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Scripts to reproduce figures for the paper

License: MIT License

Python 0.36% Jupyter Notebook 99.64%
atmospheric-modelling climate-modelling idealized-numerical-simulations numerical-modelling planetary-science ugrid unstructured-meshes

lfric_exo_bench_code's Introduction

cover image

Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere

GMD Preprint

Python 3.11 black License: MIT DOI

Repository contents

Notebooks and Python scripts are in the src/scripts/ directory, while the figures themselves are in the src/figures/ directory. The final regridded and time mean data are in the src/data/ directory.

# Figure or Table Notebook
1 3D image of the cubed sphere mesh Mesh-3D.ipynb
2 Conservation diagnostics in the Temperature Forcing cases Conservation-Diags.ipynb
3 Temperature Forcing climate External-Forcing-Plots.ipynb
4 Mean climate in the THAI cases THAI-Plots.ipynb
5 Global mean climate diagnostics table Tab04-THAI-Global-Diags.ipynb

How to reproduce figures

Set up environment

To recreate the required environment for running Python code, follow these steps. (Skip the first two steps if you have Jupyter with nb_conda_kernels installed already.)

  1. Install conda or mamba, e.g. using mambaforge.
  2. Install necessary packages to the base environment. Make sure you are installing them from the conda-forge channel.
mamba install -c conda-forge jupyterlab nb_conda_kernels
  1. Git-clone or download this repository to your computer.
  2. In the command line, navigate to the downloaded folder, e.g.
cd /path/to/downloaded/repository
  1. Create a separate conda environment (it will be called lfric_ana).
mamba env create --file environment.yml

Open the code

  1. Start the Jupyter Lab, for example from the command line (from the base environment).
jupyter lab
  1. Open notebooks within the lfric_ana environment start running the code.

System information and key python libraries

--------------------------------------------------------------------------------
  Date: Wed Apr 05 12:42:43 2023 UTC

                OS : Linux
            CPU(s) : 192
           Machine : x86_64
      Architecture : 64bit
               RAM : 503.5 GiB
       Environment : Python
       File system : ext4

  Python 3.11.2 | packaged by conda-forge | (main, Mar 31 2023, 17:51:05) [GCC
  11.3.0]

            aeolus : 0.4.16+15.gd4237f3
              dask : 2023.3.2
       esmf_regrid : 0.6.0
              iris : 3.4.1
        matplotlib : 3.7.1
             numpy : 1.24.2
          stratify : 0.2.post0
--------------------------------------------------------------------------------

How to cite this repository

@software{denis_sergeev_2023_7818107,
  author       = {Denis Sergeev},
  title        = {dennissergeev/lfric\_exo\_bench\_code: Version 0},
  month        = apr,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v0},
  doi          = {10.5281/zenodo.7818107},
  url          = {https://doi.org/10.5281/zenodo.7818107}
}

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