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Many global climate models, including the Climate Forecast System version 2 (CFSv2), have a biased representation of subseasonal modes of variability of the Indian summer monsoon. For instance, they simulate a weaker summer mean monsoon low‐pressure systems (LPS) climatology, faster than observed northward propagation of monsoon intraseasonal oscillations (MISOs), and a systematic dry bias over Indian landmass. The Bay of Bengal (BoB), with its shallow mixed layers and unique thermal stratification, significantly modulates the convective activity in this region at subseasonal‐to‐seasonal timescales through modulation of sea surface temperature. The highly stratified upper ocean in the BoB is due to the enormous freshwater it receives from rains and rivers. A river routing model is coupled to the CFSv2 to account for the riverine freshwater and the improvements in modelling the upper‐ocean structure are analysed. Model simulations indicate that inclusion of temporally varying riverine freshwater improves the upper‐ocean state in the BoB and the observed mixed‐layer temperature gradients in the Bay are simulated reasonably after incorporating the time varying river runoff. This resulted in increased LPS lifetime and track density, and enhanced rainfall over central India. Better representation of the upper‐ocean stratification in the model leads to larger post‐convection shoaling of mixed layers at intraseasonal timescales, thereby forming thick barrier layers. Enhanced air–sea interactions restricted to the shallow mixed layer are associated with stronger vorticity, specific humidity and low‐level convergence to the north of the intraseasonal convection band. This enhanced low‐level moisture convergence north of the convection centre results in realistic northward propagation of MISO and aids LPS activity. It is demonstrated that better simulation of the upper‐ocean structure in coupled climate models can improve the representation of subseasonal modes of monsoon variability. These results bear important implications for operational forecasting. [ABSTRACT FROM AUTHOR]

Accurate estimation of ET0, and determination of its trend/variability and sensitivity to changes in climate variables is essential at regional scale for different applications. These are attempted for India by considering 18 homogeneous ET0 regions formed in companion paper. Food and Agriculture Organization (FAO) recommends Penman–Monteith (PM) equation for ET0 estimation, which requires information on several climate variables. The equation cannot be used in data‐sparse areas where information/forecasts on one or more required climate variables is unavailable/unreliable. To address this, relevance vector regression (RVR) relationships are developed in this paper for the 18 homogeneous regions to arrive at FAO‐PM estimate of ET0 from subsets of its predictor climate variables, which could be typically expected in data‐sparse areas. The developed relationships are shown to be better in arriving at ET0 estimates when compared to multiple linear regression (MLR) relationships and three widely used empirical equations (Hargreaves, Mcguinness–Bordne, Priestly–Taylor). Regional trend analysis revealed that ET0 is significantly decreasing (increasing) in most regions located in north (south) India. Sensitivity of ET0 and surface runoff to changes in their predictor climate variables is determined for each region by considering third‐order Taylor series approximation of their functional relationships. Key climate variable(s) that govern ET0 changes in each region are identified. Results indicate that at annual scale solar radiation and relative humidity govern ET0 changes in regions located in south and north‐east India, whereas wind speed followed by relative humidity mostly influence the ET0 changes over other regions. Existence of evaporation paradox in four regions and validity of Bouchet's complementary relationship between ET0, actual evapotranspiration (ETa), and precipitation in 11 regions is also established. Significant divergence in trend of ET0 and ETa was evident in north‐east India. [ABSTRACT FROM AUTHOR]

Delineation of homogeneous reference evapotranspiration (ET0) regions is essential for different applications in hydro‐meteorology. In conventional regionalization approaches, lumped (time‐invariant) statistics such as mean, median or interquartile range of different hydrometeorological variables are often considered as attributes to delineate regions. Information on temporal dynamics of those variables is not utilized (as it is lost in lumped statistics), which if accounted could yield better regions. To address this, a new regionalization approach is presented in fuzzy framework in this Part 1 of a two‐part series. In the proposed approach, information on temporal dynamics of predictor climate variables influencing ET0 is used for regionalization, and the delineated regions are subsequently validated for homogeneity using the predictand (ET0) related information. Effectiveness of the approach is demonstrated through a case study on India, which yielded 18 regions. They are shown to be statistically more homogeneous in ET0 when compared to the existing agro‐ecological zones and regions formed using global fuzzy c‐means clustering method. The homogeneous ET0 regions were found to be different from homogeneous actual evapotranspiration (ETa) regions delineated over India using the fuzzy dynamic clustering approach. Various applications of the homogeneous ET0 regions formed using the proposed approach are presented in Part 2 of this series. [ABSTRACT FROM AUTHOR]

The article reports on the study of the historical climate variations and climate change in the Brahmaputra basin such as Tibetan plateau (TP), Himalayan belt and the floodplains (FP). The study analyses the historical trends in temperature and precipitation of the TP, FP, and Himalayan belt through a global 100 year monthly high resolution dataset. It shows that precipitation in the basin is determined by the strength by the monsoons. It expresses that El Niño Southern Oscillation teleconnection does not have an important role in the changes in monsoon precipitation. The study also demonstrates the relationship among air temperature, FP, TP, and the strength of the monsoon.