projected changes in arctic summer storm-track activity by
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Projected changes in Arctic summer storm-track activity by 6
CMIP3 climate models 7
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Kazuaki Nishii* ([email protected]) 10
Hisashi Nakamura* ([email protected]) 11
Research Center for Advanced Science and Technology, University of Tokyo. 12
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Yvan J. Orsolini ([email protected]) 14
Norwegian Institute for Air Research, Kjeller, Norway 15
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Corresponding author address: Kazuaki Nishii, Research Center for Advanced Science and Technology, 19
University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan. 20
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Abstract 23
Model reproducibility and future projection of summertime storm-track activity in the 24
Arctic and associated climate components are investigated on the basis of Coupled 25
Model Intercomparison Project Phase 3 (CMIP3) climate models. Most of the models 26
underestimate storm-track activity over the Arctic Ocean measured locally as the 27
variance of subweekly SLP fluctuations, and its large inter-model diversity is related to 28
that of the lower-tropospheric westerlies in the Arctic region and the storm-track 29
activity over the entire extratropics. As a multi-model mean, the CMIP3 models project 30
the enhancement of storm-track activity over the Arctic Ocean off the eastern Siberian 31
and Alaskan coasts, the region called the Arctic Ocean Cyclone Maximum (AOCM), in 32
association with the strengthening of the westerlies projected in the warmed climate. 33
This intensifying storm-track activity is likely due to enhancing land-sea contrast in 34
surface air temperature (SAT) across the Siberian coast, which reflects greater surface 35
warming over the Siberian continent with increasing surface sensible heat flux and 36
slower warming over the Arctic Ocean with reduction in sea ice cover. Although the 37
future projections of these variables exhibit large inter-model variability, their model 38
biases are nevertheless mutually correlated significantly in the same manner as their 39
multi-model means are. 40
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1. Introduction 42
The Arctic has been undergoing rapid climatic changes with warming at a rate twice 43
as fast as that of the global average in the annual mean temperature, as a result of polar 44
amplification (e.g., Screen and Simmonds 2010). In the sub-Arctic region, surface air 45
temperature (SAT), upper ocean temperature and soil temperature have also been 46
undergoing prominent warming in late summer and early autumn, when the Arctic sea 47
ice extent has recently exhibited the largest decline in the year (Comiso 2003, 2012). It 48
is hence of great importance to deepen our understanding of various components of the 49
coupled ice-ocean-atmosphere system over the Arctic and reproduce them in climate 50
models not only for winter but also for summer. 51
Migratory cyclones and anticyclones constitute a key component of the extratropical 52
atmospheric circulation, as they systematically transport heat and moisture poleward. 53
Those atmospheric disturbances tend to organize themselves into zonally elongated 54
regions, called “storm tracks”. Ulbrich et al. (2008) found an enhancing tendency in 55
wintertime midlatitude storm-track activity in future projections by climate models that 56
participated in the World Climate Research Programme’s (WCRP’s) Coupled Model 57
Intercomparison Project Phase 3 (CMIP3) multi-model dataset (Meehl et al. 2007). 58
Recently, Woollings et al. (2012) have demonstrated that uncertainties in the future 59
projections for the North Atlantic wintertime storm-track activity by the CMIP3 models 60
are linked to those in the Atlantic Meridional Overturning Circulation that changes 61
lower-tropospheric baroclinicity. Thus far, studies of storm tracks under the changing 62
climate have focused mostly on midlatitude storm tracks in the cold season. 63
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Two noteworthy exceptions are Bengtsson et al. (2006) and Orsolini and Sorteberg 64
(2009), who focused on a particular storm track that forms only in summer over 65
Northern Eurasia and the Beaufort Sea in the Arctic, termed the Arctic Ocean Cyclone 66
Maximum (AOCM; Serreze and Barrett 2008), by applying Lagrangian cyclone 67
tracking to lower-tropospheric (850-hPa) vorticity obtained from the ECHAM and 68
BCM climate models, respectively. Bengtsson et al. (2006) found a future increase in 69
summertime storm activity over the Arctic. Likewise, Orsolini and Sorteberg (2009) 70
found a future increase in the number of storms over the Arctic and along the Russian 71
Arctic coast in particular. They pointed out that the enhanced storminess is associated 72
with enhanced meridional temperature gradient between the Arctic Ocean and the 73
warmed Eurasian continent and with the enhanced subpolar westerlies as well. Figure 7 74
of Lang and Waugh (2010) hints a slight increasing tendency as multi-model ensemble 75
mean (MEM) in summertime cyclones along the AOCM in future. 76
Thus far, no such systematic multi-model comparison as Woollings et al. (2012), 77
however, has been carried out to assess relationship between model reproducibility and 78
future projection of the climatological activity of the Arctic summer storm track, 79
focusing on its linkage with the background thermal structure and mean atmospheric 80
circulation that can yield inter-model diversity in the storm-track activity. The present 81
study presents the first of such an assessment as above, including benchmarking against 82
a global atmospheric reanalysis dataset. 83
2. Methodology 84
The Japanese 25-year Reanalysis (JRA-25; Onogi et al. 2007) from 1979 through 85
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1998 is used for the benchmarking. We also use output from 20 models in the CMIP3 86
multi-model dataset for experiments under the current climatic condition (20C3M) and 87
a future climatic scenario (SRES-A1B). Daily-mean sea-level pressure (SLP) fields of 88
these models are available at the web site of the Program for Climate Model Diagnosis 89
and Intercomparison (PCMDI). The climatology for the summer season (June, July and 90
August) has been defined for the 17-year period from 1982 to 1998, based separately on 91
the JRA-25 and 20C3M data. The corresponding 17-year climatology for the 92
SRES-A1B simulation has been defined for the period from 2082 to 2098 for all the 93
models except FGOALS-g1.0, for which daily-mean fields are available only for the 94
period 2082-2084. 95
Variance of subweekly SLP fluctuations obtained through 8-day high-pass filtering is 96
evaluated as a local measure of storm-track activity as in Ulbrich et al. (2008) and 97
Woollings et al. (2012). This measure is qualitatively consistent with 850-hPa poleward 98
eddy heat flux associated with subweekly disturbances that is used, for example, by 99
Nakamura et al. (2002) for reanalysis data. In fact, we have confirmed that qualitatively 100
the same results are obtained if the poleward eddy heat flux is used as a measure of 101
local storm-track activity. The usage of this Eulerian measure enables us to evaluate 102
storm-track activity in the CMIP3 model data whose 6-hourly fields necessary for the 103
Lagrangian cyclone tracking are not available (Ulbrich et al. 2008). Thus our analysis 104
cannot treat the intensity and number of individual cyclones but can measure the 105
climatological-mean local amplitude of subweekly disturbances. 106
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3. Results 108
In summer the Arctic Ocean and its surroundings are characterized by the deep 109
westerlies in both the upper and lower troposphere (Supplement 1a and Fig. 1a). Unlike 110
in the major midlatitude storm tracks, collocation is not necessarily obvious between the 111
local axes of the low-level westerlies and storm track as measured by the subweekly 112
SLP variance based on the reanalysis data (Fig. 1b). In fact, the strongest SLP variance 113
is observed along the western Siberian coast, where the mean westerlies are relatively 114
weak. Nevertheless, to the downstream of this variance maximum, a well-defined band 115
of local maxima of the variance is observed from the central Siberian coast to the 116
maritime domain off eastern Siberia and Alaska (Fig. 1b), which is along but slightly to 117
the north of a band of local westerly maxima (Fig. 1a). This “storm track” defined as a 118
band of local SLP variance maxima roughly corresponds to the AOCM (marked by red 119
lines in Fig. 1), defined as the band of maxima of track density and intensity of 120
migratory cyclones identified through the Lagrangian tracking method, as in Figs. 1a 121
and 2a of Orsolini and Sorteberg (2009). Compared to their domain, however, our 122
AOCM domain indicated in Fig. 1 extends slightly southward in such a manner that a 123
particular region is included where significant future enhancement of storm-track 124
activity is projected as shown in Fig. 3a. 125
Compared to the “observations” based on the JRA25 reanalysis data, the storm-track 126
activity over the Arctic is generally much weaker in the CMIP3 models (Fig. 1c), and so 127
are the lower-tropospheric westerlies (Supplement 1b). In particular, the SLP variance 128
as a MEM exhibits no well-defined maximum that corresponds to the AOCM (Fig. 1c). 129
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In fact, most of the CMIP3 models have negative biases both in the storm-track activity 130
and low-level westerlies averaged over the AOCM, as indicated in their scatter plot in 131
Fig. 2a. The negative model biases of the two variables averaged within the AOCM are 132
large enough to dominate root mean square errors (RMSE) against the JRA25 data 133
defined for the AOCM. Thus metrics for model reproducibility of the two variables 134
based on the RMSE are anti-correlated with the area-averaged biases shown in Fig. 2. 135
The scatter plot in Fig. 2a also indicates large inter-model diversity in these two 136
variables, and their model biases exhibit positive correlation that exceeds the 5% 137
significance level. In fact, those models with strong negative biases in the storm-track 138
activity over the AOCM tend to strongly underestimate the lower-tropospheric 139
westerlies locally, while other models can simulate local enhancement both in the 140
storm-track activity and the westerlies around the AOCM (Suppl. Fig. 2). It is 141
noteworthy that the local storm-track activity within the AOCM shows positive 142
inter-model correlation with that activity (Fig. 2b) and the lower-tropospheric westerlies 143
(not shown) over the entire polar and subpolar regions and even in the mid-latitude 144
Atlantic, although their causal-relationship is hard to clarify. 145
As a MEM, the CMIP3 models project future enhancement of the storm-track activity 146
around the AOCM (Fig. 3a), as consistent with previous works based on individual 147
models (Bengtsson et al. 2006; Orsolini and Sorteberg 2009). This projected 148
enhancement of the storm-track activity is dynamically consistent with strengthening of 149
both the lower-tropospheric westerlies and meridional gradient in surface air 150
temperature (SAT) across the Siberian coast (Figs. 3b and 3c, respectively), as 151
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migratory cyclones and anticyclones generally develop to relax the meridional 152
temperature gradient while translating westerly momentum downward. As discussed in 153
detail later, the strengthening of meridional SAT gradient can be attributed to the 154
projected inhomogeneous surface warming that is greater over the continents than over 155
the Arctic Ocean. While these projected enhancements in the storm-track activity, 156
westerlies and land-sea thermal contrast are simulated coherently in most of the CMIP3 157
models, their inter-model variability is pronounced as indicated in a scatter plot (Fig. 158
4a). In a dynamically consistent manner, those models that project stronger (weaker) 159
enhancement of the meridional SAT gradient across the eastern Siberian coast tend to 160
project stronger (weaker) intensification of the Arctic storm-track activity (red) and the 161
sub-polar low-level westerlies (blue) both over the AOCM (Fig. 4a). The overall 162
tendency can be confirmed in inter-model regression maps of the projected changes in 163
those three variables with the change in the AOCM storm-track activity (Fig. 5). Again, 164
the projected future change in the AOCM storm-track activity is correlated positively 165
with those in the storm-track activity, 850-hPa westerlies and meridional SAT gradient 166
along the Siberian and Alaskan coasts, exceeding the 5% significance level. 167
The meridional SAT gradient across the Siberian coast is pronounced in summer 168
between the warmer Siberian continent heated by insolation to the south and the cooler 169
Arctic Ocean to the north. Consistently, the future changes in the SAT gradient across 170
the Siberian coast projected by the CMIP3 models exhibit significant positive (+0.59) 171
and negative (–0.43) correlation with the corresponding SAT changes over Siberia and 172
the Arctic Ocean off Siberia, respectively (Fig. 4b). Furthermore, the future SAT 173
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changes over Siberia exhibit significant positive correlation (+0.65) with those in local 174
surface sensible heat flux (SHF) (Fig. 4c), while the future SAT changes off the 175
Siberian coast exhibit significant negative correlation (–0.55) with those in local sea ice 176
concentration (Fig. 4d). Presumably, the positive correlation reflects large inter-model 177
diversity in the model projected thermal and hydrological changes that arise from 178
uncertainties in model representation of land surface processes. The negative correlation 179
presumably reflects large inter-model diversity in projected ice concentration changes 180
owing to uncertainties in representing ice-related processes in the CMIP3 models. Our 181
analysis suggests that the future projection of the SAT gradient across the Siberian coast 182
by a given model is determined in balance between future projections of surface SHF 183
over Siberia and sea ice loss over the Arctic Ocean. 184
4. Discussions 185
As shown in Figs. 1c and 2a and Supplement 2 as well, only a few models are found 186
to reproduce surface storm-track activity around the AOCM reasonably well in the 187
20C3M experiment, under large inter-model diversity among the CMIP3 models. In 188
contrast to the projected future changes, model biases in the storm-track activity in the 189
20C3M experiment exhibit no significant correlation with those of the meridional SAT 190
gradient across the Siberian coast (not shown). Likewise, correlation is found 191
insignificant in summertime interannual variability between the AOCM storm-track 192
activity and the local meridional SAT gradient based on the JRA-25 data (not shown). 193
Further analysis is required to determine what factor in the background state controls 194
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the summertime storm-track activity and its model biases in the Arctic. 195
Consistently with Zhang et al. (2004), interannual variability observed in the AOCM 196
storm-track activity is negatively correlated with SLP anomaly in summer over the 197
entire Arctic Ocean (Fig. 6a), whose pattern is similar to the summertime Arctic 198
Oscillation (AO; e.g., Ogi et al. 2004). In the particular phase of the AO with negative 199
SLP anomaly, the Beaufort high tends to weaken with reduced sea ice cover off the 200
Canadian, Alaskan and eastern Siberian coasts in September (not shown), which is 201
consistent with findings of Screen et al. (2011). Interestingly, the inter-model diversity 202
of the AOCM storm-track activity in the 20C3M simulation (Fig. 6b) and in the 203
projected future change (Fig. 6c) is correlated negatively with that of the SLP over the 204
Arctic Ocean and positively with the lower-tropospheric westerlies along the Arctic 205
coast (not shown) but not with that of sea ice cover over the Arctic Ocean (not shown). 206
This result suggests that the model biases and future projection of the sea ice may not be 207
explained by influence of atmospheric dynamical processes. 208
5. Concluding remarks 209
In the present study, we have found that most of the CMIP3 models have negative 210
biases in summertime storm-track activity and lower-tropospheric westerlies around the 211
Arctic Ocean and inter-model diversity between these two variables is mutually 212
correlated. We have also found that future enhancement of summertime storm-track 213
activity in the AOCM projected as a MEM by the CMIP3 models tends to be linked to 214
that of the land-sea meridional SAT gradient across the Siberian coast, the latter of 215
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which is accounted for mainly by greater surface warming over Siberia than over the 216
Arctic Ocean. We have further found fairly large inter-model diversity in the projected 217
storm-track activity to be correlated with those of the meridional SAT gradient 218
associated with the surface differential warming. Our results suggest that more reliable 219
projection of the summertime storm-track activity in the Arctic by climate models 220
requires further improvement in model representation of various processes involved in 221
the heat and moisture budgets at the land surface and physical, kinematic and 222
thermodynamic processes of sea ice. 223
Acknowledgments 224
KN and HN are supported in part by the Japanese Ministry of Environment through the 225
Environment Research and Technology Development Fund A1201 and by Japanese 226
Ministry of Education, Culture, Sports, Science and Technology (MEXT) through a 227
Grant-in-Aid for Scientific Research in Innovative Areas 2205. KN is supported by 228
MEXT also through the GRENE Arctic Climate Change Research Project. YO is 229
supported by the Norwegian Research Council East Asian DecCen Project (193690). 230
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A list of Figure captions 278
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Figure 1. (a) JJA-mean climatology of 850-hPa zonal wind velocity (m/s; contoured for 280
every 1, with shading as indicated below the panel) based on the JRA-25 reanalysis data. 281
Northward of 60°N is shown. (b) As in (a), but for variance of subweekly SLP 282
fluctuations (hPa2). (c) As in (b), but for multi-model ensemble mean of the 20C3M 283
experiment with the CMIP3 models. Red lines indicate the AOCM (70°N-85°N, 284
150°E-210°E). 285
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Figure 2. (a) Scatter plot showing inter-model correlation between JJA-mean subweekly 287
SLP variance and 850-hPa westerlies both averaged over the AOCM (70°N-85°N, 288
150°E-210°E) as the climatological statistics based on the 20C3M experiment. 289
Alphabets designate individual models and “JRA” the reanalysis data. (b) Map of local 290
inter-model regression of subweekly SLP variance (contour: hPa2) against the same 291
variance but averaged locally within the AOCM (marked with red lines), plotted for the 292
extratropical Northern Hemisphere (north of 30°N). Shading is for significance levels 293
estimated by the t-statistic (negative significance levels corresponding to the negative 294
t-values). The 60°N circle is indicated by the blue line. 295
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Figure 3. (a) Future multi-model mean change in JJA climatology of subweekly SLP 297
variance (contoured for every ±0.2 hPa2), projected as the difference between the 298
SRES-A1B and 20C3M simulations. Positive values indicate enhancement. Shading 299
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indicates significance levels estimated by the t-statistic. AOCM is indicated with red 300
lines. (b) As in (a), but for JJA-mean 850hPa westerlies (m/s). (c) As in (a), but for 301
meridional gradient of surface air temperature with sign reversed (K/1000km). 302
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Figure 4 (a) Scatter plots showing inter-model correlations between JJA-mean future 304
changes in meridional surface air temperature (SAT) gradient (K/1000km) across the 305
Siberian coast (70-75°N, 90°E-180°) and those in storm-track activity (subweekly SLP 306
variance) (red; hPa2) and 850-hPa westerlies (U850) (blue; m/s), both averaged within 307
the AOCM. Their correlation coefficients are +0.40 and +0.41, respectively. (b) As in 308
(a), but for the corresponding future changes in the SAT gradient (K/1000km) across 309
the Siberian coast against those in SAT (K) averaged over Siberia (blue; 60°N-70°N, 310
90°E-180°) and SAT over the Arctic Ocean off Siberia (red; 75°N-85°N, 90°E-180°). 311
Their correlations are +0.59 and –0.43, respectively. (c) As in (a), but the corresponding 312
future changes in surface sensible heat flux (W m-2) against SAT (K) both averaged 313
over Siberia (60°N-70°N, 90°E-180°). Their correlation is +0.63. (d) As in (a), but for 314
the corresponding future changes in sea ice concentration (%) against SAT (K) both 315
averaged over the Arctic Ocean off Siberia. Their correlation is –0.55. Note that 316
correlation of +0.38 and +0.45 corresponds to the 10% and 5% significance levels, 317
respectively, for 20 independent samples. 318
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Figure 5. (a) Map of local inter-model regression of the future change of JJA-mean 320
subweekly SLP variance (contour: every 1 hPa2) over the domain north of 60°N with 321
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that averaged within the AOCM (marked with red lines). Shading indicates significance 322
levels estimated by the t-statistic. (b, c) Same as in (a), but for JJA-mean 850-hPa 323
westerlies (contour: every 0.2 m/s) and meridional SAT gradient with sign reversed 324
(K/1000km), respectively, with the SLP variance within the AOCM. 325
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Figure 6 (a) Map of local regression of JJA-mean SLP (hPa) with JJA subweekly SLP 327
variance averaged within the AOCM (marked by red lines), both based on the JRA-25 328
data. (b, c) As in (a), but for inter-model diversity within the corresponding statistics 329
based on the 20C3M simulations and SRES-A1B future projection, respectively. 330
Shading indicates significance levels estimated by the t-statistic. 331
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Figure 1. (a) JJA-mean climatology of 850-hPa zonal wind velocity (m/s; contoured for 342
every 1, with shading as indicated below the panel) based on the JRA-25 reanalysis data. 343
Northward of 60°N is shown. (b) As in (a), but for variance of subweekly SLP 344
fluctuations (hPa2). (c) As in (b), but for multi-model ensemble mean of the 20C3M 345
experiment with the CMIP3 models. Red lines indicate the AOCM (70°N-85°N, 346
150°E-210°E). 347
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353 Figure 2. (a) Scatter plot showing inter-model correlation between JJA-mean subweekly 354
SLP variance and 850-hPa westerlies both averaged over the AOCM (70°N-85°N, 355
150°E-210°E) as the climatological statistics based on the 20C3M experiment. 356
Alphabets designate individual models and “JRA” the reanalysis data. (b) Map of local 357
inter-model regression of subweekly SLP variance (contour: hPa2) against the same 358
variance but averaged locally within the AOCM (marked with red lines), plotted for the 359
extratropical Northern Hemisphere (north of 30°N). Shading is for significance levels 360
estimated by the t-statistic (negative significance levels corresponding to the negative 361
t-values). The 60°N circle is indicated by the blue line. 362
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Figure 3. (a) Future multi-model mean change in JJA climatology of subweekly SLP 368
variance (contoured for every ±0.2 hPa2), projected as the difference between the 369
SRES-A1B and 20C3M simulations. Positive values indicate enhancement. Shading 370
indicates significance levels estimated by the t-statistic. AOCM is indicated with red 371
lines. (b) As in (a), but for JJA-mean 850hPa westerlies (m/s). (c) As in (a), but for 372
meridional gradient of surface air temperature with sign reversed (K/1000km). 373
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375 Figure 4 (a) Scatter plots showing inter-model correlations between JJA-mean future 376 changes in meridional surface air temperature (SAT) gradient (K/1000km) across the 377 Siberian coast (70-75°N, 90°E-180°) and those in storm-track activity (subweekly SLP 378 variance) (red; hPa2) and 850-hPa westerlies (U850) (blue; m/s), both averaged within 379 the AOCM. Their correlation coefficients are +0.40 and +0.41, respectively. (b) As in 380 (a), but for the corresponding future changes in the SAT gradient (K/1000km) across 381 the Siberian coast against those in SAT (K) averaged over Siberia (blue; 60°N-70°N, 382 90°E-180°) and SAT over the Arctic Ocean off Siberia (red; 75°N-85°N, 90°E-180°). 383 Their correlations are +0.59 and –0.43, respectively. (c) As in (a), but the corresponding 384 future changes in surface sensible heat flux (W m-2) against SAT (K) both averaged 385 over Siberia (60°N-70°N, 90°E-180°). Their correlation is +0.63. (d) As in (a), but for 386 the corresponding future changes in sea ice concentration (%) against SAT (K) both 387 averaged over the Arctic Ocean off Siberia. Their correlation is –0.55. Note that 388 correlation of +0.38 and +0.45 corresponds to the 10% and 5% significance levels, 389 respectively, for 20 independent samples. 390
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392
Figure 5. (a) Map of local inter-model regression of the future change of JJA-mean 393
subweekly SLP variance (contour: every 1 hPa2) over the domain north of 60°N with 394
that averaged within the AOCM (marked with red lines). Shading indicates significance 395
levels estimated by the t-statistic. (b, c) Same as in (a), but for JJA-mean 850-hPa 396
westerlies (contour: every 0.2 m/s) and meridional SAT gradient with sign reversed 397
(K/1000km), respectively, with the SLP variance within the AOCM. 398
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Figure 6 (a) Map of local regression of JJA-mean SLP (hPa) with JJA subweekly SLP 406
variance averaged within the AOCM (marked by red lines), both based on the JRA-25 407
data. (b, c) As in (a), but for inter-model diversity within the corresponding statistics 408
based on the 20C3M simulations and SRES-A1B future projection, respectively. 409
Shading indicates significance levels estimated by the t-statistic. 410
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Supplemental Material 422
423 Supplement 1. (a) JJA-mean climatology for 300-hPa westerlies based on the JRA-25 424 reanalysis (m/s), plotted for the domain north of 40°N. The blue circle represents 60°N. 425 (b) JJA-mean climatology for 850-hPa westerlies (m/s) as the CMIP3 multi-model 426 mean based on the 20C3M simulation. 427
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429 Supplement 2. As in Fig. 1b, but for JJA-mean storm-track activity of individual models 430 based on the 20C3M simulation. A Label of each panel corresponds to A: 431 BCCR-BCM2.0, B: CGCM3.1(T47), C: CGCM3.1(T63), D: CNRM-CM3.0, E: 432 CSIRO-Mk3.0, F: CSIRO-Mk3.5, G: GFDL_CM2.0, H: GFDL-CM2.1, I: GISS_AOM, 433 J: GISS-ER, K: FGOALS-g1.0, L: INGV-SXG, M: INM-CM3.0, N: IPSL-CM4, O: 434 MIROC3.2(hires), P: MIROC3.2 (medres), Q: ECHO-G, R: ECHAM5/MPI-OM, S: 435 MRI-CGCM2.3.2, T: CCSM3. 436