Dynamical relationship between Indian summer monsoon rainfall and South Asian High in seasonal coupled models

doi:10.21203/rs.3.rs-3313824/v1

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Dynamical relationship between Indian summer monsoon rainfall and South Asian High in seasonal coupled models

https://doi.org/10.21203/rs.3.rs-3313824/v1

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published 11 Nov, 2023

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The present study aims to understand the dynamical linkage between Indian summer monsoon rainfall (ISMR) and South Asian High (SAH) in four seasonal models (CANCM4, NEMO, CANSIP, and CFSv2) initialized with May conditions for 1982–2016. Observational analyses indicate that the northwest-southeast (I_NW−SE) index of SAH is strongly correlated (~ 0.62) with ISMR whereas the east-west (I_EW) index is negatively correlated (~-0.57). All the models reasonably capture the relation between ISMR and the I_NW−SE/I_EW indices with a slightly varying correlation. The positively regressed rainfall anomalies are (90%) significant during I_NW−SE years and attributed to the strong cold sea surface temperature anomalies over the equatorial eastern Pacific (i.e., La Niña) and positive vorticity associated with the strong cyclonic circulation over monsoon region. Similarly, significant negative rainfall anomalies are identified during I_EW years, strongly associated with El Niño patterns in the eastern Pacific and negative vorticity anomalies over monsoon region. The CANCM4, NEMO, and CANSIP models show strong positive (negative) regressed rainfall anomalies over India in I_NW−SE (I_EW) years, unlike the CFSv2 and observations and mainly due to strong linkage with El Niño and the Indian Ocean Dipole in the models. Overall, the high resolution CFSv2 performs better than the other models for mean rainfall and teleconnections between ISMR and SAH. Of the three models, the CANCM4 performs better in capturing dynamic circulations such as vorticity, velocity potential, stream function etc. The study highlights the seasonal model’s ability to capture the linkage of ISMR and SAH indices and helps understand rainfall variability.

South Asian High

East-west index

northwest-southeast index

Seasonal rainfall

Indian Summer Monsoon

teleconnections

The Indian Summer Monsoon (ISM) is a complex land-ocean-atmosphere complex system (Gadgil 2003). The Asian summer monsoon (ASM) produces major parts of annual rainfall during June to September (summer) months and is attributed to the significant moisture transfer from the Indian Ocean toward the Indian subcontinent (Webster et al. 1998). The annual cycle of monsoon precipitation is influenced by teleconnections such as Elnino/Lanina (Shukla and Paolino 1983; Cane et al. 1997), Indian Ocean Dipole (IOD; Saji et al. 1999; Webster et al. 1999), seasonal movement of the Inter- Tropical Convergence Zone (ITCZ; Gadgil, 2003, 2018), etc., ensuing shift in the rainbands. The summer monsoon rainfall is highly variable/diverse in the Indian Subcontinent, influencing the socio-economic conditions of the country (Gadgil and Gadgil 2006; Kulkarni et al. 2020). The Indian/South Asian monsoon exhibits dramatic wind reversals between winter and summer from the Arabian Sea through the South China Sea in response to the rise of the Tibetan Plateau (Molnar et al. 2010).

The South Asian high (SAH) is an imperative upper-level component of the Asian summer monsoon (ASM) system. The most vigorous and perpetual anticyclone at the upper level originated during boreal summer over the Tibetan Plateau in the Northern Hemisphere (Mason and Anderson 1963; Tao and Zhu 1964; Krishnamurti et al. 1973) in the early 1950s. Numerous earlier studies have shown the SAH shift to the west or east in the synoptic scale, leading to the Iranian and Tibetan modes (Luo et al. 1982; Zhang and Wu 2001). These two modes are closely related to the variation of the Indian summer monsoon (ISM) and the summer rainfall over China (Luo et al. 1982; Zhang and Wu 2001; Zhang et al. 2009). The SAH is a substantial upper-level characteristic of the ISM system (Krishnamurti and Bhalme 1976). Zhang et al. (2002) considered the SAH an important indicator for the ASM rainfall. Further, Liu et al. (2013, 2015) and Wu et al. (2015) demonstrated the importance of SAH in monsoon onset. They have shown that the monsoon onset vortex can be triggered by the pumping effect of the upper-level divergence interrelated with the SAH over the Bay of Bengal and the eastern Arabian Sea, favoring the onset of monsoon in South Asia.

A recent study has also identified that the onset of SAM has been linked to the seasonal north-westward movement of the SAH from the western Pacific to the South Asian plateaus (Wei et al. 2019a, 2021). According to Liu et al. (2001) and Wu and Liu (2003), the early north-westward shift of the SAH helps the ASM's early beginning. The diabatic heating over the Tibetan Plateau and the nearby monsoon regions determines the SAH intensity (Krishnamurti et al. 1973; Liu et al. 2001; Wu and Liu. 2003; Wu et al. 2007, 2016). In addition, a severe SAH is assisted by a strengthened upper-level easterly wind to its south, which increases vertical wind shear across South Asia (Wang and Fan 1999; Webster and Yang 1992) and allows more rainfall over the ISM region.

The prominent characteristic of the SAH is its zonal shift, which is primarily attributed to the abnormal latent heat connected with ISM rainfall (Wei et al. 2014, 2019b, 2021; Zhang et al. 2002). Recent studies have shown that the SAH's zonal and meridional shifts are closely connected with ISM rainfall variability on an interannual time scale (Wei et al. 2012, 2014, 2015, 2021). The rainfall for ISM and East Asian Summer Monsoon (EASM) can be explained by moisture transport features (Kripalani & Kulkarni 2001; Zhang 1999, 2001), the circulation pattern over the Northern Hemispheric midlatitudes (Dai et al. 2002; Ding & Wang 2005, 2007; Liu & Ding 2008; Wu 2002), and upper-tropospheric high-pressure systems (200 hPa) such as the SAH (Wei et al. 2014, 2015, 2017). Many studies have reported that SAH can influence the EASM and ISM together from the observations on inter-annual and intraseasonal scale by the zonal shift and northwest-southeast shift of the SAH during the JJA season (Wei et al. 2012, 2014, 2015).

Most studies have demonstrated the relationship between SAH and ASM rainfall; minimal studies are available for the ISMR. However, recent studies by Wei et al. (2014; 2019b, 2021) have demonstrated the relationship between the ISMR (as part of ASM rainfall) and SAH during the JJA season using the global analysis- ERA-Interim. They have identified two SAH indices, the northwest-southeast Index (I_NW−SE) and the east-west Index (I_EW), strongly influencing the ISMR during JJA. The Indian Institute of Tropical Meteorology (IITM) has been using in-house (CFSv2; Ramu et al. 2016; Rao et al. 2019) and other seasonal coupled models (North American Multi-Model Ensemble Models; Kirtman et al. 2014) to improve the ISMR seasonal predictions. This study assesses the seasonal coupled model’s ability to simulate or capture the dynamical connection between ISMR and SAH during the JJA season. The study also focuses on how the I_NW−SE and I_EW indices are dynamically connected to the ISMR variations during JJA.

The rest of the paper is organized as follows. Data, methods, and model details in this study are reported in Section 2. Results and discussions are presented in Section 3. A summary and conclusions are shown in the final section.

The present study uses three seasonal coupled models (i.e., CANCM4, NEMO, and CANSIP), of the NMME and another seasonal model, Climate Forecast System version 2 (CFSv2; Ramu et al. 2016; Rao et al. 2019) from IITM. The rainfall, SST, winds at 850 and 200 hPa, and GPH are available in these three models and hence considered in this study. Better performance of these models in the Indian region was also demonstrated by Ramu et al. (2022). The resolution and citation of the models are shown in Table 1. The model outputs from May initial conditions are considered in this study for 1982–2016. The model monthly variables such as geopotential height (GPH) at 200 hPa (GPH200), wind circulations at 850 hPa and 200 hPa troposphere are validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim; Dee et al. 2011). The model-derived monthly precipitation is validated against the Global Precipitation Climatology Project (GPCP; Adler et al. 2003). The Optimum Interpolated SST (OISST) is used to validate the sea surface temperature (SST) of the seasonal models, and details can be found in Reynolds et al. (2002).

Table 1

Description of NMME models and the CFSv2 model.
Model Name	Referred Model Name	Resolution	Reference
CANCM4i	CANCM4	1⁰ ⅹ 1^o (~ 100 km)	Kirtman et al. (2014)
CFSv2	CFSv2	~ 38 km	Rao et.al. (2019)
GEM-NEMO	NEMO	1⁰ ⅹ 1^o (~ 100 km)	Kirtman et al. (2014)
CANSIPv2	CANSIP	1⁰ ⅹ 1^o (~ 100 km)	Kirtman et al. (2014)

Generally, the interannual time scale of the GPH200 measures SAH indices which are helpful in the estimation of the strength and horizontal mobility of the SAH. The zonal index or east-west index (I_EW) is defined as the shift or difference between GPH200 in the west box (22.5–32.5°N, 55–75°E) and the east box (22.5–32.5°N, 85–105°E), following Wei et al. (2014). Similarly, the northwest-southeast index (I_NW−SE) is defined as the difference in GPH200 from the northwest box (27.5–35°N, 50–80°E) to the southeast box (20–27.5°N, 85–115°E) following Wei et al. (2015). Following Wei et al. (2019b), these two indices are de-trended and standardized. Similarly, two SST indices - IOD (Saji et al. 1999; Webster et al. 1999) and Nino 3.4 (Shukla and Paolino, 1983; Cane et al. 1997) are also examined.

Various statistical scores like mean, bias (model minus observation/analysis), standard deviation (STD) for inter-annual variability, root mean square error (RMSE), correlation coefficient (r), etc. The regression is used to examine the association between ISM and SAH in the models and the observations. The student's t-test is used to determine statistical significance for correlation and regression.

3.1 ASM Climatological characteristics

The seasonal model’s fidelity to replicating the climatological characteristics of the ASM for mean and bias of rainfall, GPH200, and wind circulation at 850 hPa and 200 hPa are presented and compared with GPCP data during the JJA season. Figure 1 shows the climatological mean and bias of GPH200 (contour) and rainfall (shaded) obtained from ERAI and seasonal models. It is noticed that the orographic regions receive more rain, for instance, the northeastern parts and Western Ghats (above 10 mm day^− 1) in India, north-eastern African region, and south-east Asian countries like Bangladesh, Myanmar, Thailand, and Cambodia (Fig. 1a). The central parts of India also receive considerable amounts of rainfall due to synoptic disturbances such as lows and depressions from the Bay of Bengal (Krishnamurthy and Ajayamohan 2010). The northwestern parts and southern peninsular India receive less rainfall during the JJA. High geopotential height at 200 hPa (12240 m) is seen over the northern Indian region, extending in an east-west direction in the ERAI analysis (Fig. 1a), and consistent with that of Wei et al. (2019). All models have captured the spatial JJA mean rainfall patterns over the entire ASM region with varying magnitudes (Fig. 1b, d, f, and h). The CANCM4 (Fig. 1b-c) and CFSv2 (Fig. 1d-e) models could show mean orographic rainfall over the northeastern and western Ghats region of the Indian subcontinent, showing wet bias (Fig. 1c, e). These two models also show wet bias in the northeastern central African region, but CFSv2 is better than CANCM4 (Fig. 1c, e). All the models, except CANCM4, show dry bias over the northeastern states of India. However, the dry bias over most Indian regions is reduced compared to other models. All models show wet bias over Myanmar, Thailand, and Cambodia except CANCM4 (Fig. 1c, e, g, i). The CFSv2 and CANSIP models reasonably capture the mean GPH and zonal extension close to verifying analysis (Fig. 1a) than other models, unlike in the observation and CANCM4, NEMO models.

Table 2 shows the mean, standard deviation, bias, RMSE, and skill of rainfall of seasonal models against GPCP rainfall over all Indian region. The observation or GPCP rainfall shows the highest mean (7.32) and highest STD (0.64) for all India rainfall. Among the models, the CFSv2 has reproduced the mean (6.309 mm day^-1) rainfall, close to the GPCP rainfall. All the models underestimated the interannual variability (STD) compared to that of GPCP. The RMSE is low for CFSv2 (1.18) and CANCM4 (1.69) models and is more for NEMO and CANSIP with similar magnitude (2.34). All the models exhibit similar skill varying between 0.4 to 0.41, except for CANCM4 (0.35). These statistics have also been prepared for the monsoon core region (not shown). Similar performance of the models has been noticed in the monsoon core regions. The CFSv2 exhibited higher skill (0.52), while the remaining models show skill between 0.3 to 0.37 (not shown).

Table 2

Statistical scores of the mean seasonal rainfall (mm day^− 1) from GPCP and seasonal models for the JJA season.
	MEAN	STD	BIAS	RMSE	Rainfall SKILL
OBSERVATION	7.32	0.64
CANCM4	5.76	0.55	-1.55	1.69	0.35
CFSv2	6.30	0.43	-1.01	1.18	0.40
NEMO	5.06	0.49	-2.25	2.34	0.41
CANSIP	5.06	0.49	-2.25	2.34	0.41

Table 3

Skill for I_NW−SE and I_EW indexes in NMME, CFSv2 models and observation.
INDEX	I_NW−SE		I_EW
	ISMR	ISMR_CORE	ISMR	ISMR_CORE
OBS	0.62	0.77	-0.57	-0.66
CANCM4	0.54	0.70	-0.65	-0.63
CFSV2	0.74	0.83	-0.55	-0.59
NEMO	0.55	0.75	-0.59	-0.69
CANSIP	0.60	0.78	-0.67	-0.74

The climatology of 850 hPa wind circulation and its bias during the JJA season from ERAI and individual models are shown in Fig. 2. The ASM region is distinguished by a strong cross-equatorial flow, prevalently known as low-level jet (LLJ)/Findlater jet (Joseph and Raman 1966; Findlater 1969) over the Arabian Sea (AS) in the analysis (Fig. 2a). This strong LLJ supports intense rainfall in the windward side of the Western Ghats (Fig. 1a). The ERAI study shows a significant cyclonic circulation over the North Bay of Bengal (Fig. 2a), which contributes to higher amounts of rain south of the monsoon trough across India (Fig. 1a). All the models reasonably captured the mean circulation over the Asian region, especially the LLJ over the AS region with varying wind speeds (Fig. 2b,d,f,h). The CANCM4 and CFSv2 (Fig. 2b, d) models are better in showing wind maxima and location of wind maxima than the other two models (Fig. 2a, b, d, f). All three NMME models exhibit easterly wind bias over the equatorial Indian Ocean, whereas CFSv2 models show strong westerly wind bias over the same region (Fig. 2c, e, g, i). Further, a weak cyclonic circulation (negative bias) is noticed over the central Bay of Bengal in all four models, which may be one of the reasons for dry bias in rainfall over the Indian land mass (refer Fig. 1c, e, g, i). The NEMO and CANSIP models underestimate the wind magnitudes over the Somalia region compared to CFSv2 and CANCM4 models.

Another important component of ASM is the upper tropospheric (200 hPa) anticyclone over the Tibetan plateau. The ERAI analysis could bring out the upper-level subtropical westerly jet and tropical easterly jet (TEJ) north and south of the Tibetan Plateau anticyclonic circulation, respectively (Fig. 3a). The strength of the TEJ plays a crucial role in ASM rainfall (Chen and Van Loon 1987; Chen and Yen 1991; Pattanaik and Satyan 2000) and it is related to strength of the LLJ, hence increases the ISMR (Tanaka 1982). Hence, proper representation of TEJ in coupled models is essential to anticipate a better prediction of the ISM. All models reasonably produced mean anticyclonic circulation over the Tibetan region with different magnitudes (Fig. 3b, d, f, h). The three NMME models have strong westerly wind bias over the Indian Ocean and easterly bias over the sub-tropical region (Fig. 3c, g, i), unlike in the CFSv2 model where the TEJ is represented with relatively less bias. The results of the CFSv2 model are consistent with the earlier study by Ramu et al. (2016). Overall, the CFSv2 model performs better than the other models in reproducing the climatological characteristics of rainfall along with ASM components in the lower and upper troposphere. Although the CANCM4 model replicates the climatological rainfall, the statistical skill appears to be poorer than the other models. The NEMO and CANSIP models resulted in higher errors and poor representation of climatological features. Note that the CFSv2 is also consistently better for inter-annual rainfall variability, indicating similar rainfall peaks as that of the GPCP observation, unlike other seasonal models. The better performance of the CFSv2 model for rainfall and monsoon components could be due to (i) higher resolution, (ii) improved physical processes, mainly reduction of upper-level anticyclonic wind bias and improved TEJ, and (iii) better ocean coupling, etc (Ramu et al. 2016).

3.1 Relationship between ISMR and SAH

This section reports the association between rainfall over India as a whole and monsoon core regions, and SAH indices in the observation and individual models during JJA season. I_NW−SE and I_EW indices are estimated as discussed in the Methodology section from NMME models and CFSv2 and compared with that of the ERAI. Figure 4 presents the relationship between observed and model-captured indices at 200 hPa GPH. All models have reasonably captured the I_NW−SE index with high correlation (r > 0.5) thus the better skill, except in the CANCM4 model (0.45) (Fig. 4a-d). The I_EW index is highly varying in the seasonal models, unlike ERAI, leading to less correlation/skill (Fig. 4e-h).

Figure 5 shows the year-to-year variability of the rainfall over the monsoon core region (22°-30°N and 75°-87°E) and both the indices (I_NW−SE and I_EW) during this study period. It is observed that both the rainfall and I_NW−SE and I_EW indices exhibit strong interannual variability. All NMME and CFSv2 models have presented in-phase variability between ISMR and I_NW−SE most of the time (Fig. 5a-e), while the I_EW index and ISMR are negatively related in the monsoon core region (Fig. 5f-j). Figure 6 shows the scatter plot between rainfall over the monsoon trough region and two SAH indices during the study period. I_NW−SE index is positively correlated (r = 0.77) with rainfall, whereas I_EW index is negatively correlated (r=-0.66) in the observation/verifying analysis (Fig. 6a, f). All seasonal prediction models have replicated almost similar relationships with slight variations (Fig. 6b-e). Similarly, the relationship with I_EW is comparable and reasonably captured in the seasonal models (Fig. 6g-j) with the observation/verifying analysis (Fig. 6f). Note that the association between the SAH indices and all India rainfall is also analyzed (Figure S2). These findings are consistent with an earlier study by Wei et al. (2014).

3.2 Teleconnection between SAH and different SST indices

The association of SAH indices with different SST indices (IOD and NINO3.4) has also been studied. Figure 7 depicts the relationship between the IOD index with I_NW−SE and I_EW index during this study period. A weak negative correlation (-0.05 and − 0.19) in both indices with IOD in the observations/verifying analysis indicates no significant relation between IOD with I_NW−SE and I_EW indices (Fig. 7a, f). However, all models show a stronger and significant (90%) negative correlation with IOD. This relation between IOD and I_EW is the opposite. Among all the seasonal models, the CFSv2 shows a better relation between IOD and I_NW−SE (Fig. 7b-e). The IOD and I_EW are positively correlated, unlike observed indicating strong (or overestimated) association (Fig. 7g-j). Better performance of CFSv2 could be attributed to (i) improved ocean coupling, which led to a realistic representation of tropical SST over the Indian Ocean (Figure S1) and a better representation of 200 hPa GPH and zonal extension (Fig. 1). The CANCM4 model follows the CFSv2 model. The higher errors due to weaker GPH200 magnitudes could be one of the reasons for the poor capturing of the association between IOD and SAH indices in NEMO and CANSIP models.

Further, the I_NW−SE and NINO3.4 relationship also represents a negative correlation in observations/verifying analysis (-0.33) and all the seasonal models (Fig. 8a-e). Among the seasonal models, the NEMO and CANSIP models highly overestimated the relation, while CFSv2 and CANCM4 are comparable with observation/verifying analysis (Fig. 8a-e). On the other hand, the association or relation between NINO3.4 and I_EW (zonal) indices is weak and positive in observation (0.13), which is strongly estimated in the seasonal models, except CANCM4 (Fig. 8f–j). Overall, the seasonal models are unable to generate the teleconnection between SST and SAH indices during the JJA season. The poor performance of the seasonal models could be mainly due to the large diversity of SST indices in representing the ISMR teleconnection with Pacific Ocean conditions. It is perceived that all the models have exhibited strong El Niño features, unlike in the observations (Ramu et al. 2022).

3.3 Relation between SAH indices and anomalies of rainfall and wind speed

Figure 9 illustrates the regressed JJA rainfall anomalies (mm day^− 1) against the SAH (I_NW−SE and I_EW) indices during the study period. Here, it is observed that positive rainfall anomalies (90% significant) are observed over the central India, eastern places of the monsoon core region, the foothills of the Himalayas, Bangladesh, Myanmar, Thailand, South China, and surrounding regions during I_NW−SE years in the observation (Fig. 9a). The study period is divided into I_NW−SE and I_EW years based on rainfall amount in a particular season, following Wei et al. (2014, 2015, 2019b, 2021). Weak I_NW−SE induced negative rainfall anomalies are observed over the Arabian Sea, the Western Ghats, most parts of northeast India, and the northwestern Pacific region in the observations (Fig. 9a). The SAH (I_NW−SE) induced rainfall anomalies over the Indian region are reasonably captured by all seasonal prediction models with different magnitudes and more spread when compared to the observation except CFSv2 (Fig. 9b-e). For example, the CANCM4 model is notable for the negative rainfall anomalies over the Arabian Sea and northwestern Pacific region and underestimated over the western Pacific Ocean, South China, and surrounding areas, whereas slightly overestimated over the eastern African, equatorial Indian Ocean and Bay of Bengal regions (Fig. 9b). The CFSv2, NEMO and CANSIP models capture the strong negative rainfall anomalies over the Arabian Sea, western equatorial Pacific Ocean, and weak negative anomalies over the northwestern pacific region (Fig. 9c, d and e). Positive rainfall anomalies were simulated over the eastern African region, south China and surrounding regions, and the east Bay of Bengal in the CFSv2 model (Fig. 9c). NEMO and CANSIP models have simulated the less positive rainfall anomalies over the eastern African region, south China sea and strong positive rainfall anomalies over the western and equatorial pacific region like observation and strong negative anomalies are Myanmar and Bangladesh and surrounding regions (Fig. 9d and e). The results from analyses and seasonal models infer that the SAH shifting towards north-westward favors the ISMR, while the shift to south-eastward suppresses the ISMR and favors the rainfall over the South China region. These features have been observed from the analyses in the earlier studies (Wei et al. 2015, 2019b), however, the seasonal models have been employed for the first time to assess this relationship in this study.

Analysis indicates that the I_EW index is negatively correlated to the JJA rainfall anomalies over central India, eastern parts of the monsoon core region, the foothills of the Himalayas, Bangladesh, Myanmar, the South China Sea, and western and equatorial Pacific Ocean (Fig. 9f). It is caused by strong negative relative vorticity over south Asian region. Further, a strong El Nino-like pattern has been seen in the eastern Pacific (Figure not shown). Strong Positive rainfall anomalies are observed over the northwestern Pacific region, and weak positive rainfall anomalies are observed over the Arabian Sea (Fig. 9f). In CFSv2, NEMO, and CANSIP models, except for the Indian subcontinent, regions like the southern Arabian Sea and the southern Indian Ocean are strongly linked with negative rainfall because there is a strong anticyclonic circulation over those regions (Fig. 10f). Thus, realistic simulation of SAH is critical in seasonal prediction models to accurate prediction of ISMR.

In this section, the lower-level (850 hPa) atmospheric relative vorticity over the Asian region during the SAH years in the JJA season (through regression) is studied to understand the possible reasons for SAH shifting and rainfall distribution (Fig. 10). The central Arabian Sea, the foothills of Himalayas, the western Pacific Ocean, and the Mongolian region are experiencing the anomalous negative relative vorticity anomalies (Fig. 10a) which may be one of the reasons for negative rainfall anomalies over those regions (Fig. 9a). The significant positive relative vorticity anomalies are seen over the monsoon trough region, the northern Arabian Sea, Saudi Arabia and the surrounding areas, the south China Sea, and the northwestern Pacific Ocean in observation (Fig. 10a) during SAH years (I_NW−SE index) which resulted into significant positive rainfall anomalies (Fig. 9a). The seasonal models are able to simulate SAH induced relative negative vorticity anomalies over the central Arabian Sea, and the Himalayan region with less magnitude less spatial distribution when compared to observations whereas positive vorticity anomalies are over the monsoon trough region with less spatial extent and shift westward compared to the observation (Fig. 10b-e). Whereas the seasonal models are unable to capture the north Bay of Bengal vorticity pattern like in the observation, except NEMO model. Unlike the observation, all models have shown substantial positive vorticity anomalies over the south Bay of Bengal. Moreover, Except for the NEMO model, all models reasonably captured the SAH induced vorticity anomalies over the western Pacific Ocean (Fig. 10b-e). Similarly, the opposite relative vorticity pattern is seen over the Asian region during the second SAH (I_EW) index years in observation and models (Fig. 10f-j). Overall, there is a large diversity to produce the SAH-induced relative vorticity anomalies during the JJA season over the Asian region in current seasonal prediction models.

The upper tropospheric velocity potential and divergent wind component have been studied to recognize the large-scale circulation patterns in both the observational analysis and the models. Figure 11 shows the regressed JJA velocity potential (shaded) and divergent flow anomalies (vectors) at 200 hPa against the SAH indices (I_NW−SE and I_EW) during the study period. In the observational analysis, noticed that a weak divergence (convergence) over the South Asian (Eastern Pacific) region during the I_NW−SE years, represented by negative (positive) velocity potential anomalies, respectively (Fig. 11a). Except CFSv2, all NMME models exhibit the westward shifting of divergent and convergent centers in the both Asian and Pacific regions with higher magnitude (Fig. 11b-e). The opposite velocity potential and divergent winds pattern are observed during I_EW years in the same season in observations (Fig. 11f). All seasonal prediction models have captured the large-scale convective centers during I_EW years in JJA season with slight westward shift and more magnitude (Fig. 11g-j). The response of tropical circulation to rainfall over the Asian region during SAH years is presented in Fig. 12 through the regression analysis of the stream function and rotational wind component at 850 hPa for observational analysis and models. The regressed anomalies of stream function are negative for cyclonic circulations observed over the Indian landmass and either side of the equator around (west) the dateline (Fig. 12a). The result is consistent with the earlier studies (Matsuno 1966; Gill 1980). Most models reasonably simulated the SAH induced two off-equatorial cyclonic circulations on either side of the equator around (west) date line with more magnitude (Fig. 12b-e). Models like CFSv2, NEMO, and CANSIP have captured the stream function and rotational with patted over the Indian Ocean and northwest Indian region like in the observations (Fig. 12c-e). Similarly, opposite rotational wind and stream function anomalies are observed over the Indo-Pacific region during SAH (I_EW Index) in the observational study (Fig. 12f). Most models can capture SAH (I_EW Index) induced stream function and rotational wind component with overestimation in the magnitude (Fig. 12g-j). Overall, all models appear to better represent the SAH-induced large-scale circulation pattern at 850 hPa over the Indo-Pacific region.

In this study, the ability of three NMME and CFSv2 seasonal models are assessed to simulate the primary mean characteristic of ISM and its teleconnection with I_NW−SE and I_EW indices during the JJA season for the period 1982–2016. All the model simulations are considered from MAY initial conditions. It is noticed that models accurately simulate the mean climatological features of ISM, like circulation at 850 and 200 hPa, rainfall, and geopotential height over the Asian region during the JJA season. Particularly, orographic rainfall (Western Ghats, Eastern Ghats) over the Indian region, cross-equatorial flow over the Arabian Sea, and easterly jet and Tibetan anticyclonic circulation at 200 hPa level are well simulated by NMME models.

The CANCM4 and CFSv2 models replicate the mean orographic rainfall over northeastern India and the western ghats. Four models can simulate more or less similar JJA mean rainfall across the northeastern central African region. However, CANCM4 can simulate significantly more rain than the other models, resulting in more wet bias. All models show wet bias over the southeastern Asian countries like Myanmar, Thailand, and Cambodia except CANCM4. The CFSv2 model exhibited a less dry bias (-1.01), while other models had comparatively higher bias for all-India rainfall. The CFSv2 model reasonably captures the mean GPH patterns close to observation.

Additionally, CANCM4 has a slightly lower skill for rainfall across the Indian land mass than the other three models. The CANCM4 and CFSv2 exhibited less bias and root mean square errors than NEMO and CANSIP models. The mean rainfall in all NMME models is less and has shown a greater inter-annual variability (standard deviation) than the CFSv2 model.

All models reasonably capture I_NW-SE index (skill is around 0.5) except the CANCM4 model, whereas I_EW has less skill.
It has been shown that there is a significant interannual variability in both rainfalls over the monsoon core region using the I_NW-SE and I_EW indices. The NMME and CFSv2 models effectively reflect the year-to-year rainfall variability across the monsoon trough region and SAH indices.
The shifting of SAH impacts the ISMR rainfall. The shift towards north-westward (south-eastward) supports (suppresses) the rainfall over the Indian region.
The relationship of ISMR is positive with the I_NW-SE index and negative with the I_EW index. A similar relationship has been seen when considering rainfall in the monsoon core region.
There is no significant relation between IOD with I_NW-SE and I_EW index in observation, but all models show negative correlation with I_NW-SE index [CANCM4 (-0.30), CFSv2 (-0.29), NEMO (-0.56), and CANSIP (-0.51)] and positive correlation with I_EW index [CFSv2 (0.28), NEMO (0.55), and CANSIP (0.43)]. It indicates all models show a strong association (overestimated) than observation. The observational analysis shows a weak negative (positive) correlation between the I_NW-SE (I_EW) index and IOD. Whereas all models exhibited strong correlations except CANCM4. The seasonal models cannot replicate the teleconnection between SAH and IOD/Nino 3.4 indices during the JJA season.
Strong positive regressed relative rainfall anomalies are linked with the strong positive lower tropospheric relative vorticity anomalies over the monsoon trough region.

Acknowledgements:

The authors acknowledge the financial and computational support of the Earth System Science Organization (ESSO), Ministry of Earth Sciences through THUMP Project (No. MoES/16/09/2018-RDEAS-THlJMP-7).

Data availability statement:

The data supporting this study's findings are freely available and the codes are available from the corresponding authors upon request. CFSv2 model data is provided by the Surya chandhrara rao from the IITM for reasonable request.

Declaration of Competing Interest

The authors declare that they have no known competing interests for this publication.

Authors' contributions:

G Sripathi: Conceptualizations, Methodology, Formal Analysis, Visualization, Writing- Original Draft, Writing -Review & Editing.

Dandi A. Ramu: Conceptualizations, Methodology, Formal Analysis, Visualization, Review & Editing.

T. S. Saikrishna: Formal Analysis, Review & Editing,

Krishna K Osuri: Conceptualizations, Methodology, Supervision, Formal Analysis, Writing Review & Editing, Funding acquisition.

A.S.Rao: Supervision, Formal Analysis, Review & Editing,

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Dynamical relationship between Indian summer monsoon rainfall and South Asian High in seasonal coupled models

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Abstract

Figures

1. Introduction

2. Data and Methodology

3. Results

3.1 ASM Climatological characteristics

3.1 Relationship between ISMR and SAH

3.2 Teleconnection between SAH and different SST indices

3.3 Relation between SAH indices and anomalies of rainfall and wind speed

Summary and conclusions

Declarations

References

Supplementary Files

Status:

Journal Publication

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