2026-10-12T01:55:13Z https://www.scidb.cn/oai

10.57760/sciencedb.07777 2024-02-04T12:20:26Z

2024-02-04 Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming doi:10.57760/sciencedb.07777 en Dynamical downscaling is an important approach to generating climate projections at a finer scale. Recently, a set of future simulations under four types of 1.5 and 2 °C global warming scenarios are available using the Nanjing University Information Science and Technology Earth System Model (NESM), which provide a new opportunity for accessing the climate impacts of 1.5 °C/2 °C global warming. However, the systematic bias of the NESM3 in the large-scale driving variables would degrade the dynamical downscaling simulation. With the aid of a novel bias correction method of the global climate model, we corrected the NESM3 bias in terms of climate mean and inter-annual variance against the European Centre for Medium-Range Weather Forecasts Reanalysis 5 dataset and then produced a set of bias-corrected datasets for dynamical downscaling. The bias-corrected NESM3 spans the historical period from 1979 to 2014 and four future scenarios (i.e., 1.5 °C overshoot from 2070 to 2100, stabilized 1.5 °C/2 °C from 2070 to 2100, and transient 2 °C from 2031 to 2061) with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected NESM3 shows better performance in terms of the climatological mean, inter-annual variability, and climate extreme events compared with the original NESM3 during the historical period. This bias-corrected dataset will provide high-quality large-scale driving fields for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios, which is expected to generate more reliable projections for regional climate and environment. dynamical downscaling; 1.5/2.0 degree global warming; mean bias correction; variance bias correction; NESM3 Meng-Zhuo Zhang Ying Han Zhongfeng Xu Weidong Guo PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1038/s41597-021-01079-3 http://www.doi.org/10.1038/s41597-024-03224-0 Science Data Bank