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Abstract. Since the 1982–83 major El Niño event, the El Niño Southern Oscillation (ENSO) has been suspected to be a major driver of coastal hazards in the U.S. Pacific Northwest (PNW). However, limited availability of observations, combined with the diversity of ENSO characteristics, has led to significant challenges constraining the teleconnections between ENSO effects and hazardous impacts on the coast. Defining these teleconnections provides key insight into internal climate dynamics and will inform hazard management. Here, we used a stochastic climate emulator to probabilistically assess the role of ENSO phase, ENSO strength, and synoptic weather in driving flooding and erosion hazard proxies on the U.S. PNW coast. When compared to the last 45 years of observations, our simulations suggest that ENSO is not necessarily a strong predictor of coastal hazards in the PNW, and instead, indicate that the relationship between ENSO and coastal hazard teleconnections is more complex than previously believed. 

Regional scale assessments of future chronic coastal hazard impacts are critical tools for adaptation planning under a changing climate. Probabilistic simulations of hazard impacts can improve these assessments by explicitly attempting to quantify uncertainty and by better simulating dependence between complex multivariate drivers of hazards. In this study, probabilistic future timeseries of total water levels (TWLs) are generated from a stochastic climate emulator (TESLA; Anderson et al., 2019) for the Cascadia region, USA for use in a chronic hazard impact assessment. This assessment focuses on three hazard metrics: collision, overtopping, and beach safety, and also introduces a novel hotspot indicator to identify areas that may experience dramatic changes in hazard impacts compared to present day conditions. Results are presented for a subset of the Cascadia region (Rockaway Beach Littoral Cell, Oregon) to demonstrate the power of the probabilistic impact assessment approach. The results highlight how useful spatially varying, scenario-based hazard impacts assessments and hotspot indicators are for identifying which areas and types of hazards may require increased adaptation support. This approach enables us to piece apart the relative uncertainty of hazards as driven by SLR versus natural variability (caused by variation in climate, weather, and hydrodynamic drivers).