School of Atmospheric Sciences, Nanjing University, Nanjing, China.
CAS Key Laboratory of Regional Climate and Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China.
Sci Data. 2024 Apr 20;11(1):399. doi: 10.1038/s41597-024-03224-0.
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.
动力降尺度对于生成更精细尺度的气候预测至关重要。最近，南京大学地球系统模型（NESM）提供了四组 1.5/2°C 全球变暖情景下的模拟结果。然而，NESM3 在大尺度驱动变量上的偏差会降低降尺度模拟的质量。我们使用一种新的偏差修正方法，针对气候平均值和年际方差，对 NESM3 的偏差进行了修正，然后为动力降尺度生成了一组偏差修正数据集。修正后的 NESM3 覆盖了 1979-2014 年的历史时期和四个未来情景（即 2070-2100 年 1.5°C 的超调、2070-2100 年稳定的 1.5/2°C 和 2031-2061 年瞬态 2°C），水平分辨率为 1.25°×1.25°，时间分辨率为每六小时一次。我们的评估表明，在历史时期的季节平均值和变率以及气候极值事件的气候平均值方面，修正后的 NESM3 优于原始 NESM3。这个修正后的数据集有望为 1.5/2°C 全球变暖下的区域气候和环境生成更可靠的预测。
