Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia

Abstract

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%.

摘要

区域降尺度协同试验(CORDEX)中的区域气候模式(RCMs),被广泛用于为特定区域提供不同排放情景下详尽的气候信息。本研究评估了三种偏差校正方法和两种多模式平均方法对东亚地区历史期(1980-2005)温度模拟的校正效果。基于最佳偏差校正和集合平均方法,预测了典型性浓度路径(RCP)4.5和8.5情景下,未来温度变化的趋势(2006-49)。结果表明:(1)驱动的全球模式和RCMs均能捕捉到年平均气温的空间分布,但在大部分地区,尤其是青藏高原地区,存在冷偏差。(2)所有偏差校正方法均能显著降低模式模拟误差。分位数映射法超过其他偏差校正方法,对所有的RCM,最大均方根误差的相对减小率分别达到59.8%(HadGEM3-RA),63.2%(MM5)、51.3%(RegCM)、80.7%(YSU-RCM)和62.0%(WRF)。(3)贝叶斯多模型平均(BMA)方法在减小校正后的气温不确定性上超过简单多模型平均(SMA)方法。相比于单个RCM,BMA方法对空间相关系数的提升率在2%-31%。(4)对于未来气温的预测,升温趋势显著,RCP8.5情景下,整个区域的升温幅度从1.2°C到3.5°C。(5)分位数映射法降低了未来气温预测的不确定性,降低了66%-94%的不确定性。