Code underlying analysis performed in
Wohland, J., Folini, D., Pickering, B., 2021. Wind speed stilling and its recovery due to internal climate variability. Earth System Dynamics. https://doi.org/10.5194/esd-2021-29
If you content of this repository or code derived from it in academic work, please cite the above publication.
| Figure | Filename | Creating python script |
|---|---|---|
| Fig. 1 | attribution_maps.jpeg | attribution.py:plot_attribution_maps |
| Fig. 2 | contribution_histograms.jpeg | attribution.py:plot_onshore_contribution_histograms |
| Fig. 3 | scatter_gothr+gsecd_abs.jpeg | attribution.py:plot_luh_vs_wind_speed_scatter |
| Fig. 4 | LUH1/LUH_change_future_ref2000.jpeg | LUH_plots.py:plot_future_LUH_change |
| Fig. 5 | global_windspeeds.jpeg | trend_maps.py:plot_global_windspeeds |
| Fig. 6 | timeseries_picontrol_Europe.jpeg | trends.py:plot_full_timeseries_with_trend_marks |
| Fig. 7a | picontrol_wind_trends_Europe_5_20y.jpeg | trends.py:plot_trend_histograms |
| Fig. 7b | CMIP6/Ensmean_picontrol_wind_trends_Europe_5.jpeg | trends.py:plot_pi_control_cmip6_trend_histograms |
| Fig. 8a | historical_wind_trends_Europe_5_all.jpeg | trends.py:plot_experiment_trend_histograms |
| Fig. 8b | rcp26_wind_trends_Europe_5_all.jpeg | trends.py:plot_experiment_trend_histograms |
| Fig. 8c | rcp45_wind_trends_Europe_5_all.jpeg | trends.py:plot_experiment_trend_histograms |
| Fig. 8d | rcp85_wind_trends_Europe_5_all.jpeg | trends.py:plot_experiment_trend_histograms |
| Appendix Fig. A1 | box_timeseries_Europe.jpeg | ensmean_timeseries.py:plot_ensemble_members_timeseries |
| Appendix Fig. A2 | picontrol_HadISD_wind_trends_Europe_5_20y.jpeg | trends.py:plot_trend_histograms |
| Appendix Fig. A3a | picontrol_wind_trends_Europe_5_15y.jpeg | trends.py:plot_trend_histograms |
| Appendix Fig. A3b | picontrol_wind_trends_Europe_5_25y.jpeg | trends.py:plot_trend_histograms |
| Appendix Fig. A4a | picontrol_wind_trends_Europe_10_20y.jpeg | trends.py:plot_trend_histograms |
| Appendix Fig. A4b | picontrol_wind_trends_Europe_15_20y.jpeg | trends.py:plot_trend_histograms |
| Appendix Figs. 5-7 | CMIP6/{Model_name}_picontrol_wind_trends_Europe_5.jpeg | trends.py:plot_pi_control_cmip6_trend_histograms |
All figures can be created at once by running the function make_all_plots.py from the command line, assuming you are in the Anaconda environment given in this repository:
cd code
python make_all_plots.py [data_path] [plots_path] [--cache_path]Where directories for input data and output plots are user-defined (default ../data and ../plots from the code directory).
--cache_path can be also defined to store some intermediate processed data.
requirements.yaml can be used to create an Anaconda environment with all the necessary packages to run the scripts in this repository (including the command line tool cdo).
Executing scripts from this repository requires (a) downloading of the required input data and (b) preprocessing the data.
To download the input, please follow the structure and recommendations given in the data subdirectories.
The MPI-GE data can be downloaded using the provided wget scripts after you have registered at ESGF (see https://esgf-data.dkrz.de/user/add/?next=http://esgf-data.dkrz.de/projects/esgf-dkrz/ to create a new profile).
The other downloads are explained in README.md files in the respective subfolders.
Input data is from 4 different sources:
- The Max Planck Institute for Meteorology (MPI-M) Grand Ensemble (MPI-GE). Ref: DOI 10.1029/2019MS001639
- The Land Use Harmonization (LUH) Project. Ref: DOI 10.1007/s10584-011-0153-2
- The Coupled Model Intercomparison Project Phase 6 (CMIP6). Ref: DOI 10.5194/gmd-9-1937-2016
- Zeng et al. (2019). Ref: DOI 10.1038/s41558-019-0622-6
To speed up Python calculations, the MPI-GE ensemble means are pre-calculated using CDO (see code/preprocess/preprocess_MPI-GE.sh).
They are stored in a subfolder ensmean of the respective experiment (e.g., rcp26/ensmean contains the ensemble mean of the rcp26 scenario).
Similarly, CDO is used to preprocess the CMIP6 data (see code/preprocess/preprocess_CMIP6.sh).
The preprocessed landmask based on MPI-GE is available in the data directory (data/runoff/landmask.nc), having been produced from raw data using code/preprocess/make_land_mask.py.
Maher, N., Milinski, S., Suarez‐Gutierrez, L., Botzet, M., Dobrynin, M., Kornblueh, L., Kröger, J., Takano, Y., Ghosh, R., Hedemann, C., Li, C., Li, H., Manzini, E., Notz, D., Putrasahan, D., Boysen, L., Claussen, M., Ilyina, T., Olonscheck, D., Raddatz, T., Stevens, B., Marotzke, J., 2019. The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability. J. Adv. Model. Earth Syst. 11, 2050–2069. https://doi.org/10.1029/2019MS001639
Hurtt, G.C., Chini, L.P., Frolking, S., Betts, R.A., Feddema, J., Fischer, G., Fisk, J.P., Hibbard, K., Houghton, R.A., Janetos, A., Jones, C.D., Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D.P., Wang, Y.P., 2011. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change 109, 117–161. https://doi.org/10.1007/s10584-011-0153-2
Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., Taylor, K.E., 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016