d22a-mcdc: Analysis Code for "Monte Carlo Drift Correction – Quantifying the Drift Uncertainty of Global Climate Models"

Usage guidelines

This repository accompanies the following manuscript:

B. S. Grandey, Z. Y. Koh, D. Samanta, B. P. Horton, J. Dauwels, and L. Y. Chew (2023), Monte Carlo drift correction – quantifying the drift uncertainty of global climate models, Geosci. Model Dev., https://doi.org/10.5194/gmd-16-6593-2023.

The manuscript serves as the primary reference. The Zenodo archive of this repository serves as a secondary reference.

The data/ folder contains post-processed CMIP6 climate model data. Users of these data should note the CMIP6 Terms of Use.

Workflow

Setup

To create a conda environment with the necessary software dependencies, use the environment.yml file:

conda env create --file environment.yml conda activate d22a-mcdc

The analysis has been performed within this environment on macOS 13 (arm64).

Windows users may need to remove the references to Climate Data Operators (cdo, python-cdo) in environment.yml. CDO is required by data_d22a.ipynb, but not by analysis_d22a.ipynb.

Preparation of data

CMIP6 climate model data have been downloaded, post-processed, and prepared as follows:

1. Data have been downloaded from the Earth System Grid Federation (ESGF) using the ESGF PyClient and Globus (see p22b-esgf-globus v0.2.0).

2. Data have been post-processed using Climate Data Operators (CDO). This includes the following steps: (i) calculate annual means, (ii) multiply each flux variable with the corresponding grid cell area, then (iii) sum globally (see p22c-esgf-processing v0.2.0).

3. Data have then been prepared for further analysis using data_d22a.ipynb (in this repository).

The NetCDF files produced by data_d22a.ipynb can be found in data/.

Analysis

Analysis of the data in data/ is performed using analysis_d22a.ipynb.

analysis_d22a.ipynb uses the functions contained in d22a.py, and it writes both figures (in figs_d22a/) and tables (in tables_d22a/).

Author

Benjamin S. Grandey (Nanyang Technological University), in collaboration with Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew.

Acknowledgements

This Research/Project is supported by the National Research Foundation, Singapore, and National Environment Agency, Singapore under the National Sea Level Programme Funding Initiative (Award No. USS-IF-2020-3).

We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.