In the coming decades, coastal flooding will become more frequent due to sea‐level rise and potential changes in storms. To produce global storm surge projections from 1950 to 2050, we force the Global Tide and Surge Model with a ∼25‐km resolution climate model ensemble from the Coupled Model Intercomparison Project Phase 6 High Resolution Model Intercomparison Project (HighResMIP). This is the first time that such a high‐resolution ensemble is used to assess changes in future storm surges across the globe. We validate the present epoch (1985–2014) against the ERA5 climate reanalysis, which shows a good overall agreement. However, there is a clear spatial bias with generally a positive bias in coastal areas along semi‐enclosed seas and negative bias in equatorial regions. Comparing the future epoch (2021–2050) against the historical epoch (1951–1980), we project ensemble‐median changes up to 0.1 (or 20%) in the 1 in 10‐year storm surge levels. These changes are not uniform across the globe with decreases along the coast of Mediterranean and northern Africa and southern Australia and increases along the south coast of Australia and Alaska. There are also increases along (parts) of the coasts of northern Caribbean, eastern Africa, China and the Korean peninsula, but with less agreement among the HighResMIP ensemble. Information resulting from this study can be used to inform broad‐scale assessment of coastal impacts under future climate change.

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.

Interest in understanding long‐term coastal morphodynamics has recently increased as climate change impacts become perceptible and accelerated. Multiscale, behavior‐oriented and process‐based models, or hybrids of the two, are typically applied with deterministic approaches which require considerable computational effort. In order to reduce the computational cost of modeling large spatial and temporal scales, input reduction and morphological acceleration techniques have been developed. Here we introduce a general framework for reducing dimensionality of wave‐driver inputs to morphodynamic models. The proposed framework seeks to account for dependencies with global atmospheric circulation fields and deals simultaneously with seasonality, interannual variability, long‐term trends, and autocorrelation of wave height, wave period, and wave direction. The model is also able to reproduce future wave climate time series accounting for possible changes in the global climate system. An application of long‐term shoreline evolution is presented by comparing the performance of the real and the simulated wave climate using a one‐line model.

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.

Shifts in the frequency of typical meteorological patterns in an ocean basin, over interannual to decadal time scales, cause shifts in the patterns of wave generation. Therefore, ocean basin‐scale climate shifts produce shifts in the wave climates affecting the coastlines of the basin. We present a hybrid methodology for downscaling observed (or predicted) climate shifts into local nearshore wave climates and then into the associated coastline responses. A series of statistical analyses translate observed (or predicted) distributions of meteorological states into the deep water wave climate affecting a coastal region and dynamical modeling combined with statistical analyses transform the deep water wave climate into the nearshore wave climate affecting a particular coastline. Finally, dynamical modeling of coastline evolution hindcasts (or predicts) how coastline shapes respond to climate shifts. As a case study, we downscale from meteorological hindcast in the North Atlantic basin since 1870 to the responses of the shape of the coast of the Carolinas, USA. We test the hindcasts using shoreline change rates calculated from historical shorelines, because shifts in coastline shape equate to changes in the alongshore pattern of shoreline change rates from one historical period to another. Although limited by the availability of historical shorelines (and complicated by historical inlet openings), the observations are consistent with the predicted signal of ocean basin‐scale climate change. The hybrid downscaling methodology, applied to the output of global climate models, can be used to help forecast future patterns of shoreline change related to future climate change scenarios.