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On the effects of constraining atmospheric circulation in a coupled atmosphere-ocean Arctic regional climate model

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Abstract

Impacts of spectral nudging on simulations of Arctic climate in coupled simulations have been investigated in a set of simulations with a regional climate model (RCM). The dominantly circumpolar circulation in the Arctic lead to weak constraints on the lateral boundary conditions (LBCs) for the RCM, which causes large internal variability with strong deviations from the driving model. When coupled to an ocean and sea ice model, this results in sea ice concentrations that deviate from the observed spatial distribution. Here, a method of spectral nudging is applied to the atmospheric model RCA4 in order to assess the potentials for improving results for the sea ice concentrations when coupled to the RCO ocean-sea ice model. The spectral nudging applied to reanalysis driven simulations significantly improves the generated sea ice regarding its temporal evolution, extent and inter-annual trends, compared to simulations with standard LBC nesting. The method is furthermore evaluated with driving data from two CMIP5 GCM simulations for current and future conditions. The GCM biases are similar to the RCA4 biases with ERA-Interim, however, the spectral nudging still improves the surface winds enough to show improvements in the simulated sea ice. For both GCM downscalings, the spectrally nudged version retains a larger sea ice extent in September further into the future. Depending on the sea ice formulation in the GCM, the temporal evolution of the regional sea ice model can deviate strongly.

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Acknowledgments

This work was carried out at the Swedish Meteorological and Hydrological Institute (SMHI) and made possible by the support of the ADSIMNOR project, funded by the Swedish research council FORMAS. We acknowledge use of the NSIDC sea ice extent (Fetterer et al. 2009).

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Berg, P., Döscher, R. & Koenigk, T. On the effects of constraining atmospheric circulation in a coupled atmosphere-ocean Arctic regional climate model. Clim Dyn 46, 3499–3515 (2016). https://doi.org/10.1007/s00382-015-2783-y

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