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  • 1
    Publication Date: 2022-04-04
    Description: Observations in polar regions show that sea ice deformations are often narrow linear features. These long bands of deformations are referred to as Linear Kinematic Features (LKFs). Viscous‐plastic sea ice models have the capability to simulate LKFs and more generally sea ice deformations. Moreover, viscous‐plastic models simulate a larger number and more refined LKFs as the spatial resolution is increased. Besides grid spacing, other aspects of a numerical implementation, such as the placement of velocities and the associated degrees of freedom, may impact the formation of simulated LKFs. To explore these effects this study compares numerical solutions of sea ice models with different velocity staggering in a benchmark problem. Discretizations based on A‐,B‐, and C‐grid systems on quadrilateral meshes have similar resolution properties as an approximation with an A‐grid staggering on triangular grids (with the same total number of vertices). CD‐grid approximations with a given grid spacing have properties, specifically the number and length of simulated LKFs, that are qualitatively similar to approximations on conventional Arakawa A‐grid, B‐grid, and C‐grid approaches with half the grid spacing or less, making the CD‐discretization more efficient with respect to grid resolution. One reason for this behavior is the fact that the CD‐grid approach has a higher number of degrees of freedom to discretize the velocity field. The higher effective resolution of the CD‐discretization makes it an attractive alternative to conventional discretizations.
    Description: Plain Language Summary: Sea ice in the Arctic and Antarctic Oceans plays an important role in the exchange of heat and freshwater between the atmosphere and the ocean and hence in the climate in general. Satellite observations of polar regions show that the ice drift sometimes produces long features that are either cracks (leads) and zones of thicker sea ice (pressure ridges). This phenomenon is called deformation. It is mathematically described by the non‐uniform way in which the ice moves. For numerical models of sea ice motion it is difficult to represent this deformation accurately. Details of the numerics may affect the way these models simulate leads and ridges, their number and length. Specifically, we find by comparing different numerical models, that the way the model variables are ordered on a computational grid to solve the mathematical equations of sea ice motion has an effect of how many deformation features can be represented on a grid with a given spacing between grid points. A new discretization (ordering of model variables) turns out to resolve more details of the approximated field than traditional methods.
    Description: Key Points: The placement of the sea ice velocity has a mayor influence on the number of simulated linear kinematic features (LKFs). The CD‐grid resolves twice as many LKFs compared to A, B, C‐grids. A, B, C‐grids on quadrilateral meshes resolve a similar number of LKFs as A‐grids on triangular meshes (with the same total number of nodes).
    Keywords: ddc:550 ; ddc:551.343
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2021-09-21
    Description: Simulation 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- 1 Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site year period from 1948 to 2007 and are performed as contributions to the second phase of the Coordinated Ocean-ice 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.
    Description: U.S. National Science Foundation (NSF) NSF U.S. Department of Energy NOAA Climate Program Office under Climate Variability Predictability Program NA09OAR4310163 Department of Climate Change and Energy Efficiency Bureau of Meteorology CSIRO National Computational Infrastructure facility at the Australian National University Research Council of Norway through the EarthClim 207711/E10 NOTUR/NorStore projects Centre for Climate Dynamics at the Bjerknes Centre for Climate Research Italian Ministry of Education, University, and Research Italian Ministry of Environment, Land, and Sea under the GEMINA project BNP-Paribas foundation via the PRECLIDE project under the CNRS 30023488 WGOMD
    Description: Published
    Description: 76-107
    Description: 4A. Clima e Oceani
    Description: JCR Journal
    Description: open
    Keywords: Global ocean–sea-ice modelling ; Ocean model comparisons ; Atmospheric forcing ; Experimental design ; Atlantic meridional overturning circulation ; North Atlantic simulations ; 03. Hydrosphere::03.03. Physical::03.03.03. Interannual-to-decadal ocean variability
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
    Publication Date: 2023-01-17
    Description: It is generally agreed that the resolution of a regular quadrilateral mesh is the side length of quadrilateral cells. There is less agreement on the resolution of triangular meshes, exacerbated by the fact that the numbers of edges or cells on triangular meshes are approximately three or two times larger than that of vertices. However, the geometrical resolution of triangular meshes, that is, maximum wavenumbers or smallest wavelengths that can be represented on such meshes, is a well defined quantity, known from solid state physics. These wavenumbers are related to a smallest common mesh cell (primitive unit cell), and the set of mesh translations that map it into itself. They do not depend on whether discrete degrees of freedom are placed on vertices, cells or edges. For equilateral triangles the smallest wavelength equals twice the triangle height. Resolutions of quadrilateral and triangular meshes approximately agree if they have the same numbers of vertices.
    Description: Plain Language Summary: Some models used in climate studies are formulated on triangular computational meshes. We discuss how to determine the smallest scales that are resolved on such meshes. They are referred to as a mesh resolution. The notion of mesh resolution is commonly used to relate climate model results simulated on different meshes.
    Description: Key Points: Geometrical resolution of an equilateral triangular mesh is defined by the height of its triangles. Quadrilateral and triangular meshes with the same number of vertices have approximately the same resolution.
    Description: Collaborative Research Centre
    Description: German Research Foundation
    Keywords: ddc:550 ; triangular meshes ; resolved wavenumbers
    Language: English
    Type: doc-type:article
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