AUTHOR=Liu Zhu , Zhang Guoping , Ding Jin , Xiao Xiong TITLE=Biases of the Mean and Shape Properties in CMIP6 Extreme Precipitation Over Central Asia JOURNAL=Frontiers in Earth Science VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.918337 DOI=10.3389/feart.2022.918337 ISSN=2296-6463 ABSTRACT=

The global climate models (GCMs) are indispensable for accurately simulating the climate variability and change, and numerous studies have assessed climatic extreme events globally and regionally. However, the shape properties of GCM precipitation extreme simulations, such as measures of asymmetry (e.g., skewness coefficient) and measures of tail heaviness (e.g., kurtosis coefficient), have received far less attention. Here, we address this issue by comparing the performance of 22 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing the statistical properties of ground observations for the period 2001–2014 over typical arid and semiarid Central Asia. We evaluated the performance of the CMIP6 models using novel methodologies to assess biases not only in mean and variation but also in higher order L-moments which involved less bias and variance than the conventional moment approach, including 1) summary statistics as expressed by univariate analysis of L-moments and 2) the bivariate kernel densities of (mean, L-variation) and (L-skewness, L-kurtosis) using the application of the highest probability region (HPR) and applying the Hellinger distance as a measure of agreement. The results show that CMIP6 simulations can reproduce the shape properties of precipitation extremes with the observational datasets and that biases are observed when the mean and variation are examined bivariate. An ensemble mean of the CMIP6 models does not improve the performance of the variation and skewness of the simulated precipitation extremes, while it only slightly constrains the mean and kurtosis error of most metrics. Our results could provide guidance for climate research and improve the statistical properties of CMIP6 models in relation to ground observations.