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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2019-325
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-325
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Dec 2019

12 Dec 2019

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A revised version of this preprint is currently under review for the journal GMD.

Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model

Eduardo Moreno-Chamarro1, Pablo Ortega1, and François Massonnet2 Eduardo Moreno-Chamarro et al.
  • 1Barcelona Supercomputing Center (BSC), Barcelona, Spain
  • 2Georges Lemître Centre for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain,Louvain-la-Neuve, Belgium

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability.

Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data. Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume.

In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.

Eduardo Moreno-Chamarro et al.

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Eduardo Moreno-Chamarro et al.

Data sets

Data and code E. Moreno-Chamarro https://doi.org/10.5281/zenodo.3540756

Model code and software

Model source code F. Massonnet https://doi.org/10.5281/zenodo.3345604

Eduardo Moreno-Chamarro et al.

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Short summary
Climate models need to capture sea ice complexity to represent it realistically. Here, we test whether the distribution of sea ice in different thickness categories in the NEMO3.6-LIM3 model impacts how sea ice variability is simulated. Comparison between simulations and satellite observations is done using k-means clustering in sea ice concentration in winter and summer between 1979 and 2014 at both poles. Though some improvements are discussed, we recommend the standard number of 5 categories.
Climate models need to capture sea ice complexity to represent it realistically. Here, we test...
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