1. Introduction
Over the past two decades, Arctic warming and Arctic sea ice loss are among the most remarkable signals of change in the climate system and have received a great deal of attention, particularly in summer. The Arctic dipole anomaly, Arctic anticyclonic surface wind anomalies, enhanced downwelling longwave radiation, and increased Atlantic and Pacific water inflow are believed to contribute to Arctic sea ice loss (Shimada et al. 2006; Wang et al. 2009; Ogi et al. 2010; Polyakov et al. 2010; Overland et al. 2012; Wu et al. 2012; Ding et al. 2017). Observations and model simulations demonstrate that anomalies in Arctic sea ice and atmospheric circulation affect summer atmospheric circulation variability over Eurasia (Wu et al. 2009, 2013; Screen 2013). Arctic sea ice loss would reduce (enhance) summer precipitation over the mid- and high latitudes of East Asia (northern Europe) (Wu et al. 2013; Screen 2013). However, what dominant features of summer Arctic temperature variability in the mid- and low troposphere that closely relate to atmospheric variability in the mid- and low latitudes of East Asia remain unclear.
Summer high temperature and heat waves have frequently occurred worldwide since the beginning of the 2000s, and they directly caused a high fatality and produced widespread economic impacts (Meehl and Tebaldi 2004; Barriopedro et al. 2011; Bador et al. 2017). Compared with 1991–2000, casualties related with high temperature and heat waves increased by more than 2000% in 2001–10 (WMO 2013). East Asia has experienced frequent heat waves since the 1990s (Ding et al. 2010). The well-documented example is the East Asian heat waves of 2013 (Min et al. 2014; Imada et al. 2014; Zhou et al. 2014). South Korea had its hottest summer nights and second hottest summer days since 1954 (Min et al. 2014), and China suffered the strongest heat wave since 1951 (Zhou et al. 2014). Anthropogenic global warming generally has been shown to increase the likelihood of summer heat waves over East Asia (Sun et al. 2014; Min et al. 2014; Coumou et al. 2014, 2015; Mann et al. 2017). Additionally, sea surface temperature (SST) anomalies, Arctic sea ice loss/snow cover melting, and precipitation anomalies in India and the South China Sea also contribute to East Asian summer high temperature and heat waves (Hu et al. 2011; Sun 2014; Min et al. 2014; Imada et al. 2014; Tang et al. 2014; Liu et al. 2015). Sun (2014) proposed that SST over the mid–North Atlantic in July 2013 was the warmest in the past 160 years, which connects to weakening of the East Asian upper-level westerly and strengthening of the northwest Pacific subtropical high (NWPSH) through a teleconnection wave train. This contributes to surface air temperature variability and heat waves over the Jianghuai–Jiangnan region of China. More direct causes can be attributed to sustained high pressure systems, particularly the NWPSH (Wang et al. 2013; Sun 2014; Imada et al. 2014; Wang et al. 2017; Freychet et al. 2017; Gao et al. 2018). It is not clear whether simultaneous Arctic atmospheric circulation anomalies during summer are linked to summer heat waves in East Asia, and this is the question explored in this study. We use National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data to identify dominant patterns of summer (JJA) atmospheric thickness (1000–500 hPa) variability north of 30°N and demonstrate the association between summer Arctic cold anomalies and East Asian heat waves.
2. Data and methods
Atmospheric data used in this study include monthly mean sea level pressure (SLP), winds, and geopotential heights from January 1979 to December 2016, and daily surface air temperatures (SATs), SLP, air temperatures, and winds at 17 pressure levels from 1 January 1979 to 31 December 2016. Data were obtained from the NCEP–NCAR Reanalysis-I (http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1), with a horizontal resolution of 2.5° × 2.5° and 17 pressure levels (1000–10 hPa) in the vertical direction. Daily SATs are used to calculate the frequency of summer (June–August) extreme heat wave events, identified when daily SATs are above 1 standard deviation for a given date and location (note similar results were obtained using a 1.5-standard-deviation threshold). Similarly, daily SLP data are used to assess the frequency of summer anomalous low pressure when daily SLP is below −1.5 standard deviations for a given date and location.
This study uses NWPSH indices, which include intensity, ridge location, and western ridge point, obtained from National Climate Center (China; http://cmdp.ncc-cma.net/Monitoring/cn_index_130.php). The NWPSH intensity index is defined as the sum of the grid area with a geopotential height ≥5880 gpm multiplied by the difference between the geopotential height (≥5880 gpm) minus 5870 gpm within 10°–60°N and 110°–180°E. The NWPSH ridge location is defined as the average of the latitudes at each longitude where zonal wind u = 0.0 and 
We use summer mean 1000–500-hPa atmospheric thickness to approximately represent a vertically averaged air temperature in the mid- and lower troposphere. The empirical orthogonal function (EOF) analysis method is applied to extract the first two dominant patterns of summer mean 1000–500-hPa atmospheric thickness variability north of 30°N for the period 1979–2016. The first two patterns account for 29% and 10% of the variance. Similarly, EOF analysis is also used to analyze the leading pattern of summer 300 hPa zonal wind variability north of 20°N, which accounts for 14% of the variance.
3. Results
a. Atmospheric thickness variability and Arctic cold anomaly
SATs are generally used to characterize Arctic warming and Arctic amplification. Because SATs are strongly influenced by sea ice concentrations and SST, however, differences in SATs between the Arctic and midlatitudes may exaggerate the thermal contrast of the column in the mid- and lower troposphere (Sellevold et al. 2016; Wu 2017). This study uses the summer 1000–500-hPa thickness to represent the mean temperature in the mid- and low troposphere (Overland and Wang 2010; Francis and Vavrus 2015), and its first two dominant patterns are shown in Figs. 1a–1c. The leading pattern displays strong interannual variability superposed on an interdecadal shift, that is, from frequent negative phases prior to 1998 to more frequent highly positive phases afterward (Fig. 1a). According to the Student’s t test, the significance of the shift exceeds the 99% confidence level. Spatially, positive thickness anomalies cover much of the domain north of 30°N, particularly over the Arctic, Eurasia, and North America where significant positive anomalies are observed (Fig. 1b). This interdecadal shift is generally consistent with changes in surface wind variability over the Arctic Ocean in both spring [April–June (AMJ)] and summer [July–September (JAS)]: An anomalous cyclone prevailed prior to the late 1990s, which was replaced by an anomalous anticyclone over the Arctic Ocean in later years (Wu et al. 2012). This interdecadal shift is consistent with the rapid declining trend in September sea ice extent. Thus, this leading pattern is closely associated with both summer Arctic warming and sea ice loss.
The first two patterns of summer (JJA) 1000–500-hPa thickness variability north of 30°N. (a) Normalized PC time series (red: PC1; blue: PC2), the red dashed line represents a linear trend in PC1, and the blue dashed line indicates the mean of PC2 averaged over 2007–12. (b) Summer 1000–500-hPa thickness anomalies, derived from a linear regression on the normalized detrended PC1, the white and black contours represent thickness anomalies at 95% and 99% confidence levels, respectively. (c) As in (b), but for the regression on the normalized PC2. (d)–(f) Summer 1000–500-hPa thickness anomalies (relative to the mean averaged over 1979–2016) in (d) 2006, (e) 2013, and (f) 2016. The first two patterns, respectively, account for 29% and 10% of the variance.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The first two patterns of summer (JJA) 1000–500-hPa thickness variability north of 30°N. (a) Normalized PC time series (red: PC1; blue: PC2), the red dashed line represents a linear trend in PC1, and the blue dashed line indicates the mean of PC2 averaged over 2007–12. (b) Summer 1000–500-hPa thickness anomalies, derived from a linear regression on the normalized detrended PC1, the white and black contours represent thickness anomalies at 95% and 99% confidence levels, respectively. (c) As in (b), but for the regression on the normalized PC2. (d)–(f) Summer 1000–500-hPa thickness anomalies (relative to the mean averaged over 1979–2016) in (d) 2006, (e) 2013, and (f) 2016. The first two patterns, respectively, account for 29% and 10% of the variance.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The first two patterns of summer (JJA) 1000–500-hPa thickness variability north of 30°N. (a) Normalized PC time series (red: PC1; blue: PC2), the red dashed line represents a linear trend in PC1, and the blue dashed line indicates the mean of PC2 averaged over 2007–12. (b) Summer 1000–500-hPa thickness anomalies, derived from a linear regression on the normalized detrended PC1, the white and black contours represent thickness anomalies at 95% and 99% confidence levels, respectively. (c) As in (b), but for the regression on the normalized PC2. (d)–(f) Summer 1000–500-hPa thickness anomalies (relative to the mean averaged over 1979–2016) in (d) 2006, (e) 2013, and (f) 2016. The first two patterns, respectively, account for 29% and 10% of the variance.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The first two patterns of summer (JJA) 1000–500-hPa thickness variability north of 30°N. (a) Normalized PC time series (red: PC1; blue: PC2), the red dashed line represents a linear trend in PC1, and the blue dashed line indicates the mean of PC2 averaged over 2007–12. (b) Summer 1000–500-hPa thickness anomalies, derived from a linear regression on the normalized detrended PC1, the white and black contours represent thickness anomalies at 95% and 99% confidence levels, respectively. (c) As in (b), but for the regression on the normalized PC2. (d)–(f) Summer 1000–500-hPa thickness anomalies (relative to the mean averaged over 1979–2016) in (d) 2006, (e) 2013, and (f) 2016. The first two patterns, respectively, account for 29% and 10% of the variance.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The first two patterns of summer (JJA) 1000–500-hPa thickness variability north of 30°N. (a) Normalized PC time series (red: PC1; blue: PC2), the red dashed line represents a linear trend in PC1, and the blue dashed line indicates the mean of PC2 averaged over 2007–12. (b) Summer 1000–500-hPa thickness anomalies, derived from a linear regression on the normalized detrended PC1, the white and black contours represent thickness anomalies at 95% and 99% confidence levels, respectively. (c) As in (b), but for the regression on the normalized PC2. (d)–(f) Summer 1000–500-hPa thickness anomalies (relative to the mean averaged over 1979–2016) in (d) 2006, (e) 2013, and (f) 2016. The first two patterns, respectively, account for 29% and 10% of the variance.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Here we focus primarily on the second thickness pattern because it is associated with summer high temperatures and heat waves in Asia and North America. This pattern displays strong interannual variability but does not exhibit any trend or shift (Fig. 1a). Spatially (Fig. 1c), negative thickness anomalies are observed in the Arctic north of 70°N, indicating Arctic cold anomalies in the mid- and low troposphere. Positive thickness anomalies appear mainly over northern Europe, northeastern Atlantic, western North America, the Bering Sea, north-central Canada, and mid- and low latitudes of East Asia. Over the eastern Pacific–North America and northern Pacific–East Asia, positive anomalies form a “comma” structure, which is associated with the upper-tropospheric steering flow anomalies (see the following section). All extreme positive anomalies occurred after 2005, that is, 2006, 2013, and 2016, with values exceeding 1.5σ, corresponding with Arctic cold anomalies (Figs. 1d–f). The negative phases of the second principal component (PC2) dominated during 2007–12, implying summer Arctic warm anomalies (Fig. 1c), consistent with the well-documented rapid Arctic sea ice loss period. Years exhibiting summer Arctic cold anomalies seem to be become more frequent in the context of Arctic warming. It should be pointed out that two thickness patterns are independent from each other and the leading thickness pattern cannot obscure the expression of the second thickness pattern. Additionally, although summer 500-hPa height anomalies exhibit a great similarity to 1000–500-hPa thickness anomalies (Figs. 1c, 2), differences are also visible, including extents and amplitudes of positive and negative anomalies.
Summer mean 500-hPa geopotential height anomalies, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Summer mean 500-hPa geopotential height anomalies, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Summer mean 500-hPa geopotential height anomalies, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
b. Dynamic linkages between Arctic cold anomalies and East Asian heat waves
Significant correlations between the leading thickness pattern and frequency of summer heat waves are mainly confined to near the Ural Mountains (40°–60°N, 40°–80°E), North America, and the Arctic, rather than over East Asia (not shown). Cold anomalies are also observed over near Alaska, but they are not significant (Fig. 1b). This study, therefore, focuses on the second thickness pattern. The positive phase of PC2 correlates significantly with heat waves over the mid- and low latitudes of Asia, particularly over the Tibetan Plateau (the third pole) and the coast of East Asia (Fig. 3a). Significant correlations also exist over most of Europe, northern Canada, southwestern United States, and northern North Pacific Ocean. Decreased heat waves are observed over the Arctic Ocean, consistent with Arctic cold anomalies (Fig. 1c). Thus, a dipole structure is apparent between the Arctic and several midlatitude continental areas, particularly with the third pole. Over East Asia, the regionally (25.71°–39.05°N, 80.625°–135°E) averaged frequency of summer heat waves exhibits an increasing trend at the 99% confidence level (Fig. 3b). The frequency of East Asian heat waves exceeded 1.0σ in 1994, 2001, 2006, 2010, 2013, and 2016 (Fig. 3b). Consequently, East Asian heat waves frequently occurred after 2005. It should be pointed out that if the criterion for heat waves is instead defined as ≥1.5 standard deviations, very similar results are obtained (not shown).
(a) Regression map of the frequency of summer heat wave events, regressed on the normalized PC2 of atmospheric thickness variability. White and black contours denote 95% and 99% confidence levels, respectively. (b) Regionally averaged heat wave frequencies (normalized time series) over the domain bounded by the green box in (a). The dashed line represents a linear trend at 99% confidence level in (b).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Regression map of the frequency of summer heat wave events, regressed on the normalized PC2 of atmospheric thickness variability. White and black contours denote 95% and 99% confidence levels, respectively. (b) Regionally averaged heat wave frequencies (normalized time series) over the domain bounded by the green box in (a). The dashed line represents a linear trend at 99% confidence level in (b).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Regression map of the frequency of summer heat wave events, regressed on the normalized PC2 of atmospheric thickness variability. White and black contours denote 95% and 99% confidence levels, respectively. (b) Regionally averaged heat wave frequencies (normalized time series) over the domain bounded by the green box in (a). The dashed line represents a linear trend at 99% confidence level in (b).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Why do Arctic cold anomalies correspond with more intense and frequent summer heat waves in the mid- and low latitudes of East Asia? We find that this association is closely related to the large-scale upper-tropospheric zonal wind variability. The positive phase of the second thickness pattern significantly strengthens upper-tropospheric westerlies over most of the Arctic (Fig. 4a), while over Eurasia, wind anomalies display a belt structure with significantly weaker westerlies in the mid- and low latitudes of Asia and Europe. This wind pattern closely resembles the leading pattern of summer 300 hPa zonal wind variability north of 20°N (not shown; r = 0.81; Fig. 4b). The area-weighted, regionally averaged 300 hPa westerly north of 70°N (Arctic westerly index) is also significantly correlated with the second thickness pattern (r = 0.88; Fig. 4c). In the upper troposphere, Arctic westerly strength is out of phase with that in the mid- and low latitudes of Asia. The normalized Arctic westerly indices exceeded 1.5σ in 2006, 2013, and 2016, corresponding with strong East Asian heat waves (Fig. 3b). Over the mid- and low latitudes of Asia, weakened upper-tropospheric steering flow (Fig. 4d) favors the stagnation of air masses and dynamically contributes to sustained high pressure anomalies in the mid- and low troposphere (Fig. 4e). These anomalies suppress convection in the lower troposphere, thereby reducing cloud cover and enhancing the downwelling surface shortwave radiation, which favors high temperatures and heat waves. Sun (2014) also showed a significantly negative correlation (r = −0.72) between July SAT averaged over the region bounded by 27.5°–32.5°N and 110°–125°E and the 200 hPa zonal wind averaged over the same region, consistent with our results here.
Weakened tropospheric westerly steering flow links East Asian heat waves to Arctic cold anomalies. (a) Spatial distribution of summer 300-hPa zonal wind anomalies, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively; the green line represents 0°–180°E at 30°N. (b) Normalized PC2 (blue line) and PC1 of summer 300-hPa zonal wind variability north of 20°N (red line; the leading wind pattern accounts for 14% of the variance; r = 0.81). (c) As in (b), but for normalized, area-weighted, and regionally averaged 300-hPa zonal winds north of 70°N (red line). Correlation between the two time series is 0.88. (d) As in (a), but for zonal wind anomalies along the longitude–pressure cross section at 30°N [green line in (a)]. The gray area indicates topography along 30°N. (e) As in (d), but for geopotential height anomalies.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Weakened tropospheric westerly steering flow links East Asian heat waves to Arctic cold anomalies. (a) Spatial distribution of summer 300-hPa zonal wind anomalies, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively; the green line represents 0°–180°E at 30°N. (b) Normalized PC2 (blue line) and PC1 of summer 300-hPa zonal wind variability north of 20°N (red line; the leading wind pattern accounts for 14% of the variance; r = 0.81). (c) As in (b), but for normalized, area-weighted, and regionally averaged 300-hPa zonal winds north of 70°N (red line). Correlation between the two time series is 0.88. (d) As in (a), but for zonal wind anomalies along the longitude–pressure cross section at 30°N [green line in (a)]. The gray area indicates topography along 30°N. (e) As in (d), but for geopotential height anomalies.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Weakened tropospheric westerly steering flow links East Asian heat waves to Arctic cold anomalies. (a) Spatial distribution of summer 300-hPa zonal wind anomalies, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively; the green line represents 0°–180°E at 30°N. (b) Normalized PC2 (blue line) and PC1 of summer 300-hPa zonal wind variability north of 20°N (red line; the leading wind pattern accounts for 14% of the variance; r = 0.81). (c) As in (b), but for normalized, area-weighted, and regionally averaged 300-hPa zonal winds north of 70°N (red line). Correlation between the two time series is 0.88. (d) As in (a), but for zonal wind anomalies along the longitude–pressure cross section at 30°N [green line in (a)]. The gray area indicates topography along 30°N. (e) As in (d), but for geopotential height anomalies.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Weakened tropospheric westerly steering flow links East Asian heat waves to Arctic cold anomalies. (a) Spatial distribution of summer 300-hPa zonal wind anomalies, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively; the green line represents 0°–180°E at 30°N. (b) Normalized PC2 (blue line) and PC1 of summer 300-hPa zonal wind variability north of 20°N (red line; the leading wind pattern accounts for 14% of the variance; r = 0.81). (c) As in (b), but for normalized, area-weighted, and regionally averaged 300-hPa zonal winds north of 70°N (red line). Correlation between the two time series is 0.88. (d) As in (a), but for zonal wind anomalies along the longitude–pressure cross section at 30°N [green line in (a)]. The gray area indicates topography along 30°N. (e) As in (d), but for geopotential height anomalies.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Weakened tropospheric westerly steering flow links East Asian heat waves to Arctic cold anomalies. (a) Spatial distribution of summer 300-hPa zonal wind anomalies, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively; the green line represents 0°–180°E at 30°N. (b) Normalized PC2 (blue line) and PC1 of summer 300-hPa zonal wind variability north of 20°N (red line; the leading wind pattern accounts for 14% of the variance; r = 0.81). (c) As in (b), but for normalized, area-weighted, and regionally averaged 300-hPa zonal winds north of 70°N (red line). Correlation between the two time series is 0.88. (d) As in (a), but for zonal wind anomalies along the longitude–pressure cross section at 30°N [green line in (a)]. The gray area indicates topography along 30°N. (e) As in (d), but for geopotential height anomalies.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Positive Arctic westerly anomalies are not confined to the upper troposphere; they are evident throughout the troposphere and stratosphere (Fig. 5). Negative westerly anomalies in the mid- and low latitudes also penetrate through the troposphere and stratosphere. Such a change is attributed to a systematic northward shift of zonal winds, dominantly characterized by a shift of the East Asian westerly jet. In fact, the Eurasian westerly jet systematically migrates northward (Fig. 4a). Additionally, a dipole structure in zonal wind anomalies is also observed over the Arctic and the third pole (Figs. 4a,d, 5), similar to the pattern in temperature anomalies discussed previously. Over the Arctic, negative temperature anomalies are completely confined to below 300 hPa and the top level of the stratosphere, with positive temperature anomalies between them (Fig. 6). Tropospheric temperature anomalies in the mid- and low latitudes are in phase with Arctic temperature anomalies in the upper troposphere and much of stratosphere. Strengthening of tropospheric westerly winds may enhance baroclinicity in the mid- and low troposphere, which dynamically favors the occurrence of anomalous low pressure during summer. Figure 7 supports this deduction. We find that significant increases in baroclinicity are observed over the Arctic Ocean, surrounded by negative anomalies outside the Arctic (Fig. 7a). Decreases in baroclinicity emerge over high latitudes of Eurasia and North America, northern North Pacific, and East Asia, generally consistent with warm areas shown in Figs. 1c and 3a. Significant increases in the frequency of anomalous low pressure are confined to the Arctic Ocean and northern North Atlantic, while negative anomalies cover most of Eurasia and North America (Fig. 7b). Over high latitudes of the continents, decreases in the frequency of anomalous low pressure may imply that thermal exchanges between the Arctic Ocean and lower latitudes weaken. It should be noted that if the threshold used to define anomalous low pressure is set to below −1.0 or −2.0 standard deviations in daily SLP anomalies, very similar results are obtained (not shown).
A systematic shift northward of zonal winds in the troposphere and stratosphere. Zonal wind anomalies along the latitude–pressure cross section at 110°E, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively. Green contours denote zonal winds averaged over 1979–2016, and purple contours are zonal winds averaged over Arctic cold summers, with the normalized PC2 ≥ 1σ (i.e., 1989, 1994, 1996, 2006, 2013, and 2016).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
A systematic shift northward of zonal winds in the troposphere and stratosphere. Zonal wind anomalies along the latitude–pressure cross section at 110°E, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively. Green contours denote zonal winds averaged over 1979–2016, and purple contours are zonal winds averaged over Arctic cold summers, with the normalized PC2 ≥ 1σ (i.e., 1989, 1994, 1996, 2006, 2013, and 2016).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
A systematic shift northward of zonal winds in the troposphere and stratosphere. Zonal wind anomalies along the latitude–pressure cross section at 110°E, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively. Green contours denote zonal winds averaged over 1979–2016, and purple contours are zonal winds averaged over Arctic cold summers, with the normalized PC2 ≥ 1σ (i.e., 1989, 1994, 1996, 2006, 2013, and 2016).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Air temperature anomalies along the latitude–pressure cross section at 110°E, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Air temperature anomalies along the latitude–pressure cross section at 110°E, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Air temperature anomalies along the latitude–pressure cross section at 110°E, derived from a linear regression on the normalized PC2. White and black contours represent anomalies at 95% and 99% confidence levels, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Spatial distribution of summer mean EGR anomalies at 600 hPa day−1, derived from a linear regression on the normalized Arctic westerly index. White and black contours denote anomalies at 95% and 99% confidence levels, respectively. (b) As in (a), but for anomalies in the frequency of the anomalous low pressure.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Spatial distribution of summer mean EGR anomalies at 600 hPa day−1, derived from a linear regression on the normalized Arctic westerly index. White and black contours denote anomalies at 95% and 99% confidence levels, respectively. (b) As in (a), but for anomalies in the frequency of the anomalous low pressure.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Spatial distribution of summer mean EGR anomalies at 600 hPa day−1, derived from a linear regression on the normalized Arctic westerly index. White and black contours denote anomalies at 95% and 99% confidence levels, respectively. (b) As in (a), but for anomalies in the frequency of the anomalous low pressure.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
4. Discussion
a. Roles of NWPSH in East Asian heat waves
Although the NWPSH is believed to contribute to East Asian heat waves (Luo and Lau 2017; Gao et al. 2018), some major characteristics of this subtropical high, including intensity and location, are not significantly correlated with the second thickness pattern or with the Arctic westerly index. We find that correlation coefficients of the second thickness pattern with the NWPSH intensity, ridge location, and western ridge point indices are 0.07, −0.09, and −0.24, respectively. While East Asia experienced abnormal high temperature and heat waves in 2013, the NWPSH was in a neutral phase (Fig. 8). The statistical association between the NWPSH and heat waves also differs from that in Fig. 3a (not shown). Thus, increasingly frequent East Asian heat waves cannot be simply attributed to the NWPSH.
(a) Normalized time series of the NWPSH intensity index. (b),(c) As in (a), but for the ridge location (latitude) and western ridge point (longitude), respectively. All data obtained from National Climate Center in China (http://cmdp.ncc-cma.net/Monitoring/cn_index_130.php).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Normalized time series of the NWPSH intensity index. (b),(c) As in (a), but for the ridge location (latitude) and western ridge point (longitude), respectively. All data obtained from National Climate Center in China (http://cmdp.ncc-cma.net/Monitoring/cn_index_130.php).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Normalized time series of the NWPSH intensity index. (b),(c) As in (a), but for the ridge location (latitude) and western ridge point (longitude), respectively. All data obtained from National Climate Center in China (http://cmdp.ncc-cma.net/Monitoring/cn_index_130.php).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
b. Possible dynamic processes linking Arctic cold anomalies
We assess the statistical relationship between summer Arctic cold anomalies in the mid- and low troposphere and East Asian heat waves. As previously noted, we find strengthened westerly winds in the Arctic along with weakened zonal winds in the mid- and low latitudes of East Asia, which we attribute to a systematic northward shift of the zonal wind belts. The present study, however, cannot identify the mechanism responsible for this shift nor for the temporal/spatial behavior of the second thickness pattern. It is possible that natural variability may be a major reason for recent changes in this pattern.
The 2013 summer mean 500-hPa temperature anomalies (relative to the summer mean averaged over 1979–2016; °C).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The 2013 summer mean 500-hPa temperature anomalies (relative to the summer mean averaged over 1979–2016; °C).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The 2013 summer mean 500-hPa temperature anomalies (relative to the summer mean averaged over 1979–2016; °C).
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The 500 hPa mean potential temperature advection and time mean of transient eddy flux divergence for summer of 2013: (a) 
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The 500 hPa mean potential temperature advection and time mean of transient eddy flux divergence for summer of 2013: (a)
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
The 500 hPa mean potential temperature advection and time mean of transient eddy flux divergence for summer of 2013: (a)
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Mean vertical velocity at 500 hPa (Pa s−1) for summer of 2013.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Mean vertical velocity at 500 hPa (Pa s−1) for summer of 2013.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Mean vertical velocity at 500 hPa (Pa s−1) for summer of 2013.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Normalized time series: summer mean AO index (red) and second thickness pattern (blue), and their correlation is 0.66.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Normalized time series: summer mean AO index (red) and second thickness pattern (blue), and their correlation is 0.66.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
Normalized time series: summer mean AO index (red) and second thickness pattern (blue), and their correlation is 0.66.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
c. Precursor to predict East Asian winter monsoon
Finally, we find that the strengthened summer Arctic westerly is significantly correlated with the ensuing Asian winter climate variability (Fig. 13). The correlation between the Arctic westerly index and the winter Siberian high index is −0.59, significant at 99% confidence level (Fig. 13a). At 500 hPa, geopotential height anomalies exhibit a tripole structure, and the positive height anomalies cover East Asia, indicating a weakened east trough (Fig. 13b). This configuration resembles the so-called Eurasian pattern (Wallace and Gutzler 1981; Wang and Zhang 2015). Negative SLP anomalies occupy the mid- and high latitudes of Eurasia (Fig. 13c). Thus, winter atmospheric circulation anomalies associated with strengthened Arctic westerly winds result in anomalous winter warming and a weakened winter monsoon over East Asia (Fig. 13d). Thus, the summer Arctic westerly index is an important precursor for winter surface air temperature anomalies over East Asia and it may prove to be useful predictor. It is not clear, however, which mechanisms are responsible for this lagged relationship. It is possible that summer high temperatures and strong heat waves in central and eastern Asia produce anomalies in soil temperature and moisture that lead to a weakened East Asian winter monsoon. This question requires further investigation.
(a) Normalized time series of the summer Arctic westerly index (red line) and the ensuing winter (DJF) Siberian high index (blue line). Their correlation is −0.59, significant at 99% confidence level. (b) Winter 500-hPa geopotential height anomalies (gpm), derived from a linear regression on the normalized summer Arctic westerly index. White and black contours represent anomalies at 95% and 99% confidence levels, respectively. (c),(d) As in (b), but for winter SLP (hPa) and SAT (°C) anomalies, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Normalized time series of the summer Arctic westerly index (red line) and the ensuing winter (DJF) Siberian high index (blue line). Their correlation is −0.59, significant at 99% confidence level. (b) Winter 500-hPa geopotential height anomalies (gpm), derived from a linear regression on the normalized summer Arctic westerly index. White and black contours represent anomalies at 95% and 99% confidence levels, respectively. (c),(d) As in (b), but for winter SLP (hPa) and SAT (°C) anomalies, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Normalized time series of the summer Arctic westerly index (red line) and the ensuing winter (DJF) Siberian high index (blue line). Their correlation is −0.59, significant at 99% confidence level. (b) Winter 500-hPa geopotential height anomalies (gpm), derived from a linear regression on the normalized summer Arctic westerly index. White and black contours represent anomalies at 95% and 99% confidence levels, respectively. (c),(d) As in (b), but for winter SLP (hPa) and SAT (°C) anomalies, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Normalized time series of the summer Arctic westerly index (red line) and the ensuing winter (DJF) Siberian high index (blue line). Their correlation is −0.59, significant at 99% confidence level. (b) Winter 500-hPa geopotential height anomalies (gpm), derived from a linear regression on the normalized summer Arctic westerly index. White and black contours represent anomalies at 95% and 99% confidence levels, respectively. (c),(d) As in (b), but for winter SLP (hPa) and SAT (°C) anomalies, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
(a) Normalized time series of the summer Arctic westerly index (red line) and the ensuing winter (DJF) Siberian high index (blue line). Their correlation is −0.59, significant at 99% confidence level. (b) Winter 500-hPa geopotential height anomalies (gpm), derived from a linear regression on the normalized summer Arctic westerly index. White and black contours represent anomalies at 95% and 99% confidence levels, respectively. (c),(d) As in (b), but for winter SLP (hPa) and SAT (°C) anomalies, respectively.
Citation: Journal of Climate 32, 4; 10.1175/JCLI-D-18-0370.1
5. Conclusions
This study reveals the first two dominant patterns of summer 1000–500-hPa thickness variability north of 30°N, and they, respectively, account for 29% and 10% of the variance. The leading thickness pattern displays strong interannual variability superposed on an interdecadal shift that occurred in the late 1990s, consistent with summer Arctic warming and rapid Arctic sea ice loss. Spatially, positive thickness anomalies cover much domain north of 30°N, particularly over the Arctic, Eurasia, and North America where significant positive anomalies are observed. The second thickness pattern exhibits strong interannual variability but does not exhibit any trend or shift. The positive phase of the second thickness pattern corresponds with significant Arctic cold anomalies in the mid- and low troposphere, which are surrounded by warm anomalies outside the Arctic. Arctic cold anomalies have occurred more frequently in the context of Arctic warming (2005–16).
The second thickness pattern is the thermodynamic expression of the leading pattern of upper-tropospheric westerly variability and shows significant positive correlations with frequencies of East Asian heat waves. In the upper troposphere, enhanced Arctic westerly winds are out of phase with winds in the mid- and low latitudes of Asia. The stronger Arctic westerly winds significantly enhance baroclinicity, which dynamically contributes to increased frequency of anomalous low pressure over the Arctic. The weaker westerly winds favor stagnation of warm air and dynamically contributes to sustained high pressure anomalies in the mid- and low troposphere, increasing the likelihood of East Asian heat waves. These wind anomalies can be attributed to a systematic northward shift of zonal winds, dominantly characterized by a shift of the East Asian westerly jet. This shift dynamically produces a dipole structure in zonal wind anomalies over the Arctic and the third pole. Dynamic analysis indicates that Arctic upward motion in the mid- and low troposphere is a major reason for summer Arctic cold anomalies. The enhanced Arctic westerly and Arctic cold anomalies during summer may provide a precursor to predict East Asian winter monsoon.
Acknowledgments
We thank Professor Jianqi Sun and two anonymous reviewers for their insightful comments, which significantly improved this manuscript. The authors are grateful to NCEP–NCEP for providing atmospheric reanalysis data. BW was supported by the National Natural Science Foundation of China (41730959, 41790472, and 41475080) and the National Key Basic Research Project of China (2015CB453200), and JF was supported by NASA Grant NNX14AH896 and funding from the Woods Hole Research Center.
REFERENCES
Bador, M., L. Terray, J. Boé, S. Somot, A. Alias, A.-L. Gibelin, and B. Dubuisson, 2017: Future summer mega-heatwave and record-breaking temperatures in a warmer France climate. Environ. Res. Lett., 12, 074025, https://doi.org/10.1088/1748-9326/aa751c.
Barriopedro, D., E. M. Fischer, J. Luterbacher, R. M. Trigo, and R. García-Herrera, 2011: The hot summer of 2010: Redrawing the temperature record map of Europe. Science, 332, 220–224, https://doi.org/10.1126/science.1201224.
Coumou, D., V. Petoukhov, S. Rahmstorf, S. Petri, and H. J. Schellnhuber, 2014: Quasi-resonant circulation regimes and hemispheric synchronization of extreme weather in boreal summer. Proc. Natl. Acad. Sci. USA, 111, 12 331–12 336, https://doi.org/10.1073/pnas.1412797111.
Coumou, D., J. Lehmann, and J. Beckmann, 2015: The weakening summer circulation in the Northern Hemisphere mid-latitudes. Science, 348, 324–327, https://doi.org/10.1126/science.1261768.
Crawford, A. D., and M. C. Serreze, 2016: Does the summer arctic frontal zone influence Arctic Ocean cyclone activity? J. Climate, 29, 4977–4993, https://doi.org/10.1175/JCLI-D-15-0755.1.
Ding, Q., and Coauthors, 2017: Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Climate Change, 7, 289–295, https://doi.org/10.1038/nclimate3241.
Ding, T., W. Qian, and Z. Yan, 2010: Changes in hot days and heat waves in China during 1961-2007. Int. J. Climatol., 30, 1452–1462, https://doi.org/10.1002/joc.1989.
Francis, J. A., and S. J. Vavrus, 2015: Evidence for a wavier jet stream in response to rapid Arctic warming. Environ. Res. Lett., 10, 014005, https://doi.org/10.1088/1748-9326/10/1/014005.
Freychet, N., S. Tett, J. Wang, and G. Hegerl, 2017: Summer heat waves over eastern China: Dynamical processes and trend attribution. Environ. Res. Lett., 12, 024015, https://doi.org/10.1088/1748-9326/aa5ba3.
Gao, M., B. Wang, J. Yang, and W. Dong, 2018: Are peak summer sultry heat wave days over the Yangtze–Huaihe River basin predictable? J. Climate, 31, 2185–2196, https://doi.org/10.1175/JCLI-D-17-0342.1.
Hoskins, B. J., and R. P. Pearce, 1983: Large-Scale Dynamical Processes in the Atmosphere. Academic Press, 397 pp.
Hu, K., G. Huang, and R. Huang, 2011: The impact of tropical Indian Ocean variability on summer surface air temperature in China. J. Climate, 24, 5365–5377, https://doi.org/10.1175/2011JCLI4152.1.
Imada, Y., H. Shiogama, M. Watanabe, M. Mori, M. Ishii, and M. Kimoto, 2014: The contribution of anthropogenic forcing to the Japanese heat waves of 2013 [in “State of the Climate in 2013”]. Bull. Amer. Meteor. Soc., 95, S52–S54.
Liu, G., R. Wu, S. Sun, and H. Wang, 2015: Synergistic contribution of precipitation anomalies over northwestern India and the South China Sea to high temperature over the Yangtze River valley. Adv. Atmos. Sci., 32, 1255–1265, https://doi.org/10.1007/s00376-015-4280-y.
Luo, M., and N.-C. Lau, 2017: Heat waves in southern China: Synoptic behavior, long-term change and urbanization effects. J. Climate, 30, 703–720, https://doi.org/10.1175/JCLI-D-16-0269.1.
Mann, M. E., S. Rahmstorf, K. Kornhuber, B. A. Steinman, S. K. Miller, and D. Coumou, 2017: Influence of anthropogenic climate change on planetary wave resonance and extreme weather events. Sci. Rep., 7, 45242, https://doi.org/10.1038/srep45242; Corrigendum, 7, 46822, https://doi.org/10.1038/srep46822.
Meehl, G. A., and C. Tebaldi, 2004: More intense, more frequent, and longer lasting heat waves in the 21st century. Science, 305, 994–997, https://doi.org/10.1126/science.1098704.
Min, S.-K., Y.-H. Kim, M.-K. Kim, and C. Park, 2014: Assessing human contribution to the summer 2013 Korea heat wave [in “State of the Climate in 2013”]. Bull. Amer. Meteor. Soc., 95, S48–S51.
Ogi, M., K. Yamazaki, and Y. Tachibana, 2004: The summertime annular mode in the Northern Hemisphere and its linkage to the winter mode. J. Geophys. Res., 109, D20114, https://doi.org/10.1029/2004JD004514.
Ogi, M., K. Yamazaki, and J. M. Wallace, 2010: Influence of winter and summer surface wind anomalies on summer Arctic sea ice extent. Geophys. Res. Lett., 37, L07701, https://doi.org/10.1029/2009GL042356.
Overland, J. E., and M. Wang, 2010: Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice. Tellus, 62A, 1–9, https://doi.org/10.1111/j.1600-0870.2009.00421.x.
Overland, J. E., J. A. Francis, H. Edward, and M. Wang, 2012: The recent shift in early summer Arctic atmospheric circulation. Geophys. Res. Lett., 39, L19804, https://doi.org/10.1029/2012GL053268.
Polyakov, I., and Coauthors, 2010: Arctic Ocean warming contributes to reduced polar ice cap. J. Phys. Oceanogr., 40, 2743–2756, https://doi.org/10.1175/2010JPO4339.1.
Screen, J. A., 2013: Influence of Arctic sea ice on European summer precipitation. Environ. Res. Lett., 8, 044015, https://doi.org/10.1088/1748-9326/8/4/044015.
Sellevold, R., S. Sobolowski, and C. Li, 2016: Investigating possible Arctic–midlatitude teleconnections in a linear framework. J. Climate, 29, 7329–7343, https://doi.org/10.1175/JCLI-D-15-0902.1.
Shimada, K., T. Kamoshida, M. Itoh, S. Nishino, E. Carmack, F. McLaughlin, S. Zimmermann, and A. Proshutinsky, 2006: Pacific Ocean inflow: Influence on catastrophic reduction of sea ice cover in the Arctic Ocean. Geophys. Res. Lett., 33, L08605, https://doi.org/10.1029/2005GL025624.
Simmonds, I., and E.-P. Lim, 2009: Biases in the calculation of Southern Hemisphere mean baroclinic eddy growth rate. Geophys. Res. Lett., 36, L01707, https://doi.org/10.1029/2008GL036320.
Sun, J., 2014: Record-breaking SST over mid-North Atlantic and extreme high temperature over the Jianghuai–Jiangnan region of China in 2013. Chin. Sci. Bull., 59, 3465–3470, https://doi.org/10.1007/s11434-014-0425-0.
Sun, Y., X. Zhang, F. W. Zwiers, L. Song, H. Wan, T. Hu, H. Yin, and G. Ren, 2014: Rapid increase in the risk of extreme summer heat in eastern China. Nat. Climate Change, 4, 1082–1085, https://doi.org/10.1038/nclimate2410.
Tang, Q., X. Zhang, and J. A. Francis, 2014: Extreme summer weather in northern mid-latitudes linked to a vanishing cryosphere. Nat. Climate Change, 4, 45–50, https://doi.org/10.1038/nclimate2065.
Vallis, G. K., 2006: Atmospheric and Oceanic Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press, 745 pp.
Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784–812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.
Wang, J., J. Zhang, E. Watanabe, M. Ikeda, K. Mizobata, J. E. Walsh, X. Bai, and B. Wu, 2009: Is the dipole anomaly a major driver to record lows in Arctic summer sea ice extent? Geophys. Res. Lett., 36, L05706, https://doi.org/10.1029/2008GL036706.
Wang, N., and Y. Zhang, 2015: Evolution of Eurasian teleconnection pattern and its relationship to climate anomalies in China. Climate Dyn., 44, 1017–1028, https://doi.org/10.1007/s00382-014-2171-z.
Wang, P., J. Tang, X. Sun, S. Wang, J. Wu, X. Dong, and J. Fang, 2017: Heat waves in China: Definitions, leading patterns, and connections to large-scale atmospheric circulation and SSTs. J. Geophys. Res. Atmos., 122, 10 679–10 699, https://doi.org/10.1002/2017JD027180.
Wang, W., W. Zhou, X. Wang, S. K. Fong, and K. C. Leong, 2013: Summer high temperature extremes in southeast China associated with the East Asian jet stream and circumglobal teleconnection. J. Geophys. Res. Atmos., 118, 8306–8319, https://doi.org/10.1002/jgrd.50633.
WMO, 2013: The global climate 2001–2010: A decade of climate extremes. WMO/TD-1103, 119 pp.
Wu, B., 2017: Winter atmospheric circulation anomaly associated with recent Arctic winter warm anomalies. J. Climate, 30, 8469–8479, https://doi.org/10.1175/JCLI-D-17-0175.1.
Wu, B., R. Zhang, B. Wang, and R. D’Arrigo, 2009: On the association between spring Arctic sea ice concentration and Chinese summer rainfall. Geophys. Res. Lett., 36, L09501, https://doi.org/10.1029/2009GL037299.
Wu, B., J. E. Overland, and R. D’Arrigo, 2012: Anomalous arctic surface wind patterns and their impacts on September sea ice minima and trend. Tellus, 64A, 18590, https://doi.org/10.3402/tellusa.v64i0.18590.
Wu, B., R. Zhang, R. D’Arrigo, and J. Su, 2013: On the relationship between winter sea ice and summer atmospheric circulation over Eurasia. J. Climate, 26, 5523–5536, https://doi.org/10.1175/JCLI-D-12-00524.1.
Zhou, T., S. Ma, and L. Zou, 2014: Understanding a hot summer in central Eastern China: Summer 2013 in contest of multimodel trend analysis [in “State of the Climate in 2013”]. Bull. Amer. Meteor. Soc., 95, S54–S57.
