Northern Hemisphere blocking climatology asswson/papers/Dunn-Sigouin-Son-JGR... · 2012-08-08 · 64 climatology and document the multi-model projection of blocking frequency and

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/,

Northern Hemisphere blocking climatology as1

simulated by the CMIP5 models2

Etienne Dunn-Sigouin,1

Seok-Woo Son1

Etienne Dunn-Sigouin, Departement of Atmospheric and Oceanic Sciences, McGill University,

805 rue Sherbrooke Ouest, Montreal, Quebec, Canada. ([email protected])

Seok-Woo Son, Departement of Atmospheric and Oceanic Sciences, McGill University, 805 rue

Sherbrooke Ouest, Montreal, Quebec, Canada. ([email protected])

1Departement of Atmospheric and

Oceanic Sciences, McGill University,

Montreal, Quebec, Canada.

D R A F T August 9, 2012, 5:20am D R A F T

X - 2 DUNN-SIGOUIN AND SON: CMIP5 BLOCKING CLIMATOLOGY

Abstract. Northern Hemisphere (NH) blocking climatology is examined3

using a subset of climate models participating in the Coupled Model Inter-4

Comparison Project phase 5 (CMIP5). Both historical and Representative5

Concentration Pathway (RCP) 8.5 integrations are analzyed to evaluate the6

performance of the CMIP5 models and to identify possible changes in NH7

blocking frequency and duration in a warmer climate. Comparison to reanal-8

ysis data reveals that CMIP5 models can reproduce the NH blocking clima-9

tology reasonably well although the frequency of Euro-Atlantic blockings,10

particularly those with relatively short duration, is underestimated in most11

models during the cold season. In several models, overestimation of the Pa-12

cific blocking frequency is also evident throughout the year. In comparison13

to historical integrations, RCP 8.5 integrations show statistically significant14

decrease in blocking frequency over both the north Pacific and north Atlantic15

regions, with a weak hint of increasing blocking frequency over western Rus-16

sia. This change is clear from autumn to winter, and dominated by changes17

in relatively short-lived blocking events.18


DUNN-SIGOUIN AND SON: CMIP5 BLOCKING CLIMATOLOGY X - 3

1. Introduction

Atmospheric blocking is one of the most dramatic examples of extratropical low-19

frequency variability. Traditionally it refers to a synoptic-scale high pressure system, often20

accompanied by low pressure system(s) at lower latitudes, that remains quasi-stationary21

for days to weeks, interrupting the zonal flow (e.g., Rex 1950). Since blocking highs are22

persistent and quasi-stationary by definition, they affect surface weather and climate in23

a major way. For instance, a blocking high often causes a significant increase in surface24

temperature beneath it and enhanced storm activity around it (e.g., Trigo et al. 2004).25

Striking examples are the 2003 European and 2010 Russian heat waves, that resulted from26

exceptionally long-lasting blocking events. Both of them lead to record breaking surface27

air temperatures and significant increases in mortality rates [Black et al., 2004; Dole et al.,28

2011].29

The crucial role that blocking plays in surface weather (especially in extreme weather)30

has underlined the need for the reliable simulation of blocking events by climate models.31

It is however well documented that climate models often underestimate Northern Hemi-32

sphere (NH) blocking frequency, especially over the North Atlantic and Europe (e.g.,33

D’Andrea et al. 1998; Doblas-Reyes et al. 2002; Scaife et al. 2010; Barriopedro et al.34

2010). While detailed causality is still an active area of research, this underestimation35

has been attributed to the limited model resolution, misrepresentation of surface bound-36

ary conditions and uncertainties in physical parametrizations that lead to the biases in37

the time-mean flow and high-frequency eddies (e.g., Matsueda et al. 2009; Scaife et al.38

2010, 2011).39



Recent studies have shown that the frequency of NH blocking events may decrease in40

the future, in response to global warming. By analyzing CMIP3 models, Barnes and41

Hartmann [2010] and Barnes et al. [2011] documented a statistically significant decrease42

in blocking frequency over both north Pacific and north Atlantic in the 21st century. They43

however found no evidence in blocking duration change. While a similar frequency change44

is also found in model sensitivity tests [Matsueda et al., 2009; Sillmann and Croci-Maspoli,45

2009], different studies show slightly different results in duration change. For example,46

Sillmann and Croci-Maspoli [2009] showed a possible increase in maximum blocking du-47

ration over the Euro-Atlantic region, whereas Matsueda et al. [2009] showed a possible48

reduction in long-lived blocking events.49

It should be noted that, despite of the importance of blocking events, only few studies50

(as listed above) have documented possible changes in blocking activities in a warmer51

climate. As a matter of fact, future projection of NH blocking highs was not commented52

in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report53

(AR4) (Solomon et al. 2007) which is largely based on the CMIP3 data. This lack of54

documentation partly results from availability of daily data. Although blocking is typically55

examined using 500-hPa geopotential height or upper-tropospheric dynamic fields such56

as potential temperature on the 2-PVU surface or upper-tropospheric PV anomalies,57

these data sets were not archived in the CMIP3 in daily or sub-daily resolution. Of the58

few studies based on CMIP3 models, Barnes et al. [2011] used zonal wind, assuming59

geostrophic balance, to characterize the NH blocking climatology in the CMIP3.60

The primary goal of this study is to examine the NH blocking climatology in the present61

and future climate as simulated by the CMIP5 models. Extending and updating previous62



studies, we evaluate state-of-the-art climate models in the context of present-day blocking63

climatology and document the multi-model projection of blocking frequency and duration64

in the 21st century. This goal is achieved by applying a hybrid blocking index [Dunn-65

Sigouin et al., 2012], that is essentially a combination of the two widely-used blocking66

indices, to daily 500-hPa geopotential height fields in both historical and RCP integrations.67

2. Data and Methodology

The model data used in this study are historical and RCP 8.5 runs from a subset of68

climate models participating in the CMIP5 [Taylor et al., 2012]. Briefly, historical runs69

are 20th-century climate integrations with all observed climate forcings. RCP 8.5 runs are70

scenario integrations with a rapid warming, specifically with an anthropogenic radiative71

forcing of 8.5 Wm−2 at 2100. All available models that archived daily geopotential height72

fields are used: i.e., a total of 17 models for historical integrations and 13 models for RCP73

8.5 integrations as of June 2012 (Table 1). If multiple realizations are available, only the74

first ensemble member is considered.75

All model output is first interpolated onto the lowest model resolution, namely 2.8◦76

latitude by 2.8◦ longitude (Table 1). Blocking statistics are then derived using this inter-77

polated data. To minimize the effect of internal variability in climatology, analyses are78

performed for a sufficiently long time period. Specifically, 40 years, 1966-2005 for histori-79

cal runs and 2060-2099 for RCP 8.5 runs, are considered. The performance of the CMIP580

models is first evaluated by comparing the blocking climatology derived from historical81

integrations to that of the National Centers for Atmospheric Prediction (NCEP) and the82

National Center for Atmospheric Research (NCAR) reanalysis (NNR, Kalnay et al. 1996).83



Future changes in blocking activities are then quantified by comparing historical runs with84

RCP 8.5 runs.85

The blocking index employed in this study is a hybrid index which combines the two86

widely-used blocking indices in a simple way [Dunn-Sigouin et al., 2012]. A contiguous87

area of blocking anomalies is first identified from the 500-hPa geopotential height field, as88

in the Dole-Gordon type blocking index [Dole and Gordon, 1983], and then a reversal of the89

meridional gradient of geopotential height, as in the Tibaldi-Molteni type blocking index90

[Tibaldi and Molteni, 1990], is evaluated on the equatorward side of the blocking anomaly91

maximum. The spatio-temporal evolution of the blocking anomalies is assured by tracking92

the amplitude (A), spatial extent (S), overlap between successive days (O) and duration93

(D). The amplitude threshold (A) is set to 1.5 standard deviation of geopotential height94

anomalies over 30◦-90◦N for a 3-month period centered at a given month. The remaining95

threshold values are set to (S)=2.5x106km2, (O)=50% and (D)=5 days, for both NNR96

and CMIP5 data. See Dunn-Sigouin et al. [2012] for the details.97

All results are shown in the multi-model mean. But, to illustrate inter-model differences98

and statistical uncertainty, individual models are also presented in the auxiliary materials.99

3. Blocking climatology

Figure 1a presents NH blocking climatology from 40-year long NNR data (see also100

Dunn-Sigouin et al. 2012). Blocking frequency is shown as the number of days per year a101

blocked area occupies each grid point. As widely documented in the literature, two active102

regions of blocking occurrence emerge: north Pacific (hereafter PA blocking) and Europe-103

northeastern Atlantic (EA blocking). The latter generally exhibits higher frequency than104

the former although the opposite is true during the summer (Fig. 2a). EA blocking also105



shows a more zonally-elongated geographical distribution than PA blocking, with a long106

tail to western Russia. In certain seasons, blocking activities over western Russia are107

separated from those over North Atlantic and western Europe (e.g., November in Fig.108

2a), and referred to as Ural blocking, .109

The blocking climatology, derived from 17 CMIP5 historical runs, is illustrated in Figs.110

1b-c. It is evident that the CMIP5 models can reproduce the overall geographical dis-111

tribution of the NH blocking activities reasonably well. Noticeable biases are however112

present in amplitude (Fig. 1c). Most of all, EA blocking frequency is underestimated by113

about 30%. This is common to all the models analyzed in this study (Fig. S1) and con-114

sistent with previous modelling studies (e.g., D’Andrea et al. 1998; Scaife et al. 2010). In115

contrast, PA blocking frequency is overestimated by the models over broad regions. While116

this bias is less robust than EA blocking bias (see shading in Fig. 1 in midlatitudes; see117

also Fig. S1), it is still significant particularly on the poleward side of the climatologi-118

cal blocking frequency maxima. A similar overestimation was also reported in a recent119

study by Dunn-Sigouin et al. [2012] as well as in a few climate models participating in120

the CMIP3 [Scaife et al., 2010].121

The above model biases in blocking frequency might be partly caused by the model122

resolution. It is well known that high-frequency eddy forcing, which is one of the most123

important maintenance mechanisms of blocking highs (e.g., Shutts 1983; Nakamura et al.124

1997), is very sensitive to the model resolution. Matsueda et al. [2009], for instance,125

presented more accurate EA blocking simulation in a higher-resolution model integration.126

However, a direct comparison between IPSL-CM5A-MR and IPSL-CM5A-LR, whose dif-127

ference is only model resolution (Table 1), shows a negligible difference in EA blocking128



frequency (Figs. S1c-d). A moderate increase in horizontal resolution instead leads to129

a slight decrease in EA blocking frequency. This result supports the finding of Scaife130

et al. [2011] that EA blocking frequency is more sensitive to surface boundary condi-131

tions than atmospheric model resolution. Matsueda et al. [2009] further indicated that132

the PA blocking frequency could be overestimated in a high-resolution model integration.133

However, no systematic relationship between PA blocking bias and model resolution is ob-134

served throught the models (Fig. S1). This is largely consistent with Dunn-Sigouin et al.135

[2012] who showed that PA blocking frequency could be overestimated even in a coarse136

resolution model integration where high-frequency eddy activities are underrepresented.137

They suggested that the biases in time-mean flow could result in model biases in blocking138

frequency.139

The seasonal cycles of blocking frequency are illustrated in Fig. 2. It presents the140

evolution of monthly-mean NH blocking frequency as a function of longitude. The number141

of blocking events are simply counted along a given longitude band from 30◦ to 90◦N for142

a given month. The longitudinal distribution of blocking frequency and its seasonality is143

reasonably well reproduced in the multi-model mean (Figs. 2a-b). Underestimation of EA144

blocking frequency is largely confined to the cold season, whereas overestimation of PA145

blocking frequency is found throughout most of the year. It is also found that, although146

the models successfully reproduce the summertime peak in PA blocking, it is delayed by147

a month from August to September.148

Figure 3 shows the number of blocking events as a function of duration. The EA and149

PA blocking events, defined over 0-90◦N and 25◦W-42◦E and 0-90◦N and 151◦E-220◦W,150

respectively, are separately illustrated along with a total number of blocking events over151



whole NH. In general, the number of blocking events exhibits an exponential decrease152

with duration (Figs. 3a-b). Comparison of CMIP5 to NNR data, however, reveals that,153

although not statistically significant, short-lived blocking events, duration shorter than 9154

days, are generally underrepresented by the models while long-lived ones are somewhat155

overrepresented (see black bars in Fig. 3c). The bias in short-lived blocking events is156

mostly due to EA blocking events (see blue bars in Fig. 3c). In most durations, PA157

blocking events show overestimation.158

To identify the possible sources of model biases in blocking frequency climatology, high159

frequency eddy fields are further examined in Fig. 4. High-frequency eddy activities are160

quantified by using the standard deviation of 500-hPa geopotential height anomalies with161

periods shorter than 7 days. While the high-frequency eddy activities are in qualitatively162

good agreement with the NNR (Figs. 4a-b), the strength of both north Atlantic and north163

Pacific storm tracks is underestimated in the CMIP5 models (Fig. 4c). This underestima-164

tion is consistent with EA blocking frequency biases (compare Figs. 1c and 4c). However,165

a similar consistency is not found with PA blocking frequency biases. This result suggests166

that the formation and maintenance mechanism(s) of blocking highs may be different over167

the two basins [Tibaldi et al., 1997; Matsueda et al., 2009; Dunn-Sigouin et al., 2012].168

4. Future changes

With model biases described above in hand, this section examines potential changes in169

blocking frequency and duration in a warmer climate. The multi-model mean climatology,170

derived from 40 years (2060-2099) of RCP 8.5 runs, are presented in Fig. 1d and compared171

with its counterpart derived from 40 years of historical runs (1966-2005) in Fig. 1e.172



Only 13 models, that have both historical and RCP 8.5 runs, are used to make a direct173

comparison.174

The geographical distribution of blocking frequency in the future climate is quite similar175

to the one in the present climate (Fig. 1d). However, blocking frequency in a warmer176

climate is slightly decreased over the northeastern Pacific and northwestern Atlantic (Fig.177

1e). While the decrease is rather weak, it is robustly found in several models (Fig. S5).178

This result agrees with previous findings [Matsueda et al., 2009; Sillmann and Croci-179

Maspoli, 2009; Barnes et al., 2011], and is consistent with high-frequency eddy changes180

which are predicted to be weakened upstream of the north Atlantic and north Pacific181

blocking regions (Figs. 4d-e).182

In contrast to blocking frequency changes over the north Pacific and north Atlantic,183

blocking frequency over eastern Europe-Russia is predicted to increase in the future.184

This increase of the so-called Ural blocking frequency is relatively weak and not quite185

robust across the models (Fig. 1e). However, a general tendency is observed in a number186

of models (Fig. S5). In some models (e.g., HadGEM2-CC), the Ural blocking frequency187

change is statistically significant and even larger than PA blocking frequency change. The188

increased high-frequency eddy activities upstream of the Ural region in a warm climate189

(Fig. 4e) further supports the potential increase in Ural blocking frequency.190

The seasonal cycle of the NH blocking activities in the RCP 8.5 runs is compared with191

their historical counterparts in Figs. 2d-e. Again, overall geographical distribution and192

seasonality are quite similar to those in the present climate. It is further found that the193

reduced blocking frequencies observed over the north Pacific and north Atlantic occur194

primarily during the fall and winter seasons, although they extend into spring over the195



north Pacific (see also Fig. S6). Figures 3d-e presents blocking frequency change as a196

function of duration. A decrease in number of blocking events is found in all durations,197

with a larger decrease in shorter-lived blocking events. Although statistically insignificant,198

this result may suggest that the mean duration of individual blocking events would slightly199

decrease in a warmer climate.200

5. Conclusions

This study examines the NH blocking climatology as simulated by the CMIP5 models.201

Both historical and RCP 8.5 runs are examined to evaluate the performance of the CMIP5202

models in comparison to NNR and to identify possible changes in blocking frequency and203

duration in a warmer climate.204

Comparison to reanalysis data reveals that the CMIP5 models can reproduce the NH205

blocking climatology reasonably well. It is however found that the EA blocking frequency206

is generally underestimated in most models during the cold seasons. This is mostly due207

to the short-lived blocking events with duration shorter than 9 days. In contrast, the208

PA blocking frequency is largely overestimated throughout the year for almost all dura-209

tions. Although this result contrasts to previous modelling studies which have typically210

documented underestimation of the blocking frequency over both the north Pacific and211

Atlantic in a moderate-resolution climate model integration (e.g., D’Andrea et al. 1998;212

Doblas-Reyes et al. 2002; Matsueda et al. 2009; Barriopedro et al. 2010), a similar result213

is documented for the latest operational model [Dunn-Sigouin et al., 2012], and for certain214

models participating in the CMIP3 [Scaife et al., 2010].215

In comparison to historical integrations, the RCP8.5 integrations show a weak hint of216

reduced blocking frequency over the north Pacific and north Atlantic especially during the217



fall and winter. This result largely confirms previous findings that are based on different218

data and different blocking indices [Matsueda et al., 2009; Sillmann and Croci-Maspoli,219

2009; Barnes and Hartmann, 2010; Barnes et al., 2011]. In contrast to north Pacific220

and north Atlantic blocking events, blocking events over eastern Europe and Russia, the221

so-called Ural blocking, are predicted to marginally increase in a warmer climate.222

Here it should be noted that the results presented in this study are largely insensitive223

to the horizontal interpolation. Although not shown, quantitatively similar results are224

found with raw data. Overall results, however, could be sensitive to the choice of blocking225

index. As an example, Fig. 5 presents the blocking climatology derived from the TM-type226

blocking index [D’Andrea et al., 1998]. While model bias and future projection of the NH227

blocking frequency are qualitatively similar to those presented in this study (compare228

Figs. 2c and 5c, and Figs. 2e and 5e), the detailed spatial distribution and seasonal229

cycles are quantitatively different. This indicates that different blocking index could lead230

to quantitatively (or even qualitatively) different results in blocking climatology and its231

relationship with regional climate systems. Further analyses are needed.232

The causes of model biases and possible reasons of future blocking changes are not233

explicitly addressed in this study. While detailed analyses are needed, preliminary results234

indicate that underestimation of blocking frequency over the Euro-Atlantic and a future235

decrease in blocking frequency over the north Atlantic and north Pacific are consistent236

with changes in upstream high-frequency eddy activities. In most CMIP5 models, high-237

frequency eddy activities are underrepresented in the present climate integrations and are238

predicted to be weakened in the future over the two basins. A similar consistency between239



high-frequency eddy activities and north Pacific blocking frequency biases, however, is not240

observed.241

Acknowledgments. We acknowledge the World Climate Research Programme’s242

Working Group on Coupled Modelling, which is responsible for CMIP, and we thank243

the climate modeling groups (listed in Table 1 of this paper) for producing and mak-244

ing available their model output. For CMIP the U.S. Department of Energy’s Program245

for Climate Model Diagnosis and Intercomparison provides coordinating support and led246

development of software infrastructure in partnership with the Global Organization for247

Earth System Science Portals.248

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Figure 1. Climatology of NH annual-mean blocking frequency: (a) NNR, (b) historical multi-

model mean, (c) historical multi-model mean - NNR, (d) RCP 8.5 multi-model mean and (e)

RCP 8.5 - historical multi-model mean. Black and colored contour intervals in (a,b,d) and (c,e)

are 4 days and 2 days, respectively. The grey contour in (c,e) denotes the zero line and shaded

areas denote differences greater than one standard deviation of individual model differences.



Figure 2. Seasonal cycle of the NH blocking frequency as a function of longitude: (a) NNR,

(b) historical multi-model mean, (c) historical multi-model mean - NNR, (d) RCP 8.5 multi-

model mean and (e) RCP 8.5 - historical multi-model mean. Black and colored contour intervals

in (a,b,d) and (c,e) are 1 day and 0.5 days per month, respectively. the grey contour in (c,e)

denotes the zero line and shaded areas denote differences greater than one standard deviation of

individual model differences.



Figure 3. Number of blocking events as a function of duration: (a) NNR, (b) historical multi-

model mean, (c) historical multi-model mean - NNR, (d) RCP 8.5 multi-model mean and (e)

RCP 8.5 - historical multi-model mean. Black, blue and red bars denote blocking events over the

NH, the EA and PA sectors, respectively. Shaded bars in (c,e) denote differences greater than

one standard deviation of individual model differences.



Figure 4. Climatology of annual-mean standard deviation of 500-hPa high-frequency eddies

with periods shorter than 7 days: (a) NNR, (b) historical multi-model mean, (c) historical multi-

model mean - NNR, (d) RCP 8.5 multi-model mean and (e) RCP 8.5 - historical multi-model

mean. Black and colored contour intervals in (a,b,d) and (c,e) are 10 m and 2 m, respectively.

The grey contour in (c,e) denotes the zero line and shaded areas denote differences greater than

one standard deviation of individual model differences.



Figure 5. Same as Fig. 2 but for the blocking frequency derived from the blocking index used

in D’Andrea et al. (1998).



Table 1. Description of CMIP5 models used in this analysis. Resolutions refer to atmospheric

model resolution and horizontal resolution is approximate for spectral models.

Model Group, country Horizontal res. (lat. x lon.) Historicalrun

RCP8.5run

BNU-ESM BNU, China T42L26 (2.8◦x2.8◦) yes yesCanESM2 CCCma, Canada T63L35 (2.8◦x2.8◦) yes yesCMCC-CM CMCC, Italy (0.75◦x0.75◦) yes yesHadGEM2-CC MOHC, UK N96L60 (1.875◦x1.25◦) yes yesIPSL-CM5A-LR IPSL, France (1.875◦x3.75◦) yes yesIPSL-CM5A-MR IPSL, France (1.25◦x2.5◦) yes yesIPSL-CM5B-LR IPSL, France (1.875◦x3.75◦) yes yesMIROC5 CCSR, Japan T85L40 (1.4◦x1.4◦) yes yesMIROC-ESM-CHEM CCSR, Japan T42L80 (2.8◦x2.8◦) yes yesMPI-ESM-LR MPI-M, Germany T63L47 (1.875◦x1.875◦) yes yesMPI-ESM-MR MPI-M, Germany T63L95 (1.875◦x1.875◦) yes yesMRI-CGCM3 MRI, Japan TL159L48 (1.125◦x1.125◦) yes yesNorESM1-M NCC, Norway f19L26 (2.5◦x1.875◦) yes yesBCC-CSM1-1 BCC, China T42L26 (2.8◦x2.8◦) yes noCCSM4 NCAR, US (0.9◦x1.25◦) yes noCNRM-CM5 CNRM, France TL127L31 (1.4◦x1.4◦) yes noMIROC-ESM CCSR, Japan T42L80 (2.8◦x2.8◦) yes no


Northern Hemisphere blocking climatology assimulated by the CMIP5 models

(Auxiliary Material)

Etienne Dunn-Sigouin1 and Seok-Woo Son1

1. Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec,Canada

Figure S1: Same as Fig. 1c except for individual models. Contour interval is 2 days peryear and shaded areas denote statistically significant differences at the 95 percent confidencelevel using a two-tailed student t-test. Zero lines are omitted.

1

Figure S2: Same as Fig. 2c except for individual models. Contour interval is 1 day per yearand shaded areas denote statistically significant differences at the 95 percent confidence levelusing a two-tailed student t-test. Zero lines are omitted.

2

Figure S3: Same as Fig. 3c except for individual models. Shaded bars denote statisticallysignificant differences at the 95 percent confidence level using a two-tailed student t-test.

3

Figure S4: Same as Fig. 4c except for individual models. Contour interval is 2 m andshaded areas denote statistically significant differences at the 95 percent confidence levelusing a two-tailed student t-test. Zero lines are omitted.

4

Figure S5: Same as Fig. 1e except for individual models. Contour interval is 2 days peryear and shaded areas denote statistically significant differences at the 95 percent confidencelevel using a two-tailed student t-test. Zero lines are omitted.

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Figure S6: Same as Fig. 2e except for individual models. Contour interval is 1 day per yearand shaded areas denote statistically significant differences at the 95 percent confidence levelusing a two-tailed student t-test. Zero lines are omitted.

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Figure S7: Same as Fig. 3e except for individual models. Shaded bars denote statisticallysignificant differences at the 95 percent confidence level using a two-tailed student t-test.

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Figure S8: Same as Fig. 4e except for individual models. Contour interval is 2 m andshaded areas denote statistically significant differences at the 95 percent confidence levelusing a two-tailed student t-test. Zero lines are omitted.

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