https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2019GL085311
This repo is setup for scientists interested to reproduce our Spring and Ilyina, 2020 paper. It contains scripts to reproduce the analysis and create the shown figures. It is inspired by Irving (2015) to enhance reproducibility in geosciences.
- Irving, Damien. “A Minimum Standard for Publishing Computational Results in the Weather and Climate Sciences.” Bulletin of the American Meteorological Society 97, no. 7 (October 7, 2015): 1149–58. https://doi.org/10/gf4wzh.
See config file scripts/asp_esmControl_ens3178_m0001.config.
- model output aggregation:
cdo - analysis:
xarray - visualisation:
matplotlib,cartopy - predictive skill analysis:
climpred - (private repo) plotting routines and data storage on supercomputer:
PMMPIESM
The results in this paper were obtained using a number of different software packages. The command line tool known as Climate Data Operators (CDO) was used to aggregate output and perform routine calculations on those files (e.g., the calculation of temporal and spatial means). For more complex analysis and visualization, a Python distribution called Anaconda was used. A Python library called xarray was used for reading/writing netCDF files and data analysis. The xarray-wraper climpred was co-developed by Aaron Spring and Riley X. Brady and is publicly available at https://climpred.readthedocs.io/. In addition to Matplotlib (the default Python plotting library), Cartopy was used to generate the figures.
- CDO: Climate Data Operators, 2018. http://www.mpimet.mpg.de/cdo.
- Hoyer, Stephan, and Joe Hamman. “Xarray: N-D Labeled Arrays and Datasets in Python.” Journal of Open Research Software 5, no. 1 (April 5, 2017). https://doi.org/10/gdqdmw.
- Hunter, J. D. “Matplotlib: A 2D Graphics Environment.” Computing in Science Engineering 9, no. 3 (May 2007): 90–95. https://doi.org/10/drbjhg.
- Met Office. Cartopy: A Cartographic Python Library with a Matplotlib Interface, 2010. http://scitools.org.uk/cartopy.
Dependencies (Packages installed) can be found in requirements.txt (conda list in requirements_final.txt). Installed via conda (see setup conda_info.txt) and pip.