A Comparative Study on the Skill of CMIP6 Models to Preserve Daily Spatial Patterns of Monsoon Rainfall Over India - Mitra, Adway (Frontiers in Climate, 2021)
South Asian monsoon is a phenomena that plays out during June-September every year, due to the northward shift of the ITCZ which causes heavy rainfall over many countries of South Asia, including India. These rains are directly related to the lives and economic well-being of over a billion people. Indian monsoon is highly heterogeneous, due to the vast physiographic variations across the country. There is considerable interest among scientists and other stake-holders about possible future changes to Indian monsoon due to worldwide climate change. Simulations of future scenarios by global climate models can provide important clues for this. However, the global climate models under the CMIP5 family were found to be considerably limited in their simulations of Indian monsoon in historical settings. Simulations by the CMIP6 family models are now available, and scientists are evaluating their ability to simulate Indian monsoon. In this work, we focus on one particular aspect of such simulation: the spatial distribution over daily rainfall over the Indian landmass during monsoon. We use a Machine Learning based probabilistic graphical model that identifies frequent spatial patterns of rainfall after creating a binary representation of the data. This model also helps us to identify spatial clusters, i.e. homogeneous regions with similar temporal characteristics of rainfall. We compare the common spatial patterns and spatial clusters identified from the simulations by different CMIP6 models to observed historical data. We find that simulations by many of the CMIP6 models are decent, but each of them also have their limitations.