India is among the monsoon-dominant tropical countries, with majority of the rural population still dependent on agriculture. During the monsoon, rainfall received over the country has greater socioeconomic importance than other meteorological variables. The agricultural production and the country’s economy, through gross domestic product (GDP), are intertwined on the stability of monsoon rainfall (Krishna Kumar et al. 2004; Gadgil and Gadgil 2006). The summer monsoon season (June to September) is the major rainy season in India, during which the region receives a mean rainfall of 852 mm (80% of annual rainfall, Parthasarathy et al. 1994) with 10% coefficient of variation (i.e. standard deviation divided by mean). During the summer monsoon, orographic influence is prominent in the distribution of rainfall over India, especially the west coast and northeast India. This is because the prevailing moisture-laden south-west monsoon winds blow almost at right angle to the Western Ghats (WG) and the Khasi-Jaintia hills (Rao 1976). Global warming plays a decisive role in the strength and variability of the future Indian summer monsoon (ISM) rainfall. Hence, even minor changes in the country’s rainfall variability (in spatial or temporal scale) due to global warming can have an immense impact on water resources and agricultural production (Gadgil 1995; Webster et al. 1998). This, in turn, affects food security, livelihood of farmers, and economy of the country.
We know that the most important feature of ISM for livelihoods is rainfall occurring during the season. Most of the state-of-the-art Coupled General Circulation Models (CGCMs) face challenges in simulating ISM rainfall (Gadgil and Sajani 1998). After identifying the physically consistent groups of Coupled Model Intercomparison Project Phase 5 (CMIP5) models with the highest reliability, Jayasankar et al. (2015) derived the future projections of Indian summer monsoon rainfall and variability. Recently, the state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) models are being used to derive the future projections summer monsoon rainfall over India (Moon and Ha 2020; Almazroui et al. 2020). The global monsoon strength and precipitation are better simulated by CMIP6 models than by CMIP5 (Wang et al. 2020). In a study, Rajendran et al. (2021) found that the multimodel ensemble mean (MME) of 61 CMIP6 models is better than that of CMIP5 models in simulating mean and variability of Indian summer monsoon rainfall over India. The present-day simulations of several CMIP6 models capture the spatio-temporal patterns of the summer monsoon rainfall over India, especially over the Western Ghats and northeast foothills of Himalayas (Gusain et al. 2019, Rajendran et al. 2021). However, many of them are having horizontal resolution of about 100 kilometers or more. Due to which they face difficulties in resolving the sharp variation of WG orography of the west coast region and fail to capture observed regional characteristics (Mishra et al. 2018). It is to be noted that CMIP5/CMIP6 models still project uniform increase in ISMR over India (Jayasankar et al. 2015; Moon and Ha 2020; Almazroui et al. 2020). Giorgi and Marinucci (1996) found that precipitation amounts are highly sensitive to the resolution of the climate model. Also, increase in resolution results in improvement in the representation of the orography and associated precipitation, especially over the WG region (Rajendran et al. 2012). High-resolution simulations are found to be very useful to derive realistic climate change information at regional scale, which is crucial for climate change impact assessments.
The high-resolution was crucial not only for realistic simulation of spatial heterogeneity of mean summer monsoon, but also for attaining useful climate change projections of its mean and extremes (Rajendran et al. 2013). Under the future warming scenario, the high-resolution (20-km) model projects an increase in rainfall over interior India, but projects significant orographic rainfall reduction over the west coast of India at the end of the 21st century (Rajendran et al. 2012, 2013). The coarse resolution CMIP5 model could not simulate the observed decreasing trend in present-day monsoon rainfall over the WG region, but this trend is well simulated in high-resolution GCMs (Rajendran et al. 2012). Hence, to study the regional scale characteristics, it is essential to use a sufficiently high-resolution model. To overcome the difficulties caused by inadequate horizontal resolution and to study the finer-scale climate features, an alternative way is to downscale those GCMs by using a high-resolution regional climate model (RCM, Wang et al. 2004; IPCC 2013). This requires a high-resolution RCM driven by the initial and lateral boundary conditions from the global GCM (Dickinson et al. 1989; Giorgi et al. 1990; Jayasankar et al. 2018; Jayasankar 2019). There are several studies, which attempted to simulate the Indian summer monsoon and its seasonal variability using RCMs through dynamical downscaling technique (Bhaskaran et al. 1996; Dash et al. 2006; Ratnam et al. 2009). RCMs were also utilized as an useful tool to study the high-resolution climate change projections over India (Kumar et al. 2006; Mishra et al. 2014; Dash et al. 2015).
To evaluate the performance of various RCMs for regional climate projections at high-resolution, World Climate Research Programme (WCRP) initiated the Coordinated Regional Climate Downscaling Experiments (CORDEX) programme (Giorgi et al. 2009). This is a framework for downscaled climate change projections from the CMIP5 GCMs. CORDEX - South Asia (SA) domain covers the Indian region with RCMs are having a spatial resolution of 0.44° (~ 50-km). The inability to represent realistic spatial distribution of the summer monsoon rainfall at regional scale by the coarse resolution CMIP5 models is considerably rectified through the dynamical downscaling with most suitable physics schemes and domain set up (Jayasankar et al. 2018). These high-resolution downscaled simulations can be effectively used to understand the climate change projection at regional scale. However, studies using CORDEX-SA models show that many models exhibit lower skill in simulating the mean, variability and extremes of ISM (Singh et al. 2017). Mishra et al. (2014) analyzed these models in the context of precipitation extremes and found that few models showed improvement in simulating the rainfall extremes over India than its parent GCM. Few studies shows that the CORDEX-SA models highly overestimate the mean precipitation over the high altitude (e.g. Himalayas) regions (Sanjay et al. 2017a; Ghimire et al. 2018). Choudhary et al. (2018) found a notable dry rainfall bias over most of India, especially over central India, from the thorough evaluation of present-day summer monsoon simulations using 11 CORDEX-SA models. However, when comparing the RCM to its parent GCM, few other studies found an improvement in the mean climate and extreme events over South Asia (Gu et al. 2012; Hassan et al. 2015). A detailed analysis of the mean summer monsoon climatology over India revealed that downscaling may not always improve the seasonal averages, and it strongly depends on the choice of the RCM and the driving GCM (Sanjay et al. 2017b). However, in a study, Jayasankar et al. (2018) showed that employing sufficiently high-resolution RCM driven by bias-corrected boundary datasets with a suitable configuration results in realistic present-day ISM simulation as well as useful regional climate change projection.
The Weather Research and Forecasting (WRF) – Advanced Research WRF (WRF-ARW) model is considered as the next generation RCM, which is the most suitable and potentially useful tool for high-resolution regional climate modelling (e.g., Paul et al. 2018) as well as dynamical downscaling (e.g., Lo et al. 2008; Jayasankar et al. 2018). Hence, a high-resolution dynamical downscaling framework for the Indian region is implemented by Jayasankar et al. (2018), in which a high-resolution RCM (i.e., WRF-ARW) is one-way nested into bias-corrected NCAR-CCSM4–one of the CMIP5 GCMs (hereafter referred to as CCSM4-WRF). This CCSM4-WRF shows high fidelity in capturing important physical and dynamical characteristics of present-day ISM and extreme rainfall events, particularly recent trends in ISM rainfall over southern WG as observed (e.g., Rajendran et al. 2012). Its skill in simulating the present-day ISM provides better confidence in its future projection at the regional scale. Using this, climate change projections of ISM under the IPCC Representative Concentration Pathways 8.5 (RCP8.5) scenario (which is a worst-case scenario) was obtained by Jayasankar et al. (2018). Though the WRF-ARW has been included as one of the RCMs in the CORDEX-SA programme, but dynamical downscaled simulations using WRF-ARW are not available yet for the CORDEX-SA domain. Therefore, it is important to assess the performance of WRF-ARW as high-resolution RCM over the SA domain. In this study, after validating the CCSM4-WRF with observational datasets, we compared the results of our 9-km CCSM4-WRF simulations over Indian region with the 50-km resolution dynamically downscaled simulations under the CORDEX-SA programme.
The scenarios are found to be the integral part of the climate change research. Similar to RCP forcing scenarios, a set of socioeconomic scenarios called Shared Socioeconomic Pathways (SSPs) was implemented (Kriegler et al. 2012; O’Neill et al. 2014; O’Neill et al. 2017); this is considered as a reference scenario for impact assessments, adaptation and mitigation. The RCP scenarios are described based on the overall warming that may occur by the end of the century as a result of GHG emissions, while SSPs look at the likelihood of potential emissions reductions. There are five SSP scenarios (SSP 1 to 5) classified in terms of socioeconomic challenges to adaptation (abscissa) and mitigation (ordinate). These five SSPs scenarios provide insights into how various rates of climate change mitigation could be accomplished by integrating RCP mitigation goals with SSPs. In these scenarios, SSP1 (good sustainability) stands for low mitigation and adaptation, SSP2 (middle of the road) stands for moderate, SSP3 (regional rivalry, unsustainable) for high mitigation and adaptation, SSP4 (increasing inequality) is dominant in adaptation challenges, and SSP5 (fossil-fueled development) is dominant in mitigation challenges (Fig. 1 of O’Neill et al. 2014). Such scenarios are used to describe how the choices of society can impact future GHG emissions. These set of SSPs can also be used to quantitatively estimate the projected changes of population, economic growth, and land use. Different studies use these scenarios for various purposes; for instance, Wiebe et al. (2015) used SSPs to analyze the effect of climate change on agricultural development and markets. In this paper, we tried to relate the projected changes in ISM rainfall and the future changes in the population and GDP under three socioeconomic pathways (SSP1, SSP2, and SSP3).
The major objective of this study is to (a) delineate the advantage of high resolution to study the regional scale climate change projection of ISM rainfall by comparing the high-resolution (9-km) dynamically downscaled CCM4-WRF simulation with 50-km resolution CORDEX-SA model simulations, (b) quantify the projected ISM rainfall changes over different homogeneous zones of India, with possible uncertainty; and c) discuss application utility of the future changes in ISM rainfall along with the projected changes of socioeconomic variable such as population and GDP over India, derived from SSP scenarios.