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

IF 6.5 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES Advances in Atmospheric Sciences Pub Date : 2024-02-06 DOI:10.1007/s00376-023-3101-y

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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.

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评估由偏差校正 CMIP6 数据集驱动的亚洲气候动态降尺度模拟的性能

摘要在本研究中，我们旨在利用经过偏差校正的新型全球气候模式（GCM）数据来驱动亚洲-西北太平洋地区的区域气候模式（RCM），从而对动态降尺度模拟进行评估。在 1980-2014 年期间进行了三次模拟，网格间距为 25 公里。第一个模拟（WRF_ERA5）由欧洲中期天气预报中心再分析 5（ERA5）数据集驱动，作为验证数据集。原始 GCM 数据集（MPI-ESM1-2-HR 模型）用于驱动第二次模拟（WRF_GCM），而第三次模拟（WRF_GCMbc）则由偏差校正后的 GCM 数据集驱动。偏差校正后的 GCM 数据具有基于 ERA5 的平均值和年际方差，以及从 18 个 CMIP6 模型的集合平均值得出的长期趋势。结果表明，与 WRF_GCM 相比，WRFGCMbc 显著降低了温度、降水、降雪、风、相对湿度和行星边界层高度等降尺度变量的气候学平均值的均方根误差（RMSE）50%-90%。同样，降尺度变量的年际至年代际方差均方根误差也降低了 30%-60%。此外，WRFGCMbc 更好地捕捉了季风环流的年周期以及季节内和逐日变化。在 WRFGCM 中，领先的经验正交函数（EOF）显示了单极降水模式。相比之下，WRF_GCMbc 成功地再现了观测到的中国东部夏季降水的三极模式。这一改进可归因于 WRF_GCMbc 在 GCM 偏差校正后更好地模拟了北太平洋西部副热带高压的位置。

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来源期刊

Advances in Atmospheric Sciences 地学-气象与大气科学

CiteScore

9.30

自引率

5.20%

发文量

154

审稿时长

6 months

期刊介绍： Advances in Atmospheric Sciences, launched in 1984, aims to rapidly publish original scientific papers on the dynamics, physics and chemistry of the atmosphere and ocean. It covers the latest achievements and developments in the atmospheric sciences, including marine meteorology and meteorology-associated geophysics, as well as the theoretical and practical aspects of these disciplines. Papers on weather systems, numerical weather prediction, climate dynamics and variability, satellite meteorology, remote sensing, air chemistry and the boundary layer, clouds and weather modification, can be found in the journal. Papers describing the application of new mathematics or new instruments are also collected here.