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Atmospheric and oceanic parameters derived from global climate model (GCM) simulations have received wide global attention and importance in representing the future world under different scenarios of greenhouse gas emissions. The present study deals with near-surface wind speed in the Bay of Bengal (BoB) obtained from CMIP5 and the upcoming CMIP6 GCMs and validation exercise clearly signify improved performance of CMIP6 GCMs over CMIP5. Multi-model ensemble mean corresponding to the four emission scenarios are constructed using the best performing models of CMIP6 family. The study reveals that near-future changes in wind speed in the BoB are moderate under the low-end scenario of SSP1-2.6. Projected wind speeds in the head BoB are expected to increase or decrease by 20% during June–July–August and December–January–February under high-end scenario by the end of twenty-first century. A positive change up to 30% in the northeast monsoon winds under SSP5-8.5 is projected in the central BoB. Irrespective of the seasons, a net increase amounting to 0.6–0.8 m/s is observed along the east coast of India under SSP2-4.5 scenario by the mid and end of the century. Maximum rise by 25% (0.5–1 m/s) in wind speed is predicted under SSP3-7.0 scenario in the near future. Further, the study points out a decline in wind speed by 0.2–0.8 m/s in the central and southern BoB under the extreme scenario of SSP5-8.5. Strengthening and weakening of winds over the BoB accounts the projected variations in temperature that resulted from global warming and subsequent changes in atmospheric circulation. [ABSTRACT FROM AUTHOR]

Global warming and associated climate change impacts have posed major threats to this 21st century's world, especially for developing countries like India, given various socio‐economic factors. Considering the extensive spatial diversity of this warming‐induced climate changes, suitable region‐specific strategies must be adopted to combat its potential implications. Therefore, it becomes crucial to identify the places most exposed to this changing scenario. This study attempts to shed some light towards this by identifying futuristic "temperature‐based hotspots" across India through a comprehensive multimodel multiscenario analysis at various spatiotemporal scales. A new and more informative index named "Temperature‐based Hotspot Index (THIn)" is proposed for this purpose, which encapsulates various attributes of changing temperature, including its mean, variability and extremes (magnitude, frequency and severity) into a single metric. Bias‐corrected future‐projected temperature data from 14 state‐of‐the‐art general circulation models (GCMs) have been considered for the analysis, which are participating in the coupled model intercomparison project version 6 (CMIP6). The overall analysis identifies temperature‐based hotspots to span predominantly in the west, north and northeast parts of the country. On the other hand, the southern and eastern part of India along the eastern coast are found to be comparatively less exposed to temperature changes in future. Overall, we expect the findings of this study to be beneficial to plan and adopt suitable region‐specific management strategies to combat the challenging future in sufficient advance. [ABSTRACT FROM AUTHOR]

Relative humidity (RH), an important climatic element influencing tree growth, is also crucial in assessing the General Circulation Models (GCMs) on global warming. However, in the absence of a relatively long record of RH in the Himalayan region, precise modelling of the climate change related processes and their impacts on this region are not well established. Here we present a new RH reconstruction for the pre-monsoon months (February–May) starting from 1800 C.E. using the tree-ring width data of subtropical Pinus roxburghii (chir-pine) from Pithoragarh, Western Himalaya, India. We found significant positive correlation between tree growth and pre-monsoon RH (n = 67, r = +0.569, p < 0.001). Based on this relationship, the developed linear regression model explains 32.5% of the total variance during the calibration period from 1949 to 2015 C.E. The region experienced 28 high and 25 low humidity years over the past 215 years. The five highest and three lowest humidity years were identified as 1831, 1836, 1881, 1977, 1982 and 1939, 1999, 2013 C.E. Further, we found a declining trend in RH during the past 35 years. This decline in RH over the Himalaya indicates that the global warming is not evident as expected by the climate modellers. The reliability of the reconstructed data is validated by the significant positive correlation with column water vapour/relative humidity gridded data of ERA-interim (850 mb) for 1979–2015 C.E. over the Himalayan region. Further, a consistent and significant positive relation with precipitation and a negative correlation with temperature over this region confirm the reliability of the reconstructed RH data. Spectral analysis reveals the existence of periodicities of 2–10 years, which may be linked with effects of El-Nino-Southern Oscillation (ENSO) over the India subcontinent. [ABSTRACT FROM AUTHOR]

Groundwater in north India remains a vital food and water security resource for more than one billion people. Both summer monsoon drying, and winter warming pose considerable challenges for rapidly declining groundwater. However, their impacts on irrigation water demands and groundwater storage under the observed and projected future climate remain unexplored. Using in situ observations, satellite data, and a hydrological model that considers the role of irrigation and groundwater pumping, we show that summer monsoon drying and winter warming accelerate groundwater depletion in north India during the observed climate, which will continue in the projected future climate. Summer monsoon precipitation has significantly (P‐value = 0.04) declined (∼8%) while winters have become warmer in north India during 1951–2021. Both satellite (GRACE/GRACE‐FO) and hydrological model‐based estimates show a rapid groundwater depletion (∼1.5 cm/year) in north India with a net loss of 450 km3 of groundwater during 2002–2021. The summer monsoon drying followed by winter warming cause a substantial reduction in groundwater storage due to reduced groundwater recharge and enhanced pumping to meet irrigation demands. Summer monsoon drying and winter warming will continue to affect groundwater storage in north India in the future. For instance, summer monsoon drying (10%–15% deficit for near‐far periods) followed by substantial winter warming (1–4°C) in the future will further accelerate groundwater depletion by increasing (6%–20%) irrigation water demands and reducing groundwater recharge (6%–12%). Groundwater sustainability measures including reducing groundwater abstraction and enhancing the groundwater recharge during the summer monsoon seasons are needed to ensure future agricultural production. Plain Language Summary: Groundwater in north India is facing rapid depletion due to summer monsoon drying and winter warming. The decline in summer monsoon precipitation and warmer winters have led to major droughts, resulting in a massive loss of groundwater between 2002 and 2021. Summer monsoon drying coupled with winter warming climate will increase irrigation water demands and reduce groundwater recharge. This necessitates the implementation of groundwater sustainability measures to ensure future agricultural production during monsoon droughts. Key Points: Examined the role summer monsoon drying and winter warming on groundwater storage in IndiaRapid groundwater depletion (∼1.5 cm/year) resulted in a net loss of 450 km3 of groundwater during 2002–2021Summer monsoon drying followed by substantial winter warming under the warming climate will further accelerate groundwater depletion [ABSTRACT FROM AUTHOR]

The Indo-Gangetic Plain (IGP), the source of grains for around 40% Indian population, is known as the breadbasket of India. The Indian Summer Monsoon Rainfall (ISMR) plays a vital role in the agricultural activities in this region. The rapid urbanization, land use and land cover change have significantly impacted the region's agriculture, water resources, and socioeconomic facets. The present study has investigated the observed and regional modeling aspects of ISMR characteristics, associated extremes over the IGP, and future perspectives under the high-emission RCP8.5-scenario. Future projections suggest a 10–20% massive decrease during pre-monsoon (March–May) and earlier ISM season months (i.e., June and July). A significant 40–70% decline in mean monsoon rainfall during the June–July months in the near future (NF; 2041–2060) has been projected compared to the historical period (1986–2005). An abrupt increase of 80–170% in mean monsoon rainfall during the post-monsoon (October–December) in the far future (FF; 2080–2099) is also projected. The distribution of projected extreme rainfall events shows a decline in moderate or rather heavy events (5 or more) in NF and FF. Further, an increase in higher rainfall category events such as very heavy (5–10) and extremely heavy rainfall (5 or more) events in NF and FF under the warmer climate is found. However, the changes are less prominent during FF compared to the NF. The mean thresholds for extremely heavy rainfall may increase by 1.9–4.9% during NF and FF. Further, the evolution patterns of various quantities, such as tropospheric temperature gradient (TG), specific humidity, and mean sea level pressure, have been analyzed to understand the physical processes associated with rainfall extremes. The strengthening in TG and enhanced atmospheric moisture content in NF and FF support the intensification in projected rainfall extremes over IGP. [ABSTRACT FROM AUTHOR]

In this study, using the NCAR Community Earth System Model (CESM1.2.2), we investigate the changes in the characteristics of the summer monsoon low pressure systems (LPS) over India in a twenty-first century climate change simulation corresponding to the RCP8.5 scenario. A slight weakening in monsoon circulation and an increase in mean summer monsoon precipitation over India are simulated in a warmer climate, consistent with several previous studies. The weakening of the monsoon circulation is associated with a pair of anticyclonic anomalies straddling the equator in the low level, weakening the cross-equatorial monsoonal flow from the southern hemisphere. These low-level circulation anomalies appear to be robust features in the equatorial Indian Ocean under climate change. An increase in moisture flux is also simulated over the Indian subcontinent. However, we find no significant change in the number of LPS or their spatial distribution under the RCP8.5 scenario. This is attributed to a small but non-significant decrease in the low-level meridional cyclonic shear in zonal winds. An increase in the intensity and frequency of extreme precipitation over India in a warmer world is simulated and is likely associated with an increase in moisture content. Our study shows that the fractional contribution of LPS to mean and extreme precipitation over India in a warmer world is likely unchanged, but the frequency and intensity of extreme events are larger. Because of the diversity in the results from single model studies on LPS characteristics in a warmer world, a future study that uses CMIP6 multi-model data would be valuable to assess the robustness and the uncertainty in changes in LPS activities under climate change. [ABSTRACT FROM AUTHOR]

This study explores the population exposure to an increasing number of hydroclimatic extreme events owing to the warming climate. It is well agreed that the extreme events are increasing in terms of frequency as well as intensity due to climate change and that the exposure to compound extreme events (concurrent occurrence of two or more extreme phenomena) affects population, ecosystems, and a variety of socioeconomic aspects more adversely. Specifically, the compound precipitation–temperature extremes (hot-dry and hot-wet) are considered, and the entire Indian mainland is regarded as the study region that spans over a wide variety of climatic regimes and wide variation of population density. The developed copula-based statistical method evaluates the change in population exposure to the compound extremes across the past (1981–2020) and future (near future: 2021–60 and far future: 2061–2100) due to climate change. The results indicate an increase of more than 10 million person-year exposure from the compound extremes across many regions of the country, considering both near and far future periods. Densely populated regions have experienced more significant changes in hot-wet extremes as compared with the hot-dry extremes in the past, and the same is projected to continue in the future. The increase is as much as sixfold in many parts of the country, including the Indo-Gangetic Plain and southernmost coastal regions, identified as the future hotspots with the maximum increase in exposure under all the projected warming and population scenarios. The study helps to identify the regions that may need greater attention based on the risks of population exposure to compound extremes in a warmer future. Significance Statement: How is the growing population being affected now, and in the future, how will it be affected due to climate change induced compound extreme events? This study explores this societal consequence in terms of population exposure for the most populous country, India. An increase of more than 10 million person-year exposure from the precipitation–temperature compound extremes across many regions is indicated. Densely populated regions are expected to experience enhanced population exposure to hot-wet extremes as compared with the hot-dry extremes. Furthermore, the maximum increase in population exposure to compound extremes is expected across the Indo-Gangetic Plain and southern coastal regions of India. The outcome of the study will be helpful for adopting socioeconomic decisions toward the welfare of society. [ABSTRACT FROM AUTHOR]

Water resources across the globe are projected to undergo significant changes as climate and socio-economic conditions change. A policy relevant question thus arises: whether climate change or population change exerts a greater control on future freshwater resources of a region? Understanding the relative importance of these factors in affecting water availability can help guide prioritization of policy level interventions. In this study, we quantify the changes in mean annual per capita water availability (PCWA) across India under 1.5 °C, 2.0 °C, and 3.0 °C levels of global warming. We utilize projections of future climate from several general circulation models (GCMs) under three different representative concentration pathways (RCPs) along with projections of future population from five socio-economic pathways (SSPs). Using the estimated PCWA from these GCM–RCP–SSP combinations, we perform a sensitivity analysis to ascertain the relative importance of climatic (precipitation and temperature change) and demographic (population) factors in affecting per capita freshwater availability in a region. Our analysis shows that PCWA over India will decrease across all warming scenarios. In addition, a transition from the 1.5 °C warmer world to the 2.0 °C warmer world leads to a reduction in PCWA for a majority (92.8%) of regions across India. The number of people likely to face severe water stress (PCWA < 500 m 3/ year / capita) under 1.5 °C, 2.0 °C, and 3.0 °C warming scenarios, are 354, 421, and 380 million, respectively. Sensitivity analysis indicated that changes in both population and mean annual precipitation are dominating factors controlling PCWA, depending upon the historical setting of the region. Regions with historically lower populations and lower aridity indices tend to be more sensitive to population changes. On the other hand, as historical population of a region increases, sensitivity to changing climate (mainly mean annual precipitation) increases. These results indicate the complex interactions between demographic and climatic changes that need to be accounted for in policies that aim to manage water scarcity by either controlling global warming or via socio-economic interventions. • We quantify the per capita water availability across India for 1.5 °C, 2.0 °C and 3.0 °C levels of global warming • Considerable reduction in per capita water availability is projected for a transition from 1.5 °C to 2.0 °C warming levels • We quantify the sensitivity of per capita water availability to changes in climate and population • Per capita water availability is most sensitive to either historical population or historical mean annual precipitation • The historical socio-economic and climatic settings of a region determine the dominance of factors [ABSTRACT FROM AUTHOR]

Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata approach, long term projections with climate change impacts are performed using statistical downscaling and urban impacts are simulated with a dynamic regional model for limited number of years covering different precipitation characteristics. The evaluation of the statistical downscaling methodology over historical time period reveals large underestimation of the extreme rainfall, which is improved effectively by applying another regression model, for extreme days. The limited runs of dynamic downscaling models with different stages of urbanization for Mumbai, India, reveal spatially non uniform changes in precipitation, occurring primarily at the higher quantiles. The statistical and dynamical outputs are further integrated using quantile transformation for precipitation projection in Mumbai during 2050s. The projections show dominant impacts of urbanization compared to those from large scale changing patterns. The uniqueness of this computationally efficient framework lies in an integration of global and local factors for precipitation projections through a conjugal statistical–dynamical approach. [ABSTRACT FROM AUTHOR]

Bhat, S.; Jain, P., and Deo, M.C., 2019. Application of regional climate models for coastal design parameters along India. Journal of Coastal Research, 35(1), 110–121. Coconut Creek (Florida), ISSN 0749-0208. Coastal structures are generally designed on the basis of significant wave height, Hs2, corresponding to a return period of 100 years. This design wave height is derived by fitting an appropriate statistical distribution to a set of long-duration Hs data, either observed or simulated, using historical wind conditions. This study investigates what might happen if wind conditions projected on the basis of regional climate models (RCMs), reflecting the effect of climate change, are instead used. At a series of 91 coastal stations spread over the entire 7000-km-long coastline of India, waves were simulated for two time slices of 27 years each in the past and future using a numerical wave model driven by an ensemble of RCMs. The RCMs were earlier run for a moderate global warming scenario. The long-term statistical analysis of resulting wave data was carried out, and Hs with a 100-year return period were derived at each location. It was noticed that if projected wind were used in the place of past wind, an increase in the magnitude of design Hs is likely to be encountered at most stations, although there might be some locations where a decrease instead of an increase might happen. Because the west coast is subjected to different wind conditions than the east coast, it also showed different behavior in terms of the change. These observations were supported by comparative analysis of trends, probability distributions, and wave rose diagrams of past and projected wave data. [ABSTRACT FROM AUTHOR]

Compound dry hot events (CDHEs), where hot events and droughts coexist, have received a lot of attention lately due to their catastrophic effects on the economy, environment and human health. In this study, we use two CDHE indices, the Standardized Compound Event Indicator (SCEI) and the Standardized Dry and Hot Index (SDHI), to assess changes in CDHE characteristics (severity, frequency, spatial extent) over the historical past and future CMIP6 simulations across the Indian subcontinent. To understand the role of the drought index selected on CDHE characterization two drought indices namely the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are employed in the calculation of the CDHE indices. Further, the role of climatic oscillations such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Equatorial Indian Ocean Monsoon Oscillation and Indian Ocean Dipole in modulating CDHE characteristics have also been explored. Results show that SPI and SPEI based CDHE indices exhibit contrasting trends in northern India, while similar trends are observed in southern India in the historical past. Also, more frequent, extended, and severe CDHEs are reported by CDHE indices calculated using SPEI than by SPI. Temperature is found to be the dominant factor contributing to increases in CDHEs in the recent past and ENSO phases significantly modulate the severity and frequency of CDHE events in India. CMIP6 simulations generally report an increase in CDHE events for a 3°C global warming scenario. Overall, our findings show that the choice of the drought index has a greater impact on CDHE characterization than the choice of the CDHE index itself. Results from this study provide useful information towards understanding the risk of CDHEs in India under global warming and urge for the development and implementation of adaptation and mitigation measures. [ABSTRACT FROM AUTHOR]

Gram pod borer, Helicoverpa armigera (Hub.) is the major insect pest of pigeonpea and prediction of number of generations (no. of gen.) and generation time (gen. time) using growing degree days (GDD) approach during three future climate change periods viz., Near (NP), Distant (DP) and Far Distant (FDP) periods at eleven major pigeonpea growing locations of India was attempted. Multi-model ensemble of Maximum (Tmax) and Minimum (Tmin) temperature data of four Representative Concentration Pathways viz., RCP 2.6, 4.5, 6.0 and 8.5 of Coupled Model Inter comparison Project 5 (CMIP5) models was adopted here. The increase in projected Tmax and Tmin are significant during 3 climate change periods (CCPs) viz., the NP, DP and FDP over base line (BL) period under four RCP scenarios at all locations and would be higher (4.7–5.1 °C) in RCP 8.5 and in FDP. More number of annual (10–17) and seasonal (5–8) gens. are expected to occur with greater percent increase in FDP (8 to 38%) over base line followed by DP (7 to 22%) and NP (5to 10%) periods with shortened annual gen. time (4 to 27%) across 4 RCPs. The reduction of crop duration was substantial in short, medium and long duration pigeonpeas at all locations across 4 RCPs and 3 CCPs. The seasonal no.of gen. is expected to increase (5 to 35%) with shortened gen. time (4 to 26%) even with reduced crop duration across DP and FDP climate periods of 6.0 and 8.5 RCPs in LD pigeonpea. More no. of gen. of H. armigera with reduced gen. time are expected to occur at Ludhiana, Coimbatore, Mohanpur, Warangal and Akola locations over BL period in 4 RCPs when normal duration of pigeonpeas is considered. Geographical location (66 to 72%), climate period (11 to 19%), RCPs (5–7%) and their interaction (0.04–1%) is vital and together explained more than 90% of the total variation in future pest scenario. The findings indicate that the incidence of H. armigera would be higher on pigeonpea during ensuing CCPs in India under global warming context. [ABSTRACT FROM AUTHOR]

We studied the potential of using machine learning to downscale global-scale climate model output towards ground station data. The aim was to simultaneously analyze both city-level air quality and regional- and global-scale radiative forcing values for anthropogenic aerosols. As the city-level air pollution values are typically underestimated in global-scale models, we used a machine learning approach to downscale fine particulate (PM 2.5) concentrations towards measured values. We first simulated the global climate with the aerosol–climate model ECHAM-HAMMOZ and corrected the PM 2.5 values for the Indian megacity New Delhi. The downscaling procedure clearly improved the seasonal variation in the model data. The seasonal trends were much better captured in the corrected PM 2.5 than in original ECHAM-HAMMOZ PM 2.5 when compared to the reference PM 2.5 from the ground stations. However, short-term variations showed less extreme values with the downscaling approach. We applied the downscaling model also to simulations where the aerosol emissions were following two different future scenarios: one following the current legislation and one assuming currently maximum feasible emission reductions. The corrected PM 2.5 concentrations for the year 2030 showed that mitigating anthropogenic aerosols improves local air quality in New Delhi, with organic carbon reductions contributing most to these improvements. In addition, aerosol emission mitigation also resulted in negative radiative forcing values over most of India. This was mainly due to reductions in absorbing black carbon emissions. For the two future emission scenarios modeled, the radiative forcing due to aerosol–radiation interactions over India was -0.09±0.26 and -0.53±0.31 W m -2 , respectively, while the effective radiative forcing values were -2.1±4.6 and 0.06±3.39 W m -2 , respectively. Although accompanied by relatively large uncertainties, the obtained results indicate that aerosol mitigation could bring a double benefit in India: better air quality and decreased warming of the local climate. Our results demonstrate that downscaling and bias correction allow more versatile utilization of global-scale climate models. With the help of downscaling, global climate models can be used in applications where one aims to analyze both global and regional effects of policies related to mitigating anthropogenic emissions. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon rainfall (ISMR, June–September) is an important factor in agricultural planning and the Indian economy. The ISMR over East India (EI: Chhattisgarh-Bihar-Jharkhand-Odisha) is particularly significant, and it can have an impact on the country's average ISMR. The current study examined projected changes in ISMR over EI with two emission scenarios, RCP4.5 and RCP8.5, for near (2017–2040) and far-future (2041–2070) projections using a set of ten CORDEX-SA Regional Climate Model (RCMs). To begin, the performance of raw and bias-corrected ISMR over EI outputs from ten CORDEX-SA RCMs was compared to a high-resolution (0.25° × 0.25°) gridded rainfall analysis dataset from the India Meteorological Department (IMD) over the hindcast period (1971–2005). Following that, bias-corrected results were used to calculate ISMR projections over EI for the near and distant futures. Most RCMs, according to the findings, can imitate the spatial pattern of ISMR across EI but are restricted in their ability to capture actual magnitudes. Notably, RCM prediction skill increased greatly after employing various bias-correction approaches, the quantile mapping (QQM) bias-correction technique outperformed other current conventional bias correction methods, and the QQM technique was employed for ISMR future projections using RCP4.5 and RCP8.5 emission scenarios. The analysis of ISMR projections over EI reveals that there will be more deficit rainfall years in the short term while more excess rainfall years in the far future. [ABSTRACT FROM AUTHOR]

The Indo‐Gangetic Plain (IGP) bears great agricultural importance and contributes to a major share of national GDP of India. In present study, a location‐specific comprehensive analysis of rainfall extremes over IGP, using second generation CORDEX‐CORE simulations in the present and future scenarios (under high emission RCP8.5 scenario) have been performed. Here, the high‐resolution CORDEX‐CORE simulations with International Centre for Theoretical Physics's regional climate model (RegCM4.7) have been considered for the detailed rainfall characteristics assessment. Twelve thresholds‐based climate indices have been analyzed to investigate the characteristics of rainfall extremes during three‐time slices: 1986–2005 (historical), 2041–2060 (near future) and 2080–2099 (far future). The RegCM4 projections suggested a substantial decline in mean Indian Summer monsoon rainfall (ISMR) and wet days (rainfall ≥ 1 mm; 7%–14%) over IGP under high‐emission RCP8.5 scenario. The contribution of 90th and 99th percentile days and total rainfall on wet days, seems to be get enhanced in future by 14%–35%, which implies the increase and intensification in rainfall extremes over IGP by the end of the 21st century. Further, the decline in ISMR and negligible changes in annual rainfall over IGP suggest the possible shift of monsoon regime during the later months of the year in warming climate. Thus, findings of present study may play a crucial role in predicting the ISMR and rainfall extremes over the IGP. Therefore, it can be useful for scientists and policymakers to plan and implement mitigation strategies. Plain Language Summary: In present study, an endeavor has been made to access the Indian Summer monsoon rainfall (ISMR) patterns and monsoon extremes over Gangetic plain which is fluvially fertile land and feeds around 40% Indian population. The International Centre for Theoretical Physics' regional climate model (RegCM4.7) robustly performs with various forcings in simulating mean precipitation and 12 different threshold‐based climate indices over Indo‐Gangetic Plain (IGP) during the historical period. However, the statistical analysis suggests RegCM4 with forcings EIN75 and MPI‐ESM‐MR followed by MIROC5 and Nor‐ESM performs robustly in simulating the rainfall patterns over the IGP. Further, various forcing combinations have produced some rainfall indices with slight differences. Therefore, a ranking‐based framework has been proposed in order to choose best forcing‐combination for future projections. A closure encounter revealed that substantial decline in mean ISMR while negligible changes in annual rainfall have been projected which resembles to the fact of shift in monsoon regime toward the later months of the year under warming climate. As far as monsoon extreme are concerned, a rise in heavy rainfall days and their intensity have been projected. Also, maximum enhancement in projected heavy rainfall characteristics have been found to be near Himalayan foothills which is may be due the increased aerosol accumulation over IGP. Key Points: The RegCM4 performs satisfactorily in simulating mean and extreme monsoonal rainfall characteristics over Gangetic PlainsProjected decline in mean monsoon rainfall whereas a rise in extreme rainfall indices over the Gangetic plain under a warming climateProjected increase in extreme rainfall indices over upper Indo‐Gangetic Plain and Himalayan foothills during future time slices under RCP8.5 scenario [ABSTRACT FROM AUTHOR]

The west‐central Himalayan River basins face significant damage caused by extreme floods annually. We selected the upper Ganga basin (UGB), the west‐central Himalayan basin, to demonstrate how maximum cumulative storm and total rainfall received during monsoon control the extreme flow variability. In addition, the downscaled CMIP6 data sets were used for projections of extremes. The results suggest that historically Alaknanda basin receives a relatively higher magnitude of monsoon and storm rainfall than the other regions in the UGB, which manifested into significant extreme flow variability at the Alaknanda outlet. The extreme flows at the UGB outlet are projected to be more frequent under warming scenarios. The nonstationary flood frequency magnitudes are higher than those obtained from the stationary frequency method, implying a need for a review of return periods used in the designs of flood control structures in the region. Plain Language Summary: Floods are frequent natural disasters in India that affect millions of people and disrupt socioeconomic conditions. Climate change and human interventions further exaggerate flood hazards in many regions of the country. The west and west‐central Indian Himalayas are the worst flood‐affected regions in recent decades. A detailed scientific analysis is, therefore, crucial for sustainable flood management. Here, we analyze the historical behavior and future projections of annual maximum flows in the upper Ganga basin (UGB; located in the west‐central Himalayan region). Our results indicate that the eastern Alaknanda basin has historically been a highly flood‐prone region in the UGB. The warming climate might further amplify the extreme flood hazards. The nonstationary flood magnitudes are also expected to intensify, posing a severe threat to the operation of hydraulic structures. These inferences would be helpful in formulating sustainable flood mitigation strategies for the west‐central Himalayan region. Key Points: Eastern Alaknanda basin is one of the worst flood‐affected regions in the upper Ganga basinWarming climates are observed to amplify the extreme flood magnitudes in the upper Ganga basinNonstationary flood frequency magnitudes are higher than those obtained from the stationary frequency method [ABSTRACT FROM AUTHOR]

The main focus of this study is on the high-impact precipitation events of the Indian summer monsoon (ISM) that trigger devastating floods and landslides and cause widespread damage to lives, agriculture, and infrastructure. Here, 16 models from Coupled Model Intercomparison Project Phase 6 (CMIP6) have been used to evaluate the projected changes in the spatio-temporal distribution of high-impact/extreme precipitation events for the ISM under a high-emission scenario during 1941–2070 with respect to the historical period of 1981–2010. The observed rain gauge-based precipitation datasets from two sources (i.e., the India Meteorological Department (IMD) and the Climate Prediction Center (CPC)) have been used to analyze the ability of CMIP6 models in simulating the extreme precipitation characteristics of the ISM for the historical period. After a comprehensive assessment analysis, it was found that four out of a total of 16 CMIP6 models performed well in simulating the spatio-temporal distribution of extreme precipitation characteristics of the ISM for the historical period. Relative future changes in the spatio-temporal distribution of extreme precipitation characteristics with respect to the historical period indicate that the frequency, intensity, and spatial extent of high-impact precipitation events will increase with global warming over large parts of India. In particular, the total number of extreme events above the 99th percentile of the reference period is projected to increase by over 60% with a robust increase in the spatial extent of the number of extreme events in central India. The number of days with more than 50 mm of precipitation is projected to rise at a rate of over 40% per season over large parts of India and a robust amplification of heavy precipitation has also been observed. The projected changes are more prominent in the central India region. Overall, our results show a significant contribution of global warming in the rise of the frequency and intensity of extreme precipitation events. Furthermore, the response to global warming is plausibly consistent among the selected models which provides greater confidence in the results and suggests the need for profound adaptation measures and better policy-making in the future. [ABSTRACT FROM AUTHOR]

Modeling and mapping the distribution of suitable habitats of aquatic plants are critical for assessing the impact of factors like changing climate on species habitat range shifts, declines, and expansions. Nymphaea is an aquatic perennial herb considered valuable because of its ornamental, economic, medicinal, and ecological importance. In India, the geographical distribution of Nymphaea is diverse, and the suitable habitats of individual species are vulnerable to the changing climate and global warming effects. Despite its increased vulnerability, only a few limited conservation efforts in aquatic environments are being made to date. In several places, the distribution of Nymphaea has been impacted by both anthropogenic and climate-related disturbances. A comprehensive strategy will be needed to meet the socio-ecological challenge of Nymphaea conservation. In this study, we employed maximum entropy (MaxEnt) method to assess how climate change affects the distribution of Nymphaea suitable habitat. The occurrence records of Nymphaea were collected from primary surveys, Global Biodiversity Information Facility (GBIF), and published works. Bioclimatic variables obtained from the Coupled Model Intercomparison Project (CMIP6) were employed as predictor variables in distribution modeling. The projections were made using three SSPs (stringent mitigation scenarios) for the future period of 2050. Our results showed shifts in the suitability ranges of Nymphaea under different projection scenarios. The study provides information about the distribution of suitable habitats for Nymphaea in India, which may be helpful for ongoing efforts to conserve and manage the aquatic plants, particularly in areas that are losing suitable climate conditions. [ABSTRACT FROM AUTHOR]

Local groundwater levels in South India are falling alarmingly. In the semi-arid crystalline Deccan plateau area, agricultural production relies on groundwater resources. Downscaled Global Climate Model (GCM) data are used to force a spatially distributed agro-hydrological model in order to evaluate Climate Change (CC) effects on local groundwater extraction (GWE). The slight increase of precipitation may alleviate current groundwater depletion on average, despite the increased evaporation due to warming. Nevertheless, projected climatic extremes create worseGWEshortages than for present climate. Local conditions may lead to opposing impacts on GWE, from increases to decreases (+/-20 mm/year), for a given spatially homogeneous CC forcing. Areas vulnerable to CC in terms of irrigation apportionment are thus identified. Our results emphasize the importance of accounting for local characteristics (water harvesting systems and maximal aquifer capacity versus GWE) in developing measures to cope with CC impacts in the South Indian region. [ABSTRACT FROM AUTHOR]

Composite seasonal scenarios (per °C change in global equilibrium mean) were developed for the Indian subcontinent for the period 20 10-2039 using 5 General Circulation Models (GCMs), namely, HadCM2, CSIRO-MK2b, CGCM1, GFDL-R15 and ECHAM4/OPYC3. These scenarios indicate a general warming over the entire region by about 0.3 to 0.6°C (± 0.2°C) with more warming in the northern part and less in the southern part of the country. The study shows pockets of negative and positive changes in rainfall over the study area. Large variations in the estimation of rainfall changes from different models also set the upper and lower limits to more extreme values. Further, scenarios indicate a warming of about 0.4 ± 0.2°C over Gangetic West Bengal (GWB) and surrounding regions in the eastern part of the country. The maximum change in rainfall over this part of the country, according to the scenarios developed, would be 4%. Sub-grid scale seasonal scenarios have been developed over GWB and surrounding regions through statistical downscaling. HadCM2 and ECHAM4/OPYC3 are considered for this purpose, as they more effectively represent spatial and temporal variations in rainfall and temperature over the area. Results reveal more warming (0.3 to 0.9°C) and more rainfall changes (-5 to + 9%) than the projections developed without downscaling the GCM output. Further, scenarios developed for the winter and pre-monsoon seasons show unusual changes in rainfall and temperature, which may be due to mesoscale activities present in the study area. [ABSTRACT FROM AUTHOR]

Turbulent mixing in the planetary boundary layer (PBL) governs the vertical exchange of heat, moisture, momentum, trace gases, and aerosols in the surface–atmosphere interface. The PBL height (PBLH) represents the maximum height of the free atmosphere that is directly influenced by Earth's surface. This study uses a multidata synthesis approach from an ensemble of multiple global datasets of radiosonde observations, reanalysis products, and climate model simulations to examine the spatial patterns of long-term PBLH trends over land between 60°S and 60°N for the period 1979–2019. By considering both the sign and statistical significance of trends, we identify large-scale regions where the change signal is robust and consistent to increase our confidence in the obtained results. Despite differences in the magnitude and sign of PBLH trends over many areas, all datasets reveal a consensus on increasing PBLH over the enormous and very dry Sahara Desert and Arabian Peninsula (SDAP) and declining PBLH in India. At the global scale, the changes in PBLH are significantly correlated positively with the changes in surface heating and negatively with the changes in surface moisture, consistent with theory and previous findings in the literature. The rising PBLH is in good agreement with increasing sensible heat and surface temperature and decreasing relative humidity over the SDAP associated with desert amplification, while the declining PBLH resonates well with increasing relative humidity and latent heat and decreasing sensible heat and surface warming in India. The PBLH changes agree with radiosonde soundings over the SDAP but cannot be validated over India due to lack of good-quality radiosonde observations. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) that make landfall over India's east coast are responsible for significant loss of life along affected coastlines. TCs forming over the Bay of Bengal (BoB) region in October, November, and December have, in the past, intensified significantly at the time of landfall. The effects of climate change on TCs of different strengths and their characteristics such as track, intensity, precipitation, and convective available potential energy over the BoB region have not been well studied. This study explores the effects of climate change on two TCs of very severe cyclonic storm (VSCS) category (TC Vardah and TC Madi), and two other TCs of extremely severe cyclonic storm (ESCS) category (TC Hudhud and TC Phailin) formed over BoB, both in the short term (2035) and long term (2075). The high-resolution Weather Research and Forecasting (WRF) model is used to simulate the TCs under current and future climate conditions. The simulated TC track and intensity in the current climate agree well with the observations. To explore the impacts of climate change on TCs, the mean climate change signal, computed from future projections of the Community Climate System Model (CCSM4) in different representative concentration pathway (RCP) scenarios, is added to current climate conditions by using the pseudo global warming method. Results show a climate change-related reduction in TC translation speed, deepening of TC core, increased maximum surface wind, and increased precipitation over land in future RCP (4.5, 6.0, and 8.5) scenarios. The TCs in future RCP scenarios are seen to be more intensified compared to current climate simulations. Results demonstrate that all VSCS and ESCS category TCs considered in this study are likely to further intensify to the next higher category level with respect to their current classification, particularly in the far future RCP 6.0 and in the far future RCP 8.5 scenarios. The cyclone damage potential index of TC Vardah, TC Hudhud, and TC Phailin is projected to increase in a future warming climate. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP6 are of interest. Here, we analyze 32 models of the latest CMIP6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with a high agreement between the models independent of the SSP if global warming is the dominant forcing of the monsoon dynamics as it is in the 21st century; the multi-model mean for JJAS projects an increase of 0.33 mm d -1 and 5.3 % per kelvin of global warming. This is significantly higher than in the CMIP5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP6 simulations largely confirm the findings from CMIP5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall. [ABSTRACT FROM AUTHOR]

Heat waves in India during the pre‐monsoon months have significant impacts on human health, productivity and mortality. While greenhouse gas‐induced global warming is believed to accentuate high temperature extremes, anthropogenic aerosols predominantly constituted by radiation‐scattering sulfate are believed to cause an overall cooling in most world regions. However, the Indian region is marked by an abundance of absorbing aerosols, such as black carbon (BC) and dust. The goal of this work was to understand the association between aerosols, particularly those that are absorbing in nature, and high‐temperature extremes in north‐central India during the pre‐monsoon season. We use 30‐year simulations from a chemistry‐coupled atmosphere‐only general circulation model (GCM), ECHAM6‐HAM2, forced with evolving aerosol emissions in an interactive aerosol module, along with observed evolving SSTs. A composite of high‐temperature extremes in the model simulations, compared to climatology, shows large‐scale conditions conducive to heat waves. Importantly, it reveals concurrent positive anomalies of BC and dust aerosol optical depths. Changes in near‐surface properties include a reduction in single scattering albedo (implying greater absorption) and enhancement in short‐wave heating rate, compared to climatological conditions. Alterations in surface energy balance include reduced latent heat flux, but increased sensible heat flux, consistent with enhanced temperatures. Thus, chemistry‐coupled GCM simulations capture an association of absorbing aerosols with high‐temperature extremes in north India, arising from radiative heating in the surface layer. [ABSTRACT FROM AUTHOR]

The changing characteristics of precipitation extremes under global warming have recently received tremendous attention, yet the mechanisms are still insufficiently understood. The present study attempts to understand these processes over India by separating the 'dynamic' and 'thermodynamic' components of precipitation extremes using a suite of observed and reanalysis datasets. The former is mainly due to changes in atmospheric motion, while the latter is driven mainly by the changes associated with atmospheric moisture content. Limited studies have attributed dynamic and thermodynamic contributions to precipitation extremes, and their primary focus has been on the horizontal atmospheric motion component of the water budget. Our study, on the other hand, implements the decomposition of vertical atmospheric motion, based on the framework proposed by Oueslati et al. (Sci Rep 9: 2859, 2019), which has often been overlooked, especially for India. With the focus on two major and recent extreme events in the Kerala and Uttarakhand regions of India, we show that the vertical atmospheric motion has a more significant contribution to the events than the horizontal atmospheric motion. Further, decomposition of the vertical atmospheric motion shows that the dynamic component overwhelms the thermodynamic component's contribution to these extreme events, which is found to be negligible. Using a threshold method to define extreme rainfall, we further extended our work to all India, and the results were consistent with those of the two considered events. Finally, we evaluate the contributions from the recently made available CMIP6 climate models, and the results are interestingly in alignment with the observations. The outcomes of this study will play a critical role in the proper prediction of rainfall extremes, whose value to climate adaptation can hardly be overemphasised. [ABSTRACT FROM AUTHOR]

Using Hadley Global Environment Model 2 - Earth System and Max Planck Institute Earth System Model simulations, we assess the impact of global warming and stratospheric geoengineering on deciduous fruit production in Himachal Pradesh (the second-largest apple-producing state in India). The impacts have been assessed for the Representative Concentration Pathways 4.5 (RCP4.5) global warming scenario, and a corresponding geoengineered scenario (G3) from the Geoengineering Model Intercomparison Project, in which stratospheric aerosols are increased for 50 years from 2020 through 2069 to balance the global warming radiative forcing, and then aerosol precursor emissions are terminated. We used the period 2055–2069 (with the largest geoengineering forcing) and the period 2075–2089 (beginning 5 years into the termination phase) and evaluated winter chill and growing season heat accumulation. We found that although stratospheric geoengineering would be able to suppress the increase in temperature under an RCP4.5 scenario to some extent during both switch-on and switch-off periods, if the geoengineering was terminated, the rate of temperature increase would be higher than RCP4.5. The agroclimatically suitable area is projected to shift northeastwards (to higher elevations) under RCP4.5 as well as G3 during both periods. However, during the switched on period, geoengineering would restrict the shift, and areas of Shimla and Mandi districts (most suitable under the current climate) would not be lost due to global warming. Even during the switched off period, before the climate returned to RCP4.5 levels, the above areas would, although to a lesser extent, have reduced harmful climate effects from global warming. However, the area of suitable land (the intersection of soil and agroclimatic suitability) would decrease in both periods for RCP4.5 as well as G3, because as more high-elevation regions become agroclimatically suitable, they do not have suitable soils to support cultivation. Geoengineering could benefit deciduous fruit production by reducing the intensity of global warming; however, if geoengineering was terminated abruptly, the rate of change in temperature would be quite high. This could lead to a rapid change in land suitability and might result in total crop failure in a shorter period compared to RCP4.5. [ABSTRACT FROM AUTHOR]

It is well recognized that the global climate has been warming and it can have adverse regional impacts in the future. Besides producing changes in mean climate, global warming can also substantially alters the weather and climate extremes. Considering the impact of weather extremes on agriculture as well as water resource management in an agrarian country like India, present study aims to characterize future changes in precipitation extremes in northeast monsoon season (October–November–December) over south peninsular India based on statistically downscaled high-resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) datasets. This NEX-GDDP dataset simulates the mean rainfall reasonably well. There may be an increase in precipitation in near (5% under RCP 4.5 and 11% under RCP 8.5) and far future (21% and 38% under RCP4.5 and RCP 8.5 respectively). Future climate projections indicate that both the intensity and frequency of precipitation extremes in most parts of the south peninsular India may increase under both the warming scenarios during northeast monsoon season. [ABSTRACT FROM AUTHOR]

This study examined the responses of Asian monsoon precipitation to global warming on the regional scale, focusing on monsoon westerlies and monsoon trough. This is because the Asian monsoon precipitation is closely associated with tropical disturbances. To reproduce convective precipitation and tropical disturbances, this study used outputs of a high-resolution climate simulation. Two sets of approximately 30-yr simulations under present-day (control) and warmer climate conditions (global warming) were conducted by the 14-km Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with explicitly calculated convection. For understanding the spatial pattern of future precipitation changes, a further set of a 5-yr simulation [sea surface temperature (SST) + 4 K] was also conducted. Overall, the Asian summer monsoon was well simulated by the model. Precipitation increased as a result of global warming along the monsoon trough, which was zonally elongated across northern India, the Indochina Peninsula, and the western North Pacific Ocean. This increased precipitation was likely due to an increase in precipitable water. The spatial pattern of the increased precipitation was associated with enhanced cyclonic circulations over a large area along the monsoon trough, although it was difficult to determine whether the large-scale monsoon westerly was enhanced. This enhancement can be explained by future changes in tropical disturbance activity, including weak tropical cyclones. However, over part of South Asia, circulation changes may not contribute to the increased precipitation, suggesting regional characteristics. The regional increase in precipitation along the monsoon trough was mostly explained by the uniform increase in SST, whereas SST spatial patterns are important over some regions. [ABSTRACT FROM AUTHOR]

Monsoon low pressure systems (MLPSs) are among the most important synoptic-scale disturbances of the South Asian summer monsoon. Potential changes in their characteristics in a warmer climate would have broad societal impacts. Yet, the findings from a few existing studies are inconclusive. We use the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model CM4.0 to examine the projected changes in the simulated MLPS activity under a future emission scenario. It is shown that CM4.0 can skillfully simulate the number, genesis location, intensity, and lifetime of MLPSs. Global warming gives rise to a significant decrease in MLPS activity. An analysis of several large-scale environmental variables, both dynamic and thermodynamic, suggests that the decrease in MLPS activity can be attributed mainly to a reduction in low-level relative vorticity over the core genesis region. The decreased vorticity is consistent with weaker large-scale ascent, which leads to less vorticity production through the stretching term in the vorticity equation. Assuming a fixed radius of influence, the projected reduction in MLPSs would significantly lower the associated precipitation over north-central India, despite an overall increase inmean precipitation. [ABSTRACT FROM AUTHOR]

Present-day simulations (1983–2003) of a global climate model of 60-km resolution with three deep convection schemes are analysed to find the best scheme for simulation of mean Indian summer monsoon rainfall (ISMR) and its variability. Multiforcing ensemble projections with the best scheme are carried out under multiple Representative Concentration Pathways (RCPs) (based on various socio-economic and technological development at the end of the century), viz. RCP2.6, RCP4.5, RCP6.0 and RCP8.5, forced with four patterns of future sea surface temperature (SST) change for each scenario; one with mean SST changes projected by 28 Coupled Model Intercomparison Project Phase-5 (CMIP5) models and the rest obtained from subgroups of CMIP5 models grouped through cluster analysis of tropical SST changes. These are analysed for future (2079–2099) changes in surface air temperature ( T s ) and rainfall which show overall increase over India except for rainfall reduction over Western Ghats. We find that combination of enhanced atmospheric water vapour content and increased vertically integrated low level moisture transport into the subcontinent as the major contributing factors for future intensification of ISMR. Extreme events show increase in warm days with significant increase in warm nights. Percentage of grid points showing increased extreme rainfall increases from low to high emission scenario. The high-resolution model enables to study projected changes over India at homogeneous zones level. The maximum increase in T s and rainfall occurs over Western Himalaya and Northeast hilly region respectively. Consistent with future increase in T s and rainfall, their extreme events also increase over all the homogeneous zones. [ABSTRACT FROM AUTHOR]

Water availability for human use in a region depends upon hydro-climatic as well associo-economic factors such as population, lifestyles, etc. It is thus useful to understand howthese factors affect water availability and how their relative importance varies across differentregions. This information may help guide prioritization of policy level interventions. Here,we quantify the changes in mean annual per capita water availability (PCWA) across Indiaunder 1.5∘C, 2.0∘C, and 3.0∘C levels of global warming. We utilize projections of futureclimate from several general circulation models (GCMs) under three different representativeconcentration pathways (RCPs) along with projections of future population from fivesocio-economic pathways (SSPs). Using the estimated mean annual PCWA from theseGCM–RCP–SSP combinations, we perform a sensitivity analysis to ascertain the relativeimportance of climatic (precipitation and temperature change) and demographic(population change) factors in affecting per capita freshwater availability in a region.Our analysis shows that a transition from the 1.5∘C warmer world to the 2.0∘Cwarmer world leads to a reduction in mean annual PCWA for a majority (92.8%) ofregions across India. The number of people likely to face severe water stress (meanannual PCWA &#x003C; 500 m3/year/capita) under 1.5∘C, 2.0∘C, and 3.0∘C warmingscenarios, are 354, 421, and 380 million, respectively. Sensitivity analysis indicated thatchanges in both population and mean annual precipitation are dominating factorscontrolling mean annual PCWA, depending upon the historical setting of the region.Regions with historically lower populations and lower aridity indices tend to be moresensitive to population changes. On the other hand, as historical population of aregion increases, sensitivity to changing climate (mainly mean annual precipitation)increases. These results indicate the complex interactions between demographic andclimatic changes that need to be accounted for in policies that aim to manage watersecurity by either controlling global warming or via socio-economic interventions. [ABSTRACT FROM AUTHOR]

The main source of sea surface temperature (SST) variability in the Tropical Atlantic at interannual time scales is the Equatorial Mode or Atlantic El Niño. It has been shown to affect the adjacent continents and also remote regions, leading to a weakened Indian Monsoon and promoting La Niña-type anomalies over the Pacific. However, its effects in a warmer climate are unknown. This work analyses the impact of the Equatorial Mode at the end of the twenty first century by means of sensitivity experiments with an atmosphere general circulation model. The prescribed boundary conditions for the future climate are based on the outputs from models participating in the coupled model intercomparison project-phase V. Our results suggest that even if the characteristics of the Equatorial Mode at the end of the twenty first century remained equal to those of the twentieth century, there will be an eastward shift of the main rainfall positive anomalies in the Tropical Atlantic and a weakening of the negative rainfall anomalies over the Asian monsoon due to the change in climatological SSTs. We also show that extratropical surface temperature anomalies over land related to the mode will change in regions like Southwestern Europe, East Australia, Asia or North America due to the eastward shift of the sea level pressure systems and related surface winds. [ABSTRACT FROM AUTHOR]

In this study, the authors have investigated the likely future changes in the summer monsoon over the Western Ghats (WG) orographic region of India in response to global warming, using time-slice simulations of an ultra high-resolution global climate model and climate datasets of recent past. The model with approximately 20-km mesh horizontal resolution resolves orographic features on finer spatial scales leading to a quasi-realistic simulation of the spatial distribution of the present-day summer monsoon rainfall over India and trends in monsoon rainfall over the west coast of India. As a result, a higher degree of confidence appears to emerge in many aspects of the 20-km model simulation, and therefore, we can have better confidence in the validity of the model prediction of future changes in the climate over WG mountains. Our analysis suggests that the summer mean rainfall and the vertical velocities over the orographic regions of Western Ghats have significantly weakened during the recent past and the model simulates these features realistically in the present-day climate simulation. Under future climate scenario, by the end of the twenty-first century, the model projects reduced orographic precipitation over the narrow Western Ghats south of 16°N that is found to be associated with drastic reduction in the southwesterly winds and moisture transport into the region, weakening of the summer mean meridional circulation and diminished vertical velocities. We show that this is due to larger upper tropospheric warming relative to the surface and lower levels, which decreases the lapse rate causing an increase in vertical moist static stability (which in turn inhibits vertical ascent) in response to global warming. Increased stability that weakens vertical velocities leads to reduction in large-scale precipitation which is found to be the major contributor to summer mean rainfall over WG orographic region. This is further corroborated by a significant decrease in the frequency of moderate-to-heavy rainfall days over WG which is a typical manifestation of the decrease in large-scale precipitation over this region. Thus, the drastic reduction of vertical ascent and weakening of circulation due to 'upper tropospheric warming effect' predominates over the 'moisture build-up effect' in reducing the rainfall over this narrow orographic region. This analysis illustrates that monsoon rainfall over mountainous regions is strongly controlled by processes and parameterized physics which need to be resolved with adequately high resolution for accurate assessment of local and regional-scale climate change. [ABSTRACT FROM AUTHOR]

In a context of increased demand for food and of climate change, the water consumptions associated with the agricultural practice of irrigation focuses attention. In order to analyze the global influence of irrigation on the water cycle, the land surface model ORCHIDEE is coupled to the GCM LMDZ to simulate the impact of irrigation on climate. A 30-year simulation which takes into account irrigation is compared with a simulation which does not. Differences are usually not significant on average over all land surfaces but hydrological variables are significantly affected by irrigation over some of the main irrigated river basins. Significant impacts over the Mississippi river basin are shown to be contrasted between eastern and western regions. An increase in summer precipitation is simulated over the arid western region in association with enhanced evapotranspiration whereas a decrease in precipitation occurs over the wet eastern part of the basin. Over the Indian peninsula where irrigation is high during winter and spring, a delay of 6 days is found for the mean monsoon onset date when irrigation is activated, leading to a significant decrease in precipitation during May to July. Moreover, the higher decrease occurs in June when the water requirements by crops are maximum, exacerbating water scarcity in this region. A significant cooling of the land surfaces occurs during the period of high irrigation leading to a decrease of the land-sea heat contrast in June, which delays the monsoon onset. [ABSTRACT FROM AUTHOR]

Conventional statistical downscaling techniques for prediction of multi-site rainfall in a river basin fail to capture the correlation between multiple sites and thus are inadequate to model the variability of rainfall. The present study addresses this problem through representation of the pattern of multi-site rainfall using rainfall state in a river basin. A model based on K-means clustering technique coupled with a supervised data classification technique, namely Classification And Regression Tree (CART), is used for generation of rainfall states from large-scale atmospheric variables in a river basin. The K-means clustering is used to derive the daily rainfall state from the historical daily multi-site rainfall data. The optimum number of clusters in the observed rainfall data is obtained after application of various cluster validity measures to the clustered data. The CART model is then trained to establish relationship between the daily rainfall state of the river basin and the standardized, dimensionally-reduced National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis climatic data set. The relationship thus developed is applied to the General Circulation Model (GCM)-simulated, standardized, bias free large-scale climate variables for prediction of rainfall states in future. Comparisons of the number of days falling under different rainfall states for the observed period and the future give the change expected in the river basin due to global warming. The methodology is tested for the Mahanadi river basin in India. [ABSTRACT FROM AUTHOR]

Climate change associated global warming, rise in carbon dioxide concentration and uncertainties in precipitation has profound implications on Indian agriculture. Maize ( Zea mays L.), the third most important cereal crop in India, has a major role to play in country’s food security. Thus, it is important to analyze the consequence of climate change on maize productivity in major maize producing regions in India and elucidate potential adaptive strategy to minimize the adverse effects. Calibrated and validated InfoCrop-MAIZE model was used for analyzing the impacts of increase in temperature, carbon dioxide (CO2) and change in rainfall apart from HadCM3 A2a scenario for 2020, 2050 and 2080. The main insights from the analysis are threefold. First, maize yields in monsoon are projected to be adversely affected due to rise in atmospheric temperature; but increased rainfall can partly offset those loses. During winter, maize grain yield is projected to reduced with increase in temperature in two of the regions (Mid Indo-Gangetic Plains or MIGP, and Southern Plateau or SP), but in the Upper Indo-Gangetic Plain (UIGP), where relatively low temperatures prevail during winter, yield increased up to a 2.7°C rise in temperature. Variation in rainfall may not have a major impact on winter yields, as the crop is already well irrigated. Secondly, the spatio-temporal variations in projected changes in temperature and rainfall are likely to lead to differential impacts in the different regions. In particular, monsoon yield is reduced most in SP (up to 35%), winter yield is reduced most in MIGP (up to 55%), while UIGP yields are relatively unaffected. Third, developing new cultivars with growth pattern in changed climate scenarios similar to that of current varieties in present conditions could be an advantageous adaptation strategy for minimizing the vulnerability of maize production in India. [ABSTRACT FROM AUTHOR]