Bias correction of CMIP6 simulations of precipitation over Indian monsoon core region using deep learning algorithms

Abstract

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).

Statistical downscaling of maximum temperature under CMIP6 global climate models and evaluation of heat wave events using deep learning methods for Indo‐Gangetic Plain

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Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

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“…Gumus, El Moçayd, et al, 2023;Gumus, Oruc, et al, 2023 applied an ANN in simulating regional climate and to analyse the climate change effect in Morocco under different scenarios. They evaluated future climate projections for 2025-2049, 2050-2074and 2075-2100. Kesavavarthini et al (2023 Boosting algorithms to project streamflow in the Swat River basin.…”

Improving CMIP6 Atmospheric River Precipitation Estimation by Cycle‐Consistent Generative Adversarial Networks

“…Kesavavarthini et al. (2023) employed a deep learning method called LSTM‐ED to correct precipitation biases in India. Specifically, Kesavavarthini et al.…”