中国东海海表温度时空演化分析

doi:10.6046/gtzyyg.2020.04.28

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摘要

为掌握中国东海2003—2018年间海表温度(sea surface temperature,SST)变化规律,分析SST变化与气候异常现象之间的联系,利用遥感数据监测东海16 a来SST时空演化特征。以2003—2018年MODIS SST产品为数据源,先通过最邻近点值替代法对数据进行修复,并用实测数据进行精度验证,利用最小二乘法、皮尔逊相关系数分析SST变化趋势,通过互相关分析研究海表温度异常(sea surface temperature anomaly,SSTA )与南方涛动指数(southern oscillation index,SOI)的相关性。结果表明: ①2003—2018年东海SST总体呈上升趋势,夏季升温趋势更加明显,长江口海域升温速度可达0.042 ℃/a以上; ②东海SST呈SE—NW分布,同纬度地区的大陆附近SST通常比其东部海域低,但4—9月杭州湾海域SST比其东部要高; ③SOI与滞后其15个月内的东海SSTA基本不相关,但与滞后其21~39个月的东海SSTA呈较强负相关,相关系数超过-0.2。研究结果可为掌握气候变化规律、预测极端天气提供参考依据。

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	王平
	毛克彪
	孟飞
	袁紫晋

关键词 ：海表温度(SST), 时空变化, 东海

Abstract：

In order to grasp the law of sea surface temperature (SST) change in the East China Sea from 2003 to 2018, the authors analyzed the relationship between SST changes and climate anomalies, and used remote sensing data to monitor the temporal and spatial evolution of SST in the East China Sea for 16 years. With the 2003—2018 MODIS SST product as the data source, the data were first repaired by the nearest neighbor point value replacement method, and the measured data were used to verify the accuracy. The least square method and Pearson correlation coefficient were used to analyze the SST change trend. Through cross-correlation analysis, the correlation between sea surface temperature anomaly (SSTA) and southern oscillation index (SOI) was studied. The results are as follows: ① SST in the East China Sea generally showed an upward trend from 2003 to 2018, and the temperature rise in summer was more obvious. The temperature rise rate in the Yangtze River estuary could reach above 0.042 ℃/a; ② SST in the East China Sea showed a SE—NW distribution, and at the same latitude, SST near the mainland was usually lower than the eastern sea area, but the SST of Hangzhou Bay area from April to September was higher than that of the eastern area; ③ SOI was basically not related to the East China Sea SSTA that was 15 months behind it, but it had a strong negative correlation with the East China Sea SSTA that was 21~39 months behind with correlation coefficient exceeding -0.2. The research results can provide a reference for grasping the laws of climate change and predicting extreme weather.

Key words： sea surface temperature(SST) spatial and temporal variability East China Sea

收稿日期: 2020-01-15 出版日期: 2020-12-23

TP79

基金资助:国家重点研发计划重点项目“全国和典型区域低温、高温灾害风险评估与图谱编制”(2019YFC1510203);中央级公益性科研院所基本科研业务费专项项目“高时空分辨率干旱监测关键参数土壤水分反演算法及应用研究”(1610132020014)

通讯作者: 毛克彪

作者简介: 王平(1997-),女,硕士研究生,主要从事农业遥感方面的研究。Email:wangping_1997@163.com。

引用本文:

王平, 毛克彪, 孟飞, 袁紫晋. 中国东海海表温度时空演化分析[J]. 国土资源遥感, 2020, 32(4): 227-235.
WANG Ping, MAO Kebiao, MENG Fei, YUAN Zijin. Spatiotemporal evolution of sea surface temperature in the East China Sea. Remote Sensing for Land & Resources, 2020, 32(4): 227-235.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.04.28 或 https://www.gtzyyg.com/CN/Y2020/V32/I4/227

Fig.1 技术路线

Fig.2 MODIS插值后SST精度验证

Fig.3 东海SST年际变化

Fig.4 2003—2018年东海年平均SST变化趋势空间分布

Fig.5 各季节东海SST年际变化

Tab.1 各季节东海SST年际变化趋势

Fig.6 东海SST月际变化曲线

Fig.7-1 东海SST空间分布

Fig.7-2 东海SST空间分布

Fig.8 东海SSTA时间序列

Tab.2 东海SSTA与厄尔尼诺年及拉尼娜年的关系

Fig.9 东海与赤道太平洋海温以及SOI互相关分析(2003—2018年)

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