1. Introduction
The warm Arctic–cold continents pattern (WACC) has been actively investigated in recent studies. WACC features Arctic warming that accompanies cold winters across midlatitude Eurasia and North America (e.g., Overland et al. 2011; Cohen et al. 2014; Mori et al. 2014; Kug et al. 2015; Sun et al. 2016). The WACC pattern has been found in the long-term trend (e.g., Kug et al. 2015; Sun et al. 2016; Sigmond and Fyfe 2016) and intraseasonal to interannual variability (e.g., Cohen et al. 2012, 2014; Mori et al. 2014, 2019; Lin 2015; Kug et al. 2015; Sorokina et al. 2016; Blackport et al. 2019; Guan et al. 2020a,b) of surface air temperatures. This broad-scale pattern not only accompanies seasonal mean climate anomalies, but also influences weather and climate extremes (e.g., Guan et al. 2020b; Cohen et al. 2021).
WACC generally consists of two counterparts according to its midlatitude climate anomalies: one over Eurasia and another over the North American sector. The WACC pattern over Eurasia has been termed the warm Arctic–cold Eurasia (WACE) pattern in previous studies (e.g., Mori et al. 2014, 2019; Cohen et al. 2012; Sorokina et al. 2016). The formation of the WACE pattern has been extensively studied, including influences of external remote forcing and internal climate variability, although disagreement between the results still exists. Specifically, surface temperature anomalies over Eurasia have been found to be driven by the anomalies of Arctic sea ice over the Barents–Kara Seas (e.g., Deser et al. 2007; Honda et al. 2009; Overland and Wang 2010; Francis and Vavrus 2012; Mori et al. 2019) and Eurasian snow cover (e.g., Cohen et al. 2012, 2014; Lin and Wu 2011; Yu et al. 2018). The remote influence can be initiated through excitation of atmospheric circulation anomalies such as those resembling the North Atlantic Oscillation (NAO; e.g., Hurrell et al. 2003) or Arctic Oscillation (AO; Thompson and Wallace 1998), Eurasian blocking, and stratospheric polar vortex anomaly (e.g., Deser et al. 2007; Honda et al. 2009; Cohen et al. 2007, 2012, 2021; Mori et al. 2014, 2019). By contrast, some other studies have suggested that the Arctic sea ice anomaly tends to contribute little to Eurasian climate anomalies, which can instead be largely attributed to internal climate variability (e.g., Screen et al. 2013; Wallace et al. 2014; Sorokina et al. 2016; Sun et al. 2016; Screen and Blackport 2019; Blackport et al. 2019). In particular, the anomalous atmospheric circulation is found to be crucial in driving surface turbulent heat flux and sea ice anomalies over the Barents–Kara Seas, as well as the WACE pattern by simultaneously driving surface temperature anomalies over Arctic and the northern midlatitudes (e.g., Sorokina et al. 2016; Blackport et al. 2019).
Similar to WACE, the WACC pattern over the North American sector may be referred to as the warm Arctic–cold North American (WACNA) pattern. The anomalous snow cover over Eurasia has been found to be related to surface temperature anomalies over North America on intraseasonal-to-interannual time scales (e.g., Lin and Wu 2011; Cohen et al. 2012; Lin 2015; Yu et al. 2018; Park et al. 2021). However, the physical process of this remote influence on the WACNA pattern is still unclear. Recent studies also indicated that reduced Arctic sea ice in the Chukchi–Bering Seas has a weak influence on cold winters over North America; instead, the anomalous atmospheric circulation can simultaneously cause the sea ice decline, Arctic warming, and cold North American winters (Blackport et al. 2019; Guan et al. 2020a). In addition, the tropical convective activity associated with the Madden–Julian oscillation (MJO; Madden and Julian 1971) as well as the interaction between the troposphere and stratosphere over mid-high latitudes are found to play important roles in sustaining the WACNA on intraseasonal time scales (Lin 2015; Guan et al. 2020b). Nevertheless, much less attention has been received on the WACNA than the WACE, especially on the formation of the WACNA pattern.
In this study, we analyze the interannual WACNA pattern by focusing on examining the pattern evolution and exploring its formation mechanism. The questions we aim to answer are as follows: 1) What are the processes of the evolution and formation of the WACNA pattern? In particular, what is the influence of large-scale atmospheric circulation anomalies on the formation of WACNA? 2) What is the relationship between the WACE and WACNA patterns? Do they occur simultaneously and covary in or out of phase? 3) What is the driving mechanism of the WACNA associated atmospheric circulation anomalies and hence the WACNA pattern?
The rest of the paper is organized as follows. Section 2 describes the observational and reanalysis datasets, an atmospheric model, and analysis methods we used. Section 3 examines the evolution and formation of the WACNA pattern, as well as the relationship between the WACE and WACNA patterns. Section 4 explores the driving role of atmospheric circulation anomalies in the WACNA pattern, including the WACNA associated large-scale circulation anomalies and their driving mechanisms. A summary is given in section 5.
2. Data and method
a. Data and climate indices
The analysis is mainly based on daily atmospheric and oceanic fields extracted from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011) in winters over the period from 1979 to 2019. Years are labeled according to the January dates in this study. The variables we used involve surface air temperature (SAT), sea level pressure (SLP), geopotential in the troposphere, outgoing longwave radiation (OLR), sea ice fraction, snow density, and sea surface temperature (SST). We use these variables obtained from the same data assimilation system to keep them dynamically consistent, and we interpolate all variables to the standard 2.5° × 2.5° grid.
Climate indices of the PNA (Pacific–North American; Wallace and Gutzler 1981), NAO (North Atlantic Oscillation; e.g., Hurrell et al. 2003), TNH (tropical–Northern Hemisphere; Mo and Livezey 1986), and WP (west Pacific; Wallace and Gutzler 1981) patterns from 1980–2019 are obtained from the Climate Prediction Center (CPC; http://www.cpc.ncep.noaa.gov/data/indices). These patterns are identified from a rotated empirical orthogonal function (REOF) analysis of monthly mean normalized 500-hPa height anomalies over the northern extratropics, based on the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (Kistler et al. 2001). In addition, the monthly Niño-3.4 index from the CPC is employed to characterize the tropical El Niño–Southern Oscillation (ENSO) variability.
We have also calculated an ERA-Interim-based PNA index by projecting monthly 500-hPa geopotential anomalies from the ERA-Interim reanalysis onto the CPC’s PNA pattern. In addition, the Asian–Bering–North American pattern (ABNA; Yu et al. 2016) index is calculated based on the ERA-Interim data. Specifically, the index is constructed as follows: 1) calculating the residual monthly 500-hPa geopotential anomalies
b. Data processing and diagnostics
To explore the evolution of the WACNA pattern and its associated atmospheric and oceanic anomalies, monthly data from December to February (DJF) over the period from 1980–2019 are examined. Anomalies are calculated relative to the 40-DJF climatology after removing the secular linear trend. We perform an empirical orthogonal function (EOF) analysis to identify the principal mode of SAT anomalies over the North American (NA) sector, which features the WACNA pattern. The leading mode is well separated from subsequent EOFs according to the criterion of North et al. (1982). We then use the corresponding principal component (PC1) as an index of the pattern. Lead–lag relationships between the PC1 index and variables of interest, calculated from moving 30-day-mean ERA-Interim data, are quantified through correlation and regression analyses. The statistical significance of a correlation is determined by a Student’s t test. The effective degrees of freedom are estimated by considering the autocorrelation of the time series of interest (Bretherton et al. 1999).
c. Atmospheric model
To confirm potential forcing influences on large-scale circulation anomalies, we perform idealized numerical experiments. The model employed is a primitive equation of the dry atmospheric model as described by Hall (2000), Hall et al. (2001), and Lin et al. (2007). It uses a diagnosed forcing, which is empirically obtained as a residual term from each tendency equation by calculating the dynamical terms of the model, together with the dissipation, using daily global analyses. Hence, the forcing involves all processes including diabatic forcing and forcing terms in the vorticity and divergence equations. The model has no explicit orography, while the time-averaged effect of the orography is accounted for by the forcing fields. The surface pressure is computed from the barometric equation with the 1000-hPa geopotential and temperature. Since the forcing is diagnosed and specific to the model, it differs from the generally used lower boundary forcing, such as SST and sea ice. However, the climatology and climate variability in the long integration of this model with a climatological forcing agree well with observations, as demonstrated in the above studies and in Yu and Lin (2013).
The model used here has a horizontal T31 triangular resolution (about 3.75°) and 10 vertical levels. The forcing fields are computed based on daily reanalysis data and averaged to obtain winter means, where the winter is defined as a 90-day period from 1 December. The winter climatology of forcing fields is an average over 30 winters from 1980 to 2010. The model outputs are also interpolated to the standard 2.5° × 2.5° grid using a bilinear interpolation.
3. Evolution and formation of the WACNA pattern
The principal mode of interannual surface temperature variability over the North American sector is identified by an EOF analysis of DJF monthly mean and area-weighted SAT anomalies over the extended NA region (20°–90°N, 150°E–40°W). The leading mode (EOF1) accounts for 30.6% of total variance and is dominated by a pair of opposite anomalous centers over the domain of interest (shown in the green box of Fig. 1; lag 0). Specifically, it consists of a large anomaly over NA with its center of action located over the Great Plains and another anomaly of opposite sign broadly spreading over the eastern Arctic and mid-high-latitude North Pacific, with its action center over the Chukchi–Bering Seas (CBS) and northeastern Siberia. The result is insensitive to a reasonable variation of the selected domain. In addition, the EOF1 mode bears close resemblance to relevant WACC patterns over the NA sector on interannual (e.g., Kug et al. 2015; Blackport et al. 2019; Guan et al. 2020a) and intraseasonal (e.g., Lin 2015; Guan et al. 2020b) time scales, and in long-term SAT trends (e.g., Kug et al. 2015; Sigmond and Fyfe 2016; Sun et al. 2016), obtained from various observational and numerically simulated datasets, indicating the robustness of the SAT variability pattern. Hence, EOF1 is employed to represent the WACNA pattern in this study. A positive phase of WACNA refers to a pattern as shown in Fig. 1 (lag 0) with positive and negative SAT anomalies over CBS and NA, respectively. The normalized principal component (PC1) series corresponding to EOF1 is then used as an index to characterize the interannual variability of the WACNA pattern.
Lead–lag regressions of SAT anomalies onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 0.5°C. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (20°–90°N, 150°E–40°W) used for the EOF analysis.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of SAT anomalies onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 0.5°C. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (20°–90°N, 150°E–40°W) used for the EOF analysis.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of SAT anomalies onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 0.5°C. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (20°–90°N, 150°E–40°W) used for the EOF analysis.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Figures 1–3 display lead–lag regressions of anomalous SAT and SLP over the Northern Hemisphere and sea ice fraction over the northern mid-high latitudes onto the PC1 index, from day −30 (lag −30) to day 10 (lag +10) at an interval of 5 days, which reveal the evolution of anomalous surface fields in association with the positive phase of the WACNA pattern. Negative (positive) days here indicate time when the anomalous field leads (lags) the PC1 index. For the simultaneous relationship (lag 0), the SAT anomalies are dominated by the WACNA pattern, with a statistically significant warming center over CBS and significant cooling over NA, as well as pronounced cooling over the Barents–Kara Seas (BKS) (Fig. 1, bottom left). WACNA is supported by anomalous large-scale atmospheric circulation as shown in the SLP anomalies, with an anomalous horseshoe-shaped high straddling over Gulf of Alaska, Alaska, and northwestern Canada (Fig. 2, bottom left). The anomalous circulation is accompanied by warm advection from the North Pacific to help maintain warming over CBS and northeastern Siberia as well as advection of cold air from Arctic to reinforce cooling over NA. The SAT anomalies are also associated with and may be locally reinforced by sea ice declines over CBS and some sea ice growth over BKS and Hudson Bay (Fig. 3, bottom left). However, recent studies indicated that the pronounced sea ice anomalies over CBS and BKS could also be induced by the large-scale circulation anomaly (Blackport et al. 2019; Guan et al. 2020a; Jin et al. 2020).
As in Fig. 1, but for SLP anomalies, with a contour interval of 1.0 hPa.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
As in Fig. 1, but for SLP anomalies, with a contour interval of 1.0 hPa.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
As in Fig. 1, but for SLP anomalies, with a contour interval of 1.0 hPa.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of anomalous sea ice fraction over the northern mid-high latitudes (poleward of 50°N) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 2.0%. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (75°–80°N, 30°–60°E) used to calculate regional mean sea ice fraction anomalies over the North Barents Sea.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of anomalous sea ice fraction over the northern mid-high latitudes (poleward of 50°N) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 2.0%. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (75°–80°N, 30°–60°E) used to calculate regional mean sea ice fraction anomalies over the North Barents Sea.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of anomalous sea ice fraction over the northern mid-high latitudes (poleward of 50°N) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 2.0%. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (75°–80°N, 30°–60°E) used to calculate regional mean sea ice fraction anomalies over the North Barents Sea.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
The evolution of WACNA (Fig. 1) exhibits significant SAT anomalies apparent upstream over northern Eurasia about one month preceding the peak of the WACNA pattern, which shows the maximum difference of 6.47°C between the positive and negative SAT anomalies over CBS and NA at lag 0. In particular, prior to the WACNA, remarkable cooling is seen over BKS and warming in its southern flank over southern Siberia. This features a negative WACE pattern (e.g., Mori et al. 2014; Sun et al. 2016). The WACE shows its pattern peak at about lag −25, with the maximum difference of 2.86°C between its opposite action centers of the SAT anomalies, and weakens subsequently. In particular, the southern center of WACE becomes less clear and statistically insignificant after lag −5. The negative WACE is supported by a negative SLP anomaly over most of Eurasia (Fig. 2), indicating the weakened Siberian high that leads to cooling over the western Arctic through anomalous cold advection over BKS and Eurasian warming in its southern flank by anomalous warm advection, together with sea ice increases over BKS (Fig. 3). Influences of the anomalous atmospheric circulation and sea ice on WACE are consistent with those demonstrated in previous studies (e.g., Mori et al. 2014, 2019; Honda et al. 2009). On the other hand, WACNA reveals a well-organized structure that is readily discernible at lag −25. The pattern gradually develops, reaches its peak at lag 0, and weakens afterward. Hence, a negative WACE tends to lead a positive WACNA by about 25 days. In association with the evolution of WACNA, a horseshoe shaped high of SLP anomalies straddling over Gulf of Alaska, Alaska and northwestern Canada is evident at lag −25, gradually develops, reaches its peak at lag −5, and weakens afterward (Fig. 2). In addition, sea ice decreases over the Bering Sea and increases over most of Hudson Bay at lag −25. The sea ice anomalies enhance, reach their peaks at lag 0, and slightly weaken afterward. Sea ice also decreases over most of the Chukchi Sea since lag −5 (Fig. 3). Overall, the anomalous temperature advection following the circulation anomalies along with local sea ice anomalies lead to the formation of the WACNA pattern.
4. Driving role of atmospheric circulation anomalies in WACNA
The above analysis indicates the influence of large-scale atmospheric circulation anomalies on the formation of WACNA. To further understand the driving mechanism of the WACNA pattern, in this section, we first examine whether the WACNA is related to dominant patterns of climate variability and then explore what drives the WACNA pattern.
a. Influences of the ABNA and PNA patterns on WACNA
We first calculate correlations between the PC1 index and climate indices of several prominent modes of low-frequency climate variability in the Northern Hemisphere. As described above, the ABNA index is created using the ERA-Interim data after linearly removing the PNA contribution. We project DJF monthly Ф500 anomalies from the ERA-Interim reanalysis onto the CPC’s PNA pattern to derive an ERA-Interim-based PNA index. The PNA indices obtained from CPC and induced from the ERA-Interim data are denoted as PNA0 and PNA, respectively. The PNA and PNA0 indices are highly correlated, based on monthly data over the 40 DJFs from 1980 to 2019 (r = 0.92; the time series are also shown in Fig. 5). This would be expected, since the PNA pattern is one of the most prominent modes of climate variability in the northern extratropics. We use the PNA index in the following calculation.
PC1 has high correlations with the ABNA and PNA indices, with negative values of −0.63 and −0.51, respectively (Table 1). This indicates that the PC1 and ABNA series have about 40% variance in common and the PC1 and PNA series have about 26% variance in common and suggests pronounced projections of the PC1 associated circulation anomalies on the ABNA and PNA patterns. The projections are clearly evident in the Ф500 anomalies regressed upon the PC1 index (Fig. 4, bottom left). This reveals that prominent anomalies exist over the centers of action of ABNA (i.e., North Asia, the Bering Sea and Bering Strait, and North America), as well as the action centers of PNA (i.e., the vicinity of Hawaii, south of the Aleutian Islands, the NA intermountain region, and the southeastern United States). In addition, PC1 is positively correlated with the TNH index (correlation coefficient r = 0.44), due to a negative relationship between the TNH and PNA patterns (Table 1). It is correlated marginally with the Niño-3.4 index at the 5% level (r = −0.21), which is likely due to a significant correlation between the PNA and Niño-3.4 indices (Table 1), indicating that the PNA pattern is influenced by the tropical ENSO variability. By contrast, the correlation between the PC1 and NAO indices is low (r = −0.09; Table 1).
As in Fig. 1, but for Φ500 anomalies with a contour interval of 100 m2 s−2.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
As in Fig. 1, but for Φ500 anomalies with a contour interval of 100 m2 s−2.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
As in Fig. 1, but for Φ500 anomalies with a contour interval of 100 m2 s−2.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Correlation coefficients among the PC1 index and several atmospheric and oceanic indices, based on monthly data over the 40 DJFs from 1980 to 2019. The effective sample size is found to be at least 105 for the 120-month time series in these calculations. The 5% significance level of a correlation is r > 0.19 for 105 degrees of freedom. Numbers in boldface type indicate a correlation significant at the 5% level.
PC1 is also correlated with the WP index (r = −0.33), since it significantly correlates with Ф500 anomalies over the Kamchatka Peninsula and the western subtropical North Pacific (Fig. 4, bottom left), both of which are the action centers of the WP pattern. However, the centers of action in association with the PC1 locate at east of the date line over the North Pacific and resemble the PNA-related anomalies (e.g., Wallace and Gutzler 1981). By contrast, the action centers associated with the WP pattern and its sea level pressure signature, the North Pacific Oscillation (NPO) pattern, mainly locate at west of the date line over the North Pacific (e.g., Linkin and Nigam 2008; Yuan et al. 2015; Dai and Tan 2019). This explains that PC1 has a closer correlation with the PNA index than the WP index (Table 1). Recent studies also suggested that an eastward shift of the NPO would have stronger impacts on North American SAT anomalies (Sung et al. 2019; Chen et al. 2018). In addition, the WP pattern can be maintained through energy conversion from climatological-mean fields and needs not to originate directly from the tropics (Tanaka et al. 2016; Baxter and Nigam 2015; Sung et al. 2020; Kim et al. 2021). The relationship between PNA/ENSO and WP has also been investigated (e.g., Dai and Tan 2016, 2019; Park et al. 2018; Yeh et al. 2018). In particular, the PNA pattern is found to play a crucial role in the northern winter climate impact and the ENSO modulation of the WP pattern (Dai and Tan 2019).
To explore the evolution of the WACNA associated large-scale atmospheric circulation anomalies, lead–lag regressions of Ф500 anomalies upon the PC1 index from day −30 to day 10 are examined (Fig. 4). The evolution reveals a negative WACE pattern, with a pronounced cyclonic anomaly over south of BKS and northern Siberia accompanied by a anticyclonic anomaly in its southern flank, which appears about one month preceding the WACNA pattern peak. The WACE also shows its anomalous circulation peak at lag −25, with the maximum Ф500 difference of 364.0 m2 s−2 between the two opposite action centers over northern Siberia and Mongolia. The WACE pattern weakens subsequently and has its southern center vanished after lag −10. On the other hand, positive Ф500 anomalies over the Gulf of Alaska, CBS, and Gulf of Mexico as well as negative anomalies over NA and the vicinity of Hawaii enhance downstream of the WACE pattern from lag −25, reach their maximum central values at lag −5, and weaken afterward. This is followed by slight increases of Ф500 anomalies over the North Atlantic after lag −5. Hence, the evolution of the WACNA associated Ф500 anomalies indicates a zonally oriented ABNA-like (Yu et al. 2018) and a tropical–extratropical related PNA-like (Wallace and Gutzler 1981) atmospheric teleconnections, which are apparent upstream and ahead of the WACNA pattern.
Figure 5 further shows normalized monthly time series of the PC1, ABNA, and PNA indices over the 40 DJF seasons, as well as lead–lag correlations between PC1 and the ABNA and PNA indices. The effective sample size is found to be at least 116 for the 120-month time series in calculation of the lead–lag correlations. The 5% significance level of a correlation is r > 0.18 for 116 degrees of freedom. The correlations are statistically significant at the 5% level from about 27 days when the ABNA index leads the PC1 index and from about 23 days when the PNA index leads the PC1 index. Additionally, the evolution of the lead–lag correlations shows that the strongest relationship occurs when the circulation pattern leads PC1 by about 5 days (r = −0.65 for ABNA and r = −0.53 for PNA). This also resembles the relationship that the wintertime SAT anomaly over the East Siberia–Chukchi Seas leads the SAT anomaly over central NA by 5 days, found by Kug et al. (2015). Overall, the time series analysis further suggests the upstream influence of the ABNA and PNA patterns on the WACNA, with the strongest relationship occurring when both circulation patterns lead the WACNA by about 5 days.
Indices of PC1, PNA, and ABNA in winter and their relationships. (top) Normalized monthly time series of the PC1 index (gray bars), the PNA indices from CPC (black) and the ERA-Interim reanalysis (red), and the ABNA index (blue) over the period from 1980 to 2019. (bottom) Lead–lag correlations between the PC1 index and the PNA (red) and ABNA (blue) indices from day −30 to day 10. Negative or positive days indicate times when the circulation index leads or lags the PC1 index, respectively. The dashed line indicates the correlation that is statistically significant at the 5% level.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Indices of PC1, PNA, and ABNA in winter and their relationships. (top) Normalized monthly time series of the PC1 index (gray bars), the PNA indices from CPC (black) and the ERA-Interim reanalysis (red), and the ABNA index (blue) over the period from 1980 to 2019. (bottom) Lead–lag correlations between the PC1 index and the PNA (red) and ABNA (blue) indices from day −30 to day 10. Negative or positive days indicate times when the circulation index leads or lags the PC1 index, respectively. The dashed line indicates the correlation that is statistically significant at the 5% level.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Indices of PC1, PNA, and ABNA in winter and their relationships. (top) Normalized monthly time series of the PC1 index (gray bars), the PNA indices from CPC (black) and the ERA-Interim reanalysis (red), and the ABNA index (blue) over the period from 1980 to 2019. (bottom) Lead–lag correlations between the PC1 index and the PNA (red) and ABNA (blue) indices from day −30 to day 10. Negative or positive days indicate times when the circulation index leads or lags the PC1 index, respectively. The dashed line indicates the correlation that is statistically significant at the 5% level.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Similar geopotential anomalies in association with the PC1 index are apparent at 200 and 500 hPa (cf. contours in Fig. 6 with Fig. 4), with differences mainly in magnitude of the anomalies, indicating an equivalent barotropic evolution of the WACNA-related large-scale circulation anomalies in the troposphere. In addition, diagnosis of the wave activity flux W for stationary Rossby waves illustrates that the propagation of wave activity is also characterized by two wave trains that dominate over the northern extratropics (Fig. 6, vectors). One follows the PNA pattern, with large W fluxes originating from the subtropical North Pacific and flowing downstream across the northeastern Pacific toward North America. Another follows the ABNA teleconnection, with large W fluxes originating from eastern Asia and flowing downstream across Bering Sea and Strait toward North America. The two wave trains originate about 25 days preceding the WACNA pattern, enhance locally without phase propagation, reach the strongest action centers over the NA sector at lag −5, and decay subsequently. Thus, the propagation of PNA-like and ABNA-like waves contributes to the formation of the WACNA. In addition, the wave activity flux analysis suggests that the main source of wave activity in association with the WACNA pattern may be traced back to the tropical–subtropical North Pacific Ocean and Eurasia.
Lead–lag regressions of Φ200 anomalies (shading; m2 s−2) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days, and corresponding wave activity fluxes poleward of 20°N (vectors; m2 s−2; flux values less than 0.5 m2 s−2 are omitted). The vector scale is shown at the lower right.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of Φ200 anomalies (shading; m2 s−2) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days, and corresponding wave activity fluxes poleward of 20°N (vectors; m2 s−2; flux values less than 0.5 m2 s−2 are omitted). The vector scale is shown at the lower right.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of Φ200 anomalies (shading; m2 s−2) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days, and corresponding wave activity fluxes poleward of 20°N (vectors; m2 s−2; flux values less than 0.5 m2 s−2 are omitted). The vector scale is shown at the lower right.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
b. Potential forcing of WACNA
To identify potential forcing sources of the WACNA associated large-scale atmospheric circulation anomalies, we examine SST and OLR anomalies in the tropical region as well as sea ice and snow density anomalies in the northern mid-high latitudes.
In association with the PC1 index, the most pronounced SST anomalies tend to be anomalous cooling in the tropical central-eastern Pacific (contours in Fig. 7), accompanied by positive OLR anomalies indicating weakened deep convection over the tropical central Pacific (shading in Fig. 7). The tropical SST and OLR anomalies are clearly evident over the whole period analyzed, with slight increases during the evolution. This suggests that there are tropical ENSO-like SST and atmospheric heating anomalies that accompany the WACNA pattern. Numerous studies have demonstrated that ENSO-related SST forcing directly impacts the large-scale atmospheric circulation, especially the PNA pattern, through its influence on diabatic heating and upper-level divergence over the tropical central-eastern Pacific (e.g., Horel and Wallace 1981; Hoskins and Karoly 1981; Trenberth et al. 1998). The PNA pattern subsequently influences SATs over the NA sector via the anomalous temperature advection (e.g., Wallace and Gutzler 1981; Higgins et al. 2002; Yu and Zwiers 2007; Martineau et al. 2021; Park et al. 2021).
Lead–lag regressions of mid-low-latitude SST (contours, with an interval of 0.1°C) and tropical OLR (shading; W m−2) anomalies onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. SST anomalies cover the region from 30°S to 60°N, with stippling indicating anomalies that are significantly different from zero at the 5% level. OLR anomalies cover the tropical region from 20°S to 20°N. The green-outlined box (5°S–5°N, 160°–90°W) and blue-outlined box (5°S–5°N, 180°–150°W) indicate the areas used to calculate regional mean SST and OLR anomalies, respectively.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of mid-low-latitude SST (contours, with an interval of 0.1°C) and tropical OLR (shading; W m−2) anomalies onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. SST anomalies cover the region from 30°S to 60°N, with stippling indicating anomalies that are significantly different from zero at the 5% level. OLR anomalies cover the tropical region from 20°S to 20°N. The green-outlined box (5°S–5°N, 160°–90°W) and blue-outlined box (5°S–5°N, 180°–150°W) indicate the areas used to calculate regional mean SST and OLR anomalies, respectively.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of mid-low-latitude SST (contours, with an interval of 0.1°C) and tropical OLR (shading; W m−2) anomalies onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. SST anomalies cover the region from 30°S to 60°N, with stippling indicating anomalies that are significantly different from zero at the 5% level. OLR anomalies cover the tropical region from 20°S to 20°N. The green-outlined box (5°S–5°N, 160°–90°W) and blue-outlined box (5°S–5°N, 180°–150°W) indicate the areas used to calculate regional mean SST and OLR anomalies, respectively.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
The evolution of the tropical SST and OLR influences on the WACNA can be further assessed by calculating the relationships between the PC1 index and both the SST anomalies over the equatorial central-eastern Pacific (5°S–5°N, 160°–90°W) and OLR anomalies over the equatorial central Pacific (5°S–5°N, 180°–150°W), as well as those between the PNA index and the regional mean SST and OLR anomalies (red and purple curves in Fig. 8). The correlations among them are all significant at the 5% level, with slight increases of the correlations between PC1 and the regional mean SST and OLR anomalies from lag −30 to lag +10 and relatively stable correlations between PNA and the tropical SST and OLR anomalies over the evolution period. This confirms the potential forcing of tropical SST and OLR anomalies on the WACNA, via the PNA pattern.
(left) Lead–lag correlations between the PC1 index and the tropical regional mean SST (red) and OLR (purple), and regional mean Siberian snow density (blue) and North Barents sea ice (green) anomalies from day −30 to day 10. (right) Lead–lag correlations between the PNA index and the tropical regional mean SST (red) and OLR (purple) anomalies and between the ABNA index and the regional mean Siberian snow density (blue) and North Barents sea ice (green) anomalies (blue) from day −30 to day 10. Negative or positive days indicate time when the regional mean forcing anomalies leads or lags the PC1 or circulation index, respectively. The dashed lines indicate the correlations that are statistically significant at the 5% level.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
(left) Lead–lag correlations between the PC1 index and the tropical regional mean SST (red) and OLR (purple), and regional mean Siberian snow density (blue) and North Barents sea ice (green) anomalies from day −30 to day 10. (right) Lead–lag correlations between the PNA index and the tropical regional mean SST (red) and OLR (purple) anomalies and between the ABNA index and the regional mean Siberian snow density (blue) and North Barents sea ice (green) anomalies (blue) from day −30 to day 10. Negative or positive days indicate time when the regional mean forcing anomalies leads or lags the PC1 or circulation index, respectively. The dashed lines indicate the correlations that are statistically significant at the 5% level.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
(left) Lead–lag correlations between the PC1 index and the tropical regional mean SST (red) and OLR (purple), and regional mean Siberian snow density (blue) and North Barents sea ice (green) anomalies from day −30 to day 10. (right) Lead–lag correlations between the PNA index and the tropical regional mean SST (red) and OLR (purple) anomalies and between the ABNA index and the regional mean Siberian snow density (blue) and North Barents sea ice (green) anomalies (blue) from day −30 to day 10. Negative or positive days indicate time when the regional mean forcing anomalies leads or lags the PC1 or circulation index, respectively. The dashed lines indicate the correlations that are statistically significant at the 5% level.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
The PC1-associated SST also exhibits anomalies beyond the tropical Pacific, especially those over the tropical Indian and Atlantic Oceans and midlatitude North Pacific. However, the SST anomalies are weaker over those regions than the tropical central-eastern Pacific. Relatively weak OLR anomalies are also evident over the tropical Indian and Atlantic Oceans (Fig. 7). The SST and OLR anomalies simultaneously appear over the tropical Indian, Pacific, and Atlantic Oceans, which are likely due to interannual relationships between various tropical ocean basins (e.g., Harrison and Larkin 1998; Lau and Nath 2003; Ham et al. 2013). By contrast, the action centers of the SST anomalies in midlatitude North Pacific are collocated with the PC1 associated SLP anomalies (cf. Figs. 7 and 2), which implies that the anomalous circulation mostly drives the SST in the North Pacific, as demonstrated in previous studies (e.g., Cayan 1992; Kushnir et al. 2002).
In the northern mid-high latitudes, one potential forcing for the large-scale circulation anomaly is the sea ice anomaly over the western Arctic. As discussed above in Fig. 3, sea ice increases over BKS are apparent throughout the WACNA evolution period especially when the anomalous sea ice leads the PC1 index by 1–2 weeks. However, the PC1-associated sea ice anomalies are not statistically significant at the 5% level over the whole BKS region (Fig. 3). Further analysis of the lead–lag relationships between PC1 and the regional mean sea ice anomalies over (75°–80°N, 30°–60°E) (a region with large sea ice anomalies) shows that the correlation is insignificant at the 5% level over the evolution period (green curve in Fig. 8, left). In addition, the anomalous sea ice over the BKS has no relation with the extratropical ABNA teleconnection (green curve in Fig. 8, right). This indicates the insignificant remote influence of the sea ice anomaly over the BKS on the WACNA-associated large-scale circulation anomalies and hence the WACNA. In fact, previous studies have demonstrated regional influences of sea ice anomalies over the BKS on the WACE-associated circulation and temperature anomalies over Eurasia (e.g., Mori et al. 2014, 2019; Deser et al. 2007).
Another potential forcing for the anomalous large-scale circulation is the snow anomaly over Eurasia. Figure 9 displays the lead–lag regressions of snow density anomalies over the Siberian region (55°–77.5°N, 50°–130°E) upon the PC1 index. Similar results can be obtained in the snow water equivalent anomalies (not shown). Preceding the positive WACNA pattern, there are snow density increases over northern Siberia (about north of 65°N) and decreases over southern-central Siberia from lag −30 to lag 0. However, the anomalies over northern Siberia and western parts of southern Siberia are statistically insignificant from zero at the 5% level, due to high interannual variability of the snow density there. By contrast, the statistically significant anomalies mainly appear over the central part of southern Siberia when the anomalous snow leads PC1 by about 2–3 weeks (Fig. 9). This is also clearly evident in the lead–lag correlations between PC1 and the regional mean snow density anomalies over the central part of southern Siberia (57.5°–65°N, 90°–120°E) (blue curve in Fig. 8, left). Furthermore, there are significant correlations between the regional mean snow anomalies and the ABNA index when the snow anomaly leads the ABNA by about 20 days (Fig. 8, right). This suggests that the snow anomalies over southern Siberia may contribute to the formation of WACNA, through the extratropical ABNA teleconnection. In addition, the result may explain the about 25-day lagged association of WACNA with WACE, because the Siberian snow decline is related to a negative WACE pattern and its featured Eurasian warming.
Lead–lag regressions of snow density anomalies over the Siberian region (55°–77.5°N, 50°–130°E) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 0.5 kg m−3. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (57.5°–65°N, 90°–120°E) used to calculate regional mean snow density anomalies.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of snow density anomalies over the Siberian region (55°–77.5°N, 50°–130°E) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 0.5 kg m−3. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (57.5°–65°N, 90°–120°E) used to calculate regional mean snow density anomalies.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
Lead–lag regressions of snow density anomalies over the Siberian region (55°–77.5°N, 50°–130°E) onto the normalized PC1 index from day −30 to day 10 at an interval of 5 days. The contour interval is 0.5 kg m−3. Stippling indicates anomalies that are significantly different from zero at the 5% level. The green-outlined box indicates the area (57.5°–65°N, 90°–120°E) used to calculate regional mean snow density anomalies.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
To further assess the influence process of Siberian snow on the WACNA, Fig. 10 (left column) shows the lagged anomalies of Φ500, SLP, and SAT regressed upon the normalized regional mean snow density over the central part of southern Siberia, with the Siberian snow leading the anomalous fields by 20 days. The sign of the regressions is inverted here to indicate the anomalous fields in association with the snow density decrease. One standard deviation of the regional mean snow density is 1.55 kg m−3. About 3 weeks after the Siberian snow anomaly, Φ500 is dominated by a zonally oriented wave train in midlatitudes over the North Pacific, North America, and the North Atlantic (Fig. 10, top left), which collocate with the action centers of the ABNA pattern over the NA sector. The Siberian snow associated Φ500 anomalies account for about ¼–⅓ times the WACNA-associated anomalies (cf. top left in Fig. 10 with bottom left in Fig. 4). Meanwhile, SLP is dominated by a center of action over Bering Sea and Alaska (Fig. 10, middle left), which is accompanied by the warm flow of marine air and southward transport of Arctic cold air that support warming over west of CBS and cooling over western-central NA, respectively (Fig. 10, bottom left). The SAT anomalies also account for ¼–⅓ times the WACNA-associated anomalies over the NA sector (cf. middle left in Fig. 10 with bottom left in Fig. 1). The SLP and SAT anomalies contribute to the corresponding WACNA associated anomalies over the NA sector but not Eurasia, indicating the downstream influence of the Siberian snow anomaly. Overall, the circulation and temperature anomalies following the Siberian snow anomaly contribute to the WACNA-associated fields and thus suggest that Siberian snow may act as a potential forcing of the WACNA pattern.
(left) Lead–lag regressions of (top) Φ500 (contour intervals of 100 m2 s−2), (middle) SLP (intervals of 1.0 hPa), and (bottom) SAT (intervals of 0.5°C) anomalies onto the normalized regional mean Siberian snow density anomalies. Siberian snow leads the anomalous fields by 20 days. Stippling indicates anomalies that are significantly different from zero at the 5% level. (right) Corresponding numerical responses to a Siberian forcing.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
(left) Lead–lag regressions of (top) Φ500 (contour intervals of 100 m2 s−2), (middle) SLP (intervals of 1.0 hPa), and (bottom) SAT (intervals of 0.5°C) anomalies onto the normalized regional mean Siberian snow density anomalies. Siberian snow leads the anomalous fields by 20 days. Stippling indicates anomalies that are significantly different from zero at the 5% level. (right) Corresponding numerical responses to a Siberian forcing.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
(left) Lead–lag regressions of (top) Φ500 (contour intervals of 100 m2 s−2), (middle) SLP (intervals of 1.0 hPa), and (bottom) SAT (intervals of 0.5°C) anomalies onto the normalized regional mean Siberian snow density anomalies. Siberian snow leads the anomalous fields by 20 days. Stippling indicates anomalies that are significantly different from zero at the 5% level. (right) Corresponding numerical responses to a Siberian forcing.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
c. Numerical evidence of Siberian snow forcing on WACNA
To confirm the snow forcing influence on the WACNA pattern, we conduct numerical experiments using the atmospheric model described in section 2. The experiment is set up with a diabatic heating perturbation over the Siberian area to mimic the snow forcing anomaly discussed above (Fig. 9). It has a horizontal elliptical form, with a semimajor axis of 30 degrees of longitude and a semiminor axis of 12.5 degrees of latitude. The heating center is located at 60°N, 100°E. The magnitude of the heating is proportional to the squared cosine of the distance from the center. To represent the effect of snow cover anomaly near the surface, the heating anomaly has a vertical profile of (1 − σ) sin[π(1 − σ)], where σ = p/p0, p is the pressure, and p0 = 1000 hPa. It peaks at σ = 0.95 (950 hPa) with a vertically averaged heating rate at the center of 0.5 K day−1. The heating perturbation is similar to heating anomalies generally appearing in the mid-high latitudes as found in previous numerical studies (e.g., Deser et al. 2007). In addition, atmospheric responses described below remain virtually unchanged with a reasonable variation of the location of the heating perturbation.
During the atmospheric adjustment period (days 1–5 of the integration), the atmospheric response originates and is mainly confined downstream of the heating perturbation as a Rossby wave response in the northern mid-high latitudes. The response enhances, especially with a zonally oriented wave train apparent over the North Pacific, North America, and the North Atlantic in days 6–10 (not shown). The wave train develops further with little phase propagation. The right panels of Fig. 10 display the responses of Φ500, SLP, and air temperature at 950 hPa at day 20.
The numerical responses simulate the main circulation and SAT anomalies that follow the Siberian snow anomaly as described above from the ERA-Interim (Fig. 10, cf. right vs left columns). In particular, a wave train can be seen in the Φ500 response downstream of the perturbation over the NA sector, with anomalous centers propagating from CBS, downstream across NA toward west of the North Atlantic (Fig. 10, top right), which is similar to the reanalysis result (Fig. 10, top left). The Φ500 responses are also apparent over the northeastern Pacific, which are likely related to geopotential anomalies resulting from the barotropic instability of the wintertime mean flow (Simmons et al. 1983), where disturbances grow by extracting kinetic energy from the mean flow. The most pronounced SLP responses appear over CBS and Sea of Okhotsk (Fig. 10, middle right), which contribute to anomalous temperature advections over the NA sector and lead to warming over west of CBS and cooling over western-central NA (Fig. 10, bottom right). Overall, the atmospheric circulation and surface temperature anomalies in response to the heating perturbation bear resemblance to the observed circulation and SAT anomalies in association with the Siberian snow anomaly. The numerical result hence confirms the forcing influence of the Siberian snow on the WACNA pattern.
5. Summary
Based on the ERA-Interim reanalysis data in winters over the 1980–2019 period, we examine the evolution of the interannual warm Arctic–cold continents pattern over the North American sector, which is referred to as WACNA, explore the driving role of large-scale atmospheric circulation anomalies in the WACNA pattern, and identify potential forcing sources of the pattern. In addition, using a primitive equation atmospheric model, we conduct numerical experiments to confirm the forcing influence of the Siberian snow anomaly on the WACNA pattern. Figure 11 presents a schematic diagram illustrating the formation of the positive phase of the WACNA. Processes with circulation anomalies of opposite signs would lead to the negative WACNA pattern.
A schematic diagram illustrating the physical processes of the formation of the WACNA pattern. Pink and blue shadings indicate surface warm and cold anomalies, respectively. The blue dashed arrow indicates the atmospheric anomalies in association with the tropical cold SST anomalies. The green dashed oval and arrow denote the Siberian snow decline and its associated atmospheric anomalies, respectively. The green horseshoe-shaped curves denote positive SLP anomalies. Red and blue arrows represent surface warm and cold advections, respectively. Red and blue dashed ovals indicate positive and negative geopotential anomalies at 500 hPa, respectively. Purple arrows represent the ABNA and PNA patterns.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
A schematic diagram illustrating the physical processes of the formation of the WACNA pattern. Pink and blue shadings indicate surface warm and cold anomalies, respectively. The blue dashed arrow indicates the atmospheric anomalies in association with the tropical cold SST anomalies. The green dashed oval and arrow denote the Siberian snow decline and its associated atmospheric anomalies, respectively. The green horseshoe-shaped curves denote positive SLP anomalies. Red and blue arrows represent surface warm and cold advections, respectively. Red and blue dashed ovals indicate positive and negative geopotential anomalies at 500 hPa, respectively. Purple arrows represent the ABNA and PNA patterns.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
A schematic diagram illustrating the physical processes of the formation of the WACNA pattern. Pink and blue shadings indicate surface warm and cold anomalies, respectively. The blue dashed arrow indicates the atmospheric anomalies in association with the tropical cold SST anomalies. The green dashed oval and arrow denote the Siberian snow decline and its associated atmospheric anomalies, respectively. The green horseshoe-shaped curves denote positive SLP anomalies. Red and blue arrows represent surface warm and cold advections, respectively. Red and blue dashed ovals indicate positive and negative geopotential anomalies at 500 hPa, respectively. Purple arrows represent the ABNA and PNA patterns.
Citation: Journal of Climate 35, 13; 10.1175/JCLI-D-21-0831.1
The interannual WACNA pattern is characterized by the dominant surface air temperature variability in winter over the NA sector, which features a dipole pattern with opposite temperature anomalies centered over the Chukchi–Bering Seas and the North American Great Plains. A negative phase of the warm Arctic–cold Eurasia, or WACE, pattern tends to lead a positive phase of the WACNA pattern by about 25 days. Conversely, A positive phase of the WACE pattern tends to lead a negative phase of the WACNA pattern by about 25 days. In association with the positive WACNA pattern, large-scale atmospheric circulation anomalies are dominated by a negative extratropical ABNA-like and a negative tropical–extratropical related PNA-like teleconnections, which appear upstream and precede a positive WACNA pattern with the strongest relationship when both circulation patterns lead the WACNA by 5 days. The circulation anomalies exhibit an equivalent barotropic structure in the troposphere. The negative ABNA- and PNA-like patterns also bring SLP anomalies with a horseshoe-shaped high straddling over Gulf of Alaska, Alaska, and northwestern Canada. The anomalous surface temperature advections that follow the circulation anomalies over the NA sector, as well as the sea ice anomalies over the Chukchi–Bering Seas and Hudson Bay, lead to the formation of the WACNA pattern (Fig. 11).
The driving force of the WACNA-associated large-scale circulation anomalies involves snow anomalies over southern Siberia as well as SST and deep convection anomalies in the tropical Pacific (Fig. 11). Specifically, a negative WACE pattern is accompanied by Eurasian warming and snow declines over southern Siberia, which play as a forcing source of the WACNA pattern. The downstream influence of the Siberian snow anomaly tends to be organized and achieved by an ABNA-like pattern over the NA sector. In particular, about 3 weeks after the Siberian snow anomaly, Φ500 is dominated by a zonally oriented wave train propagating from the North Pacific, across North America toward the North Atlantic. This likely explains the 25-day lagged association of WACNA with WACE. The circulation and SAT anomalies following the Siberian snow anomaly account for about 1/4 to 1/3 times the corresponding WACNA associated anomalies over the NA sector. In addition, the WACNA-associated circulation anomalies over the NA sector can also be influenced by SST and OLR anomalies in the tropical central-eastern Pacific, resembling the tropical ENSO variability and its associated PNA pattern impacts as demonstrated in numerous previous studies.
Biases exist in reanalyses, especially those in the Arctic (e.g., Wang et al. 2019). However, the relationship between the WACE and WACNA patterns as well as the evolution of the WACNA pattern can also be seen in the NCEP–DOE Reanalysis 2 (Kanamitsu et al. 2002) data (not shown), which are generated from a different climate center. Nevertheless, the low correlation between the Siberian snow anomaly and ABNA/WACNA indicates that the intercontinental teleconnection may also be initiated via other mechanisms. In addition, this study examines the internal climate variability in association with the WACC pattern over the NA sector. The established features of anthropogenic global warming, forced by greenhouse gas increase and other external forcing, involve Arctic/polar amplification and ENSO-like SST anomalies in the tropical Pacific (e.g., IPCC 2013). Apparently, the anthropogenic climate change would influence the internal WACC pattern, including the WACE and WACNA patterns and their relationship. The relative role of external forcing and internal variability on the WACC pattern is an interesting issue that waits to be investigated.
Acknowledgments.
We thank colleague Dr. Kian Abbasnezhadi for helpful comments on an early version of the paper, along with Jennifer Yu for assistance in producing the schematic diagram. We thank three anonymous reviewers and the editor (Dr. Isla Ruth Simpson) for their constructive suggestions and comments, which helped to improve the study.
Data availability statement.
Data and analysis methods used in this study are described in section 2.
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