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Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia
Advances in Atmospheric Sciences ( IF 5.8 ) Pub Date : 2020-10-09 , DOI: 10.1007/s00376-020-0026-6
Chenwei Shen , Qingyun Duan , Chiyuan Miao , Chang Xing , Xuewei Fan , Yi Wu , Jingya Han

Regional climate models (RCMs) participating in the Coordinated Regional Downscaling Experiment (CORDEX) have been widely used for providing detailed climate change information for specific regions under different emissions scenarios. This study assesses the effects of three common bias correction methods and two multi-model averaging methods in calibrating historical (1980–2005) temperature simulations over East Asia. Future (2006–49) temperature trends under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios are projected based on the optimal bias correction and ensemble averaging method. Results show the following: (1) The driving global climate model and RCMs can capture the spatial pattern of annual average temperature but with cold biases over most regions, especially in the Tibetan Plateau region. (2) All bias correction methods can significantly reduce the simulation biases. The quantile mapping method outperforms other bias correction methods in all RCMs, with a maximum relative decrease in root-mean-square error for five RCMs reaching 59.8% (HadGEM3-RA), 63.2% (MM5), 51.3% (RegCM), 80.7% (YSU-RCM) and 62.0% (WRF). (3) The Bayesian model averaging (BMA) method outperforms the simple multi-model averaging (SMA) method in narrowing the uncertainty of bias-corrected results. For the spatial correlation coefficient, the improvement rate of the BMA method ranges from 2% to 31% over the 10 subregions, when compared with individual RCMs. (4) For temperature projections, the warming is significant, ranging from 1.2°C to 3.5°C across the whole domain under the RCP8.5 scenario. (5) The quantile mapping method reduces the uncertainty over all subregions by between 66% and 94%.

中文翻译:

CORDEX东亚10个子区域温度变化的偏差校正和集合预测

参与协调区域降尺度实验 (CORDEX) 的区域气候模型 (RCM) 已被广泛用于提供不同排放情景下特定区域的详细气候变化信息。本研究评估了三种常见的偏差校正方法和两种多模型平均方法在校准东亚历史(1980-2005)温度模拟中的效果。代表性浓度路径 (RCP) 4.5 和 8.5 情景下的未来 (2006-49) 温度趋势是根据最佳偏差校正和集合平均方法预测的。结果表明:(1)驱动全球气候模式和RCMs可以捕捉年平均气温的空间格局,但在大多数地区,尤其是青藏高原地区存在冷偏差。(2) 所有偏差修正方法都可以显着降低模拟偏差。分位数映射方法在所有 RCM 中均优于其他偏差校正方法,五个 RCM 的均方根误差的最大相对降低达到 59.8% (HadGEM3-RA)、63.2% (MM5)、51.3% (RegCM)、80.7 % (YSU-RCM) 和 62.0% (WRF)。(3) 贝叶斯模型平均 (BMA) 方法在缩小偏差校正结果的不确定性方面优于简单的多模型平均 (SMA) 方法。对于空间相关系数,与单个 RCM 相比,BMA 方法的改进率在 10 个子区域中从 2% 到 31% 不等。(4) 对于温度预测,在 RCP8.5 情景下,整个域的升温幅度从 1.2°C 到 3.5°C 不等。
更新日期:2020-10-09
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