Summer sea ice floe size distribution in the Arctic: High-resolution optical satellite imagery and model evaluation

• Main Authors: Wang, Yanan, Hwang, Byongjun, Bateson, Adam William, Aksenov, Yevgeny, Horvat, Christopher
• Format: Text
• Language: English
• Published: 2022
• Subjects: Arctic; Sea ice
• Online Access: https://doi.org/10.5194/tc-2022-130; https://tc.copernicus.org/preprints/tc-2022-130/

The sea ice floe size distribution (FSD) is an important component for sea ice thermodynamic and dynamic processes, particularly in the marginal ice zone. Recently FSD-related processes have been incorporated in sea ice models, but the sparsity of existing observations limits the evaluation of FSD models, so hindering model improvements. In this study, three FSD models are selected for the evaluation – Waves-in-Ice module and Power law Floe Size Distribution (WIPoFSD) model and two branches of a fully prognostic floe size-thickness distribution model: CPOM-FSD and FSDv2-WAVE. These models are evaluated against a new FSD dataset derived from high-resolution satellite imagery in the Arctic. The evaluation shows an overall overestimation of floe perimeter density by the models against the observations. Comparison of the normalized distributions of the floe perimeter density with the observations show that the models exhibit much larger proportion for small floes (the radius < 10–30 m) but much smaller proportion for large floes (the radius > 30–50 m). Observations and the WIPoFSD model both show a negative correlation between sea ice concentration and the floe perimeter density, but the two prognostic models (CPOM-FSD and FSDv2-WAVE) show the opposite pattern. These differences between models and the observations may be attributed to limitations of the observations (e.g., the image resolution is not sufficient to detect small floes), or limitations of the model parameterisations, including the use of a global power-law exponent in the WIPoFSD model, as well as too-weak floe welding and enhanced wave fracture in the prognostic models.