Impact of Sea‐Ice Model Complexity on the Performance of an Unstructured‐Mesh Sea‐Ice/Ocean Model under Different Atmospheric Forcings

Abstract

We have equipped the unstructured-mesh global sea-ice and ocean model FESOM2 with a set of physical parameterizations derived from the single-column sea-ice model Icepack. The update has substantially broadened the range of physical processes that can be represented by the model. The new features are directly implemented on the unstructured FESOM2 mesh, and thereby benefit from the flexibility that comes with it in terms of spatial resolution. A subset of the parameter space of three model configurations, with increasing complexity, has been calibrated with an iterative Green's function optimization method to test the impact of the model update on the sea-ice representation. Furthermore, to explore the sensitivity of the results to different atmospheric forcings, each model configuration was calibrated separately for the NCEP-CFSR/CFSv2 and ERA5 forcings. The results suggest that a complex model formulation leads to a better agreement between modeled and the observed sea-ice concentration and snow thickness, while differences are smaller for sea-ice thickness and drift speed. However, the choice of the atmospheric forcing also impacts the agreement of the FESOM2 simulations and observations, with NCEP-CFSR/CFSv2 being particularly beneficial for the simulated sea-ice concentration and ERA5 for sea-ice drift speed. In this respect, our results indicate that parameter calibration can better compensate for differences among atmospheric forcings in a simpler model (i.e. sea-ice has no heat capacity) than in more realistic formulations with a prognostic ice thickness distribution and sea ice enthalpy.

Snow Depth Retrieval on Arctic Sea Ice Using Under-Ice Hyperspectral Radiation Measurements

“…Hence, a future aim is to retrieve high spatial coverage snow depth for large regions of Arctic sea ice which is in particular necessary to improve the description of snow processes in climate models (Webster et al, 2018) and the validation of satellite products. A product derived from this study could be probability density functions of snow depth that could serve as input for, e.g., Icepack, the column physics package of the Los Alamos sea ice model CICE (e.g., Zampieri et al, 2021). However, for that to be feasible the gaps between local scale studies as presented here and larger scales studied in models need to be minimized.…”

Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC

“…Despite the observed k s range, we still typically assume the snow thermal conductivity to be a constant in largescale sea ice models, and only Lecomte et al (2013) attempted to develop a wind-driven parameterization for this parameter. Moreover, the snow thermal conductivity is often used as a tuning knob to enhance or suppress the winter sea ice growth (Urrego-Blanco et al, 2016;Zampieri et al, 2021). This tuning approach is effective in obtaining the wanted result, which is improved sea ice simulations in terms of the pan-Arctic sea ice extent and volume.…”

On the Importance of Representing Snow Over Sea‐Ice for Simulating the Arctic Boundary Layer

“…This is an important question for NWP applications, but also for climate reanalyses, such as ERA5 or ERA-I, that are produced with NWP systems. The latter are also used as boundary conditions for uncoupled ocean and sea ice simulations (see for instance Zampieri et al [2021]). The challenges associated with the way snow heat transfer processes and the ice-snow-atmosphere coupling are represented are evaluated.…”