Skip to content

Latest commit

 

History

History
99 lines (81 loc) · 5.23 KB

README.md

File metadata and controls

99 lines (81 loc) · 5.23 KB
Raw

cover image

The impact of the explicit representation of convection on the climate of a tidally locked planet in global stretched-mesh simulations.

Published in The Astrophysical Journal arXiv preprint Python 3.12 License: MIT black

Repository contents

Notebooks and Python scripts are in the src/scripts/ directory, while the figures are in the src/figures/ directory.

# Figure Notebook
1 Summary of the simulation setup Show-Mesh-And-Cell-Sizes.ipynb
2 Clouds and precipitation in the simulations with stretched and quasi-uniform mesh Cloud-Precip-Snap-Hist.ipynb
3 Meridional and time mean profiles of vertically integrated moisture diagnostics Meridional-Mean-Cloud-Profiles.ipynb
4 Vertical profiles of time mean diagnostics in the substellar region Substellar-Vertical-Profiles.ipynb
5 Thermodynamic and circulation regime Thermodynamic-And-Circulation-Regime.ipynb
6 Maps of precipitation rate Precipitation-Maps.ipynb
7 Circulation regime bistability Show-Bistability.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 miniforge.
  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 conda-lock
  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 conda environment from the lock file.
conda-lock install --name stretched_mesh_env conda-lock.yml

Open the code

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

System information

Date: Tue Apr 30 11:43:52 2024 BST

            OS : Linux
        CPU(s) : 56
       Machine : x86_64
  Architecture : 64bit
           RAM : 502.6 GiB
   Environment : Python
   File system : ext4

Python 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0]

         numpy : 1.26.4
         scipy : 1.13.0
       IPython : 8.22.2
    matplotlib : 3.8.4
        scooby : 0.9.2

python -c 'import scooby; print(scooby.Report())'