Predictability of Extreme Sea Level Variations Along the U.S. Coastline

Extreme sea level variability (excluding the effects of mean sea level (MSL) and long‐period tidal cycles) at decadal to multidecadal time scales is significant along the U.S. coastlines and can modulate coastal flood risk in addition to long‐term MSL rise. Therefore, understanding the climatic drivers and ultimately predicting these low‐frequency variations are important. Extreme sea level indicators are used to represent the variations in 100‐year return water levels, estimated with a nonstationary extreme value analysis. Here, we develop prediction models in the frequency domain. Extreme sea level indicators (response) and potential predictors (traditional climate indices, sea level pressure [SLP], or sea surface temperature [SST]) are decomposed into subseries corresponding to predefined frequencies using discrete wavelet transform (DWT), and regression models are formulated for each frequency separately. In the case of traditional climate indices, subseries of climate indices that provide the highest correlation with the corresponding subseries of indicators are used in the regression models, and original indicators are reconstructed by aggregating predicted subseries. Tailored climate indices are developed for each frequency band by averaging wavelet decomposed subseries of SLP or SST from grid locations where correlations with corresponding decomposed subseries of extreme sea level indicators are highest and robust. Models with wavelet filtered climate indices reproduce the variability and general trends of the indicators. The use of tailored indices further improves the model performance in predicting extreme sea level variations. Model performance in terms of Nash‐Sutcliffe efficiency statistics varies from 0.54 to 0.93. Prediction of extreme sea level indicators using tailored indices derived from SLP and SST of initialized decadal climate model simulations is also tested to facilitate progress toward forecasting extreme sea level variations at decadal time scales. Plain Language Summary Decadal to multidecadal variability of extreme sea levels is often omitted in coastal flood risk assessments, yet it can modulate flood risk in addition to long‐term mean sea level rise, which is often considered as the only oceanographic driver for flood risk changes through time. In this study, we develop models to predict extreme sea level variations using traditional and tailored climate indices as predictors. Tailored climate indices are derived from gridded