Code to reproduce figures in the THAI trilogy

Python code to analyze and visualize climate model output for the TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI).

Most figures from the THAI trilogy can be reproduced using Jupyter Notebooks in this reporistory. Python scripts contain functions to preprocess and clean the data. They also contain common and model-specific variables for convenience. Contributions are welcome.

What's in this repo?

File	Purpose
names/	Model-specific names of variables and coordinates
THAI-Cloud-Distribution.ipynb	Cloud maps and meridional profiles
THAI-Global-Diag.ipynb	Global diagnostics (tables)
THAI-Meridional-Profiles.ipynb	Meridional profiles of temperature, WVP, CWP and cloud fraction
THAI-Radiation-Fluxes.ipynb	Maps of radiation fluxes at TOA and the surface
THAI-Rot-Div-Components.ipynb	Helmholtz decomposition
THAI-Save-Time-Mean.ipynb	Average data in time and save in an intermediate dataset
THAI-Save-Time-Series.ipynb	Average selected variables spatially and save in an intermediate dataset
THAI-Sfc-Temp-Hist.ipynb	Histograms of surface temperature
THAI-Substellar-Profiles.ipynb	Profiles of temperature, lapse rate, radiative heating at the substellar point
THAI-TL-Streamfunction.ipynb	Mass streamfunction in tidally locked coordinates
THAI-Time-Series.ipynb	Time series
THAI-Time-Spectrum.ipynb	Time variability as a spectrum
THAI-Zonal-Mean-Zonal-Wind.ipynb	Zonally averaged zonal wind
calc.py	Calculation of various climate diagnostics
commons.py	Common project-specific objects
const_ben1_hab1.py	Constants for the Ben 1 and Hab 1 cases
const_ben2_hab2.py	Constants for the Ben 2 and Hab 2 cases
grid.py	Operations on GCMs' grids
load_thai.py	Functions to load and merge THAI datasets
model_exocam.py	Functions relevant only to ExoCAM
model_lmdg.py	Functions relevant only to LMD-G
model_rocke3d.py	Functions relevant only to ROCKE-3D
model_um.py	Functions relevant only to the UM
mypaths.py	Paths to files and directories relevant to this project
paper.mplstyle	Matplotlib stylesheet
plot_func.py	Plotting functions

Want to re-use the code? Here are the instructions.

Set up Python

Skip the first two steps if you have Jupyter Lab with nb_conda_kernels installed already.

1. Install Miniforge.
2. Install necessary packages to the base environment. Make sure you are installing them from the conda-forge channel.

conda install jupyterlab nb_conda_kernels

3. Check out or download this repository.
4. In the command line, navigate to the downloaded folder and create a separate conda environment.

conda env create --file environment.yml

Download THAI data

All GCM THAI data are permanently available for download here, with variables described for each dataset. If you use those data in your own research, please cite the relevant paper and add the following statement:

THAI data have been obtained from https://ckan.emac.gsfc.nasa.gov/organization/thai, a data repository of the Sellers Exoplanet Environments Collaboration (SEEC), which is funded in part by the NASA Planetary Science Divisions Internal Scientist Funding Model.

Open the code

1. Start the Jupyter Lab, for example from the command line:

jupyter lab

2. Open noteboks in the thai environment and start coding.

Pre-process data

If you want to re-draw figures from the THAI trilogy papers, we recommend that you create an intermediate dataset first by averaging the THAI data in time or in space. To get a dataset of time-averaged variables, use the THAI-Save-Time-Mean.ipynb notebook. To get a dataset of selected time series, use the THAI-Save-Time-Series.ipynb notebook. Before running these notebooks, make sure the paths to THAI data are set up correctly in mypaths.py! Also note that these two notebooks use dask which uses multiple cores to speed up calculations. You can adapt the number of cores to be more suitable for your machine.

Acknowledging our work

If you use code from this repository, please cite our papers:

@article{Fauchez22_thai,
author = {Fauchez, Thomas J. and Villanueva, Geronimo L. and Sergeev, Denis E. and Turbet, Martin and Boutle, Ian A. and Tsigaridis, Kostas and Way, Michael J. and Wolf, Eric T. and Domagal-Goldman, Shawn D. and Forget, Fran{\c{c}}ois and Haqq-Misra, Jacob and Kopparapu, Ravi K. and Manners, James and Mayne, Nathan J.},
doi = {10.3847/PSJ/ac6cf1},
issn = {2632-3338},
journal = {PSJ},
month = {9},
number = {9},
pages = {213},
publisher = {IOP Publishing},
title = {{The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). III. Simulated Observables—the Return of the Spectrum}},
url = {https://iopscience.iop.org/article/10.3847/PSJ/ac6cf1 https://iopscience.iop.org/article/10.3847/PSJ/ac6cf1/meta},
volume = {3},
year = {2022},
}
@article{Sergeev22_thai,
author = {Sergeev, Denis E. and Fauchez, Thomas J. and Turbet, Martin and Boutle, Ian A. and Tsigaridis, Kostas and Way, Michael J. and Wolf, Eric T. and Domagal-Goldman, Shawn D. and Forget, Fran{\c{c}}ois and Haqq-Misra, Jacob and Kopparapu, Ravi K. and Lambert, F. Hugo and Manners, James and Mayne, Nathan J.},
doi = {10.3847/PSJ/ac6cf2},
issn = {2632-3338},
journal = {PSJ},
month = {9},
number = {9},
pages = {212},
publisher = {IOP Publishing},
title = {{The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). II. Moist Cases—The Two Waterworlds}},
url = {https://iopscience.iop.org/article/10.3847/PSJ/ac6cf2 https://iopscience.iop.org/article/10.3847/PSJ/ac6cf2/meta},
volume = {3},
year = {2022},
}
@article{Turbet22_thai,
author = {Turbet, Martin and Fauchez, Thomas J. and Sergeev, Denis E. and Boutle, Ian A. and Tsigaridis, Kostas and Way, Michael J. and Wolf, Eric T. and Domagal-Goldman, Shawn D. and Forget, Fran{\c{c}}ois and Haqq-Misra, Jacob and Kopparapu, Ravi K. and Lambert, F. Hugo and Manners, James and Mayne, Nathan J. and Sohl, Linda},
doi = {10.3847/PSJ/ac6cf0},
issn = {2632-3338},
journal = {PSJ},
month = {9},
number = {9},
pages = {211},
publisher = {IOP Publishing},
title = {{The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). I. Dry Cases—The Fellowship of the GCMs}},
url = {https://iopscience.iop.org/article/10.3847/PSJ/ac6cf0 https://iopscience.iop.org/article/10.3847/PSJ/ac6cf0/meta},
volume = {3},
year = {2022},
}