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Assessment of CORDEX-SA experiments in representing precipitation climatology of summer monsoon over India
Authors:A?Choudhary  Email author" target="_blank">A?P?DimriEmail author  P?Maharana
Institution:1.School of Environmental Sciences,Jawaharlal Nehru University,New Delhi,India;2.Laboratoire de Météorologie Dynamique (LMD),Paris,France
Abstract:The present work assesses the performance of 11 regional climate simulations in representing the precipitation patterns of summer monsoon over India for the period 1970–2005. These simulations have been carried out under Coordinated Regional Climate Downscaling Experiment–South Asia (CORDEX-SA) project. The regional climate models (RCMs) have been inter-compared as well as evaluated against the observation to identify the common weaknesses and differences between them. For this, a number of statistical analysis has been carried out to compare the model precipitation field with the corresponding observation. Model uncertainty has been also evaluated through bias studies and analysis of the spread in the ensemble mean (hereafter, ensemble). The models which perform better than the rest are identified and studied to look for any improvement in the ensemble performance. These better performing experiments (best RCM experiments) are further assessed over the monsoon core region (MCR) of India. This has been done to understand how well the models perform in a spatially homogeneous zone of precipitation which is considered to be a representative region of Indian summer monsoon characteristics. Finally, an additional analysis has been done to quantify the skill of models based on two different metrics—performance and convergence including a combination of the two. The experiment with regional model RegCM4 forced with the global model GFDL-ESM2M shows the highest combined mean skill in capturing the seasonal mean precipitation. In general, a significant dry bias is found over a larger part of India in all the experiments which seems most pronounced over the central Indian region. Ensemble on an average tends to outperform many of the individual experiments with bias of smaller magnitude and an improved spatial correlation compared with the observation. Experiments which perform better over India improve the results but only slightly in terms of agreement among experiments and bias.
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