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.
Altmetric Badge
Item Type | Article |
URI | https://reading-clone.eprints-hosting.org/id/eprint/109402 |
Item Type | Article |
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 |
Download/View statistics | View download statistics for this item |
Downloads
Downloads per month over past year
University Staff: Request a correction | Centaur Editors: Update this record