sst-scripts

Data processing scripts related to the Seedlot Selection Tool

Generating ClimateNA data for the Seedlot Selection Tool

1. Download elevation data for the area of interest (example source)
2. If necessary, combine individual datasets into a single raster
3. Clip the raster dataset as needed
4. Run the geotiff2climatena.py script to convert the clipped DEM to the ClimateNA format, with the region boundary or a continent boundaries shapefile that can be used to mask out oceans
   $ python geotiff2climatena.py path/to/clipped_dem.tif path/to/climatena_dem.csv --boundary=path/to/boundary.shp
5. Run the ClimateNA tool, using the DEM CSV file as input to generate the desired outputs. For SST, we use:

• Normal 1961-1990 / Annual variables
• More Normal Data / 1981_2010 / Annual Variables
• Future Periods / 15GCM-Ensemble_rcp45_2025 / Annual Variables
• Future Periods / 15GCM-Ensemble_rcp45_2055 / Annual Variables
• Future Periods / 15GCM-Ensemble_rcp45_2085 / Annual Variables
• Future Periods / 15GCM-Ensemble_rcp85_2025 / Annual Variables
• Future Periods / 15GCM-Ensemble_rcp85_2055 / Annual Variables
• Future Periods / 15GCM-Ensemble_rcp85_2085 / Annual Variables

6. Run the climatena2netcdf.py script to convert each of your ClimateNA outputs to NetCDF
   $ python climatena2netcdf.py path/to/clipped_dem.tif path/to/climatena_output.csv path/to/netcdf/dir
7. If needed, run the cut_to_region.py script to clip the NetCDF datasets from the previous step to smaller regions. Since you will have one NetCDF per variable, use {variable} to note where in the filename the variable is; the script will clip each NetCDF matching the file pattern.
   $ python cut_to_region.py path/to/full_netcdf_{variable}.nc path/to/clipped_netcdf_{variable}.nc