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Oceanic transport toward the Arctic Ocean consists mostly of Atlantic Water (AW hereafter) being transported by the Norwegian Atlantic Current toward higher latitudes (Helland-Hansen & Nansen, 1909). At the entrance of the Barents Sea, the AW flow divides in two branches. One branch enters the Barents Sea where it can either be transformed into dense water and exits at depths as so-called Arctic Intermediate Water (Schauer et al., 1997), or melt ice and become part of the "estuarine" component of the Arctic Ocean (Eldevik & Nilsen, 2013;Rudels, 2016;Stigebrandt, 1981). The other AW branch forms the West Spitsbergen Current which further splits into two branches. One of these recirculates and heads South, and merges with the East Greenland Current, and the other enters the Arctic through Fram Strait. The latter branch can either melt ice, or subduct below the lighter colder and fresher layer of Arctic Water. This latter branch then follows a pathway along the continental slope North of Svalbard and Franz Josef Land toward the eastern Nansen Basin, slowly losing its properties (Bluhm et al., 2020;Timmermans & Marshall, 2020).

This paper describes the developing theory and underlying processes of the microscale obstacle-resolving model MITRAS version 2. MITRAS calculates wind, temperature, humidity, and precipitation fields, as well as transport within the obstacle layer using Reynolds averaging. It explicitly resolves obstacles, including buildings and overhanging obstacles, to consider their aerodynamic and thermodynamic effects. Buildings are represented by impermeable grid cells at the building positions so that the wind speed vanishes in these grid cells. Wall functions are used to calculate appropriate turbulent fluxes. Most exchange processes at the obstacle surfaces are considered in MITRAS, including turbulent and radiative processes, in order to obtain an accurate surface temperature. MITRAS is also able to simulate the effect of wind turbines. They are parameterized using the actuator-disk concept to account for the reduction in wind speed. The turbulence generation in the wake of a wind turbine is parameterized by adding an additional part to the turbulence mechanical production term in the turbulent kinetic energy equation. Effects of trees are considered explicitly, including the wind speed reduction, turbulence production, and dissipation due to drag forces from plant foliage elements, as well as the radiation absorption and shading. The paper provides not only documentation of the model dynamics and numerical framework but also a solid foundation for future microscale model extensions.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Abstract. This paper describes the developing theory and the underlying processes of the microscale obstacle resolving model MITRAS Version 2. MITRAS calculates wind, temperature, humidity and precipitation fields as well as transport within the obstacles layer using Reynolds averaging. It explicitly resolves obstacles, including buildings and overhanging obstacles, to consider their aerodynamic and thermodynamic effects. Buildings are represented by impermeable grid cells at the building positions so that the wind speed vanishes and zero turbulent kinetic energy is assumed in these grid cells. Wall functions are 15 used to calculate appropriate turbulent fluxes. Most exchange processes at the obstacle surfaces are considered in MITRAS, including convective and radiative processes in order to obtain an accurate surface temperature. MITRAS is also able to simulate the effect of wind turbines. They are parameterized using the actuator-disk concept to account for the reduction in wind speed. The turbulence generation in the wake of a wind turbine is parameterized by adding an additional part to the turbulence mechanical production term in the turbulent kinetic energy equation. Effects of trees are considered explicitly, 20 including the wind speed reduction, turbulence production, and dissipation due to drag forces from plant foliage elements as well as the radiation absorption and shading. The paper is providing not only a documentation of the model dynamics and numerical framework but also a solid foundation for future microscale model extensions.

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Abstract. Sea ice models fill many purposes. They are used in global climate models or for short term forecasts to plan shipping routes. No matter what their output is used for, understanding the cause for there variability is crucial. In the this study two commonly used sea-ice models, CICE and SI3 were compared after running both models with similar boundary condition, on the same grid, with the same forcing and initialised with the same data, with the aim to understand how the two models differ from each other when forced equally. The set-up also allows linking certain model biases observed in both models to the external forcing. We found that the models compare well to sea ice concentration, sea ice thickness and snow thickness observations, with small regional differences, which could be linked to different model processes. The processes with the highest influence are the drag formulation, the albedo, and the treatment of snow. We find that the treatment of snow has a significant influence on the difference in sea ice thickness between the models, even though their forcing is equal.