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  • View in gallery

    SAT anomalies (°C) in winter of (a) 2000/01 and (c) 1988/89 and in summer of (b) 2001 and (d) 1989 relative to the climatology during 1980–2017. (e) Normalized time series of the detrended region-mean SAT anomalies in winter (45°–75°N, 90°–130°E) and the following summer (25°–75°N, 95°–130°E) during 1980–2017.

  • View in gallery

    The first SVD mode (SVD1) between interannual variations of winter [D(−1)JF(0)] and the following summer [JJA(0)] SAT over northeast Eurasia during 1980–2017. (a) Winter SAT homogeneous regression map (°C). (b) Summer SAT homogeneous regression map (°C). (c) Normalized EC time series for winter and summer SAT of the SVD1. Winter (summer) SAT homogeneous regression map is obtained by regressing the winter (summer) SAT anomalies onto the corresponding EC time series. Stippling in (a) and (b) indicates anomalies significant at the 95% confidence level. (d) Moving correlation between the EC time series of winter and summer SAT with a running window of 19 (blue line), 21 (red line), and 25 years (black line). Horizontal lines in (d) indicate the correlation coefficient significant at the 90% confidence level. Years labeled in (c) and (d) correspond to the summertime.

  • View in gallery

    (a) Winter [D(−1)JF(0)] SAT anomalies (°C) regressed upon the normalized principal component (PC) time series corresponding to the first empirical orthogonal function (EOF) mode of winter SAT anomalies during 1980–2017. (b) As in (a), but for the following summer [JJA(0)] SAT anomalies. Stippling indicates anomalies significant at the 95% confidence level.

  • View in gallery

    SAT anomalies (°C) in (a) SON(−1), (b) D(−1)JF(0), (c) MAM(0), and (d) JJA(0) regressed upon the normalized EC time series of summer SAT. Stippling indicates anomalies significant at the 95% confidence level.

  • View in gallery

    Latitude–time cross section of SAT anomalies averaged along 80°–140°E. Stippling indicates anomalies significant at the 95% confidence level.

  • View in gallery

    (left) 850-hPa winds (m s−1, scale at top right) and (right) 300-hPa geopotential height (m) anomalies in (a),(b) D(−1)JF(0), (c),(d) MAM(0), and (e),(f) JJA(0) regressed upon the normalized EC time series of summer SAT during 1980–2017. Also shown in (f) are the summer 300-hPa wave activity flux anomalies (m2 s−2, scale at top right). Shading in (a), (c), and (e) indicates either zonal or meridional wind anomalies significant at the 95% confidence level. Stippling in (b), (d), and (f) indicates geopotential height anomalies significant at the 95% confidence level.

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    Summer TCC (%) and surface downward SWR (W m−2) anomalies regressed upon the normalized EC time series of summer SAT. Stippling indicates anomalies significant at the 95% confidence level.

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    Anomalies of (left) SCE (%) and (right) SWE (mm) in (a),(b) D(−1)JF(0), (c),(d) MAM(0), and (e),(f) JJA(0) regressed upon the normalized EC time series of summer SAT. Stippling indicates anomalies significant at the 95% confidence level.

  • View in gallery

    Anomalies (°C) of SST in (a) D(−1)JF(0), (b) MAM(0), and (c) JJA(0) regressed upon the normalized EC time series of summer SAT. Stippling indicates anomalies significant at the 95% confidence level. Black boxes in (a) denote the regions employed to define the winter [D(−1)JF(0)] NAT SST index.

  • View in gallery

    SST (°C) and 1000-hPa wind (m s−1) anomalies in (a) D(−1)JF(0), (b) JFM(0), (c) FMA(0), (d) MAM(0), (e) AMJ(0), (f) MJJ(0), and (g) JJA(0) regressed upon the D(−1)JF(0) NAT SST index. Stippling indicates SST anomalies significant at the 95% confidence level.

  • View in gallery

    (left) SST tendency (°C month−1) and (right) surface net heat flux (°C month−1) anomalies in (a),(h) D(−1)JF(0), (b),(i) JFM(0), (c),(j) FMA(0), (d),(k) MAM(0), (e),(l) AMJ(0), (f),(m) MJJ(0), and (g),(n) JJA(0) regressed upon the D(−1)JF(0) NAT SST index. Stippling indicate anomalies significant at the 95% confidence level. Unit of the surface net heat flux has been converted to the unit of SST tendency (a constant mixed layer depth of 30 m is assumed).

  • View in gallery

    Surface (left) LHF and (right) SWR anomalies in (a),(h) D(−1)JF(0), (b),(i) JFM(0), (c),(j) FMA(0), (d),(k) MAM(0), (e),(l) AMJ(0), (f),(m) MJJ(0), and (g),(n) JJA(0) regressed upon the D(−1)JF(0) NAT SST index. Stippling indicates anomalies significant at the 95% confidence level. Units are W m−2.

  • View in gallery

    Anomalies (10−3 Pa s−1) of vertical p velocity averaged between 600 and 400 hPa in JJA(0) regressed upon the D(−1)JF(0) NAT SST index. Stippling indicates anomalies significant at the 95% confidence level.

  • View in gallery

    Barotropic model height perturbation (m) averaged from days 31 to 40 as a response to prescribed convergence anomaly (black contours with an interval of 2 × 10−6 s−1) over the subtropical North Atlantic with the center at 30°N, 30°W.

  • View in gallery

    Schematic diagram displaying the physical processes linking interannual variations of the winter and summer SAT anomalies over northeast Eurasia.

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Why Does a Colder (Warmer) Winter Tend to Be Followed by a Warmer (Cooler) Summer over Northeast Eurasia?

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  • 1 Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 2 School of Earth Sciences, Zhejiang University, Hangzhou, and Center for Monsoon System Research, and State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 3 Center for Monsoon System Research, and State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 4 Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Abstract

This study reveals a pronounced out-of-phase relationship between surface air temperature (SAT) anomalies over northeast Eurasia in boreal winter and the following summer during 1980–2017. A colder (warmer) winter over northeast Eurasia tends to be followed by a warmer (cooler) summer of next year. The processes for the out-of-phase relation of winter and summer SAT involve the Arctic Oscillation (AO), the air–sea interaction in the North Atlantic Ocean, and a Eurasian anomalous atmospheric circulation pattern induced by the North Atlantic sea surface temperature (SST) anomalies. Winter negative AO/North Atlantic Oscillation (NAO)-like atmospheric circulation anomalies lead to continental cooling over Eurasia via anomalous advection and a tripolar SST anomaly pattern in the North Atlantic. The North Atlantic SST anomaly pattern switches to a dipolar pattern in the following summer via air–sea interaction processes and associated surface heat flux changes. The summer North Atlantic dipolar SST anomaly pattern induces a downstream atmospheric wave train, including large-scale positive geopotential height anomalies over northeast Eurasia, which contributes to positive SAT anomalies there via enhancement of downward surface shortwave radiation and anomalous advection. Barotropic model experiments verify the role of the summer North Atlantic SST anomalies in triggering the atmospheric wave train over Eurasia. Through the above processes, a colder winter is followed by a warmer summer over northeast Eurasia. The above processes apply to the years when warmer winters are followed by cooler summers except for opposite signs of SAT, atmospheric circulation, and SST anomalies.

Corresponding author: Renguang Wu, renguang@zju.edu.cn

Abstract

This study reveals a pronounced out-of-phase relationship between surface air temperature (SAT) anomalies over northeast Eurasia in boreal winter and the following summer during 1980–2017. A colder (warmer) winter over northeast Eurasia tends to be followed by a warmer (cooler) summer of next year. The processes for the out-of-phase relation of winter and summer SAT involve the Arctic Oscillation (AO), the air–sea interaction in the North Atlantic Ocean, and a Eurasian anomalous atmospheric circulation pattern induced by the North Atlantic sea surface temperature (SST) anomalies. Winter negative AO/North Atlantic Oscillation (NAO)-like atmospheric circulation anomalies lead to continental cooling over Eurasia via anomalous advection and a tripolar SST anomaly pattern in the North Atlantic. The North Atlantic SST anomaly pattern switches to a dipolar pattern in the following summer via air–sea interaction processes and associated surface heat flux changes. The summer North Atlantic dipolar SST anomaly pattern induces a downstream atmospheric wave train, including large-scale positive geopotential height anomalies over northeast Eurasia, which contributes to positive SAT anomalies there via enhancement of downward surface shortwave radiation and anomalous advection. Barotropic model experiments verify the role of the summer North Atlantic SST anomalies in triggering the atmospheric wave train over Eurasia. Through the above processes, a colder winter is followed by a warmer summer over northeast Eurasia. The above processes apply to the years when warmer winters are followed by cooler summers except for opposite signs of SAT, atmospheric circulation, and SST anomalies.

Corresponding author: Renguang Wu, renguang@zju.edu.cn

1. Introduction

Surface air temperature (SAT) anomalies and the accompanying extreme cold spells and heat waves have substantial impacts on human health, agriculture, ecosystem, and socioeconomic development (Dong et al. 2009; IPCC 2013; Basu 2009; Kysely and Kim 2009; Ye et al. 2012; Guo et al. 2017; Stott et al. 2004; Cattiaux et al. 2010; Otomi et al. 2013). For example, the extreme low temperature and associated severe freezing snow weather over southern China in January of 2008 led to substantial damage to the production and transportation of electric power and resulted in many casualties (Zhou et al. 2009; Wang et al. 2009). The record-breaking high temperature in the summer of 2010 over many parts of East Asia caused many forest fires and severely destroyed local agriculture and ecosystem (Barriopedro et al. 2011; Matsueda 2011). Therefore, it is important to improve the understanding of the Eurasian SAT variations and the associated controlling factors.

A number of studies have examined Eurasian SAT variations during different seasons (Gong et al. 2001; Miyazaki and Yasunari 2008; Zveryaev and Gulev 2009; Wang et al. 2010). Zveryaev and Gulev (2009) investigated the dominant modes of SAT variations over Europe in four seasons. They indicated that the first empirical orthogonal function (EOF) modes of European SAT in different seasons are mainly featured by a coherent spatial pattern and have a close relation with the North Atlantic Oscillation (NAO)/Arctic Oscillation (AO). AO is the leading mode of atmospheric interannual variability over extratropical Northern Hemisphere (Thompson and Wallace 1998, 2000). NAO is regarded as a regional manifestation of the AO over the North Atlantic region, characterizing by a meridional dipole anomaly pattern (Hurrell and van Loon 1997; Thompson and Wallace 1998). Miyazaki and Yasunari (2008) indicated that the first EOF mode of SAT anomalies over Asia and surrounding oceans in boreal winter has a close relation with the AO. Previous studies generally indicated that negative (positive) phase of the winter AO can lead to negative (positive) SAT anomalies over most parts of Eurasia (Thompson and Wallace 1998; Gong et al. 2001; Wu and Wang 2002). S.-F. Chen et al. (2016, 2018a, 2019) indicated that the AO and Scandinavian teleconnection pattern have dominant impacts on SAT anomalies in boreal spring and autumn over the mid–high latitudes of Eurasia via wind-induced horizontal temperature advection. The Scandinavian teleconnection pattern is characterized by a main center of anomalies around Scandinavia and two centers of opposite sign with weaker amplitudes around the west Europe and eastern Russia–western Mongolia (Barnston and Livezey 1987). Studies indicated that atmospheric circulation anomalies related to Eurasian atmospheric wave trains, such as the circumglobal teleconnection pattern and Silk Road pattern, can exert significant impacts on summer SAT anomalies over Eurasia (Ding and Wang 2005; W. Chen et al. 2016; Xu et al. 2019). In addition, studies suggested that the North Atlantic sea surface temperature (SST) anomalies influence Eurasian SAT and rainfall via triggering atmospheric wave trains (Wu et al. 2011; Monerie et al. 2018; Dunstone et al. 2018).

The abovementioned studies mainly investigated SAT variations over Eurasia during individual seasons. Several recent studies indicated that there exists a close connection of SAT anomalies over Eurasia between boreal winter and spring (Chen et al. 2018b; Zhang et al. 2019). Chen et al. (2018b) showed that the North Atlantic tripolar SST anomalies can maintain the NAO-like meridional atmospheric circulation anomalies from winter to the following spring via a positive air–sea interaction process and contribute to the persistence of the SAT warming/cooling over Eurasia. Zhang et al. (2019) identified a persisting north–south dipole mode of SAT anomaly pattern from winter to spring over Eurasia, which is attributed to the maintenance of a dipole pattern of the Eurasian snow water equivalent (SWE) anomalies.

At present, few studies have examined the possible connection between Eurasian SAT anomalies in boreal winter and the following summer. There is evidence for out-of-phase SAT anomalies over Eurasia between winter and the following summer. For example, negative SAT anomalies control northeast Eurasia in the winter of 2000/01 (Fig. 1a), whereas positive SAT anomalies extend from Siberia to northern China in summer of 2001 (Fig. 1b). A similar switch of SAT anomalies (in opposite direction) is observed from the winter of 1988/89 to summer of 1989 (Figs. 1c,d). The above cases have a common feature that the SAT anomalies in winter are followed by opposite SAT anomalies in summer in the next year over a large part of mid–high-latitude Eurasia. The correlation coefficient of the raw (but long-term trend has been removed) SAT anomalies averaged over northeast Eurasia between winter (45°–75°N, 90°–130°E) and the following summer (25°–75°N, 95°–130°E) is about −0.41 for the period 1980–2017 (Fig. 1e), significant at the 95% confidence level. The above evidence suggests that there exists a robust out-of-phase relation between SAT in winter and the following summer over northeast Eurasia. This study examines the possible factors responsible for this cross-season connection.

Fig. 1.
Fig. 1.

SAT anomalies (°C) in winter of (a) 2000/01 and (c) 1988/89 and in summer of (b) 2001 and (d) 1989 relative to the climatology during 1980–2017. (e) Normalized time series of the detrended region-mean SAT anomalies in winter (45°–75°N, 90°–130°E) and the following summer (25°–75°N, 95°–130°E) during 1980–2017.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

The rest of this study is organized as follows. Section 2 describes the data and methodology employed in this study. Section 3 examines the possible connection of SAT anomalies over Eurasia in boreal winter and the following summer. Section 4 discusses the plausible mechanism linking Eurasian winter and summer SAT. Section 5 provides a summary and discussion.

2. Data and methodology

a. Data and methods

This study employs monthly mean geopotential height, horizontal winds, SAT, surface winds, total cloud cover (TCC), and surface downward shortwave radiation (SWR) from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996; downloaded from http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.html). The NCEP–NCAR data are available from January 1948 to the present. Geopotential height and winds have a horizontal resolution of 2.5° × 2.5°. SAT, TCC, and SWR are on T62 Gaussian grids (Kalnay et al. 1996).

We use monthly mean global gridded SAT data from the University of Delaware Air Temperature and Precipitation, version 5.01, with a horizontal resolution of 0.5° × 0.5° from January 1900 to December 2017 (Matsuura and Willmott 2009). (The University of Delaware data were downloaded from https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html.)

The present study uses the weekly snow cover extent (SCE) and SWE from the Northern Hemisphere 25-km Equal-Area Scalable Earth Grid (EASE-Grid) weekly Snow Cover and Sea Ice Extent, version 3, product (Brodzik and Armstrong 2013). [These EASE-Grid snow data were extracted from the National Snow and Ice Data Center (NSIDC) via ftp://sidads.colorado.edu/pub/DATASETS.] The SCE data cover the period of 1973–2014, and SWE data span from 1979 to 2007. The raw weekly mean SCE and SWE data have been converted into monthly mean on a regular horizontal resolution of 1° × 1°.

Monthly mean SST data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST, version 5 (ERSSTv5) (Huang et al. 2017). (The ERSSTv5 data were downloaded from https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html.) This SST dataset spans from January 1854 to the present and has a horizontal resolution of 2° × 2°. The monthly mean sea ice cover concentration (SIC) data were obtained from the Met Office Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset version 1.1 (Rayner et al. 2003). The HadISST dataset is available on a horizontal resolution of 1° × 1° and from January 1870 to the present (downloaded through https://www.metoffice.gov.uk/hadobs/hadisst/).

The factors and physical processes of SAT variations may not be the same for different time scales (e.g., interannual, interdecadal, and long-term trend). This study focuses on investigating the SAT variation on the interannual time scale. In particular, we concentrate on the relationship between interannual variations of winter and the following summer SAT. Interannual component of a specific variable is obtained by subjecting the original anomaly field to a 2–9-yr Lanczos bandpass filter (Duchon 1979). Use of a 2–7- or 2–11-yr bandpass filter leads to very similar results (not shown). Significant levels of the linear correlation and regression coefficients are estimated according to the two-tailed Student’s t test.

b. Wave activity flux

This study employs the wave activity flux defined by Takaya and Nakamura (2001) to describe propagation of stationary Rossby waves. This wave activity flux is expressed as follows:

W=12|U|{U(υ2ψυx)+V(uυ+ψux)U(uυ+ψux)+V(u2+ψuy)foRapN2Ho[U(υTψTx)+V(uTψTy)]},

where U = (U, V) denotes climatological mean winds and V = (u′, υ′) represents anomalous geostrophic winds; ψ′ denotes anomalous geostrophic streamfunction; Ra, N, p, T′, Ho, and fo are gas constant of the dry air, Brunt–Väisälä frequency, pressure normalized by 1000 hPa, anomalous air temperature, scale height, and the Coriolis parameter at 45°N, respectively. Subscripts x and y denote the derivatives in the zonal and meridional components, respectively. Climatological mean is calculated based on the period of 1980–2017.

c. Barotropic model

In this study, we use a linear barotropic model to examine role of the upper-level convergence/divergence anomalies induced by SST anomalies in the subtropical northeastern Atlantic Ocean in forming atmospheric wave train over the North Atlantic and mid–high latitudes of Eurasia that has a quasi-barotropic vertical structure. Studies have indicated that positive (negative) SST anomalies in the tropical and subtropical region could lead to anomalous divergence (convergence) in the upper troposphere that further acts as an effective source of stationary Rossby wave (Watanabe 2004; Hodson et al. 2010; Wu et al. 2011; Zuo et al. 2013). The barotropic model follows a simple barotropic vorticity equation as follows (Sardeshmukh and Hoskins 1988; Watanabe 2004; Zuo et al. 2013):

t2ψ+J(ψ¯,2ψ)+J(ψ,2ψ¯+f)+α2ψ+ν6ψ=S,

where ψ¯ and ψ′ represent the basic-state and perturbation streamfunction, respectively; J and f are the Jacobian operator and Coriolis parameter, respectively; and S′ denotes the anomalous vorticity source induced by the divergent component of the circulation. The barotropic model encompasses a linear damping that indicates the Rayleigh friction and a biharmonic diffusion. The biharmonic diffusion coefficient ν is used to dampen the eddy with time scale of 1 day, and value of the Rayleigh friction coefficient α is set to 10 (day)−1, which ensure the stability of system in the integration (Watanabe 2004; Zuo et al. 2013). Solution of the Eq. (2) is determined by a combination of the given basic-state and vorticity perturbation S′. In this study, we choose the basic state at the 300-hPa level for the period 1980–2017 based on the NCEP–NCAR reanalysis. Previous study indicated that results of the barotropic model simulation do not show obvious differences for basic states chosen from the upper troposphere (e.g., from 350 to 200 hPa) (O’Reilly et al. 2018). Note that the basic state is selected from the upper troposphere because this is where strongest anomalous divergence/convergence is induced by tropical and subtropical SST anomalies (e.g., Krishnamurti et al. 2013; Sun et al. 2015; O’Reilly et al. 2018).

3. Linkage between winter and the following summer SAT over Eurasia

We use the singular value decomposition (SVD) technique (also called the maximum covariance analysis) to obtain the covarying pattern between interannual variations of SAT over the mid–high latitudes of northeastern Eurasia in winter [D(−1)JF(0)] and the following summer [JJA(0)]. The SVD analysis can capture the coherent spatial patterns between two anomaly fields (Bretherton et al. 1992; Wallace et al. 1992; Cherry 1996; Von Storch and Zwiers 1999). The region of the SVD analysis extends from 30° to 75°N and from 80° to 140°E. Results obtained in the following analysis are not sensitive to a slight change in the region of the SVD analysis. Figure 2 shows the first SVD (SVD1) mode of SAT anomalies over northeastern Eurasia in winter and the following summer for the period 1980–2017. The SVD1 explains approximately 61.1% of the total squared covariance.

Fig. 2.
Fig. 2.

The first SVD mode (SVD1) between interannual variations of winter [D(−1)JF(0)] and the following summer [JJA(0)] SAT over northeast Eurasia during 1980–2017. (a) Winter SAT homogeneous regression map (°C). (b) Summer SAT homogeneous regression map (°C). (c) Normalized EC time series for winter and summer SAT of the SVD1. Winter (summer) SAT homogeneous regression map is obtained by regressing the winter (summer) SAT anomalies onto the corresponding EC time series. Stippling in (a) and (b) indicates anomalies significant at the 95% confidence level. (d) Moving correlation between the EC time series of winter and summer SAT with a running window of 19 (blue line), 21 (red line), and 25 years (black line). Horizontal lines in (d) indicate the correlation coefficient significant at the 90% confidence level. Years labeled in (c) and (d) correspond to the summertime.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

A prominent feature in winter SAT anomalies in the SVD1 is the large negative SAT anomalies north of 42.5°N over Eurasia (Fig. 2a). Positive SAT anomalies appear south of 40°N, in particular, around 90°–100°E (Fig. 2a). Summer SAT anomaly distribution in the SVD1 is featured by a coherent SAT warming over northeast Eurasia, with two centers of large loading around 40° and 70°N, respectively (Fig. 2b). The correlation coefficient between the expansion coefficient (EC) time series of winter and summer SAT is 0.65, significant at the 99% confidence level according to the two-tailed Student’s t test. Most of the years of large EC time series in winter correspond to large values of the summer EC time series, including 1989 and 2001 as mentioned in the introduction. This suggests a strong connection between winter and the following summer SAT anomalies over most parts of northeast Eurasia. This connection appears stable during the analysis period. This is demonstrated by Fig. 2d that displays the moving correlation coefficients between EC time series of winter and summer SAT with different lengths of windows. The negative correlation remains significant during the analysis period (Fig. 2d). In the following analysis, the summer SAT EC time series is used as an index to represent the summer coherent SAT anomaly pattern.

We have also employed an EOF analysis to extract the leading patterns of SAT anomalies over northeast Eurasia (30°–75°N, 80°–140°E) in winter and summer during 1980–2017. The results are shown in Figs. 3a and 3b, respectively. Apparently, the EOF1 pattern of the winter SAT (Fig. 3a) bears a close resemblance to the spatial distribution of the winter SAT anomalies in SVD1 (Fig. 2a). In addition, the EOF1 pattern of the summer SAT is similar to the distribution of summer SAT anomalies in SVD1 (Figs. 2b and 3b). The correlation coefficient between the principal component time series corresponding to EOF1 of winter (summer) SAT anomalies and the EC time series of SVD1 of winter (summer) SAT reaches 0.88 (0.73). This indicates that the SVD1 patterns in Figs. 2a and 2b are also the dominant modes of winter and summer SAT anomalies.

Fig. 3.
Fig. 3.

(a) Winter [D(−1)JF(0)] SAT anomalies (°C) regressed upon the normalized principal component (PC) time series corresponding to the first empirical orthogonal function (EOF) mode of winter SAT anomalies during 1980–2017. (b) As in (a), but for the following summer [JJA(0)] SAT anomalies. Stippling indicates anomalies significant at the 95% confidence level.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

We further examine the temporal evolution of SAT anomalies in association with the SVD1. Figure 4 exhibits SAT anomalies from autumn [SON(−1)] in preceding year to simultaneous summer [JJA(0)] obtained by regression upon the normalized EC time series of summer SAT. Over the mid–high latitudes of Eurasia, significant cooling is observed over the Russian far east and the region between the Lake Baikal and Caspian Sea in autumn (Fig. 4a). Most parts of Eurasia north of 42.5°N are covered by significant negative SAT anomalies in winter (Fig. 4b). In spring, SAT anomalies over northeast Eurasia become very weak and there is warming around 60°E and cooling over north Europe (Fig. 4c). In the following summer, notable SAT warmings are apparent over Siberia and midlatitude Asia (Fig. 4d).

Fig. 4.
Fig. 4.

SAT anomalies (°C) in (a) SON(−1), (b) D(−1)JF(0), (c) MAM(0), and (d) JJA(0) regressed upon the normalized EC time series of summer SAT. Stippling indicates anomalies significant at the 95% confidence level.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

The pronounced out-of-phase relation of winter and summer SAT variations over northeast Eurasia is clearly illustrated by the latitude–time cross section of SAT anomalies averaged between 80° and 140°E. Significant negative SAT anomalies are seen around 42.5°–70°N from the period of October–December (OND)(−1) to January–March (JFM)(0) (Fig. 5). SAT anomalies are weak during the transitional period from winter to the following summer [i.e., around MAM(0)]. Notable warming occurs north of 30°N from the period of April–June (AMJ)(0) to the period of July–September (JAS)(0) with two maximum centers around 35° and 70°N, respectively. Above analysis indicates that SAT anomalies over northeast Eurasia in winter have a significant negative correlation with the following summer SAT anomalies. A colder (warmer) winter over northeast Eurasia has a strong tendency to be followed by a warmer (cooler) summer. In addition, according to Fig. 1e (raw SAT anomalies but with long-term trend removed), 13 out of 20 years with negative winter SAT anomalies are followed by positive summer SAT anomalies over northeast Eurasia. This indicates that the proportion is about 65% for colder winters followed by warmer summers over northeast Eurasia.

Fig. 5.
Fig. 5.

Latitude–time cross section of SAT anomalies averaged along 80°–140°E. Stippling indicates anomalies significant at the 95% confidence level.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

4. Factors for the out-of-phase relation of winter and summer SAT over Eurasia

In this section, we first document the evolution of atmospheric circulation anomalies to explain the formation of the winter and summer SAT anomalies over Eurasia. Then, we analyze the evolution of anomalous lower boundary conditions in relation to the changes in atmospheric circulation anomalies from winter to the following summer. After that, we illustrate the role of summer North Atlantic SST anomalies in Eurasian atmospheric circulation anomaly pattern in simultaneous summer. Notice that the following descriptions correspond to colder winters followed by warmer summers, but also apply to warmer winters followed by cooler summers except for opposite signs of anomalies.

a. Evolution of atmospheric circulation anomalies

Formations of winter and summer SAT anomalies over Eurasia are closely related to atmospheric circulation changes. Figure 6 displays regression maps of 850-hPa winds and 300-hPa geopotential height anomalies from winter to the following summer onto the normalized EC time series of summer SAT during 1980–2017. The spatial patterns of atmospheric circulation anomalies at 850 and 300 hPa are similar, indicating a quasi-barotropic vertical structure of atmospheric circulation anomalies in the three seasons. In winter, the high-latitude region is dominated by large positive geopotential height anomalies and there are negative geopotential height anomalies over the midlatitude North Atlantic–west Europe and the Lake Baikal and positive geopotential height anomalies over eastern Europe (Figs. 6a,b). This geopotential height anomaly pattern resembles that of the negative phase of the AO (Thompson and Wallace 1998, 2000). In addition, a meridional contrast of geopotential height anomalies is seen over the North Atlantic, similar to the NAO (Fig. 6a; Hurrell and van Loon 1997).

Fig. 6.
Fig. 6.

(left) 850-hPa winds (m s−1, scale at top right) and (right) 300-hPa geopotential height (m) anomalies in (a),(b) D(−1)JF(0), (c),(d) MAM(0), and (e),(f) JJA(0) regressed upon the normalized EC time series of summer SAT during 1980–2017. Also shown in (f) are the summer 300-hPa wave activity flux anomalies (m2 s−2, scale at top right). Shading in (a), (c), and (e) indicates either zonal or meridional wind anomalies significant at the 95% confidence level. Stippling in (b), (d), and (f) indicates geopotential height anomalies significant at the 95% confidence level.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

Many studies have indicated that negative phase of the AO reduces westerly winds over the mid–high latitudes of Eurasia and leads to negative SAT anomalies over most parts of the mid–high-latitude Eurasia as the weakened circumpolar westerly wind provides a favorable condition for colder air from Arctic to penetrate to the mid–high latitudes of Eurasia (Thompson and Wallace 1998, 2000; Gong et al. 2001; Wu and Wang 2002; Chen et al. 2018a; He et al. 2019). Indeed, anomalous northeasterly lower-level winds control high-latitude eastern Eurasia (Fig. 6a). These anomalous winds bring colder air from the Arctic, leading to lower SAT over northeast Asia (Fig. 4b). The correlation coefficient between the wintertime AO index and the EC time series of winter SAT corresponding to the SVD1 is 0.68 for the period 1980–2017. This suggests that the winter negative AO-related atmospheric circulation anomalies play a crucial role in forming the continental-scale SAT cooling over Eurasia (Figs. 4b and 6a,b).

In spring, the Arctic region is covered by negative geopotential height anomalies (Figs. 6c,d). Large negative geopotential height anomalies are seen over northern Europe and positive geopotential height anomalies are observed around Lake Balkhash (Figs. 6c,d). The notable negative geopotential height anomalies over northern Europe (Figs. 6c,d) are accompanied by an increase in total cloud cover and a decrease in surface downward shortwave radiation (Bieli et al. 2015; S.-F. Chen et al. 2016), which explains negative SAT anomalies there (Fig. 4c). By contrast, the pronounced positive geopotential height anomalies around Lake Balkhash (Figs. 6c,d) are accompanied by a decrease in total cloud cover and an increase in downward shortwave radiation (not shown), which contributes to positive SAT anomalies there (Fig. 4c). In addition, southerly wind anomalies to the west flank of the positive geopotential height anomalies (Figs. 6c,d) lead to SAT increase via carrying warmer air northward (Fig. 4c) (S.-F. Chen et al. 2016, 2018a,b). Thus, the radiative and advective effects contribute to SAT anomalies in different regions. Geopotential height anomalies in spring over northeast Eurasia are weak (Figs. 6c,d), corresponding to weak SAT anomalies there (Fig. 4c).

In summer, a wave train–like structure is observed over the mid–high latitudes of Eurasia as indicated by the wave activity flux, with positive geopotential height anomalies over northern Europe and northeast Eurasia and negative geopotential height anomalies to the north of Lake Balkhash (Figs. 6e,f). Such a wave train is usually regarded as a stationary Rossby wave (Barnston and Livezey 1987; Scaife et al. 2017; O’Reilly et al. 2018). Studies have demonstrated that extreme high temperature events over Eurasia are closely related to local positive geopotential height anomalies (Gong et al. 2004; Zhu et al. 2012; Gao et al. 2014; Chen and Lu 2015; W. Chen et al. 2016; Xu et al. 2019). Positive geopotential height anomalies over northeast Eurasia are accompanied by decreased total cloud cover (Fig. 7a) and enhanced surface downward solar radiation (Fig. 7b), leading to warmer SAT (Fig. 4d). In addition, the southerly wind anomalies over East Asia may also contribute to positive SAT anomalies there via wind-induced horizontal temperature advection (Figs. 4d and 6e). Hence, formation of positive SAT anomalies over most parts of northeast Eurasia in summer (Fig. 4d) is associated with the large-scale atmospheric circulation anomalies (Figs. 6e,f) (Bieli et al. 2015; S.-F. Chen et al. 2016).

Fig. 7.
Fig. 7.

Summer TCC (%) and surface downward SWR (W m−2) anomalies regressed upon the normalized EC time series of summer SAT. Stippling indicates anomalies significant at the 95% confidence level.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

Above analysis indicates that formations of winter and summer SAT anomalies over Eurasia are related to atmospheric circulation anomalies. In particular, the continental-scale SAT cooling over Eurasia in winter is related to the negative AO-type atmospheric circulation anomalies. In summer, the marked SAT warming over most parts of northeast Eurasia is related to local positive geopotential height anomalies.

b. Evolution of anomalous lower boundary conditions

What is responsible for the change in atmospheric circulation anomaly pattern from winter to summer that leads to opposite winter and summer SAT anomalies over northeast Eurasia? As the atmospheric anomalies cannot persist through processes in the atmosphere only, the link of atmospheric circulation anomaly pattern between winter and summer is likely related to lower boundary condition changes. Studies indicated that boundary forcings (such as the Arctic sea ice, snow cover, and sea surface temperature) may play a role in linking atmospheric circulation anomalies during different seasons (Ogi et al. 2003, 2004; Chen et al. 2018b; Zhang et al. 2019). In the following, we examine each of these boundary forcings to investigate which one may be responsible for the change in atmospheric circulation anomaly pattern from winter to summer.

First, we have examined the Arctic SIC anomalies in association with the EC time series of summer SAT. It turns out that the Arctic SIC anomalies are generally weak and insignificant from preceding autumn to simultaneous summer (not shown). This implies that Arctic sea ice changes may not be able to explain the out-of-phase relation of the SAT anomalies over northeast Eurasia between winter and the following summer.

Second, we examine snow anomalies over the mid–high-latitude Eurasia. Figure 8 displays SCE and SWE anomalies from winter to summer obtained by regression upon the normalized EC time series of summer SAT. In winter, significant positive SWE anomalies are seen around 50°–70°N, 50°–120°E (Fig. 8b) corresponding to SAT cooling there (Fig. 4b). However, SCE anomalies are small north of 50°N in winter (Fig. 8a). Thus, the snow-albedo effect cannot explain the formation of the SAT anomalies over most parts of the mid–high-latitude Eurasia in winter (Fig. 4b). In spring and summer, SWE anomalies are weak over most parts of Eurasia (Figs. 8d,f). Spring SCE anomalies over Eurasia are featured by a tripole pattern, with positive anomalies over western and eastern Eurasia and negative anomalies to north of the Caspian Sea (Fig. 8c), which, to a large extent, is similar to the spatial distribution of spring SAT anomalies (Fig. 4c). Notable below-normal SCE anomalies are seen over northern Russian in summer (Fig. 8e) where positive SAT anomalies are located (Fig. 4d). This suggests a contribution of snow-albedo effect to the positive SAT anomalies over northern Russia in summer. However, it cannot explain the formation of the large-scale SAT anomalies over most parts of Eurasia in summer (Fig. 4d). On the other hand, the formation of the SCE anomalies may be due to SAT changes. For example, below-normal (above normal) SAT favors the accumulation (elimination) of snow. Furthermore, as indicated by previous studies (Hu and Feng 2004; Nakamura et al. 2019; Zhang et al. 2019), memory effect of the land process (e.g., soil temperature) should contribute to the persistence of local SAT anomalies. Hence, land processes in association with the snow cover anomalies over Eurasia are not able to explain the out-of-phase relation of Eurasian winter and summer SAT anomalies over northeast Asia. It is unclear whether Eurasian snow cover anomalies may contribute to the winter–summer SAT anomalies over Eurasia via other processes, which remains to be explored.

Fig. 8.
Fig. 8.

Anomalies of (left) SCE (%) and (right) SWE (mm) in (a),(b) D(−1)JF(0), (c),(d) MAM(0), and (e),(f) JJA(0) regressed upon the normalized EC time series of summer SAT. Stippling indicates anomalies significant at the 95% confidence level.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

Third, we examine SST anomalies in the North Atlantic. Previous studies have demonstrated that the North Atlantic SST anomalies can impact Eurasian climate (including precipitation and SAT) by triggering eastward-propagating atmospheric wave train (e.g., Wu et al. 2009; Wu et al. 2011; Zuo et al. 2013; S.-F. Chen et al. 2016; Wu and Chen 2016; Zhao et al. 2019). The 300-hPa wave activity flux in summer (Fig. 6f) suggests that the atmospheric wave train over Eurasia may be originated from the North Atlantic. Figure 9 displays regression maps of SST in the North Atlantic Ocean from winter to the following summer onto the normalized EC time series of summer SAT. Note that SST anomalies in the tropical Pacific are weak and thus not shown. The correlation coefficients of the EC time series for summer SAT with the Niño-3.4 index in preceding winter, spring and simultaneous summer are 0.1, 0.04, and −0.03, respectively. Here, the Niño-3.4 index is defined as area-mean SST anomalies over region of 5°S–5°N, 120°–170°W, which is generally used to characterize the ENSO variability.

Fig. 9.
Fig. 9.

Anomalies (°C) of SST in (a) D(−1)JF(0), (b) MAM(0), and (c) JJA(0) regressed upon the normalized EC time series of summer SAT. Stippling indicates anomalies significant at the 95% confidence level. Black boxes in (a) denote the regions employed to define the winter [D(−1)JF(0)] NAT SST index.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

A tripolar SST anomaly pattern is observed in the North Atlantic in winter and spring, with warming in the tropics and mid–high latitudes of northern Atlantic and cooling in the subtropical Atlantic (Figs. 9a,b). In comparison, the tripolar SST anomaly pattern in the North Atlantic shifts slightly westward in spring compared to that in winter (Figs. 9a,b). In summer, the region south of 60°N in the North Atlantic is covered by a zonally dipolar pattern, with SST cooling in the subtropical northeastern Atlantic with a northeastward extension to west coast of Europe and SST warming around 35°–45°N, 35°–75°W (Fig. 9c).

According to Fig. 9a, a North Atlantic (NAT) SST index is defined as the difference of winter SST anomalies averaged over the region of 17.5°–25°N, 35°–60°W and 32°–47.5°N, 12.5°–40°W (black boxes). Positive phase of the winter NAT SST index corresponds to SST warming in the subtropics and cooling in the midlatitudes of the North Atlantic. Correlation coefficient of the winter NAT SST index with the EC time series of SAT in the following summer reaches 0.5 over 1980–2017, significant at the 99% confidence level. This suggests that the winter NAT index has a close relation with the following summer SAT anomalies over northeast Eurasia.

What is the possible process that connects the winter North Atlantic SST and following summer SAT anomalies over northeast Eurasia? To address this issue, we first examine evolutions of SST anomalies from winter to the following summer. Following Chen et al. (2020a), the signal of winter Niño-3.4 index has been linearly removed from the winter NAT SST index and other variables to avoid the potential interruption of the ENSO signal (Curtis and Hastenrath 1995; Klein et al. 1999; Alexander et al. 2002). For example, the part of the winter NAT SST index that is linearly related to the winter Niño-3.4 index is removed using a linear regression as follows:

NATSSTres=NATSSTR×Niño3.4.

Here, Niño3.4 and NTASST represent the winter Niño-3.4 and NAT SST indices, respectively; NATSSTres denotes the part of the winter NAT SST index that is linearly unrelated to the winter Niño-3.4 index; and R denotes the linear regression coefficient of the winter NAT SST index onto the winter Niño-3.4 index.

Figure 10 shows evolutions of SST anomalies from winter to the following summer obtained by regression upon the winter NAT SST index. In winter [D(−1)JF(0)], significant positive SST anomalies are apparent in the subtropical North Atlantic and in the region to the south of Greenland, and pronounced negative SST anomalies are present in the midlatitudes (Fig. 10a). This SST anomaly pattern in the North Atlantic maintains to the following spring but with a weaker amplitude of the positive SST anomalies in the subtropics (Figs. 10a–d). Negative SST anomalies in the midlatitudes shift southward from MAM(0) to AMJ(0) (Figs. 10d,e). Positive SST anomalies in the subtropics almost disappear in AMJ(0) (Fig. 10e). Significant negative SST anomalies appear in the subtropical northeastern Atlantic in May–July (MJJ)(0) and intensify in JJA(0) (Figs. 10f,g). Strong negative SST anomalies are also observed around 50°N, and positive SST anomalies appear around 40° and 60°N in MJJ(0) and JJA(0), with a stronger amplitude in JJA(0) (Figs. 10f,g).

Fig. 10.
Fig. 10.

SST (°C) and 1000-hPa wind (m s−1) anomalies in (a) D(−1)JF(0), (b) JFM(0), (c) FMA(0), (d) MAM(0), (e) AMJ(0), (f) MJJ(0), and (g) JJA(0) regressed upon the D(−1)JF(0) NAT SST index. Stippling indicates SST anomalies significant at the 95% confidence level.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

What induces the change of the North Atlantic SST anomalies from winter to summer? Previous studies have indicated that development and evolution of SST anomalies in the North Atlantic are tightly coupled with the overlying atmospheric circulation anomalies (Czaja and Frankignoul 1999, 2002; Rodwell and Folland 2002; Czaja et al. 2003; Visbeck et al. 2003; Wu and Liu 2005; Huang and Shukla 2005, Hu and Huang 2006; Peng et al. 2003; Pan 2005; Chen et al. 2020b). This suggests an important role of air–sea interaction processes in the evolution of the SST anomalies in the North Atlantic from winter to the following summer. Figure 11 displays evolutions of the SST tendency and net surface heat flux (NHF) anomalies from winter to the following summer. Values of the surface heat fluxes are positive (negative) when their directions are downward (upward), which contribute to SST warming (cooling). Here, the SST tendency at a given month is calculated as the difference of the SST in the succeeding month minus that in the preceding month divided by two. Note that the unit of the surface net heat flux has been converted to the unit of SST tendency (°C month−1) for convenience of comparison via dividing it by ρoCpH. Here, H represents the mixed layer depth, which is set to be 30 m following Chen et al. (2015); ρo and Cp denote the water density and specific heat at constant pressure for the ocean, respectively. In D(−1)JF(0), negative SST tendency is observed off the west coasts of North Africa and Europe (Fig. 11a). The SST tendency anomalies display a tripole pattern from JFM(0) to MJJ(0), with negative SST tendency in the regions south of 30°N and around 60°N and positive SST tendency around 30°–50°N (Figs. 11b–f). In JJA(0), negative SST tendency still exists in the subtropics but with a weaker amplitude, and positive SST tendency appears in most parts of the mid–high latitudes (Fig. 11g). The negative SST tendency in the subtropical North Atlantic (Figs. 11b–f) explains the decrease in the amplitude of positive SST anomalies from D(−1)JF(0) to MAM(0) (Figs. 10a–d) and the formation of negative SST anomalies in the subtropics in MJJ(0) and JJA(0) (Figs. 10f,g).

Fig. 11.
Fig. 11.

(left) SST tendency (°C month−1) and (right) surface net heat flux (°C month−1) anomalies in (a),(h) D(−1)JF(0), (b),(i) JFM(0), (c),(j) FMA(0), (d),(k) MAM(0), (e),(l) AMJ(0), (f),(m) MJJ(0), and (g),(n) JJA(0) regressed upon the D(−1)JF(0) NAT SST index. Stippling indicate anomalies significant at the 95% confidence level. Unit of the surface net heat flux has been converted to the unit of SST tendency (a constant mixed layer depth of 30 m is assumed).

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

Spatial patterns of the SST tendency in most parts of the North Atlantic (Figs. 11a–g) are similar to those of surface NHF anomalies (Figs. 11h–n). For example, the negative SST tendencies in the subtropical North Atlantic (Figs. 11b–g) in JFM(0) to JJA(0) are collocated with negative surface NHF anomalies (Figs. 11i–n). Positive SST tendencies around 30°–50°N from MAM(0) to JJA(0) correspond to positive surface NHF anomalies there. These correspondences suggest that the evolutions of the SST anomalies in the North Atlantic from winter to summer are closely related to surface net heat flux changes (Figs. 10 and 11).

Formation of the surface NHF anomalies are closely related to the atmospheric anomalies. Note that surface NHF encompasses four components, including surface latent heat flux (LHF), sensible heat flux (SHF), SWR, and longwave radiation (LWR). As amplitudes of surface SHF and LWR anomalies are much weaker compared to those of LHF and SWR anomalies, we only present evolutions of anomalies of LHF (Figs. 12a–g) and SWR (Figs. 12h–n). Comparison of Figs. 11h–n with Fig. 12 indicates that surface NHF changes are mainly contributed by LHF anomalies. In D(−1)JF(0), SWR anomalies are weak. Positive and negative LHF anomalies are seen around 60°N and 40°–50°N, respectively. The easterly wind anomalies around 60°N to the north side of the anomalous cyclone (Fig. 10a) are opposite to climatological mean winds (not shown). This reduces surface wind speed and contributes to decrease in upward LHF around 60°N (Fig. 12a). The northerly wind anomalies over the midlatitudes North Atlantic carry more cold and dry air southward, increase the sea–air humidity difference and result in increase in the upward LHF (Fig. 12a). The negative LHF anomalies over the subtropical northern Atlantic from JFM(0) to JJA(0) are related to anomalous northeasterly winds to the eastern side of the anomalous anticyclone around 30°N (Figs. 10b–g). The anomalous northeasterly winds (Figs. 10b–g) increase surface wind speed and thus lead to increase in upward LHF (Figs. 12b–g) and NHF (Figs. 11i–n). By contrast, the southwesterly wind anomalies (Figs. 10b–g) to the western side of anomalous anticyclone carry more warm air northward, which reducing the sea–air humidity difference and leading to decrease in upward LHF (Figs. 12b–g) and NHF (Figs. 11i–n). The anomalous cyclone off the west coast of North Africa in AMJ(0), MJJ(0), and JJA(0) induces increase in TCC (not shown) and decrease in downward SWR (Figs. 12l–n), which also contribute positively to the NHF changes (Figs. 11l–n). Note that the SST cooling in the subtropical northeastern Atlantic in MJJ(0) and JJA(0) could induce an anomalous anticyclone to its northwest side via a Rossby wave–type atmospheric response (Czaja and Frankignoul 1999; Huang and Shukla 2005; S.-F. Chen et al. 2016), which in turn help to maintain the anticyclonic anomaly over the subtropical North Atlantic (Figs. 10f,g). The above analysis confirms that the evolution of the North Atlantic SST anomalies from winter to summer is closely associated with the air–sea interaction over the North Atlantic.

Fig. 12.
Fig. 12.

Surface (left) LHF and (right) SWR anomalies in (a),(h) D(−1)JF(0), (b),(i) JFM(0), (c),(j) FMA(0), (d),(k) MAM(0), (e),(l) AMJ(0), (f),(m) MJJ(0), and (g),(n) JJA(0) regressed upon the D(−1)JF(0) NAT SST index. Stippling indicates anomalies significant at the 95% confidence level. Units are W m−2.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

c. Role of summer North Atlantic SST anomalies

Many previous studies have indicated that the North Atlantic SST anomalies could exert impacts on the Eurasian climate via triggering atmospheric Rossby wave train (Czaja and Frankignoul 2002; Wu et al. 2009; Wu et al. 2011; Zuo et al. 2013; S.-F. Chen et al. 2016; Monerie et al. 2018; and references therein). In particular, it is indicated that SST anomalies in the tropical and subtropical regions are able to excite anomalous vertical motion and atmospheric convection that reaches up to the upper troposphere (Figs. 10g and 13) (Ting 1996; Hodson et al. 2010; Sun et al. 2015). The induced anomalous divergence/convergence over the upper troposphere then acts as an important source of Rossby wave (Wu et al. 2011; Zuo et al. 2013; Watanabe 2004; Chen and Huang 2012). Since the atmospheric wave train over the North Atlantic and mid–high latitudes of Eurasia in Figs. 6e and 6f is quite similar to a stationary wave with an equivalent barotropic vertical structure, the processes for the formation of the atmospheric anomalies can be investigated based on a barotropic vorticity equation, as in previous studies (Watanabe 2004; Wu et al. 2011; Zuo et al. 2013; Sun et al. 2015; O’Reilly et al. 2018). In the following, we perform model experiments with a barotropic model (Sardeshmukh and Hoskins 1988) to confirm the role of the summer North Atlantic SST anomalies in the formation of the atmospheric circulation anomalies over Eurasia. Studies have demonstrated that the barotropic model can well capture the essential dynamics of the atmospheric circulation response to the given heating related to SST anomalies (Wu et al. 2011; Zuo et al. 2013; Sun et al. 2015; S.-F. Chen et al. 2016). Detailed descriptions of the barotropic model are provided in section 2.

Fig. 13.
Fig. 13.

Anomalies (10−3 Pa s−1) of vertical p velocity averaged between 600 and 400 hPa in JJA(0) regressed upon the D(−1)JF(0) NAT SST index. Stippling indicates anomalies significant at the 95% confidence level.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

We perform two experiments: one with climatological summer mean vorticity as the basic state plus prescribed convergence as the forcing (EXPA) and the other with climatological summer mean vorticity as the basic state plus prescribed divergence as the forcing (EXPB). The convergence (divergence) anomaly in EXPA (EXPB) is prescribed over subtropical northeastern Atlantic with a maximum intensity of 7 × 10−6 s−1 (−7 × 10−6 s−1) at 30°N and 30°W based on the spatial distribution of vertical motion anomalies shown in Fig. 13. Above two experiments are integrated for 40 days. As indicated by previous studies (Sardeshmukh and Hoskins 1988; Wu et al. 2011; S.-F. Chen et al. 2016), the barotropic model experiment can reach equilibrium state after several days. Figure 14 shows the difference of response between EXPA and EXPB averaged over model days 31–40 with black contours indicating the difference of the prescribed convergence and divergence anomalies.

Fig. 14.
Fig. 14.

Barotropic model height perturbation (m) averaged from days 31 to 40 as a response to prescribed convergence anomaly (black contours with an interval of 2 × 10−6 s−1) over the subtropical North Atlantic with the center at 30°N, 30°W.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

The barotropic model simulation reproduces reasonably the observed geopotential height anomalies over the North Atlantic though the centers shift slightly northeastward (Figs. 11a and 12). The bias in the location of height anomalies over the North Atlantic may be partly due to a lack of air–sea interaction over the North Atlantic (Czaja and Frankignoul 2002; Hu and Huang 2006) as well as a weak gradient of the vorticity in the basic state in the North Atlantic as indicted by O’Reilly et al. (2018). The spatial distribution of height anomalies over Eurasia in Fig. 13, to a large extent, resembles the observed (Fig. 6e), with positive height anomalies extending eastward from north Europe to Russian far east (Figs. 6f and 13). Hence, the result of the barotropic model experiments verifies that the formation of the atmospheric wave train over Eurasia, in particular, the large-scale positive geopotential height anomalies over northeast Eurasia may be contributed by vertical motion and related adiabatic heating anomalies induced by the summer North Atlantic SST anomalies.

Notice that significant vertical motion anomalies are also apparent over the mid–high-latitude North Atlantic (Fig. 13). One may ask whether they contribute to the observed wave trains. To address this question, we have performed additional barotropic model simulations. We imposed two convergent forcing over the North Atlantic, with one forcing over the subtropical northeastern Atlantic (centered at 30°N, 30°W, similar to that in Fig. 14), and the other forcing over the midlatitude northern Atlantic (centered at 45°N, 15°W). The simulated geopotential height anomalies show large differences from those in the observations and in Fig. 14. This suggests that the formation of the summertime atmospheric circulation anomalies over Eurasia is primarily attributed to the upper-level convergence anomalies induced by the SST cooling in the subtropical northeastern Atlantic Ocean. SST anomalies in the mid–high latitudes may be not able to excite a deep convection and lead to divergence/convergence anomalies in the upper troposphere, and thus, they may play little role in the generation of the atmospheric wave train over Eurasia.

5. Summary and discussions

This study examines the possible connection of Eurasian SAT in boreal winter and the following summer based on observational data during 1980–2017. We reveal a prominent out-of-phase relation between winter and the following summer SAT over northeast Eurasia (mainly encompassing northeast Asia and northeast part of Russia). A colder (warmer) winter tends to be followed by a warmer (cooler) summer in the above regions. Such a relationship provides useful information in the cross-season prediction of climate anomalies over Eurasia.

The out-of-phase relation of northeast Eurasian winter and summer SAT is shown to be related to a change in the North Atlantic SST anomaly pattern from winter to summer and the impacts of the North Atlantic SST anomalies on Eurasian atmospheric circulation. Figure 15 summarizes the possible physical processes linking a colder winter to a warmer summer over northeast Eurasia. In winter, the negative AO-like atmospheric circulation anomalies contribute to continental-scale cooling over most parts of Eurasia via anomalous cold advection. Over the North Atlantic, a tripolar SST anomaly pattern coexists with a NAO-like meridional atmospheric anomaly pattern. Via the ocean–atmosphere interaction process, the North Atlantic tripole SST anomaly pattern switches to a dipolar pattern in the following summer. The summer North Atlantic SST anomalies induce an atmospheric wave train over Eurasia, with positive geopotential height anomalies over northeast Eurasia, which lead to pronounced positive SAT anomalies there via increasing downward shortwave radiation and anomalous warm advection. Through the above processes, winter Eurasian SAT is connected to the following summer SAT with an out-of-phase relation.

Fig. 15.
Fig. 15.

Schematic diagram displaying the physical processes linking interannual variations of the winter and summer SAT anomalies over northeast Eurasia.

Citation: Journal of Climate 33, 17; 10.1175/JCLI-D-20-0036.1

The present analysis unravels the role of the change in the North Atlantic SST anomalies in the switch of northeast Eurasian SAT anomalies via atmospheric circulation changes. It remains to be explored whether there are other factors that may contribute to this connection. Furthermore, it is unclear whether long historical simulations of coupled climate models (e.g., those participating in CMIP5/CMIP6) can reproduce the observed connection between winter and summer SAT anomalies over northeast Eurasia. The above issues will be further pursued in the near future.

The present analysis is limited to the period 1980–2017. One may ask whether the anticorrelation between winter and the following summer SAT over northeast Asia is robust. To address this question, we have examined moving correlation of the winter and summer SAT anomalies over northeast Asia using a longer period of time series (1948–2017). Results indicate that there exists an obvious interdecadal change in the relationship around the late 1970s (not shown). The correlation is weak before the late 1970s. The factors responsible for the interdecadal change in the connection around the late 1970s will be investigated in a further study.

Acknowledgments

We thank three anonymous reviewers for their constructive suggestions and comments, which help to improve the paper. This study is jointly supported by the National Natural Science Foundation of China Grants 41530425, 41775080, 41721004, and 41605050. The NCEP–NCAR data are obtained from https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html. The ERSST, version 5, SST data are derived from https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html. The SAT data provided by the University of Delaware are extracted from https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html. Arctic SIC data are obtained from https://www.metoffice.gov.uk/hadobs/hadisst/. The SCE and SWE data are obtained from ftp://sidads.colorado.edu/pub/DATASETS.

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