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In global atmospheric modeling, the differences between nonhydrostatic (NH) and hydrostatic (H) dynamical cores are negligible in dry simulations when grid spacing is larger than 10 km. However, recent studies suggest that those differences can be significant at far coarser resolution when moisture is included. To better understand how NH and H differences manifest in global fields, we perform and analyze an ensemble of 28 and 13 km seasonal simulations with the NH and H dynamical cores in the Energy Exascale Earth System Model global atmosphere model, where the differences between H and NH configurations are minimized. A set of idealized rising bubble experiments is also conducted to further investigate the differences. Although NH and H differences are not significant in global statistics and zonal averages, significant differences in precipitation amount and patterns are observed in parts of the tropics. The most prominent differences emerge near India and the Western Pacific in the boreal summer, and the central‐southern Indian Ocean and Pacific in the boreal winter. Tropical differences influence surrounding regions through modification of the regional circulation and can propagate to the extratropics, leading to significant temperature and geopotential differences over the middle to high latitudes. While the dry bubble experiments show negligible deviation between H and NH dynamics until grid spacing is below 6.25 km, precipitation amount and vertical velocity are different in the moist case even at 25 km resolution. Plain Language Summary: This paper compares the nonhydrostatic and hydrostatic dynamical cores in the Energy Exascale Earth System Model global atmosphere model. The rainfall difference between the two dynamical cores is noticeable at 28 km resolution, which is much coarser than the conclusions in the traditional studies. The difference maxima are located over the tropical oceans where the moist processes amplify the difference. The difference maxima in summer and winter emerge over different locations indicating season variations. Key Points: Experiments are performed to compare the hydrostatic (H) and nonhydrostatic (NH) dynamic cores in the Simple Cloud‐Resolving Energy Exascale Earth System Model Atmosphere ModelRegions with significant differences between H and NH dynamical cores are identifiedThe physical mechanisms driving these differences are investigated [ABSTRACT FROM AUTHOR]

Precipitation can induce a surface sensible heat flux since the raindrops are generally cooler than the surface. This precipitation‐induced sensible heat flux (QP) is typically ignored in models. However, during heavy rainfall, QP can be large and may not be negligible such as over India during the summer monsoon season. We provide the first results of incorporating QP in a simulation that shows ∼2% (∼5%) reduction in precipitation over India compared to the simulation without QP during a monsoonal active phase in 2017 (2018). This reduction was primarily due to a reduction in vertical advection of moisture. Additionally, QP modified the spatial distribution of precipitation with 40% of the geographical area encountering alterations of at least 20% in precipitation. This change in precipitation distribution across the region can have important implications for regional agriculture and irrigation practices. Changes in the partitioning of surface heat flux components due to QP is also discussed. Plain Language Summary: Rainfall during the monsoon season in India has widespread influences on agriculture and water supply. Therefore, understanding and predicting monsoon rainfall is of utmost importance. Among many parameters, surface energy budget influences precipitation. One of the components of the surface energy budget is the sensible heat flux due to precipitation (QP), which arises because the temperature of raindrops is typically different (cooler) than the temperature of the surface. The QP is thought to be small and is neglected in climate models. By incorporating QP in a regional climate model, we show its influence on monsoon precipitation and surface energy budget. We found that QP reduces precipitation by ∼2% and affects the spatial distribution of precipitation, which may have implications for regional agriculture and irrigation strategies. We also show that QP leads to significant changes in the magnitude and spatial distribution of surface energy budget terms. Key Points: Precipitation‐induced surface sensible heat flux is typically neglected in numerical weather and climate modelsModel simulations show that rain‐induced sensible heat flux reduces monsoonal precipitation and changes its spatial distributionChanges in the spatial distribution of monsoonal rain can have important implications for regional agriculture and irrigation practices [ABSTRACT FROM AUTHOR]

Uttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs. Plain Language Summary: Uttar Pradesh is the most populated state in India, with most of the population working in the agriculture sector and having a low income. The state's vulnerability to climate change is high due to inadequate infrastructure and heavy dependence on agriculture. Rice is a crucial crop for the state, but this study shows that climate change will decrease rice yields in the future, especially for irrigated rice, due to higher temperatures and shorter growing seasons. While rainfed rice yields may increase in some regions due to increased rainfall, rice production is expected to decline overall. Key Points: Rice yield (combining irrigated and rainfed) in Uttar Pradesh, India, is projected to decrease in the future for SSP2‐4.5 and SSP5‐8.5With a projected increase in rainfall, rainfed rice yield increases in rainfall deficit zones, and irrigation decreases under both shared socioeconomic pathwaysPlanting in the early season could reduce the amount of yield loss for irrigated rice [ABSTRACT FROM AUTHOR]

In contrast to the "wet gets wetter and dry gets drier" paradigm, here, using observations and climate model simulations, we show that the mean rainfall over the semi‐arid northwest parts of India and Pakistan has increased by 10%–50% during 1901–2015 and is expected to increase by 50%–200% under moderate greenhouse gas (GHG) scenarios. The GHG forcing primarily drives the westward expansion of the Indian summer monsoon rainfall (ISMR) and is facilitated by a westward expansion of the Indian Ocean (IO) warm pool. Mechanistically, the westward expansion of ISMR is a consequence of the episodic genesis over IO and the northward propagation of an expanded Inter‐Tropical Convergence Zone on a sub‐seasonal time scale. While an adaptation strategy to increased hydrological disasters is a must, harnessing the augmented rainfall would lead to a substantial boost in food productivity, bringing transformative changes in the socio‐economic condition of people in the region. Plain Language Summary: An apparent eastward shift of the Indian monsoon has led to the arid conditions in the west and north‐west regions of India where monsoon was once active, and the Indus Valley civilizations thrived (5,300–3,300‐year BP). A reversal of the process and a westward expansion of the present‐day Indian monsoon would transform the west and north‐west India to a humid "monsoonal" climate and provide food security to the expanding population of the country. The present study shows that the Indian monsoon is indeed expanding to the west with 10% decrease of mean rainfall in the northeast and 25% increase in the west and north‐west during the historical period with a potential of 50%–100% increase in the north‐west under SSP2–4.5. Harvesting the increased rainfall has the potential for significant increase in food productivity bringing in transformative changes in the socio‐economic condition of people of the region. Key Points: Global warming could potentially cause a westward expansion of the Indian monsoonRainfall in northwest India and Pakistan has increased and will continue to rise due to greenhouse gasesIncreased monsoon rainfall can improve food productivity and socio‐economic conditions in the region [ABSTRACT FROM AUTHOR]

Aerosol‐cloud interactions, also known as aerosol indirect effect (AIE), substantially impact rainfall frequency and intensity. Here, we analyze NEX‐GDDP, a multimodel ensemble of high‐resolution (0.25°) historical simulations and future projections statistically downscaled from 21 CMIP5 models, to quantify the importance of AIE on extreme climate indices, specifically consecutive dry days (CDD), consecutive wet days (CWD), and simple daily intensity index (SDII). The 21 NEX‐GDDP CMIP5 models are classified into models with reliable (REM) and unreliable (UREM) monsoon climate simulated over India based on their simulations of the climate indices. The REM group is further decomposed based on whether the models represent only the direct (REMADE) or the direct and indirect (REMALL) aerosol effects. Compared to REMADE, including all aerosol effects significantly improves the model skills in simulating the observed historical trends of all three climate indices over India. Specifically, AIE enhances dry days and reduces wet days in India in the historical period, consistent with the observed changes. However, by the middle and end of the 21st century, there is a relative decrease in dry days and an increase in wet days and precipitation intensity. Moreover, the REMALL simulated future CWD and CDD changes are mostly opposite to those in REMADE, indicating the substantial role of AIE in the future projection of dry and wet climates. These findings underscore the crucial role of AIE in future projections of the Indian hydroclimate and motivate efforts to accurately represent AIE in climate models. Plain Language Summary: We investigate the impacts of aerosol on India's wet and dry climate. High‐resolution downscaled CMIP5 models were used to calculate extreme indices like CDD (consecutive dry days), CWD (consecutive wet days), SDII (precipitation intensity). From the group of 22 models, 12 reliable models were chosen based on their fidelity to the observations. Amongst the reliable models, certain models incorporate only aerosol‐radiation interaction (REMADE), while others have both aerosol‐radiation and aerosol‐cloud interaction (REMALL). We found that the simulated trends in the REMAll were similar to the observed trends. In the current period (1975–2005), the aerosol‐cloud interactions led to the reduction in rainfall (both frequency and intensity wise) and enhanced the dry days, however in the future projections, the reduction in aerosol emissions leads to a wetter climate (increase in wet days and rainfall intensity) over India. Key Points: Using statistical metrics downscaled CMIP5 models are classified into reliable models for better simulating the wet and dry indices over IndiaDownscaled reliable models that included all aerosol effects (AIE + ADE) improved the simulated trends in the historical period than the models with only ADEThere is a general drying‐up (frequency‐wise and intensity‐wise) in the historical period. In the future projections, the reduction in aerosols lead to a wetter climate (frequency‐wise and intensity‐wise) [ABSTRACT FROM AUTHOR]

India is largely devoid of high‐quality and reliable on‐the‐ground measurements of fine particulate matter (PM2.5). Ground‐level PM2.5 concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM2.5 estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM2.5 over four Indian sub‐regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM2.5 estimates. We use the ranking scores produced from the AutoML algorithm and Spearman's rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM2.5 as a first step toward estimating particle composition. Our comparison of AutoML‐derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user‐chosen models. The idealized pseudo‐observations (chemical‐transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research. Plain Language Summary: Ground‐level fine particle (PM2.5) concentrations are frequently estimated with freely available satellite Aerosol Optical Depth (AOD) products. We focus on India where sparse ground‐based monitoring leaves gaps in our understanding of particle concentrations and the relative importance of different sources. We use an atmospheric chemistry model to test whether satellite retrievals of tropospheric trace gas columns can provide information on the origins of PM2.5 and improve satellite‐derived PM2.5. We created an Automated Machine Learning workflow to evaluate the utility of incorporating multiple trace gas columns in PM2.5 estimates, which represents nonlinear relationships between predictands and predictors while freeing users from selecting and tuning a specific machine learning model. On daily and monthly time scales, we quantify the relative information content of trace gas columns, AOD, meteorological fields, and emissions. We find that incorporating trace gas columns improves PM2.5 estimates and may also enable inference of broad characteristics of particle composition. Key Points: We developed an Automated Machine Learning workflow to evaluate the utility of incorporating multiple trace gas columns in PM2.5 estimatesTropospheric trace gas columns contain signatures of PM2.5 precursors and improve PM2.5 estimatesWe infer or link the possible relative importance of primary versus secondary sources of PM2.5 using Automated Machine Learning and Spearman's rank correlation [ABSTRACT FROM AUTHOR]

Deep convection associated with tropical cyclones leads to stratosphere‐troposphere exchange (STE), which affects the upper‐tropospheric ozone concentrations in the vicinity of the cyclones. This study estimates the ozone enhancements over India due to the North Indian Ocean (NIO) cyclones‐driven STE. Indicators such as stratospheric fraction and potential vorticity calculated using the reanalysis data sets suggest that roughly 70% of the cyclones show anomalously high stratospheric intrusions. Aircraft observations over different locations across India also show elevated ozone concentrations in the mid‐to‐upper troposphere on cyclone days. Further, ozone and stratospheric ozone tracer concentrations from Goddard Earth Observing System‐Chemistry simulations and the Copernicus Atmosphere Monitoring Service reanalysis data sets show up to 40 ppb of excess upper tropospheric ozone over India, of which stratospheric ozone accounts for roughly 60%. Stratospheric intrusion due to the Bay of Bengal and the Arabian Sea cyclones affected the upper tropospheric ozone amounts over North and South India, respectively. The stratospheric ozone was observed to propagate downwards into the troposphere, often reaching ∼600 hPa and, in some cases, even the surface. Plain Language Summary: This study estimates the increase in the amount of ozone in the upper half of the lower atmosphere ("upper troposphere") during the North Indian Ocean (NIO) cyclones. Ozone in the upper atmosphere ("stratosphere") protects the Earth from harmful ultraviolet radiation but affects human and ecosystem health when present in the lower atmosphere ("troposphere"). Generally, the ozone in the lower atmosphere occurs due to chemical reactions of gases and sometimes due to the transport from the ozone‐rich upper atmosphere. However, there is a barrier between the upper and the lower atmospheres, also known as the tropopause. Using observations and atmospheric models, we study the penetration of ozone from the upper atmosphere to the lower atmosphere, during the NIO cyclones which disturb the separation barrier. We studied 20 cyclones and found that they disturb the barrier between the upper and the lower atmosphere significantly over India and a large amount of ozone moves between them, so much so that it almost doubles the ozone concentration in the upper half of the lower atmosphere. A warmer world in the future could lead to more cyclones which could increase ozone in the lower atmosphere. Key Points: Stratosphere‐troposphere exchange (STE) over India due to the North Indian Ocean cyclones is studied using reanalysis data and model outputEnhanced stratospheric airmass (∼20%) and ozone (∼40 ppb) were evident in the upper troposphere and lower stratosphereSTE due to the Bay of Bengal and Arabian Sea cyclones influence ozone amplification over northern and southern India, respectively [ABSTRACT FROM AUTHOR]

Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed. Plain Language Summary: Land‐atmosphere interactions such as soil moisture‐precipitation feedback occur at a wide range of spatial scales. For example, spatial variability of surface fluxes such as radiation and precipitation can influence surface latent and sensible heat fluxes, which further influence turbulent mixing processes in the boundary layer and cloud and precipitation. Current global climate models (GCMs) have difficulties in representing land‐atmosphere interactions at scales smaller than the GCM grid (subgrid scales). To meet this challenge, we employ a novel framework, called multiple atmosphere multiple land (MAML), in a superparamaterized version of E3SM (SP‐E3SM) in which a cloud resolving model is embedded within each GCM grid to better resolve clouds and convection. MAML explicitly simulates the subgrid variabilities of land states and fluxes at scale of ∼1 km and provides feedback to the atmosphere. The use of MAML is shown to have a limited effect on the strength of land‐atmosphere coupling due to the current one‐way subgrid land‐atmosphere interaction setup. Despite the limited effect, MAML increases the rainfall in India, Amazon, and Central Africa, which reduces the dry bias in the model. Key Points: A multiple atmosphere multiple land (MAML) framework of land‐atmosphere coupling is implemented in the super‐parameterized E3SM (SP‐E3SM)MAML increases precipitation in India, Central Africa, and Amazon by increasing water vapor and hence large‐scale moisture convergenceMAML's impact on land‐atmosphere interactions is limited by the current setup of one‐way coupling on cloud‐resolving scales [ABSTRACT FROM AUTHOR]

Comprehensive characterization of turbulent fluxes and land‐atmosphere interactions at a semi‐arid region in the Deccan Plateau of peninsular India is presented using 2‐year eddy covariance measurements. Contrary to previously reported results over the Indian region, surface energy partitioning is more into sensible heat flux than latent heat flux, even during the monsoon season. Irrespective of the seasons, the soil moisture and its response to rainfall drive the land‐atmosphere interactions and along with the net radiation, play crucial roles in deciding the surface fluxes and its partitioning. Time series comparison with ERA5 reanalysis data reveals the inadequate representation of land‐atmosphere interactions in ERA5 reanalysis over the peninsular Indian region, particularly the intensity and duration of rainfall and the feedbacks of soil moisture to the atmosphere. The surface energy balance closure improves by ∼3% when the soil heat storage term is considered and is the highest when the height and scatter of the roughness elements around the measurement site are the lowest. The influence of rainfall on energy balance closure is lower as compared to land surface heterogeneity. The present study also fills an existing gap of validating the surface fluxes and its partitioning in ERA5 reanalysis in the tropics and will provide deeper insights on the land‐atmosphere interactions in tropical semi‐arid regions. Plain Language Summary: The interactions between the surface of the Earth and the atmosphere above it controls the weather and climate of a region to a large extent. Accurately capturing such interactions in weather and climate models is arduous due to (a) the limited measurements on the partitioning of solar energy that reaches the surface and (b) the surface heterogeneities. Such measurements over different terrains and its comparison with various global data sets are essential to improve the climate predictions. Making use of 2‐year observations, we report the anomalous partitioning of surface energy at a semi‐arid region in India and how the rainfall and soil moisture play crucial roles in regulating it. Large differences between the observations and a modeled data set in the duration and intensity of rainfall and the energy partitioning at the surface are delineated along with the significant influence of the vegetation and soil heat storage. These results will be helpful in improving the weather and climate model simulations over the semi‐arid regions in the tropics. Key Points: Contrary to previous reports over the Indian region, partitioning of surface fluxes is more to sensible heat flux than latent heat fluxMeasurements reveal large differences in the rainfall duration and intensity and land‐atmosphere interactions compared to reanalysis dataEnergy balance closure is inversely proportional to the height of the roughness elements and their scatter around the measurement site [ABSTRACT FROM AUTHOR]

The Paleocene‐Eocene Thermal Maximum (PETM) is associated with major extinctions in the deep ocean, and significant paleogeographic and ecological changes in surface ocean and terrestrial environments. However, the impact of the associated environmental change on shelf biota is less well understood. Here, we present a new PETM record of a low paleolatitude shallow‐marine carbonate platform from Meghalaya, NE India (eastern Tethys). The biotic assemblage was distinctly different to other Tethyan PETM records dominated by larger benthic foraminifera and calcareous algae both in the Paleocene and Eocene. A change in taxa and forms indicating deeper waters with a concurrent decrease in abundance of shallow water algae suggests a sea‐level rise during the onset of the PETM. The record is lacking the ecological change from corals to larger foraminiferal assemblages and the Lockhartia dominance, characteristic of several other sections in the Tethys. Comparison with a global circulation model (GCM) indicates high regional temperatures in the Thanetian which may have excluded corals from the region. Furthermore, the regional circulation pattern is isolating the site from the wider Paratethys. Our study highlights the need for a diverse global perspective on shallow‐marine response to the PETM and the strength of coupling data to global climate models for interpretation. Key Points: Shallow‐marine Paleocene‐Eocene Thermal Maximum (PETM) successions are rare; here, we presented from the low paleolatitude NE India (eastern Tethys)The absence of coral reefs in NE India, in contrast to other Tethyan records, was driven by very high temperaturesLinking biotic records of this section with climate modeling allow to interpret the biotic differences across the Tethyan region [ABSTRACT FROM AUTHOR]

Studies on multivariate drought assessment and the climate change impact on a river basin scale are limited in India. Drought monitoring is a challenging subject due to its dependence on different climatic variables. To overcome this, a copula‐based probabilistic multivariate drought index (MDI) has been developed which simultaneously represents the meteorological, hydrological and agricultural drought phenomenon. The four variate Archimedean copula is used in this study to integrate the precipitation, evapotranspiration, soil moisture and streamflow. Hydrologic variables like evapotranspiration, soil moisture and streamflow were simulated using the Soil and Water Assessment Tool model. The future MDI is also analysed to identify the impact of climate change on drought phenomenon using Coupled Model Intercomparison Project‐6 Global Climate Models under four Socio‐Economic Shared Pathways. Drought characteristics like severity and duration were evaluated to identify the present and future drought events. The precipitation and minimum and maximum temperatures were identified to have increasing tendencies in the future scenarios. Most of the future scenarios showed lower drought duration and severity when compared to the reference period. The drought duration and severity are likely to decrease in the future time scales especially under the high emission scenarios. The present study used a novel approach to examine the drought from various perspectives and the study will be useful for drought mitigation and adaptation strategies over the basin. [ABSTRACT FROM AUTHOR]

The Indian monsoon system impacts over 1 billion people, and rainfall from its southwest monsoon is critical for agriculture. Cool sea surface temperatures (SSTs) in the Arabian Sea have been associated with reduced monsoon rainfall and vice versa, although this relationship is difficult to examine due to scarce and short instrumental records. We utilize laminated Pakistan Margin sediments to reconstruct southwest monsoon rainfall and Arabian Sea SSTs at sub‐annual to multi‐annual resolution for the twentieth century. We applied paired sub‐mm mapping techniques to intact sediment core surfaces to measure elemental abundances and lipid biomarkers. The first principal component of the elemental results (PC1) explains 47% of the elemental variance and is driven by detrital lithogenic elements K, Ti, and Fe. PC1 is positively correlated with homogeneous India June‐September rainfall departures from 1903 to 1928 and 1949–1970 on a multi‐annual scale. Correlations in the remaining timespans are either negative or absent, likely driven by a combination of dam and barrage construction on the Hub and Indus Rivers and negative rainfall departures. We propose PC1 as a proxy for southwest monsoon rainfall on multi‐annual timescales prior to ∼1930. A dampened annual SST cycle is resolved with the alkenone U37K′ ${\mathrm{U}}_{37}^{{\mathrm{K}}^{\prime }}$ paleothermometer, which also captures the interannual‐decadal trends of twentieth century Arabian Sea SSTs. We propose the crenarchaeol‐caldarchaeol tetraether index, sourced from planktonic Thaumarchaeota, as a delayed upwelling proxy for the western Arabian Sea. Our reconstructions corroborate the established relationship of reduced Arabian Sea upwelling leading to warmer SSTs and increased monsoon rainfall, and vice versa. Plain Language Summary: The Indian monsoon impacts over 1 billion people, particularly through its summer monsoon rains that are critical for agriculture. Summer monsoon rainfall is difficult to predict because it is not spatially or temporally consistent each year, and climate models do not simulate it well. However, it is known that cool seawater surface temperatures in the Arabian Sea in summer lead to reduced monsoon rainfall, while warm seawater surface temperatures in the summer lead to more rainfall. Marine algae respond to seawater temperature changes by altering the proportions of molecules in their cells, which are preserved in seafloor sediments. We used this relationship to reconstruct approximately monthly Arabian Sea water surface temperature for the twentieth century. Seawater surface temperatures in the Arabian Sea are made cooler in the spring and summer when cold, deep waters are brought up to the sea surface during upwelling. We reconstructed upwelling by measuring changes in the molecules produced by planktonic archaea in their cell membranes. We reconstructed yearly monsoon rainfall using the elemental signature of river sediments preserved in the same seafloor sediments. Our reconstructions confirm that increased upwelling cools the Arabian Sea and reduces monsoon rainfall and vice versa. Key Points: We present an annually‐resolved, detrital lithogenic element‐based proxy for southwest Indian monsoon rainfallAlkenone paleothermometer records a muted seasonal sea surface temperature cycle; archeal lipid distribution an indirect upwelling proxyOur reconstructions capture the upwelling‒sea surface temperature‒monsoon rainfall relationship identified in models and observations [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are routinely applied for regional climate assessments. The RCM simulated rainfall typically overpredict the light rain/drizzle events. To correct the typical errors noted in RCM rainfall simulations, in this study, three bias‐correction methods: linear scaling (SCL), local intensity scaling (LOCI), and empirical quantile mapping (EQM), have been employed. These methods are used to correct monsoon rainfall simulations from 7 RCMs across 14 agroclimatic zones (ACZs) in India from 1970 to 2005. The corrected rainfall data were compared to the observations obtained from India Meteorological Department. The performance of the three methods was assessed using: probability distribution function, consecutive dry day index (CDD), R95 (rainfall distribution at 95th percentile), and spatial correlation. The results vary spatially across the different ACZs. Overall, SCL method is more effective followed by EQM while LOCI was relatively less effective in correcting the errors. Spatial analysis of the rainfall indicates notable improvements over the Western Himalayan Region, which has a complex topography and land use. Error metricsreveal broad improvements across different ACZs, except for Central Plateau Hill Region, East Coast Plain Hill Region, and Southern Plateau Hill Region. The SCL and EQM perform well, and the results are particularly good for simulating wet days (R95), while no distinct variation was found among correction methods to reduce dry bias (CDD). The results provide ACZ and region‐specific utilization of an effective bias correction technique for impact assessment studies in the Indian monsoon region. Key Points: Linear scaling (SCL) and empirical quantile mapping (EQM) performed well, particularly for simulating wet days (R95)Local intensity scaling (LOCI) method was relatively less effective in correcting the errorsNo distinct variation noted among correction methods to reproduce dry bias (CDD) [ABSTRACT FROM AUTHOR]

This study, for the first time, investigates the physical association between Predecessor Rain Events (PREs) and peak runoff generation in seven catchments over the Upper Mahanadi River basin (UMRB), India. A statistical–dynamical framework is developed to assess the compounding impact of PREs (as preconditioned events) versus riverine floods during both retrospective and projected climate. Based on models' fidelity to capture historical climatology, we select four out of nine Global Climate Models (GCMs) during historical (1980–2005), and the three projected time windows, that is, near future (2010–2039), mid‐century (2040–2069), and the far future (2070–2099) planning horizons with RCP8.5 emission scenario. We assess changes in compound flood hazards in historical versus projected periods using a newly proposed Bivariate Hazard Ratio (BHR) index, which represents the ratio between bivariate return periods during the projected versus the historical time windows. Assessing bivariate return periods (characterized by 'AND' operator) of rainfall‐driven compound floods shows decreased flood hazard in the mid‐century and far‐future planning horizons. Accounting ranges of uncertainty from climate model simulations and the propagation of uncertainty across the numerical model chain, overall we show considering PRE as the covariate, floods in larger catchments show an increase in compound flood hazard in the projected period. [ABSTRACT FROM AUTHOR]

Drought frequency and intensity have increased in recent decades and are expected to escalate in future under the changing climate scenario. However, a wide range of uncertainty exists regarding the risk, variability and severity of the drought. This study evaluates the future drought and associated uncertainty over homogeneous regions of India using the suites of CMIP5 global climate models (GCMs) and CORDEX‐SA regional climate models (RCMs). The drought characteristics and its projected future changes are analysed using probability density functions derived from hydroclimatic parameters, including the standardized precipitation index (SPI) and the standardized precipitation‐evapotranspiration index (SPEI). Besides, uncertainties from various sources such as inter‐model variability, indices type, timescale, adapted methods, and time‐slices are explored under the RCP8.5 emission scenario to the end of the 21st century. Our study reveals large biases in the individual model; however, both the multi model ensembles (GCM and RCM) generally demonstrate better performance with respect to observation. In particular, the RCM ensemble showed limitations in capturing the regional precipitation pattern while temperature and potential evapotranspiration (PET) showed considerable enhancement concerning GCMs. SPI (SPEI) generally exhibited enhanced wetness (dryness) derived from increased precipitation (PET), although a few discrepancies were noticed. The regional heterogeneity was also found to exist, although some robust changes were noticed in drought frequency and severity with different return periods. Our finding underscores a wide range of uncertainties in drought projection, with maximum contribution from indices selection followed by model variability whereas other sources have the least contribution. The primary drivers for all these uncertainty sources arise due to variations among models simulated hydroclimatic variables that need to be parameterized more precisely for sustainable drought management. [ABSTRACT FROM AUTHOR]

The skill of General Circulation Models (GCMs) in mimicking the observed climate is assessed through various procedures and are ranked. The performance of a GCM is site‐specific and the ranking pattern varies spatially. In general, a set of best performing GCMs is extracted to study the impact of climate change. As, there is no universally accepted ranking procedure, the ranking of GCMs is prone to subjectivity. In this study, it is aimed to address the effect of this subjectivity on the GCM rankings. The past performance of GCMs from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the maximum and minimum temperature across India are evaluated and ranked by different ranking procedures. These ranking procedures involve combinations of various components present in the ranking procedure such as model evaluation criteria, criteria weightage allocation methods, Multicriteria Decision Making methods (MCDM) and reference gridded datasets. Different criteria and methods involved in the ranking procedures are carefully selected to address the subjectivity involved in ranking of GCMs. The effect of each individual component on the ranking pattern is systematically analysed and the spatial distribution of grids with same ranking patterns across all the combinations are considered as grids with same ranking. The performance of best performing GCM is attributed to homogenous climatic zones and its corresponding topological features. An ensemble of frequently performing top five GCMs among 16 different ranking procedures are extracted for each climate zone as the most suitable set of GCMs. [ABSTRACT FROM AUTHOR]

India is one of the most drought‐ravaged countries in the world and faces at least one drought in one region or another in every 3 years. There is no single reliable approach in characterizing future droughts. To understand future drought risk, potential changes of drought properties and characteristics are analysed in this study. Using Fuzzy c‐means clustering approach, homogeneous drought regions are identified in the Godavari river basin and therefore, optimum number of clusters were assigned as four. The 12‐month standardized precipitation index (SPI) using precipitation data from India Meteorological Department (IMD) and Global Climate Model (GCM)—MIROC‐ESM‐CHEM is calculated for the homogeneous regions of the Godavari basin. The best fit copula for observed and simulated severity and duration are: Region 1—Clayton, Regions 2 and 3—Gumbel, Region 4—Frank copula. Severity‐duration‐frequency (SDF) and severity‐area‐frequency (SAF) curves were developed and analysed using the best fit copulas. The research findings conclude that moderate and severe droughts are frequently increasing for future periods (2006–2099) compared to the historic period (1962–2005). Droughts with high severity and high mean interarrival time are observed as expected in the future. For the Godavari basin, the SDF curves were concave upwards indicating an increase in severity with an increase in duration. The rate of increase of severity is small for shorter durations compared to that of longer‐duration drought. Thus, more prolonged drought events in the 21st century are likely to occur. The SAF curves with steeper slopes and high variability in topographical and hydrological characteristics have been observed over the Godavari basin. From these curves, for a specified percentage of area and return period, the drought severity can be calculated and the information can be used for crop management and agricultural water demands. Overall, the findings of this research offer a view of likely scenarios of drought in the Godavari basin. [ABSTRACT FROM AUTHOR]

This study presents the interannual variation in the summer monsoon rainfall during the El Nino Southern Oscillation (ENSO) phases, that is, El Nino and La Nina throughout the Indian subcontinent and its subregions during 1986–2010. The analysis includes the performance assessments of the regional climate model (RegCM4) in simulating Indian summer monsoon rainfall through comparison with India Meteorological Department rainfall data. Observation clearly demonstrates that the El Nino/La Nina produced weaker/strong monsoon rainfall over India and it increases the interannual variability of the southwest monsoon. The capability and skill of RegCM4's Grell and Mix99 cumulus parameterization scheme in simulating Indian summer monsoon rainfall associated with extreme climate are usually higher in "dry" conditions rather below average for the accurate simulation of "wet" conditions. Also, model performance was good in defining the characteristics, spatial distribution, and trend of a dry spell during ENSO phases with observation. The model‐simulated spatial and temporal value of wet spell during ENSO produces overestimated value over all India. Plain Language Summary: This study has been performed with the motivation of understanding the southwest monsoon drought/flood characteristics associated with El Nino and La Nina events. Climate change variability studies are of utmost importance for the planning of agriculture and economic policy. With this motivation, an attempt has been made to study the distinguishing features of Indian summer monsoon rainfall during El Nino and La Nina years over the Indian subcontinents and its subregions with Regional Climate Model (RegCM4). The finding of our study highlights the capability and skill of RegCM4's Grell cumulus parameterization scheme in simulating Indian summer monsoon associated with extreme climate are usually higher in "dry" condition rather below average for the accurate simulation of "wet" condition. The index of maximum count of consecutive dry days and consecutive wet days helps in accessing the deficient/excess rainfall over a regionalized and localized area. Key Points: The modulation of spatial and temporal south west monsoon rainfall during ENSO phases are prominentEl Nino and La Nina episodes control the probability of occurrences and length of wet spell and dry spell during monsoonThe skill of RegCM4 in simulating ISMR during the extreme ENSO phases are better in "dry" conditions rather below average for the accurate simulation of "wet" conditions [ABSTRACT FROM AUTHOR]

With the recognition that the circulation is strongly coupled with the Indian summer monsoon rainfall (ISMR), a number of large-scale circulation indices have been proposed to represent the Indian summer monsoon to aid diagnosis of teleconnections with ISMR and evaluation of simulations by climate models. The circulation indices, however, correlate poorly with ISMR raising doubts on the coupling between the two. In this study, we identify reasons behind the poor correlations and estimate a potential limit on this correlation between circulation indices and ISMR. One mechanistic reason for poor correlations between circulation indices and ISMR is that the period and variance explained by the oscillatory modes of circulation indices differ significantly from those of ISMR leading to their phases also differing between modes of circulation indices and corresponding modes of ISMR. Further, the wind anomaly patterns associated with wind indices project strongly on the climatological monsoon circulation unlike that associated with the ISMR, another reason for their poor correlation with ISMR. As a result, all circulation indices underestimate the negative correlation between June–September sea-surface temperature (JJAS SST) over the Niño3.4 and ISMR and fail to simulate the lead–lag relationship between the two. A conservative estimate of the interannual variance explained by coupling of the JJAS rainfall over continental India andwinds in the neighbourhood is strong, explaining about 55% variance distributed in three leading coupled modes. Therefore, the effectiveness of any circulation indices in explaining the ISMR variance is potentially limited to that associated with the dominant mode (∼30%). The local response of winds to deep convection over the waters of the Indian Ocean and western Pacific warm pool leads to a strong increasing trend in some of the circulation indices and adds “noise” modes contributing to the de-correlation between circulation indices and ISMR. [ABSTRACT FROM AUTHOR]

Drought patterns are better understood by analysing the joint dependence among long‐term drought characteristics: severity, duration, and frequency. Changes in these drought characteristics are also likely due to potential climate change, for instance, increased drought severity and duration, increased frequency of drought events, and increased spatial extent of drought impacts. In this study, we performed a regional‐scale analysis of potential changes in meteorological drought characteristics in a tropical region in India under a warming future climate scenario. The meteorological drought characteristics were computed using the Standardized Precipitation Index. The Coordinated Regional Downscaling Experiment South Asia Regional Climate Model future precipitation simulation under the RCP 4.5 scenario was bias‐corrected and utilized for future drought frequency analysis. Regionalization of meteorological drought characteristics was performed using the simple k‐means clustering technique to identify the homogeneous regions for baseline and future periods. Bivariate copula was utilized to model the dependence among the drought characteristics in order to develop the S‐D‐F curves. The changes in regionalization and regional S‐D‐F curves under future potential climate change were then investigated. The results of the study suggest an increase in drought severity by 2–3% for long‐term droughts (duration more than 5 months) in the future as indicated by upward shift and changes in the slope of the regional S‐D‐F curves. There is a projected increase in the occurrence of long‐term droughts in the future when compared to short‐term (duration of 1 to 3 months) droughts. We also observed a considerable spatial shift of drought hotspots, the severe drought‐prone regions lie in the eastern part of the study region in the future. Future projections of regional S‐D‐F curves and Spatio‐temporal drought characteristics obtained from the proposed framework may be utilized for devising drought mitigation strategies. [ABSTRACT FROM AUTHOR]

Recent studies examine the potential for large urban fires ignited in a hypothetical nuclear exchange of one hundred 15 kt weapons between India and Pakistan to alter the climate (e.g., Mills et al., 2014, https://doi.org/10.1002/2013EF000205, and Reisner et al., 2018, https://doi.org/10.1002/2017JD027331). In this study, the global climate forcing and response is predicted by combining two atmospheric models, which together span the micro‐scale to global scale processes involved. Individual fire plumes are modeled using the Weather Research and Forecasting (WRF) model, and the climate response is predicted by injecting the WRF‐simulated black carbon (BC) emissions into the Energy Exascale Earth System Model (E3SM) atmosphere model Version 1 (EAMv1). Consistent with previous studies, the radiative forcing depends on smoke quantity and injection height, examined here as functions of fuel loading and atmospheric conditions. If the fuel burned is 1 g cm−2, BC is quickly removed from the troposphere, causing no global mean climate forcing. If the fuel burned is 16 g cm−2 and 100 such fires occurred simultaneously with characteristics similar to historical large urban firestorms, BC reaches the stratosphere, reducing solar radiation and causing cooling at the Earth's surface. Uncertainties in smoke composition and aerosol representation cause large uncertainties in the magnitude of the radiative forcing and cooling. The approximately 4 yr duration of the radiative forcing is shorter than the 8 to 15 yr that has previously been simulated. Uncertainties point to the need for further development of potential nuclear exchange scenarios, quantification of fuel loading, and improved understanding of fire propagation and aerosol modeling. Plain Language Summary: If the detonation of nuclear weapons causes large fires, the smoke emissions could block sunlight and affect the global climate. A commonly studied scenario is the climate impact that would be caused by the detonation of one hundred 15 kt nuclear weapons in a "regional nuclear exchange" between India and Pakistan (Mills et al., 2014, https://doi.org/10.1002/2013EF000205, Reisner et al., 2018, https://doi.org/10.1002/2017JD027331). We simulate the global climate impacts of this scenario using new models for predicting the fire plume and climate and find that, when smoke from the fires remains in the lower troposphere, it is quickly removed and the climate impact is minimal. Conversely, when fires inject smoke into the upper troposphere or higher, more smoke is transported to the stratosphere where enough light is blocked to cause global surface cooling. Our simulations show that the smoke from 100 simultaneous firestorms would block sunlight for about 4 yr, instead of the 8 to 15 yr predicted in other models. Climate impacts are also shown to be sensitive to assumptions about the composition of the smoke. Additionally, we show that the global effects of the fires are sensitive to fuel availability and consumption, factors which are uncertain for cities in India and Pakistan. Key Points: Fire plume and climate modeling of a regional nuclear exchange finds potential global cooling, shorter in duration than previously assessedAerosol emissions from fires and their representation in a climate model contribute uncertainty to the climate responseA wide range of climate impacts are simulated depending on fuel availability at the detonation sites [ABSTRACT FROM AUTHOR]

Dry and wet extremes affect agricultural production, infrastructure, and socioeconomic well‐being of about 1.4 billion people in India. Despite the profound implications of dry and wet extremes, their changes in the observed and projected climate in India are not well quantified. Here, using the observations from multiple sources, we show that the area affected by dry extremes during the monsoon season (June–September) and water‐year (June–May) has significantly increased (~1% per decade; p value < 0.05) over the last six decades (1951–2015) in India. On the other hand, the area affected by wet extremes does not exhibit any significant trend over the same time period. Dry and wet extremes in the monsoon season are corroborated with the positive phase and negative phase of the sea surface temperature (SST) anomalies in the tropical Pacific Ocean (Niño 3.4 region). Global climate models (GCMs) project an increase of more than 25–30% (±3–6%) in the combined area affected by the dry and wet extremes in India by the end of the 21st century. The frequency of both dry and wet extreme years is also projected to increase in the majority of India (>80%) under a warmer world if the global mean temperature rises above 1.5°C (or 2°C) from a preindustrial level. Moreover, the population exposed to the dry and wet extremes is likely to increase threefold under the projected 2°C warmer world. Therefore, limiting global mean temperature rise below 2°C can substantially reduce the area and population exposure due to dry and wet extremes in India. Key Points: The area affected by dry extremes has significantly increased (~1% per decade) during 1951–2015 in IndiaClimate models project increase in the combined area affected by the dry and wet extremes in India (25–30%) by the end of the 21st centuryThe population exposed to the dry and wet extremes is likely to increase threefold under a (2°C) warmer world climate [ABSTRACT FROM AUTHOR]

This study aims to project and characterize the climate extremes in Ravi River Basin (RRB) which is considered as a data poor transboundary basin located in India and Pakistan. Performance of the three GCDs against observation data was evaluated at three stations. A quantile mapping technique was used to correct the biases of four regional climate models (RCMs) and climate extremes were analysed for future period (2020–2095). Seven temperature and rainfall based indices that represent warm and wet characteristics of climate were chosen. Four statistical parameters and spatial maps were evaluated for the base period 1982–2005. The CPC‐NOAA and PU had the best performance for temperature and rainfall data regarding the time series analysis. The quantile mapping improved three important aspects of the climate cycle; the transitions from dry to wet and wet to dry seasons and peaks as well. At spatial scale, quantile mapping well captured the spatial distribution of the eleven indices other than RX1Day, CWD and FD0. The results show that warm and wet extremes will increase in future at 5% significance level across the entire basin with large changes in north east. The changes will be large for RCP8.5 scenario compare to RCP4.5 scenario and choice of the scenarios has dominant contribution in uncertainty than choice of the models. [ABSTRACT FROM AUTHOR]

The role of the Arabian Sea (AS) warming on the Indian summer monsoon rainfall is investigated using regional climate model‐Regional Climate Model version 4.7 of International Centre for Theoretical Physics. The model realistically simulates the rainfall, temperature, low‐level winds and vertically integrated moisture flux over the Indian region during the years 2006–2015. It is found that the AS warming decreases the rainfall over most parts of the India, which is mainly due to decrease in the convective precipitation over these regions. The AS warming reduces the magnitude of westerly winds in the southern Bay of Bengal and AS; enhances the moisture convergence over the AS; and causes the prohibition of the moisture‐laden winds from the AS to reach to the Indian land regions resulting into decreased precipitation. The effect of anti‐cyclonic circulation produced over the central India as a result of the AS warming is to further reduce the monsoon rainfall. It is also shown that the AS warming increases the extreme rainfall over most parts of India. [ABSTRACT FROM AUTHOR]

This study assesses the performance of regional climate model version 4 (RegCM4) in simulating the monsoon onset over Kerala (MOK). It also examines any possible relationship between the onset dates with the summer monsoon rainfall over India as whole as well as each grid points of the India land points and also the moisture inflow into Indian subcontinent. A 30‐year long simulation starting from 1979 till 2008 was carried out with the lateral boundary forcings provided by European Centre for Medium Range Weather Forecasts Reanalysis (ERA‐interim) at 25 km horizontal resolution. The simulated climatological MOK date is found to be 28th May, while as per the India Meteorological Department, climatological normal onset date is 1st June. The model has performed well in simulating the inter‐annual variation of MOK during the study period. The correlation coefficient between model simulated and observed MOK is 0.83 significant at 95% confidence level. In both model and observations, the MOK is weakly correlated with All India Summer Monsoon Rainfall. Again, the model skill was examined through equitable threat score (ETS). The ETS score is high for normal (0.48) and delayed (0.42) onset years, while the score is very low in early onset years. The spatial patterns of rainfall over central India are very similar in early and normal onset years. The model has performed well in reproducing the moisture inflow in to the Indian subcontinent from all the directions in most of the years, but there is no one‐to‐one relation between different categories of MOK years with total rainfall and net moisture inflow. Based on this study, it is found that RegCM4 reproduces different aspects of MOK reasonably well. Topography of model domain (in meters) over which RegCM4 has been integrated. Location of 14 stations over which the model simulated rainfall has been compared with the IMD 0.5 gridded rainfall to identify the monsoon onset over Kerala. [ABSTRACT FROM AUTHOR]

A series of numerical experiments are carried out to investigate the sensitivity of a landfalling monsoon depression to land surface conditions using the Weather Research and Forecasting (WRF) model. Results suggest that precipitation is largely modulated by moisture influx and precipitation efficiency. Three cloud microphysical schemes (WSM6, WDM6, and Morrison) are examined, and Morrison is chosen for assessing the land surface-precipitation feedback analysis, owing to better precipitation forecast skills. It is found that increased soil moisture facilitates Moisture Flux Convergence (MFC) with reduced moisture influx, whereas a reduced soil moisture condition facilitates moisture influx but not MFC. A higher Moist Static Energy (MSE) is noted due to increased evapotranspiration in an elevated moisture scenario which enhances moist convection. As opposed to moist surface, sensible heat dominates in a reduced moisture scenario, ensued by an overall reduction in MSE throughout the Planetary Boundary Layer (PBL). Stability analysis shows that Convective Available Potential Energy (CAPE) is comparable in magnitude for both increased and decreased moisture scenarios, whereas Convective Inhibition (CIN) shows increased values for the reduced moisture scenario as a consequence of drier atmosphere leading to suppression of convection. Simulations carried out with various fixed soil moisture levels indicate that the overall precipitation features of the storm are characterized by initial soil moisture condition, but precipitation intensity at any instant is modulated by soil moisture availability. Overall results based on this case study suggest that antecedent soil moisture plays a crucial role in modulating precipitation distribution and intensity of a monsoon depression. [ABSTRACT FROM AUTHOR]

A direct numerical simulation (DNS) with the decaying turbulence setup has been carried out to study cloud-edge mixing and its impact on the droplet size distribution (DSD) applying thermodynamic conditions observed in monsoon convective clouds over Indian subcontinent during the Cloud Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX). Evaporation at the cloud-edges initiates mixing at small scale and gradually introduces larger-scale fluctuations of the temperature, moisture, and vertical velocity due to droplet evaporation. Our focus is on early evolution of simulated fields that show intriguing similarities to the CAIPEEX cloud observations. A strong dilution at the cloud edge, accompanied by significant spatial variations of the droplet concentration, mean radius, and spectral width, are found in both the DNS and in observations. In DNS, fluctuations of the mean radius and spectral width come from the impact of small-scale turbulence on the motion and evaporation of inertial droplets. These fluctuations decrease with the increase of the volume over which DNS data are averaged, as one might expect. In cloud observations, these fluctuations also come from other processes, such as entrainment/mixing below the observation level, secondary CCN activation, or variations of CCN activation at the cloud base. Despite large differences in the spatial and temporal scales, the mixing diagram often used in entrainment/mixing studies with aircraft data is remarkably similar for both DNS and cloud observations. We argue that the similarity questions applicability of heuristic ideas based on mixing between two air parcels (that the mixing diagram is designed to properly represent) to the evolution of microphysical properties during turbulent mixing between a cloud and its environment. [ABSTRACT FROM AUTHOR]

Since 1960s, India experiences a series of extreme drought and flood events during the summer months. The Humid Subtropical Climatic Zone ( HSTC), which comprises the Indo-Gangetic Plain region of India, is highly vulnerable to the climatic extremities. This region is the home for approximately 40% of total population and yields approximately 50% of total agricultural production of India. We investigate the historical variation in dry/wet conditions and project the future changes in extreme events under two different scenarios of the Coupled Model Inter-comparison Project 5 models. First, the model parameters, that is, precipitation ( P) and temperature ( T) are bias corrected with respect to observation data and finally six models are selected, which are in right phase with the observation for composite analysis. Next, we calculate the potential evapotranspiration (PET) and the Standardized Potential Evapotranspiration Index to characterize the extreme events. Both P and PET are projected to increase in the HSTC zone; however, both the wet and dry conditions demonstrate a persistent increase in future. In relative terms, P increases faster than PET along the Gangetic Plain region (wet condition) and decreases in the southern and eastern part of the region (dry condition). The mitigating effect (RCP4.5 scenario) of precipitation increase will be overridden by strengthened PET and extreme dry condition project markedly under RCP8.5 scenario. The features are consistent with the increase/ decrease in multimodel mean SPEI, consistent with the spatial pattern of P−PET. The area affected due to wet and dry events will be relatively higher under the RCP4.5 and RCP8.5 scenario, respectively. [ABSTRACT FROM AUTHOR]

The advance of the onset of the Indian monsoon is here explained in terms of a balance between the low-level monsoon flow and an overrunning intrusion of mid-tropospheric dry air. The monsoon advances, over a period of about 6 weeks, from the south of the country to the northwest. Given that the low-level monsoon winds are westerly or southwesterly, and the mid-level winds northwesterly, the monsoon onset propagates upwind relative to mid-level flow, and perpendicular to the low-level flow, and is not directly caused by moisture flux toward the northwest. Lacking a conceptual model for the advance means that it has been hard to understand and correct known biases in weather and climate prediction models. The mid-level northwesterlies form a wedge of dry air that is deep in the far northwest of India and over-runs the monsoon flow. The dry layer is moistened from below by shallow cumulus and congestus clouds, so that the profile becomes much closer to moist adiabatic, and the dry layer is much shallower in the vertical, toward the southeast of India. The profiles associated with this dry air show how the most favourable environment for deep convection occurs in the south, and onset occurs here first. As the onset advances across India, the advection of moisture from the Arabian Sea becomes stronger, and the mid-level dry air is increasingly moistened from below. This increased moistening makes the wedge of dry air shallower throughout its horizontal extent, and forces the northern limit of moist convection to move toward the northwest. Wetting of the land surface by rainfall will further reinforce the north-westward progression, by sustaining the supply of boundary-layer moisture and shallow cumulus. The local advance of the monsoon onset is coincident with weakening of the mid-level northwesterlies, and therefore weakened mid-level dry advection. [ABSTRACT FROM AUTHOR]

ABSTRACT The western Himalayas (WH) is characterized by heterogeneous land surface characteristics and topography. During winter (December, January, and February-DJF), eastward moving low-pressure synoptic weather systems, called Western Disturbances (WDs) in Indian parlance, cause the majority of the precipitation mostly in the form of snow. The interplay between land surface/topography and WDs greatly controls precipitation distribution over the region. This study seeks to evaluate this using a mosaic-type parameterization of subgrid-scale topography and land use (sub-BATS) for regional climate simulation with a regional climate model (RegCM3). The model coarse grid cell size in the control simulation is 60 km while the subgrid cell size is 10 km. This study compares two 22 year simulations (1980-2001) during winter (DJF). The first simulation is without (CONT) and the second is with (SUB) the fine scale subgrid scheme. Representing the fine scale processes using the subgrid scheme SUB experiment simulates reduced precipitation by approximately 2 mm d−1 with comparison to CONT experiment. This estimation of reduced and closer to the corresponding observed precipitation is important for regional water budget over the WH which is primarily governed by topographic and land surface disaggregation. Validation with corresponding observations over similar elevations shows that SUB displays an improvement over CONT experiment. This relevant decrease of precipitation in SUB using disaggregation-reaggregation methodology for initial model input fields in subBATS scheme is due to better representation of the WH topography. In case of temperature, SUB experiment displays colder bias (∼2-4 °C) than the CONT over the Himalayas. This preliminary finding is important for studying regional water balance, snow melt accumulation in following summer period. [ABSTRACT FROM AUTHOR]

In this study, the performance of the weather research and forecasting (WRF) ARW regional model was evaluated for simulating the regional scale precipitation during Indian summer monsoon (ISM) at 30 km resolution over seven different homogeneous rainfall zones falling under different climatic (perhumid, humid, dry/moist subhumid, dry/moist semiarid, arid) regions of India. Seasonal scale simulations were made for ten summers (JJAS months) over 2000-2009 using the boundary conditions derived from the National Centers for Environmental Prediction (NCEP) reanalysis data. Sensitivity experiments were conducted with three convection schemes (Kain-Fritsch, KF; Betts-Millor-Janjic, BMJ; Grell-Devenyi, GD). Simulated regional climate was evaluated by comparison of precipitation with 0.5° India Meteorological Department (IMD) gridded rainfall data over land, Tropical Rainfall Measuring Mission (TRMM) rainfall data over the ocean and atmospheric circulation fields with 1° NCEP global final analysis (FNL). Although all the simulations showed spatio-temporal rainfall patterns, BMJ had least bias towards dryness whereas KF had moist bias and GD had higher dry bias. BMJ could simulate low, moderate and high rainfall reasonably well with relatively higher correlations and threat scores, lower bias and mean absolute errors in most zones as compared to better simulation of heavy precipitation events with KF and low rainfall days alone with GD scheme. The better performance of BMJ scheme is evident owing to better simulation of surface pressure, temperature, and geopotential, lower and upper atmospheric flow fields. Simulations revealed a relatively less intensive heat, weaker low-level westerly winds, weaker north-south geopotential gradients, weaker subtropical easterlies in the El Niño years than in the La Niña years, which indicate the model is able to simulate the interannual variations in monsoon characteristics. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]