4.6 Article

Quantifying tropical cyclone intensity change induced by sea surface temperature

Journal

INTERNATIONAL JOURNAL OF CLIMATOLOGY
Volume 42, Issue 9, Pages 4716-4727

Publisher

WILEY
DOI: 10.1002/joc.7499

Keywords

quantifiable solution; sea surface temperature; tropical cyclone intensity

Funding

  1. Basic Scientific Fund for National Public Research Institutes of China [2020Q05]
  2. National Natural Science Foundation of China [41706034, 41976003, 42030405]
  3. National Key Research and Development Program of China [2019YFC1510101]
  4. Key-Area Research and Development Program of Guangdong Province [2020B1111020001]
  5. National Science and Technology Major Project [2016ZX05057015]
  6. NSFC-Shandong Joint Fund for Marine Science Research Centers [U1606405]
  7. National Program on Global Change and Air-Sea Interaction [GASI-IPOVAI-01-05, GASI-03-01-01-02]

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The relationship between sea surface temperature (SST) and tropical cyclone (TC) intensity change is influenced by the current TC intensity. The study identified a threshold SST (TSST) that affects TC intensity, with TCs intensifying or weakening depending on whether SST is higher or lower than TSST. A formula was developed to quantify TC intensity change using SST and current TC intensity, which replicated 99.46% of observed TC intensity changes, providing a potential alternative to linear regression models for improving intensity forecasts.
The relationship between sea surface temperature (SST) and tropical cyclone (TC) intensity change exhibits a strong dependence on the current TC intensity. Using western North Pacific TC observations from 1982 to 2018, a threshold SST (TSST) is identified as the SST required to maintain TC intensity. TSST increases with TC intensity, with TCs intensifying and weakening when SST is higher and lower than TSST, respectively. Across the dataset, mean TC intensity change is proportional to the difference between SST and TSST. This study also formulates an equation to quantify TC intensity change using SST and current TC intensity, which replicates 99.46% of the mean observed TC intensity changes. This equation could serve as an alternative to the linear regression-based relationship between SST and TC intensity change that is widely used in statistical-dynamical intensity models, thereby improving intensity forecasts.

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