David M Chen ([email protected]) 21 January, 2021
ggcmi2agmip bundles the functions required to process GGCMI outputs from ISIMIP3b, located either on the PIK/ISIMIP cluster or locally. If run locally, note that the path structure for the GGCMI data needs to be set as on cluster, i.e. starting with the crop model name as so: “/LPJmL/phase3b/mri-esm2-0/ssp585/mai” (/model name/phase3b/climate model/ssp scenario/crop) . Spam files do not require specific folder structure as they are single files.
Install the development version from GitHub with:
#install package
devtools::install_github("caviddhen/ggcmi2agmip")
library(ggcmi2agmip)Tne processing is run using the processIsimip() function, with preferred paths and parameters. See default configuration below for running on cluster. Parameters that end in “.path” are paths where the spam files are found on cluster. If processing is done locally, please see Files required… below
general.path: general path where the crop model outputs are stored, described as above
models: chooses the crop model to process
rcpso: rcp scenarios
gcms: climate model
co2_scen: “2015co2” (co2 effect off) or “default” (co2 effect on)
time: time scale to process (comparing 30 year average centred around 2030,2050, or 2080.)
# run processing
processIsimip(general.path = "/p/projects/macmit/data/GGCMI/AgMIP.output/",
models = c("LPJmL","EPIC-IIASA","pDSSAT","GEPIC","LPJ-GUESS"),
rcpso = c("historical","picontrol","ssp126","ssp585"),
gcms = c("gfdl-esm4","mpi-esm1-2-hr","mri-esm2-0","ukesm1-0-ll"),
co2_scen = c("2015co2","default"),
time = c("2030s","2050s","2085s", "2100"),
lpj.spam.path = "/p/projects/macmit/data/GGCMI/fast-track/yields/yield_shifter_econ_models/some_spam/",
adm.path= "/p/projects/macmit/data/GGCMI/AgMIP.output/processed/masks/aggr/",
rice.path = "/p/projects/macmit/data/GGCMI/AgMIP.input/phase3/crop_calendar/",
wheat.path = "/p/projects/macmit/data/GGCMI/AgMIP.input/phase3/landuse/winter_spring_wheat_separation/",
output.path = "/p/projects/landuse/users/davidch/AgMIP_impacts/ISIMIP3b")If processing on a local computer, then spam files are required from cluster, with the path to these files listed in the default settings above.
lpjml spam for Irrigated and Rainfed cropping area: lpj.spam.path/crop“.R_physical_area” and “.I_physical_area” for all crops
adm file for mapping country names and codes: adm.path/“gadm0.meta.csv”
rice spam for first and second cropping of rice: rice.path/"_rf_ggcmi_crop_calendar_phase3_v1.01.nc4" and "_ir_ggcmi_crop_calendar_phase3_v1.01.nc4
**wheat spam for spring and winter wheat **: wheat.path/winter_and_spring_wheat_areas_phase3.nc4
processIsimip() saves the processed output files in the output.path given. All values are in calories dry matter. Currently, the function produces, for each time period, for co2 scenario, ssp scenario, and for irrigated and rainfed crops, a table of crop model and crop by country:
ISI-MIP3_production_changes: total amount of calories produced in future time vs 2000s
ISI-MIP3_percent_changes_30-year_average: Average percent change over 30 years for time step t vs 2000s
ISI-MIP3_percent_changes_30-year_annual: Annual percent change over 30 years for time step t vs 2000s
ISI-MIP3_growth_rates_30-year_average: Average growth rates by crop model and crop for time step t vs 2000s
ISI-MIP3_percent_shocks_30-year_annual: The change from one year to the previous year in the 30-year time step t