基于多模式的青藏高原前冬降水预测性能评估

doi:10.13866/j.azr.2023.07.01

摘要/Abstract

摘要：

基于目前主流气候业务模式系统（BCC_CSM 1.1、ECMWF_System 5、NCEP_CFSv 2、TCC_MRI-CGCM 3），从确定性预报角度评估多种模式对青藏高原前冬降水的预测性能。结果表明：多模式均能体现出青藏高原前冬（11—12月）降水空间分布型，但对降水量级存在普遍高估的现象，其中，来自BCC的模式能反映出降水全区一致型和南北反向型主模态的时空演变特征，EC模式高估第一模态的主导作用。多模式对降水历史回报以正技巧为主，BCC模式的预报技巧最优，TCC仅对青藏高原北部预测较好。从可预报性来源的角度分析发现，赤道中东太平洋海温指数（Ni?o 3.4）和印度洋偶极子（IOD）正位相对提升模式预测技巧具有很好的指示性；BCC模式能预测出2018年高原前冬降水异常趋势，在于其对影响青藏高原降水异常的关键环流型具有一定预测能力。

关键词: 降水, 预测性能, 模式评估, 青藏高原

Abstract:

The prediction performance of four seasonal prediction model systems (BCC_CSM 1.1, ECMWF_System 5, CFSv 2, and TCC_MRI-CGCM 3) was evaluated from a deterministic perspective. Focusing on the spatial distribution and temporal variation of precipitation climatology in early winter, BCC_CSM 1.1 exhibited the best prediction performance among the models. Additionally, TCC_MRI-CGCM 3 performed well in capturing the interannual variability of precipitation, followed by BCC_CSM 1.1. All models effectively simulated regional-uniform precipitation, with BCC_CSM 1.1 exhibiting the highest time correlation coefficient and TCC_MRI-CGCM 3 showing the highest pattern correlation coefficient. Although all models could reproduce the north-south reverse mode, BCC_CSM 1.1 outperformed the other models in terms of reproducing the spatial pattern and interannual variation, given the model’s strong prediction capability for the Eurasian (EU) teleconnection pattern and western Pacific subtropical high, as well as accurate simulations of the physical processes of El Ni?o-Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) that impact early winter Tibetan Plateau precipitation.

Key words: precipitation, prediction ability, model assessment, Tibetan Plateau

申红艳, 温婷婷, 赵仙荣, 冯晓莉. 基于多模式的青藏高原前冬降水预测性能评估[J]. 干旱区研究, 2023, 40(7): 1027-1039.

SHEN Hongyan, WEN Tingting, ZHAO Xianrong, FENG Xiaoli. Evaluation of multi-model precipitation simulation over the Tibetan Plateau in early winter[J]. Arid Zone Research, 2023, 40(7): 1027-1039.

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数据来源	名称	预报时效	模式分辨率/（°）	历史回报时段
国家气候中心（BCC）	CSM 1.1	11个月	1×1	1983—2010年
欧洲中期天气预报中心（ECMWF）	System 5	7个月	1×1	1981—2010年
美国国家环境预报中心（NCEP）	CFSv 2	9个月	1×1	1982—2010年
东京气候中心（TCC）	MRI-CGCM 3	7个月	1×1	1979—2010年

名称	第一模态/%	第二模态/%	第三模态/%	累积方差贡献率/%
OBS	32.7	11.8	6.9	51.4
BCC_CSM 1.1	33.7	21.3	10.1	65.1
EC_System 5	42.9	15.9	8.4	67.2
NCEP_CFSV 2	28.1	14.4	9.2	51.7
TCC_MRI-CGCM 3	38.1	19.5	10.9	68.5

名称	第一模态	第二模态	第三模态	第一时间系数	第二时间系数	第三时间系数
BCC_CSM 1.1	0.359^*	0.755^***	0.002	0.409^**	0.303	0.131
EC_System 5	0.382^**	0.646^***	0.106	0.279	-0.105	0.141
CFS_NCEP_CFSV 2	0.061	0.541^***	0.391^**	0.305^*	-0.103	0.317^*
TCC_TCC_MRI-CGCM 3	0.510^***	0.443^**	0.070	0.403^**	0.088	0.013