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2001 El Nino and the southern oscillation - surface air temperature implications for western Canada
Budikova, Dagmar
Budikova, D. (2001). El Nino and the southern oscillation - surface air temperature implications for western Canada (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/11622 http://hdl.handle.net/1880/40763 doctoral thesis
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El Nino and the Southern Oscillation - Surface Air Temperature
Implications for Western Canada
by
Dagmar Budikova
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
DEPARTMENT OF GEOGRAPHY
CALGARY, ALBERTA
January, 2001
© Dagmar Budikova 2001 ABSTRACT
The ENSO cycle made up of warm, cold and neutral phases is a primary contributor to global interannual climate variability. The release of excess energy from the Pacific Ocean to the atmosphere modifies air circulation patterns as far as Canada. Detailed understanding of El Nino's signatures on Canadian climates would help farmers, foresters, fishermen, and numerous government agencies design practices to minimize costs and maximize profit from El Nino. The objective of this study is to further the understanding of El Nino's influence on surface air temperatures across Western Canada in space and time. Twelve events between 1950 and 1996 were used. Statistical analyses and GIS technology helped fulfill the objective. Results indicate that there is a significant reduction in year-to-year and place-to-place surface air temperature signal variation throughout the area from neutral to El Nino periods, especially during winters. This suggests that the primary mechanism of air circulation across the study area is significantly moderated during El Nino periods, as weather becomes generally more stable. There are three sub-regions within the study area that show distinct El Nino signatures. These are Regions I, and II that are situated south of 51°N, and Region III, that represents the northwestern section of the area, above 51°N. Within each sub-region, two signal types exist and are distinguished by winter conditions. Warm winters, referred to as Type W, are most prominent to the southern sections of the area, whereas cold winters, or Type C signals are more common to the north. The presence of these signal types has been tied to El Nino. Findings converge to suggest that El Nino-related winter surface air temperatures over Western Canada may be predictable several seasons in advance, more so than during neutral periods.
in ACKNOWLEDGEMENTS
Several parties and individuals deserve to be acknowledged for their support of this work. The Meteorological Service of Canada, formerly known as Atmospheric Environment Canada provided generous funding for a period of three years through a series of post-graduate scholarships in Atmospheric Sciences. The Department of Geography provided financial support through teaching assistantships, graduate research scholarships and an honorary stipend. I am equally grateful to Lawrence Nkemdirim, my supervisor for monetary help and for devoting the necessary time to help bring this work to completion. During my stay at Calgary, I quickly learnt that our Department had invaluable support staff. The help of at least some of these people should also be recognized. Medina Hanson's superior knowledge of GIS and related technologies sped up the process of locating digital basemaps. Robin Poitras' knowledge of graphic arts and photography and his willingness to lend a hand at crucial moments always helped me look better when showing off my work to the outside world. Rick Smith, whose outstanding knowledge in data modeling, computer maintenance and what seemed an unlimited willingness to listen helped me get through the day-to-day challenges Thank you all.
IV To my parents - Jiri & Vojteska
Thank you
v TABLE OF CONTENTS
Approval Page "
Abstract »i
Acknowledgements iv
Dedication v
Table of Contents vi
List of Tables x
List of Figures xni
List of Videos xxiii
List of Symbols and Acronyms xxiv
1. EL NINO/SOUTHERN OSCILLATION (ENSO) 1
1.1. INTRODUCTION 1 1.2 EL NINO/SOUTHERN OSCILLATION - HISTORY 1 1.2.1. A "composite" El Nino 7 1.3. GLOBAL EL NINO CLIMATE-RELATED IMPACTS 10 1.4. THEORIES OF TELECONNECTIONS 12 1.5. EL NINO VARIABILITY 19 1.5.1. Observed changes in the character of ENSO 19 1.5.2. Recent theories on ENSO within global climate change 20
2. ENSO AND CLIMATE OF WESTERN CANADA 23
2.1 INTRODUCTION 23 2.2 STUDY AREA: WESTERN CANADA 23 2.2.1 Physiography 23 2.2.2 Vegetation 28 2.2.3 Climate 29 2.2.3.1 General Circulation 29 2.2.3.2 Surface Temperature and Precipitation Patterns 31 2.2.3.3 Climate Regions 33 2.2.3.3.1 Pacific Canada 33
vi 2.2.3.3.2 Cordilleran Canada 35 2.2.3.3.3 The Prairies 36 2.2.3.3.4 Arctic and Boreal climate 36 2.3 SOCIO-ECONOMIC IMPACTS OF EL NINO 37 2.4 EL NINO AND CANADIAN CLIMATE 38 2.5 THE PROBLEM 40 2.6 PROJECT OBJECTIVES AND METHODOLOGIES 41
3. DATA ISSUES 42
3.1 DATA ACQUISITION AND PREPARATION 42 3.2 QUALITY CONTROL 42 3.1.1 Data Homogenization 45 3.1.2 Data Estimation 51 3.3 TREND REMOVAL 55 3.4 SPATIAL INTERPOLATION 57
4. PRINCIPAL METHODS 65
4.1 INTRODUCTION 65 4.2 DATABASE ESTABLISHMENT 65 4.3 GEOGRAPHIC INFORMATION SYSTEMS (GIS) 71 4.3.1 Data exploration, visualization, and analysis using GIS 72 4.3.2 Dynamic mapping 73 4.4 EIGENVECTORREGIONALIZATION 75 4.5 MANN-WHITNEY U TEST 76 4.6 PAIRED DATA ANALYSIS 77 4.6.1 Pearson's correlation coefficient 77 4.6.2 Spearman's correlation coefficient 78 4.6.3 Cross-correlation 78
5. EL NINO DEFINITION AND SIGNAL CALCULATION 79
5.1 INTRODUCTION 79 5.2 DEFINITIONS OF ENSO 79 5.3 RESEARCH ENSO DEFINITION 82 5.3.1 Classification of S ST time series 82 5.3.2 El Nino in Canada 84 5.3.2.1 El Nino years 85 5.3.2.2 La Nina years 86 5.3.2.3 Neutral years 87 5.3.3 El Nino signal calculations 89 5.3.4 Calculation of El Nino signal in surface temperature records 89
vii 5.3.5 Signal analysis 94 5.3.5.1 General synthesis of El Nino-related signal 94 5.3.5.2 Differentiation of El Nino, La Nina and neutral signal time series 94 5.3.5.3 El Nino signal and SSTs in tropical Pacific ocean 96 5.4 SUMMARIES AND CONCLUSIONS 98
6. SIGNAL VARIATION ANALYSIS 100
6.1 INTRODUCTION 100 6.2 COMPOSITE MAPS 100 6.2.1 El Nino periods 102 6.2.2 Comparison to Neutral periods 106 6.3 SUMMARIES AND CONCLUSIONS 107
7. SIGNAL REGIONALIZATION AND CLUSTERING I 108
7.1 INTRODUCTION l°8 7.2 EIGENVECTORREGIONALIZATION 108 7.3 TEMPORAL CLUSTERING 113 7.3.1 Region I 115 7.3.2 Region 11 12° 7.3.3 Region III 123 7.4 SUMMARIES AND CONCLUSIONS 128
8 GRID-SCALE SURFACE AIR TEMPERATURE ANALYSIS 134
8.1 INTRODUCTION 134 8.2 METHODOLOGY 134 8.3 REGION 1 135 8.3.1 Type W signal 135 8.3.2 Type C signal 144 8.4 REGION II 144 8.4.1 Type W signal 144 8.4.2 Type C signal 153 8.5 REGION m 153 8.5.1 Type W signal 153 8.5.2 Type C signal 162 8.6 SUMMARY AND CONCLUSIONS 162
9. SPATIO-TEMPORAL SIGNAL ANALYSIS: REGION 1 166
9.1 INTRODUCTION 166 9.2 CONSTRUCTION OF ANIMATED SEQUENCE 166
viii 9.3 TYPE W SIGNAL 169 9.4 SUMMARY AND CONCLUSIONS 170
10. ASSOCIATION BETWEEN EL NINO AND SIGNALS IN SURFACE AIR TEMPERATURE 172
10.1 INTRODUCTION 172 10.2 DATA 172 10.2.1 Sea Surface Temperature Anomalies (SSTAs) 173 10.2.2 Southern Oscillation Index (SOI) 173 10.2.3 Sea Level Pressure over Indonesia (ZIND) 177 10.2.4 Sea Level Pressure over the Eastern Pacific Basin (ZEAS) 177 10.2.5 Equatorial Southern Oscillation Index (ESOI) 179 10.2.6 Multivariate ENSO Index (MEI) 182 10.3 METHODS 183 10.4 RESULTS AND DISCUSSION 188 10.4.1 Region I 188 10.4.1.1 Winter [DJF(+1)] 188 10.4.1.2 Fall [SON(0)] 189 10.4.1.2.1 Event Classification 191 10.4.1.3 Spring [MAM(+1)] 195 10.4.1.4 Summer [JJA( 11)| 195 10.4.2 Region II 195 10.4.2.1 Winter [DJF(+1)] 195 10.4.2.1.1 Event Classification 197 10.4.2.2 Fall [SON(0)] 197 10.4.2.3 Spring [MAM(+1)] 200 10.4.2.4 Summer [JJA(+1)] 209 10.4.3 Region III 210 10.4.3.1 Winter [DJF(+1)] 210 Event Classification 211 10.4.3.2 Fall [SON(0)] 213 10.4.3.3 Spring [MAM(+1)J 215 10.4.3.4 Summer [JJA(+1)] 215 10.5 SUMMARIES AND CONCLUSIONS 215
11. SUMMARY AND CONCLUSIONS 222
11.1 INTRODUCTION 222 11.2 PRINCIPAL FINDINGS AND THEIR SIGNIFICANCE 223 11.3 UNANSWERED/NEW QUESTIONS STEMMING FROM THIS RESEARCH 224
REFERENCES 226
ix LIST OF TABLES
Table Description Page
Table 1-1 Upper-level northern-hemispheric circulation anomalies associated with an El Nino event 14
Table 2-1 Mean monthly precipitation and temperature anomalies for Southern Alberta during the course of a major El Nino event 39
Table 3-1 Types of quality control procedures imposed on data 42
Table 3-2 Final stations chosen for analysis of surface temperature 52
Table 3-3 Magnitude of trend (°C/year) removed from original surface temperature data. 1950-1996 59
Table 5-1 El Nino periods. Months associated with El Nino conditions in the tropical Pacific 86
Table 5-2 La Nina periods. Months associated with La Nina conditions in the tropical Pacific 88
Table 5-3 Neutral periods during which the tropical Pacific was neither in El Nino nor La Nina phases 88
Table 5-4 Mean monthly and seasonal El Nino signal measured during El Nino years in surface temperature records 90
Table 5-5 Descriptive statistics of mean signal during Neutral, El Nino and La Nina years across Western Canada 95
Table 5-6 Multiple comparison test - ANOVA results to test difference between El Nino, La Nina and Neutral surface temperature signal magnitudes across Western Canada 95
Table 6-1 Results of signal standard deviation analysis for Western Canada 103
Table 7-la Results of Mann-Whitney statistics for Region I - Type W signal 117
Table 7-lb Results of Mann-Whitney statistics for Region I - Type C signal 117
x Table Description Page
Table 7-1 c Results of Mann-Whitney statistics for Region I - All 12 El Nino events 119 Table 7-2a Results of Mann-Whitney statistics for Region II - Type W signal 119
Table 7-2b Results of Mann-Whitney statistics for Region II - Type C signal 122
Table 7-2c Results of Mann-Whitney statistics for Region II - All 12 El Nino
events 122
Table 7-3a Results of Mann-Whitney statistics for Region III - Type W signal .... 125
Table 7-3b Results of Mann-Whitney statistics for Region III - Type C signal 125
Table 7-3c Results of Mann-Whitney statistics for Region III - All 12 El Nino
events 126
Table 7-4 El Nino event classification in each region 129
Table 7-5 Frequency of occurrence of Type W and Type C El Nino events
between 1950 and 1996 across regions of Western Canada 132
Table 8-1 Key El Nino months for each region and El Nino type 163
Table 10-1 Association between El Nino signal during SON (+1) season and selected tropical Pacific variables 184 Table 10-2 Association between El Nino signal during DJF (+1) season and selected tropical Pacific variables 185 Table 10-3 Association between El Nino signal during MAM (+1) season and selected tropical Pacific variables 186 Table 10-4 Association between El Nino signal during JJA (+1) season and selected tropical Pacific variables 187
Table 10-5 Sea level pressure tendencies between DJF(0) and FMA(0) across the equatorial Pacific 192
Table 10-6 Number of tropical variables reported to have significant relationship to surface temperature signal data across Western Canada during El Nino years 219
xi Table Description Page
Table 10-7 Type of tropical variable reported to have significant relationship to surface temperature signal data across Western Canada during El Nino years 220
Table 10-8 Frequency of each tropical variable reported to have significant relationship to surface temperature signal data across Western Canada during El Nino and non-ENSO periods 220
X!l LIST OF FIGURES
Figure Description Page
Figure 1-1 Sea surface temperature conditions across tropical Pacific Ocean -
neutral and El Nino periods 2
Figure 1-2 The Southern Oscillation 4
Figure 1 -3 Oceanic and atmospheric conditions common to the tropical Pacific during non-El Nino and during El Nino periods 5 Figure 1-4 Time series of standardized sea level pressure values at Darwin, Australia, and standardized sea surface temperature values in central tropical Pacific ocean 6
Figure 1-5 Seasonal sea surface temperature changes during a typical El Nino episode as measured along the coast of South America 9
Figure 1-6 El Nino-related impacts on global climates 11
Figure 1-7 A schematic diagram of the Pacific North American (PNA) pattern of middle and upper-tropospheric geopotential height anomalies during northern hemisphere winter that coincides with El Nino 15
Figure 1-8 Positive phase of the Tropical/Northern Hemisphere (TNH) teleconnection pattern for January 18
Figure 1-9 Positive phase of the Western Pacific (WP) teleconnection pattern for January 18
Figure 2-1 Map of Western Canada 24
Figure 2-2 Topography of Western Canada 25
Figure 2-3 Hydrology of Western Canada 27
Figure 2-4 Vegetation classes of study area 30
Figure 2-5 Climate regions of study area 30
xiii Figure Description Page
Figure 2-6 Mean annual temperature and total precipitation map of study area 32
Figure 2-7 Climographs for principal stations across study area 34
Figure 3-1 Control chart used to examine datasets for outlier values 44
Figure 3-2 Surface temperature station groups defined for homogenization and
estimation procedures 47
Figure 3-3 Control charts used to test for homogeneity in temperature records 49
Figure 3-4 Station distribution and data availability. Surface temperature 50
Figure 3-5 Surface temperature data for January at Calgary International Airport. Shown are original and detrended time series 56 Figure 3-6 Mean amount of monthly and seasonal trend (°C/year) removed from
original surface temperature data 58
Figure 3-7 Spatial interpolation of station surface temperature data 64
Figure 4-1 Types of data sorts used in climate research - Station sort 66
Figure 4-2 Types of data sorts used in climate research - Time series sort 67
Figure 4-3 Types of data sorts used in climate research - Space-time sort 69
Figure 4-4 Final format of surface temperature database 70
Figure 5-1 Partitioning of tropical Pacific basin into 4 Nino regions that provide
information regarding the state of ENSO 81
Figure 5-2a Time series of Nino 34 region SST anomalies 83
Figure 5-2b Time series of Nino 3 region SST anomalies 83
Figure 5-3 A composite of El Nino-related SST anomalies between 1950 and 1996 for the Nino 34 region 84 Figure 5-4 Sea surface temperature anomalies during May 1988 - La Nina 87
xiv Figure Description Page
Figure 5-5 Proportion of months classified as El Nino, La Nina, and Neutral periods between 1950 and 1996 89
Figure 5-6 Mean monthly time series of El Nino signals between Sep(0) and Aug(+1) for Neutral, El Nino, and La Nina years across Western Canada 95
Figure 5-7 Association between mean monthly El Nino surface temperature signal across Western Canada and sea surface temperature anomalies measured across Nino 34 region between Sep(0) and Aug(+1) 97
Figure 5-8 Association between time series of El Nino surface temperature signal in Western Canada and sea surface temperature anomalies across Nino 34 region (January 1950 to December 1996) 97
Figure 6-1 Assessment of spatial and temporal variation of El Nino surface temperature related signal across Western Canada 101
Figure 6-2 Signal standard deviation for all 12 El Ninos across Western Canada - January 104
Figure 6-3 Mean monthly and seasonal values of signal standard deviation for
Western Canada for all 12 El Nifios between Sep(O) and Aug(+1) 105
Figure 6-4 Monthly change in standard deviation from neutral to El Nino years .... 106
Figure 7-1 Analysis of El Nino signal in surface temperature records across Western Canada . An overview 108 Figure 7-2 Eigenvector regionalization analysis of El Nino signal in surface air temperature data across Western Canada 109
Figure 7-3 Relationship between calculated component score values and mean
regional monthly El Nino signal for component 1 110
Figure 7-4 Results of El Nino regionalization via eigenvector analysis Ill
Figure 7-5 Loading plots from eigenvector regionalization analysis results 112
xv Figure Description Page
Figure 7-6 Clustering of similar patterns of evolution between Sep(O) and Aug (+1) for each of the three significant regions in Western Canada 114
Figure 7-7 Mean monthly component scores for each of the two types of El Nino signal (W and C) for Region I 115
Figure 7-8a Type W evolutionary pattern of El Nino across Region I 116
Figure 7-8b Type C evolutionary pattern of El Nino across Region I 116
Figure 7-9 El Nino events that did not fit into either Type W or Type C El Nino evolutionary pattern in Region I 118
Figure 7-10 Mean monthly component scores for each of the two types of signal (W
and C) for Region II 120
Figure 7-11 a Type W evolutionary pattern of El Nino across Region II 121
Figure 7-1 lb Type C evolutionary pattern of El Nino across Region II 121
Figure 7-12 Mean monthly component scores for each of the two types of El Nino
(W and C) for Region III 123
Figure 7-13a Type W evolutionary pattern of El Nino across Region III 124
Figure 7-13b Type C evolutionary pattern of El Nino across Region III 124
Figure 7-14 El Nino events that did not fit into either Type W or C El Nino evolutionary patterns for Region III 127 Figure 7-15a Standardized anomalies in 500 hPa height surfaces during events when all 3 subregions experienced warm winters 130 Figure 7-15b Standardized anomalies in 500 hPa height surfaces during events when 2 out of 3 subregions experienced warm winters 130
Figure 7-15c Standardized anomalies in 500 hPa height surfaces during events when all 3 subregions experienced cold winters 131
xvi Figure Description Page
Figure 7-15d Standardized anomalies in 500 hPa height surfaces during events when
all 2 out of 3 subregions experienced cold winters 131
Figure 8-1 Schematic of grid-scale El Nino signal analysis 134
Figure 8-2a El Nino signal magnitude and Mann-Whitney significance - Region I. Type W signal. Sep(0)-Nov(0) 136 Figure 8-2b El Nino signal magnitude and Mann-Whitney significance - Region I. Type W signal. Dec(0)-Feb(+1) 137
Figure 8-2c El Nino signal magnitude and Mann-Whitney significance - Region I. Type W signal. Mar(+1)-May(+1) 138
Figure 8-2d El Nino signal magnitude and Mann-Whitney significance - Region I. Type W signal. Jun(+1)-Aug(+1) 139
Figure 8-3a El Nino signal magnitude and Mann-Whitney significance - Region I. Type C signal. Sep(0)-Nov(0) 140
Figure 8-3b El Nino signal magnitude and Mann-Whitney significance - Region I. Type C signal. Dec(0)-Feb(+1) 141
Figure 8-3c El Nino signal magnitude and Mann-Whitney significance - Region I. Type C signal. Mar(+1)-May(+1) 142
Figure 8-3d El Nino signal magnitude and Mann-Whitney significance - Region 1 Type C signal. Jun(+1)-Aug(+1) 143
Figure 8-4a El Nino signal magnitude and Mann-Whitney significance - Region II. Type W signal. Sep(0)-Nov(0) 145
Figure 8-4b El Nino signal magnitude and Mann-Whitney significance - Region II. Type W signal. Dec(0)-Feb(+1) 146
Figure 8-4c El Nino signal magnitude and Mann-Whitney significance - Region II. Type W signal. Mar(+1)-May(+1) 147
Figure 8-4d El Nino signal magnitude and Mann-Whitney significance - Region II. Type W signal. Jun(+1)-Aug(+1) 148
xvii Figure Description Page
Figure 8-5a HI Nino signal magnitude and Mann-Whitney significance - Region 11 Type C signal. Sep(0)-Nov(0) 149
Figure 8-5b El Nino signal magnitude and Mann-Whitney significance - Region II. Type C signal. Dec(0)-Feb(+1) 150
Figure 8-5c El Nino signal magnitude and Mann-Whitney significance - Region II. Type C signal. Mar(+1)-May(+1) 151
Figure 8-5d El Nino signal magnitude and Mann-Whitney significance - Region II. Type C signal. Jun(+1)-Aug(+1) 152
Figure 8-6a El Nino signal magnitude and Mann-Whitney significance - Region III. Type W signal. Sep(0)-Nov(0) 154
Figure 8-6b El Nino signal magnitude and Mann-Whitney significance - Region III. Type W signal. Dec(0)-Feb(+1) 155
Figure 8-6c El Nino signal magnitude and Mann-Whitney significance - Region III. Type W signal. Mar(+1)-May(+1) 156
Figure 8-6d El Nino signal magnitude and Mann-Whitney significance - Region III. Type W signal. Jun(+1)-Aug(+1) 157
Figure 8-7a El Nino signal magnitude and Mann-Whitney significance - Region III. Type C signal. Sep(0)-Nov(0) 158
Figure 8-7b El Nino signal magnitude and Mann-Whitney significance - Region III. Type C signal. Dec(0)-Feb(+1) 159
Figure 8-7c El Nino signal magnitude and Mann-Whitney significance - Region III. Type C signal. Mar(+1)-May(+1) 160
Figure 8-7d El Nino signal magnitude and Mann-Whitney significance - Region
III. Type C signal. Jun(+1)-Aug(+1) 161
Figure 8-8 Positive phases of WP and NP patterns during July 164
Figure 9-1 Type W El Nino composite maps for Region I 167
xviii Figure Description Page
Figure 9-2 A frame from the animated sequence of Type W El Nino 168
Figure 10-la Time series of Nino 1.2 SST As. 1950-96 174
Figure 10-lb Time series of Nino 4 SSTAs 1950-96 174
Figure 10-2 Typical atmospheric and oceanic conditions across the tropical Pacific
Ocean around El Nino times 175
Figure 10-3 Time series of Southern Oscillation Index. 1950-96 176
Figure 10-4 Time series of sea level pressure over Indonesia. 1958-96 178
Figure 10-5 Areas where sea level pressures were recorded for Indonesia and the
Eastern Pacific, used to compute ESOI values 178
Figure 10-6 Time series of sea level pressure over Eastern Pacific 1958-96 178
Figure 10-7 Time series of Equatorial SOI. 1958-96 180
Figure 10-8 Time series of MEI. 1950-96 180
Figure 10-9 Orientation map of the tropical Pacific Basin 181 Figure 10-10a Scatter diagram showing relationships between Region I DJF(+1)
temperature signal and DJF(0) ZEAS 188 Figure 10-10b Scatter diagram showing relationships between Region I SON(+l) temperature signal and FMA(0) ESOI 190
Figure 10-10c Scatter diagram showing relationships between Region I SON(+l) temperature signal and SST A Nino 4 (JJA(0)) 190
Figure 10-10d Scatter diagram showing relationships between Region I JJA(+1) temperature signal and MEI (SON(0)) 193
Figure 10-11 Procedure used to aid in the "classification" of El Nino events for Region I 194
xix Figure Description Page
Figure 10-12a Scatter diagram showing relationships between Region II DJF(+1)
temperature signal and DJF(O) ZEAs 196
Figure 10-12b Scatter diagram showing relationships between Region II DJF(+1) temperature signal and MJJ(O) ZIND 196
Figure 10-13 Procedure used to aid in the "classification" of El Nino events for Region II 198
Figure 10-14a Scatter diagram showing relationships between Region II SON(+l) temperature signal and JJA(O) SSTA Nino 4 199
Figure 10-14b Scatter diagram showing relationships between Region II SON(+l) temperature signal and MJJ(0) SOI 199
Figure 10-15a Scatter diagram showing relationships between Region II MAM(+1) temperature signal and MAM(+1) MEI 201
Figure 10-15b Scatter diagram showing relationships between Region II MAM(+1) temperature signal and M AM(+1) S ST A Nino 3 201
Figure 10-15c Scatter diagram showing relationships between Region II MAM(+1) temperature signal and MAM(+1) SSTA Nino 1.2 202
Figure 10-15d Scatter diagram showing relationships between Region II MAM(+1) temperature signal and MAM(+1) SSTA Nino 4 202
Figure 10-15e Scatter diagram showing relationships between Region II MAM(+1) temperature signal and MAM(+1) S STA Nino 3.4 203
Figure 10-15f Scatter diagram showing relationships between Region II MAM(+1)
temperature signal and MAM(+1) ZEAs 203
Figure 10-15g Scatter diagram showing relationships between Region II MAM(+1) temperature signal and FMA(+1) MEI 204
Figure 10-15h Scatter diagram showing relationships between Region II MAM(+1) temperature signal and FMA(+1) SSTA Nino 4 204
XX Figure Description Page
Figure 10-15i Scatter diagram showing relationships between Region II MAM(+1) temperature signal and FMA(+1) S ST A Nino 3.4 205
Figure 10-15j Scatter diagram showing relationships between Region II MAM(+1) temperature signal and FMA(+1) SSTA Nino 3 205
Figure 10-15k Scatter diagram showing relationships between Region II MAM(+1) temperature signal and FMA(+1) SSTA Nino 1.2 206
Figure 10-151 Scatter diagram showing relationships between Region II MAM(+1) temperature signal and JFM(+1) SSTA Nino 3 206
Figure 10-15m Scatter diagram showing relationships between Region II MAM(+1) temperature signal and JFM(+1) SSTA Nino 1.2 207
Figure 10-15n Scatter diagram showing relationships between Region II MAM(+1) temperature signal and JFM(+1) SSTA Nino 3.4 207
Figure 10-15o Scatter diagram showing relationships between Region II MAM(+1) temperature signal and JFM(+1) SOI 208
Figure 10-16 Scatter diagram showing relationships between Region II JJA(+1) temperature signal and FMA(+1) S STA Nino 1.2 209
Figure 10-17 Scatter diagram showing relationships between Region III DJF(+1) temperature signal and DJF(0) ZEAS 211
Figure 10-18 Procedure used to aid in the "classification" of El Nino events for Region III 212
Figure 10-19 Scatter diagram showing relationships between Region III SON(+l) temperature signal and MJJ(0) SOI 213
Figure 10-20 Scatter diagram showing relationships between Region III MAM(+1) temperature signal and JAS(0) SSTA Nino 1.2 214
Figure 10-2la Scatter diagram showing relationships between Region III JJA(+1) temperature signal and MJJ(+1) MEI 216
xxi Figure Description Page
Figure 10-21b Scatter diagram showing relationships between Region III JJA(+1) temperature signal and AMJ(+1) SSTA Nino 3 216
Figure 10-21c Scatter diagram showing relationships between Region III JJA(+1) temperature signal and AMJ(+1) Nino 3.4 217
Figure 10-21 d Scatter diagram showing relationships between Region III JJA(+1) temperature signal and AMJ(+1) MEI 217
Figure 10-21e Scatter diagram showing relationships between Region III JJA(+1) temperature signal and MAM(+1) S ST A Nino 3 218
Figure 10-2If Scatter diagram showing relationships between Region III JJA(+1) temperature signal and M AM(+1) S ST A Nino 3.4 218
xxii LIST OF VIDEOS
Video 9-1 Animated sequence showing Type W El Nino signal in surface temperature records across Region I between Sep(O) and Aug (+1) CD-I
xxiii LIST OF SYMBOLS AND ACRONYMS
Symbols and Definition acronyms P Regression coefficient e Standard error of estimate v Interpolated value of surface temperature 0 Sample standard deviation \ El Nino signal (°C) r| Neutral value - Obtained from non-ENSO years K Signal composite (C)
pc Cross-correlation coefficient
po Value of predictand variable when predictor variable is zero
Ys Predictand station Predictor station Absolute value a Variable denotation A Surface air temperature over the tropical Pacific Ocean AES Atmospheric Environment Service - now Meteorological Service of Canada Apr April Aug August b Variable denotation c Cloudiness anomaly over tropical Pacific Ocean C Degrees Celsius cov Covariance between two variables CPC Climate Prediction Center (NOAA) d Difference between variable a and variable b in computation of r Dec December DJI Northern Hemisphere winter season that consists of mean values calculated from months of December, January and February EAS Eastern Pacific ENSO El Nino Southern Oscillation ESOI Equatorial Southern Oscillation Index Feb February GIS Geographic Information Systems H Center of high pressure system hPa hecto-Pascals 1 Number of stations used to predict the value of temperature at
xxiv Definition another station Indonesia January Northern Hemisphere summer season that consists of mean values calculated from months of June, July and August Japan Meteorological Agency July June Center of low pressure system Latitude Lamont-Doherty Earth Observatory of Columbia University. Longitude Month Data matrix Northern Hemisphere spring season that consists of mean values calculated from months of March, April and May March Millibars Sample size Total number of El Nino months of a certain type i.e. January. In this study, the maximum number is 12 as that many El Niiios were recorded between 1950 and 1996 National Oceanic and Atmospheric Administration November North Pacific Oscillation Observed value October Atmospheric pressure above sea level (hPa) Principal Component Principal Components Analysis Pacific North American Pattern Pacific North American Pattern Index Quality Control Spearman's rank correlation coefficient Pearson's correlation coefficient Reversed PNA Station Population standard deviation Standard error of mean
xxv Symbols and Definition acronyms Sep September SLP Sea level pressure (hPa) S-mode Decomposition used in PCA where spatial complexity of dataset is reduced using temporal attributes SOI Southern Oscillation Index SON Northern Hemisphere fall season that consists of mean values calculated from months of September, October, and November SPCA Standardized Principal Components Analysis SST Sea surface temperature (°C) SSTA Sea surface temperature anomaly (°C) t Time ts t-statistic that analyzes significance of R and r T Surface temperature (°C) T-mode Decomposition used in PCA where temporal complexity of dataset is reduced using spatial attributes TNH Tropical/Northern Hemisphere Pattern U Zonal wind anomaly (m/s) V Meridional wind anomaly (m/s) WMO World Meteorological Organization WP West Pacific Oscillation Pattern ^ Mean value Y Year Year (+1) Time marked by El Nino dissipation in the tropical Pacific Year (0) Time marked by the development and maturity of El Nino in the tropical Pacific Z Standardized value
xxvi 1
1. EL NINO/SOUTHERN OSCILLATION (ENSO)
1.1. Introduction
The modifications of oceanic and atmospheric conditions caused by the El Nino/Southern Oscillation (ENSO) phenomenon are known to create significant anomalies in climate throughout the equatorial Pacific, its birthplace. Very strong correlations between ENSO and climate anomalies also exist in areas far from the tropical Pacific. The presence of abnormally high sea-surface temperatures (SSTs) that have become synonymous with El Nino release large amounts of latent heat into the local atmosphere, that in turn causes significant heating of the equatorial troposphere. With time, these tropical anomalies propagate within large-scale atmospheric and oceanic circulation patterns throughout the globe. In the end few places around the globe fail to feel the impact of El Nino events in their climate and society El Nino's impact on local climates has been studied in detail around the globe. Detailed understanding of the phenomenon's local climate signatures may now become increasingly more important, as recent El Ninos are suspect have become stronger, occurred more often and have displayed new modes of evolution. Detailed understanding of El Nino's signatures on Canadian climates would help farmers, foresters, fishermen, and numerous government agencies design practices to minimize costs and maximize profit from El Nino. The objective of this study is to further the understanding of El Nino's influence on surface air temperatures across Western Canada.
1.2. El Nino/Southern Oscillation - History
The El Nino/Southern Oscillation phenomenon (ENSO) is the strongest source of natural variability in Earth's climate system on interannual time scales (Philander 1998) Although it originates in the equatorial latitudes of the Pacific Ocean, its climatic impact Figure 1-1: Above: Typical sea-surface temperature conditions (°C) across tropical Pacific Ocean. Middle: Sea-surface temperatures (°C) as measured during October 1997 - El Nino period Below: Sea-surface temperature anomalies (SSTA) (°C) during October 1997. Courtesy of NOAA. The geographic projection of these captions is not known and is not important. The purpose of the maps is to display general spatial patterns, like warm SSTAs in the eastern equatorial Basin It is not of interest to identify precise locations, distances or directions. Similar logic applies to other map captions throughout the thesis. 3 can be felt globally. Variations in major rainfall systems that are attributed to El Nino range from droughts and fires in Indonesia and Australia to storms and floods in Ecuador and the US (Glantz etal. 1987). The history of El Nino dates back to the sixteenth century. Peruvian fishermen and Spanish explorers that traveled the waters of the Pacific ocean noticed an annual occurrence of a warm, south flowing ocean current that moderated the normally low sea- surface temperatures (SSTs) along the coast of South America (figure 1-1). Because this invasion of warm waters was observed around Christmas, the occurrence was named "baby boy Jesus", in Spanish El Nino. Scientific inquiry into the phenomenon began, although unknowingly, at the beginning of the last century. In his study of Indian monsoons in 1923, Gilbert Walker (Walker 1923) discovered an irregular standing oscillation in atmospheric pressure that spans the equatorial Pacific basin from east to west. He found that when sea level pressure (SLP) is high over the Pacific Ocean, it tends to be low in the Indian Ocean. Later, the best association of this state was found between Tahiti, French Polynesia and Darwin, Australia. Figure 1-2 illustrates that the correlation between these two locales' sea level pressure records is -0.84. This pattern of sea level pressure has been since referred to as the Southern Oscillation (SO).
With the increasing use of computers and availability of remotely sensed images of the earth in the 1960s came a fresh opportunity to study the El Nino phenomenon in a new light. Bjerknes (1969) discovered an important association between the Southern Oscillation and sea surface temperature variations in the tropical Pacific. He found that during El Nino years, unusually high surface pressures over the western and low surface pressures over the south-eastern tropical Pacific coincide with heavy rainfall, unusually warm surface waters, and relaxed trade winds in the central and eastern tropical Pacific (figure 1-3). Figure 1-4 demonstrates the tight association between sea surface temperatures in the central Pacific Ocean and sea level pressures at Darwin, Australia. Due to this coupling, Bjerknes named this atmosphere-ocean interaction El Nino/Southern Oscillation (Bjerknes 1969). Around the same time, scientists also discovered that the anomalously warm ocean waters common to El Ninos are not restricted to the coast of South America as 4
Year
Figure 1-2: The Southern Oscillation. Above: Correlations (xlO) of annual mean sea level pressure with the pressure at Darwin. Correlations exceed ±0.4 in the shaded regions (From Trenberth and Shea 1987). Below: Sea level pressure fluctuations between 1930 and 1996 at Tahiti (red line) and Darwin (green line). Data courtesy ofNOAA. 5
EI Nifte Conditions
Australi
12STE SO'3W
WWWi TAG
Figure 1-3: Above: Oceanic and atmospheric conditions common to the tropical Pacific during non-El Nino years. Below: Conditions typically present throughout the tropical Pacific during El Nino years. Continents are shown in brown. Sea surface temperature magnitudes are shown from blue (cool) to deep red (warm). White arrows designate the direction of surface ocean currents. The convective loop shown above the ocean waters represents the relative position of surface conversion (L) (clouds) and diversion (H) (down arrows). This atmospheric air circulation pattern has been referred to as the Walker circulation. During an El Nino year, the Walker cell is displaced from the western Pacific towards the dateline. With it also move the rainfall regions, such that the typically "wet" areas of eastern Australia become dry, and the normally "dry" central- eastern Pacific becomes wet during El Nino events. Courtesy of NOAA. Darwin SLP NIN03 SST 1882 -1996
• 1 I I I I I u 1885 1390 1805 1900 1905 1910 1915 1920
_j i i i i i i i 1960 1965 1970 1975 1960 1985 1990 1995
Figure 1-4: Time series of standardized sea level pressure at Darwin, Australia and sea surface temperature anomalies in central tropical Pacific Ocean (Nino-3 region) between 1882 and 1996. Courtesy o/LDEO. 7 previously believed. During El Nino years, unseasonably warm surface ocean waters extend far westward from the South American coast to the date line (Glantz 1996).
1.2.1. A "composite" El Nino
El Nino episodes vary significantly in magnitude, spatial extent and duration. However, the timing of different episodes can be remarkably similar (figure 1-5) (Philander 1990), making composite analyses possible. The following paragraphs provide a summary of such a study carried out by Rasmusson and Carpenter (1982). With the onset of the northern winter season (Dec-Jan-Feb), the Inter-tropical Convergence Zone (ITCZ) migrates southeastwards, causing temporary modifications of atmospheric and oceanic conditions in the tropical Pacific Ocean. These entail the weakening of easterly trade winds, the reduction of sea-surface pressure gradients between the eastern and western pacific basin, and the displacement of convective zones (rain zones) along the Ocean. Reduced evaporation and upwelling, thickening of the thermocline, and a rise in sea level height along the South American coast accompany such changes. As a result, around Christmas time, the waters off the Peruvian coast temporarily warm up and produce an annual mini-El Nino episode. With the return of the ITCZ to the higher latitudes during the following spring season (March-April-May), the easterlies once again strengthen, allowing elements to return to normal.
For unknown reasons nonetheless, every three (3) to seven (7) years, the ITCZ does not return to the north with the Sun (Philander 1990). What results, is an amplification of the annual cycle that triggers the onset of a major El Nino event in the basin. The prolonged weakening of the trade winds results in a further alteration of atmospheric and oceanic conditions above and within the entire tropical Pacific basin. The principal atmospheric anomalies include those observed in sea level pressures, surface and upper level wind patterns, and atmospheric circulation patterns. Oceanic conditions during El Nino events display distinct patterns in sea-surface and sub-surface temperatures, sea- surface slope and oceanic circulation patterns. 8 According to Rasmusson and Carpenter (1982), towards the end of a year that precedes an El Nino event, the end of year (-1), sea level pressures across central and southeastern tropical Pacific decrease. This is followed by the weakening of the easterly trade winds to the west of the date line, where the sea-surface temperatures become higher than normal. Next, there is an amplification of the warm phase of the seasonal cycle in the eastern tropical Pacific during the early calendar months of year (0), the El Nino year. This results in sea-surface temperatures and rainfall in the eastern basin attaining exceptionally high values at the time of their seasonal maxima. These conditions persist in the east for several months while the seasonal northward migration of the ITCZ is inhibited. Not only is the northward movement of the ITCZ absent but so is the usual westward propagation of the cooling phase of the seasonal cycle. The persistence of high sea-surface temperatures, therefore, implies that sea-surface temperature anomalies, relative to the climatology, propagate westward. The speed of propagation is between 50 and 100 cm/s (Philander 1990). While warm, wet conditions prevail in the eastern equatorial Pacific, the convective zone over the western Pacific moves eastward starting in April of year (0), decreasing the amount of local rainfall. As shown in figure 1-3, at this time, the warm waters also progress as a Kelvin wave, causing the Walker convection region to move with it towards the date line, increasing sea level pressure over Indonesia. This causes the thermocline, sea-level height, and the SSTs in the western Pacific to be reduced as well. At this time, in the area between 165°E and the South American coast, heavy rainfall and northerly wind anomalies are present. By July of year (0), anomalous conditions in the eastern equatorial Pacific have peaked, although a secondary maximum can appear late in year (0) and early in year (+1), the mature phase of El Nino. In the western and central Pacific, on the other hand, anomalous conditions continue to amplify until the end of the year. A precursor to the end of El Nino is the appearance of cold surface waters in the eastern Pacific towards the middle of year (+1). Eighteen (18) months after the onset, 'normal' conditions are re• established over the entire Pacific Ocean (Philander 1990).
In essence, the sea-surface temperature rise in the eastern tropical Pacific ocean, or the presence of an El Nino event, may be caused by either the reduced upwelling in the 9
] MMJ SNJMMJ SNJMMJSN
Figure 1-5: Above: The seasonal sea surface temperature changes (dashed line) during a typical El Nino episode as measured along the coast of South America between 3Sand 12S. Below: Sea surface temperature departures from the seasonal cycle, along the same track during El Nino episodes in 1951, 1953, 1957, 1963,1965,1969 and 1972. The figure also shows variations during the preceding and following years. From Philander (1990). 10 region, or by the spreading of warm waters from the western to the eastern portions of the basin. The weakening of the easterlies that is responsible for the reduction of the sea level gradient between the east and west basin causes the warm SSTs commonly present in the west to be brought eastward, further augmenting the already anomalous warm conditions there, bringing El Nino to life. Both phenomena are caused by the reduction in the strength of the easterly trade winds due to a prolonged anomalous southerly displacement of the ITCZ.
1.3. Global El Nino climate-related impacts
The oceanic and atmospheric modifications of the tropical Pacific Ocean that result from the presence of an El Nino event in the region impact on atmospheric and oceanic circulation patterns around the globe especially around the Pacific rim and the tropics. Bjerknes (1969) found the existence of very strong correlations between El Ninos and climate anomalies in areas far from the equatorial Pacific. Figure 1-6 shows the typical response of tropical and extra-tropical temperature and precipitation records to El Nino during northern-hemisphere winter (DJF) and summer (JJA) seasons. Spatially, climate modifications are greatest in the tropical Pacific basin, where El Nino resides. It is generally wetter and warmer than usual along the eastern Pacific coast during the winter season, and in the central section of the basin during the entire duration of an El Nino event. Dry conditions prevail in the western sections of the basin, along southwest Asia and northern Australia throughout the duration of an event (Glantz et al. 1987). Figure 1-6 also shows significant modifications in climate away from the tropics. Most of the extra- tropical impacts observed in surface temperature and precipitation occur as the El Nino event matures during the winter season. Most areas in these regions experience anomalously warm seasons. Generally, only areas in the Southern Hemisphere (SH) feel the impact of El Nino throughout the entire life span of a warm event. The impact of El Nino events on global precipitation patterns have been studied extensively both on large and regional scales, as the impacts on the hydrological cycle produce more severe social and economic hardships than do those produced by temperature modifications. Generally, December-February
Figure 1-6: El Nino-related impacts on global climates. (Courtesy of NOAA). Based on Ropelewski and Halpert (1987) and prepared after the 1982/83 event. 12 the impact on precipitation varies by location and by the position within a continent. However, the direction of impact is mixed, as is the timing (Ropelewski and Halpert 1987, Nkemdirim and Budikova 1996). As a general rule, those areas that have positive precipitation anomalies, also show below normal temperature anomalies during ENSO events. Increased cloud cover reduces incident global irradiance and moisture abundance increases the latent heat fraction at the expense of sensible heat. Based on the evidence the thermal insulation provided by clouds does not adequately compensate for losses in irradiance; but there are exceptions, especially in Europe where results appear to be opposite during the winter
1.4. Theories of teleconnections
The 'abnormal' oceanic and atmospheric conditions associated with El Nino events are known to create climatic anomalies throughout the equatorial Pacific. The presence of unusually high sea-surface temperatures in the eastern equatorial Pacific region during an El Nino event has been shown to alter circulation patterns in the areas of its origin, as can be observed through the anomalies in the El Nino Walker circulation (Figure 1-3). During El Nino years, the region of convection and abundant rainfall above the tropical Pacific Ocean shifts eastward from the Australian coast toward the dateline. This results in a temporary modification of rainfall patterns across the region. The usually dry region of northern Peru experiences flooding while the normally wet regions of Indonesia report droughts.
This energy from the ocean surface propagates to the upper levels of the equatorial troposphere due to the release of latent heat from the ocean, which leads to altered atmospheric circulation patterns and temporary climatic modifications around the globe (Hoskins and Karoly 1981; Hoerling and Kumar 1997). Bjerknes and Walker (1923) were among the first to find the existence of very strong correlations between El Nino and climate anomalies in areas far from the equatorial Pacific. Such relationships between extratropical climate and oceanic/atmospheric anomalies observed in the equatorial regions of the Pacific have been referred to as teleconnections (Glantz et al. 1987). Teleconnection 13 patterns reflect large-scale changes in the atmospheric wave and jet stream patterns, and influence temperature, rainfall, storm tracks, and jet stream location/ intensity over vast areas. Thus, they are often the culprit responsible for abnormal weather patterns occurring simultaneously over seemingly vast distances. Today, there are some climate signatures of El Nino worldwide that are widely accepted (Glantz 1996). There has, however, been much controversy regarding the modes of precise 'energy transfers' from the tropics to the mid-latitudes during El Nino years. The debate began in the late 1960s when Bjerknes suggested that the heating of the tropical troposphere in the eastern equatorial Pacific accelerates the Hadley circulation. This, he then suggested, should intensify the transport of heat and momentum to the mid- latitudinal regions. In consequence, the subtropical jet stream and westerlies should then strengthen (Bjerknes 1966). Little changed about this viewpoint until research performed on three-dimensional Rossby-way propagation in the early 1980s (Simmons et al. 1983; Webster 1982), which disproved Bjerknes' hypothesis. The new studies claimed that the meridional Hadley cell is baroclinic in nature, and is therefore trapped in the tropics, negating its role in extratropical teleconnection (Lim and Chang 1983). This theory has since proven unsatisfactory because the pattern is the same for all El Nino episodes even though the position of the heat source in the tropics changes from event to event (Philander, 1990).
Later in the mid-80s, Yarnal (1985) claimed that the anomalous zone of active convection that develops in the central equatorial Pacific during El Nino episodes disturbs the atmosphere and propagates a low-frequency tropospheric Rossby-wave train to the extratropics, giving rise to circulation anomalies throughout the region of North America listed in Table 1-1. The resulting northern hemispheric circulation pattern is associated with: 1) an exceptionally high upper level ridge over the north-western USA and southwestern Canada; and 2) deepened upper level troughs over the Aleutian area and the south-eastern USA. This pattern has come to be known as the Pacific/North American (PNA) teleconnection pattern, first mentioned by Wallace and Gutzler (1981) and shown in figure 14
1-7. Wallace and Gutzler (1981) first presented a PNA index (PNAI), that was calculated as a linear
Table 1-1: Upper-level northern-hemispheric circulation anomalies associated with an El Nino event. Aleutian Low Polar Jet Stream Subtropical Jet Stream • Strengthening •Enhanced meridional flow •Strengthening •Southern i) anomalous ridging over regions •Southerly displacement displacement of NW Canada •Deepening and eastward ii) anomalous troughing in eastern displacement of trough over parts of Canada and over the North western USA and region of Pacific Gulf of Mexico. function of standardized winter 700 hPa height anomalies (z-scores) at four pattern centers across the Northern Hemisphere:
0 0 PNA = 1/4 [Pz (20°N,160°W) - Pz(45 N,65 W) + Pz(55°N?115°W) - Pz (30°N,85°W)] 1-1
These four centers are located near Hawaii (20°N, 160°W), North Pacific Ocean (45°N, 165°W), in Alberta (55°N, 115°W) and in the Gulf Coast region of the United States (30°N, 85°W). Positive index values indicate the PNA pattern, whereas negative values are typical of the RPNA (reversed PNA) pattern. During the PNA pattern meridional upper-level flows shown in arrows in figure 1-7 are dominant across the USA and Canada. During RPNA upper-level flows reverse, such that the observed areas of high pressure during PNA pattern become dominated by a low-pressure and vice versa.
The PNA circulation can be generated by mechanisms such as orography, land- surface heating contrasts and barotropic instability. Nonetheless, many studies have shown that this observed pattern resembles a year-round dominant response of Northern Hemisphere atmosphere to El Ninos (Livezey and Mo 1987). The most consistent response, however, occurs during winter months (DJF) (Shukla and Wallace 1983; Chen 1982), because the extratropical Rossby waves are generated when the heat source in the tropical Pacific is overlain by westerlies. Consequently, strong teleconnection patterns are primarily suspected to develop during the winter when the westerly winds migrate 15
Figure 1-7: A schematic diagram of the Pacific North American (PNA) pattern of middle and upper-tropospheric geopotential height anomalies during the Northern Hemisphere winter that coincides with El Nino conditions in the tropical Pacific. The arrows depict a midtropospheric streamline as distorted by the anomaly pattern, with pronounced "troughing" over the central Pacific and "ridging" over western Canada. Cloudiness and rainfall are enhanced over the shaded areas. The dots indicate the stations used in the computation of the pattern values (From Philander 1990) 16 equatorwards (Webster 1982). Overall, El Nino has been found to account for approximately 20% of the variance in the centers of action of the PNA pattern (Philander 1990). How can the appearance of such a pattern be explained? Elementary textbooks in meteorology show that the existence of the jet streams can be attributed to the horizontal variations in temperature and pressure such that the jets are located on the boundary that separates the cold air to the north from the warm air to the south. Moreover, the strength of a jet is also related to the temperature gradient across this boundary. Thus, the stronger the north-south temperature contrasts the faster the wind blow within the jet. Finally, as the leading edge of the cold air extends into the lower latitudes, the jet stream also moves south with it (Ahrens 1994). From the above arguments, the strengthening of the subtropical jet becomes evident, due to the anomalously warm atmosphere that is created as a result of the warm SSTs in the tropical Pacific region. Such conditions in turn increase the previously mentioned temperature gradient between the extratropical latitudes and the tropics, enhancing the wind speeds within the subtropical jet as well as promoting its southerly displacement. The southeastern displacement and strengthening of the Aleutian low may be responsible for the observed anomalous meandering of the polar jet.
Hoerling and Ting (1994) tried to examine the dynamics of the atmospheric responses during several large-scale warmings and coolings in the tropical Pacific aimed at determining the origin of the extratropical wave train. When exploring the possible mechanisms by which events in the tropics can control the behavior of transient eddies in the extratropics, the authors found that a simple steady equatorial heat source was sufficient to initiate a process that included a forced tropical anticyclone phase that shifts east of the west Pacific high, resulting in an eastward extension of the subtropical Pacific jet. Such a mechanism was effective in displacing the climatological deformation zone of mid- latitudes eastward. The authors further argue that this process could be as effectively initiated by an equatorial heat source positioned near the date line, which explained the reproducibility of the observed extratropical transient eddy behavior during the several ENSO despite the large SST variability. In their conclusions, Hoerling and Ting (1994) cautiously point out that more studies need to be done to test their claims, and that 17
...given the inter-El Nino variability in SSTs and the fact that several modes of extratropical response are observed, it is quite possible that the relative importance of individual atmospheric forcing mechanisms can vary among El Ninos...
Since the original Hadley cell theory of extratropical teleconnection in the early 1970s, scientists have been trying to find a plausible explanation of the mechanisms through which the circulation anomalies arise in the mid to high-latitudes of the North American continent. Although the theory of the PNA pattern has been most widely used in explaining the climatic anomalies associated with El Nino episodes, the pattern is not yet well understood. To date no one is certain about how exactly the anomalously warm waters in the eastern tropical Pacific cause circulation patterns observed over the USA and Canada during an El Nino winter. No one is also sure how El Nino begins.
As mentioned earlier, in addition to the PNA other factors change the circulation pattern over North America during El Nino years. The West Pacific Oscillation (WP), and the Tropical/Northern Hemisphere (TNH) pattern are just as likely to manifest themselves during El Nino years as is the PNA pattern alone (Hoerling and Ting 1994). The Tropical/ Northern Hemisphere pattern was first classified by Mo and Livezey (1986), and appears as a prominent mode of variability from November-February. The pattern consists of one primary pressure anomaly center over the Gulf of Alaska and a separate anomaly center of opposite sign over the Hudson Bay, shown in figure 1-8. Pronounced negative phases of the TNH pattern are often observed during December and January when Pacific warm (ENSO) episode conditions are present (Barnston et al. 1991). The WP pattern previously described by both Barnston and Livezey (1987) and Wallace and Gutzler (1981) is a primary mode of low-frequency variability over the North Pacific in all months. During winter and spring, the pattern consists of a north-south dipole of anomalies, with one center located over the Kamchatka Peninsula and another broad center of opposite sign covering portions of southeastern Asia and the low latitudes of the extreme western North Pacific (figure 1-9). 18
Figure 1-8: Positive phase of the Tropical/Northern Hemisphere (TNH) teleconnection pattern for the month of January. Shown are height anomalies (m) as seen in scale below.
Figure 1-9: Positive phase of the Western Pacific (WP) teleconnection pattern for the month of January. Shown are the height anomalies (m) as seen in scale above. Both images are courtesy of NOAA (http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.html) 19
1.5. El Nino variability
1.5.1. Observed changes in the character of ENSO
Over the past 5 years, a number of studies have emerged that suggest the possibility of long-term changes in El Nino's character. Among the features are: 1) frequency of occurrence, 2) intensity, and 3) evolutionary characteristics of ENSO (Trenberth and Hoar 1996; IPCC 1997; Trenberth and Hoar 1997; Rajagopalan et al. 1997; Cane et al. 1997; Hunt 1999). These studies suggest the possibility of such changes occurring as a result of the widely-debated anthropogenically-induced climate change. Since the late 1970s, the number of El Nino events has markedly exceeded the number of La Nina events measured in the tropical Pacific Ocean. According to Trenberth (1997), after 1976 there have only been four La Nina events (1984-85, 1988-89, 1995-96 and 1998-99) as opposed to five El Nino years (1976-77, 1982-83, 1986-87, 1990-95 and 1997-98). Also, the 1982-83 and 1997-98 events had the biggest magnitudes on record, and the 1990-95 was the longest on record (comprised of three events as defined by the authors). The periodicity of ENSO has also varied since 1950. Between 1950 and 1975, the phenomenon exhibited a periodicity of about five (5) years. Since then, the period has shortened to almost four (4) years (IPCC 1996; Hunt 1999). Hunt (1999) explains as follows:
...initiation of an episode of the warming of the tropical eastern Pacific surface waters and associated atmospheric and oceanic circulation change, known as El Nino, appear to be a partly random prcKess (Trenberth 1991), modulated by several temporal cycles. These modulations include an approximately centennial-scale variability with an interval of more than 30 years during which El Nino occurrences are rare. Both, in the late 19th century and in the late 20th century, El Nino occurrence frequencies appear elevated, especially with respect to the 1930s-1950s when El Nino occurrence was quite rare. The late 20th century El Nino frequency of occurrence particularly during the 1990s appears to be even higher than that of the late nineteenth century...
There appears to have been a distinct change in the ENSO cycle in 1976/77. Since then, there have been relatively more frequent El Nino episodes. The SOI has 20 tended to be negative for extended periods, especially during the 1990-95 period. During this time period, the sea-surface temperature anomaly values in the central Pacific Ocean (Nino 3.4 region) remained above 0 at all times. Precipitation over land in many areas where dry conditions usually accompany El Nino episodes, such as Indonesia and northeast Australia, has been low. Finally, Wang (1995) has brought to attention an observed modification of the evolutionary patterns of El Nino events in the tropical Pacific before and after 1976. Rasmusson and Carpenter (1982) made a comprehensive description of a composite ENSO scenario based on six events during 1950 and 1976. All the above authors found that for most significant warm episodes before the late 1970s, onset of an El Nino event was characterized by a warming of the eastern coastal Pacific Ocean that was subsequently followed by a warming of the central basin. After the 1976 event, however, El Nino evolutionary patterns behave quite differently from the canonical scenario of Rasmusson and Carpenter (1982), as the central Pacific warming has not been preceded by South American coastal warming as described in the composites. The recent ENSO behavior appears to be unusual in the context of the instrumental record years. Trenberth and Hoar (1996) suggested that the 1990 to 1995 behavior had a probability of natural occurrence of about once in 2,000 years. Present studies have been assessing the relative strengths of the two strongest El Nino episodes in over 120 years - that of the 1977-78, 1982-83 and 1997-98 events. Though much discussion goes on with regards to the precise quantification of their respective strengths, the fact that these two events occurred only 15 years apart are both without doubt 1-in-100 years events.
1.5.2. Recent theories on ENSO within global climate change
There has been considerable debate as to whether these recent changes in El Nino's character can be attributed to global warming. Is there a correlation between global warming and changes in the long-term behavior of El Nino? Trenberth and Hoar's (1997) conclusions are compatible with the assumption that global warming produced an increase in El Nino at the end of this century superimposed on centennial-scale variabil ty (Hunt 21 1999). It has also been suggested by Ely et al. (1993) that the frequency of occurrence of El Nino correlates with positive temporal gradient on the earth's temperature. Hunt (1999) also suggests that ENSO index fluctuations should be more frequent with global warming. Dickinson et al. (1996) state that
... global warming not only affects ENSO by affecting the background state but indeed much of the effects of greenhouse warming might be modulated through changes in the magnitude and regularity of the warm and cold phases of ENSO...
In order to study the changes in climate under global warming, it is important that the GCMs which are used for the prediction of global climate under the 2XCO2 scenario be able to successfully incorporate the ENSO cycle. The ENSO variability in the eastern tropical Pacific has generally been underestimated in the models. Large discrepancies, as much as by a factor of two, occur over the tropical oceans. In general, multi-decadal integrations with these models display interannual variability in the tropical Pacific that resembles some aspects of the observed variability associated with ENSO, although typically to a lesser amplitude (Gates et al. 1996). Regardless of the limitations, scientists have been trying to gain insight into the problem by attempting to find answers to questions such as: 1) Will ENSO continue to occur? 2) If so, will it be more intense? 3) Will it change in frequency and/or duration? 4) Will there be a change in tropical and extratropical teleconnection patterns?
Under the CO2 climate warming, sea-surface temperatures are suspected to rise by about 1°C (Meehl et al. 1993). Any interannual variability will in turn be superimposed on the higher SST mean. ENSO events will most probably continue to occur under the new climatic equilibrium, since paleoclimatic evidence points to the presence of ENSO in many past climates. However, the establishment of a new global climatic equilibrium will produce a different mean state in the upper ocean, that may affect the frequency of ENSO's occurrence (Meehl et al. 1993). Meehl et al. (1993), also claim that the intensity of ENSO events may also change in the warmer world. ENSOs are suspected to become more profound, as the models show that precipitation anomalies of the tropical regions are somewhat larger in the increased CO2 climate, due to intensified anomalous east-west 22
Walker circulation. In turn, large-scale anomalously wet areas will become wetter, and dry areas will become drier. Possibly altered extratropical circulation patterns due to increased CO2 will be responsible for creating new mid-latitude teleconnection patterns during ENSO events. Liang et al. (1996) found that under the CO2 climate, teleconnection patterns such as the Pacific/North American (PNA) pattern, Eurasian (EP) pattern, North Atlantic oscillation (NAO) and North Pacific oscillation (NPO) patterns will still occur. However, the authors also found systematic changes in the PNA and EP during the winter seasons. The action centers shift to the east and the anomalies weaken over land, such that for example, the January PNA signal is weaker over North America, producing smaller local precipitation anomalies. The modeling results of the same study also show a possible northward shift of the Hadley circulation in both the northern and southern hemispheres. The authors claim that this shift may result from a predicted increased surface radiative forcing where summertime conditions appear earlier in the year. On the other hand, Nkemdirim (1996) suggests a possibility that the subtropical high-pressure cell may, in fact, displace southwards towards the equator, in both hemispheres, in turn shrinking the tropics and the ecosystems within. The author claims that although
... evidence of a shrinking tropics is early and inconclusive the current pattern of drought and precipitation on both sides of the sub-tropical highs hint on a possible equatorward migration of deserts and near desert conditions and, by implication, a lesser true tropical environment...
Further analyses of Liang et al. (1996) show that the Hadley circulation becomes weaker while the subtropical westerly jet intensifies. Such changes would occur due to the decrease in the meridional temperature gradient in the lower troposphere as a result of the greenhouse radiative forcing. The weakening of the Hadley circulation may play a role in generating the weaker interannual teleconnection anomalies. Consequently, both Nkemdirim (1996) and Liang etal. (1996) agree that the role of the tropics in extra-tropical climates may be reduced under the new 2XCO2 climate equilibrium. 23
2. ENSO AND CLIMATE OF WESTERN CANADA
2.1 Introduction
Because Western Canada is located several thousand kilometers away from the birthplace of El Nino, its signature in regional climates has been complex. Is it possible to clarify some of this variation and directly attribute it to El Nino? The answer to these questions would improve decision-making methods in climate-sensitive sectors of the society, making economic and social El Nino impacts more manageable.
2.2 Study area: Western Canada
Figure 2-1 displays the study area, including principal cities, political boundaries, and statistics. The region of interest, Western Canada, lies approximately between 90°W and 135°W, a region that spans 45° of longitude and encompasses 14° of latitude between 49°N to 62°N. Its land and water area totals almost 3,000,000 square kilometers. Politically this area includes the prairie provinces of Saskatchewan, Manitoba, Alberta to the east; British Columbia, and southern Yukon to the west; and Northwest Territories and Nunavut to the north. About one-third of Canada's population resides there. Figures 2-2 through 2-5 summarize key features of the area's macro-geography - topography, hydrology, and principal regions of climate and vegetation.
2.2.1 Physiography
The physiography of any region helps define its meso- and micro-climates. Western Canada encompasses a vast diversity of landscapes, which include complex mountains of the Western Cordillera, plateaus, valleys and basins, that stretch from Figure 2-1: Map of the study area - Western Canada comprising of Manitoba, Saskatchewan, Alberta, British Columbia, southern Yukon, and Northwest Territories. Longitude (West)
Figure 2-3: Hydrology of Western Canada. 26 Alaska to Mexico (Hare and Thomas 1979) (figure 2-2). The Rocky Mountains run through the area, in a NW-SE direction from Yukon through British Columbia into the Unites States, with over 60 peaks which reach 3,000 m above sea level, and others that exceed over 4,000 m above sea level. The interior plains are situated between the Canadian Shield that dominates the northern region of Canada (i.e. Northwest Territories), and the Western Mountains. These are characteristic of a gentler landscape of glacial sedimentary origin. Hare and Thomas (1979) describe this region as a gently concave basin. Generally, the landscape rises in all directions from Hudson Bay, to about 1000 m above sea level in the foothill regions of Alberta, to about 500 m in the interior. Flat-lying sedimentary rocks mostly distinguish this region. The prairie provinces are a part of the interior Plains and consist largely of undulating grassy or cultivated till plains with countless small ponds and low hills.
Numerous seas and thousands of lakes and rivers that intertwine the region help define its physiography (figure 2-3) and influence regional climates. The Arctic Ocean to the north is permanently ice-covered, though it thaws a little in the summer. To the northeast Hudson Bay remains cold throughout the summer even after the ice has dispersed. Perhaps the greatest influence on climates is felt in the west because of the region's proximity to the Pacific Ocean (Hare and Thomas 1979). The western coast of Canada is one of the wetter places in North America, and the Rocky mountains are close enough to the coast to receive a significant amount of moisture each year from the prevailing westerly winds. The western slopes of the Rockies are, therefore, wetter than the eastern slopes in Alberta.
Hundreds of lakes and rivers, most of which have formed as a result of glaciation, flow through the region into the oceans. Major lakes considerably impact local climates and hydrology. In the central northern parts of the region lie Lake Athabasca and Lake Reindeer in Saskatchewan. Thousands of lakes can be seen on a detailed map of Manitoba (not shown here), that make up the boggy, low-lying areas of north-eastern Manitoba. Important parts of this system include Lake Winnipeg and Winnipegois in the southern parts of the province. These are the two largest lakes of the "thousand lakes of Manitoba". Figure 2-3: Hydrology of Western Canada. 28 The study area is intertwined with hundreds of rivers of glacial origin that have become an intricate part of not only the landscape but the natural ecosystems. These are shown in figure 2-3. The rivers deliver melt-water from the mountains of British Columbia into forested areas of the north, and prairie grasslands. Among these are the North and South Saskatchewan Rivers that flow through the interior plains. The latter flows from southeastern Alberta through south central Saskatchewan, where it meets the North Saskatchewan River that originates in the Rocky Mountains of British Columbia and travels northeast through Edmonton and east into Saskatchewan's Prince Albert. The two rivers merge in its vicinity, and flow into Lake Winnipeg in Manitoba. Athabasca River originates in the Rocky Mountains of British Columbia, but flows northwards through Alberta into Lake Athabasca. Peace River flows from the mountains of British Columbia and travels through Alberta into Lake Athabasca. The principal transport of water from the mountains into surrounding areas in British Columbia is the Fraser River that travels from the heart of the Rocky Mountains southwestwards through the Fraser Valley and Vancouver into the Pacific Ocean (Kendrew and Kerr 1955).
2.2.2 Vegetation
Figure 2-4 shows that the area spans 7 major vegetation districts, from sub• tropical vegetation in the extreme west to Arctic tundra in the north west. Included are: 1) coastal; 2) sub-alpine; 3) montane; 4) forest; 5) grassland; 6) tundra; and 7) woodland ecosystems regions (Hare and Thomas 1979). The extreme West Coast of British Columbia is covered by Mediterranean vegetation - an abundance of lush green coastal vegetation. A combination of heavy rainfall and mild temperatures in the coastal region support some of the most temperate rain forests in the world (Gadd 1995). Below the tree line, the Rocky Mountains are covered with coniferous forests, sub-alpine vegetation, and grassy and semi-arid open landscapes in the driest interior valleys. Much of the montane area in central British Columbia grows no vegetation. Tundra covers highly elevated regions of southern British Columbia. Extreme north of 29 the province is occupied by boreal forest. The natural vegetation of Alberta, Saskatchewan and Manitoba comprises of grassland to the south and boreal forest to the north (Gadd 1995).
2.2.3 Climate
The complexity of local physiography gives rise to heterogeneous climate regions (Hare and Thomas 1979). Five (5) distinct climate regions dominate the study area, as illustrated in figure 2-5. These include 1) the Pacific region; 2) the Cordillera, 3) the Prairies; and 4) the Arctic and 5) Boreal climates.
2.2.3.1 General Circulation
The area lies within the belt of circumpolar westerlies. Generally, the polar front jet meanders high above the surface between 9 and 11 km, along the Halifax-Montreal- Winnipeg-Vancouver line (Hare and Thomas 1979). During the winter, it is displaced south, as the leading edge of the cold air extends to the subtropics. With the return of warmer weather to the higher latitudes during the Northern Hemisphere spring, however, the jet returns as well (Ahrens 1994).
Three sets of standing disturbances determine the climate: 1) the Aleutian Low; 2) the Arctic High; and 3) the Pacific High. Their relative positions and strengths display temporal variability. The Aleutian low is very deep in the winter and much weaker in the summer. During winter it is located off the coast of northern British Columbia, whereas during the summer months it resides over northeastern Asia. The Pacific High replaces the Aleutian low during the summer seasons, when it is positioned furthest north and extends high into the atmosphere. In the winter it is split into two or more cells, and lies far to the west of North America allowing the Aleutian low to 'take control' over the climate. The Arctic high, the last of the standing features, dominates the climate of the Figure 2-4: Vegetation classes of the study area.
Figure 2-5: Climate regions of study area. 31 study area mainly during the cool months of the year, when it extends from the ocean as a ridge southwards towards Keewatin (Hare and Thomas 1979).
2.2.3.2 Surface Temperature and Precipitation Patterns
It is evident from figure 2-6a that there is considerable spatial variability in mean annual temperature and mean total precipitation values across the area. Mean annual temperatures show a latitudinal gradient, decreasing from south to north, from 4.5°C in the extreme southwest (Vancouver, British Columbia), to -6°C in the northeast (Churchill, Manitoba). The presence of the mountain range in the west gives rise to a SW-NE "tilt" in the contour lines. The month-to-month variability in mean temperature across the region is evident from figure 2-7 that shows climographs for eight principal stations across the area. Moving from west to east, it can be seen that the variability in annual temperature values increases, due to the increasing distance to significant water bodies.
The heterogeneous character of the landscape is equally reflected in the spatial variability in total annual precipitation shown in figure 2-6b. The western side of the mountains including the West Coast is significantly wetter than the leeward side. The mean total annual precipitation for Vancouver is 1068 mm. In contrast, in Calgary it is measured at 437 mm, a difference of about 60%. The moist and relatively warm Pacific air is driven by the circumpolar westerlies from the west coast, over the mountains, into the eastern regions of Canada. As it hits the peaks of the mountain range, it is forced to rise, cool, and release latent heat on the western side. By the time it reaches the foothills of Alberta, the airstream is dry. The Fraser Valley situated in the middle of British Columbia, a region where the mountain range around it relaxes, is clearly observed in figure 2-6b. A large portion of the airstream's moisture is released very close to the coast, where high elevations can be observed. The remainder of the water is released as it passes the highest peaks of the Rocky Mountain range east of the Fraser Valley. This process produces the spatial rainfall variability pattern observed. As a result, to the east, 32
Longitude (West)
1 1 -140 -130 -120 -110 -100 i 1 If !
Temperature (C) Longitude (West) I i
-140 i|[ 300 0 225 0 250 0 275 0 r 125 0 150 0 175 0 o to , 3o 2o ifio S
Precipitation (mm)
Figure 2-6: Above (a): Mean annual temperature. Below (b): Total mean annual precipitation. 33 the Prairie provinces of eastern Alberta, Manitoba and Saskatchewan are much drier. Only the extreme eastern parts of Manitoba, including Winnipeg, are somewhat wetter. The total mean precipitation for Winnipeg is 535.2 mm. The difference comes from the relative close proximity of the Great Lakes system to the southeast. The month-to-month variability in total precipitation for eight major stations is again displayed in figure 2-7. With the exception of coastal British Columbia (Vancouver), most other stations receive their greatest amounts of precipitation during the warmest seasons of the year. Vancouver shows the opposite pattern, as the majority of its precipitation falls during the fall and winter seasons. Edmonton, Regina and Winnipeg display a high variability of rainfall from one season to the next. Most of their precipitation falls as rainfall during the late spring and early summer seasons. The extreme northern parts of the region, signified by Whitehorse and Yellowknife, that are located just north of the northern extent of the study area, show the least amounts of precipitation over all seasons, in comparison to all the other stations. The paragraphs below describe in detail the various climate regions of the study region, that give rise to the annual values in temperature and precipitation measured throughout the region.
2.2.3.3 Climate Regions
2.2.3.3.1 Pacific Canada
From the leeward side of the Rocky Mountains western coast of Canada is dominated by the onshore flow of pacific airstreams. The west facing mountain slopes confront these along the whole coast, and are usually cloud-covered and wet because of the upward drift of the moist air. The eastern faces of the mountains on the other hand have a much less rainy climate. In these areas, these airstreams are usually descending and this tends to disperse cloud and lessen the cyclonic rainfall. Finally, the Gulf Islands, the Saanich peninsula of Vancouver Island and small areas of the Lower Mainland coast Figure 2-7: Climographs for principal stations selected across Western Canada. 35
display Canada's nearest imitation of a Mediterranean environment, with rain almost confined to the cooler seasons, and a blazing sun in summer. In general, Pacific Canada experiences two characteristic periods of rainfall during a given year. During the fall and winter seasons, the pacific westerlies across the region are continuous. At very frequent intervals cyclonic storms push through (caused by presence of the Aleutian low relatively close to northeastern Pacific). In the spring the westerlies weaken, temperatures rise, rain becomes lighter and less frequent until the end of August. This is due to the occurrence of the ridge that lies off the coast, which is swept by persistent, cool and cloudy northwesterlies (Hare and Thomas 1979).
2.2.3.3.2 Cordilleran Canada
Between the crest line of the Coast Range and the eastern foot of the Rockies lies a broad region of plateaus, valleys, and mountains, called the Cordilleran Canada (Hare and Thomas 1979) (figure 2-3; 2-5). Although the climate within this region becomes Boreal above about 60° N, this discussion will concentrate on climates below this line. In general, the climate of this region is controlled by the same pacific disturbances that govern the coastal belt. Although pacific airstreams are still common at the surface, continental airstreams are more frequent, and control the winter circulation. The deep valleys in the interior of this region are dry due to the descent of the pacific airstreams. There are two (2) major continental airstreams that influence the climate of the Cordillera: 1) an air flow from the southeast that originates in the Great Basin states of the USA and 2) Arctic air that originates in Arctic Canada. The former is common during all seasons, dry and cool during winters, but hot and dry during the summer seasons. The latter predominates only in the cooler months, and brings cold and dry conditions (Hare and Thomas 1979). 36 2.2.3.3.3 The Prairies
Having a continental climate, Manitoba, Saskatchewan and parts of eastern Alberta encounter bitterly cold winters, short but warm summers, and a light precipitation regime. It is a subhumid climate. There are no major relief features in the prairie climatic region. In the eastern part of the prairie region, especially in summer, there are frequent influxes of moist air from the south and southeast, allowing a near humid type of climate. Further west, however, the region in summer is usually blanketed by modified Maritime Polar or Maritime Arctic air which has crossed the western Cordillera, and thus is much warmer and drier than it was along the Pacific coast. The heart of the prairie dry country is the south Saskatchewan River basin along the Alberta-Saskatchewan border where annual precipitation averages less than 300 mm. The variation of precipitation throughout the year is typically continental with moderately heavy falls during the summer, even by continental humid climate standards, but with very light falls in the winter months.
Most invasions of Arctic air sweep eastwards as they come down from the north to the eastern Prairies, Saskatchewan and Manitoba. As a result, Alberta has relatively warmer winters. In addition, as southern Alberta is at relatively high altitude, the depth of Arctic air over that area is not as great nor is the duration of its presence as long as in central Saskatchewan. Another reason for warmer temperatures in Alberta is due to Chinook winds, which affects southern Alberta several times each winter.
2.2.3.3.4 Arctic and Boreal climate
The Arctic climatic zone covers all areas north of the tree line, and permafrost. A Boreal climate is interlocked with the Boreal forest formation that in Canada stretches from Alaska to Newfoundland. Within the study region, Edmonton, Winnipeg, and Prince Albert are close to the southern limits of the Boreal climate region. Beside large 37 variations in annual solar radiation, the second major control of these climates is the circulation of the atmosphere. Arctic airstreams coming from the Arctic Ocean dominate the climate. These air streams tend to keep out warmer air that would import heat, and tend to spread out over a larger area the influence of the cold surface of the Arctic. Year-round Arctic air stream dominance is the special mark of the Arctic climates in Canada. Boreal climates are dominated by the Arctic airstreams only in winter and spring. In summer and fall they are replaced on the average by westerly currents coming mostly from the Pacific.
2.3 Socio-economic impacts of El Nino
El Nino-related climate fluctuations result in considerable impacts on both the natural and social environments. During its 15-month life span, an estimated 2,100 people died as a result of conditions directly related to the 1997/98 El Nino. The same event also affected about 117 million, displaced 4.9 million, and caused a total of $34 billion (US) in socio-economic and structural losses around the globe. This made it the most destructive El Nino event in history. The previously proclaimed as the most severe El Nino event occurred just 15 years prior in 1982-83. During that time, an estimated 2,000 people died as a result of related impacts. The event's global economic costs were estimated at $18 billion (US) (WMO 1998a; 1998b).
According to Shabbar (1998), El Nino-induced mild weather in Southern British Columbia during the 1997/98 event significantly reduced motor vehicle accidents. The Insurance Corporation of B.C. reported a saving of $3 million per day. Owing to a mild winter, the central interior of British Columbia also experienced premature thawing of logging roads, which necessitated imposition of severe restrictions on the amount of wood that could be hauled out of the forests by logging trucks. This led to a shutdown of a number of sawmills in northern B.C., augmenting the area's already chronic economic problems. Owing to the mild December weather, the ice wine industry of Ontario reported losses in the $10 - $15 million range. The El Nino induced warmth did not allow grapes to reach the critical harvesting temperature of -8°C for several consecutive days. 38
Starlings that, because of the warm weather, did not make their usual migration south consumed some of the crop. In southern Alberta's Porcupine Hills, mid-December of 1997 was fanned by strong winds that promoted grass fires across the already dry prairie. The blaze burnt more than 200 square kilometers of farmland and torched six houses. It destroyed 100 head of cattle and hundreds of kilometers of fencing and winter hay supplies of many farmers (Shabbar 1998).
2.4 £1 Nino and Canadian climate
El Nino signatures in Canadian climates are complex (AES 1994). Most visible El Nino signatures across the study area occur between Dec(0) and Mar(+1). Studies of El Ninos' impacts on local climates have been documented in the literature since the 1980s. These accounts range from very local to countrywide examinations; from analyses of few El Ninos, to examinations of events from over 100 years of record. One of the earliest precipitation studies completed by Cowan (pers. comm.) showed that winters El Nino events tend to be dry in Southern Alberta. 31 out of 40 years had below normal precipitation between Nov (0) and Mar (+1). Although northern parts of Alberta recover quickly, southern Alberta usually remains dry until the end of June. Ropelewski and Halpert (1986) and later Halpert and Ropelewski (1992), showed Western Canada to be unseasonably warm during 80% of El Ninos between 1875 and 1990. Yarnal and Diaz (1986) addressed signatures in surface temperature and precipitation along the western Pacific coastline. Between 1939 and 1976, El Nino winters from Vancouver Island to the northern edge of British Columbia, were significantly warmer, +0.5 a above normal, accompanied by wet conditions, reaching up to +0.5 a above normal.
Kiladis and Diaz (1989) showed that during El Nino winters, Edmonton, Alberta and Winnipeg, Manitoba experience significant positive temperature anomalies of +2.1°C (significant at 99.9% confidence level) and +1.6°C (significant at 99.8% confidence level), respectively. During the spring, these magnitudes decline to +0.7°C and +0.9°C 39 for Edmonton and Winnipeg, respectively. The authors found El Nino winters to be associated with negative surface air temperature anomalies, of -0.9°C in Prince Albert,
Saskatchewan. These magnitudes were found significant at 96.5%. Smit (1989) analyzed the impact of El Nino events on precipitation and temperature across Southern Alberta. His results are shown in Table 2-1. He found El Nino winters to be relatively dry and spring wet. Greatest anomalies in surface air temperature were found during January and February.
Table 2-1: Mean monthly precipitation and temperature anomalies for Southern Alberta daring the course of a major El Nino event. Anomalies Month Precip. Temp. (Year +1) (%) (°C) January 59 4.8 February 120 1.4 March 79 0.0 April 81 1.1 May 68 0.8 June 105 0.1 July 114 0.5 August 110 0.4 September 103 -0.6 October 55 0.0 November 135 1.5 December 70 -0.8
A later publication by Bonsai and Chakravarti (1993) claimed that anomalously warm waters that arise in the North Pacific during El Nino years might be responsible for dry spells across the Canadian prairies. Smit (1989) showed that the mean monthly frequency of southern Alberta's Chinook hours was affected by strong El Nino events of 1958, 1973 and 1983. Between January and March Chinook hour-frequency anomalies varied in the order of +15%, -47%, and -48%, respectively. In their study of 69 weather stations across the country, Shabbar et al. (1997) found significant negative winter precipitation anomalies along the southern edge of the 40 country, from the West Coast up to the Great Lakes region. Elevated precipitation amounts were found during the same season across central Northwest Territories. The authors showed these anomalies to last between late autumn to early spring. In a similar Canadian-wide analysis of 5°xl0° grid cells, but of surface temperature records, Shabbar and Khandekar (1996) found significant positive temperature anomalies that gradually spread eastward from the west coast of Canada to the Labrador coast between late autumn and early spring following the onset of El Nino. Significant air temperature anomalies occur during the January-March season, of about +1°C to +1.5°C across regions of Yukon, British Columbia, Alberta, Saskatchewan, Manitoba and western Ontario. Significant air temperature anomalies that exceed +1°C, have been found during spring in the extreme eastern parts of Canada, in Labrador and eastern Quebec. Byrne and Berg (1998) analyzed surface temperature anomaly patterns associated with major El Nino events across the Canadian prairies. Similarly to other studies they found "..substantive warming." throughout the region during strong El Nino winters. Precipitation analyses revealed a slight decrease in precipitation during winters in the southern portions of the prairies.
2.5 The problem
The study of El Nifios' impacts on Canadian climates is important both from scientific and socio-economic perspectives. Scientists have studied El Nino's signature in local climates for over two decades now and repeatedly document its complexity. This may partially be due to the physical and climatic intricacy of the region, but also due to its location several thousand kilometers from the equatorial Pacific where El Nino occurs. The signatures in surface air temperatures across Western Canada comprise of considerable variability in the strength of the signal among events, considerable variability of the strength from place to place, and reversal of sign of the signal across space and over time. A clarification of this variation should help improve skills in the 41 prediction of ENSO impacts on surface air temperature in different parts of Western Canada.
2.6 Project objectives and methodologies
The key objective of this study includes a detailed account of the spatio-temporal variation of El Nino signatures in surface air temperature records across Western Canada. Such a problem prompts the answer to several questions, or mini-objectives that should to be addressed. These include the following:
1. What is the inter-El Nino variability of the signal, in time and space? 2. Are there any coherent types of El Nino signals across the area? 3. What is the spatial and temporal extent of these signals? 4. Is it possible to connect the observed signatures back to the tropical Pacific, so that any existing cause-and-effect relationships can begin to be investigated?
Several methodologies were used to answer these questions. Station data of monthly surface air temperature records were used to calculate the El Nino signal between 1950 and 1996. Spatio-temporal nature of the variability in this signal was investigated using a combination of Geographical Information Systems (GIS) technology and multivariate statistical methods. Large-scale and grid-scale composite analyses were used to visualize the spatial and temporal extent of the signals. Small-sample statistical analyses accompanied these results to assess the deviation of the magnitudes from expected values. Whenever possible, temporal animations were constructed to facilitate a better understanding of the signals' spatio-temporal characteristics. The research concluded with an attempt to tie the observed signal patterns back to the El Nino by exploring lead-lag relationships between temperature signals across the study area and several key tropical variables associated with the warm-events. -
42
3. DATA ISSUES
3.1 Data acquisition and preparation
Surface air temperature data for Canada was obtained from Atmospheric Environment Service (AES) of Canada's compact disk (CD) collection. It is called the Canadian Monthly Climate Data and 1961-90 normals (AES 1994). The archive contains records for over 6900 observing sites across Canada. The values are monthly averages where a set of observations is available for the entire data collection period, sometimes dating back to 1875. Since the last archived year in this set is 1993, updates up to and including 1996 were purchased from AES directly. Over 600 weather stations maintained by Atmospheric Environment Canada across the study area had some type of record between 1950 and 1994.
3.2 Quality Control
Extensive data quality control (QC) was performed on the raw data purchased from AES. The types of quality control procedures are shown in table 3-1. These included the elimination of invalid entries and outliers, missing data analysis, data homogenization, estimation and temporal de-trending.
Table 3-1: Types of quality control procedures imposed on data. QC procedure Invalid entry elimination Missing data analysis Elimination of outliers Data homogenization Data estimation Temporal de-trending of monthly data 43 Initially, over 600 stations showed records for surface air temperature between 1950 and 1996. Quality control procedures were initiated by the elimination of "invalid" entries from the database. These included values that were estimated by AES. Because a separate data-estimation procedure was performed here, such data values were eliminated to keep the final data set methodologically consistent. Secondly, the dataset was queried to reveal the amount of missing entries for each station. Any station with less than one-half (50%) of its data complete was eliminated During analyses, mean monthly values replaced missing entries. To prevent the substituted data from altering the original 'behavior' of the series, such as reduction of variance, attention had to be paid to the amount of estimation allowed. Sensitivity tests were performed to determine the point at which the variability of the series was significantly altered as a result of the substitution of means for missing values. In this procedure, one station was chosen from each of the four climate provinces of the study area (i.e. Cordillera, Prairie, Boreal, and Pacific regions), based on the completeness of data records. Stations with the most complete record were chosen. These included Comox for Cordillera, Vancouver International Airport for the Pacific division, Fort Reliance for Boreal, and Morden for the Prairie region. January and July records were used. For each of these stations and months, the mean value of the complete sample was calculated. Entries were then removed from the original series and substituted with the original series mean value.
The variances of the two series (i.e. original and the new modified) were tested using one-way ANOVA (Kvanli 1988). Increasing number of records randomly chosen were gradually replaced by mean values until the test revealed a significant difference in the variability between the two samples, at the 95% confidence level. At that point it was concluded that the substitution caused a significant change in the character of the original series, and no more data could be replaced with a mean The results showed that on average, one-half of the values could be replaced before a marked change in variability of the monthly temperature records were observed. A conservative decision was made to only retain stations that had at least 50% of un-estimated data to preserve the temporal 44 character of the series, but at the same time to prevent excessive station elimination that would compromise the spatial aspect of the analyses. Next, the data were tested for outliers, entries that were unusually large or small when compared to other records within the same series. According to a statistical empirical rule (Kvanli 1988) for a given dataset, almost all records (approximately 99.7%) should lie within 3 standard deviations from a mean value. To confirm the suitability of this limit for the dataset at hand, the database was queried randomly for various station records. The examination of the descriptive statistics of monthly data, revealed that over 95% of all records fell within 2 standard deviation limits. This limit was expanded to 3 standard deviations in the actual testing, to minimize the elimination
Point outside control limits -1936 + 3 sigma upper control limit
> -a 0* 9—* •• • • inS -1 • •• .
-3 sigma lower control limit
TtCOCMtOOTfCOWUJOTfOOC^tO _ ooesa>o>ooQT-T-cscMCM«
Year
Figure 3-1: A control chart used to examine datasets for outlier values. July, Calgary International Airport. Only one problem is visible in this scatterplot. In 1936, the value 3.2 exceeds the upper control limit of +3.0 standard deviations. This point was further examined by checking records for the same months for other surrounding stations. It was found that July 1936 was an unusually warm month throughout the prairies. Consequently, this data entry was deemed valid and was retained. of valid data, but to optimize the search for possible invalid entries. The limit was used 45 to construct control charts for all stations' standardized monthly data to reveal suspect data as points that cross the upper and lower confines. Control charts are XY plots where the deviation from a mean value is plotted for each successive interval (year). Figure 3-1 shows the results for the month of July at Calgary International Airport. All points but one in 1936, when the standardized value of surface air temperature was at 3.16, fall within the upper and lower control limits. To assess the validity of this point, surrounding stations' 1936 records were examined. Several stations were abnormally warm during July of that year including Lethbridge and Pekisko in Alberta, and Morden in Manitoba. Consequently, this entry was valid and the data was retained. In other instances, however, when data fell outside the control limits, but no surrounding stations showed similar anomalies, the data were eliminated from the database. After these steps, 124 stations remained suitable for further analyses of surface air temperature data. This data were further tested for homogeneity, and temporal trends.
3.1.1 Data Homogenization
Homogeneity of a data's time-series implies that the measurements in the set must each have been obtained in the same manner, or in comparable circumstances. Heterogeneity can, therefore, arise from a shift of instruments that yields two or more distinct sets of measurements before and after the move (Linacre 1992). The data was subjected to homogeneity tests, as AES did not correct for any possible discontinuities. Discontinuities within time series can arise due to instrumentation change. (Metcalfe et al. 1997). Unfortunately, there are no records pertaining to instrumentation changes in the station data catalogues. Because the station catalogues only showed dates of station moves, but not instrumentation adjustments, station relocation was the only "proof that was available and, hence, the only justification for correcting data. In turn, station data was only adjusted when evidence that supported the correction existed. In other instances, it was dropped from study. Another possible source of data discontinuity can come from the merging of two station time series. During data archiving, AES assigns 7 digit numbers as permanent 46 identifiers of a site at which official weather observations have been taken. While building the database, and upon the examination of the climatological station catalogues (AES 1989 abc), it was found that occasionally, different station names and identifiers were used for the same site or two distinct sites in very close proximity. Hay River station that collected data between 1893 and 1943 at 115.57° longitude and 60.51° latitude, relocated in 1943 to 115.67° longitude and 60.50° latitude where it has been collecting data since. At the time of move, the station was renamed to Hay River Airport, and was assigned a new AES identifier. Because of the close proximity of these two stations, the data were merged and a new station, with a new identifier was created. The database was queried to see which other stations can be "safely" joined together, and new station identifier with a suffix of 0.5 was appended to the code. Homogeneity testing was performed to check if the creation of the new "joined" station was plausible. Several steps were followed to check each station's time series for evidence of discontinuity. It was assumed that significant discontinuities or breaks in a climatological record that resulted from station move, instrumentation change, or series 'joint' would become visible in mean annual values. Stations were clustered into distinct 'temperature regions' such that every station's annual time series was correlated with the annual time series of all other stations, and those that showed largest correlations were grouped together. Other factors that were taken into consideration when establishing station groups included similarities in vegetation boundaries, topography, principal surface circulation patterns (wind patterns), and the proximity to large bodies of water. All such conditions contribute to the definition of a regional climate (Ahrens 1994). The cut-off station correlation value was 0.90, because after the inspection of all the station correlations, it was able to best separate the groups from one another. At 0.85 several stations could belong to more than one group. The chosen value eliminated this problem. A total of 26 regions were defined (figure 3-2). In four instances two stations defined a region. The series within each region were tested for discontinuities using control charts. Stations that themselves defined a region could not be tested for inhomogeneity via these methods. Here, discontinuities were examined visually. The remaining stations' homogeneity was tested in several steps. Legend
O • 1 GWhitehqj Wat 2 60.0O j _Eort Smith ::; 4 5 hurchill • 6 Qjease Lake o 1 Irochet • & 9 10 Peace River ! B°,t McMurray O 11 12 13 • 14 15 •he Pas • 16 VVe ^org^ «^)Of)tor e ^ #rlfce Albert O i7 O 18 I 19 • 20 • © o 21 °ooo Regirwisgina) ( 22 50.00— o°8° > O ; 23 > (§fvinni • 2" thbridge OO Van ' 0 • 25 26
-130.00 -120.00 -110.00 -100.00 Longitude
Figure 3-2'.Surface air temperature station groups (1 through 26) defined for homogenization and estimation procedures. Each station group is shown in a distinct colour from surrounding areas. Stations defined as a black star did not fit into any other group ofstations. 48
First, the regional annual mean value was computed by averaging all station values that belonged to the group, excluding the station to be tested. Second, the difference between the test station and regional values was computed for each year. Third, the differences were standardized. Fourth, these were plotted in temporal sequence. Finally, upper and lower 3 standard deviation confidence limits were calculated for each station and shown in the plot (figure 3-3). These limits were chosen to detect obvious lack of homogeneity, and at the same time prevent unnecessary data tampering. Control charts were constructed. These were defined as XY plots, where a station's value at each time t (year) was plotted around a mean annual value for the region to which the station was assigned. In turn, the standardized deviation of a station's time series from the regional mean value was examined.
A control plot that represented a homogeneous time series displayed randomly distributed values about the regional mean, and lay within the control limits. A random distribution about the mean value was tested using a simple regression procedure (Kvanli 1988). For a randomly distributed data series, the regression trend was statistically insignificant at a minimum 95% confidence level. If discontinuity was present, the plot showed 'breaks' in the series, or points lay outside of the prescribed boundaries, because the 'behavior' of the station data was not consistent throughout its record.
When non-homogeneity was found in a station's series, the difference before and after the "break" was then tested for significance (95%) by using a t-test for the difference in means (Kvanli 1988). If the difference was significant, the station records were examined for a possible reason of non-homogeneity around the time of the discontinuity. If records showed a station move, the data was adjusted. Adjustment was done for the shorter time period that spanned before or after the move. The series was "corrected" by adding or subtracting the mean difference measured before and after the break. The station was then re-tested. When it showed signs of discontinuity, but there was no evidence of a station move within the catalogues, the station was dropped from the study. Figure 3-3 shows a sample of a homogeneous record. Surface air temperature data recorded at Dease Lake, B.C., is randomly scattered around the mean annual regional value. The scatter remains well within the limits. The time series of Edson, 49
3.5 Upper Control Limit 3 — — — — — — H. J
2.5
2
1.5
1
0.5
0 H * H 1 1 1 1- H 1 1 h -+TJ-I 1 1 »* I -0.5 • • • -1 +
-1.5
-2
-2.5
-3 Lower Control Limit -3.5
(0 00 O CO O CN Tj- «> OT oioioionnoooioinw in (o o O*)Aa)0>O*)0>O>O>O)G)
Upper Control Limit
0 2 1 Mean fluctuation before move Mean fluctuation after move
I I I 10 1 I T j -«t - I
Lower Control Limit
8 S 5 * 0)0>O0)0)0)0>Cr)O0)0>O>O}0>0)0>0)0> en o> A oi Year
Figure 3-3: Control charts used to test for homogeneity in temperature records. Above: Homogeneous series - Dease Lake, B. C. Standardized deviations from the regional mean value fall well within the confidence limits and were found to be randomly scattered about the mean value at 99.9% confidence level. Below: Inhomogeneous series - Edson, Alberta. Data discontinuity is evident in 1970, when station began recording lower temperature values than previously. Station catalogue confirmed station relocation. Watson Lake ^fVhltehorse -f ort Smlth-
Dease Lake hurchill Brochet
^eace River McMurray ^ 4
RupeMI Data completeness ^ 4 *r^e George^ <^ito)i the Pas Prince Albfert (Fraction)
4 4 0.75 to 0.92 Creek 41 ^ary < < 0.92 to 0.95 IV ^ ^ * * < 0.95 to 0.97 <4ethbrjdge 4^ 4-4 < 0.97 to 0.98 0.98 to 1.00
-130 -120 -110 -100 Longitude
Figure 3-4: Station distribution and data availability across Western Canada for monthly surface air temperature
o 51 Alberta, on the other hand displays a clear discontinuity. Figure 3-3 shows that around 1970, a sharp discontinuity appears within the dataset. Prior to 1970, the deviations of the data from the regional value are on average greater than after 1970. The catalogue proved that the station in Edson moved in 1970. When tested statistically, this deviation was significant enough to warrant series adjustment. A total of 22 stations showed correctable discontinuity problems, and 5 stations had to be deleted due to non-correctable problems. After quality control procedures, the final number of stations that remained suitable for further analyses was 119. Table 3-2 shows the final list of stations and quality control procedures performed on data. Contained are the station name, its assigned code, longitude, latitude, the province in which it is located, the temperature homogeneity group to which it belongs, whether it showed any problems with homogeneity (Phom), and statistics of data estimation and record completeness. Figure 3-4 shows the final station distribution.
3.1.2 Data Estimation
To assess the association between air temperature records across the study area and conditions in the tropical Pacific during El Nino events, data for several atmospheric and oceanic variables directly related to El Nino need to be collected. Prior to 1950 most indicators of El Nino-related conditions in the tropical Pacific Ocean were not measured or are not reliable (Fletcher 1985). Consequently, data for this project was restricted to between 1950 and 1996, a total of 47 years. During this time period, 12 El Nino events were recorded in the Pacific Ocean (discussed in Chapter 5). Because of such limited sample size, it was of interest to utilize as much data as possible within the available time frame. It is for this reason that data were estimated whenever possible. Data estimation of surface air temperature records was done using least-squared multi-variate linear regression (Kvanli 1988). A model for each station was developed using each of the prior-derived 26 groups. Estimation was performed on monthly time-
series. A station's temperature data (Ys) for each month was estimated as a function of the other stations' temperature data (xO within the same group such that Table 3-2: Final stations chosen for analysis of surface temperature * 1950-9 6 Station Name Code Long Lat Prov Group PHoin Estimated* Complete' ABBOTS FORD A 228 -122.22 49.20 BC 5 • 0.8% 98.0% ANEROID 488 -107.18 49.43 SASK 18 • 8.1% 96.6% ATHABASCA 2 74.5 -113.32 54.49 ALTA 16 0.0% 91.0% BANFF 62 -115 34 51.11 ALTA 14 • 1.5% 94.6% BARKER VILLE 214 -121.31 53.40 BC 8 • 7.4% 94.4% BARRJERE 333 -120.70 51.11 BC 11 • 19.6% 92.6% BEAVER MINES 63 -114.10 49.28 ALTA 14 1.0% 97.5% BEECHY 490 -107.19 50.46 SASK 18 • 7.8% 87.7% BELLA COOIA 177 -126.41 52.22 BC 7 1.4% 95.8% BIG CREEK 208 -123.40 51.40 BC 7 • 23.3% 92.9% BLUE RIVER NORTH 335.5 -119.17 52.90 BC 13 6.1% 94 1% BRANDON CDA 384 -99.59 49.52 MAN 22 • 3.9% 97.0% BROADVIEW A 446.5 -102.32 50.15 SASK 22 2.4% 90.7% BROCHET A 435 -101.41 57.53 MAN 24 • 29.9% 89.2% BURNS LAKE DECKER LA 216.5 -125.48 54.15 BC 6 • 24.5% 93.2% CALGARY INTL A 39 -114 10 51.70 ALTA 15 • 0.0% 99 2% CAMPSIE 76 -114.41 54.80 ALTA 12 • 3.5% 96.5% CAMROSE 4 -112.49 53.20 ALTA 16 • 7.6% 92.4% CAPE ST JAMES CS 172 -131.10 51.56 BC 2 • 6.8% 92.6% CARLYLE 447 -102.16 49.38 SASK 22 • 4.6% 93.1% CARWAY 41 -113.22 49.00 ALTA 14 • 10.8% 91.4% CEYLON 449.5 -104.39 49.23 SASK 21 • 0.0% 75.0% CHURCHILL A 436 -94.40 58.44 MAN 25 • 0.0% 99.3% COLD LAKE A 124 -110.17 54.25 ALTA 20 • 5.4% 97.0% COMOXA 131 -124.54 49.43 BC 5 0.2% 100.0% CORONATION A 6 -111.27 52.40 ALTA 15 yl 0.0% 91.4% CORTES ISLAND 132 -125.20 50.60 BC 5 • 16.6% 96.1% CRANBROOKA 323.5 -115.47 49.37 BC 13 • 6.9% 98.8% CRESTON 305 -116.31 49.60 BC 13 • 1.4% 97.5% DAUPHIN A 425 -100.30 51.60 MAN 22 • 0.3% 92.4% DAWSON CREEK A 367.5 -120.11 55.44 BC 12 • 17.1% 95.9% DEASELAKE 374 -130.00 58.25 BC 4 • 0.2% 96.3% EDMONTON MUNICIPAL A 10 -113.31 53.34 ALTA 16 n 0.0% 99.2% EDSONA 81.5 -116.28 53.35 ALTA 16 m 0.2% 91.2% ENTRANCE 82 -117.41 53.23 ALTA 16 • 31.6% 92.9% ESTEVAN POINT 152 -126.33 49.23 BC 2 • 1.2% 97.1% FAIR VIEW 108 -118.23 56.40 ALTA 12 • 7 8% 93.4% FLIN FLON 430 -101.51 54.46 MAN 20 • 10.1% 98.3% FORT CHTPKWYAN A 110 5 -111 70 58 46 ALTA 17 34.0% 959% FORT MACLEOD NORTH 49.5 -113.17 49.52 ALTA 14 K 3.0% 91.4% Phom = Data homogeneity problems. Table 3-2: Final stations chosen for analysis of surface temperature *1950-96 Station Name Code Long Lat Prov Group Pliom Estimated* Complete*
FORT MCMURRAY A 381 -111.13 56.39 ALTA 17 0.2% 99.0% FORT NELSON A 375 -122.35 58.50 BC 9 • 2.5% 98.0% FORT RELIANCE 575 -109.10 62.43 NWT 17 • 15.9% 99.0% FORT RESOLUTION A 578 -113.41 61.11 NWT 17 • 38.7% 96.3% FORT SMITH A 583 -111.57 60.10 NWT 17 • 0.0% 97.5% FORT ST JAMES 218 -124.15 54.27 BC 8 • 1.9% 96.8% FORT ST JOHN A 369 -120.44 56.14 BC 12 • 0.0% 95.4% GERMANSEN LANDING 370 -124.42 55.47 BC 8 • 4.4% 91.6% GOLDEN A 361 -116.59 51.18 BC 13 3.9% 97.3% GRAND FORKS 298 -118.28 49.20 BC 10 • 1.7% 96.3% GRANDE PRAIRIE A 114 -118.53 55.11 ALTA 12 •J 0.0% 98.6% GRAVELBOURG 494 -106.33 49.53 SASK 18 • 9.6% 93.6% GREAT FALLS 419 -96.00 50.28 MAN 23 8.1% 91.7% HAINES JUNCTION 552 -137.35 60.46 YT 4 27.9% 91.4% HAY RIVER A 585.5 -115.47 60.50 NWT 17 • 0.5% 98.3% HOPE A 264 -121.29 49.22 BC 10 • 0.2% 93.6% INDIAN HEAD CDA 457 -103.40 50.32 SASK 21 2.9% 91.0% ISLAND FALLS 534 -102.21 55.32 SASK 20 • 2.9% 92.6% JASPER 66 -118.40 52.53 ALTA 13 0.0% 94.1% KAMLOOPS A 344.5 -120.27 50.42 BC 11 • 0.0% 98.3% LACOMBE CDA 29 -113.45 52.28 ALTA 15 • 0.5% 91.4% LANGARA 173 -133.30 54.15 BC 1 • 3.5% 98.6% LEADER 2 501.5 -109.32 50.53 SASK 18 • 21.3% 89.5% LKTHBRIDGE CDA 52 -112.47 49.42 ALTA 14 • 5.1% 87.5% LOST RIVER 542 -104.20 53.17 SASK 20 • 11.3% 98.5% LYTTON 267.5 -121 35 50.14 BC 11 9.3% 95.1% MCINNES ISLAND 188 -128.43 52.16 BC 2 • 10.1% 93.4% MJDALE 465 -103.24 49.24 SASK 22 • 8.4% 93.6% MOOSE JAW A 466 -105.33 50.20 SASK 21 • 3.5% 93.1% MOOSOMIN 467 -101.40 50.80 SASK 22 • 8.4% 94.3% MORDEN CDA 408 -98.50 49.11 MAN 23 • 2.4% 98.6% MUENSTER 468 -105.00 52.11 SASK 19 • 9.1% 95 1% NORTH BATTLEFORD A 516 -108.15 52.46 SASK 19 • 0.3% 98.5% ORMISTON 470 -105.22 49.43 SASK 21 • 6.6% 82.6% OUTLOOK PFRA 524 -107.30 51.29 SASK 19 • 8.8% 87.7% PACHENA POINT 155 -125.60 48.43 BC 2 • 6.3% 98.1% PEACE RIVER A 116 -117.26 56.14 ALTA 12 • 17.4% 95.6% PEKISKO 69 -114.25 50.22 ALTA 14 • 5.6% 93.8% PENNANT 504 -108.14 50.32 SASK 18 4.6% 85.6% PERSON 392 -101.16 49.11 MAN 22 • 3.2% 96.3% Table 3-2: Final stations chosen for analysis of surface temperature *1950-96 Station Name Code Long Lat Prov Group PHom Estimated* Complete*
PORT HARDY A 145 -127.22 50.41 BC 3 • 0.2% 98.5% PRINCE ALBERT A 527 -105.41 53.13 SASK 20 • 0.7% 99.8% PRINCE GEORGE A 222 -122.41 53.53 BC 8 0.5% 99.2% PRINCE RUPERT A 191.5 -130.26 54.18 BC 2 0.3% 93.1% PRINCETON A 290 -120.31 49.28 BC 10 • 0.3% 97.6% RANFURLY 14 -111.39 53.27 ALTA 16 • 8.1% 90.7% REGINAA 474 -104.40 50.26 SASK 21 0.2% 95.3% REVELSTOKE A 364.5 -118.11 50.58 BC 11 0 0.3% 93.6% SALMON ARM 2 351.5 -119.17 50.42 BC 11 19.3% 95.3% SANDSPIT A 175 -131.49 53.15 BC 2 • 0.0% 94.9% SASKATOON A 528 -106.41 52.10 SASK 19 0 4.1% 93.8% SMITHERS A 202 -127.11 54.49 BC 6 • 2.5% 99.7% SPRAGUE 414 -95.36 49.10 MAN 23 • 2.2% 96.3% SUFFIELD A 56 -111.11 50.16 ALTA 18 • 1.7% 97.6% SUMMERLAND CDA 292 -119.39 49.34 BC 10 • 4.9% 96.6% SWANRTVER 429 -101.60 52.30 MAN 26 • 14.5% 97.1% TAHTSA LAKE WEST 210 -127.42 53.37 BC 6 • 5.6% 94.3% TATLAYOKO LAKE 212 -124.24 51.40 BC 7 0 0.7% 95.3% TERRACEA 199.5 -128.35 54.28 BC 6 • 4.9% 99.0% TESLIN A 558 -132.45 60.10 YT 4 • 11.7% 95.6% THE PAS A 433 -101.60 53.58 MAN 20 • 0.7% 99.8% TOFINOA 163 -125.46 49.50 BC 3 • 14.0% 90.9% TROCHU TOWN 36.5 -113.13 51.50 ALTA 15 19.4% 95.4% TUGASKE 478 -106.18 50.53 SASK 18 • 4.9% 97.6% VANCOUVER INTL A 257 -123.10 49.11 BC 5 0.5% 99.5% VAVENBY 355 -119.47 51.35 BC 11 • 14.5% 99.3% VERNON COLDSTREAM R 295 -119.12 50.14 BC 11 • 1.7% 96.8% VTRDEN 398 -100.56 49.51 MAN 22 • 13.5% 96.5% WARFIELD 319 -117.45 49.60 BC 10 n 4.6% 97.5% WASECA 518 -109.24 53.80 SASK 20 • 4.9% 99.0% WATROUS 479 -105.28 51.40 SASK 19 • 1.5% 89.7% WATSON LAKE A 559 -128.49 60.70 YT 9 • 4.7% 98.0% WHJTECOURT A 100.5 -115.47 54.90 ALTA 12 • 2.9% 99.3% WMTEHORSE A 561 -135.40 60.43 YT 4 • 1.9% 96.8% WHJTESAND DAM 540 -103.90 56.14 SASK 24 • 0.0% 77.0% WINNIPEG INTL A 415 -97.14 49.54 MAN 23 • 0.7% 92.7% WISTARIA 213 -126.13 53.49 BC 6 0 2.2% 97.3% YELLOWKNIFE A 594 -114.27 62.28 NWT 17 • 0.0% 99.0% YORKTONA 486 -102.28 51.16 SASK 26 • 0.8% 99 3% 55
Ys = B0+/3lXsl + B2%s2 +....Bi%Sl ±e 3-1
where p\ represent the regression coefficients, and e is the standard error of the prediction. The number of stations used depended on the number of stations within a group. The minimum number was two, the maximum four. A final model had to satisfy the following criteria to be classified as acceptable: 1) The R2 value of the model had to exceed 0.8; 2) the minimum number of degrees of freedom in the model had to be at least 10; 3) residual plots had to be random. If one or more of these criteria were not met, the model was discarded and new predictors were searched for by selecting new stations as predictors. Otherwise, no data estimation was performed. The original dataset was 87% complete. An average of 8% of data was estimated making the data record 95% complete.
3.3 Trend Removal
During the preparation of data for analysis, statistically significant temporal trends were identified in records of many stations across the study area. Vast amount of literature documents similar presence of temporal trends within surface temperature records around the globe (Houghton etal. 1996; Jones and Briffa 1992; Mann etal. 1998). It was decided to remove temporal trends from the monthly datasets, so that the extracted El Nino signal magnitudes would not be affected by such a change not directly related to ENSO. The presence of trend would alter its magnitude and inter-El Nino variation characteristics. Signal estimates (Chapter 5) for earlier events would be considerably underestimated, and those calculated for later El Nifios would have been overestimated. Positive trend would increase the mean monthly signal and the variation from one El Nino to another. Traditional statistical time series analyses such as cross-correlation and eigenvector regionalization also require that the data utilized in the testing or model creation be stationary (Davis 1973). With the presence of a temporal trend within the dataset, the results of these analyses and tests would not be reliable. The trend was removed by fitting a polynomial model of a second degree, to the monthly series. The model was of the following format:
2 YT=pxty + B2ty + B0±e 3-2
where YT represents the data to be predicted, the trend value, ty represents time in years, 0i and p2 signify the regression coefficients and e is the standard error of the regression mean value. Residual values of each model were then used in subsequent analyses.
-10-
O
CD -15 i— 03
& -20 E CD —I -25 Recorded Value (C)
C CD -30 Detrended Value (C) 1950 1956 1962 1968 1974 1980 1986 1992 1953 1959 1965 1971 1977 1983 1989 1995
Year
Figure 3-5: Surface temperature data for January at Calgary International Airport, Alberta. Original series is shown in red Detrended series via polynomial filter is shown in green. Average trend correction for this station was 0.162°C per year. Similar analyses were performed for all other stations and months. Figure 3-5 shows the record for Calgary International Airport, Alberta for January before and after the de-trending routine was applied. The temporal trend in the original series was found significant at the 99% confidence level. After the application of the polynomial filter, the series was stationary. Similar analyses were performed for all other stations and months of the year. Table 3-3 shows the results, including seasonal averages. Figure 3-6 displays the monthly and seasonal summaries in graphical format. Greatest change in surface air temperatures between 1950 and 1996 was found during winter (DJF) and spring months (MAM). These magnitudes are positive, unlike during the fall (SON) when negative trends were found to be common throughout the study area. On average, the summer season shows little change.
3.4 Spatial interpolation
The last step of data verification included the projection of the data onto regular grids. Several spatial interpolation methods such as inverse distance, kriging, nearest neighbor and minimum curvature (Keckler 1997) were tested. A total of 144 surface air temperature maps (12 El Nino events for 12 months - Chapter 5) were produced. Test runs were developed where 36 surfaces were randomly chosen, testing three surfaces for each month (3X12). For each method and each surface, the interpolated grid file values
(Tt) were calculated for every original station point, (Ts), which enabled the computation of a residual values (e) at that location:
Tt=Ts±e 3-3
The goal of the testing was to choose a gridding method that would reduce the mean error of interpolation (s) to less than 10%. The residuals had to be randomly distributed through both time and space, and the distribution of their magnitude should be normal, such that most residuals clustered around the mean residual value (close to 0%), with 58
Figure 3-6: Mean amount of trend (°C/year) removed from original surface air temperature data. Above: Monthly trend. Below: Seasonal trend. Table 3-3: Magnitude of trend (C/year) removed from original surface air temperature data. 1950-1996.
Station name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJF MAM JJA SON ABBOTS FORD A 0 023 0.020 0 064 0 043 0 022 0 028 0.020 0.038 0.019 0.008 -0.008 -0.034 0 003 0 043 0 029 0 006 ANEROID 0 129 0.095 0 148 0 098 0 055 0 040 -0.013 -0.001 0.009 -0.015 -0.011 -0.033 0 064 0 101 0 009 -0 006 ATHABASCA 2 0 072 0.064 0 038 0 045 0 009 -0.00 3 0.010 0.020 0.013 0.001 0.009 0.024 0 054 0 031 0 009 0 008 BANFF 0 087 0.035 0 104 0 085 0 024 0 021 -0.015 0.007 0.013 0.005 -0.011 -0.038 0 028 0 071 0 004 0 002 BARKERVILLE 0 071 0.016 0 078 0 062 0 030 0 030 0.007 0.038 0.018 0.015 -0.010 -0.018 0 023 0 057 0 025 0 007 BARRIERE 0 038 0.015 0 070 0 037 0 016 0 025 0.002 0.031 0.000 -0.002 -0.004 -0.025 0 009 0 041 0 019 -0 002 BEAVER MINES 0 107 0.024 0 049 0 060 0 021 -0.01 7 -0.020 -0.008 -0.002 -0.008 -0.016 -0.037 0 031 0 043 -0 015 -0 008 BEECHY 0 128 0.091 0 165 0 100 0 054 0 043 -0.007 0.000 0.018 0.001 0.021 -0.003 0 072 0 106 0 012 0 013 BELLA COOLA 0 088 0.002 0 055 0 040 0 024 0 015 0.014 0.035 0.014 0.004 0.012 0.012 0 034 0 039 0 022 0 010 BIG CREEK 0 098 -0.009 0 078 0 042 0 008 0 015 0.006 0.033 0.000 -0.006 -0.004 -0.011 0 026 0 043 0 018 -0 003 BLUE RIVER NORTH 0 030 -0.003 0 030 0 028 0 034 0 022 -0.004 0.023 0.006 0.007 -0.009 -0.049 -0 007 0 030 0 014 0 001 BRANDON CDA 0 106 0.081 0 099 0 073 0 073 0 026 -0.017 -0.015 0.002 -0.067 -0.040 -0.062 0 042 0 082 -0 002 -0 035 BROADVIEW A 0 124 0.069 0 057 0 092 0 079 0 038 0.004 0.000 0.021 -0.038 -0.020 -0.061 0 044 0 076 0 014 -0 012 BROCHETA 0 120 0.022 0 111 0 159 0 136 0 059 0.009 0.008 -0.021 -0.066 -0.065 0.022 0 054 0 135 0 025 -0 051 BURNS LAKE DECKER LAKE 0 134 0.030 0 073 0 052 0 026 0 038 0.033 0.049 0.047 0.027 0.019 0.011 0 058 0 050 0 040 0 031 CALGARY INT'L A 0 162 0.056 0 114 0 106 0 032 0 027 -0.020 0.003 0.009 -0.009 -0.009 0.009 0 076 0 084 0 003 -0 003 CAMPSIE 0 174 0.029 0 118 0 086 0 019 0 025 -0.003 0.008 0.000 -0.020 -0.017 0.004 0 069 0 074 0 010 -0 012 CAMROSE 0 179 0.051 0 117 0 099 0 027 0 030 -0.012 -0.011 0.017 0.008 0.002 0.026 0 085 0 081 0 002 0 009 CAPE ST JAMES CS 0 045 0.021 0 051 0 032 0 017 0 019 0.025 0.022 0.026 0.027 0.015 0.023 0 030 0 033 0 022 0 023 CARLYLE 0 119 0.092 0 142 0 090 0 076 0 045 0.024 0.018 0.028 -0.049 -0.001 -0.012 0 066 0 102 0 029 -0 007 CARWAY 0 099 0.002 0 125 0 086 0 025 0 018 -0.021 -0.013 -0.008 -0.017 -0.043 -0.027 0 024 0 078 -0 005 -0 022 CEYLON 0 158 0.083 0 142 0 110 0 079 0 036 0.048 0.036 0.007 -0.026 0.037 -0.091 0 050 0 110 0 040 0 006 CHURCHILL A 0 059 0.028 0 032 0 042 0 058 0 005 -0.008 -0.003 -0.014 -0.051 -0.072 -0.054 0 011 0 044 -0 002 -0 045 COLD LAKE A 0 131 0.057 0 134 0 095 0 021 0 021 -0.005 0.003 0.000 -0.038 -0.033 -0.040 0 049 0 083 0 006 -0 024 COMOX A 0 022 0.004 0 013 0 007 0 008 0 012 0.004 0.031 0.017 0.012 0.006 -0.016 0 004 0 009 0 016 0 011 CORONATION A 0 224 0.007 0 231 0 165 0 018 0 009 -0.071 -0.026 -0.019 -0.042 -0.051 -0.042 0 063 0 138 -0 029 -0 037 CORTES ISLAND 0 033 0.019 0 054 0 036 0 017 0 019 0.012 0.028 0.022 0.026 0.015 -0.004 0 016 0 036 0 020 0 021 CRANBROOK A 0 028 0.026 0 095 0 065 0 028 0 025 0.005 0.024 0.020 0.012 0.003 -0 068 -0 004 0 063 0 018 0 011 CRESTON 0 008 0.007 0 076 0 053 0 018 0 027 -0.025 0.010 0.002 0.018 0.010 -0.050 -0 012 0 049 0 004 0 010 DAUPHIN A 0 138 0.078 0 126 0 067 0 059 0 006 -0.014 -0.015 -0.001 -0.070 -0.033 -0.030 0 062 0 084 -0 008 -0 035 DAWSON CREEK A 0 222 0.052 0 137 0 075 0 021 0 026 0.002 0.008 0.021 -0.008 -0.026 0.030 0 101 0 078 0 012 -0 005 DEASE LAKE 0 150 0.011 0 084 0 049 0 014 0 006 0.008 -0.006 -0.006 -0.010 -0.024 0.031 0 064 0 049 0 003 -0 013 EDMONTON MUNICIPAL A 0 204 0.060 0 128 0 099 0 032 0 029 -0.004 0.011 0.007 -0.009 -0.015 0.020 0 094 0 086 0 012 -0 006 EDSON A -0.01 2 0.008 0 012 0 028 0 016 0 020 -0.010 0.005 -0.002 -0.031 -0.044 -0.006 -0 003 0 019 0 005 -0 025 ENTRANCE 0 160 0.065 0 081 0 066 0 024 0 007 -0.007 0.012 0.015 -0.005 -0.001 0.043 0 089 0 057 0 004 0 003 ESTEVAN POINT 0 021 0.010 0 051 0 028 0 017 0 008 0.006 0.011 0.001 0.006 -0.001 -0.015 0 006 0 032 0 009 0 002 FAIRVIEW 0 224 0.055 0 120 0 107 0 019 0 026 0.005 0.009 0.015 -0.017 0.006 0.041 0 107 0 082 0 013 0 001 FLIN FLON 0 140 0.060 0 112 0 088 0 065 0 015 -0.003 0.015 0.013 -0.045 -0.026 -0.017 0 061 0 088 0 009 -0 019 Table 3-3: Magnitude of trend (C/year) removed from original surface air temperature data. 1950-1996.
Station name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJF MAM JJA SON FORT CHIPEWYAN A 0 153 0.060 0 098 0 111 0.030 0.017 0.010 0.002 0.011 -0.024 -0.053 -0.018 0 065 0 080 0 010 -0.022 FORT MACLEOD NORTH 0 135 0.034 0 110 0 096 0.029 0.022 -0.040 -0.011 -0.005 -0.036 -0.052 -0.055 0 038 0 078 -0 010 -0.031 FORT MCMURRAY A 0 184 0.076 0 137 0 109 0.052 0.036 0.011 0.011 0.014 -0.040 -0.037 -0.017 0 081 0 099 0 020 -0.021 FORT NELSON A 0 158 0.035 0 120 0 096 0.020 0.022 -0.004 -0.004 0.021 -0.006 -0.045 0.051 0 081 0 079 0 005 -0.010 FORT RELIANCE 0 083 0.062 0 056 0 081 0.045 0.024 0.014 -0.024 -0.014 -0.037 -0.052 -0.009 0 046 0 061 0 004 -0.034 FORT RESOLUTION A 0 149 0.079 0 100 0 112 0.039 0.011 0.007 -0.019 -0.013 -0.037 -0.086 -0.001 0 076 0 083 0 000 -0.045 FORT SMITH A 0 150 0.080 0 116 0 133 0.044 0.016 0.005 -0.007 0.008 -0.022 -0.044 -0.001 0 077 0 098 0 005 -0.019 FORT ST JAMES 0 121 0.024 0 063 0 057 0.037 0.037 0.032 0.067 0.044 0.024 0.010 0.018 0 055 0 052 0 045 0.026 FORT ST JOHN A 0 237 0.047 0 127 0 093 0.013 0.019 0.002 0.005 0.014 -0.003 -0.037 0.047 0 110 0 078 0 009 -0.009 GERMANSEN LANDING 0 139 0.030 0 082 0 071 0.028 0.025 0.018 0.042 0.021 0.014 0.001 0.007 0 059 0 060 0 028 0.012 GOLDEN A 0 027 -0.012 0 055 0 044 0.002 -0.002 -0.029 -0.003 -0.018 -0.014 0.001 -0.036 -0 007 0 034 -0 011 -0.010 GRAND FORKS 0 011 0.018 0 075 0 049 0.028 0.030 -0.004 0.027 0.013 0.013 0.005 -0.035 -0 002 0 051 0 018 0.010 GRANDE PRAIRIE A 0 089 0.035 0 093 0 061 0.020 0.023 -0.008 0.001 0.008 -0.025 -0.025 0.027 0 050 0 058 0 005 -0.014 GRAVELBOURG 0 115 0.078 0 129 0 088 0.040 0.028 -0.008 -0.012 -0.002 -0.021 -0.016 -0.032 0 054 0 085 0 003 -0.013 GREAT FALLS 0 109 0.081 0 037 0 062 0.090 0.004 -0.009 -0.007 0.002 -0.072 -0.048 -0.024 0 055 0 063 -0 004 -0.039 HAINES JUNCTION 0 146 0.013 0 116 0 041 0.024 0.005 0.019 0.015 -0.017 0.013 0.021 0.143 0 101 0 060 0 013 0.005 HAY RIVER A 0 166 0.066 0 087 0 105 0.037 0.020 -0.007 -0.012 0.000 -0.027 -0.056 0.016 0 083 0 076 0 001 -0.028 HOPE A 0 046 0.008 0 069 0 037 0.007 0.008 -0.010 0.026 0.019 0.014 0.001 -0.017 0 012 0 038 0 008 0.011 INDIAN HEAD CDA 0 099 0.058 0 130 0 089 0.072 0.036 0.022 0.000 0.007 -0.048 -0.010 -0.053 0 035 0 097 0 019 -0.017 ISLAND FALLS 0 109 0.001 0 071 0 078 0.061 0.033 0.002 0.004 -0.012 -0.074 -0.038 -0.034 0 025 0 070 0 013 -0.041 JASPER 0 139 0.029 0 084 0 071 0.022 0.025 -0.007 0.010 0.002 -0.007 0.000 -0.013 0 051 0 059 0 010 -0.002 KAMLOOPS A 0 052 0.036 0 074 0 047 0.018 0.024 0.003 0.036 0.012 0.008 0.000 -0.027 0 020 0 046 0 021 0.007 LACOMBE CDA 0 148 0.032 0 105 0 088 0.015 0.008 -0.042 -0.015 -0.010 -0.018 -0.031 -0.004 0 058 0 069 -0 016 -0.019 LANGARA 0 044 0.014 0 038 0 021 0.008 0.011 0.010 0.007 0.000 0.015 0.010 0.025 0 028 0 022 0 009 0.008 LEADER 2 0 134 0.068 0 131 0 089 0.041 0.020 -0.023 -0.009 -0.013 -0.031 -0.032 -0.033 0 057 0 087 -0 004 -0.025 LETHBRIDGE CDA 0 180 0.054 0 144 0 094 0.041 0.037 -0.009 -0.001 0.006 -0.022 -0.024 -0.037 0 066 0 093 0 009 -0.013 LOST RIVER 0 137 0.063 0 133 0 098 0.057 0.028 -0.005 0.001 0.015 -0.041 -0.028 -0.031 0 057 0 096 0 008 -0.018 LYTTON 0 041 -0.006 0 015 0 030 -0.008 0.012 -0.027 0.014 -0.007 -0.003 -0.009 -0.048 -0 004 0 013 0 000 -0.006 MCINNES ISLAND 0 052 0.020 0 055 0 026 0.009 0.008 0.017 0.006 -0.002 0.013 0.014 0.012 0 028 0 030 0 010 0.008 MIDALE 0 091 0.064 0 116 0 067 0.054 0.020 0.000 -0.002 0.012 -0.045 -0.016 -0.036 0 040 0 079 0 006 -0.017 MOOSE JAW A 0 118 0.073 0 130 0 076 0.047 0.026 -0.015 -0.014 -0.008 -0.037 -0.026 -0.044 0 049 0 084 -0 001 -0.024 MOOSOMIN 0 117 0.060 0 132 0 066 0.058 0.022 -0.018 -0.014 0.003 -0.059 -0.019 -0.034 0 048 0 085 -0 003 -0.025 MORDEN CDA 0 115 0.066 0 096 0 076 0.080 0.008 -0.007 -0.014 0.008 -0.069 -0.033 -0.029 0 051 0 084 -0 004 -0.031 MUENSTER 0 159 0.054 0 115 0 108 0.050 0.033 -0.014 -0.010 0.004 -0.029 -0.052 -0 026 0 062 0 091 0 003 -0.026 NORTH BATTLEFORD A n 123 0.031 0 129 0 099 0.032 0.009 -0.032 -0.019 -0.009 -0.049 -0.046 -0.059 0 031 0 087 -0 014 -0.035 ORMISTON 0 129 0.084 0 135 0 095 0.055 0.015 -0.011 -0.011 -0.006 -0.039 -0.021 -0.049 0 055 0 095 -0 002 -0.022 OUTLOOK PFRA 0 137 0.076 0 151 0 087 0.033 0.037 -0.017 -0.007 0.005 -0.024 -0.027 -0.052 0 054 0 090 0 004 -0.016 PACHENA POINT 0 023 0.032 0 062 0 048 0.028 0.028 0.028 0.027 0.023 0.016 0.007 -O005 0 016 0 046 0 028 0.015 Table 3-3: Magnitude of trend (C/year) removed from original surface air temperature data. 1950-1996.
Station name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJF MAM JJA SON PEACE RIVER A 0 201 0.054 0.126 0 105 0.026 0.029 0.009 0.012 0.012 -0.025 0.003 0.043 0 099 0.086 0,017 -0.003 PEKISKO 0 106 0.017 0.102 0 089 0.024 0.013 -0.018 -0.003 -0.004 -0.027 -0.030 -0.032 0 030 0.072 -0.003 -0.020 PENNANT 0 027 0.098 0.023 0 046 0.049 0.037 -0.003 0.004 0.011 -0.012 0.001 -0.024 0 034 0.039 0.013 0.000 PIERSON 0 117 0.094 0.116 0 075 0.070 0.037 0.004 -0.007 0.021 -0.052 -0.011 -0.039 0 058 0.087 0.012 -0.014 PORT HARDY A 0 031 0.014 0.051 0 031 0.021 0.004 0.019 0.015 0.000 0.002 0.006 -0.006 0 013 0.034 0.013 0.002 PRINCE ALBERT A 0 125 0.068 0.152 0 098 0.051 0.031 -0.001 -0.002 0.004 -0.036 -0.026 -0.026 0 056 0.100 0.009 -0.019 PRINCE GEORGE A 0 130 0.030 0.121 0 096 0.053 0.044 0.018 0.066 0.041 -0.004 -0.003 -0.009 0 051 0.090 0.043 0.011 PRINCE RUPERT A 0 028 -0.006 0.027 0 014 0.021 0.016 0.020 0.014 -0.002 -0.011 -0.013 0.003 0 008 0.020 0.017 -0.009 PRINCETON A 0 039 0.011 0.083 0 042 0.018 0.026 -0.008 0.027 0.002 0.017 0.022 -0.036 0 005 0.048 0.015 0.014 RANFURLY 0 203 0.047 0.133 0 098 0.037 0.040 -0.002 0.009 0.008 -0.031 -0.029 0.000 0 083 0.089 0,015 -0.017 REGINAA 0 101 0.068 0.080 0 090 0.056 0.008 -0.014 -0.015 -0.002 -0.039 -0.024 -0.047 0 041 0.075 -0.007 -0.022 REVELSTOKE A 0 040 -0.016 0.035 0 048 0.019 0.019 0.009 -0.011 0.011 0.002 0.018 -0.007 0 006 0.034 0.006 0.010 SALMON ARM 2 0 034 0.029 0.054 0 040 0.028 0.041 0.009 0.035 0.018 0.015 0.007 -0.029 0 011 0.041 0.028 0.013 SANDS PIT A 0 040 0.015 0.041 0 021 0.016 0.016 0.012 0.019 0.018 0.022 0.013 0.021 0 025 0.026 0.016 0.018 SASKATOON A 0 135 0.057 0.055 0 085 0.039 0.031 -0.008 -0.005 -0.001 -0.039 -0.032 -0.067 0 042 0.060 0.006 -0.024 SMITHERSA 0 102 0.024 0.064 0 045 0.018 0.018 0.019 0.031 0.013 -0.003 0.004 0.004 0 044 0.042 0.023 0.004 SPRAGUE 0 086 0.079 0.094 0 060 0.073 0.003 -0.002 0.006 0.012 -0.066 -0.057 -0.030 0 045 0.076 0.002 -0.037 SUFFIELD A 0 148 0.073 0.141 0 094 0.036 0.025 -0.030 -0.015 -0.010 -0.029 -0.029 -0.023 0 066 0.090 -0.007 -0.023 SUMMERLAND CDA 0 018 0.006 0.055 0 027 0.006 0.015 -0.023 0.008 -0.007 0.005 0.002 -0.047 -0 008 0.029 0.000 0.000 SWAN RIVER 0 146 0.083 0.135 0 079 0.064 0.022 -0.011 -0.003 0.021 -0.045 -0.031 -0.026 0 068 0.093 0.003 -0.018 TAHTSA LAKE WEST 0 120 0.023 0.096 0 051 0.003 0.012 0.001 0.026 0.014 0.020 0.024 0.035 0 059 0.050 0.013 0.019 TATLAYOKO LAKE 0 010 0.005 0.033 0 023 0.013 0.018 -0.007 0.022 0.010 0 000 -0.010 -0.038 -0 008 0.023 0.011 0.000 TERRACE A 0 055 0.001 0.056 0 057 0.038 0.016 0.012 0.020 0.006 0.006 0.004 0.010 0 022 0.050 0.016 0.005 TESLIN A 0 144 -0.001 0.088 0 058 0.023 -0.003 0.011 0.008 -0.006 0.009 -0.034 0.052 0 065 0.056 0.005 -0.010 THE PAS A 0 139 0.063 0.123 0 088 0.056 0.007 -0.014 0.003 0.000 -0.054 -0.027 -0.015 0 062 0.089 -0.001 -0.027 TOFINO A 0 026 0.009 0.044 0 023 0.002 0.002 0.002 0.011 0.009 0.004 0.000 -0.015 0 007 0.023 0,005 0.004 TROCHU TOWN 0 161 0.051 0.134 0 069 0.027 0.023 -0.025 -0.005 -0.001 -0.009 -0.020 0.004 0 072 0.077 -0.002 -0.010 TUGASKE 0 131 0.079 0.150 0 090 0.051 0.038 -0.008 -0.001 0.004 -0.027 -0.021 -0.037 0 058 0.097 0,010 -0.015 VANCOUVER INT'L A 0 011 0.006 0.042 0 020 0.006 0.013 0.004 0.022 0.012 0.009 -0.004 -0 026 -0 003 0.023 0.013 0.006 VAVENBY 0 039 0.015 0.073 0 053 0.022 0.024 -0.005 0.021 -0.015 -0.015 -0.005 -0.025 0 009 0.050 0,013 -0.011 VERNON COLDSTREAM RANCH 0 018 0.005 0.066 0 040 0.009 0.017 -0.005 0.027 0.010 0.009 -0.003 -0.044 -0 007 0.038 0.013 0.005 VIRDEN 0 151 0.145 0.113 0 082 0.066 0.014 -0.017 -0.009 0.020 -0.050 -0.021 -0.014 0 094 0.087 -0.004 -0.017 WARFIELD 0 007 0.007 0.061 0 032 0.001 0.013 -0.031 0.005 -0.011 0.000 -0.018 -0.043 -0 010 0.031 -0.004 -0.010 WASECA 0 160 0.053 0.135 0 105 0.036 0.036 -0.007 0.006 0.010 -0.026 -0.028 -0.036 0 059 0.092 0.012 -0.015 WATROUS 0 152 0.084 0.164 0 099 0 053 0.040 0.006 0.003 0.015 -0.022 -0.033 -0.057 0 060 0.106 0.016 -0.013 WATSON LAKE A 0 164 0.004 0.070 0 046 0.020 -0.012 -0.001 -0.011 -0.012 -0.010 -0.054 0.031 0 066 0.045 -0.008 -0.025 WHITECOURT A 0 217 0.075 0.144 0 109 0.043 0.038 0.012 0.023 0.019 0.014 0.024 0.068 0 120 0.098 0.024 0.019 WHITEHORSE A 0 141 -0.006 0.090 0 044 0.012 -0.018 -0.008 -0.006 -0.020 -0.002 -0.021 0093 0 076 0.048 -0.011 -0.014 Table 3-3: Magnitude of trend (C/year) removed from original surface air temperature data. 1950-1996.
Station name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJF MAM JJA SON WINNIPEG INT'L A 0.089 0.067 0.089 0.060 0.07O -0.001 -0.012 -0.016 -0.005 -0.085 -0.052 -0.040 0.039 0.073 -0.010 -0.047 WISTARIA 0.126 0.008 0.022 0.046 0.017 0.011 0.003 0.035 0.021 0.018 0.033 0.038 0.057 0.028 0.016 0.024 WRIGLEY A 0.248 0.053 0.120 0.089 0.033 0.019 0.022 -0.005 0.012 -0.033 -0.066 0.040 0.114 0.081 0.012 -0.029 YELLOWKNIFE A 0.102 0.067 0.071 0.106 0.036 0.004 0.003 -0.014 -0.006 -0.015 -0.035 -0.002 0.056 0.071 -0.002 -0.019 YORKTONA 0.139 0.062 0.119 0.079 0.048 0.006 -0.030 -0.029 -0.010 -0.059 -0.040 -0.052 0.050 0.082 -0.018 -0.036 MEAN 0.108 0.041 0.093 0.070 0.034 0.020 -0.002 0.007 0.006 -0.017 -0.016 -0.013 0.045 0.066 0.008 -0.009 STANDARD DEVIATION 0.059 0.032 0.040 0.030 0.023 0.013 0.017 0.018 0.013 0.026 0.024 0.035 0.031 0.027 0.013 0.018 63 only a few with magnitudes either extremely large or extremely small. Consequently, the amount of error contained in the data was not allowed to show temporal nor spatial dependence. The procedure that most satisfied these requirements was minimum curvature. Minimum curvature is a spatial interpolation method employed in earth sciences. This method is able to generate the smoothest possible surfaces while attempting to honor the original data as closely as possible. The method produces the grids by repeatedly applying an equation over them in an attempt to smooth them. Each pass over the grids is counted as one iteration. The grid node values are recalculated until successive changes in the values are less than the maximum residual of 10%, or until the maximum number of iterations is reached (Keckler 1997; Briggs 1974). The surfaces used in this study were created by the minimum curvature interpolation method, with 10,000 iterations, errors less than 10% for all variables, and anisotropy ratio equal to 1.0. Figure 3-7 shows the results of the test runs. Included is the histogram of the residuals, and its mean monthly distribution. The majority of residuals computed from the 36 maps clustered around the mean value, with only a small percentage of residuals having significantly different magnitudes from the central value. The monthly bar charts all revealed the temporal randomness of the residuals. Spatial analyses of residuals were also performed, and showed no trends significant at 99% confidence levels. The stations were interpolated onto 100 X 46 grids (columns X rows), a spacing of 0.5 longitude by 0.5 latitude.
119 stations were found suitable for analysis after an initial quality control assessment of over 600 stations. In spite of this loss, this study used a significantly higher number of stations when compared to any other studies with comparable spatial extent. Shabbar et al. (1997) and Shabbar and Khandekar (1996) for example, analyzed surface temperature and precipitation anomaly data across entire Canada. The region that delimited Western Canada of this work was defined in the former study by 22 stations, 80% less than used in this study and in the latter by 5°xl0° grid cells. The 119 stations of this study were projected onto regular surfaces with grid sizes of 0.5°x0.5°. 64
1 2 3 4 5 6 7 8 9 10 11 12
Month
Std. Dev = 1.08 Mean = ,04 N-36.00 •3.00 -2.50 -2.00 -1.50 -1.00 -.50 0.00 .50 1.00 1.50 2.00
Histogram of temperature residuals (%)
Figure 3-7: Spatial interpolation of station surface air temperature data. Shown are statistics obtained from the chosen gridding procedure - minimum curvature. Above: Surface temperature mean residual values for each month of the year. Below: Mean histogram of surface temperature residual values. Residuals have no temporal dependence. Majority of residuals computed clustered around a mean value, with only a small proportion showing magnitudes much different from the central value. 65
4. PRINCIPAL METHODS
4.1 Introduction
The study was initiated with the development of the surface air temperature database. Specific considerations discussed here had to be given to the spatio-temporal nature of the data so that it could be examined interchangeably within statistical and GIS environments. Several methodologies that were applied throughout the work will also be presented here. These included GIS, eigenvector regionalization, non-parametric Mann- Whitney U test, Pearson and Spearman's correlation coefficients and cross correlation.
4.2 Database establishment
Variability in regional and local air temperature conditions was studied over a 40- year period of record. A surface air temperature database was created to access, store, query, and extract data in suitable formats for statistical and processing purposes. Because the data were both spatial and temporal, special considerations were taken in the design of the database. Both the temporal and the spatial dimensions needed to be readily accessed. Not all database designs allow for flexible transformations between various formats without extensive labor and/or additional programming. It was of interest to build a spatio-temporal database from which both the spatial and temporal sorts were easily extracted. In order to analyze or visualize the spatial arrangement of air temperature values across Western Canada for January 1983, for instance, the data needed to come in a spatial station-sort format
Figure 4-1: Types of data sorts used in climate research - Station sort. A ^Airport. Surface temperature at Calgary International Airport 1885-1996 (page 1) Year Value 1885 -12 5 1886 -18 7 1887 -15 7 1888 -184 1889 -7 0 1890 -19 7 1891 -2 7 1892 -8 4 1893 -9 3 1894 -12 7 1895 -156 1896 -15 7 1897 -108 1898 -6 1 1899 -104 1900 -5 5 1901 -8 5 1902 -6 5 1903 -5 7 1904 -7 9 1905 -122 1906 -8 8 1907 -21 2 1908 -3 7 1909 -15 7 1910 -60 1911 -18 3 1912 -106 1913 -13 0 1914 -8 6 1915 -67 1916 -22 0 1917 -103 1918 -10 5 1919 -0 6 1920 -101 1921 -8 0 1922 -8 2 1923 -84
Figure 4-2: Types of data sorts used in climate research - Time series sort. 68 simultaneously, the data had to be rearranged in another manner. In one possible format, the temporal dimension was taken as the attribute variable, so that the final data table was in the following format
Microsoft Access database (Simpson and Olson 1996) was chosen to store and maintain the data. It is a database management system, or DBMS. The software is readily available, affordable, accessible, and easy to learn. Access is also a very versatile program, in that it is possible to export data in many formats. It is directly compatible with numerous analytical softwares, including the GIS software IDRISI (Eastman 1997), and SPSS (Norusis 1993), the principal analytical package used.
Access is a development system that includes the Visual Basic programming language and other tools for setting up sophisticated applications. Aside from storage capabilities, the software helps manage data through objects like tables, queries, built-in macros, and forms, just to name a few (Simpson and Olson 1996). Finally, the ACCESS database is compatible with advanced statistical and GIS software packages such as ESRI's GEOD AT ABASE, a data management and modeling module of ARCINFO (Zeiler 1999), and spatial statistics software S-PLUS (MathSoft 1998), now incorporated into ARC VIEW (Ormsby and Alvi 1999). Such features further enhanced the capabilities of the database by allowing for efficient data movement between the statistical and GIS environments, significantly improving the research process. Temperature at various locations (v?) over period of time (page 1) Date vl4 v4 v6 V10 v29 v30 v36-5 v39 v41 v49-5 v50
1/1/50 149 •147 6 1 152 -14 5 -15 1 -15 5 -156 15 4 -175 -15 1 2/1/50 -2 5 -3 3 -2.5 -3 6 -14 -0.4 -1.3 -0.2 1.9 2.5 -1.2 3/1/50 -3 1 -3 6 -5 5 -3.7 -2.9 -3.0 -3.7 -4.6 -3.3 1.8 -2.7 4/1/50 -22 -2.5 -4 2 -2.8 -2.0 -17 -2.7 -2.1 -1.9 0.0 -1.4 5/1/50 -08 -12 -1.8 -14 -1.4 -13 -15 -1 5 -09 -14 -11 6/1/50 0 8 0 1 00 0.5 0.4 1.2 0.0 -0.7 -09 0.9 -0.1 7/1/50 1.5 0.1 0.1 0.3 0 5 0.4 0 1 -0 8 -0.2 1.3 -0.1 8/1/50 -0.9 -1.9-1.6 -2.4 -10 -1.2 -1.5 -1.7 -0.5 0.1 -1.5 9/1/50 1.8 08 1.1 1.2 1 1 1.7 08 0 5 1 5 1 5 07 10/1/50 -3 0 -2 9-2 1 -36 -2 8 -3 0 -27 -2.2 -11 -0 1 -1.6 11/1/50 -5.4 -5.1 -4.4 -6.1 -4 9 -4.5 -4.7 -3 8 -1.4 -1.1 -3 9 12/1/50 07 -03 0.9 -0.5 1.0 2.2 10 2.1 4.9 3.7 1.6 1/1/51 -3 8 -4.1 4.5 -4.7 -4.1 -2.8 -3.9 -3 0 -3.0 -1.7 -2.5 2/1/51 -3 8 -4 1 -4 2 -4.0 -3 8 -3 1 -4 1 -42 -2 9 -3 7 -5.1 3/1/51 -7.0 -6 5 9 4 -7.8 -7.4 -7.8 -7.5 -6.8 -3.7 -7.8 4/1/51 -16 -0 9 -3.2 -1.9 -1.4 -0 8 -1.8 -1.0 -11 -0.7 -1.1 5/1/51 0.5 0.3 0.5 0.1 0.4 0.4 0.4 0 3 0.4 1 1 06 6/1/51 -3.2 -3.1 3 4 -3.0 -2.7 -3.2 -3 3 -3 9 -3 4 -3 1 -3 7 7/1/51 -0.5 0.5 0.0 -09 03 00 0.1 -0.3 0.3 0.4 -0.3 8/1/51 -2.8 -2.0 -2.0 -2.8 -1.7 -2.3 -2.2 -2.7 -2.4 -20 -30 9/1/51 -1.9 -1.8 1.6 -2.0 -1.8 -1.6 -19 -17 -1.8 -1.9 -20 10/1/51 -4.3 -4.4 -5.0 -4.9 -5.1 -5.4 -5 4 -5 3 -4.4 -2 7 -5 7 11/1/51 -18 -12 -0 6 -15 -0 1 08 0 1 1 0 09 1 7 1 7 12/1/51 -4.5 -3.3 -2.4 -3.9 -3.5 -3 3 -5.0 -3.2 -39 1/1/52 -5.8 -5.2 -5.8 -6.3 -6.3 -5.1 -60 -5.4 -3.5 -3.9 -4 8 2/1/52 1.0 0.0 0 9 0.5 -1.1 1.3 -0.5 -0.4 0.2 1.9 -1.4 3/1/52 -3.1 -3.5 -5.9 -3.8 -50 -3 6 -50 -46 -2.3 1 2 -5 1 4/1/52 3 4 3 9 2.1 3 2 10 3 5 1 6 29 2.7 39 3 8 5/1/52 0.5 0 9 1.2 0.6 0.8 1 2 09 0.6 10 0.3 1 4 6/1/52 -16 -1.4 -1.4 -1.4 -1.1 -1.3 -1.4 -15 -10 -1.6 -15 7/1/52 -1.4 -1.0 0.8 -1.3 -0.4 -0 5 -0.7 -1.1 -1.3 -1.3 -1 5 8/1/52 -13 -1.0 0 7 -09 -0.4 -0 5 -0 8 -11 -0.7 -0.6 -14
Figure 4-3: Types of data sorts used in climate research - Space-time sort. 70
Mean Daily Temperature (C) 1876-1996 (page 1) Station Code Date Value 1 9/1/40 13 5 1 10/1/40 6.4 1 5/1/41 9.1 1 6/1/41 14.2 1 7/1/41 17.6 1 8/1/41 14.2 1 9/1/41 6 6 1 10/1/41 6.0 1 5/1/42 8.0 1 6/1/42 11.5 1 7/1/42 15.4 1 8/1/42 14.2 1 6/1/43 9.7 1 8/1/43 13.7 1 5/1/44 10.6 1 8/1/44 13.7 1 6/1/45 11.3 1 7/1/45 15.2 1 8/1/45 14.8 1 9/1/45 8.4 1 6/1/46 11.3 1 7/1/46 15 5 1 8/1/46 13 5 1 9/1/46 9.9 1 5/1/47 8 8 1 6/1/47 115 1 7/1/47 16.1 1 8/1/47 12.2 1 5/1/48 9.5 1 6/1/48 139 1 7/1/48 14.7 1 8/1/48 13.7
Figure 4-4: Final format of surface temperature database. 71
4.3 Geographic Information Systems (GIS)
GIS technology integrates the use of computer hardware and software in order to capture, manipulate, process, and display geo-referenced data (Berry 1993). Processing includes computer mapping, spatial database management, and cartographic modeling. According to Goodchild (1993), the use of GIS in North America dates back to the mid- 1960s. In Canada it was first utilized by the Canadian Land Inventory to compute estimates of land area available for certain types of uses. Almost at the same time, US researchers used GIS to extract data from large stores, making it available for analysis, and mapping (Coppock and Rhind, 1991). These databases were then utilized to study associations between population distributions, employment and transportation routes. GIS has evolved dramatically since the 1960s. Its first commercial successes came in the early 1980s, primarily in resource management. Today, the GIS community includes specialists in many application fields. Among these are local government officials, urban and regional planners, land records administrators, the oil and gas industry, and many others. These may be geographers, planners, resource managers, environmental modelers, geologists, epidemiologists, soil scientists, and representatives of the disciplines that work with geographical data (Goodchild 1993; MacEachren 1994a; Dent 1990).
In this study, GIS was used as a tool for data exploration, visualization and analysis. It aided in the understanding of the signal spatial and temporal characteristics. The technology proved valuable in providing answers to posed questions, helping devise new questions and hypotheses that eventually led towards the formulation of new methodologies. 72
4.3.1 Data exploration, visualization, and analysis using GIS
Scientific visualization has been a major topic in computing over the past decade. Though its origins are often traced to the publication of the National Science Foundation report on Visualization in Scientific Computing (Haber and McNabb 1990; McCormick et al. 1987), James Clerk Maxwell built 3D clay models to help understand the behavior of a function of two variables, and the Rutherford and Culham Laboratories in the UK produced animated film sequences of their data in the 1960s (see Wood et al. 1994 for discussion). According to MacEachren and Ganter (1990) and Nyerges (1993), the use of GIS in exploratory analysis with analytical techniques is on the horizon. In visual exploratory research, scientists produce a visual representation of data in order to search for unknowns and to help formulate hypotheses (MacEachren 1995; Kineman 1993; Haber and Nabb 1990).
The term "scientific visualization" has taken on the narrower meaning of advanced computer technology to facilitate "making visible" scientific data and concepts ... visualization tools facilitate exploration of both data and the general problem context... data-intensive problems driven by a rapid increase in computational modeling cannot be addressed by just thinking - concrete visual displays must replace or supplement mental visualization when information volume and problem complexity makes mental visualization alone impractical (MacEachren 1994b).
Because of the widening interest in large-scale or even global patterns, popularity of scientific visualization has been increasing among earth scientists, with the growing need to envision large and complex data sets from countless perspectives (DiBiase et al. 1992). The process of scientific visualization incorporates computer software that is used to permit visual data analysis, that includes both static and dynamic displays, as well as data interaction (Wood et al. 1994; Palmer 1992). According to MacEachren et al. (1992), scientific visualization can act as a 'brainstorming' environment that stimulates creative scientific enquiry, which can be enhanced with a variety of visual representations of data that can then be further 73 explored. The basic set of exploration data analysis techniques have included statistical graphs, histograms, scatterplots, scatterplot matrices, influence plots, density ellipses, and cluster diagrams (Haber and McNabb 1990). Maps are an integral part of the process of spatial data handling, and, therefore, are also used to visualize spatial data, and to reveal and understand spatial distributions and relationships. According to Kraak (1999), modern cartography provides three roles for visualization: 1) to present spatial information or to create well-designed maps; 2) to analyze spatial data by accessing individual map components to extract information and functions in order to process, manipulate, or summarize that information; 3) to explore spatial data visually by animations or linked views.
4.3.2 Dynamic mapping
Dynamic mapping or temporal sequence animation was used to study the spatio- temporal character of the signal across the study area to allow any signal progression to be detected. Animated time series have been used successfully in numerous global change studies where researchers aimed to better understand the locational change of a given attribute variable over time (DiBiase et al, 1992; MacEachren 1995; Weber and Buttenfield 1993; Peuquet 1999). Three characteristics of features can be depicted through time series animations: 1) feature existence; 2) attributes of features, and 3) changes in feature existence or attributes (Monmonier 1990; MacEachren 1994a). Animation is based on a principle that the eye-brain mechanism retains, for an instant images of objects it has seen after the objects have been removed. Therefore, if the eye is shown a series of static views, or "frames" of objects at a rapid rate (typically 30 frames per second), with the objects changing positions only slightly from frame to frame, the illusion of fluid-like motion is created in the brain (Campbell and Egbert 1990). The eye- brain system is particularly adept at monitoring continuous movement. Doerling (1992) claims that the most successful cartographic animations that he has made were those that exploited this property. 74 Unlike a static map, animation focuses on the change through time in a spatial arrangement. Its greatest use has proven to be in the display of the evolution of spatio- temporal phenomena over long periods and high temporal resolutions (Koussoulakou and Kraak 1992). It enhances visual thinking and has, therefore, the potential to stimulate scientific insight. MacEachren (1995) extensively praises animation, especially in its interactive mode to provide state-of-the-art capabilities of scientific visualization of geographic phenomena. Thrower (1959) and Corawell and Robinson (1966) were among the first to investigate the viability of animation with regard to cartography and geography. Despite their potential, however, use of animation techniques in cartography remained very sporadic until the mid-1980s, when the costs associated with their production became more feasible and GIS use in spatial sciences was expanding (Koussoulakou and Kraak 1992). Assembling a series of individual maps and importing them into a program that has the capabilities to "play" them creates cartographic animation. Within the GIS environment, using such programs as IDRISI (Eastman 1997), animations of individual frames are constructed from digital maps that may be readily modified, updated, and reorganized. This capability is a feature very important in effective scientific visualization.
The number of frames in animation is one of the factors that can have an effect on the change being depicted. Greater number of frames usually enhances smooth progression through time, whereas a relatively small number of frames can make the spatial progression of a given feature more difficult to detect. One way to solve such a problem is to create in-between frames whenever appropriate. This procedure is called tweening (MacEachren 1994b). It involves automatic creation of in-between frames for the purpose of showing a smooth transition. In animated maps, manipulating the data used to construct the different frames does this. Temporal interpolation, as this process may be thought of, has been one method of manipulating data to increase the number of frames in animation. It can be done either linearly, or by using an appropriate function that describes the temporal change in the attribute being mapped. In-between frames can be created using GIS software, where mathematical manipulation of maps is possible. 75
This process, however, must be performed very carefully, so as not to create transitions that may not exist. The goal of tweening is to 'bring out' existing features, not to create them.
4.4 Eigenvector regionalization
The spatial variability of the signal prompted an interest to determine the existence of homogeneous regions within the study area that show similar signal response(s). Statistical regionalization via Principal Components Analysis (PCA) in the S-mode was chosen here as the principal investigation tool. The method is described in detail in Yarnal (1993). Three key products are routinely derived from an eigenvector regionalization that employs PCA: 1) the principal components; 2) the component- loadings matrix; and 3) the component-scores matrix. In this mode, the data matrix consists of time series data (cases) at various locations (variables). A dispersion correlation matrix is computed from the data matrix, such that each location's time series is correlated to all other locations' time series. This results in an M x M correlation matrix. PCA procedure is used to run the regionalization. The dispersion matrix is used to compute one principal component for each of the locations. To increase interpretability these results are rotated using varimax or orthogonal rotation to identify spatial modes of variation. The first extracted component explains the most variation in the data, and each successive component describes progressively lower amount of variation. A component-loadings matrix is calculated through the analysis and describes the way that each variable weighs (or loads) on each component. To interpret the rotated results, the loadings are plotted in space. Multiplying the dispersion matrix by the loadings matrix produces the component-scores matrix, a time series of component scores that underline relationships between the observations and the components (Yarnal 1993). 76
4.5 Mann-Whitney U test
The Mann-Whitney U test is a non-parametric statistic for independent samples described in detail in Kvanli (1988). Mann-Whitney tests that two sampled populations are equivalent in location. If the populations are identical in location, the ranks should be randomly mixed between the two samples. The number of times a score from group 1 precedes a score from group 2 and the number of times a score from group 2 precedes a score from group 1 are calculated. The Mann-Whitney U statistic is the smaller of these two numbers. Significance of results are assessed based on the value of U that is transformed into a Z statistic, and the sample size of each group. The significance level based on the asymptotic distribution of a test statistic is calculated when the data set is large. When the data set is small, sparse, contains many ties, is unbalanced, or is poorly distributed, an exact significance value based on the exact distribution of a test statistic is calculated instead.
The test was used in this study to assess the statistical significance of El Nino-related signals. These were compared to those observed during neutral periods. This test was used for two principal reasons: 1) the population distribution of the signal is unknown; 2) the sample size is small (i.e. 12 at most, if all El Nino events in a region group into 1 pattern of evolution). The relevant statistics included the U, Z and exact significance values (p-values) that were computed for a 1-tailed test. It was of interest to know if a particular month is significantly colder or warmer than the mean (1-tailed), not just different from the neutral value (2-tailed). P-values equal to or less than 0.1 were used as the cut-off values for the calculated differences between El Nino and non-ENSO periods. When p-values exceeded 0.1, the differences between El Nino and non-ENSO periods were deemed non-existent. When, the p-value was at 0.1 or smaller, the signal was classified as either considerably higher or lower than the non-ENSO set. A p-value of 0.1 signified that the conclusion was being made correctly approximately 90 out of 100 times. 77
4.6 Paired data analysis
4.6.1 Pearson's correlation coefficient
Sometimes referred to as ordinary correlation coefficient, Pearson's correlation, R, measures association between two variables, a and b, computed as the ratio of the sample covariance (cov) of the two variables to the product of the two standard deviations (sd):
^=cov(a,Z>) 41
sd sdh
The upper and lower limits of this formula are +1 and -1 that denote perfect positive and negative relationships, respectively. The square of the Pearson correlation, R2, specifies the proportion of the variability of one of the two variables that is linearly accounted for, or described, by the other. Pearson's correlation coefficient is neither robust nor resistant. It is not robust because strong but nonlinear relationships between the two variables may not be recognized. It is not resistant since it can be extremely sensitive to outliers (Wilks 1995). Statistical significance of the coefficient's magnitude can be assessed as follows:
* = 4-2 yl\-R2 where ts is the t-statistics that tests the null hypothesis of no relationship. Coefficients with corresponding significance values of less than 0.05 or lower were used to denote strong relationships. 78
4.6.2 Spearman's correlation coefficient
According to Wilks (1995), a robust and resistant alternative to R is the Spearman rank correlation coefficient, r, which is computed using ranks of data
6Yd2 r = 1 H, 4-3 N(N2 -1)
Similarly for R, values of r vary between -1 and 1 for perfect negative and positive relationships, respectively. This method of measuring association between two variables is not as sensitive to outlying values and better depicts relationships that may not be linear. Spearman's correlation has also been often used for small sample size datasets (N<30), especially when the variables may not come from a normally distributed population. In such instances, r measures the relationship between two variables more reliably than does R (Kvanli 1988). Statistical significance is assessed in a similar manner to that of Pearson's correlation coefficient. Again, significance value of 0.05 or lower was used to denote strong relationships.
4.6.3 Cross-correlation
A cross-correlation procedure determines the association between two variables, but at various time lags. The purpose is to determine how far one series leads another. The procedure reveals the correlation between two series at the same time and also with each series leading by one or more time segments (lags). The cross correlation procedure can only be applied to stationary series, where the mean and variance remain constant throughout the length of the series. 79
5. EL NINO DEFINITION AND SIGNAL CALCULATION
5.1 Introduction
Signal calculation necessitated that a quantitative definition of ENSO be first specified. This included numerical definitions for: 1) the warm-phase of ENSO, or El Nino; 2) the cold-phase of ENSO, or La Nina; and 3) the non-ENSO phase, or times when neither El Nino nor La Nina have been present in the tropical Pacific basin. All months between January 1950 and December 1996 were divided into one of the three groupings.
5.2 Definitions of ENSO
Throughout the study of the ENSO phenomenon, numerous attempts have been made to provide quantitative definitions for it. Initially, El Nino was almost exclusively defined as an annual coastal phenomenon of tropical South America that significantly impacted natural and human systems in and around the immediate area of Peru, and Ecuador. Definitions were descriptive and qualitative. Sailors associated El Ninos' arrivals with drops in fish anchovy catches each year around Christmas time. However, prosperity occurred in other areas such that El Nino years were known as anos de abundacia (years of abundance) when
...the sea ..was., full of wonders, the land even more so. First of all, the desert ...became... a garden... The soil ...was... soaked by the heavy downpour, and within a few weeks the whole country ...was... covered by abundant pasture. The natural increase of flocks ...was... practically doubled and cotton can be grown in places where in other years vegetation ...seemed... impossible (Murphy 1926).
Even after oceanographers realized that the unusually warm surface water off the coast of Peru during the abundance years extended thousand of kilometers offshore well 80 throughout the basin in the 1960s, El Nino definitions remained descriptive and continued to incorporate only conditions in the eastern Pacific basin well until the late 1980s. Quinn et al. (1978) and Quinn and Neal (1987) provided the first "semi• quantitative" definition of ENSO by classifying events since 1726 as measures of their intensities on scale of 1 to 4 (strong, moderate, weak, and very weak). The measures used to define the El Nino and its intensity were primarily based on qualitative phenomena along the coast of South America (Trenberth 1997). The Scientific Committtee for Ocean Research working group defined El Nino as:
... the appearance of anomalously warm water along the coast of Ecuador and Peru, as far south as Lima (12 °S). This means a normalized sea surface temperature (SST) anomaly exceeding one standard deviation for at least 4 consecutive months. This normalized SST anomaly should occur at least at three of five Peruvian coastal stations... (SCOR 1983)
This definition, as argued by Trenberth (1997), however, again incorporates only conditions along the eastern Pacific coast, and does not address the developments of the phenomenon in the remainder of the ocean. Ropelewski and Jones (1987) used a 5- month running mean of the Southern Oscillation Index (SOI) to define El Nino events. Around the same time, Kiladis and van Loon (1988) used SOI combined with an SST anomaly index for the eastern tropical Pacific (within 4° of the equator from 160°W to the South American coast) to define an "event" and required that the SST anomalies had to be positive for at least three seasons and be at least 0.5°C above the mean, while the SOI had to remain negative and below -1.0 for the same duration (Trenberth 1997). Over the past decade, the definition of El Nino has become increasingly more quantitative. In the 1980s, the tropical Pacific basin was divided into 2 major El Nino regions, Nino-l&2, and Nino-3 (figure 5-1). The former monitors sea-surface- temperature conditions in the eastern tropical Pacific basin, a region between 0°- 10°S and 80°W-90°W. The latter acknowledges the importance of conditions throughout the mid- basin of the tropics, during the development of ENSO. This region has since been 81 extensively used in forecasting the El Nino phenomena. This area lies between 5°N-5°S and 150°W-90°W. Accordingly, the Japan Meteorological Agency (JMA) defined an El Nino as periods during which the 5-month running means of the monthly SST anomalies in the Nino-3 region exceed +0.5°C or more for at least 6 consecutive months. The periods that qualify define the El Nino periods and provide a quantitative measure of its intensity (Trenberth 1997).
Figure 5-1: Partitioning of tropical Pacific basin into 5 Nino regions that provide information regarding the state of ENSO. Base map courtesy ofNOAA.
Recent research of Wang (1995), Trenberth (1991), Trenberth and Hoar (1996), and Barnston and Chelliah (1997), provides evidence that the key region for coupled atmospheric-ocean interactions in ENSO lies further west of the Nifio-3 region. Negative correlations of smoothed SOI and SSTs exceed -0.8 only through a broad region from about 120° to 180°W and 5°N to 10°S. Trenberth and Hoar (1996) named this region Nino 3.4 as it only partially encompasses the Nino 3 region and extends into the Nino 4 region (figure 5-1). The Nino 4 region monitors ocean-atmosphere conditions in the region between 5°N-5°S and 160°E-150°W. Niflo-3.4 straddles an area between 5°N-5°S and 120°W to 170°W, and tracks conditions between Nino-3 and Nino-4 regions. 82
Trenberth (1997) decided to use the Nino 3.4 region to define El Nino events since 1950. He plotted 5-month running means of sea-surface-temperature data between 1950 and 1997. The base climatology chosen for this definition of ENSO was 1950 to 1979. Values that exceeded thresholds of ±0.4°C were used to indicate ENSO years.
5.3 Research ENSO definition
El Nino and La Nina periods were defined here similarly to that of Trenberth (1997) with minor adjustments. Both the Nino-3 and Nifio-3.4 regions were used as key areas that describe the state of the basin and monitor the state of ENSO. The corresponding monthly sea-surface-temperature time series from 1950 to 1996 were used. Anomalies were computed using the entire time series. For each Nino region, a 5-month running mean was computed. From this plot, ENSO periods were denoted as those when SST As in either region exceeded a threshold value of ±0.6°C (± 1 standard deviation) for 6 consecutive months or longer. The 5-month running mean time series along with the limits were plotted for each Nino region and can be seen in figure 5-2a and figure 5-2b.
5.3.1 Classification of SST time series
The ±0.6°C control limits were used as threshold values, to classify the two series into El Nino, La Nina and neutral months. The limit had to be continuously exceeded for 6 or more months in at least one of the two regions, for an event to qualify as either La Nina or El Nino. Otherwise, the time period was classified as neutral. Since both the Nino-3 and Nino-3.4 regions are indicative of the development of an ENSO event, warm or cold, all recognized events from either series were taken into consideration 83
-2.6 lilliiilliilfllilllilllliillllliiliiilllillllll Figure 5-2a: Time series of Nino-3.4 region SST anomalies as 5-month running means relative to the J950-96 base period. Periods during which SSTAs exceeded the + Jo- limits, a threshold of +0.6X1, for 6 consecutive months or longer were classified as El Nino months, and times during which SSTa values crossed the -la limit for 6 consecutive months or longer were classified as IM Nina years.
HunnnuunnnnnnunHHunHinun Figure 5-2b: Similar to figure 5-2a. Shown are SSTa time series across Nino-3 region as 5-month running means relative to the 1950-96 base period. 84
5.3.2 El Niflo in Canada
The construction of signal composites required that a typical El Nino cycle be identified for the study area and the tropical Pacific region. ENSO events are typically
Year(-1) Year(O) Year(+1) -0.4 ^ — Q. > Q. > != >< -= Q. = ra a = 01 o 01 o « TO = oj 3 S | "> CO Z » 1 | 3 « 2 S 5 ^ v) FVbnth
Figure 5-3: ^4 composite of El Nino-related SST anomalies between 1950 and 1996for the Nino 3.4 region. Typically, El Nino events begin to develop during the spring, attain El Nino 'strength' during the summer, and reach maturity during the following winter. SSTAs re-attain neutral values during the following spring and completely diminish by the following winter (Dec(+1)). In red is also shown the time period when El Nino signals are being analyzed across Western Canada. SSTA data courtesy of CPC (1999).
fully developed throughout the Pacific basin during northern hemisphere summer to early autumn, mature during winter and terminate during the following spring (i.e. SSTAs cross the +0.6°C threshold) (figure 5-3). In Canada, the maximum climate-related 85 impacts resulting from ENSO are felt after an event has reached maturity in the Pacific basin early during year (+1) (Chapter 1). Analyses were initiated just after El Nino's onset in the Pacific (Sep(O)) to several months after the event has diminished in the spring (Aug (+1)). This included a total of 12 consecutive months, namely Sep(O), Oct(0), Nov(O), Dec(O), Jan(+1), Feb(+1), Marf+l), Apr(+1), May(+1), Jun(+1), Jul(+1), and Aug(+1). Such schema allowed for more emphasis to be placed on the capture of the time when El Nino impact is greatest in Canada, and to prevent the overlap between El Nino, La Nina and neutral periods. Similar methodology and reasoning was applied in Shabbar et al. 1997 and Mo et al. (1998).
5.3.2.1 El Nino years
A total of 12 El Nino events were isolated from the two series and are shown in table 5-1. Also shown are the total number of consecutive months during which SSTAs in either region exceeded the ±0.6°C threshold around the time of the event as defined here (i.e. Sep(O) - Aug(+1)), and the region that proved the event to exist. These results can also be confirmed from figures 5-2a and 5-2b.
With the exception of the 1986-88 event that lasted over 12 months, the Sep (0) - Aug (+1) limits were appropriate. The 1986 event lasted between September 1986 and May 1988. The year 1993 measured SSTAs higher than normal, even exceeding the ±0.6 threshold. However, this only occurred for 3 months in Nino-3.4 region and 4 months in Nino-3 region. As a result, this time period did not qualify as an El Nino phase. The extracted years were found to be in general agreement with those isolated by Trenberth (1997). 86
Table 5-1: El Nino periods. Months associated with El Nino conditions in the tropical Pacific^ No. Event # consec. # consec. Source Months months >0.6°C >0.6°C Nifio-3 Nifio-3.4 1 Sep 1951 - Aug 1952 6 2 Nino 3 2 Sep 1957-Aug 1958 11 11 Nino 3, Nino 3.4 3 Sep 1963 - Aug 1964 5 6 Nino 3.4 4 Sep 1965-Aug 1966 10 10 Nino 3, Nino 3.4 5 Sep 1968-Aug 1969 6 6 Nino 3, Nino 3.4 6 Sep 1969-Aug 1970 6 4 Nino 3 7 Sep 1972-Aug 1973 10 9 Nino 3, Nino 3.4 8 Sep 1976-Aug 1977 7 4 Nino 3 9 Sep 1982-Aug 1983 16 12 Nino 3, Nino 3.4 10 Sep 1986-Mar 1988 16 17 Nino 3, Nino 3.4 11 Sep 1991 - Aug 1992 13 13 Nino 3, Nino 3.4 12 Sep 1994-Aug 1995 3 6 Nino 3.4
5.3.2.2 La Nina years
The ENSO phenomenon has two phases, a warm phase called El Nino during which abnormally warm sea surface temperatures are present across the tropical Pacific ocean (Chapter 1), and a cold phase called La Nina, associated with unseasonably cool waters throughout the basin (figure 5-4) that last for several months at a time. The oceanic and atmospheric modifications that are typical during El Nino events across the tropical Pacific ocean are also present during La Nina events, but of the opposite sign. For instance, during the course of a typical La Nina, surface pressure is abnormally high over the eastern but abnormally low over the western equatorial Pacific, while trade winds are more intense than normal and the sea surface temperatures and rainfall are low throughout the central and eastern tropical Pacific.
10 La Nina events were isolated. The precise times, and details regarding the period's inclusion as La Nina events, are shown in Table 5-2. The 1988/89 event was unique to all others, as it was not confined to the prescribed limits of Sep (0) to Aug (+1). The registration of the 1988/89 La Nina event began in June 1988 instead of September 87
Sea Surface Temperature Anomaly
Figure 5-4: Sea-surface temperature anomalies during May 1988 - La Nina. Courtesy ofNOAA.
1988 due to an overrunning El Nino event of 1986. Three other events, 1954/56, 1970/72 and 1984/86 spanned a 24-month period. These years were found in general agreement with those isolated by Trenberth (1997).
5.3.2.3 Neutral years
The remaining months were classified as neutral years, or periods when the tropical Pacific Ocean was not in either extreme phase of ENSO (table 5-3). A total of 564 months are counted between Jan-50 and Dec-96. Out of those, 151 months (27%) were classified as El Nino, 143 months (25%) were classified as La Nina, and the remaining 270 months, (48%) as non-ENSO (figure 5-5). Table 5-2: La Nina periods. Months associated with La Nina conditions in the tropical Pacific. No. Event #consec. # consec. Source months months <-0.6°C <-0.6°C Niiio-3 Nino-3.4 1 Sep 1949-Aug 1950 6 9 Nino 3, Nino 3.4 2 Sep 1954-Aug 1956 19 20 Nino 3, Nino 3.4 3 Sep 1964-Aug 1965 10 9 Nino 3, Nino 3.4 4 Sep 1967-Aug 1968 8 3 Nino 3 5 Sep 1970-Aug 1972 20 17 Nino 3, Nino 3.4 6 Sep 1973 - Aug 1974 11 12 Nino 3, Nino 3.4 7 Sep 1975 - Aug 1976 12 13 Nino 3, Nino 3.4 8 Sep 1984-Aug 1985 18 8 Nino 3, Nino 3.4 9 Jun 1988-Aug 1989 13 13 Nino 3, Nino 3.4 10 Sep 1995 -Aug 1996 5 7 Nino 3.4
Table 5-3: Neutral periods during which the tropical Pacific was neither in El Nino nor La Nina phases. Period Sep 1950 -Aug 1951 Sep 1952 -Aug 1954 Sep 1955 -Aug 1957 Sep 1958 -Aug 1963 Sep 1966 -Aug 1967 Sep 1974 -Aug 1975 Sep 1977 -Aug 1982 Sep 1983 -Aug 1984 Sep 1985 -Aug 1986 Apr 1988 -May 1988 Sep 1989 -Aug 1991 Sep 1992 -Aug 1994 Sep 1996 -Dec 1996 89
5.3.3 El Nino signal calculations
Once the series was classified into El Nino, La Nina and neutral periods, it was possible to calculate the signal from the temperature records. The signal was determined by comparing mean monthly values in neutral years to values during El Nino years. The mean neutral value for each month had to be computed based Figure 5-5: Proportion of months classified on the neutral months. as El Nino, La Nina, and non-ENSO (neutral) periods between 1950 and 1996. Total number of months = 564.
5.3.4 Calculation of El Nino signal in surface temperature records
For a monthly surface air temperature record (T), the El Nino signal (^) was calculated by subtracting the monthly mean neutral value at a station (r|) from each corresponding monthly value at each station (o) such that
Ts = To-1n 5-1
These values provided an idea of the strength of El Nino's influence and represented some sort of "pseudo-anomalies". Table 5-4 shows the signal magnitudes during El Nino years for each month between Sep(O) and Aug(+1) and each season at every station across the study area. Summary statistics are provided at the bottom. These include the monthly and seasonal mean values, standard deviation, and the number of stations that recorded positive, negative or zero signals. Table 5-4: Mean monthly and seasonal El Nino signal measured during El Nino years in surface temperature records.
STATION NAME Lat Long Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec SON DJF MAM JJA Annual ABBOTSFORD A 49.20 -122.22 0.5 1.1 0.5 0.0 0.2 0.8 -0.1 -0.2 0.3 0.1 0.4 0.5 0.3 0.7 0.2 0.2 0.4 ANEROID 49.43 -107.18 1.2 1.9 0.5 0.5 0.8 00 -0.2 -0.2 04 -0.3 0 7 2.1 0.3 1.7 0.6 -0.1 0.6 ATHABASCA 2 54.49 -113.32 0.2 2.8 0.1 09 04 0.5 -0.3 -0.7 04 -0 1 0.7 1.3 0.3 1.4 0.5 -0.2 0.5 BANFF 51.11 -115.34 06 1 7 08 05 08 08 -0.2 0 1 0.2 00 09 1.1 0.4 1.1 0.7 0.2 0.6 BARKERVILLE 53.40 -121.31 1 2 19 0.7 03 03 1 2 -0.1 -0.4 0.3 02 0.9 0.9 0.5 1.3 04 0.2 0.6 BARRIERE 51.11 -120.70 1.7 2.2 1.0 0.4 0.6 1.2 0.0 -0.4 0.5 00 0.7 1.3 0.4 1.8 0.7 0.3 0.8 BEAVER MINES 49.28 -114.10 1.5 2.3 0.3 0.5 1.0 05 -0 1 -0.1 0.4 00 1.1 1.6 0.5 1.8 0.6 0.1 0.8 BEECHY 50.46 -107.19 1.6 2.1 0.9 0.6 0.7 0.0 -0.2 -0.4 0.1 0.0 0.8 2.4 0.3 2.1 0.7 -0.2 07 BELLA COOLA 52.22 -126.41 0.7 1.5 0.7 0.2 0.1 0.8 0.0 -0.4 0.5 0.1 1.0 0.8 0.6 1.0 0.3 0.1 0.5 BIG CREEK 51.40 -123.40 0.3 1.7 0.7 0.3 0.3 09 0.0 -0.2 0.5 -0.2 12 1.4 0.5 1.1 04 0.2 0.6 BLUE RIVER NORTH 52.90 -119.17 1 3 2.0 1.0 04 0.3 1.0 -0 1 -0.1 06 00 0.9 1.0 0.5 14 06 03 0.7 BRANDON CDA 49.52 -99.59 1 2 16 1.0 0.5 04 -0.3 00 0.0 0.2 0 1 0.6 2.4 0.3 1.7 0.6 -0.1 0.6 BROADVIEW A 50.15 -102.32 1 0 12 05 0.5 02 -0.3 -0.2 -0 1 -0 1 -0.4 0.4 1.7 0.0 1.3 04 -0.2 04 BROCHET A 57.53 -101.41 2.3 1.5 1.5 0.7 0.3 0.0 0.2 -0.1 0.1 0.3 -0.5 0.4 -0.1 1 4 0.8 0.0 05 BURNS LAKE DECKER LAKE 54.15 -125.48 1 0 1 9 05 04 03 09 0.1 -0.5 0.3 -0.2 1.5 1.2 0.5 1 3 04 0.1 0.6 CALGARY INT'L A 51.70 -114.10 1 0 2.0 0.5 0.5 0.8 0.5 -0.3 -0.4 0.3 -0.4 1.0 1.7 0.3 1.6 0.6 0.0 06 CAMPSIE 54.80 -114.41 08 20 02 0.5 04 04 -0.3 -0.5 0.4 -0.2 0.7 1.1 0.3 1.3 04 -0 1 0.5 CAMROSE 53.20 -112.49 08 20 0.0 13 0.5 04 -0.2 -0.3 -0.2 -0.6 0.3 1.4 -0.2 14 0.6 -0 1 04 CAPE ST JAMES CS 51.56 -131.10 04 1.1 06 0.3 00 03 -0.1 -0.3 0.3 04 0.8 0.4 0.5 06 0.3 0.0 0.3 CARLYLE 49.38 -102.16 1.3 1.6 1.1 0.5 0.5 00 -0 1 0 1 0 1 -0.3 0.6 1.9 0.1 1.6 0.7 0.0 0.6 CARWAY 49.00 -113.22 1.4 1 9 08 0.5 10 0.5 -0.4 -0.2 0.3 -0.6 0.9 1.4 0.2 1.6 0.8 0.0 0.6 CEYLON 49.23 -104.39 10 2.3 07 0.6 1.0 -0.1 00 0.2 -0.2 -0.4 0.4 0.6 -0.1 13 0.8 00 0.5 CHURCHILL A 58.44 -94.40 1 7 05 1 4 03 -0.8 -0.3 -0.4 -0 1 00 02 -0.8 0.9 -0.2 1 0 03 -0.3 02 COLD LAKE A 54.25 -110.17 1.0 2.3 06 0.7 0.6 0.2 -0.3 -0.7 0.3 -0.3 0.9 2.0 0.3 1.8 0.6 -0.3 0.6 COMOX A 49.43 -124.54 0.5 0.9 06 0.2 0.3 0.9 00 -0.3 0.4 01 0.5 0.6 0.3 0.7 04 0.2 04 CORONATION A 52.40 -111.27 08 2.3 06 0.8 0.7 0.5 0.1 -0 1 0.1 -0.3 0.5 1.4 0.1 1.5 0.7 0.1 0.6 CORTES ISLAND 50.60 -125.20 0.5 0.9 0.6 0.3 0.3 0.7 -0.2 -0.2 0.3 0.1 0.5 0.5 0.3 0.6 0.4 0.1 0.4 CRANBROOK A 49.37 -115.47 07 1.5 05 04 0.7 08 -0.2 -0.2 0.4 -0.2 0.4 0.8 0.2 1.0 0.5 02 0.5 CRESTON 49.60 -116.31 0.5 1 0 0.6 0.1 08 0.9 0.1 0.2 0.2 -0.1 0.4 0.4 0.2 0.6 05 04 0.4 DAUPHIN A 51.60 -100.30 1.5 1 4 0.3 06 0.1 -0.4 -0 ! -0.1 0.0 -0.3 -0.3 1.6 -0.2 1.5 0.3 -0.2 0.4 DAWSON CREEK A 55.44 -120.11 1 0 2 1 -0 1 09 04 04 -0.3 -0.7 0.3 0 1 1.0 1.5 0.5 1.6 04 -0.2 0.6 DEASE LAKE 58.25 -130.00 08 1.6 05 0.7 0 1 0.8 -0 1 -0.8 04 0.2 0.9 1.4 0.5 1.3 04 -0.1 0.5 EDMONTON MUNICIPAL A 53.34 -113.31 1.1 2.0 0.6 0./' 06 0.6 -0.2 -0.4 0.3 -0.2 0.9 1.8 0.4 1.6 0b 0.0 Ub EDSON A 53.35 -116.28 1 0 19 0.7 0.8 04 0.4 -0.2 -0.3 0.3 0.2 1.0 1.0 0.5 1.3 0.7 0.0 0.6 ENTRANCE 53.23 -117.41 0.9 2.0 0.1 0.3 0.2 0.2 -0.3 -0.4 -0.1 -0.2 1.4 1.6 0.4 1.5 0.2 -0.2 0.5 Table 5-4: Mean monthly and seasonal El Nino signal measured during El Nino years in surface temperature records.
STATION NAME Lat Long Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec SON DJF MAM JJA Annual ESTEVAN POINT 49.23 -126.33 0.5 0.9 0.5 0.1 0.2 0.4 0.0 -0.1 0.2 0.2 0.5 0.3 0.3 0.6 0.3 0.1 0.3 FAIRVIEW 56.40 -118.23 1.1 2.3 0.1 0.6 0.2 0.2 -0.4 -0.4 0.2 0.0 0.7 1.3 0.3 i.6 0.3 -0.2 0.5 FLIN FLON 54.46 -101.51 1.7 1.1 06 0.5 04 -0.1 -0.2 -0 1 0.0 00 -0.2 1.9 0.0 1.6 0.5 -0.1 05 FORT CHIPEWYAN A 58.46 -111.70 1.9 14 08 05 0 1 -0 1 -0.3 -0.4 0.1 0 1 04 1.9 0.2 1.7 05 -0.3 0.5 FORT MACLEOD NORTH 49.52 -113.17 1.2 24 03 06 1.1 03 -0.3 -0.2 0.0 -0.4 1 0 1.7 0.2 1.8 0 7 -0.1 0.7 FORT MC MURRAY A 56.39 -111.13 1.9 2.2 0.6 0.6 0.3 0.0 -0.2 -0.6 0.3 0.1 0.4 1.8 0.2 1.9 0.5 -0.2 0.6 FORT NELSON A 58.50 -122.35 0.6 1.6 01 0.6 0.0 0.4 -0.3 -0.6 0.2 0.5 0.5 1.5 0.4 1.3 0.2 -0.2 0.4 FORT RELIANCE 62.43 -109.10 1.6 06 0.5 0.2 -0.2 0.2 04 -0.3 -0.2 05 -0 1 1.1 0.1 1 1 0.2 0.1 0.4 FORT RESOLUTION A 61.11 -113.41 1.2 1.0 05 06 0.1 0.0 -0.1 -0.6 -0.1 0.6 0 1 1.5 0.2 1.2 0.4 -0.2 0.4 FORT SMITH A 60.10 -111.57 1.8 1 6 08 05 0.1 0.0 -0.2 -0.5 0.0 0.2 0.3 1.7 0.2 1.7 04 -0.3 05 FORT ST JAMES 54.27 -124.15 1.5 2.2 0.8 04 04 0.8 0.1 -0.7 0.4 -0.1 1.2 1.9 0.5 1.8 0.5 01 0.7 FORT ST JOHN A 56.14 -120.44 0.9 2.3 -0.2 0.7 0.2 03 -0.3 -0.6 0.2 02 0.7 1.3 0.4 1.5 0.2 -0.2 0.5 GERMANSEN LANDING 55.47 -124.42 1.4 2.0 0.5 0.5 0.2 0.9 -0 1 -0.8 0.5 -0 1 1.2 1.4 0.5 1.6 04 00 06 GOLDEN A 51.18 -116.59 1.3 1 8 1.2 0.3 0.5 0.9 -0.2 -0.3 0.4 -0.1 0.8 1.2 0.4 1.4 06 01 0.6 GRAND FORKS 49.20 -118.28 0.8 1.5 08 03 0.7 1 0 -0.1 0.0 02 0 1 0.7 1.0 0.4 1.1 06 03 06 GRANDE PRAIRIE A 55.11 -118.53 0.7 2.0 0.1 0.6 04 03 -0.2 -0.6 0.4 -0.3 0.9 1.5 0.3 14 04 -0 1 0.5 GRAVELBOURG 49.53 -106.33 1.1 2.0 0.8 0.4 0.7 -0.1 -0.2 0 1 0.2 -0.4 0.6 2.2 0.1 1.8 06 00 0.6 GREAT FALLS 50.28 -96.00 1.6 1.2 1.1 0.4 0.1 -0 1 0.0 0.0 0.2 -0.2 -0.6 1.8 -0.2 1.5 05 0.0 0.4 HAINES JUNCTION 60.46 -137.35 0.2 2.5 -0.2 0.8 -0.3 04 -0 1 -0.7 0.8 03 0.6 1.8 0.6 1.5 0.1 -0 1 0.5 HAY RIVER A 60.50 -115.47 06 1.3 0.1 00 0.4 0.3 -0 1 -0.7 0.1 0.5 0 1 1.8 0.2 1 2 02 -0.2 0.4 HOPE A 49.22 -121.29 02 14 05 0.2 0.5 1.0 -0.1 -0.1 04 0.1 0.6 0.4 0.4 0.7 04 0.3 0.4 INDIAN HEAD CDA 50.32 -103.40 1.0 15 08 0.5 02 -0.2 0.0 0.1 0.0 -0.3 0.7 1.9 0.1 1.4 0.5 00 0.5 ISLAND FALLS 55.32 -102.21 20 1 5 1 1 09 04 -0 1 on -0? -0 1 02 -0.6 1.6 -0.2 1.7 0.8 -0.1 0.6 JASPER 52.53 -118.40 1.0 2.2 0.9 0.2 04 0.9 -0.1 -0.2 0.5 -0.1 1.0 1.3 0.4 1.5 0.5 02 0.7 KAMLOOPS A 50.42 -120.27 14 20 0.8 0.2 0.6 1.1 -0.2 -0.2 0.4 -0.2 0.7 1.4 0.3 1.6 05 0.2 0.7 LACOMBE CDA 52.28 -113.45 0.8 2.2 06 0.9 0.6 0.5 -0 1 -0.2 0.1 -0.2 0.6 1.3 0.2 1.4 0.7 0.0 0.6 LANGARA 54.15 -133.30 0.3 1.1 0.5 0.2 0.1 0.3 -0.1 -0.3 0.2 0.2 0.7 0.5 0.3 0.6 0.2 00 0.3 LEADER 2 50.53 -109.32 1 4 2 1 1 3 0.7 07 0.5 -0.3 -0.4 0 1 -0.7 0.9 2.4 0.1 2.0 0.9 •0.1 0.7 LETHBRIDGE CDA 49.42 -112.47 09 1.7 0.3 0.7 1.3 05 -0.4 -0.1 0.1 -0.5 1.0 1.3 0.2 1.3 0.7 00 0.6 LOST RIVER 53.17 -104.20 1 4 1.8 0.4 0.5 0.5 -0.2 -0.2 -0.3 0.3 -0 1 0.5 1.6 0.2 1.6 0.5 -0.2 0.5 LYTTON 50.14 -121.35 0.7 16 0.7 0.2 0.7 1.1 -0.2 0.0 0.3 0.0 1 0 0.9 0.4 1 1 0.5 03 06 MCINNES ISLAND 52.16 -128.43 0.3 1.1 0.6 0.2 0.0 0.4 -0.2 -0.2 0.1 00 0.7 0.5 0.3 0.6 03 0.0 0.3 MIDALE 49.24 -103.24 1.2 1.8 U.9 O.b 0.6 -0.1 -0.1 0.2 0.1 -0.4 0.6 1.9 0.1 1.6 0.7 0.0 0.6 MOOSE JAW A 50.20 -105.33 1.3 1 9 0.9 0.4 0.7 -0.2 -0.2 -0.3 0.3 -0.4 08 2.0 0.3 1.7 0 7 -0.2 0.6 MOOSOMIN 50.80 -101.40 1 7 1 6 0.8 0.7 02 -0.2 -0.2 00 02 -0.2 06 1.8 0.2 1.7 06 -0 1 0.6 Table 5-4: Mean monthly and seasonal El Nino signal measured during El Nino years in surface temperature records.
STATION NAME Lat Long Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec SON DJF MAM JJA Annual MORDEN CDA 49.11 -98.50 1.5 1.5 0.9 0.8 0.5 -0.3 0.0 0.0 0.3 -0.3 0.2 2.3 0.1 1.8 0.7 -0.1 0.6 MUENSTER 52.11 -105.00 1 6 1 9 06 06 06 -0.2 -0.3 -0.4 03 -0.4 04 2.2 0.1 1 9 06 -0.3 06 NORTH BATTLEFORD A 52.46 -108.15 1 0 2 1 06 0 7 06 0 1 -0 1 -0.3 03 -0.2 09 2.3 0.3 1 8 06 •0.1 0 7 ORMISTON 49.43 -105.22 08 24 09 06 09 00 -0.2 0.0 02 -0.3 0 7 1.8 0.2 1.7 08 -0 1 06 OUTLOOK PFRA 51.29 -107.30 1.1 18 0.7 0.5 06 -0.1 -0.2 -0.3 0.2 -0.4 0.5 2.0 0.1 1.6 06 -0.2 0.5 PACHENA POINT 48.43 -125.60 05 09 04 0.1 00 06 00 -0 1 02 01 0.5 0.3 0.3 06 0.2 0 1 03 PEACE RIVER A 56.14 -117.26 08 24 0 4 08 02 0 1 -0.4 -0.5 03 -0.1 0.7 18 0.3 1 7 05 -0.3 06 PEKISKO 50.22 -114.26 1 0 26 04 04 08 04 -0.2 -0.2 04 -0 1 10 1.2 0.4 1 6 05 0.0 06 PENNANT 50.32 -108.14 06 22 1 3 08 08 04 -0.2 -0.3 0.1 -0.3 0.8 1.6 0.2 1 4 1.0 -0 1 06 PIERSON 49.11 -101.16 1.6 1.7 06 05 05 -0.2 -0.2 -0 1 0 1 -0 1 0.7 2.3 0.2 1 9 0.5 -0.2 06 PORT HARDY A 50.41 -127.22 04 1.1 0.5 0 1 0 1 0.5 -0.1 -0.3 0.3 00 05 0.3 0.3 06 03 0.0 0.3 PRINCE ALBERT A 53.13 -105.41 1 2 1 9 05 06 05 -0 1 -0.2 -0.5 02 -0.3 04 2.0 0.1 17 0.5 -0.3 05 PRINCE GEORGE A 53.53 -122.41 1 7 22 1 0 04 05 09 00 -0.7 05 -0 1 1.2 2.1 0.5 20 06 0 1 08 PRINCE RUPERT A 54.18 -130.26 02 15 0.5 0.1 00 05 -0.2 -0.3 0.3 0.0 0.7 0.3 0.3 0.7 0.2 0.0 0.3 PRINCETON A 49.28 -120.31 01 1 3 0.9 0.3 0 7 1.3 -0.1 0 1 0 4 -0 1 08 0.8 0.4 07 0 6 04 05 RANFURLY 53.27 -111.39 07 20 0.3 1.1 04 04 -0.1 -0 1 0 1 -0 1 08 1.5 0.3 14 06 0.1 06 REGINA A 50.26 -104.40 1 7 1 9 06 04 03 -0.3 -0.2 -0 1 02 -0.3 0.7 1.9 0.2 1 8 04 -0.2 06 REVELSTOKE A 50.58 -118.11 08 1 3 09 04 05 1.1 0 1 02 04 0 1 0.7 0.8 0.4 10 06 04 06 SALMON ARM 2 50.42 -119.17 1.1 1 6 08 03 06 1.1 -0 1 -0.2 04 -0 1 0.9 1.0 0.4 1.2 0.6 0.3 06 SANDSPIT A 53.15 -131.49 03 12 06 03 00 0.5 0.0 -0.4 0.3 03 0.9 0.4 0.5 06 03 00 0.4 SASKATOON A 52.10 -106.41 1 0 1 9 03 0.6 04 -0 1 -0.3 -0.3 0.2 -0.4 0.3 1.9 0.0 1 6 04 -0.3 04 SMITHERS A 54.49 -127.11 09 1 8 0 7 03 03 1 0 00 -0.7 03 -0.1 1 4 1.2 0.6 1 3 04 0 1 06 SPRAGUE 49.10 -95.36 1 5 1 4 1.5 04 05 -0.4 02 -0.1 0.2 GC -0.2 2.0 0.0 1 7 0.8 •0.1 0.6 SUFFIELD A 50.16 -111.11 1 9 2.2 0.6 0 7 1 0 0.5 -0.2 0.0 05 -0.3 1 0 2.5 0.4 2 2 08 0.1 09 SUMMERLAND CDA 49.34 -119.39 0.5 1.3 08 04 08 12 -0.1 00 04 -0.1 06 0.7 0.3 08 06 0.4 05 SWAN RIVER 52.30 -101.60 1.5 1.7 0.3 0.2 0 1 -0.3 0.0 -0 1 02 -0 1 0 1 1.7 0.1 1 6 02 -0 1 04 TAHTSA LAKE WEST 53.37 -127.42 04 1 8 0 7 0 2 00 05 -0 1 -0.7 0 4 01 1 0 0.7 05 1 0 0 J -0 1 0 4 TATLAYOKO LAKE 51.40 -124.24 0? 1 4 06 02 04 1 0 0 1 -03 04 -0 1 1 5 1.2 0.6 09 04 0.3 06 TERRACE A 54.28 -128.36 06 I 4 0.6 0.3 0.0 0.9 -0.2 -0.8 0.3 -0 1 09 1.1 0.4 1.0 0.3 0.0 04 TESLIN A 60.10 -132.46 1 0 22 03 0.6 0.1 07 -0.2 -1.0 04 02 1.1 1.7 0.6 16 03 -01 06 THE PAS A 53.58 -101.60 1 5 1 4 0 7 05 03 -0.3 -0.3 -0 1 0 1 00 0 1 1.7 0.1 15 05 -0.2 05 TOFINO A 49.50 -125.46 04 09 05 0.1 02 06 00 -0.3 03 02 06 0.3 0.3 05 0 2 0.1 0.3 TROCHU TOWN 51.50 -113.13 09 20 0.8 0.9 0 7 06 -0.3 -0.3 02 -0.3 0.9 1.9 0.3 16 08 00 07 TUGASKE 50.53 -106.18 1 8 22 1.0 06 06 -0 1 -0.2 -0.3 02 -0.3 08 2.3 0.2 2 1 0 7 -0.2 07 VANCOUVER INT'L A 49.11 -123.10 05 09 0.5 0.1 02 0.6 -0.1 -0.3 0.2 00 03 0.5 0.2 06 0.3 0 1 03 Table 5-4: Mean monthly and seasonal El Nino signal measured during El Nino years in surface temperature records.
STATION NAME Lat Long Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec SON DJF MAM JJA Annual VAVENBY 51.35 -119.47 1.8 2.1 0.9 0.3 0.6 1.2 0.1 -0.3 0.4 -0.1 0.9 1.5 0.4 1.8 0.6 0.3 0.8 VERNON COLDSTREAM RANCH 50.14 -119.12 07 1.5 10 0.3 0.7 1.2 -0 1 -0.1 0.4 -0.1 0.7 1.0 0.4 1.0 0.6 0.3 06 VIRDEN 49.51 -100.56 1 1 16 0.3 0.5 04 0.0 0.0 0 1 0.1 -0 1 0.4 2.2 0.1 16 04 0.0 05 WARFIELD 49.60 -117.45 04 12 0.7 0.2 09 1.0 -0.4 0.0 0.4 02 0.5 0.6 0.4 0.8 0.6 0.2 05 WASECA 53.80 -109.24 0.8 2.1 0.5 0.8 0.6 0.2 -0.2 -0.5 0.3 -0.3 0.8 1.7 0.3 1.5 0.6 -0.2 0.6 WATROUS 51.40 -105.28 14 2.0 0.5 0.5 0.6 -0.1 -0.2 -0.3 0.3 -0.4 0.3 1.8 0.1 1.7 0.5 -0.2 0b WATSON LAKE A 60.70 -128.49 0.7 1.5 0.0 0.6 0.0 0.6 -0.3 -0.9 0.2 0.5 0.6 1.7 0.4 1.3 02 -0.2 04 WHITECOURT A 54.90 -115.47 05 1 9 03 0.6 0.5 0.4 -0.2 -0.5 0.5 -0.3 0.8 1.6 0.3 1.3 05 -0.1 05 WHITEHORSE A 60.43 -135.40 0.7 26 0.2 08 -0 1 0.7 -0.1 -1.0 0.6 0.5 0.8 2.4 0.6 1.9 0.3 -0.1 07 WHITESAND DAM 56.14 -103.90 1.7 0.9 09 0.6 0.1 -0.3 -0.4 -0.3 -0.4 -0.1 -0.5 1.4 -0.3 1 3 05 -0.3 03 WINNIPEG INT'L A 49.54 -97.14 1.5 13 1.0 0.3 0.1 -0.6 -0.1 -0 1 -0 1 -0.3 -0 1 1.7 -0.2 15 0.5 -0.2 04 WISTARIA 53.49 -126.13 10 2.0 0.7 04 03 0.9 00 -0.7 04 0 1 1.4 1.3 0.6 1.4 0.5 0.1 0.7 YELLOWKNIFE A 62.28 -114.27 1 5 1.0 05 02 -0 1 0 1 -0 1 -0.6 0.0 06 0.1 1.6 0.2 1 4 02 -0.2 04 YORKTON A 51.16 -102.28 1.5 1.6 0.4 0.6 0.4 -0.1 0.0 -0.1 0.2 -0.1 0.7 2.1 0.3 1.7 0.4 0.0 0.6 MEAN 1.0 1.7 0.6 0.5 0.4 0.4 -0.1 -0.3 0.2 -0.1 0.6 1.4 0.3 1.4 0.5 0.0 0.5 STANDARD DEVIATION 0.5 0.5 0.3 0.2 0.3 0.5 0.1 0.3 0.2 0.3 0.4 0.6 0.2 0.4 0.2 0.2 0.1 NUMBER OF POSITIVE 119 119 114 117 105 80 10 10 103 53 109 119 106 119 119 40 119 NUMBER OF NEGATIVE 0 0 3 0 5 30 90 99 9 52 10 0 9 0 0 55 0 ZERO 0 0 2 2 9 9 19 10 7 14 0 0 4 0 0 24 0 94
5.3.5 Signal analysis
5.3.5.1 General synthesis of El Nino-related signal
Table 5-4 shows that on average, positive signals are common to the area during El Nino years with a mean annual magnitude of +0.5°C. The direction of the annual signals can be attributed to conditions observed during fall (SON), winter (DJF), and spring (MAM) when positive signals are common to the area. A total of 89%, 100% and 100% stations show positive signals for SON, DJF and MAM, respectively. This pattern, however, falls apart during the summer (JJA), as the percentage of stations recording positive signals drops to 34. At this time of the year, the majority of stations (46%) record negative magnitudes and 20% seem unaffected by El Nino, as the mean signal is 0.0°C. Signals of +0.3°C are typical to the area during SON(0), which rise and reach greatest magnitudes of+1.4°C in DJF(+1). These then drop to +0.3°C in MAM(+1) and reach 0.0°C in JJA(+1). Mean signals observed during December, January and February are +1.4°C, +1.0°C, and +1.7°C, respectively. These months show the least amount of variability in the mean value of 0.6°C for Dec(+1), and 0.5°C for Jan(+1) and Feb(+1).
5.3.5.2 Differentiation of El Nino, La Nina and neutral signal time series
After the signal was computed it was of interest to determine whether the values during neutral, El Nino, and La Nina years were statistically different from each other. Figure 5-6 shows the plot of each of the three classes. The monthly signal represents the mean values of all 119 stations. The line that depicts El Nino periods confirms issues revealed in Table 5-4. El Nino-related signals resemble a "bell-shape" curve with a maximum positive magnitudes around Feb(+1) and two minima, one during the fall and the other during following summer. Negative signals are apparent during Jul(+1) and Aug(+1). Month
Figure 5-6: Mean monthly time series of El Nino signals between Sep(O) andAug(+l) for neutral, El Nino and La Nina years across Western Canada.
Table 5-5: Descriptive statistics of mean signal during Neutral, El Nino and La Nina years across Western Canada.
Class N (months) Mean Standard Deviation Neutral 273 0.028 2.147 El Nino 148 0.450 2.108 La Nina 143 -0.594 2.113 Total 564 -0.021 2.158
Table 5-6: Multiple Comparisons Test - ANOVA results. Shown is the 2-tailed significance level that tested whether the three time series of El Nino signal are statistically similar across Western Canada.
Neutral El Nino La Nina Neutral 1.000 El Nino 0.052 1.000 La Nina 0.000 0.000 1.00 % La Nina years resemble a mirror image of El Nino periods (figure 5-6). The onset of cooler seasons beginning in Nov(O) is characterized by the invasion of negative signals to the area. These persist until spring (Apr(+1)), when neutral magnitudes return. Signals during neutral periods show little deviation from zero, or the neutral line throughout the year. During neutral times, the mean annual signal value was calculated at 0.028°C with a standard deviation of 2.147°C (Table 5-5). The results shown in Table 5-5 show that during El Nino (La Nina) years, the mean annual signal value was +0.450°C (-0.594°C) with standard deviation of 2.108°C (2.113°C). Greatest differences between the two series are evident between late fall and early spring (figure 5-6).
The mean signal series were then subjected to an ANOVA (Kvanli 1988) test which revealed that they are significantly different. The exact significant levels are summarized in Table 5-6. In spite of the variability, there is a significant difference between the monthly neutral, El Nino and La Nina signal series at the 95% confidence level or better. It can be concluded that the oceanic and atmospheric modifications observed across the tropical Pacific ocean during El Nino and La Nina periods coincide with significant surface temperature 'behavior' alterations across Western Canada.
5.3.5.3 El Nino signal and SSTs in tropical Pacific ocean
The "bell-shape" feature of surface air temperature signals observed during El Nino years can also be seen in anomalies of sea surface temperatures in the equatorial Pacific. Figure 5-7 shows a comparative plot with SSTA in the Nino 3.4 region. Best association is obtained when SST As lead by 1 month. An R2 of 0.55 was obtained at this lag, statistically significant at the 99.9% confidence level. SSTAs in this region explain about 55% of the variation in Western Canadian signals. Figure 5-8 shows the sequence plots of the two variables. The R2 value at 0-lag was 0.10, statistically significant at the 99.9% confidence level. A significant positive linear relationship exists between sea surface temperature conditions in the Nino 3.4 region and surface air temperatures across the study area. 97
Figure 5-7: Association between mean monthly El Nino surface temperature signal across Western Canada and sea surface temperature anomalies measured across central tropical Pacific (Nino 3.4 region). Mean values are computed based on El Nino events between 1950 and 1996. R2 at O-lag cross-correlation 0.45, significant at 99% confidence level. SSTA leading by 1-month, R2 = 0.55 significant at 99.9% confidence level (SSTA data courtesy ofCPC (1999)).
Figure 5-8: Association between 6-month running mean time series of El Nino surface temperature signal in Western Canada and sea surface temperature anomalies across the central tropical Pacific (Nino 3.4 region) between January 1950 and December 1996. R2 at O-lag was calculated at 0.104. This value was found statistically significant at the 99.9% confidence level. (SSTA data courtesy ofCPC (1999)). 98
5.4 Summaries and Conclusions
According to the ENSO definition, 12 El Ninos occurred between 1950 and 1996: the 1951/52, 1957/58, 1963/64, 1965/66, 1968/69, 1969/70, 1972/73, 1976/77, 1982/83, 1986/87, 1991/92 and 1994/95 events. Equally important to the denotation of signal were neutral periods characterized by Pacific conditions in neither state of El Nino nor La Nina. These included the following years: 1950/51, 1952/54, 1955/57, 1958/63, 1966/67, 1974/75, 1977/82, 1983/84, 1985/86, mid-1988, 1989/91, 1992/94, mid-late 1996. This constituted about one half of the total time. One quarter of the time, El Nino was present. A total of twelve months of the El Nino cycle were chosen for this study, from its development in the Pacific in the fall, around Sep(0) through to complete dissipation in the following summer, Aug(+1). This time period allows for the propagation of energy from the tropics to extratropical latitudes. An examination of the signal's "character" revealed significant differences among the El Nino, La Nina and neutral classes. Signals are strongly associated with SSTA conditions in the Nino 3.4 region. Higher SSTAs are synonymous with positive signal magnitudes and vice versa. This finding confirms the argument of Barnston and Chelliah (1997) who claim that not only is the anomaly observed in this region of the tropical Pacific an excellent indicator of ENSO, but also a good indicator of climate impacts around the world. Barnston and Smith (1996) found the central-west basin to provide highest skill in their model that forecasts South American surface temperatures between January and March when compared to those attained in other regions of the tropical ocean. When searching for a simultaneous relationship between boreal winter Pacific SSTs and Northern Hemisphere 700 mb height, Graham and Barnett (1995) found best- defined teleconnections to SSTs between 135° and 180°W. Positive signals decrease in magnitude as follows: DJF>MAM>SON>JJA. During the summer months, the majority of stations experience near-neutral to negative signals. The strong cool season anomaly is consistent with the General Circulation within which the ENSO phenomenon exists. The poleward flux of heat is greatest in the 99 winter and spring due to the larger temperature gradients. In the summer, this gradient flattens, hence the reduced transport polewards. This study calculates El Nino signal in a unique manner to other Canadian literature on the subject. Earlier published studies of Ropelewski and Halpert (1986), Yarnal and Diaz (1986), Kiladis and Diaz (1989), AES (1994), Shabbar and Khanderkar (1996), Shabbar et al. (1997), and Byrne and Berg (1998) analyzed the climatological anomaly values of either surface temperature and/or precipitation observed during various El Nino periods. In these studies, monthly climatological values, or normals, were computed using all months within a certain time period, for example, 1961-1990. This record, however, contains periods when the Pacific was both in an extreme state and in a neutral state. So, when striving to quantify the impact of El Nino on a climatic variable using this procedure, the true impact value is 'contaminated' due to the presence of ENSO periods in the monthly normal values that are then compared to the observed magnitudes. When El Nino's signatures are determined by comparing the observed value against a mean value computed from neutral periods only, a more precise measure of El Nino impact is, therefore, obtained. The only drawback with this method is that the signal is still impure. Such a calculation assumes that the signal magnitudes result chiefly due to the El Nino event present in the Pacific. Thus, it is assumed here that the climate observed across the study region during El Nino periods is present as a result of the El Nino phenomenon in the Pacific basin, with no other climate determinants present during those time periods. This is however incorrect, since El Nino events can be occurring simultaneously with other events that may act to significantly alter climates around the world. One example includes the explosion of Mount Pinatubo in the Philippines during 1991. This eruption caused a cooling in the tropics and extratropics despite of the presence of an El Nino event for a few years (Glantz 1996). 100
6. SIGNAL VARIATION ANALYSIS - EL NINO VERSUS NEUTRAL PERIODS
6.1 Introduction
The mean state of surface climate (i.e. air temperatures) is elevated during El Ninos. But, how consistent is this modification among the twelve events? AES (1994) and other Canadian studies revealed considerable differences in climate responses from one event to another. It was the goal of this chapter to examine the nature of this variation from one event to the next, and from one location to another during warm and neutral periods.
Composite maps have been used in climate research to illustrate average or typical 'behaviors' of countless number of variables (Wilks 1995). Climatic atlases contain maps of monthly, annual or daily mean values of temperatures, pressures, wind speeds and directions, and rainfall patterns, to name a few. All such illustrations are derived by averaging long-term records throughout the displayed geographic area. These representations were produced here digitally to investigate the 'behavior' of El Nino's signal in time and space, by exploring the variability inherent within the maps. Data exploration, visualization and analysis within GIS were used to move the issue forward.
6.2 Composite maps
Composite maps have been repeatedly used in studies that attempted to understand and quantify El Nino's impact on Canadian climates (Shabbar and Khandekar 1996; Shabbar etal. 1997; Byrne and Berg 1998; Ropelewski and Halpert 1996). This 'typical' behavior, however, comprises of certain amount of variability that arises from dissimilarities among the maps used to compute the composites. And, the greater the amount of variability present or the more dissimilar the maps are the less representative 101
Station data for western Canada Monthly values between 1950-96 of El Nino signal
Extraction of El Nino years (months)
Calculation of monthly El Nino composite map based on all El Nino years
GIS
Spatial and temporal variability analysis (variance of signal between events) (variance of signal between locations)
Signal standard deviation analysis
Calculate values for each monthly composite map between Sep(0) and Aug(+1)
Calculate and plot mean regional value with standard deviation of the mean for each month
Each monthly map will provide idea regarding the variation of signal at each grid point between two events
Figure 6-1: Assessment of spatial and temporal variation of El Nino surface temperature related signal across Western Canada. 102 the final composites. Investigating the nature of the maps through intra- and inter-map variability assessment can provide insight into the complexity of the signals.
Each monthly composite (£m) of the El Nino signal (£) was initially computed as the arithmetic mean of all available maps for each month:
n = 12
n=1 6-1
The mean value for each month becomes a function of the values from all maps, and the number of maps (i.e. 12) from which the representative map is constructed. The following paragraphs will illustrate the methods (figure 6-1) and results obtained from the variation analysis.
6.2.1 El Nino periods
The spatio-temporal signal variance was explored by using the image calculator in IDRISI (Eastman 1997). Signal variability from one event to another at each grid point, one month at a time was examined using the standard deviation of corresponding magnitudes.
]£W-(f;r«,))2/i2
Tjj represents the mean monthly El Nino surface air temperature signal obtained from all 12 available years (y). Values were calculated from the 12 images (12 events) in each month. Figure 6-2 shows values calculated for January. The 95% trimmed mean value for the image is 5.1°C. Highest variation of 8°C can be observed in a northwest- 103 southeast trajectory, from southern Yukon through Alberta and into southwestern Saskatchewan. Least amount of variation from one January to another is seen along the coast of BC and throughout Manitoba. There values average at 3.5°C during El Nino periods. Table 6-1 summarizes the remaining monthly and seasonal results. The mean value similarly represents the 95% trimmed mean of each monthly image, and the standard deviation of this mean represents the variation in the trimmed mean from one station to the next.
Table 6-1: Results of standard deviation analysis for Western Canada. On the left are shown results from El Nino years and on the right results obtained for neutral years (non-ENSO years). Variation between various El Nino events and between various neutral years was examined for each month and season of the year. Also shown are the standard deviation values of the mean signal variation, depicting the spatial variability during each month/season.
Month El Nino years Neutral years o a
Mean Ospace Mean Qspace Sep(0)* 2.1 0.6 1.5 0.3 Oct(0)* 2.1 0.8 1.4 0.4 Nov(0)* 2.1 0.7 3.4 0.8 Dec(0)* 3.4 0.8 3.8 1.1 Jan(+1)* 5.1 1.5 4.1 1.0 Feb(+1)* 2.9 1.0 3.9 1.2 Mar(+1) 2.5 1.2 2.5 0.9 Apr(+1) 2.0 1.0 2.1 0.7 May(+1)* 1.6 0.6 1.4 0.3 Jun(+1)* 1.5 0.3 1.3 0.2 Jul(+1) 1.2 0.3 1.1 0.2 Aug(+1) 1.6 0.5 1.6 0.3 SON 2.1 0.7 2.1 0.5 DJF 3.8 1.1 3.9 0.5 MAM 2.0 0.9 2.0 0.7 JJA 1.4 0.3 1.4 0.3 Annual 2.3 0.8 2.3 0.5 * denotes differences between El Nino and neutral years found significant at the 99% confidence level. 104 Cool months of the year between Dec(+1) and Mar(+1) show the greatest spatial
-130 -120 -110 -100 West Longitude (degrees)
Figure 6-2: Signal standard deviation in °C for all 12 El Nino events for January across Western Canada. Mean value of image is 5.1 °C calculated from data that fall within the 95th percentile.
and temporal variation in temperature signal. Least amount of variation occurs during warm summer months between May(+1) and Aug(+1). Highest values are observed during Jan(+1) at 5.1±1.5°C, lowest in July at 1.2±0.3°C. From a seasonal perspective, variation between events and locations increases in the following order: JJA How do these values compare to those observed during neutral periods? Are they higher, lower or the same? How does the variation of surface air temperature signals 105 Figure 6-3: Above: Mean monthly value of standard deviation for Western Canada for all 12 El Nino events. Error bars show the standard deviation of mean value. Below: Mean seasonal values. 106 during non-ENSO years compare to that during El Nino periods? Similar analyses were performed for the neutral years. 6.2.2 Comparison to neutral periods Key results are shown in table 6-1 and figures 6-3 and 6-4. Annual and seasonal surface air temperature signal values show no difference in year-to-year and grid-to-grid variation between non-ENSO and El Nino years. Mean annual standard deviation during El Nino years is similar to that during non-ENSO periods, at 2.3°C. Spatial variation of 0.6°C is observed during neutral years, which insignificantly increases to 0 8°C during El 40 20 § I - j n -20 M c S M £ -40 aDt ) (C0 .c O -60 -80 o ~S. Distinctions become apparent when examining monthly observations. Marked reductions in year-to-year variation values can be observed during cool months between Nov(+l) and Feb(+1), with the exception of Jan(+1), when variation increases significantly by 19%. The overall reduction in mean signal variation from one neutral to El Nino periods remains at 21%. All differences are statistically significant at the 99% confidence interval using the student's t-test (Kvanli 1988). Early fall marked by Sep(0) and Oct(0) notes positive change in variation at 29% and 32%, respectively, also found statistically significant at the 99% confidence level. Similar modifications were also observed in early summer when in May(+1) and Jun(+1) year-to-year variation increases by 13% during El Nino periods. Mar(+1), Apr(+1), Jul(+1) and Aug(+1) are the only periods when the presence of El Nino does not significantly influence the year-to-year variation of surface air temperature. The former show a slight but insignificant decrease of 5%, the latter a minor increase of 2%. 6.3 Summaries and Conclusions GIS was used to explore, visualize and analyze the spatio-temporal variation in signal through digital composite maps. Analyses of signal standard deviation show year- to-year variance to be significantly higher during non-ENSO than during El Nino periods during cool months of the year, between Nov(O) and Feb(+1). This suggests that during El Nino periods the primary mechanism which usually drives the local climate during neutral years is moderated or possibly over-ridden during warm events. The formation of the upper-level high pressure cell over the area in the fall of an EI Nino reduces the influence exerted on local climates by the Arctic High allowing warm Pacific air to continuously reach deeper into the continent. This acts to 'stabilize' the complexity of winter climate throughout the study area, possibly making the signals across the area more "predictable" and consistent during winter periods. 108 7. SIGNAL REGIONALIZATION AND CLUSTERING I: LARGE- SCALE ANALYSIS 7.1 Introduction El Nino signal The previous section quantified the 1950-1996 spatio-temporal variation present in signals in the study area. The current objective here is to Signal Reg onalization (Statistical re gionalization) further explore the fluctuations observed during (Chaf rter 7) El Nino periods. To do so, new questions were posed and methods developed to move the issues Temporal Clustering (Chapter 7) forward. These means and corresponding results are presented in the current and subsequent New comp osite maps sections of the thesis as illustrated in figure 7-1. for each sub-regie> n and signal type (Chaprte r 8) 7.2 Eigenvector regionalization Significance assessment (Chaprte r 8) The observed signal variability from one S patio-tempora signal analysis place to another across the area prompted an Reg on II (Chaf rter 9) interest to isolate any homogeneous sub-regions that show similar response(s). Statistical Relating winter signals to regionalization via PCA was used with monthly Pacific conditions (Chapter 10) signal time series at each station between Jan-50 and Dec-96. Steps with major results are Figure 7-1: Analysis of signal. An summarized in figure 7-2. overview. Figure 7-3 shows that there exists a strong relationship between the scores and signal, with the signal (°C) explaining about 71% of variation in the component scores. Positive scores are associated with positive signals, and negative scores with negative 109 signals. Component scores with values around El Nino signal in zero generally represent neutral signals. The monthly surface temperature data of Western Canada magnitude and sign of the scores can help 1950-96 determine characteristics. Eigenvector Regionalization Results of the analysis are shown in figure 7-4 and figure 7-5a-d. The latter shows the S-mode decomposition loading plots (spatial arrangements) of the first 564X119 matrix time(months)Xstations four components, used in the interpretation of the extracted regions. The former, a graph, depicts Correlation matrix the first six components' eigenvalue and 119x119 stationsXstations cumulative percent variation. Also shown is a table of eigenvalues, total amount of variation Principal Component Analysis explained and the cumulative percentages after rotation. These results, along with a working Varimax Rotation knowledge of the problem were used as criteria in 3 chosen components deciding how many components to retain. The 81.5% of total variance examination of the plots in figure 7-4 suggests that 3 Principal regions numerically, the first four components should be retained. A major break in slope in each of the Loading maps curves is observed after the fourth component. Together the first four components account for a Region defined where loading map values > 0.499 significant portion of total variance (i.e. 88.2%). The fourth component is also easily interpretable, Figure 7-2: Eigenvector regionalization analysis of El a coherent region north of the 58th parallel east of Nino signal in surface 120° longitude (figure 7-5d). It represents the temperature data across Western Canada. boreal climate. However, the fourth component was very poorly represented in the study, with less than 5% of stations being from this region. For this reason, the fourth component was dropped, and only the first three components that explain a total of 81.5% of variation in the original dataset were retained. 110 S228B8338SSS2S£8S?$8?SS* Icccceeccccecseccsesceen a a a a a (5 a a e , 5- ] • 4 - • • i J" i 2 " 'in it** Hn» s 10 i . • • : CS - 0.3153«signal ^ • -3- R? • 0.7048 * I El Nino signal (C) Figure 7-3: Relationship between component scores obtained from eigenvector regionalization, component 1, named Region I or Canadian Prairies. Above: Time series of 6-month running mean values between 1950 and 1996 of El Nino signal as extracted from Region I surface temperature records and the corresponding component scores obtained from the regionalization analysis. Below: Scatterplot between component scores and El Nifio signal (°C). There is a linear relationship between the two variables, such that high values in one signify high values in the other and vice versa. The R2 value was determined statistically significant at the 99.9% confidence level. Ill Rotation Sums of Squared Load ings Component Eigenvalue % of Variance Cumulative % 1 41.06 34,50 34.507 2 34.04 28.61 63.118 3 21.86 18.36 81.488 4 7.99 6.71 88.202 5 1.803 1.51 89.717 6 1.639 1.377 91.095 Figure 7-4: Results of the El Nino signal regionalization via eigenvector analysis. Above: A plot showing eigenvector values associated with each component number and the cumulative amount of variance explained by the component (1 through 6 for illustration). Below: Table summarizing the result statistics plotted in the figure.. 112 c: Component 3: Region III - 18.4% of variance d: Component 4: Region IV - 6.7% of variance 1 . • aappooe - D O O -* ^ o -* K> G» *. o> < 4 ft * O Cam pan ant Icedrtgs Figure 7-5 a-d: Component loading maps from regionalization analysis of El Nino signal in surface air temperature data across Western Canada. Regions are denoted by values that exceed 0.499. 113 Loading plots help interpret the meaning of each component. Correlation values exceeding 0.499 were used to depict the regions within each map. Component 1 shows highest values throughout the Prairie Provinces. It includes southern portions of Manitoba, Saskatchewan, and most of Alberta, with the exception of the extreme northwest and the Rocky Mountains. This region named Region I, accounted for 34.5% of the total variance. The second region, Region II represents the southern coast and interior British Columbia that spans from the foothills of Alberta to the Pacific Ocean, below 55°N, accounting for 28.6% of total variance. The third region, Region III that explains 18.4% of the variance represents northwest British Columbia, southern Yukon and northwestern Alberta. 7.3 Temporal Clustering After the sub-regions were extracted the next step involved the study of evolutionary patterns of the signals in time within each of them. It was of interest to isolate any similar sequences of monthly signal evolution observed from one El Nino to the next in each sub-region. The component-score time-series obtained for each sub- region from the regionalization analysis were used to accomplish this. The schematic of this step is shown in figure 7-6. Component scores for months between Sep(0) and Aug(+1) were extracted from the original 564-month time series, plotted as sequences and grouped according to similarities. A group of similar events was considered a "signal type" if at least three events (i.e. 25% of total) demonstrated similar behavior over the 12 month period. Once the patterns were extracted, the score values for each "type" were tested for significance, using neutral periods in the comparisons. The Mann- Whitney U test of independent samples was used. 114 Component-score time series from each 3 regions extracted from regionalization 564 months X 3 regions Extraction of 12 El Nino periods Sep(O) thro jgh Aug(+1) Grouping of evolutionary patterns according to similarities (i.e. winter conditions) Production of new composite factor scores for each month based on relevant events that grouped into similar types of El Nino Test if mean component score during El Nino periods is same as that measured during non-ENSO periods Mann-Whitney U test -1-tailed p-value 0.10 - signals same Figure 7-6: Clustering of similar patterns of evolution between Sep(0) and Aug(+1) for each of the three significant regions in western Canada. 7.4.1 Region I Upon examination of the -Typ«W component scores' time series it -TYWC was discovered that most events I 0.5 u N cluster into two principal patterns 8 £ o of evolution. These are 0 1 distinguished by conditions 8 -OS observed during JFM(+1). Relatively cool winters define the 1 * I Type C, whereas warmer winters Figure 7-7: Mean monthly component scores for define the Type W signal. The each of the two types of El Nino signal (W and C) majority of El Nino events (9/12), for Region I. show significant signals during fall (i.e. SON(O)). Type W is typically accompanied by cooler than neutral fall seasons that precede the major signals, whereas warmer than neutral fall conditions prevail during Type C events. Based on these findings, the signal types were classified here by the fall-winter pattern. Sequences that showed 2 of 3 cold months (JFM) to have a neutral to positive score AND neutral to negative score during 2 of 3 preceding fall months (SON) were classified as type W signals. All patterns that showed 2 of 3 cool months (JFM) to have neutral to negative component score AND neutral to positive component scores during 2 of 3 preceding fall months are referred to as Type C signals. A total of 9 events clustered into these groups. Figure 7-7 shows the mean monthly values of the scores for these signal types. Figure 7-8a and figure 7-8b, show in more detail each of the types. Tables 7-la and 7-lb summarize the results from significance tests. Months that showed mean scores significantly higher or lower (p<0.10) than neutral values were highlighted in the graphs in red and in the tables in bold. 50% of events grouped into Type W. These included the 1951/52, 1957/58, 1972/73, 1982/83, 1986/87, and 1991/92 events. The events* evolution resembled a "bell curve" with two minimums, one during the fall and another during the summer following Error Bars=Stardard| Error of Man Sigtfcart at SC%or better • 85-87 • 3- + 91/92 0 57/55 2 0 72ff3 uo • 82/83 —IVfean w c c o Q. E o o -1 -2 o Nbntfi 2- 1.5- 1 -I A Q5- A \14 r-flV co 1-0.5- • 0 A I -1- o -1.5- BMrto 0 94/96 -2 • D 68/89 . Error Bars =Sfand^ error of trie ms6riA -25 A • Sgificart at 90% cr better * Msan -3 0 o o o. ^ (0 "5 1 s i j i < Msdh Figure 7-8 a, b: 7j/?e above) and Type C (b: below) evolutionary types ofElNifio common to 9/12 El Nino events observed between 1950 and 1996 across Region I. Table 7-la: Results of Mann-Whitney statistics for Region I - Type W El Nino. Month Mann-Whitney UJ U Z sig Asymp** Exactf Sep (0) 21.0 -2.584 0.005 0.004 Oct (0) 38.0 -1.669 0.047 0.051 Nov (0) 40.0 -1.561 0.059 0.064 Dec (0) 53.0 -0.861 0.193 0.207 Jan (+1) 17.0 -2.743 0.003 0.002 Feb (+1) 33.0 -1.938 0.026 0.027 Mar (+1) 49.0 -1.077 0.141 0.151 Apr (+1) 55.0 -0.754 0.225 0.238 May (+1) 68.0 -0.054 0.478 0.489 Jun (+1) 60.0 -0.336 0.368 0.382 Jul (+1) 37.0 -1.624 0.052 0.056 Aug (+1) 65.0 -0.056 0.477 0.489 % dfnemrai = 23 except for Jan, Jun, Jul, Aug where dfneutrai=22, dfnm0=6; **l-tailed; t 1-tailed. Table 7-1 b: Results of Mann-Whitney statistics for Region I - Type C El Nino. Month Mann-Whitney UJ U Z sig Asymp** Exacrf Sep (0) 14.0 -1.645 0.050 0.056 Oct (0) 24.0 -0.843 0.199 0.211 Nov (0) 18.0 -1.324 0.092 0.105 Dec (0) 22.0 -1.003 0.158 0.176 Jan (+1) 18.0 -1.254 0.105 0.119 Feb (+1) 32.0 -0.201 0.420 0.440 Mar (+1) 10.0 -1.966 0.024 0.026 Apr(+1) 27.0 -0.602 0.274 0.297 May (+1) 11.0 -1.886 0.029 0.032 Jun (+1) 31.0 -0.167 0.433 0.453 Jul(+1) 32.0 -0.084 0.466 0.484 Aug (+1) 13.0 -1.672 0.047 0.053 Jdfneutrai = 23 for all months except Jan, Jun, Jul, Aug where dfneutrai=22, dfnin0=3; **1-tailed; f 1-tailed. 118 an event. A single maximum is observed in winter. 25% of events defined the Type C pattern. These included the 1968/69, 1969/70 and 1994/95 events. The remaining 25% of events did not fall within these two patterns, nor did they make up a distinct group of their own. These are shown in figure 7-9. None of these displays the major "attributes" of C, or W Figure 7-9 : El Nino events that did not fit into either types The 1963/64 event Type W or Type C signal evolutionary patterns in Region I. was associated with strongly positive signals during the fall season which were followed by strongly positive signatures during the following winter. During the 1965/66 event signals demonstrated a similar problem such that strongly negative signals found during September and November were followed by strongly negative signatures during the following winter. Finally, during 1976/77, unique developments of temperature signals were observed in this region. Strongly negative Jan(+1) magnitudes were 'surrounded' by strongly positive signals during both Dec(0) and Feb(+1). Despite this fact, however, it is evident that similarly to all other years, these 3 events were also associated with strong deviations from the expected neutral values during the key seasons. Table 7-lc shows the statistics (i.e. p-values) computed when all 12 events were grouped together. The results demonstrate that when all of the studied El Nino events were grouped together, with the exception of February, none of the other signals are significantly different from the expected neutral values. Thus, grouping of all El Nino events together seems to have "washed out" the important signals imbedded within the temperature records. 119 Table 7-lc : Results of Mann-Whitney statistics for Region I -All 12 El Nino events. Month Mann-Whitney UJ U Z sig Asymp** Exactf Sep (0) 116.0 -0.765 0.222 0.230 Oct (0) 104.0 -1.182 0.118 0.124 Nov (0) 117.0 -0.730 0.233 0.241 Dec (0) 117.0 -0.730 0.233 0.241 Jan (+1) 108.0 -0.865 0.193 0.201 Feb (+1) 95.0 -1.494 0.067 0.070 Mar (+1) 136.0 -0.07 0.472 0.479 Apr (+1) 119.0 -0.660 0.254 0.263 May (+1) 129.0 -0.313 0.377 0.385 Jun (+1) 131.0 -0.036 0.485 0.493 Jul(+1) 120.0 -0.432 0.332 0.341 Aug (+1) 128.0 -0.144 0.442 0.450 % dfneutrai = 23 except for Jan, Jun, Jul, Aug where df„eutrai=22, dfnino=12; **Mailed; t 1-tailed. Table 7-2a: Results of Mann-Whitney statistics for Region II - Type WEI Nino. Month Mann-Whitney U$ U Z sig Asymp** Exactf Sep(0) 70.0 -1.404 0.080 0.085 Oct(0) 103.0 -0.021 0.491 0.500 Nov(0) 82.0 -0.901 0.184 0.193 Dec(0) 70.0 -1.404 0.080 0.085 Jan(+1) 66.0 -1.436 0.075 0.080 Feb(+1) 51.0 -2.200 0.014 0.013 Mar(+1) 51.0 -2.20 0.014 0.013 Apr(+1) 96.0 -0.314 0.376 0.386 May(+1) 79.0 -1.027 0.152 0.160 Jun(+1) 61.0 -1.654 0.049 0.050 Jul(+1) 98.0 -0.044 0.483 0.491 Aug(+1) 91.0 -0.348 0.364 0.374 % dfneutrai = 23 except for Jan, Jun, Jul, Aug w lere dfneutrai=22, dfnino=9; **1-tailed; f 1-tailed. 120 7.4.1 Region H All 12 series clustered into one of Type C and W signals. Figure 7-10 shows the mean temporal plots for each type. The signals are defined by winter conditions, the DJF(+1) season. Initial signals appear in December All sequences that showed 2 of 3 DJF months to have a neutral to positive scores were grouped into Type W signal. All patterns that showed 2 of 3 months to Figure 7-10: Mean monthly component scores for each , , „. ofthe two types ofsignal (Wand C) for Region II. have neutral to negative J J ° /J ° scores were marked as Type C signals. All El Nino events clustered to either one of these two groups. Figure 7-1 la and 7-1 lb show each of these patterns in more detail. Tables 7-2a through 7-2c summarize the significance tests. 9/12 (75%) events make up the Type W signal. The 1957/58, 1963/64, 1965/66, 1969/70, 1976/77, 1982/83, 1986/87, 1991/92 and 1994/95 events define it. Figures 7-10 and 7-1 la show that with minor exceptions, this "warm winter" El Nino is characterized by neutral to positive signals throughout the entire year. Table 7-2a shows that signal deviations measure significantly higher than those recorded during non-ENSO periods during 6 months of the year and include Sep(0), Dec(0), Jan(+1), Feb(+1), Mar(+1) and Jun(+1). 25% of events make up the Type C pattern. These include the 1951/52, 1968/69, and 1972/73 events. This type is characterized by neutral to negative signals throughout most of the year, with maximum negative magnitudes observed during DJF(+1). Significance tests are shown in table 7- 2b. One month of the fall season (Oct(0)) displays signal magnitudes significantly lower than the expected. During the winter season, Dec(+1) demonstrates the greatest 121 4 Error Bars = Standard error of the mean El Nino 3.5 X 91/§2 • Significant at 90% or better * 94/S5 3 o 57/58 A 63/64 2.5 • 76/77 • 86/87 2 • 69/70 + 82/83 - 65/6'6 1.5 ••-Mean 1 0.5 H 0 -0.5 -1 -1.5 -2 J S S- Month 2.3 - El Nino Error Bars = Standard error of the mean + 51/52 • significant at 90% or better O 68/69 1.3 - O 72/73 -•-Mean a* 0.3 - o ino -0.7 - a E o u -1.7 -2.7 -3.7 + + < Month Figure 7-11 a, b: Type W (a: above) and Type C (b: below) evolutionary patterns of El Nino common to all 12 El Nifio events recorded between 1950 and 1996 across Region II. Table 7-2b: Results of Mann-Whitney statistics for Region II - Type C El Nino. Month Mann-Whitney U{ U Z sig Asymp* * Exactf Sep 25.0 -0.762 0.223 0.245 Oct 23.0 -1.485 0.069 0.078 Nov 25.0 -0.762 0.223 0.245 Dec 8.0 -2.127 0.016 0.016 Jan 18.0 -1.254 0.105 0.119 Feb 24.0 -0.843 0.199 0.221 M ar 24.0 -0.843 0.199 0.221 Apr 30.0 -0.361 0.359 0.381 M ay 22.0 -1.003 0.158 0.176 Jun 30.0 -0.251 0.401 0.422 Jul 31.0 -0.167 0.433 0.45.3 Aug 27.0 -0.502 0.308 0.331 = Jdfneutrai 23 for all months except Jan. Jun, Jul, Aug where dfneutrai=22, dfnino=3; **l-tailed; f 1-tailed. Table 7-2c: Results of Mann-Whitney statistics for Region II - All 12 El Nino events. Month Mann-W tiitney UJ U Z sig A sy mp** Exactf Sep(0) 114.0 -0.834 0.202 0.210 Oct(0) 119.0 -0.660 0.254 0.263 Nov(0) 107.0 -1.077 0.140 0.147 Dec(0) 131.0 -0.243 0.404 0.412 Jan(+1) 114.0 -0.649 0.258 0.267 Feb(+1) 96.0 -1.460 0.072 0.075 Mar(+1) 75.0 -2.189 0.014 0.014 Apr(+1) 126.0 -0.417 0.338 0.347 May(+1) 101.0 -1.286 0.100 0.104 Jun(+1) 91.0 -1.478 0.070 0.073 Jul(+1) 129.0 -0.108 0.457 0.765 Aug(+1) 130.0 -0.072 0.471 0.479 % dfneutrai = 23 except for fan, Jun, . >ul, Aug where dfneutrai=22, dfnino=12; **l-tailed; f 1-tailed. 123 deviations from neutral values. Table 7-2c shows similar test results when event clustering was not fendered. Feb(+1), and Mar(+1), show signal values significantly below expected, magnitudes. During the month of June (+1), however, these are found to reach magnitudes well above neutral values. These months are also identified in the Type W signal. What can not be seen here, however, is the uncovered importance of the fall months of Sep(O) (i.e. Type W) and Oct (0) (i.e. Type C), as well as that of Dec(0) (i.e. Type W & C) and Jan(+1) (i.e. Type W). 7.4.1 Region DJ Winter (DJF (+1)) conditions once again helped define the Type W and C signals in this sub-region. All patterns that showed 2 of 3 DJF months to have a neutral to strongly positive scores defined the Type W signal. All sequences that showed 2 of 3 Worth DJF months to have a neutral to Figure 7-12: Mean monthly component scores for strongly negative scores defined each of the two types of signal (W and C) for Region the Type C signal. Figure 7-12 III shows the mean scores for both types. 11 out of all 12 events (91.7%) grouped into either one of these two groups. The figure demonstrates these two patterns to be very complementary to one another throughout the El Nino cycle. Figures 7-13a and 7-13b show the detailed plot of signal type and tables 7-3a through 7-3 c summarize the statistical tests. Just under one half, 5 out of the total 12 events (41.6%), made up Type W signal. This includes the 1963/64, 1969/70, 1976/77, 1986/87 and 1991/92 periods. This type is characterized by neutral fall temperature signals that precede the maximum positive 124 4 El Error Bars • Standard error of the mean 3.5 A 86787 • Significant at 90% or better O 91/92 3 ft 4 *V» O O 69/70 2.5 • 76/77 X 63/64 2 -•-Mean 3 2 Month El Nino • 94/95 O 65/66 o A 82/83 Error Bars = Standard error of the mean * • Significant at 90% or better jV™ Month Figure 7-13 a,b: 7y/?e JPYa: above) and Type C (b: below) evolutionary types of El Mho common to 11/12 El Nifio events observed between 1950 and 1996 across Region III. Table 7-3a: Results of Mann-Whitney statistics for Region III - Type WEI Nino. Month Mann-Whitney Uf U Z sig Asymp** Exactf Sep(O) 51.0 -0.210 0.348 0.363 Oct(0) 54.0 -0.210 0.412 0.431 Nov(O) 55.0 -0.150 0.440 0.454 Dec(O) 36.0 -1.290 0.098 0.107 Jan(+1) 44.0 -0.687 0.246 0.262 Feb(+1) 24.0 -2.009 0.022 0.023 Mar(+1) 54.0 -0.210 0.417 0.431 Apr(+1) 45.0 -0.750 0.226 0.241 May(+1) 16.0 -2.489 0.006 0.005 Jun(+1) 29.0 -1.623 0.051 0.056 Jul(+1) 46.0 -0.562 0.287 0.303 Aug(+1) 50.0 -0.312 0.377 0.393 % dfneutrai = 23 except for Jan, Jun, Jul, Aug where df„6Utrai=22, dfnino=5; **1 -tailed; t 1-tailed. Table 7-3b: Results of Mann-Whitney statistics for Region III - Type C El Nino. Month Mann-Whitney UJ U Z sig Asymp** Exactf Sep(O) 56.0 -0.700 0.242 0.255 Oct(0) 46.0 -1.238 0.108 0.116 Nov(O) 53.0 -0.861 0.199 0.207 Dec(O) 39.0 -1.615 0.053 0.057 Jan(+1) 27.0 -2.184 0.014 0.014 Feb(+1) 58.0 -0.592 0.227 0.290 Mar(+1) 34.0 -1.884 0.030 0.031 Apr(+1) 67.0 -0.108 0.457 0.468 May(+1) 67.0 -0.108 0.457 0.937 Jun(+1) 65.0 -0.056 0.477 0.489 Jul(+1) 62.0 -0.224 0.411 0.424 Aug(+1) 18.0 -2.687 0.003 0.002 I dfneutrai = 23 for all months except Jan, Jun, Jul, Aug where dfneutrai=22, dfnmo=6; **1-tailed; f 1-tailed. 126 Table 7-3c: Results of Mann-Whitney statistics for Region III -All 12 El Nino events. Month Mann-W litney UJ U Z sig Asymp** Exactf Sep(O) 107.0 -1.077 0.140 0.147 Oct(0) 112.0 -0.904 0.183 0.498 Nov(O) 135.0 -0.104 0.458 0.466 Dec(O) 127.0 -0.382 0.351 0.359 Jan(+1) 110.0 -0.793 0.214 0.222 Feb(+1) 125.0 -0.452 0.326 0.334 Mar(+1) 89.0 -1.703 0.044 0.046 Apr(+1) 136.0 -0.070 0.472 0.479 May(+1) 86.0 -1.807 0.035 0.036 Jun(+1) 115.0 -0.613 0.270 0.278 Jul(+1) 130.0 -0.072 0.471 0.479 Aug(+1) 79.0 -1.910 0.028 0.029 % dfneutrai = 23 except for Jan, Jun, Jul, Aug where df„eutrai=22, df„ino=12; **l-tailed; f 1-tailed. 127 signal during the following winter (DJF(+1)). Signals gradually increase in magnitude until February, when the maximum magnitudes are reached. Thereafter, these begin to decline until May and June, when a secondary "peak" is observed. During this time, Figure 7-14: El Nino events that did not fit into either the signal is strongly Type W or Type C El Nino evolutionary patterns - negative. Thereafter, Region III. conditions recover so that by August (+1), signals reach neutral values. One half (i.e. 6/12) of events defined the Type C signal. These included 1951/52, 1965/66, 1968/69, 1972/73, 1982/83 and 1994/95. This type is initially characterized by neutral conditions during the fall. After Nov (0), however, magnitudes drop to below neutral values and remain there until Jan(+1) when maximum negative signals are attained. Magnitudes are significantly lower than expected during Dec(+1) and Jan(+1). After this time, the signals begin a gradual recovery until March when a secondary minimum signal was attained. Thereafter, conditions remain neutral until late summer, when significant negative signatures reappear in August. Only the 1957/58 event did not fall into either one of the two types. Its evolutionary sequence is shown in figure 7-14. Although 2 out of 3 DJF months do fall below the neutral value, the event can not be placed into Type C, as the January signal is strongly positive. Table 7-3 c summarizes the significance tests when no evolutionary groups are developed. March, May and August all show significant deviations from the expected values. These have also been extracted from both types. None of the significant winter signals are detected in this procedure. 128 7.4 Summaries and Conclusions Signal regionalization and component score clustering revealed three distinct sub- regions each with two types of signal, W and C. The largest, Region I, includes the Prairie Provinces. Southern British Columbia, located west of the foothills of the Rocky Mountains below 52°N, defines Region II. Region III represents northern British Columbia, southern Yukon, and the Grand Prairie region of Alberta, an area west of 110° longitude, and north of 52° latitude. The signal types resemble 'bell-shaped' mirror images of each other. The most pronounced ditferences between them occur in winter. Type W signals are characterized by positive, and Type C by negative magnitudes. Table 7-4 summarizes the winter signals to reveal spatial heterogeneity across the study area The 1968/69 event recorded negative signals across the area whereas in 33% of events, positive signal values occurred throughout. These include the 1963/64, 1976/77, 1986/87, and 1991/92 events. The remaining 58% of evens displayed some degree of spatial variation in the sign of the winter signal. Warm-winter conditions prevailed in two of the three sub-regions during the 1951/52, 1965/66, 1972/73, and 1994/95 events. In each case Region III was dominated by negative signals. Cold- winter conditions were observed in two of the three areas during the 1957/58, 1969/70, and 1982/83 events. During these El Ninos Region II always recorded positive signals. Table 7-5 summarizes the signal type frequency. The relative importance of Type W signals diminishes northward. Warm-winter events are most common to Regions II and I, at a frequency of 75% and 67%, respectively. In Region III only 42% of all El Nino events were warm-winter events. The relative 'importance' of Type C declines from Region III, to I to II with frequencies of 58%, 33% and 25%, respectively. The greatest number of events that did not fit into either Type C or W types are observed across Region I, followed by Region III and finally Region II, where all sequences were classified into either type. These results suggest that signal variability increases from the coast towards the interior of the continent. 129 Table 7-4: El Nino event classification in each region (i.e. El Nino Type (Wor C)) Region Sum El Nino I II III #c #w '51/52 W c c 2 1 '57/58 W w N-c 1 2 '63/64 N-w* w w 0 3 '65/66 N-c w c 2 1 '68/69 C c c •69/70 C w w i 2 '72/73 w c c 2 1 '76/77 N-w w w 0 '82/83 W w c 1 2 '86/87 w w w 0 3 '91/92 w w w o 3 '94/95 c w c 2 1 * Not classified into either Cold or Warm type (Neither), but the winter season was warmer than neutral (-w) or colder (-c) than neutral or non-ENSO periods. Figure 7-15 a-d shows standardized geopotential height surfaces at 500 hPa during event winters when a. all 3 regions experience warm winters; b. 2 out of the 3 regions experience warm winters; c. all 3 regions experience cold winters; d. 2 out of 3 regions experience cold winters. Warm-winter events are coincidental with a mid-tropospheric high-pressure cell over the area and a low pressure center over mid-Pacific. This pattern resembles the PNA (figure 1-7). The stronger and more developed the high-pressure area is over the region, the greater the likelihood of having a warm winter throughout Western Canada. During cool winters, a low-pressure cell resides over the area. It is most developed over Region II. Similarly to the high-pressure cell, its strength and spatial extent appears to influence the presence of negative signals throughout. This arrangement of pressures patterns resembles the negative phase of the TNH (figure 1-8) with strong troughing over Region II and ridging over eastern North America and mid-Pacific. PNA allows for a steady influx of warm subtropical air into the region, whereas cold polar air permeates the area during a negative phase of TNH. The dominance of these patterns reduces the influence 130 • 170 -l«t -ISO 430 -120 -111 -100 -» -SO -"() -170 -160 -150 -140 -130 -120 -110 -100 -90 -SO Figure 7-15a-b: Standardized anomalies of500 hPa surface, a: During events when 3 sub-regions experienced warm winters. (3w) b: During events when 2 out of 3 regions experienced cold winters. (2wlc). Positive values are shown in red, negative in blue. Anomalies computed from 1961-90 base period. 131 -170 -160 -150 -WO -136 -120 -110 -100 -90 -80 Figure 7-15c-d: Standardized anomalies of500 hPa surface, c: During events when 3 sub-regions experienced cold winters. (3c) d: During events when 2 out of 3 regions experienced cold winters. (2clw). Positive values are shown in red, negative in blue. 132 of other systems on the area, like the Arctic High pressure that typically alternates with Pacific airstreams during non-El Nino periods specifically in the Prairies. Region III is located on the northern edge of the jet stream that meanders from lower latitudes of the Pacific around the high-pressure cell situated over the area, into the Prairies. This places the region outside the area that receives the greatest influx of warm subtropical air, hence the subdued temperature signals. Table 7-5: Frequency of occurrence of Type W and Type C signals between 1950 and 1996 across regions of Western Canada. Region Frequency of occurrence El Nino Type Type W TypeC Not Warm Cool • classified Winters* Winters** Region I 50% 25% 25% 67% 33% Region II 75% 25% 0% 75% 25% Region III 42% 50% 8% 42% 58% *2 of 3 JFM (region I) or DJF (regions II, III) months above neutral ** 2 of 3 JFM (region I) or DJF (regions II, III) months below neutral The timing of maximum signals differs from one sub-region to another. JFM(+1) is the key season for Region I and DJF(+1) in Regions II and III. Several explanations can be put forth for this observation. Typically, a weather disturbance takes from a couple to 10 days to travel from the coast across the mountains and into the prairies (Hare and Thomas 1979). Taking into consideration that monthly mean signals were used to compute these statistics, it is not possible to determine when during December the energy reaches the west coast and when it arrives to the east in January. It is possible that the signal arrives in the last week of the month at the west coast and a week later in January it reaches the prairies. It is also possible that the 1-month lag time is real and can be attributed to surface energy balance. Between December and February, the earth's surface is likely to be snow-covered in regions east of the mountains. This is not true in the west. When the 'El Nino' energy arrives through general circulation to the coast, it can be shortly used to heat the atmosphere. This energy will, however, have to be initially used to melt snow and ice in the Prairies. Surface albedo values will decline as snow melts, gradually allowing the energy to be absorbed by the surface. These 133 processes will take place before the energy will be used to heat the atmosphere. This may take additional time and appear as lag in the data. Moreover, figure 7-15 shows sea surface temperature anomalies measured in tenths of degree Celsius off the coast of British Columbia during the winter period. The elevated magnitudes are apparent from Washington to Alaska. Thus, the west coast receives energy from El Nino not only via the atmosphere but also through the ocean current that can be used immediately to heat the atmosphere and raise the signal magnitudes over Regions II and III. This energy is not available to the area east of the mountains. Finally, Type W and C signals were compared to unclassified magnitudes. Signal 'averaging' masks the evolutionary patterns in all areas and none of the winter characteristics are captured. These results support the idea of the importance of investigating such signatures by keeping the winter period in mind, and that El Nino signals are not just random manifestations of climatic perturbations. Although the study area is located thousands of kilometers away from the birthplace of El Nino, there appears to be a consistent 'response' to it. 134 8. GRID-SCALE SURFACE AIR TEMPERATURE ANALYSIS 8.1 Introduction Chapter 7 examined the structure of temperature signals on a large-scale. Regional values of each composite were discussed in terms of one value represented by a component score. The current analysis parallels that investigation but on a local level. Grid-scale analysis of surface temperature signal evolution 8.2 Methodology Spatial & Temporal Variation (variation from one grid to the next and from one month to the next) Figure 8-1 summarizes the steps 6 patterns taken to analyze signal variability on grid- of evolution (3 regions & 2 patterns of evolution for each) scale. Local variation of the signal was IL "NeW El Nino signal (C) Assessment of signal examined for each pattern of evolution. Composites magnitude at each (for each El Nino month) grid point To this end, new composites were (for each El Nino type) produced for each month by taking an 119x12 matrix 119x12 matrix (stations x months) (stations x months) average of signals that belonged to each of Gridding Mann-Whitney the two types. For instance, the Jan(+1) (Minimum curvature) independent sample test for each month at each station composite (Kc) for Type C signal for a station located in Region I was produced Local representation p-value <0.1 denoted of El Nino signal El Nino signals to be for each month different from neutral as follows: Sep (O)-Aug (+1) periods Result gridding (Minimum curvature) Jan 1969 £ Jan 1995 KcJan = 8-1 Local representation of monthly El Nino signal mgnitude in relation to neutral periods where Jan 1969, 1970 and 1995 are months Figure 8-1: Schematic of grid-scale El that make up Type C signal. This was Nino signal analysis. repeated for the three regions and Type W signal and the station values were gridded onto regular surfaces. The magnitude maps 135 are shown on the left-hand side of Figures 8-2 through 8-7. Positive signals are shown in red, negative in blue. A black thick line represents magnitude of zero degrees Centigrade. The Mann-Whitney test was used to assess statistical significance of the signals at each station. Results were gridded onto regular surfaces using Minimum curvature method. Figures 8-2 through 8-7 on the right display the maps of significance values. 8.3 Region I 8.3.1 Type W signal All months between Sep(O) and Nov(O) are characterized by significant cool signals throughout the majority of southern prairies. In Sep(O) magnitudes of less than - 1°C are observed in that area. The cool conditions spread towards the northwest in Oct(0) and to the east during Nov(O). During Oct(0), southwestern Manitoba, majority of Saskatchewan, and all of Alberta are dominated by negative signals that range between - 1.2°C in the east and -2°C to the west. The eastward spread during Nov(O) brings significantly cool conditions to Manitoba where signals drop to below -2.5°C. Seasonal conditions return to the area in Dec(O). Jan(+1) is marked by the influx of positive and significant signals to Region I, as the majority of the area is encompassed by magnitudes in excess of +4°C. These magnitudes are, however, short-lived as by Feb(+1) significant values are now restricted to the southern Prairies. Significant signals of magnitudes above 2.5°C briefly reappear throughout Manitoba and Saskatchewan during Mar(+1). During Apr(+1) these weaken throughout the majority of the area, such that significant values are now restricted to areas north of 55°. By May(+1) signals exceeding +1°C are only found in the foothills of Alberta. In Jun(+1) the entire area regains seasonal values. Significant negative signals between -0.5°C and -1°C only briefly appear in the southern Prairies during mid-summer in Jul(+1). Signal (C) Mann-Whitney Significance -120 -110 -100 -120 -110 -100 Oct(0) -120.0 -110.0 -100.0 -120 -110 -100 Nov(O) Figure 8-2a: El Nino signal magnitude and Mann-Whitney significance (J-tailed). Region I. Type WEI Nino signal. Sep(0)-Nov(0). 137 Signal (C) Mann-Whitney Significance -120 -110 -100 -120 -110 -100 Dec(0) 60— 55-H 50-4 -120 -110 -100 -120 -110 -100 Jan(+1) Feb(+1) Figure 8-2b: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region I. Type W El Nino signal Dec(0)-Feb(+1). 138 Signal (C) Mann-Whitney Significance Mar(+1) -120 -110 -100 -120 -110 -100 Apr(+1) -120 -110 -100 -120 -110 -100 May(+1) Figure 8-2c: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region I. Type W El Nino signal. Mar(+1)-May(+1). 139 Signal (C) Mann-Whitney Significance -120 -110 -100 -120 -110 -100 Jul(+1) -120 -110 -100 -120 -110 -100 Aug(+1) Figure 8-2d: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region I. Type WEl Nino signal. Jun(+1)-Aug(+1). Signal (C) Mann-Whitney Significant -120 -110 -100 -120 -110 -100 -120 -110 -100 -120 -110 -100 Oct(0) Nov(0) Figure 8-3a: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region I. Type C El Nino signal. Sep(0)-Nov(0). 141 Signal (C) Mann-Whitney Significance -120 -110 -100 -120 -110 -100 Dec(0) -120 -110 -100 -120 -110 -100 Jan(+1) -120 -110 -100 -120 -110 -100 Feb(+1) Figure 8-3b: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region I. Type C El Nino signal. Dec(0)-Feb(+1). 142 Signal (C) Mann-Whitney Significance -120 -110 -100 -120 -110 -100 Mar(+1) -120 -110 -100 -120 -110 -100 Apr(+1) -120 -110 -100 -120 -110 -100 May(+1) Figure 8-3c: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region I. Type C El Nino signal. Mar(+ 1)-May(+ J) 143 Figure 8-3d: El Nino signal magnitude and Mann-Whitney significance (I-tail). Region I. Type C El Nino signal Jun(+ J)-Aug( f I). 144 8.3.2 Type C signal Sep(O) is characterized by neutral to positive signals throughout the area. Significant magnitudes below -1.5°C are restricted to eastern Manitoba. During Oct(0) significant negative signals between -1°C and -1.5°C are observed in the foothills of Alberta. Thereafter, the strength briefly dissipates to within neutral values until Jan(+1) when negative magnitudes in the area regain significant levels and expand towards the northeast. But, by Feb(+1), neutral conditions once again return to the area. Early spring in Mar(+1) is marked by brief appearance of significant negative magnitudes to the southern prairies where values drop below -3°C. These completely dissipate by Apr(+1). Thereafter, significant magnitudes of +1°C temporarily reappear in Jun(+1) throughout the Grand Prairie region of Alberta. 8.4 Region II 8.4.1 Type W signal SON(O) is characterized by neutral to positive signal magnitudes throughout the area. Significant values of +0.7°C are only observed along the coastal area west of 120°, and in the central interior of British Columbia (BC) north of 51° during Sep(0). The south-central interior of the area attains significant magnitudes of +2.5°C during Dec(0). These strengthen towards the east, where they reach significant values of +3°C in areas parallel to the highest peaks of the Canadian Rocky Mountain range in Jan(+1). Magnitudes also continue to strengthen along the coast and throughout the interior of the region in Feb(+1), when values reach +1.5°C and +2.5°C in each area, respectively. By Mar(+1) the signals again dissipate to neutral values. These conditions last throughout the area until May(+1), when positive signals of +0.5°C reappear in the extreme south. 145 Signal (C) Mann-Whitney Significance -130 -120 -110 -130 -120 Oct(0) Nov(0) Figure 8-4a: El Nino signal magnitude and Mann-Whitney significance (l-tail). Region II. Type WEl Nino signal. Sep(0)-Nov(0). 146 Feb(+1) Figure 8-4b: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region II, Type WEl Nino signal. Dec(0)-Feb(+1). 147 Signal (C) Mann-Whitney Significance 5M: ! h-55 5 -110 .130 -120 -110 Mar(+1) 55 ! -55 50H HO I—i—r~ -130 110 -110 _130 Apr(+1) 55- : jir -55 &0H j.r-50 -3- C i—r -110 -110 May(+1) Figure 8-4c: El Nino signal magnitude and Mann-Whitney significance (I-tail). Region II. Type W El Nino signal. Mar(+l)-May(+I). 148 Signal (C) Mann-Whitney Significance Jun(+1) -130 -120 -110 -130 -120 -110 Jul(+1) -130 -120 -110 -130 -120 -110 Aug(+1) Figure 8-4 d: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region II. Type WEl Nino signal. Jun(+1)-Aug(+I). 149 Signal (C) Mann-Whitney Significance 55-4: :r-55 5CH ;h50 / \ j 77 i—r -130 —r—r—rn—i—|~T -110 -120 -110 Sep(0) 55-4 -55 50H -50 i r i—i—f—i—I—r~r -110 -130 -120 -110 Oct(0) 55-4; 50-H Nov(0) Figure 8-5a: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region II. Type C El Nino signal. Sep(0)-Nov(0). 150 Signal (C) Mann-Whitney Significance -130 -120 -130 -120 -110 Jan(+1) -130 -120 -130 -120 -110 Feb(+1) Figure 8-5b: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region II. Type C El Nino. Dec(0)-Feb(+1). 151 Signal (C) Mann-Whitney Significance 55-H 50H 55-4 50—\ Apr(+1) 55--!. h55 50H h50 110 May(+1) Figure 8-5c: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region II. Type C El Nino signal. Mar(+1)-May(+1). 152 Signal (C) Mann-Whitney Significance -130 -120 -130 -120 -110 Jul(+1) h55 -130 -120 -130 -120 -110 Aug(+1) Figure 8-5d: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region II. Type C El Nino signal Jun(+I)-Aug(+I). 153 In Jun(+1) these continue to intensify to +1°C and spread throughout the interior and into Alberta. By Jul(+1), however, temperature conditions throughout the region once again return to expected values. 8.4.2 Type C signal During early fall, the region is dominated by negative signals that strengthen and become significant in Oct(0). At this time, values of -1.5°C dominate the eastern portions of the area, whereas magnitudes of -0.5°C are common to the west. Neutral signals briefly return to the region in Nov(O). DJF(+1) is dominated by negative signals. In Dec(O) significant magnitudes exceed -4°C to the east and -2°C along the coast. With the exception of the southern coast, these magnitudes, however, diminish in Jan(+1). Between Feb(+1) and Apr(+1) signals gradually return to their neutral values throughout. Significant positive signals between +0.5°C and +1.0°C appear south of 52° during May(+1) and remain there through Jun(+1). 8.5 Region III 8.5.1 Type W signal Sep(O) is characterized by significant positive signals of +0.5°C along the coastal mountain region of BC north of 55°. These, however quickly dissipate so that by Oct(0) neutral conditions return to the area, and last until Dec(O). The beginning of the winter season in Dec(O) is marked by positive and significant signals throughout the area that remain in the region until early spring. At this time, the significant values exhibit a north-south gradient, such that magnitudes in excess of +4°C are observed throughout southern Yukon, whereas the Queen Charlotte Islands to the south measure values of +2°C These continue to strengthen so that by Feb(+1) magnitudes of +5°C are common 154 Signal (C) Mann-Whitney Significance -60 :j -55 -110 -130 \ ir -110 Sep(0) 60- 55— -110 -130 Oct(O) 60- 55— Nov(0) Figure 8-6a: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region III. Type W El Nino signal. Sep(0)-Nov(0). 155 Signal (C) Mann-Whitney Significance 60- -60 55— :-55 -110 Dec(+1) 60- 55— -110 Jan(+1) 60- '9> i 55- :-55 T—i—r -130 -120 -110 -130 -120 -110 Feb(+1) Figure 8-6b: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region III. Type WEI Nino signal. Dec(0)-Feb(+1). 156 Signal (C) Mann-Whitney Significance Aprf+1) Figure 8-6c: El Nino signal magnitude and Mann-Whitney significance (I-tail). Region III. Type W El Nino signal. Mar(+I)-May(+1). 157 Figure 8-6d: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region III. Type WEI Nino signal Jun(+ 1)-Aug(+1). 158 Signal (C) Mann-Whitney Significance •130 -120 -110 -130 -120 -110 Sep(0) Nov(O) Figure 8-7a: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region III Type C El Nino signal. Sep(0)-Nov(0). 159 Signal (C) Mann-Whitney Significance -130 -120 -110 -130 -120 -110 Dec(O) -130 -120 -110 -130 -120 -110 Feb(+1) Figure 8-7b: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region III. Type C El Nino signal. Dec(0)-Feb(+1). 160 May(+1) Figure 8-7c: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region III. Type C El Nino signal. Mar(+1)-May(+1). 161 Aug(+1) Figure 8-7d: El Nino signal magnitude and Mann-Whitney significance (1-tail). Region III Type C El Nino. Jun(+1)-Aug(+1). 162 to the interior, and +3°C along the coast. The signals dissipate with the onset of spring in Mar(+1). In Apr(+1), significant positive magnitudes greater than + 1°C briefly reappear in central Alberta. In Jun(+1) significant signals re-appear in north-west Alberta and eastern BC. At this time, signals reach values in excess of 0.5°C south of 59° and east of 125°. In Jul(+1) significantly negative signals of-0.5°C appear throughout the interior north of 55°. These signals continue to intensify to -1°C throughout the east in Aug(+1). 8.5.2 Type C signal Neutral to negative signal magnitudes dominate the region in Sep(O). Cool conditions become especially notable west of 125° where magnitudes reach -0.6°C During Nov(O), negative signals are observed in north-central Alberta and southern Yukon, where they strengthen to -3°C until Jan(+1). Significant negative signals are also again observed across coastal and interior BC during Dec(O) when magnitudes reach -2.5°C. By Feb(+1) significantly cooler conditions, below -1.5°C are restricted to the northern fringes of the area, north of 60°. Thereafter, these signals dissipate and neutral conditions return to the area until May(+1), when significant positive magnitudes of +0.6°C briefly reappear in the interior and throughout eastern parts of central and northern BC and north-western Alberta. Jul(+1) and Aug(+1) are on the other hand marked by the reappearance of negative magnitudes in the area. In Jul(+1) signals of -0.5°C dominate eastern BC and western Alberta. During Aug(+1) they encompass north-central BC and southern Yukon. 8.6 Summary and Conclusions Table 8-1 lists key results obtained here. In italics are months when in excess of 30% of a region's area shows magnitudes significantly different from neutral values. In 163 each sub-region, at least one month during year (0) and one month during each season in year (+1) shows this to be true. Once attained, these signals are retained for uninterrupted 3 months in Type W and 2 months during Type C types. The period between Dec(0) and Feb(+1) is crucial for the coastal regions, whereas Jan(+1) through Mar(+1) is key to Region I for both signal types, for reasons discussed in Chapter 7. Table 8-1: Key El Nino months for each region and signal type. Region Signal Month(s) for Year(+1) Month(s) for type (+ /- sign shows signal direction) Year (0} I Warm Jan(+), Feb(+), Mar(+), Apr(+) Sep(-), Oct(-), May(+), Jul(-) Nov(-) Cold Jan(-), Mar(-), Jun(+) Sep(-), Oct(+) n Warm Dec(+), Jan(+), Feb(+), Jun(+) Sep(+) Cold Dec(-), May(+), Aug(-) Oct(-) m Warm Dec(+), Jan(+), Feb(+), May(-) Sep(+) Jun(+), Jul(-), Aug(-) Cold Dec(-), Jan(-), May(+), Jul(-) Oct(-), Nov(-) Aug(-) Greatest spatial extent of the positive (negative) winter signals coincides with the presence of the high (low) pressure centers over the area. Yarnal (1985), Yarnal and Diaz (1986) and Shabbar and Khanderkar (1996) found the most significant positive surface air temperature anomalies throughout the area to be associated with the maximum extent of the PNA pattern. It is during this period that a maximum influx of southern or northern air into the area occurs, allowing for a considerable spread of significant signals throughout the region. Although the majority of significant signals are observed during winter, the summer season is also important. Negative (positive) signals appear in Region I during Type W (C) for one month during the mid-season. In Region II, though also short-lived, positive (negative) signals dominate during Type W (C) events. Summer signals are most important in Region III, however, where they manifest themselves as negative peaks that last in the area throughout the summer season in both types. No explanation has been found in the literature for this observation, perhaps because seasons outside of NP Pattern Figure 8-8: Positive phases of the Western Pacific (WP) and North Pacific (NP) patterns during July. Shown are height anomalies (m). Courtesy ofNOAA. 165 winter have not yet been addressed. The WP and North Pacific (NP) patterns shown in figure 8-8 are typically observed during El Nino summer periods, and both are chiefly characterized by a deep low-pressure center over western Siberia and north Pacific, respectively (Wallace and Gutzler 1981; and Bell and Janowiak 1995). The presence of such circulation patterns would bring relatively cool temperatures to Region III, especially if they are slightly displaced eastwards. Finally, significant signal magnitudes appear in all three areas during at least one month of the fall season that precedes the major impact. The most prominent of these was obtained for Region I, where especially for type W, large portions of the area are dominated by significant signals. These signals are negatively related to those that follow in the winter. It is unclear why this correlation exists. 166 9. SPATIO-TEMPORAL SIGNAL ANALYSIS: REGION I 9.1 Introduction All signal inquiries performed thus far have included a variety of separate spatial and temporal examinations. This segment of the research continues with the exploration type W composite maps for Region I, in a way that allows for the spatial and temporal constituents of the signal to be investigated simultaneously. This task was accomplished through the use of GIS. Region I was chosen because it was found to cover a significant portion of the total study area that allowed signal progression to be detected. Type W was selected because it represents over one half of events. Computer animation with GIS was used as the principal methodology where time was incorporated as an additional dimension in the geographic analysis. 9.2 Construction of animated sequence IDRISI GIS provided the capability to construct the original and in-between frames, and to view them in sequence through its Media Viewer (Eastman 1997). Macromedia was used to build the final sequence with titles, legends, and scale bars, options not available through IDRISI. The original frames (figures 9-1) were organized in sequence from Sep(O) through Aug(+1). Tweening was used to create smoother transitions by averaging successive frames. For instance, averaging the Sep(O) and Oct(0) composites created the first in-between frame. This frame was inserted between the two original frames. The IDRISI animation that consisted of the original frames only was then compared to the new one to make certain that the two animations remained comparable. The resulting set of maps was then played in sequence using the IDRISI Media Viewer. 167 Jan(+1) Feb(+1) Mar(+1) Apr(+1) May(+1) Jun(-i-l) Jul(+1) Aug(+1) <_5 .4 -3 -2 -1 0 +1 +2 +3 +4 >5 Surface Temperature El Nino signal (C) Figure 9-1: Type WEI Nino composite maps for Region I. 168 IDRlSFs capabilities are limited with respect to its abilities to incorporate other layers like political boundaries to the animation, to make the final movie more effective for a general audience. The addition of signal scale and other annotations was not possible, and the execution of the sequence required the presence of the IDRISI software. Figure 9-2: A frame from the animated sequence of Type W temperature signals across Region I of Western Canada. No alternate format export options are available. To solve this problem the final animation was created in a Macromedia. This involved taking the original images, and exporting them in Bitmap (BMP) format into Macromedia where the additional graphics were created for each frame. A progression bar was made in order to show the image that was playing at any particular instance. During animation, the progression bar moves along with it. Titles and a color scale bar and other annotations were also added. The final sequence was played in succession at 5 169 seconds per frame. The color scheme of the maps ranged from dark red, through yellow, to dark blue, to signify strong positive, neutral and strongly negative temperature signals, respectively. A single frame shows the animation design in figure 9-2 and the entire animation sequence can be found in video 9-1, on a compact disk included at the back of this thesis. 9.3 Type W signal A typical early fall season prior to the major El Nino signal onset is characterized by neutral to slightly negative signals throughout Region I. At this time, the most intense signals of magnitudes up to -1.5°C are present in southern Alberta and throughout southwestern parts of Saskatchewan. Subsequently, these strengthen and migrate towards the northwest, such that by Nov(O) negative signals reach minimum magnitudes of -4°C and maximum spatial extent. The end of the fall season is marked by the initial appearance of neutral to positive signals to the west, along the foothills of the Rocky Mountains and throughout the extreme southern Alberta. The onset of winter in Dec(O) is characterized by strengthening and eastward progression of these positive signals through the entire area that push out the negative signals east towards the Hudson Bay area. By Jan(+1), Region I is dominated by strongly positive El Nino-related surface temperature signals, now in excess of +5°C. During Feb(+1) the intensity temporarily weakens everywhere to about +2°C. Mar(+1) is marked by signal re-intensification, as magnitudes rise to above +3°C throughout Manitoba and Saskatchewan. At this time, however, Alberta is recovering from the winter signals. By Apr(+1) neutral conditions return to the rest of Region I as well, as signal magnitudes decline to or close to their expected levels of about +1°C in the south and +2°C throughout the central regions. By May(+1), the entire area shows a permanent recovery from the winter signals. 170 Early El Nino summers are characterized by a brief reappearance of negative signals throughout eastern portions of the region. Without any apparent changes in strength, these spread westwards from the Hudson Bay area through Manitoba and into eastern Saskatchewan in Jun(+1). In Jul(+1) a brief strengthening of the negative signals to -1°C is observed throughout southern prairies and across the foothills of Alberta. These, however, dissipate by the end of the season. 9.4 Summary and Conclusions A temporal animation was constructed to study the space-time behavior of Type W signals for Region I. Positive signals that initially appear during Nov(0) in Alberta drive out negative signals that dominate the region between Sep(0) and Nov(0). Between Nov(0) and Jan(+1), the positive signals travel through the region, and intensify. They completely dissipate between May(+1) and Jul(+1). Positive signals arrive from the west and spread towards the east. Several months later in the spring, however, these are replaced by negative signals that arrive from the Hudson's Bay area in the east, and progress towards the west. Information from monthly composite maps was examined, allowing only the movement of large-scale features to be detected. The sensible heat arrives to the area from the west with the jet stream. Several months later, it retreats to the west, after the PNA pattern relaxes to the west. Precise progression and thus detailed timing could not be detected in these animations because of the crude temporal resolution. The energy resides over the area for approximately 4 months, during which its intensity appears to fluctuate. This variation as that observed between January and March can be attributed to temporary disturbances such the Arctic High that may reside over the area for several days and would appear to 'remove' this excess heat from the atmosphere. With its retreat, however, the presence of the energy would again become 'noticeable'. However, it is also likely that the fluctuations are artifacts of the gridding process, especially when stations had missing data, which was substituted for by a mean signal value. The gridded signal for the 'area', which is represented by that station, would then be underestimated in the composite producing a pulsating effect of signals in the animation process. 172 10. ASSOCIATION BETWEEN EL NINO AND SIGNALS IN SURFACE AIR TEMPERATURE 10.1 Introduction Figures 5-la and 5-lb suggest that in Region I, Nino 3 and Nino 3.4 SSTAs may be connected to the signal types. Weak El Niiios coincide with Type C; strong El Ninos with Type W events. But, although these signal types are observed during El Ninos, are they physically connected? Are there any tropical Pacific variables, oceanic and/or atmospheric that are significantly associated to the two signal types in Canada? It is the objective of this chapter to find such variables via cross-correlation and bivariate scatterplot analyses Mean seasonal regional values of surface air temperature signals are examined alongside of seasonal values of several oceanic and atmospheric variables that have been used to monitor El Nino conditions. 10.2 Data The association was investigated using three types of variables. The first set "follows" the oceanic conditions, and includes SSTA time series from four El Nino regions across the tropical basin (figure 5-1) Another set of variables, used to monitor the atmospheric component of El Nino, includes a series of sea level pressure records, one from eastern Pacific, one from Indonesia, and two sea level pressure indices, namely the Equatorial Southern Oscillation Index (ESOI) and the traditional Southern Oscillation Index (SOI) that monitor basin-wide conditions. The third type of variable chosen is an index that honors the ocean-atmosphere connection of ENSO, and is derived from oceanic and atmospheric variables. It is the Multivariate ENSO Index (MEI) (Wolter 1987; Wolter and Timlin, 1993). 173 10.2.1 Sea Surface Temperature Anomalies (SSTAs) Monthly series of sea surface temperature for the four regions across the equatorial Pacific Ocean (figure 5-1) were obtained from the Climate Prediction Center (http://www.cpc.ncep.noaa.gov/data/indices/). Scientists have used these records to monitor ENSO since the mid-1990s. Monthly anomaly values were computed for each series based on the 1961-1990 base period. Each spans 564 months between January 1950 and December 1996. The values were collected as point data from buoys, ships and satellites, and are archived as part of the Comprehensive Ocean-Atmosphere Dataset, (http://www.cdc.noaa.gov/coads/; Fletcher 1985; Woodruff et al.. 1993) maintained by the National Climatic Data Center (NCDC) (http://www.ncdc.noaa.gov/). The point data was interpolated onto regular 2° x 2° surfaces using the Optimum Interpolation (01) method (Reynolds and Smith, 1994). Figures 5-2a and 5-2b show 5-month running means of anomalies for Nino 3 and Nino 3.4 regions between January 1950 and December 1996. Similarly, figure 10-la and 10-lb show the 5-month running mean time series for Nino 1.2 and Nino 4 regions. Figure 10-2 shows the typical conditions that help define El Nino periods. Associations between SSTAs throughout the tropical Pacific Ocean and signal values recorded across Western Canada were investigated using 3-month running mean values (i.e. running seasonal mean values). 10.2.2 Southern Oscillation Index (SOI) Southern Oscillation Index (SOI) (figure 10-3) was developed as one of the very first monitoring variables of El Nino. It measures the sea level pressure difference between central and western Pacific just south of the equator. Values used in this research have been derived in the following manner: SOI - ZSLPTihiti ZSLPDarsvui 10-1 Figure 10-lb: Similar to figure 10-la. Shown are SSTA time series across Nino-4 region as 5-month running means relative to the 1961-90 base period. 175 —MB —ZIND (Z) —ESOI (Z) —ZEAS (Z) SOI (Z) Figure 10-2: Typical atmospheric and oceanic conditions across the tropical Pacific Ocean around El Nino times. With the exception of MEI, shown are the 3-month moving mean values for each variable. 176 where Z represent standardized anomaly values for sea level pressures at Tahiti (SLPTahiti) and Darwin (SLPoarwnOsuch that for each month „ SLPTahiti — SLPTahiti , n - Zs™= 10-2 °SLPTahiti where SLPTahiti and c?sLPTahiti signify the mean monthly and standard deviation of long- term values, respectively, computed from the 1961-1990 base period. Standardized pressure values from Darwin have been computed in a similar manner, using sea level pressure data for that location instead. The data for this variable has been obtained from CPC at http://www.cpc.ncep.noaa.gov/data/indices/index.html. In this study, associations between SOI and signal values were investigated using 3-month running mean SOI values. 8 Figure 10-3: 5-month running mean of SOI between January 1950 and December 1996. El Nino periods are synonymous with negative values. 177 10.2.3 Sea Level Pressure over Indonesia (ZIND) Standardized values of the sea level pressure recorded over Indonesia (ZIND) are available from CPC starting January 1958. The time series is shown in figure 10-4. Due to the length of record, the use of this variable excludes the 1951/52 and 1957/58 events. ZIND monitors atmospheric conditions over the extreme western Pacific basin (figure 10- 5). Mean values (ZIND) and the standard deviation (OSLPIND) were calculated with respect to the 1961-90 base period and the final index was derived in the following manner. SLPTNJJ SLPjjjp 10-3 SLPIND Associations between this variable and signal values across Western Canada were investigated using 3-month running mean values. 10.2.4 Sea Level Pressure over the Eastern Pacific Basin (ZEAS) Another variable directly modified during El Nino is sea-level pressure over the Eastern Pacific (figure 10-5). Standardized values between January 1958 and December 1996 are available from the Climate Prediction Center. The 1951/52 and 1957/58 events were again excluded from analyses. Monthly values represent standardized (ZEAS) sea level pressure records (ZEAS) recorded over the eastern Pacific basin that have been calculated as follows: SLP, SLP, EAS "EAS EAS 10-4 SLP EAS 178 Figure 10-4: 5-month running mean of standardized values of sea level pressure recorded above Indonesia between January 1958 and December 1996. Base period: 1961-1990. Figure 10-5: Areas where sea level pressures were recorded for Indonesia and the Eastern Pacific, used to compute the Equatorial SOI values. Figure 10-6: 5-month running mean of standardized values of sea level pressure recorded above the eastern Pacific basin between January 1958 and December 1996. Base period: 1961-1990. SLPEAS and OSLPEAS represent the mean and standard deviation values derived from the 1960-91 base period. Figure 10-6 shows the 5-month running mean values. El Nino periods are marked by strongly negative values. Associations were investigated using 3-month running mean values. 10.2.5 Equatorial Southern Oscillation Index (ESOI) Equatorial Southern Oscillation Index (ESOI) tracks the contrast of atmospheric pressure states across the equatorial Pacific basin from east to west (figure 10-5). An index (ESOI) is calculated from standardized values of differences between standardized values of pressure over Indonesia (ZIND) and the eastern Pacific (ZEAS)- It is computed in two steps: 1. ZEAS - IND = ZEAS — ZIND 10-5 where ZEAS-IND represents the difference between standardized monthly values of sea level pressure recorded across the eastern Pacific (ZEAS) and Indonesia (ZIND), respectively. This difference is then once again standardized to compute the final ESOI value 2. ESOI = Zeas-ind Zeas-1nd 10-6 ^ZEAS-IND Mean values were computed using the 1961-1990 base period. A 5-month running mean time series of ESOI between January 1958 and December 1996 is shown in figure 10-7. Due to the length of the time series, the use of this variable excluded the 1951/52 and 1957/58 events from analyses. 180 L rim iPTirW CN (O CO ^ Figure 10-7: 5-month running mean of ESOI. January 1958 to December 1996. Base period: 1961-90. -2.0 -L //////////////////////// Figure 10-8: 5-month running mean of MEI. January 1958 to December 1996. 181 18 19 2D 21 32. 23 24 25 2€ 27 ZB 21 2d Figure 10-9: Orientation map of the tropical Pacific basin. Bold capital letters denote the approximate centers of gravity of MEI key regions: P+ and P- are the western and eastern sea-level pressure dipoles, U represents westerly zonal wind anomalies in the central Pacific, V+ (V-) indicate southerly (northerly) meridional wind anomalies in the south-western (northern) portion of the domain. A denotes the location of surface air temperature measurement and c represents cloudiness anomalies over the central Pacific during ENSO events. Rectangular boxes represent sea surface temperature regions, red Niho-4, blue Nino-3, and yellow Nino-12 (Wolter and Timlin 1998). Base map courtesy ofNOAA. 182 The nature of this index is similar to that of SOI, as they both monitor sea level pressure conditions across the tropical Pacific. The major difference lies in the locales used to compute the two indices. SOI uses two point values, both in the Southern Hemisphere, shown as RD and RT in figure 10-5 for Darwin, and Tahiti, respectively. Moreover, Tahiti monitors pressures closer to the central basin, whereas ESOI monitors areas directly over the equator, and precisely over the eastern and western basin. In spite of these slight differences in measurement and great similarities to El Nino response, the two time series may produce distinct relationships with surface air temperatures across Western Canada. Associations were investigated using 3-month running mean values. 10.2.6 Multivariate ENSO Index (MEI) The Multivariate ENSO Index (MEI) is the most recently developed indicator of ENSO's state developed by Klaus Wolter and associates (Wolter 1987; Wolter and Timlin 1993). Unlike all other indices, this one incorporates several facets of the phenomenon, oceanic and atmospheric in nature. They chose six variables from across the equatorial Pacific. These include sea-level pressure, zonal (U) and meridional (V) components of surface wind, sea surface temperature, surface air temperature (A) and total cloudiness fraction of the sky (c) (Wolter and Timlin 1998). These observations were collected and published in the Comprehensive Ocean-Atmosphere Data Set (COADS) described in more detail in Section 10.2.1. Figure 10-8 shows the relative locations where each of the variables is measured. Index values are computed separately for each of twelve sliding bi-monthly seasons (Dec/Jan, Jan/Feb,..., Nov/Dec) (Wolter 1987). Its series between Dec/Jan 1950 and Nov/Dec of 1996 are shown in figure 10-9. Values are computed as the first unrotated Principal Component (PC) of all six observed fields combined This is accomplished by first normalizing the total variance of each field, and then performing the extraction of the first PC on the co-variance matrix of the combined fields (Wolter and Timlin 1993). All seasonal values are standardized with respect to each season and to 183 the 1950-93 reference period. El Nino periods are defined by positive values of MEI (figure 10-2) This is, in part, due to the fact that the majority of the variables, help define El Nino. During El Ninos, SLP, U, SST-4, C, SST-3, SA, SST-12 and V+ all exhibit large positive anomalies. Only P- and V- do not. Associations between MEI and signal values across Western Canada were investigated using the bi-monthly values. 10.3 Methods The study was approached via statistics, through cross-correlation analyses and bi-variate scatterplots (Utts 1996; Davis 1973). Mean seasonal values of surface air temperature signals were examined against mean seasonal values of chosen oceanic and atmospheric variables in the tropical Pacific Ocean at various lag times using Spearman's correlation coefficient. Any relationship found significant at level 0.05 was considered strong. Each association was plotted on a scatter diagram to isolate outlying values and investigate groupings that may mask relationships, especially during the winter season. These were investigated by color-coding signal types, W in red and, C in blue. Region I (III) also had three (one) event(s) that did not fit within either W or C criteria. Since their winter signatures were either cold or warm, these years were shown in the scatterplots as either NW in hollow red, or NC in hollow blue. Tables 10-1 through 10-4 show the results for each of the four seasons, namely fall preceding major El Nino impact (SON(0)), El Nino winter (DJF(+1)), spring (MAM(+1)), and following summer (JJA(+1)). Similar statistics were computed and reported in the tables for neutral periods to determine if the relationship observed during El Nino years also existed during non-ENSO periods. 184 Table 10-1: Association between El Nino signal during the SON (0) season and selected tropical Pacific variables. REGION I Variable Lag Period Cross-corr. p-value Sample size N4 3 El Nino -0.580 0.024 12 Non-ENSO 0.167 0.218 23 ESOI 7 El Nino 0.612 0.030 10 Non-ENSO -0.180 0.230 19 REGION II Variable Lag Period Cross-corr. p-valuc Sample size N4 3 El Nino -0.657 0.010 12 Non-ENSO 0.214 0.158 23 SOI 4 El Nino 0.573 0.026 12 Non-ENSO -0.043 0.421 23 REGION III Variable Lag Period Cross-corr. p-value Sample size SOI 4 El Nino 0.566 0.027 12 Non-ENSO -0.039 0.428 23 185 Table 10-2: Association between El Nino signal during the DJF (+1) season and selected tropical Pacific variables. REGION I Variable Lag Period Cross-corr. p-value Sample size ZEAS 12 El Nino 0.745 0.007 10 Non-ENSO -0.233 0.182 17 REGION II Variable Lag Period Cross-corr. p-value Sample size ZIND 7 El Nino 0.728 0.009 10 Non-ENSO -0.086 0.372 17 ZEAS 12 El Nino 0.576 0.041 !() Non-ENSO 0.J 03 0.319 17 REGION III Variable Lag Period Cross-corr. p-value Sample size ZEAS 12 El Nino 0.733 0.008 10 Non-ENSO -0.258 0.168 17 186 Table 10-3: Association between El Nino signal during the MAM (+1) season and selected tropical Pacific variables. REGION II Variable Lag Period Cross-corr. p-value Sample size MEI 0 El Nino 0.902 0.000 12 Non-ENSO ft 577 0.002 23 N3 0 El Nino 0.860 0.000 12 Non-ENSO 0.407 0.027 23 N12 0 El Nino 0.874 0.000 12 Non-ENSO 0.393 0.032 23 N4 () El Nino 0.657 0.010 12 Non-ENSO 0.166 0.225 23 N34 0 El Nino 0.797 0.001 12 Non-ENSO 0.397 0.037 23 ZEAS 0 El Nino -0.829 0.001 11 Non-ENSO -0.283 0.120 19 MEI 1 El Nino 0.860 0.000 12 Non-ENSO 0.503 0.007 23 N4 1 El Nino 0.650 0.011 12 Non-ENSO 0.222 0.154 23 N34 1 El Nino 0.804 0.001 12 Non-ENSO 0.401 0.029 23 N3 1 El Nino 0.909 0.000 12 Non-ENSO 0.336 0.058 23 N12 1 El Nino 0.909 0.000 12 Non-ENSO 0.394 0.031 25 N3 2 El Nino 0.832 0.000 12 Non-ENSO ft/45 0.255 23 N12 2 El Nino 0.818 0.001 12 Non-ENSO 0.380 0.037 25 N34 2 El Nino 0.678 0.008 12 Non-ENSO 0.225 0.151 23 SOI 2 El Nino -0.713 0.005 12 Non-ENSO -ft5i? 0.006 25 REGION III Variable Lag Period Cross-corr. p-value Sample size N12 8 El Nino 0.517 0.042 12 Non-ENSO 0.405 0.028 23 187 Table 10-4: Association between El Nino signal during the JJA (+1) season and selected tropical Pacific variables. REGION I Variable Lag Period Cross-corr. p-value Sample size MEI 9 El Nino 0.510 0.045 12 Non-ENSO -0.129 0.283 22 REGION II Variable Lag Period Cross-corr. p-value Sample size N12 4 El Nino 0.510 0.045 12 Non-ENSO -0.142 0.265 22 REGION III Variable Lag Period Cross-corr. p-value Sample size MEI 1 El Nino 0.713 0.005 12 Non-ENSO -0.027 0.453 22 N3 2 El Nino 0.671 0.008 12 Non-ENSO 0.113 0.308 22 N34 2 El Nino 0.657 0.010 12 Non-ENSO -0.012 0.179 22 MEI 2 El Nino 0.678 0.008 12 Non-ENSO -0.072 0.376 22 N3 3 El Nino 0.706 0.005 12 Non-ENSO -0.016 0.471 22 N34 3 El Nino 0.755 0.002 12 Non-ENSO -0.085 0.353 22 188 10.4 Results and Discussion 10.4.1 Region I 10.4.1.1 Winter [DJF(+1)] Sea level pressure over the eastern Pacific basin (ZEAS) during DJF(O) are strongly related to signal values across this region (figure 10-5) one year later (figure 10- 10a). Accompanying statistical results are summarized in table 10-2. High pressures over the tropical Pacific correspond to positive signals and vice versa. Exceptions are observed during the 1968/69 and 1969/70 events. During the former, ZEAS values were too high for the corresponding signals observed later. The opposite was true for the Figure 10-10a: Scatter diagram showing relationships between Region I DJF(+1) temperature signal and DJF(0) ZEAS- Arrows indicate pressure tendencies between DJF(0) andFMA(O). 1969/70 even, when ZEAs values were too low for the signal magnitudes that followed. The 1968/69 event preceded the 1969/70 episode with no recovery in-between. During DJF(O) of 1968, ZEAS were close to neutral, showing only a mild recovery from the previous event. With the exception of the 1972/73 event, all warm-winter El Ninos are preceded by high pressure readings across the eastern Pacific. Five out of the six Type W signals cluster with positive ZEAs values. C or NC signals exhibit no relation to ZEAS, as they scatter randomly at neutral to negative values of ZEAS- Events that develop from positive anomalies in sea level pressure are followed by positive signals one year later. And, events that develop from negative sea level pressure anomalies are followed by negative signals. But what about those El Nino events where DJF(O) ZEAS hovered around zero? This is the case with the 1968/69, 1994/95 and 1983/84 events (figure 10-10a). The answer lies in the eastern Pacific sea level pressure "tendencies" between DJF(O) and FMA(O). 10.4.1.2 Fall [SON(0)J Table 10-1 summarizes results for fall and figures 10-10b and 10-10c show the scatterplots. Two Pacific variables are related to signals across this region at this time of year, ESOI and SSTAs across the Nino 4 region. ESOI is important during FMA(0), and shows a positive relationship to Canadian signals approximately seven seasons before SON(0). Positive values are followed by positive signals, negative, by negative signals. The only exception to this rule is the 1969/70 event when the ESOI value was negative at -0.6. This can be accounted for by the fact that another El Nino immediately preceded this event. Negative ESOI values (figure 10-2) testify that the basin had not fully recovered from the preceding event before the onset of the 1969/70 episode. In general the scatterplot reveals a strong grouping of Type W and NC signals. Negative signals are associated with ESOI values that fall below 0.6. All Type C and NW El Ninos characterized by positive SON(0) signals cluster above this threshold. The 1963/64 and 190 63 O 2H 94 O CO 68| 1 H 76 O O c o o H 69 TYPEJ a: 82 To • w c 86 O NW 55 -1 H • 91 O NC 72 65 lil -2 O • C -.7 -.5 -.3 -.1 .1 .3 .5 .7 .9 -.6 -.4 -.2 .0 .2 .4 .6 .8 1.0 ESOI (Z) - FMA (0) 63 O 94 o CO 68 e> 1 76 O 57 O CM 69 • 82 TYPEJ a: 86 to -1 • w c 91 652 "w O NW O NC UJ -3 • C 0.0 SSTA Nino 4(C)- JJA (0) Figure 10-10: Scatter diagram showing relationships between Region I SON(0) temperature signal and b: ESOI (FMA(0)) ZEAS; C: SSTA Nino 4 (JJA(0)). 191 1976/77 NW events record positive signals but show ESOI values above 0.6. The 1965/66 NC event shows a negative magnitude and ESOI value below 0.6. Figure 10-2 shows that prior to the onset of an El Nino event during FMA(0), seasonal ESOI values begin to decline from positive values that average about 0.7 towards neutral and into negative magnitudes once the event is well underway. Results presented here suggest that Type W signals, or cool SON(0) seasons in Region I, are followed by equatorial sea level pressure conditions that show significant El Nino development in the basin during the previous spring. Type C signals, however, are common to years when ESOI values are strongly positive during FMA(O), typical to neutral or La Nina conditions. SSTAs become relevant in JJA(O) when high values (> 0.5°C) precede Type W or NC signals. Relatively low SSTAs (< 0.5°C) are followed by Type C or NW signals. Type W signals are typically observed during years when El Nino is well developed in the Pacific by JJA(O). Less-developed events at this time of year are followed by Type C signals. 10.4.1.2.1 Event Classification Is it possible to use the information presented so far to devise a single method by which the events can be assigned to their respective "classes" from known conditions in the tropical Pacific some time prior? More specifically, can each of the El Nino events be "classified" into one of the four groups, namely Type W, C, NW, or NC by knowing what happens in the tropics several seasons before? When considering the results for ESOI during FMA (0) and comparing them to the ZEAS values observed just two seasons before in DJF(0), it becomes obvious that something significant happens to the pressure characteristics across the basin between mid-winter and early spring, just prior to the onset of an El Nino. Sea level pressures over the eastern Pacific and Indonesia are examined more closely in table 10-5. Pressures over Indonesia typically increase from DJF(0) to FMA(0) during most events. 192 During the same time period, however, pressures in the east display distinct tendencies in warm- and cold-winter years in Canada. With the exception of the 1972/73 event, falling Table 10-5: Sea level pressure tendencies between DJF(O) and FMA(O) across the equatorial Pacific. Shown are Region 1 warm-winter El Nino periods in regular print and Region I cold winter El Nino periods in bold. Nino DJF(O) FMA(0) FMA(0)-DJF(0) ZEAS ZIND ESOI ZEAS ZIND ESOI ZEAS ZIND ESOI 87 1.13 -0.60 0.90 0.50 -0.43 0.60 -0.63 0.17 -0.30 64 0.97 -1.33 1.40 0.13 -1.20 0.90 -0.84 0.13 -0.50 77 0.83 -1.70 1.27 0.23 -0.97 0.77 -0.60 0.73 -0.50 92 0.33 0.03 0.13 0.00 0.03 0.00 -0.33 0.00 -0.13 83 0.20 0.10 0.43 0.17 -0.03 0.10 -0.03 -0.13 -0.33 73 -0.93 -1.87 0.67 -0.47 -0.97 0.30 0.46 0.90 -0.37 95 0.03 -1.23 0.97 0.53 -0.53 0.67 0.50 0.70 -0.30 69 0.10 -0.63 0.50 0.93 -0.53 0.97 0.83 0.10 0.47 66 -1.10 -2.03 0.63 -0.97 -1.80 0.57 0.13 0.23 -0.06 70 -2.23 -1.23 -1.07 -0.87 0.03 -0.60 1.36 1.26 0.47 ZEAS during this time period precedes all warm-winter events. Similarly, all cold winters in Region I are led by rising ZEAS- When ZEAS during DJF(0) hover around zero, the outcome is inconclusive. But the grouping of such events (i.e. 1994/95, 1968/69, and 1982/83) becomes clearer once the pressure tendencies are taken into consideration. The 1994/95 and 1968/69 cold-winter events are both preceded by rising pressure tendencies in the eastern tropical Pacific. Falling pressure tendencies common to all other warm- winter El Ninos also led the 1982/83 event. SSTAs recorded in the Nino 4 region of the Pacific during summer are able to confirm this information or may provide a clearer picture where previous results may be inconclusive. Since ZEAS conditions in DJF(0) are easily distinguished between NC and NW events, the distinction between Type W and NC and Type C and NW should not be a difficult one at this point. 193 Figure 10-10d: Scatter diagram showing relationships between Region I JJA(+I) temperature signal and SON(O) MEI. Figure 10-11 summarizes the steps that help classify the studied El Nino events. According to these criteria, the 1972/73 event is classified as NC, instead of Type W, and the 1969/70's classification by this method results in a NC event instead of a Type C. Although a warm winter is predicted correctly for the 1986/87 event, the indecisive value of ESOI during FMA(0), coupled with the low SSTA in JJA(O), mis-classifies this event as NW, making an error with respect to the SON (0) conditions. All remaining 7 events are classified correctly by this schema, a success rate of 70%. When taking only the winter season into consideration, the success rate increases to 90%. 194 Region I El Nino signal Classification ZEAS DJF(O) >0.0 =0.0 <0 Warm-winter ZEAS Cold Winter Tendencies between DJF(O) & FMA(O) I X 1 ESOI Decreasing Increasing ESOI FMA(O) FMA(O) <0.6 =0.6 >0.6 Warm-winter Cold-winter 0.6 >0.6 Type W SSTA Type NW Type NC SSTA TypeC Nino4 Nino4 JJA(O) JJA(0) >0.5 =0.5 <0.5 >0.5 =0.5 <0.5 Type W ??? Type NW TypeNC ??? TypeC Figure 10-11: Procedure used to classify El Nino events for Region I. 195 10.4.1.3 Spring [MAM(+1)J No variable from the Pacific Ocean shows related behavior to signal magnitudes across this region during El Nino spring. 10.4.1.4 Summer [JJA(+1)] At this time of year, signals strongly correlate to MEI values measured during SON(0), nine months before. Statistics (Table 10-4) and scatterplots (figure 10-10d) summarize the results. With the exception of the 1969/70 event, all other events conform to this relationship. More developed events are associated with high signal values across Region I, and vice versa. 10.4.2 Region II 10.4.2.1 Winter [DJF(+1)] Table 10-2 and figure 10-12a,b summarize the results. ZEAS 12 months ahead, in DJF(0) is positively correlated with signal magnitudes in Region II one year later. Positive values in ZEAS are followed by positive signal magnitudes. With the exception of two events (i.e. 1969/70 and 1965/66) all other Type W signals are preceded by positive values of ZEAS- Moreover, years when DJF(+1) signals exceed +2°C, all follow ZEAS values that are not only positive, but also show negative ZEAS tendencies between DFJ(0) and FMA(0) (table 10-5). All other winters when signals are near neutral or negative, are preceded by neutral to negative ZEAs in DJF(0), and rising pressures over the Eastern Pacific between DJF(0) and FMA(0). Sea level pressure anomalies over Indonesia (ZIND) during MMJ(0) display a strong positive relationship to DJF(+1) 196 84 83 LL —i Q 70 O 95 66 c 4 73 g or -2 ro „ c -3 TVPE_I 69 , 8 * • W 7 -5-1 LU • c —i— -2.5 -2.0 -1.5 -1.0 -.5 0.0 1.0 1.5 ZEAS - DJF(O) 5 4 92 3H 87 77 83 64 • • —> 2 Q 70 I 1 95 O 66 0 c o -1 9 -2 Q. TO C -3 CO (0 -4 TYPE, o c 69 -5-I • w UJ -6 11 * r"" • c -1.5 -1.0 -.5 0.0 1.0 ZIND - MJJ(0) Figure 10-12: Scatter diagram showing relationships between Region II DJF(+1) temperature signal and a: DJF(0) ZEAS! b: MJJ(0) Zjm- Arrows show ZEAS pressure tendencies between DJF(0) and FMA(0). 197 signals. With the exception of the 1963/64 and 1965/66 events, the scatterplot reveals that ZIND values above -0.5 coincide with Type W signals during the following winter And, both of the analyzed Type C El Nino events of this region are led by ZIND values below -0.5. 10.4.2.1.1 Event Classification These connections to the Pacific Ocean can be used together similarly to Region I, to help distinguish between the two signal types from Pacific conditions. Figure 10-13 summarizes the schema. This procedure utilizes sea level pressure information from both regions of the Pacific, first during DJF(O) for the east, followed by MJJ(O) for the west Out of the ten analyzed events only one, the 1965/66 event, is misclassified as Type C. Considering signal magnitudes, when ZEAS are negative and the pressure between DJF(O) and FMA(O) is increasing, such conditions are the precursors to winters that show no significant positive or negative signals. The only exception is the 1968/69 event. Nine of 10 events are correctly classified from Pacific conditions. Only the 1968/69 event is incorrect as neutral instead of cold. The separation of Type C and W events is difficult, as only three El Ninos define the former type, and only 2 of those are depicted on the scatterplots. Although the 1972/73 cold event fits within both of the described relationships, the 1968/69 event fails to do so. As in Region I, this event repeatedly stands out as an outlier in all winter relationships. 10.4.2.2 Fall [SON(0)| Similarly to Region I, sea surface temperature anomalies across Nino 4 region in JJA(0) show a significant relationship to the surface temperature signals calculated across this area during SON(0). The scatterplot shown in figure 10-14 reveals this association to be negative, such that highly positive signals are coincidental with negative anomalies 198 Region II El Nino signal Classification ZEAS DJF(O) <0 >0 ZIND ZEAS ZIND MJJ(O) DJF(O) MJJ(O) Tendencies Increasing Decreasing ZIND Type W MJJ(O) Winter signal > 2C <-0.5 -0.5 >-0.5 Type C ??? Type W Winter signal between OC and 2C Figure 10-13: Procedure used classify El Nino events for Region II. 199 O CO O c o a: (0 C E? w o c UJ SSTA Nino 4 (C) - JJA(0) 1.5 63 0 1.0 69 O 5 c 94 m o 68 0.0 65 86 or • • "its c 51 82 « o c 72 • HI -1.0 -3 -1 SOI - MJJ(O) Figure 10-14: Scatter diagram showing relationships between Region II SON(0) temperature signal and a: JJA(0) SSTA Nino 4; b: MJJ(0) SOI. 200 and vice versa. The 1968/69, 1976/77 and 1986/87 events were found to deviate from this relationship. The second significant variable is SOI. Negative MJJ(O) SOI values are followed by negative signals during SON(0). The closer to zero the SOI values are at this time of the year, the more positive the signal magnitudes are subsequently recorded in this area. It should be noted that only two exceptions to this "rule" occur. These are the 1986/87 and 1968/69 El Ninos. During both events, positive SOI values are observed that are both followed by signal values close to zero across Region II. Both variables confirm that well-developed events or events that were unusually strong by mid-summer of year zero, are typically followed by cool El Nino fall seasons across this region. During times when the Pacific basin is in a neutral or La Nifia state during at this time, signals during the following fall are close to expected values. 10.4.2.3 Spring |MAM(+1)] El Nino spring shows the greatest number of Pacific variables strongly related to temperature signals across this region of Western Canada. Table 10-4 and figure 10-15a through 10-15o show the results. SSTAs from all four regions of the Pacific strongly positively correlate at zero lags. High MAM(+1) SSTAs in all regions coincide with high signal values. Eastern-Pacific sea level pressures also display a strong simultaneous relationship to signal magnitudes at this time of year (figure 10-15f) Highly positive signal values are associated with highly negative ZEAs values during the same season. In addition, there is also a strong positive relationship between MAM(+1) MEI and signals across Region II. The 1982/83 El Nino event does not to conform to any of the described relationships, with the exception to Nino 4 region. In that case, it was the 1966/67 event that does fit the shown relationship. At increasing lags, MEI and SSTAs from Nino 3, 3.4 and Nino 4 are closely associated with MAM(+1) signals during FMA(+1) via a positive relationship. The 1982/83 El Nino strongly deviates from this pattern in Nino 3, 1.2, and MEI series. Likewise, the 1966/67 event does not conform to the pattern in the Nino 4 region. 201 2.0 58 1.5 92 1.0 69 83 87 O 52 * • c .5 73 o MEI - MAM(+1) 2.0 58 + 1.5 H < 92 1.0 69 83 U 87 52 c o .5 73 O) 0 95 or 0.0 c 70 !_ (/> o -.5 c Z 64 UJ -1.0 •1.0 -.5 0.0 .5 1.0 1.5 2 0 SSTA Nino 3 (C) - MAM (+1) Figure 10-15: Scatter diagram showing relationships between Region II MAM(+1) temperature signal and a: MAM(+I) MEI; b: MAM(+1) SSTA Nino 3. 202 2.0 58 1.5 + 92 < I 1.0 83 ft o 52 73 o 66 77 95 or o.o TO 70 C O) CO o -.5 c 64 rjj -1.0 -2-10 SSTA Nino 12 (C) - MAM (+1) 2.0 53 1.5 if < 92 1 1.0 83 69 87 O 52 c .5 o 73 77 66 95 0.0 c 70 O) in cO 64 • -1.0 .2 0.0 .2 .4 1.0 SSTA Nino 4 (C) - MAM(+1) Figure 10-15: Scatter diagram showing relationships between Region II MAM(+1) temperature signal and c: MAM(+1) SSTA Nino 1.2; d:MAM(+l) SSTA Nino 4. 203 2.0 58 + 1.5 J if 92 < 1.0 I 69 83 87 O 52 .5 4 c 73 0 77 66 cn 95 • -1-0 -.5 0.0 1.0 1 5 SSTA Nino 34 (C) - MAM(+1) 2.0 58 1.5 2 92 < 1.0 83 69 87 O c g 73 en 66 77 95 rr 0.0 J TO 70 C Ol to -.5 o c 64 z Qj -10 -2.0 -1.5 -1.0 0.0 5 ZEAS (Z) - MAM(+1) Figure 10-15: Scatter diagram showing relationships between Region II MAM(+1) temperature signal and e: MAM(+1) SSTA Nino 3.4; f: MAM(+1) ZEAS- 204 2.0 58 1.5 < 92 I 1.0 69 83 87 o 5? c o 73 a> 77 66 95 Of 0.0 J ro 70 c g> c 64 Z Qj -10 0.0 1.0 1.5 2 0 2.5 3 0 MEI - FMA(+1) 2.0 58 7 1.5 92 < 1.0 83 69 87 O 52 c o 73 D) 77 66 or 95 0.0 c 70 cn 'to o _ 64 LU -1.0 -.5 0.0 .5 1.0 1 5 SSTA Nino 4 (C) - FMA(+1) Figure 10-15: Scatter diagram showing relationships between Region II MAM(+1) temperature signal andg: FMA(+1) MEI; h: FMA(+1) SSTA Nino 4. 205 2.0 58 1.5 92 < 83 1.0 H 69 87 O 52 = .5 c 73 o 77 66 '9 •95 or o.o H ro 70 c C 64 Z _] -1.0 -.5 0.0 1.0 1.5 2.0 SSTA Nino 34 (C) - FMA(+1) 2.0 58 1.5 92 < 1.0 69 83 87 o 52 5H c 73 o O) 77 66 CD 95 or 0.0 H ro 70 c O) o -.5^ c 64 LLI -1.0 0.0 1.0 1.5 2.0 2.5 SSTA Nino 3 (C) - FMA(+1) Figure 10-15: Scatter diagram showing relationships between Region II MAM(+1) temperature signal and i: FMA(+1) SSTA Nino 3.4; j: FMA(+1) SSTA Nino 3. 206 2 0 58 • + 1.5 92 • 1.0 • 69 83 87 • O 52* • • c .5 - 73 o • cn 66 77 c 64 Z • iij -10 -1 0 -.5 0.0 1 0 1.5 20 2.5 3 0 SSTA Nino 12 (C) - FMA(+1) 2.0 58 1.5 - 92 < 1.0 - 69 83 I 87 O 52 c .5 - 73 o 77 66 f 95 OH 0.0- ro 70 fe CO .5- 64 Z Qj -1.0 0.0 .5 1 0 1.5 2 0 2.5 3 0 SSTA Nino 3(C)- JFM(+1) Figure 10-15: Scatter diagram showing relationships between Region II MAM(+1) temperature signal and k: FMA(+J) SSTA Nino 1.2; I: JFM(\ 1) SSTA Nino 3. 207 2.0 58 • + 1.5 92 < • 1.0 69 83 i 87 • • o 52 • • 73 c • o 66 77 en 95 • • 0.0 • a: 70 15 • c if) o c: 64 • m -1.0 -1.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 SSTA Nino 12 (C) - JFM(+1) 20 58 1.5 2 92 83 < 1.0 69, 1 87 O 52 c 73 o 77 a) 99 66 a: 0.0 70 c 01 -.5 O c: 64 • -1.0 0.0 .5 1.0 1.5 2.0 2 5 SSTA Nino 34 (C) - JFM(+1) Figure 10-15: Scatter diagram showing relationships between Region II MAM(+1) temperature signal and m: JFM(+1) SSTA Nino 1.2; n: JFM(+1) SSTA Nino 3.4. 208 JFM(+1) SSTAs in Nino 1.2 and 3 continue to show a strong positive relationship with signal values during MAM(+1) in Region II, with the exception of 1982/83 event. SOI during JFM(+1) also shows a significant association, such that low values in SOI are Figure 10-15: Scatter diagram showing relationships between Region IIMAM(+1) air temperature signal and o: JFM(+1) SOI. associated with positive signals. The 1982/83 event again stands out of this relationship. Stronger positive signals during this season typically coincide with or slightly follow more developed and/or strong El Nino events. Table 10-3 shows that several of the identified relationships are not restricted to El Nino years. Most of the lag-0 relationships are held during non-ENSO periods. These include associations with MEI, Nino 3,1.2, and 3.4. In turn, these variables cannot be attributed towards a possible cause of signal magnitudes during El Nino periods. 209 Similar conclusions can be made with respect to the significant relationships found at other lags. These include MEL Nino 1.2 and Nino 3.4 at lag-1, and Nino 1.2, and SOI, both at lag-2. 10.4.2.4 Summer [JJA(+1)] Fewer Pacific variables are strongly related to signals across Region II during El Nino summer than are found during the spring season. Table 10-4 and figure 10-16 show the results. The only relevant relationship exists with FMA(+1) Nino 1.2 SSTAs. 2.5 58 • 2.0 + 1.5 < 1.0 O 70 92 • • C .5 o 69 77 • 87 • • E o.o 52 CD 83 C 95 • O) • • CO 6 73 64 2 • m • • -1.0 -1.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 SSTA Nino 12 (C) - FMA(+1) Figure 10-16: Scatter diagram showing relationships between Region II JJA(+1) temperature signal and SSTA Nino 1.2 during FMA(+1). 210 Warm waters in the eastern Pacific during FMA(+1) precede positive signals in JJA(+1). The scatterplot shows that the four coldest summers in this region occurred when SSTAs fell below zero during FMA(+1). These included the 1963/64, 1965/66, 1972/73 and 1994/95 events. Although in 1970 negative SSTAs were observed in Nino 1.2, these conditions were followed by positive signals. By FMA(+1) this part of the Pacific is typically past its secondary maximum that takes place around DJF(+1) (Figure 10-2). Around FMA(+1) equatorial waters begin to recover. The closer to neutral the eastern basin is during this time of the year, the cooler the following summer in Region II will be. On the other hand, the warmer the waters of this region are after Christmas and during the following spring, the more positive the signals across Region II during the following summer. The 1982/83 event does not fit this relationship. Extremely high values in SSTA in FMA(+1) resulted in only slightly negative signals during the following summer. 10.4.3 Region III 10.4.3.1 Winter [DJF(+lj] As for the other two regions during this season, ZEAS shows a strong association with signals across this area, 12 running seasons in advance. Table 10-2 and figure 10-17 show the results. A positive relationship between ZEAS during DJF(0) and signal magnitudes in DJF(+1) is observed. The four largest DJF(0) ZEAS values coincide with the four largest positive signal values (>3°C). As in the other regions, the exceptions are the 1968/69 and 1969/70 events. ZEAs tendencies observed between DJF(0) and FMA(0) help separate the 10 events into two groups. Four out of five cold-winter (warm-winter) events are preceded by rising (falling) tendencies during the two-month period. Despite of their winter characteristics, falling (rising) pressure tendencies precede the 1969/70 (1982/83) warm-winter (cool-winter) events. 211 77 87. 64 •+ •I % 70 • 83 + 2 t •I 95» O 7.3f •t 66 I g C_ 4 TO C O) TYPE, 69 • w z: [0 -8 •t • c —"i— —i 1— "i r -2.5 -2.0 -1.5 -1.0 -.5 0.0 .5 1.0 1.5 ZEAS - DJF(O) Figure 10-17: Scatter diagram showing relationships between Region III DJF(+1) temperature signal and DJF(O) ZEAS- 10.4.3.1.1 Event Classification Figure 10-18 shows the chart that helps to "classify" the events into Type W or C signals, based on the sea level pressure conditions across the eastern equatorial Pacific and its tendencies between DJF(0) and FMA(0). This method correctly relates the conditions in the Pacific to those observed in Canada in 8 out of 10 (80%) events. The two events not grouped correctly are 1982/83 and 1969/70. The former is mis-grouped into Type W, and the latter into Type C signal. ZEAS during DJF(0) for 1982 was 212 Region III El Nino Signal Classification ZEAS DJF(O) >0 =0 <0 Type W ZEAS TypeC Tendencies DJF(O) - FMA(O) Increasing Decreasing Type C Type W Figure 10-18: Procedure used classify El Nino events for Region III. 213 recorded at 0.20 and the pressure tendency between DJF(O) and FMA(O) was the closest to zero of all 10 events at -0.03. During DJF(0) of 1969, ZEAS was the lowest recorded during all 10 events at -2.30, due to the preceding El Nino conditions immediately prior to the 1968/69 event. Despite of increasing ZEAS tendencies over the next few months, in this case warm winter in Region III followed. 10.4.3.2 Fall [SON(0)) Figure 10-19: Scatter diagram showing relationships between Region III SON(0) temperature signal andMJJ(O) SOI. 214 SOI is the only Pacific variable to show any significant association to Region Ill's signal magnitudes during the fall. Table 10-1 and figure 10-19 show the results. A significant positive relationship exists when SOI precedes SON(0) signals by four running months. Low index values during MJJ(O) correspond to low SON(+l) signal values, and vice versa. With the exception of 1986/87 and 1968/69 events, all other El Nino fall seasons are preceded by negative SOI values, which range between -3 and -0.5. Both of the observed positive SOI values are accompanied by neutral signals. Figure 10-20: Scatter diagram showing relationships between Region IIMAM( + 1) signal and JAS(0) SSTA in Nino 1.2 region. 215 10.4.3.3 Spring [MAM(+1)] In the majority of instances, very weak associations (p-value >0.05) exist between spring (+1) signals and conditions across the Pacific. The only exception is SSTA across Nino 1.2. Figure 10-20 and table 10-3 show the results. With the exception of the 1963/64 event, positive JAS(O) SSTAs are followed by positive signal magnitudes. 10.4.3.4 Summer [JJA(+1)] Mid-summer season shows the greatest number of significant relationships to exist between the equatorial Pacific and signal magnitudes across this region. Table 10-4 and figures 10-2la through 10-2If show the results. Strongest positive associations exist between local signals and MJJ(+1) MEI, MAM(+1) Nino 3 and Nino 3.4 SSTAs. Significant relationships also exist with Nino 3.4, MEI and Nino 3 at lag-2. Only the 1957/58 El Nino persistently fails to fit any of the relationships. At each instance, the signals reported in Canada are too high for the preceding tropical conditions. When looking at the plot of MEI (MJJ(+1)), it can be seen that 1958 was the warmest summer. The expected corresponding MEI value should be highest as well. 10.5 Summaries and Conclusions The objective of this chapter was to tie the W and C signals back to El Nino. Of primary importance for all regions was the connection during the DJF(+1) season. But signal behavior during other seasons was also investigated. Nine tropical Pacific variables were used to investigate their behavior relative to that of the air temperature signals during El Nino and non-ENSO periods. To attribute relationships to El Nino, significant associations had to be confined to those years. 216 2.0 58 1.5 < 1.0 H O 92 83 c 70 IB o 0.0 A 87 g> 52 69 © 64 95 77 • or 66 ro -5 4 c CI "(0 73 c -1.0- ED -1.5 -1.0 - 5 0.0 1.0 1.5 2.0 MEI-MJJ(+1) 2.0 58 1-5 4 < 1.0 4 _ O .5J 92 83 c 70 o 0.0 87 52 69 » 64 95 77 or 6^ ro -5 c CO '«/> 73 O -1.0-1 CD -15 -1.5 -1.0 -.5 0.0 1.0 1.5 20 SSTA Nino3(C)-AMJ(+1) Figure 10-21: Scatter diagram showing relationships between Region III JJA(M) temperature signal and a: MJJ(+1) MEI; b: AMJ(+1) SSTA Nino 3. 217 2.0 58 1.5 < 1.0 i O .5 92 ,83 70 O 00 3, 52 69 64 95 771 a: IS -5-I ac CO 73 O -1.0 CD -15 -1.0 00 1 0 1 5 SSTA Nino 34 (C) - AMJ(+1) 2.0 58 m 15 < 1.0 S 5 92 83 70 c q 0.0 87 52 69 |64 95 •77 C_ 6? TO - 5 C O) CO 73 -1.0 4 UJ -15 -1 - .5 0 0 1 0 1.5 2.0 2 5 MEI-AMJ(+1) Figure 10-21: Scalier diagram showing relationships between Region III JJA(+1) temperature signal and c: AMJ( i I) Nino 3.4 SSTAs; d: AMJ(+1) MEI. 218 2.0 58 1.5 H < 1.0 I 92 83 70 .2 o.o -I 87 52 69 64 95 77 or 6? ro -5 c w 73 Q -1.0-1 • -15 -1.0 -.5 0 0 1.0 1.5 2.0 SSTA Nino 3 (C) - MAM(+1) 2.0 58 1.5 < 1.0 92 83 c 70 87 .2 o.o 52 69 64 95 77 or 66 ro -.5 c o> en 73 -1.0 co z ED -1.5 0.0 1 0 1.5 SSTA Nino 34 (C) - MAM(+1) Figure 10-21: Scatter diagram showing relationships between Region III JJA(+1) temperature signal and e: MAM(+1) Nino 3 SSTAs; f: MAM(+1) Nino 3.4 SSTAs. 219 Table 10-6: Number of equatorial Pacific variables reported to have significant relationship to air temperature signal data of Western Canada during El Nino and non- ENSO periods. Region El Nino Season Total El Nino (El Niiio+non-ENSO) SON DJF MAM JJA [°) (+D I 2 1 0 1 4 II 2 2 5 (9)* 1 10(19) III 1 1 0(1)* 6 8(9) Total 5 4 5(15) 8 23 (32) •Values shown in brackets denote number of variables also found significant during non-ENSO periods. Table 10-6 shows the number of Pacific variables that show significant relationships during each season. Results for DJF(+1) for all regions and SON(0) for Region I are printed in bold to highlight the seasons that distinguish between signal types. Sea level pressure conditions across the eastern Pacific basin help distinguish between Type W and C signals during winter in all three areas (table 10-7). These conditions are key precursors to El Nino (Wright 1986). The DJF(0) season prior to the development of a major event is typically marked by a steady decline of sea-level-pressures across this area. These conditions appear equally crucial to the development of surface air temperature signals across Western Canada one year later. During the spring, "connections" to the Pacific are almost exclusively restricted to Region II. By summer, significant associations to the tropical Pacific are mostly restricted to Region III. Region II shows the greatest number of associations to the Pacific during El Nino years, followed by Region III and then I. Strongest teleconnections exist during winter attenuating polewards and towards the east. This is consistent with existing literature of Bjerknes (1969), Wallace and Gutzler (1981) and many others who identified the winter season as key to the ability of energy to propagate from the tropics to extratropical latitudes of North America, chiefly through the PNA pattern. All teleconnections established during winter are from sea level pressure patterns across the eastern and western sections of the tropical basin (table 10-7). Teleconnections from the tropical Pacific identified during fall, spring and summer come 220 from oceanic and atmospheric 'sources'. 75% (6/8 - MEI included) of the tropical variables significant at lag times greater than 6 months are of atmospheric origin. At lags of less than 6 months, on the other hand, it is oceanic variables that exhibit closer relationships. Out of the total of 26 tropical variables significant at lag times less than 6 months, only 8 (MEI included) are of atmospheric origin. It is unclear why these differences may be present. Table 10-7: Type of tropical variable found correlated with temperature signal values Region El Nino Season SON DJF MAM JJA (0) (+D (+D (+D I SST/SLP SLP SST/MEI II SST/SLP SLP SST/SLP/MEI SST III SLP SLP — SST/MEI Table 10-8 examines which of the variables have the greatest correspondence to Canadian signals. ESOI, ZIND and SSTAs across Nino 1.2 are least important, followed by SOI, SSTAs in Nino 3.4 and MEI. The most frequently identified and thus most important variables are SSTAs in Nino 4 and 3 regions, and sea level pressure conditions across the eastern Pacific basin (ZEAS)- Sea-surface-temperature conditions throughout central and western tropical Pacific Ocean exhibit greatest teleconnectivity to surface Table 10-8: Frequency of each tropical variable reported significant in each region of Western Canada during El Nino years only, excluding relationships found significant Tropical Sub-Re ?ion of Western Canada Variable I II III Total ESOI 1 0 0 1 ZIND 0 1 0 1 Nino 1.2 0 1 0 1 SOI 0 1 1 2 Nino 3.4 1 0 2 3 MEI 1 0 2 3 Nino 4 1 3 0 4 ZEAS 1 2 1 4 Nino 3 0 2 2 4 221 temperature signals across Western Canada. This finding is consistent with literature that emphasizes the importance of the Nino 3 region in relation to extratropical teleconnections from the El Nino region. These are Wang (1995), Trenberth (1991), Barnston and Chelliah (1997), Barnston and Smith (1996), and Wolter and Timlin (1998). The apparently successful identification of these relationships, especially during winter, provides supportive evidence towards the hypothesis that the two signal types are related to El Nino. This is further enforced by the fact that similar relationships do not exist during non-El Nino periods. 222 11. SUMMARY AND CONCLUSIONS 11.1 Introduction The El Nino/Southern Oscillation phenomenon is the greatest contributor to inter- annual climate variability around the globe. The study of climate variability that arises due to its presence in the equatorial Pacific basin has proven important to the scientific community and society in general. The temporary warming of the basin that lasts between 12 and 18 months results in a release of large amounts of latent and sensible heat from vast areas of the ocean, altering atmospheric air circulation patterns above the sea. Between six and eight months after its birth, El Nino's presence begins to be felt as far away as Canada, exerting its influence on climate and local economies there. Previous studies demonstrated climatological impacts of El Nino to be strongest in Western Canada during winters, the time when the event reaches maturity in the Pacific. At that time, anomalously warm and dry conditions have been found to prevail in the lowland areas east of the mountains south of 60°, and warm and wet conditions along the west coast. The objective of this research was to further the understanding of El Nino's influence on climates across Western Canada. Current literature acknowledges the presence of considerable variability in El Nino signatures throughout the area This research examined and clarified the issues of such variation in surface air temperature signals, paying special attention to its spatial and temporal characteristics. Questions regarding signal magnitude and sign fluctuations from one event to another, as well as from one place to another were addressed. 223 11.2 Principal findings and their significance The key findings of the research are as follows: 1) There is a significant reduction in year-to-year and place-to-place surface air temperature signal variation from neutral to El Nino periods between November and February throughout the study area. 2) During El Ninos three sub-regions show distinct signal 'behaviors' over time. These are Regions I and II, both situated south of 51°, and Region III, located in the northwest above 51°N. 3) Within each sub-region, two patterns of signal evolution exist. These are distinguished by warm (Type W) and cold (Type C) winters. Type W is most prominent to Regions I and II, whereas type C predominates in the north, Region III. 4) These winter signals are positively cross-correlated with sea level pressure patterns in the eastern tropical Pacific (SLPEAS) 12 months ahead. During El Nino periods, air circulation across the area is significantly modified especially during the cool months of the year. During winter when El Nino signatures reach maximum magnitudes, weather across the area becomes less variable and more stable both from one event to the next and from one place to another. At these times, two types of mid-tropospheric circulation patterns dominate the area and are closely linked to the two signal types. The first, resembling the PNA pattern, is characterized by a high (low) pressure cell centered over Western Canada (mid-Pacific) and is observed when large parts of the area experience positive winter temperature signals or unusually warm conditions. The second which occurs during cool winters features a prominent trough over the west coast and strong ridging over the central Pacific and eastern Canada. This arrangement of pressures patterns resembles the negative phase of the TNH. The former arrangement of air circulation allows for a steady influx of warm subtropical air into the region, whereas cold polar air permeates the area during the latter. The dominance of these patterns reduces the influence of other systems on the area, like the Arctic High pressure that typically alternates with Pacific airstreams during non-El Nino periods specifically in the 224 Prairies. Region III is located on the northern edge of the jet stream that meanders from lower latitudes of the Pacific around the high-pressure cell situated over the area, into the Prairies. This places the region outside the area that receives the greatest influx of warm subtropical air, hence the subdued temperature signals. The state of the equatorial Pacific just prior to the full development of El Nino events plays a large role in the development of signals across the study area. Events associated with strongly positive SLPEAS conditions between December and February of the year during which El Nino develops have the greatest chance of being followed by relatively cool winters across the study area. Episodes that are preceded by abnormally low SLPEAS are likely to be followed by warm winters in Western Canada. The apparently successful establishment of a relationship between winter surface air temperature signals in Western Canada and events in the equatorial Pacific gives credibility to the hypothesis that the two signal types are significantly El Nino driven. This development points to a future in which surface air temperature in Western Canadian winters may be predicted with greater accuracy in El Nino years than in neutral years. 11.3 Unanswered/new questions stemming from this research Although this thesis helped answer several questions about El Ninos' impacts on Western Canadian climates, it also raises new issues. Two signal types were identified, but the mechanisms underlying the physical connection between them and the tropical Pacific remains unknown. The relationships between Western Canadian signals and conditions in the equatorial Pacific were determined using coarse-grid statistics. But, do similar relationships hold at the local scale? This question would be of interest to further assess potential predictability of site-specific signal. Thirdly, similar questions to those posed here could also be asked for La Nina. Are the responses similar to that of El Nino? What asymmetries exist between El Nino and La Nina and where and when are they most prominent? Finally, the scientific community has discussed possible changes in key characteristics of El Nino events that may result from global warming. One of the 225 concerns or areas of interest includes the debate regarding possible modifications to extratropical teleconnections. Can this change be now detected in our climate records? How, if at all would the signal characteristics change under a new climatic equilibrium? Would the impact and variability increase or decrease? The answers to these questions will depend upon our understanding of past and current El Nino signatures in our climate. 226 REFERENCES Ahrens, C. D. 1994. Meteorology today. An introduction to weather, climate and the environment. Fifth Edition. West Publishing Company. 591pp. Alexander, M. A. 1990. Simulation of the response of the North Pacific Ocean to the anomalous atmospheric circulation associated with El Nino. Climate Dynamics. 5: 53-65. Atmospheric Environment Service (AES). 1989a. Climatological station catalogue. British Columbia. Downsview. Ontario.57 pp. Atmospheric Environment Service (AES). 1989b. Climatological station catalogue. The North. Yukon and the Northwest Territories. Downsview. Ontario. 57 pp. Atmospheric Environment Service (AES). 1989c. Climatological station catalogue. Prairie Provinces. Downsview. Ontario.57 pp. Atmospheric Environment Service (AES). 1994. A note on El Nino. In Climatic perspectives. September Issue. Report of the Atmospheric Environment Canada, p. 9-10. Atmospheric Environment Service (AES). 1994. Canadian Monthly Climate Data and the 1961-1990 Normals. CD-ROM. Toronto: Environment Canada. Barnston, A. G. and Chelliah, M. 1997. Documentation of a highly ENSO-related SST region in the Equatorial Pacific. Atmosphere-Ocean. 35: 367-383. Barnston, A. G, and T. M. Smith. 1996. Specification and prediction of global surface temperature and precipitation from global SST using CCA. Journal of Climate. 9: 2660-2697. Bell, G. D., and Janowiak, J. E. 1995. Atmospheric circulation associated with the Midwest floods of 1993. Bulletin of American Meteorological Society. 76: 681- 695. Berry, J. K. 1993. Cartographic modeling: The analytical capabilities of GIS. In Goodchild, M. F., Parks, B. O., and Steyaert, L. T. (eds). Environmental modeling with GIS. New York. Oxford University Press, pp 58-74. 227 Bjerknes, J. 1969. Atmospheric teleconnections from the equatorial Pacific. Monthly Weather Review. 97: 163-172. Bjerknes,! (1966). Atmospheric telecormections from the Equatorial Pacific. Monthly Weather Review. 97: 163-172. BonsaL B. R., Chakravarti, A. K., and Lawford, R. G. 1993. Telecormections between North Pacific SST anomalies and growing season extended dry spells on the Canadian Prairies. International Journal of Climatology. 13: 865-878. Briggs, I. C. 1974. Machine contouring using minimum curvature. Geophysics. 39: 39- 48. Byrne, J. M., and Berg, A. A. 1998. Spatial analysis of temperature and precipitation anomalies on the Canadian Prairies during two strong El Nino events. Canadian Water Resources Journal. 23: 231-243. Cambell, C. S., and Egbert, S. L. 1990. Animated cartography-thirty years of scratching the surface. Cartographica. 27: 24-46. Cane, M. A., Clement, A. C, Kaplan, A., Kkushnir, Y., Pozdnyakov, D., Seager, P., and Zebiak, S., 1997. Twentieth century sea surface temperature trends. Science. 275: 957-960. Climate Prediction Center (CPC). 1999. CPC-Current Monthly Atmospheric and SST Index Values, http://nic.fb4.noaa.gov/data/cddb/. Comprehensive Ocean-Atmosphere Dataset (COADS) homepage: http: //www. cdc.noaa.gov/coads/. Coppock, J.T., and Rhind, D. W. 1991. The history of GIS. In Maguire, D. J., Goodchild, M. F., and Rhind, D. W. (eds). Geographical Information Systems: Principles and Applications. (2 volumes). London: Longman, and New York: Wiley, pp. 21-43. Cornwell, B., and Robinson, A. 1966. Possibilities for computer animated films in cartography. Cartographic journal. 3: 79-82. Davis, J. C. 1973. Statistics and data analysis in geology. John Wiley & Sons Inc. 550 pp. Dent, B. D. (1990). Cartography: Thematic map design. Second Edition. USA: C. 228 Brown Publishers. 433 pp. DiBiase, D., MacEachren, A. M., Krygier, J. B., and Reeves, C. 1992. Animation and the role of map design in scientific visualization. Cartography and Geographic Information Systems. 19: 201-214. Dickinson, R. E., Meleshko, V., Randall, D., Sarachik, E., Silva-Dias, P., and Slingo, A. (1996). Climatic Processes. In Climate Change 1995: The Science of climate change. Contribution of Working Group I to the Second Assessment Report of the IPCC. Cambridge University Press, p. 193-229. Doerling, D. 1992. Stretching space and splicing time: from cartographic animation to interactive visualization. Cartography and Geographic Information Systems. 19: 215-227. Eastman, J. R. 1997. Idrisi for Windows. User's Guide. Version 2.0. Clark Labs for Cartographic Technology and Geographic Analysis. Clark University. IDRISI productions. Ely, L. E., Enzel, Y., Baker, V. R., and Cayan, D R. 1993. A 5000-year record of extreme floods and climate change in the southwestern United States. Science. 262: 410-412. Fletcher, J. 1985. CO ADS - Comprehensive Ocean-Atmosphere Data Set. Release 1. Boulder. Colorado. CIRES/ERL/NCAR/NCDC. Gadd, B. 1995. Handbook of the Canadian Rockies. Second Edition. Corax Press. 831 PP Gates, W. L., Henderson-Sellers, A., Boer, G. J., Folland, C. K., Kitoh, A., McAvaney, B. J., Semazzi, F., Smith, N., Weaver, A. J., and Zeng, Q. -C. 1996. Climate Models - Evaluation. In Climate Change 1995: The Science of climate change. Contribution of Working Group I to the Second Assessment Report of the IPCC. Cambridge University Press, p. 233-276. Glantz, M. H. 1996. Currents of change: El Nino's impact on climate and society. Cambridge University Press. 194 pp. Glantz, M., Katz, R., and Krenz, M. 1987. Climate crisis. The societal impacts associated with the 1982-83 worldwide climate anomalies. United Nations Publications. 105 pp. Goodchild, M. J. 1993. The state of GIS for Environmental problem-solving. In 229 Goodchild, M. F., Parks, B. O, and Steyaert, L. T. (eds). Environmental modeling with GIS. New York. Oxford University Press. pp7-13. Graham, N. E, and T. P. Barnett. 1995. ENSO andENSO-relatedpredictability. Part II: Northern Hemisphere 700-mb height predictions based on a hybrid coupled ENSO model. Journal of Climate. 8: 544-549. Haber, R. B., and McNabb, D. A. 1990. Visualization idioms: a conceptual model for scientific visualization systems. In Neilson, G. N, Shriver, B., Rosenblum, L. J. (eds). Visualization in Scientific Computing. IEEE Computer Society Press, pp. 74-93. Halpert, M. S., and Ropelewski, C. F. 1992. Surface temperature patterns associated with the Southern Oscillation. Journal of Climate. 5: 577-593. Hare, F. K., and Thomas, M. K. 1979. Climate Canada. Second Edition. John Wiley & Sons Canada Limited. 225 pp. Hoerling, MP., and Ting, M. 1994. Organization of extratropical transients during El Nino. Journal of Climate. 7: 745-766. Hoskins, B. J., and Karoly, D. J. (1981). The steady linear response of a spherical atmosphere to thermal and orographic forcing. Journal of Atmospheric Sciences. 38: 1179-1196. Houghton, J. T., Filho, L. G. M., Callander, B. A., Harris, N, Kattenberg, A., and Maskell, K. 1996. Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change, pp. 572. Cambridge University Press. Cambridge. GB. Hunt, A. G. 1999. Understanding a possible correlation between El Nino occurrence frequency and global warming. Bulletin of the American Meteorological Society. 80: 297-300. Intergovernmental Panel on Climate Change (IPCC). 1997. Climate Change 1995: The science of climate change. Contribution of Working Group I to the Second Assessment Report of the IPCC. Summary for Policymaker. 67 pp. Jones, P. D., and Briffa, K. R. 1992. Global surface air temperature variations during the 20th century: Part 1-Spatial, temporal and seasonal details. Holocene. 1: 165-179. Keckler, D. 1997. Surfer for Windows. Contouring and 3D surface mapping. Version 6. 230 User's Guide. Golden Software Inc.. 356 pp. Kendrew, W. G, and Kerr, D. 1955. The climate of British Columbia and the Yukon Territory. Edmond Cloutier. Ottawa. 222 pp. Kiladis, G. N., and Diaz, H. F. 1989. Global climatic anomalies associated with extremes in the Southern Oscillation. Journal of Climate. 2: 1,069-1,089. Kiladis, G. N., and van Loon, H. 1988. The Southern Oscillation. Part VII: Meteorological anomalies over the Indian and Pacific sectors associated with the extremes of the oscillation. Monthly Weather Review. 116: 120-136. Kineman, J. J. 1993. What is a scientific database? Design considerations for global characterization in the NOAA-EPA global ecosystems database project. In Goodchild, M. F., Parks, B. O, and Steyaert, L. T. (eds). Environmental modeling with GIS. New York. Oxford University Press, pp 373-378. Koussoulakou, A., and Kraak, M. J. 1992. Spatio-temporal maps and cartographic communication. The Cartographic Journal. 29: 101-109. Kraak, M. J. 1999. Visualizing spatial distributions. In Longely, P. A., Goodchild, M. F., Maguire, D. J., and Rhind, D. W. (eds). Geographical Information Systems (2 volumes). New York: John Wiley & Sons, Inc.. pp. 157-173. Kvanli, A. H. 1988. Statistics. A computer Integrated Approach. New York: West Publishing Company. 935 pp. Liang, X-Z., Wang, W-C, and Dudek, M. P. 1996. Northern Hemispheric interannual teleconnection patterns and their changes due to the greenhouse effect. Journal of Climate. 9: 465-479. Lim, H., and Chang CP. (1983). Dynamics of teleconnections and Walker circulations forced by equatorial heating. Journal of Atmospheric Sciences. 40: 1897-1915. Linacre, E. 1992. Climate data and resources. A reference and guide. London. Routledge, Chapman and Hall Inc. 366 pp. Livezey, R. E., and Mo, K. C. 1987. Tropical-extratropical teleconnections during the Northern Hemisphere winter. Part IT. Relationships between monthly mean Northern Hemisphere circulation patterns and proxies for tropical convection. Monthly Weather Review. 115: 3,115-3,132. 231 MacEachren, A. M. 1994a. Time as a cartographic variable. In Hearnshaw, H. M. and Unwin, D. J. (eds). Visualization in Geographic Information Systems. New York: John Wiley and Sons Pp. 115-130. MacEachren, A. M. 1995. Some Truth with Maps: A primer on symbolization and design. USA: Association of American Geographers. 119 pp. MacEachren, A. M. 1994b. How Maps Work. Representation, Visualization and Design. USA: The Guilford Press. 510 pp. MacEachren, A. M., Buttenfield, B., Campbell, J., DiBiase, D., Monmonier, M. 1992. Visualization. In Abler, R., Marcus, M., and Olson, J. (eds) Geography's inner worlds: Pervasive themes in contemporary American geography. Rutgers University Press. New Brunswick. New Jersey. Pp. 99-137. MacEachren, M. A., and Ganter, J. H. 1990. A pattern identification approach to cartographic visualization. Cartographica. 27: 64-81. Mann, M. E., Bradley, R. S., and Hughes, M. K. 1998. Global-scale temperature patterns and climate forcing over the past six centuries. Nature. 392: 779-787. MathSoft, Inc. 1998. S-Plus for ArcView GIS. User's Guide. Version 1.1 Data Analysis Products Division. MathSoft Inc. Seattle. Washington. 122 pp. McCormick, B. H, Defant, M. D., and Brown, M. D. (eds). 1987. Visualization in scientific computing. Special issue ACM SIGGRAPH Computer Graphics 21(6). Meehl, G. A., Branstator, G. W, and Washington, W. M. 1993. Tropical Pacific interannual variability and CO2 climate change. Journal of climate. 6: 42-63. Metcalfe, JR., Routledge, B., and Devine, K. 1997. Rainfall measurement in Canada: Changing observational methods and archive adjustment procedures. Journal of Climate. 10: 92-101. Mo, K. C, and Livezey, R. E. 1986. Tropical-extratropical geopotential height teleconnections during the Northern Hemisphere winter. Monthly Weather Review. 114: 2,488-2,515. Mo, R., Fyfe, J., and Derome J. 1998. Phase-locked and asymmetric correlations of the wintertime atmospheric patterns with the ENSO. Atmosphere-Ocean. 36: 213- 239. 232 Monmonier, M. 1990. Strategies for the visualization of geographic time-series data. Cartographica. 27: 30-45 Murphy, R. C. 1926. Oceanic and climatic phenomena along the West Coast of South America during 1925. Geographical Review, pp. 26-54. Nkemdirim, L. C. 1996. Will climate change marginalise the tropics? In Commission on Climatology of the International Geographical Union. Climate. Climate Change and Impacts (Focus on Central and Latin America), p. 1-9. Nkemdirim, L. C, and Budikova, D. 1996. Ihe El Nino-Southern Oscillation has a truly global impact: A preliminary report on the ENSO project of the Commission on Climatology. Scientific Article. Bulletin of the International Geographical Union (IGU). Issue #46. p. 15-21. Norusis, M. J. 1993. SPSS for Windows. Base system user's guide. Release 6.0. SPSS Inc. 828 pp Nyerges, T. L. 1993. Understanding the scope of GIS: Its relationship to environmental modeling. In Goodchild, M. F., Parks, B. O., and Steyaert, L. T. (eds). Environmental modeling with GIS. New York. Oxford University Press pp 75- 93. Ormsby, T., and Alvi, J. 1999. Extending ArcView GIS. ESRI Press. Redlands. CA. 517 pp. Palmer, T. C. 1992 A language for molecular visualization. IEEE Computer Graphics and Applications. 12(3): 23-32. Peuquet, D. J. 1999. Time in GIS and geographical databases In Longley, P. A., Goodchild, M. F., Maguire, D. J., and Rhind, D. W. (eds). Geographical Information Systems. Volume 1. Principal and Technical Issues. New York: John Wiley and Sons Inc. 91-102. Philander, S. G. 1990. El Nino, La Nina, and the Southern Oscillation. London: Academic Press Inc. 293 pp. Philander, S. G. 1998. Learning from El Nino. Weather. 53: 270-274. Quinn, W. H., and Neal, V. T. 1987 El Nino occurrences over the past four and a half centuries. Journal of Geophysical Research. 82 (C13): 14,449-14,461. 233 Quinn, W. H., Zopf, D. O, Short, K. S., and Yang Duo, R. T. W. 1978. Historical trends and statistics of the Southern Oscillation, El Nino, and Indonesian droughts. Fishery Bulletin. 76: 663-678. Rajagopalan, B., Lall, U., and Cane, M. A. 1997. Anomalous ENSO occurrence: An alternate view. Journal of Climate. 10: 2,351-2,357. Rasmusson, E. M., and Carpenter, T. H. 1982. Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Nino. Monthly Weather Review. 110: 354-384. Rasmusson, E. M., and Wallace, J. M. 1983. Meteorological aspects of the El Niho/Southern Oscillation. Science. 222: 1195-1202. Reynolds, R. W. and T. M. Smith. 1994. Improved global sea surface temperature analyses using optimum interpolation. Journal of Climate. 7: 929-948. Ropelewski, C. F., and Halpert, M. S. 1986. North American precipitation and temperature patterns associated with the El Niho/Southern Oscillation (ENSO). Monthly Weather Review. 114: 2,352-2,362. Ropelewski, C. F., and Halpert, M. S. 1987. Global and regional scale precipitation and temperature patterns associated with the El Nino-Southern Oscillation. Monthly Weather Review. 115: 1,606-1,625. Ropelewski, C. F., and Jones P. D. 1987. An extension of the Tahiti-Darwin Southern Oscillation Index. Monthly Weather Review. 115: 2,161 -2,165. SCOR 1983. Prediction of El Nino. Proceeding Number 19. Paris. France. Scientific Committee for Ocean Research Working Group 55. 47-51. Shabbar, A. 1998. Canadian perspective on El Nino, http://wwwl.tor.ec.gc.ca/elnino/. Shabbar, A., and Khandekar, M. 1996. The impact of El Nino Southern Oscillation on the temperature field over Canada. Atmosphere-Ocean. 34: 401-416. Shabbar, A., Bonsai, B , and Khandekar, M. 1997. Canadian precipitation patterns associated with the Southern Oscillation. Journal of Climate. 10: 3,016-3,027. Simmons, A., Wallace, J. M., and Branstator, G. W. (1983). Barotropic wave propagation and instability, and atmospheric teleconnection patterns. Journal of Atmospheric Science. 40: 1363-1392. 234 Simpson, A., and Olson, E. 1996. Mastering Access 97. Fourth Edition. USA: San Francisco. Sybexlnc. 1,140 pp. Smit, A. I. 1989. Southern Alberta's climate in the eighties place in a historical context. M. Sc. Thesis. Department of Geography. University of Calgary. 163 pp. Thrower, N. J. W. 1959. Animated cartography. The Professional Geographer. 11: 9- 12. Trenberth, K. E. 1991. General characteristics of the El Nino Southern Oscillation. In Glantz, M. H, Katz, R. W., and Nicholls, N. (eds.) Teleconnections linking worldwide climate anomalies.. Cambridge University Press. 13-42. Trenberth, K. E. 1997. The definition of El Nino. Bulletin of the American Meteorological Society. 78(12): 2,771-2,777. Trenberth, K. E., and Hoar, T. J. 1996. The 1990-95 El Nino-Southern Oscillation event: Longest on record. Geophysical Research Letters. 23: 57. Trenberth, K. E., and Hoar, T. J. 1997. El Nino and climate change. Geophysical Research Letters. 24: 3,057-3,060. Utts, J. M. 1996. Seeing through statistics. Duxbury Press. 451pp. Walker, G. T. 1923. Correlation in seasonal variations of weather. VIII. A preliminary study of world weather. Memoirs of the Indian Meteorological Department. 24: 75-131. Wallace, J. M., and Gutzler, D. S. 1981. Teleconnections in geopotential height field during the Northern Hemisphere winter. Monthly Weather Review. 109: 784- 812. Wang, B. 1995. Interdecadal changes in El Nino onset in the last four decades. Journal of Climate. 8: 267-285. Weber, C. R. and Buttenfield, P. 1993. A cartographic animation of average yearly surface temperatures for the 48 contiguous United States: 1897-1986. Cartography and geographic information systems. 20: 141-150. 235 Webster, P. J. (1982). Seasonality in the local and remote atmospheric response to sea surface temperature anomalies. Journal of Atmospheric Sciences. 39: 41-52. Wilks, D. S. 1995. Statistical Methods in the Atmospheric Sciences. An Introduction. New York: Academic Press. 456 pp. Wolter, K. 1987. The Southern Oscilllation in surface circulation and climate over the Atlantic, eastern Pacific, and Indian Oceans, as captured by cluster analysis. Journal of Climatology and Applied Meteorology. 26: 540-558 Wolter, K., and Timlin, M. S. 1998. Measuring the strength of ENSO events: How does the 1997/98 rank? Quaternary Journal of the Royal Meteorological Society. 125: 315-324. Wolter, K., and Timlin, M. S. 1993. Monitoring ENSO in COADS with a seasonally adjusted principal component index. In: Proceedings of the 17th Climate Diagnostics Workshop. Norman. Oklahoma, pp. 52-57. Wood, M, and Brodlie, K. 1994. ViSC and GIS: Some fundamental considerations. In Visualization in Geographic Information Systems. Hearnshaw., H. M., and Unwin, D. J. (eds). New York: John Wiley & Sons. pp. 3-8. Woodruff, SD., Lubker, S.J, Wolter, K., Worley, S.J., and Elms, J.D., 1993. Comprehensive Ocean-Atmosphere Data Set (COADS) Release la: 1980-92. Earth System Monitor. 4: 1-8. National Climatic Data Center (NCDC) homepage: http://www.ncdc.noaa.gov/. World Meteorological Organization (WMO). 1998a. 1998 Global surface temperature - highest by a wide margin according to WMO. Annual statement on global change. Geneva. December 17, 1998. Report #626. World Meteorological Organization (WMO). 1998b. El Nino contributed to record global warmth in 1997 according to WMO. Annual statement on global climate. Geneva. January 20, 1998. Report #610. Wright, P. B. 1986. Precursors of the Southern Oscillation. Journal of Climatology. 6: 17-30. Yarnal, B. 1985. Extratropical teleconnections associated with ENSO events. Progress in Physical Geography. 9: 315-352. Yarnal, B. 1993. Synoptic Climatology in Environmental Analysis. A primer. Belhaven Press. London. 191 pp. 236 Yarnal, B., and Diaz, H. F. 1986. Relationships between extremes of the Southern Oscillation and the winter climate of the Anglo-American Pacific coast. Journal of Climatology. 6: 197-219. Zeiler, M. 1999. Modeling our world. The ESRI guide to geodatabase design. ESRI Press. Redlands. CA. 199 pp.