Estimating The Sea Ice Floe Size Distribution Using Satellite Altimetry: Theory, Climatology, and Model Comparison

Abstract

Abstract. In sea-ice-covered areas, the sea ice floe size distribution (FSD) plays an important role in many processes affecting the coupled sea-ice-ocean-atmosphere system. Observations of the FSD are spare – traditionally taken via a pain-staking analysis of ice surface photography – and the seasonal and inter-annual evolution of floe size regionally and globally is largely unknown. Frequently, measured FSDs are assessed using a single number, the scaling exponent of the closest power law fit to the observed floe size data, although in the absence of adequate datasets there have been limited tests of this power-law hypothesis. Here we derive and explain a mathematical technique for deriving statistics of the sea ice FSD from polar-orbiting altimeters, satellites with sub-daily return times to polar regions with high along-track resolutions. Applied to the CryoSat-2 radio altimetric record, covering the period from 2010–2018, and incorporating 11 million individual floe samples, we produce the first climatology and seasonal cycle of sea ice floe size statistics. We then perform the first pan-Arctic test of the power law hypothesis, finding limited support in the range of floe sizes typically analyzed in photographic observational studies. We compare the seasonal variability in observed floe size to fully coupled climate model simulations including a prognostic floe size and thickness distribution and coupled wave model, finding good agreement in regions where modeled ocean surface waves cause sea ice fracture.

Wave–sea-ice interactions in a brittle rheological framework

“…Its main characteristic is certainly its cut-off floe size that depends both on sea ice properties and on the wave field. This redistribution scheme could easily be adapted in case progress were made on the relative importance of the parameters affecting this cut-off floe size and the associated redistribution, either from observations thanks to new available FSD datasets (Hwang et al, 2017;Horvat et al, 2019), or from discrete element modelling (Herman, 2018). Note also that alternatives exist to the redistribution scheme of Zhang et al (2015), and in particular the one suggested by Horvat and Tziperman (2015) that makes use of the whole wave frequency spectrum.…”

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

“…Maximum-likelihood estimation (MLE) was used to estimate the most probable value of α and the most probable onset (if any), A 0 , of power-law behavior in the area distribution. The MLE estimation was performed for each sub-region (n = 13), physiographic terrain class (n = 5), and over the entire dataset [56][57][58]. To understand how the choice of sub-region boundary might have influenced sampling, a bootstrap method [59] was used to estimate p-values and one-standard-deviation confidence intervals for α and A 0 as follows: 1000 subsets for A > A 0 were generated using the MLE estimates of α.…”