Abstract
Abstract
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 El Niño-Southern Oscillation (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.
Funder
the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering
the Netherlands Organization for Scientific Research
the Weather and Climate Science for Service Partnership for Southeast Asia
Postgraduate Research & Practice Innovation Program of Jiangsu Province
National Natural Science Foundation of China
the UK Met Office Climate Science for Service Partnership for China
National Key Research and Development Program of China
Subject
Public Health, Environmental and Occupational Health,General Environmental Science,Renewable Energy, Sustainability and the Environment
Cited by
2 articles.
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