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  • 1
    Publication Date: 2023-01-25
    Description: We describe the ocean general circulation model Icosahedral Nonhydrostatic Weather and Climate Model (ICON‐O) of the Max Planck Institute for Meteorology, which forms the ocean‐sea ice component of the Earth system model ICON‐ESM. ICON‐O relies on innovative structure‐preserving finite volume numerics. We demonstrate the fundamental ability of ICON‐O to simulate key features of global ocean dynamics at both uniform and non‐uniform resolution. Two experiments are analyzed and compared with observations, one with a nearly uniform and eddy‐rich resolution of ∼10 km and another with a telescoping configuration whose resolution varies smoothly from globally ∼80 to ∼10 km in a focal region in the North Atlantic. Our results show first, that ICON‐O on the nearly uniform grid simulates an ocean circulation that compares well with observations and second, that ICON‐O in its telescope configuration is capable of reproducing the dynamics in the focal region over decadal time scales at a fraction of the computational cost of the uniform‐grid simulation. The telescopic technique offers an alternative to the established regionalization approaches. It can be used either to resolve local circulation more accurately or to represent local scales that cannot be simulated globally while remaining within a global modeling framework.
    Description: Plain Language Summary: Icosahedral Nonhydrostatic Weather and Climate Model (ICON‐O) is a global ocean general circulation model that works on unstructured grids. It rests on novel numerical techniques that belong to the class of structure‐preserving finite Volume methods. Unstructured grids allow on the one hand a uniform coverage of the sphere without resolution clustering, and on the other hand they provide the freedom to intentionally cluster grid points in some region of interest. In this work we run ICON‐O on an uniform grid of approximately 10 km resolution and on a grid with four times less degrees of freedom that is stretched such that in the resulting telescoping grid within the North Atlantic the two resolutions are similar, while outside the focal area the grid approaches smoothly ∼80 km resolution. By comparison with observations and reanalysis data we show first, that the simulation on the uniform 10 km grid provides a decent mesoscale eddy rich simulation and second, that the telescoping grid is able to reproduce the mesoscale rich circulation locally in the North Atlantic and on decadal time scales. This telescoping technique of unstructured grids opens new research directions.
    Description: Key Points: We describe Icosahedral Nonhydrostatic Weather and Climate Model (ICON‐O) the ocean component of ICON‐ESM 1.0, based on the ICON modeling framework. ICON‐O is analyzed in a globally mesoscale‐rich simulation and in a telescoping configuration. In telescoping configuration ICON‐O reproduces locally the eddy dynamics with less computational costs than the uniform configuration.
    Description: https://swiftbrowser.dkrz.de/public/dkrz_07387162e5cd4c81b1376bd7c648bb60/kornetal2021
    Description: https://mpimet.mpg.de/en/science/modeling-with-icon/code-availability
    Keywords: ddc:551.46 ; ocean modeling ; ocean dynamics ; unstructured grid modeling ; local refinement ; structure preservation numerics
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2024-02-21
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉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.〈/p〉
    Description: 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.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉The type of spatial discretization used in CD‐grid approximations is important for the amount of simulated local kinematic features (LKFs)〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The CD‐grid discretization based on nonconforming finite elements simulates the highest amount of LKFs〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The CD‐grids resolve more LKFs than A‐grids, B‐grids, or C‐grids〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: German Research Foundation
    Description: https://doi.org/10.5281/zenodo.7662610
    Description: https://doi.org/10.5281/zenodo.7646908
    Description: https://data.mendeley.com/datasets/7h9hkjvx48/1
    Keywords: ddc:550.724 ; sea‐ice dynamics ; CD‐grids ; linear kinematic features ; nonconforming finite elements
    Language: English
    Type: doc-type:article
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  • 3
    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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  • 4
    Publication Date: 2021-11-01
    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.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 5
    Publication Date: 2022-08-05
    Description: This work documents the ICON‐Earth System Model (ICON‐ESM V1.0), the first coupled model based on the ICON (ICOsahedral Non‐hydrostatic) framework with its unstructured, icosahedral grid concept. The ICON‐A atmosphere uses a nonhydrostatic dynamical core and the ocean model ICON‐O builds on the same ICON infrastructure, but applies the Boussinesq and hydrostatic approximation and includes a sea‐ice model. The ICON‐Land module provides a new framework for the modeling of land processes and the terrestrial carbon cycle. The oceanic carbon cycle and biogeochemistry are represented by the Hamburg Ocean Carbon Cycle module. We describe the tuning and spin‐up of a base‐line version at a resolution typical for models participating in the Coupled Model Intercomparison Project (CMIP). The performance of ICON‐ESM is assessed by means of a set of standard CMIP6 simulations. Achievements are well‐balanced top‐of‐atmosphere radiation, stable key climate quantities in the control simulation, and a good representation of the historical surface temperature evolution. The model has overall biases, which are comparable to those of other CMIP models, but ICON‐ESM performs less well than its predecessor, the Max Planck Institute Earth System Model. Problematic biases are diagnosed in ICON‐ESM in the vertical cloud distribution and the mean zonal wind field. In the ocean, sub‐surface temperature and salinity biases are of concern as is a too strong seasonal cycle of the sea‐ice cover in both hemispheres. ICON‐ESM V1.0 serves as a basis for further developments that will take advantage of ICON‐specific properties such as spatially varying resolution, and configurations at very high resolution.
    Description: Plain Language Summary: ICON‐ESM is a completely new coupled climate and earth system model that applies novel design principles and numerical techniques. The atmosphere model applies a non‐hydrostatic dynamical core, both atmosphere and ocean models apply unstructured meshes, and the model is adapted for high‐performance computing systems. This article describes how the component models for atmosphere, land, and ocean are coupled together and how we achieve a stable climate by setting certain tuning parameters and performing sensitivity experiments. We evaluate the performance of our new model by running a set of experiments under pre‐industrial and historical climate conditions as well as a set of idealized greenhouse‐gas‐increase experiments. These experiments were designed by the Coupled Model Intercomparison Project (CMIP) and allow us to compare the results to those from other CMIP models and the predecessor of our model, the Max Planck Institute for Meteorology Earth System Model. While we diagnose overall satisfactory performance, we find that ICON‐ESM features somewhat larger biases in several quantities compared to its predecessor at comparable grid resolution. We emphasize that the present configuration serves as a basis from where future development steps will open up new perspectives in earth system modeling.
    Description: Key Points: This work documents ICON‐ESM 1.0, the first version of a coupled model based on the ICON framework. Performance of ICON‐ESM is assessed by means of CMIP6 Diagnosis, Evaluation, and Characterization of Klima experiments at standard CMIP‐type resolution. ICON‐ESM reproduces the observed temperature evolution. Biases in clouds, winds, sea‐ice, and ocean properties are larger than in MPI‐ESM.
    Description: European Union H2020 ESM2025
    Description: European Union H2020 COMFORT
    Description: European Union H2020ESiWACE2
    Description: Deutsche Forschungsgemeinschaft TRR181
    Description: Deutsche Forschungsgemeinschaft EXC 2037
    Description: European Union H2020
    Description: Deutscher Wetterdienst
    Description: Bundesministerium fuer Bildung und Forschung
    Description: http://esgf-data.dkrz.de/search/cmip6-dkrz/
    Description: https://mpimet.mpg.de/en/science/modeling-with-icon/code-availability
    Description: http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=RUBY-0_ICON-_ESM_V1.0_Model
    Keywords: ddc:550.285 ; ddc:551.63
    Language: English
    Type: doc-type:article
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