Study of seasonal climatology and interannual variability over India and its subregions using a regional climate model (RegCM3)

The temporal and spatial variability of the various meteorological parameters over India and its different subregions is high. The Indian subcontinent is surrounded by the complex Himalayan topography in north and the vast oceans in the east, west and south. Such distributions have dominant influence over its climate and thus make the study more complex and challenging. In the present study, the climatology and interannual variability of basic meteorological fields over India and its six homogeneous monsoon subregions (as defined by Indian Institute of Tropical Meteorology (IITM) for all the four meteorological seasons) are analysed using the Regional Climate Model Version 3 (RegCM3). A 22-year (1980–2001) simulation with RegCM3 is carried out to develop such understanding. The National Centre for Environmental Prediction/National Centre for Atmospheric Research, US (NCEP-NCAR) reanalysis 2 (NNRP2) is used as the initial and lateral boundary conditions. The main seasonal features and their variability are represented in model simulation. The temporal variation of precipitation, i.e., the mean annual cycle, is captured over complete India and its homogenous monsoon subregions. The model captured the contribution of seasonal precipitation to the total annual precipitation over India. The model showed variation in the precipitation contribution for some subregions to the total and seasonal precipitation over India. The correlation coefficient (CC) and difference between the coefficient of variation between model fields and the corresponding observations in percentage (COV) is calculated and compared. In most of the cases, the model could represent the magnitude but not the variability. The model processes are found to be more important than in the corresponding observations defining the variability. The model performs quite well over India in capturing the climatology and the meteorological process. The model shows good skills over the relevant subregions during a season.