activity : nca4-projected-change-in-seasonal-precip-panel-3-process

nca4-projected-change-in-seasonal-precip-panel-3-process

Methodology:

1. For each model at each grid point, the total spring precipitation under the RCP8.5 scenario was calculated for each year.
2. For each model at each grid point, the mean spring precipitation under the RCP8.5 scenario was calculated for the period of 2070–2099, and the mean spring precipitation for the reference period was calculated for the period of 1986-2015. To calculate the mean values for the reference period, the RCP8.5 scenario data for 2006-2015 were appended to the historical data for 1986-2005, and then the 30-year mean was calculated using the period of 1986-2015.
3. At each grid point, the difference in projected spring precipitation was calculated for 2070–2099 minus 1986–2015.
4. For each model, these data were re-gridded to a common grid.
5. At each grid point, the mean spring precipitation difference was computed by averaging the following models:
ACCESS1-0, ACCESS1-3, bcc-csm1-1, BNU-ESM, CanESM2, CCSM4, CNRM-CM5, CSIRO-Mk3-6-0, FGOALS-g2, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-R-p1, HadGEM2-AO, HadGEM2-ES, inmcm4, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3.
6. For each model at each grid point, the spring precipitation under the pre-industrial control runs (at least 500 years long) was calculated. The first 100 years of the pre-industrial runs were ignored. Variability was calculated for every grid point as the standard deviation of non-overlapping 20-year means, multiplied by the square root of 2. This is by definition the standard deviation of the difference between two independent 20-year averages having the same variance, and estimates the variation of the difference that would be expected due to unforced internal variability. The median across all models of that quantity is used.
7. The mean spring precipitation difference was plotted for grid points in the Contiguous U.S., Alaska, Hawai’i, Puerto Rico, and the U.S. Virgin Islands, with model agreement indicated as follows:
• "Whited out" – large changes where multi-model average change is greater than double the standard deviation of the 20-year mean from control runs and less than 90 percent of the models agree on the sign.
• Stippling – large changes where multi-model average change is greater than double the standard deviation of the 20-year mean from control simulations runs, and 90 percent of the models agree on the sign.
• Hatching – No significant change where multi-model average change is less than the standard deviation of the 20-year means from control simulations runs.
• Color only – changes are between one standard deviation and two standard deviations.

Multi-model mean CMIP5 spring temperature projections under the RCP8.5 scenario were plotted for the Contiguous U.S., Alaska, Hawai’i, Puerto Rico, and the U.S. Virgin Islands, for 2070–2099 relative to 1976–2005. Hatching/stippling, etc. was applied to indicate model agreement (as described in the methodology).

Methodology Contact: Kenneth E. Kunkel, North Carolina State University

Processes

The duration of this activity was 120 hours.

Interim artifacts generated by this activity :
access1-0.gs; access1-3.gs; bcc-csm1-1-t.gs; bnu-t.gs; can-t.gs ccsm4-t.gs cnrm-cm5-t.gs csiro-mk3-6-0-en01-t.gs fgoals-g2-t.gs gfdl-cm3-t.gs gfdl-esm2g-t.gs gfdl-esm2m-t.gs giss-e2r-t.gs hadgem2-a0-t.gs hadgem2-es-t.gs inmcm4-t.gs ispl-cm5a-lr-t.gs miroc5-t.gs miroc-esm-chem-t.gs miroc-esm-t.gs mpi-esm-lr-t.gs mpi-esm-mr-t.gs mri-cgcm3-t.gs,intp.gs intp-1.gs,mme.gs,w.f,pcp-rcp85-wseasons-2070-2099.txt,RCP85_Precip_MAM.ai, RCP85_Precip_MAM_AK.ai, RCP85_Precip_MAM_HI.ai, RCP85_Precip_MAM_PR.ai
Output artifacts generated by this activity :
Change Climate_proj change seasonal precip_v4.png; on TSU Resources site
Tools

Computing environment : Red Hat Enterprise Linux Server release 7.3

Software used : FORTRAN: G95 (GCC 4.0.3 (g95 0.94!) Jan 17 2013),Grid Analysis and Display System (GrADS) Version 2.0.2

Visualization software used : Grid Analysis and Display System (GrADS) Version 2.0.2

Data Bounding

The input object was time bounded starting from January 01, 1986 (00:00 AM)

The input object was time bounded ending at December 31, 2099 (00:00 AM)

The input object was bounded spatially:

This activity resulted in the following :

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