The Alfred Wegener Institute Climate Model (AWI-CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice-ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max-Planck-Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI-CM performs better than the average of CMIP5 models. AWI-CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea ice extent in September over the past 30 years is 20-30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present-day climate under the strong emission scenario SSP585 are similar to the multi-model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2 to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical and Southern Ocean. Furthermore, in the northern mid-latitudes in boreal summer and autumn as well as in the southern mid-latitudes a more zonal atmospheric flow is projected throughout the year.

A new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea-surface salinity and sea-level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea-ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere-ocean-ice interaction occurs. One-year-long sea-ice forecasts initialized on January 1st, April 1st, July 1st and October 1st from 2003 to 2019 are described. To correct systematic forecast errors, sea-ice concentration from 2011 to 2019 is calibrated by trend-adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea-ice edge raw forecast skill is within the range of operational global subseasonal-to-seasonal forecast systems, outperforming a climatological benchmark for about two weeks in the Arctic and about three weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea-ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations.

Many state-of-the-art climate models do not simulate the Atlantic Water (AW) layer in the Arctic Ocean realistically enough to address the question of future Arctic Atlantification and its associated feedback. Biases concerning the AW layer are commonly related to insufficient resolution and exaggerated mixing in the ocean component as well as unrealistic Atlantic-Arctic Ocean exchange. Based on sensitivity experiments with FESOM1.4, the ocean-sea ice component of the global climate model AWI-CM1, we show that even if all impediments for simulating AW realistically are addressed in the ocean model, new biases in the AW layer develop after coupling to an atmosphere model. By replacing the wind forcing over the Arctic with winds from a coupled simulation we show that a common bias in the atmospheric sea level pressure (SLP) gradient and its associated wind bias lead to differences in surface stress and Ekman transport. Fresh surface water gets redistributed leading to changes in steric height distribution. Those changes lead to a strengthening of the anticyclonic surface circulation in the Canadian Basin, so that the deep counterflow carrying warm AW gets reversed and a warm bias in the Canadian Basin develops. An underestimation of sea ice concentration can significantly amplify the induced ocean biases. The SLP and anticyclonic wind bias in the Nordic Seas weaken the cyclonic circulation leading to reduced AW transport into the Arctic Ocean through Fram Strait but increased AW transport through the Barents Sea Opening. These effects together lead to a cold bias in the Eurasian Basin.

Landfast ice is nearly immobile sea ice attached to the coast. Despite the important role of landfast ice in coastal climates, landfast ice is not well simulated in current sea ice models and needs to be parameterized. The mechanism for landfast ice formation is linked to the local geography. Grounded ice ridges act as anchoring points in shallow water. Sea ice arching between offshore island chains can lead to landfast ice in deep water. Previous studies successfully represented landfast ice in shallow marginal seas using bathymetry information to implement a grounding scheme, but this method fails in deep regions. This paper develops a new parameterization for coarse resolution sea ice models using lateral drag as a function of sea ice thickness, drift velocity, and coastline length. The new parameterization is tested in a 36 km pan-Arctic sea ice-ocean simulation. The simulated landfast ice in the model run is compared to observations from satellite data. With the lateral drag parameterization, representation of landfast ice improves in deep marginal seas. The combination of the lateral drag parameterization and a grounding scheme leads to a realistic landfast ice distribution in most Arctic regions.