Potential Predictability of Arctic sea-ice linear kinematic features in high-resolution ensemble simulation

Linear kinematic features (LKFs) in sea ice, potentially important for short-term forecasts and for climate simulations, emerge as viscous-plastic sea ice models are used at high resolution (~ 4.5 km). Here we analyze the short-range (up to 10 days) potential predictability of LKFs in Arctic sea ice using an ocean/sea-ice model with a grid point separation of 4.5 km. We analyze the sensitivity of predictability to idealized initial perturbations, mimicking the uncertainties in sea ice analyses, and to growing uncertainty of the atmospheric forcing caused by the chaotic nature of the atmosphere. For the latter we use different members of ECMWF ensemble forecasts to drive ocean/sea-ice forecasts. For our analysis, we diagnose LKFs occurrence and investigate different sea ice characteristics. We find that forcing uncertainty (due to limited atmospheric predictability) largely determines LKF predictability on the 10-day time scale. When it comes to metrics, we demonstrate that spatial correlation, although a useful metric to measure some aspects of deformation field similarity, fails to detect LKF similarity when LKFs are only slightly shifted in space. The Modified Hausdorff Distance (MHD) appears to be a more appropriate metric, but it can be misleading if the LKF density is very high, for example due to artificial LKFs caused by spurious small-scale perturbations of the sea-ice initial state