The new spectral ocean surface gravity wave model ICON-waves

ICON-waves is a newly developed spectral ocean surface wave model within the Icosahedral Nonhydrostatic (ICON) modelling framework. It predicts the spatio-temporal evolution of the two-dimensional wave spectrum in terms of spectral variance density, adding a previously missing physical process to the ICON framework by accounting for wave-induced feedbacks in the air-sea exchange of heat, mass, and momentum.

The model builds on the existing ICON infrastructure, such as the unstructured icosahedral-triangular grid, the coupling software YAC, and the finite-volume method for scalar transport currently used in the atmospheric component. The spectral wave modelling approach, including formulations for wind input, dissipation, and nonlinear wave-wave interaction, is adopted from the operational model WAM (Wave Model Cycle 4), which is used at Deutscher Wetterdienst (DWD) in a standalone setup for daily maritime forecasts. ICON-waves is intended to replace WAM in future operational forecasting at DWD.

Unlike WAM, ICON-waves enables simultaneous two-way coupling with both the atmospheric and oceanic components of ICON, allowing for a more realistic representation of wave-mediated atmosphere-ocean interactions. Technically, embedding ICON-waves into the ICON framework creates synergies in code maintenance and testing, infrastructure development, and portability across a range of computing architectures.

We present the physical and numerical formulation of the model alongside first results from standalone and coupled atmosphere–wave simulations evaluated against observations and the operational WAM model. In standalone mode, ICON-waves demonstrates performance comparable to the current operational system. When coupled to the atmosphere, ICON-waves enables a more physically consistent representation of air-sea interactions, altering surface momentum fluxes and sea-level pressure patterns, particularly under strong wind conditions. While these results are promising, further work will focus on fully exploiting the potential of the coupled system, including additional tuning and optimization for operational forecasting.