a b s t r a c tSimulation characteristics from eighteen global ocean-sea-ice coupled models are presented with a focus on the mean Atlantic meridional overturning circulation (AMOC) and other related fields in the North Atlantic. These experiments use inter-annually varying atmospheric forcing data sets for the 60-year period from 1948 to 2007 and are performed as contributions to the second phase of the Coordinated Oceanice Reference Experiments (CORE-II). The protocol for conducting such CORE-II experiments is summarized. Despite using the same atmospheric forcing, the solutions show significant differences. As most models also differ from available observations, biases in the Labrador Sea region in upper-ocean potential temperature and salinity distributions, mixed layer depths, and sea-ice cover are identified as contributors to differences in AMOC. These differences in the solutions do not suggest an obvious grouping of the models based on their ocean model lineage, their vertical coordinate representations, or surface salinity restoring strengths. Thus, the solution differences among the models are attributed primarily to use of different subgrid scale parameterizations and parameter choices as well as to differences in vertical and horizontal grid resolutions in the ocean models. Use of a wide variety of sea-ice models with diverse snow and sea-ice albedo treatments also contributes to these differences. Based on the diagnostics considered, the majority of the models appear suitable for use in studies involving the North Atlantic, but some models require dedicated development effort.

[1] We present high resolution simulations and observational data as evidence of a fast current flowing along the shelf break of the Siberian and Alaskan shelves in the Arctic Ocean. Thus far, the Arctic Circumpolar Boundary Current (ACBC) has been seen as comprising two branches: the Fram Strait and Barents Sea Branches (FSB and BSB, respectively). Here we describe a new third branch, the Arctic Shelf Break Branch (ASBB). We show that the forcing mechanism for the ASBB is a combination of buoyancy loss and non local wind, creating high pressure upstream in the Barents Sea. The potential vorticity influx through the St. Anna Trough dictates the cyclonic direction of flow of the ASBB, which is the most energetic large scale circulation structure in the Arctic Ocean. It plays a substantial role in transporting Arctic halocline waters and exhibits a robust seasonal cycle with a summer minimum and winter maximum. The simulations show the continuity of the FSB all the way around the Arctic shelves and the uninterrupted ASBB between the St. Anna Trough and the western Fram Strait. The BSB flows continuously along the Siberian shelf as far as the Chukchi Plateau, where it partly diverges from the continental slope into the ocean interior. The Alaskan Shelf break Current (ASC) is the analog of the ASBB in the Canadian Arctic. The ASC is forced by the local winds and high upstream pressure in Bering Strait, caused by the drop in sea surface height between the Pacific and Arctic Oceans.

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Abstract. The Coupled Model Intercomparison Project phase 6 (CMIP6) HighResMIP is a new experimental design for global climate model simulations that aims to assess the impact of model horizontal resolution on climate simulation fidelity. We describe a hierarchy of global coupled model resolutions based on the Hadley Centre Global Environment Model 3 – Global Coupled vn 3.1 (HadGEM3-GC3.1) model that ranges from an atmosphere–ocean resolution of 130 km–1∘ to 25 km–1∕12∘, all using the same forcings and initial conditions. In order to make such high-resolution simulations possible, the experiments have a short 30-year spinup, followed by at least century-long simulations with constant forcing to assess drift. We assess the change in model biases as a function of both atmosphere and ocean resolution, together with the effectiveness and robustness of this new experimental design. We find reductions in the biases in top-of-atmosphere radiation components and cloud forcing. There are significant reductions in some common surface climate model biases as resolution is increased, particularly in the Atlantic for sea surface temperature and precipitation, primarily driven by increased ocean resolution. There is also a reduction in drift from the initial conditions both at the surface and in the deeper ocean at higher resolution. Using an eddy-present and eddy-rich ocean resolution enhances the strength of the North Atlantic ocean circulation (boundary currents, overturning circulation and heat transport), while an eddy-present ocean resolution has a considerably reduced Antarctic Circumpolar Current strength. All models have a reasonable representation of El Niño–Southern Oscillation. In general, the biases present after 30 years of simulations do not change character markedly over longer timescales, justifying the experimental design.

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International audienceSimulated inter-annual to decadal variability and trends in the North Atlantic for the 1958–2007 period from twenty global ocean – sea-ice coupled models are presented. These simulations are performed as contributions to the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II). The study is Part II of our companion paper (Danabasoglu et al., 2014) which documented the mean states in the North Atlantic from the same models. A major focus of the present study is the representation of Atlantic meridional overturning circulation (AMOC) variability in the participating models. Relationships between AMOC variability and those of some other related variables, such as subpolar mixed layer depths, the North Atlantic Oscillation (NAO), and the Labrador Sea upper-ocean hydrographic properties, are also investigated. In general, AMOC variability shows three distinct stages. During the first stage that lasts until the mid- to late-1970s, AMOC is relatively steady, remaining lower than its long-term (1958–2007) mean. Thereafter, AMOC intensifies with maximum transports achieved in the mid- to late-1990s. This enhancement is then followed by a weakening trend until the end of our integration period. This sequence of low frequency AMOC variability is consistent with previous studies. Regarding strengthening of AMOC between about the mid-1970s and the mid-1990s, our results support a previously identified variability mechanism where AMOC intensification is connected to increased deep water formation in the subpolar North Atlantic, driven by NAO-related surface fluxes. The simulations tend to show general agreement in their temporal representations of, for example, AMOC, sea surface temperature (SST), and subpolar mixed layer depth variabilities. In particular, the observed variability of the North Atlantic SSTs is captured well by all models. These findings indicate that simulated variability and trends are primarily dictated by the atmospheric datasets which include the influence of ocean dynamics from nature superimposed onto anthropogenic effects. Despite these general agreements, there are many differences among the model solutions, particularly in the spatial structures of variability patterns. For example, the location of the maximum AMOC variability differs among the models between Northern and Southern Hemispheres

[1] As a part of Arctic Ocean Intercomparison Project, results from five coupled physical and biological ocean models were compared for the Arctic domain, defined here as north of 66.6°N. The global and regional (Arctic Ocean (AO)-only) models included in the intercomparison show similar features in terms of the distribution of present-day water column-integrated primary production and are broadly in agreement with in situ and satellite-derived data. However, the physical factors controlling this distribution differ between the models. The intercomparison between models finds substantial variation in the depth of winter mixing, one of the main mechanisms supplying inorganic nutrients over the majority of the AO. Although all models manifest similar level of light limitation owing to general agreement on the ice distribution, the amount of nutrients available for plankton utilization is different between models. Thus the participating models disagree on a fundamental question: which factor, light or nutrients, controls present-day Arctic productivity. These differences between models may not be detrimental in determining present-day AO primary production since both light and nutrient limitation are tightly coupled to the presence of sea ice. Essentially, as long as at least one of the two limiting factors is reproduced correctly, simulated total primary production will be close to that observed. However, if the retreat of Arctic sea ice continues into the future as expected, a decoupling between sea ice and nutrient limitation will occur, and the predictive capabilities of the models may potentially diminish unless more effort is spent on verifying the mechanisms of nutrient supply. Our study once again emphasizes the importance of a realistic representation of ocean physics, in particular vertical mixing, as a necessary foundation for ecosystem modeling and predictions.

Abstract. Until recently, the Arctic Basin was generally considered to be a low productivity area and was afforded little attention in global-or even basin-scale ecosystem modelling studies. Due to anthropogenic climate change however, the sea ice cover of the Arctic Ocean is undergoing an unexpectedly fast retreat, exposing increasingly large areas of the basin to sunlight. As indicated by existing Arctic phenomena such as ice-edge blooms, this decline in sea-ice is liable to encourage pronounced growth of phytoplankton in summer and poses pressing questions concerning the future of Arctic ecosystems. It thus provides a strong impetus to modelling of this region.The Arctic Ocean is an area where plankton productivity is heavily influenced by physical factors. As these factors are strongly responding to climate change, we analyse here the results from simulations of the 1/4 • resolution global ocean NEMO (Nucleus for European Modelling of the Ocean) model coupled with the MEDUSA (Model for Ecosystem Dynamics, carbon Utilisation, Sequestration and Acidification) biogeochemical model, with a particular focus on the Arctic basin. Simulated productivity is consistent with the limited observations for the Arctic, with significant production occurring both under the sea-ice and at the thermocline, locations that are difficult to sample in the field.Results also indicate that a substantial fraction of the variability in Arctic primary production can be explained by two key physical factors: (i) the maximum penetration of winter mixing, which determines the amount of nutrients available for summer primary production, and (ii) short-wave radiation at the ocean surface, which controls the magnitude of phytoplankton blooms. A strong empirical correlation was Correspondence to: E. E. Popova (ekp@noc.soton.ac.uk) found in the model output between primary production and these two factors, highlighting the importance of physical processes in the Arctic Ocean.