default.cfg 2.41 KB
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##################
#### SETTINGS ####
##################

cfg <- list()

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cfg$regionmapping <- "config/regionmappingH12.csv"
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# which LPJmL data set should be used
# see cmuellers landuse folder for available options
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# use '+' for add-on settings specified in mrcommons::toolLPJmLVersion
cfg$lpjml       <- c(natveg    = "LPJmL4_for_MAgPIE_44ac93de+oldGSWP3",
                     crop      = "ggcmi_phase3_nchecks_9ca735cb+oldGSWP3",
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                     cgrazing  = "LPJmL_cgrazing",
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                     mowing    = "LPJmL_mowing")

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# climate scenario specification (in the form of <gcm>:<scenario>)
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# available gcms: UKESM1-0-LL, MRI-ESM2-0, MPI-ESM1-2-HR, IPSL-CM6A-LR, GFDL-ESM4
# available rcps: ssp126, ssp245, ssp370, ssp585, 
#                 ssp119 (only MRI, IPSL, UKESMI),
#                 ssp460 (only MRI, IPSL) 
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cfg$climatetype <- "GFDL-ESM4:ssp370"
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#ISIMIP scenarios, with format:  "yields:EPIC-IIASA:ukesm1-0-ll:ssp585:default:3b"
# available GGCMs: EPIC-IIASA, pDSSAT, CYGMA1p74, full list here: /p/projects/macmit/data/GGCMI/AgMIP.output
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# available gcms: UKESM1-0-LL, MRI-ESM2-0, MPI-ESM1-2-HR, IPSL-CM6A-LR, GFDL-ESM4
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# available rcps: ssp126, ssp370 (only for some), ssp585
# available co2 fert: default, 2015co2
cfg$isimip <- NULL
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# data revision
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cfg$revision <- 4.59
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# developer suffix for regional revision
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cfg$dev      <- ""
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#  aggregation clustering type, which is a combination of a single letter, indicating the cluster methodology, and a number,
# indicating the number of resulting clusters. Available methodologies are hierarchical clustering (h), normalized k-means clustering
# (n) and combined hierarchical/normalized k-means clustering (c). In the latter hierarchical clustering is used to determine the
# cluster distribution among regions whereas normalized k-means is used for the clustering within a region.
cfg$ctype <- "c200"
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# Should specific regions be resolved with more or less detail
# Values > 1 mean higher share, < 1 lower share
# e.g. cfg$clusterweight <- c(LAM=2) means that a higher level of detail for
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# region LAM
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# for m clustering this weights are directly linked to the number of clusters within a region
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# if set to NULL all weights will be assumed to be 1.
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# examples:
# c(LAM=1.5,SSA=1.5,OAS=1.5)
# c(LAM=2,SSA=2,OAS=2)
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#cfg$clusterweight <- c(SSA=30, MEA=15, OAS=20, CHA=26, IND=22, REF=11, NEU=6, EUR=15, LAM=25, USA=17, CAZ=12, JPN=1)
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cfg$clusterweight <- NULL
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# number of cores
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cfg$n_cores <- 10