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Frequency-intensity-distribution bias correction and trend analysis of high-resolution CMIP6 precipitation data over a tropical river basin

Abstract

Advancements in computational power have enabled general circulation models (GCMs) to simulate climate variables at a higher resolution. However, GCM outputs often deviate from the observed climatological data and therefore need bias correction (BC) before they are used for impact studies. While there are several BC methods, BCs considering frequency, intensity and distribution of rainfall are few. This study proposes a BC method which focuses on separately correcting the frequency, intensity and distribution of precipitation. This BC was performed on high-resolution daily precipitation simulations of Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 with a 20-km grid size (MRI-AGCM3-2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6) on Netravati basin, a tropical river basin in India. The historical rain gauge station data was considered for testing the effectiveness of the BC method applied. The quantile–quantile (Q–Q) plot, Taylor diagram, Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS) and correlation coefficient (R) are employed for the evaluation of the BC method. Higher R and R2 and lower RMSE, MAE and PBIAS values were observed for the bias-corrected GCM data than raw simulation. The PBIAS reduced from 15.6 to 6% when BC was applied. The analysis suggested that the proposed method effectively corrects the bias in rainfall over the basin. Furthermore, an attempt has been made to analyse the trend of historical and future rainfall in the basin. The analysis revealed a declining trend of precipitation in monsoon months with the magnitude of 12.44 mm and 56.7 mm in the historical and future periods respectively. This study demonstrates that BC should be applied before the use of GCM simulated precipitation for any analysis or impact studies to improve the predictions.

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Data availability

The GCM data used is available online on the World Climate Research Programme (WCRP) climate data portal (https://esgf-node.llnl.gov/search/cmip6/). The observed data from rain gauge stations are procured from IMD.

Code availability

The authors have used customised MATLAB scripts for performing various stages of bias correction and for generation of figures. Trend analysis was done using pyMannKendall package in python.

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Acknowledgements

We acknowledge the data of daily precipitation from the India Meteorological Department (IMD). Further, we acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access and the multiple funding agencies who support CMIP6 and ESGF. The authors would also like to thank the Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, India, for providing the necessary support to carry out this research work.

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DMJ conceptualised this research work. DMJ implemented the coding part and prepared the main text and figures for the research work. DMJ and GSD reviewed the manuscript and contributed to the final version of the manuscript. GSD supervised the research work. Both the authors provided critical feedback and helped shape the research, analysis and manuscript.

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Correspondence to Dinu Maria Jose.

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Jose, D.M., Dwarakish, G.S. Frequency-intensity-distribution bias correction and trend analysis of high-resolution CMIP6 precipitation data over a tropical river basin. Theor Appl Climatol 149, 683–694 (2022). https://doi.org/10.1007/s00704-022-04078-5

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  • DOI: https://doi.org/10.1007/s00704-022-04078-5