Multivariate bias corrections of CMIP6 model simulations of compound dry and hot events across China

• Published in: Environmental research letters Vol. 17; no. 10; pp. 104005 - 104014
• Main Authors: Meng, Yu, Hao, Zengchao, Feng, Sifang, Guo, Qiang, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.10.2022
• Subjects: Bias; Climate change; Climate models; Climate policy; CMIP6; compound events; Drought; dry and hot; Environmental policy; Error correction; Global warming; Mitigation; Multivariate analysis; multivariate bias correction; Performance evaluation; Precipitation; precipitation-temperature dependence; Root-mean-square errors; Simulation; Temperature dependence; China
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/ac8e86

Abstract Climate model simulations provide useful information to assess changes in climate extremes (e.g. droughts and hot extremes) under global warming for climate policies and mitigation measures. Due to systematic biases in climate model simulations, bias correction (BC) methods have been employed to improve simulations of climate variables such as precipitation and temperature. Previous studies mostly focus on individual variables while the correction of precipitation-temperature (P-T) dependence, which is closely related to compound dry and hot events (CDHEs) that may lead to amplified impacts, is still limited. In this study, we evaluated the performance of the multivariate BC (MBC) approach (i.e. MBCn and MBCr) for adjusting P-T dependence and associated likelihoods of CDHEs in China based on 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) models with observations from CN05.1. Data for the period 1961–1987 were used for model calibrations and those for 1988–2014 were used for model validations. Overall, the MBC can improve the simulation of P-T dependence and associated CDHEs with large regional variations. For P-T dependence, the median values of root mean squared error (RMSE) for corrected simulations show a decreased bias of 5.0% and 4.3% for MBCn and MBCr, respectively, compared with those of raw CMIP6 models. For the likelihood of CDHEs, a decrease of 1.0% and 7.2% in RMSE is shown based on the MBCn and MBCr, respectively. At the regional scale, the performance of the MBC varies substantially, with the reduced RMSE up to 34.8% and 18.7% for P-T dependence and likelihood of CDHEs, respectively, depending on regions and MBC methods. This study can provide useful insights for improving model simulations of compound weather and climate extremes for impact studies and mitigation measures.