A mass and energy conservation analysis of drift in the CMIP6 ensemble: supplementary metadata

This repository contains the details of the computational aspects of the following study: Irving D, Hobbs W, Church J & Zika J (in press). A mass and energy conservation analysis of drift in the CMIP6 ensemble. Journal of Climate. doi:10.1175/JCLI-D-20-0281.1 The figures in the paper were generated by running a series of Python scripts at the command line. Those commands (denoted by the $ prompt) are outlined below with additional notes where necessary.Any scripts in this repository that aren't referred to by name below are modules that are used by some/all of the scripts.The software environment that the scripts were executed in is described by environment.yml. That environment can be installed using conda:$ conda env create -f environment.yml(See https://docs.conda.io/en/latest/ for details.)Figure 1$ python plot_global_budget_variables.py ACCESS-CM2 r1i1p1f1 cmip6 --compfile figure1.pngIn addition to creating a plot for one particular model, the plot_global_budget_variables.py script writes a text file containing the various trends/drifts and regression coefficients presented in the paper. The trends and coefficients for every model were stored in drifts.csv and regression_coefficients.csv respectively, which were then used to create most of the figures below.Figure 2 $ python plot_ohc_masso.py figure2.png --models ACCESS-CM2 ACCESS-ESM1-5 BCC-CSM2-MR BCC-ESM1 CNRM-CM6-1 CNRM-ESM2-1 E3SM-1-0 E3SM-1-1 EC-Earth3 EC-Earth3-Veg GFDL-CM4 GISS-E2-1-G GISS-E2-1-G-CC HadGEM3-GC31-LL IPSL-CM6A-LR MPI-ESM-1-2-HAM MPI-ESM1-2-HR MPI-ESM1-2-LR UKESM1-0-LL --runs r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f2 r1i1p1f2 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f1 r1i1p1f2 --colors tab:blue tab:blue tab:orange tab:orange tab:green tab:green tab:red tab:red tab:purple tab:purple tab:brown tab:brown tab:pink tab:pink tab:olive tab:olive tab:cyan tab:cyan tab:gray --linestyles solid dotted solid dotted solid dotted solid dotted solid dotted solid dotted solid dotted solid dotted solid dotted solid --ohc_outlier 1e24 --runmean_window 10Figure 3$ python plot_ohc_drift.py drifts.csv figure3.pngFigure 4 (and Table 4)$ python plot_conservation_scatter.py drifts.csv figure4.png In addition to creating a plot, the plot_conservation_scatter.py script outputs the ensemble median and interquartile range values shown in Table 4.Figure 5$ python plot_leakage.py drifts.csv figure5.pngFigure 6$ python plot_regression_boxplot.py regression_coefficients.csv figure6.pngFigure S1$ python plot_regression_coefs.py drifts.csv energy figureS1.png --cmip_line 21.5Figure S2$ python plot_regression_coefs.py drifts.csv mass figureS2.png --cmip_line 21.5