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SI3 (Sea Ice modelling Integrated Initiative) is the sea ice engine of NEMO (Nucleus for European Modelling of the Ocean). It is adapted to regional and global sea ice and climate problems. It is intended to be a flexible tool for studying sea ice and its interactions with the other components of the Earth System over a wide range of space and time scales. For these applications, a 2+1D continuum approach is currently the most appropriate, SI3 is based on the Arctic Ice Dynamics Joint EXperiment (AIDJEX) framework, combining the ice thickness distribution framework, the conservation of horizontal momentum, an elastic-viscous plastic rheology, and energy-conserving halo-thermodynamics. Prognostic variables are the two-dimensional horizontal velocity field, ice volume, area, enthalpy, salt content, snow volume and enthalpy, and melt pond volume and area. In the horizontal direction, the model uses a curvilinear orthogonal grid. In the vertical direction, the model uses equally-spaced layers. In thickness space, the model uses thickness categories with prescribed boundaries. Various physical and numerical choices are available to describe sea ice physics. SI3 is interfaced with the NEMO ocean engine, and, via the OASIS coupler, with several atmospheric general circulation models. It also supports two-way grid embedding via the AGRIF software.

In theory, the same sea-ice models could be used for both research and operations, but in practice, differences in scientific\ud and software requirements and computational and human resources complicate the matter. Although sea-ice modeling tools\ud developed for climate studies and other research applications produce output of interest to operational forecast users, such as\ud ice motion, convergence, and internal ice pressure, the relevant spatial and temporal scales may not be sufficiently resolved.\ud For instance, sea-ice research codes are typically run with horizontal resolution of more than 3 km, while mariners need\ud information on scales less than 300 m. Certain sea-ice processes and coupled feedbacks that are critical to simulating the\ud Earth system may not be relevant on these scales; and therefore, the most important model upgrades for improving sea-ice\ud predictions might be made in the atmosphere and ocean components of coupled models or in their coupling mechanisms,\ud rather than in the sea-ice model itself. This paper discusses some of the challenges in applying sea-ice modeling tools\ud developed for research purposes for operational forecasting on short time scales, and highlights promising new directions in\ud sea-ice modeling.

Global and regional ocean and sea ice reanalysis products (ORAs) are increasingly used in polar research, but their quality remains to be systematically assessed. To address this, the Polar ORA Intercomparison Project (Polar ORA-IP) has been established following on from the ORA-IP project. Several aspects of ten selected ORAs in the Arctic and Antarctic were addressed by concentrating on comparing their mean states in terms of snow, sea ice, ocean transports and hydrography. Most polar diagnostics were carried out for the first time in such an extensive set of ORAs. For the multi-ORA mean state, we found that deviations from observations were typically smaller than individual ORA anomalies, often attributed to offsetting biases of individual ORAs. The ORA ensemble mean therefore appears to be a useful product and while knowing its main deficiencies and recognising its restrictions, it can be used to gain useful information on the physical state of the polar marine environment. We acknowledge Dr. Benjamin Rabe and the two anonymous reviewers for their comments that significantly improved the manuscript. EU-COST EOS-1402 Ocean Synthesis action is acknowledged for their support, in particular to assist the organisation of the Polar ORA-IP meetings, both physical and virtual, which were crucial for the study. Work of Uotila was supported by the Finnish Academy (Grants 264358 and 283034) and by the EU MCSA grant 707262-LAWINE. Chevallier, Fučkar, Haines and Massonnet have received funding from the European Union’s Horizon 2020 Research and Innovation programme through Grant agreement No. 727862 APPLICATE. Fučkar was a Juan de la Cierva-incorporacion fellow supported by the Spanish government. Goosse is a research director and Massonnet a post-doctoral researcher with the FRS/FNRS, Belgium. The ORA and MMM data used in this study are provided by Hamburg University on the ORA-IP web-site at https ://icdc.cen.uni-hambu rg.de/1/daten /reana lysis -ocean /oraip .html.

We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.

We establish the first inter-model comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea-ice extent and volume, there is potential predictive skill for lead times of up to three years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea-ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea-ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea-ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.

We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates leads to good assimilation performance for sea-ice concentration and thickness, both in identical-twin experiments and when assimilating sea-ice observations. The simulation of other Arctic surface fields in the coupled model is, however, not significantly improved by the assimilation. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that an adjustment of mean ice thickness in the analysis update is essential to arrive at plausible state estimates. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that assimilation with proportional mean-thickness updates outperforms the other two methods considered. The method described here is very simple to implement, and gives results that are sufficiently good to be used for initialising sea ice in a global climate model for seasonal to decadal predictions.

We examine the recovery of Arctic sea ice from prescribed ice-free summer conditions in simulations of 21st century climate in an atmosphere-ocean general circulation model. We find that ice extent recovers typically within two years. The excess oceanic heat that had built up during the ice-free summer is rapidly returned to the atmosphere during the following autumn and winter, and then leaves the Arctic partly through increased longwave emission at the top of the atmosphere and partly through reduced atmospheric heat advection from lower latitudes. Oceanic heat transport does not contribute significantly to the loss of the excess heat. Our results suggest that anomalous loss of Arctic sea ice during a single summer is reversible, as the ice-albedo feedback is alleviated by large-scale recovery mechanisms. Hence, hysteretic threshold behavior (or a "tipping point") is unlikely to occur during the decline of Arctic summer sea-ice cover in the 21st century.

Scientists in the Sea Ice Prediction Network share and discuss their user-oriented forecasts of seasonal sea ice in a changing Arctic.