Abstract
The need for skillful seasonal prediction of coastal sea level anomalies (SLAs) has become increasingly evident as climate change has increased coastal flooding risks. Here, we evaluate nine current forecast systems by calculating deterministic and probabilistic skill from their retrospective forecasts (“hindcasts”) over 1995–2015, for lead times up to 6–9 months, at two U.S. tide gauge stations (Charleston, South Carolina, and San Diego, California). Additionally, we assess local skill enhancement by two postprocessing/downscaling techniques: an observation-based multivariate linear regression and a hybrid dynamical approach convolving sea level sensitivity to surface forcings with predicted surface forcing variations. All these approaches face challenges stemming from whether modeled SLAs sufficiently represent observed local coastal SLA variations because of ocean model limitations and inadequacies in model initialization and ensemble spread. Some of these issues also complicate the ability of the postprocessing techniques to improve probabilistic skill, even though they do somewhat improve deterministic skill. In general, deterministic hindcast skill is considerably higher for San Diego than Charleston, as expected from the stronger influence of El Niño–Southern Oscillation (ENSO). However, ensemble spread metrics such as forecast reliability and sharpness remain low for both locations, highlighting model deficiencies in representing uncertainty. Additionally, evaluating how well any technique predicts seasonal coastal sea level variations is complicated by the forced trend component, particularly how it is estimated. Moreover, model skill is matched by a stochastically forced multivariate linear prediction model constructed from observations, suggesting that substantial improvement remains for predicting coastal seasonal SLAs, which could also include leveraging other predicted fields, including sea level pressure and prevailing winds.
Significance Statement
Coastal floodings have occurred more frequently in the last few decades, and it is anticipated that the number of such hazardous events will increase. Therefore, accurate and reliable forecasting of coastal water level is becoming increasingly important. This study thoroughly evaluated some current forecast techniques for sea level at two pilot study locations on the U.S. Coast (Charleston, South Carolina, and San Diego, California) and found that those techniques are still not capable to produce usable forecasting of anomalous sea level 3 months in advance, due to model inadequacy. The current generation of forecasting models was not designed for coastal sea level prediction, and we propose a few potential improvements that can potentially advance our capability in coastal sea level and inundation forecasting in the near future.
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