General Circulation Models or Global Climate Models (GCMs) output consists of inevitable bias due to insufficient knowledge about parameterization schemes and other mathematical computations that involve thermodynamical and physical laws while designing climate models. Indian summer monsoon (southwest monsoon) accounts for 75%-90% of the annual rainfall over most climatic zones of India during the months, June, July, August, and September, which has a direct impact on the agricultural economy of India. The aim of this study is to bias correct the Coupled Model Intercomparison Project Phase -6 (CMIP6) GCMs' precipitation data for the historical period from 1985 to 2014 and two Shared Socioeconomic Pathways (SSP) SSP1-2.6 and SSP5-8.5, from the period 2015 to 2100, with reference to the India Meteorological Department (IMD) observed rainfall gridded dataset. The datasets used are for the rain-bearing Indian southwest monsoon season from the months, June to September. Monsoon Core Region is selected to carry out the bias correction using a couple of deep learning algorithms, namely one-dimensional Convolutional Neural Network (CNN1D) and Long Short-Term Memory Encoder-Decoder (LSTM-ED) Neural Network. The performance of both algorithms is evaluated with metrics. The LSTM-ED algorithm yielded better results with least error output. The bias-corrected data obtained using the LSTM-ED algorithm is then compared with IMD observed rainfall data for the climatic events such as ENSO (El Niño and La Niña) and Positive and Negative IOD (Indian Ocean Dipole).
Present study commences from the time series analysis of evaporation data sets obtained from the Coupled Modeled Inter comparison Project of Phase 5 (CMIP5) for the study period 1979 to 2100 under the RCP 4.5 and 8.5 emission scenarios over Interior Peninsular region during the Northeast monsoon (October to December) period. Further, a comparative analysis has been carried out with the evapotranspiration (ET) estimated from the Hargreaves and Samani (1982) using the temperature data of India Meteorological Department for the period 1979 to 2005. Our results show that evaporation trends are increasing with more prominence in RCP 8.5 scenario. This increase in evaporation has been attributed to increase in air temperature which is an undisputed fact under future climate change scenario. Different climate models of CMIP5 show mixed response by displaying the positive and negative correlations with the Hargreaves ET over the study region. The results of the study will be useful in understanding the bias between the modeled data sets and the estimates of ET from the observations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.