The El Niño-Southern Oscillation is the dominant mode of interannual climate variability across the Pacific Ocean basin, with influence on the global climate. The two end members of the cycle, El Niño and La Niña, force anomalous oceanographic conditions and coastal response along the Pacific margin, exposing many heavily populated regions to increased coastal flooding and erosion hazards. However, a quantitative record of coastal impacts is spatially limited and temporally restricted to only the most recent events. Here we report on the oceanographic forcing and coastal response of the 2015–2016 El Niño, one of the strongest of the last 145 years. We show that winter wave energy equalled or exceeded measured historical maxima across the US West Coast, corresponding to anomalously large beach erosion across the region. Shorelines in many areas retreated beyond previously measured landward extremes, particularly along the sediment-starved California coast.

Ocean‐basin‐scale climate variability produces shifts in wave climates and water levels affecting the coastlines of the basin. Here we present a hybrid shoreline change—foredune erosion model (A COupled CrOss‐shOre, loNg‐shorE, and foreDune evolution model, COCOONED) intended to inform coastal planning and adaptation. COCOONED accounts for coupled longshore and cross‐shore processes at different timescales, including sequencing and clustering of storm events, seasonal, interannual, and decadal oscillations by incorporating the effects of integrated varying wave action and water levels for coastal hazard assessment. COCOONED is able to adapt shoreline change rates in response to interactions between longshore transport, cross‐shore transport, water level variations, and foredune erosion. COCOONED allows for the spatial and temporal extension of survey data using global data sets of waves and water levels for assessing the behavior of the shoreline at multiple time and spatial scales. As a case study, we train the model in the period 2004–2014 (11 years) with seasonal topographic beach profile surveys from the North Beach Sub‐cell (NBSC) of the Columbia River Littoral Cell (Washington, USA). We explore the shoreline response and foredune erosion along 40 km of beach at several timescales during the period 1979–2014 (35 years), revealing an accretional trend producing reorientation of the beach, cross‐shore accretional, and erosional periods through time (breathing) and alternating beach rotations that are correlated with climate indices.

A recent 35‐year endpoint shoreline change analysis revealed significant counterclockwise rotations occurring in north‐central Oregon, USA, littoral cells that extend 10s of kilometers in length. While the potential for severe El Niños to contribute to littoral cell rotations at seasonal to interannual scale was previously recognized, the dynamics resulting in persistent (multidecadal) rotation were unknown, largely due to a lack of historical wave conditions extending back multiple decades and the difficulty of separating the timescales of shoreline variability in a high energy region. This study addresses this question by (1) developing a statistical downscaling framework to characterize wave conditions relevant for longshore sediment transport during data‐poor decades and (2) applying a one‐line shoreline change model to quantitatively assess the potential for such large embayed beaches to rotate. A climate INdex was optimized to capture variability in longshore wave power as a proxy for potential LOngshore Sediment Transport (LOST_IN), and a procedure was developed to simulate many realizations of potential wave conditions from the index. Waves were transformed dynamically with Simulating Waves Nearshore to the nearshore as inputs to a one‐line model that revealed shoreline rotations of embayed beaches at multiple time and spatial scales not previously discernible from infrequent observations. Model results indicate that littoral cells respond to both interannual and multidecadal oscillations, producing comparable shoreline excursions to extreme El Niño winters. The technique quantitatively relates morphodynamic forcing to specific climate patterns and has the potential to better identify and quantify coastal variability on timescales relevant to a changing climate.

Rising seas coupled with ever increasing coastal populations present the potential for significant social and economic loss in the 21^st century. Relatively short records of the full multi‐dimensional space contributing to total water level (TWL) coastal flooding events (astronomic tides, sea level anomalies, storm surges, wave runup, etc.) results in historical observations of only a small fraction of the possible range of conditions that could produce severe flooding. The Time‐varying Emulator for Short‐ and Long‐Term analysis of coastal flood hazard potential (TESLA‐flood) is presented here as a methodology capable of producing new iterations of the sea‐state parameters associated with the present‐day Pacific Ocean climate to simulate many synthetic extreme compound events. The emulator utilizes weather typing of fundamental climate drivers (sea surface temperatures, sea level pressures, etc.) to reduce complexity and produces new daily synoptic weather chronologies with an auto‐regressive logistic model accounting for conditional dependencies on the El Niño Southern Oscillation (ENSO), the Madden‐Julian Oscillation (MJO), seasonality, and the prior two days of weather progression. Joint probabilities of sea‐state parameters unique to simulated weather patterns are used to create new time series of the hypothetical components contributing to synthetic TWLs (swells from multiple directions coupled with water levels due to wind setup, temperature anomalies, and tides). TESLA‐flood reveals the importance of considering the multi‐variate nature of extreme coastal flooding, while progressing the ability to incorporate large‐scale climate variability into site specific studies assessing hazards within the context of predicted climate change in the 21^st century.

Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21^st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.