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中国周边海域海面温度日变化对区域气候的影响
Influence of Diurnal Variation of SST on the Regional Climate over Coastal Waters of China

查看参考文献38篇

文摘 利用区域气候模式,分别以逐时海面温度(sea surface temperature, SST)数据及逐日SST数据作为模式的海表温度进行强迫,开展了1991~2010年共计20年的数值模拟,探讨SST日变化对中国区域气候变化的影响。对比结果表明,两组试验均能合理地再现中国区域气候的主要气候态特征。同时发现,两组试验模拟的气候特征在我国沿海区域以及近海洋面上存在明显差异:考虑SST日变化之后,2 m气温和感热通量差异呈现夏季(冬季)升高(降低)为主的趋势;潜热通量则与之相反;低层风场差异在夏季以海洋吹向大陆的东南风为主,冬季则以陆面吹向海洋的西北风差异为主;另外,水汽输送差异呈气旋式(反气旋式)时,降水出现正差异(负差异)。 SST日变化对上述气候因子的影响在夏季更为显著。
其他语种文摘 In order to clarify the influence of diurnal variation of sea surface temperature (SST) on regional climate over coastal waters of China, two sensitivity experiments have been conducted using a regional climate model forced by both hourly-mean SST and daily-mean SST for 20 years (1991–2010). Although the main characteristics of climate over the coastal region of China can be well simulated by these two sensitivity experiments, there still exist some significant differences between them. For the sensitivity experiment forced by hourly-mean SST, the temperature at 2 m (T2) and the sensible heat flux tend to have positive anomalies in the summer and negative anomalies in the winter, while the tendency of the latent heat flux is opposite. In the summer, southeasterly wind anomalies blowing from the ocean to the land prevail in the lower levels; in the winter, northwesterly wind anomalies blowing from the land to the ocean are the primary features. For water vapor transport, positive/negative rain anomalies always correspond to cyclonic/anticyclonic circulation anomalies. The influence of diurnal variation of SST over the coastal waters of China on these climatic variables are much more significant in the summer than in the winter.
来源 气候与环境研究 ,2019,24(2):214-226 【核心库】
DOI 10.3878/j.issn.1006-9585.2018.18087
关键词 海面温度 ; 日变化 ; 中国近海区域 ; 区域气候
地址

国家海洋环境预报中心, 北京, 100081

语种 中文
文献类型 研究性论文
ISSN 1006-9585
学科 大气科学(气象学)
基金 全球变化与海气相互作用专项 ;  国家自然科学基金
文献收藏号 CSCD:6451004

参考文献 共 38 共2页

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引证文献 3

1 韩莹 一种基于深度学习的海表温度混合预测方法 海洋环境科学,2022,41(5):791-798
CSCD被引 2

2 杜家豪 基于全局跨尺度时空注意力的深度神经网络海表面温度预测模型 海洋环境科学,2023,42(6):944-954
CSCD被引 2

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论文科学数据集

1. SSTG 数据集:全球海面温度格网数据(2002–2019)

数据来源:
国家青藏高原科学数据中心
PlumX Metrics
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