Multidecadal Variations in the East Asian Winter Monsoon and Their Relationship with the Atlantic Multidecadal Oscillation since 1850

Jiapeng Miao aInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Dabang Jiang aInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
cCAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China

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Abstract

This study investigates the characteristics and physical mechanisms of the multidecadal variations in the East Asian winter (December–February) monsoon (EAWM) since 1850 based on multiple observational and reanalysis datasets. The results indicate that the EAWM undergoes multidecadal weakening during the periods of 1869–1919 and 1986–2004 but strengthening during the period of 1920–85. Similar evolutions can be observed in the time series of the area-averaged winter surface air temperature over East Asia. Associated with the EAWM multidecadal variations, a quasi-barotropic Rossby wave train originating from the subtropical North Atlantic propagating across the Eurasian continent to Northeast Asia also experiences phase shifting at the same time. In its positive phase, the low-level anticyclonic anomaly over the northern Eurasian continent causes a stronger Siberian high; the mid- and high-level cyclonic anomalies over Northeast Asia deepen the East Asian trough and strengthen the East Asian jet stream, respectively. Thus, the positive phase of the wave train is conducive to stronger EAWMs and vice versa. The diagnostic analysis of the Rossby wave source indicates that the upper-tropospheric divergence anomalies over the North Atlantic can favor the excitation of this wave train, and the feedback forcing of high-frequency eddies plays important roles in its maintenance. In addition, the phase shifting of the Atlantic multidecadal oscillation (AMO) can induce a similar Rossby wave train across the Eurasian continent, through which it further modulates the multidecadal variations in the EAWM. Warm phases of the AMO are favorable for a stronger EAWM and colder midlatitude Eurasian continent and vice versa.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Jiapeng Miao, miaojiapeng@mail.iap.ac.cn; Dabang Jiang, jiangdb@mail.iap.ac.cn

Abstract

This study investigates the characteristics and physical mechanisms of the multidecadal variations in the East Asian winter (December–February) monsoon (EAWM) since 1850 based on multiple observational and reanalysis datasets. The results indicate that the EAWM undergoes multidecadal weakening during the periods of 1869–1919 and 1986–2004 but strengthening during the period of 1920–85. Similar evolutions can be observed in the time series of the area-averaged winter surface air temperature over East Asia. Associated with the EAWM multidecadal variations, a quasi-barotropic Rossby wave train originating from the subtropical North Atlantic propagating across the Eurasian continent to Northeast Asia also experiences phase shifting at the same time. In its positive phase, the low-level anticyclonic anomaly over the northern Eurasian continent causes a stronger Siberian high; the mid- and high-level cyclonic anomalies over Northeast Asia deepen the East Asian trough and strengthen the East Asian jet stream, respectively. Thus, the positive phase of the wave train is conducive to stronger EAWMs and vice versa. The diagnostic analysis of the Rossby wave source indicates that the upper-tropospheric divergence anomalies over the North Atlantic can favor the excitation of this wave train, and the feedback forcing of high-frequency eddies plays important roles in its maintenance. In addition, the phase shifting of the Atlantic multidecadal oscillation (AMO) can induce a similar Rossby wave train across the Eurasian continent, through which it further modulates the multidecadal variations in the EAWM. Warm phases of the AMO are favorable for a stronger EAWM and colder midlatitude Eurasian continent and vice versa.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Jiapeng Miao, miaojiapeng@mail.iap.ac.cn; Dabang Jiang, jiangdb@mail.iap.ac.cn

1. Introduction

The East Asian winter (December–February) monsoon (EAWM) is a complex three-dimensional system over East Asia involving the Siberian high, the Aleutian low, the northerly winds along the East Asian coast in the lower troposphere, the East Asian trough (EAT) in the middle troposphere, and the East Asian jet stream (EAJS) in the upper troposphere (Chen and Sun 1999; Jhun and Lee 2004). The EAWM exhibits obvious interannual variations, and a strong EAWM is conducive to cold-air outbreaks and intense snowfall over East Asia (Guo 1994; Gu et al. 2008; Zhou 2011). An anomalous EAWM can modulate the East Asian summer monsoon through oceanic processes and Eurasian snow anomalies (Chen et al. 2000; Lu et al. 2020). In addition, the EAWM also impacts remote climate systems, such as the North Pacific storm track activity (Lee et al. 2010), the Australian summer monsoon (Zhang and Zhang 2010), and even the North American surface air temperature (SAT) via a modulation on the Rossby wave trains related to El Niño–Southern Oscillation (Ma et al. 2018). At the interdecadal time scale, as presented in many studies, the EAWM began to weaken after the mid-1980s (e.g., Wang and Fan 2013; Ding et al. 2015; Miao et al. 2018), favoring more snowfall over Northeast China (Wang and He 2013) and more haze days in eastern China (Wang and Chen 2016). In the mid-2000s, the EAWM system, especially the Siberian high, turned to strengthen, leading to more colder than normal winters over northern East Asia (Wang and Chen 2014a; Miao and Wang 2020). Altogether, the anomalous EAWM has great impacts on the East Asian and even remote regions’ climates. It is thus essential to understand the characteristics and physical mechanisms of EAWM variations at various time scales.

Previous studies have explored the physical mechanisms for the EAWM interdecadal weakening in the mid-1980s from various perspectives. In terms of atmospheric internal dynamics, the propagation of quasi-stationary planetary waves to the stratosphere along the polar waveguide became weaker, the propagation to the upper troposphere along the low-latitude waveguide became stronger, and the wave amplitude at approximately 45°N was weakened after the mid-1980s, all of which contributed to the interdecadal weakening of the EAWM (L. Wang et al. 2009). Observational studies also show that the strengthening of the Arctic Oscillation after the mid-1980s can give rise to EAWM weakening (He and Wang 2012, 2013), which is further supported by analysis based on multiple coupled climate models (Miao et al. 2020). From the perspective of oceanic causes, the sea surface temperature (SST) over the southwestern North Pacific shifted to a warm phase around the mid-1980s, playing a vital role in the EAWM weakening through changing air–sea interactions (Sun et al. 2016). In addition to internal factors, external forcings were also suggested to play important roles in the interdecadal weakening of EAWM (Miao et al. 2018); the atmospheric greenhouse gas changes weakened the EAT, and the natural external forcings reduced the meridional shear of the EAJS, both of which acted to weaken the Siberian high. However, the aforementioned studies have mainly focused on the EAWM interdecadal changes after 1950, when reanalysis datasets and observations commonly started. The interdecadal–multidecadal variations in the EAWM over the entire twentieth century or even earlier period have received little attention due to the limitations in data availability.

Few studies have extended the analysis of EAWM variations to the early twentieth century using long-observed gridded sea level pressure (SLP) datasets. More specifically, Shi et al. (1996) analyzed the EAWM changes during 1873–1989 using the EAWM index reflecting the land–sea zonal SLP gradient based on previous monthly SLP data from the U.K. Met Office and noted that the EAWM shows abrupt strengthening in the late 1950s and abrupt weakening in the early 1980s. Meanwhile, the EAWM has exhibited quasi-biennial, low-frequency (3–7 years) and interdecadal (above 10 years) oscillations in the past century (Xu et al. 1999). It should be noted that these studies were conducted by using earlier generations of data. In addition, the atmospheric and oceanic processes responsible for the EAWM variations during the twentieth century are not clear. Thus, in this study, we investigate the characteristics and possible drivers of EAWM variations by utilizing multiple EAWM indices and datasets.

The remainder of this study is organized as follows. Section 2 gives a description of the data and method used. In section 3, we illustrate the EAWM variations over the past century and the associated atmospheric and oceanic processes. The conclusions and discussion are presented in section 4.

2. Data and method

The Hadley Centre gridded SLP dataset (HadSLP2r) is adopted to calculate the EAWM index from 1850 to the present (Allan and Ansell 2006). Two reanalysis products with sufficient length are also used to verify the EAWM index and to investigate the associated large-scale atmospheric circulations, namely, the ECMWF Twentieth Century Reanalysis (ERA-20C) covering the period of 1900–2010 (Poli et al. 2016) and the NOAA–CIRES Twentieth Century Reanalysis version 2c (20CRv2c) available from 1851 to 2014 (Compo et al. 2011). Meanwhile, the three latest near-term reanalysis datasets assimilating more observations are compared to these results: JRA-55 from 1958 to present (Kobayashi et al. 2015), NCEP–DOE from 1979 to the present (Kanamitsu et al. 2002), and ERA-Interim from 1979 to the present (Dee et al. 2011). In addition, two gridded SAT datasets covering the entire twentieth century are also used to illustrate the time-evolving East Asian winter SAT, which reflects the EAWM intensity from a different perspective. They are HadCRUT5, available from 1850 to the present (Morice et al. 2021), and GISTEMP, available from 1880 to the present (Hansen et al. 2010). A set of homogenized monthly SAT series at 28 stations in China back to the nineteenth century are also adopted for validation (Cao et al. 2017; Li et al. 2018), and the number of stations increases from 1 in 1873 to 28 in 1924 (Fig. S1a in the online supplemental material). Furthermore, three SST datasets are used to investigate the roles of SST variability in the EAWM variations, including the NOAA’s Extended Reconstruction SST version 3b (ERSSTV3b) from 1854 to the present (Smith et al. 2008), the Hadley Centre’s Sea Ice and Sea Surface Temperature (HadISST) from 1870 to the present (Rayner et al. 2003), and the Centennial in situ Observation-Based Estimates (COBE) SST version 2 from 1850 to the present (Hirahara et al. 2014).

In this study, we choose the SLP-based EAWM index due to the better quality and availability of SLP than the winds or geopotential height in the early decades of the twentieth century (see Table S1 in the online supplemental material). The first type reflects east–west pressure gradients over Asia-Pacific regions. Guo (1994) defined an EAWM index as the sum of zonal SLP differences (110° minus 160°E) over 10°–60°N. Shi et al. (1996) followed this method and further normalized the SLP before the calculation to reduce the impacts of variance differences at each latitude. Chan and Li (2004) defined the EAWM index in more direct ways as the regionally averaged SLP difference between the East Asian continent and the North Pacific. The second type considers land–sea pressure gradients in both east–west and north–south directions (Wang and Chen 2014b). The winter season in this study presents 3-month averages; for example, the winter of 2018 refers to the means of December 2018, January 2019, and February 2019.

Pearson’s linear correlation was used to quantify the relationships between the EAWM indices and the East Asian SAT, the statistical significance of which was determined by a two-sided t test. Epoch difference analysis is also adopted to address the atmospheric circulation anomalies for strong and weak EAWM regimes, with the statistical significance being calculated using the two-sided t test. In addition, regression analysis is performed to illustrate the atmospheric processes and SST variability associated with the EAWM variations, with all related variables being detrended and 21-yr low-pass Lanczos filtered to emphasize the multidecadal component. The effective degree of freedom is then calculated as
Ne=No1+2i=110aibi,
where No is the sample number, and ai and bi represent autocorrelations at i lags of two series, respectively (Quenouille 1952).
The wave activity flux presented by Takaya and Nakamura (2001) is adopted to diagnose the propagation of Rossby waves, that is,
Fx=pcosϕ2|U|{Ua2cos2ϕ[(ψλ)2ψ2ψλ2]+Va2cosϕ[ψλψϕψ2ψλϕ]},
Fy=pcosϕ2|U|{Ua2cosϕ[ψλψϕψ2ψλϕ]+Va2[(ψϕ)2ψ2ψϕ2]},
where p = pressure (1000 hPa)−1, U = (U, V, 0)T is the basic flow, ϕ is latitude, λ is longitude, a is Earth’s radius, and ψ is the geostrophic streamfunction.
The Rossby wave source (RWS) is defined as
S=(Vχζa)=ζaVχVχζa,
where ζa is the absolute vorticity and Vχ is the divergent wind (Sardeshmukh and Hoskins 1988). The first term on the right side is associated with vortex stretching, and the second term denotes the advection of absolute vorticity by divergent flow.

The Atlantic multidecadal oscillation (AMO) index is used to address the relationship between the AMO and EAWM. It is defined following Trenberth and Shea (2006) as SST anomalies averaged over the North Atlantic (0°–60°N, 80°W–0°) relative to 1901–70 (roughly one full cycle) with global (60°S–60°N) mean SST being removed. In addition, the Decadal Climate Prediction Project Component C (DCPP-C) in the framework of phase 6 of the Coupled Model Intercomparison Project (CMIP6) released a set of idealized pacemaker experiments to investigate the roles of North Atlantic SSTs in driving global or regional climate variations (Boer et al. 2016; Eyring et al. 2016). Specifically, two large ensembles of 10-yr-long simulations were carried out by each model, in which positive Atlantic multidecadal variability (AMV+) and negative AMV (AMV−) anomaly patterns were superimposed on model climatology over the North Atlantic, and the atmosphere and ocean were fully coupled and free to evolve elsewhere. Thus, the AMV-forced signals are illustrated as ensemble mean differences between the AMV+ and AMV− experiments. It should be noted that the AMO index used in our study is slightly different from the AMV index in the DCPP-C project defined by Ting et al. (2009). Nevertheless, the associated anomalous SST patterns over the North Atlantic are very similar.

3. Results

a. Observed multidecadal variation of the EAWM

Many previous studies have tried to quantify the strength of the EAWM and its variability with an appropriate index (e.g., He and Wang 2012), which is usually classified into four categories: the land–sea SLP contrast, the low-level wind fields, the EAT, and the EAJS. In this study, we select SLP-based EAWM indices due to SLP’s better availability and quality before the 1950s. The SLP-based indices take into account only the east–west SLP gradient or both the east–west and north–south SLP gradients (see section 2). In comparison, these EAWM indices show different relationships with the SAT over East Asia (Fig. 1). The EAWM index defined by Wang and Chen (2014b) exhibits a stronger correlation than the others, and its correlation coefficient with the East Asian averaged SAT reaches −0.75 (significant at the 99% level). When this EAWM index is positive, negative SAT anomalies occur over the Siberian region, eastern China, the Korean Peninsula, Japan, and the surrounding oceans. The cooling is strongest over the Siberian region, North China, and the Korean Peninsula, where the correlation coefficient is even lower than −0.6. In comparison, the correlation coefficients between SAT averaged over East Asia and the EAWM index defined by Guo (1994), Shi et al. (1996), and Chan and Li (2004) are −0.32, −0.50, and −0.56, respectively. In detail, the former index captures the SAT variations mainly over the northern Eurasian continent, while the latter two indices reflect the SAT variations over mid- and low-latitude East Asian regions. Therefore, we choose the Wang and Chen (2014b) index in the subsequent analysis.

Fig. 1.
Fig. 1.

Correlation maps between the detrended SLP-based EAWM indices and winter SAT during the period of 1950–2018. The correlation coefficients between the detrended EAWM indices and East Asian (20°–50°N, 100°–145°E; black box) averaged winter SAT are illustrated in the top right of each panel. The SLP and SAT are from the HadSLP2r and HadCRUT5 datasets, respectively. Areas with significant values exceeding 95% confidence level are dotted.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

As shown in Fig. 2, the EAWM index exhibits both interannual and decadal–multidecadal variations during 1850–2018. Meanwhile, there exists a significant positive trend during this period, which is 0.4 (100 yr)−1 (at the 90% confidence level). The results from the latest reanalysis datasets (i.e., JRA-55, NCEP–DOE, and ERA-Interim) are consistent with those from HadSLP2r in recent decades. This means that the EAWM robustly weakens around the mid-1980s, which has been widely investigated in previous studies. In this study, we mainly focus on the multidecadal variations in the EAWM, and the linear trend is thus removed. The detrended low-pass filtered EAWM index is below normal during 1869–1919 and 1986–2004 and above normal during 1920–85. In other words, the EAWM begins to strengthen around 1920 onward and turns to weaken around 1986 at the multidecadal time scale. Both the observational dataset (i.e., HadSLP2r) and reanalysis datasets (i.e., 20CRv2c and ERA-20C) support this phenomenon. In addition, considering that a stronger EAWM usually favors a colder East Asia and vice versa and that observational SAT data are available for a long time, we also examine the variations in detrended SAT averaged over East Asia (20°–50°N, 100°–145°E). The East Asian SAT exhibits multidecadal variations similar to those of the EAWM. It shifts from warmer to colder conditions around 1920 in the GISTEMP dataset and slightly earlier in the HadCRUT5 dataset. Then, warmer conditions again occurred after 1986 in both gridded datasets. The correlation coefficients between the detrended low-pass filtered EAWM index from HadSLP2r and the averaged SAT over East Asia from HadCRUT5 and GISTEMP are −0.42 (p = 0.1) and −0.63 (p = 0.03), respectively. At the same time, the results derived from station observation in China also show similar characteristics (Fig. S1b).

Fig. 2.
Fig. 2.

(top) The EAWM index (bars) and its multidecadal component (black line) during the period of 1850–2018 based on the HadSLP2r dataset, with results from other three reanalysis datasets (i.e., JRA-55, NCEP-DOE, ERA-Interim) overlaid. The linear trend is plotted as a dashed line. (middle) As in the top panel, except that the EAWM index is detrended, and the datasets used are HadSLP2r, 20CRv2c, and ERA-20C. (bottom) Detrended time series of East Asian (20°–50°N, 100°–145°E) averaged winter SAT anomalies (bars; units: °C) and its multidecadal component (black line) based on the HadCRUT5 dataset, with the result from GISTEMP overlaid (red line). All three plots share the same baseline period of 1981–2010.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

Given the complexity of the EAWM system, we further examine the associated changes in the EAWM-related subsystems from the lower to upper troposphere. Figures 3a and 3b show the epoch difference maps of winter SLP for strong-minus-weak EAWM regimes: 1920–85 minus 1869–1919, and 1920–85 minus 1986–2004. As expected, positive SLP anomalies are observed over the Eurasian continent north of 40°N in both maps, indicating a strengthening of the Siberian high. Note that the SLP increases with larger magnitudes in the recent epoch difference than in the early one, with maximum values exceeding 1 hPa over the northern Eurasian continent (Fig. 3c). In addition, negative SLP values are clearly seen over South China, the northwestern North Pacific, the Maritime Continent, and the tropical Indian Ocean in the recent epoch difference, which further increases the land–sea SLP contrast and thus induces a stronger EAWM (Figs. 3b,c). Correspondingly, northeasterly and northerly wind anomalies cover the mid- and high-latitude Eurasian continent in two epoch differences, with larger magnitudes in the recent epoch than in the early one (Figs. 3d–f). In the middle troposphere, significant negative 500-hPa geopotential height anomalies exist around Northeast China and the Okhotsk Sea in the earlier epoch difference, and around Northeast China and Japan in the recent one (Figs. 3g,h). In contrast, positive values can be seen to the north in both epoch differences. This means that the EAT deepens significantly, which favors a stronger EAWM circulation. In the upper troposphere, the zonal wind increases at the band of 30°–40°N where the EAJS is located but decreases both south and north of the EAJS (Figs. 3j,k). Thus, anomalous cyclonic vorticity is enhanced north of the EAJS, conducive to the development of the EAT and cold-air outbreaks over East Asia. In comparison, the EAT and the EAJS strengthen with larger magnitudes in the recent epoch (Figs. 3i,l). On the whole, based on the 20CRv2c dataset, we find that all members of the EAWM system experienced multidecadal changes around 1920 and 1986, with the recent changes being more intense than the early ones. Actually, the results from the ERA-20C dataset are similar (Fig. S2). Northerly wind anomalies can also be observed over the northern Eurasian continent, and both the EAT and EAJS are strengthened during 1920–85 relative to the two weak regimes. At the same time, although different analysis periods are applied due to temporal length discrepancy between two reanalysis datasets, the results are still similar even under the constraint of shared analysis periods (figure not shown), and this conclusion is suitable for the following analysis using both 20CRv2c and ERA-20C.

Fig. 3.
Fig. 3.

(left) Epoch difference maps of winter sea level pressure (units: hPa), 850-hPa wind (units: m s−1), 500-hPa geopotential height (units: m), and 300-hPa zonal wind (units: m s−1) between periods of strong (i.e., 1920–85; P2) and weak (i.e., 1869–1919; P1) EAWM. (center) As in the left column, but between the strong period (i.e., 1920–85; P2) and another weak period (i.e., 1986–2004; P3) EAWM. (right) Difference maps between the center and left column. All data are detrended before the composite analysis. Areas with significant values exceeding the 95% confidence level are dotted or shaded in gray. The sea level pressure is from the HadSLP2r dataset, while others are from the 20CRv2c dataset.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

As discussed above, a stronger EAWM is usually associated with a colder than normal East Asia. Figure 4 illustrates the epoch difference maps of SAT for strong-minus-weak EAWM regimes. In the earlier epoch difference, significant negative SAT anomalies appear over the Eurasian continent in the HadCRUT5 dataset (Fig. 4a). In the GISTEMP dataset, the cooling covers most of the Eurasian continent and surrounding oceans, with maximum values above 0.6°C over the Siberian region and Northeast Asia (Fig. 4d). In the recent epoch difference, the cooling is stronger in both the HadCRUT5 and GISTEMP datasets (Figs. 4b,e). Specifically, the SAT decreases by more than 0.9°C in the band of 40°–60°N over the Eurasian continent and is even larger than 1.5°C over the Siberian region. Meanwhile, the SAT decreases by 0.3°–0.6°C over East Asia. Logically, the stronger cooling in the recent epoch difference is consistent with the greater EAWM strengthening relative to the early one (Figs. 4c,f). Similar results hold in two reanalysis datasets (i.e., ERA-20C and 20CRv2c) (Fig. S3).

Fig. 4.
Fig. 4.

(left) Epoch difference maps of winter SAT (units: °C) between periods of strong (i.e., 1920–85; P2) and weak (1869–1919 for HadCRUT5; 1880–1919 for GISTEMP; P1) EAWM from the HadCRUT5 and GISTEMP datasets. (center) As in the left column, but between the strong period (i.e., 1920–85; P2) and a different weak period (i.e., 1986–2004; P3) EAWM. (right) Difference maps between the center and left column. All data are detrended before the composite analysis. Areas with significant values exceeding 95% confidence level are dotted.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

Overall, the EAWM system and the relevant East Asian SAT experienced obvious multidecadal variations, which shifted from weak to strong regimes around 1920 and weakened again after 1986. The question is how atmospheric process and SST variability contribute to the multidecadal variations in the EAWM. These issues are addressed in the following sections.

b. Associated large-scale atmospheric circulation anomalies

Figure 5a shows the regression map of winter 300-hPa geopotential height (Z300) on the EAWM index during 1851–2013 using the 21-yr low-pass filtered detrended data to emphasize the multidecadal component. In general, when the EAWM index is higher, the Z300 anomalies appear in zonal belts, with negative ones mainly along 45°N and positive north of 60°N, partly resembling the Northern Hemisphere annular mode except over the Pacific. At the same time, positive Z300 anomalies are present to the west of Sahara and around the Barents–Kara Seas, while negative anomalies are evident over the Mediterranean and northeastern Asia. The positive–negative–positive–negative anomalies highly resemble a Rossby wave pattern stretching across the Eurasian continent. For the wind fields, the alternating occurrence of anomalous anticyclones and cyclones also illustrates a downstream propagation of the Rossby wave train originating from the North Atlantic (Fig. 6a). To confirm this, we further calculate the horizontal wave activity flux anomalies based on the regressed Z300 on the EAWM index, which is a useful diagnostic tool for illustrating stationary wave propagation (Takaya and Nakamura 2001). As shown in Fig. 6b, the Rossby wave originating from the subtropical North Atlantic (especially for that to the west of Sahara) splits into two branches. One propagates northeastward to the Ural Mountains and then turns southeastward to Northeast Asia. This is consistent with the results derived from Z300 and wind field anomalies clarified above, referred to as the extratropical Eurasian teleconnection (ETET) pattern in this study. The other moves eastward along 30°N, where the jet stream is located, to South China. However, this branch cannot be obviously observed in the ERA-20C dataset (Figs. S4a and S5). In addition, there exists a wave pattern originating from South China to North America in the 20CRv2c dataset (Figs. 5a and 6), while the corresponding wave train is not clear in the ERA-20C dataset (Fig. S5). Given all that, only the ETET pattern can be observed robustly in both the 20CRv2c and ERA-20C datasets. To depict this pattern, we define a teleconnection index by the area-averaged Z300 difference between positive centers [i.e., to the west of Sahara (20°–35°N, 30°–10°W) and the northern Ural Mountains (60°–80°N, 50°–90°E)] and negative centers [i.e., the Mediterranean (40°–60°N, 10°W–30°E) and northeastern Asia (35°–50°N, 110°–140°E)]. As illustrated in Fig. 7, the normalized ETET index shows multidecadal variations during 1851–2013 in both the 20CRv2c and ERA-20C datasets. It fluctuates in phase with the EAWM index, which also undergoes negative phases during 1869–1919 and 1986–2004 but positive during 1920–85. This means that the ETET pattern plays vital roles in the multidecadal variations of the EAWM. Actually, this wave train exhibits a quasi-barotropic structure, obviously seen in the upper to lower tropospheric geopotential height anomalies (Fig. 5). Upper-level cyclonic anomalies over northeastern Asia lead to a stronger EAJS, and midlevel anomalies favor a deeper EAT. Meanwhile, anticyclonic anomalies over the northern Eurasian continent at lower levels are conducive to the development of the Siberian high. Thus, all members of the EAWM system are strengthened at the positive phase of the ETET pattern. The subsequent question is how the ETET wave train is excited.

Fig. 5.
Fig. 5.

Regression maps of winter (a) 300-, (b) 500-, and (c) 850-hPa geopotential height (units: m) on the EAWM index during the period of 1851–2013. Both the geopotential height and the EAWM index are detrended and 21-yr low-pass filtered. The geopotential height is from the 20CRv2c dataset, while the EAWM index is calculated based on the HadSLP2r dataset. Areas with significant values exceeding 95% confidence level are dotted.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

Fig. 6.
Fig. 6.

Regression maps of winter (a) 300-hPa wind fields (units: m s−1) and (b) 300-hPa quasigeostrophic streamfunction (contours; units: 105 m2 s−1) and wave activity flux (vectors; units: m2 s−2) on the EAWM index during the period of 1851–2013. Blue and red lines in (b) denote negative and positive values, respectively, and the interval is 105 m2 s−1. Letters A and C in (a) represent anticyclone and cyclone, respectively. All variables and the EAWM index are detrended and 21-yr low-pass filtered. The variables are from the 20CRv2c dataset, while the EAWM index is calculated based on the HadSLP2r dataset. Areas with significant values exceeding 95% confidence level are gray shaded.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

Fig. 7.
Fig. 7.

Detrended time series of the ETET index (bars) and its multidecadal component (black line) during the period of 1851–2013 based on the 20CRv2c dataset, with results from the ERA-20C dataset overlaid (green line). The reference period is 1981–2010.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

Considering that the ETET pattern emanates from the North Atlantic, we further examine the RWS anomalies associated with the EAWM there. As shown in Fig. 8a, significant negative RWS anomalies are located in the midlatitude North Atlantic, while positive anomalies are located over the northeastern and southeastern North Atlantic in the 20CRv2c dataset. Similar results can be observed in the ERA-20C dataset, although the negative anomalies shift southeastward (Fig. S6a). The RWS distributions are consistent with the results in Martineau et al. (2020), which mainly focus on the modes of atmospheric variability (i.e., North Atlantic Oscillation). As presented in Sardeshmukh and Hoskins (1988), the RWS is composed of two terms (i.e., vortex stretching and the advection of absolute vorticity by divergent flow). For both reanalysis datasets, vortex stretching plays a dominant role in the RWS anomalies (Fig. 8b; see also Fig. S6b). In other words, the upper-tropospheric divergence anomalies over the North Atlantic induce RWSs, which further force Rossby wave trains downstream. The anomalous divergence could be triggered by the precipitation anomalies over the North Atlantic, which can be obviously observed in both 20CRv2c and ERA-20C datasets (figure not shown). In addition, the feedback forcing of high-frequency eddies plays important roles in the maintenance of the Rossby wave train (Peng et al. 2003; Msadek et al. 2011; Peings and Magnusdottir 2014; Martineau et al. 2020). There are also RWSs over the Eurasian continent contributing to maintaining the wave train, but these RWSs could be associated with vertical motions produced by the propagating wave train and not necessarily indicate a true wave source (figure not shown).

Fig. 8.
Fig. 8.

Regression maps of winter 300-hPa (a) Rossby wave source (units: 10−11 s−2), (b) vortex stretching (units: 10−11 s−2), and (c) advection of absolute vorticity by divergent flow (units: 10−11 s−2) on the EAWM index during the period of 1851–2013. All variables and the EAWM index are detrended and 21-yr low-pass filtered. The variables are from the 20CRv2c dataset, while the EAWM index is calculated based on the HadSLP2r dataset. Areas with significant values exceeding 95% confidence level are dotted.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

c. Roles of the AMO

As presented in earlier studies, the SST over the North Atlantic undergoes low-frequency variations at the multidecadal time scale, widely known as the AMO. Whether the AMO affects the multidecadal variations in the extratropical Eurasian teleconnection (ETET) pattern and in turn the EAWM is further investigated in this section.

Figure 9 shows the regression maps of winter SST on the EAWM index with both being detrended and 21-yr low-pass filtered. When the EAWM is stronger, positive SST anomalies exist over almost the entire North Atlantic, with the largest magnitudes south of Greenland. The anomalous SST pattern highly resembles the warm phase of the AMO, which is illustrated in previous studies (e.g., Trenberth and Shea 2006; Deser et al. 2010). The AMO index is thus calculated to quantitatively demonstrate the relationship between EAWM intensity and AMO. As plotted in Fig. 10, the AMO shifts from cold to warm phases around the mid-1920s and then returns to the cold phase again after the mid-1960s. The correlation coefficients between the low-pass filtered EAWM and AMO indices are 0.50 (p = 0.07), 0.46 (p = 0.11), and 0.40 (p = 0.14) in the ERSSTV3b, HadISST, and COBE datasets, respectively. This means that the AMO could have significant impacts on the EAWM at the multidecadal time scale.

Fig. 9.
Fig. 9.

Regression maps of winter SST (units: °C) on the EAWM index during the periods of (a) 1870–2018, (b) 1854–2018, and (c) 1850–2018 from the (a) HadISST, (b) ERSSTV3b, and (c) COBE datasets. The SST and the EAWM index are detrended and 21-yr low-pass filtered. The EAWM index is calculated based on the HadSLP2r dataset. Areas with significant values exceeding 95% confidence level are dotted.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

Fig. 10.
Fig. 10.

Time series of the EAWM index (red line) and the winter AMO index (blue line) during the period of 1850–2018. The EAWM index is detrended, and both the EAWM and AMO indices are 21-yr low-pass filtered. The EAWM index is calculated based on the HadSLP2r dataset, while the AMO index is calculated based on the ERSSTV3b, HadISST, and COBE datasets.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

We further illustrate the regression maps of large-scale atmospheric circulations on the AMO index to address how the AMO affects the EAWM. As shown in Figs. 11a and 11b, during the warm phase of the AMO positive Z300 anomalies and anticyclonic circulation can be seen to the west of Sahara and around the Ural Mountains, while anomalous negative Z300 and cyclones are located over the Mediterranean and Northeast Asia in the 20CRv2c dataset. Similar results hold in ERA-20C, although the confidence level is lower (Fig. S7). Furthermore, the horizontal wave activity flux also demonstrates a wave train from the subtropical North Atlantic to northeastern Asia (Fig. 11c). This wave pattern highly resembles the ETET pattern controlling the multidecadal variations in the EAWM discussed above. This means that the phase shifting of the AMO contributes to the variations in the ETET pattern. The correlation coefficients between the low-pass filtered ETET and AMO indices are 0.71 (p = 0.01), 0.62 (p = 0.05), and 0.61 (p = 0.03) in the ERSSTV3b, HadISST, and COBE datasets, respectively (Fig. S8). At the same time, there also exists a Rossby wave originating from South China through the North Pacific to western North America in the 20CRv2c dataset but cannot be obviously seen in the ERA-20C dataset (Fig. 11c and Fig. S7). After all that, the AMO has significant impacts on the ETET pattern in both the 20CRv2c and ERA-20C datasets.

Fig. 11.
Fig. 11.

Regression maps of winter 300-hPa (a) geopotential height (units: m °C−1), (b) wind fields (units: m s−1 °C−1), and (c) quasigeostrophic streamfunction (contours; units: 105 m2 s−1 °C−1) and wave activity flux (vectors; units: m2 s−2 °C−1) on the AMO index during the period of 1870–2013. Letters A and C in (b) represent anticyclone and cyclone, respectively. Blue and red lines in (c) denote negative and positive values respectively, and the interval is 105 m2 s−1. All variables are detrended and 21-yr low-pass filtered, and the AMO index is 21-yr low-pass filtered. The variables are from the 20CRv2c dataset, while the AMO index is calculated based on the HadISST dataset. Areas with significant values exceeding 95% confidence level are dotted or shaded in gray.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

As expected, the AMO phase shifting-induced ETET pattern further modulates the EAWM subsystems. Figure 12 shows the regression maps of the East Asian atmospheric circulations on the AMO index with both being 21-yr low-pass filtered first. When the AMO lies in warm phases, positive and negative SLP anomalies appear north and south of 40°N, respectively, over Asian–Pacific regions. The Siberian high is thus strengthened significantly, and the land–sea pressure contrast increases, both of which favor a stronger EAWM (Fig. 12a). Under this condition, anticyclonic circulation anomalies are located over the northern Eurasian continent, and northeasterly and northerly winds are obviously seen over eastern Asia (Fig. 12b). Meanwhile, the EAT gets deeper and the EAJS gets stronger under the warmer AMO phase, which are also favorable to EAWM strengthening (Figs. 12c,d). The results from ERA-20C are similar to those from 20CRv2c (Fig. S9). Therefore, a warmer phase of the AMO is conducive to a stronger EAWM circulation at the multidecadal time scale, and vice versa. As a result, colder conditions are observed over the midlatitude Eurasian continent when the AMO is in warm phases in both the 20CRv2c and ERA-20C reanalysis datasets (Figs. 13a,b). In comparison, the SAT decreases with larger magnitudes in 20CRv2c, with values mostly larger than 3.2°C °C−1 and ranging from 1.6° to 3.2°C °C−1 in ERA-20C. Additionally, the cooling is even larger than 4.8°C °C−1 over Northeast Asia in 20CRv2c. Nevertheless, the two reanalysis datasets both favor a colder Eurasian continent at midlatitudes under a warmer AMO phase. To confirm this, we also illustrate the epoch difference maps of winter SAT between warm and cold AMO phases in the HadCRUT5 and GISTEMP datasets, which are not suitable for regression analysis due to missing values in early periods. It is found that there also exists a midlatitude cooling phenomenon over the Eurasian continent in these two gridded datasets, with maximum centers shifting westward relative to the reanalysis data (Figs. 13c,d). Actually, station observation data over China also support the cooling condition, especially over midlatitude East China (Fig. S10). Overall, the warm phase of the AMO gives rise to a stronger EAWM and colder midlatitude Eurasian continent, with the ETET pattern acting as an essential bridge.

Fig. 12.
Fig. 12.

Regression maps of winter (a) SLP (units: hPa °C−1), (b) 850-hPa wind fields (units: m s−1 °C−1), (c) 500-hPa geopotential height (units: m °C−1), and (d) 300-hPa zonal wind (units: m s−1 °C−1) on the AMO index during the period of 1870–2013. All variables are detrended and 21-year low-pass filtered, and the AMO index is 21-yr low-pass filtered. The SLP is from the HadSLP2r dataset, while other variables are from the 20CRv2c dataset. The AMO index is calculated based on the HadISST dataset. Areas with significant values exceeding 95% confidence level are dotted or shaded in gray.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

Fig. 13.
Fig. 13.

Regression maps of winter SAT (units: °C °C−1) on the AMO index during the periods of (a) 1870–2013 based on the 20CRv2c dataset and (b) 1900–2009 based on the ERA-20C dataset. The SAT is detrended and 21-yr low-pass filtered, and the AMO index is 21-yr low-pass filtered before the regression analysis. The AMO index is calculated based on the HadISST dataset. Epoch difference maps of winter SAT (units: °C) between periods of warm (i.e., 1930–60) and cold (i.e., 1890–1920 and 1965–95) AMO phases based on the (c) HadCRUT5 and (d) GISTEMP datasets. The SAT is detrended before the composite analysis. Areas with significant values exceeding 95% confidence level are dotted.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

4. Conclusions and discussion

In this study, we investigate the multidecadal variations in the EAWM since 1850 and the associated physical mechanisms using long-term reanalysis and gridded datasets. Considering the better availability and quality of SLP than other elements in earlier decades of the twentieth century, we mainly analyze the SLP-based EAWM index to illustrate the EAWM evolution. At the multidecadal time scale, the EAWM intensity is weaker during 1869–1919 and 1986–2004 but stronger during 1920–85. Similar characteristics hold in the time series of the East Asian averaged SAT, with obvious multidecadal warmer conditions for the EAWM weakening periods and colder conditions for the EAWM strengthening periods. Therefore, the EAWM began to strengthen after 1920 and turned to weaken again after 1986. Correspondingly, the Siberian high and low-level monsoon circulations are strengthened during the EAWM stronger phase relative to both the earlier and recent weaker phases. Meanwhile, the EAT is deeper and the EAJS is stronger during the EAWM stronger phase. In comparison, changes in the EAWM subsystems are at larger magnitudes for the recent than for the earlier phase shifts. Further diagnostic analysis indicates that a Rossby wave train [called the extratropical Eurasian teleconnection (ETET) in this study] originating from the subtropical North Atlantic to Northeast Asia could be responsible for the EAWM variations. Furthermore, the phase shifting of AMO induces an ETET-like pattern and thus contributes to the EAWM variations. This means that the low-frequency variations in the EAWM could partly be predictable due to the influence of the AMO.

As discussed above, the ETET pattern acts as a bridge between the AMO and the EAWM variations. It is thus crucial for climate models trying to perform accurate predictions over East Asia at multidecadal time scales to represent AMO-induced teleconnection patterns. The newly released DCPP-C idealized AMV experiments in the framework of CMIP6 are examined on the impacts of the AMO through teleconnection (Boer et al. 2016; Eyring et al. 2016). Figure 14 shows the ensemble mean differences in winter Z300 departure from the zonal average between the AMV+ and AMV− experiments. The Z300 departure rather than Z300 itself is used here to reduce the thermal expansion effect of troposphere-associated surface warming (Fig. S11). In all four models, a wavelike pattern originates from the North Atlantic to East Asia (Fig. 14). The patterns are similar in the CNRM-CM6-1 and IPSL-CM6A-LR models but different from the other two models. However, none of them resembles the ETET pattern, which means that the models are inadequate in their representation of AMO-induced teleconnections. A recent study carrying out AMO sensitivity experiments using the Community Atmosphere Model version 3.5 also suggests that the AMO-related wave train is hard to capture (Zhou et al. 2020). Thus, the models could not reproduce the strengthened Siberian high during the warm phase of the AMO (Fig. S12). Negative SLP anomalies exist over the Eurasian continent in all four models. Correspondingly, the weakened Siberian high favors warmer conditions over East Asia, opposite to the observed ones (Fig. S13). Further research is needed to better understand the reasons behind the model inadequacy.

Fig. 14.
Fig. 14.

Ensemble-mean differences of winter Z300 zonal departure (units: m) between the AMV+ and AMV− experiments as obtained from four models of the CMIP6 DCPP component C. Areas with significant values exceeding 95% confidence level are dotted.

Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-21-0073.1

Actually, the impacts of the AMO on the East Asian winter climate have been discussed in previous studies (e.g., Li and Bates 2007; Y. Wang et al. 2009). Similarly, they also found a weaker Siberian high and colder conditions north of 40°N over East Asia during the warm phase of the AMO in the observations. Based on the atmospheric general circulation model (AGCM) experiments, however, they suggested that the AMO warm phase favors negative SLP anomalies ranging from midlatitude North Atlantic to midlatitude Eurasia and a weaker Siberian high, and leads to warmer Eurasian troposphere and weaker land–sea thermal contrast. That is, the results from AGCMs suggest a weaker EAWM during the AMO warm phase, in contrast with the observational ones. As discussed above, different models show various teleconnection patterns associated with the AMO, and uncertainties exist in the modeling results. On the other hand, given the limited temporal length of the instrumental record, the observational impacts of the AMO on the EAWM should be further addressed by collecting paleo reconstructions of the AMO and EAWM indices in future work. In addition, partly due to the impacts of the AMO on the background winter climate over East Asia, the relationship between El Niño–Southern Oscillation and EAWM exhibits low-frequency oscillations. Specifically, during the La Niña winters, the EAWM strengthens at the positive phase of the AMO but weakens at the negative one (e.g., Geng et al. 2017; Hao and He 2017).

The mechanisms for recent Eurasian cooling have been widely investigated but are still under debate. Whether Arctic sea ice loss or the atmospheric internal dynamics played an essential role cannot be determined due to disagreements among model simulations and between modeling and observational results (e.g., Ogawa et al. 2018; Blackport et al. 2019; Mori et al. 2019; He et al. 2020; Xu et al. 2021). From other perspectives, Luo et al. (2017) suggested that the phase transition of the AMO can cause recent Eurasian cooling through its impact on temperature and sea ice over the Barents–Kara Seas, which in turn affects the meridional temperature gradient, the westerly winds, and the Ural blocking events. As clarified in our study, the warm phase of the AMO is favorable to colder central Eurasia, while the cold phase is conducive to warmer conditions there. This means that the AMO transition from cold to warm phases leads to Eurasian cooling through the ETET pattern during 1995–2014. This is consistent with the result from Luo et al. (2017) but through different physical mechanisms.

We have also examined the roles of Pacific SST patterns in the EAWM multidecadal variations. Neither the Pacific decadal oscillation nor the interdecadal Pacific oscillation is highly correlated with the EAWM index, with the absolute value of correlation coefficients being no more than 0.15 (Fig. S14). Aside from oceanic processes, anthropogenic forcings (e.g., greenhouse gases, anthropogenic aerosols), natural external forcings (e.g., volcanic eruptions and solar activities) and other internal factors (e.g., Arctic sea ice and Eurasian snow cover) may also modify EAWM at multidecadal time scales. For instance, our previous studies show that greenhouse gases and natural external forcings play important roles in the EAWM weakening after 1986 (Miao et al. 2018). However, whether external forcing agents affect the whole period since 1850 remains unclear. In addition, the winter sea ice concentration averaged over the Barents Sea (70°–80°N, 30°–60°E) also exhibits multidecadal variations similar to the EAWM in both the COBE and HadISST datasets (Fig. S15), although the data in previous decades could have large uncertainties. As such, the possible roles of other factors aside from oceanic factors on the multidecadal variations in the EAWM deserve future investigations.

Acknowledgments

We sincerely thank three anonymous reviewers and the editor for their valuable comments and suggestions. This research is supported by the National Natural Science Foundation of China (41991284) and the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0101). The authors declare that they have no conflict of interest.

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