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and Ice Sheets","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# Yelmo\n\nYelmo is a 3D ice-sheet-shelf model solving\nfor the coupled dynamics and thermodynamics of the ice sheet system. Yelmo\ncan be used for idealized simulations, stand-alone ice sheet simulations\nand fully coupled ice-sheet and climate simulations.\n\nThe physics and design of the model are described in the following article:\n\n\u003e Robinson, A., Alvarez-Solas, J., Montoya, M., Goelzer, H., Greve, R., and Ritz, C.: Description and validation of the ice-sheet model Yelmo (version 1.0), Geosci. Model Dev., 13, 2805–2823, [https://doi.org/10.5194/gmd-13-2805-2020](https://doi.org/10.5194/gmd-13-2805-2020), 2020.\n\nThe (growing) model documentation is provided help with proper use of the model,\nand can be found at:\n\n [https://palma-ice.github.io/yelmo-docs](https://palma-ice.github.io/yelmo-docs)\n \nWhile the model has been designed to be easy to use, there\nare many parameters that require knowledge of ice-sheet\nphysics and numerous parameterizations. It is not recommended to use the ice\nsheet model as a black box without understanding of the key parameters that\naffect its performance.\n\nNote that the test cases shown by Robinson et al. (2020) can be reproduced following the\ninstructions below in the section \"Test cases\".\n\nTo get started with compiling and running the model, see the quick-start\ninstructions below. Or go to the documentation directly: [https://palma-ice.github.io/yelmo-docs/getting-started/](https://palma-ice.github.io/yelmo-docs/getting-started/).\n\n# Getting started\n\nHere you can find the basic information and steps needed to get **Yelmo** running.\n\n## Super-quick start\n\nA summary of commands to get started is given below. For more detailed information see subsequent sections.\n\n```\n# Clone repository\ngit clone git@github.com:palma-ice/yelmo.git\n\n# Enter directory and run configuration script\ncd yelmo\npython config.py config/pik_ifort \n\n# Compile the benchmarks program\nmake clean \nmake benchmarks \n\n# Run a test simulation of the EISMINT1-moving experiment\n./runylmo -r -e benchmarks -o output/eismint1-moving -n par-gmd/yelmo_EISMINT-moving.nml\n\n# Compile the initmip program and run a simulation of Antarctica\nmake initmip \n./runylmo -r -e initmip -o output/ant-pd -n par/yelmo_initmip.nml -p ctrl.clim_nm=\"clim_pd\"\n```\n\n## Dependencies\n\nSee: [Dependencies](https://palma-ice.github.io/yelmo-docs/dependencies/) for installation tips.\n\n- NetCDF library (preferably version 4.0 or higher)\n- LIS: [Library of Iterative Solvers for Linear Systems](http://www.ssisc.org/lis/)\n- [Optional] Python 3.x, which is only needed for automatic configuration of the Makefile and the use of the script `runylmo` for job preparation and submission.\n- [Optional] 'runner' Python library: [https://github.com/alex-robinson/runner](https://github.com/alex-robinson/runner). Used for changing parameters at the command line using `runylmo`, and for running ensembles. \n\n## Directory structure\n\n```fortran\n    config/\n        Configuration files for compilation on different systems.\n    input/\n        Location of any input data needed by the model.\n    libs/\n        Auxiliary libraries nesecessary for running the model.\n    libyelmo/\n        Folder containing all compiled files in a standard way with\n        lib/, include/ and bin/ folders.\n    output/\n        Default location for model output.\n    par/\n        Default parameter files that manage the model configuration.\n    src/\n        Source code for Yelmo.\n    tests/\n        Source code and analysis scripts for specific model benchmarks and tests.\n```\n\n## Usage\n\nFollow the steps below to (1) obtain the code, (2) configure the Makefile for your system,\n(3) compile the Yelmo static library and an executable program and (4) run a test simulation.\n\n### 1. Get the code.\n\nClone the repository from [https://github.com/palma-ice/yelmo](https://github.com/palma-ice/yelmo):\n\n```\ngit clone git@github.com:palma-ice/yelmo.git $YELMOROOT\ncd $YELMOROOT\n```\n\nwhere `$YELMOROOT` is the installation directory.\n\nIf you plan to make changes to the code, it is wise to check out a new branch:\n\n```\ngit checkout -b user-dev\n```\n\nYou should now be working on the branch `user-dev`.\n\n### 2. Create the system-specific Makefile.\n\nTo compile Yelmo, you need to generate a Makefile that is appropriate for your system. In the folder `config`, you need to specify a configuration file that defines the compiler and flags, including definition of the paths to the `NetCDF` and `LIS` libraries. You can use another file in the config folder as a template, e.g.,\n\n```\ncd config\ncp pik_ifort myhost_mycompiler\n```\n\nthen modify the file `myhost_mycompiler` to match your paths. Back in `$YELMOROOT`, you can then generate your Makefile with the provided python configuration script:\n\n```\ncd $YELMOROOT\npython config.py config/myhost_mycompiler\n```\n\nThe result should be a Makefile in `$YELMOROOT` that is ready for use.\n\n#### Alternative configuration - quickstart with Docker and VS Code\n\nInstead of a manual install, one way to get up and running quickly with Yelmo is with VS Code and Docker. It works on any plattform and uses a Linux based container. You don't need to know Docker or VS Code to get started. Just install the following:\n\n1. [Docker](https://docs.docker.com/engine/install/)\n2. [VS Code](https://code.visualstudio.com) \n3. [install the remote development extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack)\n\nThen make sure that Docker is running and start VS Code. \nOpen the folder with the Yelmo code. Say Yes, when VS Code asks you if you want to open it in the container.\n\nNow you can directly go to step 3 below, just make sure that you use the terminal in VS Code.\n\n### 3. Compile the code.\n\nNow you are ready to compile Yelmo as a static library:\n\n```\nmake clean    # This step is very important to avoid errors!!\nmake yelmo-static [debug=1]\n```\nThis will compile all of the Yelmo modules and libraries (as defined in `config/Makefile_yelmo.mk`),\nand link them in a static library. All compiled files can be found in the folder `libyelmo/`.\n\nOnce the static library has been compiled, it can be used inside of external Fortran programs and modules\nvia the statement `use yelmo`.\nTo include/link yelmo-static during compilation of another program, its location must be defined:\n\n```\nINC_YELMO = -I${YELMOROOT}/include\nLIB_YELMO = -L${YELMOROOT}/include -lyelmo\n```\n\nAlternatively, several test programs exist in the folder `tests/` to run Yelmo\nas a stand-alone ice sheet.\nFor example, it's possible to run different EISMINT benchmarks, MISMIP benchmarks and the\nISIMIP6 INITMIP simulation for Greenland, respectively:\n\n```\nmake benchmarks    # compiles the program `libyelmo/bin/yelmo_benchmarks.x`\nmake mismip        # compiles the program `libyelmo/bin/yelmo_mismip.x`\nmake initmip       # compiles the program `libyelmo/bin/yelmo_initmip.x`\n```\n\nThe Makefile additionally allows you to specify debugging compiler flags with the option `debug=1`, in case you need to debug the code (e.g., `make benchmarks debug=1`). Using this option, the code will run much slower, so this option is not recommended unless necessary.\n\n### 4. Run the model.\n\nOnce an executable has been created, you can run the model. This can be\nachieved via the included Python job submission script `runylmo`. The following steps\nare carried out via the script:\n\n1. The output directory is created.\n2. The executable is copied to the output directory\n3. The relevant parameter files are copied to the output directory.\n4. Links to the input data paths (`input` and `ice_data`) are created in the output directory. Note that many simulations, such as benchmark experiments, do not depend on these external data sources, but the links are made anyway.\n4. The executable is run from the output directory, either as a background process or it is submitted to the queue via `sbatch` (the SLURM workload manager).\n\nTo run a benchmark simulation, for example, use the following command:\n\n```\n./runylmo -r -e benchmarks -o output/test -n par/yelmo_EISMINT.nml\n```\n\nwhere the option `-r` implies that the model should be run as a background process. If this is omitted, then the output directory will be populated, but no executable will be run, while `-s` instead will submit the simulation to cluster queue system instead of running in the background. The option `-e` lets you specify the executable. For some standard cases, shortcuts have been created:\n\n```\nbenchmarks = libyelmo/bin/yelmo_benchmarks.x\nmismip     = libyelmo/bin/yemo_mismip.x\ninitmip    = libyelmo/bin/yelmo_initmip.x\n```\nThe last two mandatory arguments `-o OUTDIR` and `-n PAR_PATH` are the output/run directory and the parameter file to be used for this simulation, respectively. In the case of the above simulation, the output directory is defined as `output/test`, where all model parameters (loaded from the file `par/yelmo_EISMINT.nml`) and model output can be found.\n\nIt is also possible to modify parameters inline via the option `-p KEY=VAL [KEY=VAL ...]`. The parameter should be specified with its namelist group and its name. E.g., to change the resolution of the EISMINT benchmark experiment to 10km, use:\n\n```\n./runylmo -r -e benchmarks -o output/test -n par/yelmo_EISMINT.nml -p ctrl.dx=10\n```\n\nSee `runylmo -h` for more details on the run script. \n\n## Test cases\n\nThe published model description includes several test simulations for validation\nof the model's performance. The following section describes how to perform these\ntests using the same model version documented in the article. From this point,\nit is assumed that the user has already configured the model for their system\n(see https://palma-ice.github.io/yelmo-docs) and is ready to compile the mode.\n\n### 1. EISMINT1 moving margin experiment\nTo perform the moving margin experiment, compile the benchmarks\nexecutable and call it with the EISMINT parameter file:\n\n```\nmake benchmarks\n./runylmo -r -e benchmarks -o output/eismint-moving -n par-gmd/yelmo_EISMINT_moving.nml\n```\n\n### 2. EISMINT2 EXPA\nTo perform Experiment A from the EISMINT2 benchmarks, compile the benchmarks\nexecutable and call it with the EXPA parameter file:\n\n```\nmake benchmarks\n./runylmo -r -e benchmarks -o output/eismint-expa -n par-gmd/yelmo_EISMINT_expa.nml\n```\n\n### 3. EISMINT2 EXPF\nTo perform Experiment F from the EISMINT2 benchmarks, compile the benchmarks\nexecutable and call it with the EXPF parameter file:\n\n```\nmake benchmarks\n./runylmo -r -e benchmarks -o output/eismint-expf -n par-gmd/yelmo_EISMINT_expf.nml\n```\n\n### 4. MISMIP RF\nTo perform the MISMIP rate factor experiment, compile the mismip executable\nand call it with the MISMIP parameter file the three parameter permutations of interest (default, subgrid and subgrid+gl-scaling):\n\n```\nmake mismip\n./runylmo -r -e mismip -o output/mismip-rf-0 -n par-gmd/yelmo_MISMIP3D.nml -p ydyn.beta_gl_stag=0 ydyn.beta_gl_scale=0\n./runylmo -r -e mismip -o output/mismip-rf-1 -n par-gmd/yelmo_MISMIP3D.nml -p ydyn.beta_gl_stag=3 ydyn.beta_gl_scale=0\n./runylmo -r -e mismip -o output/mismip-rf-2 -n par-gmd/yelmo_MISMIP3D.nml -p ydyn.beta_gl_stag=3 ydyn.beta_gl_scale=2\n```\nTo additionally change the resolution of the simulations change the parameter `mismip.dx`, e.g. for the default simulation with 10km resolution , call:\n\n```\n./runylmo -r -e mismip -o output/mismip-rf-0-10km -n par-gmd/yelmo_MISMIP3D.nml -p ydyn.beta_gl_stag=0 ydyn.beta_gl_scale=0 mismip.dx=10\n```\n\n### 5. 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