Modelling ocean waves in ice-covered seas

• Published in: Applied ocean research Vol. 83; pp. 30 - 36
• Main Author: Shen, Hayley H.
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.02.2019; Elsevier BV
• Subjects: Calibration; Data; Ice; Modelling; Ocean waves; Sea ice; Surface water waves; Theories; Wave propagation; Wave spectra; Models; Ocean waves; Sea ice
• ISSN: 0141-1187; 1879-1549
• DOI: 10.1016/j.apor.2018.12.009

Over the past decade there has been a rapid growth of interest in wave propagation through ice covers. This paper summarizes the author’s observation of the modeling efforts on this topic. Models can be theory-based, data-driven, or a combination of the two. A pure data-driven model relies on a large amount of observations and is only becoming available recently. Theory-based models on the other hand have a long history. They are always a simplified version of the reality. As our knowledge grows, theories become more complicated. A theory for waves-in-ice that captures all possible processes does not exist. However, when integrated with observation through calibration, these combined theory + data-based models may be used with some confidence. In this paper, different models, their basic concepts, their calibration and validation are discussed. The present theory-based models do not have the correct spectral attenuation trend as observed from field or laboratory experiments. Hence, through calibration they may fit different parts of the wave spectra but not all. Pure data-driven models can reproduce the correct trend, but its dependability outside the situation where the data are collected is uncertain. In addition to offering tools to forecast waves-in-ice, these model building and validating efforts point to missing mechanisms that should be carefully studied. Despite the many challenges towards building a satisfactory general waves-in-ice model, significant progress has been made for models that work reasonably well in the marginal ice zone. We anticipate much more data will become available in the coming years to help us improve the existing models.