| WEI Jun,LIU Xin,JIANG Guoqing. 2018. Parameterizing sea surface temperature cooling induced by tropical cyclones using a multivariate linear regression model. Acta Oceanologica Sinica, 37(1):1－10 |
| Parameterizing sea surface temperature cooling induced by tropical cyclones using a multivariate linear regression model |
| 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 |
| Received:January 23, 2017 |
| DOI：10.1007/s13131-018-1153-0 |
| Key words:tropical cyclones SST cooling regression model parameterization |
| 中文关键词: 热带气旋 海表降温 回归模型 参数化 |
| 基金项目: |
| Author Name | Affiliation | E-mail | | WEI Jun | Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, Peking University, Beijing 100871, China | junwei@pku.edu.cn | | LIU Xin | 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 | | | JIANG Guoqing | Laboratory 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℃ 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. |
| 中文摘要: |
| 本文采用了线性回归分析和温度方程收支分析的方法，提出一个多元线性回归方程来参数化由热带气旋（TC）引起的海表降温（SSTC）的计算方案。在TC引起的SSTC机制中，垂直混合机制、上升流和热通量是最重要的三个机制。本文采用了海表高度（SSH）、风速、风场涡度、TC移动速度和海表热通量经验性的参数化了这三个机制，并基于此建立了一个多元线性回归方程来计算在TC条件下的海表降温。本文将2001-2010发生在西北太平洋的100个TC个例中实际观测到的SSTC，与本文建立的回归模型所计算出的SSTC进行对比。结果显示，通过本文所提出的回归模型计算出的SSTC与实际观测到的SSTC符合的非常好，平均误差为0.07℃，绝对误差为0.72℃。结果同时还显示了，垂直混合机制是引起SSTC最重要的机制，此机制所能引起的SSTC大约为总SSTC的55%。由艾克曼抽吸所引起的上升机制所引起的SSTC大约占总SSTC的18%，这种机制所引起的SSTC主要分布在TC路径的中央。海表热通量所引起的SSTC大约占总SSTC的26%，并且这种机制的作用在热带地区和大陆架地区会有所增强。海洋中一些特殊的热力学结构，可以通过方程中的SSH被分析出来，这些热力学结构在SSTC的空间分布中起到了重要的作用。这个TC条件下的回归模型还可以用在天气预报中来提高海表温度的参数化方案估计。 |
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