| 基于回归模型的热带气旋条件下的海表降温参数化方案设计 |
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| 引用本文： | 韦骏,刘欣,蒋国庆.基于回归模型的热带气旋条件下的海表降温参数化方案设计[J].海洋学报(英文版),2018,37(1):1-10. |
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| 作者姓名： | 韦骏 刘欣 蒋国庆 |
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| 作者单位： | 北京大学大气与海洋科学系, 气候与海气实验室, 北京, 100871,中国气象科学研究院, 灾害天气国家重点实验室, 北京, 100081北京大学大气与海洋科学系, 气候与海气实验室, 北京, 100871,北京大学大气与海洋科学系, 气候与海气实验室, 北京, 100871 |
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| 摘 要： | Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature(SST) cooling induced by tropical cyclones(TCs). Three major dynamic and thermodynamic processes governing the TC-induced SST cooling(SSTC), vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables:sea surface height(SSH), wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model,plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.
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| 关 键 词： | 热带气旋 海表降温 回归模型 参数化 |
| 收稿时间： | 2017/1/23 0:00:00 |
Parameterizing sea surface temperature cooling induced by tropical cyclones using a multivariate linear regression model |
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| WEI Jun,LIU Xin and JIANG Guoqing.Parameterizing sea surface temperature cooling induced by tropical cyclones using a multivariate linear regression model[J].Acta Oceanologica Sinica,2018,37(1):1-10. |
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| Authors: | WEI Jun LIU Xin and JIANG Guoqing |
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| Institution: | 1.Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, Peking University, Beijing 100871, China2.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaLaboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, Peking University, Beijing 100871, China |
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| Abstract: | Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature (SST) cooling induced by tropical cyclones (TCs). Three major dynamic and thermodynamic processes governing the TC-induced SST cooling (SSTC), vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables: sea surface height (SSH), wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001–2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72°C between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model, plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes. |
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| Keywords: | tropical cyclones SST cooling regression model parameterization |
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