Revisiting the Northern Mode of East Asian Winter Monsoon Variation and Its Response to Global Warming

Hainan Gong Center for Monsoon System Research, and LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Lin Wang Center for Monsoon System Research, and LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Wen Zhou Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China
City University of Hong Kong Shenzhen Research Institute, Shenzhen, China

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Wen Chen Center for Monsoon System Research, and LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
School of Earth Science, University of the Chinese Academy of Sciences, Beijing, China

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Renguang Wu Center for Monsoon System Research, and LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Lin Liu State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Debashis Nath Center for Monsoon System Research, and LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Marco Y.-T. Leung Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China

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Abstract

This study revisits the northern mode of East Asian winter monsoon (EAWM) variation and investigates its response to global warming based on the ERA dataset and outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. Results show that the observed variation in East Asian surface air temperature (EAT) is tightly coupled with sea level pressure variation in the expanded Siberian high (SH) region during boreal winter. The first singular value decomposition (SVD) mode of the EAT and SH explains 95% of the squared covariance in observations from 1961 to 2005, which actually represents the northern mode of EAWM variation. Meanwhile, the first SVD mode of the EAT and SH is verified to be equivalent to the first empirical orthogonal function mode (EOF1) of the EAT and SH, respectively. Since the leading mode of the temperature variation is significantly influenced by radiative forcing in a rapidly warming climate, for reliable projection of long-term changes in the northern mode of the EAWM, we further employ the EOF1 mode of the SH to represent the northern mode of EAWM variation. The models can well reproduce this coupling between the EAT and SH in historical simulations. Meanwhile, a robust weakening of the northern mode of the EAWM is found in the RCP4.5 scenario, and with stronger warming in the RCP8.5 scenario, the weakening of the EAWM is more pronounced. It is found that the weakening of the northern mode of the EAWM can contribute 6.7% and 9.4% of the warming trend in northern East Asian temperature under the RCP4.5 and RCP8.5 scenarios, respectively.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wen Chen, cw@post.iap.ac.cn

Abstract

This study revisits the northern mode of East Asian winter monsoon (EAWM) variation and investigates its response to global warming based on the ERA dataset and outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. Results show that the observed variation in East Asian surface air temperature (EAT) is tightly coupled with sea level pressure variation in the expanded Siberian high (SH) region during boreal winter. The first singular value decomposition (SVD) mode of the EAT and SH explains 95% of the squared covariance in observations from 1961 to 2005, which actually represents the northern mode of EAWM variation. Meanwhile, the first SVD mode of the EAT and SH is verified to be equivalent to the first empirical orthogonal function mode (EOF1) of the EAT and SH, respectively. Since the leading mode of the temperature variation is significantly influenced by radiative forcing in a rapidly warming climate, for reliable projection of long-term changes in the northern mode of the EAWM, we further employ the EOF1 mode of the SH to represent the northern mode of EAWM variation. The models can well reproduce this coupling between the EAT and SH in historical simulations. Meanwhile, a robust weakening of the northern mode of the EAWM is found in the RCP4.5 scenario, and with stronger warming in the RCP8.5 scenario, the weakening of the EAWM is more pronounced. It is found that the weakening of the northern mode of the EAWM can contribute 6.7% and 9.4% of the warming trend in northern East Asian temperature under the RCP4.5 and RCP8.5 scenarios, respectively.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wen Chen, cw@post.iap.ac.cn

1. Introduction

The East Asian winter monsoon (EAWM) is associated with the most active planetary-scale circulation system during boreal winter, known as the Siberian high (SH). It brings severe cold surges and heavy snowfall over the East Asian region and exerts strong socioeconomic impacts on East Asian countries (e.g., Ding 1994; Chang et al. 2006; Chen et al. 2000, 2005, 2013; Wang et al. 2009a,b; Zhou et al. 2009; Wei et al. 2011; Huang et al. 2012). To depict the variability of the EAWM, many EAWM indices have been proposed in the previous studies (e.g., Guo 1994; Cui and Sun 1999; Chen et al. 2000; Hu et al. 2000; Jhun and Lee 2004; Wang and Jiang 2004; Li and Yang 2010; Wang and Chen 2014). However, it is noted that there are some inconsistencies among these indices, especially for the indices defined based on the different latitudes (Wang and Chen 2010). In fact, the EAWM has a large meridional extent stretching from the high latitudes to the tropics; thus, its features may differ between extratropical and tropical East Asia due to the different circulation patterns that prevail in these two regions (e.g., Wu et al. 2006; Chen and Li 2007; Wang et al. 2010; Sohn et al. 2011; Cheung et al. 2012; Chen et al. 2014a,b; Jia et al. 2014, 2015; Leung et al. 2015; Park and Ahn 2016; Oh et al. 2017; Ma et al. 2018). Based on this consideration, recent studies have revealed that the EAWM indeed exists in two distinct modes (e.g., Wang et al. 2009a; Wang et al. 2010; Sohn et al. 2011). Considering the importance of the degree of coldness in winter weather severity, and that there is better spatial homogeneity in surface air temperature (SAT) variability than in surface winds over complex terrain, Wang et al. (2010) successfully captured the dominant variability of the EAWM based on the East Asian SAT (EAT) in observations. They verified that winter EAT variation is dominated by two distinct modes, the northern mode and the southern mode. The northern mode is the most dominant mode and accounts for almost half of the total temperature variability in East Asia and explains the majority of the local temperature variation north of 30°N. Meanwhile, the northern mode is found to be closely tied to extratropical circulation, which is complicated because of the varied internal atmospheric processes. The southern mode is not only related to variability in the circulation at the middle and high latitudes but is also affected by tropical factors such as east-central Pacific SST anomalies (Wang et al. 2010). Therefore, the factors for the southern mode of the EAWM seem to be more complicated than those for the northern mode, which may be involved in the complicated interaction between the tropical and extratropical circulations.

Although there are some advantages to using temperature variation to measure EAWM variability in observations, the temperature variability in East Asia actually results from the impact of the EAWM and is not the source of the EAWM variation. Moreover, the long-term temperature trend is significantly influenced by direct radiative forcing due to increased greenhouse gases (GHGs; IPCC 2013). As such, the temperature field may not be suitable to reflect long-term changes in the EAWM under rapid climate warming in the future. Therefore, it is of particular importance to understand the underlying mechanisms dominating the observed temperature variability over East Asia. Many studies have indicated that the SH is the source of cold-air outbreaks and exerts pronounced impacts on temperature variability in East Asia (e.g., Ding 1994; Gong et al. 2001; Wu and Wang 2002; Wang and Chen 2010). However, the role of SH in the dominant observed temperature variability over East Asia is unclear and has not been explored yet. Hence, in this study, the possible coupled relationship between the SH and EAT is first investigated. If a certain coupled relationship between SH variation and EAT variation is verified, and these coupling modes are both found equivalent to the leading mode for the SH and EAT variations, the projection of the dominant modes of the EAWM will be more reliable based on SH variation than that using EAT, because the evident global warming signal is included in the EAT variations in the warmer climate. Since the EAWM is an atmospheric circulation system, the dynamic field factor (e.g., SH) could show better representation than the thermal field factor (e.g., EAT) to reflect the true variations of the EAWM in a warmer climate.

The purpose of this study is to revisit the underlying mechanisms for the dominant variation in EAT and to reveal the coupling modes between EAT and SH in observations. Furthermore, we present the projection of long-term changes in the northern mode of the EAWM in different GHG emission scenarios and discuss the relative contribution of EAWM-related warming to the total warming in East Asia under the specified scenarios. Section 2 describes the reanalysis datasets, phase 5 of the Coupled Model Intercomparison Project (CMIP5) models, and methodology. Section 3 presents the results related to the coupled variation between the EAT and SH and the projection of its long-term changes. The main conclusions and discussion are provided in section 4.

2. Data and methods

a. Data

In this study, the observational proxies of the atmospheric variables are from the monthly mean data of the European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA) dataset, including ERA-40 and ERA-Interim, with a 2.5° × 2.5° horizontal resolution (Uppala et al. 2005; Dee et al. 2011). The ERA-Interim datasets are used to extend the ERA-40 dataset from September 2002 to the present. The first realizations of historical simulations and the representative concentration pathway (RCP) 4.5 (RCP4.5) and 8.5 (RCP8.5) experiments from 33 coupled atmosphere–ocean general circulation models (CGCMs) participating in CMIP5 are used in this study (Taylor et al. 2012). The brief descriptions of the 33 CMIP5 models including their names, modeling centers, and horizontal and vertical resolutions are listed in Table 1 (see http://cmip-pcmdi.llnl.gov/cmip5/availability.html for details). To compare model results with observations, the atmospheric variables from model outputs are bilinearly interpolated to a resolution of 2.5° × 2.5°.

Table 1.

The climate modeling centers and CMIP5 models used in this study. The horizontal resolution and vertical levels of each model are also listed.

Table 1.

b. Analysis methods

The empirical orthogonal function (EOF) and singular value decomposition (SVD) methods are employed in this study. EOF analysis is helpful in objectively identifying the principal (spatially uncorrelated) modes of variability of a given field. The SVD method is used to isolate the important coupled modes of variability between two fields (e.g., Cherry 1996; Wu et al. 2003). In consideration of the common availability of data, the observational data and historical simulations of CMIP5 are obtained for the period from 1961 to 2005. The projection period in the RCP4.5 and RCP8.5 scenarios is from 2006 to 2099. Winter means are constructed by averaging the monthly mean data from December, January, and February (DJF). As per convention, the winter of 1961 refers to the 1960/61 winter. Regression and correlation analysis are used and the significance of the results is evaluated with the two-tailed Student’s t test. The multimodel ensemble (MME) is calculated by simply averaging over the models with equal weighting.

3. Results

a. The coupled mode of the East Asian SAT and Siberian high

Figure 1 shows the distribution of the winter mean sea level pressure (SLP) and 1000-hPa winds over the Eurasian continent based on the ERA dataset. It can be seen that a strong SH is centered near Lake Baikal. The strong northwesterlies along the eastern flank of the SH bring cold air from the Siberian cold dome southward to East Asia and cool most of East Asia (e.g., Gong et al. 2001; Wang and Chen 2010). To reveal the linkage between the variations of the SH and EAT, SVD analysis is performed by analyzing the covariance matrices of the two fields. Figure 2 shows the heterogeneous correlations of the first SVD mode between the SLP in the expanded Siberian high region (25°–75°N, 60°–140°E; see blue box in Fig. 1) and EAT (0°–60°N, 100°–140°E) in the ERA dataset. The first coupled mode accounts for 95% of the total covariance between the EAT and the SH anomalies. The first SVD mode of EAT is characterized by the largest negative correlation around 60°N, and the amplitude decreases southward, signifying a cold-air pathway intruding into East Asia from the north (Fig. 2a). This correlation pattern in EAT is quite consistent with the northern mode of the EAWM proposed by Wang et al. (2010). Meanwhile, the correlation pattern for SLP characterizes a large-scale strong positive correlation north of 45°N, with a major ridge extending southward from central Siberia to northeastern China (Fig. 2b). This distribution of the SLP correlation pattern is also consistent with the correlation pattern in EAT, both reflecting a cold-air intrusion from central Siberia to northern East Asia and inducing a cold winter in northern East Asia. The time evolution of the SAT anomalies in the first SVD mode is quite consistent with that of the SLP anomalies, with both experiencing a decreasing trend during recent decades, and the correlation coefficient between the temporal coefficients of the two fields is 0.90 (Fig. 2c). Thus, the obtained correlation pattern for SAT reveals that the evolution of SAT in northern East Asia is closely associated with the SH. To verify the robustness of this result, we perform a parallel analysis using the data from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR; Kalnay et al. 1996) and the Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. 2015) for the same period. The results show that the coupling mode of EAT and SH display major features consistent with those based on the ERA (not shown). Thus, this coupling mode is robust and supported by different datasets. Although the coupling mode of the SH and EAT is verified by a statistical decomposition, the causality is certain; that is, the temperature advection associated with the divergent flow from the SH to East Asia influences the EAT variations to a great extent. This result indicates that the northern mode of the EAT may be determined by the dominant variability of the SH, which represents the southward intrusion of the cold air from Siberia.

Fig. 1.
Fig. 1.

Climatology (1961–2005) of winter (DJF)-mean SLP (shading) and 1000-hPa winds (vectors; m s−1) in ERA data.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

Fig. 2.
Fig. 2.

(a),(b) Heterogeneous correlation patterns of the leading SVD mode and (c) corresponding time coefficients of the winter-mean EAT and SLP in the expanded SH domain for the period of 1961–2005.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

To verify this hypothesis, the EOF analysis is performed on the SH and EAT in the ERA dataset separately for the comparison between the first SVD mode and the first EOF mode in the two fields (Fig. 3). The first EOF mode of the EAT and SH explains ~48.2% and 62.2% of the total variance of SAT and SLP anomalies, respectively. The distributions of both the SAT and SLP anomalies are almost totally consistent with the first SVD mode (Figs. 3a,b). The temporal correlation coefficient between the first SVD mode and the corresponding principal component (PC1) in the EAT and SH is 0.998 and 0.990, respectively (Figs. 3c,d). This result suggests that the first SVD mode of the EAT and SH is actually identical to the first EOF mode of the EAT and SH, respectively. Therefore, the northern mode of the EAWM defined as the first EOF mode of the EAT variation in Wang et al. (2010) is mainly determined by the dominant variability of the SH. This hypothesis is confirmed by the high degree of consistency of PC1 of the EAT to that of the SH (Fig. 4a).

Fig. 3.
Fig. 3.

(a) Spatial patterns and (c) corresponding time coefficient of the first EOF mode of the winter-mean EAT in observations during 1961–2005. (b),(d) As in (a) and (c), respectively, but for SLP in the expanded SH domain. The dashed lines in (c) and (d) represent the time coefficient of the first SVD mode of the EAT and SH, respectively. The percentages of the explained variances of the EOF modes are given at the top-right corners of (a)–(d).

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

Fig. 4.
Fig. 4.

(a) Principal component of the first EOF mode for the EAT and SH, respectively. The red lines represent the decadal components. (b),(c) The EAT anomalies regressed onto the normalized PC1s of the EAT and SH, respectively. Dots indicate significance exceeding the 95% confidence level.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

The temporal behaviors of the dominant EAT and SH modes both show a sharp decrease over the past several decades (Fig. 4a). This feature is consistent with previous findings that the EAWM weakened from the mid-1960s until the end of the twentieth century (e.g., Wang et al. 2009b; Wang et al. 2010; Wu et al. 2011, 2015; Liu et al. 2018). Some previous studies have demonstrated that the weakening of the EAWM may be attributed to the decrease of the autumn snow cover over the Eurasian continent and Arctic sea ice over the polar region (e.g., Wang et al. 2010; Wu et al. 2011, 2015). Meanwhile, the changes of a large-scale atmospheric circulation pattern such as the Arctic Oscillation (AO; He et al. 2017) and quasi-stationary planetary wave activity in the Northern Hemisphere (Wang et al. 2009b) may also contribute to the weakening of the EAWM during recent decades. The temporal correlation between the PC1 of the EAT and PC1 of the SH is 0.87, and it reaches 0.98 on the interdecadal time scale obtained by a 9-yr low-pass filter. The above results support the previous findings that the SH can influence the temperature variability in East Asia significantly during boreal winter (e.g., Gong et al. 2001; Wu and Wang 2002; Wang and Chen 2010). The consistent long-term changes in the leading mode of the EAT and SH confirm the reliability of the northern mode of the EAT; that is, the long-term trend of the dominant EAT variability may not be the result of direct radiative forcing from increased GHGs in observations.

Figure 4b shows the SAT anomalies regressed onto the normalized PC1 of the SH. It can be seen that the SAT anomalies related to the leading mode of the SH are quite similar to those of the northern mode of the EAT, with the coldest anomalous SAT located north of Lake Baikal and extending southeastward to northern China (Fig. 4c), which reflects the northern pathway of the cold-air outbreak from central Siberia to northern East Asia. Moreover, the climate anomalies related to the dominant variability of the SH are all consistent with those related to the northern mode of the EAT, with an amplified SH accompanied by cold-air intrusion from Siberia to northern East Asia at the surface (Figs. 5a,b). A westward shift of the East Asian trough in the middle troposphere and an accelerated East Asian jet stream in the upper troposphere (Figs. 5c,d) is noticeable. These results suggest that the northern mode of the EAT actually results from the dominant observed variation of the SH, which represents the intrusion of the cold air from high latitudes. In other words, the EAT and SH variations are different manifestations of the same phenomenon, that is, the northern mode of the EAWM.

Fig. 5.
Fig. 5.

(a) SLP (shading) and 1000-hPa wind (vectors; m s−1) anomalies regressed onto the normalized PC1 of the EAT in observations. (c) As in (a), but for the 500-hPa geopotential height (contours; 5-gpm interval) and 200-hPa zonal winds (shading). (b),(d) As in (a) and (c), respectively, but for the anomalies regressed onto the normalized PC1 of the SH.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

b. Long-term trend of the northern mode of the EAWM under the RCP4.5 and RCP8.5 scenarios

The above results suggest that the northern mode of the EAWM can also be represented by the dominant variability of the SH. The cold temperature advection accompanied by the southward intrusion of the cold-air outbreak from central Siberia to northern East Asia induces the pronounced temperature anomaly in northern East Asia. It is noted that although the long-term changes in the EAT seem to be less affected by direct radiative forcing due to the relatively small anthropogenic forcing in the present climate, they may be significantly influenced by rapid climate warming in the future. Many studies have warned that the global mean temperature will increase more than 4 K in the end of the twenty-first century if the anthropogenic emission of GHGs is not limited (e.g., IPCC 2013). This strengthening of warming is about 4 times greater than that in the past 100 years. Climate models are an essential tool to study the monsoon variability and climate change. Although some studies have analyzed the future changes of the East Asian winter climate under different emission scenarios (e.g., Hong et al. 2017), limited study has been carried out to investigate the northern mode of the EAWM. Also, the relative contributions of the long-term change of the northern mode of EAWM and direct radiative forcing by the GHG forcing to the long-term change of EAT are not explored yet. Therefore, in this study, the 33 available CMIP5 models are employed to investigate the ability of the models in simulating the coupling mode of the EAWM and projecting the long-term changes of this mode under the RCP scenarios.

Previous studies have indicated that the CMIP5 models can well reproduce the climatology and interannual variability of the EAWM (e.g., Gong et al. 2014, 2015; Wei et al. 2014). In this study, for the consistency of the results in the historical simulation and RCPs scenarios, the sign of the pattern and the PC in the northern mode of EAT are reversed in the historical simulations compared with those in observations. Results show that the MME of CMIP5 models also simulates a reliable spatial pattern of the northern mode of EAT variation in the historical simulations (Fig. 6a). The pattern correlation of the northern mode of EAT between the MME and observations is −0.9. This EAT anomaly pattern can be well reproduced by the almost all the models and the intermodel spread is very small (not shown). Meanwhile, the weakening of the northern mode of the EAWM is also well captured by the MME, with an evident warming trend in the corresponding time series (Fig. 6d). This suggests that the northern mode of the EAT can be well reproduced by the CMIP5 models in the historical simulation, especially the long-term changes. However, the MME of the first EOF mode of the EAT shows that almost all of the warming in East Asia actually represents typical global warming features, with a consistent SAT anomaly over all of East Asia under the RCP4.5 and RCP8.5 scenarios instead of a pure EAWM-related SAT anomaly pattern. These features are also basically consistent among the simulations of the individual model (not shown). This is supported by the fact that the first EOF mode of MME can explain much of the total EAT variance in RCP4.5 and RCP8.5, which is 62.9% and 78.7%, respectively. However, the magnitude of the variance is much larger than that in the observations and historical simulations (Figs. 6b,c). Meanwhile, much stronger long-term trends are displayed in the corresponding PC1 of EAT in the RCP4.5 and RCP8.5 scenarios than in the historical simulations (Figs. 6d,f). This result further suggests that the leading mode of the EAT in the rapidly warming climate actually mainly reflects the signal of global warming instead of the EAWM variations. For the dominant mode of SH, the MME can well reproduce the SLP and SAT anomaly patterns related to the dominant mode of the SH, and both show the typical cold-air outbreak from Siberia to northern East Asia in the historical simulation (Figs. 7a,d). These features are also basically consistent among the simulations of the individual model (not shown). Meanwhile, the SLP and SAT anomaly patterns associated with the dominant mode of the SH remain relatively stable during the historical simulations and the RCP4.5 and RCP8.5 scenarios (Fig. 7). This indicates that the pattern of the northern mode of EAWM variation in a warming climate is consistent with that in the historical simulations as well as the observations. These results further suggest that the dominant pattern of EAWM variation cannot be truly reflected in the leading temperature variation in a rapidly warming climate. Therefore, to obtain a relatively reliable projection of the northern mode of the EAWM, the dynamic field of the SH is more suitable than temperature to represent EAWM variation in future projections. Hence, the dominant mode of the SH is employed to investigate possible changes in the northern mode of the EAWM in the RCP4.5 and RCP8.5 scenarios.

Fig. 6.
Fig. 6.

(left) (a) Spatial pattern and (d) corresponding time coefficient of the first EOF mode of the EAT in the historical simulation during 1961–2005 in the MME of 33 CMIP5 models. (center),(right) As in (left), but for the whole period from (b),(e) historical to RCP4.5 and (c),(f) historical to RCP8.5 scenarios. The percentages of the explained variances of the EOF modes is given at the top-right corners of (a)–(f).

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

Fig. 7.
Fig. 7.

(a) SLP anomalies regressed onto the normalized PC1 of the SH in the historical simulation in the MME of 33 CMIP5 models. (b),(c) As in (a), but for the whole period from historical to RCP4.5 and historical to RCP8.5 scenarios, respectively. (d)–(f) As in (a)–(c), but for the EAT anomalies regressed onto the normalized PC1 of the SH.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

Before the projection, the ability of the CMIP5 models to simulate the coupled relationship between the EAT and SH is first evaluated using the models’ historical simulations during 1961–2005. Figures 8a and 8b show the heterogeneous correlations of the first SVD mode between the expanded SH and EAT in the historical simulation during 1961–2005 in the MME of 33 CMIP5 models. The MME can well reproduce the coupling mode of SH and EAT. The correlation patterns are quite consistent with those in observations, both reflecting the cold-air pathway intruding from high latitudes to East Asia (Figs. 8a,b). These features are also well reproduced by the individual models (not shown). Figure 8c further presents the temporal correlation between PC1 of the SH and PC1 of the EAT in each model in the historical simulations. It shows that all the models capture the strong coupling between the dominant variations of the EAT and SH. The correlation coefficients range from 0.65 to 0.93, all exceeding the 99.9% confidence level, and about two-thirds (20 of 33) of the models exceed 0.8 (Fig. 8c). This result further suggests that the CMIP5 models can well reproduce the tightly coupled association between the EAT and SH. In other words, the northern mode of the EAWM is well captured by the CMIP5 models. Furthermore, the pattern correlations between the simulated SAT anomalies related to the dominant SH variation and the observational SAT anomalies associated with the northern mode of the EAT exceed 0.73 in all models, and most of the models (28 of 33) exceed 0.8 (Fig. 8d). This further confirms that the dominant mode of the SH can well represent the observational northern mode of the EAT as well as the EAWM in all CMIP5 models.

Fig. 8.
Fig. 8.

(a),(b) Heterogeneous correlation patterns of the leading SVD mode of the EAT and SH in the historical simulation in the MME of 33 CMIP5 models during 1961–2005.(c) Temporal correlation coefficients between PC1 of the EAT and PC1 of the SH during 1961–2005 in CMIP5 models. (d) Pattern correlations between the EAT anomalies associated with the leading mode of the SH in each model, and the EAT anomalies related to the first EOF mode of the EAT in observations.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

Previous studies have demonstrated that the MME is an effective method to not only reproduce the capacity of models’ simulations but also show a better performance than individual models in the prediction and projection (e.g., Krishnamurti et al. 1999). Although there is some intermodel spread, it is much smaller than the common features of models’ simulations (not shown). Moreover, for the projection, the MME with large samples can significantly eliminate the effects of internal variations from different models and thus reduce the uncertainty of models’ simulation and show a more reliable projection of climate system response to the external forcing (e.g., GHG forcing). Therefore, the MME is employed to examine the responses of the leading mode of the EAWM to the GHG forcings in the different RCP scenarios. Figure 9 presents a time series corresponding to the dominant mode of the SH, EAT, and area-averaged SAT in northern East Asia [northern EAT (NEAT); 30°–60°N, 100°–140°E] in the MME under the RCP4.5 and RCP8.5 scenarios. For convenience of comparison, the time series for each of the models is normalized by dividing by its standard deviation prior to the MME analysis. Since the MME of the 33 models is wide enough to reduce internal variability, the MME can represent the true response of the EAWM to global warming caused by anthropogenic forcing. The time evolution of the MME in the NEAT and PC1 of the EAT are similar and both represent a rapid increasing trend, with 2.46 and 1.98 standard deviations per 100 years [std dev (100 yr)−1], respectively, under the RCP4.5 scenario (Fig. 9a). Although there are different structures, physical parameterizations, resolutions, and so on among models, nearly all the models simulate an equivalent warming trend of PC1 of EAT and NEAT in both the RCP4.5 and RCP8.5 scenarios with much smaller intermodel spread, especially in the RCP8.5 scenario (not shown). In contrast, it is noted that although the trend in PC1 of the SH is decreasing, with −0.2 std dev (100 yr)−1 and also significant at the 99% confidence level, it is much weaker compared to that of the NEAT and PC1 of EAT (Fig. 9a). This suggests that the northern mode of the EAWM weakens slightly under the RCP4.5 scenario, and the leading mode of the EAT actually reflects mainly the long-term trend of the northern EAT forced by the increase in GHGs instead of real EAWM variation. With the occurrence of stronger warming in the RCP8.5 scenario, a much higher degree of consistency between the NEAT and PC1 of EAT is seen (Fig. 9b), which further confirms that the leading mode of EAT in a rapidly warming climate represents mainly the long-term trend of the regional SAT forced by anthropogenic influence rather than the dominant variation of the EAWM. In addition, the trend of PC1 in the SH also decreases, with 0.39 std dev (100 yr)−1, which is almost twice that in the RCP4.5 scenario. This indicates that the amplitude of the weakening northern mode of the EAWM is much larger than that in the RCP4.5 scenario, suggesting a stronger EAWM-related circulation response to more rapid warming in the future climate. Most of the models can simulate a decreasing trend of PC1 of the SH in both the RCP4.5 and RCP8.5 scenarios, but especially in the RCP8.5 scenario. It is noted that the intermodel spread of the long-term trends of PC1 of SH show a relatively larger intermodel spread than those of temperature, which may be attributed to the relatively strong internal variability of the atmospheric circulation fields such as SLP in mid- and high latitudes. This result also suggests that the MME should be used to reduce the uncertainty of projection as much as possible. Furthermore, to verify the robustness of the results of the projection based on MME in all 33 models, the 20 best models with stronger coupling between SH and EAT (Fig. 8c) and larger pattern correlation coefficients between the simulated SAT anomalies related to the dominant SH variation and the observational SAT anomalies associated with the northern mode of the EAT (Fig. 8d) are further selected for the projection. The main results are consistent with those based on all 33 models (not shown), confirming the robustness of the projection.

Fig. 9.
Fig. 9.

(a) Future change in PC1 of the SH (blue line), PC1 of the EAT (red line), and NEAT (black line) during 1961–2099 in the historical and RCP4.5 scenario. (b) As in (a), but for the RCP8.5 scenario.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0136.1

The weakening EAWM can also induce warming in East Asia. The relative contributions of the weakened northern mode of the EAWM-induced SAT warming and the warming caused by direct radiative forcing in northern East Asia are further investigated. The northern mode of the EAWM-related temperature trend is calculated as follows. First, we reconstruct the three-dimensional temperature anomalies related to the northern mode of the EAWM based on the related temperature anomaly pattern and the time series of the northern mode of the EAWM. Second, we calculate the temperature anomaly trend over northern East Asia in the reconstructed data. The result indicates that the NEAT increases 2.98 K (100 yr)−1 in the RCP4.5 scenario, whereas the northern mode of the EAWM-related SAT trend increases 0.2 K (100 yr)−1 and contributes about 6.7% of the total trend of the NEAT. In the RCP8.5 scenario, the trend of the NEAT reaches 5.23 K (100 yr)−1, almost double that in the RCP4.5 scenario due to the stronger GHGs emission prescribed in the RCP8.5 scenario. The NEAT warming caused by the northern mode of the EAWM is about 0.49 K (100 yr)−1 in the RCP8.5 scenario and accounts for 9.4% of the total trend of the NEAT, which is larger than that in the RCP4.5 scenario. These results suggest that the weakening of the northern mode of the EAWM can indeed contribute to the warming of the NEAT under the RCP scenarios to a certain extent, but this contribution is relatively small, and thus the rapid warming of the NEAT under the RCP4.5 and RCP 8.5 scenarios is still caused mainly by direct radiative forcing from increased GHGs.

The present study does not consider the southern mode of the EAWM, which explains 22.5% of the total variance of the EAT. In fact, the second SVD mode of the EAT and SH also shows a pattern somewhat similar to that of the second EOF mode of the EAT and SH, respectively. The temporal correlation coefficient of PC2 of the EAT and PC2 of the SH is 0.68, exceeding the 99.9% confidence level. However, the southern mode of the EAWM is not only influenced by extratropical circulation but is also impacted by tropical air–sea interactions such as El Niño–Southern Oscillation (ENSO). Therefore, it is difficult to select a single dynamic factor to measure the southern mode of East Asian temperature variation. Moreover, the second SVD mode of the EAT and SH cannot be well reproduced in most of the models. Therefore, possible changes in the southern mode of the EAWM in the RCP scenarios need to be systematically investigated in future studies.

4. Conclusions and discussion

In this study, we identify a clear coupled relationship between the EAT and SH in both observations and CMIP5 models. The first SVD mode explains 95% of the squared covariance between the EAT and SH in observations, which is characterized by cold-air intrusion from the SH southward toward northern East Asia. The correlation coefficient between the temporal evolutions of the two fields reaches 0.9. Furthermore, the first SVD mode of the EAT and SH is verified to be equivalent to the first EOF mode of the EAT and SH, respectively. Meanwhile, PC1 of the EAT and PC1 of the SH have a high correlation, 0.87, and clearly capture the same long-term changes. This suggests that the northern mode of the EAT is actually the result of the dominant mode of the SH. Moreover, this coupled mode can be well reproduced in the CMIP5 models.

In the RCP4.5 and RCP8.5 scenarios, the first EOF mode of the EAT shows typical global warming features, with a consistent SAT anomaly over all of East Asia, and it explains much more variance than that of the observations. The first EOF mode of the EAT actually reflects the long-term trend of the NEAT in the RCP scenarios instead of the EAWM in the RCP4.5 and RCP8.5 scenarios. In view of the inapplicability of the temperature field to represent the long-term changes of the EAWM in a rapidly warming climate, the dynamic aspect of the northern mode of the EAWM is employed to investigate its long-term changes. In addition, the multimodel ensemble-mean approach has been verified to be a better method than individual models to reduce the errors compared with the individual models. Meanwhile, since the internal variability varies among models, the MME can also reduce the uncertainty of models’ projections and perform a more reliable response of the climate system to the external forcing (e.g., GHG forcings). Therefore, the MME is used to project the future changes of the dominant mode of EAWM variations in this study. A robust weakening of the northern mode of the EAWM is seen in the RCP4.5 scenario, and with strengthened warming in the RCP8.5 scenario, the weakening of the EAWM is also more pronounced. But the amplitudes are relatively weak compared to those of the temperature field in both the RCP4.5 and RCP8.5 scenarios. In addition, the warming trend of the NEAT contributed by the weakening of the northern mode of the EAWM is 6.7% and 9.4% in the RCP4.5 and RCP8.5 scenarios, respectively, suggesting that the warming of the NEAT is still caused mainly by direct radiative forcing from increased GHGs.

Previous studies have indicated that SH variations are related not only to the underlying surface changes such as the snow cover on the Eurasian continent and sea ice over the polar region in the prior autumn and simultaneous winter (e.g., Wang et al. 2010; Wu et al. 2011, 2015; Wu 2017) but also to the large-scale atmospheric teleconnection pattern such as AO (e.g., He et al. 2017; Gong et al. 2017, 2018). Thus, the relationship between the changes of SH and AO as well as the underlying surface snow cover and Arctic sea ice should be further investigated in future studies.

Acknowledgments

We thank the editor Mathew Barlow and three anonymous reviewers for their constructive suggestions and comments, which helped to improve the paper. This work was supported by the National Natural Science Foundation of China (41721004, 41661144016, 41605060, and 41530425), and Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 11305715 and 11335316). The authors declare that they have no conflict of interest.

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Save
  • Chang, C.-P., Z. Wang, and H. Harry, 2006: The Asian winter monsoon. The Asia Monsoon, B. Wang, Ed., Praxis, 89–127.

    • Crossref
    • Export Citation
  • Chen, W., and T. Li, 2007: Modulation of Northern Hemisphere wintertime stationary planetary wave activity: East Asian climate relationships by the quasi-biennial oscillation. J. Geophys. Res., 112, D20120, https://doi.org/10.1029/2007JD008611.

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    • Search Google Scholar
    • Export Citation
  • Chen, W., H. F. Graf, and R. H. Huang, 2000: The interannual variability of East Asian winter monsoon and its relation to the summer monsoon. Adv. Atmos. Sci., 17, 4860, https://doi.org/10.1007/s00376-000-0042-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, W., S. Yang, and R.-H. Huang, 2005: Relationship between stationary planetary wave activity and the East Asian winter monsoon. J. Geophys. Res., 110, D14110, https://doi.org/10.1029/2004JD005669.

    • Search Google Scholar
    • Export Citation
  • Chen, W., J. Feng, and R. Wu, 2013: Roles of ENSO and PDO in the link of the East Asian winter monsoon to the following summer monsoon. J. Climate, 26, 622635, https://doi.org/10.1175/JCLI-D-12-00021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Z., R. Wu, and W. Chen, 2014a: Distinguishing interannual variations of the northern and southern modes of the East Asian winter monsoon. J. Climate, 27, 835851, https://doi.org/10.1175/JCLI-D-13-00314.1.

    • Crossref
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  • Chen, Z., R. Wu, and W. Chen, 2014b: Impacts of autumn Arctic sea ice concentration changes on the East Asian winter monsoon variability. J. Climate, 27, 54335450, https://doi.org/10.1175/JCLI-D-13-00731.1.

    • Crossref
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    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheung, H. N., W. Zhou, H. Y. Mok, and M. C. Wu, 2012: Relationship between Ural–Siberian blocking and the East Asian winter monsoon in relation to the Arctic Oscillation and the El Niño–Southern Oscillation. J. Climate, 25, 42424257, https://doi.org/10.1175/JCLI-D-11-00225.1.

    • Crossref
    • Search Google Scholar
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    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
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    • Crossref
    • Export Citation
  • Gong, D.-Y., S.-W. Wang, and J.-H. Zhu, 2001: East Asian winter monsoon and Arctic Oscillation. Geophys. Res. Lett., 28, 20732076, https://doi.org/10.1029/2000GL012311.

    • Crossref
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  • Gong, H., L. Wang, W. Chen, R. Wu, K. Wei, and X. Cui, 2014: The climatology and interannual variability of the East Asian winter monsoon in CMIP5 models. J. Climate, 27, 16591678, https://doi.org/10.1175/JCLI-D-13-00039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gong, H., L. Wang, W. Chen, D. Nath, G. Huang, and W. Tao, 2015: Diverse influences of ENSO on the East Asian–western Pacific winter climate tied to different ENSO properties in CMIP5 models. J. Climate, 28, 21872202, https://doi.org/10.1175/JCLI-D-14-00405.1.

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  • Gong, H., L. Wang, W. Chen, X. Chen, and D. Nath, 2017: Biases of the wintertime Arctic Oscillation in CMIP5 models. Environ. Res. Lett., 12, 014001, https://doi.org/10.1088/1748-9326/12/1/014001.

    • Crossref
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  • Gong, H., L. Wang, W. Chen, and D. Nath, 2018: Multidecadal fluctuation of the wintertime Arctic Oscillation pattern and it implication. J. Climate, 31, 55955608, https://doi.org/10.1175/JCLI-D-17-0530.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Q.-Y., 1994: Relationship between the variations of East Asian winter monsoon and temperature anomalies in China (in Chinese). Quart. J. Appl. Meteor., 5, 218225.

    • Search Google Scholar
    • Export Citation
  • He, S., Y. Gao, F. Li, H. Wang, and Y. He, 2017: Impact of Arctic Oscillation on the East Asian climate: A review. Earth Sci. Rev., 164, 4862, https://doi.org/10.1016/j.earscirev.2016.10.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, J.-Y., J.-B. Ahn, and J.-G. Jhun, 2017: Winter climate changes over East Asian region under RCP scenarios using East Asian winter monsoon indices. Climate Dyn., 48, 577595, https://doi.org/10.1007/s00382-016-3096-5.

    • Crossref
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  • Fig. 1.

    Climatology (1961–2005) of winter (DJF)-mean SLP (shading) and 1000-hPa winds (vectors; m s−1) in ERA data.

  • Fig. 2.

    (a),(b) Heterogeneous correlation patterns of the leading SVD mode and (c) corresponding time coefficients of the winter-mean EAT and SLP in the expanded SH domain for the period of 1961–2005.

  • Fig. 3.

    (a) Spatial patterns and (c) corresponding time coefficient of the first EOF mode of the winter-mean EAT in observations during 1961–2005. (b),(d) As in (a) and (c), respectively, but for SLP in the expanded SH domain. The dashed lines in (c) and (d) represent the time coefficient of the first SVD mode of the EAT and SH, respectively. The percentages of the explained variances of the EOF modes are given at the top-right corners of (a)–(d).

  • Fig. 4.

    (a) Principal component of the first EOF mode for the EAT and SH, respectively. The red lines represent the decadal components. (b),(c) The EAT anomalies regressed onto the normalized PC1s of the EAT and SH, respectively. Dots indicate significance exceeding the 95% confidence level.

  • Fig. 5.

    (a) SLP (shading) and 1000-hPa wind (vectors; m s−1) anomalies regressed onto the normalized PC1 of the EAT in observations. (c) As in (a), but for the 500-hPa geopotential height (contours; 5-gpm interval) and 200-hPa zonal winds (shading). (b),(d) As in (a) and (c), respectively, but for the anomalies regressed onto the normalized PC1 of the SH.

  • Fig. 6.

    (left) (a) Spatial pattern and (d) corresponding time coefficient of the first EOF mode of the EAT in the historical simulation during 1961–2005 in the MME of 33 CMIP5 models. (center),(right) As in (left), but for the whole period from (b),(e) historical to RCP4.5 and (c),(f) historical to RCP8.5 scenarios. The percentages of the explained variances of the EOF modes is given at the top-right corners of (a)–(f).

  • Fig. 7.

    (a) SLP anomalies regressed onto the normalized PC1 of the SH in the historical simulation in the MME of 33 CMIP5 models. (b),(c) As in (a), but for the whole period from historical to RCP4.5 and historical to RCP8.5 scenarios, respectively. (d)–(f) As in (a)–(c), but for the EAT anomalies regressed onto the normalized PC1 of the SH.

  • Fig. 8.

    (a),(b) Heterogeneous correlation patterns of the leading SVD mode of the EAT and SH in the historical simulation in the MME of 33 CMIP5 models during 1961–2005.(c) Temporal correlation coefficients between PC1 of the EAT and PC1 of the SH during 1961–2005 in CMIP5 models. (d) Pattern correlations between the EAT anomalies associated with the leading mode of the SH in each model, and the EAT anomalies related to the first EOF mode of the EAT in observations.

  • Fig. 9.

    (a) Future change in PC1 of the SH (blue line), PC1 of the EAT (red line), and NEAT (black line) during 1961–2099 in the historical and RCP4.5 scenario. (b) As in (a), but for the RCP8.5 scenario.

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