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Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios.
Zhang, Meng-Zhuo; Han, Ying; Xu, Zhongfeng; Guo, Weidong.
Afiliação
  • Zhang MZ; School of Atmospheric Sciences, Nanjing University, Nanjing, China.
  • Han Y; CAS Key Laboratory of Regional Climate and Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China. hanying@tea.ac.cn.
  • Xu Z; CAS Key Laboratory of Regional Climate and Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China.
  • Guo W; School of Atmospheric Sciences, Nanjing University, Nanjing, China.
Sci Data ; 11(1): 399, 2024 Apr 20.
Article em En | MEDLINE | ID: mdl-38643170
ABSTRACT
Dynamical downscaling is vital for generating finer-scale climate projections. Recently, a set of simulations under four types of 1.5/2 °C global warming scenarios are available with Nanjing University of Information Science and Technology Earth System Model (NESM). However, NESM3's bias in large-scale driving variables would degrade downscaled simulations. We corrected NESM3 bias in terms of climate mean and inter-annual variance against ERA5 using a novel bias correction method and then produced a set of bias-corrected datasets for dynamical downscaling. The bias-corrected NESM3 spans the historical period for 1979-2014 and four future scenarios (i.e., 1.5 °C overshoot for 2070-2100, stabilized 1.5/2 °C for 2070-2100, and transient 2 °C for 2031-2061) with 1.25° × 1.25° horizontal resolution at six-hourly intervals. Our evaluation suggests that bias-corrected NESM3 outperforms the original NESM3 in the climatological mean of seasonal mean and variability, as well as climate extreme events during the historical period. This bias-corrected dataset is expected to generate more reliable projections for regional climate and environment under 1.5/2 °C global warming.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article