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  • View in gallery
    Fig. 1.

    Climatology of the DJF accumulated (a) precipitation (mm), (b) temperature, and (c) Palmer drought severity index. (d) Averaged DJF SWC precipitation and Palmer drought severity index. Precipitation is averaged at the stations in the pink box.

  • View in gallery
    Fig. 2.

    Climatology of the DJF (a) 850-hPa geopotential height (gpm) and wind (m s−1; vectors), (b) 700-hPa geopotential height (gpm) and wind (m s−1; vectors), (c) vertically integrated moisture flux (kg s−1 m−1), and (d) 700-hPa moisture flux (g m kg−1 s−1).

  • View in gallery
    Fig. 3.

    (a) Year-to-year variation in the normalized SWCPI and PSACI. (b) The 21-yr running correlation between the SWCPI and PSACI. The thin dashed lines indicate correlation coefficients exceeding the 95% confidence level, and the bold dashed lines denote correlation coefficients exceeding the 99% confidence level.

  • View in gallery
    Fig. 4.

    Correlations between precipitation in SWC and the PSACI in (a) P1 and (b) P2. Values of 0.38, 0.44, 0.56, and 0.68 represent correlation coefficients exceeding the 90%, 95%, 99%, and 99.9% confidence levels in (a). Values of 0.37, 0.43, 0.55, and 0.67 denote correlation coefficients exceeding the 90%, 95%, 99%, and 99.9% confidence levels in (b).

  • View in gallery
    Fig. 5.

    Regression of the 700-hPa moisture flux (g m kg−1 s−1; vectors) and divergence (10−6 g kg−1 s−1; shading) anomalies against the SWCPI in (a) P1 and (b) P2. Bold arrows indicate values exceeding the 95% confidence level in either the zonal or the meridional component.

  • View in gallery
    Fig. 6.

    Regression of the 700-hPa moisture flux (g m kg−1 s−1; vectors) and divergence (10−6 g kg−1 s−1; shading) anomalies against the PSACI in (a) P1 and (b) P2. Regression of the 500-hPa vertical velocity (10−2 Pa s−1; shading) and 200-hPa divergent wind (m s−1; vectors) anomalies against the PSACI in (c) P1 and (d) P2. Bold arrows indicate values exceeding the 95% confidence level in either the zonal or the meridional component.

  • View in gallery
    Fig. 7.

    (a) Vertical–meridional cross sections of the vertical velocity (10−2 Pa s−1; shading) and WV (vectors) zonally averaged between 97.5° and 107.5°E. Regression of the vertical velocity (10−2 Pa s−1; shading) and WV (vectors) anomalies zonally averaged between 97.5° and 107.5°E against the PSACI in (b) P1 and (c) P2. Gray shading represents the terrain.

  • View in gallery
    Fig. 8.

    Regression of SST (°C; shading) and 850-hPa wind (m s−1; vectors) anomalies against the ONI in (a) P1 and (b) P2. Regression of SLP (hPa; contour), 700-hPa moisture flux (g m kg−1 s−1; vectors) and divergence (10−6 g kg−1 s−1; shading) anomalies against the ONI in (c) P1 and (d) P2. Bold arrows indicate values exceeding the 95% confidence level in either the zonal or the meridional component. Green stippling denotes the value of shadings exceeding the 95% confidence level in (a) and (b). Red lines indicate that the value is above 0, and green lines indicate values below 0 in (c) and (d). The contour interval of the SLP is 0.2. Thicker red lines denote larger SLP anomalies.

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Interdecadal Changes in the Impact of the Philippine Sea Anticyclone on Boreal Winter Precipitation in Southwestern China

Zongjian KeaLaboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China

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Xingwen JiangbInstitute of Plateau Meteorology, China Meteorological Administration, Chengdu, Sichuan, China

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Jinming FengcKey Lab of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Zunya WangaLaboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China

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Abstract

In the last two decades, southwestern China (SWC) has experienced severe droughts, which are always accompanied by severe deficiencies in precipitation. In this study, we found that the interannual variability in boreal winter precipitation in SWC is modulated by the Philippine Sea anomalous anticyclone (PSAC). The interannual relationship between the PSAC and SWC precipitation experienced an interdecadal change around the early 1980s. The correlation between them was enhanced in the period from 1981 to 2001 (P2) compared to the period from 1961 to 1980 (P1). In P1, the moisture transported by the PSAC mainly affected eastern China, as the PSAC was located over the northern Philippine Sea, and the moisture budget of SWC was dominated by moisture transport at the western boundary. The PSAC, however, strengthened and shifted southwestward in P2, accompanied by a deepened India–Burma trough. As such, the PSAC transported moist air from the western North Pacific and the Indian Ocean into SWC through its southern boundary. Meanwhile, the stronger PSAC in P2 was accompanied by an upper-level convergence from the western North Pacific to the Bay of Bengal, which induced an upper-level divergence and ascending motion over SWC. Thus, the PSAC caused a significant increase in precipitation in P2. Stronger air–sea interactions in the western North Pacific induced by El Niño–Southern Oscillation may be responsible for the enhancement and southwestward shift of the PSAC in P2 compared to that in P1.

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

Corresponding author: Dr. Xingwen Jiang, xingwen.jiang@yahoo.com

Abstract

In the last two decades, southwestern China (SWC) has experienced severe droughts, which are always accompanied by severe deficiencies in precipitation. In this study, we found that the interannual variability in boreal winter precipitation in SWC is modulated by the Philippine Sea anomalous anticyclone (PSAC). The interannual relationship between the PSAC and SWC precipitation experienced an interdecadal change around the early 1980s. The correlation between them was enhanced in the period from 1981 to 2001 (P2) compared to the period from 1961 to 1980 (P1). In P1, the moisture transported by the PSAC mainly affected eastern China, as the PSAC was located over the northern Philippine Sea, and the moisture budget of SWC was dominated by moisture transport at the western boundary. The PSAC, however, strengthened and shifted southwestward in P2, accompanied by a deepened India–Burma trough. As such, the PSAC transported moist air from the western North Pacific and the Indian Ocean into SWC through its southern boundary. Meanwhile, the stronger PSAC in P2 was accompanied by an upper-level convergence from the western North Pacific to the Bay of Bengal, which induced an upper-level divergence and ascending motion over SWC. Thus, the PSAC caused a significant increase in precipitation in P2. Stronger air–sea interactions in the western North Pacific induced by El Niño–Southern Oscillation may be responsible for the enhancement and southwestward shift of the PSAC in P2 compared to that in P1.

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

Corresponding author: Dr. Xingwen Jiang, xingwen.jiang@yahoo.com

1. Introduction

In the last two decades, severe droughts have occurred frequently in southwestern China (SWC) and have received widespread attention (Barriopedro et al. 2012). Drought events can occur in any season and sometimes last for two or three seasons, resulting in devastating disasters. Drought events in SWC are ascribed to sustained high temperatures and deficiencies in precipitation (Li et al. 2011; Yang et al. 2012; Lu et al. 2011). As climatological precipitation in SWC reaches a minimum in boreal winter, precipitation deficiencies frequently cause drought (Feng et al. 2014).

Variabilities in boreal winter precipitation in SWC are significantly modulated by atmospheric circulation anomalies both locally and remotely, for example, the India–Burma trough (IBT) (Qin et al. 1991; Wang et al. 2011; Li and Zhou 2016) and the East Asian winter monsoon (Wang and Chen 2010). The IBT is located to the south of the Tibetan Plateau and dominates the mean water vapor transport (WVT) from the Indian Ocean to SWC. Qin et al. (1991) ascribed heavy winter precipitation over southwestern China to the mutual influences of warm and moist air from the Bay of Bengal transported by the IBT and cold air transported from higher latitudes. The magnitude of the IBT experienced an apparent interdecadal change in approximately 1977/78, and the interannual relationship between the IBT and precipitation over SWC is more significant in stronger IBT decades (Wang et al. 2011). The interannual variability in the IBT is linked to the Philippine Sea anomalous anticyclone (PSAC; Li and Zhou 2016), which is usually accompanied by El Niño–Southern Oscillation (ENSO). Although it is well known that the PSAC significantly affects the interannual variability in precipitation in southeastern China by modulating the WVT from the western North Pacific to southeastern China, the relationship between the PSAC and precipitation in SWC remains unclear.

Interdecadal changes in the climate system were noticed in previous studies (Nitta and Yamada 1989; Trenberth and Hurrell 1994; Graham 1994; Zhang et al. 1997). Interdecadal changes also exist in the interannual relationships between the regional climate and major climate phenomena (Kumar et al. 1999; Wu and Wang 2002; Zhu and Yang 2003; Chang et al. 2004; B. Wang et al. 2008; L. Wang et al. 2008; Huang et al. 2010; Xie et al. 2010; Chu et al. 2018; Hu et al. 2020). For example, an enhanced relationship between the East Asian summer monsoon and ENSO in the previous winter (Wu and Wang 2002; B. Wang et al. 2008) has been accompanied by a weakened relationship between the Indian summer monsoon rainfall and ENSO since the 1970s (Kumar et al. 1999). Ding et al. (2010) suggested that the strengthening linkage between ENSO and the East Asian summer monsoon may be due to the increase in ENSO variability in association with tropical IO warming. It has been demonstrated that autumn precipitation in SWC experienced a notable shift in the mid-1990s, accompanied by decadal changes in large-scale atmospheric circulation and sea surface temperature (SST) in the tropical warm pool (Wang et al. 2018). Did the relationship between winter precipitation in SWC and large-scale circulation experience any interdecadal changes?

The rest of this paper is structured as follows. Section 2 briefly describes the datasets used in this study. Section 3 shows the interannual and interdecadal impacts of the PSAC on boreal winter precipitation in SWC and its link to ENSO. Finally, conclusions and a discussion are presented in section 4.

2. Data and methods

Precipitation data from 753 stations in China used in this study are obtained from the China Meteorological Data Sharing Service System, collected and quality-controlled by the National Meteorological Information Center of China Meteorological Administration, covering the period from December 1961 to February 2016. The National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Global Reanalysis 1 (NCEP-1) dataset (Kalnay et al. 1996) used in this study includes monthly wind, sea level pressure, geopotential height, specific humidity, and vertical velocity values, with a horizontal resolution of 2.5° × 2.5°. The moisture flux is calculated from the monthly mean data, and the vertically integrated moisture flux represents the accumulation from the surface to 300 hPa. SST data with a horizontal resolution of 2° × 2° are derived from the merged extended reconstructed SST version 3b dataset (Smith et al. 2008). The oceanic Niño index (ONI), defined as the mean SSTA in the Niño-3.4 region (5°S–5°N, 170°–120°W), is derived from Climate Prediction Center (CPC; http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/detrend.nino34.ascii.txt) data and is used as an index to depict ENSO variability in this study. The monthly self-calibrated Palmer drought severity index (PDSI) is derived with a horizontal resolution of 2.5° × 2.5° covering the period from 1850 to 2014 from https://rda.ucar.edu/datasets/ds299.0/. The climatology represents the period from 1981 to 2010. The winter of a specific year refers to December of the current corresponding year and January and February of the next year (DJF). The two-sided Student’s t test was used to check the statistical significance of the correlation and regression.

3. Results

a. Climatological precipitation and atmospheric circulation over SWC

The boreal winter precipitation amount in SWC is generally below 100 mm, which is apparently less than that in southeastern China (Fig. 1a). On the other hand, the surface air temperature is prominently high in SWC, particularly in Yunnan Province (Fig. 1b). Therefore, drought tends to occur more frequently in boreal winter in SWC than in southeastern China at the same latitudes, as revealed by the spatial variability in the Palmer drought severity index (Fig. 1c). A comparison of the spatial patterns of the precipitation, surface air temperature, and Palmer drought severity index suggests that low precipitation mainly accounts for the higher frequency of climatological drought observed in SWC.

Fig. 1.
Fig. 1.

Climatology of the DJF accumulated (a) precipitation (mm), (b) temperature, and (c) Palmer drought severity index. (d) Averaged DJF SWC precipitation and Palmer drought severity index. Precipitation is averaged at the stations in the pink box.

Citation: Journal of Hydrometeorology 22, 6; 10.1175/JHM-D-20-0281.1

To further analyze the interannual relationship between precipitation and drought in SWC, we define the SWC precipitation index (SWCPI) as the regionally averaged precipitation amount over the area over 21°–27°N and 97.5°–107.5°E, including 34 gauge stations. As shown in Fig. 1d, the SWCPI exhibits strong interannual variability. The precipitation amount in SWC is less than 20 mm in some years. The SWCPI is significantly correlated with the regionally averaged Palmer drought severity index over the same region, with a correlation coefficient of 0.42 from 1961 to 2013. It can also be seen that both the precipitation amount and Palmer drought severity index experienced an apparent decadal decrease after the 1990s. Thus, boreal winter drought in SWC is significantly affected by precipitation on both interannual and interdecadal time scales.

Why is the boreal winter precipitation amount low in SWC? Fig. 2 shows the climatological geopotential height, winds, moisture flux, and its divergence at various levels. The geopotential height in the lower troposphere is high in the subtropics, with a center located near the coast of southeastern China at the 850-hPa level. An anticyclone can be seen over Southeast Asia in the lower troposphere. On the other hand, geopotential height is low over the northern Bay of Bengal (Fig. 2a), and a trough is formed to the south of the Tibetan Plateau, which is the so-called IBT (Fig. 2b). As SWC is located to the east of the IBT and at the northwestern edge of the anticyclone, low-level southwesterlies or westerlies prevail over SWC. As water vapor is concentrated at low levels, southerly or southwesterly moisture transport dominates the vertically integrated moisture flux over SWC (Figs. 2c,d). Although the moisture flux is large over SWC, moisture generally diverges in the lower troposphere (figures not shown), consistent with the low observed precipitation amount.

Fig. 2.
Fig. 2.

Climatology of the DJF (a) 850-hPa geopotential height (gpm) and wind (m s−1; vectors), (b) 700-hPa geopotential height (gpm) and wind (m s−1; vectors), (c) vertically integrated moisture flux (kg s−1 m−1), and (d) 700-hPa moisture flux (g m kg−1 s−1).

Citation: Journal of Hydrometeorology 22, 6; 10.1175/JHM-D-20-0281.1

Figure 2c indicates that moist air is transported into SWC mainly through its southern boundary. Therefore, the positions and magnitudes of the IBT and the anticyclone over Southeast Asia are important as they influence precipitation over SWC by modulating the moisture budget. The impacts of the IBT on SWC have been discussed by some studies (Wang et al. 2011; Li and Zhou 2016), so we mainly discuss the influence of the anticyclone over Southeast Asia on SWC precipitation in the following subsection.

b. Impact of the Philippine Sea anomalous anticyclone on the interannual variability in SWC precipitation

Wang and Zhang (2002) found that the anticyclone over Southeast Asia strengthens during ENSO events, and low-level circulation anomalies are featured by the PSAC, which serves as a medium between ENSO and East Asian climate. A strong PSAC is accompanied by above-normal boreal winter precipitation over southern China (Zhang 1999; Zhang et al. 2015; Gao et al. 2018; Ke et al. 2019).

To investigate the impact of the PSAC on precipitation over SWC, we use the PSAC index (PSACI) defined by normalized mean SLP anomalies in the western North Pacific region (10°–20°N, 120°–150°E) to measure the variability of the PSAC following the methods used by Wang and Zhang (2002). Figure 3a shows the interannual variability in the SWCPI and PSACI. The SWCPI basically varies in phase with the PSACI, with a correlation coefficient of 0.31. This suggests that the PSAC may affect SWC precipitation. The relationship between the SWCPI and PSACI, however, exhibits apparent interdecadal variations, as presented in the 21-yr running correlation. The correlation between the SWCPI and PSACI was low before the early 1980s and after the early 2000s but was high and significant during the rest of the studied period (Fig. 3b).

Fig. 3.
Fig. 3.

(a) Year-to-year variation in the normalized SWCPI and PSACI. (b) The 21-yr running correlation between the SWCPI and PSACI. The thin dashed lines indicate correlation coefficients exceeding the 95% confidence level, and the bold dashed lines denote correlation coefficients exceeding the 99% confidence level.

Citation: Journal of Hydrometeorology 22, 6; 10.1175/JHM-D-20-0281.1

To investigate the possible impact of the PSAC on SWC precipitation and its interdecadal change around the early 1980s, we analyze their relationships and underlying processes for two periods: 1961–80 (P1) and 1981–2001 (P2). The correlation of the SWCPI with the PSACI is −0.1 and 0.68 for P1 and P2, respectively. The spatial patterns of the correlation between the PSACI and precipitation in SWC indicate that the PSAC is accompanied by below-normal precipitation in P1 in almost the entirety of SWC but by significant above-normal precipitation in P2 (Fig. 4). These features further confirm the interdecadal change that occurred around the early 1980s in the relationship between precipitation in SWC and the PSAC.

Fig. 4.
Fig. 4.

Correlations between precipitation in SWC and the PSACI in (a) P1 and (b) P2. Values of 0.38, 0.44, 0.56, and 0.68 represent correlation coefficients exceeding the 90%, 95%, 99%, and 99.9% confidence levels in (a). Values of 0.37, 0.43, 0.55, and 0.67 denote correlation coefficients exceeding the 90%, 95%, 99%, and 99.9% confidence levels in (b).

Citation: Journal of Hydrometeorology 22, 6; 10.1175/JHM-D-20-0281.1

Why does the PSAC exert a more significant impact on SWC precipitation in P2 than in P1? To answer this question, we first analyze the changes in WVT; these changes can directly lead to changes in precipitation. As moisture concentrates in the lower troposphere and the altitude in SWC is basically higher than 1000 m, we analyze 700-hPa WVT anomalies associated with the PSAC.

Figure 5 displays the regressed 700-hPa moisture flux against the SWCPI for P1 and P2. In both P1 and P2, anomalous anticyclonic WVT dominates the western North Pacific, while anomalous cyclonic WVT occurs over the northern Bay of Bengal. However, the center and magnitude of the anticyclonic or cyclonic WVT are different in P2. Compared to P1, the anticyclonic WVT over the western North Pacific in P2 is stronger, with a southwestward shift of its center, and the cyclonic WVT over the northern Bay of Bengal is also stronger in P2 than in P1. As such, stronger southerly WVT occurs over southern SWC, and moisture converges over the entirety of the SWC in P2 but only over central and eastern SWC in P1. We also analyze the vertically integrated water vapor flux related to the SWCPI for P1 and P2; the values are similar to those at 700 hPa, consistent with the study of Li and Zhou (2016), who reported that the contribution of WVT over SWC mainly derives from that at middle and low levels, particularly near 700 hPa. Thus, the 700-hPa WVT and its divergence anomalies related to the SWCPI difference between P1 and P2 are consistent with the observed difference in the precipitation anomalies.

Fig. 5.
Fig. 5.

Regression of the 700-hPa moisture flux (g m kg−1 s−1; vectors) and divergence (10−6 g kg−1 s−1; shading) anomalies against the SWCPI in (a) P1 and (b) P2. Bold arrows indicate values exceeding the 95% confidence level in either the zonal or the meridional component.

Citation: Journal of Hydrometeorology 22, 6; 10.1175/JHM-D-20-0281.1

Figures 6a and 6b show the regressed 700-hPa moisture flux against the PSACI. There is anticyclonic circulation from the western North Pacific to the northern Indian Ocean in both P1 and P2. The anticyclonic circulation in P1 is larger than that in P2. In P1, the anticyclonic circulation has a center around the Philippines, and the transition from easterlies to westerlies occurs at approximately 10°N. SWC is located in the northern part of the anticyclonic circulation and is controlled by a local ridge. In P2, the anticyclonic circulation is smaller but stronger than that of P1, with centers over the southern South China Sea and to the south of the Bay of Bengal. There is cyclonic circulation to the south of SWC. As such, there is a strong moisture convergence over SWC in P2 and a generally weak moisture divergence in P1. The difference in the 700-hPa moisture flux divergence anomaly between P1 and P2 is consistent with the difference in precipitation. A comparison of the WVT in SWC indicates that the strong convergence observed in P2 is ascribed to southerly moisture transport.

Fig. 6.
Fig. 6.

Regression of the 700-hPa moisture flux (g m kg−1 s−1; vectors) and divergence (10−6 g kg−1 s−1; shading) anomalies against the PSACI in (a) P1 and (b) P2. Regression of the 500-hPa vertical velocity (10−2 Pa s−1; shading) and 200-hPa divergent wind (m s−1; vectors) anomalies against the PSACI in (c) P1 and (d) P2. Bold arrows indicate values exceeding the 95% confidence level in either the zonal or the meridional component.

Citation: Journal of Hydrometeorology 22, 6; 10.1175/JHM-D-20-0281.1

The differences in dynamical features are also favorable for more precipitation over SWC in P2 than in P1 (Figs. 6c,d). As the PASC is mainly driven by local diabatic cooling due to suppressed local precipitation, the western North Pacific is dominated by a local descending motion and upper-level convergence. However, a strong upper-level wind convergence is also present in the central Bay of Bengal, accompanied by anomalous divergent northerlies to the south of the Tibetan Plateau, that favors the upper-level divergence and middle-level ascending motion over SWC. The strong upper-level convergence observed over the central Bay of Bengal is dynamically consistent with the strong low-level anticyclone in P2.

Figure 7a depicts the climatological zonally averaged vertical circulation over the longitudes of SWC. Air generally ascends around the equator and sinks over the tropics and subtropics, while air ascends in the lower troposphere over SWC. The regressed vertical circulation against PSACI shows apparent differences between P1 and P2. In P1, an anomalous ascending motion dominates most of the troposphere, while an anomalous descending motion is noticed in the lower troposphere over SWC (Fig. 7b). In P2, an anomalous descending motion can be seen over most of the tropics, while an anomalous ascending motion dominates over SWC except on the southern slopes of the Tibetan Plateau (Fig. 7c). Comparisons between Figs. 7a and 7c indicate that the anomalous ascending motion observed over SWC in P2 may be caused by the anomalous descending motion over the tropics associated with the enhancement and southward shift of the anomalous anticyclone from the western North Pacific to the northern Indian Ocean. The anomalous meridional circulation favors an increased amount of moist air from the tropics entering and ascending over SWC, resulting in above-normal precipitation. The above analyses indicate that the change in the relationship of the PSAC with SWC precipitation between P1 and P2 can be ascribed to the change in circulation anomalies over the Bay of Bengal and the Indo-China Peninsula associated with the PSAC. Compared to P1, the PSAC is stronger and shifts southward in P2, as does the anomalous anticyclone over the Bay of Bengal and the Indo-China Peninsula. On the one hand, the southward shift of the anomalous anticyclone favors the transport of tropical warm and moist air to SWC. On the other hand, the strong anomalous anticyclone can suppress local convection and lead to strong upper-level convergence, which favors strong upper-level divergence over SWC. Thus, the southward shift of the strong anomalous anticyclone in P2 favors above-normal precipitation over SWC thermodynamically and dynamically.

Fig. 7.
Fig. 7.

(a) Vertical–meridional cross sections of the vertical velocity (10−2 Pa s−1; shading) and WV (vectors) zonally averaged between 97.5° and 107.5°E. Regression of the vertical velocity (10−2 Pa s−1; shading) and WV (vectors) anomalies zonally averaged between 97.5° and 107.5°E against the PSACI in (b) P1 and (c) P2. Gray shading represents the terrain.

Citation: Journal of Hydrometeorology 22, 6; 10.1175/JHM-D-20-0281.1

c. Roles of ENSO

Why does the PSAC have different features between P1 and P2? Previous studies reported that the intensity and position of the PSAC are linked to the ENSO cycle (Zhang et al. 1996; Wang et al. 2000). Tropical SST development in the central-eastern Pacific usually triggers the PSAC during boreal autumn–winter by a wind-evaporation/entrainment–SST feedback mechanism (Wang et al. 2000). The intensity of the PSAC is influenced by both cold SST anomalies in the western North Pacific and warm SST anomalies in the central-eastern Pacific (Wang et al. 2000; Ke et al. 2019). The correlation coefficients between the ONI and PSACI are 0.83 and 0.84 for P1 and P2, respectively, indicating that an ENSO event is basically accompanied by the PSAC in the two periods. The regressed low-level wind pattern against the ONI also shows anomalous anticyclonic circulations extending from the western North Pacific to the Bay of Bengal for the two periods (Figs. 8a,b). However, the magnitudes and positions of the anomalous anticyclonic circulations have distinct differences between P1 and P2; these differences are similar to those related to the PSAC.

Fig. 8.
Fig. 8.

Regression of SST (°C; shading) and 850-hPa wind (m s−1; vectors) anomalies against the ONI in (a) P1 and (b) P2. Regression of SLP (hPa; contour), 700-hPa moisture flux (g m kg−1 s−1; vectors) and divergence (10−6 g kg−1 s−1; shading) anomalies against the ONI in (c) P1 and (d) P2. Bold arrows indicate values exceeding the 95% confidence level in either the zonal or the meridional component. Green stippling denotes the value of shadings exceeding the 95% confidence level in (a) and (b). Red lines indicate that the value is above 0, and green lines indicate values below 0 in (c) and (d). The contour interval of the SLP is 0.2. Thicker red lines denote larger SLP anomalies.

Citation: Journal of Hydrometeorology 22, 6; 10.1175/JHM-D-20-0281.1

The SST anomalies regressed against ONI indicate that the warm SST anomalies in the central and eastern Pacific have no large differences between P1 and P2. However, the cold SST anomalies in the western North Pacific are stronger in P2 than in P1, while the SST anomalies in the Indian Ocean are warmer in P1 (Figs. 8a,b). As local cold SST anomalies could suppress local convection and favor a strong low-level anticyclone by diabatic cooling, the colder SST anomalies observed in P2 are accompanied by lower sea level pressures and a stronger low-level anomalous anticyclone in the western North Pacific. The westward extension of the anomalous anticyclone to the Bay of Bengal may be explained as a Rossby wave response to diabatic cooling in the western North Pacific. As a result, the anomalous anticyclone over the Bay of Bengal is stronger in P2 than in P1 (Figs. 8c,d). Therefore, the difference in the anomalies from the western North Pacific to the Bay of Bengal may be mainly due to the different responses of the air–sea interactions in the western North Pacific.

As the relationship between the PSAC and SWC precipitation is different between P1 and P2, it is expected that the relationship between ENSO and SWC precipitation is also different. Indeed, there is no apparent linear relationship between ENSO and SWC precipitation (R = −0.19) in P1. However, an obvious linear relationship is noticed between them in P2, with a significant correlation coefficient of 0.56. This indicates that SWC tended to receive above-normal precipitation during El Niño events in P2 but below-normal precipitation during La Niña events. The 700-hPa moisture flux regressed against the ONI also supports the change in the relationship of ENSO and SWC precipitation between P1 and P2. The low-level moisture air converges over SWC in P2 but diverges in P1. The moist air convergence observed in P2 is ascribed to the southward shift of the anticyclone over the Bay of Bengal and the Indo-China Peninsula. Thus, the response of SWC precipitation to the ENSO is dependent on the response of the anomalous anticyclone from the western North Pacific to the Bay of Bengal to the ENSO.

4. Conclusions and discussion

ENSO exerts a significant impact on boreal winter in southeastern China, and the PSAC is a key factor during these processes. However, the relationship between the PSAC and precipitation in SWC is not well understood, whereas previous studies have emphasized the impact of the IBT on SWC precipitation. The impact of the PSAC on boreal winter precipitation in SWC is investigated in this study. A significant positive correlation is found between the PSAC and SWC precipitation, and this correlation experienced an apparent interdecadal change around the early 1980s. In contrast to the first period (P1, 1961–80), the SWC precipitation was highly correlated with the PSAC in the second period (P2, 1981–2001). This change is due to the changes in the position and magnitude of the PSAC and the associated anomalies over the Bay of Bengal. The anomalous anticyclone observed from the western North Pacific to the Bay of Bengal becomes stronger and shifts southward in P2, resulting in a low-level moisture convergence and upper-level divergence over SWC and thus above-normal precipitation in SWC.

PSAC tended to form during ENSO events in both P1 and P2. While the SST anomalies in the central and eastern Pacific associated with ENSO are similar in P1 and P2, the cold SST anomalies in P2 are stronger than those in P1 and result in a stronger PSAC shifting southwestward and extending westward to the Bay of Bengal. Thus, ENSO exerts a significant impact on SWC precipitation in P2. The reasons why both the air and sea in the western North Pacific had strong responses to the ENSO in P2 deserve further study.

A previous study reported that the IBT plays an important role in interannual variability in boreal winter SWC precipitation, and the IBT is modulated by the ENSO via the PSAC (Li and Zhou 2016). A significant correlation (R = 0.38) existed between the IBT and the PSAC from 1961 to 2015. This relationship also experienced interdecadal changes. There was no apparent linear relationship between them in P1 (R = 0.12), but a significant correlation was observed in P2 (R = 0.50). As such, the relationship between the IBT and SWC precipitation also exhibited an interdecadal change around the early 1980s. The possible reasons for this interdecadal change in the relationship between the PSAC and the IBT guarantee further investigation.

To verify whether the above findings are dependent on the data used in this study, we also performed calculations using Japanese 55-year Reanalysis (JRA-55) data from the Japan Meteorological Agency (Kobayashi et al. 2015; Harada et al. 2016). Comparisons of the results calculated from NCEP-1 and JRA-55 indicate that the two datasets show consistent results. For example, the correlation coefficient of the PSAC between the two datasets is 0.98. Thus, the findings presented in this study are robust without data dependence.

Acknowledgments

We thank the three anonymous reviewers for their constructive comments, which improved the overall quality of the paper. This research was jointly supported by the National Key R&D Program of China (2018YFC1505603), Public Welfare Industry (Meteorological) Research Projects (GYHY201306024), the National Natural Science Foundation of China (Grants U20A2097, 42075045), and the State Oceanic Administration International Cooperation Program (GASI-IPOVAI-03). The authors thank the support of the Innovation Team of Climate Prediction Theory and Application of China Meteorological Administration, the Innovation Team of Subseasonal to Seasonal Climate Prediction of Sichuan Meteorological Bureau, and the Climate Science for Service Partnership (CSSP).

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