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Seasonal predictability of high sea level frequency using ENSO patterns along the U.S. West Coast

Author:
Khouakhi, Abdou, Villarini, Gabriele, Zhang, Wei, Slater, Louise J.
Source:
Advances in water resources 2019 v.131 pp. 103377
ISSN:
0309-1708
Subject:
El Nino, assets, climate, coastal zone management, coasts, cold, correlation, ecosystems, floods, gauges, models, planning, prediction, saltwater intrusion, sea level, storms, tides, water resources, United States
Abstract:
High sea levels can be conducive to coastal flooding, coastal erosion and inland salt-water intrusion, and thus pose a significant threat to coastal communities, ecosystems and coastal assets. Increases in high water levels have been attributed largely to rising mean sea levels associated with intra-seasonal to interannual climate modes of variability such as the El Niño-Southern Oscillation (ENSO). Here, we examine the predictability of the seasonal frequency of high sea levels using the Niño3.4 index. Different high sea level quantities are considered at 23 tide gauges along the U.S. West Coast, including storm surge and nuisance (minor) floods. At each site, we develop a statistical probabilistic forecasting model for seasonal high sea level frequency during the cold period of October-March. As predictors, we compare the use of (1) seasonal Niño3.4 index observations over the warm antecedent period of July-September and (2) seasonal Niño3.4 index forecasts from the North American Multi-Model Ensemble (NMME) over the cold concurrent period of October-March. Results indicate that the Niño3.4 observations are a good predictor of seasonal high sea level frequency, especially for predicting the storm surge frequency. Correlation coefficients between the observed and modelled seasonal storm surge frequency range from 0.6 to 0.95 at most of the 23 tide gauges. In the predictive model, when using NMME Niño3.4 index, correlation coefficients range between approximately 0.4 and 0.7 at the southern gauges for Niño3.4 index forecasts initialized from October to June (the skill decreases with lead time). Our results provide insights into the seasonal predictability of high sea levels using ENSO patterns which is important for planning and coastal management.
Agid:
6510194