Abstract. Two hindcast simulations are performed with the global, ocean-sea ice models NEMO-LIM2 and NEMO-LIM3 driven by atmospheric reanalyses and climatologies. The two simulations differ only in their sea ice component, while all other elements of experimental design (resolution, initial conditions, atmospheric forcing) are kept identical. The main differences in the sea ice models lie in the formulation of the subgrid-scale ice thickness distribution, of the thermodynamic processes, of the sea ice salinity and of the sea ice rheology. To assess the differences in model skill over the period of investigation, we develop a set of metrics for both hemispheres, comparing the main sea ice variables (concentration, thickness and drift) to available observations and focusing on both mean state and seasonal to interannual variability. Based upon these metrics, we discuss the physical processes potentially responsible for the differences in model skill. In particular, we suggest that (i) a detailed representation of the ice thickness distribution increases the seasonal to interannual variability of ice extent, with spectacular improvement for the simulation of the recent observed summer Arctic sea ice retreats, (ii) the elastic-viscous-plastic rheology enhances the response of ice to wind stress, compared to the classical viscous-plastic approach, (iii) the grid formulation and the air-sea ice drag coefficient affect the simulated ice export through Fram Strait and the ice accumulation along the Canadian Archipelago, and (iv) both models show less skill in the Southern Ocean, probably due to the low quality of the reanalyses in this region and to the absence of important small-scale oceanic processes at the models' resolution (∼1 • ).

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tSea ice variability in the Southern Ocean has a complex spatio-temporal structure. In a global warming context, the Antarctic sea ice cover has slightly expanded over the recent decades. This increase in sea ice extent results, however, from the sum of positive and negative regional trends and is influenced by a wide range of modes of climate variability. An additional view on sea ice thickness and volume changes would improve our understanding. Still, no large-scale multi-decadal well-sampled record of Antarctic sea ice thickness exists to date. To address this issue, we assimilate real sea ice concentration data into the ocean-sea ice model NEMO-LIM2 using an ensemble Kalman filter and demonstrate the positive impacts on the global sea ice cover. This paper reports the 1980-2008 evolution (monthly anomalies, trends plus their uncertainty ranges) of sea ice volume and thickness in different sectors of the Southern Ocean. We find that the global Antarctic sea ice volume has risen at a pace of 355 AE 338 km 3 /decade (5:6 AE 5:3%/decade) during this period, with an increase in the Ross and Weddell Seas (150 AE 124 and 209 AE 362 km 3 /decade, respectively) and a decrease in the Amundsen-Bellingshausen Seas (À45 AE 54 km 3 /decade). Sea ice volume anomalies co-vary well with extent anomalies, and exhibit yearly to decadal fluctuations. The results stress the need to analyze sea ice changes at the regional level first and then at the hemispheric level.

Landfast ice is sea ice that forms and remains fixed along a coast, where it is either attached to the shore or held between shoals or grounded icebergs. The current generation of sea ice models is not capable of reproducing certain aspects of landfast ice behavior, for example the persistence of landfast sea ice under the effect of offshore winds. The authors develop a landfast sea ice model by adding tensile strength to the viscous–plastic as well as two versions of the elastic–viscous–plastic sea ice rheologies. One-dimensional implementations of these rheologies are used to explore the ability of coastal sea ice to resist offshore winds over extended times. While all modified rheologies are capable of maintaining landfast ice–like structures in the model, only the viscous–plastic rheology fulfills theoretical expectations. The elastic–viscous–plastic rheologies show initial elastic waves that weaken the ice and thus reduce its capacity of maintaining landfast ice. Further, special care has to be taken when implementing the most commonly used version of the elastic–viscous–plastic rheology because the standard set of parameters is not adequate for landfast sea ice modeling.

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using iceocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an ensemble Kalman filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged) data into a global coupled oceansea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the use of synthetic data (i.e. model-generated data), and secondly, through the assimilation of real satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005-2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

Decadal prediction is one focus of the upcoming 5th IPCC Assessment report. To be able to interpret the results and to further improve the decadal predictions it is important to investigate the potential predictability in the participating climate models. This study analyzes the upper limit of climate predictability on decadal time scales and its dependency on sea ice albedo parameterization by performing two perfect ensemble experiments with the global coupled climate model EC-Earth. In the first experiment, the standard albedo formulation of EC-Earth is used, in the second experiment sea ice albedo is reduced. The potential prognostic predictability is analyzed for a set of oceanic and atmospheric parameters. The decadal predictability of the atmospheric circulation is small. The highest potential predictability was found in air temperature at 2 m height over the northern North Atlantic and the southern South Atlantic. Over land, only a few areas are significantly predictable. The predictability for continental size averages of air temperature is relatively good in all northern hemisphere regions. Sea ice thickness is highly predictable along the ice edges in the North Atlantic Arctic Sector. The meridional overturning circulation is highly predictable in both experiments and governs most of the decadal climate predictability in the northern hemisphere. The experiments using reduced sea ice albedo show some important differences like a generally higher predictability of atmospheric variables in the Arctic or higher predictability of air temperature in Europe. Furthermore, decadal variations are substantially smaller in the simulations with reduced ice albedo, which can be explained by reduced sea ice thickness in these simulations.