- Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II
- Featured Publications
- All Publications
activity : 59cb391b-nca3-cmip5-r1-process
Multi-model mean CMIP5 (RCP 6.0 scenario) winter precipitation projections were plotted for North America, for 2071-2099 relative to 1970-1999.
1. For each model at each grid point, the mean winter precipitation under the RCP 6.0 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 winter precipitation was calculated under the RCP 6.0 scenario for 2071-2099 and under the historical scenario for 1970-1999 for the same subset of CMIP3 models. 4. At each grid point, the mean winter precipitation for the two periods was computed by averaging the following models: bcc-csm1-1, CCSM4, CESM1-CAM5, CSIRO-Mk3-6-0, FIO-ESM, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-R, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM-CHEM, MIROC-ESM, MRI-CGCM3, NorESM1-ME, and NorESM1-M. 5. At each grid point, the difference in projected winter 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 :
pr_rcp60_1970-1999_2071-2099_percent_change.nc_hatched_North_America_AR5_white.nc Custom python codes written by Michael Wehner config.py plot_north_american_categories.py plot_north_american_precip.py plot_hawaii_precip.py Wintertime_precip.eps Wintertime_categories.eps rcp60.png APP_precip_winter projections_V6.png
Computing environment : Linux (Red Hat Enterprise Linux Server release 6.4)
Software used : Python (v2.7.6)
Visualization software used : Python 2.7.6, Matplotlib 1.3.1, Basemap 1.0.7
Notes : Python
This activity resulted in the following :
Alternatives : JSON YAML Turtle N-Triples JSON Triples RDF+XML RDF+JSON Graphviz SVG