CMIP6不同分辨率全球气候模式对中国降水模拟能力评估

doi:10.12006/j.issn.1673-1719.2021.005

摘要/Abstract

摘要：

基于参与CMIP6高分辨率模式比较计划（HighResMIP）9个模式组的18个全球气候模式模拟数据,通过与CN05.1观测资料的对比,评估了不同分辨率气候模式对中国区域1961—2014年降水特征的模拟能力。结果表明：低、高分辨率模式均能模拟出中国区域多年平均降水的总体空间分布特征,以及降水冬弱夏强的季节变化特征,但对降水的模拟都存在系统性偏多的误差;与低分辨率模式结果相比,高分辨率模式对降水空间分布的模拟有明显改善,在青藏高原、华北、华南地区降水模拟的系统性偏差明显减小;与低分辨率模式结果相比,高分辨率模式对年循环变化的模拟效果也更好,多年平均1月及9—12月逐月降水以及年降水的模拟误差均有所减小。对于年际、年代际的前两个主导空间模态,低、高分辨率模式大多无法模拟年代际的第一模态,但对于年际前两个模态以及年代际第二模态,分辨率提高可使半数左右模式组的模拟能力有所改善。

关键词: HighResMIP, 高分辨率, 降水, 评估

Abstract:

Based on the 18 global climate models with different horizontal resolutions from the High Resolution Model Intercomparison Project (HighResMIP) of CMIP6, precipitation over China in the period of 1961-2014 was compared against the observation of CN05.1. Results show that all of the models can reproduce the spatial pattern of mean annual precipitation and the seasonal variation of dry winter and wet summer over China. Compared with the model with low-resolution, the high-resolution model can significantly improve the spatial distribution of precipitation, and the bias in the Tibetan Plateau, North China and South China is significantly reduced. The annual cycle of precipitation is also better simulated by the high-resolution compared to the low-resolution models, with lower bias in January, from September to December, and the annual precipitation. In addition, for the first and second leading modes of interannual and interdecadal, most of the low- and high-resolution models fail to simulate the first mode of interdecadal scale. But for the second mode of interdecadal, the first and second mode of interannual, the performance of about half models are improved with the increase of horizontal resolution.

Key words: HighResMIP, High-resolution, Precipitation, Evaluation

胡一阳, 徐影, 李金建, 韩振宇. CMIP6不同分辨率全球气候模式对中国降水模拟能力评估[J]. 气候变化研究进展, 2021, 17(6): 730-743.

HU Yi-Yang, XU Ying, LI Jin-Jian, HAN Zhen-Yu. Evaluation on the performance of CMIP6 global climate models with different horizontal resolution in simulating the precipitation over China[J]. Climate Change Research, 2021, 17(6): 730-743.

图/表 11

表1 CMIP6中HighResMIP计划提供的9组全球气候模式基本信息

Table 1 Basic information on 9 sets of global climate models provided by HighResMIP in CMIP6

图1 观测与模拟的1961—2014年平均年降水量分布及二者相对偏差分布注：打点处代表相对偏差的同号率>78%。

Fig. 1 Observation (a) and low-resolution MME (b) and high-resolution MME (c) of mean annual precipitation, and the relative deviation (d, e) between simulated results and observation in 1961-2014 (The dots indicate that 78% or more of models agree on the sign of biases)

图2 低分辨率模式(a) 与高分辨率模式(b)模拟的1961—2014年平均年总降水与观测值的相对偏差分布注：序号对应表1的模式序号。

Fig. 2 The spatial distribution of model biases in mean annual precipitation in 1961-2014. (a) Low-resolution models, (b) high-resolution models (The number corresponds to Table 1)

图3 低分辨率与高分辨率模式模拟1961—2014年中国地区多年平均的逐月降水分布盒须图注：紫色盒须对应低分辨率模式,黑色盒须对应高分辨率模式;箱体上下线代表75%与25%的分位数。

Fig. 3 Box-and-whisker plots for multi-year average monthly precipitation distribution in China simulated by low-resolution models (purple) and high-resolution models (black) during 1961-2014 (The upper and lower boundaries of the box represent the 75% and 25% quantiles respectively)

图4 低分辨率(a)、高分辨率(b)模式模拟1961—2014年中国地区多年平均季节、年降水相对于观测值的Taylor图注：0代表MME,1~9对应表1的模式序号。

Fig. 4 Taylor diagram for low-resolution models (a), high-resolution models (b) simulated seasonal and annual precipitation in China during 1961-2014 compared with the observation (The different colors represent the season, and the different numbers represent the model)

图5 观测(a)以及低分辨率模式(b)与高分辨模式(c)模拟的1961—2014年中国东部夏季降水年际变化模态EOF1 注：序号0为MME,其余序号对应表1的模式序号;打点表示回归计算可通过95%信度检验,绿色框表示模拟与观测间存在正的空间相关且可通过95%信度检验。

Fig. 5 Low-resolution models (b) and high-resolution models (c) simulated EOF1 of interannual variation of summer precipitation spatial distribution in eastern China during 1961-2014 and observation (a). (b0 and c0 are MME. Dot area in panels indicates that they are all statistically significant at the 95% confidence level. The green box indicates that the spatial positive correlation with observation is statistically significant at the 95% confidence level)

图6 同图5,但为EOF2

Fig. 6 As in Fig. 5 except for EOF2

表2 年际变化EOF1和EOF2在各模式组与观测之间的空间相关系数

Table 2 The spatial correlation coefficients of the first and second dominant mode of interannual variation by each model groups and observation

图7 同图5,但为年代际变化EOF1

Fig. 7 As in Fig. 5 except for the EOF1 of interdecadal variation

图8 同图7,但为年代际变化EOF2

Fig. 8 As in Fig. 7 except for EOF2

表3 年代际变化EOF1和EOF2在各模式组与观测之间的空间相关系数

Table 3 The spatial correlation coefficients of the first and second dominant mode of interdecadal variation by each mode model groups and observation

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