부산대학교 도서관

Linear kinematic features (LKFs) are found everywhere in the Arctic sea‐ice cover. They are strongly localized deformations often associated with the formation of leads and pressure ridges. In viscous‐plastic (VP) sea‐ice models, the simulation of LKFs depends on several factors such as the grid resolution, the numerical solver convergence, and the placement of the variables on the mesh. In this study, we compare two recently proposed discretization with a CD‐grid placement with respect to their ability to reproduce LKFs. The first (CD1) is based on a nonconforming finite element discretization, whereas the second (CD2) uses a conforming subgrid discretization. To analyze their resolution properties, we evaluate runs from different models (e.g., FESOM, MPAS) on a benchmark problem using quadrilateral, hexagonal and triangular meshes. Our findings show that the CD1 setup simulates more deformation structure than the CD2 setup. This highlights the importance of the type of spatial discretization for the simulation of LKFs. Due to the higher number of degrees of freedom, both CD‐grids resolve more LKFs than traditional A, B, and C‐grids at fixed mesh level. This is an advantage of the CD‐grid approach, as high spatial mesh resolution is needed in VP sea‐ice models to simulate LKFs. Plain Language Summary: Sea ice in the polar regions is an important component of the climate system. Satellite images demonstrate that the sea‐ice cover can contain long features, such as cracks or leads and areas of increased sea‐ice density known as pressure ridges. In order to simulate these features, mathematical equations that describe the drift of ice are solved on a computational grid. A recent study showed that the simulation of these features on a grid with a given spacing is influenced by the way the variables are placed on grid cells. Locating them at the edge midpoints of the cells leads to simulations with more features than placing the variables on vertices or centers of cells. In this contribution, we show that, along with the placement, also the mathematical method used to approximate the equations on the computational grid plays a pivotal role on the number of simulated features. Key Points: The type of spatial discretization used in CD‐grid approximations is important for the amount of simulated local kinematic features (LKFs)The CD‐grid discretization based on nonconforming finite elements simulates the highest amount of LKFsThe CD‐grids resolve more LKFs than A‐grids, B‐grids, or C‐grids [ABSTRACT FROM AUTHOR]