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1

Impacts of projected Arctic sea ice loss on daily weather patterns over North

2

America3

Melissa Gervais ,a Lantao Sun,b Clara Deser,c4

a Department of Meteorology and Atmospheric Science and The Institute for Computational and5

Data Science6b Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

7c National Center for Atmospheric Research, Boulder, Colorado

8

Corresponding author: Melissa Gervais, mmg62@psu.edu
19ABSTRACT: Future Arctic sea ice loss has a known impact on Arctic Amplification (AA) and10

mean atmospheric circulation. Furthermore, several studies have shown it leads to a decreased

11

variance in temperature over North America. In this study, we analyze results from two fully-

12

coupled Community Earth System Model (CESM) Whole Atmosphere Community Climate Model

13(WACCM4) simulations with sea ice nudged to either the ensemble mean of WACCM historical14

runs averaged over the 1980-1999 period for the control (CTL) or projected RCP8.5 values over15

the 2080-2099 period for the experiment (EXP). Dominant large-scale meteorological patterns

16(LSMPs) are then identified using self-organizing maps applied to winter daily 500 hPa geopotential

17′ ) over North America. We investigate how sea ice loss (EXP-CTL) impacts
height anomalies (𝑍500

18

the frequency of these LSMPs and, through composite analysis, the sensible weather associated19with them. We find differences in LSMP frequency but no change in residency time indicating

20

there is no stagnation of the flow with sea ice loss. Sea ice loss also acts to de-amplify and/or shift

21′
the 𝑍500
that characterize these LSMPs and their associated anomalies in potential temperature22

at 850hPa. Impacts on precipitation anomalies are more localized and consistent with changes in23

anomalous sea level pressure. With this LSMP framework we provide new mechanistic insights,

24demonstrating a role for thermodynamic, dynamic and diabatic processes in sea ice impacts on

25

atmospheric variability. Understanding these processes from a synoptic perspective is critical as

26

some LSMPs play an outsized role in producing the mean response to Arctic sea ice loss.

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SIGNIFICANCE STATEMENT:The goal of this study is to understand how future Arctic sea

28

ice loss might impact daily weather patterns over North America. We use a global climate model29

to produce on set of simulations one where sea ice is similar to present conditions and another30

that represents conditions at the end of the 21st century. Daily patterns in large-scale circulation

31

at roughly 5.5km in altitude are then identified using a machine learning method. We find that32

sea ice loss tends to de-amplify these patterns and their associated impacts on temperature nearer

33

the surface. Our methodology allows us to probe more deeply into the mechanisms responsible

34

for these changes, which provides a new way to understand how sea ice loss can impact the daily

35

weather we experience.

36

1. Introduction

37

The Arctic Sea has experienced a significant decline in sea ice extent with trends of38−4.36%/decade and greatest losses in the Barents/Kara Seas and Beaufort Sea (Comiso et al.

392017). Climate models project that the Arctic will become seasonally ice free by the mid 21st40

century (Wang and Overland 2012), albeit with large uncertainty due to internal variability (Jahn41et al. 2016). This sea ice loss is greatest in September; however, the impact on the atmosphere is42largest in winter when turbulent heat fluxes from the ocean to the atmosphere are greatest (Deser43

et al. 2010; Singarayer et al. 2006).

44

One robust impact of sea ice loss on the atmosphere is Arctic amplification (AA), where the45

Arctic warms faster than the global mean (Screen and Simmonds 2010; Barnes and Screen 2015;46

Dai et al. 2019). The AA signal can be seen in observations (e.g. Serreze et al. 2009; Screen

47

and Simmonds 2010) and modeling studies (e.g. Holland and Bitz 2003; Deser et al. 2010). The

48

increased atmospheric temperatures associated with AA are largest near the surface and during the49

winter months (e.g. Serreze et al. 2009; Holland and Bitz 2003; Deser et al. 2010). Although the50causes of AA and their relative importance remain an active area of research (Smith et al. 2019),

51

several feedback mechanisms operating at low and high latitudes have been shown to contribute,

52

including: the surface albedo feedback, the lapse rate feedback, and the Planck feedback (Pithan

53

and Mauritsen 2014). Additional processes such as increased atmospheric transport of heat and54

moisture associated with remote SSTs have also been shown to play an important role in producing3

55

the AA signal and in particular its extension to higher altitudes (Screen et al. 2012; Perlwitz et al.

562015).

57

The increased turbulent heat fluxes associated with Arctic sea ice loss result in the development

58

of localized thermal low pressure anomalies over the region of sea ice loss (Alexander et al.

59

2004; Gervais et al. 2016; Smith et al. 2017). The remote circulation response; however, is more

60

uncertain (Smith et al. 2019). AA is associated with a general reduction in meridional temperature

61

gradient and increase in mean column thickness over the Arctic which, through thermal wind62

arguments, is expected to weaken the midlatitude westerlies (Vihma 2014). This leads to the63

tug-of-war paradigm, where sea ice loss is expected to shift the midlatitude jets equatorward,

64

while greenhouse gas forcing separate of sea ice loss acts to shift them poleward (e.g. Deser et al.

65

2015; Oudar et al. 2017; McCusker et al. 2017; Blackport and Kushner 2017). Fully coupled and66

atmosphere-only simulations with imposed future sea ice loss show broadly consistent impacts on

67

the atmospheric circulation including a weakened Icelandic Low, an intensified Aleutian Low and68

Siberian High, and an equatorward shifted and often weakened zonal mean mid-latitude jet (Screen

69

et al. 2018). However, Peings et al. (2021) showed that even with the large imposed future sea ice

70

loss internal variability can play an important role in determining the atmospheric response.

71

The further impact of Arctic sea ice loss on atmospheric variability has become an important

72

topic of discussion and disagreement. Francis and Vavrus (2012) hypothesized that AA leads to73

a reduction in the midlatitude westerlies and consequently more meanders in the jet. Although74

issues with the methodology they used were highlighted in subsequent papers (Barnes 2013; Screen

75

et al. 2013), the topic of Arctic midlatitude linkages has been the subject of considerable research

76

and has been summarized in numerous review articles (Cohen et al. 2014; Vihma 2014; Barnes

77

and Screen 2015; Screen et al. 2018). More recently, Blackport and Screen (2020) extended the78

observational analysis to present day and found that the observed trends in waviness are no longer

79

significant, although the AA signal has continued to increase. They conclude that the causal link80

is likely that periods of increased waviness leads to periods of increased AA due to enhanced

81

meridional temperature and moisture fluxes. Much of this previous work on Arctic sea ice loss and82

atmospheric variability has focused on the historical period; however, in the future we expect sea

83

ice loss to be much greater and the mechanisms through which it impacts atmospheric variability84

may differ from those discussed above.

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Atmospheric variability can be characterized in a variety of ways that may capture different

86

aspects and come with their own advantages or disadvantages. Many studies have utilized variance87

or standard deviation and found a reduction in the standard deviation of surface temperature with88

Arctic sea ice loss that they attribute to a reduction of the meridional temperature gradient (Screen

89

2014; Screen et al. 2015; Collow et al. 2019; Dai and Deng 2021). This metric is straightforward

90

and provides useful general information about changes in temperature distribution at each location.

91

A variety of metrics have been employed to examine changes in the waviness or sinuosity of the92

mid-latitude flow (e.g. Francis and Vavrus 2012, 2015; Cattiaux et al. 2016), in particular in the93

observations, often departures of a single geopotential height contour from its zonal mean value

94

are used. However, early applications of such methods (Francis and Vavrus 2012) have been

95

shown to be sensitive to analysis parameters chosen (Barnes 2013; Screen et al. 2013) thus careful

96

attention must be paid in their application to ensure robustness across seasons and with mean

97

warming (Cattiaux et al. 2016). These metrics provide useful information about the amplitude of98

spatial patterns across the Northern Hemisphere. However, neither standard deviation nor sinuosity

99

provide information about spatial patterns, and both are limited in terms of the ability to probe

100

more deeply into the physical mechanisms responsible.

101

Alternatively, the identification of large-scale meteorological patterns (LSMPs) and their changes

102

can provide key information about regional atmospheric variability. LSMPs can be manually103

identified through synoptic typing; however for large datasets objective classification methods

104

such as k-means or self-organizing maps (SOM) can be employed (Grotjahn et al. 2016). SOM

105

is a machine learning method that can effectively identify archetypal patterns and classify data

106

into these categories. A benefit of the SOM method is that it does not require patterns to be107

orthogonal, unlike the more traditional method of empirical orthogonal functions (EOFs). As a108

result, the SOM method can produce LSMPs (SOM nodes) that are more realistic (Grotjahn et al.

1092016). Much like classic synoptic typing analysis, composite analysis of diagnostic fields can be

110

applied to identified LSMPs. This provides a framework through which physical understanding of111

these patterns and their sensible weather impacts can be ascertained, which is not possible using

112

measures of variability such as standard deviations or sinuosity.

113

This study will examine the impact of future Arctic sea ice loss on LSMPs of mid-tropospheric114

circulation over North America. We will employ two fully coupled climate model simulations with

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nudged sea ice to historical or projected end of 21st century conditions, thus changes are much

116

larger than the observed trend. Self-organizing maps will be used to identify LSMPs of 500hPa

117

geopotential height anomalies and examine their changes in frequency and pattern with sea ice loss.

118

Composite analysis of these LSMPs will be used to investigate the sensible weather conditions

119

associated with these LSMPs including low-level potential temperature and precipitation. Finally,120

the impact of sea ice loss will be viewed through the lens of these LSMPs to better understand121

processes tied to atmospheric variability.

122

2. Data and Methods

123

a. Model Simulations

124

To investigate the contribution of sea ice loss to atmospheric variability, we employed a pair of125

two Community Earth System Model (CESM) (Hurrell et al. 2013) simulations with constrained126

sea ice. The model setup utilizes the Whole Atmosphere Community Climate Model (WACCM4),

127

the Parallel Ocean Program Version 2 (POP2), the Community Land Model Version 4 (CLM4), and128

the Los Alamos Sea Ice Model (CICE4) component models. The atmosphere and land components

129

both have horizontal resolutions of 1.9◦ ×2.5◦ , and the ocean and sea ice components have roughly

130

1◦ resolutions. The Whole Atmosphere Community Climate Model (WACCM4) is a high-top

131model with 66 vertical pressure levels reaching 5.96 × 10−6 hPa (approximately 140 km). The

132

added vertical resolution and extension to higher heights leads to a better representation of the133

stratosphere. This is important for studying the impact of sea ice loss as troposphere-stratosphere134

interactions are known to be an important mechanism through which sea ice loss impacts the135

atmosphere (Sun et al. 2015). The model also includes a sophisticated stratospheric chemistry

136

package which provides more realistic conditions in the upper-atmosphere (Marsh et al. 2013).

137

The CICE4 model includes a thermodynamic component that calculates growth rates of snow and138

ice, an ice dynamics component that utilizes realistic ice physics based on ice mass and velocity,

139

a thickness parameterization that quantifies ice strain and thickness, and a transport model that140

simulates ice advection (Hunke et al. 2015).

141

Both experiments are fully-coupled with radiative forcing held constant at the year 2000. The

142

control simulation (CTL) sea ice is nudged to the ensemble mean of the WACCM historical runs

143

averaged over 1980-1999 and the experiment simulation (EXP) is nudged to projected RCP 8.5
6

a.

b.

160

Fig. 1. a) Monthly mean sea ice extent (millions of km2 ) defined as the total area of grid boxes having at least

161

15% sea ice concentration for the CTL (green) and EXP (purple) experiments. b) Mean difference in winter sea

162

ice (December-February) concentration (%) between the EXP and CTL experiments.

144

values over 2080-2099. The nudging method is described in Deser et al. (2015) and utilizes

145

spatially and seasonally varying long wave radiative fluxes (LRF) in each grid cell of the sea ice

146

model to force the sea ice to mimic historical and projected sea ice conditions. The LRF is applied147

only to the sea ice model where there is sea ice. The magnitude of the downward LRF is larger for148

months of greater ice thickness and coverage, and vice versa. Although energy is not conserved

149

using this method, water is conserved between the sea ice and ocean model components. The

150

experiments are both 300 years in duration, but we disregard the first 100 years for spin-up time151

and retain only the last 200 years for the analysis.

152

One advantage of this coupled model configuration is that SSTs are free to vary. This allows

153

for more realistic SSTs that are free to increase as the sea ice edge retreats and maintains dynamic

154

atmosphere-ocean variability. Ocean-atmosphere coupling has been shown to be important for

155

generating a more realistic response to sea ice loss that extends to lower latitudes and higher

156

altitudes (Deser et al. 2015) and in producing a reduced summer storminess in the mid-to-late157

21st century due to Arctic sea ice (Kang et al. 2023). Although the SSTs will differ between the158

simulations, they are still a direct bi-product of changes in sea ice as this is the only difference

159

between the two model set-ups.
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b. Self-Organizing Maps Algorithm

164

The SOM methodology works by repeatedly introducing input data vectors and adjusting a set

165

of nodes to better match these input data. Each SOM node is the same size as an input data vector

166

and is initialized prior to training, in this case with random data. These nodes are then updated

167

throughout the training. To accomplish this, the SOM algorithm determines a best matched unit

168

(BMU) for a specific training step (𝑡) by finding the map node (𝑚 𝑐 ) with the smallest Euclidean

169

distance to the input data vector (𝑥(𝑡)). The SOM is then updated using the following relation:𝑚𝑖 (𝑡 + 1) = 𝑚𝑖 (𝑡) + 𝛼(𝑡) · ℎ𝑐𝑖 (𝑡) · (𝑥(𝑡) − 𝑚𝑖 (𝑡)),

(1)

170

where ℎ𝑐𝑖 (𝑡) is the neighborhood function that defines the relative influence on different map nodes,

171

and 𝛼(𝑡) is the learning rate parameter that defines how much the map nodes are updated (Vesanto172

et al. 2000; Kohonen 2001). For the neighborhood function we use the Epanechnikhov function

173

defined as:ℎ𝑐𝑖 = 𝑚𝑎𝑥(0, 1 −2𝑑 𝑐𝑖𝜎(𝑡) 2

),

(2)

174

where d is the distance between a given node (𝑖) and the BMU (𝑐). For the Epanechnikov function,175

the BMU is modified the most and this decreases with distance away from the BMU. Nodes outside

176of the radius of influence (𝜎(𝑡)) are left unchanged. We use the diameter of the SOM as the177

initial radius of influence and decrease the value with each training iteration to eventually reach 1.

178

Here we conduct two trainings with different initial 𝜎(𝑡). The first training is important for broad

179

organization and in this case has an initial 𝜎(𝑡) value of 5. The second training is utilized for fine180

tuning and has an initial 𝜎(𝑡) of 2. For the learning rate parameter we use an inverse function of181

training time defined as:𝑡𝛼(𝑡) = 𝛼0 /(1 + 100 ),
𝐿

(3)

182

where 𝛼0 is the initial learning rate for each training and L is the total number of training steps (t)183

in each training. Here we use 𝛼0 = 0.1 for the first training and 𝛼0 = 0.01 for the second training.

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184

There are three measures used to assess SOM map quality: topological error, quantization error,

185

and the Sammon map. Quantization error is the average Euclidean distance between the input

186

data and their associated BMU, thus describing how similar the map nodes are to the input data

187

vectors. The topological error is defined as the percentage of input data vectors for whom the188

next best match unit is not a neighbor to the BMU and thus quantifies how well-ordered the189

SOM is. The Sammon map is a nonlinear mapping that visually represents the relative locations

190of the SOM map nodes. Over-training a SOM can result in a quantization error that continues

191

to decrease at the expense of a twisted Sammon map and higher topological error. The SOM

192

shown here is well constructed, meaning that it has a balance of low quantization error and low

193

topological error (<15%) and a flat Sammon map (not shown). More information about the SOM

194

method is available in Kohonen (2001). The SOM Program Package is publicly accessible at195

http://www.cis.hut.fi/research/som-research/.

196c. Creation of Final SOM

197

In this study, SOM is used to identify large-scale patterns of daily winter 500 hPa geopotential

198′ ) over North America. Analysis is conducted over the winter (December toheight anomalies (𝑍500199

February) season when the impact of sea ice loss on atmospheric circulation is greatest. The data200

is also confined the region of 25◦ N to 75◦ N and 180◦ E to 20◦ E to focus on the North American201

mid-latitude response to sea ice loss and identify patterns of variability on synoptic spatial scales.

202

We are interested in identifying changes in large-scale patterns separately from the mean response203

to sea ice loss. As such, anomalies are computed for each simulation (CTL and EXP) separately.

204

A daily climatology is computed for each simulation by averaging each calendar day over all

205

200 model years. Anomaly fields are then created by subtracting the daily climatology, for the206

corresponding simulation and calendar day, from each day of the simulation. This procedure takes

207

into account the seasonal cycle of 𝑍500 so that anomalies are identified across all months and208

effectively removes the seasonally varying mean response to sea ice loss. For subsequent analysis,209

the term “anomalies” will refer to the difference in any field relative to its seasonally varying

210

′ .
climatology and these will be denoted with a prime, for example 𝑍500

211

There are several options for pre-processing input data depending on the research question. In

212′
this study, the 𝑍500
fields are normalized by removing the mean of the time series and dividing

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by the standard deviation at each grid point prior to training. This ensures that locations that214

experience greater variability do not have a larger impact on the SOM classification. The data are

215

then multiplied by the cosine of the latitude to account for grid box area changes with latitude.

216

Input data for the SOM consists of model output from both the CTL and EXP simulations to ensure217

all patterns of variability present in each simulation are represented in the final SOM.

218

The SOM algorithm includes several user defined parameters, the most notable being the number219

of map nodes (archetypal patterns). Here the number of map nodes is determined through testing220

a variety of different SOM sizes. A final SOM size is chosen that is the smallest size that is able

221

to identify all patterns that are physically relevant to the research question. After testing different

222

SOM sizes, a 5×3 grid of map nodes for a total of 15 nodes was chosen for this study. For well223

constructed SOMs, such as that presented here, Gervais et al. (2016) found that changes in user

224

defined parameters (e.g. neighborhood function and learning rate parameter) made little difference225

in the final SOM.

226

d. SOM Analysis

227

Once a SOM is trained, the final nodes or LSMPs are no longer modified and each day input

228

data vector (or day of data in this case) is compared to the final SOM and assigned a BMU. This

229

enables a multitude of additional analyses to explore the LSMPs. The frequency of occurrence230

of each LSMP is computed as the total number of BMUs for a given node divided by the total

231

number of input days for the entire SOM. This can provide information about which LSMPs are232

most common. We can also obtain a more complete understanding of the physical processes

233

associated with each node through compositing of any variable of interest. These composites (𝑆)234

are computed for a given node by averaging all days that are assigned as a BMU for that node. For

235

both the frequency ( 𝑓 ) and composite, calculations can include all of the input data or only the236

BMUs associated with either the CTL ( 𝑓𝐶𝑇 𝐿 or 𝑆𝐶𝑇 𝐿 ) or EXP ( 𝑓 𝐸 𝑋 𝑃 or 𝑆 𝐸 𝑋 𝑃 ).

237

Differences in atmospheric variability between experiments can arise from either differences in238

the frequency of SOM nodes (Δ 𝑓 = 𝑓 𝐸 𝑋 𝑃 − 𝑓𝐶𝑇 𝐿 ) or differences in their pattern (Δ𝑆 = 𝑆 𝐸 𝑋 𝑃 − 𝑆𝐶𝑇 𝐿 ).

239

The relative importance of changes in frequency versus change in pattern will depend on the SOM

240

size. With a smaller SOM we would expect changes in pattern to be greater and for a larger SOM

241

we would expect to see more changes in frequency. Examining both metrics together provides a
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complete view of changes in the variability (Gervais et al. 2020). Throughout the paper, these243

differences will be described as the impact of sea ice loss on the either the frequency or pattern.

244

Significant differences in frequency are evaluated using a permutation test. Here BMUs from245

both simulations are randomly assigned to new “CTL” and “EXP” labels and a new Δ 𝑓 is computed.

246

This process is repeated 1000 times in order to create a null distribution of Δ 𝑓 values. If the true Δ 𝑓

247

lies outside the 2.5𝑡ℎ or 97.5𝑡ℎ percentiles, the frequency differences are deemed significant. This

248

process is repeated for each node. Statistical significance for Δ𝑆 at each grid point is determined249

using a student’s t-test at a 95% confidence level with a null hypothesis of zero.

250

The SOM categorizes each day into different LSMPs with a given 𝑓 and 𝑆. Thus, the seasonal251

mean field of a given experiment can be approximated as the sum of the frequencies of each252

node times their composite. As discussed in Gervais et al. (2020), the total difference between

253

simulations (Δ( 𝑓 𝑆)) for all nodes can then be approximately decomposed into contributions from254

changes in frequency and pattern as follows:Δ( 𝑓 𝑆) = Δ 𝑓 𝑆 𝑎𝑣𝑔 + 𝑓𝑎𝑣𝑔 Δ𝑆

255

where,Δ( 𝑓 𝑆) =𝑛∑︁𝑓𝑒𝑖 𝑆 𝑒𝑖 −

𝑖=1

𝑛
∑︁

(4)

𝑓𝑐𝑖 𝑆 𝑐𝑖

(5)

𝑆 𝑒𝑖 + 𝑆 𝑐𝑖
2

(6)

(𝑆 𝑒𝑖 − 𝑆 𝑐𝑖 )

(7)

𝑖=1

256

Δ 𝑓 𝑆 𝑎𝑣𝑔 =𝑛∑︁( 𝑓𝑒𝑖 − 𝑓𝑐𝑖 )

𝑖=1
257

𝑓𝑎𝑣𝑔 Δ𝑆 =𝑛∑︁𝑓𝑒𝑖 + 𝑓𝑐𝑖
𝑖=1

2

258

In these equations, 𝑛 is the number of SOM nodes (which in the case of our SOM is 15), and259

the indices 𝑐 and 𝑒 indicate the CTL and EXP simulations respectively. This decomposition can

260

be conducted for any variable of choice to understand the impact of frequency versus pattern261

associated with these LSMPs.

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3. Results and Discussion

263

a. Winter Atmospheric Response to Sea Ice Loss

264

The atmospheric response to future sea ice loss will be defined in this study as the difference

265

between the CTL and EXP simulations (EXP - CTL). The differences in sea ice cover between

266

the simulations are seasonally varying with the greatest differences in September coinciding with267

the seasonal sea ice minimum (Fig. 1a). Although sea ice loss is greatest in September, the mean268

impact on atmospheric circulation is greatest in the winter, consistent with previous studies (Vihma

2692014). This seasonality of the atmospheric response can be seen in the monthly mean differences270

in 500 hPa geopotential height (𝑍500 ) and sea level pressure (SLP) between the simulations (Fig.

271

S1). The winter mean atmospheric response to future sea ice loss shows a clear signal of Arctic

272

Amplification with warmer potential temperatures at 850 hPa (Θ850 ) that are greatest at the high273

latitudes (Fig. 2a). Consistent with an increase in mean column temperature, we find a similar

274

pattern in the geopotential heights in the mid-troposphere (𝑍500 , Fig. 2b).

275

During the winter, differences in sea ice between the CTL and EXP are concentrated in the276

marginal sea ice zone with reductions of up to 100% sea ice cover (Fig. 1b). The local response to277

sea ice loss can be clearly seen in the surface fluxes and SLP. Over the marginal seas where sea ice

278

loss is greatest and the atmosphere is exposed to more open ocean, there is a substantial increase279

in turbulent heat flux (defined as the sum of the sensible and latent heat flux) from the ocean to the280

atmosphere (Fig. 2f). Over the Bering/Beaufort Sea and Hudson Bay this change in turbulent heat

281

flux reaches 100 W m−2 . Consistent with a large decrease in surface albedo with a greater fraction282

of ice free ocean there is a large increase in net absorbed shortwave radiation at the surface with283

sea ice loss (Fig. 2h). The warmer surface temperatures of an ice free ocean, are associated with a284

larger net surface outgoing longwave radiation (Fig. 2g). Finally, there is a local reduction in SLP

285

concentrated near regions of sea ice loss (Fig. 1e) consistent with a thermal low response (Fig. 2e).

286

For example, over the Hudson Bay there are large negative SLP anomalies that reach −5 hPa. Over287

and downstream of these regions of newly open ocean in the Bering/Beaufort Seas and Hudson Bay

288

there is enhanced total cloud cover (Fig. 2i) and precipitation ((Fig. 2f,j) consistent with enhanced

289

sensible and latent heat flux associated with a transition to ice free conditions (Fig. 2f).

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In the mid-latitudes, negative anomalies in the winter mean 𝑍500 and SLP response indicate

291

a deepening of the Aleutian low in the North Pacific (Fig. 2b,e). This is dynamically consis-292

tent with an intensification and elongation of the Pacific jet, where we would expect a corre-293

sponding eastward displacement of an enhanced secondary circulation favoring a more intense294

troposphere-deep cyclonic circulation. Here we identify the jet using the wind speed on the dy-

295

namic tropopause, where the dynamic tropopause is defined as the 2 potential vorticity unit (PVU;296

1 PVU=10−6 K kg−1 m−2 s−1 ) surface (Fig. 2c). The dynamic tropopause is an ideal surface upon

297

which to examine mid-latitude jets as this is where the jet is maximized and it rises with the298increasing column temperature (Hoskins et al. 1985) thus ensuring that the differences are due to299changes in the jet rather than the height of the tropopause. Coinciding with the elongated North

300

Pacific jet and deeper Aleutian low, we see an increase in precipitation that extends to the west

301

coast of North America (Fig. 2j).

311

Over the Atlantic, the response to sea ice loss includes an increase in 𝑍500 (Fig. 2b) over312

Greenland and an equatorward shift of the North Atlantic jet, as seen in the dipole of wind speed313

on the dynamic tropopause (Fig. 2c), consistent with several previous studies (Deser et al. 2015;314

Sun et al. 2015; Blackport and Kushner 2017, 2018; Screen et al. 2018; Ronalds et al. 2020).

315

Furthermore, we find a dipole in precipitation over the North Atlantic as would be expected from316

an equatorward shift of the storm track along with the jet (Fig. 2j). The winter mean SLP response317

shows no clear change in the Icelandic Low (Fig. 2e).

318

b. Identification of Large-Scale Patterns

319

To understand the impact of sea ice loss on LSMPs, we begin by first identifying dominant large-

320

′
′
scale patterns of 𝑍500
using SOM (Fig. 3). Fig. 3 shows the 𝑍500
SOM nodes (LSMPs) in color321

and composites of 𝑍500 in the control simulation (𝑆𝐶𝑇 𝐿 ) in black lines. In general, LSMPs on the322

left side of the SOM have amplified climatological ridges (troughs) over western (eastern) North

323

America and vice versa on the right side of the SOM. Enhancement of the ridge/trough patterns

324

shifts from being further east in LSMPs at the top of the SOM (e.g. LSMP [1,1]) to further west

325

at the bottom (e.g. LSMP [5,1]). Similarly, negative (positive) anomalies over the climatological326

ridge (trough) shift from being to the west in LSMP [1,3] to further east in LSMP [5,3].
13

a.

Θ850

b.

Z500

c.

DT Wind

d.

Z50

e.

SLP

f.

THFLX

g.

LW

h.

SW

i.

CLDTOT

j.

PCP

302

Fig. 2. Mean winter differences between simulations (EXP - CTL) in color and climatology in black contours

303

for a) Θ850 with climatology contoured every 5 K, b) 𝑍500 with climatology contoured every 100 m, c) wind speed

304

on the dynamic tropopause (DT WIND) with climatology contoured every 5 m/s, d) 50hPa geopotential height305(𝑍50 ) with climatology contoured every 100 m, e) SLP with climatology contoured every 4 hPa, f) turbulent

306

heat flux (THFLX) with climatology contoured every 10 W m s−2 , g) Surface longwave radiation (LW) with307

climatology contoured every 5 W m s−2 , h) Surface shortwave radiation (SW) with climatology contoured every308

2 W m s−2 , i) Total cloud cover (CLDT) with climatology contoured every 5%, and j) Precipitation (PCP) with309

climatology contoured every 2 mm d−1 . Insignificant differences at the 5% significance level according to a

310

resampling test are stippled.

14

327

The LSMPs [1,1] and [2,1] in the upper left corner have a pattern similar to the positive phase of328

the Pacific North American Pattern (PNA, Wallace and Gutzler (1981)) with negative anomalies in329

the Pacific and Eastern North America and positive anomalies over Alaska / the Pacific Northwest.

330

Conversely, LSMP [5,3] in the bottom right corner resembles the negative PNA. LSMPs [1,1],

331[1,2] and [1,3] include anomalies over the North Atlantic that are consistent with a negative Arctic

332

Oscillation (AO,Thompson and Wallace (1998)) or North Atlantic Oscillation (NAO, Hurrell

333′
near Iceland while LSMP [1,3](1995)) like pattern. LSMPs [1,1] and [1,2] have positive 𝑍500

334

has a center of action shifted further west. LSMPs [1,2] and [1,3] have negative anomalies over335

the subtropical North Atlantic. In contrast, LSMPs [3,2] and [4,2] have weak positive AO/NAO-

336

like anomalies. Although the NAO is an important feature of the Northern Hemisphere climate

337

variability and exerts an impact on North American weather, our SOM is trained with data over

338

North America and therefore we expect variability over the North Atlantic will have a limited339

presence as compared to other sources. LSMPs [4,1] and [5,1] have a strong positive anomaly over340

Alaska that acts to amplify and shift the climatological ridge over the Rockies further east, while341

LSMPs [3,3], [4,3], and [5,3] have a negative anomaly over Alaska. LSMPs [1,1], [1,2], [2,1],

342

and [2,2] exhibit a strengthened Aleutian Low, while LSMPs [4,3], [5,1], [5,2], and [5,3] exhibit a343

weakened Aleutian low. Nodes in the center of the SOM have weaker patterns overall.

346

To obtain further understanding of the synoptic conditions associated with each map LSMP and

347

their sensible weather impacts, we compute control simulation composites (𝑆𝐶𝑇 𝐿 ) for additional

348

variables. LSMPs in the top left of the SOM (namely LSMPs [1,1], [1,2], [2,1], [2,2]) have deeper

349

Aleutian lows as shown in their sea level pressure anomalies (SLP′, Figs. 4, 5) consistent with the350′
negative values in 𝑍500
SOM (Fig. 3). Those on the right side of the SOM (namely LSMPs [3,3],351[4,3], [5,3]) have Aleutian Lows that are shifted further east toward the continent and coupled with352

a high over the subtropics (Fig. 4, 5). This high/low pressure couplet of SLP over the Gulf of353

Alaska and west coast of North America acts to generate westerly lower-tropospheric winds through354

geostrophic balance arguments. This in turn can act to enhance the transport of warm maritime air

355

into the continent, which is seen in the positive Θ′850 values over western North America associated356

with these LSMPs (Fig. 4). LSMPs on the top and left side of the SOM are generally colder,357

specifically nodes [1,1], [1,2], [3,1], and [4,1]. These are associated with either an enhancement of358

the climatological high pressure and ridge over western North America (LSMPs [1,1], [2,1], [3,1],

15

344

′
Fig. 3. SOM of DJF 𝑍500
(color, m) over North America with the DJF climatological mean 𝑍500 (black

345

contours every 100 m).

359[4,1]) and/or a weakened Iceland Low (LSMPs [1,1] and [2,1]) consistent with the negative phase360

of the NAO (Fig. 4). LSMPs [1,2] and [4,1] are associated with particularly deep cold anomalies

361

down to −2◦ C.

362

Circulation patterns can also play a key role in the precipitation over the continental US. LSMPs363

with strong Aleutian lows that are closer to the continent ([2,2], [2,3], [3,2], [3,3]) are associated364with enhanced precipitation along the west coast whereas nodes with weaker Aleutian Lows ([4,1],

365[5,1], [4,2], [5,2]) have less precipitation along the west coast (Fig. 5 and contours in Fig. 9).
16

366

Enhanced precipitation in the Southeastern US is found in LSMPs [5,1], [5,2], [5,3] and [1,3], all367

of which are characterized by a trough over the Southeastern US (Fig. 5 and contours in Fig. 9).

368

In contrast, precipitation is reduced in LSMPs [2,1], [3,1], and [3,2] where the trough is located369

offshore (Fig. 5 and contours in Fig. 9).

370

371

′
Fig. 4. CTL composites of Θ850
(color, ◦ C), SLP′ (black contours every 4 hPa, dashed negative from 2 hPa),

and wind speed on the dynamic tropopause (green contours every 5 m s−1 from 35 m s−1 )374

Fig. 6a,b shows the associated frequency of each map node in the CTL and EXP simulations.

375

All LSMPs in Fig. 3 are present in both the CTL and EXP simulations. In the CTL simulation,

376

LSMPs [3,2], [4,2], and [4,3] occur most often. The LSMPs that occur least often are [1,1] and
17

372

Fig. 5. CTL composites of total precipitation (color, mm d−1 ), SLP (black contours every 4 hPa), and wind

373

speed on the dynamic tropopause (magenta contours every 5 m s−1 from 35 m s−1 )377[1,2], both of which are characterized a deepened Aleutian low, cold Θ′850 over North America,378

and high SLP′ over northeastern Canada and Greenland. In the EXP simulation, LSMPs [2,2],379[4,2], and [4,3] occur most often, while LMSPs [1,1], [2,1], and [3,1] occur least often. The mean

380

residency time, defined as the number of consecutive days spent in a given map node, are shown in

381

Fig. 6d,e for the CTL and EXP simulations respectively. Mean residency times range from 3.2-4.3

382

days with LSMP [5,1] having the highest and LSMP [4,2] the lowest residency time for both the383

CTL and EXP simulations. It should be noted that for both the frequency and residency time the
18

384

values will change depending on the SOM size (decreasing with increasing SOM size), therefore

385

the actual values are less meaningful than how they might change between the CTL to the EXP

386

simulations.

CTL

b.

EXP

c.

CTL - EXP

Frequency

a.

e.

f.

Residency

d.

387

Fig. 6. Heatmaps of frequency of occurrence (top row) of each node in the CTL (a), EXP (b), and their388

difference (c) and mean residency time (bottow row) for each node in the CTL (d), EXP (e), and their difference

389(f). Differences are only shown when significant at the 95% level using a permutation test.

390

c. Impact of Sea Ice Loss on LSMP Frequency and Residency

391

To understand the impact of sea ice loss on LSMPs, we will first discuss the impact on their392

frequency of occurrence and residency. Fig. 6c demonstrates the difference in frequency of each

393

LSMP between the CTL and EXP. LSMP [3,2] decreases in frequency by -0.6% while LSMPs [1,2]394

and [2,2] increase in frequency by 0.7% and 0.9% respectively. These changes may seem small;395

however, relative to the CTL frequency of 6.4% in LSMP [2,2], for example, the fractional increase396

is 14%. All of these LSMPs ([1,2], [2,2], and [3,2]) have anomalously strong Aleutian Lows but
19

397

LSMPs [1,2] and [2,2] are stronger than LSMP [1,3] (Figs. 3 and 4) therefore these changes in398

frequency imply that patterns with deepened Aleutian Lows become more common with sea ice

399

loss. It should be noted that here we have already removed the seasonal mean difference between

400

the experiments that was characterized by a mean deepening of the Aleutian Low and that this401

result shows further changes in how often these deepened Aleutian Low patterns occur. We also402

see that LSMP [5,1] decreases in frequency while LSMP [4,1] increases in frequency with sea ice

403′
over Alaska than node [4,1] (Fig. 3), this can beloss. Since node [5,1] has a larger positive 𝑍500

404

interpreted as the positive anomaly over Alaska becoming de-amplified.

405

Unlike the frequency, only LSMP [2,2] experiences a significant change in mean residency time,406

with an increase of 0.3 days. This LSMP also exhibited an increase in frequency, indicating that407

some of the increase in frequency is due to enhanced persistence. Since these LSMPs capture

408

synoptic spatial scale variability, they include patterns associated with Rossby wave propagation409

across North America. The overall lack of change in residency times across the SOM implies that410

there is no general change in the speed of wave propagation owing to sea ice loss.

411

d. Impact of Sea Ice Loss on LSMP Pattern412

To complete our investigation of sea ice impacts on LSMPs, we examine differences in LSMP

413

composite mean (Δ𝑆) for a variety of atmospheric variables. The impact of Arctic sea ice loss is414′
to weaken the 𝑍500
in many LSMPs, which can be interpreted as a reduction in variability (Fig.

415

7). The best example of this is LSMP [1,2], where the magnitude of the gradient associated with416′
the −NAO-like dipole in 𝑍500
between the Icelandic Low and Subtropical High is reduced by417′
approximately 15% with sea ice loss. In many cases, the Δ𝑆 of 𝑍500
are not centered on the CTL

418′ and thus are better characterized as a shift in location, for example the anomalous
composites 𝑍500

419

ridging along the west coast in LSMPs [4,1], [5,1], and [5,2] is shifted further south. A few LSMPs

420′
are amplified with sea ice loss, for example, the positive 𝑍500
in the subtropical Pacific in LSMPs421[4,3] and [5,3] are deepened and in [5,3] extended further east toward the continent. LSMP [1,3]422′also has negative Δ𝑆 of 𝑍500
in the North Pacific consistent with a deepened Aleutian Low. In all423′
cases, the Δ𝑆 of 𝑍500
are smaller than the CTL composites and so there is no change in the sign424′
of the patterns. This is necessarily true for 𝑍500
since the SOM is trained and BMUs are assigned

20

425

based on this field. However, for other fields, LSMP composites may see larger changes if the426′ patterns change.
conditions associated with these 𝑍500

427

To understand the impact of sea ice loss on the sensible weather associated with these circulation428

patterns, we examine Δ𝑆 of Θ′850 (Fig. 8) and precipitation anomalies (Fig. 9). The most striking

429

impact of sea ice loss on Θ′850 is in LSMP [1,2] (Fig. 8). This LSMP was associated with deep cold430

anomalies up to -1.75◦ C in the CTL simulation. However, the impact of sea ice loss far exceeds this431

at up to +4◦ C in Δ𝑆 of Θ′850 , resulting in a change in sign of Θ′850 associated with this LSMP in the432

CTL relative to the EXP. LSMP [4,1] that is also associated with strong cold anomalies over North

433

America reaching -2◦ C in the CTL simulation experiences a large decrease in magnitude with sea

434

ice loss of up to +1.5◦ C. Both LSMPs [4,1] and [1,2] increase in frequency with sea ice loss, so435

the circulation patterns typically associated with deep cold anomalies become more common with436

sea ice loss; however, they are much less cold or, in the case of [1,2], now associated with a warm437

Θ′850 .

438

Looking across the entire SOM, we see that a reduction in the amplitude of Θ′850 associated439′ LSMPs is ubiquitous (Fig. 8). Other LSMPs associated with large cold anomalies
with these 𝑍500

440

(LSMPs [1,1], [2,1], [5,1], and [2,3]) become warmer and those associated with warmer anomalies

441

become colder. Several of these LSMPs (namely [3,3], [4,3], [5,3]), are not associated with442

significant changes in frequency (Fig. 6), so their contribution to changes in variability is solely

443

through a change in pattern. This de-amplification of Θ′850 is consistent with the general reduction444′
in 𝑍500
across the SOM owing to sea ice loss. One explanation is that the reduction of horizontal

445

temperature gradients owing to AA may lead to a reduction in anomalous temperature advections

446

occurring in these nodes, even though the mean impact of AA is already removed by virtue of447

computing the anomalies. This can result in reduced Θ′850 and through hypsometric arguments in

448

′ .
a corresponding reduction in 𝑍500

449

The impact of sea ice loss on precipitation anomalies associated with these LSMPs is less450

robust and more localized (Fig. 9). LSMPs [3,1], [4,1], and [4,2] all experience a small decrease451

in precipitation along the California coast, acting to amplify the precipitation anomalies values452

typically associated with these LSMPs (Fig. 9). This is consistent with a positive SLP′ that acts to453

further reduce the transport of moist air to the region (Fig. S2). The opposite is true for LSMPs

454[1,1] and [2,2] (Fig. 9, S2). LSMP [1,3] experiences an increase in Δ𝑆 of precipitation anomalies

21

455

in the southeastern US (Fig. 9) consistent with the enhanced troughing (Fig. 7, S2) occurring in

456

proximity to the Gulf of Mexico and Atlantic Basin, well known moisture sources for the region.

457

The opposite is true for LSMP [3,3].

458′
Fig. 7. CTL Composites of 𝑍500
(contours, every 50 m from ±50 m, dashed negative) and difference in459′ (color, stippled insignificant using Student’s t-test).
composites (EXP-CTL) of 𝑍500

465

e. Mechanisms Responsible for LSMP [1,2] Pattern Changes

466

Given the striking changes in LSMP [1,2] and in particular the associated Θ′850 , a deeper

467

investigation into mechanisms operating in this node is warranted. First, it is important to recognize
22

460

′
Fig. 8. CTL Composites of Θ850
(contours, every 0.25◦ C from ±0.25◦ C, dashed negative) and difference in461′
composites (EXP-CTL) of Θ850
(color, stippled insignificant using Student’s t-test).

468

that the LSMPs identified in this study are from anomalous 𝑍500 fields relative to the respective

469′ ). Thus, these patterns represent atmospheric variability
climatologies of each simulation (i.e. 𝑍500

470

separate from mean impacts of sea ice loss. However, when it comes to understanding the impacts

471

of these LSMPs on fields such as Θ850 , the mean impacts of sea ice loss can still be important. As

472such, in the ensuing analysis we will be examining both composites of total fields (e.g. 𝑍500 ) and

473

′ ).
anomaly fields (e.g. 𝑍500

23

462

Fig. 9. CTL Composites of precipitation anomalies (contours, every 1mm d−1 from ±1mm d−1 , dashed463

negative) and difference in composites (EXP-CTL) of precipitation anomalies (color, stippled insignificant using

464

Student’s t-test).

474

Fig. 10 shows the CTL and EXP composite of Θ850 and SLP. In the CTL simulation, high SLP

475

over the center of the continent and low SLP over the North Atlantic implies a north-northeasterly

476

geostrophic wind. Coupled with the strong meridional background temperature gradient between

477

the pole and the midlatitudes, there is implied geostrophic cold air advection over northeastern

478

North America. Furthermore, the anticyclonic circulation around the high pressure system would
24

479

aid in transporting this cold air throughout North America. This helps explain why LSMP [1,2] is480

associated with deep cold continental temperatures.

481

In the EXP simulations, the background temperature gradient from equator to pole is weakened,482

as is expected with AA (Fig. 10). This in and of itself would cause a reduction in cold air advection

483

in this LSMP. However, we also see that the high pressure over Hudson Bay is weakened resulting484

in a slackening of the SLP gradient over eastern Canada and a weakening of the implied north-

485

northeasterly geostrophic wind by roughly 30%. Furthermore, the overall reduction in the strength486

of the high pressure system would reduce the typical transport of this cold air into the interior of

487

North America. This can be seen, for example, in the slacking of the meridional pressure gradient488

from Hudson Bay to the Gulf of Mexico coast. Therefore, though we could ascribe the changes in489

cold air advection to mean AA and the weakened temperature gradient (a thermodynamic impact),490

these changes in SLP also imply a large role for dynamical impacts.

491

As discussed previously, there is an increase in mean winter turbulent heat flux and decrease492

in mean winter SLP between the two simulations over Hudson Bay (Fig. 2), consistent with a493

local thermal low pressure response to sea ice loss. The difference in CTL and EXP LSMP [1,2]494composites of SLP′ are insignificant over much of the North American continent (Fig. 11c).

495

Furthermore, the effect of turbulent heat flux is smaller in LSMP [1,2] (Fig. 11d) potentially owing

496

to the warmer Θ′850 reducing the ocean-atmosphere temperature gradients (Fig. 11a). This implies

497

that much of the differences in the SLP gradients discussed above are owing to differences in the498mean climatology between the CTL and EXP simulations and how this projects onto the LSMP

499

[1,2] circulation pattern rather than changes in SLP that are specific to this LSMP. For this node500

in particular, where the high pressure in this region is an important factor, this mean change acts

501

to reduce the zonal SLP gradient and consequently the strong cold air advection in northeastern

502

North America that characterizes the LSMP.

503

In addition to changes in temperature advection, diabatic processes may also play a role in the504

increased Θ′850 associated with LMSP [1,2]. There is an increase in total cloud cover anomalies505

and precipitation anomalies downstream (south) of Hudson Bay with sea ice loss (Fig. 11e,f). This506

is expected given the mean increase in moisture and heat flux (Fig. 2f) from the ice-free surface

507

with sea ice loss (Fig. 1b). This increase in clouds and precipitation relative to other LSMPs is508

associated with less incoming net short wave radiation and less upward longwave radiation (Fig.

25

509

11g,h). Furthermore, we would expect an increase in diabatic heating to be associated with cloud510

and precipitation generation, though this cannot be directly confirmed with the variables saved in

511

these model simulations. These results imply a role of diabatic processes in addition to temperature

512

advection in producing the large differences in Θ′850 in LSMP [1,2].
CTL

EXP

EXP-CTL

513

Fig. 10. Node [1,2] composites of Θ850 (color) and SLP (black contours every 4hPa) for a) CTL, b) EXP and

514c) their difference (EXP - CTL). For a) and b) SLP contours are every 4hPa and for c) SLP contours are every

515

1hPa with dashed negative values and the 0 contour omitted.

26

a.

Θ′850

b.

′
Z500

c.

SLP′

d.

THFLX′

e.

CLDT′

f.

PCP′

g.

SW′

h.

LW′

516

Fig. 11. Node [1,2] CTL composites (contours) and differences (EXP-CTL) in composites (color) for a)

517

′
′
Θ850
(contours every 0.25◦ C from ±0.25◦ C, dashed negative), b) 𝑍500
(contours every 50 m from ±50 m,

518

dashed negative), c) SLP′ (contours every 2 hPa from ±2 hPa, dashed negative),d) turbulent heat flux anomalies

519(THFLX′ , contours every 20 W m s−2 from ±20 W m s−2 , dashed negative), e) total cloud cover anomalies

520(CLDT′ , contours every 5% from ±5%, dashed negative), f) precipitation anomalies (PCP′ , contours every

521

1mm d−1 from ±1mm d−1 , dashed negative), g) incoming shortwave radiation anomalies (SW′ , positive down,

522

contours every 5 W m s−2 from ±5W m s−2 , dashed negative), and h) outgoing longwave radiation anomalies

523(LW′ , positive up, contours every 5 W m s−2 from ±5W m s−2 , dashed negative) . All figures have insignificant

524

differences at the 5% level computed using a Student’s t-test stippled.

27

525

f. Contributions of changes in LSMPs to mean DJF response to sea ice loss

526

AA is one of the most notable impacts of Arctic sea ice loss. In Fig. 2a we can see this reflected527

in the DJF seasonal mean difference between the CTL and EXP (ΔΘ850 ). As described above, some528

LSMPs are associated with greater changes Θ′850 than others (e.g. LSMP [1,2]). Decomposing the529

DJF mean Θ′850 response by LSMP contribution can provide an avenue into better understanding

530

of how synoptic scale processes relate to mean Θ′850 response and elucidate additional mechanisms

531

responsible for AA that might otherwise be obscured.

532

As discussed in the methods section, the mean difference between experiments can be approxi-533

mated as those arising due to changes in frequency versus pattern of the LSMPs (Eqn. 4). For Θ850534

the contribution from changes in frequency are much smaller than from changes in pattern (not535

shown). On the left side of equation 4, Δ( 𝑓 𝑆) is an approximation of the seasonal mean difference

536

between experiments for a given variable (e.g. ΔΘ850 ). Substituting these assumptions, we can537

re-write equation 4 for Θ850 as:
ΔΘ850 ≈

𝑛∑︁

𝑓𝑎𝑣𝑔,𝑖 Δ𝑆𝑖

(8)

𝑖=1
538

where 𝑓𝑎𝑣𝑔,𝑖 is the mean frequency of occurrence over the CTL and EXP simulations and Δ𝑆𝑖 is the539

composite mean Θ850 of EXP minus that of CTL for a given node 𝑖. Expanding out the summation,

540

dividing both sides by ΔΘ850 and multiplying by 100 we can obtain the percent contribution of541

each node to ΔΘ850 .
100 ≈𝑓𝑎𝑣𝑔,15 Δ𝑆15
𝑓𝑎𝑣𝑔,2 Δ𝑆2𝑓𝑎𝑣𝑔,1 Δ𝑆1· 100 +
· 100 + ... +
· 100
ΔΘ850
ΔΘ850
ΔΘ850

(9)

542

In Fig. 12, each of the terms of the right hand side are plotted, showing the percent contribution543

of each node to the mean DJF Θ850 response. The sum of all the percent contributions over all

544

nodes is approximately equal to 100 (±5%) at each grid point location, confirming that changes in

545

composite are indeed the greatest contributor to the mean Θ850 response.

546

To avoid the creation of artificially high values of percent contribution where ΔΘ850 is very

547

small, grid points where ΔΘ850 is not statistically significant are masked out in Fig. 12. This is548

computed using a permutation test applied at each grid point to determine if the mean of DJF days

549

used for the SOM analysis in the CTL are different from the EXP simulation with a significance

28

550

level of 95%. This is similar to test used in Fig. 2 except there the DJF seasonal mean is computed

551

first and the null hypothesis is that the seasonal means are the same.

552

If each of these nodes contributed equally to the mean Θ850 response, we would expect the553

percent contribution over 15 nodes to be 6.6% at each grid point. To test this, we compute a null

554

distribution of percent contributions using a permutation test. Here the percent contributions are

555

computed as in equation 9 but using an average frequency of 6.6% and the number of days per

556

composites equal to the frequency times the number of input data vectors. We then shuffle the SOM

557

node labels and choose a new set of CTL and EXP randomly without replacement and compute558

the difference in their composites. This process is repeated 500 times and if the actual percent

559

contribution to the mean Θ850 response is greater than the 97.5th or less than the 2.5th percentiles

560

of this null distribution, it is considered significant at the 95% level.

561

The results show that there are indeed nodes that contribute much more significantly to mean562

Θ850 response than others. LSMP [1,2] stands out for its significant contributions to mean DJF

563

Θ850 response over the majority of North America ranging from 20-50%. Over Northern Canada

564(including the Northwest Territories, Nunavut, and Northern Quebec) where mean Θ850 response565

is greatest, LSMP [1,2] contributes up to 20% of the total mean Θ850 response. This is more than566

double the rate if there was an equal distribution across nodes. LSMP [4,1] also has a notable

567

increase in contribution to the mean Θ850 response of up to 15% over the Yukon and Northwest

568

Territories. It should be noted that these two LSMP were associated with deep cold anomalies in569

the CTL simulations (Fig. 4) in these regions. This implies that processes specific to these LSMPs,

570

such as those outlined in Section 3c, are important for creating the mean Θ850 response and can

571

occur on synoptic time scales.

578

In the midlatitudes, the mean Θ850 response is much smaller and the contributions of LSMPs is579

larger. LSMP [1,2] contributes up to 50% to the mean Θ850 response in the southern United States.

580

This implies that LSMP [1,2] plays an important role in propagating the mean Θ850 response into581

the mid-latitudes. There are also notable positive contributions to the mean Θ850 response in the582

southern United States from LSMPs [1,1], [2,3], and [4,2] as well as negative contribution other583

LSMPs including [2,1], [2,2], [3,1], [3,2], [3,3], [4,1], and [4,3]. This is consistent with the general584

reduction in intensity in Θ850 across LSMPs identified in Fig. 8. It should be noted that in these585

regions where ΔΘ850 is smaller, the percent contribution will be much larger for the same Δ𝑆𝑖 .

29

586

One interpretation of these results is therefore that when the mean signal is smaller the impact ofinternal variability will be larger.

572

Fig. 12. Percent contribution of changes in each LSMP composite pattern to mean Θ850 in DJF (color).

573

For reference, the DJF mean difference between CTL and EXP (ΔΘ850 ) are provided in contours every 0.5◦ 𝐶574

beginning at 0.5◦ 𝐶 as shown in Fig. 2a in color. Locations where ΔΘ850 is not significantly different at the

575

95% confidence level as determined using a permutation test are masked out. Stippling shows regions were the576

percent contribution of changes in LSMP composite are not significant at a 95% level as determined using a

577

permutation test.

587

30

588

4. Conclusions

589

The goal of this study was to identify the impact of future sea ice loss on large-scale meteorological590

patterns (LSMPs) and their associated sensible weather impacts. We analyze output from two

591

fully-coupled CESM-WACCM simulations, one with sea ice nudged to the ensemble mean of the592

WACCM historical runs averaged over 1980-1999, and the other simulation nudged to projected593

RCP 8.5 values over 2080-2099. A machine learning method, self-organizing maps (SOMs), is594

used to identify LSMPs of anomalous 500 hPa in both experiments. Composite analysis of days

595

assigned to these LSMPs is then used to understand the associated sensible weather conditions.

596

To identify the impact of sea ice loss on LSMPs, we quantify differences in how often these597

LSMPs occur (frequency) and for how many consecutive days data are classified in these LSMPs

598(residency). There are significant changes in LSMP frequency, most notably with two patterns

599

associated with the coldest potential temperatures at 850 hPa (Θ850 ) becoming more common in600

the future. However, there were little changes in the residency across the set of LSMPs, indicating

601

that there is no general change in the speed of propagation of Rossby waves or stagnation of the602

flow with sea ice loss.

603

The impact of sea ice loss on LSMP patterns and their associated sensible weather impacts were604

identified by taking differences in composites of the CTL and EXP simulations for a variety of

605

variables. In general, sea ice loss tends to de-amplify and in some cases shift the LSMP patterns,

606′ . The impact of sea ice loss on Θ′
as seen in the composites differences in 𝑍500
850 is generally

607′ . This is consistent with previous
consistent with the general reduction in amplitude of the 𝑍500

608

studies that suggested that decreases in the variance of temperature can occur due to the mean AA

609(Screen 2014; Screen et al. 2015; Collow et al. 2019; Dai and Deng 2021). Since the amplitude of610

tropospheric waves can generally be attributed to the displacement of air masses, it makes sense611

that with a reduction in the background temperature gradient associated with AA we would find a612

commensurate reduction in amplitude of LSMPs and their associated Θ′850 . There are less robust613

and more localized impacts of sea ice loss on precipitation anomalies associated with the LSMPs

614

that are generally consistent with SLP′ changes.

615

One LSMP in particular, LSMP [1,2], exhibits a striking change in associated Θ′850 with sea ice

616

loss. In the CTL simulation, this LSMP is associated with deep cold anomalies of Θ′850 reaching617−1.75◦ C; however, with Arctic sea ice loss there is an increase in Θ′850 exceeding 4◦ C. LSMP [1,2]

31

618

is associated with a ridge of higher pressure over the center of the continent that would facilitate

619

Northerly flow of cold Arctic air deep into the continental US and Canada in the CTL simulation,620

as can be seen in the cold anomalies across much of the North American continent. AA reduces621

the meridional temperature gradient and thus would result in a reduction in cold air advection622

associated with this LSMP.

623

In this framework, it is possible to further identify the coincident impact of dynamical forcing.

624

With Arctic sea ice loss, there are enhanced turbulent heat fluxes from the newly ice free Hudson625

Bay and the resulting local thermal low pressure anomaly in the wintertime. This results in both626

a reduction in the southward extent of the high SLP ridge and a weakening of the localized SLP

627

gradient, consequently limiting the geostrophic meridional flow. Since these SLP changes are

628

related to a local thermal response to sea ice loss that are geographically tied to Hudson Bay,629

they are likely robust to internal variability unlike many other dynamical impacts of sea ice loss.

630

The combined impact of these two changes in the background mean state, both dynamical and631

thermodynamical, would result in a reduction in cold air advection. This analysis indicates that632

when it comes to the sensible weather impacts associated with LSMPs, there is an interplay between

633

changes in the mean state and changes in the LSMP.

634

We further identify diabatic forcing mechanisms that may increase the Θ′850 in this LSMP. With

635

Arctic sea ice loss, there is an increase in total cloud cover anomalies downstream of Hudson Bay

636

with a coinciding decrease in anomalous shortwave radiation reaching the surface and increase in637

anomalous longwave radiation down. Along with this increase total cloud cover anomalies there638

is also an enhancement of precipitation anomalies, both of which are likely associated with latent639

heating although this cannot not be confirmed given the fields available in our simulations.

640

Given the association of LSMP [1,2] with large changes in Θ′850 owing to sea ice loss, a follow-

641

on question was how important this specific LSMP is to the overall mean Θ850 response which642

is largely an AA signal. We find that in the Canadian north where the mean Θ850 response is643

large, this single LSMP accounts for up to 20% of the total signal. This is significantly larger

644

than the 6.6% that would be expected if that signal were equally distributed among all the LSMPs.

645

Although the mean Θ850 response is weaker in the midlatitudes, the role of LSMP [1,2] is even646

greater reaching 50% in the southern United States. This implies that LSMP [1,2] play an out-sized

647

role in the mean Θ850 response and its propagation further south.

32

648

Although we haven’t examined extreme temperature events in this study, LSMP [1,2] does

649

resemble the broad-scale patterns associated with cold-air outbreaks over the Eastern US (e.g.

650Walsh et al. 2001). Previous literature has highlighted the role of AA in reducing the intensity651

of cold air outbreaks over North America (Screen et al. 2015; Ayarzagüena and Screen 2016);

652

however, this analysis demonstrates that further investigation including the role of dynamics and653

diabatic effects in cold air outbreaks could yield new insight into the problem.

654

The results in this study demonstrate that there are notable changes in LSMPs and their associated655

sensible weather with Arctic sea ice loss. However, here we have shown results from just a single

656

set of climate model simulations. Future work conducing a similar analysis with a suite of climate

657

model experiments, such as those available in the Polar Amplification Model Intercomparison

658

Project (PAMIP, Smith et al. (2019)), would help confirm the robustness of these results.

33

659

Acknowledgments. We would like to thank two anonymous reviewers for their helpful comments

660

as well as Kevin Bowley for insightful discussions regarding this research. This research was

661

supported by NSF Grant AGS-2236771. NCAR is sponsored by the National Science Foundation

662

under Cooperative Agreement 1852977. We would like to acknowledge high-performance com-663

puting support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR’s Computational664

and Information Systems Laboratory, sponsored by the National Science Foundation. Additional

665

computations for this research were performed on the Pennsylvania State University’s Institute for

666

Computational and Data Sciences’ Roar supercomputer.

667

Data availability statement.

668

SOM used to identify the LSMPs are openly available from the Penn State DataCommons at669

https://doi.org/10.26208/144H-0X26. The Self-Organizing Map Program Package (SOM PAK;

670

Kohonen (2001)) is available at http://www.cis.hut.fi/research/som-research/.

671

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