On the time and length scales of the Arctic sea ice thickness anomalies: a study based on fourteen reanalyses

Abstract

Abstract. The ocean-sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of fourteen reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites and ice boreholes. Second, based on the same reanalyses, we intent to characterize the time (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation 5 of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective time and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the time and length scales: reanalyses built with sea ice data assimilation present shorter time and length scales. The mean time and length scales for reanalyses with data assimilation vary from 2.5-5.0 months and 337.0-732.5 km, respectively, 10 while reanalyses with no data assimilation are characterized by values from 4.9-7.8 months and 846.7-935.7 km, respectively.

Southern Ocean sea ice concentration budgets of five ocean-sea ice reanalyses

“…Compared with in-situ or satellite observations, ice-ocean reanalysis is not only continuous in time and space, but also shows inherent ocean model physics (Storto et al 2019). In view of the above advantages, ice-ocean reanalysis is widely used to study sea ice trends and sea ice-atmosphere interactions in polar regions (e.g., Ponsoni et al 2018;Spreen et al 2011;Jaiser et al 2016), or as initial and boundary conditions for sea ice and atmosphere forecasting models (e.g., Guemas et al 2016), and numerical weather prediction models (e.g., Dee et al 2011). However, due to differences in model formulation, boundary conditions and data assimilation approaches, there is also significant spread between reanalysis products themselves (Balmaseda et al 2015).…”