Faculty Publications

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    Bias Correction and Trend Analysis of Temperature Data by a High-Resolution CMIP6 Model over a Tropical River Basin
    (Korean Meteorological Society, 2022) Jose, D.M.; Dwarakish, G.S.
    Technological advancements like increase in computational power have led to high-resolution simulations of climate variables by Global Climate Models (GCMs). However, significant biases exist in GCM outputs when considered at a regional scale. Hence, bias correction has to be done before using GCM outputs for impact studies at a local/regional scale. Six bias correction methods, namely, delta change (DC) method, linear scaling (LS), empirical quantile mapping (EQM), adjusted quantile mapping (AQM), Gamma-Pareto quantile mapping (GPQM) and quantile delta mapping (QDM) were used to bias correct the high-resolution daily maximum and minimum temperature simulations by Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 (MRI-AGCM3–2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6), of Netravati basin, a tropical river basin on the south-west coast of India. The quantile-quantile (Q–Q) plots and Taylor diagrams along with performance indicators like Nash–Sutcliffe efficiency (NSE), the Root-Mean Square Error (RMSE) or Root-Mean Square Deviation (RMSD), the Mean Absolute Error (MAE), the Percentage BIAS (PBIAS) and the correlation coefficient (r) were used for the evaluation of the performance of each bias correction method in the validation period. Considerable reduction in the bias was observed for all the bias correction methods employed except for the LS method. The results of QDM method, which is a trend preserving bias correction method, was used for analysing the trend of future temperature data. The trend of historical and future temperature data revealed an increasing trend in the annual temperature. An increase of 0.051 °C and 0.046 °C is expected for maximum and minimum temperature annually during the period 2015 to 2050 as per RCP 8.5 scenario. This study demonstrates that the application of a suitable bias correction is needed before using GCM projections for climate change studies. © 2021, Korean Meteorological Society and Springer Nature B.V.
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    Integrated assessment of bias correction techniques, CMIP6 model rankings, and multi-model ensemble optimisation across diverse temporal scales for regional climate projection in Kerala, Southwestern India
    (Springer Science and Business Media Deutschland GmbH, 2025) Athithottam, S.M.; Ramesh, H.
    In the context of climate change, CMIP6 (Coupled model intercomparison project phase 6) General Circulation Models (GCMs) are indispensable for projecting global and regional climate impacts, including temperature rise, precipitation variability, and extreme weather events. These models serve as the basis for Intergovernmental Panel on Climate Change (IPCC) assessments and are crucial for informing mitigation and adaptation strategies. However, their coarse resolution and systematic biases limit their direct application in local-scale climate impact studies. This motivates the present study, which aims to enhance the reliability of CMIP6 precipitation projections over Kerala, a monsoon-dominated, topographically complex region susceptible to rainfall variability. This study employs the CRITIC–TOPSIS (Criteria Importance through intercriteria correlation and technique for order of preference by similarity to ideal solution) framework to comprehensively evaluate bias correction methods, GCM performance, and multi-model ensembling (MME) techniques across multiple temporal scales. Observed daily rainfall data from the India Meteorological Department (IMD) serve as the reference for model evaluation. This integrated, data-driven approach enables robust ranking and selection of optimal models and techniques for regional application. The findings reveal considerable variability in model performance across time scales. ACCESS-ESM1-5 performs consistently well, while MRI-ESM2-0 and HadGEM3-GC31-LL are more suited to long-term projections. IITM-ESM and CMCC-CM2-SR5 show strength in short- to medium-term applications. Advanced ensemble methods, such as Support Vector Machines, Gradient Boosting Machines, Random Forests, and LightGBM, outperform simpler methods in capturing rainfall variability. The study’s results provide practical guidance for selecting climate models and designing ensemble strategies, particularly for hydrological forecasting, infrastructure planning, and climate risk assessment in Kerala and similar monsoon-prone regions. Overall, this research contributes to advancing regional climate modelling practices and supports informed, climate-resilient decision-making at policy and planning levels. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.