A new method for parameterization of wave dissipation by sea ice

We present a method for predicting wave dissipation by sea ice that is based on the dimensional analysis of data with a scaling defined by ice thickness. Applying the method to an extensive dataset from the measurements during the Polynyas, Ice Production, and seasonal Evolution in the Ross Sea (PIPERS) cruise in 2017, we derive a new model of wave dissipation which describes a nonlinear dependence on ice thickness, and reveals the interrelation between the dependences on ice thickness and on wave frequency. This nonlinear dependence on ice thickness can have important implications on predicting low-frequency waves. The root-mean-square error of the prediction is significantly reduced using the new model, compared with other existing parametric models that are also calibrated for the PIPERS dataset. The new model also explicitly describes a condition of similarity between largeand small-scale observations, which is shown to exist when various laboratory datasets collapse onto the prediction. Thus, the new model improves the estimate of wave dissipation by ice across multiple scales.

Automated summary

This study presents a method for predicting wave dissipation by sea ice using dimensional analysis and derives a new model that describes the nonlinear dependence on ice thickness and reveals the interrelation with wave frequency. The new model improves the prediction accuracy for low-frequency waves and reduces the root-mean-square error compared to existing parametric models. It also describes a condition of similarity between large and small-scale observations, enhancing the estimation of wave dissipation by ice across multiple scales.