Incorporating dependence on ice thickness in empirical parameterizations of wave dissipation by sea ice

TL;DR

This paper develops an improved empirical model for wave energy dissipation by sea ice that explicitly includes ice thickness, aiming to enhance prediction accuracy in ice-infested regions.

Contribution

It introduces a new dissipation parameterization incorporating ice thickness alongside wave frequency, showing improved fit on the original dataset.

Findings

Including ice thickness reduces data scatter.

Non-dimensionalization aids data scale collapse.

Mixed results in independent dataset evaluations.

Abstract

This study is part of an effort to improve the Navy's ability to forecast wind-generated ocean waves in ice-infested regions, and here we are attempting to further this goal by improving prediction of dissipation of wave energy by sea ice. Rogers et al. (2021) presented new estimates of frequency-dependent dissipation of wave energy by sea ice, based on model-data inversion, and studied the correlation with various other parameters, such as ice thickness and sea state variables. Here, we use that dataset to propose a new dissipation parameterization which explicitly incorporates the dependence on the ice thickness, in addition to the wave frequency. The goal is to determine whether a parameterization dependent on wave frequency and ice thickness can be more accurate than one dependent only on wave frequency. Due to the dominant impact of frequency and confounding difficulties of field measurements, this is not a foregone conclusion. A parameterization is developed using the non-dimensionalization approach proposed by Yu et al. (2019). We find that the non-dimensionalization does result in significant scale collapse of the data, and inclusion of ice thickness does improve accuracy, most evidenced by reduced scatter when applied to the same dataset. However, evaluations against independent datasets are mixed. Possible reasons for this are discussed.