Observed Atmospheric Coupling Between Barents Sea Ice and the Warm-Arctic Cold-Siberian Anomaly Pattern

Observed Atmospheric Coupling between Barents Sea Ice and the Warm-Arctic Cold-Siberian Anomaly Pattern

Sorokina, S. A., Li, C., Wettstein, J. J., & Kvamstø, N. G. (2016). Observed Atmospheric Coupling between Barents Sea Ice and the Warm-Arctic Cold-Siberian Anomaly Pattern. Journal of Climate, 29(2), 495-511. doi:10.1175/JCLI-D-15-0046.1

10.1175/JCLI-D-15-0046.1 American Meteorological Society

Version of Record http://cdss.library.oregonstate.edu/sa-termsofuse 15 JANUARY 2016 S O R O K I N A E T A L . 495

Observed Atmospheric Coupling between Barents Sea Ice and the Warm-Arctic Cold-Siberian Anomaly Pattern

SVETLANA A. SOROKINA Nansen Environmental and Remote Sensing Center, and Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway

CAMILLE LI Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway

JUSTIN J. WETTSTEIN College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon, and Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway

NILS GUNNAR KVAMSTØ Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway

(Manuscript received 9 January 2015, in ﬁnal form 7 July 2015)

ABSTRACT

The decline in Barents Sea ice has been implicated in forcing the ‘‘warm-Arctic cold-Siberian’’ (WACS) anomaly pattern via enhanced turbulent heat ﬂux (THF). This study investigates interannual variability in winter [December–February (DJF)] Barents Sea THF and its relationship to Barents Sea ice and the large- scale atmospheric ﬂow. ERA-Interim and observational data from 1979/80 to 2011/12 are used. The leading pattern (EOF1: 33%) of winter Barents Sea THF variability is relatively weakly correlated (r 5 0.30) with Barents Sea ice and appears to be driven primarily by atmospheric variability. The sea ice–related THF variability manifests itself as EOF2 (20%, r 5 0.60). THF EOF2 is robust over the entire winter season, but its link to the WACS pattern is not. However, the WACS pattern emerges consistently as the second EOF (20%) of Eurasian surface air temperature (SAT) variability in all winter months. When Eurasia is cold, there are indeed weak reductions in Barents Sea ice, but the associated THF anomalies are on average negative, which is inconsistent with the proposed direct atmospheric response to sea ice variability. Lead–lag correlation analyses on shorter time scales support this conclusion and indicate that atmospheric variability plays an important role in driving observed variability in Barents Sea THF and ice cover, as well as the WACS pattern.

1. Introduction multiyear ice cover is shrinking rapidly in winter as well (Comiso 2012). Wintertime Arctic sea ice area declines Arctic sea ice has decreased in all seasons during re- are concentrated in the Barents Sea (Serreze et al. 2009; cent decades. The largest declines in areal extent have Screen and Simmonds 2010a; Parkinson and Cavalieri occurred during summer and early autumn (up to 2 2012). The diminishing Arctic sea ice cover has been 10% decade 1; Serreze et al. 2007), but the thicker accompanied by near-surface warming in the high latitudes, particularly during the winter season (e.g., Screen and Simmonds 2010b), but the midlatitudes have re- Denotes Open Access content. cently experienced some anomalous winters with se- vere cold spells and above-normal snow cover in parts of Europe, Russia, and North America (e.g., Cohen et al. Corresponding author address: Dr. Svetlana A. Sorokina, Geo- physical Institute, University of Bergen, P.O. Box 7803, 5020 2007; Cattiaux et al. 2010; Ghatak et al. 2010; Guirguis Bergen, Norway. et al. 2011; Orsolini et al. 2012; Coumou and Rahmstorf E-mail: [email protected] 2012). The cold Eurasian surface temperatures are part

DOI: 10.1175/JCLI-D-15-0046.1

Ó 2016 American Meteorological Society 496 JOURNAL OF CLIMATE VOLUME 29 of what has been termed the ‘‘warm-Arctic cold-Siberian’’ (Jaiser et al. 2012; Orsolini et al. 2012; Cohen et al. 2014; (WACS) anomaly pattern (or slight variations thereof; Kim et al. 2014; García-Serrano et al. 2015; King et al. see Inoue et al. 2012; Mori et al. 2014). The relationship 2015). Others suggest that it is a simultaneous response between this WACS pattern and Barents Sea ice con- to winter Barents Sea ice anomalies (Petoukhov and ditions is the focus of this study, which probes reanalysis Semenov 2010; Hori et al. 2011; Inoue et al. 2012; Outten data for clues about how the two are linked. and Esau 2012). The link to winter sea ice anomalies has Various mechanisms have been proposed to explain been shown to be more robust in at least one comparison how the WACS pattern is generated. Early on, Honda (Tang et al. 2013). Furthermore, while some studies fo- et al. (2009) pointed to the fact that, where sea ice re- cus on interannual covariability in sea ice and Eurasian treats, we expect enhanced ocean-to-atmosphere energy winters (Honda et al. 2009; Petoukhov and Semenov loss in the form of turbulent heat fluxes (THFs). They 2010; Hori et al. 2011; Inoue et al. 2012), others examine suggested that such a THF perturbation triggers a sta- potential linkages to strong negative trends in sea ice tionary Rossby wave train that amplifies the climatological- cover (Outten and Esau 2012; Mori et al. 2014). Most mean wintertime high pressure over Siberia and enhances recently, a series of observational and modeling studies northerly cold-air advection over eastern Eurasia. Other has questioned whether there is adequate evidence that studies focus on the role of the THF perturbation in sea ice influences atmospheric circulation, blocking, and modifying the near-surface meridional temperature Eurasian winter temperatures at all (Hopsch et al. 2012; gradient, leading to altered cyclone pathways and a cold Barnes 2013; Screen and Simmonds 2013; Barnes et al. anticyclonic flow anomaly north of the Eurasian conti- 2014; Wallace et al. 2014; Woollings et al. 2014). nent (Inoue et al. 2012), or weakened zonal winds and Despite unresolved issues about the mechanism, sea- reduced heat transport from the North Atlantic to the sonality, and time scale, many of the existing hypotheses interior of the Eurasian continent (Petoukhov and attempting to explain the WACS pattern and its vari- Semenov 2010; Outten and Esau 2012). A more global ability share a common starting point: an enhancement of (less regional) alternative suggests that the modified ocean-to-atmosphere THFs in late autumn to winter as a (weaker) meridional temperature gradient causes the direct consequence of reduced sea ice. These enhanced atmospheric flow to be wavier, thus leading to more THFs are hypothesized to create a large-scale atmo- extreme midlatitude weather in general (Francis and spheric response leading to the WACS pattern. This Vavrus 2012). study will explore the robustness of this causal chain in A different class of studies examines the phenomenon reanalysis data by examining the THF covariability as- of cold Eurasian winters independently of Arctic sea ice. sociated with both Barents Sea ice conditions and cold These studies attribute cold winters to persistent atmo- Eurasian winters. spheric blocking but not necessarily in connection with Studies using AGCMs provide qualified support for declining ice cover. Wintertime blocking patterns in the causal chain summarized in the previous para- recent cold years (e.g., 2005/06, 2009/10) are shown to be graph. Movements of the sea ice edge are found to linked to sudden stratospheric warmings and/or sea affect the spatial distribution of the THF field, surface temperature anomalies (Scaife and Knight 2008; producing a distinctive dipole signature with enhanced Croci-Maspoli and Davies 2009; Cattiaux et al. 2010), ocean-to-atmosphere THF in regions of ice loss next to with Rossby wave trains playing a possible amplifying the new ice edge and reduced THF next to the old ice role over Siberia (Takaya and Nakamura 2005; Park edge (e.g., Alexander et al. 2004; Deser et al. 2004; et al. 2011; Cheung et al. 2013; Sato et al. 2014). While Magnusdottir et al. 2004; Singarayer et al. 2006; Seierstad such atmospheric circulation changes could be forced by and Bader 2009; Budikova 2009; Deser et al. 2010). sea ice changes (Mori et al. 2014), they could also be an However, the large-scale atmospheric response to expression of natural internal variability or reflect a di- these THF anomalies is weak compared to natural var- rect atmospheric response to external radiative forcing. iability (Screen et al. 2014); varies considerably among The range of proposed mechanisms, including some AGCM studies (Bader et al. 2011); exhibits nonlinear that do not explicitly require a role for sea ice, has led to behavior (e.g., Lau and Holopainen 1984; Peng et al. some debate over details of the relationship between the 1997; Deser et al. 2007, 2010; Liptak and Strong 2014); WACS pattern and sea ice cover. Some studies suggest and depends strongly on the magnitude, location, and that the WACS pattern is a delayed wintertime atmo- polarity of the sea ice changes (e.g., Honda et al. 1999; spheric response to reduced sea ice cover in the pre- Magnusdottir et al. 2004; Deser et al. 2004; Kvamstø ceding autumn (Honda et al. 2009; Francis et al. 2009; et al. 2004), as well as the season (e.g., Alexander et al. Overland and Wang 2010; Overland et al. 2011) possibly 2004; Bhatt et al. 2008; Seierstad and Bader 2009; Deser via an indirect response involving the stratosphere et al. 2010). 15 JANUARY 2016 S O R O K I N A E T A L . 497

Furthermore, AGCM experiments cannot capture the 3, and the discussion is presented in section 4. A sum- two-way interactions that link the ocean and the atmo- mary completes the paper. sphere in nature. The uncoupled AGCM setup allows us to investigate the atmospheric response to variability 2. Data and methods in the surface ocean (sea surface temperature and sea ice). It does not account for the other direction of cou- We use data from the European Centre for Medium- pling, known to be important on weekly to interannual Range Weather Forecasts interim reanalysis (ERA- time scales (Bjerknes 1964; Cayan 1992; Deser et al. Interim; Dee et al. 2011) from 1979/80 to 2011/12. We 2000; Gulev et al. 2013), namely, the atmospheric driv- focus analyses on the 3-month winter season from ing of surface ocean variability via changes in near- December to February (DJF), but the results and surface winds, temperature gradients, and humidity conclusions are also valid over an extended winter gradients across the ocean–atmosphere interface (Fang season (October–February), a point we will return to in and Wallace 1994; Prinsenberg et al. 1997; Deser et al. the discussion section. Monthly and daily THF, sea 2000; Rigor et al. 2002; Sorteberg and Kvingedal 2006; surface temperature (SST), surface air temperature Wu and Zhang 2010; Germe et al. 2011; Wettstein and (SAT), 10-m near-surface wind in the zonal (U)and Deser 2014). As a result, uncoupled experiments often meridional (V) directions, sea level pressure (SLP), exhibit unrealistically strong and persistent ocean-to- and geopotential height at 500 hPa (Z500) are analyzed atmosphere fluxes and reduced internal atmospheric in the 1.5831.58-resolution ERA-Interim. The THF is variability (Barsugli and Battisti 1998). The THF defined as the sum of the surface latent and sensible anomalies in AGCM studies are by definition caused heat fluxes. Positive THF values indicate heat transfer by the prescribed surface ocean conditions, and thus from the ocean to the atmosphere. Sea ice concentra- they cannot be used as evidence for the proposed tion (SIC) is estimated from passive microwave satel- causal chain. litedataona25km3 25 km grid (NSIDC; Cavalieri In the current study, we explore the coupled in- et al. 1996). This satellite-based SIC is independent teractions between Barents Sea ice cover, THF, and the from, but consistent with, SIC obtained from ERA- large-scale atmospheric circulation during winter to Interim for the Barents Sea as a whole (r 5 0.98 for a further examine the causal chain linking Barents Sea raw correlation using monthly data, r 5 0.97 when ice conditions to the WACS pattern. We focus on THF linearly detrended) and also in terms of the geo- in the Barents Sea, the region with the most pro- graphic distribution of the sea ice edge (Fig. 1). All nounced winter sea ice variability, as the coupling results and conclusions are similar when SIC from agent between the ocean and atmosphere. Specifically, ERA-Interim is used instead of the NSIDC data. We we explore whether Barents Sea THF variability is define two regions (both indicated in Fig. 6)forthe driven by sea ice or atmospheric variability in the ob- analyses: the Barents Sea (678–818N, 128–738E) and servational record. Whereas previous observational the Eurasian region (458–818N, 128–1208E). The Eur- studies investigated the WACS pattern in association asian region includes mid- and high-latitude conti- with a few extremely low Barents Sea ice years (Honda nental Eurasia and the adjacent Arctic seas. Note that et al. 2009; Hori et al. 2011; Inoue et al. 2012)orin the Barents Sea region is wholly subsumed within the association with trends in Barents Sea ice (Outten and northwest quadrant of the larger Eurasian region. Esau 2012; Outten et al. 2013), we focus on interannual The results in this study are not very sensitive to the variability during all years of the satellite record, ex- exact definition of the regions, several of which were cluding the linear trend. By removing the trend, we tested and all of which produced qualitatively similar maximize the statistical degrees of freedom and isolate results. The Barents Sea ice index (ICEBar)isdefined mechanisms that can produce the WACS pattern in- as the detrended area-averaged SIC over the Barents dependent of lower-frequency climate change. This al- Sea region, with positive ICEBar corresponding to less lows us to evaluate a candidate null hypothesis that the Barents Sea ice. WACS pattern can be attributed at least in part to in- Unless otherwise stated, results in this study are based ternal atmospheric variability. The corollary of our de- on monthly anomalies calculated by removing the linear trending decision is that the results of this study are not trend from each month individually and then remov- directly comparable to an examination of coupled in- ing the climatological-mean seasonal cycle from the teractions in the context of long-term, externally forced detrended data. Standard empirical orthogonal function climate change. (EOF)/principal component (PC) analysis is performed The paper is organized as follows: Data and methods on monthly anomalies of 1) THF over the Barents Sea are described in section 2, results are reported in section and 2) SAT over the Eurasian region. Gridpoint values 498 JOURNAL OF CLIMATE VOLUME 29

FIG. 1. (a) Time series of DJF Barents Sea ice area in NSIDC (black) and ERA-Interim (purple). The correlation between time series is 0.98 for the seasonal-mean time series (also 0.98 for the corresponding monthly time series). Dots indicate greater (red) or less (blue) than average Barents Sea ice area for the linearly detrended data. (b) Climatological-mean SIC (color shading) from NSIDC and the 15% isopleth of SIC from NSIDC (black) and ERA-Interim (purple). The Barents Sea domain (678–818N, 128–738E) is indicated by the yellow box. of the anomaly ﬁelds are weighted by the square root of series are referred to as PCs. Broader climate relation- the cosine of latitude (equivalent to area weighting the ships are explored by regressing monthly anomalies of variance) before performing the EOF analysis. EOFs relevant variables onto standardized PCs and ICEBar, presented in this study are well separated from each such that the resulting regression maps are displayed in other and lower-order EOFs according to the criterion physical units per standard deviation of the index. described by North et al. (1982). The resulting spatial Later in this study we create ‘‘synthetic’’ time series of patterns are referred to as EOFs and the associated time the WACS pattern at daily (WACSdsyn) and seasonal 15 JANUARY 2016 S O R O K I N A E T A L . 499

(WACSssyn) time scales. In both cases, the synthetic time series is created by projecting Eurasian SAT anomaly patterns at these two time scales onto the second EOF of Eurasian DJF SAT variability (i.e., what is deﬁned in the results section as the WACS pattern). For WACSdsyn, the linear trend and climatological daily mean are removed to create the anomaly patterns, and these are used in a variety of lead–lag correlation and regression analyses to provide additional context to our evaluation of the causal chain described in the introduction section.

For WACSssyn, only the climatological seasonal mean is removed to create the anomaly patterns, and we assess the variance associated with the trend and in the linearly detrended WACSssyn time series. The conclusions of the current study have been found to be robust when other satellite-based [OAFlux (Yu FIG. 2. Standardized time series of DJF ICEBar (black), THF et al. 2008) and HOAPS (Andersson et al. 2011)] and PC1 (blue), and THF PC2 (red) based on seasonal-mean data with the seasonal cycle removed. Positive ICE corresponds to reanalysis-based [NCEP1 (Kalnay et al. 1996)] THF Bar less Barents Sea ice area. Correlation values are r(PC1, ICEBar) 5 products are used instead of ERA-Interim data. The 0.46 (0.30 for the corresponding monthly time series) and

OAFlux and HOAPS products are thought to give im- r(PC2, ICEBar) 5 0.63 (0.60 for the corresponding monthly proved heat flux estimates compared to reanalysis time series). products, but a disadvantage is that they mask out data in areas with sea ice concentration above 50%, which Alexander et al. 2004; Deser et al. 2010). Recall that in- makes it impossible to examine comprehensively the creases in ICEBar correspond to reductions in Barents- associations between THF and SIC variability. averaged SIC. Similar THF and SIC signatures appear in association with the second pattern of Barents Sea THF variability (THF EOF2; Fig. 3c), suggesting that THF 3. Results EOF2 captures at least some aspects of the atmo- Because ocean-to-atmosphere THF has been widely spheric response to sea ice reductions. The leading pattern invoked as the intermediary for communicating sea ice (THF EOF1), however, tells a different story (Fig. 3b). changes to the atmosphere (cf. references in the in- THF EOF1 describes a broad, Barents-wide reduction in troduction section), we first examine the leading patterns THF (reduced ocean-to-atmosphere heat loss in the yellow of winter (DJF) THF variability and their association box) that is associated with relatively weak sea ice re- with sea ice cover in the Barents Sea. Figure 2 shows the ductions. The THF–sea ice relationship (reduced THF with leading Barents Sea THF PCs along with the ICEBar in- reduced sea ice) in Fig. 3b is incompatible with the expected dex defined in section 2. The first PC (PC1) is associated relationship associated with an atmospheric response to sea with 33% of the total DJF variability in Barents Sea THF, ice reduction (enhanced THF with reduced sea ice). but it is relatively weakly correlated with the ICEBar time The strength of winter THF depends on near-surface series (r 5 0.30). The second PC (PC2) is associated with wind speeds and on the temperature difference between less of the variability (20%), but it is more strongly cor- the (warm) ocean and (cold) atmosphere (Maykut 1982; related with the ICEBar (r 5 0.60). The relationships be- Schröder et al. 2003; Alexander et al. 2004). If THF is tween Barents Sea THF PCs and ICEBar are robust not enhanced as a result of a reduction in sea ice cover (as only for DJF but also throughout the extended [October– represented by ICEBar and THF PC2), then the ocean February (ONDJF)] winter season (Table 1). provides more heat to the atmosphere and we expect

The spatial patterns associated with ICEBar and the warm SAT anomalies. This relationship is shown in THF PCs support the idea that THF PC2 is more con- Figs. 3a and 4d and 3c and 4f for ICEBar and THF PC2, nected to sea ice cover than THF PC1. The THF signa- respectively, meaning that the variability described by ture of ICEBar is familiar (Fig. 3a)—strongly enhanced the two indices is thus far consistent with an atmospheric ocean-to-atmosphere THF over areas of reduced SIC response to Barents Sea ice perturbations. In contrast, accompanied by moderate reductions in THF directly to both THF and sea ice cover are reduced with THF PC1 the south, indicating a northward shift in intense turbu- increases (Fig. 3b), while SAT anomalies are also warm lent heat loss from the ocean associated with the sea ice (Fig. 4e). This suggests that the THF reductions associ- edge (cf. Deser et al. 2004; Magnusdottir et al. 2004; ated with THF PC1 are likely driven by atmospheric 500 JOURNAL OF CLIMATE VOLUME 29

TABLE 1. Correlations between Barents Sea THF PCs and the The regression analyses in Fig. 5 show that sea ice– ICEBar for various months from October to February. THF PCs related THF variability is present in all winter months and the ICEBar time series are based on detrended monthly data and is consistently associated with warming over the with the seasonal cycle removed. Barents Sea, but the large-scale circulation patterns vary r(PC1, ICEBar) NSIDC r(PC2, ICEBar) NSIDC substantially. As established in the introduction section, (ERA-Interim) (ERA-Interim) the variation from month to month could be due to Oct 0.30 (0.23) 0.43 (0.47) nonlinearities in the dynamical response of the atmo- Nov 0.16 (0.13) 0.74 (0.79) sphere or to internal variability, which is known to play a Dec 0.24 (0.29) 0.45 (0.51) role even in the presence of strong forcing (Deser et al. Jan 0.25 (0.20) 0.73 (0.77) Feb 0.38 (0.35) 0.57 (0.58) 2004, 2012; Wettstein and Deser 2014). Internal vari- ONDJF 0.23 (0.21) 0.57 (0.58) ability is especially important on larger spatial scales; so NDJF 0.24 (0.22) 0.60 (0.63) even if the regional signature of THF PC2 can be un- DJF 0.30 (0.29) 0.60 (0.64) derstood as a simple atmospheric response to sea ice ON 0.21 (0.17) 0.50 (0.55) variability, hemispheric signatures like the WACS pattern seem to be more complicated. changes, in particular a warming of the overlying atmo- Following this idea, we consider variability associated sphere that reduces the ocean–atmosphere temperature with the large-scale WACS pattern (Inoue et al. 2012; difference. Oceanic (SST) correlations with THF PC1 are Mori et al. 2014) identified independently of Barents Sea generally much weaker than the corresponding atmo- ice variability. The WACS pattern emerges consistently spheric (SAT) correlations (cf. Figs. 4b and 4e), which is as the second EOF (20%) of monthly Eurasian SAT to be expected if the ocean is responding to atmospheric variability in all winter months (Figs. 6d–f).1 The SAT changes. In comparison, THF PC2 exhibits spatially co- and SLP regressions onto the WACS index are sub- herent oceanic and atmospheric patterns and slightly stantially stronger and generally more robust across all stronger correlations with SST than with SAT (cf. Figs. 4c winter months than the comparable regressions onto and 4f), in concert with an atmospheric response to ocean THF PC2 (Figs. 5d–i) or ICEBar (not shown). In its changes, such as Barents Sea ice variability. Together, the positive phase, the WACS pattern exhibits robust out- analyses in Figs. 2–4 suggest an active role for the atmo- of-phase SAT anomalies between warmer temperatures sphere in driving a substantial portion of THF variability over the Barents–Kara Seas and colder temperatures over the Barents Sea. over Eurasia. It is consistently associated with a prom- Before exploring the atmosphere’s role further, we inent SLP high over the Eurasian continent and south- check the robustness of the sea ice–related THF vari- erly near-surface wind anomalies over the Barents Sea ability (i.e., THF PC2) in individual winter months from December to January (Figs. 6g–i). When Eurasia is (Fig. 5). The localized dipole-like structure in THF ap- cold, Barents Sea ice concentration is somewhat lower pears in all months, and is consistently associated with than average, but the associated Barents Sea THF enhanced ocean-to-atmosphere THF over areas of re- anomalies are, on average, negative (Figs. 6a–c) for each duced SIC (Figs. 5a–c) and warmer SATs over the Barents winter month. This is the opposite of what would be Sea (Figs. 5d–f). On a hemispheric scale, the SAT re- expected if the WACS pattern were a direct atmo- gressions in all months show a generally cooler Eurasian spheric response to reduced Barents Sea ice. Thus, at- continent similar to the WACS pattern described by mospheric variability appears not only to shape the Inoue et al. (2012), but the cooling is only statistically WACS pattern itself, but also to contribute substantially significant in December (Figs. 5d–f). In contrast, there is to observed THF variability over the Barents Sea and substantial variation in the large-scale atmospheric circu- large-scale SAT variability. lation patterns associated with THF PC2 (Fig. 5g–i). Causality is better explored by examining lead–lag There are few robust features in the SLP regressions relationships between the WACS pattern and atmo- across all months, and we do not observe the expected spheric or oceanic indicators on shorter time scales. linear atmospheric response to enhanced local heating— Figure 7 shows lagged correlations between a variety a negative SLP anomaly directly downstream of a heat of indicators and a daily index of the WACS pattern source (e.g., Hoskins and Karoly 1981). We see some evi- (WACSdsyn as defined in the data and methods section). dence of northerly cold-air advection over Siberia, in agreement with the circulation pattern described by Honda et al. (2009) and other studies listed in the introduction 1 The first EOF (40%) of Eurasian SAT is associated with the section. Similar results are obtained if the ICEBar index is temperature signature of the North Atlantic/Arctic Oscillation (cf. used instead of THF PC2, particularly during midwinter. Thompson and Wallace 1998). 15 JANUARY 2016 S O R O K I N A E T A L . 501

FIG. 3. DJF THF (color shading) and SIC regressions (blue contours indicate reduced sea ice, red contours

indicate increased sea ice; contour interval 5%) onto (a) ICEBar (b) THF PC1, and (c) THF PC2. White dots indicate regression values that are not significant using a two-sided t test and a 0.05 significance threshold. Only significant values are plotted for SIC. The Barents Sea domain is indicated by the yellow box. Baffin Bay is the only area with positive (red) SIC regressions.

On daily time scales, the Barents-averaged THF previous results, that the WACS pattern does not orig-

(THFdBar; blue curve) leads the WACS pattern, with inate directly from enhanced THF caused by Barents reduced ocean-to-atmosphere heat ﬂuxes peaking Sea ice reduction. 2 days before the WACS pattern. This echoes the mes- The corresponding spatial patterns at various lags sage from the monthly analysis (Fig. 6), particularly that (Fig. 8) provide some ﬁnal clues about how the WACS the WACS pattern does not appear to be a direct at- pattern, THF, and atmospheric circulation over the mospheric response to reduced Barents Sea ice (and Barents Sea are linked. The patterns are consistent with increased THF) on short (daily) or long (monthly) time the results shown in Fig. 7: the WACS pattern is asso- scales. Note that because THF PC1 represents a basin- ciated with negative Barents Sea THF anomalies (re- wide pattern of THF variability in the Barents Sea, it is duced ocean-to-atmosphere THF) and a relatively weak very similar to THFdBar except for a change in sign reduction in the Barents Sea ice cover (Figs. 8a–c), (see Fig. 3b). The lagged correlation curves associated which together are inconsistent with the notion that the with the daily version of these two indices are also very WACS pattern is a direct atmospheric response to re- similar except for a change in sign (r 520.96; see also duced Barents Sea ice. The spatial distribution and Fig. 3b). On the other hand, the daily version of the diminishing intensity of THF anomalies over time THF PC2 index, which represents sea ice–related THF (Figs. 8a–c) are instead consistent with atmospheric variability, exhibits low correlations with WACSdsyn changes driving ocean-to-atmosphere THF variability at all lags (red curve). The lagged correlation between (e.g., Bjerknes 1964; Cayan 1992; Gulev et al. 2013). The

ICEdBar and WACSdsyn (black curve) peaks at zero associated near-surface signature exhibits southerly and lag,butitisstrongerwithICEdBar lagging rather than southwesterly winds (Figs. 8d–f), consistent with warm leading, casting further doubt on the idea that the temperature advection over the Barents Sea. The large-

WACS pattern is a direct consequence of reduced scale surface (SLP) and midtropospheric (Z500) anom- Barents Sea ice. alies (Figs. 8g–i) resemble patterns associated with wave Finally, southerly wind anomalies over the Barents propagation and winter atmospheric blocking (e.g., Park region (VdBar; green curve) also peak 2 days before et al. 2011; Cheung et al. 2013) and, to some degree, with WACSdsyn. Supposing that these wind anomalies are the east Atlantic pattern (Wallace and Gutzler 1981; associated with an anomalous atmospheric circulation Smoliak 2009; Wettstein and Wallace 2010). It is also pattern, this could suggest 1) advection of warm air from noteworthy that the strongest Z500 anomalies occur the south into the Barents Sea region, 2) a basinwide prior to the strongest (lag 0) correlation between the reduction of THF as a result of a correspondingly WACS pattern and Barents Sea ice reductions (cf. weakened temperature contrast between the warmer Figs. 7 and 8g–i), further supporting the interpretation ocean and colder atmosphere, and 3) a more slowly that the large-scale atmospheric wave exists prior to any evolving decrease in Barents Sea ice cover driven me- atmospheric changes initiated by Barents Sea ice (and chanically by southerly winds pushing the sea ice edge the associated THF) variability. Our interpretation northward. This chain of events is consistent with the of the collective results is that the WACS pattern re- lead–lag relationships in Fig. 7 and suggests, along with ﬂects a large-scale pattern of intrinsic coupled climate 502 JOURNAL OF CLIMATE VOLUME 29

FIG. 4. (top) DJF correlations (color shading) between SST and (a) ICEBar, (b) THF PC1, and (c) THF PC2 with SIC regressions (blue contours indicate reduced sea ice, red contours indicate increased sea ice; interval 5%) onto

the same indices. (bottom) DJF correlations (color shading) between SAT and (d) ICEBar, (e) THF PC1, and (f) THF PC2 with 10-m wind regressions (vectors) onto the same indices. White dots indicate values of the shaded fields that are not significant using a two-sided t test and a 0.05 significance threshold. Only significant values are 2 plotted for SIC and 10-m winds. Vectors representing wind speeds below 0.5 m s 1 are omitted for clarity. variability that is related to both the atmospheric forcing of Francis et al. 2009; Overland and Wang 2010; Overland and the atmospheric response to Barents Sea ice variability. et al. 2011; Jaiser et al. 2012; Orsolini et al. 2012; Cohen et al. 2014; Kim et al. 2014; García-Serrano et al. 2015; King et al. 2015). Because the previous section focuses 4. Discussion on synchronous analyses during the DJF season, it does In the previous section, we investigated interactions not address the possibility of this autumn-to-winter between the observed Barents Sea ice cover and large- linkage. To explore this issue, Fig. 9 shows lagged re- scale atmospheric circulation with a focus on ocean-to- gressions between the winter (DJF)-mean WACS index atmosphere THF as the coupling agent between the with THF, SIC, SST, and SLP anomalies in the months underlying ocean and the overlying atmosphere. The leading up to winter. The DJF WACS pattern is indeed goal was to evaluate a candidate null hypothesis that the associated with negative SIC anomalies (in the Barents, observed interannual variability in the winter WACS Kara, Laptev, and east Siberian Seas) and positive THF pattern might primarily be an expression of atmospheric anomalies in the vicinity of the SIC anomalies during variability rather than an atmospheric response to sea October and November, but many of these anomalies ice variability. Our results suggest that we cannot reject are insignificant. this null hypothesis, and that atmospheric circulation The October–November regression results (Figs. 9a,b) variability does in fact play a critical role in determining are consistent with the suggestion that negative SIC/ the SLP, SAT, THF, and even the sea ice distribution positive THF anomalies during autumn can influence the both over the Barents Sea and farther afield. wintertime circulation but still do not provide adequate A number of studies mentioned in the introduction grounds for a direct causal interpretation. The geo- section suggest that the Barents Sea ice variability in graphical distribution of the fields (SIC, THF, SLP, and October and November (ON) causes a delayed response 10-m winds) is transitory over the late fall and early in the winter atmospheric circulation (Honda et al. 2009; winter and the THF anomalies are displaced from the 15 JANUARY 2016 S O R O K I N A E T A L . 503

FIG. 5. Monthly regressions onto standardized and contemporary monthly THF PC2. (top) THF (color shading) and SIC (blue contours indicate reduced sea ice, red contours indicate increased sea ice; contour interval 5%), (middle) SAT (color shading), and (bottom) SLP (color shading) and 10-m winds (vectors). White dots indicate values of the shaded fields that are not significant using a two-sided t test and a 0.05 significance threshold. Only 2 significant values are plotted for SIC and 10-m winds. Vectors representing wind speeds below 0.5 m s 1 are omitted for clarity. strongest SIC anomalies, suggesting an important role for and the relatively few degrees of freedom, a clear nonlinear ice-circulation effects, internal atmospheric diagnosis of the causal linkage between autumn ice variability, or both. Many studies also point to more anomaliesandmidwinterWACSanomaliesisper- complicated, indirect pathways linking SIC and circula- haps unobtainable using reanalysis. tion via autumn stratospheric anomalies that propagate Sea ice loss is prominent in the observed record and downward to create the tropospheric signals later in is also consistently projected for the future within winter (Jaiser et al. 2012; Orsolini et al. 2012; Cohen et al. coupled GCM experiments forced by anthropogenic 2014; Kim et al. 2014; García-Serrano et al. 2015; King increases in greenhouse gases. The associated atmo- et al. 2015). While the downward propagation of stratospheric response is reflected in various aspects of cli- spheric anomalies is clear in observations, the ques- mate, both locally and remotely (e.g., Serreze et al. tion of whether they are caused by sea ice anomalies 2009; Simmonds and Keay 2009; Honda et al. 2009; is not yet settled (see discussion in King et al. 2015). Francis et al. 2009; Screen and Simmonds 2010a,b; Given the apparent complexity of the mechanisms Overland and Wang 2010; Overland et al. 2011; Blüthgen 504 JOURNAL OF CLIMATE VOLUME 29

FIG.6.AsinFig. 5, but all regressions are onto the WACS index (PC2 of Eurasian SAT) instead of THF PC2. The domain of the Eurasian region (458–818N, 128–1208E) is indicated in (f). et al. 2012; Jaiser et al. 2012; Orsolini et al. 2012; Screen the corresponding detrended analysis, r 5 0.39 (0.33 for et al. 2013; Mori et al. 2014). To more completely the monthly time series). More importantly, both syn- evaluate the relationship between the WACS pattern, chronous and lagged regressions onto the WACS pat- Barents Sea ice, and Barents Sea THF, we performed a tern are largely similar between raw and detrended full set of parallel analyses on raw (not detrended) analyses (not shown). In addition, an intraseasonal variables. analysis like in Fig. 9 but performed on raw (not de- The main conclusions of the current study remain trended) data returns very similar results to those when trends are retained (not shown). Consistent with shown in Fig. 9, suggesting the trend is perhaps not Mori et al. (2014), we ﬁnd trends toward a positive DJF critically important in understanding the DJF WACS WACS pattern and increased Barents Sea ice loss, both pattern. of which are particularly evident from 2004 onward. An analysis of the total seasonally averaged variance Attributing causality to this relationship is difﬁcult, associated with the linear trend and in linearly detrended however, because of few degrees of freedom and be- data suggests that at least a third of the observed 1979/ cause the correlation between the raw seasonal time 80 to 2011/12 variance in the winter Barents Sea ice is as- series, r 5 0.57(0.47forthemonthlytimeseries), sociated with the linear trend (Table 2). The trend ac- represents substantially more shared variance than in counts for a small fraction of the variance in the DJF 15 JANUARY 2016 S O R O K I N A E T A L . 505

FIG. 7. Lagged correlations between the synthetic daily PC2 of Eurasian SAT (WACSdsyn) and a number of other daily indices averaged over the Barents Sea region for DJF 1979/80 to

2011/12: THF (THFdBar, blue; positive indicates ocean-to-atmosphere heat ﬂux), THF PC2dsyn (red; positive index associated with lower sea ice and a THF dipole similar to that shown in

Fig. 3c), Barents Sea ice area (ICEdBar, black; positive index indicates less ice area), and 10-m winds (VdBar, green; positive index indicates southerly winds). The autocorrelation of WACSdysn is shown in orange. The shading shows the 95% conﬁdence interval on the correlations calculated using Fisher’s z transform.

WACS index (8%) and hardly any of the variance in motivated our study, and it is critical for understanding winter Barents Sea THF (1%). The mismatch in the rel- our results in the context of previous work. Studies ative importance of the trend further suggests it is unlikely based on observations ﬁnd compelling correlations be- that the WACS pattern results from a direct linear at- tween sea ice and atmospheric circulation variability mospheric response to sea ice loss or variability. In short, (e.g., Honda et al. 2009; Hori et al. 2011; Inoue et al. our decision to explore the interannual variability allows 2012; Outten and Esau 2012; Mori et al. 2014), but for a focus on the mechanistic relationships between the causality is challenging to determine in nature because atmospheric circulation anomalies, sea ice variability, and of the many complicated forcing/response interac- the connection of both to the upper ocean using the tions and the relatively few years of data. Studies based maximum number of degrees of freedom. on uncoupled modeling experiments do isolate an The goal of identifying the atmospheric response to atmospheric response by design—they prescribe an ice Barents Sea ice variability in the fully coupled system anomaly under an atmospheric model and examine the 506 JOURNAL OF CLIMATE VOLUME 29

FIG. 8. Lagged regressions onto the daily WACSdsyn index (lags expressed in days, similar to Fig. 7; positive lag indicates WACSdsyn leading). (a)–(c) THF (color shading with red indicating ocean-to-atmosphere heat ﬂux) and SIC (blue contours indicated reduced sea ice, red contours indicate increased sea ice; contour interval 5%); (d)–

(f) SAT (color shading) and 10-m winds (vectors); and (g)–(i) SLP (color shading) and Z500 (solid/dashed contours 2 indicate positive/negative values; contour interval 5 20 m). Vectors representing wind speeds below 0.5 m s 1 are omitted for clarity. resulting changes to the atmospheric circulation. While (Deser et al. 2004, 2012; Wettstein and Deser 2014; there is undoubtedly a WACS-like response to pre- Screen et al. 2014). Our focus on the surface turbulent scribed Barents Sea ice reductions, the particular result heat ﬂuxes allows us to use the reanalysis-based data, is somewhat model dependent (Honda et al. 2009; which represent the fully coupled system, without Petoukhov and Semenov 2010; Orsolini et al. 2012; having to assume that observed atmospheric circula- Mori et al. 2014). It is also unclear how much of the tion changes are purely a response to sea ice forcing. atmospheric response is due to the prescribed sea ice Our results are generally more consistent with studies loss as compared with other surface (SST, snow cover) attributing cold Eurasian winters to atmospheric cir- changes, and how much is directly attributable to culation variability (e.g., Hurrell 1995; Jeong and Ho changes in radiative forcing. Further complicating the 2005; Zhang et al. 2008; Cattiaux et al. 2010; Park et al. task is the presence of strong natural variability and 2011; Sato et al. 2014) than with those attributing them rich two-way ocean–atmosphere interactions in nature to Arctic sea ice variability. Relationships between 15 JANUARY 2016 S O R O K I N A E T A L . 507

FIG. 9. Lagged regressions onto the seasonal-mean DJF WACS pattern (SAT PC2) of the following fields, for both the contemporary DJF season and during the preceding October and November. (top) THF (color shading) and SIC (blue contours indicate reduced sea ice, red contours indicate increases sea ice; contour interval 5%); (middle) SAT (color shading); and (bottom) SLP (color shading) and 10-m winds (vectors). White dots indicate values of the shaded fields that are not significant using a two-sided t test and a 0.05 significance threshold. Only significant values are plotted for 10-m wind regressions. All values are plotted for SIC anomalies since many of them are not significant. 2 Vectors representing wind speeds below 0.5 m s 1 are omitted for clarity. The domain of the Eurasian region (458– 818N, 128–1208E) is indicated in (f).

ocean circulation, SST, Barents Sea ice, and Barents the ocean, ice, and atmosphere are continuously inter- Sea THF (Årthun et al. 2012; Sato et al. 2014)are acting and none of the three components is obviously certainly intriguing, but cleanly assessing their relative dominant. Atmospheric circulation variability is driven importance in a coupled WACS response is inherently by many factors, sea ice being just one of these (see also difﬁcult with low-frequency variability and so few years Deser et al. 2012; Wettstein and Deser 2014). The results of data. collected here demonstrate that the proposed direct link The implications of this observational study must be between variability in Barents Sea ice and the WACS viewed strictly in the context of the fully coupled cli- pattern is at best an incomplete characterization of na- mate system on interannual time scales. In this context, ture. Our study suggests that a broader and more 508 JOURNAL OF CLIMATE VOLUME 29

TABLE 2. Fraction of total monthly variance associated with the Acknowledgments. Although CL and JJW are listed s2 s2 linear trend ( trend) and with the linearly detrended data ( detrend). alphabetically, they contributed equally to the manuscript. ICEBar, THFBar, and SATBar are the negative sea ice concentra- We wish to thank J. Kristiansen, P. Kushner, three anony- tion, turbulent heat ﬂux, and surface air temperature, respectively, averaged over the Barents Sea region (67–818N, 12–73 8E). mous reviewers, and the editor for their helpful comments.

WACSssyn is a measure of the strength of the WACS pattern We acknowledge the European Centre for Medium-Range (see section 2). Weather Forecasts for providing the ERA-Interim data

2 2 and the National Snow and Ice Data Center for pro- Index (DJF) strend (%) sdetrend viding the sea ice data. This work was supported by the ICEBar NSIDC 50 50 NordForsk TRI program under project GREENICE ICE ERA-Interim 33 67 Bar 61841 (CL, JJW), the Research Council of Norway under WACSssyn 892 THFBar 199projects CLIMARC 225032 and Bjerknes Compensation SATBar 18 82 227137 (SAS), and the Centre for Climate Dynamics (SKD) at the Bjerknes Centre (CL). JJW acknowledges support from the G. Unger Vetlesen Foundation. rigorous diagnosis of the causes of Eurasian continental REFERENCES temperature extremes is warranted. Alexander, M. A., U. S. Bhatt, J. E. Walsh, M. S. Timlin, J. S. Miller, and J. D. Scott, 2004: The atmospheric response to realistic Arctic sea ice anomalies in an AGCM during winter. 5. Summary J. Climate, 17,890–905,doi:10.1175/1520-0442(2004)017,0890: TARTRA.2.0.CO;2. Our study examines relationships between Barents Andersson, A., C. Klepp, K. Fennig, S. Bakan, H. Grassl, and Sea ice, turbulent heat flux (THF), and the ‘‘warm- J. Schulz, 2011: Evaluation of HOAPS-3 ocean surface fresh- Arctic cold-Siberian’’ (WACS) pattern. Our main find- water flux components. J. Appl. Meteor. Climatol., 50, 379–398, doi:10.1175/2010JAMC2341.1. ings are as follows: Årthun, M., T. Eldevik, L. H. Smedsrud, Ø. Skagseth, and R. B. Ingvaldsen, 2012: Quantifying the influence of Atlantic heat d The leading pattern (EOF1) of winter Barents Sea on the Barents Sea ice variability and retreat. J. Climate, 25, THF variability appears to be driven primarily by 4736–4743, doi:10.1175/JCLI-D-11-00466.1. atmospheric rather than sea ice variability. The sea Bader, J., M. D. S. Mesquita, K. I. Hodges, N. Keenlyside, ice–related THF variability appears as EOF2. S. Østerhus, and M. Miles, 2011: A review on Northern Hemi- d The sea ice–related THF variability (EOF2) is robust sphere sea-ice, storminess and the North Atlantic Oscillation: over the entire winter season, but its link to cold Observations and projected changes. Atmos. Res., 101, 809–834, doi:10.1016/j.atmosres.2011.04.007. anomalies over Siberia is not. Barnes, E. A., 2013: Revisiting the evidence linking Arctic ampli- d The WACS pattern is a robust pattern of temperature fication to extreme weather in midlatitudes. Geophys. Res. variability (EOF2 of winter Eurasian SAT) and is Lett., 40, 4734–4739, doi:10.1002/grl.50880. associated with weak reductions in Barents Sea ice ——, E. Dunn-Sigouin, G. Masato, and T. Woollings, 2014: Ex- cover but also reduced ocean-to-atmosphere THF. ploring recent trends in Northern Hemisphere blocking. Geo- phys. Res. Lett., 41, 638–644, doi:10.1002/2013GL058745. The sense of these relationships is inconsistent with Barsugli, J. J., and D. S. Battisti, 1998: The basic effects of the proposed pathways of the WACS pattern being a atmosphere–ocean thermal coupling on midlatitude variability. direct atmospheric response to Barents Sea ice J. Atmos. Sci., 55, 477–493, doi:10.1175/1520-0469(1998)055,0477: reductions. TBEOAO.2.0.CO;2. Bhatt, U. S., M. A. Alexander, C. Deser, J. E. Walsh, J. S. Miller, The role of Barents Sea ice variability in forcing the M. S. Timlin, J. Scott, and R. A. Tomas, 2008: The atmospheric WACS pattern appears to be minor in the detrended response to realistic reduced summer Arctic sea ice anomalies. analyses performed here. One consistent interpretation Arctic Sea Ice Decline: Observations, Projections, Mechanisms, of the results is that large-scale atmospheric circulation and Implications, Geophys. Monogr., Vol. 180, Amer. Geophys. Union, 91–110, doi:10.1029/180GM08. variability produces southerly wind anomalies that Bjerknes, J., 1964: Atlantic air-sea interaction. Advances in push the Barents Sea ice edge northward, advect warm Geophysics, Vol. 10, Academic Press, 1–82, doi:10.1016/ air into the region, and reduce Barents Sea THF, and S0065-2687(08)60005-9. that are ultimately associated with cold Siberian tem- Blüthgen, J., R. Gerdes, and M. Werner, 2012: Atmospheric re- peratures downstream. The analyses leave open the sponse to the extreme Arctic sea ice conditions in 2007. Geo- phys. Res. Lett., 39, L02707, doi:10.1029/2011GL050486. possibility that Barents Sea ice variability drives an Budikova, D., 2009: Role of Arctic sea ice in global atmospheric atmospheric response that feeds back on preexisting circulation: A review. Global Planet. Change, 68, 149–163, atmospheric circulation anomalies. doi:10.1016/j.gloplacha.2009.04.001. 15 JANUARY 2016 S O R O K I N A E T A L . 509

Cattiaux, J., R. Vautard, C. Cassou, P. Yiou, V. Masson-Delmotte, summer Arctic sea-ice extent. Geophys. Res. Lett., 36, L07503, and F. Codron, 2010: Winter 2010 in Europe: A cold extreme doi:10.1029/2009GL037274. in a warming climate. Geophys. Res. Lett., 37,L20704, García-Serrano, J., C. Frankignoul, G. Gastineau, and A. de la doi:10.1029/2010GL044613. Cámara, 2015: On the predictability of the winter Euro- Cavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. J. Zwally, Atlantic climate: Lagged influence of autumn Arctic sea ice. 1996: Sea ice concentrations from Nimbus-7 SMMR and J. Climate, 28, 5195–5216, doi:10.1175/JCLI-D-14-00472.1. DMSP SSM/I passive microwave data, version 1. National Germe, A., M.-N. Houssais, C. Herbaut, and C. Cassou, 2011: Snow and Ice Data Center, Boulder, CO, digital media. [Avail- Greenland Sea sea ice variability over 1979–2007 and its link able online at http://nsidc.org/data/nsidc-0051.] to the surface atmosphere. J. Geophys. Res., 116, C10034, Cayan, D. R., 1992: Latent and sensible heat flux anomalies over doi:10.1029/2011JC006960. the northern oceans: The connection to monthly atmo- Ghatak, D., A. Frei, G. Gong, J. Stroeve, and D. Robinson, 2010: spheric circulation. J. Climate, 5, 354–369, doi:10.1175/ On the emergence of an Arctic amplification signal in terres- 1520-0442(1992)005,0354:LASHFA.2.0.CO;2. trial Arctic snow extent. J. Geophys. Res., 115, D24105, Cheung, H. N., W. Zhou, Y. Shao, W. Chen, H. Y. Mok, and M. C. doi:10.1029/2010JD014007. Wu, 2013: Observational climatology and characteristics of Guirguis, K., A. Gershunov, R. Schwartz, and S. Bennett, 2011: wintertime atmospheric blocking over Ural–Siberia. Climate Recent warm and cold daily winter temperature extremes in Dyn., 41, 63–79, doi:10.1007/s00382-012-1587-6. the Northern Hemisphere. Geophys. Res. Lett., 38, L17701, Cohen, J., M. Barlow, P. J. Kushner, and K. Saito, 2007: doi:10.1029/2011GL048762. Stratosphere–troposphere coupling and links with Eurasian Gulev, S. K., M. Latif, N. Keenlyside, W. Park, and K. P. land surface variability. J. Climate, 20, 5335–5343, doi:10.1175/ Koltermann, 2013: North Atlantic Ocean control on surface 2007JCLI1725.1. heat flux on multidecadal timescales. Nature, 499, 464–467, ——, and Coauthors, 2014: Recent Arctic amplification and extreme doi:10.1038/nature12268. mid-latitude weather. Nat. Geosci., 7,627–637,doi:10.1038/ Honda, M., K. Yamazaki, H. Nakamura, and K. Takeuchi, 1999: ngeo2234. Dynamic and thermodynamic characteristics of atmospheric re- Comiso, J. C., 2012: Large decadal decline of the Arctic multi- sponse to anomalous sea-ice extent in the Sea of Okhotsk. year ice cover. J. Climate, 25, 1176–1193, doi:10.1175/ J. Climate, 12, 3347–3358, doi:10.1175/1520-0442(1999)012,3347: JCLI-D-11-00113.1. DATCOA.2.0.CO;2. Coumou, D., and S. Rahmstorf, 2012: A decade of weather extremes. ——, J. Inoue, and S. Yamane, 2009: Influence of low Arctic sea-ice Nat. Climate Change, 2, 491–496, doi:10.1038/nclimate1452. minima on anomalously cold Eurasian winters. Geophys. Res. Croci-Maspoli, M., and H. C. Davies, 2009: Key dynamical fea- Lett., 36, L08707, doi:10.1029/2008GL037079. tures of the 2005/06 European winter. Mon. Wea. Rev., 137, Hopsch, S., J. Cohen, and K. Dethloff, 2012: Analysis of a link 664–678, doi:10.1175/2008MWR2533.1. between fall Arctic sea ice concentration and atmospheric Dee, and Coauthors, 2011: The ERA-Interim reanalysis: Config- patterns in the following winter. Tellus, 64A, 18624, doi:10.3402/ uration and performance of the data assimilation system. tellusa.v64i0.18624. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828. Hori, M. E., J. Inoue, T. Kikuchi, M. Honda, and Y. Tachibana, 2011: Deser, C., J. E. Walsh, and M. S. Timlin, 2000: Arctic sea ice vari- Recurrence of intraseasonal cold air outbreak during the 2009/ ability in the context of recent atmospheric circulation trends. 2010 winter in Japan and its ties to the atmospheric condition over J. Climate, 13,617–633,doi:10.1175/1520-0442(2000)013,0617: the Barents-Kara Sea. SOLA, 7, 25–28, doi:10.2151/sola.2011-007. ASIVIT.2.0.CO;2. Hoskins, B. J., and D. J. Karoly, 1981: The steady linear response of a ——, G. Magnusdottir, R. Saravanan, and A. Phillips, 2004: The spherical atmosphere to thermal and orographic forcing. J. Atmos. effects of North Atlantic SST and sea ice anomalies on the Sci., 38, 1179–1196, doi:10.1175/1520-0469(1981)038,1179: winter circulation in CCM3. Part II: Direct and indirect com- TSLROA.2.0.CO;2. ponents of the response. J. Climate, 17,877–889,doi:10.1175/ Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscil- 1520-0442(2004)017,0877:TEONAS.2.0.CO;2. lation: Regional temperatures and precipitation. Science, 269, ——, R. A. Tomas, and S. Peng, 2007: The transient atmospheric 676–679, doi:10.1126/science.269.5224.676. circulation response to North Atlantic SST and sea ice Inoue, J., M. E. Hori, and K. Takaya, 2012: The role of Barents Sea anomalies. J. Climate, 20, 4751–4767, doi:10.1175/JCLI4278.1. ice in the wintertime cyclone track and emergence of a warm- ——, ——, M. Alexander, and D. Lawrence, 2010: The seasonal Arctic cold-Siberian anomaly. J. Climate, 25, 2561–2568, atmospheric response to projected Arctic sea ice loss in the late doi:10.1175/JCLI-D-11-00449.1. twenty-first century. J. Climate, 23,333–351,doi:10.1175/ Jaiser, R., K. Dethloff, D. Handorf, A. Rinke, and J. Cohen, 2012: 2009JCLI3053.1. Impact of sea ice cover changes on the Northern Hemisphere ——, A. S. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty atmospheric winter circulation. Tellus, 64A, 11595, doi:10.3402/ in climate change projections: The role of internal variability. tellusa.v64i0.11595. Climate Dyn., 38, 527–546, doi:10.1007/s00382-010-0977-x. Jeong, J.-H., and C.-H. Ho, 2005: Changes in occurrence of cold Fang, Z., and J. M. Wallace, 1994: Arctic sea ice variability on a surges over East Asia in associated with Arctic Oscillation. timescale of weeks and its relation to atmospheric forcing. Geophys. Res. Lett., 32, L14704, doi:10.1029/2005GL023024. J. Climate, 7, 1897–1914, doi:10.1175/1520-0442(1994)007,1897: Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re- ASIVOA.2.0.CO;2. analysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, Francis, J. A., and S. J. Vavrus, 2012: Evidence linking Arctic doi:10.1175/1520-0477(1996)077,0437:TNYRP.2.0.CO;2. amplification to extreme weather in mid-latitudes. Geophys. Kim, B.-M., S.-W. Son, S.-K. Min, J.-H. Jeong, S.-J. Kim, X. Zhang, Res. Lett., 39, L06801, doi:10.1029/2012GL051000. T. Shim, and J.-H. Yoon, 2014: Weakening of the stratospheric ——, W. Chan, D. J. Leathers, J. R. Miller, and D. E. Veron, 2009: polar vortex by Arctic sea-ice loss. Nat. Commun., 5, 4646, Winter Northern Hemisphere weather patterns remember doi:10.1038/ncomms5646. 510 JOURNAL OF CLIMATE VOLUME 29

King, M. P., M. Hell, and N. Keenlyside, 2015: Investigation of the off Labrador and Newfoundland from 1962 to 1992. Can. atmospheric mechanisms related to the autumn sea ice and J. Fish. Aquat. Sci., 54, 30–39, doi:10.1139/f96-150. winter circulation link in the Northern Hemisphere. Climate Rigor, I. G., J. M. Wallace, and R. L. Colony, 2002: Response of sea Dyn., doi:10.1007/s00382-015-2639-5, in press. ice to the Arctic Oscillation. J. Climate, 15, 2648–2663, Kvamstø, N. G., P. Skeie, and D. B. Stephenson, 2004: Impact of doi:10.1175/1520-0442(2002)015,2648:ROSITT.2.0.CO;2. Labrador sea-ice extent on the North Atlantic oscillation. Int. Sato, K., J. Inoue, and M. Watanabe, 2014: Influence of the Gulf J. Climatol., 24, 603–612, doi:10.1002/joc.1015. Stream on the Barents Sea ice retreat and Eurasian coldness Lau, N.-C., and E. O. Holopainen, 1984: Transient eddy forcing of during early winter. Environ. Res. Lett., 9, 101003, doi:10.1088/ the time-mean flow as identified by geopotential tendencies. 1748-9326/9/8/084009. J. Atmos. Sci., 41, 313–328, doi:10.1175/1520-0469(1984)041,0313: Scaife, A. A., and J. R. Knight, 2008: Ensemble simulations of the TEFOTT.2.0.CO;2. cold European winter of 2005–2006. Quart. J. Roy. Meteor. Liptak, J., and C. Strong, 2014: The winter atmospheric response to Soc., 134, 1647–1659, doi:10.1002/qj.312. sea ice anomalies in the Barents Sea. J. Climate, 27, 914–924, Schröder, D., T. Vihma, A. Kerber, and B. Brümmer, 2003: On the doi:10.1175/JCLI-D-13-00186.1. parameterization of turbulent surface fluxes over heteroge- Magnusdottir, G., C. Deser, and R. Saravanan, 2004: The effects of neous sea ice surfaces. J. Geophys. Res., 108, 3195, North Atlantic SST and sea ice anomalies on the winter circu- doi:10.1029/2002JC001385. lation in CCM3. Part I: Main features and storm track charac- Screen, J. A., and I. Simmonds, 2010a: Increasing fall-winter energy teristics of the response. J. Climate, 17, 857–876, doi:10.1175/ loss from the Arctic Ocean and its role in Arctic temperature 1520-0442(2004)017,0857:TEONAS.2.0.CO;2. amplification. Geophys. Res. Lett., 37, L16707, doi:10.1029/ Maykut, G. A., 1982: Large-scale heat exchange and ice production 2010GL044136. in the central Arctic. J. Geophys. Res., 87, 7971–7984, doi:10.1029/ ——, and ——, 2010b: The central role of diminishing sea ice in JC087iC10p07971. recent Arctic temperature amplification. Nature, 464, 1334– Mori, M., M. Watanabe, H. Shiogama, J. Inoue, and M. Kimoto, 1337, doi:10.1038/nature09051. 2014: Robust Arctic sea-ice influence on the frequent Eurasian ——, and ——, 2013: Exploring links between Arctic amplification cold winters in past decades. Nat. Geosci., 7, 869–873, doi:10.1038/ and midlatitude weather. Geophys. Res. Lett., 40, 959–964, ngeo2277. doi:10.1002/grl.50174. North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982: ——, ——, C. Deser, and R. Tomas, 2013: The atmospheric re- Sampling errors in the estimation of empirical orthogonal sponse to three decades of observed Arctic sea ice loss. functions. Mon. Wea. Rev., 110, 699–706, doi:10.1175/ J. Climate, 26, 1230–1248, doi:10.1175/JCLI-D-12-00063.1. 1520-0493(1982)110,0699:SEITEO.2.0.CO;2. ——, C. Deser, I. Simmonds, and R. Tomas, 2014: Atmospheric Orsolini, Y. J., R. Senan, R. E. Benestad, and A. Melsom, 2012: impacts of Arctic sea-ice loss, 1979–2009: Separating forced Autumn atmospheric response to the 2007 low Arctic sea ice change from atmospheric internal variability. Climate Dyn., extent in coupled ocean–atmosphere hindcasts. Climate Dyn., 43, 333–344, doi:10.1007/s00382-013-1830-9. 38, 2437–2448, doi:10.1007/s00382-011-1169-z. Seierstad, I. A., and J. Bader, 2009: Impact of a projected future Arctic Outten, S., and I. Esau, 2012: A link between Arctic sea ice and sea ice reduction on extratropical storminess and the NAO. Cli- recent cooling trends over Eurasia. Climatic Change, 110, mate Dyn., 33, 937–943, doi:10.1007/s00382-008-0463-x. 1069–1075, doi:10.1007/s10584-011-0334-z. Serreze, M. C., M. M. Holland, and J. C. Stroeve, 2007: Perspec- ——, R. Davy, and I. Esau, 2013: Eurasian winter cooling: In- tives on the Arctic’s shrinking sea-ice cover. Science, 315, tercomparison of reanalyses and CMIP5 data sets. Atmos. Oce- 1533–1536, doi:10.1126/science.1139426. anic Sci. Lett., 6, 324–331, doi:10.1080/16742834.2013.11447102. ——, A. P. Barrett, J. C. Stroeve, D. M. Kindig, and M. M. Holland, Overland, J. E., and M. Wang, 2010: Large-scale atmospheric cir- 2009: The emergence of surface-based Arctic amplification. culation changes are associated with the recent loss of Arctic Cryosphere, 3, 11–19, doi:10.5194/tc-3-11-2009. sea ice. Tellus, 62A, 1–9, doi:10.1111/j.1600-0870.2009.00421.x. Simmonds, I., and K. Keay, 2009: Extraordinary September Arctic ——, K. R. Wood, and M. Wang, 2011: Warm Arctic – cold con- sea ice reductions and their relationships with storm behavior tinents: Climate impacts of the newly open Arctic Sea. Polar over 1979–2008. Geophys. Res. Lett., 36, L19715, doi:10.1029/ Res., 30, 15 787, doi:10.3402/polar.v30i0.15787. 2009GL039810. Park, T.-W., C.-H. Ho, and S. Yang, 2011: Relationship between Singarayer, J. S., J. L. Bamber, and P. J. Valdes, 2006: Twenty-first- the Arctic Oscillation and cold surges over East Asia. century climate impacts from a declining Arctic sea ice cover. J. Climate, 24, 68–83, doi:10.1175/2010JCLI3529.1. J. Climate, 19, 1109–1125, doi:10.1175/JCLI3649.1. Parkinson, C. L., and D. J. Cavalieri, 2012: Arctic sea ice variability Smoliak, B. V., 2009: A Eurasian pattern of Northern Hemisphere and trends, 1979–2010. Cryosphere Discuss., 6, 931–956, wintertime sea level pressure variability. M.S. thesis, Dept. of doi:10.5194/tcd-6-931-2012. Atmospheric Sciences, University of Washington, 116 pp. Peng, S. L., W. A. Robinson, and M. P. Hoerling, 1997: The Sorteberg, A., and B. Kvingedal, 2006: Atmospheric forcing on the modeled atmospheric response to midlatitude SST anomalies Barents Sea winter ice extent. J. Climate, 19, 4772–4784, and its dependence on background circulation states. doi:10.1175/JCLI3885.1. J. Climate, 10, 971–987, doi:10.1175/1520-0442(1997)010,0971: Takaya, K., and H. Nakamura, 2005: Mechanisms of intraseasonal TMARTM.2.0.CO;2. amplification of the cold Siberian high. J. Atmos. Sci., 62, Petoukhov, V., and V. A. Semenov, 2010: A link between reduced 4423–4440, doi:10.1175/JAS3629.1. Barents–Kara Sea ice and cold winter extremes over northern Tang, Q., X. Zhang, X. Yang, and J. A. Francis, 2013: Cold winter continents. J. Geophys. Res., 115, D21111, doi:10.1029/ extremes in northern continents linked to Arctic sea ice loss. 2009JD013568. Environ. Res. Lett., 8, 014036, doi:10.1088/1748-9326/8/1/014036. Prinsenberg, S. J., I. K. Peterson, S. Narayanan, and J. U. Umoh, Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscilla- 1997: Interaction between atmosphere, ice cover, and ocean tion signature in the wintertime geopotential height and 15 JANUARY 2016 S O R O K I N A E T A L . 511

temperature fields. Geophys. Res. Lett., 25, 1297–1300, Woollings, T., B. Harvey, and G. Masato, 2014: Arctic warming, doi:10.1029/98GL00950. atmospheric blocking and cold European winters in CMIP5 Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the models. Environ. Res. Lett., 9, 014002, doi:10.1088/1748-9326/ geopotential height field during the Northern Hemisphere 9/1/014002. winter. Mon. Wea. Rev., 109, 784–812, doi:10.1175/ Wu, Q., and X. Zhang, 2010: Observed forcing-feedback processes 1520-0493(1981)109,0784:TITGHF.2.0.CO;2. between Northern Hemisphere atmospheric circulation and ——, I. M. Held, D. W. J. Thompson, K. E. Trenberth, and J. E. Arctic sea ice coverage. J. Geophys. Res., 115,D14119, Walsh, 2014: Global warming and winter weather. Science, doi:10.1029/2009JD013574. 343, 729–730, doi:10.1126/science.343.6172.729. Yu, L., X. Jin, and R. A. Weller, 2008: Multidecade global flux datasets Wettstein, J. J., and J. M. Wallace, 2010: Observed patterns of from the objectively analyzed air-sea fluxes (OAFlux) project: month-to-month storm track variability and their relationship Latent and sensible heat fluxes, ocean evaporation, and related to the background flow. J. Atmos. Sci., 67, 1420–1437, surface meteorological variables. Woods Hole Oceanographic doi:10.1175/2009JAS3194.1. Institution OAFlux Project Tech. Rep. OA-2008-01, 64 pp. ——, and C. Deser, 2014: Internal variability in projections of Zhang, X., A. Sorteberg, J. Zhang, R. Gerdes, and J. C. Comiso, twenty-first century Arctic sea ice loss: Role of the large-scale 2008: Recent radical shifts of atmospheric circulations and atmospheric circulation. J. Climate, 27, 527–550, doi:10.1175/ rapid changes in Arctic climate system. Geophys. Res. Lett., JCLI-D-12-00839.1. 35, L22701, doi:10.1029/2008GL035607.