cmip6hackathon-utci

CMIP6 Hackathon UTCI codebase developing with colleagues from the Project 10 team

Contents

• utci_model_projection_quickplot.py - single (model)(projection) run version with flags for land masking and thresholding
• utci_over_32C_ipcc_regions.py - python script to read in SSP projections plot the mean UTCI>32C masked by IPCC AR5 (or AR6) regions. Illustrative example with the Amazon Basin masked.
• utci_over_32C.py - python script to read in SSP projections and extract the UTCI>32C exceedences. Calculates the latitudinally weighted zonally-averaged mean UTCI>32C edge for the NH and SH. Calculates the mean weighted zonally-average. Calculates the global area-averaged weighted mean timeseires.
• utci_over_32C_time_fraction.py - python script to read in SSP projections and create a boolean array of UTCI>32C exceedences for calculation of time fraction statistics. Calculates the latitudinally weighted zonally-averaged mean UTCI>32C edge for the NH and SH. Calculates the mean weighted zonally-average. Calculates the global area-averaged weighted mean timeseires.
• load_baselines_and_projections.py - python script to lazy load all model baselines and projections into a dataframe and write out netcdfs containing the (model)(projection) area-averaged mean and gridded timeseries with the option of land masking as input to animate_anomalies.py.
• animate_anomalies.py - python script to load the (model)(projection) netCDF area-averaged means and monthly gridded timeseries data and plot monthly gridded anomalies for production of animated GIFs.

Instructions for use

The first step is to clone the latest cmip6hackathon-utci code and step into the installed Github directory:

$ git clone https://github.com/patternizer/cmip6hackathon-utci.git
$ cd cmip6hackathon-utci

Then create a DATA/ directory and copy to it the required datasets listed in the code.

Using Standard Python

The code is designed to run in an environment using Miniconda3-latest-Linux-x86_64 (see requirements.txt)

$ python utci_model_projection_quickplot.py
$ python utci_over_32C_ipcc_regions.py
$ python utci_over_32C.py
$ python utci_over_32C_time_fraction.py
$ python load_baselines_and_projections.py (prerequisite for `animate_anomalies.py`
$ python animate_anomalies.py

License

To be confirmed (probably CC-BY 4.0) but for now Open Government License.

Contact information

• Michael Taylor