气候变化领域集成服务门户(CCPortal): Bias Correction in CMIP6 Models Simulations and Projections for Brazil's Climate Assessment

CCPortal

DOI	10.1007/s41748-023-00368-8
	Bias Correction in CMIP6 Models Simulations and Projections for Brazil's Climate Assessment
	Brumatti, Livia Maria; Commar, Luiz Felipe Sant'Anna; Neumann, Nathan de Oliveira; Pires, Gabrielle Ferreira; Avila-Diaz, Alvaro
发表日期	2024
ISSN	2509-9426
EISSN	2509-9434
起始页码	8
结束页码	1
卷号	8 期号:1
英文摘要	Recently, the impacts of climate change have become increasingly evident, affecting both population and economic sectors, highlighting the need for developing future-oriented adaptation strategies in response to the predicted intensification of its impacts. Climate models' projections are powerful tools for evaluating future climate change impacts and developing adaptation strategies. However, potential biases within these projections may affect climate risk assessment in hydrology and agriculture. In this way, this study aimed to find a suitable bias-correction method from the Coupled Model Intercomparison Project Phase 6 climate and extreme climate variables for Brazil, providing the best models for each climate variable. We evaluated the performance and error of two types of bias-correction methods commonly applied in the literature: linear scaling and quantile mapping. The results showed that the non-parametric quantile mapping methods performed better for most climate variables. The linear scaling method performed slightly better for the maximum consecutive dry days index in certain models and regions. Nevertheless, the improvement was minimal compared to the raw data, indicating that bias correction has limited capacity to improve indexes that climate models represent poorly. The best models varied according to the climate variable, but ACCESS-ESM1-5, NorESM2-MM, CanESM5, EC-Earth3, and CMCC-ESM2 predominated in the variables' ranking after bias correction. Our study supplies insight into the suitable bias-correction method and model selection across different variables and the Brazilian region. This level of detailed information promotes informed decision-making in climate risk assessment studies regarding agriculture, energy production, society, and disasters.
英文关键词	CMIP6; Bias correction; Climate change; Climate risk assessment
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001144325500001
来源期刊	EARTH SYSTEMS AND ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/309786
作者单位	Universidade Federal de Vicosa; Universidad del Rosario
推荐引用方式 GB/T 7714	Brumatti, Livia Maria,Commar, Luiz Felipe Sant'Anna,Neumann, Nathan de Oliveira,et al. Bias Correction in CMIP6 Models Simulations and Projections for Brazil's Climate Assessment[J],2024,8(1).
APA	Brumatti, Livia Maria,Commar, Luiz Felipe Sant'Anna,Neumann, Nathan de Oliveira,Pires, Gabrielle Ferreira,&Avila-Diaz, Alvaro.(2024).Bias Correction in CMIP6 Models Simulations and Projections for Brazil's Climate Assessment.EARTH SYSTEMS AND ENVIRONMENT,8(1).
MLA	Brumatti, Livia Maria,et al."Bias Correction in CMIP6 Models Simulations and Projections for Brazil's Climate Assessment".EARTH SYSTEMS AND ENVIRONMENT 8.1(2024).