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Forecast of the Indian summer monsoon on an extended range (beyond the conventional one-week lead time) is critical for an agronomic economy like India. Although dynamic models have been quite successful in capturing and representing monsoon circulation, they fail to sustain this skill beyond the standard weather time scale (7–10 days). As such, the present study is directed at developing a computationally feasible, yet reliable method of statistical downscaling that further improves the present skill of global dynamic models for extended range forecasting. We quantitatively demonstrate the feasibility of this post-processing statistical module for improving the predictability of the dynamic Extended Range Prediction System (ERPAS), which is developed by Indian Institute of Tropical Meteorology, Pune, India and now operational in the country. It first develops climate cluster(s) (rainfall states in the present case), then builds a statistical relationship between these clusters and a set of appropriate climate variables using a robust and advanced classification technique known as Extreme Gradient Boosting (XGboost), and eventually delivers the real-valued precipitation at individual grid cells via a non-parametric regression. The module is able to skilfully capture the active and break phases of the Indian summer monsoon, and also subsequently project them for the ensuing regression component of the module. This approach shows to significantly boost the prediction skill over the Core Monsoon Zone of Indian mainland up to a lead time of 4 weeks. Our statistical downscaled model is comparable in the week 1 lead time but outperforms global models for 2nd, 3rd, and 4th weeks lead time. Nonetheless, this performance is retained only in the northern and central region, and not ubiquitous over the rest of India.

Assessing impacts of climate change on hydrology involves global scale climate projections by General Circulation Models (GCMs), downscaling of global scale projections to regional scale by statistical methods or regional climate models and then use of regional outputs in hydrological simulations. Hydrological simulations considers varying inputs starting with soil characteristics, land cover, vegetation types, control structures to social parameters such as human interventions, irrigation and water use. This makes the model highly parametrized and at the same time highly uncertain due to the non-availability of majority of input parameters. Here, we compare the contributions of uncertainty from hydrological parameterization in the hydrological projections of climate change to that generated from the use of multiple climate models. The Ganga River Basin in India was selected as the study region. For regional climate change projections, we use dynamic downscaling outputs from Coordinated Regional Climate Downscaling Experiment (CORDEX) and statistical downscaling outputs from a transfer function forced with 3 GCMs, Institut Pierre Simon Laplace (IPSL), European Consortium Earth System Model (EC-EARTH) and MPI (Max Plank Institut) ESM (Earth System Model). Monte-Carlo Simulations (MCS) are performed with 1000 generated sets of sensitive model parameters for each of the GCM-regional model combination. We find that the observed time series of river discharge is reproduced well but with bias in low-flow conditions. This is probably associated with human intervention and poor representation of baseflow in VIC due to the neglected groundwater storage which feed the surface water during low flow condition. The future projections show that the major uncertainty lies across climate models for all the four seasons (MAM, JJAS, ON and DJF) and for all the hydrological variables, soil moisture, evapotranspiration (ET), water yield and river discharge. The uncertainty resulting from the MCS is quite small as compared to the climate model uncertainty. We are unable to find any added value in hydrological simulations by rigorous hydrological calibration and parameterization in absence of many required data, when the forcing meteorological data has huge uncertainty. Our findings highlight the need of convergence of climate models before the studies on hydrological impacts assessment and subsequent development of adaptation strategies.

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.

The regional water storage shifting causes nonstationary spatial distribution of droughts and flooding, leading to water management challenges, environmental degradation and economic losses. The regional water storage shilling is becoming evident due to the increasing climate variability. However, the previous studies for climate drivers behind the water storage shifting are not rigorously quantified. In this study, the terrestrial water storage (TWS) spatial shilling pattern during 2002-2017 over the China-India border area (CIBA) is developed using the Gravity Recovery and Climate Experiment (GRACE). suggesting that the Indus-Ganges-Brahmaputra basin (IGBB) was wetting while the central Qinghai-Tibet Plateau (QTP) was drying. Similar drying and wetting patterns were also found in the precipitation, snow depth, Palmer Drought Severity Index (PDSI) and potential evaporation data. Based on our newly proposed Indian monsoon (IM) and western North Pacific monsoon (WNPM) variation indices, the water shifting pattern over the CIBA was found to be affected by the weakening of the variation of IM and WNPM through modulating the regional atmospheric circulation. The weakening of IM and WNPM variations has shown to be attributed to the decreasing temperature gradient between the CIBA and the Indian Ocean, and possibly related to increasing regional temperatures associated with the increasing global temperature. As the global warming intensifies, it is expected that the regional TWS shifting pattern over the CIBA will be further exaggerated, stressing the need of advancing water resources management for local communities in the region. (C) 2019 Elsevier B.V. All rights reserved.

Increase in human-induced climate warming is unequivocal and is subsequently causing an increase in the magnitude of precipitation extremes around the globe, inducing substantial damages to the socioeconomic sectors. Thus, a reliable projection of extreme precipitation scenarios is crucial for designing suitable adaptation strategies. At present, this is being attempted with the use of general circulation models (GCMs) projections. However, the GCMs simulate the extreme precipitation events rather poorly, especially over the tropical regions, and also suffer from frequent lack of reliability at local/regional scales. Therefore, it is of paramount significance to identify the relevant physical parameters for the extreme precipitation scenarios, and further implement these parameters in downscaling approaches to significantly improve the impact assessment at a regional scale. Previous studies have reported that the dynamic component (mainly vertical wind velocity) has a significant influence on the precipitation extremes over South Asian regions, along with the thermodynamic component. This indicates that the consideration of vertical wind velocity for projecting the precipitation extremes may significantly increase the efficacy of the downscaling approach, a contemplation that provoked its inclusion in statistical downscaling in this study. The methodology was demonstrated over the Mahanadi river basin (India), which often experiences heavy rainfall during the monsoon and post-monsoon disturbances originating from the Bay of Bengal (BoB). We observed that the dynamic component plays a crucial role in changing the pattern of precipitation extremes over the basin. Further, we noticed that inclusion of this dynamic component in statistical downscaling significantly improved the extreme precipitation projections. Based on these observations, we projected (2026–2055) the mean and extreme precipitation events, considering six most efficient CMIP5 models for the Indian subcontinent under RCP 4.5 and RCP 8.5 scenarios. The outcomes of this study can be utilized in deriving reliable water resource management practices.

Seasonal prediction of Indian Summer Monsoon Rainfall (ISMR, rainfall during June to September over India) has remained an important scientific challenge for decades, due to its complex multi-scale nature. Statistical and dynamical seasonal ISMR predictions have traditionally relied on the relatively small variability of the spatially averaged monsoon rainfall over India, known as All India Monsoon Rainfall (AIMR). While this has served to mitigate socioeconomic impacts to some extent, overall prediction skill has remained relatively low (Wang et al. in Nat Commun 6:7154. https://doi.org/10.1038/ncomms8154 , 2015) while the spatial variability is anything but small. Here we find that the classification of deficit/ dry or surplus/ wet monsoon years based on AIMR does not add value at a regional scale due to the very high heterogeneity of monsoon rainfall, even in the extreme years. To demonstrate the need and the potential to predict this important spatial heterogeneity, we improve the classification of monsoon years by focusing on the spatial patterns of rainfall anomalies within different meteorological subdivisions of India. We apply the k-means clustering methodology and also offer cluster validation. Cluster validation reveals the existence of nine clusters of monsoon years with distinct spatial patterns of monsoon rainfall anomalies. The composite anomalies of sea surface temperature (SST) and winds during March to May (MAM) and June to September (JJAS) show distinct hydroclimatic teleconnections indicating potential predictability of regional monsoon rainfall at seasonal scale. To demonstrate the potential prediction pathways for spatial patterns of ISMR, we develop a statistical seasonal prediction model based on Classification and Regression Tree (CART) between SST over different oceanic regions as predictors and monsoon classes as predictands for the period 1901–2010. Search for the potential regressors reveals the importance of new predictors such as Atlantic Nino and SST over North Pacific region along with conventional predictors such as El Nino Southern Oscillation (ENSO), Indian Ocean Dipole/Zonal Mode (IODZM), etc. Validation of the method is performed for 2011–2015 and the model is able to predict the regional pattern of monsoon rainfall for 4 out of the 5 years. The purpose of this prediction exercise is to demonstrate the need to focus on the process and predictive understanding of these clusters and their predictability.

Recent studies point to combined effects of changes in regional land-use, anthropogenic aerosol forcing and sea surface temperature (SST) gradient on declining trends in the South Asian monsoon (SAM). This study attempted disentangling the effects produced by changes in SST gradient from those by aerosol levels in an atmospheric general circulation model. Two pairs of transient ensemble simulations were made, for a 40-year period from 1971 to 2010, with evolving versus climatological SSTs and with anthropogenic aerosol emissions fixed at 1971 versus 2010, in each case with evolution of the other forcing element, as well as GHGs. Evolving SST was linked to a widespread feedback on increased surface temperature, reduced land–sea thermal contrast and a weakened Hadley circulation, with weakening of cross-equatorial transport of moisture transport towards South Asia. Increases in anthropogenic aerosol levels (1971 versus 2010), led to an intensification of drying in the peninsular Indian region, through several regional pathways. Aerosol forcing induced north–south asymmetries in temperature and sea-level pressure response, and a cyclonic circulation in the Bay of Bengal, leading to an easterly flow, which opposes the monsoon flow, suppressing moisture transport over peninsular India. Further, aerosol induced decreases in convection, vertically integrated moisture flux convergence, evaporation flux and cloud fraction, in the peninsular region, were spatially congruent with reduced convective and stratiform rainfall. Overall, evolution of SST acted through a weakening of cross-equatorial moisture flow, while increases in aerosol levels acted through suppression of Arabian Sea moisture transport, as well as, of convection and vertical moisture transport, to influence the suppression of SAM rainfall.

This study is oriented towards quantification of the skill addition by regional climate models (RCMs) in the parent general circulation models (GCMs) while simulating wind speed and wind potential with particular reference to the Indian offshore region. To arrive at a suitable reference dataset, the performance of wind outputs from three different reanalysis datasets is evaluated. The comparison across the RCMs and their corresponding parent GCMs is done on the basis of annual/seasonal wind statistics, intermodel bias, wind climatology, and classes of wind potential. It was observed that while the RCMs could simulate spatial variability of winds, well for certain subregions, they generally failed to replicate the overall spatial pattern, especially in monsoon and winter. Various causes of biases in RCMs were determined by assessing corresponding maps of wind vectors, surface temperature, and sea-level pressure. The results highlight the necessity to carefully assess the RCM-yielded winds before using them for sensitive applications such as coastal vulnerability and hazard assessment. A supplementary outcome of this study is in form of wind potential atlas, based on spatial distribution of wind classes. This could be beneficial in suitably identifying viable subregions for developing offshore wind farms by intercomparing both the RCM and GCM outcomes. It is encouraging that most of the RCMs and GCMs indicate that around 70% of the Indian offshore locations in monsoon would experience mean wind potential greater than 200 W/m2.

While satellite data provides a strong robust signature of urban feedback on extreme precipitation; urbanization signal is often not so prominent with station level data. To investigate this, we select the case study of Mumbai, India and perform a high resolution (1 km) numerical study with Weather Research and Forecasting (WRF) model for eight extreme rainfall days during 2014–2015. The WRF model is coupled with two different urban schemes, the Single Layer Urban Canopy Model (WRF-SUCM), Multi-Layer Urban Canopy Model (WRF-MUCM). The differences between the WRF-MUCM and WRF-SUCM indicate the importance of the structure and characteristics of urban canopy on modifications in precipitation. The WRF-MUCM simulations resemble the observed distributed rainfall. WRF-MUCM also produces intensified rainfall as compared to the WRF-SUCM and WRF-NoUCM (without UCM). The intensification in rainfall is however prominent at few pockets of urban regions, that is seen in increased spatial variability. We find that the correlation of precipitation across stations within the city falls below statistical significance at a distance greater than 10 km. Urban signature on extreme precipitation will be reflected on station rainfall only when the stations are located inside the urban pockets having intensified precipitation, which needs to be considered in future analysis.

We analyse the bias present in the Indian Summer Monsoon Rainfall (ISMR), as simulated by Climate Forecast System Model 2 (CFSv2), the operational model used for monsoon forecasts in India. In the simulations, the precipitation intensity is redistributed within the ITCZ band with southward shifts of precipitation maxima. We observe weakening of maximum intensity of precipitation over the region between 20°N and 14°N. In the simulations by CFSv2, there exists two rain bands: the northern one located slightly southward compared to reanalysis dataset and the southern one over the equator with intensified precipitation. This results in dry bias over land and wet bias over the ocean. We use a Dynamic Recycling Model, based on Lagrangian approach, to investigate the role of various moisture sources in generating these biases. We find that, the dry bias during June exists due to the delayed monsoon onset and reduced moisture flow from the Arabian Sea. As the monsoon progresses, deficiency in the simulated contributions from South Indian Ocean becomes the key source of bias. The reduced supply of moisture from oceanic sources is primarily attributed to the weaker northward transport of moisture flux from the Southern Ocean, associated with a weaker southward energy flux. Inefficiency of the model in simulating the heating in Tibetan plateau during the pre-monsoon period leads to this reduced cross equatorial energy flow. We also find that, towards the end of monsoon season, moisture contributions from land sources namely, Ganga Basin and North-Eastern forests become significant and underestimations of the same in the simulations by CFSv2 result into biases over Central and Eastern India.

India is a major agrarian country strongly impacted by spatio-temporal variations in the Indian monsoon. The impact assessment is usually accomplished by implementing projections from general circulation models (GCMs). Unfortunately, these projections cannot capture the dynamicity of the monsoon and require either statistical (SD) or dynamical (DD) downscaling of the GCM projections to a finer resolution. Both downscaling techniques can capture the spatio-temporal variation in climatic variables but are marred by uncertainty in the projections resulting from the choice of the GCM and downscaling method, which affects climate change adaptations. Here, we assessed uncertainties in the projections of hydro-climatic variables over India by considering multiple downscaling techniques, multiple GCMs, and their combined effects (referred as the total uncertainty). Multiple hydrological variables were simulated by implementing the variable infiltration capacity model that considered outputs from DD (derived by the coordinated regional climate downscaling experiment, CORDEX) and SD forced with multiple GCM simulations. Our results showed that the SD projections captured the observed spatio-temporal variability of hydro-climatic variables more efficiently than the DD projections. Importantly, contribution from the downscaled projections to the total uncertainty was significantly smaller compared to the inter-GCM uncertainty. We believe uncertainty analysis is an important component of good scientific practice; however, several researchers appear to be rather reluctant to embrace the concept of uncertainty in making projections, predictions, and forecasting. It remains a common practice to show climate change exercises to decision-makers/stakeholders, without uncertainty bounds. Here, a successful attempt was made to identify the key sources of uncertainty and adequately bracket the uncertainty, indicating a requirement of the code of practice to provide formal guidance, particularly for climate-change impact assessments. This consequently emphasized the importance of follow-up research to understand the inter-GCM uncertainty, which has a significant impact on sustainable agriculture and water resources management in India.

A probabilistic framework for evaluating changes in offshore wind potential under a changing climate is presented considering three general circulation models (GCMs), two representative concentration pathways and two statistical downscaling techniques. The analysis was done with respect to three specific locations along the Indian coastline, identified earlier for future offshore energy extraction. The uncertainty among the GCMs was addressed and the choice of an appropriate GCM was made by combining analysis of GCM uncertainty and model reliability. The regional scale wind was derived from the selected GCM using one hard computing method, namely bilinear interpolation and quantile mapping, and one soft method, an artificial neural network. The efficiency of these methods was assessed using certain performance criteria for the historical period and a convergence criterion over the future period. To evaluate wind potential, the GCM output was downscaled from daily to hourly, taking into account the removal of gusts because of long interval averaging. The wind potential for past (1979–2005) and future (2006–2032) time slices was determined using both hard and soft methods as well as two representative concentration pathways. The resulting four outcomes at annual and seasonal scale were compared with the extrapolated trend of past wind potential derived from both GCM and reanalysis data. Finally, the most efficient strategy was evaluated and the actual extractable power was estimated considering a standard power curve. It is concluded that at all the three locations the annual average wind potential will substantially increase, benefitting the offshore industry.

Regional simulations of the seasonal Indian summer monsoon rainfall (ISMR) require an understanding of the model sensitivities to physics and resolution, and its effect on the model uncertainties. It is also important to quantify the added value in the simulated sub-regional precipitation characteristics by a regional climate model (RCM), when compared to coarse resolution rainfall products. This study presents regional model simulations of ISMR at seasonal scale using the Weather Research and Forecasting (WRF) model with the synoptic scale forcing from ERA-interim reanalysis, for three contrasting monsoon seasons, 1994 (excess), 2002 (deficit) and 2010 (normal). Impact of four cumulus schemes, viz., Kain–Fritsch (KF), Betts–Janjic–Miller, Grell 3D and modified Kain–Fritsch (KFm), and two micro physical parameterization schemes, viz., WRF Single Moment Class 5 scheme and Lin et al. scheme (LIN), with eight different possible combinations are analyzed. The impact of spectral nudging on model sensitivity is also studied. In WRF simulations using spectral nudging, improvement in model rainfall appears to be consistent in regions with topographic variability such as Central Northeast and Konkan Western Ghat sub-regions. However the results are also dependent on choice of cumulus scheme used, with KF and KFm providing relatively good performance and the eight member ensemble mean showing better results for these sub-regions. There is no consistent improvement noted in Northeast and Peninsular Indian monsoon regions. Results indicate that the regional simulations using nested domains can provide some improvements on ISMR simulations. Spectral nudging is found to improve upon the model simulations in terms of reducing the intra ensemble spread and hence the uncertainty in the model simulated precipitation. The results provide important insights regarding the need for further improvements in the regional climate simulations of ISMR for various sub-regions and contribute to the understanding of the added value in seasonal simulations by RCMs.

Southeast India experienced a heavy rainfall during 30 Nov–2 Dec 2015. Particularly, the Chennai city, the fourth major metropolitan city in India with a population of 5 million, experienced extreme flooding and causalities. Using various observed/reanalysed datasets, we find that the concurrent southern Bay of Bengal (BoB) sea surface temperatures (SST) were anomalously warm. Our analysis shows that BoB sea surface temperature anomalies (SSTA) are indeed positively, and significantly, correlated with the northeastern Indian monsoonal rainfall during this season. Our sensitivity experiments carried out with the Weather Research and Forecasting (WRF) model at 25 km resolution suggest that, while the strong concurrent El Nino conditions contributed to about 21.5% of the intensity of the extreme Chennai rainfall through its signals in the local SST mentioned above, the warming trend in BoB SST also contributed equally to the extremity of the event. Further, the El Nino southern oscillation (ENSO) impacts on the intensity of the synoptic events in the BoB during the northeast monsoon are manifested largely through the local SST in the BoB as compared through its signature in the atmospheric circulations over the BoB.

Indian summer monsoon rainfall extremes and their changing characteristics under global warming have remained a potential area of research and a topic of scientific debate over the last decade. This partially attributes to multiple definitions of extremes reported in the past studies and poor understanding of the changing processes associated with extremes. The later one results into poor simulation of extremes by coarse resolution General Circulation Models under increased greenhouse gas emission which further deteriorates due to inadequate representation of monsoon processes in the models. Here we use transfer function based statistical downscaling model with non-parametric kernel regression for the projection of extremes and find such conventional regional modeling fails to simulate rainfall extremes over India. In this conjuncture, we modify the downscaling algorithm by applying a robust regression to the gridded extreme rainfall events. We observe, inclusion of robust regression to the downscaling algorithm improves the historical simulation of rainfall extremes at a 0.25° spatial resolution, as evaluated based on classical extreme value theory methods, viz., block maxima and peak over threshold. The future projections of extremes during 2081–2100, obtained with the developed algorithm show no change to slight increase in the spatial mean of extremes with dominance of spatial heterogeneity. These changing characteristics in future are consistent with the observed recent changes in extremes over India. The proposed methodology will be useful for assessing the impacts of climate change on extremes; specifically while spatially mapping the risk to rainfall extremes over India.

Increases in global temperature over recent decades and the projected acceleration in warming trends over the 21 century have resulted in a strong need to obtain information about future temperature conditions. Hence, skillful decadal temperature predictions (DTPs) can have profound societal and economic benefits through informed planning and response. However, skillful and actionable DTPs are extremely challenging to achieve. Even though general circulation models (GCMs) provide decadal predictions of different climate variables, the direct use of GCM data for regional-scale impacts assessment is not encouraged because of the limited skill they possibly exhibit and their coarse spatial resolution. Here, we focus on 14 GCMs and evaluate seasonally and regionally averaged skills in DTPs over the continental United States. Moreover, we address the limitations in skill and spatial resolution in the GCM outputs using two data-driven approaches: (1) quantile-based bias correction and (2) linear regression-based statistical downscaling. For both the approaches, statistical parameters/relationships, established over the calibration period (1961-1990) are applied to retrospective and near future decadal predictions by GCMs to obtain DTPs at '4 km' resolution. Predictions are assessed using different evaluation metrics, long-term statistical properties, and uncertainty in terms of the range of predictions. Both the approaches adopted here show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DTPs from 14 GCMs with a spatial resolution of 4 km, which can be used as a key input for impacts assessments.

With an installed capacity of over 19,000 MW, the wind power currently accounts for almost 70% of the total installed capacity among the renewable energy sector in India. The extraction of wind power mainly depends on prevailing meteorology which is strongly influenced by monsoon variability. The monsoon season is characterized by significant fluctuations in between periods of wet and dry spells. During the dry spells, the demand for power from agriculture and cooling equipment increases, whereas during the wet periods, such demand reduces, although, at the same time, the power supply increases because of strong westerly winds contributing to an enhanced production of wind energy. At this backdrop, we aim to assess the impact of intra-seasonal wind variability on the balance of energy supply and demand during monsoon seasons in India. Further, we explore the probable cause of wind variability by relating it to El Nino events. It is observed that the active and break phases in wind significantly impact the overall wind potential output. Although the dry spells are generally found to reduce the overall wind potential, their impact on the potential seems to have declined after the year 2000. The impact of meteorological changes on variations in wind power studied in this work should find applications typically in taking investment decisions on conventional generation facilities, like thermal, which are currently used to maintain the balance of power supply and demand.

The present study investigated the relationship between the El Nino Southern Oscillation (ENSO) and heat wave characteristics, such as duration, annual number of heat wave days and maximum temperature of heat wave events, in India. El Nino is associated with an eastward shift in the Walker circulation and breakdown of circulation patterns, resulting in delay in the onset of the Indian Summer Monsoon (ISM). Because most heat waves in India occur during the pre-monsoon season, the present study showed that heat waves during El Nino years were longer and hotter, and it is argued that this is related to a delay in the onset of the ISM. Further, it was found that the shift in the circulation pattern due to El Nino resulted in (1) the weakening of southwesterlies in the Arabian Sea, and (2) the occurrence of a large number of clear sky days over India. These explain the occurrence of warmer and longer duration heat waves during El Nino years. These results imply that, if El Nino activity increases in the future, heat waves in India are likely to intensify.

The South Asian summer monsoon is a complex coupled human‐natural system that poses unique challenges for understanding its evolution alongside increasing anthropogenic activities. Rapid and substantial changes in land‐use, land‐management and industrial activities over the subcontinent, and warming in the Indian Ocean, have influenced the South Asian summer monsoon. These might continue to be significant drivers in the near‐term along with rising global greenhouse gas emissions. Deciphering the region's vulnerability to climate change requires an understanding of how these anthropogenic activities, acting on a range of spatial scales, have shaped the monsoon spatially and temporally. This review summarizes historical changes in monsoon rainfall characteristics, associated mechanisms, and the role of anthropogenic forcings, focusing on subseasonal variability and extreme events. Several studies have found intensified subseasonal extremes across parts of India and an increase in spatial variability of rainfall despite an overall weakening of seasonal rainfall in the monsoon core. However, understanding these changes remains challenging because of uncertainties in observations and climate models. The mechanisms and relative influences of various anthropogenic activities, particularly on subseasonal extremes, remain relatively underexplored. Large biases in the representation of relevant processes in global climate models limit the ability to attribute historical changes and make reliable projections. Nevertheless, recent advances in modeling these processes using higher‐resolution modeling frameworks provide new tools to investigate the Indian summer monsoon's response to various anthropogenic forcings. There is an urgent need to understand how these forcings interact to shape climate variability and change in this vulnerable region. This article is categorized under: Paleoclimates and Current Trends > Earth System Behavior

The Indian Summer Monsoon Rainfall (ISMR) is one of the most complex, yet stable climatic phenomena and it has enormous importance to the rhythms of life in the Indian subcontinent. The summer monsoon in Indian subcontinent exhibits a wide range of temporal variability that is often viewed as a manifestation of disturbances originated as a result of coupled ocean–land–atmosphere interactions. Although the large-scale processes through the Sea Surface Temperature (SST) variations predominantly control the atmospheric circulations leading to the summer monsoon rainfall, the regional land surface feedbacks can also influence the rainfall through surface variables such as snow, soil moisture, and evapotranspiration. Evapotranspiration over a region supplies moisture to the atmosphere, which may lead to precipitation in the same region or different regions. Therefore, any disturbance in the large-scale and regional-scale processes (that are associated with the ISMR) is reflected in terms of anomalous quantity of atmospheric moisture transport from the different evaporative sources to the Indian subcontinent. The chapter primarily discusses the role of the land surface feedback (through evapotranspiration) and the atmospheric moisture transport from different subcontinental terrestrial sources in the seasonal and sub-seasonal variability of ISMR.

A World Urban Data Analysis and Portal Tool (WUDAPT) based Local Climate Zone (LCZ) classification and mapping is done for Mumbai, India. Prior WUDAPT-based urban studies have primarily focused on urban heat island (UHI) assessment, and this is the first study to assess the impact on rainfall simulations. Accordingly, in this study, the impact of incorporating the LCZ map in the Weather Research and Forecasting (WRF) model for simulating very heavy rainfall events over Mumbai is assessed. The classified LCZ map is incorporated in the WRF model, and the model performance is compared against the Control, for four recent very heavy rain events. Comparing the results from paired simulations reveals that the rainfall amount and spatial variability is significantly different when the LCZ framework is used. Implementation of WUDAPT data, introduces heterogeneity in the morphological characteristics of the city, which contributes to microscale feedbacks that appear to organize and impact the mesoscale convergence/divergence and convection fields in the WRF model. The study results show variability within the cases highlighting the need for additional investigations in different cities. Overall, the incorporation of WUDAPT LCZs in WRF results in notably improving the model performance for heavy rainfall simulation in urban areas.

This study assesses the improvements in climate model (CM) simulations for indicative characteristics representing complex dynamics of Indian summer monsoon rainfall for period 1951–2005 in CMIP6 (Coupled Model Intercomparison Project Phase 6). To our knowledge, this is the first preliminary study which compares the performance of models available in CMIP5 and CMIP6 consortium and their multi-model average (MMA). We find a significant improvement in CMIP6 models in capturing the spatiotemporal pattern of monsoon over Indian landmass, especially in the Western Ghats and North-East foothills of Himalayas. We also show that present-day global warming of 0.6 °C over Indian landmass is remarkably consistent with the changes in annual maximum 1-day precipitation in MMA of CMIP6, which are in agreement with the observations. Our results suggest that added value in CMIP6 models precipitation simulations is not consistent within CMs used in present study. However, it still provides a precedent to the scientific community in performing future studies on climate change impact assessment.

Monsoon climate over India has high degree of spatio-temporal heterogeneity characterized by the existence of multi-climatic zones along with strong intra-seasonal, seasonal, and inter-annual variability. Vegetation growth of Indian forests relates to this climate variability, though the dependence structure over space and time is yet to be explored. Here, we present a comprehensive analysis of this association with quality-controlled satellite-based remote sensing dataset of vegetation greenness and radiation along with station based gridded precipitation datasets. A spatio-temporal time-frequency analysis using wavelets is performed to understand the relative association of vegetation growth with precipitation and radiation at different time scales. The inter-annual variation of forest greenness over the Tropical India are observed to be correlated with the seasonal monsoon precipitation. However, at inter and intra-seasonal scales, vegetation has a strong association with radiation in regions of high precipitation like the Western Ghats, Eastern Himalayas, and Northeast hills. Forests in Western Himalayas were found to be correlated more on the winter precipitation from western disturbances than the south west monsoon precipitation. Our results provide new and useful region-specific information for dynamic vegetation modelling in the Indian monsoon region that may further be used in understanding global vegetation-land-atmosphere interactions.

Indian summer monsoon rainfall (ISMR) being mainly controlled by the large-scale oceanic processes is also influenced by the land–atmosphere interactions. At the regional scale, land–atmosphere interactions govern the fate of precipitation by modulating regional hydro-climate variables such as evapotranspiration, snow cover, soil moisture, and surface temperature. The contribution of local evapotranspiration to the precipitation in the same region is known as precipitation recycling. The quantitative estimates of recycling in terms of regional ratio highlight the importance of land-surface feedback in the regional precipitation. In the present chapter, we discuss the role of land-surface feedback (through evapotranspiration) over the India subcontinent during summer monsoon rainfall. We also discuss how a significant change in land-surface condition due to changes in land use–land cover (LULC) could affect the summer monsoon rainfall in India with the help of a sensitivity experiment.

A complete understanding of urban environment becomes increasingly important in the present world where more than half of the human population resides in the urban areas. A warmer surface temperature exists in the urban area due to change in land cover and population density, indicated as urban heat island (UHI). Traditionally, UHI is linked with air temperature differences between urban and rural areas using pairs of in situ climatology data. The term surface UHI (SUHI) is used to explicitly distinguish UHIs measured using land surface temperature (LST). SUHII is a widely recognized surrogate index indirectly addressing the heat island effect. The urban climate largely differs from the non-urban or rural climate affecting not only the temperature but cloudiness, precipitation, water and air quality as well. The long-term monitoring of UHI is essential to better understand the urban climatology. Understanding the UHI behaviour has a concern primarily for the human health and energy consumption. The factors driving UHI is an important aspect of climate research. Several attempts have been made employing a range of methods to investigate the energy balance of global urban centres. India ranking at second largest urban population of the world is projected to have the highest urban growth rate in the next 30 years. The UHI studies in India have been undertaken mostly within individual cities using varied methodology. This chapter provides a concise compilation different studies characterizing UHI for urban centres of India. The chapter briefly reports results from the first assessment of seasonal and diurnal characteristics of SUHI overall large urban centres of India carried out by Shastri et al. in Sci Rep 7:40178 (2017).

Atmospheric and Ocean reanalysis products are handy tools for studying the past climate. However, the choice of these products could influence the outcome of the study. In this study, we compared two popular Ocean reanalysis products (ORAS4 and GECCO2) in the context of the Indian Ocean (IO) heat content variability. The region of interest is the southern tropical IO (STIO) as the region features a seasonal upwelling zone and the Indonesian Throughflow (ITF) is nearly aligned with it. The comparison mainly includes the trends in sea surface height anomaly, IO response to Interdecadal Pacific Oscillation (IPO), and the recent changing behavior of Indo-Pacific Oceans. Among the drivers of southern tropical Indian Ocean subsurface heat content (STIOSHC), the local atmospheric wind forcing seems to be contributing more to its variability in ORAS4 where as ITF seems to be contributing more in GECCO2. To get a deeper understanding of the local and remote influences, we distinguish the interannual variability in the STIOSHC due to local atmospheric contribution and ITF contribution by setting up an Indian Ocean model using ROMS and carrying out some experiments. We show that the interannual variability in STIOSHC contributed by local winds is higher in ORAS4 when compared to GECCO2 and it is mainly reflected in the western IO. In the eastern IO, in ORAS4, the local wind and ITF contributions to the STIOSHC variability are found to be comparable whereas in GECCO2, the ITF contribution is greater than that of ORAS4. In GECCO2, the local wind and ITF contributions are found to be more or less same whereas in ORSA4, the local wind contribution is noticeably higher than the ITF contribution. Also, the local wind and ITF contribution to STIOSHC are found to be out of phase with dominant time periods of 8 to 15 years. The high STIOSHC during first (second) half of the warming hiatus period is found to be contributed by ITF (local surface forcing). The sea surface height anomalies and sea surface temperature anomalies (SSTA) in ORAS4 at few key locations in the Indian Ocean are found to be more in line with observations than those of GECCO2. We posit that ORAS4 is more reliable than GECCO2 over the IO.

The Indian Monsoon Region (IMR) has undergone notable land cover and land use change (LCLUC) in response to a dramatic increase in population and economic growth. While the changes in characteristics of LCLUC have been documented in various studies, the realistic representation of these changes within a dynamic hydroclimatic framework remains a challenge to the scientific community. This chapter highlights that representing LCLUC changes and feedbacks should be an integral component of short, medium, and long-range predictions over the IMR. Realistic representation of the land conditions can enhance model predictions at different scales. Yet, the LCLUC information is not well represented in atmospheric models, principally due to lack of details in land surface datasets and also due to the limitations of the model parameterizations. The need for developing gridded, high-resolution datasets and models that can ascribe to the changes and build off the sensitivity to the land changes emerge as a focus area. This chapter presents the nature of land cover feedbacks and their influence on the prediction of hydroclimatological extremes over the IMR. It is not intended as a comprehensive review, but more as a synthesis of contemporary studies that have addressed the issue of land surface responses and their feedback as part of land–atmospheric interactions over the region. Current understanding from studies on the impact of LCLUC over different scales of environmental processes and associated principles is also summarized with a particular focus on improving the hydroclimatic predictions over the IMR by considering coupled land–atmosphere interactions.

We show that 21st century increase in radiative forcing does not significantly impact the frequency of South Asian summer monsoon depressions (MDs) or their trajectories in the Coupled Model Intercomparison Project Phase 5 general circulation models (GCMs). A significant relationship exists between the climatological occurrences of MDs and the strength of the background upper (lower) tropospheric meridional (zonal) winds and tropospheric moisture in the core genesis region of MDs. Likewise, there is a strong relationship between the strength of the meridional tropospheric temperature gradient in the GCMs and the trajectories of MDs over land. While monsoon dynamics progressively weakens in the future, atmospheric moisture exhibits a strong increase, limiting the impact of changes in dynamics on the frequency of MDs. Moreover, the weakening of meridional tropospheric temperature gradient in the future is substantially weaker than its inherent underestimation in the GCMs. Our results also indicate that future increases in the extreme wet events are dominated by nondepression day occurrences, which may render the monsoon extremes less predictable in the future. Plain Language Summary Monsoon depressions (MDs) are one of the most important synoptic scale transient weather systems that transport large amount of moisture over the South Asian landmass and contribute significantly to the total precipitation. In this study, we investigate representation of MDs in the Coupled Model Intercomparison Project Phase 5 general circulation models, their simulated responses to increase in radiative forcing during the 21st century under Representative Concentration Pathway 8.5, and the resulting changes in precipitation characteristics at intraseasonal time scales. We show that 21st century increase in radiative forcing does not significantly impact the frequency of South Asian summer monsoon depressions or their trajectories in the general circulation models. Our results indicate a significant relationship between the climatological occurrences of MDs and the background upper (lower) tropospheric meridional (zonal) winds and tropospheric moisture in the core genesis region of MDs. While monsoon dynamics progressively weakens in the future, atmospheric moisture exhibits a strong increase, limiting the impact of changes in dynamics on the frequency of MDs. Our results also indicate that future increase in the extreme wet events is dominated by nondepression day extremes, which may render the monsoon extremes less predictable in the future.

Impacts of climate change are typically assessed with fairly coarse resolution General Circulation Models (GCMs), which are unable to resolve local scale features that are critical to precipitation variability. GCM simulations must be downscaled to finer resolutions, through statistical or dynamic modelling for further use in hydrologic analysis. In this study, we use a linear regression based statistical downscaling method for obtaining monthly Indian Summer Monsoon Rainfall (ISMR) projections at multiple spatial resolutions, viz., 0.05°, 0.25° and 0.50°, and compare them. We use 19 GCMs of Coupled Model Intercomparison Project Phase 5 (CMIP5) suite and combine them with multi model averaging and Bayesian model averaging. We find spatially non-uniform changes in projections at all resolutions for both combinations of projections. Our results show that the changes in the mean for future time periods (2020s, 2050s, and 2080s) at different resolutions, viz., 0.05°, 0.25° and 0.5°, obtained with both Multi-Model Average (MMA) and Bayesian Multi-Model Average (BMA) are comparable. We also find that the model uncertainty decreases with projection times into the future for all resolutions. We compute Signal to Noise Ratio (SNR), which represents the climate change signal in simulations with respect to the noise arising from multi-model uncertainty. This appears to be almost similar at different resolutions. The present study highlight that, a mere increase in resolution by a way of computationally more expensive statistical downscaling does not necessarily contribute towards improving the signal strength. Denser data networks and finer resolution GCMs may be essential for producing usable rainfall and hydrologic information at finer resolutions in the context of statistical downscaling.

Impacts of climate change on Indian Summer Monsoon Rainfall (ISMR) and the growing population pose a major threat to water and food security in India. Adapting to such changes needs reliable projections of ISMR by general circulation models. Here we find that, majority of new generation climate models from Coupled Model Intercomparison Project phase5 (CMIP5) fail to simulate the post-1950 decreasing trend of ISMR. The weakening of monsoon is associated with the warming of Southern Indian Ocean and strengthening of cyclonic formation in the tropical western Pacific Ocean. We also find that these large-scale changes are not captured by CMIP5 models, with few exceptions, which is the reason of this failure. Proper representation of these highlighted geophysical processes in next generation models may improve the reliability of ISMR projections. Our results also alert the water resource planners to evaluate the CMIP5 models before using them for adaptation strategies.

The difference in land surface temperature (LST) between an urban region and its nearby non–urban region, known as surface urban heat island intensity (SUHII), is usually positive as reported in earlier studies. India has experienced unprecedented urbanization over recent decades with an urban population of 380 million. Here, we present the first study of the diurnal and seasonal characteristics of SUHII in India. We found negative SUHII over a majority of urban areas during daytime in pre-monsoon summer (MAM), contrary to the expected impacts of urbanization. This unexpected pattern is associated with low vegetation in non-urban regions during dry pre-monsoon summers, leading to reduced evapotranspiration (ET). During pre-monsoon summer nights, a positive SUHII occurs when urban impacts are prominent. Winter daytime SUHII becomes positive in Indo-Gangetic plain. We attribute such diurnal and seasonal behaviour of SUHII to the same of the differences in ET between urban and non-urban regions. Higher LST in non-urban regions during pre-monsoon summer days results in intensified heatwaves compared to heatwaves in cities, in contrast to presumptions made in the literature. These observations highlight the need for re-evaluation of SUHII in India for climate adaptation, heat stress mitigation, and analysis of urban micro-climates.

Socioeconomic challenges continue to mount for half a billion residents of central India because of a decline in the total rainfall and a concurrent rise in the magnitude and frequency of extreme rainfall events. Alongside a weakening monsoon circulation, the locally available moisture and the frequency of moisture-laden depressions from the Bay of Bengal have also declined. Here we show that despite these negative trends, there is a threefold increase in widespread extreme rain events over central India during 1950–2015. The rise in these events is due to an increasing variability of the low-level monsoon westerlies over the Arabian Sea, driving surges of moisture supply, leading to extreme rainfall episodes across the entire central subcontinent. The homogeneity of these severe weather events and their association with the ocean temperatures underscores the potential predictability of these events by two-to-three weeks, which offers hope in mitigating their catastrophic impact on life, agriculture and property., Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India. Here the authors identify a threefold increase in widespread extreme rains over the region during 1950–2015, driven by an increasing variability of the low-level westerlies over the Arabian Sea.

Dynamic Regional Climate Models (RCMs) work at fine resolution for a limited region and hence they are presumed to simulate regional climate better than General Circulation Models (GCMs). Simulations by RCMs are used for impacts assessment, often without any evaluation. There is a growing debate on the added value made by the regional models to the projections of GCMs specifically for the regions like, United States and Europe. Evaluation of RCMs for Indian Summer Monsoon Rainfall (ISMR) has been overlooked in literature, though there are few disjoint studies on Indian monsoon extremes and biases. Here we present a comprehensive study on the evaluations of RCMs for the ISMR with all its important characteristics such as northward and eastward propagation, onset, seasonal rainfall patterns, intra-seasonal oscillations, spatial variability and patterns of extremes. We evaluate nine regional simulations from Coordinated Regional Climate Downscaling Experiment and compare them with their host Coupled Model Intercomparison Project-5 GCM projections. We do not find any consistent improvement in the RCM simulations with respect to their host GCMs for any of the characteristics of Indian monsoon except the spatial variation. We also find that the simulations of the ISMR characteristics by a good number of RCMs, are worse than those of their host GCMs. No consistent added value is observed in the RCM simulations of changes in ISMR characteristics over recent periods, compared to past; though there are few exceptions. These results highlight the need for proper evaluation before utilizing regional models for impacts assessment and subsequent policy making for sustainable climate change adaptation.

Three key issues of moisture supply and Indian summer monsoon rainfall (ISMR) variability are discussed in the present work: identification of the oceanic and terrestrial sources of moisture; the extent to which each source affects the ISMR; and their individual contributions to the interannual variability of ISMR. The modified Dynamic Recycling Model, based on a Lagrangian trajectory approach, is used to estimate the relative contributions from 27 terrestrial and oceanic moisture source regions to the monsoon during 1979–2013. ERA-Interim data are used for the study. The results show that the ISMR is strongly influenced by the land–ocean–atmosphere interactions, and a significant fraction of atmospheric moisture to the ISMR comes from five main moisture sources: the western Indian Ocean (WIO), central Indian Ocean (CIO), upper Indian Ocean (UIO), Ganges basin (GB), and Red Sea and the neighboring gulf (RDG). The moisture flux from WIO is very high during the initial period of monsoon seasons. From the mid-monsoon season, the contribution from this moisture source decays and land sources through evapotranspiration (ET) become more active. Early decay of moisture contributions from the WIO and the GB is observed during weak monsoon years. El Niño years are associated with low contributions of moisture from all sources, whereas warm Indian Ocean years are associated with low moisture flux from the major sources except WIO. ISMR is characterized by the prolonged and increasing moisture supply from WIO during the first half of the monsoon along with contributions from GB during the end of season. The results are consistent across several reanalyses (CFSR, ERA-Interim, and MERRA).

Summer Monsoon Rainfall over the Indian subcontinent displays a prominent variability at intraseasonal timescales with 10–60 day periods of high and low rainfall, known as active and break periods, respectively. Here, we study moisture transport from the oceanic and terrestrial sources to the Indian landmass at intraseasonal timescales using a dynamic recycling model, based on a Lagrangian trajectory approach applied to the ECMWF–ERA–interim reanalysis data. Intraseasonal variation of monsoon rainfall is associated with both a north-south pattern from the Indian landmass to the Indian Ocean and an east-west pattern from the Core Monsoon Zone (CMZ) to eastern India. We find that the oceanic sources of moisture, namely western and central Indian Oceans (WIO and CIO) contribute to the former, while the major terrestrial source, Ganga basin (GB) contributes to the latter. The formation of the monsoon trough over Indo-Gangetic plain during the active periods results in a high moisture transport from the Bay of Bengal and GB into the CMZ in addition to the existing southwesterly jet from WIO and CIO. Our results indicate the need for the correct representation of both oceanic and terrestrial sources of moisture in models for simulating the intraseasonal variability of the monsoon.

Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.

Global and local environmental changes are likely to introduce nonstationarity in the characteristics of Indian Summer Monsoon Rainfall (ISMR) extremes. Here, we perform a nonstationary frequency analysis on ISMR extremes in a Generalized Additive Model for Location, Scale and Shape (GAMLSS) framework with a cluster of 74 models, considering nonstationarity in different possible combinations. Interestingly, we observe significant nonstationarity in ISMR extremes in urbanizing/developing-urban areas (transitioning from rural to urban), compare to completely urbanized or rural areas. This presents a postulation that the extent of urbanization plays a significant role in introducing nonstationarity in ISMR extremes. We emphasize the effect of urbanization in changing the character of ISMR extremes, which further needs a scientific re-evaluation by implementing physics-based modeling. The impact of these observational studies will be critical in correcting the bias of model projections of ISMR.

Literature found that the increasing concentration of Anthropogenic Aerosol (AA) is the key reason behind the weakening trend of Indian Summer Monsoon Rainfall (ISMR), based on the Coupled Model Intercomparison Project Phase5 (CMIP5) simulations with AA-only forcing. Here, we re-examine and find that AA-only simulations show country-wide drying, in contrast to the observed east-west asymmetry in the recent ISMR trend. For further evaluation, we decompose the changes in moisture convergence during summer monsoon into dynamic and thermodynamic components. We find that multi-run ensemble averages for individual CMIP5 models do not capture the observed dominance of the changes in dynamic component over the thermodynamic one. An optimal fingerprinting technique for detection and attribution also fail to attribute the changes in ISMR to AA, either because of large internal variability and/or intermodel spread. This implies the need for more careful assessment of AA-only simulations for the ISMR before attributing the changes to AA.

To understand the improvements in the simulations of Indian summer monsoon rainfall (ISMR) by Coupled Model Intercomparison Project 5 (CMIP5) over CMIP3, a comparative study is performed with the original and statistically downscaled outputs of five General Circulation Models (GCMs). We observe that multi-model average of original CMIP5 simulations do not show visible improvements in bias, over CMIP3. We also observe that CMIP5 original simulations have more multi-model uncertainty than those of CMIP3. The statistically downscaled simulations show similar results in terms of bias; however, the uncertainty in CMIP5 downscaled rainfall projections is lower than that of CMIP3.

[1] Spatially aggregated extreme rainfall over India shows an increasing trend over the last 50 years of the 20th century, while fine-resolution analysis reveals a spatially nonuniform trend. Analysis of 104 years (1901–2004) extreme precipitation over India at 1° resolution, using Extreme Value Theory, reveals that post-1950 extreme rainfall characteristics (intensity, duration, and frequency) and their trends are significantly different from those of before 1950, with spatial nonuniformity. Majority of the locations in India have post-1950 trends of extremes with a different sign as compared to pre-1950. Further investigations with statistical change point analysis reveal that fraction of grid points having post-1975 changes is maximum. Majority of the urbanized areas have post-1975 change points, while the same is not true for nonurban areas. As urbanization has intensified during 1971–1981 in India, these results indicate the possible impacts of urbanization on extreme rainfall trends and patterns.

[1] Climate change impacts assessment involves downscaling of coarse-resolution climate variables simulated by general circulation models (GCMs) using dynamic (physics-based) or statistical (data-driven) approaches. Here we use a statistical downscaling technique for projections of all-India monsoon rainfall at a resolution of 0.5° in latitude/longitude. The present statistical downscaling model utilizes classification and regression tree, and kernel regression and develops a statistical relationship between large-scale climate variables from reanalysis data and fine-resolution observed rainfall, and then applies the relationship to coarse-resolution GCM outputs. A GCM developed by the Canadian Centre for Climate Modeling and Analysis is employed for this study with its five ensemble runs for capturing intramodel uncertainty. The model appears to effectively capture individual station means, the spatial patterns of the standard deviations, and the cross correlation between station rainfalls. Computationally expensive dynamic downscaling models have been applied for India. However, our study is the first to attempt statistical downscaling for the entire country at a resolution of 0.5°. The downscaling model seems to capture the orographic effect on rainfall in mountainous areas of the Western Ghats and northeast India. The model also reveals spatially nonuniform changes in rainfall, with a possible increase for the western coastline and northeastern India (rainfall surplus areas) and a decrease in northern India, western India (rainfall deficit areas), and on the southeastern coastline, highlighting the need for a detailed hydrologic study that includes future projections regarding water availability which may be useful for water resource policy decisions.

Prediction of monthly mean sea surface temperature (SST) values has many applications ranging from climate predictions to planning of coastal activities. Past studies have shown usefulness of neural networks (NNs) for this purpose and also pointed to a need to do more experimentation to improve accuracy and reliability of the results. The present work is directed along these lines. It shows usefulness of the nonlinear autoregressive type of neural network vis-à-vis the traditional feed forward back propagation type. Neural networks were developed to predict monthly SST values based on 61-year data at six different locations around India over 1 to 12 months in advance. The nonlinear autoregressive (NAR) neural network was found to yield satisfactory predictions over all time horizons and at all selected locations. The results of the present study were more attractive in terms of prediction accuracy than those of an earlier work in the same region. The annual neural networks generally performed better than the seasonal ones, probably due to their relatively high fitting flexibility.

[1] Hydrologic impacts of global climate change are usually assessed by downscaling large-scale climate variables, simulated by general circulation models (GCMs), to local-scale hydrometeorological variables. Conventional multisite statistical downscaling techniques often fail to capture spatial dependence of rainfall amounts as well as hydrometeorological extremes. To overcome these limitations, a downscaling algorithm is proposed, which first simulates the rainfall state of an entire study area/river basin, from large-scale climate variables, with classification and regression trees, and then projects multisite rainfall amounts using a nonparametric kernel regression estimator, conditioned on the estimated rainfall state. The concept of a common rainfall state for the entire study area, using it as an input for projections of rainfall amount, is found to be advantageous in capturing the cross correlation between rainfalls at different downscaling locations. Temporal variability and extremities of rainfall are captured in downscaling with multivariate kernel regression. The proposed model is applied for downscaling daily monsoon precipitation at eight locations in the Mahanadi River basin of eastern India. The model performance is compared, with a recently developed conditional random field based as well as with established multisite downscaling models, and is found to be superior. Analysis of future rainfall scenarios, projected with the developed downscaling model, reveals considerable changes in rainfall intensity and dry and wet spell lengths, among other things, at different locations. An increasing trend of rainfall is projected for the lower (southern) Mahanadi River basin, and a decreasing trend is observed in the upper (northern) Mahanadi River basin.