Identifying and Using Suitable Oceanic Domains for Prediction of the Indian Monsoon Rainfall over Tamil Nadu

Abstract

Rainfall prediction are vital for agriculture which is one of the primary sectors greatly affected by climate variability and extremes. Agriculture plays a vital role in shaping the economy of India which is often affected by monsoon. Sea surface temperature (SST) plays a vital role in rainfall predictability over the land surface. A total of twelve different domains of oceanic influences of SST on monsoon rainfall over Tamil Nadu were selected for analysis. The SST of different lead times (February, March, April, and May for southwest monsoon (SWM) and June, July, August, and September for northeast monsoon (NEM) from the ERSSTv5 and ECMWF-SEAS5 model with the Canonical Correlation Analysis (CCA) were used in the Climate Predictability Tool (CPT) to identify the best predictor domains for the prediction of SWM and NEM rainfall over Tamil Nadu. The model training utilized the first 40 years (1981-2020) SST and rainfall data and prediction was done for the 2021 seasons. The results of the study revealed from Kendall tau goodness index and CCA score, the predictor domains comprised of a combination of oceanic domains, this were the Indian, Arabian, Bay of Bengal, and Pacific Oceans recorded the best CCA score and the goodness index. Is therefore recommended that, these domains which have the highest overall predictability can be used by the National meteorological services to early warning and monsoon rainfall information over Tamil Nadu.