Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

Abstract

In this study, we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model (GCM) data to drive a regional climate model (RCM) over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRFGCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRFGCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRFGCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

摘要

为了评估全球模式(GCM)误差订正对动力降尺度模拟的影响,本研究开展了三组25km分辨率的1980-2014年动力降尺度模拟试验,三个试验分别利用原始的GCM模拟数据(WRF_GCM)、经过误差订正GCM数据(WRF_GCMbc)和ERA5再分析数据(WRF_ERA5)作为大尺度驱动场。经过误差订正的GCM数据具有ERA5基准的平均值、年际变化和来自18个CMIP6模型集合平均的长期趋势。评估结果表明,与WRF_GCM相比,WRF_GCMbc显著降低了区域气候平均态的均方根误差(RMSE),温度、降水、雪、风、相对湿度和行星边界层高度等变量的RMSE降低了50%至90%。类似地,降尺度变量的年际至年代际方差的RMSE降低了30%-60%。此外,WRF_GCMbc显著提高了对季风年循环、季节内变率和日际温度变率的模拟能力。在WRF_GCM试验中,中国东部夏季降水表现为单极型降水模态。相反,WRF_GCMbc试验则成功模拟出观测中的中国东部夏季降水三极型模态,这一改进可以归因于GCM误差订正改善了区域模式中的西北太平洋副热带高压位置。

Electronic Supplementary Material to

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Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate