Drivers of Tropical Cyclone‐Induced Ocean Cooling in Different Seasons Over the Northwest Pacific From Explainable Machine Learning

Abstract This study develops an interpretable machine learning framework combining extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to explain the seasonal and regional variations in tropical cyclone (TC)‐induced sea surface temperature (SST) cooling in the Northwest Pacific. The framework adopts TC characteristics (e.g., intensity, translation speed, size) and pre‐storm ocean conditions (e.g., mixed layer depth, ocean thermal structure) as predictors and skillfully reproduces the spatial structure of cooling in different seasons and regions. The method identifies the drivers of the cooling in both the marginal and the open seas and quantifies their respective contributions by explicitly accounting for both the seasonal and regional variations. The drivers vary with seasons, with TC characteristics explaining 43%–56% of the variance in cooling in the open sea and 47%–52% in the marginal sea while oceanic drivers contribute 23%–41% and 31%–35%, respectively. Our results reveal that the seasonality of TC intensity and mixed layer depth are the most important contributors to the seasonal variations in the cooling over the marginal and open seas.

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Publication Details

Journal
Journal of Geophysical Research Machine Learning and Computation
Published
2025-12-01
DOI
https://doi.org/10.1029/2025jh000746
Citations
2
Primary Topic
Tropical and Extratropical Cyclones Research
Type
article
Field-Weighted Citation Impact
2.60

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article

Drivers of Tropical Cyclone‐Induced Ocean Cooling in Different Seasons Over the Northwest Pacific From Explainable Machine Learning

Gregory R. Foltz, Yi Sui, Hongxing Cui, Danling Tang et al.
2 citations
Journal of Geophysical Research Machine Learning and Computation
Tropical and Extratropical Cyclones Research
2.60
article

Drivers of Tropical Cyclone‐Induced Ocean Cooling in Different Seasons Over the Northwest Pacific From Explainable Machine Learning

Gregory R. Foltz, Yi Sui, Hongxing Cui, Danling Tang, Wei Mei, Ibrahim Hoteit, Han Zhang, Xiaowei Gu, Hongbin Liu, Chao Li
article en
2 citations

Abstract

Abstract This study develops an interpretable machine learning framework combining extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to explain the seasonal and regional variations in tropical cyclone (TC)‐induced sea surface temperature (SST) cooling in the Northwest Pacific. The framework adopts TC characteristics (e.g., intensity, translation speed, size) and pre‐storm ocean conditions (e.g., mixed layer depth, ocean thermal structure) as predictors and skillfully reproduces the spatial structure of cooling in different seasons and regions. The method identifies the drivers of the cooling in both the marginal and the open seas and quantifies their respective contributions by explicitly accounting for both the seasonal and regional variations. The drivers vary with seasons, with TC characteristics explaining 43%–56% of the variance in cooling in the open sea and 47%–52% in the marginal sea while oceanic drivers contribute 23%–41% and 31%–35%, respectively. Our results reveal that the seasonality of TC intensity and mixed layer depth are the most important contributors to the seasonal variations in the cooling over the marginal and open seas.

Journal of Geophysical Research Machine Learning and ComputationVol. 2(4)
South China Agricultural University (CN), Hong Kong University of Science and Technology (HK), Ministry of Natural Resources (CN), Fudan University (CN), NOAA Atlantic Oceanographic and Meteorological Laboratories (US), University of Surrey (GB), Zhejiang Ocean University (CN), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), Second Institute of Oceanography (CN), Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (CN), King Abdullah University of Science and Technology (SA), Zhejiang University (CN)
Natural Science Foundation of Guangdong Province
Openalex Percentile: Top 9%
Tropical and Extratropical Cyclones Research
2.60
DOIOpen Access PDFOpenAlex
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