Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g., Pacific Ocean). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications.

Hurricanes interact with the Gulf Stream in the South Atlantic Bight (SAB) through a wide variety of processes, which are crucial to understand for prediction of open-ocean and coastal hazards during storms. However, it remains unclear how waves are modified by large-scale ocean currents under storm conditions, when waves are aligned with the storm-driven circulation and tightly coupled to the overlying wind field. Hurricane Matthew (2016) impacted the U.S. Southeast coast, causing extensive coastal change due to large waves and elevated water levels. The hurricane traveled on the continental shelf parallel to the SAB coastline, with the right side of the hurricane directly over the Gulf Stream. Using the Coupled Ocean–Atmosphere–Wave–Sediment Transport modeling system, we investigate wave–current interaction between Hurricane Matthew and the Gulf Stream. The model simulates ocean currents and waves over a grid encompassing the U.S. East Coast, with varied coupling of the hydrodynamic and wave components to isolate the effect of the currents on the waves, and the effect of the Gulf Stream relative to storm-driven circulation. The Gulf Stream modifies the direction of the storm-driven currents beneath the right side of the hurricane. Waves transitioned from following currents that result in wave lengthening, through negative current gradients that result in wave steepening and dissipation. Wave–current interaction over the Gulf Stream modified maximum coastal total water levels and changed incident wave directions at the coast by up to 20°, with strong implications for the morphodynamic response and stability of the coast to the hurricane.

Barrier islands are especially vulnerable to hurricanes and other large storms, owing to their mobile composition, low elevations, and detachment from the mainland. Conceptual models of barrier‐island evolution emphasize ocean‐side processes that drive landward migration through overwash, inlet migration, and aeolian transport. In contrast, we found that the impact of Hurricane Dorian (2019) on North Core Banks, a 36‐km barrier island on the Outer Banks of North Carolina, was primarily driven by inundation of the island from Pamlico Sound, as evidenced by storm‐surge model results and observations of high‐water marks and wrack lines. Analysis of photogrammetry products from aerial imagery collected before and after the storm indicate the loss of about 18% of the subaerial volume of the island through the formation of over 80 erosional washout channels extending from the marsh and washover platform, through gaps in the foredunes, to the shoreline. The washout channels were largely co‐located with washover fans deposited by earlier events. Net seaward export of sediment resulted in the formation of deltaic bars offshore of the channels, which became part of the post‐storm berm recovery by onshore bar migration and partial filling of the washouts with washover deposits within 2 months. This event represents a volumetric setback in the overwash/rollover behavior required for barrier transgression, but the new ponds and lowland habitats may provide beneficial habit for endangered species and will likely persist for years.

Historically, the sparseness of in situ open‐ocean wave and weather observations has severely limited the forecast skill of weather over the ocean with major social and economic consequences for coastal communities and maritime industries. Ocean surface waves, specifically, are important for the interaction between atmosphere and ocean, and thus key in modeling weather and climate processes. Here, we investigate the improvements achievable from a large distributed sensor network combined with advances in assimilation strategies. Wave spectra from a global network of over 600 Sofar Spotter buoys are assimilated into an operational global wave forecast via optimal interpolation to update model spectra to best fit observations. We demonstrate end‐to‐end improvements in forecast skill of significant wave height of 38%, and up to 45% for other bulk parameters. This shows distributed observations of the air‐sea interface, with advances in assimilation strategies, can reduce uncertainty in forecasts to dramatically improve earth system modeling.

Coastal hazards emerge from the combined effect of wave conditions and sea level anomalies associated with storms or low‐frequency atmosphere‐ocean oscillations. Rigorous characterization of wave climate is limited by the availability of spectral wave observations, the computational cost of dynamical simulations, and the ability to link wave‐generating atmospheric patterns with coastal conditions. We present a hybrid statistical‐dynamical approach to simulating nearshore wave climate in complex coastal settings, demonstrated in the Southern California Bight, where waves arriving from distant, disparate locations are refracted over complex bathymetry and shadowed by offshore islands. Contributions of wave families and large‐scale atmospheric drivers to nearshore wave energy flux are analyzed. Results highlight the variability of influences controlling wave conditions along neighboring coastlines. The universal method demonstrated here can be applied to complex coastal settings worldwide, facilitating analysis of the effects of climate change on nearshore wave climate.

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical-downscaling model-based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.