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The Maritime Continent (MC), located in the heart of the Indo-Pacific warm pool, plays an important role in the global climate. However, the future MC climate is largely unknown, in particular the ENSO-rainfall teleconnection. ENSO induces a zonal dipole pattern of rainfall variability across the Indo-Pacific Ocean, i.e., positive variability in the Tropical Pacific and negative variability towards the MC. Here new CMIP6 models robustly project that, for both land and sea rainfall, the negative ENSO teleconnection over the MC (drier/wetter during El Niño/La Niña) could intensify significantly under the SSP585 warming scenario. Strengthened teleconnection may cause enhanced droughts and flooding, leading to agricultural impacts and altering rainfall predictability over the region. Models also project that the Indo-Pacific rainfall center and the zero-crossing of dipole-like rainfall variability both shift eastward, which adjustments are more notable during boreal summer than winter. All these projections are robustly supported by the model agreement and scale up with the warming trend.

Lung cancer is the leading cause of cancer-related deaths. Lung cancer cells develop resistance to apoptosis by suppressing the secretion of the tumor suppressor Par-4 protein (also known as PAWR) and/or down-modulating the Par-4 receptor GRP78 on the cell surface (csGRP78). We sought to identify FDA-approved drugs that elevate csGRP78 on the surface of lung cancer cells and induce Par-4 secretion from the cancer cells and/or normal cells in order to inhibit cancer growth in an autocrine or paracrine manner. In an unbiased screen, we identified crizotinib (CZT), an inhibitor of activated ALK/MET/ROS1 receptor tyrosine kinase, as an inducer of csGRP78 expression in ALK-negative, KRAS or EGFR mutant lung cancer cells. Elevation of csGRP78 in the lung cancer cells was dependent on activation of the non-receptor tyrosine kinase SRC by CZT. Inhibition of SRC activation in the cancer cells prevented csGRP78 translocation but promoted Par-4 secretion by CZT, implying that activated SRC prevented Par-4 secretion. In normal cells, CZT did not activate SRC and csGRP78 elevation but induced Par-4 secretion. Consequently, CZT induced Par-4 secretion from normal cells and elevated csGRP78 in the ALK-negative tumor cells to cause paracrine apoptosis in cancer cell cultures and growth inhibition of tumor xenografts in mice. Thus, CZT induces differential activation of SRC in normal and cancer cells to trigger the pro-apoptotic Par-4-GRP78 axis. As csGRP78 is a targetable receptor, CZT can be repurposed to elevate csGRP78 for inhibition of ALK-negative lung tumors.

The unified model (Version: UM10.4) wind gust prediction system using rose nesting suite at 300 m resolution over the Incheon international airport (IIA) is carried out, and the prediction results of multiple domain nesting with input initial and lateral boundary conditions from the Korea meteorological administration (KMA) operational global data assimilation and prediction system (GDAPS) are compared with the operational local data assimilation and prediction system and the observed automated weather stations (AWS) dataset. The 300 m nested prediction system over the IIA is built from the 17 km GDAPS output in KMA; it is downscaled with a multiple nesting domains to 300 m resolution from the GDAPS, here after called as IIA-300 m. The model results are validated against the AWS to see the accuracy of the IIA-300 m model’s skill in predicting the wind gust over the IIA. The nesting suite is run with the default ancillary files created by the central ancillary program 9.0 (CAP 9.0) as well as modified ancillary files created by modifying the land points over IIA region using an ancillary editor. The UM model wind prediction is done with both ancillary files to see the improvements achieved by adding additional land points over the IIA domain. Further the vegetation fraction from the international geosphere-biosphere program (IGBP) data classified as 9 tiles in the ancillary file is replaced by the environmental-geographic information system (E-GIS) data to see the effect on the UM model’s wind gust predictions. The prediction of two wind gust events on 30-August-2016 and 02-October-2016 reveals only modest improvements in the prediction with respect to AWS observations, even after the modification of ancillaries with land points over the IIA domain, and only minor improvements are noted for the vegetation fraction replacement from IGBP to E-GIS vegetation dataset over the Incheon International Airport and exhibited only a marginal wind speed decrease due to warming up of the land part, particularly on the peripheries of the IIA-domain.

Year-to-year variations of the Maritime Continent (MC, 80E-160E & 18S-26N) rainfall is strongly influenced by ENSO variability. Seasonal predictability of the MC rainfall heavily relies on climate models’ ability to simulate realistic ENSO developments and its teleconnection. Here we analyze 32 available state-of-the-art CMIP6 models, and find that most models are able to simulate the observed negative ENSO-rainfall teleconnection [i.e., drier than normal during El Niño and wetter than normal during La Niña] over the MC during the boreal winter (DJF, when ENSO normally peaks). Using the sign-adjusted bias analysis for the historical period [1980-2014], we show that CMIP6 models tend to systematically underestimate the negative correlation in the central MC and overestimate the positive correlation in the eastern MC due to the westward intrusion of the positive correlation within the tropical Pacific. In regard to changes in the ENSO-rainfall teleconnection over the MC under global warming, the multi-model mean suggests that, by the end of the 21st century [2065-2099] under the highest emission scenario (SSP585), the negative ENSO-rainfall teleconnection over the western and central MC will strengthen while the positive teleconnection over the eastern MC will weaken. These spatially opposing changes of ENSO teleconnection under global warming could induce dramatic multi-sectoral impacts within the MC.

This study employs the Unified Model (UM) with the rose nesting suite at 300-m resolution to construct a high spatial and temporal resolution wind simulation model for predicting wind gusts over the Incheon International Airport (IIA). The model can enable efficient aircraft operation and avert accidents due to sudden wind gusts. Simulation results with different inputs from the Korea Meteorological Administration (KMA) operational data assimilation and prediction systems are compared with an observed dataset. The 300-m nested prediction systems are built using the 17 km Global Prediction System (GDAPS) and 1.5 km Local Prediction System (LDAPS) of KMA. They are downscaled to 300 m resolution using five and three nesting domains from the GDAPS and LDAPS, respectively. The model results are validated against automated weather stations (AWS) to determine the accuracy of the UM for simulating high-resolution winds over the IIA. Both nesting suites are identical, with the only difference being their initial (IC) and lateral boundary conditions (LBC). The major difference between LDAPS and GDAPS downscaled model results is that the GDAPS downscaled system has a lower wind direction RMSE and the LDAPS downscaled system has a lower wind speed RMSE for up to 48 hours of verification against observations; thus, it is better than the GDAPS downscaled system. Two case studies were performed; one for wind gust conditions and one for vertical wind shear over the IIA. The 300 m model performs better in both cases, making it useful for wind gust and wind shear predictions over the airport.

In this study, the El Nino Southern Oscillation (ENSO) phase index is used for water management over the Kotmale reservoir in Sri Lanka. Daily rainfall data of 9 stations over the Kotmale catchment during 1960–2005 June-September (JJAS) season is investigated over the Kotmale catchment. The ENSO phases are identified based on the 0.5°C sea surface temperature (SST) anomaly over Nino 3 region. The study has brought out few stations showing increasing and a few decreasing seasonal rainfall trends for JJAS season, while there is no change in the annual rainfall for the catchment. Monthly and seasonal rainfall of all the selected stations showed negative correlation with the sea surface temperature (SST) over the Nino-3 region index during JJAS season with varying magnitudes. During the warm phase of ENSO, below average rainfall is prominent for JJAS season over many stations. The rainfall especially during early September showed a significant below average rainfall during the warm ENSO phase. The seasonal rainfall during neutral and cold ENSO phases does not experience similar significant changes as seen during warm ENSO phase. Inflow of the Kotmale reservoir shows decreasing trend for the period of 1960–2005 in the observation from all stations collectively.

This study makes use of temperature and precipitation from CMIP5 climate model output for climate change application studies over the Indian region during the summer monsoon season (JJAS). Bias correction of temperature and precipitation from CMIP5 GCM simulation results with respect to observation is discussed in detail. The non-linear statistical bias correction is a suitable bias correction method for climate change data because it is simple and does not add up artificial uncertainties to the impact assessment of climate change scenarios for climate change application studies (agricultural production changes) in the future. The simple statistical bias correction uses observational constraints on the GCM baseline, and the projected results are scaled with respect to the changing magnitude in future scenarios, varying from one model to the other. Two types of bias correction techniques are shown here: (1) a simple bias correction using a percentile-based quantile-mapping algorithm and (2) a simple but improved bias correction method, a cumulative distribution function (CDF; Weibull distribution function)-based quantile-mapping algorithm. This study shows that the percentile-based quantile mapping method gives results similar to the CDF (Weibull)-based quantile mapping method, and both the methods are comparable. The bias correction is applied on temperature and precipitation variables for present climate and future projected data to make use of it in a simple statistical model to understand the future changes in crop production over the Indian region during the summer monsoon season. In total, 12 CMIP5 models are used for Historical (1901–2005), RCP4.5 (2005–2100), and RCP8.5 (2005–2100) scenarios. The climate index from each CMIP5 model and the observed agricultural yield index over the Indian region are used in a regression model to project the changes in the agricultural yield over India from RCP4.5 and RCP8.5 scenarios. The results revealed a better convergence of model projections in the bias corrected data compared to the uncorrected data. The study can be extended to localized regional domains aimed at understanding the changes in the agricultural productivity in the future with an agro-economy or a simple statistical model. The statistical model indicated that the total food grain yield is going to increase over the Indian region in the future, the increase in the total food grain yield is approximately 50 kg/ ha for the RCP4.5 scenario from 2001 until the end of 2100, and the increase in the total food grain yield is approximately 90 kg/ha for the RCP8.5 scenario from 2001 until the end of 2100. There are many studies using bias correction techniques, but this study applies the bias correction technique to future climate scenario data from CMIP5 models and applied it to crop statistics to find future crop yield changes over the Indian region.

Ground-based India Meteorological Department, Aphrodite (Aphro) and satellite-based observations Tropical Rainfall Measuring Mission 3B42V6 and V7 and GPCP, high-resolution Climate Prediction Center merged daily precipitation estimates based on satellite and rain gauge data available for the period 2001–2007 and reanalysis products (ERA-INTERIM and MERRA) are analysed to determine the spatio-temporal variability of heavy precipitation during the summer monsoon season over the Indian subcontinent. The purpose of this work is to compare the representation of heavy precipitation by different datasets with different resolutions, and the focus of this study is to compare the heavy rainfall data over the Central Indian land and different subdivisions within India. Day-to-day variation in rainfall activity over the Central Indian region obtained from these datasets is compared among the different datasets from gauge observation, satellite, merged products and reanalysis products. The study showed that, over most of India, the mean and extremes in rainfall are captured well by the all the datasets. However, considerable differences exist among the datasets. The spatial and temporal variation of the summer monsoon rainfall is examined by computing various indices using different datasets for wet years (2003, 2005, 2006) and dry years (2001, 2002, 2004). The spatial pattern of the rainy days and heavy precipitation indices follows the spatial pattern of the seasonal rainfall. Large interannual variability is observed in the spatial distribution of the indices of precipitation extremes. The heavy precipitation (90 percentile) indices over Central India show low/high values during drought/excess years and also follow the mean precipitation indices in all the datasets. The study shows that gauge and remote sensing through satellite and gauge satellite merged products play an important role in monitoring climate and provide continuous datasets at high resolution and also useful in studying the climate of data-sparse regions. Comparison with the reanalysis products shows promising signs of capturing heavy precipitation in ERA-INT and MERRA dataset during the monsoon season. In spite of varied resolutions the datasets have consensus in capturing the light, moderate and heavy precipitation; however, considerable differences exist among the datasets.

The warm (cold) phase of El Nino (La Nina) and its impact on all Indian Summer Monsoon rainfall (AISMR) relationship is explored for the past 100 years. The 103-year (1901–2003) data from the twentieth century reanalysis datasets (20CR) and other major reanalysis datasets for southwest monsoon season (JJAS) is utilized to find out the simultaneous influence of the El Nino Southern Oscillation (ENSO)-AISMR relationship. Two cases such as wet, dry monsoon years associated with ENSO(+) (El Nino), ENSO(−) (La Nina) and Non-ENSO (neutral) events have been discussed in detail using observed rainfall and three-dimensional 20CR dataset. The dry and wet years associated with ENSO and Non-ENSO periods show significant differences in the spatial pattern of rainfall associated with three-dimensional atmospheric composite, the 20CR dataset has captured the anomalies quite well. During wet (dry) years, the rainfall is high (low), i.e. 10 % above (below) average from the long-term mean and this wet or dry condition occur both during ENSO and Non-ENSO phases. The Non-ENSO year dry or wet composites are also focused in detail to understand, where do the anomalous winds come from unlike in the ENSO case. The moisture transport is coherent with the changes in the spatial pattern of AISMR and large-scale feature in the 20CR dataset. Recent 50-year trend (1951–2000) is also analyzed from various available observational and reanalysis datasets to see the influence of Indo-Pacific SST and moist processes on the South Asian summer monsoon rainfall trend. Apart from the Indo-Pacific sea surface temperatures (SST), the moisture convergence and moisture transport among India (IND), Equatorial Indian Ocean (IOC) and tropical western pacific (WNP) is also important in modifying the wet or dry cycles over India. The mutual interaction among IOC, WNP and IND in seasonal timescales is significant in modifying wet and dry cycles over the Indian region and the seasonal anomalies.

Performance of twenty-eight state of art atmosphere general circulation models (AGCMs) in simulating regional characteristic features of summer monsoon rainfall and circulation over the south Asian and the western-north Pacific regions are examined. AGCMs depict good representation of climatological spatial distribution of monsoon rainfall over both the regions. Interestingly, the year-to-year variability of the south Asian (Indian) monsoon rainfall is well simulated by the models compared to the circulation features. Whereas, for the western-north Pacific region, the AGCMs depict poor representation of monsoon rainfall variability, but circulation features are simulated with better skill. Thus suggesting that, models with good performance of monsoon rainfall variability need not necessarily have better representation of monsoon circulation features. Further analysis indicate that most models with good skill in simulating monsoon variability have better representation of monsoon-SST teleconnections with the Indo-Pacific SSTs. For the south Asian region, better simulating models have good representation of monsoon-ENSO teleconnection whereas most models have poor representation of monsoon-IOD relationship, suggesting requirement for realistic representation of these teleconnections in the models. For the western-north Pacific region, most models have unrealistic relationship of monsoon rainfall with SSTs during preceding winter and concurrent monsoon season, indicating that the poor (better) skill in simulating rainfall (circulation) probably arise from the unrealistic (realistic) representation of monsoon-SST teleconnections. Thus, the importance of realistic representation of monsoon-SST teleconnections in the climate models have been brought out for better simulation of monsoon variability over the south Asian and the western-north Pacific regions.

The relationship between the warm phase of El Nino southern oscillation (ENSO) and Indian summer monsoon rainfall is explored through seven coupled global climate models (CGCMs), which are semi-operational at APEC Climate Center (APCC). The 23-year (1983–2005) hindcast datasets of individual model ensembles derived from May initial conditions for southwest monsoon season (JJAS) are utilized to find out the simultaneous influence of El Nino–ISMR relationship in 1990s, which is observed to be weaker than present decades. The hindcast of ISMR climatology derived from seven individual models viz. APCC, NCEP, POAMA, SINT, SUT1, PNU and UHT1 appears to be reasonably simulated; in particular, about 50 % of mean departure is evident in most CGCMs. In addition, four of six El Nino years during the aforementioned period are well depicted in most of the CGCMs, while the years 1994 and 1997 are not represented well by these seven individual models. The warm SST anomaly aligned with surplus precipitation over tropical equatorial Pacific region simulated using APCC, NCEP, POAMA, SINT and SUT1 is relatively better than that simulated in PNU and UHT1 and it is closer to observation. The El Nino–ISMR teleconnection skills both monthly to seasonal scale are very poor in PNU as well as UHT1 and their RMSEs are 3.84 and 3.77 higher than APCC, NCEP, POAMA, SINT and SUT1 models. The authors developed two Multi-Model Ensembles (MMEs) that were simple composites of ensemble forecast from seven models (APCC, NCEP, POAMA, SINT, SUT1, PNU and UHT1) referred to as MME1, and from five models (APCC, NCEP, POAMA, SINT and SUT1) are referred to as MME2. Importantly, the one-month lead MME2 prediction of anomaly correlation coefficient (ACC) and its adverse impacts is reasonably better than MME1 prediction. However, there are some limitations in capturing SST forcing fields over Indian Ocean region in both MMEs. Among the seven models, SINT has the highest pattern correlation of precipitation over the Indian monsoon region.

This paper evaluates the performance of 29 state-of-art CMIP5-coupled atmosphere–ocean general circulation models (AOGCM) in their representation of regional characteristics of monsoon simulation over South Asia. The AOGCMs, despite their relatively coarse resolution, have shown some reasonable skill in simulating the mean monsoon and precipitation variability over the South Asian monsoon region. However, considerable biases do exist with reference to the observed precipitation and also inter-model differences. The monsoon rainfall and surface flux bias with respect to the observations from the historical run for the period nominally from 1850 to 2005 are discussed in detail. Our results show that the coupled model simulations over South Asia exhibit large uncertainties from one model to the other. The analysis clearly brings out the presence of large systematic biases in coupled simulation of boreal summer precipitation, evaporation, and sea surface temperature (SST) in the Indian Ocean, often exceeding 50 % of the climatological values. Many of the biases are common to many models. Overall, the coupled models need further improvement in realistically portraying boreal summer monsoon over the South Asian monsoon region.

This paper evaluates the performance of a suite of state-of-art coupled atmosphere–ocean general circulation models (AOGCMs) in their representation of regional characteristics of hydrological cycle and temperature over South Asia. Based on AOGCM experiments conducted for two types of future greenhouse gas emission scenarios (RCP4.5 and RCP8.5) extending up to the end of 21st century, scenarios of temperature and hydrological cycle are presented. The AOGCMs, despite their relatively coarse resolution, have shown a reasonable skill in depicting the hydrological cycle over the South Asian region. However, considerable biases do exist with reference to the observed hydrological cycle and also inter-model differences. The regional climate change scenarios of temperature (T), atmospheric water balance components, precipitation, moisture convergence and evaporation (P, C and E) up to the end of the 21st century based on CMIP5 modeling experiments conducted for (RCP4.5 and RCP8.5) indicate marked increase in both rainfall and temperature into the 21st century, particularly becoming conspicuous after the 2050s. The monsoon rainfall and atmospheric water balance changes under RCP4.5 and RCP8.5 scenarios are discussed in detail in this paper. Spatial patterns of rainfall change projections indicate maximum increase over South Asia in most of the models. Model simulations under scenarios of increased greenhouse gas concentrations suggests that the intensification of the hydrological cycle is driven mainly by the increased moisture convergence due to increase in the water holding capacity of the atmosphere in a warmer environment, the intensification of the hydrological cycle is greater for RCP8.5 compared to RCP4.5, also fewer models indicate increased variance of temperature and rainfall in a warmer environment. While the scenarios presented in this study are indicative of the expected range of rainfall and water balance changes, it must be noted that the quantitative estimates still have large uncertainties associated with them. Five best model mean reveals the general consensus among the AOGCM results and gives the best estimate of the future projection over the South Asian monsoon region.

The study focuses on understanding the variations of precipitation during summer monsoon season and its impact on Kharif and Rabi foodgrain yield over India. Total foodgrain yield over India during Kharif (summer) season is directly affected by variations in the summer monsoon precipitation (June–September). An increase (decrease) in rainfall is generally associated with an increase (decrease) in foodgrain yield. A similar correspondence during the Rabi (winter) foodgrain yield is not evident. The Rabi crop is not directly affected by variations in the post-monsoon precipitation (October–December) alone, also the summer season precipitation influences the Rabi crop through water and soil moisture availability over many parts of India. Though the reduction of rainfall activity during the entire summer monsoon season leads to reduction in crop yields, the occurrence of prolonged rainfall breaks also causes adverse effect on the crop growth resulting in reduced crop yields.

In the present research to identify moist processes that initiate and maintain extended monsoon breaks over South Asia moisture and moist static energy (MSE) budgets are performed on the newly available European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim) and ensemble integrations from a coupled model. The hypothesis that interaction between moist physics and regional circulation and the role of cloud–radiation feedbacks are important is tested. Budget diagnostics show that dry advection is the principal moist process to initiate extended breaks. Its sources are (i) regional anticyclonic circulation anomalies forced by equatorial Indian Ocean negative rainfall anomalies advect low MSE air from north to central India, and (ii) rainfall enhancement over tropical west Pacific forces cyclonic circulation anomalies to its northwest as a Rossby wave response, and the northerlies at the poleward flank of this circulation advect air of low MSE content from north. The dominance of anomalous wind acting on climatological moisture gradient is confirmed from an examination of the moisture advection equation. A partition of various flux terms indicates that over central India, due to an increase in upwelling shortwave and longwave fluxes, radiative cooling increases during extended breaks. Here, enhanced rainfall over the equatorial Indian Ocean promotes anomalous radiative warming due to trapping of upwelling fluxes. The differential radiative heating anchors a local Hadley circulation with descent over central India. A direct implication of this research is that observational efforts are necessary to monitor the three-dimensional moisture distribution and cloud–radiation interaction over the monsoon region that would aid in better understanding, modeling, and predicting extended monsoon breaks.

The India Meteorological Department (IMD) gridded rainfall dataset, the 47 Bangladesh gauge rainfall observations and the Tropical Rainfall Measuring Mission (TRMM) 3B42V6 satellite data are used in the present analysis. The nearest neighbour interpolation scheme is used, wherein the interpolated values are computed from a weighted sum of observations. The Bangladesh daily gauge measured rainfall is interpolated into regular grids of 0.5° × 0.5° resolution every day from January 1988 to December 2007 and appended with the daily gridded dataset of the IMD over the Indian region. A similar resolution dataset of 0.5° × 0.5° for the TRMM-3B42V6 data from January 1998 to December 2007 is created from the original data of 0.25° × 0.25° resolution. To produce a merged rainfall product, all the gridded datasets are merged. The merging of datasets is done in such a way as to include the highest rainfall at each grid point from the three products. Based on the three available sets of daily observations (IMD dataset (1° × 1°), TRMM-3B42 (0.25° × 0.25°) and 46 daily station observations over Bangladesh), a dataset of 0.5° × 0.5° resolution on a daily scale is generated. The focus of this study is to compare the TRMM-3B42V6 rainfall data over the Ganga, Brahmaputra and Meghna (GBM) domain with observed point gauge data, and assess the possibility of using them for application in real time flood forecasting as well as to serve as a comparison tool for the baseline simulation of high resolution atmospheric models aimed at flood forecasting and climate change projections. Copyright © 2012 Royal Meteorological Society

This paper evaluates the performance of eight state-of-art IPCC-AR4 coupled atmosphere-ocean general circulation models in their representation of regional characteristics of atmospheric water balance over South Asia. The results presented here are the regional climate change scenarios of atmospheric water balance components, precipitation, moisture convergence and evaporation (P, C and E) up to the end of the twenty-second century based on IPCC AR4 modelling experiments conducted for (A1B) future greenhouse gas emission scenario. The AOGCMs, despite their relatively coarse resolution, have shown a reasonable skill in depicting the hydrological cycle over the South Asian region. However, considerable biases do exist with reference to the observed atmospheric water balance and also inter-model differences. The monsoon rainfall and atmospheric water balance changes under A1B scenario are discussed in detail. Spatial patterns of rainfall change projections indicate maximum increase over northwest India in most of the models, but changes in the atmospheric water balance are generally widespread over South Asia. While the scenarios presented in this study are indicative of the expected range of rainfall and water balance changes, it must be noted that the quantitative estimates still have large uncertainties associated with them.

The atmospheric water balance over dierent domains within the South Asian monsoon region has been studied using moisture convergence ðCÞ computed from JRA-25, ERA-40 and NCEP/NCAR reanalysis datasets, GPCP precipitation data ðPÞ and evaporation data ðEÞ as a residual of these two parameters. The seasonal clima- tology of P, C, and E for the selected regions shows generally large contribution of E to P. The inter-annual characteristics of P, C and E over selected key domains within the South Asian monsoon region have also been examined for both the early (June and July: JJ) and late summer (August and September: AS) monsoon periods from 1979 to 2000. The spatial and temporal characteristics of the hydrological cycle and the contribution of E and C to P are discussed in detail. One important aspect on the seasonal timescale is that from the dry regions in the northwest to the central and the wettest northeast regions, the monthly variations of E or C are large during the monsoon months specific to those regions. However, the interannual variability of P over each domain is not necessarily influenced by the same criteria like C or E, which influences the mean seasonal precipitation. It is also evident that the structure of variability for early (JJ) and late (AS) summer precipitation is dierent over the South Asian monsoon region. Over northwest India E is dominant on the seasonal timescale, but C contributes higher to interannual variability of P. On the other hand, over central India C is dominant during early summer (JJ) on the seasonal timescale, but E contributes higher to P variability on the interannual timescale, and during late summer (AS) E is dominant on the seasonal timescale, but C contributes higher to P variability on the inter- annual timescale. Over northeast India, C is dominant on the seasonal timescale, but E contributes higher to in- terannual variability of P. The importance of land-atmosphere interaction over each domain is discussed. The regionality in the mechanism of precipitation generation and its contribution to the India summer monsoon precipitation variability are also discussed in detail. The role of evaporation variability of precipitation is stronger over the Bay of Bengal sector and the role of convergence on the interannual variability of precipitation is stronger over the Arabian Sea sector.

In this study we have investigated atmospheric water balance over South Peninsular India and Sri Lanka during the months October to December (OND) using computed moisture convergence (C) and residual evaporation (E) from National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data and Global Precipitation and Climatology Project (GPCP) precipitation data. The interannual signatures of OND precipitation, moisture convergence and evaporation over the South Peninsular India and Sri Lanka have been captured. The spatial and temporal characteristics of the hydrological cycle and the contribution of evaporation (E) and convergence (C) to precipitation (P) are discussed in detail. Over the South Peninsular India and Sri Lanka, evaporation (E) dominates during the entire monsoon months (OND). However, the interannual variability of precipitation over the domain is not necessarily influenced by the same criteria which influences the mean seasonal precipitation. The moisture from the Indian Ocean (IO) sector also modulates the precipitation over this region on a year-to-year basis. It has been noted that the positive northeast monsoon rainfall (NEMR) is associated with El Nino coupled with IO dipole, but negative NEMR is weakly associated only with La Nina over South Peninsular India and Sri Lanka. There also exists a significant land-atmospheric interaction over the region in modulating the hydrological cycle on a year-to-year basis. Copyright © 2008 Royal Meteorological Society