Abstract

Using long-term observational data and numerical model experiments, this study found that the Atlantic multidecadal oscillation (AMO) affects the influence of ENSO-like sea surface temperature anomalies (SSTAs, which contain the variability of both El Niño–Southern Oscillation and Pacific decadal oscillation) on the interannual change in the East Asian winter monsoon (EAWM). In the observations, the out-of-phase relationship between the variations in ENSO and the EAWM was significantly intensified when the AMO and ENSO-like SSTAs were in phase. Warmer-than-normal winters occurred across East Asia when the ENSO-like SSTAs and AMO were positively in phase, with a significantly weakened Siberian high and anomalous anticyclones over the western North Pacific. The opposite patterns occurred under negative in-phase conditions. In contrast, when the ENSO-like and AMO SSTAs were out of phase, the anomalies related to the EAWM tended to exhibit relatively weaker features. Numerical model experiments confirmed these observational results. When the models were perturbed with warm ENSO-like SSTAs and warm AMO SSTAs, the atmosphere showed a weakened Siberian high, strong anticyclonic anomalies over the Philippine Sea, a weakened East Asian trough, and dominant positive temperature anomalies over East Asia, implying a weaker EAWM. Reverse responses to negative in-phase temperature anomalies were observed. However, the atmospheric signals that responded to the out-of-phase conditions were less robust. This phenomenon may be attributed to the superposition of the interannual variability of the EAWM caused by ENSO-like SSTAs upon the influence of AMO on background Eurasian climate and the Walker circulation response to the heating source provided by the AMO, which induced changes in ENSO-like variability through the surface wind anomalies and modulated the anomalous anticyclone/cyclone over the Philippine Sea in warm–cold ENSO-like events.

1. Introduction

The East Asian winter monsoon (EAWM) is one of the dominant systems in midlatitude areas during the boreal winter and has pronounced impacts on weather and climate in many Asian countries (e.g., Fan 2009; Huang et al. 2003; He and Wang 2012; Li and Wang 2013, 2014; Li et al. 2014; L. Wang et al. 2009). Many studies have examined the dynamic interactions between the EAWM and the variability in sea surface temperature anomalies (SSTAs) in the Pacific; anomalies characterized by El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO; e.g., Kim et al. 2014; Ji et al. 1997; Krishnamurthy and Krishnamurthy 2014; Li 1990; Li and Mu 2000; Zhang et al. 1996). An intense EAWM leads to anomalous westerly winds over the equatorial western Pacific region, causing eastward propagation of positive subsurface ocean temperature anomalies in the warm pool region and further leading to El Niño events (Li and Mu 2000; Tomita and Yasunari 1996). The opposite occurs with a weak EAWM, leading to La Niña events (Tourre and Kushnir 1999; Wang et al. 2000). On the other hand, ENSO induces anomalistic circulation, which leads to an irregular EAWM (e.g., Ashok et al. 2007; Lau and Nath 2000; Zhou and Wang 2008). For example, in an El Niño winter, an anomalous low-level anticyclone around the Philippine Sea impairs the northerlies over the coast of East Asia, inducing a weak EAWM (e.g., Li 1990; Wang et al. 2000; Zhang et al. 1996). The PDO is an ENSO-like interdecadal change in the background state of the Pacific (e.g., Gershunov and Barnett 1998; Chang et al. 2000). Atmospheric circulation patterns linked to the EAWM have broadly analogous responses to ENSO and the PDO (Kim et al. 2014; Zhou et al. 2007). Studies on the “teleconnection” between the PDO phenomenon and the EAWM by Jhun and Lee (2004), Zhou et al. (2007), and Ding et al. (2014) demonstrated that when the PDO is in a negative phase, the EAWM is enhanced, and vice versa.

In recent decades, the covariability of the EAWM with Pacific SSTAs has experienced reciprocating changes. A study by Zhou et al. (2007) noted that the EAWM and ENSO are not consistently highly correlated and that there may be interdecadal variations in their relationship. The PDO is an important factor in the interdecadal variation of the ENSO–EAWM relationship (e.g., Wang et al. 2008; Yoon and Yeh 2010; He and Wang 2013; He et al. 2013; Kim et al. 2014; Krishnamurthy and Krishnamurthy 2014). The out-of-phase relationship between variations in ENSO and the EAWM has weakened since the 1970s (Wang et al. 2008; Wang and He 2012; He and Wang 2013; He et al. 2013). Wang et al. (2008) suggested that interannual changes in the ENSO–EAWM relationship may have been caused by the transition of the PDO from a low phase to a high phase in the late 1970s. He and Wang (2013) and He et al. (2013) further indicated that there is a low-frequency oscillation in the ENSO–EAWM relationship, with a period of approximately 50 yr. This relationship may have been intensified owing to a transition to a negative PDO in the early 2000s and an enhancement of the Walker circulation in the late 1990s. Additionally, some studies insist that the change in the ENSO–EAWM relationship is attributable to the combined effect of ENSO and the PDO (e.g., Latif and Barnett 1996; Gu and Philander 1997; Zhang et al. 1997; Kim et al. 2014). When ENSO and the PDO are in phase, the EAWM tends to exhibit strong features. In contrast, when ENSO and the PDO are out of phase, the EAWM does not display significant anomalies (Kim et al. 2014).

The physical mechanisms for the modulation of the PDO on the ENSO–EAWM relationship provided by previous studies are multifarious. On the one hand, many studies have proposed that the PDO can contribute to the ENSO–EAWM relationship via the modulation of the PDO on the background circulation conditions, which include the North Pacific Oscillation, Pacific–North America (PNA) teleconnection, and a synergistic impact on the EAWM-related circulation (Gershunov and Barnett 1998; Wang et al. 2008; L. Wang et al. 2009; Alexander et al. 2010; He and Wang 2013; He et al. 2013; Kim et al. 2014). On the other hand, the interdecadal variation in sea surface temperatures (SSTs) in the Pacific could be affected by the PDO, causing changes in the ENSO–EAWM relationship (Zhou et al. 2007; Kim et al. 2014). The PDO can drive decadal-scale changes in the intensity of ENSO via its effect on the trade winds, which then alter the east–west slope of the thermocline in the tropics (Pierce et al. 2000; Barnett et al. 1999; Newman et al. 2003; Verdon and Franks 2006; D’Aleo and Easterbrook 2010; Chen and Sun 2015). The midlatitude decadal SSTAs in the Kuroshio Extension, incited by the PDO, is a key system bridging ENSO and the EAWM (Schneider et al. 1999; Kim et al. 2014; Zhou et al. 2007; Krishnamurthy and Krishnamurthy 2014).

Indeed, there is also a teleconnection between Atlantic and Pacific SSTs. Previous studies have indicated that the Atlantic Ocean acts as a pacemaker for the Pacific SST mean state and variability (Wang et al. 2010a; Simpkins et al. 2014; Ham et al. 2013; Kang et al. 2014; Dong and Sutton 2002; Dong et al. 2006; Zhang and Delworth 2007; Xiang et al. 2013; Chung and Li 2013). The authors attributed the ENSO phenomenon triggered by the Atlantic multidecadal oscillation (AMO) to the changes in convection over the equatorial Atlantic and equatorial Pacific, acting through the Walker circulation, wave trains, and surface winds. Additionally, Zhang and Delworth (2007) pointed out that the North Atlantic SST anomaly associated with the AMO leads to a weakening–strengthening of the winter storm track over both the North Atlantic and North Pacific midlatitudes that results in a shift of the westerly wind, and thus induces sea level pressure (SLP) anomalies over Aleutian and PNA pattern anomalies over the North Pacific, causing a PDO-like SST variation. Furthermore, the AMO favors a low-frequency variability of winter climate in East Asia that resembles a EAWM-related change in climatic background, such as warmer winters over East Asia and a weakening of the Mongolian cold high during the warm phase of the AMO (Guo et al. 1996; Li and Bates 2007; Han et al. 2011; Wang et al. 2004; G. L. Wang et al. 2009; Y. M. Wang et al. 2009; Li et al. 2009; Chylek et al. 2014; Jiang et al. 2014). Thus, it is reasonable to speculate that the AMO may have the ability to modulate the Pacific SST–EAWM relationship.

Bearing in mind the above, we wished to examine whether the AMO can modify the Pacific SST–EAWM relationship. A few studies have mentioned the low-frequency oscillation of the ENSO–EAWM teleconnection might be attributed to the modulation of the AMO, without providing details (He and Wang 2013; He et al. 2013; Wang et al. 2013). A recent study conducted by Kim et al. (2016) suggested that the PDO and AMO could induce changes in the tropical Pacific mean state that drove the anomalous ENSO-induced convection to be shifted to the west–east and then migrated the zonal displacement of the lower-tropospheric northwest Pacific anticyclone, and that the shift of the lower-tropospheric northwest Pacific anticyclone changed the ENSO–EAWM relationship. In consideration of the coincident influence of the ENSO and PDO on the EAWM, and the modulation of the PDO on the ENSO-EAWM relationship, we regarded ENSO and PDO as a whole (ENSO-like SSTAs, which consist of ENSO variability on the interannual time scale and PDO variability on the decadal time scale) in this study. Hence, the main purpose of the present study was to identify the combined effect of the ENSO-like SSTAs and the AMO on the interannual change in the EAWM, based on both observations and simulations. In addition, we provide a hypothetical mechanism for the influence of the AMO on the relationship between the ENSO-like SSTAs and EAWM.

2. Datasets and methods

Two monthly mean meteorological datasets were used in this study, covering the period from December 1901 to February 2005. One is the gridded historical land surface temperature dataset from the Climate Research Unit at the University of East Anglia, which is referred to as CRU Time Series, version 3.2 (TS3.2; CRU 2012; Mitchell and Jones 2005). The other is the NOAA-funded Cooperative Institute for Research in Environmental Sciences Twentieth Century Reanalysis dataset, referred to as the NOAA dataset (dataset produced by NOAA-CIRES 2012; Compo et al. 2011), which includes 2-m air temperature, sea level pressure, zonal wind, and meridional wind data. We also used the NOAA Extended Reconstructed SST, version 4, dataset in this study. In addition to the observation and reanalysis data, three climate indexes were used in this study. The AMO index was calculated from area-weighted SSTs averaged over the North Atlantic from 0° to 70°N using Kaplan’s SST dataset (Enfield et al. 2001; available at http://www.esrl.noaa.gov/psd/data/timeseries/AMO/). The Niño-3.4 index was an index of area-average SSTAs in the Niño-3.4 region (5°N−5°S, 170°−120°W). The PDO index was obtained from the JISAO at the University of Washington (http://jisao.washington.edu/pdo).

We also provide simulated results to improve the validity of the results derived from the observations. The simulations were from a series of idealized experiments by the U.S. Climate Variability and Predictability Program (U.S. CLIVAR) Drought Working Group U.S. CLIVAR Drought Working Group 2008. A rotated empirical orthogonal function (REOF) analysis was applied to the annual mean SST of HadISST for the period of 1901–2004. The REOFs resulted from linear transformations of EOFs, based on varimax rotation (Richman 1986), revealing more robust spatial patterns (Cheng et al. 1995). Figure 1 shows the second and third leading REOFs of the annual mean SST and their associated principal components (PCs). The ENSO-like pattern (Fig. 1a), which resembles the ENSO pattern in the tropical Pacific and the PDO pattern in the subtropical North Pacific (Schubert et al. 2009; Hao et al. 2015), is abbreviated as P. The Atlantic pattern (Fig. 1b) that resembles the AMO is abbreviated as A (Enfield et al. 2001). The ENSO-like and Atlantic SSTA patterns were multiplied by +2 (−2) standard deviations of the associated PCs or 0 to form the warm (cold) or neutral phase of ENSO-like and Atlantic pattern forcing for the climate models. In these experiments, the models were forced by the various combinations of the differential patterns. The idealized experiments analyzed in this paper were perturbed by the following SST boundary conditions: 1) PwAn (PcAn): the warm (cold) phase of ENSO-like SSTAs and neutral Atlantic SSTAs added to the climatology of monthly mean SST; 2) PnAw (PnAc): the warm (cold) phase of Atlantic SSTAs and neutral ENSO-like SSTAs added to the climatology of monthly mean SST; 3) PwAw (PwAc): the warm (cold) phase of Atlantic SSTAs and the warm phase of ENSO-like SSTAs added to the climatology of monthly mean SST; 4) PcAw (PcAc): the warm (cold) phase of Atlantic SSTAs and the cold phase of ENSO-like SSTAs added to the climatology of monthly mean SST; and 5) PnAn (regarded as the control run): the combination of the neutral Atlantic pattern and the neutral ENSO-like pattern. To reinforce the robustness of the model results, we utilized experiments from three individual models, including the idealized SST experiments that used the National Center for Atmospheric Research Community Climate Model 3 performed by the Climate Group of Lamont-Doherty Earth Observatory at Columbia University; the idealized SST experiments that used the Community Atmosphere Model, version 3.5, performed by the Community Climate System Model Climate Variability Working Group at the National Center for Atmospheric Research; and the idealized SST experiments that used the Atmospheric Model, version 2.1 (AM2.1), developed at the Geophysical Fluid Dynamics Laboratory. In this study, the simulated results are the ensemble results, that is, the average of the results from the three models. Details of the models and experiments used were described in Schubert et al. (2009).

Fig. 1.

Eigenvectors of the (a) second and (b) third leading modes, and (c),(d) the associated PCs, respectively, from the REOF analysis of the annual mean SST based on 1901–2004 for the global ocean. The gravest modes are (a) the ENSO-like pattern and (b) the Atlantic pattern. The values of variance explained are (c) 20.5% and (d) 5.8%, respectively (Schubert et al. 2009). The Pacific and Atlantic patterns reflect the 2 standard deviation forcing amplitudes applied to the models.

Fig. 1.

Eigenvectors of the (a) second and (b) third leading modes, and (c),(d) the associated PCs, respectively, from the REOF analysis of the annual mean SST based on 1901–2004 for the global ocean. The gravest modes are (a) the ENSO-like pattern and (b) the Atlantic pattern. The values of variance explained are (c) 20.5% and (d) 5.8%, respectively (Schubert et al. 2009). The Pacific and Atlantic patterns reflect the 2 standard deviation forcing amplitudes applied to the models.

In this study, winter was defined as December–February (DJF) of a certain year. For example, 1901 refers to the boreal 1901/02 winter. Note that we focus on the interannual variability of the EAWM. Therefore, an 11-yr high-pass filter was applied to the observational data to obtain the high-frequency component. Observational anomalies were presented as the values following the removal of the DJF-mean climatology data for the period 1901–2004. Simulated anomalies were the differences between the PxAy (x and y can be labeled as c, w, or n) runs and PnAn runs. Linear trends in the observational data and indexes were removed before performing other analyses.

3. Modulation of the Pacific–EAWM relationship by AMO

a. Observed results

We used a 9-yr high-pass filter to resolve the time series of the ENSO-like SSTAs and found that the high-frequency component of the ENSO-like SSTAs was highly related to the Niño-3.4 index with a correlation coefficient of 0.65 (Fig. 2a). We also found that the residual low-frequency component of the ENSO-like SSTAs and the PDO index showed a high consistent variability with a correlation coefficient of 0.67 (Fig. 2b). Besides, the correlation coefficient between the time series of ENSO-like SSTAs and PDO + Niño-3.4 index was also high with a coefficient of 0.66 (Fig. 2c), confirming the fact that the ENSO-like mode consists of ENSO variability on the interannual time scale and PDO variability on the decadal time scale.

Fig. 2.

(a) The high-frequency component of the ENSO-like SSTAs calculated by a 9-yr high-pass filter (blue) and the normalized Niño-3.4 index (red) for the winters 1901–2004. (b) The residual decadal component of the ENSO-like SSTAs (blue) and normalized PDO index (red) expressed by a 9-yr running mean for the winters 1901–2004. (c) The time series of the ENSO-like SSTAs (blue) and normalized Niño-3.4 + PDO index (red) for the winters 1901–2004.

Fig. 2.

(a) The high-frequency component of the ENSO-like SSTAs calculated by a 9-yr high-pass filter (blue) and the normalized Niño-3.4 index (red) for the winters 1901–2004. (b) The residual decadal component of the ENSO-like SSTAs (blue) and normalized PDO index (red) expressed by a 9-yr running mean for the winters 1901–2004. (c) The time series of the ENSO-like SSTAs (blue) and normalized Niño-3.4 + PDO index (red) for the winters 1901–2004.

During the boreal winter, surface air temperature (SAT) over East Asia deeply reflects the intensity of the EAWM (Chen et al. 2005). Figure 3 depicts a composite of the SAT anomaly in the positive (negative) phases of ENSO-like events which were identified by PC2 according to a threshold of equal or greater (less) than 0.5 (−0.5) standard deviations (Fig. 1b). To confirm the robustness of the impact of the ENSO-like pattern on the East Asian winter SAT, we utilized both the observational dataset (CRU TS3.2) and the reanalysis dataset (derived from NOAA). During a warm ENSO-like winter, a significant positive SAT anomaly appeared in Mongolia, the Southeast Asian countries, South China Sea, and the eastern Asian coastal area, similar to the conditions concurrent with a weakened EAWM (Figs. 3a,c; He and Wang 2013; He et al. 2013). In contrast, a significantly negative SAT anomaly occurred over Southeast Asian countries and the South China Sea in a cold ENSO-like winter (Figs. 3b,d). Obviously, the ENSO-like SSTAs, which represent ENSO on interannual time scales and the PDO on decadal time scales, have a significantly negative connection with the interannual variability of the EAWM.

Fig. 3.

(a) Composite maps of differences in the winter SAT (°C) between warm ENSO-like SSTAs and climatology, using the CRU TS3.2 dataset. (b) As in (a), but between cold ENSO-like SSTAs and climatology. (c),(d) As in (a) and (b), respectively, but using the NOAA dataset. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

Fig. 3.

(a) Composite maps of differences in the winter SAT (°C) between warm ENSO-like SSTAs and climatology, using the CRU TS3.2 dataset. (b) As in (a), but between cold ENSO-like SSTAs and climatology. (c),(d) As in (a) and (b), respectively, but using the NOAA dataset. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

Additionally, the AMO is a remarkable multidecadal fluctuating signal for the Northern Hemisphere climate. We analyzed the composite anomalies of the 11-yr running mean SAT during different phases of the AMO (Fig. 4). The selective positive phases (1930–59, 1995–2004) and negative phases (1915–25, 1965–90) were based on the AMO index (not shown in the study). The spatial structure of the composite SAT anomaly was characterized by significant positive anomalies over the south of East Asia (between 10° and 40°N) and negative anomalies over the north of East Asia during the warm phase of the AMO (Fig. 4a; calculated from CRU TS3.2 dataset). In contrast, significant negative SAT anomalies swept across the eastern and southern areas of East Asia during the cold phase of the AMO (Fig. 4b). Results derived from the NOAA dataset showed similar features (Figs. 4c,d). Notably, the positive and negative phases of AMO seemed to induce opposite SAT anomalies over the southern parts of Asia, especially south of 40°N. These results agree well with previous studies (e.g., Li et al. 2009; Li and Bates 2007; G. L. Wang et al. 2009; Goswami et al. 2006; Wang et al. 2010b). Here, the AMO seems to be a potentially determining factor for the background climate conditions in East Asia. The warm (cold) AMO may reinforce the impact of the warm (cold) ENSO-like pattern on the EAWM. On the contrary, the warm (cold) AMO may impair the impact of the cold (warm) ENSO-like pattern on the EAWM.

Fig. 4.

As in Fig. 3, but between warm AMO and climatology.

Fig. 4.

As in Fig. 3, but between warm AMO and climatology.

To explore the combined effect of the AMO and ENSO-like SSTAs on the interannual variability of the EAWM, a conditional composite analysis for the four categories (i.e., warm AMO + warm ENSO-like SSTAs, cold AMO + warm ENSO-like SSTAs, warm AMO + cold ENSO-like SSTAs, cold AMO + cold ENSO-like SSTAs) was conducted (Figs. 5 and 6). Figure 5 shows the composites of anomalous SAT during the boreal winter, calculated from the CRU TS3.2 dataset. We found that the anomalous SAT over East Asia was negligible when the AMO and ENSO-like SSTAs were out of phase (Figs. 5b,d; i.e., cold AMO + warm ENSO-like SSTAs, warm AMO + cold ENSO-like SSTAs). Interestingly, a wide range of significant SAT anomalies emerged over East Asia when the AMO and ENSO-like SSTAs were in phase (Figs. 5a,c; warm AMO + warm ENSO-like SSTAs, cold AMO + cold ENSO-like SSTAs). Clearly, the impact of the in-phase combination of AMO and ENSO-like SSTAs was much stronger and more significant than the out-of-phase combination of AMO and ENSO-like SSTAs. During the warm ENSO-like events concurrent with the positive phase of the AMO, the warmer-than-normal winter conditions occurring over Mongolia, Southeast Asian countries, China, the east Asian coastal area, and Japan were much more dominant than those without AMO classification, both in magnitude and extent (Fig. 5a vs Fig. 3a). Similarly, during cold ENSO-like events concurrent with the negative phase of the AMO, the colder-than-normal winter conditions located in southern China and South Asian countries were more pronounced than those without an AMO classification (Fig. 5d vs Fig. 3b). This result showed that the anomalous SAT anomalies over East Asia and South Asia were more significant and intense under the in-phase combined effect of the AMO and ENSO-like SSTAs compared with out-of-phase combinations. Likewise, the composites SAT anomalies derived from the NOAA dataset were characterized by similar features (Fig. 6), indicating a robust impact of the in-phase combinations of the AMO and ENSO-like SSTAs.

Fig. 5.

Conditional composite maps of winter SAT (°C) for (a) warm AMO + warm ENSO-like SSTAs, (b) warm AMO + cold ENSO-like SSTAs, (c) cold AMO + warm ENSO-like SSTAs, and (d) cold AMO + cold ENSO-like SSTAs, using the CRU TS3.2 dataset. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

Fig. 5.

Conditional composite maps of winter SAT (°C) for (a) warm AMO + warm ENSO-like SSTAs, (b) warm AMO + cold ENSO-like SSTAs, (c) cold AMO + warm ENSO-like SSTAs, and (d) cold AMO + cold ENSO-like SSTAs, using the CRU TS3.2 dataset. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

Fig. 6.

As in Fig. 5, but, using the NOAA dataset.

Fig. 6.

As in Fig. 5, but, using the NOAA dataset.

Furthermore, we analyzed the same conditional composite anomalies of SLP and horizontal wind velocity at 850 hPa (850 wind) using the NOAA dataset (Figs. 7 and 8). Figure 7 indicates the composite anomalies in the warm ENSO-like winter during different phases of the AMO. One could find that significant large-scale low-pressure anomalies covered eastern Russia when both the AMO and ENSO-like SSTAs were in positive phase (Fig. 7a). The low-pressure anomaly, appearing in a warm ENSO-like winter during the positive phase of the AMO, weakened the Siberia high (SH) and then obstructed cold air moving to the south. Meanwhile, significant high-pressure anomalies occurred over the western North Pacific (Fig. 7a). One center of the high-pressure anomalies was located over the Philippine Sea and the other center was found over the Kuroshio–Oyashio Extension (Fig. 7a), which is consistent with two significant anomalous anticyclones over the Philippine Sea and the Kuroshio–Oyashio Extension at 850 hPa (Fig. 7c). The western North Pacific anticyclones favored anomalous southwesterly winds along the East Asian coastal areas, causing the weakening of the EAWM (Wang et al. 2000). However, in a warm ENSO-like winter concurrent with the negative phase of the AMO, the low-pressure anomalies over the East Asian continent were insignificant and weak. Moreover, the anomalous anticyclone over the western North Pacific was located farther southward, with only one anomalous center located in the Philippine Sea (Figs. 7b,d). Such an SLP anomaly pattern is unfavorable for the impact of ENSO on the EAWM (He et al. 2013; He and Wang 2013). As illustrated by Fig. 7d, the anomalous anticyclones over the Philippine Sea and the Kuroshio–Oyashio Extension at 850 hPa were weak and therefore the anomalous southwesterly winds over southern parts of East Asia were statistically insignificant. This explained why the warm ENSO-like SSTAs did not exert a deep impact on the East Asian winter SAT during the negative phase of the AMO.

Fig. 7.

Conditional composite maps of (a) SLP (hPa) and (c) horizontal wind vectors at 850 hPa (m s−1) for warm AMO + warm ENSO-like SSTAs, using the NOAA dataset. (b),(d) As in (a) and (c), but for cold AMO + warm ENSO-like SSTAs. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

Fig. 7.

Conditional composite maps of (a) SLP (hPa) and (c) horizontal wind vectors at 850 hPa (m s−1) for warm AMO + warm ENSO-like SSTAs, using the NOAA dataset. (b),(d) As in (a) and (c), but for cold AMO + warm ENSO-like SSTAs. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

Fig. 8.

As in Fig, 7, but for warm AMO + cold ENSO-like SSTAs.

Fig. 8.

As in Fig, 7, but for warm AMO + cold ENSO-like SSTAs.

Figure 8 represents the anomalous atmospheric circulation when a cold ENSO-like pattern occurred with a negative or positive AMO phase. Significant positive SLP anomalies were observed in eastern Russia in cold ENSO-like winters, concurrent with a negative phase of the AMO (Fig. 8b). This indicated a strengthening of the SH, promoting the southward invasion of cold air. Meanwhile, significant negative SLP anomalies were located in the western North Pacific (Fig. 8b). Consistent with changes in the SLP, a significant anticyclonic anomaly was found over eastern Russia and a strong cyclonic anomaly emerged over the western North Pacific, leading to anomalous northerlies bringing more cold air from the north to the south (Fig. 8d). Nonetheless, in a cold ENSO-like winter during the positive phase of the AMO, there was not a prominent anomalous atmospheric circulation, except a slight cyclonic anomaly over the Philippine Sea (Figs. 8a,c). It was evident that warm ENSO-like SSTAs, occurring in a warm phase of the AMO, were closely associated with significant anomalous anticyclones over the western North Pacific. The opposite was true for cold ENSO-like SSTAs and the cold phase of the AMO. These anticyclones were relatively weaker and statistically insignificant in warm (cold) ENSO-like winters, concurrent with a cold (warm) phase of the AMO. In summary, the observational results indicated that the interannual change in the EAWM under the in-phase combinations of the AMO and ENSO-like SSTAs was more pronounced than under the out-of-phase combinations.

b. Simulated results

In addition to the diagnostic analysis method, model simulation was another important method for confirming observational results. The idealized experiments carried out by U.S. CLIVAR gave us an optional way to explore the response of the EAWM to different combinations of forcing from the AMO and ENSO-like SSTAs. First, we verified the potential impacts of ENSO-like SSTAs, which were tested by perturbing the models with PwAn and PcAn SSTA forcing. Figure 9 displays the differences in the SAT and EAWM-related circulation between the PwAn–PcAn run and the control run in the winter. The atmospheric circulation showed a dramatic response to the ENSO-like SSTA forcing. In a warm ENSO-like scenario, an anomalous Philippine Sea anticyclone (Fig. 9b) associated with the significant positive SLP anomalies over the Philippine Sea (Fig. 9c) resulted from a Rossby wave response to suppressed convective heating, which is forced by both the in situ ocean surface cooling and the subsidence forced remotely by the central Pacific warming (Matsuno 1966; Gill 1980; Wang et al. 2000). Meanwhile, in the midtroposphere, a weakened East Asian trough (EAT) was induced by the ENSO-like SSTAs (Fig. 9d), which drove a reduced SH (Fig. 9b) and an anomalous anticyclone over the Kuroshio–Oyashio Extension (Fig. 9c; Wang et al. 2000; Hao et al. 2015). Both the weakened SLP gradient between the Asian continent and the adjacent oceans and the anomalous southwesterly wind over East Asian coast created by the anomalous Philippine Sea anticyclone and anomalous Kuroshio–Oyashio Extension anticyclone could obstruct colder and drier air from accessing East Asia. Thus, some significant positive SAT anomalies, with magnitudes above 0.8°C, were found, located around northwestern China, and the others appeared over the East Asian marginal seas, suggesting a weaker-than-normal EAWM (Fig. 9a). The reverse occurred in a cold ENSO-like scenario (Figs. 9e–h). This result certifies that warm ENSO-like SSTAs can independently induce a weakened EAWM and vice versa, as shown by the observations.

Fig. 9.

Conditional composite maps of (a) SAT (°C), (b) SLP (hPa), (c) horizontal wind vectors at 850 hPa (m s−1), and (d) Z500 (m) obtained by subtracting the average of the control runs from the average of the PwAn runs. (e)–(h) As in (a)–(d), but subtracting the average of the control runs from the average of the PcAn runs. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

Fig. 9.

Conditional composite maps of (a) SAT (°C), (b) SLP (hPa), (c) horizontal wind vectors at 850 hPa (m s−1), and (d) Z500 (m) obtained by subtracting the average of the control runs from the average of the PwAn runs. (e)–(h) As in (a)–(d), but subtracting the average of the control runs from the average of the PcAn runs. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

We further verified the potential impact of AMO-related SSTAs, which were tested by perturbing the models with PnAw and PnAc SSTA forcing. Figure 10 represents the differences in SAT and atmospheric circulation between the PnAw/PnAc run and the control run in winter. In the PnAw run, a significant cyclonic anomaly dominated in Siberia and anticyclonic anomalies occurred over the western North Pacific in the lower troposphere, which were accompanied by significant anomalous southwesterly winds in northern Russia and anomalous southeasterly winds in Southeast Asia (Figs. 10b,c). In the middle troposphere, slight positive anomalies over East Asia indicated a weakening of the EAT (Fig. 10d). As a result, a positive SAT anomaly, induced by the warm AMO, was dominant over East Asia, with three centers located in Mongolia; Heilongjiang, China; and India (Fig. 10a). These results agreed well with previous studies (e.g., Li et al. 2009; Li and Bates 2007; G. L. Wang et al. 2009; Goswami et al. 2006; Wang et al. 2010b). This result implies that warm AMO SSTAs can also weaken the EAWM. In contrast, cold AMO SSTAs induced a stronger EAWM, although the response of the EAWM to the AMO SSTAs was asymmetric. For example, significant low-pressure anomalies over the western North Pacific, with anomalous northwesterly winds along the coast of East Asia, caused colder-than-normal conditions in southern East Asia (Figs. 10e–h).

Fig. 10.

As in Fig. 9, but obtained by subtracting the average of the control runs from the average of the PnAw runs.

Fig. 10.

As in Fig. 9, but obtained by subtracting the average of the control runs from the average of the PnAw runs.

The individual effects of the AMO and ENSO-like SSTAs on the EAWM simulated by the models were consistent with observations. We further explored their combined effect, which was tested by perturbing the models with PwAw, PwAc, PcAw, and PcAc SSTA forcing. This is the most important issue that studies need to examine. In the PwAw scenario, a robust and significant negative SLP anomaly emerged over Mongolia–Siberia, where the Siberian high was generally located (Fig. 11b). Meanwhile, prominent responses of the near-surface atmospheric flow over the western tropical and subtropical Pacific took the form of a robust anomalous Philippine anticyclone and a strong anomalous Kuroshio–Oyashio Extension anticyclone (Figs. 11b,c). Consequently, significant anomalous southwesterly winds dominated Siberia and coastal areas of East Asia (Fig. 11c). At 500 hPa, significant positive height anomalies occupied most parts of China, Korea, and Japan, revealing a weaker-than-normal EAT (Fig. 11d). These atmospheric conditions were conducive to a weaker EAWM, leading to warmer winters in East Asia (Fig. 11a). In contrast, the response of the EAWM to the PwAc forcing was weaker. In the PwAc scenario, the low-pressure anomalies over Siberia and the high-pressure anomalies over the Kuroshio–Oyashio Extension were negligible and insignificant. The significant anomalous anticyclone over the Philippine Sea and the 500-hPa geopotential height (Z500) anomalies over East Asia and the western North Pacific were located farther southward and were weaker than those under the PwAw forcing (Figs. 11f–h). In accordance with the anomalous SLP and geopotential height fields, the anomalous southeasterly over coastal areas of East Asia was weaker than those under the PwAw forcing. Therefore, the positive SAT anomalies were located farther northward and were mainly confined to Siberia (Fig. 11e). The model simulations confirmed speculation that the warm (cold) phase of the AMO might reinforce (cripple) the impact of warm ENSO-like SSTAs on the EAWM.

Fig. 11.

As in Fig. 9, but obtained by subtracting the average of the control runs from the average of the PwAw runs.

Fig. 11.

As in Fig. 9, but obtained by subtracting the average of the control runs from the average of the PwAw runs.

Figure 12 displays the differences in the EAWM-related atmosphere between the PcAw/PcAc run and the control run. Notably, the anomalous SAT and circulation patterns in Fig. 12 were almost opposite of those in Fig. 11. When a cold ENSO-like pattern was accompanied by a positive phase of the AMO (indicated as PcAw), there were statistically significant small-scale high-pressure anomalies centered in Lake Baikal, Siberia, and low-pressure anomalies located over the western North Pacific (Fig. 12b). These intensified the meridional and zonal pressure gradients and caused the anomalous northeasterly winds over East Asian coastal areas (10°–30°N; Fig. 12c). Additionally, the EAT was slightly weakened (Fig. 12d). Thus, the negative SAT anomalies were barely observed on the East Asian continent and appeared only in the East Asian marginal seas (Fig. 12a). Nevertheless, when both the AMO and ENSO-like SSTAs were in negative phases (indicated as PcAc), the magnitude of high-pressure anomalies over Siberia, low-pressure anomalies over the western North Pacific, and the negative geopotential height anomalies at 500 hPa were twice as large as those under PcAw forcing (Figs. 12f,h). Of note, the significant negative SLP and 500-hPa geopotential height anomalies over the western North Pacific extended farther northward, inducing significant anomalous winds northeasterly over the entire East Asian coastal areas and anomalous northwesterly winds around Siberia (Fig. 12g). Consequently, statistically significant negative SAT anomalies appeared not only in the western North Pacific and South Asia but also in southern China and Mongolia (Fig. 12e). This result implies that the impact of the cold phase of ENSO-like SSTAs on the EAWM would be more dominant under a cold AMO scenario than under a warm AMO scenario.

Fig. 12.

As in Fig. 9, but obtained by subtracting the average of the control runs from the average of the PcAw runs.

Fig. 12.

As in Fig. 9, but obtained by subtracting the average of the control runs from the average of the PcAw runs.

Both the observational and simulated results suggested that the ENSO-like and AMO SSTAs could independently impact the EAWM to some extent. However, there were differing effects of the various combinations of the AMO and ENSO-like SSTAs on the SAT and EAWM. Based on the previous studies and our results, we inferred a hypothesis for the dynamical mechanisms involved in the teleconnection. The possible mechanisms are listed below (Fig. 13):

  1. In warm ENSO-like events, an anomalous anticyclone occurred in Philippine Sea—a Rossby wave response to the suppressed convective heating—that was induced by both the in situ ocean surface cooling and equatorial central Pacific heating (Matsuno 1966; Gill 1980; Wang et al. 2000). Additionally, a weakened EAT forced by the ENSO-like warming impaired large-scale sinking motion in the rear of the trough to drive a reduced SH and weakened ascending motion in front of the trough to induce an anomalous anticyclone over the Kuroshio–Oyashio Extension (Wang et al. 2000; Hao et al. 2015). The reduced SH, the anomalous Philippine Sea anticyclone, and the anomalous Kuroshio–Oyashio Extension anticyclone caused warmer winters in East Asia. The reverse occurred in a cold ENSO-like scenario.

  2. The AMO could influence the winter surface temperature in East Asia through an atmospheric bridge—the North Atlantic Oscillation (e.g., Li et al. 2009; Li and Bates 2007; G. L. Wang et al. 2009; Goswami et al. 2006; Wang et al. 2010b). The warm AMO induced a weakened Mongolian cold high and then favored a warm winter in East Asia on an interdecadal time scale, and vice versa.

  3. There might be two ways to accomplish the combined effect of the ENSO-like and AMO SSTAs on the interannual variability of the EAWM. On the one hand, in the warm phase of the AMO, the weakened Mongolian cold high and positive SAT anomalies in East Asia on the interdecadal time scales might provide back ground climatic conditions to superpose upon the anomalous EAWM forced by ENSO-like SSTAs, which reinforced (crippled) the low-pressure (high pressure) anomalies over Mongolia–Siberia and strengthened (weakened) the positive (negative) SAT anomalies over East Asia in warm (cold) ENSO-like events. As a result, the EAWM was intensely weakened when both the AMO and ENSO-like SSTAs were in positive phases, while the anomalies related to the EAWM were insignificant when a cold ENSO-like winter coupled with a positive AMO. In the same way, when the AMO and ENSO were negatively in phase, they deeply affected the EAWM, while the influence was weak when a warm ENSO winter was coupled with negative AMO.

  4. On the other hand, the AMO provided a heating source for the Walker circulation (Wang et al. 2010a). Generally, a warm (cold) ENSO-like weakened (strengthened) the Walker circulation and there were positive (negative) velocity potential anomalies and anomalous convergence (divergence) wind at the upper-troposphere over the western Pacific (He and Wang 2013; Fig. 10a). Both observed and simulated results showed that the Walker circulation response to the ENSO-like SSTAs was intensified (impaired) when the ENSO-like and AMO SSTAs were in phase (out of phase) (Fig. 14). Compared with the situation during the cold phase of the AMO, the Walker circulation anomalies induced by the warm ENSO-like SSTAs during the warm phase of the AMO were relatively stronger (Figs. 14a,c). Thus, the Walker circulation anomalies forced by the positive in-phase combination of the AMO and ENSO-like SSTAs could lead to stronger divergent wind anomalies on the surface over the northwestern Pacific, which could further drive a stronger warm ENSO-like variability through the surface westerly anomalies over the equatorial Pacific (Fig. 15a vs Fig. 15c). Similarly, the cold (warm) AMO could reinforce (weaken) the Walker circulation anomalies associated with a cold ENSO-like pattern (Figs. 14b,d, 15b,d), which results in an increase (decrease) in the cold ENSO-like variability. More intense variability of the ENSO-like under the in-phase combinations implied deeper anomalous anticyclones–cyclones over Philippine Sea and Kuroshio–Oyashio Extension. Meanwhile, anomalous sinking (ascending) motion over the Philippine Sea caused by the positive (negative) in-phase combinations was more potent compared with the anomalous motion under the out-of-phase combinations (Fig. 14). It suggests that an anomalous anticyclone/cyclone over the Philippine Sea under the in-phase combinations is stronger than under the out-of-phase combinations. In summary, deeper anomalous anticyclones–cyclones over the Philippine Sea and Kuroshio–Oyashio Extension responded to the positive–negative in-phase combination of the AMO and ENSO-like SSTAs, acting through the Walker circulation; thus, the change in the EAWM was therefore more intense.

Fig. 13.

A schematic diagram for the combined effect of the warm ENSO-like and warm AMO SSTAs on the interannual variability of the EAWM.

Fig. 13.

A schematic diagram for the combined effect of the warm ENSO-like and warm AMO SSTAs on the interannual variability of the EAWM.

Fig. 14.

(a) Composite winter-mean 200-hPa velocity potential (contours with an interval of 1.2 × 10−6 m2 s−1) and divergent wind vectors (m s−1) between the warm AMO + warm ENSO-like events and cold AMO + warm ENSO-like events, using the NOAA dataset. Green and yellow shadings indicate the 95% confidence level. (b) As in (a), but between cold AMO + cold ENSO-like events and warm AMO + cold ENSO-like events. (c) As in (a), but between the PwAw and PwAc runs. (d) As in (c), but between the PcAc and PcAw runs.

Fig. 14.

(a) Composite winter-mean 200-hPa velocity potential (contours with an interval of 1.2 × 10−6 m2 s−1) and divergent wind vectors (m s−1) between the warm AMO + warm ENSO-like events and cold AMO + warm ENSO-like events, using the NOAA dataset. Green and yellow shadings indicate the 95% confidence level. (b) As in (a), but between cold AMO + cold ENSO-like events and warm AMO + cold ENSO-like events. (c) As in (a), but between the PwAw and PwAc runs. (d) As in (c), but between the PcAc and PcAw runs.

Fig. 15.

Conditional composite maps of SSTAs (°C) for (a) warm AMO + warm ENSO-like SSTAs, (b) warm AMO + cold ENSO-like SSTAs, (c) cold AMO + warm ENSO-like SSTAs, and (d) cold AMO + cold ENSO-like SSTAs, using the NOAA Extended Reconstructed SST, version 4, dataset. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

Fig. 15.

Conditional composite maps of SSTAs (°C) for (a) warm AMO + warm ENSO-like SSTAs, (b) warm AMO + cold ENSO-like SSTAs, (c) cold AMO + warm ENSO-like SSTAs, and (d) cold AMO + cold ENSO-like SSTAs, using the NOAA Extended Reconstructed SST, version 4, dataset. Light, medium, and dark shadings indicate the 90%, 95%, and 99% confidence levels, respectively.

4. Summary and discussions

This study reveals the combined effects of the AMO and ENSO-like SSTAs on the interannual variability of the EAWM, based on both observations and numerical model experiments. First, we examined the individual effect of the AMO and ENSO-like SSTAs on the EAWM using observations. We found that ENSO-like SSTAs had a negative relationship with the EAWM and that the AMO could drive a low-frequency change in the EAWM, as has been suggested in previous studies (e.g., Li et al. 2009; Li and Bates 2007; G. L. Wang et al. 2009; Goswami et al. 2006; Wang et al. 2010b).

To investigate the combined effect of the AMO and ENSO-like SSTAs on the interannual variability of the EAWM, we analyzed the conditional composite of the four categories (i.e., warm AMO + warm ENSO-like SSTAs, cold AMO + warm ENSO-like SSTAs, warm AMO + cold ENSO-like SSTAs, cold AMO + cold ENSO-like SSTAs). Observations showed that the interannual variability of the EAWM depended on the combination of the AMO and ENSO-like SSTAs. When the AMO and ENSO were positively in phase, the increasing of the SAT over East Asia was strong and statistically significant in winter, implying a weakened EAWM. The opposite occurred when the AMO and ENSO were negatively in phase. By contrast, the SAT anomalies were less robust when the AMO and ENSO-like SSTAs were out of phase. This discrepancy might be attributed to the different EAWM-related circulation anomalies between the in-phase combinations of the AMO and ENSO-like SSTAs and the out-of-phase combinations. A warm ENSO-like winter accompanied by a warm AMO was connected with a robust weakened SH, a robust anomalous Philippine anticyclone, and a strong anomalous Kuroshio–Oyashio Extension anticyclone, which led to fewer cold surges over the East Asian continent. The opposite occurred in response to the negative in-phase combination of the ENSO-like and AMO SSTAs. However, the atmospheric anomalies corresponding to the out-of-phase AMO and ENSO-like SSTAs were relatively weaker and insignificant. The results allowed us to realize that the AMO variability can promote or disrupt the relationship between the EAWM and ENSO-like SSTAs.

Fortunately, the idealized numerical experiments carried out by U.S. CLIVAR demonstrate the validity of the observations. First, model simulations suggested that the ENSO-like and AMO SSTAs could independently impact the EAWM to some extent. Warm ENSO-like forcing could incite significant warm temperature anomalies in northern China and the East Asian marginal seas, low-pressure anomalies around Siberia, anomalous anticyclones over the western North Pacific and southwesterly wind anomalies along the East Asian coast areas in the lower troposphere, and a weakened EAT in the middle troposphere. Reversed atmospheric signal in the cold ENSO-like scenario drove cold temperature anomalies in South Asian countries. Moreover, the warm AMO SSTAs could also drive changes in the EAWM, and vice versa. For example, an anomalous warm SAT dominates over the northern East Asian continent when forced by warm AMO SSTAs. Second, model simulations further confirmed the combined effect of the AMO and ENSO-like SSTAs on the EAWM. When both the AMO and ENSO-like SSTAs were in positive phases, a strongly weakened EAWM responded to the SSTAs, including significant positive SAT anomalies on the East Asian continent and its marginal seas, a weaker SH, anomalous anticyclones over the western North Pacific with southwesterly wind anomalies and a weakened EAT, and vice versa under the combination when both were in negative phases. However, the responses of the SAT and EAWM-related circulation to the out-of-phase combinations of AMO and ENSO-like SSTAs were weaker than the responses to in-phase combinations.

Notably, the response of the interannual variability of the East Asian temperature to the positive–negative in-phase combination of the AMO and ENSO-like SSTAs was asymmetric. The composite differences of the SAT under the positive in-phase combination were found to be stronger than the difference under the negative in-phase combination. Under the positive in-phase combination, significant temperature anomalies occurred farther north over East Asia. This discrepancy might be attributable to the asymmetric effect of ENSO-like SSTAs on East Asian temperature. Many previous studies have indicated that the effect of El Niño on the EAWM was different from the effect of La Niña (e.g., Wang et al. 2000; Zhang et al. 1997; Cai et al. 2010; Zhang et al. 2015). In particular, there were strong anomalous anticyclones over the western North Pacific in an El Niño winter but weak anomalous cyclones in a La Niña winter, which was the key system that bridges ENSO and the EAWM. In this paper, both the observations and the simulations showed that the impact of ENSO-like SSTAs on the EAWM was significantly asymmetric; that is, warm ENSO-like SSTAs induced strong SAT anomalies in East Asia, while the SAT anomalies forced by the cold ENSO-like SSTAs were weak. The asymmetric effect of ENSO-like SSTAs on the EAWM, superimposed onto the effect of the AMO on the background winter climate in East Asia, may be a reason for the asymmetry in the combined effect of the AMO and ENSO-like SSTAs on the interannual variability of the EAWM.

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grants 41421004 and 41210007).

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