1

Uncertainty in the winter tropospheric2

response to Arctic Sea ice loss: the role of3stratospheric polar vortex internal variability

45

Lantao Sun*

6

Department of Atmospheric Science, Colorado State University,

7

Fort Collins, CO

8Clara Deser and Isla Simpson

9

National Center for Atmospheric Research Climate & Global Dynamics,

10

Boulder, CO

11

Michael Sigmond

12

Canadian Centre for Climate Modelling and Analysis, Victoria, British

13

Columbia, Canada.

14
15

Submitted July 9, 2021; Revised Dec 20, 2021

16

Journal of Climate

17
18
19

* Corresponding author: Dr. Lantao Sun, Department of Atmospheric Science, Colorado

20

State University, Fort Collins, CO. Lantao.Sun@colostate.edu.

21

ABSTRACT

22

Arctic sea ice has declined rapidly over the past four decades and climate models

23

project a seasonally ice-free Arctic Ocean by the middle of this century, with attendant

24

consequences for regional climate. However, modeling studies lack consensus on how the25large-scale atmospheric circulation will respond to Arctic sea ice loss. In this study, the26

authors conduct a series of 200-member ensemble experiments with the Community

27

Atmosphere Model version 6 (CAM6) to isolate the atmospheric response to past and28

future sea ice loss following the Polar-Amplification Model Inter-comparison Project

29(PAMIP) protocol. They find that the stratospheric polar vortex response is small compared30

to internal variability, which in turn influences the signal-to-noise of the wintertime

31

tropospheric circulation response to ice loss. In particular, a strong (weak) stratospheric32

polar vortex induces a positive (negative) tropospheric Northern Annular Mode (and North

33

Atlantic Oscillation), obscuring the forced component of the tropospheric response, even34

in 100-member averages. Stratospheric internal variability is closely tied to upward wave

35

propagation from the troposphere and can be explained by linear wave interference

36

between the anomalous and climatological planetary waves. Implications for the detection

37of recent observed trends and model realism are also presented. These results highlight the38

inherent uncertainty of the large-scale tropospheric circulation response to Arctic sea ice

39

loss arising from stratospheric internal variability.

140

1. Introduction

41

Arctic sea ice has declined rapidly over the past four decades (Fetterer et al. 2017) and42

the vast majority of climate models participating in the Coupled Model Intercomparison

43

Project Phase 6 (CMIP6) project a seasonally ice-free Arctic Ocean by 2050 in all emission

44

scenarios (Notz and SIMIP community 2020). The loss of sea ice and its consequences for45

the global climate system are one of the grand science challenges of the World Climate

46

Research Programme (WCRP). Sea ice loss causes near-surface warming and increased

47

precipitation at high latitudes, especially in winter (e.g., Screen and Simmonds 2010; Deser48

et al. 2010) due to enhanced upward fluxes of heat and moisture from the ocean to the49

atmosphere. The remote response, however, is more complex in its physical mechanisms,

50

and there is less consensus among different studies (e.g., Cohen et al. 2020). In particular,

51

whether observed Arctic sea ice loss has contributed to colder winters over North America

52

and Eurasia in recent decades remains a topic of extensive debate (Overland et al. 2011;53

Cohen et al. 2013; Mori et al. 2014; Perlwitz et al. 2014; McCusker et al. 2016; Sun et al.

54

2016; Ogawa et al. 2018; Blackport et al. 2019; Dai and Song 2020), highlighting the55

challenge of distinguishing the sea ice loss effect from internal variability and the56

incomplete mechanistic understanding of the underlying processes (Barnes and Screen

572015).

58

Previous modeling studies have revealed aspects of the dynamical mechanisms

59

connecting Arctic sea ice loss to large-scale atmospheric circulation changes. For example,

60

Deser et al. (2004) suggested there is a direct and an indirect component of the winter61

atmospheric circulation response to Arctic sea ice loss. The direct component is localized

62

to the vicinity of the ice loss region and exhibits a baroclinic structure in the vertical with2

63

a surface trough and upper-level ridge. The indirect component is hemisphere in scale and64barotropic in the vertical throughout the troposphere resembling the Northern Annular

65

Mode (NAM; Thompson and Wallace 2000) or North Atlantic Oscillation (NAO; Hurrell

661995). More recently, it has been argued that Arctic sea ice loss can also cause a weakening

67of the stratospheric polar vortex, which subsequently induces a negative phase of the68

tropospheric NAM/NAO in late winter (Peings and Magnusdottir 2014; Kim et al. 2014;69

Sun et al. 2015). This “stratospheric pathway” is distinct from the “tropospheric pathway”

70(i.e., that presented in Deser et al. 2004), and has been suggested to play a dominant role71

in the inferred atmospheric circulation response to observed sea ice loss in the Barents-

72

Kara Seas (Jaiser et al. 2013; Wu and Smith 2016; Nakamura et al. 2016; Zhang et al. 2017,732018). It is also consistent with the observed increased frequency of January-February

74

weak polar vortex states in recent decades (Kretschmer et al. 2018). However, the75

stratospheric response to autumn sea ice loss and its subsequent influence on the wintertime

76

tropospheric response was found to be small compared to the concurrent effect of winter77

ice loss (Sun et al. 2015; Blackport and Screen 2019). Moreover, atmospheric models

78

forced with observed or projected future Arctic sea ice loss simulate a range of stratospheric79

polar vortex responses, including weakening (Kim et al. 2014; Nakamura et al. 2016),

80

strengthening (Cai et al. 2012; Scinocca et al. 2009; Screen et al. 2013; Sun et al. 2014;81

England et al. 2018) and no change (Dai and Song 2020). This range of responses may

82

have many contributing factors, including structural differences among models in their83

representation of the mean state (Smith et al. 2017) and eddy-mean flow feedbacks (e.g.,84

Smith et al., 2021), differences in the regional patterns of imposed sea ice loss (Peings and85

Magnusdottir 2014; Sun et al. 2015; McKenna et al. 2017; Screen 2017), and inadequate3

86

sampling of internal variability that obscures the forced response (Seviour 2017). Without

87

modeling consensus, it is difficult to interpret observed relationships between sea ice loss

88

and winter atmospheric circulation (e.g., ice loss in the Barents-Kara Seas and NAO:

89

Peings 2019; Warner et al. 2020).

90

To account for the sources of divergent stratospheric and tropospheric responses among91

different studies, there is an urgent need for coordinated model experiments. The CMIP6

92

Polar Amplification Model Intercomparison Project (PAMIP) aims to investigate the93

causes and consequences of polar amplification through a coordinated set of numerical

94

model experiments (Smith et al. 2019). So far, early comparisons have been conducted to95

address the robustness of sea ice-induced atmospheric circulation change and its underlying

96

mechanisms (Ronald et al. 2020; Smith et al. 2021).

97

As mentioned above, low signal-to-noise ratio is one major obstacle for the detection

98of an atmospheric circulation response to Arctic sea ice loss (Liang et al. 2020). While99

some common features of the atmospheric response to projected late 21st century Arctic100

sea ice loss have been found across models (Screen et al. 2018; Hay et al. 2018; Hay et al.

1012021), there are substantial discrepancies among modeling studies that consider the102

response to observed (and thus weaker) Arctic sea ice loss (e.g., Screen et al. 2014). In

103

particular, Sun et al. (2018) investigated the atmospheric circulation response to transient104

sea ice change from 1990-2090 and found that the forced signal is small compared to the105

noise from internal variability, especially in the early decades when the ice loss is more106

limited. To better isolate the signal, a useful approach is to use large ensembles of107

simulations so as to reduce the noise, in analogy with the initial-condition Large Ensemble

108

framework used in climate change studies (Kay et al. 2015; Maher et al. 2019; Deser et al.

4

109

2020). In particular, the minimum ensemble size needed to detect a statistically significant

110

atmospheric circulation response signal was found to exceed 50 in the case of observed

111

Arctic sea ice loss (Screen et al. 2014), and 200 according to newer calculations (Labe112

2020).

113

More recently, Peings et al. (2021; hereafter P21) examined the atmospheric circulation

114

response to future-minus-preindustrial Arctic sea ice loss based on simulations following

115

the PAMIP time-slice protocol with uncoupled and coupled versions of Specified

116

Chemistry-Whole Atmosphere Community Climate Model Version 4 (SC-WACCM4),117

and found significant inconsistencies among three discrete 100-member ensembles. They

118

identified tropospheric dynamics and ENSO-induced teleconnections as the main sources

119

of internal variability leading to the inconsistencies.

120

This study utilizes the PAMIP time-slice simulations with Community Atmosphere

121

Model version 6 (CAM6) to investigate the effect of Arctic sea ice loss from pre-industrial

122

to present-day, and from present-day to middle 21st century, under two different

123background SST states. Our study differs from P21 in the following ways. Firstly, we

124

explore role of stratospheric internal variability in the tropospheric response to past and125

future Arctic sea ice loss, whereas P21 focused on the role of tropospheric internal

126

variability in the response to middle 21st century – minus – preindustrial sea ice loss under127

a single background SST state. Second, we go beyond the discrete case study approach of128

P21 by conducting a random sampling bootstrapping procedure to more robustly quantify

129

the spread in tropospheric circulation responses to Arctic sea ice loss arising from internal

130

variability. Third, while P21 and our results both suggest that it is challenging to make131

causal inferences based on analysis of observations alone due to confounding of internal

5

132

variability in short (< 50 years) records, our results explicitly highlight the stratospheric133

pathway which has received much attention in the observational literature.

134

The rest of this paper is organized as follows. Section 2 contains a description of the135

model and the PAMIP experimental protocol. Section 3 presents results on the relative

136

magnitudes of the stratospheric response to Arctic sea ice loss versus internal variability,137

the influence of stratospheric internal variability on the tropospheric response to sea ice

138

loss and its mechanism. Section 4 discusses the implications for detection of recent

139

observed trends and model realism. Summary and discussion follow in Section 5.

140
141

2. Model and Experimental Design

142

a. Model description

143

CAM6 is the atmospheric model component of Community Earth System Model 2

144(CESM2) developed at the National Center for Atmospheric Research (NCAR). It uses the145

same Finite Volume (FV) dynamical core as previous versions, but includes the unified

146

turbulence scheme “Cloud Layers Unified By Binormals” (CLUBB; Larson 2017) as well147

as updates to almost all of the parameterizations (Danabasoglu et al. 2020). CAM6 ranks

148

within the top 10% of the CMIP6 models in representing many features of the atmospheric149

circulation such as storm tracks, stationary waves and blocking (Simpson et al. 2020).

150

CAM6 has a horizontal resolution of 1.25o in longitude and 0.9o in latitude, and 32 vertical151

levels with the model top at 2.26 hPa (Danabasoglu et al. 2020). As a “low-top” model, it152

does not generate a Quasi-Biennial Oscillation (QBO). The SSW frequency in CAM6 is

153

also underestimated (Ayarzaguena et al. 2020), likely resulting from a too strong mean

6

154

polar vortex. However, as we shall show, the interannual variability of the stratospheric155

polar vortex and its downward influence on the troposphere are generally realistic.

156
157

b. PAMIP experimental protocol

158

We conduct atmosphere-only time slice experiments following PAMIP protocols. In

159

these experiments, the radiative forcing is fixed at year 2000, and sea-surface temperatures

160(SST) and sea ice concentrations (SIC) are prescribed at preindustrial (pi), present-day (pd)161

and future (fut) conditions (Smith et al. 2019). The pd SST and SIC are taken from162

observations, while the pi and fut lower boundary forcings are constructed from the 31

163

historical and Representative Concentration Pathway 8.5 (RCP8.5) simulations

164

respectively from the corresponding 31 Coupled Model Intercomparison Project Phase 5165(CMIP5) models (Smith et al. 2019). For each ensemble member, the pi period is defined166

as the 30 years when the global mean temperature (GMT) equals 13.67 oC, obtaining by

167

removing an estimated global warming index (Haustein et al. 2017) of 0.57 oC from the168

observed 1979-2008 value (14.24 oC). The fut period is defined as the 30 years when the169

GMT first exceeds the pi GMT by +2 oC, typically near 2030-2060 (Hausfather 2020).

170

Thus, the magnitude of the sea-ice loss from pd to fut prescribed in PAMIP is substantially171

smaller than the end of 21st century ice loss commonly used in previous studies (e.g., Screen

172

et al. 2018). The sea ice thickness is set to be uniformly 2 m in the Arctic and 1 m in the173

Antarctic.

174

Here we conduct the following PAMIP experiments (nomenclature follows that in175

Table 1 of Smith et al. 2019): 1.1: pdSST-pdSIC; 1.2: piSST-piSIC; 1.3: piSST-pdSIC; 1.4:

176

futSST-pdSIC; 1.5: pdSST-piArcSIC; and 1.6: pdSST-futArcSIC (note that for 1.5 and 1.6,

7

177

pd SIC is prescribed in the Antarctic). We also conduct an additional experiment: futSST-

178

futArcSIC (with pd SIC prescribed in the Antarctic). Each simulation starts on April 1st,179

2000, initialized from ensemble member 001 of the CAM6 Atmospheric Model

180

Intercomparison Project (AMIP) historical simulation, and runs for 14 months; we discard

181

the first 2 months as spin-up from our analysis. For each experiment, we conduct a 200-

182

member ensemble using the method of “pertlim” (order 10-14 K perturbation to the initial

183

atmospheric temperature field following Kay et al., 2015). Additional details of the PAMIP

184

protocols and the SST/SIC forcing can be found in Table 1 and Smith et al. (2019).

185

We calculate the atmospheric response to past (pdSIC minus piSIC: Δpast) and future

186(futSIC minus pdSIC: Δfut) Arctic sea ice loss from the 200-member ensemble means of187

each experiment as follows:

188

ΔpastpiSST = 1.3 (piSST-pdSIC) minus 1.2 (piSST-piSIC),

(1)

189

ΔpastpdSST = 1.1 (pdSST-pdSIC) minus 1.5 (pdSST-piArcSIC),

(2)

190

ΔfutpdSST = 1.6 (pdSST-futArcSIC) minus 1.1 (pdSST-pdSIC),

(3)

191

ΔfutfutSST = (futSST-futArcSIC) minus 1.4 (futSST-pdSIC).

(4)

192

Note that there are two background SST states for the atmospheric circulation responses to193both past and future Arctic sea ice loss. This allows us to investigate whether the responses

194

are sensitive to the background SST state (e.g., Smith et al. 2017). The False Discovery

195

Rate (FDR) with control level αFDR=0.2 has been applied to the 90% statistical significance196

level on the two-sided Student’s t-test to measure the field significance of the responses

197(Wilks 2016).

198
199

c) Random sampling procedure

8

200

The PAMIP protocol recommends a minimum ensemble size of 100 members (Smith201

et al. 2019). We therefore begin our investigation by examining the sensitivity of the results

202

based on the first 100 members and the second 100 members of our 200-member Δpast and203

Δfut ensembles, similar to P21’s approach. The marked differences between these two

204

independent 100-member sub-ensembles leads us to then conduct a more systematic

205

assessment of sampling uncertainty due to internal variability using a random sampling206(bootstrapping) procedure (Mudelsee 2010). For each Δpast and Δfut ensemble, we

207

randomly selected 100 members with replacement from the full 200 members, computed

208

the average of each random 100-member subset, and repeated the entire procedure 1000

209

times. We then used these 1000 random 100-member averages to compute a probability

210

distribution function (PDF) for each of the Δpast and Δfut ensembles.

211
212

d) Characterizing stratospheric internal variability

213

Following convention, we define an index of the strength of the winter (December-

214

February average; DJF) stratospheric polar vortex as the zonal-mean zonal wind at 10 hPa

215

and 60oN (hereafter, U10). We compute U10 for each of the 1000 bootstrapped samples

216

of 100-member averages, and use these to compute PDFs. We also calculate analytical

217

PDFs of 100-member averages of U10 for each of the Δpast and Δfut ensembles, assuming

218

a Gaussian distribution with mean XN (equal to the 200-member average) and a standard

219

deviation σN:

220"

𝜎! = 𝜎 ∙ %! ,

(5)

9

221

where 𝜎 is the standard deviation of U10 across the original 200 members of each ensemble

222

and N = 100. The factor of √2 accounts for the fact that we are calculating the standard

223

deviation of the difference between a pair of simulations. As shown in Section 3b, the two

224

approaches generate very similar PDFs. Finally, we compute correlation and regression

225

coefficients between U10 and other quantities of interest across the 1000 bootstrapped226

samples of 100-member averages.

227
228

3. Results229

a. Surface atmospheric response to past and future Arctic sea ice loss

230

The loss of Arctic SIC (%) prescribed in the Δpast and Δfut ensembles occurs mainly

231

within the central Arctic in summer and autumn, and in the marginal ice zones (e.g., Sea

232

of Okhotsk, Bering Sea and Labrador Sea) in winter and spring (Fig. 1a). In the Δfut

233

ensemble, the SIC reduction in summer and autumn extends all the way to the North Pole234

(Fig. 1a), with a corresponding increase (up to 140%) in the areal extent of ice loss from

235

July-December compared to the Δpast ensemble (Fig. 1b). The loss of Arctic SIC (%)

236

prescribed in the Δpast and Δfut ensembles shares regional and seasonal characteristics

237

with observed SIC trends since 1979 (Fig. S1).

238

Sea ice loss results in a net upward energy flux from the ocean to the atmosphere, which

239

in turn is responsible for the near-surface warming over the Arctic and high-latitude240

continents, as well as the increase in Arctic precipitation (Fig. 2). These responses exhibit

241

similar seasonal cycles, with a minimum in summer and a maximum in winter; note the242roughly two-month delay between the peak sea ice loss (October) and the largest
10

243

atmospheric response (December) for the future ice loss case, in agreement with previous

244

studies245

temperature/precipitation responses are approximately twice as large for future ice loss

246

compared to past ice loss, in keeping with the relative magnitudes of sea ice reduction, and247

show little sensitivity to the background SST conditions, especially for the past ice loss

248

case (Fig. 2). The slightly weaker (~10%) but statistically significant responses for the249

future ice loss case under future SSTs compared to present-day SSTs is most apparent in

250

October-December (Fig. 2 and S2). It is likely related to enhanced poleward atmospheric251

energy transport, which warms the Arctic free troposphere (Audette et al. 2021), thus

252

reducing the upward energy transfer from the ocean surface. Outside the Arctic, the253

atmospheric responses to the same Arctic sea ice loss but under different background SST

254

conditions are largely indistinguishable (not shown). Moreover, internal variability of the255

surface energy flux response based on 100-member averages is relatively small and thus

256

may not contribute much to the spread in atmospheric response (see error bars in top panel

257

of Fig. 2). In the remainder of this study, we focus on the winter season when the258

atmospheric response is largest.

(e.g.,

Deser

et

al.

2010).

The

wintersurfaceenergy

flux

and

259
260

b. Stratospheric internal variability and its influence on the tropospheric response261

To demonstrate the relative magnitudes of the mean stratospheric response to Arctic262

sea ice loss and its internal variability based on 100-member averages, we show PDFs of263

the 1000 U10 bootstrapped samples (histograms) along with the analytical distribution

264(thick black curves) for each of the four ice loss cases in DJF (Fig. 3). The mean U10265

response of the 1000 100-member averages is -1.6, -0.7, -0.4, and +0.5 m s-1 for ΔpastpiSST,

11

266

ΔpastpdSST, ΔfutpdSST and ΔfutfutSST, respectively. The difference in U10 responses between

267

the two SST states is statistically insignificant for both Δpast and Δfut. In comparison, the268

standard deviation of the 1000 100-member averages ranges from 1.4 - 1.6 m s-1 (similar

269

values obtain for the analytical solution) for all four sea ice cases, indicating a low signal-

270

to-noise ratio for 100-member averages. Due to the low signal-to-noise, by chance, the first271

and second 100-member averages of U10 (e.g., members 1-100 and 101-200) can differ

272

significantly, as is the case for ΔpastpiSST (~ -5.2 vs. 1.9 m s-1) and ΔfutpdSST (~ -3.0 vs. 2.3

273

m s-1), or be nearly indistinguishable, as is the case for ΔpastpdSST (-0.9 vs. -0.4 m s-1) and274

ΔfutpdSST (0.5 vs. 0.5 m s-1)1. Since the four sea ice cases have very similar magnitudes of275

internal variability, these differences highlight the importance of assessing the full276

distribution via random subsampling rather than relying on two discrete (albeit fully

277

independent) estimates. Indeed, the first and second 100-member averages of U10 for

278

ΔpastpiSST slightly exceed two standard deviations of the PDF, indicating that they are low

279

probability occurrences. Below, we use this case (ΔpastpiSST) as an example to illustrate

280

how the random sampling of internal stratospheric variability in any 100-member average281

affects the apparent tropospheric response to Arctic sea ice loss.

282

The vertical structure of the DJF zonal-mean zonal wind response in ΔpastpiSST based283

on the full 200-member average shows a statistically significant deceleration of the mean284

westerlies throughout the troposphere and stratosphere at high latitudes (60-80oN;

285

maximum values around -2.5 m s-1 at 10 hPa), and very little response equatorward of 50oN

286

(Fig. 4a). Inspection of the first and second 100-member averages reveals striking

1

It is noted that there is a very low probability that two of the four cases show significant differences between
the first and second 100-member averages. We have exhaustively checked that there were no other underlying
reasons for this, and put it down to chance.

12

287

differences in both the stratosphere and troposphere. The polar vortex weakens

288

substantially in the first 100 members but strengthens in the second 100 members, albeit289

not significantly (Figs. 4b and c). The tropospheric circulation response in the first 100-290

member average shows a meridional dipole with deceleration (acceleration) centered at291

60oN (35oN), resembling the full 200-member average but with larger magnitudes. By

292

contrast, the second 100-member average lacks any statistically significant signal in the293

extratropical troposphere. These results indicate that using two discrete and independent294

estimates can lead to non-robustness of atmospheric circulation responses to past and future295

Arctic sea ice loss in the PAMIP experiments when based on 100 members.

296

It is well-known that NH stratospheric polar vortex is dynamically coupled to the297

troposphere in boreal winter (e.g., Baldwin et al. 2021). Given that the differences between

298

the first and second 100-member averages span the stratosphere and troposphere, one may

299

surmise that there is a connection between the internal variability in the two domains. To

300

examine this, we plot the monthly evolution of U(z) at 60oN (a proxy for the NAM; Butler

301

et al. 2017) for the first and second 100-member averages and their difference (Fig. 5 a-c).

302

U(z) shows a distinct evolution in the two 100-member averages, consistent with Fig. 4,

303

and their difference is characterized by maximum easterly anomalies peaking in

304

January/February in the stratosphere and February/March at the surface, suggestive of a305

downward influence.

306

To further explore stratosphere-troposphere coupling more generally, we regress

307

monthly U(z) response at 60oN onto the normalized DJF U10 response across the 1000308

bootstrapped samples of 100-member averages. We then add or subtract the regression309

coefficients (multiplied by 2) to the 200-member average to form the 2.5th-to-97.5th

13

310

percentile confidence intervals on the mean response based on 100-member averages (Figs.

311

5d and e). The results based on regression analysis bear a striking resemblance to the first312

and second 100-member averages, indicating that most of the difference between the first313

and second 100-member averages arises from a random, albeit extreme, sampling of314

stratospheric polar vortex internal variability. Using Pearson correlations in place of315

regressions reveals a strong connection (r = 0.5 – 0.6) between DJF U10 and near-surface

316

U response in January, February and March, lending further support to the conjecture that317

stratospheric internal variability exerts a substantial influence on the troposphere (Fig. 5g).

318

Figure 6 shows the ΔpastpiSST geopotential height response in the stratosphere (10 hPa

319

in DJF) and troposphere (500 hPa, 850 hPa and 1000 hPa in January-March; JFM) for the320

first and second 100-member averages and their difference. Consistent with Figs. 4 and 5,

321

the winter polar vortex weakens (consistent with a positive geopotential height anomaly)322

in the first 100 members but strengthens (consistent with a negative geopotential height323

anomaly) in the second 100 members. In the troposphere, the first 100-member average

324shows a barotropic pattern with positive height anomalies over the Arctic and negative325

height anomalies over North Atlantic midlatitudes, projecting onto the negative phase of326

the NAO. By contrast, the second 100-member average exhibits a more baroclinic vertical327

structure in the Nordic Seas, with a negative anomaly near the surface and positive anomaly

328

in the upper levels, similar to the direct response in Deser et al. (2004). The circulation329

response in the North Pacific is also opposite between the two 100-member averages.

330

Figure 7 shows the corresponding regression maps between U10 and geopotential

331

height based on the 1000 bootstrapped samples of 100-member averages. The patterns in332

the Mean+2σ and Mean-2σ regression maps are largely similar to the first and second 100-

14

333

member averages, respectively, except over the lower latitude North Pacific (20-40oN). In

334

this region, the difference between the two 100-member averages shows an anomalous

335

ridge which is absent from the regression pattern (Figs. 6c and 7c). In addition, the336

anomalous trough over the North Atlantic extends further into the Mediterranean Sea in337

the regression pattern compared to the difference between two sub-ensembles. Overall, the338

regression pattern (Fig. 7c) closely resembles the NAM throughout the depth of the339

troposphere, while the difference between the two sub-ensembles exhibits additional340

regional features (Fig. 6c). The magnitude of the correlation coefficients between U10 and

341

geopotential height across the bootstrapped samples exceeds 0.9 at 10 hPa and reaches 0.6

342

in the troposphere over the main centers of action of the NAM (Fig. S3). Taken together,343

the results shown in Figs. 5-7 indicate that internal variability of the stratospheric polar344

vortex exhibits a strong connection to the tropospheric NAM, but that the troposphere also

345

contains its own intrinsic modes of variability.

346

Stratospheric internal variability can also introduce uncertainty in the surface climate

347

response to Arctic sea ice loss estimated from 100-member averages. For example, the348

pattern and amplitude of the surface air temperature (SAT) response in JFM shows striking

349

differences between the two sub-ensembles of ΔpastpiSST (Figs. 8a and b; shading). In

350

particular, the second 100-member average exhibits strong and statistically significant

351

warming over northern Eurasia (maximum values exceeding 1.5oC over eastern Siberia),

352

whereas the first 100-member average shows weak and statistically insignificant anomalies353

of both signs in this region. Notable differences in warming amplitude between the two354

sub-ensembles are also found over Greenland and northeastern Canada. These SAT

355

differences are clearly linked to the different circulation responses in the two sub-

15

356

ensembles noted earlier, in particular the opposing SLP responses over the Arctic (contours

357

in Figs. 8a and b). Indeed, the difference between the two sub-ensembles shows evidence358

of dynamically-induced cooling over northern Eurasia, and warming over eastern Canada

359

and Greenland, in association with the negative NAM-like SLP pattern (Fig. 8c). The

360

regression analysis based on U10 (Mean-2σ and Mean+2σ: Figs. 8d and e) reproduces361

almost all of the SAT and SLP features seen in the two sub-ensembles, and the difference362

pattern (Fig. 8f) shows the canonical SAT and SLP signatures associated with a weakened

363

stratospheric polar vortex (e.g., Polvani et al. 2017).

364

Similar to SAT, the JFM precipitation responses in the two sub-ensembles of ΔpastpiSST

365

show striking differences, with opposite-signed meridional dipole patterns over the North

366

Pacific and North Atlantic that result from the opposing circulation responses (Figs. 9a and

367b). The regression analysis reproduces many of the features of the sub-ensembles over the368

North Atlantic and Arctic, but shows less agreement over the North Pacific (Figs. 9d and

369e). This is even more evident in the difference field: the regression analysis strongly

370

underestimates the magnitude of the precipitation and SLP dipoles over the North Pacific,

371

and the North Atlantic SLP trough extends farther to the east, which results in enhanced

372

precipitation over the eastern North Atlantic and Iberian Peninsula, compared to the sub-373

ensembles (Fig. 9f). These discrepancies highlight that while the stratosphere plays an374

important role, intrinsic variability of the tropospheric circulation also contributes to375

uncertainty in the simulated precipitation response to Arctic sea ice loss based on 100-376

member averages.

377

The influence of stratospheric internal variability on the tropospheric circulation and378

surface climate responses to Arctic sea ice loss is also apparent for the other sea ice loss

16

379

cases, based on regression analysis of the bootstrapped samples of 100-member averages

380(Figs. 10, S4 and S5). Note that for these cases, the first and second 100-member averages

381

do not necessarily mirror the Mean+2σ and Mean-2σ regression results, consistent with the382chance sampling of U10 documented earlier (Figs. 3 b-d). All cases, however, show a383

consistent NAM/NAO regression pattern with corresponding fingerprints in SAT and

384

precipitation (panel c in Figs. 10, S4 and S5). Likewise, the pattern and amplitude of the385

corresponding correlation results are also similar across the 4 cases. In particular, the386

correlation magnitudes reach ~ 0.6 for SAT in northeastern Siberia and Greenland and for

387

precipitation in Northern Europe and the Mediterranean region (Fig. S6). The consistency388

of the results across the 4 cases underscores the role of stratospheric internal variability in

389

causing uncertainty in the surface climate response to Arctic sea ice loss when based on

390

100-member averages.

391
392

c. Mechanism of stratospheric internal variability

393

Previous studies have established a connection between the strength of the stratospheric394

polar vortex and upward planetary wave propagation from the troposphere (e.g., Polvani

395

and Waugh 2004), indicating that stratospheric internal variability may originate, at least396

in part, in the troposphere. To investigate this possible dynamical linkage in ΔpastpiSST, we

397

examine the monthly evolution of the zonal-mean eddy heat flux response near 100 hPa

398(model 0.1 hybrid sigma level) as a function of latitude in the two 100-member sub-399

ensembles and the Mean+2σ and Mean-2σ regression results (Fig. 11). Consistent with the400

polar vortex response, the first 100-member average shows increased and statistically401

significant extratropical upward wave propagation in November-January (NDJ), while the

17

402

second 100-member average shows insignificant and slightly decreased upward403

propagation in this season (Figs. 11a and b). Similar results are found in the regression

404

analysis (Fig. 11 d and e). Therefore, the different polar vortex responses between the first405

and second sub-ensembles (recall Fig. 5a and b) is tied to internal variability of upward406

wave propagation. Similar results are found for the other sea ice loss cases (Fig. S7).

407

Changes in upward wave propagation have been found to be closely related to408interactions between the anomalous and climatological planetary waves (in particular,409

zonal wave numbers one and two), which has been referred to as the linear wave

410

interference theory (e.g., Garfinkel et al. 2010; Fletcher and Kushner 2011; Smith and411

Kushner 2012). This suggests that the different responses in upward wave propagation

412

between the first and second 100 member averages of ΔpastpiSST are also connected to413

constructive or destructive linear wave interference for zonal wave numbers one and two.

414

Indeed, the NDJ anomalous wave-1 is in phase with its climatology (constructive

415

interference) poleward of 40oN in the first 100-member average, but no such relationship

416

exists in the second 100-member average (Figs. 12a and b). Similarly, while anomalous417

wave-2 is in quadrature with the climatology in the first 100-member average, it is out of418

phase with the climatology (destructive interference) in the second 100-member average

419(Figs. 12 a and b). As a result, there will be more wave-1 and wave-2 upward wave

420

propagation in the first 100-member average than the second. The regression analysis

421

shows similar results regarding wave-1 and wave-2 interference characteristics (Figs. 12d422

and e), confirming that internally-driven planetary wave interference can largely explain423

the different upward wave propagation and polar vortex responses in 100-member averages.

424

Similar features can be also seen in the other sea ice loss cases (Figs. S8 and S9), suggesting

18

425

that this is a universal mechanism connecting internal variability of the stratosphere and426

troposphere.

427
428

d. Role of the ensemble size in stratospheric internal variability

429

While the preceding analysis has focused on 100-member averages, we next investigate

430

the role of ensemble size in determining the magnitude of stratospheric internal variability

431

and its impact on the assessment of the tropospheric response to sea ice loss. To do this,432

we calculate the 2.5th – 97.5th percentile (-2σ to +2σ) range of U10 as a function of sample

433

size (number of ensemble members averaged) using the analytical formula in Eqn. 5 for434

both model and reanalysis. The U10 interannual σ in pdSST-pdSIC (assuming it is435

equivalent to the standard deviation across 200 ensemble members) is 10.0 m s-1 (similar

436

values obtain in the other PAMIP experiments), which is very close to the value of 9.6 m

437

s-1 found in the National Centers for Environmental Prediction (NCEP)-NCAR Reanalysis

438(Kalnay et al. 1996) based on detrended data during 1966-2020 when the data are most439

reliable (see Fig. S10). Thus, the curves showing the 2.5th – 97.5th percentile ranges of U10

440

response as a function of ensemble size are nearly identical for CAM6 and Reanalysis (Fig.

441

13a). This means that the signal-to-noise ratios of the U10 responses in each of the 4 sea

442

ice loss cases would be similar if the “observed” noise were substituted for the model’s443

noise. In particular, we note that the 2σ uncertainty based on 200-member averages (2.0444m s-1 for CAM6, and 1.9 m s-1 for Reanalysis; Fig. 13a) exceeds the magnitude of the 200-

445

member averaged U10 response for each of the 4 ice loss cases (-1.6, -0.7, -0.4, 0.5 m s-1

446

for ΔpastpiSST, ΔpastpdSST, ΔfutpdSST and ΔfutfutSST, respectively). Thus, our conclusions

447

regarding the effect of stratospheric internal variability on the signal-to-noise of the U10

19

448

response to ice loss remain valid if using “observed” noise. It should be noted that our449

estimate of interannual U10 σ reflects internal atmospheric variability only in CAM6,

450

whereas in Reanalysis, coupled ocean-atmosphere and land-atmosphere interactions may

451

also contribute. However, in this regard, we note that the interannual U10 σ in a long pre-452

industrial control simulation of CAM6 is only 2% smaller than that of the fully-coupled

453

Community Earth System Model version 2 (CESM2).

454

Next, we assess the impact of internal U10 variability on the response of the NAO

455(normalized PC1 of SLP in the Atlantic-European sector) as a function of sample size based456

on CAM6 and Reanalysis data. As before, we compute the regression coefficient of the457

normalized JFM NAO Index onto the normalized DJF U10 Index based on interannual

458

anomalies (detrended in the case of Reanalysis), and use this value in Eqn. 5 (Section 2d).

459

For a sample size of one, the proportion of internal NAO variability associated with U10460

is slightly smaller for Reanalysis data (1.4) than for the model (1.8), and the difference461(scaled by462

model overestimates the linkage between the stratospheric polar vortex and the surface

463

NAO, or such linkage is represented correctly in the model but there is just more, unrelated

464

variability in the observations. For the case of a 200-member average, the minimum465

ensemble size needed for the JFM NAO response in CAM6 to be significantly different

466

from zero at the 95% confidence level is 165, 1296, 144, and 73 for ΔpastpiSST, ΔpastpdSST,

467

ΔfutpdSST and ΔfutfutSST, respectively, due to the high level of stratospherically-induced

468

noise in the NAO.

#√!

) is maintained for all sample sizes (Fig. 13b). This indicates that either the

469
470

4. Implications for attributing observed trends to Arctic sea ice loss
20

471

Motivated by empirical observational studies examining the relationship between

472

trends in atmospheric circulation and Arctic warming (or sea ice loss) with inferences about473

causality (e.g., Francis and Vavrus 2012), we proceed to apply our CAM6 results to the474interpretation of observed trends in recent decades. We first note that the “past” sea ice loss

475

adopted for the PAMIP protocol is approximately equivalent to the observed [merged

476

Hadley-Optimum Interpolation (OI) sea ice concentration; Hurrell et al. (2008)] linear

477

change during 1971-2020 for the November-March average (almost identical ice area loss

478

with similar spatial distribution; Fig. S11). Using the analytical approach of Thompson et479

al. (2015) and Deser et al. (2017), we calculate the 95% margin-of-error (e) on a 50-year

480

linear trend from the statistics of the interannual σ and 1-year lag autocorrelation (r1) for a

481

Gaussian timeseries as follows. Equation (6) shows the dependence of e on σ and r1:482

𝑒 = 𝑡% 𝑛& 𝜎'(% 𝛾(𝑛& , 𝑟# )𝑔(𝑛& ) ,

(6)

483

where tc=2 (95% confidence level), nt is the length of the trend in years (50), 𝜎'(% is the484

value of the surface anomaly (SLP, SAT or precipitation) associated with a one σ anomaly

485

of U10, and the other terms are defined as follows.
)! *"

486

𝛾(𝑛& , 𝑟# ) = [487

𝑔(𝑛& ) = %) ' *) ,

"#$"
)! +
,*"
"%$"

#"

!

!

"

]& ,

(7)

(8)

488

Figure 14 shows maps of the 95% margin-of-error on a 50-year trend in SLP (top row),489

SAT (middle row) and precipitation (bottom row) associated with internal variability of490

the stratospheric polar vortex (U10) based on Reanalysis (first column) and CAM6 (middle

491

column) following the procedure outlined above. Overall, CAM6 shows a realistic

492

depiction of the patterns and magnitudes seen in Reanalysis, including the NAO and its
21

493

fingerprint on SAT and precipitation. There are some notable differences as well. For

494

example, the observed SLP pattern more closely resembles the regional NAO while the495

model’s pattern is more similar to the hemispheric NAM. As a result, the precipitation

496

values over the North Pacific differ between Reanalysis and CAM6. These discrepancies

497

may be related to the additional influence of El Niño-Southern Oscillation (ENSO) on U10498

variability in Reanalysis that is lacking in CAM6 due to the use of a climatological seasonal499

cycle as the SST boundary condition, or it may be a reflection of the well-known bias of500

atmospheric models to simulate a more zonally-symmetric NAM structure of variability

501

compared to the more regionally-confined NAO structure found in observations (Flato et502

al. 2013). Over the North Atlantic midlatitudes, the SLP trough is shifted eastward in

503

CAM6 compared to Reanalysis, with impacts on precipitation over the Mediterranean

504

region. This bias appears to be a common issue in climate models (e.g., Sigmond et al.

505

2013; Ayarzaguena et al. 2020).

506

Despite some regional differences in magnitude and pattern, the margin-of-error on the507

surface climate trends associated with U10 in both CAM6 and Reanalysis substantially508

exceed the magnitude of the CAM6 response to the observed Arctic sea ice loss trend

509

during 1971-2020, obtained by combining the 200-member averages of ΔpastpiSST and510

ΔpastpdSST (see above; Fig. 14c). Therefore, unless the model’s forced response is511

substantially underestimated (as some studies suggest: e.g., Mori et al. 2019a) or the model

512

substantially overestimates stratospheric internal variability (e.g., signal-to-noise paradox

513

documented in Smith et al. 2020), it will be difficult to isolate the forced surface climate

514

response to recent Arctic sea ice loss from stratospherically-induced internal variability in

515

observations.

22

516
517

5. Summary and discussion

518

a. Summary

519

In this study, we conduct PAMIP time-slice experiments with CAM6 to investigate the520

atmospheric response to past and projected Arctic sea ice loss, with a specific focus on the521role of stratospheric internal variability on the uncertainty in the tropospheric circulation522

and surface climate response based on 100-member ensembles. Our focus on 100-member

523

averages is motivated by the PAMIP protocol recommendation. We note that increasing524

the sample size N will reduce the regression magnitudes by a factor of525S12c), but will not affect the correlation magnitudes. Our main findings are summarized526

as follows.

527

1. Consistent with previous studies, the seasonal cycle of the Arctic and terrestrial high-528

latitude SAT and precipitation responses is approximately in phase with that of the529

Arctic surface energy flux response, which maximizes in winter (October-March),

530

rather than that of the ice loss, which peaks in autumn (September-October). The

531

internal variability in Arctic surface energy flux response based on 100-member

532

averages is small.

#√!

(Figs. 5f and533

2. Unlike the surface responses, the magnitude of the winter stratospheric polar vortex

534

response to both past and future Arctic sea ice loss is small compared to its internal

535

variability, even when based on 100-member averages. The stratospheric internal

536

variability is associated with large uncertainty in the tropospheric circulation response537

to sea ice loss, and can completely obscure the forced NAM/NAO circulation response

23

538

and associated impacts on air temperatures over Eurasia and eastern North America

539

and precipitation over the North Atlantic and Mediterranean Sea based on 100-member

540

averages.

541

3. Internal variability of the winter stratospheric polar vortex is related to antecedent542

conditions in tropospheric wave activity. As such, it can be explained by internal543

variability in upward wave propagation and linear wave interference theory.

544

4. CAM6 exhibits realistic magnitudes of interannual variability in the winter545

stratospheric polar vortex and its connection to the surface NAO when compared546

against detrended Reanalysis data during 1966-2020. If the interannual variability of547

the stratospheric polar vortex is mainly due to internal atmospheric dynamics

548(including coupling with the troposphere), then Reanalysis data can be used to assess549

the uncertainty of the atmospheric circulation response to Arctic sea ice loss based on

550

100-member averages following the analytical approach developed by Thompson et al.

551(2015). The resulting signal-to-noise for the winter (JFM) SLP response to past Arctic552

sea ice loss is low (< 1) based on 100-member (or 200-member) averages, indicating

553

that unless the model’s forced response is substantially underestimated or the model554

substantially overestimates stratospheric internal variability, it will be difficult to555

isolate the forced response to recent Arctic sea ice loss in nature.

556
557

b. Discussion

558

The results presented above highlight a number of issues. The first one concerns the559

observed relationship between Arctic sea ice and winter NAO/NAM (e.g., Cohen 2016),

560

which has been hypothesized to reflect a causal influence of Arctic sea ice loss via a

24

561

stratospheric pathway (Ruggieri et al. 2016) and argued to be one of the dominant climate

562

impacts in recent years (Jaiser et al. 2016). In agreement with Barnes and Screen (2015)563

and P21, but based on an explicit consideration of the stratospheric pathway, our results564

caution that empirical analyses that rely on only four decades of observations may face565

serious sampling issues. Indeed, while Arctic sea ice extent has declined more or less566

continuously since 1979, the stratospheric polar vortex does not show any significant trend

567

over this time period, but instead exhibits mainly interannual to decadal variability (Fig.

568

S10).

569

weakened when more recent data (e.g., years 2013-2020) are included in the analysis

570(Smith et al. 2021), suggesting that part of the relationship may result from stratospheric

571

internal variability and its downward influence on the troposphere.

Moreover, the observed connection between Arctic sea ice and NAM/NAO has

572

The second issue regards the relative roles of stratospheric versus tropospheric573

pathways for the forced response to Arctic sea ice loss. When averaged over all 200

574

members, the polar vortex response shows large variation across the four sea ice loss cases575(from -1.6 to 0.5 m s-1; Fig. 3), while the tropospheric circulation responses are quite

576

similar (not shown). Thus, in CAM6, the atmospheric circulation change due to Arctic sea

577

ice loss is mainly dominated by the tropospheric pathway, with a relatively smaller role for578

the stratospheric pathway. Interestingly, a recent PAMIP multimodel analysis also found579

divergent stratospheric polar vortex responses but robust tropospheric responses (Smith et580

al. 2021). Note that these findings do not negate the importance of considering sampling

581

variability in the stratospheric polar vortex response and its impact on the troposphere.

582

The third issue concerns the relative roles of internal variability in the stratosphere583

versus troposphere. P21 found that the tropospheric circulation, either due to intrinsic

25

584

atmospheric dynamics or ENSO-related teleconnections, is the main source of internal

585

variability in their PAMIP experiments with SC-WACCM4, while we find that internal

586

variability of the stratospheric polar vortex plays a dominant role in our CAM6 experiments.

587

These results do not contradict each other and can be easily reconciled. As shown in Fig.

588

3, the polar vortex response difference between the first and second 100 members can be589

large or small solely by chance. When the two 100-member averages show similar U10590

responses, like in the ΔpastpdSST and ΔfutfutSST sea ice loss cases, the contribution of internal

591

variability unrelated to the stratosphere will manifest itself in a manner similar to that in592

P21 (not shown). On the other hand, when the two 100 member-averages of U10 show

593

large differences, as in ΔpastpiSST and ΔfutpdSST, the difference in the tropospheric responses

594

will manifest primarily as an NAO/NAM pattern induced by stratospheric internal

595

variability. More importantly, we have shown that a random selection of 100 members of596

any of the CAM6 PAMIP sea ice loss experiments is likely to contain substantial

597

stratospheric internal variability, with consequences for the signal-to-noise of the598

tropospheric and surface climate responses. We note that our results are in agreement with599

newer PAMIP modeling studies, for example Streffing et al. (2021) who used the Open600

Integrated Forecasting System, albeit with a focus on the sensitivity to horizontal resolution

601

as opposed to stratospheric internal variability per se. It is also worth mentioning that602

stratospheric internal variability is not the only source of uncertainty in the tropospheric603

response to Arctic sea ice loss, but it is substantial (Fig. S13; also inferred from Fig. S6).

604

The fourth issue concerns the mechanisms of stratospheric internal variability. In

605

addition to the eddy heat flux diagnostics (Fig. 11), we also examined the November-

606

January wave activity flux (Plumb 1985) and find very different upward wave propagation

26

607

responses over Siberia between the two 100-member sub-ensembles, underscoring the608important role of tropospheric internal variability in modulating upward wave propagation

609

characteristics through interference with the climatological planetary waves (not shown).

610

Further research is needed to assess additional mechanisms such as troposphere-

611

stratosphere resonance of free modes (Tung and Lindzen, 1979; Plumb, 1981; Esler and

612

Scott, 2005) and interactions between the polar vortex and Aleutian anticyclone (O’Neill

613

and Pope, 1988; Scott and Dritschel, 2006).

614

Our results come with several caveats. CAM6 is a “low-top” model, and as such, it615

does not internally generate a QBO and thus lacks mechanisms that may be sensitive to the616phase of the QBO (e.g., Labe et al. 2019). Although the magnitude of its stratospheric

617

internal variability is realistic (Figures 13 and 14), the mean winter polar vortex is too

618

strong, causing the simulated frequency of sudden stratospheric warmings to be619

underestimated (Ayarzaguena et al. 2020). It is still unclear whether the stratospheric620

response to Arctic sea ice loss may depend on the basic state bias in the polar vortex (e.g.,621

Sigmond et al. 2010; Sun et al. 2015). Finally, our atmosphere-only model experiments do

622

not consider the role of ocean-atmosphere coupling, which has been shown to amplify the623

response to Arctic sea ice loss (Deser et al. 2016) and expand its reach to the entire globe

624(e.g., Deser et al., 2015; Tomas et al. 2016; Wang et al., 2018; Peings et al., 2021).

625

Last but not least, it is still an open question whether models underestimate the626

atmospheric response to Arctic sea ice loss (e.g., Mori et al. 2019a) and associated signal-627

to-noise ratio (e.g., Smith et al. 2020). So far there is still no consensus (e.g., Screen and

628Blackport 2019; Mori et al. 2019b), but non-stationarity of the connection between sea ice

629

reduction and atmospheric circulation (Kolstad and Screen 2019; Blackport and Screen

27

630

2020) appears to imply large internal variability in observations. In other words, the631

connection may be an artifact of sampling.

632
633

Acknowledgements

634

We appreciate the thoughtful comments and suggestions of three anonymous reviewers,

635

and constructive discussions with Dr. Walter A. Robinson (NCSU). NCAR is sponsored636

by the National Science Foundation. We would like to acknowledge the use of637

computational

638

Supercomputing Center provided by the National Science Foundation and the State of639

Wyoming, and supported by NCAR's Computational and Information Systems Laboratory.

resources(doi:10.5065/D6RX99HX)

at

the

NCAR-Wyoming

640
641
642
643

Data Availability Statement
The PAMIP data with CAM6 are accessible on the Earth System Grid Federation
website (https://doi.org/10.22033/ESGF/CMIP6.2197).

644

REFERENCES

645

Ayarzagüena, B., Charlton-Perez, A. J., Butler, A. H., Hitchcock, P., Simpson, I. R.,

646

Polvani, L. M., et al., 2020: Uncertainty in the response of sudden stratospheric warmings

647

and stratosphere-troposphere coupling to quadrupled CO2 concentrations in CMIP6

648

models. Journal of Geophysical Research: Atmospheres, 125, e2019JD032345.

649

https://doi.org/10.1029/2019JD032345

28

650

Audette, A., Fajber, R., Kushner, P., Wu, Y., Peings, Y., Magnusdottir, G., Sigmond, M.,

651

Sun, L., Eade, R., 2021: Opposite responses of the dry and moist eddy heat transport into

652

the

653

https://doi.org/10.1029/2020GL089990.

654

Baldwin, M. P., Ayarzagüena, B., Birner, T., Butchart, N., Butler, A. H., Charlton-Perez,

655

A. J., et al., 2021: Sudden stratospheric warmings. Reviews of Geophysics, 59,656

e2020RG000708. https://doi.org/10.1029/2020RG000708.

657

Barnes, E. A., and J. A. Screen, 2015: The impact of Arctic warming on the midlatitude

658

jetstream: Can it? Has it? Will it?, WIREs Clim. Change, 6, 277–286.

659

Blackport, R., & Screen, J. A., 2019: Influence of Arctic sea ice loss in autumn compared660

to that in winter on the atmospheric circulation. Geophysical Research Letters, 46, 2213–

661

2221. https://doi.org/10.1029/2018GL081469

662

Blackport, R. & J.A. Screen, 2020: Weakened evidence for mid-latitude impacts of Arctic

663

warming. Nature. Clim. Change, 10, 1065-1666.

664

Blackport, R., J.A. Screen, K. van der Wiel & R. Bintanja, 2019: Minimal influence of665

reduced Arctic sea ice on coincident cold winters in mid-latitudes. Nature Clim. Change,

666

9, 697-704.

667

Butler, A. H., J. P. Sjoberg, D. J. Seidel, and Karen H. Rosenlof, 2017: A sudden668

stratospheric warming compendium, Earth Syst. Sci. Data, doi:10.5194/essd-9-63-2017.

Arctic

in

the

PAMIP

experiments.

GeophysicalResearch

Letters,29

669

Cai, D., M. Dameris, H. Garny, and T. Runde, 2012: Implications of all season Arctic sea-670

ice anomalies on the stratosphere. Atmospheric Chemistry and Physics, 12, 11819–11831,671

doi:10.5194/acp-12-11819-2012.

672

Cohen J., 2016: An observational analysis: tropical relative to Arctic influence on

673

midlatitude weather in the era of Arctic amplification. Geophys Res Lett, 43:5287–5294.

674

https://doi.org/10.1002/2016GL069102.

675

Cohen, J., J. Jones, J.C. Furtado, and E. Tziperman. 2013. Warm Arctic, cold continents:

676

A common pattern related to Arctic sea ice melt, snow advance, and extreme winter677

weather. Oceanography 26(4):150–160, http://dx.doi.org/10.5670/oceanog.2013.70.

678

Cohen, J., Zhang, X., Francis, J. et al., 2020: Divergent consensuses on Arctic amplification679

influence on midlatitude severe winter weather. Nat. Clim. Chang. 10, 20–29.

680

https://doi.org/10.1038/s41558-019-0662-y

681Dai, A., Song, M., 2020: Little influence of Arctic amplification on mid-latitude climate.

682

Nat. Clim. Change. 10, 231–237. https://doi.org/10.1038/s41558-020-0694-3

683Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards,

684

J., et al., 2020: The Community Earth System Model Version 2 (CESM2). Journal of

685

Advances

686

10.1029/2019MS001916.

687

Deser, C., F. Lehner, K. B. Rodgers, T. Ault, T. L. Delworth, P. N. DiNezio, A. Fiore, C.

688

Frankignoul, J. C. Fyfe, D. E. Horton, J. E. Kay, R. Knutti, N. S. Lovenduski, J. Marotzke,inModeling

Earth

Systems,

12,

e2019MS001916.

https://doi.org/

30

689

K. A. McKinnon, S. Minobe, J. Randerson, J. A. Screen, I. R. Simpson and M. Ting, 2020:

690

Insights from earth system model initial-condition large ensembles and future prospects.

691

Nat. Clim. Change, doi: 10.1038/s41558-020-0731-2.

692

Deser, C., G. Magnusdottir, R. Saravanan, and A. Phillips, 2004: The effects of North

693

Atlantic SST and sea-ice anomalies on the winter circulation in CCM3, Part II: Direct and694

indirect components of the response. J. Climate, 17, 877-889, doi:10.1175/1520-

695

0442(2004)017<0857:TEONAS>2.0.CO;2.

696

Deser, C., R. A. Tomas, M. A. Alexander, and D. Lawrence, 2010: The Seasonal

697

Atmospheric Response to Projected Arctic Sea Ice Loss in the Late Twenty-First Century.

698

J. Climate, 23, 333–351, doi:10.1175/2009JCLI3053.1.

699

Deser, C., R. A. Tomas, and L. Sun, 2015: The role of ocean-atmosphere coupling in the700

zonal-mean atmospheric response to Arctic sea ice loss. J. Climate, 28, 2168-2186, doi:701

10.1175/JCLI-D-14-00325.1.

702

Deser, C., L. Sun, R. A. Tomas, and J. Screen, 2016: Does ocean coupling matter for the703

northern extratropical response to projected Arctic sea ice loss?, Geophys. Res. Lett., 43,704

2149–2157, doi:10.1002/2016GL067792.

705

Deser, Clara, Hurrell, James W., and Phillips, Adam S., 2017, The role of the North

706

Atlantic Oscillation in European climate projections. Climate Dynamics Vol. 49, No. 9-10,707

pp 3141, 1432-0894

708

England, M., Polvani, L., & Sun, L., 2018: Contrasting the Antarctic and Arctic

709

Atmospheric Responses to Projected Sea Ice Loss in the Late Twenty-First Century,

710

Journalof

Climate,31(16),

6353-6370.

Retrieved

Apr

18,

2021,

from
31

711

https://journals.ametsoc.org/view/journals/clim/31/16/jcli-d-17-0666.1.xml

712

Esler, J. G., and R. K. Scott (2005), Excitation of transient Rossby waves on the713

stratospheric polar vortex and the barotropic sudden warming, J. Atmos. Sci., 62, 3661–

714

3682.

715

Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel. 2017: updated

716

daily. Sea Ice Index, Version 3. [sea ice extent]. Boulder, Colorado USA. NSIDC: National

717

Snow and Ice Data Center. doi: https://doi.org/10.7265/N5K072F8. [July 16, 2021

718

Accessed].

719

Flato, G., J. Marotzke, B. Abiodun, P. Braconnot, S.C. Chou, W. Collins, P. Cox, F.

720

Driouech, S. Emori, V. Eyring, C. Forest, P. Gleckler, E. Guilyardi, C. Jakob, V. Kattsov,

721

C. Reason and M. Rummukainen, 2013: Evaluation of Climate Models. In: Climate

722

Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth

723

Assess- ment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D.

724

Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and

725

P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New

726

York, NY, USA.

727

Fletcher, C. G. and P. J. Kushner, 2011: The Role of Linear Interference in the Annular

728

Mode Response to Tropical SST Forcing. J. Climate, 24, 778-794.

729

Francis, J. A., and S. J. Vavrus, 2012: Evidence linking Arctic amplification to extreme

730

weather in mid-latitudes, Geophys. Res. Lett., 39, L06801, doi:10.1029/2012GL051000.

731

Garfinkel, C. I., D. L. Hartmann, and F. Sassi, 2010: Tropospheric Precursors of

32

732

Anomalous Northern Hemisphere Stratospheric Polar Vortices. J. Climate, 23, 3282-3299.

733

Hausfather, Zeke, 2020: Analysis: When might the world exceed 1.5oC and 2oC of global734

warming?

735

https://www.weforum.org/agenda/2020/12/analysis-world-paris-agreement-climate-736

targets-change-emissions-global-warming/

737

Haustein, K., M. R. Allen, P. M. Forster, F. E. L. Otto, D. M. Mitchell, H. D. Matthews

738

and D. J. Frame, 2017: A real-time Global Warming Index, Scientific Reports, 7, 15417,

739

doi:10.1038/s41598-017-14828-5.

740

Hay, S., P. Kushner, R. Blackport and K. McCusker, 2018: On the relative robustness of741

the atmospheric circulation response to high-latitude and low-latitude warming, Geophy.

742

Res. Lett., https://doi.org/10.1029/2018GL077294.

743

Hay, S., P. Kushner, R. Blackport, K. McCusker, T. Oudar, L. Sun, M. England, C. Deser,

744

J. Screen and L. Polvani, 2021: Separating the influences of low-latitude warming and sea-745

ice loss on Northern Hemisphere climate change, Journal of Climate, in revision.

746

Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation: Regional747

temperatures and precipitation. Science, 269, 676-679.

748

Hurrell, James W., James J. Hack, Dennis Shea, Julie M. Caron, James Rosinski, 2008: A

749

New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community

750

Atmosphere Model. J. Climate, 21, 5145–5153. doi: 0.1175/2008JCLI2292.1.

751

Jaiser, Ralf, K. Dethloff and D. Handorf, 2013: Stratospheric response to Arctic sea ice

752

retreat and associated planetary wave propagation changes. 65. Tellus A, doi:World

economyforum,

last

accessed

on

June

2,

2021

at

33

753

http://dx.doi.org/10.3402/tellusa.v65i0.19375.

754

Jaiser, R., Nakamura, T., Handorf, D., Dethloff, K., Ukita, J., and Yamazaki, K., 2016:

755

Atmospheric winter response to Arctic sea ice changes in reanalysis data and model

756

simulations, J. Geophys. Res. Atmos., 121, 7564– 7577, doi:10.1002/2015JD024679.

757

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M.,

758

Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W.,

759

Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R.,760& Joseph, D., 1996: The NCEP/NCAR 40-Year Reanalysis Project, Bulletin of the761

American Meteorological Society, 77(3), 437-472.

762

Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J., Bates, S.,

763

Danabasoglu, G., Edwards, J., Holland, M. Kushner, P., Lamarque, J.-F., Lawrence, D.,764

Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and M.

765

Vertenstein, 2015: The Community Earth System Model (CESM) Large Ensemble Project:

766

A Community Resource for Studying Climate Change in the Presence of Internal Climate

767

Variability, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-13-

768

00255.1

769

Kim, B.-M., S.-W. Son, S.-K. Min, J.-H. Jeong, S.-J. Kim, X. Zhang, T. Shim, and J.-H.

770

Yoon, 2014: Weakening of the stratospheric polar vortex by Arctic sea-ice loss. Nature

771

communications, 5, 4646, doi:10.1038/ncomms5646.

772

Kolstad E. W. and J. A. Screen, 2019: Nonstationary relationship between autumn Arctic773

sea ice and the winter North Atlantic oscillation. Geophysical Research Letters, 46, 7583–

774

7591. https://doi.org/10.1029/2019GL083059.
34

775

Kretschmer, M. et al., 2018: More frequent weak stratospheric polar vortex states linked to776

mid-latitude cold extremes. Bull. Am. Meteorol. Soc. 99, 49–60 (2018).

777

Labe, Z. M., Y. Peings, and G. Magnusdottir, 2019: The effect of QBO phase on the778

atmospheric response to projected Arctic sea ice loss in early winter. Geophysical Research

779

Letters, DOI:10.1029/2019GL083095

780

Labe, Z. M., 2020: The effects of Arctic sea-ice thickness loss and stratospheric variability

781

on mid-latitude cold spells. University of California, Irvine. Doctoral Dissertation.

782

Larson, V. E., 2017: CLUBB-SILHS: A parameterization of subgrid variability in the783

atmosphere. arXiv:1711.03675v2 [physics.ao-ph].

784

Liang, Y.-C., Y.-O. Kwon, C. Frankignoul, G. Danabasoglu, S. Yeager, A. Cherchi, Y.

785

Gao, G. Gastineau, R. Ghosh, D. Matei, J. V. Mecking, D. Peano, L. Suo, and T. Tian,786

2019: Quantification of the Arctic sea ice-driven atmospheric circulation variability in

787

coordinated788

doi:1029/2019GL085397

789

Maher, N., Milinski, S., Suarez- Gutierrez, L., Botzet, M., Dobrynin, M., Kornblueh, L., et790

al., 2019: The Max Planck Institute Grand Ensemble: Enabling the exploration of climate

791

system variability. Journal of Advances in Modeling Earth Systems, 11, 2050–2069.

792

https://doi.org/10.1029/2019MS001639

793

McCusker, Kelly E., J. C. Fyfe, M. Sigmond, 2016: Twenty-five winters of unexpected794

Eurasian cooling unlikely due to Arctic sea ice loss, Nature Geoscience,large

ensemblesimulations,GeophysicalResearch

Letters,

47,

35

795

doi:10.1038/ngeo2820.

796

McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M.,

797

2018: Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere798

impacts.

799

https://doi.org/10.1002/2017GL076433

800

Mori, M., M. Watanabe, H. Shiogama, J. Inoue, and M. Kimoto, 2014: Robust arctic sea-801

ice influence on the frequent Eurasian cold winters in past decades. Nature Geoscience, 7,

802

869–873, doi:10.1038/ngeo2277.

803

Mori, M., Kosaka, Y., Watanabe, M., Nakamura, H. & Kimoto, M., 2019a: A reconciled804

estimate of the infuence of Arctic sea-ice loss on recent Eurasian cooling. Nat. Clim.

805

Change 9, 123–129.

806

Mori, M., Kosaka, Y., Watanabe, M. et al., 2019b: Reply to: Is sea-ice-driven Eurasian807

cooling

808

https://doi.org/10.1038/s41558-019-0636-0

809

Mudelsee M, 2010: Climate Time Series Analysis: Classical Statistical and Bootstrap

810

Methods. First edition. Springer, Dordrecht, 474pp

811

Nakamura T, Yamazaki K, Iwamoto K, Honda M, Miyoshi Y, Ogawa Y, Tomikawa Y,

812

Ukita J, 2016: The stratospheric pathway for Arctic impacts on midlatitude climate.

813

Geophys Res Lett 43:3494–3501. https://doi.org/10.1002/2016GL068330

814

Notz, D., & SIMIP Community, 2020: Arctic sea ice in CMIP6. Geophysical Research

Geophysical

too

weak

in

Researchmodels?.

Letters,

Nat.

Clim.

45,

Chang.

945–954.

9,

937–939.

36

815

Letters, 47, e2019GL086749. https://doi.org/10.1029/2019GL086749.

816

Ogawa, F., and Coauthors, 2018: Evaluating impacts of recent Arctic sea ice loss on the817

Northern Hemisphere winter climate change. Geophys. Res. Lett., 45, 3255–3263,

818

https://doi.org/ 10.1002/2017GL076502.

819

O'Neill, A., and V. D. Pope (1988), Simulations of linear and nonlinear disturbances in the820

stratosphere, Q. J. R. Meteorol. Soc., 114, 1063–1110.

821

Overland, J. E., Wood, K. R., M. Wang, 2011: Warm Arctic-cold continents: Climate

822

impacts of the newly open arctic sea. Polar Research, 30(SUPPL.1), 1–14.

823

http://doi.org/10.3402/polar.v30i0.15787.

824

Peings, Y. and G. Magnusdottir, 2014: Response of the Wintertime Northern Hemisphere

825

Atmospheric Circulation to Current and Projected Arctic Sea Ice Decline: A Numerical

826

Study with CAM5. J. Climate, 27, 244-264, doi: 10.1175/JCLI-D-13-00272.1.

827

Peings, Y., 2019: Ural blocking as a driver of early-winter stratospheric warmings.

828

Geophysical Research Letters,46, 5460–5468. https://doi.org/10.1029/2019GL082097.

829

Peings, Y., Z.M. Labe, and G. Magnusdottir, 2021: Are 100 ensemble members enough to830

capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate,831

DOI:10.1175/JCLI-D-20-0613.1

832

Perlwitz, J., M. Hoerling, and R. Dole, 2015:, Arctic tropospheric warming: Causes and833

linkages to lower latitudes. J. Climate, 28, 2154–2167, https://doi.org/10.1175/JCLI-D-14-

834

00095.1.
37

835

Plumb, R. A. (1981), Instability of the distorted polar night vortex: A theory of836

stratospheric warmings, J. Atmos. Sci., 38, 2514– 2531.

837

Polvani, L., Sun, L., Butler, A. H., Richter, J. H., & Deser, C., (2017: Distinguishing838

stratospheric sudden warmings from ENSO as key drivers of wintertime climate variability

839

over

840

https://doi.org/10.1175/JCLI-D-16-0277.1

841

Polvani, L. M. and D. W. Waugh, 2004: Upward wave activity flux as precursor to extreme

842

stratospheric events and subsequent anomalous surface weather regimes. J. Climate, 17,843

3548–3554.

844

Ronalds B., E.A. Barnes, R. Eade, Y. Peings, M. Sigmond, 2020: North Pacific zonal wind845

response to sea ice loss in the Polar Amplification Model Intercomparison Project and its

846

downstream implications. Climate Dynamics, https://doi.org/10.1007/s00382-020-05352-

847

w

848

Ruggieri, P., R. Buizza, and G. Visconti, 2016: On the link between Barents-Kara sea ice

849

variability and European blocking, J. Geophys. Res. Atmos., 121, 5664–5679,

850

doi:10.1002/2015JD024021.

851

Scinocca, J. F., M. C. Reader, D. A. Plummer, M. Sigmond, P. J. Kushner, T. G. Shepherd,852

and R. Ravishankara, 2009: Impact of sudden Arctic sea-ice loss on stratospheric polar853

ozone Geophys. Res. Lett., 36, L24701, doi:10.1029/ 2009GL041239.

854

Scott, R. K., and D. G. Dritschel (2006), Vortex-vortex interactions in the winter855

stratosphere, J. Atmos. Sci., 63, 726–740.

the

North

AtlanticandEurasia,J.

Climate,30(6),

1959-1969.

38

856

Screen, J. A., 2017: Simulated atmospheric response to regional and pan-Arctic sea ice loss.

857

Journal of Climate, 30, 3945–3962. https://doi.org/10.1175/JCLI-D-16-0197.1.

858

Screen, J.A. & R. Blackport, 2019: Is sea-ice-driven Eurasian cooling too weak in models?

859

Nature Clim. Change, 9, 934-936.

860

Screen, J. A., C. Deser, I. Simmonds, and R. Tomas, 2014: Atmospheric impacts of Arctic

861

sea-ice loss, 1979-2009: Separating forced change from atmospheric internal variability.

862

Clim. Dyn., 43, 333-344., doi: 10.1007/s00382-013-1830-9.

863

Screen J., C. Deser, D. M. Smith, X. Zhang, R. Blackport, P. J. Kushner, T. Oudar, K. E.

864

McCusker and L. Sun, 2018: Consistency and discrepancy in the atmospheric response to865

Arctic sea ice loss across climate models, Nature Geoscience, doi:10.1038/s41561-018-

866

0059-y.

867

Screen, J. a. and I. Simmonds, 2010: The central role of diminishing sea ice in recent Arctic

868

temperature amplification. Nature, 464, 1334–7, doi:10.1038/nature09051.

869

Screen, J. a., I. Simmonds, C. Deser, and R. Tomas, 2013: The Atmospheric Response to870

Three Decades of Observed Arctic Sea Ice Loss. J. Climate, 26, 1230–1248,

871

doi:10.1175/JCLI-D-12-00063.1.

872

Seviour, W. J. M., 2017: Weakening and shift of the Arctic stratospheric polar vortex:

873

Internal variability or forced response?, Geophys. Res. Lett., 44, 3365– 3373,

874

doi:10.1002/2017GL073071.

875

Sigmond, M. and J. F. Scinocca, 2010: The Influence of Basic State on the Northern

876

Hemisphere Circulation Response to Climate Change. J. Climate, 23, 1434–1446.
39

877

Sigmond, M., J. F. Scinocca, V.V. Kharin, T.G. Shepherd, 2013: Enhanced seasonal878

forecast skill following stratospheric sudden warmings, Nature Geosci., 6, 98-102.

879

Simpson, I. R., Bacmeister, J., Neale, R. B., Hannay, C., Gettelman, A., Garcia, R. R., et880

al., 2020: An evaluation of the large-scale atmospheric circulation and its variability in

881

CESM2 and other CMIP models. Journal of Geophysical Research: Atmospheres, 125,882

e2020JD032835. https://doi.org/ 10.1029/2020JD032835

883

Smith, D. M., Nick J. Dunstone, Adam A. Scaife, Emma K. Fiedler, Dan Copsey, and884

Steven C. Hardiman, 2017: Atmospheric Response to Arctic and Antarctic Sea Ice: The

885

Importance of Ocean–Atmosphere Coupling and the Background State. Journal of Climate,886

30:12, 4547-4565.

887

Smith, D.M., Scaife, A.A., Eade, R. et al., 2020: North Atlantic climate far more888

predictable than models imply. Nature 583, 796–800. https://doi.org/10.1038/s41586-020-

889

2525-0.

890

Smith, D. M., Screen, J. A., Deser, C., Cohen, J., Fyfe, J. C., García-Serrano, J., Jung, T.,

891

Kattsov, V., Matei, D., Msadek, R., Peings, Y., Sigmond, M., Ukita, J., Yoon, J.-H., and892

Zhang, X., 2019: The Polar Amplification Model Intercomparison Project (PAMIP)

893

contribution to CMIP6: investigating the causes and consequences of polar amplification,

894

Geosci. Model Dev., 12, 1139-1164, https://doi.org/10.5194/gmd-12-1139-2019.

895

Smith, D. M., R. Eader, M. Andrews, H. Ayres, A. Clark, S. Chripko, C. Deser, N. J.

896

Dunstone, J. Garcia-Serrano, G. Gastineau, L. S. Graff, S. Hardiman, B. He, L. Hermanson,

897

T. Jung, J. Knight, X. Levine, G. Magnusdottir, E. Manzini, D. Matei, M. Mori, R. Msadek,

40

898

P. Ortega, Y. Peings, A. A. Scaife, J. A. Screen, T. Semmler, M. Sigmond, J. Streffing, L.

899

Sun, A. Walsh, 2021: Observationally constrained multi-model ensemble atmospheric900

response to future Arctic sea ice loss, Nature communication, in review.

901

Smith, K. L. and P. J. Kushner, 2012: Linear interference and the initiation of extratropical902

stratosphere-troposphere interactions. Journal of Geophysical Research, 117, D13107,

903

doi:10.1029/2012JD017587.

904

Streffing, J., Semmler, T., Zampieri, L., & Jung, T. (2021). Response of Northern

905

Hemisphere Weather and Climate to Arctic Sea Ice Decline: Resolution Independence in

906

Polar Amplification Model Intercomparison Project (PAMIP) Simulations, Journal of907

Climate, 34(20), 8445-8457.

908

Sun, L., Alexander, M. A., & Deser, C., 2018: Evolution of the global coupled climate909

response to Arctic sea ice loss during 1990-2090 and its contribution to climate change.

910

Journal of Climate, 31(19), 7823–7843. https://doi.org/10.1175/JCLI-D-18-0134.1.

911

Sun, L., C. Deser, L. M. Polvani, and R. Tomas, 2014: Influence of projected Arctic sea

912

ice loss on polar stratospheric ozone and circulation in spring. Environmental Research

913

Letters, 9, 084016, doi:10.1088/1748-9326/9/8/084016.

914

Sun, L., C. Deser and R. A. Tomas, 2015: Mechanisms of stratospheric and tropospheric915

circulation response to projected Arctic sea ice loss, J. Climate, 28, 7824–7845.doi:916

http://dx.doi.org/10.1175/JCLI-D-15-0169.1.

917

Sun, L., J. Perlwitz and M. Hoerling, 2016: What caused the recent "Warm Arctic, Cold

41

918

Continents" trend pattern in winter temperatures? Geophys. Res. Lett., doi:919

http://dx.doi.org/10.1002/2016GL069024.

920

Thompson, D. W. J. and J. M. Wallace, 2000: Annular Modes in the Extratropical921

Circulation. Part I: Month-to-Month Variability. J. Climate, 13, 1000–1016.

922

Thompson, D. W. J., Barnes, E. A., Deser, C., Foust, W. E., & Phillips, A. S., 2015:

923

Quantifying the Role of Internal Climate Variability in Future Climate Trends, Journal of924

Climate, 28(16), 6443-6456.

925

Tomas, R. A., C. Deser, and L. Sun, 2016: The role of ocean heat transport in the global

926

climate response to projected Arctic sea ice loss. J. Climate, 29, 6841–6859,

927

https://doi.org/10.1175/ JCLI-D-15-0651.1.

928

Tung, K. K., and R. S. Lindzen (1979), A theory of stationary long waves. Part I: A simple

929

theory of blocking, Mon. Weather Rev., 107, 714–734.

930

Wang, K., C. Deser, L. Sun, and R. Tomas, 2018: Fast response of the tropics to an abrupt

931

loss of Arctic sea ice via ocean dynamics. Geophys. Res. Lett., 45, 4264–4272,

932

https://doi.org/ 10.1029/2018GL077325.

933

Warner, J.L., J.A. Screen & A.A. Scaife, 2020: Links between Barents-Kara sea ice and934

the extratropical atmospheric circulation explained by internal variability and tropical935

forcing. Geophys. Res. Lett., 47, e2019GL085679.

936

Wilks, D. S., 2016: "The stippling shows statistically significant grid points": How research

937

results are routinely overstated and overinterpreted, and what to do about it. Bull. Amer.

938

Meteor. Soc., 97, 2263-22.
42

939

Wu, Y. and K. L. Smith, 2016: Response of Northern Hemisphere Midlatitude Circulation

940

to Arctic Amplification in a Simple Atmospheric General Circulation Model. J. Clim., 29,941

2041-2058.

942

Zhang, P., Wu, Y. and K. L. Smith, 2017: Prolonged Effect of the Stratospheric Pathway

943

in Linking Barents-Kara Sea Sea Ice Variability to the Midlatitude Circulation in a

944

Simplified Model. Clim. Dyn., https://doi.org/10.1007/s00382-017-3624-y.

945

Zhang, P., Wu, Y., Simpson, I. R., Smith, K. L., Zhang, X., De, B., & Callaghan, P., 2018:946

A stratospheric pathway linking a colder Siberia to Barents-Kara Sea sea ice loss. Science

947

Advances, 4(7), eaat6025. 915 https://doi.org/10.1126/sciadv.aat6025

43

948
949
950

Table 1. PAMIP atmosphere-only time-slice experiments conducted in this study using
Community Atmosphere Model version 6 (see text for details of the experimental design).
Nomenclature follows that in Smith et al. (2019).
No.

Experiment name

SST forcing

SIC forcing

1.1

pdSST-pdSIC

present-day
conditions

present-day conditions

1.2

piSST-piSIC

preindustrial
conditions

preindustrial conditions

1.3

piSST-pdSIC

preindustrial
conditions

present-day conditions

1.4

futSST-pdSIC

future conditions

present-day conditions

1.5

pdSST-piArcSIC

present-day
conditions

preindustrial conditions in
the Arctic, present-day
conditions in the Antarctic

1.6

pdSST-futArcSIC

present-day
conditions

future conditions in the
Arctic, present-day
conditions in the Antarctic

futSST-futArcSIC

future conditions

future conditions in the
Arctic, present-day
conditions in the Antarctic

Other details

Year-2000 radiative
forcing; 14-monthtime-slice run withthe first 2 months
discarded as spin-up;
ensemble size of 200;
set ice thickness set tobe 2 m in the Arctic
and 1 m in the
Antarctic

951

44

952
953
954
955
956
957
958
959

Figure 1. (a) Seasonal maps of prescribed Arctic SIC loss (%) in the PAMIP experiments
for the past (preindustrial to present-day) and future (present-day to +2oC warming above
preindustrial level). Summer, autumn, winter and spring seasons are defined to be theaverage of July-September (JAS), October-December (OND), January-March (JFM) and
April-June (AMJ), respectively. (b) Monthly Arctic sea ice area loss (106 km2) for the past
(blue) and future (red); note the inverted y-axis scale.

45

960
961
962
963
964
965
966
967
968
969
970
971
972

Figure 2. Monthly responses to past (blue) and future (red) Arctic sea ice loss for (fromtop to bottom): Arctic net upward surface energy flux (W m-2; positive upward); Arctic
surface air temperature (oC); Arctic precipitation (mm day-1); and high-latitude (poleward
of 50oN) terrestrial surface air temperature (oC). The surface energy flux is the sum of the
turbulent (sensible plus latent) heat flux and the long-wave radiative flux. Solid and dashedblue curves indicate the past sea ice loss with preindustrial and present-day background
SSTs, while solid and dashed red curves indicate the future sea ice loss with present-day
and future background SSTs. The Arctic responses are averaged over all grid boxes
containing a minimum of 15% present-day climatological SIC in March. Vertical line
segments show the two standard deviation error bars based on the boostrapped 100member averages (see text for details).

46

973
974
975
976
977
978
979
980
981

Figure 3. Probability density function (PDF) of 100-member averages of DJF U10 for: (a)ΔpastpiSST; (b) ΔpastpdSST; (c) ΔfutpdSST and (d) ΔfutfutSST. For each case, the histogram isevaluated by randomly selecting 100 members with replacement from the 200-member
ensembles and repeating this procedure 1000 times. Curves are calculated analytically fromthe interannual standard deviation (see text). Horizontal green (black) bar indicates +/- one(two) standard deviations of the PDF. Vertical dashed red (blue) line indicates the averageover the first (second) 100 members.

47

982
983
984
985
986
987
988

Figure 4. DJF zonal-mean zonal wind response (shading; m s-1) as a function of pressure(hPa; y-axis) and latitude (x-axis) for ΔpastpiSST based on: (a) all 200 members; (b)members 1-100; and (c) members 101-200. Black contours (interval of 10 m s-1; zero
contour is thickened) indicate the climatology. Stippling indicates the 90% statisticalsignificance based on a two-sided student’s t-test and false discovery rate (FDR; Wilks
2016).

48

989

990
991
992
993
994
995
996
997
998
999

Figure 5. (Top) 60oN zonal-mean zonal wind response (m s-1) in ΔpastpiSST as a functionof month (x-axis) and pressure (y-axis) for: (a) first 100-member average; (b) second 100member average and (c) their difference. (Bottom) Regression analysis of 60oN zonalmean zonal wind response onto U10 (defined as DJF zonal-mean zonal wind at 10 hPa and60oN) across the 1000 bootstrapped samples of 100-member averages. Panels d, e and fshow the Mean-2σ, Mean+2σ and their difference, respectively. Panel g shows thecorrelation of the zonal-mean zonal wind response with U10 across the 1000 bootstrappedsamples. Stippling indicates the 90% statistical significance based on a two-sided student’s
t-test and false discovery rate.

49

1000
1001
1002
1003
1004
1005
1006

Figure 6. Geopotential height response (m) in ΔpastpiSST for: (a) first 100-member average;(b) second 100-member average and (c) their difference. Rows 1-4 show the 10-hPa DJF
response and the 500-hPa, 850-hPa and 1000-hPa responses in JFM, respectively. Stippling
indicates the 90% statistical significance based on a two-sided student’s t-test and false
discovery rate.

50

1007
1008
1009
1010
1011
1012
1013

Figure 7. Regression analysis of geopotential height response onto U10 in ΔpastpiSST acrossthe 1000 bootstrapped samples. Panels a, b and c show the Mean-2σ, Mean+2σ and theirdifference, respectively. Rows 1-4 show the 10-hPa DJF response and the 500-hPa, 850hPa and 1000-hPa responses in JFM, respectively. Stippling indicates the 90% statisticalsignificance based on a two-sided student’s t-test and false discovery rate.

51

1014
1015
1016
1017
1018
1019
1020
1021
1022

Figure 8. (Top) JFM SAT (shading; oC) and SLP (contours; interval of 0.5 hPa) responses
in ΔpastpiSST for: (a) first 100-member average; (b) second 100-member average and (c)their difference. (Bottom) regression analysis of JFM SAT response onto U10 in ΔpastpiSST
across the 1000 bootstrapped samples of 100-member averages. Panels d, e and f show theMean-2σ, Mean+2σ and their difference, respectively. Red and blue contours denotepositive and negative values, respectively. Zero contour line has been omitted. Stippling
indicates the 90% statistical significance based on a two-sided student’s t-test and false
discovery rate.

52

1023
1024
1025
1026
1027
1028
1029
1030
1031
1032

Figure 9: (Top) JFM precipitation (shading; mm day-1) and SLP (contours; interval of 0.5hPa) responses in ΔpastpiSST for: (a) first 100-member average; (b) second 100-member
average and (c) their difference. (Bottom) regression analysis of JFM precipitation
response onto U10 in ΔpastpiSST across the 1000 bootstrapped samples of 100-member
averages. Panels d, e and f show the Mean-2σ, Mean+2σ and their difference, respectively.
Red and blue contours denote positive and negative values, respectively. Zero contour line
has been omitted. Stippling indicates the 90% statistical significance based on a two-sided
student’s t-test and false discovery rate.

53

1033
1034
1035
1036
1037
1038
1039
1040
1041
1042

Figure 10. Regression analysis of JFM response onto U10 in ΔfutpdSST across the 1000
bootstrapped samples of 100-member averages. Rows 1-3 show the SLP (shading; hPa),
SAT (shading; oC) and precipitation (shading; mm day-1) responses, respectively. Panels a,b and c show the Mean-2σ, Mean+2σ and their difference, respectively. The SLP responses(contours; interval of 0.5 hPa) are overlaid on the SAT and precipitation panels. Red andblue contours denote positive and negative values, respectively. Zero contour line has been
omitted. Stippling indicates the 90% statistical significance based on a two-sided student’s
t-test and false discovery rate.
54

1043
1044
1045
1046
1047
1048
1049
1050
1051

Figure 11. (Top) eddy heat flux response near 100 hPa (model hybrid 0.1 sigma level;
shading; K m s-1) in ΔpastpiSST as a function of month (x-axis) and latitude (y-axis) for: (a)first 100-member average; (b) second 100-member average and (c) their difference.
(Bottom) Regression analysis of eddy heat flux response near 100 hPa onto U10 across the1000 bootstrapped samples of 100-member averages. Panels d, e and f show the Mean-2σ,
Mean+2σ and their difference, respectively. Stippling indicates the 90% statisticalsignificance based on a two-sided student’s t-test and false discovery rate. The
climatologies (contours; interval of 15 K m s-1) are overlaid on the panels a, b, d and e.

55

1052
1053
1054
1055
1056
1057
1058
1059
1060
1061

Figure 12. (Top two rows) NDJ zonal wave-1 and wave-2 responses (shading; m) at 300
hPa in ΔpastpiSST for: (a) first 100-member average; (b) second 100-member average and(c) their difference. The climatologies (contours; interval of 50 m) are overlaid on panelsa, b and c. (Bottom two rows) Regression analysis of NDJ zonal wave-1 and wave-2
responses at 300 hPa onto U10 in ΔpastpiSST across the 1000 bootstrapped samples of 100member averages. Panels d, e and f show the Mean-2σ, Mean+2σ and their difference,
respectively. The climatologies (contours; interval of 50 m) are overlaid on the panels d, e
and f. Solid and dashed contours denote positive and negative values, respectively. Zero
contour line has been omitted.

56

1062
1063
1064
1065
1066
1067
1068

Figure 13. (a) 2.5th-to-97.5th percentile (2σ) range of variability in DJF U10 (m s-1) as afunction of the number of ensemble members averaged (“sample size”) in Reanalysis data
(red curve) and CAM6 (black curve). (b) As in (a), but for the JFM NAO response
associated with +/- two standard deviation anomaly of DJF U10. Gray curve indicates thetotal NAO internal variability (+/-2σ) in Reanalysis and CAM6 (identical because each
NAO time series is standardized by its interannual standard deviation).

57

1069
1070
1071
1072
1073
1074
1075
1076

Figure 14. (a) 95% marginal error (2𝜎) of the JFM linear trend during the past 50 years(1971-2020) for (from top to bottom): SLP (shading; hPa), SAT (shading; oC) and
precipitation (shading; mm day-1) based on (a) NCEP-NCAR Reanalysis and (b) CAM6
(see text for details). The SLP trends are overlaid as contours (interval of 0.5 hPa) on the
SAT and precipitation panels. (c) The estimated forced response to Arctic sea ice loss overthe past 50 years from the CAM6 experiments (see text for details). Stippling indicates the
90% statistical significance based on a two-sided student’s t-test and false discovery rate.
58