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1. Introduction
The Tibetan Plateau (TP) is the origin of many Asian rivers, including the Yangtze, Yellow River, Yarlung Zangbo, Indus, and Lancang–Mekong, and provides water to more than 1.9 billion people (over 20% of the global population) (Immerzeel et al. 2010, 2020). One of the most important factors modifying the TP hydroclimate is precipitation, which mainly occurs in summer. The interannual variability of TP precipitation in boreal summer is modulated by many factors, such as El Niño–Southern Oscillation (ENSO; S. Liu et al. 2020; Hu et al. 2021), the sea surface temperature anomaly (SSTA) in the North Atlantic (Gao et al. 2013), the convection over the western Maritime Continent (Jiang et al. 2016; Jiang and Ting 2017), the Indian Ocean SSTA (Hu and Duan 2015; Sun and Wang 2019; Ren et al. 2020), and the North Atlantic Oscillation (NAO). The NAO receives the most attention because of its robust negative correlation with the leading mode of the TP precipitation in boreal summer. It can stimulate a Rossby wave train with an anomalous cyclone over the western TP and thus induces the positive TP precipitation anomaly (Liu and Yin 2001; Zhu et al. 2011; Liu and Duan 2012; Liu et al. 2015; Z. Q. Wang et al. 2017, 2018; Shang et al. 2021).
However, it is worth noting that this robust correlation between the NAO and TP precipitation exists only in June, rather than the whole of boreal summer (see Table S1 in the online supplemental material). Considering the strong subseasonal dependency of regional climate anomalies (Wang et al. 2009, 2013; Jiang et al. 2016; Xue et al. 2018; Dong and He 2020; Piao et al. 2020), the remote drivers of the TP precipitation anomaly may change in July and August. In July, the TP precipitation may be affected by the SSTA in the western Maritime Continent (Fig. S1), which can induce the local convection anomaly. In turn, it can trigger an anticyclone anomaly to the southern TP by exciting an anomalous Hadley cell. As a result, a positive precipitation anomaly appears over the southeastern TP (Jiang et al. 2016; Jiang and Ting 2017). In August, typically, a consistent positive correlation can be observed between the southeastern TP precipitation anomaly and the SSTA in the tropical Indian Ocean and tropical North Atlantic (TNA). In addition, the variance of TP precipitation in August is the largest of the year [Fig. 1 in Zhang and Duan (2021)], indicating its substantial interannual variability. Therefore, the August TP precipitation holds paramount importance for human life and production, the hydrological cycle, and terrestrial ecosystem sustainability (Wan et al. 2017). Moreover, the TP condensation latent heating caused by precipitation in August is also the largest of the year [Fig. 1 in Zhao et al. (2018); Fig. 2 in Duan and Zhang (2022)], which can influence the intensity and location of the South Asian high (Wu et al. 2015b, 2016, 2018), the seasonal evolution of the Asian summer monsoon (Duan and Wu 2005, Duan et al. 2011, 2013, 2020; Wu et al. 2012, 2015a; G. X. Wu et al. 2017; Hu and Duan 2015; Ge et al. 2019), and even global climate change (Wu et al. 2009, 2015a; P. Zhao et al. 2019; Y. M. Liu et al. 2020; Yang et al. 2020). Therefore, addressing the mechanism of interannual variability of the TP precipitation in August is of great significance for local and global climate systems.
The Indian Ocean basin mode (IOBM) is the dominant pattern of the tropical Indian Ocean SSTA. The warm Indian Ocean SSTA can excite a Kelvin wave in the lower atmosphere with anomalous easterlies from the Indian Ocean to the western Pacific, which cause an anomalous anticyclone over the Bay of Bengal and western Pacific, inducing southwesterly water vapor transport toward the southeastern TP and thus increasing the positive precipitation anomaly over that region (Hu and Duan 2015; Sun and Wang 2019; Ren et al. 2020). In addition, in the upper troposphere, the warm Indian Ocean SSTA can induce a coupled south–north anticyclone–cyclone structure over the TP, with a positive geopotential height anomaly in the south and negative geopotential height anomaly in the north, which leads to the divergence of the subtropical westerly jet in the eastern TP and thus strengthens the ascending motion locally. Thereby, the positive precipitation anomaly over the eastern TP is strengthened (Hu and Duan 2015; S. F. Liu et al. 2020).
Recently, a growing number of studies have found a strengthening of the relationship between the TNA SSTA and summertime western North Pacific subtropical high (WNPSH) after the early 1980s (Chen et al. 2014; Hong et al. 2014a; Chang et al. 2016). The warm TNA SSTA can trigger an eastward-extending Kelvin wave with low-level easterly anomalies over the tropical Indo-Pacific, and thereby strengthen the WNPSH (Lu and Dong 2005; Zhang et al. 2017; Rong et al. 2010). In addition, anomalous easterlies can also partially contribute to the tropical Indian Ocean warm SSTA (Li et al. 2015; Yu et al. 2015; Rong et al. 2010). The anomalous easterlies caused by the Indian Ocean SSTA overlap with those caused by the TNA SSTA and hence favor the development of the WNPSH, which can further influence East Asian climate, decreasing the tropical cyclone genesis frequency in the western North Pacific (Huo et al. 2015; Yu et al. 2015) and facilitating extreme rainfall over the Yangtze River valley, such as the record-breaking event in June 2020 (Zheng and Wang 2021). Moreover, the warm TNA SSTA can force a westward-extending zonally vertical overturning circulation anomaly, with the ascending (descending) branch in the tropical Atlantic (central Pacific). The anomalous descending motion further triggers an anomalous anticyclone in the western North Pacific as a Rossby wave response, and therefore enhances the WNPSH (Chen et al. 2014; Hong et al. 2014a,b; Chang et al. 2016; Jin and Huo 2018; Zhao et al. 2019a,b; Yuan and Yang 2020; Feng and Chen 2021). Adjacent to the Asian monsoon region, TP precipitation may also be affected by the TNA SSTA. However, so far, the influence of the TNA SSTA on TP precipitation anomaly remains poorly understood.
Our recent study has demonstrated that, in October, the interannual variability of TP precipitation is mainly influenced by the tropical Pacific–Indian Ocean SSTA (Zhang and Duan 2021). However, with seasonal evolution and various background circulation, the dominant drivers of TP precipitation anomaly may vary in different months. Therefore, this study aims to investigate the physical mechanism by which the TNA SSTA influences the August TP precipitation anomaly and its combined effect with the tropical Indian Ocean SSTA. This will provide a scientific basis to improve the capability of precipitation seasonal forecasting in and around the TP.
The remainder of the paper is organized as follows. In section 2, the datasets and numerical models used in this study are introduced. Section 3, using data diagnosis, analyzes the physical mechanism through which the IOBM and TNA SSTA individually affect the August TP precipitation anomaly, respectively. In section 4, numerical experiments with the Community Atmosphere Model, version 5 (CAM5.0) are conducted, to further verify the mechanisms of IOBM and TNA SSTA affecting the TP precipitation anomaly. Finally, the summary and discussion are given in section 5.
2. Data and model
a. Datasets
The following datasets were used:
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Monthly sea surface temperature data from the Hadley Center Sea Ice and Sea Surface Temperature, version 1.1 (HadISST1) dataset (Rayner et al. 2003), with a horizontal resolution of 1.0° × 1.0°.
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Monthly variables including wind, humidity, and geopotential height obtained from the Japan Meteorological Agency Japanese 55-year Reanalysis (JRA-55) dataset (Kobayashi et al. 2015), with a horizontal resolution of 1.25° × 1.25°.
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Multiple precipitation datasets, including monthly precipitation data from a gridded dataset interpolated from observational data of more than 2400 stations in China, with a horizontal resolution of 0.25° × 0.25° (CN05.1; Wu and Gao 2013); monthly precipitation from the Global Precipitation Climatology Project (GPCP) dataset (Huffman et al. 2001), with a horizontal resolution of 2.5° × 2.5°; and daily precipitation from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) V1101 product, with a horizontal resolution of 0.25° × 0.25° and covering the domain 15°–55°N, 60°–150°E (Yatagai et al. 2012).
All datasets cover the period from 1980 to 2018, except APHRODITE (1980–2015).
b. Index definitions
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The TP precipitation index is defined as the time series of the domain-averaged TP (defined as 26°–34°N, 88°–104°E) precipitation anomaly based on the CN05.1 data.
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The TNA SSTA index is defined as the domain-averaged SSTA over 0°–20°N, 80°–25°W (Hong et al. 2014a; Chang et al. 2016).
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The IOBM index is defined as the domain-averaged SSTA over 20°S–20°N, 40°–110°E (Xie et al. 2009).
This study focuses on the interannual variability, and hence all data used were Lanczos-filtered for 2–9 years after removing the linear trend.
c. Model
The AGCM employed here is CAM5.0 (Neale et al. 2012), the atmospheric component of CESM1.2.0 (the Community Earth System Model, version 1.2.0; Hurrell et al. 2013), developed by NCAR (the National Center for Atmospheric Research; for more details, see http://www.cesm.ucar.edu/models/cesm1.2/). CAM5 updated several important physical parameterization schemes, such as a new moist turbulence scheme, an updated shallow convection scheme, improved stratiform microphysical processes, a revised cloud macrophysics scheme, and a new three-mode modal aerosol scheme. As such, CAM5.0 is the first version of CAM that can simulate cloud–aerosol indirect radiative effects (Neale et al. 2012). In this study, the configuration of the horizontal resolution is f19_g16 (1.9° latitude × 2.5° longitude) and the vertical resolution is 30 hybrid levels. The model component is “F_2000” with the external forcing fixed at the year-2000 level. The specific experiment designs are introduced in section 5a.
3. Relationship of the IOBM and TNA SSTA with TP precipitation
Before investigating the relationship between August TP precipitation and remote SSTA forcing, it is necessary to identify the chief characteristics of the temporal and spatial variation of the August TP precipitation. Figure 1 shows the first empirical orthogonal function (EOF1) of the TP precipitation anomaly with multiple precipitation datasets, characterized by the variance center over the southeastern TP (Figs. 1a–c). The corresponding first principal components (PC1) among different datasets are highly correlated (Fig. 1d), indicating that the TP precipitation variability is not dataset dependent. In addition, in the CN05.1 dataset, the domain-averaged (26°–34°N, 88°–104°E) TP precipitation index can well represent its PC1, with a correlation coefficient of 0.99 (exceeding the 99.9% confidence level; Fig. 1d), indicating that the precipitation anomaly in the southeastern TP can well reflect the precipitation variation in August of the whole TP. Because CN05.1 is a high-resolution dataset interpolated from station observations and there are relatively dense meteorological stations over the eastern central TP (Wu and Gao 2013), it is the most reliable dataset to describe the southeastern TP precipitation, compared with other datasets (Zhang and Duan 2021). Therefore, the TP precipitation index obtained from CN05.1 is used to analyze the precipitation variability over the southeastern TP, which is the focus region in the following.
Spatial patterns of the first empirical orthogonal function (EOF1) mode of August TP precipitation (units: mm day−1) derived from (a) CN05.1 data, (b) APHRODITE data, and (c) GPCP data. The numbers in the upper-right corners indicate the percentage of variance explained by EOF1. (d) Normalized first principal component (PC1) of August TP precipitation derived from CN05.1 data (blue line), APHRODITE data (red line), and GPCP data (green line). The red box in (a) represents the southeastern TP where the domain-averaged TP precipitation anomaly index is defined [pink bar in (d)] using CN05.1 data. The correlation coefficient between the TP precipitation index and the PC1 from CN05.1 data is shown in the upper-right corner of (d). The bold black curve in (a)–(c) represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
To explore the relationship between the tropical SSTA and TP precipitation, the correlations between the SSTA and the August TP precipitation index are calculated (Fig. 2). The simultaneous correlation map (Fig. 2a) shows that both the tropical Indian Ocean and TNA are the significant relevant areas. In the tropical Indian Ocean, the correlation presents a basin warming pattern (i.e., the IOBM), and the correlation coefficient between the TP precipitation and IOBM index is 0.412 (exceeding the 99% confidence level; Table 1), explaining 17% of the variance of TP precipitation. In the TNA region, the correlation presents a warm TNA SSTA mode, with the correlation coefficient between TP precipitation and the TNA SSTA index being 0.452 (exceeding the 99% confidence level; Table 1), explaining 20% of the variance of TP precipitation. Due to the huge thermal inertia of seawater, it is not a surprise that a similar correlation pattern appeared in the tropical Indian Ocean from May and in the TNA from June onward (Fig. 2b). Therefore, we can conclude that the SSTA in the tropical Indian Ocean and North Atlantic appear first, and then affect the August TP precipitation anomaly.
Correlations between the August TP precipitation index and the tropical sea surface temperature anomaly (SSTA) in (a) August, (b) the monthly evolution of tropical SST averaged over 0°–20°N. The green boxes in (a) represent the tropical Indian Ocean (left box) and tropical North Atlantic (right box). The bold blue curve is the TP domain with an altitude > 2000 m. Black (blue) stippling in (a) and (b) indicates regions with statistical significance above the 95% (90%) confidence level according to the Student’s t test. (c) Lead–lag correlations between the TP precipitation and IOBM index (red line)/TNA SSTA index (blue line). The numeral 0 denotes the simultaneous correlation in August. The short- and long-dashed lines indicate the 99% and 95% confidence levels according to the Student’s t test, respectively.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
Simultaneous correlations of the IOBM and TNA SSTA indices with TP precipitation in August, along with the partial correlation coefficients between the TP precipitation and the IOBM (TNA SSTA) index after removing the TNA SSTA (IOBM). Three asterisks (***) indicate a coefficient exceeding the 99% confidence level.
Note that the correlation pattern of the tropical SSTA evolution presents a decaying phase of El Niño (Fig. 2b; see also Fig. S2). The positive (negative) SSTAs in the central-eastern Pacific (western North Pacific) decay gradually and disappear in August (July). The positive SSTA in the Indian Ocean peaks in May and then maintains its impact, which becomes stronger in August. Meanwhile, the positive SSTA in the TNA region gradually strengthens and peaks in August. This suggests that the oceanic forcing in the TNA cannot be neglected. After removing the influence of ENSO in the preceding winter (December–February), the partial correlation of both the IOBM and the TNA SSTA with the TP precipitation is greatly reduced (Table S2). This indicates that the IOBM and the TNA SSTA may act as El Niño’s capacitors to some extent (Xie et al. 2009; Rong et al. 2010; L. Wang et al. 2017; S. F. Liu et al. 2020; Feng and Chen 2021; Zheng and Wang 2021), prolonging the effect of ENSO on the TP precipitation anomaly. Particularly, after removing ENSO, the IOBM can explain only 2.3% of the TP precipitation variance (Table S2), which verifies the increased lagged impact of ENSO on the warm SSTA in the Indian Ocean after the mid-1970s (Xie et al. 2010; Hong et al. 2014a). In contrast, the influence of ENSO on the TNA SSTA has been weakening since the late 1970s (Hong et al. 2014a), which explains why the partial correlation between the TNA SSTA index and TP precipitation in August still exceeds the 90% confidence level after removing the Niño-3.4 signal (explaining 8.5% of the TP precipitation variance, about half of that before partial correlation; Table S2). This indicates that the remote impact of the TNA SSTA alone on the TP precipitation has been becoming more important in recent decades. Indeed, the IOBM and TNA SSTA have their own variabilities caused by local air–sea interaction (Huang et al. 2004), which are independent of ENSO. Also, ENSO in the preceding winter explains only 6.8% of the TP precipitation variance. Therefore, this study focuses mainly on the individual and combined effects of the IOBM and TNA SSTA on the TP precipitation anomaly.
Figure 2c shows the lead–lag relationship of the IOBM and TNA SSTA indices with TP precipitation, from which we can see significant correlation above the 95% confidence level in both cases. The IOBM (TNA SSTA) index leads the TP precipitation by three (two) months and lasts until August, suggesting that the IOBM (TNA SSTA) may act as a precursor signal to the interannual variation of the TP precipitation.
To further explore the anomalous precipitation and circulation pattern around the TP related to the IOBM and TNA SSTA, Fig. 3 shows the simultaneous regression of precipitation along with the lower-level horizontal wind on the TP precipitation, IOBM, and TNA SSTA indices, respectively. During the positive phase of the IOBM or TNA SSTA, significant positive precipitation anomaly occurs over the southeastern TP, accompanied by the anomalous anticyclone over the Bay of Bengal (BOBAC) (Figs. 3b,c), which resembles the regression on the TP precipitation index (Fig. 3a). Therefore, the IOBM and TNA SSTA indices can represent the dominant tropical SSTA signals associated with the TP precipitation anomaly. Moreover, given the lead correlation between the IOBM (TNA SSTA) index and TP precipitation, the lead regressed pattern of the precipitation and circulation in August against the IOBM (TNA SSTA) index in May (June) is almost the same as the simultaneous regression in August (Fig. S3). This further implies that the IOBM and TNA SSTA indices can be used as potential predictors for the TP precipitation anomaly in August.
(a) Regression of the August precipitation (shading; units: mm day−1) and 850-hPa horizontal wind (vectors; units: m s−1) over Asia against the TP precipitation index. (b) As in (a), but for the IOBM index. (c) As in (a), but for the TNA SSTA index. The global precipitation data in this study are derived from GPCP. The black vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% (90%) confidence level according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
Notably, there is a robust simultaneous relationship between the IOBM and TNA SSTA indices, with a correlation coefficient of 0.48 (exceeding the 99% confidence level), suggesting the influences of the IOBM and TNA SSTA on the TP precipitation anomaly may not be independent of one another. In fact, the relationship between TP precipitation and the IOBM is no longer robust after removing the TNA SSTA by partial correlation, and the same is also true for the TNA SSTA after removing the effects of IOBM (Table 1). Recently, several studies have found that the TNA SSTA can partially contribute to the warm SSTA in the tropical Indian Ocean (Rong et al. 2010; Li et al. 2015; Yu et al. 2015). Therefore, it is necessary to consider the potential connection between these two modes and their combined effects on the TP precipitation anomaly. Next, the physical mechanisms through which the IOBM and TNA SSTA affect the TP precipitation are further discussed, respectively.
4. Physical processes connecting the IOBM and TNA SSTA with TP precipitation
a. Impacts of the IOBM on TP precipitation
When the IOBM is in a positive phase, in the lower troposphere, the warm Indian Ocean SSTA forces an equatorial Kelvin wave accompanied by easterly anomalies to the east, inducing Ekman divergence with a BOBAC (Fig. 4b; Gill 1980; Yang et al. 2007; Xie et al. 2009, 2016). With such a circulation regime, anomalous southwesterlies to the western BOBAC deliver abundant water vapor from the tropical Indian Ocean to the southeastern TP, thus causing convergence of the local water vapor flux (Fig. S4a) and positive precipitation anomaly over the southeastern TP (Fig. 3b). These results are basically consistent with previous studies (Hu and Duan 2015; Sun and Wang 2019; Ren et al. 2020).
Regression of the (a) 200-hPa geopotential height (shading; units: gpm) and horizontal wind (vectors; units: m s−1) and (b) 450-hPa vertical velocity (shading; units: Pa s−1) and 850-hPa horizontal wind (vectors; units: m s−1) against the IOBM index in August. The purple vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% (90%) confidence level according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
In the upper troposphere, the warm Indian Ocean SSTA stimulates a Gill-type response with a positive geopotential height anomaly and an anticyclone anomaly over the southern TP (Gill 1980; Yang et al. 2007). Meanwhile, over the northeastern TP, a significant negative geopotential height anomaly accompanied by an anomalous cyclone can be detected, which is induced by the positive TP condensation heating. The positive TP precipitation anomaly causes the positive TP heating. In turn, as a strong negative vorticity source, it can stimulate a barotropic Rossby wave train with the anomalous cyclone over the northeastern TP (Duan and Wu 2005a; Hu and Duan 2015). Such a coupled south–north anticyclone–cyclone structure leads to a strengthened Asian subtropical westerly jet (Fig. 4a), corresponding to divergent airflows over the southeastern TP. As a result, ascending motion forms over the southeastern TP (Fig. 4b). This further facilitates the positive precipitation anomaly in this region (S. F. Liu et al. 2020).
As mentioned in section 3, after removing the TNA SSTA signal, the correlation coefficient between the IOBM and TP precipitation decreases significantly (0.19) and can only explain 3.6% of the of the TP precipitation variance. Also, after removing the TNA SSTA signal, the intensity of the BOBAC is greatly weakened, and the positive precipitation anomaly over the southeastern TP is substantially reduced (Fig. S5). This indicates that the IOBM is closely related to the TP precipitation, but it cannot solely account for the interannual variation of TP precipitation. Therefore, a combined effect with the TNA SSTA included seems to be necessary to address the mechanism of the TP precipitation anomaly, and the connection between the IOBM and TP precipitation may be largely modulated by the TNA SSTA. Next, we further explore the physical mechanism of TNA SSTA on the TP precipitation anomaly, which is also the focus of this study.
b. Impacts of the TNA SSTA on TP precipitation
Figure 5 shows the regression of the streamfunction on the TNA SSTA index in the lower and upper troposphere. The streamfunction anomalies are characterized by a quadrupole structure in the lower troposphere (850 hPa)—that is, a pair of cyclonic circulation anomalies over the eastern Pacific–western Atlantic and an anticyclonic circulation anomaly in a large domain extending from the Bay of Bengal to the western Pacific (Fig. 5a). In the upper troposphere (200 hPa), the streamfunction anomalies are almost opposite, characterized by a pair of large-scale anomalous anticyclones extending from the eastern tropical Pacific to the Atlantic, accompanied by a pair of anomalous cyclones over the Bay of Bengal and western Pacific (Fig. 5b). This stationary equatorial wave pattern is known as a medium of the teleconnection between the TNA SSTA and TP precipitation, and the mechanism involved is further addressed in the following subsections.
Regression of the (a) 850- and (b) 200-hPa streamfunction (shading; units: 106 m2 s−1) against the TNA SSTA index in August. The black (blue) stippled regions indicate statistical significance above the 95% (90%) confidence level according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
1) Eastward pathway: Kelvin wave response
The equatorial wave response to the TNA SSTA can be illustrated by the regressed tropospheric (850–200-hPa) temperature anomalies against the TNA SSTA index (Fig. 6a). The tropospheric temperature field displays a typical Gill-type response pattern to the equatorially asymmetric warm SSTA in the TNA region, with a Rossby wave response over the eastern Pacific and Atlantic and a warm Kelvin wave wedge penetrating into the equatorial western Pacific (Gill 1980; Xing et al. 2014). When the TNA SSTA is in a positive phase, the warm SSTA in the TNA region favors local positive precipitation anomalies, and the associated diabatic heating induces striking easterly anomalies to its eastern side from Africa to the western Pacific as a Kelvin wave response. The resultant surface Ekman divergence induces the development of a BOBAC (Fig. 6b; Rong et al. 2010; Zheng and Wang 2021). Furthermore, the anomalous easterlies caused by the TNA SSTA overlap with those caused by the IOBM, which further enhances the BOBAC via Kelvin wave adjustment, and thereby the southerly anomalies to the west side of the BOBAC transport more water vapor to the southeastern TP.
Regression of the (a) tropospheric (850–250-hPa averaged) temperature (shading; units: °C), (b) precipitation (shading; units: mm day−1) and 850-hPa horizontal wind (vectors; units: m s−1) against the TNA SSTA index in August. The black vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% (90%) confidence level according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
In addition, the anomalous easterlies induced by the TNA SSTA can also increase the SSTA in the tropical Indian Ocean to amplify the effect of the IOBM on the TP precipitation. To illustrate this, a simultaneous regression of the SSTA and surface latent heat flux along with 10-m winds on the TNA SSTA index is shown in Fig. 7. Obviously, the easterly anomalies weaken the summer monsoon southwesterly and thus increase the downward surface latent heat flux over the Indian Ocean by reducing surface evaporation, particularly over the Bay of Bengal and southwestern Maritime Continent (Fig. 7b), hence resulting in the positive SSTA (Wang et al. 2006; Du et al. 2009; Xie et al. 2009, 2016; Kosaka et al. 2013). This suggests that the positive TNA SSTA may partially contribute to the warm Indian Ocean SSTA (Lu and Dong 2005; Rong et al. 2010; Li et al. 2015; Yu et al. 2015) and further strengthens the positive precipitation anomaly over the southeastern TP. This also explains, to some extent, why the IOBM can affect TP precipitation in August, instead of June and July, although the IOBM can persist to the whole of boreal summer.
Regression of the (a) SSTA (shading; units: °C) and (b) surface latent heat flux (shading; units: W m−2) along with 10-m winds (vectors; units: m s−1) against the TNA SSTA index in August. The positive shading in (b) represents downward surface latent heat. The purple vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% (90%) according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
It is worth noting that there is no significant wind anomaly over Africa (Fig. 6b). This is likely because the anomalous westerlies induced by the Indian Ocean warm SSTA are offset by the anomalous easterlies generated by the warm TNA SSTA, thereby resulting in no significant wind anomaly over Africa (Li et al. 2015; Rong et al. 2010).
2) Westward pathway: Walker circulation anomaly
Figure 8 shows the regressed pattern of velocity potential and divergent wind in the upper and lower troposphere and the zonal vertical circulation. When the TNA SSTA is in a positive phase, in the upper level, there is a large-scale anomalous divergence over the Atlantic, and a large-scale convergence over the central Pacific. The anomalous circulation patterns are reversed in the lower level, with convergent flows over the Atlantic and divergent flows over the Pacific. The anomalous convergent and divergent centers are located between approximately 5° and 15°N (Figs. 8a,b). Correspondingly, there is anomalous ascending motion and enhanced convection over the TNA region. In contrast, anomalous descending motion and suppressed convection appear over the central Pacific (Fig. 8c). The zonal gradients of the velocity potential near the equator are associated with the Walker circulation anomaly. This is probably a westward pathway through which the TNA SSTA influences the TP precipitation anomaly and can be interpreted as follows.
Regression of the (a) 200- and (b) 850-hPa velocity potential (shading; units: 106 m2 s−1) and the divergent wind (vectors; units: m s−1) against the TNA SSTA index in August. (c) As in (a) and (b), but for the regressed vertical velocity (shading; units: m s−1) and zonal vertical circulation (vectors; units: m s−1; vertical speed is scaled by 1450 to facilitate visualization) averaged between 5° and 15°N. The black vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% confidence level according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
During the positive phase of the TNA SSTA, the warm in situ SSTA results in local positive precipitation anomalies (Fig. 6b). The associated diabatic heating gives rise to a pair of lower-tropospheric cyclonic circulation anomalies to its western side (Figs. 5a and 6b) as a Rossby wave response. The accompanying anomalous convergence and ascending motion over the TNA region induce the Walker circulation anomaly, and hence suppress the convection over the central Pacific (Fig. 8c), which in turn forces a Rossby wave response with the anomalous anticyclone over the Bay of Bengal and western Pacific (Fig. 6b; Chen et al. 2014; Hong et al. 2014a,b; Chang et al. 2016; Jin and Huo 2018; Zhao et al. 2019a,b). As a result, the BOBAC is strengthened and thus increases the southeastern TP precipitation anomaly.
c. Multiple regression model for the TP precipitation anomaly
The correlation coefficient between the hindcast and the observed ITP is 0.503, explaining 25% of the variance of TP precipitation, which is considerably higher than that of any single factor (Table 2). This further indicates the combined effect of the IOBM and the TNA SSTA on the TP precipitation anomaly. Particularly, the regression coefficients of both the single and multiple regression are nearly equal (Table 2), indicating that the contributions of the IOBM and TNA SSTA to the TP precipitation anomaly are comparable. This provides the basis for the design of the SSTA sensitivity runs described below.
Correlation coefficients between the observed TP precipitation index and the IOBM, TNA SSTA, and the combined indices in August, along with their explained variances for the TP precipitation anomaly. Three asterisks (***) indicate a coefficient exceeding the 99% confidence level. Note that the IIOBM, ITNA, and ITP indices are normalized time series.
5. Numerical experiments
In this section, the results from a series of AGCM experiments using CAM5.0 are analyzed to further verify the physical mechanisms through which the IOBM and TNA SSTA affect the TP precipitation anomaly in late summer.
a. Design
The AGCM experiments, including a control run and three sets of sensitivity runs, are summarized in Table 3. The control run (CTRL) is forced by the climatological annual cycle of SST with the external forcing fixed at the year-2000 level. CTRL is integrated for 32 years, and the first 2 years are used as spinup. Accordingly, the first day of each year of outputs from the last 30 years is used as the initial conditions to restart the sensitivity experiments. To remove the influence of atmospheric noise, each set of sensitivity experiments consists of 30 members with the initial conditions provided by CTRL, and the responses of these members are averaged.
Designs of the AGCM experiments.
In section 4, the data diagnosis suggested that the IOBM influences the TP precipitation anomaly by triggering a BOBAC via Kelvin wave adjustment. In addition, the TNA SSTA affects the TP precipitation anomaly through two pathways. The TNA SSTA generates overlapping and amplification effects on the IOBM influencing TP precipitation by inducing an eastward-extending Kelvin wave. The westward pathway is the one through which the TNA SSTA influences TP precipitation by triggering a Walker circulation anomaly and the subsequent Rossby wave response. Therefore, the IOBM and TNA SSTA have a combined effect on the TP precipitation anomaly, and considering only one signal, the intensity and significance of the TP precipitation anomaly are expected to be greatly weakened.
To identify the individual and combined contributions of the SSTAs in the tropical Indian Ocean and the TNA, three sets of sensitivity experiments, with the external forcing identical to CTRL, are conducted. Each set of sensitivity experiments consists of a positive SSTA experiment and a negative SSTA experiment, and is integrated from 1 June to 1 September. The results of these sensitivity experiments are the difference between positive and negative experiments. The SSTA forcing is the composite SSTA of the positive (negative) IOBM/TNA SSTA years with the time-dependent SSTA indices greater (less) than + (−)1.0 standard deviation from July to August. Figure S6 shows the August SSTA in each sensitivity experiment.
The SSTA forcing in the first set of sensitivity experiments is the model climatology plus the total SSTA in both the tropical Indian Ocean and the tropical North Atlantic (IOBM&TNA), while the sea surface temperature in the rest of ocean areas is kept the same as the model climatology (Figs. S6a,b). A buffer zone (zonal and meridional ranges are both 5°) is constructed between the inner and outer boxes, with the restoration weight linearly reduced from one to zero (Zhou et al. 2016). The second and third sets of sensitivity experiments follow the same design as the first, but retain only the SSTA forcing in the tropical Indian Ocean (IOBM; Figs. S6c,d) and tropical North Atlantic (TNA; Figs. S6e,f), respectively.
b. Results
The differences in the precipitation and lower-tropospheric horizontal wind between the positive and negative anomalies of the three sensitivity experiments are shown in Fig. 9. In the IOBM&TNA sensitivity experiment, the responses are highly consistent with the observational results (Fig. 3a), including the anomalous easterlies from the tropical western Pacific to the Bay of Bengal, the anomalous BOBAC, and the positive precipitation anomaly over the southeastern TP. In addition, the negative precipitation anomalies over the Bay of Bengal and western North Pacific are also reproduced by the experiment (Fig. 9a). Although the response patterns of the circulation and precipitation in the IOBM alone experiment (Fig. 10b) and TNA alone experiment (Fig. 10c) are basically similar to that in the IOBM&TNA experiment, the intensity, range, and significance of these responses are greatly reduced, especially in the southeastern TP. Therefore, the combined effects of the IOBM and TNA SSTA are vital to explain the circulation and precipitation anomaly over and around the TP. Next, the individual physical mechanisms through which the IOBM and TNA SSTA affect the TP precipitation anomaly are investigated.
Differences of August precipitation (shading; units: mm day−1) and 850-hPa horizontal wind (vectors; units: m s−1) over Asia between the positive and negative sensitivity experiments of the (a) IOBM&TNA experiment, (b) IOBM experiment, and (c) TNA experiment. The black vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% (90%) confidence level according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
Differences of August (a) 200-hPa geopotential height (shading; units: gpm) and horizontal wind (vectors; units: m s−1) and (b) 450-hPa vertical velocity (shading; units: Pa s−1) and horizontal wind (vectors; units: m s−1) between the positive and negative IOBM sensitivity experiments. The purple vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% (90%) confidence level according to Student’s t test. The bold black curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
In the IOBM alone sensitivity experiment, the circulation and precipitation response resemble the observations in section 4a (Figs. 4a,b). The anomalous Asian subtropical westerly over the TP with the coupled anticyclone (positive geopotential height) and cyclone (negative geopotential height) structure along the meridional direction from south to north in the upper level (Fig. 10a), and the ascending motion over the southeastern TP in the middle level (Fig. 10b), can be reproduced, but the response of the positive geopotential height is slightly southward. In the lower level, striking easterly anomalies prevail from the tropical western Pacific to the Bay of Bengal, which is controlled by an anomalous anticyclonic circulation (Fig. 10b), and thus cause the convergence of water vapor flux over the southeastern TP (Fig. S4b). These features are consistent with the observational results (Figs. 4a,b; see also Fig. S4a).
In the TNA alone sensitivity experiment, the quadrupole streamfunction anomaly, with twin cyclonic circulation anomalies ranging from the eastern Pacific to the Atlantic and twin anticyclonic circulation anomalies extending from the Bay of Bengal to the western Pacific, can be well simulated in the lower troposphere (Fig. 11a). The striking easterly anomalies prevailing from Africa to the western Pacific trigger a BOBAC and thus enhance the positive precipitation over the southeastern TP (Fig. 11b). These features are highly consistent with the observational results (Fig. 6b), which verifies the overlapping effect of the TNA SSTA on the IOBM influencing the TP precipitation. In addition, the positive surface latent heat flux anomaly over the eastern Indian Ocean is successfully reproduced (Fig. 11c), which verifies that the TNA SSTA can increase the warm Indian Ocean SSTA by reducing the surface evaporation and further amplifying the effect of the IOBM on the TP precipitation. Therefore, these experimental results successfully reproduce the eastward pathway of the TNA SSTA affecting TP precipitation.
Differences of the August (a) 850-hPa streamfunction (shading; units: 106 m2 s−1), (b) precipitation (shading; units: mm day−1) and 850-hPa horizontal wind (vectors; unit: m s−1), and (c) surface latent heat flux (shading; units: W m−2) between the positive and negative TNA sensitivity experiments. The positive shading in (c) represents downward surface latent heat. The black vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% (90%) confidence level according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000 m.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
In addition, the westward pathway of the TNA SSTA affecting TP precipitation can be found in Fig. 12. The experiment reproduces the dipole mode of the large-scale circulation anomaly with divergence (convergence) over the Atlantic and convergence (divergence) over the central Pacific in the upper (lower) troposphere (Figs. 12a,b). Correspondingly, an anomalous Walker circulation with ascending motion over the TNA region and anomalous descending motion over the central Pacific is evident (Fig. 12c). Therefore, the TNA alone experiment well verifies the eastward and westward pathways through which the TNA SSTA influences the TP precipitation anomaly.
Differences of the August (a) 200- and (b) 850-hPa velocity potential (shading; unit: 106 m2 s−1) and the divergent wind (vectors; units: m s−1), (c) the vertical velocity (shading; units: m s−1) and zonal vertical circulation (vectors; units: m s−1; vertical speed is scaled by 780 to facilitate visualization) averaged between 5° and 15°N between the positive and negative TNA sensitivity experiments. The black vectors indicate statistical significance above the 95% confidence level, and the black (blue) stippled regions indicate statistical significance above the 95% (90%) confidence level according to the Student’s t test. The bold blue curve represents the TP domain with an altitude > 2000.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
In contrast to the data diagnosis (Figs. 5a and 6b), the maximum anomalous anticyclone center and negative precipitation anomaly center are not located in the western Pacific, but in the Bay of Bengal (Fig. 11b). Given that the development of the western Pacific anomalous anticyclone is a process of strong atmosphere–ocean interaction (Du et al. 2009; Wu et al. 2010; Kosaka et al. 2013; Xie et al. 2016; Chang et al. 2016), which is not included in these AGCM experiments, the intensity and location of the western Pacific anticyclone anomaly are slightly different from the observations. In addition, compared with the widespread and strong descending motion near the date line in the observation (Fig. 8c), there is weak ascending motion anomaly over there in the TNA alone experiment (Fig. 12c). In the observation, the anomalous descending motion in Pacific is induced by both the warm SSTA in the Indian Ocean, western Pacific, and TNA regions (Fig. 2a), whereas in the TNA alone experiment the SSTA forcing exists only in the TNA region. As a result, only the anomalous descending motion in the central Pacific matches the observation.
6. Summary and discussion
a. Summary
This study aims to explore the individual and combined effects of the tropical Indian Ocean Basin mode (IOBM) and tropical North Atlantic (TNA) SSTA on the interannual variability of the August TP precipitation through data diagnosis and numerical simulations using CAM5.0. A schematic diagram depicting the physical mechanisms responsible for the southeastern TP precipitation anomaly is shown in Fig. 13. The major conclusions can be summarized as follows:
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The interannual variation of the TP precipitation in late summer (August) is characterized by a maximum variance center over the southeastern TP. It is influenced significantly by both the IOBM and TNA SSTA. Statistically, a robust positive correlation exists between the August precipitation anomaly over the southeastern TP and the IOBM (TNA SSTA), with a leading signal of the IOBM (TNA SSTA) appearing 3 (2) months before.
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When the IOBM is in a positive phase, the tropical Indian Ocean warm SSTA induces an anomalous anticyclone over the Bay of Bengal (BOBAC) via Kelvin wave–induced Ekman divergence. As a result, anomalous southwesterlies to the western BOBAC convey abundant water vapor to the southeastern TP, favoring the positive precipitation anomaly over that region.
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The TNA SSTA can affect the TP precipitation anomaly through eastward and westward pathways. In the positive phase of the TNA SSTA, the tropical North Atlantic warm SSTA induces an eastward-extending Kelvin wave accompanied by easterly anomalies from Africa to the western Pacific, which overlap with the counterparts caused by the positive IOBM, thus strengthening the BOBAC via Kelvin wave adjustment. As a result, the positive precipitation anomaly over the southeastern TP is enhanced. In addition, the anomalous easterlies induced by the warm TNA can increase the tropical Indian Ocean SSTA by reducing surface evaporation and thus amplifying the effect of the IOBM on TP precipitation. Therefore, the eastward pathway generates overlapping and amplification effects on the IOBM influencing TP precipitation. In addition, the warm SSTA in the TNA region can trigger a westward-extending Walker circulation anomaly to suppress the convection over the central Pacific, which in turn forces a BOBAC as a Rossby wave response. As a result, the positive precipitation anomaly over the southeastern TP is further increased.
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Numerical experiments further confirmed the combined effect of the IOBM and TNA SSTA on the TP precipitation anomaly. If only one of them is considered, the intensity, spatial range, and significance of the TP precipitation anomaly are greatly reduced. Therefore, it is necessary to simultaneously consider the contributions of the IOBM and TNA SSTA when studying the circulation and precipitation variability over and around the TP in late summer.
Schematic diagram illustrating the physical mechanisms responsible for the combined effect of positive Indian Ocean Basin mode (IOBM) and tropical North Atlantic (TNA) SSTA on the southeastern TP precipitation in late summer. 1) The mechanism of the IOBM influencing TP precipitation: the positive IOBM (red shading over the tropical Indian Ocean) induces an anomalous anticyclone over the Bay of Bengal (BOBAC, shallow blue circle) via Kelvin wave–induced Ekman divergence (dark and dashed purple circle), and thus causes the positive precipitation anomaly over the southeastern TP (dark green shading over the TP). 2) The TNA SSTA affects the TP precipitation anomaly through eastward and westward pathways. In the eastward pathway, the warm TNA SSTA (red shading over the TNA region) triggers an eastward-extending Kelvin wave (shallow and solid purple circle), which overlaps with that caused by the IOBM, and thus strengthens the BOBAC. As a result, the positive precipitation anomaly over the southeastern TP is further enhanced. In addition, the Kelvin wave trigged by TNA SSTA induces anomalous easterlies over the tropical Indo-Pacific, which contribute to the warm Indian Ocean SSTA and thereby amplify the effect of the IOBM influencing TP precipitation. That is, the eastward pathway generates the overlapping and amplification effects on the IOBM influencing TP precipitation. In the westward pathway, the warm TNA SSTA generates a westward-extending Walker circulation anomaly (dark blue arrows), suppressing the convection over the central Pacific, which in turn triggers a Rossby wave response with a BOBAC, and hence increases the positive precipitation anomaly over the southeastern TP significantly. Therefore, the IOBM and TNA SSTA have a combined effect on the TP precipitation anomaly and neither factor alone can significantly affect the TP precipitation anomaly.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0990.1
b. Discussion
This study only explores the effects of positive phases of the IOBM and TNA SSTA on the TP precipitation anomaly by regression analysis. To discuss whether a reverse case exists during negative phases, Fig. S7 shows the composite August precipitation and lower-level circulation anomalies in the positive and negative phases of the IOBM and TNA SSTA, separately. The composite differences between the positive and negative phases of the IOBM and TNA (Figs. S7a,d) are basically consistent with their regression (Figs. 3b,c). A significant positive precipitation anomaly occurs over the southeastern TP, accompanied by a BOBAC. In addition, the patterns of the BOBAC and TP precipitation anomaly in the positive and negative composites of the IOBM and TNA SSTA are basically opposite. However, the intensity of these anomalies in the negative phase is slightly weakened compared with that in the positive phase (Figs. S7b,c,e,f). Therefore, it can be concluded that the positive and negative phases of the IOBM and TNA SSTA have basically symmetrical effects on the TP precipitation anomaly in late summer. For brevity, the discussion about the negative phases is not included in the text.
Note that the water vapor transported to the TP comes not only from the anomalous southwesterlies induced by the IOBM and TNA SSTA (Figs. 3b,c) but also from strong westerly anomalies over the northern Arabian Sea and Indian Peninsula (Fig. 3c), which may be influenced by other driving factors. But that is beyond this study, and deserves further study. In addition, it is worth noting that the IOBM and TNA SSTA mainly influence the TP precipitation in August, instead of June and July, although the IOBM-like and TNA-like SSTA has appeared from June (Fig. 2). This is partly due to the subseasonal evolution of the climatological circulation and the development of the IOBM and TNA SSTA. As mentioned in section 1, the strong subseasonal variability results in the TP precipitation anomaly being affected by the NAO in June and the anomalous convection over the western Maritime Continent in July. On the other hand, this might also be related to the development of the IOBM and TNA SSTA. For the IOBM, as mentioned in section 4b, the warm TNA SSTA may partially contribute to the warm Indian Ocean SSTA in August and further amplify the effect of the IOBM on the TP precipitation. For the TNA SSTA, its enhancement may be affected by both ENSO (Rong et al. 2010; L. Wang et al. 2017; Zheng and Wang 2021) and local air–sea interaction (Huang et al. 2004). As a result, the strengthened TNA SSTA is strong enough to increase TP precipitation in August. Nevertheless, the detailed physical mechanism of the TNA SST development is beyond this study and deserves further investigation.
In this study, we concluded that the IOBM and TNA SSTA affect the TP precipitation anomalies through exciting the BOBAC, which is located to the west of the western North Pacific anticyclone (WNPAC). Compared with BOBAC, the driving factors and related physical mechanisms influencing the WNPAC are more complicated. Besides their common driving factors (i.e., the IOBM and TNA SSTA), the WNPAC can be enhanced by the local cold SSTA in western North Pacific (Wang et al. 2000, 2003, 2013; B. Wang et al. 2017; Xiang et al. 2013), the central Pacific cold SSTA (Xiang et al. 2013; Wang et al. 2013), zonal SST gradient between tropical IO and central Pacific (Chen et al. 2012; Cao et al. 2013), and the low moist enthalpy advection (B. Wu et al. 2017). Whether these factors can also affect the BOBAC and thus the TP precipitation anomaly needs further investigations.
The physical mechanisms by which the TNA SSTA affects the BOBAC and WNPAC are almost same in this study, through either a Kelvin wave response to its east or a Rossby wave response to its west. However, the relative importance of the eastward and westward pathways is unknown. Lu and Dong (2005) pointed out that the TNA SSTA affects WNPAC through exciting an eastward Kelvin wave, which propagates faster than a Rossby wave. However, Chang et al. (2016) argued that the prevailing circulation induced by TNA SSTA features Rossby waves with strong off-equator rotational winds, rather than equatorial Kelvin waves that have maximum wind speed at the equator and decay exponentially with increasing latitudes. As mentioned in the introduction, recent studies have verified the two physical mechanisms, but their relative importance is still debated. For the BOBAC and WNPAC, the contribution from each pathway may vary due to their different location, which deserves further exploration in the future to accurately simulate and forecast TP precipitation.
Previous studies have demonstrated that the TP diabatic heating (partially contributed by condensation latent heat release from precipitation) acts as a regional driver of precipitation anomalies in East Asia (Duan and Wu 2005; Wang et al. 2014, 2018; Hu and Duan 2015; Wu et al. 2015a; G. X. Wu et al. 2017; Duan et al. 2017; Xie and Duan 2017; He et al. 2019; Liu et al. 2020a). But to what degree can the TP be regarded as a bridge connecting the global SSTA and East Asian summer monsoon? This study only shows the influence of the IOBM and TNA SSTA on the TP precipitation anomaly from the perspective of large-scale zonal circulation through the Kelvin wave response and Walker circulation anomaly. However, some other studies have found another possible mechanism for the influence of the Indian Ocean SSTA on TP precipitation. The warm SSTA in the Indian Ocean strengthens the local Hadley circulation between the Maritime Continent and the Bay of Bengal, resulting in local intense descending motion and thus inducing a BOBAC that influences the TP precipitation anomaly (Jiang et al. 2016). Therefore, could the TNA SSTA influence Maritime Continent convection and further influence TP precipitation through the anomalous Hadley cell? Further studies are needed to address these issues. Moreover, a fully coupled land–atmosphere–ocean model could be employed in the future to conduct experiments that investigate the interaction between the global SSTA and TP climate anomalies.
Acknowledgments.
This work was supported by the National Natural Science Foundation of China (Grants 41725018, 91937302, and 42030602).
Data availability statement.
All the data are available from the following websites: the HadISST1 data from https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html; the APHRODITE and GPCP precipitation data from http://aphrodite.st.hirosaki-u.ac.jp/download/ and https://psl.noaa.gov/data/gridded/data.gpcp.html, respectively; and the JRA-55 data from https://rda.ucar.edu/datasets/ds628.1/#!description. The monthly CN05.1 precipitation and model data are available at http://data.lasg.ac.cn/dam/zhangp/2021_JC_Zhang-Duan/.
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