Assessment of model performance of precipitation extremes over the mid-high latitude areas of Northern Hemisphere: from CMIP5 to CMIP6

ABSTRACT

This study explores the model performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating precipitation extremes over the mid–high latitudes of Asia, as compared with predecessor models in the previous phase, CMIP5. Results show that the multimodel ensemble median generally outperforms the individual models in simulating the climate means of precipitation extremes. The CMIP6 models possess a relatively higher capability in this respect than the CMIP5 models. However, discrepancies also exist between models and observation, insofar as most of the simulated indices are positively biased to varying degrees. With respect to the temporal performance of indices, the majority are overestimated at most time points, along with large uncertainty. Therefore, the capacity to simulate the interannual variability needs to be further improved. Furthermore, pairwise and multimodel ensemble comparisons were performed for 12 models to evaluate the performance of individual models, revealing that most of the new-version models are better than their predecessors, albeit with some variance in the metrics amongst models and indices.

GRAPHICAL ABSTRACT

摘要

本文基于亚洲中高纬地区逐日降水观测资料和CMIP6中12个全球模式资料, 采用泰勒图等方法, 系统评估CMIP6模式对该地区极端降水的模拟能力, 并与CMIP5结果进行对比。结果表明, 相较于CMIP5模式, CMIP6模式能够更好地模拟极端降水指数气候平均态以及趋势变化特征, 与观测相关系数更高。多模式集合平均在模拟极端降水方面普遍优于单个模式。为进一步评估单个模型的性能, 与CMIP5中的旧版本模式进行了两两比较。尽管针对不同的模式和评估指标结果存在一些差异, 但大多数新版本模式模拟极端降水能力较CMIP5有所增强。

KEYWORDS:

• Precipitation extremes
• CMIP5
• CMIP6
• the mid-high latitudes of Asia

关键词:

• 极端降水
• CMIP5
• CMIP6
• 亚洲中高纬

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This research was jointly supported by the National Natural Science Foundation of China [grant numbers 41991284 and 41922034], the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDA23090102], and the National Key Research and Development Program of China [grant number 2016YFA0602401].