Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods

Lianyi Guo, Zhihong Jiang (), Deliang Chen, Hervé Treut and Laurent Li
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Lianyi Guo: Key Laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology
Zhihong Jiang: Key Laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology
Deliang Chen: University of Gothenburg
Hervé Treut: Sorbonne Université, Ecole Normale Supérieure, Ecole Polytechnique
Laurent Li: Sorbonne Université, Ecole Normale Supérieure, Ecole Polytechnique

Climatic Change, 2020, vol. 162, issue 2, No 27, 623-643

Abstract: Abstract Four bias correction methods, i.e., gamma cumulative distribution function (GamCDF), quantile–quantile adjustment (QQadj), equidistant cumulative probability distribution function (CDF) matching (EDCDF), and transform CDF (CDF-t), to read are applied to five daily precipitation datasets over China produced by LMDZ4-regional that was nested into five global climate models (GCMs), BCC-CSM1-1m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR, and MPI-ESM-MR, respectively. A unified mathematical framework can be used to define the four bias correction methods, which helps understanding their natures and essences for identifying the most reliable probability distributions of projected climate. CDF-t is shown to be the best bias correction method based on a comprehensive evaluation of different precipitation indices. Future precipitation projections corresponding to the global warming levels of 1.5 °C and 2 °C under RCP8.5 were obtained using the bias correction methods. The multi-method and multi-model ensemble characteristics allow to explore the spreading of projections, considered a surrogate of climate projection uncertainty, and to attribute such uncertainties to different sources. It was found that the spread among bias correction methods is smaller than that among dynamical downscaling simulations. The four bias correction methods, with CDF-t at the top, all reduce the spread among the downscaled results. Future projection using CDF-t is thus considered having higher credibility.

Keywords: Climate downscaling; Bias correction; Daily precipitation; 1.5 °C and 2 °C global warming; Climate projection uncertainty (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10584-020-02841-z

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