual injection rate of 3 Tg of SO2, so we refer to this ensemble in the rest of
150 the paper as 3 Tg. This ensemble is conducted in the same manner as the Arctic geoengineering
151 scenario described by Robock et al. [2008], albeit with different initial conditions and tuning
152 parameters, which we describe below. Another ensemble, referred to as AMJ, involved daily
153 injections of the same amount at the same spatial points, but only in the months April, May, and
154 June. This results in a total injection rate of 0.75 Tg of SO2 per year. The third ensemble,
155 referred to as Apr, also results in a total injection rate of 0.75 Tg of SO2 per year, but the daily
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156 injections occurred only in the month of April. This corresponds to a daily rate of injection of
157 0.0246 Tg, or three times the rate of the previous ensembles. The specifications for the different
158 ensembles are summarized in Table 1.
159 3. Climate Forcing
160 Figures 1 and 2 show the seasonal cycle, averaged over the second decade of simulation,
161 of sulfate aerosol optical depth and radiative forcing once the aerosol layer has achieved a steady
162 state. As with high latitude volcanic eruptions [Kravitz and Robock, 2010] and prior simulation
163 of Arctic geoengineering [Robock et al., 2008], the bulk of the aerosol cloud is mostly confined
164 to latitudes north of 30°N.
165 There is a large seasonal dependence of aerosol optical depth and, consequently, aerosol
166 radiative forcing. All geoengineering simulations show a strong summer peak in optical depth
167 during the summer, which is due to the chemical conversion time of SO2 to sulfate of 30-40 days
168 [McKeen et al., 1984]. In contrast to 3 Tg, AMJ and Apr show all sulfate aerosols are deposited
169 out of the atmosphere throughout the winter and are replenished each spring. In 3 Tg, aerosols
170 are created year-round in the mid-latitudes and throughout the summer, autumn, and early spring
171 in the high latitudes, resulting in a higher peak of aerosol optical depth and even some
172 maintenance of aerosol optical depth in the winter. AMJ and Apr do not have these sources of
173 year-round replenishment, so the aerosols are subjected to unmitigated large-scale removal, and
174 the magnitude of injection is insufficient to allow the aerosols to persist into the following spring
175 [Kravitz and Robock, 2010].
176 Even though the amount of SO2 injected is the same in both AMJ and Apr, the AMJ
177 ensemble reaches higher peaks of aerosol optical depth and radiative forcing. The AMJ peak
178 occurs in July, which is consistent with the one-month chemical lifetime of stratospheric SO2.
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179 This means that the daily injection rate and subsequent SO4 formation rate in the AMJ
180 experiment is greater than the late spring/early summer aerosol deposition rate at high latitudes.
181 The optical depths at mid-latitudes are nearly identical between the two spring injection
182 experiments, suggesting the difference between these two scenarios mostly occurs at high
183 latitudes. The optical depth from Apr is higher throughout the spring than AMJ, since the total
184 amount of SO2 injected under Apr is larger than AMJ until the end of June.
185 4. Climate Response
186 With both simulations of volcanic eruptions [Kravitz and Robock, 2010] and
187 geoengineering [e.g., Robock et al., 2008], an introduction of sulfate aerosols into the
188 stratosphere would cause an observable climate response if the injection is large enough, at the
189 proper time of year [Kravitz and Robock, 2010], or if the injection is continued for a long enough
190 period of time [Robock et al., 2010]. The main purpose of geoengineering would be to cool the
191 planet, so one of the primary climate responses should be a reduction in surface air temperature,
192 as is seen from large volcanic eruptions. This modifies dynamics in the Asian/African monsoon
193 region by reducing the land-ocean temperature gradient. Since this gradient is the primary driver
194 of monsoonal precipitation, geoengineering should weaken the monsoon. We discuss this
195 mechanism of precipitation reduction in more detail in the following section.
196 Figure 3 shows globally averaged temperature and precipitation over the simulated period
197 for all experiments. All three geoengineering scenarios show some cooling below A1B
198 temperatures, the strongest and most discernible cooling being from 3 Tg. The results from this
199 experiment are very similar to those in Robock et al. [2008]. Conversely, and contrarily to the
200 findings of Robock et al., none of the simulations, including the 3 Tg experiment, show a
201 reduction in globally averaged precipitation from a global warming situation, although all of the
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202 ensembles do show statistically significant anomalies when compared to the control ensemble.
203 Trenberth and Dai [2007] argue that geoengineering would indeed cause a reduction in
204 precipitation, as is seen in the case of large volcanic eruptions, which is in concordance with the
205 results of Robock et al.
206 The classical climate responses to large high latitude volcanic eruptions have spatial
207 patterns as well as global average anomalies. Specifically, these patterns are summer cooling
208 over the Northern Hemisphere continents and a weakening of the Indian/African monsoon
209 system, both of which were found by Robock et al. [2008] in their simulations of Arctic
210 geoengineering. Similarly, in our 3 Tg experiment, we found summer cooling over the Northern
211 Hemisphere continents (Figure 4). We found a weak reduction of the summer monsoon system
212 (Figure 5). Robock et al. found a much stronger reduction, but we suspect the differences
213 between their results and ours are due to model tuning, which took place between the simulations
214 performed by Robock et al. and this current set of simulations. We discuss this in greater detail
215 in Section 5. However, the Hadley Centre climate model, when performing tropical injection
216 simulations, did not show a significant precipitation response [Jones et al., 2010], in contrast to
217 Robock et al. and the arguments of Trenberth and Dai [2007]. Therefore, we are unable to
218 ascertain whether monsoonal disruption is a robust feature of geoengineering.
219 Both AMJ and Apr appear to show winter cooling over the Northern Hemisphere
220 continents. However, winter temperatures have a much higher natural variability than summer
221 temperatures, so these anomalies are not necessarily indicative of any climate response to spring
222 geoengineering. Our analysis (not pictured) of dynamics and snow and ice coverage did not
223 reveal any potential forcing that would cause such temperature anomalies. Moreover,
224 temperature anomalies of the magnitude seen in the DJF panels of Figure 4 are similar in
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225 magnitude to Antarctic temperature anomalies in the JJA panels, which would not be affected by
226 Arctic geoengineering. This leads us to conclude the Northern Hemisphere winter temperature
227 anomalies are due to internal variability.
228 5. The Effects of Model Tuning
229 As we discussed in Section 2, the model was tuned between the time Robock et al. [2008]
230 conducted their simulations and we conducted ours. This tuning was performed to resolve a
231 large temperature trend in the control run of Robock et al., but it also resulted in different
232 precipitation results between their Arctic geoengineering experiment and our 3 Tg experiment,
233 which replicates their experiment. To better determine how model tuning had an impact on our
234 precipitation results, we compared our control run ensemble with that of Robock et al. [2008].
235 Because the tuning parameters modify the critical humidities for ice cloud and water cloud
236 condensation, we chose to analyze total cloud cover differences between the two control runs.
237 Figure 6 shows the resulting effects of model tuning, which results in a large reduction of
238 summer monsoonal precipitation over Southeast Asia, India, and the Sahel. It also results in an
239 increase in cloud cover during the boreal summer over nearly all of the oceans, in some locations
240 by up to 10%. This resulted in a weaker monsoon than in the simulations of Robock et al.
241 [2008], meaning any further weakening due to a geoengineering aerosol layer would not show as
242 prominently in the results.
243 Because of these changes, we investigated how tuning the model impacted the model’s
244 accuracy when compared to climate data. To perform this analysis, we obtained two control runs
245 from Robock et al. [2008] of 40 years each. We also obtained monthly precipitation data from
246 the Global Precipitation Climatology Centre (GPCC), covering the years 1999-2007 [Schneider
247 et al., 2008; Rudolf and Schneider, 2005]. These years were chosen because this is the most
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248 recent precipitation data available from GPCC that would not have been directly affected by the
249 large El Niño of 1997-1998. To analyze total cloud cover, we obtained data from the
250 International Satellite Cloud Climatology Project (ISCCP) covering the same time period
251 [Rossow et al., 1996].
252 Figure 7 shows the comparison between both sets of simulations (pre- and post-tuning)
253 with the GPCC precipitation data. This figure only shows data over land areas, as precipitation
254 data was not available over the oceans. Figure 8 shows the same comparison but with the ISCCP
255 cloud coverage data. Qualitatively, both sets of simulations appear to show similar results.
256 Comparing with Figure 6, the anomalies in Figures 7 and 8 are much larger, indicating the
257 differences between the versions of the model are a great deal smaller in magnitude than the
258 differences between the model and observations.
259 To assess which version of the model more accurately represents these fields, we
260 calculated the difference between the model and the observations at each grid point to form root
261 mean square errors for each month of the climatologies. The results of these calculations are
262 reported in Table 2. The climatology of the model post-tuning shows lower errors than pre-
263 tuning for nearly all months and on average for both precipitation and cloud cover. Dividing by
264 the total number of grid boxes (land only for precipitation and globally for cloud cover), we can
265 obtain an average error for a given grid box, which is reported in the last line of Table 2. Using
266 reference values of 1 m of precipitation per year and 60% cloud cover in a given location, these
267 reported errors are quite large. An error of 2.65 mm day-1 of precipitation corresponds to
268 approximately 967 mm a-1, or approximately 97% of the reference value. An error of 15.98%
269 cloudiness is approximately 26% of the reference value. However, Figures 7 and 8 show these
270 values are heavily influenced by localized regions with very large error. Calculating from the
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271 totals, the difference between the model versions is approximately 3% for both precipitation and
272 cloud cover.
273 6. Saving the Arctic Sea Ice
274 Because of the negative radiative forcing in the Arctic due to the sulfate aerosols, the loss
275 of Arctic sea ice should slow under geoengineering, with a possible re-growth of sea ice. Table
276 3 shows average values and standard deviations of Northern Hemisphere September sea ice area
277 coverage for all of the experiments. Under A1B, Arctic sea ice coverage (averaged over the
278 second decade of the simulation) is reduced from the control case by over 105 km2 according to
279 the model results. Under 3 Tg, sea ice actually increases in areal extent. In AMJ and Apr, sea
280 ice coverage is still reduced from the control case, but not as much as in A1B. Apr appears to be
281 slightly more effective at preserving sea ice than AMJ, suggesting having a higher radiative
282 forcing in the early spring is more important to maintaining September sea ice coverage than
283 having a lower radiative forcing for a longer period of time. Of all of these values, only AMJ
284 and 3 Tg are separable by more than one standard deviation.
285 Vinnikov et al. [1999] performed long control run simulations using the GFDL model
286 [Manabe et al., 1991, 1992; Manabe and Stouffer, 1997; Haywood et al., 1997] to determine
287 whether a given Northern Hemisphere sea ice trend over a certain time interval would occur by
288 chance. Using the second decade of our simulations, with 6 ensemble members each, we can
289 compare the trends in Table 3 with their results for a trend over a 60 year period. The units
290 given in Vinnikov et al. for a trend are million square km per 10 years, so our results are directly
291 comparable by multiplying their values by 10. Converting the results of Vinnikov et al., over a
292 60 year time interval, a trend of approximately 0.5 105 km2 per 10 years will occur by chance
293 less than 10% of the time, and a trend of approximately 1.5 105 km2 per 10 years will occur by
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294 chance less than 0.1% of the time. With the exception of the Apr ensemble, all of our
295 simulations fall within this envelope of values. Specifically, the trend of 1.32 105 km2 over
296 simulation years 11-20, which is the result of the A1B ensemble, will occur by chance less than
297 0.5% of the time. For the geoengineering experiments, the trend of 0.77 105 km2 over
298 simulation years 11-20, which is the result of the 3 Tg ensemble, will occur by chance less than
299 3% of the time, and the trend of 0.63 105 km2 over simulation years 11-20, which is the result
300 of the AMJ ensemble, will occur by chance less than 5% of the time.
301 Comparing differences between the ensembles makes the results more stark. The trend
302 difference between the 3 Tg and A1B ensembles of 2.02 105 km2 over the second decade is
303 highly unlikely to have occurred by natural climate variability. Similarly, the difference between
304 the AMJ and A1B ensembles of 0.69 105 km2 over the second decade would occur less than
305 5% of the time by natural variability, and the difference between Apr and A1B of 1.02 105 km2
306 over the second decade would occur less than 1% of the time by natural variability. Due to the
307 large standard deviations of these trends, as reported in Table 3, we are hesitant to make firm
308 conclusions from this data. However, our results would suggest that a comparison between our
309 simulations and the results of Vinnikov et al. [1999] implies geoengineering in the Arctic spring
310 will prevent some loss of sea ice compared to an A1B scenario.
311 The version of ModelE used to generate this output has a problem with the sea ice
312 module which results in a reduced climate sensitivity in the Arctic, meaning the loss of sea ice
313 under anthropogenic warming is slower than should be experienced [G. Schmidt, personal
314 communication]. The effect of this model issue on our results would be a smaller response of
315 sea ice coverage to radiative forcing than should be experienced, meaning were the issue not
316 present, sea ice loss under the A1B scenario would be greater, and the recovery of the sea ice
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317 under geoengineering would be more pronounced. This could also have the effect of making the
318 sea ice recovery from geoengineering more statistically significant, although we are unable to
319 quantify this potential change in our results.
320 7. Discussion and Conclusions
321 We conclude that geoengineering with stratospheric aerosols in the Arctic spring, despite
322 taking advantage of the months of maximum insolation, is not as effective as year-round SO2
323 injections of the same daily rate at cooling the planet or preventing the loss of Arctic sea ice.
324 However, each of our spring injection experiments shows cooler temperatures and more Arctic
325 sea ice than the A1B ensemble, which are two of the desired purposes of geoengineering. Only
326 our 3 Tg experiment shows even a slight monsoonal reduction. We argue this difference
327 between our results and those of Robock et al. [2008] is due to model tuning, but we are unable
328 to conclude whether geoengineering would indeed cause a monsoonal disruption. According to
329 our model results, if the primary goal of a geoengineering policy were to save the Arctic sea ice,
330 the spring injection scenarios presented here appear to be well suited to satisfying this policy.
331 The effectiveness of AMJ and Apr could be increased by magnifying the summer
332 radiative forcing to levels seen in 3 Tg. This could be accomplished by increasing the daily rate
333 of injection or, in analogy to Kravitz and Robock [2010], extending the time of year over which
334 the injections occur. This latter means is potentially the more effective one, as it will counteract
335 some of the aerosol deposition that occurs throughout the year, and it will allow for aerosol
336 creation during months that have sunlight but do not experience maximum insolation. In either
337 case, this would involve an increase in the annual amount of SO2 injected, although the total
338 amount would still be less than year-round injection. Based on our findings here and the results
339 of Kravitz and Robock, we postulate a near optimal injection scenario would be at the same daily
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340 rate as the 3 Tg experiment, but with daily injections over the 6 month period of mid-March
341 through mid-September. However, further experimentation is needed to assess this hypothesis.
342 This study has a strong need for follow-up and improvement upon the results presented
343 here. First, to make the results comparable to those of Robock et al. [2008], the spring injection
344 experiments could be run with the version of the model prior to tuning to analyze precipitation
345 effects of geoengineering. However, the model was tuned to resolve a strong temperature trend,
346 and as we show, this tuning affected the model results. The model’s accuracy in resolving
347 precipitation and cloud coverage appears to agree better with observations as a result of the
348 tuning, but these fields are too different from the observations to conclusively assert that tuning
349 improved the model output. Moreover, geoengineering simulations of a similar nature need to be
350 conducted by multiple modeling groups to conclusively determine whether monsoonal disruption
351 is a robust feature of geoengineering. The Geoengineering Model Intercomparison Project
352 (GeoMIP) [Kravitz et al., 2010b] has placed this as one of its priorities, so this question may
353 soon be resolved. Second, this same study could be repeated on a different version of the model
354 in which the sea ice issue has been resolved in order to verify our hypotheses about how it
355 affected our results. We could additionally extend our runs past 2026 to obtain better statistical
356 significance.
357 Finally, Heckendorn et al. [2009] argue the aerosol size we used in our study is likely
358 much smaller than the aerosol size that would actually result from stratospheric geoengineering.
359 Using a more representative size would likely require larger injection amounts, as the scattering
360 efficiency and aerosol lifetime would both be reduced. However, since the aerosol layer in the
361 spring injection runs is replenished every year (Figure 2), perhaps the results of Heckendorn et
362 al. would not apply to these experiments, but would only apply to year-round injection scenarios.
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363 Although this study is part of an investigation into the optimal means by which the
364 climate could be geoengineered, we can still find many reasons why geoengineering may not be
365 a good idea [e.g., Robock, 2008]. Also, compared to some large volcanic eruptions, for which
366 the current observation system already has large gaps [Kravitz et al., 2010a], the amount of
367 climate interference suggested in this paper is small and would be difficult to observe and
368 analyze [Robock et al., 2010].
369
370 Acknowledgments. We thank Georgiy Stenchikov for his help in the initial stages of this study
371 and Gavin Schmidt for detailed description of the Arctic climate sensitivity issue in ModelE.
372 Model development and computer time at Goddard Institute for Space Studies are supported by
373 National Aeronautics and Space Administration climate modeling grants. This work is
374 supported by NSF grant ATM-0730452.
375
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475 Table 1. Specifications for the ensembles used in this experiment. All ensembles have 6
476 members and span the years 2007-2026 (20 year simulations). Each ensemble is described by its
477 background greenhouse gas (GHG) scenario, the daily rate of injection of SO2 into the lower
478 stratosphere of one grid box centered at 66°N, 122.5°E, the days of the year over which the SO2
479 was injected, and the total rate of injection per year.
480
Ensemble GHG Daily injection Days of injection Total annual
background rate (Tg day-1) in a given year injection rate (Tg a-1)
constant 2007 Control 0 N/A 0 conditions
A1B A1B 0 N/A 0
3 Tg A1B 0.0082 Jan 1 - Dec 31 3.0
AMJ A1B 0.0082 Apr 1 - Jun 30 0.75
Apr A1B 0.0246 Apr 1 - Apr 30 0.75
481
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482 Table 2. Errors for control run climatologies compared with data. Pre-tuning results are
483 simulations performed by Robock et al. [2008] and are calculated from two 40-year simulations.
484 Post-tuning results are simulations from this experiment, i.e., six 20-year simulations. GPCC
485 data [Schneider et al., 2008; Rudolf and Schneider, 2005] are a climatology calculated from the
486 years 1999-2007. These data were regridded by box averaging to the model’s resolution (4°
487 latitude by 5° longitude) and are only available over land areas. ISCCP data [Rossow et al.,
488 1996] are a climatology calculated over the same period. They were also regridded by box
489 averaging to the model’s resolution, and data are available globally. Error values were
490 calculated by taking the root mean square of the differences in climatologies for each grid, with
491 averaging done over the total number of grid boxes at this resolution (3312 for the cloud cover
492 variable to represent global coverage, and 1108 for precipitation to represent land areas only).
493 Values are rounded to two decimal places.
494
Precipitation Precipitation Cloud Cover (%) Cloud Cover (%) (mm/day) (mm/day) Pre-tuning Post-tuning Month Pre-tuning Post-tuning minus ISCCP minus ISCCP minus GPCC minus GPCC
January 2.61 2.54 17.51 17.73
February 2.61 2.53 16.41 16.41
March 2.52 2.44 15.79 14.53
April 2.70 2.62 15.40 14.29
May 2.81 2.75 15.99 15.02
June 2.80 2.72 16.56 15.71
July 3.30 3.12 17.32 16.57
August 3.27 3.12 16.77 16.03
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September 2.95 2.84 15.91 14.86
October 2.51 2.46 15.17 14.67
November 2.46 2.34 17.15 17.46
December 2.43 2.32 18.19 18.43 Annual 2.75 2.65 16.52 15.98 Average
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495 Table 3. Northern Hemisphere September sea ice coverage anomalies for all detrended
496 ensembles. All values have units 105 km2 and are rounded to two decimal places. Standard
497 deviation (σ) is calculated from the difference between September for each year and the average
498 September value, giving 120 degrees of freedom for the years 1-20 average (6 ensemble
499 members) and 60 degrees for the years 11-20 average.
500
Average σ Average σ Ensemble (Years 1-20) (Years 1-20) (Years 11-20) (Years 11-20)
A1B - Control -1.19 9.71 -1.32 3.37
3 Tg - Control 0.30 2.08 0.77 0.54
AMJ - Control -0.42 2.20 -0.63 0.83
Apr - Control -0.30 3.02 -0.30 1.03
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501
502
503 Figure 1. Seasonal cycle of sulfate aerosol optical depth (λ=550 nm) and surface shortwave
504 radiative forcing at the surface due to sulfate aerosols. Seasonal cycle is calculated from an
505 average of years 11-20 of the simulation. Shaded areas are ±1 standard deviation, calculated
506 from the different ensemble members (6 degrees of freedom). All values shown are Northern
507 Hemisphere averages.
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508
509
510 Figure 2. Seasonal cycle of sulfate aerosol optical depth and surface shortwave radiative forcing
511 at the surface due to sulfate aerosols. Seasonal cycle is calculated from an average of years 11-
512 20 of the simulation. All plots shown are zonal averages. The Southern Hemisphere is not
513 shown, as all values are zero at those latitudes.
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514
515 Figure 3. Globally averaged surface air temperature and precipitation due to geoengineering.
516 All values shown are running 12 month means, averaged from the current month through the
517 following 11 months (t+0 through t+11). Yellow shading shows the range of the individual
518 ensemble members of A1B minus control. Orange lines indicate ±1.96σ of a 12 month running
519 mean of the variability of the control ensemble.
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520
521
522 Figure 4. Spatial plots of surface air temperature, showing differences between the
523 geoengineering ensembles and the A1B ensemble. All panels show averages of years 11-20 of
524 the simulations. The left column shows summer averages (JJA), and the right column shows
525 winter averages (DJF). Grey hatching is meant to obscure values that are not statistically
526 significant at the 95% confidence level, calculated using a Student's t test.
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527
528 Figure 5. Spatial plots of precipitation, showing differences between the ensembles. All panels
529 show summer (JJA) averages, temporally averaged over years 11-20 of the simulations. Grey
530 hatching is meant to obscure values that are not statistically significant at the 95% confidence
531 level, calculated using a Student's t test.
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532
533 Figure 6. Comparison of control run ensembles from Robock et al. [2008], which were
534 performed before model tuning, and this series of experiments, which were done after model
535 tuning. The ensemble of Robock et al. has two members of 40 years each. This series has six
536 members of 20 years each. Both panels show summer (JJA) averages. Grey hatching denotes
537 values that are not significant at the 95% confidence interval, as determined by a Student's t-test.
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538
539
540 Figure 7. Spatial patterns of precipitation climatologies from Robock et al. [2008] and this set of
541 experiments compared with gridded precipitation data from GPCC, which was regridded to
542 match the model's resolution of 4° latitude by 5°longitude. Only values over land are shown, as
543 GPCC does not have precipitation data available for the oceans. Climatologies were calculated
544 from two 40-year control ensembles in Robock et al. and six 20-year ensembles for this
545 experiment. GPCC climatology was calculated from the years 1999-2007.
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546
547
548 Figure 8. Spatial patterns of total cloud cover climatologies from Robock et al. [2008] (pre-
549 tuning) and this set of experiments (post-tuning) compared with gridded precipitation data from
550 ISCCP, which was regridded to match the model's resolution of 4° latitude by 5° longitude.
551 Climatologies were calculated from two 40-year control ensembles in Robock et al. and six 20-
552 year ensembles for this experiment. ISCCP climatology was calculated from the years 1999-
553 2007.
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