Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Abstract

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.

A Climate Index Optimized for Longshore Sediment Transport Reveals Interannual and Multidecadal Littoral Cell Rotations

“…The quantitative link between the specific coastal process of interest and the spatial climate is unique relative to other statistical downscaling techniques. Other studies have developed techniques linking meteorological conditions with wave distributions (e.g., Antolinez et al, ; Rueda et al, ) by performing clustering analyses of the meteorology a priori to knowledge of wave conditions and forming coincident H s , T p , and θ distributions directly dependent on the atmospheric clusters. The present study differs from such previous work by clustering with respect to ∑ P l and is therefore identifying a specific nearshore process and the coincident meteorological conditions relevant to that process as opposed to vice versa.…”

Downscaling Changing Coastlines in a Changing Climate: The Hybrid Approach

“…To translate these typical SSTs into wave conditions affecting specific coastal locations, we use a dynamical model that propagates the waves in the SST across continental shelf bathymetry and around any obstructions, taking local wind conditions into account. This versatile “hybrid” downscaling approach, mixing statistical and dynamical modeling, can be used to generate wave data with a range of different temporal resolutions, and the wave and wind data can be converted to other forcing variables, including total water level (Rueda et al, ). This approach could also be used to address future wave climate change, using the meteorological conditions output from global climate models (Perez et al, ).…”

Can beaches survive climate change?

“…Using an ensemble of computationally expensive models (e.g., 3‐D physics‐based models) becomes significantly less practical. Therefore, ensemble model predictions will likely use computationally efficient process‐based models and/or statistical downscaling [ Antolínez et al , ; Rueda et al , ] versus dynamical downscaling (i.e., nested models).…”