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  • View in gallery

    Correlations between the AO index and surface air temperature anomalies in China for (a) December, (b) January, and (c) February. Regions inside the white dashed contour indicate significance at the 95% confidence level based on a two-tailed Student’s t test.

  • View in gallery

    Regressions of the 500-hPa geopotential height anomalies (gpm) onto the AO index for (a) December, (b) January, and (c) February. (d)–(f) As in (a)–(c), but for the 300-hPa zonal wind anomalies (m s−1). Shading indicates significance at the 95% confidence level based on a two-tailed Student’s t test. Purple contour in (d)–(f) denotes the climatic mean of the 300-hPa zonal wind with a value of 30 m s−1.

  • View in gallery

    As in Fig. 1, but using the residual surface air temperature anomalies after removing the MEJS signal.

  • View in gallery

    Correlations of the MEJS index with (a) raw surface air temperature anomalies and (b) residual surface air temperature anomalies after removing the AO signal in January. Regions inside the white dashed contour indicate significance at the 95% confidence level based on a two-tailed Student’s t test.

  • View in gallery

    Regressions of the 300-hPa velocity potential (contour; 105 m2 s−1) and RWS (shading; 10−11 s−2) anomalies onto the AO index for (a) December, (b) January, and (c) February. (d)–(f) As in (a)–(c), but for the anomalies of near-surface (σ = 0.995) velocity potential (contour; 105 m2 s−1) and sea level pressure (shading; hPa). Vector indicates divergent wind component (m s−1). Purple contour in (a)–(c) are as in Fig. 2. Boxes in (a) and (b) indicate the North Atlantic and Mediterranean region for defining the regionally averaged RWS indices, respectively.

  • View in gallery

    Composite anomalies of the sea level pressure and near-surface (σ = 0.995) velocity potential (contour; 105 m2 s−1) and divergent wind (vector; m s−1) for the extreme AO phase accompanied with (a) extreme and (d) normal RWS anomalies over the Mediterranean region in January. Years for composites are shown in Table 2. (b),(e) As in (a),(d), but for the anomalies of 300-hPa velocity potential (contour; 105 m2 s−1), divergent wind (vector; m s−1), and RWS (shading; 10−11 s−2). (c),(f) As in (a),(d), but for the 300-hPa zonal wind anomalies (contour; m s−1). Shading in (c) and (f) indicates significance at the 95% confidence level based on a two-tailed Student’s t test. Purple contour in (b),(c),(e),(f) are as in Fig. 2.

  • View in gallery

    Responses of (a) the streamfunction (contour; 106 m2 s−1) and wave activity flux (vector; m2 s−2) and (b) the zonal wind (contour; m s−1) to the idealized positive vorticity forcing over the northeastern Atlantic in a linearized barotropic model. Shading indicates climatic mean of the 300-hPa zonal wind larger than 30 m s−1, and a dot denotes the center of the forcing in the model. (c),(d) As in (a),(b), but for the forcing located in the Mediterranean region.

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Contrasting Impacts of the Arctic Oscillation on Surface Air Temperature Anomalies in Southern China between Early and Middle-to-Late Winter

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  • 1 Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, and Joint Center for Global Change Studies, Beijing, China
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Abstract

In the boreal winter, the Arctic Oscillation (AO) evidently acts to influence surface air temperature (SAT) anomalies in China. This study reveals a large intraseasonal variation in the relationship between the winter AO and southern China SAT anomalies. Specifically, a weak in-phase relationship occurs in December, but a significant out-of-phase relationship occurs in January and February. The authors show that the linkage between the AO and southern China SAT anomalies strongly depends on the AO-associated changes in the Middle East jet stream (MEJS) and that such an AO–MEJS relationship is characterized by a significant difference between early and middle-to-late winter. In middle-to-late winter, the Azores center of high pressure anomalies in the positive AO phase usually extends eastward and yields a significantly anomalous upper-level convergence over the Mediterranean Sea, which can excite a Rossby wave train spanning the Arabian Sea and intensify the MEJS. In early winter, however, the Azores center of the AO is apparently shifted westward and is mainly confined to the Atlantic Ocean; in this case, the associated change in the MEJS is relatively weak. Both observational diagnoses and experiments based on a linearized barotropic model suggest that the MEJS is closely linked to the AO only when the latter generates considerable upper-level convergence anomalies over the Mediterranean Sea. Therefore, the different impacts of the AO on the MEJS and the southern China SAT anomalies between early and middle-to-late winter are primarily attributed to the large intraseasonal zonal migrations of the Azores center of the AO.

Denotes Open Access content.

Corresponding author address: Hong-Li Ren, National Climate Center, China Meteorological Administration, 46 Zhongguancun, Haidian District, Beijing 100081, China. E-mail: renhl@cma.gov.cn

Abstract

In the boreal winter, the Arctic Oscillation (AO) evidently acts to influence surface air temperature (SAT) anomalies in China. This study reveals a large intraseasonal variation in the relationship between the winter AO and southern China SAT anomalies. Specifically, a weak in-phase relationship occurs in December, but a significant out-of-phase relationship occurs in January and February. The authors show that the linkage between the AO and southern China SAT anomalies strongly depends on the AO-associated changes in the Middle East jet stream (MEJS) and that such an AO–MEJS relationship is characterized by a significant difference between early and middle-to-late winter. In middle-to-late winter, the Azores center of high pressure anomalies in the positive AO phase usually extends eastward and yields a significantly anomalous upper-level convergence over the Mediterranean Sea, which can excite a Rossby wave train spanning the Arabian Sea and intensify the MEJS. In early winter, however, the Azores center of the AO is apparently shifted westward and is mainly confined to the Atlantic Ocean; in this case, the associated change in the MEJS is relatively weak. Both observational diagnoses and experiments based on a linearized barotropic model suggest that the MEJS is closely linked to the AO only when the latter generates considerable upper-level convergence anomalies over the Mediterranean Sea. Therefore, the different impacts of the AO on the MEJS and the southern China SAT anomalies between early and middle-to-late winter are primarily attributed to the large intraseasonal zonal migrations of the Azores center of the AO.

Denotes Open Access content.

Corresponding author address: Hong-Li Ren, National Climate Center, China Meteorological Administration, 46 Zhongguancun, Haidian District, Beijing 100081, China. E-mail: renhl@cma.gov.cn

1. Introduction

The Arctic Oscillation (AO) is the dominant mode of winter atmospheric low-frequency variability in the extratropical Northern Hemisphere (NH), and its typical structure is characterized by a pressure seesaw between the Arctic and middle latitudes of the NH (Thompson and Wallace 1998, 2000). The atmospheric circulation anomalies associated with the AO over the North Atlantic sector greatly resemble those associated with the North Atlantic Oscillation (NAO). The time series for the two similar phenomena are highly correlated in boreal winter and are usually combined in climate studies (Wallace 2000; Watanabe 2004).

Changes in the phases of the AO/NAO are usually accompanied by large-scale weather and climate anomalies in the northern continents, including North America, North Africa, Greenland, and Eurasia, during winter (Hurrell 1995; Hurrell et al. 2003; Thompson and Wallace 1998, 2001; Huang et al. 2006; Wang and Chen 2010). Additionally, the winter AO/NAO can have a delayed impact on the subsequent summer atmospheric circulation and local climate variability in the middle and high latitudes of the NH via the role of the AO/NAO-induced underlying surface anomalies, such as the North Atlantic sea surface temperature, the Arctic sea ice, and the Eurasian snow cover, which can persist from winter to summer (e.g., Gong and Ho 2003; Ogi et al. 2003; Sung et al. 2006; Zuo et al. 2012, 2013).

It has been indicated that variations in the East Asian winter monsoon (EAWM) and winter surface climate are intimately related to the AO/NAO variability on the interannual and interdecadal time scales. The atmospheric circulation anomalies corresponding to the negative AO/NAO phase tend to favor a strong EAWM and cold surface air temperature (SAT) anomalies over East Asia in winter, and vice versa (Wu and Huang 1999; Gong et al. 2001; Wu and Wang 2002; Gong and Wang 2003). The variability of the occurrence of the East Asian cold surge event in winter, which is one of the most prominent extreme weather events with tremendous socioeconomic impacts on East Asian countries, is closely linked to the AO/NAO (Jeong and Ho 2005; Hong et al. 2008). Additionally, the wintertime East Asian cold surges during the negative AO phase tend to have larger amplitudes and longer durations than those during the positive AO phase (Park et al. 2011, 2014).

The above-mentioned studies generally suggest that the substantial impacts of the AO/NAO on the winter mean SAT anomalies in China are mainly confined to the north (Wu and Wang 2002). However, Wen et al. (2009) found that the AO acted as an important factor for the long-lasting snowstorms that affected central-southern China in January 2008; this is consistent with the results of Zhang et al. (2008). Also, Tan et al. (2010) reported an intimate relationship between the NAO and SAT anomalies in central and southern China in January. These findings imply that the relationship between the AO/NAO and the SAT and/or precipitation anomalies in central and southern China in terms of the seasonal mean may be different from that in terms of the monthly mean. In other words, the impacts of the AO/NAO on the climate anomalies in central and southern China most likely exhibit intraseasonal variations during wintertime that require further examination.

Furthermore, the mechanisms responsible for the impacts of the AO/NAO on the winter climate variability in southern China are possibly different from those in northern China during winter. The former tends to be related to changes in the atmospheric circulation over southern Eurasia, such as the Middle East jet stream (MEJS), as mentioned by Wen et al. (2009), whereas the latter is primarily linked to the large-scale circulation anomalies over the Eurasian middle-to-high latitudes, such as the Siberian high and East Asian coastal trough (Gong et al. 2001; Wu and Wang 2002; Chen and Kang 2006). Both the Siberian high and East Asian coastal trough tend to be weaker during the positive AO/NAO phase, and as a result the winter SAT anomalies over East Asia are warmer than the normal, and vice versa. Some studies have suggested that the link of the AO with circulation changes over the Eurasian middle-to-high latitudes and East Asian climate variability could be modulated by planetary wave activity involving dynamical coupling between the stratosphere and troposphere during boreal winter (Chen et al. 2005; Chen and Kang 2006; Takaya and Nakamura 2013; Nath et al. 2014). Also, studies showed that the positive (negative) phase of the AO/NAO tends to be accompanied by a wave train spanning the North Atlantic to the Arabian Sea and by a concurrently intensified (weakened) MEJS during winter (Yang et al. 2004; Xu et al. 2012; Gong et al. 2014). The MEJS has a substantial influence on the winter SAT and precipitation anomalies in southern China by affecting the cold air activity and water vapor transport from the north Indian Ocean to East Asia (Yang et al. 2004; Wen et al. 2009). In this case, the AO/NAO could exert a remote impact on the climate variability of southern China by modifying the MEJS. However, how the AO/NAO induces changes in the Asian subtropical atmospheric circulation (e.g., anomalies in the MEJS) remains unclear.

These findings motivated us to comprehensively investigate the relationship between the AO/NAO and SAT anomalies in southern China during winter and the associated physical mechanisms. This study focuses on revealing the intraseasonal variations in such a relationship and on demonstrating the physical mechanism generating these variations. The remainder of the paper is organized as follows. Section 2 introduces the data and methods. Section 3 presents the relationships between the monthly AO and SAT anomalies in China during winter. Then, the possible mechanism responsible for the relationship between the AO and southern China SAT anomalies and for the intraseasonal contrast of the relationship is explored in section 4, followed by a summary and discussion in section 5.

2. Data and methods

The winter months addressed in this study are December, January, and February. Monthly atmospheric circulations, including geopotential height, zonal and meridional wind components, and sea level pressure (SLP), are derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996). These data have a horizontal resolution of 2.5° × 2.5°. In addition, gauge-based observations of monthly SATs for approximately 670 stations in China are provided by the China Meteorological Administration. The monthly AO and NAO indices are obtained from the National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center (CPC) (NOAA/CPC 2015a,b). The strength of the MEJS is measured by averaging the zonal wind at 300 hPa over the subtropical western Asia region (25°–35°N, 50°–80°E). All the data used in this study cover the period of 1979–2011. Linear trends in the time series are removed prior to the correlation/regression analysis.

The correlation between the NAO and AO indices is 0.78 in December, 0.72 in January and 0.85 in February during 1979–2011, which are all significant at the 95% confidence level based on a two-tailed Student’s t test. Because of the high correlation between the AO and NAO indices during boreal winter, we show only the results associated with the AO index in the present study.

3. Relationship between the monthly AO and SAT anomalies in China

Figure 1 shows the correlations between the monthly AO index and SAT anomalies in China for each winter month. It can be observed that correlations between the AO and SAT anomalies in northern China were significantly positive in each month (Figs. 1a–c). However, the correlation patterns between the AO and SAT anomalies in central and southern China showed different features between early and middle-to-late winter. In December, weakly positive correlations were located in central and southern China, except in west-southwestern China (Fig. 1a). In contrast, negative correlations were observed in the same regions in January (Fig. 1b), which exceeded the 95% confidence level for a large part of central and southern China, and negative correlations were also observed in southern China in February but were only significant in southwestern China (Fig. 1c).

Fig. 1.
Fig. 1.

Correlations between the AO index and surface air temperature anomalies in China for (a) December, (b) January, and (c) February. Regions inside the white dashed contour indicate significance at the 95% confidence level based on a two-tailed Student’s t test.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00687.1

Overall, these results reveal that a positive relationship of the AO with the SAT anomalies in northern China clearly exists in each winter month, but the relationship of the AO with the SAT anomalies in central and southern China exhibits an evident difference between early and middle-to-late winter.

4. Possible physical mechanism

The previous results show that the relationship of the AO with southern China SAT anomalies exhibits a large contrast between early and middle-to-late winter (i.e., it is weakly positive in early winter but negative in middle-to-late winter). Additionally, as mentioned in the introduction, the fundamental mechanism linking southern China SAT anomalies to the AO variability is still unclear. Therefore, the possible mechanism responsible for the relationship between the AO and southern China SAT anomalies and for the intraseasonal contrast of the relationship is explored in this section.

a. Monthly atmospheric circulation anomalies associated with the AO

First, the characteristics of monthly atmospheric circulation anomalies associated with the AO index during winter were investigated. Figure 2 displays the patterns of the monthly geopotential height anomalies at 500 hPa (H500) and the zonal wind anomalies at 300 hPa (U300) associated with the AO for each winter month.

Fig. 2.
Fig. 2.

Regressions of the 500-hPa geopotential height anomalies (gpm) onto the AO index for (a) December, (b) January, and (c) February. (d)–(f) As in (a)–(c), but for the 300-hPa zonal wind anomalies (m s−1). Shading indicates significance at the 95% confidence level based on a two-tailed Student’s t test. Purple contour in (d)–(f) denotes the climatic mean of the 300-hPa zonal wind with a value of 30 m s−1.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00687.1

In the positive phase of the AO, above-normal H500 anomalies were located over East Asia in each month (Figs. 2a–c); these anomalies constrain the southward invasion of polar cold air masses and favor warm SAT anomalies in northern China and vice versa. Therefore, the consistent patterns of atmospheric circulation anomalies over East Asia associated with the AO contribute substantially to the nearly steady impact of the AO on the SAT anomalies in northern China during all winter months. However, such circulation patterns are not suitable for explaining why the relationship of the AO with SAT anomalies in southern China exhibit a large contrast between early and middle-to-late winter.

Another notable feature in the H500 anomalies also observed in Figs. 2a–c is the wave train pattern spanning the North Atlantic and Europe through subtropical North Africa and western Asia in each month. In the positive phase of the AO, positive H500 anomalies lie over the midlatitude North Atlantic, western Europe, and the Arabian Sea, whereas negative H500 anomalies are observed over North Africa and western Asia. A similar wave train pattern can be more clearly observed in the U300 anomalies in each month (Figs. 2d–f), accompanied by an intensified subtropical westerly jet stream over North Africa to western Asia. An opposite pattern can be observed in the negative phase of the AO.

However, note that in Fig. 2, the centers of the wave train patterns associated with the AO obviously shift eastward in middle-to-late winter, relative to early winter. For example, the upstream center of the H500 wave train pattern is mainly confined to the North Atlantic in December (Fig. 2a), but it extends eastward into western Europe in both January and February (Figs. 2b,c). These features are clearer in the U300 wave train patterns associated with the AO (Figs. 2d–f). In the positive phase of the AO, westerly anomalies that are distributed over North Africa in December (Fig. 2d) can lead to an intensified North African jet stream, and they shift eastward into western Asia in January and February (Figs. 2e,f) and produce an intensified MEJS. As shown in Table 1, the correlation between the AO and the MEJS is 0.59 in January and 0.45 in February but only 0.32 in December (R0.05 = 0.35), confirming the evident change in the AO–MEJS relationship between early and middle-to-late winter.

Table 1.

Correlations between the monthly AO index and the regionally averaged Rossby wave source index over the Mediterranean region (MRWS; 27.5°–45°N, 15°–35°E) and North Atlantic (NRWS; 30°–45°N, 25°–5°W), and the MEJS index. Significance at the 95% and 99% confidence level are denoted by a single and double asterisk, respectively, based on a two-tailed Student’s t test.

Table 1.

We also noted that the change in the AO–MEJS relationship is consistent with that for the AO–southern China SAT relationship between early and middle-to-late winter. As mentioned in previous studies, the MEJS has a substantial impact on the SAT anomalies in southern China (e.g., Wen et al. 2009) and may play a role in linking the latter to the AO variability during winter. Therefore, we hypothesize that the different impacts of the AO on the southern China SAT anomalies between early and middle-to-late winter may be attributed to the intraseasonal change in the AO–MEJS relationship.

b. Role of the MEJS in linking the AO with southern China SAT anomalies

To clarify the role of the MEJS in linking the AO and southern China SAT anomalies, Fig. 3 displays the correlations between the AO and the residual China SAT anomalies after removing the MEJS signal by regressing linearly the SAT anomalies onto the MEJS index. A comparison between Fig. 1 and Fig. 3 reveals significantly positive correlations between the AO and SAT anomalies in northern China with and without the MEJS signal in each winter month. Thus, the impact of the AO on the SAT anomalies in northern China is likely independent of the MEJS variability during winter.

Fig. 3.
Fig. 3.

As in Fig. 1, but using the residual surface air temperature anomalies after removing the MEJS signal.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00687.1

In contrast, the negative correlations between the AO and SAT anomalies in southern China nearly vanish after removing the MEJS signal in both January and February (Figs. 3b,c); thus, the linkage of the southern China SAT anomalies with the AO strongly depends on the MEJS variability in middle-to-late winter. The correlation patterns between the AO and China SAT anomalies are highly similar before and after removing the MEJS signal in December, which can presumably be attributed to the reduced covariability between the AO and MEJS in early winter. Though the AO-related changes in MEJS are relatively weak in December, a weakened trough over East Asia can be clearly observed (Fig. 2a). Also, it is noted that the East Asian coastal trough in December extends southward compared to that in middle-to-late winter (Figs. 2a–c). These results indicate that the in-phase relationship between the AO and SAT anomalies in southern China appears to be linked with the East Asian coastal trough in early winter (Gong et al. 2001; Wu and Wang 2002; Chen and Kang 2006).

Furthermore, relationships of the MEJS index with raw SAT anomalies and residual SAT anomalies after removing the AO signal are examined. With a focus on January, when the AO–SAT relationship is highest, we find that the pattern of AO–SAT correlations (Fig. 1b) well resembles that of MEJS–SAT correlations (Fig. 4a) in central and southern China, except that the latter has a higher amplitude. Also, the pattern of correlations between MEJS and SAT anomalies in central and southern China show a great resemblance before and after removing the AO signal (Fig. 4), indicating that the MEJS–SAT relationship in central and southern China are not evidently influenced by the AO.

Fig. 4.
Fig. 4.

Correlations of the MEJS index with (a) raw surface air temperature anomalies and (b) residual surface air temperature anomalies after removing the AO signal in January. Regions inside the white dashed contour indicate significance at the 95% confidence level based on a two-tailed Student’s t test.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00687.1

Therefore, these results confirm the key role of the MEJS in linking the AO with SAT anomalies in southern China in middle-to-late winter. This is evidently distinct from the mechanism responsible for the linkage between the AO and SAT anomalies in northern China, where variations in the atmospheric circulation over Eurasian middle-to-high latitudes are important (Gong et al. 2001; Wu and Wang 2002).

c. Possible mechanism responsible for the AO–MEJS linkage

Because the impact of the AO on the SAT anomalies in southern China strongly depends on the MEJS variability, the questions that arise are how the MEJS is connected to the AO variability and why the relationship exhibits an apparent difference between early and middle-to-late winter. As indicated in Fig. 2, the AO-associated changes in the MEJS are concurrent with a wave train pattern with an equivalent barotropic vertical structure over the North Atlantic–Eurasia region, implying that the dynamic processes that generate the anomalous wave train pattern are most likely related to an anomalous vorticity source (Branstator 2002; Watanabe 2004). Hence, to explore the possible mechanisms responsible for the linkage between the MEJS and AO, we investigated the effect of the anomalous upper-level divergence and Rossby wave source (RWS; Sardeshmukh and Hoskins 1988) associated with the AO.

Figure 5 shows the AO-related patterns of diverse variables in each winter month. The most notable feature in Fig. 5 is the obvious upper-level convergence and RWS anomalies over the Euro-Atlantic midlatitudes in the positive AO phase, which is consistent with the divergence anomalies in the near-surface layer. Additionally, the near-surface divergence (convergence) anomalies are consistent with the high (low) SLP anomalies that are associated with the AO. This indicates that the upper-level divergence and RWS anomalies can be induced by the near-surface Ekman pumping associated with the AO (Feldstein 2003). However, we show that these anomalies are characterized by a large intraseasonal contrast in the present study. Specifically, in December, the high SLP anomalies located over the Azores and the adjacent regions, representing the Azores center of the AO, are mainly confined to the ocean (Fig. 5d) and are concurrent with the obvious upper-level convergence and positive RWS anomalies over the northeastern Atlantic (Fig. 5a). In contrast, in January and February, the high SLP anomalies evidently extend eastward (Figs. 5e,f). As a result, the obvious upper-level convergence and positive RWS anomalies shift eastward into the Mediterranean region in middle-to-late winter (Figs. 5b,c).

Fig. 5.
Fig. 5.

Regressions of the 300-hPa velocity potential (contour; 105 m2 s−1) and RWS (shading; 10−11 s−2) anomalies onto the AO index for (a) December, (b) January, and (c) February. (d)–(f) As in (a)–(c), but for the anomalies of near-surface (σ = 0.995) velocity potential (contour; 105 m2 s−1) and sea level pressure (shading; hPa). Vector indicates divergent wind component (m s−1). Purple contour in (a)–(c) are as in Fig. 2. Boxes in (a) and (b) indicate the North Atlantic and Mediterranean region for defining the regionally averaged RWS indices, respectively.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00687.1

Also note that, in Figs. 5a–c, the AO-related upper-level convergence and RWS anomalies are mainly located to the north (or northeast) of the Asian subtropical jet stream entrance in all winter months. Because of the waveguide effect of the westerly jet stream, the RWS anomalies near the jet entrance can induce a set of Rossby wave trains that are oriented along the jet (Branstator 2002; Watanabe 2004). As observed in Figs. 2 and 5, the upper-level RWS anomalies, located in the northeastern Atlantic and the Mediterranean Sea, are both accompanied by a set of wave trains over the downstream regions. Therefore, such a close relationship between the AO and upper-level RWS anomalies over the Euro-Atlantic region and its apparent intraseasonal contrast may have implications for the intraseasonal contrast in the AO–MEJS relationship and, subsequently, in the relationship between the AO and southern China SAT anomalies during winter. In other words, the impact of the AO on the MEJS and the southern China SAT anomalies may be related to the upper-level RWS anomalies over the Euro-Atlantic region.

To obtain further evidence for the relationship between the AO and the upper-level RWS anomaly and its intraseasonal contrast, we calculate correlations between the monthly AO index and the 300-hPa RWS anomaly time series that are regionally averaged over the northeastern Atlantic (30°–45°N, 5°–25°W; see the box in Fig. 5a) and the Mediterranean regions (27.5°–45°N, 15°–35°E; see the box in Fig. 5b). These two RWS time series are referred to as the NRWS and MRWS indices. As observed in Table 1, the AO index is positively correlated with both the NRWS and MRWS indices for each winter month; the correlation with the MRWS index is as high as 0.71 in January. The correlations between the AO index and the NRWS and MRWS indices range from 0.49 to 0.68 in December and February, and all are significant at the 99% confidence level. Overall, these results confirm the close relationship between the AO and the upper-level RWS anomalies over the Euro-Atlantic region and their large intraseasonal contrast during winter. Additionally, the correlation of the AO with the Mediterranean (North Atlantic) RWS anomaly in middle-to-late winter is higher (lower) than that in early winter; this finding is substantially consistent with the eastward extension of the Azores center of the AO in middle-to-late winter.

The previous results reveal that the AO has a close relationship with the Mediterranean RWS anomaly in middle-to-late winter, and the large intraseasonal contrasts in the relationship are consistent with those of the AO–MEJS relationship. Therefore, the Mediterranean RWS anomaly may play a crucial role in activating the wave train pattern and MEJS variability associated with the AO. To validate this interpretation, a composite analysis is conducted for the atmospheric circulation anomalies in terms of the two AO phases and MRWS indices. Here, only January is considered because it exhibits the highest AO–MRWS correlation. The composite anomalies are separated into two types: one based on years that have extremely positive (negative) AO and positive (negative) MRWS (referred to as type A) and another that has an extremely positive/negative AO but a normal MRWS (referred to as type B). The former consists of three extremely positive and negative years, respectively, and the latter consists of four extremely negative years (see Table 2). All of the extreme years are involved in the composite analysis, and to facilitate the comparison the signs of the atmospheric circulation anomalies are reversed for all the extremely negative years.

Table 2.

Years with respect to the AO and MRWS indices in January. Standard deviation is denoted by σ.

Table 2.

When both the AO phase and MRWS are positive, the AO-associated high SLP anomalies over the Azores extend eastward into Europe (Fig. 6a); this finding is consistent with the significant convergence and positive RWS anomalies at 300 hPa over the Mediterranean Sea (Fig. 6b). Additionally, a wave train pattern of composite anomalies of zonal wind at 300 hPa exists from the North Atlantic to the Arabian Sea (Fig. 6c), which is concurrent with an intensified MEJS. These results are substantially in agreement with those from the regression analyses, as shown in Figs. 2e and 5b. In contrast, when the positive AO phase accompanies a normal MRWS, the high SLP anomalies over the Azores are mainly confined to the ocean (Fig. 6d) such that the obvious upper-level convergence and RWS anomalies are observed over the west coast of North Africa (Fig. 6e). As a consequence, the associated wave train pattern of the composite 300-hPa zonal wind anomalies is mainly confined to the Euro-African region (Fig. 6f); in this case, the North Africa jet stream intensifies, but no significant changes occur in the MEJS. In summary, these results confirm that the AO can exert a substantial impact on the MEJS only when the former accompanies obvious RWS anomalies over the Mediterranean region.

Fig. 6.
Fig. 6.

Composite anomalies of the sea level pressure and near-surface (σ = 0.995) velocity potential (contour; 105 m2 s−1) and divergent wind (vector; m s−1) for the extreme AO phase accompanied with (a) extreme and (d) normal RWS anomalies over the Mediterranean region in January. Years for composites are shown in Table 2. (b),(e) As in (a),(d), but for the anomalies of 300-hPa velocity potential (contour; 105 m2 s−1), divergent wind (vector; m s−1), and RWS (shading; 10−11 s−2). (c),(f) As in (a),(d), but for the 300-hPa zonal wind anomalies (contour; m s−1). Shading in (c) and (f) indicates significance at the 95% confidence level based on a two-tailed Student’s t test. Purple contour in (b),(c),(e),(f) are as in Fig. 2.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00687.1

Based on these observational results, we design two idealized experiments using a linear barotropic model forced by the vorticity anomalies prescribed over the northeastern Atlantic and the Mediterranean region. The model is linearized with respect to the January climatic mean of the 300-hPa streamfunction, which is derived from the NCEP–NCAR reanalysis. More details relating to dynamic framework for the model and experiment setup can be found in reports by Watanabe (2004) and Zuo et al. (2013). Figure 7 displays the responses of the streamfunction, wave activity flux (Takaya and Nakamura 2001), and zonal wind to the idealized vorticity forcing. Results show that clear wave train patterns accompanying energy transport from the forcing area to the subtropical North African–Asian region can be observed in both of the idealized experiments, which are nearly out of phase. When the vorticity forcing is placed in the northeastern Atlantic, an anticyclonic response appears over North Africa (Fig. 7a), resulting in enhanced westerlies over the entrance region of the African–Asian jet stream (Fig. 7b). Further, when the vorticity forcing is over the Mediterranean Sea, the anticyclonic response moves into the Arabian Sea (Fig. 7c) and leads to an intensified MEJS (Fig. 7d). These results are consistent with the observational results shown in Fig. 6. Also, similar results can be obtained when using the climatic mean of December or February in the linearized model (not shown). Therefore, the similarities between the model simulations and observations further confirm the key role of the Mediterranean RWS anomalies in inducing the MEJS variability associated with the AO during winter.

Fig. 7.
Fig. 7.

Responses of (a) the streamfunction (contour; 106 m2 s−1) and wave activity flux (vector; m2 s−2) and (b) the zonal wind (contour; m s−1) to the idealized positive vorticity forcing over the northeastern Atlantic in a linearized barotropic model. Shading indicates climatic mean of the 300-hPa zonal wind larger than 30 m s−1, and a dot denotes the center of the forcing in the model. (c),(d) As in (a),(b), but for the forcing located in the Mediterranean region.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00687.1

5. Summary and discussion

This study revealed the different impacts of the AO on southern China SAT anomalies between early and middle-to-late winter and suggested a possible mechanism that causes such a difference. We examined the relationships between the monthly winter AO and SAT anomalies in China based on observational and reanalysis datasets during 1979–2011. The results showed a significant in-phase relationship between the monthly AO and SAT anomalies in northern China during December, January, and February that are consistent with the results derived from the winter-mean data. However, such a relationship greatly changes over central and southern China, where large intraseasonal variations occur. The relationships between the AO and southern China SAT anomalies are weak and positive in early winter (December) but become negative in middle-to-late winter (January and February), and in particular such an out-of-phase relationship is strong and significant in midwinter.

The linkage of the AO with SAT anomalies in southern China is strongly dependent on the AO-associated MEJS changes in middle-to-late winter. In other words, the MEJS serves as a bridge that links southern China SAT anomalies to the AO variability. Such a mechanism is evidently different from the one responsible for the connection between the AO and northern China SAT anomalies, where the latter is directly related to the AO-associated changes in large-scale atmospheric circulations over the Eurasian middle-to-high latitudes, such as the Siberian high and East Asian coastal trough (Gong et al. 2001; Wu and Wang 2002). The AO–MEJS relationship is also characterized by a large intraseasonal contrast during winter (i.e., it is weak in early winter but significant in middle-to-late winter). This finding is consistent with the intraseasonal contrast in the relationship between the AO and SAT anomalies in southern China during winter.

Furthermore, the observations and linear barotropic model simulations suggest that the AO-associated change in the MEJS is concurrent with a wave train pattern induced by the upper-level convergence anomaly over the Euro-Atlantic region. In middle-to-late winter, the Azores high SLP anomalies in the positive AO phase appear to extend eastward into Europe and lead to upper-level convergence anomalies over the Mediterranean region via the AO-associated near-surface Ekman pumping. The upper-level convergence anomalies are located just to the north of the subtropical westerly jet stream, which acts as a waveguide, and as a consequence they excite a set of wave trains and intensify the MEJS. An opposite scenario is observed in the negative AO phase. In early winter, however, the Azores high SLP anomalies in the positive AO phase are mainly confined to the Atlantic Ocean; as a result, the upper-level convergence anomalies shift westward, and the associated wave train pattern does not significantly change the MEJS compared with that in middle-to-late winter. Therefore, the different impacts of the AO on the MEJS and on southern China SAT anomalies between early and middle-to-late winter are largely attributed to the apparent intraseasonal zonal migrations of the Azores center of the AO.

Because of the high correlation between the AO and NAO, we also examined the relationships between the monthly NAO and SAT anomalies in China during winter and found that the pattern of the monthly SAT anomalies in central and southern China, in association with the NAO, well resembles that for the AO in each winter month. This result is consistent with the similarity between the monthly atmospheric circulation anomalies associated with the NAO and AO over a large part of the Euro-Atlantic region (not shown). But it is notable that the negative correlations of the NAO with the SAT anomalies in southern China are more significant than those for the AO in January and February, whereas the positive correlations in northeastern China are less significant in all the winter months. Therefore, the AO and NAO have similar impacts on the SAT anomalies in southern China during winter, except that a difference in the magnitude of the impact exists.

A question that remains unclear is why intraseasonal variation occurs in the Azores center of the AO/NAO during winter; this is a challenging issue but one beyond the scope of this paper. Early studies noted that the Azores center of the NAO generally shifts eastward from summer to winter (Barnston and Livezey 1987; Glowienka-Hense 1990; Portis et al. 2001). Indeed, some recent studies of methodologies for anatomizing the dynamical feedback show that the low-frequency variability (e.g., AO/NAO) harvests energy from synoptic eddies (Ren et al. 2009, 2011, 2012), which may help answer this question. Undoubtedly, such a question will be explored in further studies.

Additionally, early studies revealed that the wintertime spatial structure of the NAO interannual variability shifted significantly eastward in the last two decades of the twentieth century compared to the preceding two decades, which obviously resulted in changes in the relationship between the NAO and SAT anomalies over Europe (e.g., Hilmer and Jung 2000; Jung et al. 2003). Then another question is the following: How stationary is the relationship between the AO/NAO and SAT anomalies in southern China during winters? We found that the relationship between the AO and SAT anomalies during 1951–78 is nearly in contrast to that during 1979–2011 in southern China for each individual winter month (not shown). As expected, the relationship between the winter AO and SAT anomalies in southern China appears to be interdecadal nonstationary. However, more studies are needed to explore the interdecadal variations in the relationship between the wintertime AO/NAO and SAT anomalies in China for the past six decades.

Acknowledgments

This work was jointly supported by the National Basic Research program of China under Grant 2013CB430203, the Meteorological special program under Grant GYHY201406022, and the National Natural Science Foundation of China under Grants 41205058 and 41375062.

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