Description: The ability of state‐of‐the‐art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic‐CORDEX initiative. Some models employ large‐scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA‐Interim, National Centers for Environmental Prediction‐Climate Forecast System Reanalysis, National Aeronautics and Space Administration‐Modern‐Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency‐Japanese 55‐year reanalysis) in winter and summer for 1981–2010 period. In addition, we compare cyclone statistics between ERA‐Interim and the Arctic System Reanalysis reanalyses for 2000–2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large‐scale spectral nudging show a better agreement with ERA‐Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables.

Description: Models from phase 5 of the Coupled Model Inter-comparison Project (CMIP5) show substantial biases in the deep ocean that are larger than the level of natural variability and the response to enhanced greenhouse gas concentrations. Here, we analyze the influence of horizontal resolution in a hierarchy of five multi-resolution simulations with the AWI Climate Model (AWI-CM), the climate model used at the Al-fred Wegener Institute, Helmholtz Centre for Polar and Ma-rine Research, which employs a sea ice–ocean model com-ponent formulated on unstructured meshes. The ocean grid sizes considered range from a nominal resolution of ∼ 1◦ (CMIP5 type) up to locally eddy resolving. We show that increasing ocean resolution locally to resolve ocean eddies leads to reductions in deep ocean biases, although these im-provements are not strictly monotonic for the five different ocean grids. A detailed diagnosis of the simulations allows to identify the origins of the biases. We find that two key re-gions at the surface are responsible for the development of the deep bias in the Atlantic Ocean: the northeastern North Atlantic and the region adjacent to the Strait of Gibraltar. Furthermore, the Southern Ocean density structure is equally improved with locally explicitly resolved eddies compared to parameterized eddies. Part of the bias reduction can be traced back towards improved surface biases over outcrop-ping regions, which are in contact with deeper ocean layers along isopycnal surfaces. Our prototype simulations provide guidance for the optimal choice of ocean grids for AWI-CM to be used in the final runs for phase 6 of CMIP (CMIP6) and for the related flagship simulations in the High Resolution Model Intercomparison Project (HighResMIP). Quite remarkably, retaining resolution only in areas of high eddy activity along with excellent scalability characteristics of the unstructured-mesh sea ice–ocean model enables us to per-form the multi-centennial climate simulations needed in a CMIP context at (locally) eddy-resolving resolution with a throughput of 5–6 simulated years per day.

Description: Climate simulations for the North Atlantic and Europe for recent and future conditions simulated with the regionally coupled ROM model are analyzed and compared to the results from the MPI‐ESM. The ROM simulations also include a biogeochemistry and ocean tides. For recent climate conditions, ROM generally improves the simulations compared to the driving model MPI‐ESM. Reduced oceanic biases in the Northern Atlantic are found, as well as a better simulation of the atmospheric circulation, notably storm tracks and blocking. Regarding future climate projections for the 21st century following the RCP 4.5 and 8.5 scenarios, MPI‐ESM and ROM largely agree qualitatively on the climate change signal over Europe. However, many important differences are identified. For example, ROM shows an SST cooling in the Subpolar Gyre which is not present in MPI‐ESM. Under the RCP8.5 scenario, ROM Arctic sea ice cover is thinner and reaches the seasonally ice‐free state by 2055, well before MPI‐ESM. This shows the decisive importance of higher ocean resolution and regional coupling for determining the regional responses to global warming trends. Regarding biogeochemistry, both ROM and MPI‐ESM simulate a widespread decline in winter nutrient concentration in the North Atlantic of up to ~35%. On the other hand, the phytoplankton spring bloom in the Arctic and in the North‐Western Atlantic starts earlier and the yearly primary production is enhanced in the Arctic in the late 21st century. These results clearly demonstrate the added value of ROM to determine more detailed and more reliable climate projections at the regional scale.

Description: Simulating Arctic Ocean mesoscale eddies in ocean circulation models presents a great challenge because of their small size. This study employs an unstructured‐mesh ocean‐sea ice model to conduct a decadal‐scale global simulation with a 1‐km Arctic. It provides a basinwide overview of Arctic eddy energetics. Increasing model resolution from 4 to 1 km increases Arctic eddy kinetic energy (EKE) and total kinetic energy (TKE) by about 40% and 15%, respectively. EKE is the highest along main currents over topography slopes, where strong conversion from available potential energy to EKE takes place. It is high in halocline with a maximum typically centered in the depth range of 70–110 m, and in the Atlantic Water layer of the Eurasian Basin as well. The seasonal variability of EKE along the continental slopes of southern Canada and eastern Eurasian basins is similar, stronger in fall and weaker in spring.

Description: The transient climate response (TCR) is 20% higher in the Alfred Wegener Institute Climate Model (AWI-CM) compared to the Max Planck Institute Earth System Model (MPI-ESM) whereas the equilibrium climate sensitivity (ECS) is by up to 10% higher in AWI-CM. These results are largely independent of the two considered model resolutions for each model. The two coupled CMIP6 models share the same atmosphere-land component ECHAM6.3 developed at the Max Planck Institute for Meteorology (MPI-M). However, ECHAM6.3 is coupled to two different ocean models, namely the MPIOM sea ice-ocean model developed at MPI-M and the FESOM sea ice-ocean model developed at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI). A reason for the different TCR is related to ocean heat uptake in response to greenhouse gas forcing. Specifically, AWI-CM simulations show stronger surface heating than MPI-ESM simulations while the latter accumulate more heat in the deeper ocean. The vertically integrated ocean heat content is increasing slower in AWI-CM model configurations compared to MPI-ESM model configurations in the high latitudes. Weaker vertical mixing in AWI-CM model configurations compared to MPI-ESM model configurations seems to be key for these differences. The strongest difference in vertical ocean mixing occurs inside the Weddell and Ross Gyres and the northern North Atlantic. Over the North Atlantic, these differences materialize in a lack of a warming hole in AWI-CM model configurations and the presence of a warming hole in MPI-ESM model configurations. All these differences occur largely independent of the considered model resolutions.