Storm surge variability and prediction from ENSO and tropical cyclones

Tan, Y., Zhang, W., Feng, X. ORCID: https://orcid.org/0000-0003-4143-107X, Guo, Y. and Hoitink, A. J. F. (2023) Storm surge variability and prediction from ENSO and tropical cyclones. Environmental Research Letters, 18 (2). 024016. ISSN 1748-9326 doi: 10.1088/1748-9326/acb1c8

Abstract/Summary

Storm surges are among the deadliest natural hazards, but understanding and prediction of year-to-year variability of storm surges is challenging. Here, we demonstrate that the interannual variability of observed storm surge levels can be explained and further predicted, through a process-based study in Hong Kong. We find that ENSO exerts a compound impact on storm surge levels through modulating tropical cyclones (TCs) and other forcing factors. The occurrence frequencies of local and remote TCs are responsible for the remaining variability in storm surge levels after removing the ENSO effect. Finally, we show that a statistical prediction model formed by ENSO and TC indices has good skill for prediction of extreme storm surge levels. The analysis approach can be applied to other coastal regions where tropical storms and the climate variability are main contributors to storm surges. Our study gives new insight into identifying “windows of opportunity” for successful prediction of storm surges on long-range timescales.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/109402
Identification Number/DOI	10.1088/1748-9326/acb1c8
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Institute of Physics
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