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  • 1
    Publication Date: 2010-04-28
    Description: The representation of polynyas in viscous-plastic dynamic-thermodynamic sea-ice models is studied in a simplified test domain, in order to give recommendations about parametrisation choices. Bjornsson et al. (2001) validated their dynamic-thermodynamic model against a polynya flux model in a similar setup and we expand on that work here, testing more sea-ice rheologies and new-ice thickness formulations. The two additional rheologies tested give nearly identical results whereas the two new-ice thickness parametrisations tested give widely different results. Based on our results we argue for using the new-ice thickness parametrisation of Hibler (1979). We also implement a new parametrisation for the parameter h0 from Hibler's scheme, based on ideas from a collection depth parametrisation for flux polynya models.
    Print ISSN: 1994-0416
    Electronic ISSN: 1994-0424
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 2
    Publication Date: 2015-10-30
    Description: The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes, and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model, called neXtSIM in order to address this. neXtSIM is a continuous and fully Lagrangian model, and the equations are discretised with the finite-element method. In this model, sea ice physics are driven by a synergic combination of two core components: a model for sea ice dynamics built on a new mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The results of a thorough evaluation of the model performance for the Arctic are presented for the period September 2007 to October 2008. They show that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is a very promising tool for simulating the sea ice over a wide range of spatial and temporal scales.
    Print ISSN: 1994-0432
    Electronic ISSN: 1994-0440
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
    Publication Date: 2009-11-24
    Description: The response of a viscous-plastic dynamic-thermodynamic sea-ice model to a constant wind forcing is tested in an idealised setting. Bjornsson et al. (2001) have shown that the granular model of Tremblay and Mysak (1997) gives good results in such a set-up compared to a polynya flux model. Here it is shown that these results can be duplicated using the computationally more efficient elliptic yield curve approach by Hibler (1979) and modified Coulombic yield curve by Hibler and Schulson (2000). Some care is, however, required regarding the parametrisations of the ellipse. In addition it is shown that the new ice thickness formulation of Mellor and Kantha (1989) does not allow for proper polynya formation in the bay. In contrast the new ice thickness formulation of Hibler (1979) is found to give good results. Finally we propose a parametrisation of the ice demarcation thickness (h0) in Hibler's formulation, based on wind speed and ice thickness.
    Print ISSN: 1994-0432
    Electronic ISSN: 1994-0440
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
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    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Publication Date: 2023-06-22
    Description: We present our vision on how to advance short-term sea-ice forecasting with deep learning, based on two specific examples. To incorporate multifractal, anisotropic, and stochastic-like processes in sea ice, we envision the combination of geophysical sea-ice models together with neural networks in a hybrid modelling setup. On the one hand, deep learning can surrogate computationally expensive sea-ice models, like neXtSIM. This not only allows us to speed-up simulations by orders of magnitude, but also to improve forecasts of sea-ice thickness by up to 35 % compared to persistence on a daily timescale. On the other hand, deep learning can parametrize subgrid-scale processes in sea-ice models and correct persisting model errors, improving the forecasts by up to 70 % across all model variables on an hourly timescale. Based on these results, we conclude that hybrid modelling with deep learning can lead to major advancements in sea-ice forecasting.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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