Search

India has good reasons to be concerned about climate change as it could adversely affect the achievement of vital national development goals related to socio-economic development, human welfare, health, energy availability and use, and infrastructure. The paper attempts to develop a framework for integrated impact assessment and adaptation responses, using a recently built railroad coastal infrastructure asset in India as an example. The framework links climate change variables — temperature, rainfall, sea level rise, extreme events, and other secondary variables — and sustainable development variables — technology, institutions, economic, and other policies. The study indicates that sustainable development variables generally reduce the adverse impacts on the system due to climate change alone, except when they are inadequately applied. The paper concludes that development is a vital variable for integrated impact assessment. Well crafted developmental policies could result in a less-GHG intensive future, enhanced adaptive capacities of communities and systems, and lower impacts due to climate change. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, precipitation outputs from retrospective seasonal forecasts made by nine General Circulation Models ( GCMs) are used to investigate historical Indian summer monsoon seasonal rainfall variability and predictability over India. The observed data is taken from the India Meteorological Department whereas GCMs are obtained from the International Research Institute for Climate and Society, Columbia University, the National Center for Environmental Prediction, and the Japan Agency for Marine Earth Science and Technology. The study focusses on June- September precipitation hindcasts initialized from the 1 May. First, the mean climatology, variance of interannual variability ( IAV), and long-term trends for the nine GCMs were evaluated. Then Empirical Orthogonal Function ( EOF) is used to extract major climate modes and spectral analyses method is used to investigate the temporal properties of the leading principal components. It is found that the models are able to reproduce the climatology and IAV to varying degrees. The EOF and spectral analyses of models hindcast reveal that these models are capable of predicting the observed precipitation variability to some extent. In order to study the remote response, the correlation co-efficient between model predicted rainfall and sea surface temperature ( SST) have been calculated. The results suggest that the models show exaggerated remote response to ENSO SST forcing and the Indian Ocean Dipole Mode index has less predictive skill compared to ENSO. The correlation values between the model predicted Monsoon Hadley Index ( MHI) and observed MHI reveals that only a few of them could exhibit large scale circulation features well. [ABSTRACT FROM AUTHOR]

Wind climate along the southwest coast of India (Kerala coast) has been analysed using 41 years of Climate Forecast System Reanalysis (CFSR) winds to delineate long‐term trends and variability. The study reveals significant decreasing trends in annual mean wind speeds, of the order of −2.0 to −2.5 cm·s−1·year−1, for the period 1979–2019. Southwest monsoon has contributed the highest weakening trends (−3.0 to −4.0 cm·s−1·year−1) due to significant decrease in the southwesterly/westerly wind speeds. The wind climate and trends during the northeast monsoon and premonsoon seasons have been characterized largely by the prevalence of sea breeze and shamal–makran wind systems. We find that the intensity of both sea breeze and shamal–makran wind systems reduces from north to south along the Kerala coast, which is in consistent with the earlier studies. Decadal oscillations in wind speeds, driven by the decadal variability in large‐scale atmosphere–ocean circulations, are evident. Recent changes in the frequency of occurrence of Indian Ocean Dipole in the Indian Ocean have determined the overturning trends since 2010, and resulted in increasing trends in the current decade along the central and northern coasts of Kerala. Furthermore, interannual variability in wind speeds has been linked to the El Niño–Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD). [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]

The Indian coastal regions encountered frequently by extreme weather events such as heavy precipitation events (HPE). In the present study, an assessment has been made to understand the climatological relationship between atmospheric rivers (ARs) and HPEs for Indian coastal regions. For this purpose, India Meteorological Department gridded rainfall dataset, and four different reanalyses datasets for the occurrence of ARs were analysed for different seasons during 1980–2015. The influence of ARs towards the cause of HPEs was stronger over the West coast (WC) compared to the East coast of India (ECI). Over the WC the association between HPE with ARs is ~40% (for rainfall events >124.4·mm·day−1) than East coast (EC) by ~28%. The 64.4–124.4·mm·day−1 events are higher over the WC by ~18% compared to ~5% over the EC. While for the events (<64.4·mm·day−1), the percentage of occurrence is moreover the WC (~7%) compared to the EC (~2%). The study reveals that ARs is in good correlation with the occurrence of HPE. Using Poisson regression model, investigated the relationship between the AR totals in the summer and Northeast monsoon and the large‐scale climate indices. A sufficiently significant negative (positive) dependency observed between ARs and the Southern Oscillation Index (SOI) (Indian Ocean Dipole [IOD] and Niño 3.4 indices) with a larger rate of AR occurrence associated with lower SOI values and larger frequency of AR occurrence associated with larger IOD and Niño 3.4 values for both the coasts of India. [ABSTRACT FROM AUTHOR]

By reflecting some incoming solar radiation, stratospheric aerosol intervention using SO2 would reduce global mean temperature. Previous research has shown that multiple injection latitudes can be used to maintain not only global mean temperature, but also interhemispheric and equator‐to‐pole temperature gradients. However, the regional climate response depends not only on where the SO2 is injected, but also on when. We show here that even with these same objectives and same choices of latitudes, injecting in only one season instead of continuously throughout the year results in significant differences in regional climate, for instance in the magnitude of precipitation changes over India. The differential outcomes highlight the potential for underlying trade‐offs, with different choices regarding deployment leading to a different distribution of benefits or harms. This aspect of climate engineering should be considered in developing governance and emphasizes the need for a deeper understanding of the mechanisms underlying the regional responses. Plain Language Summary: Injecting sulfate in the stratosphere has been suggested as a quick, temporary solution to the warming produced by the increase in greenhouse gases. This method would however come with some drawback that could become important at a regional scale. We show here that some of what are considered drawbacks of sulfate injection interventions (a reduction in precipitation over India or the Amazon Basin, or an imperfect recovery of sea ice at high northern latitudes) are dependent on the strategy used (injecting all year round versus injecting in only one season per each hemisphere). The presence of regional trade‐offs between different strategies indicates that this is an important point when considering an eventual global governance of this method. Key Points: Injecting in autumn results in more precipitation over India than annually constant injection and increases Arctic September sea iceInjecting in spring results in less precipitation changes over the Amazon Basin and requires much less SO2 to cool the surfaceUnderlying trade‐offs exist between strategies that meet different regional objectives: this has implications for any governance effort [ABSTRACT FROM AUTHOR]

Changes in precipitation patterns as a result of increased atmospheric water vapour in a warming environment over the Indian region have been reported in the past few decades. However, these studies have not focused much on exploring the changes in atmospheric water vapour in a changing climate. The present study focuses on examining the variability of integrated precipitable water (IPW) over the Indian region derived from the Modern Era Retrospective‐Analysis for Research and Application (MERRA 2) model in a warming environment. The results suggest that the IPW has increased significantly in recent decades. Changes in the IPW are consistent with changes in regional warming over India. The results also point out an increase of about 4.98% ± 1.26% in the IPW for a 1°C increase in regional warming. The variability of IPW has been examined in a warming climate. An increase in regional temperature shows coherent variation with IPW. A decadal increase of about 1.69% has been reported in the IPW. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) serve as an important tool to perform high resolution climate simulations for a limited area. However, there remains a crucial issue regarding the efficacy of RCMs that whether they are able to produce added value (AV) in representing the regional climatic features compared to their coarse scale driving Global climate models (GCMs). The present study aims at assessing the AV of a set of RCM simulations performed under the Coordinated Regional Climate Downscaling Experiments‐South Asia (CORDEX‐SA) compared with their respective driving GCMs in representing the Indian summer monsoon (ISM) precipitation and its dynamical features. For this purpose, the performance of RCMs and GCMs is compared for the present day climate (1970–2005) against observations and reanalysis datasets in terms of various ISM features, that is, the spatial distribution of precipitation, the evolution of vertical shear of zonal wind and the onset phase of ISM in terms of delayed or early arrival of monsoon. The RCMs show a clear improvement upon their corresponding driving GCMs in simulating spatial features of precipitation distribution that are characteristically associated with ISM. Regionally, the RCMs also show skill in capturing the precipitation amount as a reduction in bias is found compared with their driving GCMs. There is a significant improvement in the simulation of onset dates of ISM by only some of the experiments particularly, RegCM4 driven with IPSL‐CM5A‐LR. The study concludes that the present set of CORDEX‐SA RCM simulations do indeed add value to their driving GCMs for a number of features associated with the ISM precipitation but this value varies with region, driving GCM and the particular feature of ISM under focus. Simulated climatology (1970–2005) of JJAS mean precipitation (mm day−1) by (A) IMD observation, (a1–l1) GCMs and their corresponding (a2–l2) CORDEX‐SA RCMs grouped together in a box. The orographic feature in spatial variability of ISM is evident in IMD—a characteristic which seems to be better captured here by RCMs as compared to their parent GCMs. [ABSTRACT FROM AUTHOR]

The study evaluates the drought characteristics in India over projected climatic scenarios in different time frames, that is, near‐future (2010–2039), mid‐future (2040–2069), and far‐future (2070–2099) in comparison with reference period (1976–2005). Standardized Precipitation Evapotranspiration Index (SPEI), a multi‐scalar drought index was used owing to its robustness in capturing drought conditions while accounting the temperature. Gridded rainfall and temperature data provided by India Meteorological Department (IMD) was used to perform bias correction of nine Global Climate Models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) project. Quantile mapping was used to correct the daily rainfall data at seasonal scale whereas daily temperature data was corrected at monthly scale. Multi‐Model Ensemble (MME) was prepared for different homogeneous monsoon regions of India, namely Hilly Regions (HR), Central Northeast (CNE), Northeast (NE), Northwest (NW), West Central (WC), and Peninsula (PS). Taylor diagram statistics were used for the preparation of MME. The regional climate cycle obtained from MME was found to be in good agreement with observed cycle derived from IMD data. The Mann–Kendal trend test was employed to detect the trend in drought severity and magnitude whereas L‐moments based frequency analysis was used to assess the magnitude of extreme drought severity under different time frames. The study reveals an increasing trend in drought severity, duration, occurrences, and the average length of drought under warming climate scenarios. Furthermore, the area under "above moderate drought" (i.e., severe and extreme drought combined) condition was also found to be increasing in projected climate. Under warming climate scenario, that is, RCP 4.5 and RCP 8.5, more numbers of drought occurrences were found in future time frames as compared to baseline period, that is, 1976–2005. Besides, average drought length is also found to be increasing with more area being affected from above moderate drought than moderate drought in future. Increased drought severity in warming future as revealed from the analysis may further aggravate the problem for water resources managers and policy makers. [ABSTRACT FROM AUTHOR]

Since 2000, the European Union (EU), a leader in global climate politics, has been looking for alternative avenues to enforce climate relations with other countries. Employing a multi-level governance lens, this article focuses on the EU-India bilateral relations on energy and climate. The analysis finds strong opportunities for mutually beneficial cooperation on low carbon development, a potential that has not yet been realized. The stimulating and accelerating impact that multi-level reinforcement could have in the energy and climate relations between the EU and India has not been exploited by either side yet. The various forms of cooperation that include the national government and the states in India offer more room for linkages which might move the Indian/European climate cooperation ahead further. Copyright © 2017 John Wiley & Sons, Ltd and ERP 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]

ABSTRACT The spatio-temporal variability of aerosols over Indian subcontinent is mainly due to transported dust from adjacent deserts (Thar and Middle East deserts), local emission due to anthropogenic activities and prevailing meteorological conditions. On large scale, the quantification of transported and locally emitted aerosol from these regions is complicated. Here, we used empirical orthogonal function ( EOF) analysis to identify these regions and their variability by using 33 years of Total Ozone Monitoring Spectrometer ( TOMS) satellite data. The maximum variability in aerosol is explained by first two EOF modes (70.31 and 20.57%) over Indian subcontinent. The major aerosol, i.e. transported dust from adjacent deserts confined to NW India and Pakistan, is observed in first leading mode, whereas biomass burning, industrial and dense populated region of southeast and eastern region of the Indo-Gangetic Plain ( IGP) are revealed in the second dominant mode. The EOF analysis is carried out specifically for the pre-monsoon and monsoon seasons over Indian subcontinent as maximum aerosol loading is observed during this period. The region of NW India, IGP, Pakistan and northern Arabian Sea explains maximum variability in both seasons. The first three leading modes and their relationship with different atmospheric and surface variables are carried out for pre-monsoon and monsoon seasons. This study explains the potential role of aerosols on reduction in cloudiness, increased shortwave at the ground, land-surface tropospheric warming and its feedback to other related processes. This study strongly suggests that there is a need for further appropriate observational as well as modelling study on the role of semi-direct aerosol effect over Indian subcontinent. [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]

ABSTRACT Sea surface temperature (SST) in the region off southern tip of India (STI, 75-83°E, 5-8°N) exhibited a prominent variability in the intraseasonal time scale (both 30-90 d and 10-30 d band) during boreal summer. Mechanisms associated with this intraseasonal variability are studied using three-dimensional ocean general circulation model (OGCM) sensitivity experiments, satellite observed outgoing longwave radiation, SST and winds for the period 1998-2007. The background oceanic structure of the STI characterized by a shallow thermocline and moderate mixed layer provided ideal conditions for strong oceanic sub-surface processes. The model mixed layer heat budget reveals that the oceanic processes such as horizontal advection and vertical processes are the dominant mechanisms in the STI region as compared with air-sea flux. Sensitivity experiments with the OGCM reveals that the ocean dynamical processes contribute to most of the intraseasonal SST variability and the wind stress contributes to 85% of the variability whereas surface flux contributes to only 15% for the 30-90 d SST variability. Higher amplitude of surface flux perturbation and its contribution to SST in the 10-30 d as compared with the 30-90 d band are evident in the model experiment and are consistent with the observational analysis. There is year-to-year variability in the relative role of horizontal and vertical processes for different intraseasonal SST events over STI. [ABSTRACT FROM AUTHOR]

Climatic variability and rise in temperature are considered as the key determinants to the transmission of malaria. In the present study, the trends in the cases of malaria caused by Plasmodium falciparum and Plasmodium vivax were investigated by using the nonparametric Mann-Kendall test after removing the effect of significant lag-1 serial correlation from the time series of cases of malaria incidence by pre-whitening in annual, seasonal, and monthly time scales at Bikaner, located in the Thar Desert of Rajasthan, in northwest India. Multi-collinearity within the datasets under consideration was investigated by means of correlation matrix, the Bartlett sphericity test, and the Kaiser-Meyer-Olkin measure of sampling adequacy, subsequent to which it was removed by using principal component analysis. Finally, artificial neural network models were employed to predict cases of malaria incidence caused by P. falciparum and P. vivax at various scales. During the last 34 years from 1975 to 2008, P. falciparum malaria incidence cases have been found to increase significantly corresponding to monthly (April and September) and seasonal (monsoon) time scales over Bikaner. On the other hand, no significant trends were observed in P. vivax malaria cases at Bikaner. Concomitant increases in P. falciparum cases of malaria incidence and observed temperature increases at Bikaner hint that P. falciparum malaria may have grown significantly under the warming climate of the Thar Desert. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Limited area models (LAMs) currently provide the only computationally feasible tool to simulate weather processes at very high spatial resolution of 1-2 km. Unlike in a global circulation model (GCM), however, the limited domains, and the associated artificial lateral boundaries, introduce additional uncertainties and errors into the simulation of LAMs. Domain size and grid spacing (horizontal resolution) both influence the spectrum of resolved scale and the nature of scale interaction in the model dynamics. A comparative, comprehensive and quantitative estimation of the relative role of domain size, grid spacing and initial condition in the simulation of mesoscale events is, however, lacking. This issue is investigated using a mesoscale model (MM5V3) with respect to three high impact weather (heavy rainfall) events in the tropics with a series of ensemble (five initial conditions) simulations. A high-resolution (10 km) benchmark simulation is used to establish the model's performance in simulating heavy rainfall events which occurred over the west coast of India during 26-27 July 2005. As the study's emphasis is on relative roles and not on the precise forecast, sensitivity simulations are carried out at coarser resolutions in view of the large number of simulations involved. Results show that, along with initial conditions and grid size, the size of the domain also significantly affects simulated quantities such as total rain and maximum rain. The results also show that domain size plays as important a role as that of grid spacing and initial condition in the simulation of high impact weather events. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The prediction of Asian summer monsoon rainfall on various space and time scales is still a difficult task. Compared to the mid-latitudes, proportional improvement in the skill in prediction of monsoon rainfall in medium range has been less in recent years. Global models and data assimilation techniques are being further improved for monsoons and the tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and reducing the model uncertainties. As major centres are exchanging the model output in near real time, MME is a viable, inexpensive, way for enhancing the forecast skill. Apart from a simple ensemble mean, the MME predictions of large-scale monsoon precipitation in the medium range was carried out during the 2009 monsoon at NCMRWF/MoES, India. The neural network weights were obtained and a neural network was trained based upon forecast data from four global models for the 2007 and 2008 monsoons in order to develop the multi-model ensemble system. The skill score for country and sub-regions, indicates that a multi-model ensemble forecast has a higher skill than individual model forecasts and also higher skill than the simple ensemble mean in general. Although the skill of the global models falls beyond day 3, a significant improvement could be seen by employing the MME technique up to day 5. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The article discusses the unpredictability of severe rainfall events during the southwest monsoon season in India. The study covers the daily rainfall data during the monsoon season covering the period 1951 to 2005. The article also includes spatial distribution of extreme rainfall events, association of frequency of synoptic scale systems with extreme rainfall events, and interannual variability of composite anomalies and extreme rainfall events.

The article offers information on the dynamic and thermodynamic models for short term prediction of premonsoon convective developments over Kolkata, India. Binary Logistic Regression Model have been utilized to predict the convective developments in the premonsoon period. The study shows that there is an occurrence of convective developments in the evening. Two conventional variable selection has been used to generate the Binary Logistic Regression Model. The development of Binary Logistics Regression Model is intended to forecast the risk of occurrence of premonsoon convective developments.

[1] Climatological mean estimates of forest burning and crop waste burning based on broad assumptions of the amounts burned have so far been used for India in global inventories. Here we estimate open biomass burning representative of 1995–2000 from forests using burned area and biomass density specific for Indian ecosystems and crop waste burning as a balance between generation and known uses as fuel and fodder. High-resolution satellite data of active fires and land cover classification from MODIS, both on a scale of 1 km × 1 km, were used to capture the seasonal variability of forest and crop waste burning and in conjunction with field reporting. Correspondence in satellite-detected fire cycles with harvest season was used to identify types crop waste burned in different regions. The fire season in forest areas was from February to May, and that in croplands varied with geographical location, with peaks in April and October, corresponding to the two major harvest seasons. Spatial variability in amount of forest biomass burned differed from corresponding forest fire counts with biomass burned being largest in central India but fire frequency being highest in the east-northeast. Unutilized crop waste and MODIS cropland fires were predominant in the western Indo-Gangetic plain. However, the amounts of unutilized crop waste in the four regions were not strictly proportional to the fire counts. Fraction crop waste burned in fields ranged from 18 to 30% on an all-India basis and had a strong regional variation. Open burning contributes importantly (about 25%) to black carbon, organic matter, and carbon monoxide emissions, a smaller amount (9–13%) to PM2.5 (particulate mass in particles smaller than 2.5 micron diameter) and CO2 emissions, and negligibly to SO2 emissions (1%). However, it cannot explain a large ‘missing source’ of BC or CO from India. [ABSTRACT FROM AUTHOR]

The pre-monsoon thermal field over the Indian landmass has an important bearing on the land-sea heating contrast in the region, consequently influencing the establishment, advance and overall performance ofthe Indian summer monsoon rainfall. This paper examines the relationship between the pre-monsoon thermal field over India and the following summer monsoon rainfall, in order to identify possible predictors for long-range forecasting of Indian summer monsoon rainfall. Based on the spatial patterns of correlations of monsoon rainfall with maximum and minimum temperatures at 121 stations well distributed over India, during the recent period 1951--80, two predictors showing a significant contribution to the variance in monsoon rainfall have been identified. They are (i) March minimum temperature in east peninsula India and (ii) May minimum temperature in west central India. These two predictors have performed very well in terms of their significant contribution to the multiple regression models during 1951--1987, vis-a-vis several other known predictors. They have also shown a consistently significant relationship with the monsoon rainfall during the recent period, from the mid-1940s till the end of the data period. A stepwise regression model for long-range forecasting of all-India summer monsoon rainfall, involving three regional predictors, has been developed, and has shown a multiple correlation of 0.89. ) 1997 by the Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

The ability of the current generation of climate models, in their long-term simulations, to replicate the observed atmospheric behaviour on a wide range of spatial and time scales provides support in applying these models to the greenhouse gas-induced climate change projections on regional scales. A series of long time-slice control experiments have been performed recently with the ECHAM3 atmospheric general circulation model at T21, T42 and T106 horizontal resolutions (30 years each with T21 and T42 and 5 years with T106). All these model experiments use a common sea-surface temperature climatology in their control experiment inferred from a coupled ocean--atmosphere climate model (ECHAM1 + LSG) experiment. In this paper, we examine the ability ofthe ECHAM3 model to simulate the Indian monsoon climatology at thesethree different horizontal resolutions. Because the Indian summer monsoon circulation evolves through a characteristic sequence of events, it is important that the climate models should be able to realistically portray these important features of the circulation over adequate spatial and time scales. We focus on the model's simulation of selected variables representative of the thermal, dynamic and hydrological components in zonal mean cross-sections and area-averaged monthly as well as seasonal regional distributions. Generally, with respect tolarge-scale features of the circulation, the largest differences among the simulations occur at T42 relative to T21. At both T21 and T42 horizontal resolution, however, the model does not have a high degreeof correspondence with observations as regards the spatial distribution of mean sea-level pressure, surface air temperature, 850 hPa. winds and precipitation. On regional scales, T106 resolution best captures both the spatial and temporal characteristics of the Indian climatology. Both the diurnal and seasonal cycles of area-averaged surface air temperature over the region simulated by the model at T106 resolution [ABSTRACT FROM AUTHOR]