activity : 12961b12-nca3-cmip5-r1-process

12961b12-nca3-cmip5-r1-process

Multi-model mean CMIP5 (RCP 2.6 scenario) summer precipitation projections were plotted for North America, for 2071-2099 relative to 1970-1999.

1. For each model at each grid point, the mean summer precipitation under the RCP 2.6 scenario was calculated. 2. For each model, these data were re-gridded to a common grid. 3. For each model at each grid point, the mean summer precipitation under the RCP 2.6 scenario was calculated for two periods: 1970-1999 and 2071-2099. 4. At each grid point, the mean summer precipitation for the two periods was computed by averaging the following models: bcc-csm1-1, BNU-ESM, CanESM2, CCSM4, CESM1-CAM5, CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, FGOALS-g2, FIO-ESM, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-Rp1, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MIROC-ESM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, NorESM1-ME, and NorESM1-M. 5. At each grid point, the difference in projected summer precipitation was calculated for 2071-2099 minus 1970-1999. 6. Data were plotted for all grid points in North America with hatching/white-out applied as follows: If the average change is twice as large as the average 20-year standard deviation from the reference period model run and 90% of the models agree in sign, then hatching is applied to those grid points. If the average change is less than the average 20-year standard deviation from the reference period model run, then those grid points are whited out (Methodology as used in IPCC AR5 WG1 report, chapter 12).

The duration of this activity was 100 hours.

Output artifacts generated by this activity :
config.py
plot_north_american_categories.py
plot_north_american_precip.py
plot_hawaii_precip.py
pr_rcp26_2071-2099_north_american_percent_change.eps
pr_rcp26_2071-2099_north_american_HI_categories.eps
pr_rcp26_2071-2099_north_american_HI_percent_change.eps
2-15_c.png
CS_seasonal precip projections RCP-NO TITLE_V8.png

This activity resulted in the following :

Computing environment : TBD

Software used : TBD

Visualization software used : Python 2.7.6, Matplotlib 1.3.1, Basemap 1.0.7

Notes : TBD

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