Nothing
R/models2wux.R
In wux: Wegener Center Climate Uncertainty Explorer
# ----------------------------------------------------------------
# $Author: thm $
# $Date: 2015-04-03 12:24:30 +0200 (Fri, 03 Apr 2015) $
# $Rev: 338 $
# ----------------------------------------------------------------
## ----------------------------------------------------------------
## Process of reading in climate data and storing them to a data.frame
## object will take place here.
##
## - models2wux, the 'main' WUX data input routine.
## - Helperfunctions for models2wux.
## ----------------------------------------------------------------
models2wux <- function(userinput,
modelinput = NULL
) {
## Creates a dataframe containing climate change signals of climate models
## listed in user.input.
##
## Args:
## userinput: Filename ot list object from input created by the user.
## The file contains a
## general.input section with tuning parameters and
## a model.input section listing the climate models
## to be processed.
## modelinput: filepath to model.input file. default is NULL,
## which reads in the internal IniModelsDictionary file.
##
## Returns:
## Dataframe containing climate change signals for each model.
##
## History:
## 2010-10-20 | Original code (thm)
## 2010-11-03 | user.input sourced from Models2Wux
## model.input inserted in user.input
## renaming of several variables (thm)
## 2010-11-17 | model.info read in after reading in parameter data (thm)
## 2010-11-29 | some additional user.input - model.input keywords added
## 2011-02-11 | user.input handling changes, renaming from 'Modes2Wux'
## to 'models2wux'
## 2011-04-29 | structural changes for generating WUX dataframe
## 2011-09-14 | added display.subregions keyword to models2wux function
## 2011-09-22 | added case filenames = NA (thus not reading in any data)
## | plus: return value added (it got lost somewhere...)
## 2014-11-12 | alternative model.input pathway introduced (thm)
## 2014-11-14 | rename argument "input" to "userinput" (thm)
## 2014-11-19 | now omitting argument does.plot.subregions (thm)
## 2014-11-21 | platform independent (thm)
## 2015-03-31 | new handling of subregion plotting
cat("\n")
## get user.input list from 'input.filename'
if (!is.list(userinput)) {
user.input.sourced <- source(userinput, local = TRUE)
user.input <- user.input.sourced$value
} else {
user.input <- userinput
}
## -------------------------------------------
## ++++++++++ READING GENERAL INPUT ++++++++++
## -------------------------------------------
############## EXTRACTING INFORMATION FROM user.input FILE ##############
## create outdir for data if path not existing
save.as.data <- user.input$save.as.data
if ( !file.exists(dirname(save.as.data)) )
dir.create(dirname(save.as.data), recursive = TRUE)
## create outdir for plots if path not existing
if ( !is.null(user.input$plot.subregions$save.subregions.plots) ) {
save.subregions.plots <- user.input$plot.subregions$save.subregions.plots
if ( !file.exists(save.subregions.plots) )
dir.create(save.subregions.plots, recursive = TRUE)
} else {
save.subregions.plots <- NULL
}
## user input for area fraction
if ( !is.null(user.input$area.fraction) ) {
does.area.fraction <- user.input$area.fraction
} else {
does.area.fraction <- FALSE
}
## user input for land mask
if ( !is.null(user.input$use.land.mask) ) {
use.land.mask <- user.input$use.land.mask
print("USING LAND-SEA MASK")
} else {
use.land.mask <- FALSE
}
## user input for area weighting
if ( !is.null(user.input$spatial.weighting) ) {
does.spatial.weighting <- user.input$spatial.weighting
} else {
does.spatial.weighting <- FALSE
}
reference.period <- user.input$reference.period
scenario.period <- user.input$scenario.period
periods <- c("reference.period" = reference.period,
"scenario.period" = scenario.period)
## user input for area weighting
if ( !is.null(user.input$na.rm) ) {
na.rm <- user.input$na.rm
} else {
na.rm <- FALSE
}
## subregion specification
subregions <- user.input$subregions
## temporal aggregation specification
temporal.aggr <- user.input$temporal.aggregation
## generate list for dataframes for different parameters
parameter.names <- user.input$parameter.names
wux.df.list <- vector("list", length(parameter.names))
names(wux.df.list) <- parameter.names
## -------------------------------------------
## +++++++++++ READING MODEL DATA ++++++++++++
## -------------------------------------------
############## LOOP OVER PARMS, MODELS, PERIODS AND EXTRACT DATA ##############
## first loop over specified parameters
for ( parameter.longname in parameter.names ) {
cat("\n******************************************************************")
cat("****\n", "PROCESSING PARAMETER \"", parameter.longname,
"\"\n", sep = "")
cat("******************************************************************")
cat("****\n")
## initialize WUX dataset
wux.df <- data.frame()
## climate model names from user.input
user.model.names <- user.input$climate.models
## load Model Dictionary, i.e. a named list containing all the information
## for known models as filenames, directories and further metainformation
model.input <- ReadFromModelDictionary(user.model.names, modelinput)
model.names <- names(model.input)
## second loop over specified models
for (modelname.counter in model.names) {
## Need parameter short name (i.e. variable name from NetCDF file)
## to extract data.
## If "parameters" tag is provided in InitModelDictionary, use its value
## as NetCDF variable name.
model.pars <- model.input[[modelname.counter]]$parameters
parameter.shortname <- GetParameterShortName(parameter.longname,
model.pars)
## getting general model info for data to be read in
institute.name <- model.input[[modelname.counter]]$institute
global.model.name <- model.input[[modelname.counter]]$gcm
gcm.run <- model.input[[modelname.counter]]$gcm.run
rcm.name <- model.input[[modelname.counter]]$rcm
emission.scenario <- model.input[[modelname.counter]]$emission.scenario
is.corrected <- model.input[[modelname.counter]]$corrected.data
resolution <- model.input[[modelname.counter]]$resolution
what.timesteps <- model.input[[modelname.counter]]$what.timesteps
## optional input for models having other periods
model.ref.per <- model.input[[modelname.counter]]$reference.period
model.scn.per <- model.input[[modelname.counter]]$scenario.period
## optional input for model having different period lengths per file
years.in.ref.per.files <-
model.input[[modelname.counter]]$years.in.ref.per.files
years.in.scn.per.files <-
model.input[[modelname.counter]]$years.in.scn.per.files
## optional input for respecification of NetCDF entries
time.units <- model.input[[modelname.counter]]$time.units
calendar <- model.input[[modelname.counter]]$calendar
count.first.time.value <-
model.input[[modelname.counter]]$count.first.time.value
lonlat.var.name <-
model.input[[modelname.counter]]$lonlat.var.name
## location of gridfile
## model grid
grid.filename <-
file.path(model.input[[modelname.counter]]$gridfile.path,
model.input[[modelname.counter]]$gridfile.filename)
## model subregion
subregion.filename <-
file.path(model.input[[modelname.counter]]$subregion.path,
model.input[[modelname.counter]]$subregion.filename)
## in case no subregionfiles are passed, we flag filename with "FALSE"
if (length(subregion.filename) == 0)
subregion.filename <- FALSE
## land mask
land.mask <- model.input[[modelname.counter]]$land.mask
land.mask.name <- model.input[[modelname.counter]]$land.mask.name
## third loop over periods (reference period and scenario period)
for (periods.counter in periods) {
cat("\n-------------------------------------------------------------")
cat("---------\n\n PROCESSING MODEL \"", modelname.counter,
"\" FOR PERIOD \"", periods.counter, "\"\n", sep = "")
## convert periods to POSIXct dates
period.begin <- ISOdate(strsplit(periods.counter, "-")[[1]][1],
1, 1, 0)
period.end <- ISOdate(strsplit(periods.counter, "-")[[1]][2],
12, 31, 23)
## flag whether in reference period or not
current.period.char = names(which(periods == periods.counter))
if (current.period.char == "reference.period") {
is.refperiod <- "yes"
## in case model has specific reference period, use it here
if (!is.null(model.ref.per))
periods.counter <- model.ref.per
} else {
is.refperiod <- "no"
## in case model has specific scenario period, use it here
if (!is.null(model.scn.per))
periods.counter <- model.scn.per
}
## retrieving filenames whose data will be read in and processed
## get required files to cover the period
cat(" GETTING REQUIRED FILENAMES FOR PERIOD", "\n", sep = "")
current.model.input <- model.input[[modelname.counter]]
filenames <- GetFileNames(current.model.input,
period.begin, period.end,
parameter.shortname,
period = current.period.char,
calendar = calendar,
count.first.time.value =
count.first.time.value,
time.units = time.units,
what.timesteps = what.timesteps)
cat(paste(" ", filenames, "\n", sep=""))
## read in data, corresponding grids and aggregate over time to
## get corresponding seasonal values
if ( !all(is.na(filenames)) ) {
data.aggr.list <-
GetAggregatedSpatioTemporalData(filenames,
model.name = modelname.counter,
plot.subregion = user.input$plot.subregion,
grid.filenames = grid.filename,
subregions = subregions,
parameter.name = parameter.shortname,
interpolate.to.grid = FALSE,
temporal.aggr = temporal.aggr,
startdate = period.begin,
enddate = period.end,
## '...' for ReadNetCdfTimeData
time.units = time.units,
calendar = calendar,
count.first.time.value =
count.first.time.value,
lonlat.var.name = lonlat.var.name,
## for AggregateTemporal(...)
what.timesteps = what.timesteps,
na.rm = na.rm,
## '...' for GetSubregionShapes
area.fraction = does.area.fraction,
spatial.weighting = does.spatial.weighting,
## '...' for GetAgg***Data.nointerpol
use.land.mask = use.land.mask,
land.mask = land.mask,
land.mask.name = land.mask.name)
## get number of datapoints read in each subregion (not used further)
n.of.pixels <-
lapply(data.aggr.list, function(z) {
lapply(z, function(x) {lapply(x, function(y) length(!is.na(y)))})})
## aggregate over spatial component
data.aggr.list <- lapply(data.aggr.list,
function(x)
lapply(x$data,
AggregateWeightedSubregions,
weight=x$weight)
)
## check if dealing with a time series
if (sum(which(unlist(sapply(temporal.aggr,
"[", "time.series")) == FALSE)) == 0) {
is.time.series <- TRUE
} else {
is.time.series <- FALSE
}
## get dataframe for particular model & period & parameter
model.data <- MakeModelDataFrame(modelname.counter,
institute.name,
rcm.name,
global.model.name,
gcm.run = gcm.run,
is.reference.period = is.refperiod,
time.step = periods.counter,
subregion = names(data.aggr.list),
is.corrected = is.corrected,
resolution = resolution,
season = sort(names(data.aggr.list[[1]])),
is.time.series = is.time.series,
data = data.aggr.list,
parameter.name = parameter.longname,
emission.scenario = emission.scenario)
## append to dataset of previos models
wux.df <- rbind(wux.df, model.data)
}
}
}
## now we have all models read in for this specific parameter, so we plug
## the data.frame to a list to merged to a dataframe again later
wux.df.list[[parameter.longname]] <- wux.df
}
############## MERGING ALL CREATED data.frames AND SAVE THEM ON DISK ##########
## merge datasets with different parmers to single data.frame
wux.df <- MergeParameterDataframes(wux.df.list, parameter.names)
## getting climate change signal calculating the diff of scen.per and ref.per.
if (!is.time.series) {
## wux.df has still seperated data for ref.per and scen.per. To get the
## climate change signal we calculate the difference of these two periods
## (and for precipitation the perentual change as well)
wux.diff.df <- WuxDataDiff(wux.df, parameter.names)
write.table(wux.diff.df,
file = paste(save.as.data, "_diff.csv", sep = ""), sep=";")
}
## storing WUX dataset to harddisk
cat("\n-------------------------------------------------------------------")
cat("---\n")
cat("\n", "SAVING DATA", "\n", sep = "")
write.table(wux.df, file = paste(save.as.data, ".csv", sep = ""), sep=";")
## time to say goodbye
cat("\n*******************************************************************")
cat("***\n")
cat(" YOUR WUX DATA FRAME HAS BEEN CREATED SUCCESSFULLY :-) ")
cat(" \n")
cat("********************************************************************")
cat("**\n")
if (!is.time.series) {
class(wux.df) <- append("wux.df", class(wux.df))
return(wux.diff.df)
} else {
class(wux.df) <- append("wux.ts.df", class(wux.df))
return(wux.df)
}
}
ReadFromModelDictionary <- function(modelnames, modelinput = NULL){
## Extracts model information from InitModelDictionary for given models.
##
## Args:
## modelnames: Character vector of model names (acronyms) to be extracted
## from InitModelDictionary, or model ensemble name for multiple
## climate models, which then has to be defined here
## (eg. "ensembles").
## modelinput: filepath to model.input file. default is NULL,
## which reads in the internal IniModelsDictionary file.
##
## Returns:
## Named list of model information needed for models2wux.
##
## History:
## 2010-10-27 | Original code (thm)
## ... | many things have happened
## 2012-08-29 | extended CMIP5 ensemble, alphabetized vectors (msu)
## 2014-04-14 | extended CMIP5 ensemble. up to date ensemble! (thm)
## 2014-11-12 | alternative model.input possible (thm)
## check wheteher user specified a specific model input (NULL means no)
if (is.null(modelinput)){
## read in whole InitModelDictionary model.list
model.input <- InitModelDictionary()
} else {
cat(is.list(modelinput))
## check whether modelinput is a directory or a list object
if (is.list(modelinput)) {
## this is a list object
model.input <- modelinput
} else {
## this is a file to be read in
model.input.sourced <- source(modelinput, local = TRUE)
model.input <- model.input.sourced$value
}
if (!is.list(model.input))
stop("YOUR model.input FILE IS WRONG. BE SURE YOU HAVE SPECIFIED A LIST CALLED \"model.input\". SEE ?models2wux FOR DETAILS. ")
}
## known model ensembles from the CMIP3 project and the "ENSEMBLES" project
##-------------------------------------------------------------------
## CMIP3 A1B scenario:
cmip3.sresa1b.labels <- c("cmip3-sresa1b", "CMIP3-SRESA1B")
cmip3.sresa1b.modelnames <- c("bccr_bcm2_0-r1",
"cccma_cgcm3_1-r1",
"cccma_cgcm3_1-r2",
"cccma_cgcm3_1-r3",
"cccma_cgcm3_1-r4",
"cccma_cgcm3_1-r5",
"cccma_cgcm3_1_t63-r1",
"cnrm_cm3-r1",
"csiro_mk3_0-r1",
"csiro_mk3_5-r1",
"gfdl_cm2_0-r1",
"gfdl_cm2_1-r1",
"giss_aom-r1",
"giss_aom-r2",
"giss_model_e_h-r1",
"giss_model_e_h-r2",
"giss_model_e_h-r3",
"giss_model_e_r-r1",
"giss_model_e_r-r2",
"giss_model_e_r-r3",
"giss_model_e_r-r4",
"giss_model_e_r-r5",
"iap_fgoals1_0_g-r1",
"iap_fgoals1_0_g-r2",
"iap_fgoals1_0_g-r3",
"ingv_echam4-r1",
"inmcm3_0-r1",
"ipsl_cm4-r1",
"miroc3_2_hires-r1",
"miroc3_2_medres-r1",
"miroc3_2_medres-r2",
"miroc3_2_medres-r3",
"miub_echo_g-r1",
"miub_echo_g-r2",
"miub_echo_g-r3",
"mpi_echam5-r1",
"mpi_echam5-r2",
"mpi_echam5-r3",
"mpi_echam5-r4",
"mri_cgcm2_3_2a-r1",
"mri_cgcm2_3_2a-r2",
"mri_cgcm2_3_2a-r3",
"mri_cgcm2_3_2a-r4",
"mri_cgcm2_3_2a-r5",
"ncar_ccsm3_0-r1",
"ncar_ccsm3_0-r2",
"ncar_ccsm3_0-r3",
"ncar_ccsm3_0-r5",
"ncar_ccsm3_0-r6",
"ncar_ccsm3_0-r7",
"ncar_ccsm3_0-r9",
"ukmo_hadcm3-r1",
"ukmo_hadgem1-r1")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## CMIP3 A2 scenario:
cmip3.sresa2.labels <- c("cmip3-sresa2", "CMIP3-SRESA2")
cmip3.sresa2.modelnames <- c("bccr_bcm2_0-sresa2-r1",
"cccma_cgcm3_1-sresa2-r1",
"cccma_cgcm3_1-sresa2-r2",
"cccma_cgcm3_1-sresa2-r3",
"cccma_cgcm3_1-sresa2-r4",
"cccma_cgcm3_1-sresa2-r5",
"cnrm_cm3-sresa2-r1",
"csiro_mk3_0-sresa2-r1",
"csiro_mk3_5-sresa2-r1",
"gfdl_cm2_0-sresa2-r1",
"gfdl_cm2_1-sresa2-r1",
"giss_model_e_r-sresa2-r1",
"ingv_echam4-sresa2-r1",
"inmcm3_0-sresa2-r1",
"ipsl_cm4-sresa2-r1",
"miroc3_2_medres-sresa2-r1",
"miroc3_2_medres-sresa2-r2",
"miroc3_2_medres-sresa2-r3",
"miub_echo_g-sresa2-r1",
"miub_echo_g-sresa2-r2",
"miub_echo_g-sresa2-r3",
"mpi_echam5-sresa2-r1",
"mpi_echam5-sresa2-r2",
"mpi_echam5-sresa2-r3",
"mri_cgcm2_3_2a-sresa2-r1",
"mri_cgcm2_3_2a-sresa2-r2",
"mri_cgcm2_3_2a-sresa2-r3",
"mri_cgcm2_3_2a-sresa2-r4",
"mri_cgcm2_3_2a-sresa2-r5",
"ncar_ccsm3_0-sresa2-r1",
"ncar_ccsm3_0-sresa2-r2",
"ncar_ccsm3_0-sresa2-r3",
"ncar_ccsm3_0-sresa2-r4",
"ncar_ccsm3_0-sresa2-r5",
"ukmo_hadcm3-sresa2-r1",
"ukmo_hadgem1-sresa2-r1")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## CMIP3 B1 scenario:
cmip3.sresb1.labels <- c("cmip3-sresb1", "CMIP3-SRESB1")
cmip3.sresb1.modelnames <- c("bccr_bcm2_0-sresb1-r1",
"cccma_cgcm3_1-sresb1-r1",
"cccma_cgcm3_1-sresb1-r2",
"cccma_cgcm3_1-sresb1-r3",
"cccma_cgcm3_1-sresb1-r4",
"cccma_cgcm3_1-sresb1-r5",
"cccma_cgcm3_1_t63-sresb1-r1",
"cnrm_cm3-sresb1-r1",
"csiro_mk3_0-sresb1-r1",
"csiro_mk3_5-sresb1-r1",
"gfdl_cm2_0-sresb1-r1",
"gfdl_cm2_1-sresb1-r1",
"giss_aom-sresb1-r1",
"giss_aom-sresb1-r2",
"giss_model_e_r-sresb1-r1" ,
"iap_fgoals1_0_g-sresb1-r1",
"iap_fgoals1_0_g-sresb1-r2",
"iap_fgoals1_0_g-sresb1-r3",
"inmcm3_0-sresb1-r1",
"ipsl_cm4-sresb1-r1",
"miroc3_2_hires-sresb1-r1",
"miroc3_2_medres-sresb1-r1",
"miroc3_2_medres-sresb1-r2",
"miroc3_2_medres-sresb1-r3",
"miub_echo_g-sresb1-r1",
"miub_echo_g-sresb1-r2",
"miub_echo_g-sresb1-r3",
"mpi_echam5-sresb1-r1",
"mpi_echam5-sresb1-r2",
"mpi_echam5-sresb1-r3",
"mri_cgcm2_3_2a-sresb1-r1",
"mri_cgcm2_3_2a-sresb1-r2",
"mri_cgcm2_3_2a-sresb1-r3",
"mri_cgcm2_3_2a-sresb1-r4",
"mri_cgcm2_3_2a-sresb1-r5",
"ncar_ccsm3_0-sresb1-r1",
"ncar_ccsm3_0-sresb1-r2",
"ncar_ccsm3_0-sresb1-r3",
"ncar_ccsm3_0-sresb1-r4",
"ncar_ccsm3_0-sresb1-r5",
"ncar_ccsm3_0-sresb1-r6",
"ncar_ccsm3_0-sresb1-r7",
"ncar_ccsm3_0-sresb1-r9",
"ukmo_hadcm3-sresb1-r1")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## CMIP5
## rcp26
cmip5.rcp26.labels <- c("cmip5-rcp26", "CMIP5-RCP26")
cmip5.rcp26.modelnames <- c("BCC-CSM1-1-r1i1p1_rcp26",
"BCC-CSM1-1-m-r1i1p1_rcp26",
"BNU-ESM-r1i1p1_rcp26",
"CanESM2-r1i1p1_rcp26",
"CanESM2-r2i1p1_rcp26",
"CanESM2-r3i1p1_rcp26",
"CanESM2-r4i1p1_rcp26",
"CanESM2-r5i1p1_rcp26",
"CCSM4-r1i1p1_rcp26",
"CCSM4-r2i1p1_rcp26",
"CCSM4-r3i1p1_rcp26",
"CCSM4-r4i1p1_rcp26",
"CCSM4-r5i1p1_rcp26",
"CCSM4-r6i1p1_rcp26",
"CESM1-CAM5-r1i1p1_rcp26",
"CESM1-CAM5-r2i1p1_rcp26",
"CESM1-CAM5-r3i1p1_rcp26",
"CNRM-CM5-r1i1p1_rcp26",
"CSIRO-Mk3-6-0-r10i1p1_rcp26",
"CSIRO-Mk3-6-0-r1i1p1_rcp26",
"CSIRO-Mk3-6-0-r2i1p1_rcp26",
"CSIRO-Mk3-6-0-r3i1p1_rcp26",
"CSIRO-Mk3-6-0-r4i1p1_rcp26",
"CSIRO-Mk3-6-0-r5i1p1_rcp26",
"CSIRO-Mk3-6-0-r6i1p1_rcp26",
"CSIRO-Mk3-6-0-r7i1p1_rcp26",
"CSIRO-Mk3-6-0-r8i1p1_rcp26",
"CSIRO-Mk3-6-0-r9i1p1_rcp26",
"EC-EARTH-r12i1p1_rcp26",
"EC-EARTH-r8i1p1_rcp26",
"FGOALS-g2-r1i1p1_rcp26",
"FGOALS-s2-r1i1p1_rcp26",
"FIO-ESM-r1i1p1_rcp26",
"FIO-ESM-r2i1p1_rcp26",
"FIO-ESM-r3i1p1_rcp26",
"GFDL-CM3-r1i1p1_rcp26",
"GFDL-ESM2G-r1i1p1_rcp26",
"GFDL-ESM2M-r1i1p1_rcp26",
"GISS-E2-H-r1i1p1_rcp26",
"GISS-E2-H-r1i1p2_rcp26",
"GISS-E2-H-r1i1p3_rcp26",
"GISS-E2-R-r1i1p1_rcp26",
"GISS-E2-R-r1i1p2_rcp26",
"GISS-E2-R-r1i1p3_rcp26",
"HadGEM2-AO-r1i1p1_rcp26",
"HadGEM2-ES-r1i1p1_rcp26",
## "HadGEM2-ES-r2i1p1_rcp26", making touble with dates in NetCDF file
## "HadGEM2-ES-r3i1p1_rcp26", making touble with dates in NetCDF file
"HadGEM2-ES-r4i1p1_rcp26",
"IPSL-CM5A-LR-r1i1p1_rcp26",
"IPSL-CM5A-LR-r2i1p1_rcp26",
"IPSL-CM5A-LR-r3i1p1_rcp26",
"IPSL-CM5A-LR-r4i1p1_rcp26",
"IPSL-CM5A-MR-r1i1p1_rcp26",
"MIROC5-r1i1p1_rcp26",
"MIROC5-r2i1p1_rcp26",
"MIROC5-r3i1p1_rcp26",
"MIROC-ESM-r1i1p1_rcp26",
"MIROC-ESM-CHEM-r1i1p1_rcp26",
"MPI-ESM-LR-r1i1p1_rcp26",
"MPI-ESM-LR-r2i1p1_rcp26",
"MPI-ESM-LR-r3i1p1_rcp26",
"MPI-ESM-MR-r1i1p1_rcp26",
"MRI-CGCM3-r1i1p1_rcp26",
"NorESM1-M-r1i1p1_rcp26",
"NorESM1-ME-r1i1p1_rcp26")
##-------------------------------------------------------------------
## rcp26 including models with shorter simulations (usually until 2035)
## with MIROC5_r4i1p1_rcp26
## MIROC5_r5i1p1_rcp26
cmip5.rcp26.shortperiods.labels <- c("cmip5-rcp26-shortperiods", "CMIP5-RCP26-SHORTPERIODS")
cmip5.rcp26.shortperiods.modelnames <- c("BCC-CSM1-1-r1i1p1_rcp26",
"BCC-CSM1-1-m-r1i1p1_rcp26",
"BNU-ESM-r1i1p1_rcp26",
"CanESM2-r1i1p1_rcp26",
"CanESM2-r2i1p1_rcp26",
"CanESM2-r3i1p1_rcp26",
"CanESM2-r4i1p1_rcp26",
"CanESM2-r5i1p1_rcp26",
"CCSM4-r1i1p1_rcp26",
"CCSM4-r2i1p1_rcp26",
"CCSM4-r3i1p1_rcp26",
"CCSM4-r4i1p1_rcp26",
"CCSM4-r5i1p1_rcp26",
"CCSM4-r6i1p1_rcp26",
"CESM1-CAM5-r1i1p1_rcp26",
"CESM1-CAM5-r2i1p1_rcp26",
"CESM1-CAM5-r3i1p1_rcp26",
"CNRM-CM5-r1i1p1_rcp26",
"CSIRO-Mk3-6-0-r10i1p1_rcp26",
"CSIRO-Mk3-6-0-r1i1p1_rcp26",
"CSIRO-Mk3-6-0-r2i1p1_rcp26",
"CSIRO-Mk3-6-0-r3i1p1_rcp26",
"CSIRO-Mk3-6-0-r4i1p1_rcp26",
"CSIRO-Mk3-6-0-r5i1p1_rcp26",
"CSIRO-Mk3-6-0-r6i1p1_rcp26",
"CSIRO-Mk3-6-0-r7i1p1_rcp26",
"CSIRO-Mk3-6-0-r8i1p1_rcp26",
"CSIRO-Mk3-6-0-r9i1p1_rcp26",
"EC-EARTH-r12i1p1_rcp26",
"EC-EARTH-r8i1p1_rcp26",
"FGOALS-g2-r1i1p1_rcp26",
"FGOALS-s2-r1i1p1_rcp26",
"FIO-ESM-r1i1p1_rcp26",
"FIO-ESM-r2i1p1_rcp26",
"FIO-ESM-r3i1p1_rcp26",
"GFDL-CM3-r1i1p1_rcp26",
"GFDL-ESM2G-r1i1p1_rcp26",
"GFDL-ESM2M-r1i1p1_rcp26",
"GISS-E2-H-r1i1p1_rcp26",
"GISS-E2-H-r1i1p2_rcp26",
"GISS-E2-H-r1i1p3_rcp26",
"GISS-E2-R-r1i1p1_rcp26",
"GISS-E2-R-r1i1p2_rcp26",
"GISS-E2-R-r1i1p3_rcp26",
"HadGEM2-AO-r1i1p1_rcp26",
"HadGEM2-ES-r1i1p1_rcp26",
"HadGEM2-ES-r2i1p1_rcp26",
"HadGEM2-ES-r3i1p1_rcp26",
"HadGEM2-ES-r4i1p1_rcp26",
"IPSL-CM5A-LR-r1i1p1_rcp26",
"IPSL-CM5A-LR-r2i1p1_rcp26",
"IPSL-CM5A-LR-r3i1p1_rcp26",
"IPSL-CM5A-LR-r4i1p1_rcp26",
"IPSL-CM5A-MR-r1i1p1_rcp26",
"MIROC5-r1i1p1_rcp26",
"MIROC5-r2i1p1_rcp26",
"MIROC5-r3i1p1_rcp26",
"MIROC5-r4i1p1_rcp26",
"MIROC5-r5i1p1_rcp26",
"MIROC-ESM-r1i1p1_rcp26",
"MIROC-ESM-CHEM-r1i1p1_rcp26",
"MPI-ESM-LR-r1i1p1_rcp26",
"MPI-ESM-LR-r2i1p1_rcp26",
"MPI-ESM-LR-r3i1p1_rcp26",
"MPI-ESM-MR-r1i1p1_rcp26",
"MRI-CGCM3-r1i1p1_rcp26",
"NorESM1-M-r1i1p1_rcp26",
"NorESM1-ME-r1i1p1_rcp26")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## rcp45
cmip5.rcp45.labels <- c("cmip5-rcp45", "CMIP5-RCP45")
cmip5.rcp45.modelnames <- c("ACCESS1-0-r1i1p1_rcp45",
"ACCESS1-3-r1i1p1_rcp45",
"BCC-CSM1-1-r1i1p1_rcp45",
"BCC-CSM1-1-m-r1i1p1_rcp45",
"BNU-ESM-r1i1p1_rcp45",
## "CanCM4-r10i1p1_rcp45", too short timeseries...
## "CanCM4-r1i1p1_rcp45",
## "CanCM4-r2i1p1_rcp45",
## "CanCM4-r3i1p1_rcp45",
## "CanCM4-r4i1p1_rcp45",
## "CanCM4-r5i1p1_rcp45",
## "CanCM4-r6i1p1_rcp45",
## "CanCM4-r7i1p1_rcp45",
## "CanCM4-r8i1p1_rcp45",
## "CanCM4-r9i1p1_rcp45",
"CanESM2-r1i1p1_rcp45",
"CanESM2-r2i1p1_rcp45",
"CanESM2-r3i1p1_rcp45",
"CanESM2-r4i1p1_rcp45",
"CanESM2-r5i1p1_rcp45",
"CCSM4-r1i1p1_rcp45",
"CCSM4-r2i1p1_rcp45",
"CCSM4-r3i1p1_rcp45",
"CCSM4-r4i1p1_rcp45",
"CCSM4-r5i1p1_rcp45",
"CCSM4-r6i1p1_rcp45",
"CESM1-BGC-r1i1p1_rcp45",
"CESM1-CAM5-r1i1p1_rcp45",
"CESM1-CAM5-r2i1p1_rcp45",
"CESM1-CAM5-r3i1p1_rcp45",
"CESM1-WACCM-r2i1p1_rcp45",
## "CESM1-WACCM-r3i1p1_rcp45", too short timeseries...
## "CESM1-WACCM-r4i1p1_rcp45",
"CMCC-CM-r1i1p1_rcp45",
"CMCC-CMS-r1i1p1_rcp45",
"CNRM-CM5-r1i1p1_rcp45",
"CSIRO-Mk3-6-0-r10i1p1_rcp45",
"CSIRO-Mk3-6-0-r1i1p1_rcp45",
"CSIRO-Mk3-6-0-r2i1p1_rcp45",
"CSIRO-Mk3-6-0-r3i1p1_rcp45",
"CSIRO-Mk3-6-0-r4i1p1_rcp45",
"CSIRO-Mk3-6-0-r5i1p1_rcp45",
"CSIRO-Mk3-6-0-r6i1p1_rcp45",
"CSIRO-Mk3-6-0-r7i1p1_rcp45",
"CSIRO-Mk3-6-0-r8i1p1_rcp45",
"CSIRO-Mk3-6-0-r9i1p1_rcp45",
"EC-EARTH-r12i1p1_rcp45",
"EC-EARTH-r13i1p1_rcp45",
"EC-EARTH-r14i1p1_rcp45",
## "EC-EARTH-r1i1p1_rcp45", too short timeseries...
"EC-EARTH-r2i1p1_rcp45",
"EC-EARTH-r6i1p1_rcp45",
## "EC-EARTH-r7i1p1_rcp45", ## missing historical runs
"EC-EARTH-r8i1p1_rcp45",
"EC-EARTH-r9i1p1_rcp45",
"FGOALS-g2-r1i1p1_rcp45",
"FIO-ESM-r1i1p1_rcp45",
"FIO-ESM-r2i1p1_rcp45",
"FIO-ESM-r3i1p1_rcp45",
## "GFDL-CM2-1-r10i1p1_rcp45", too short timeseries...
## "GFDL-CM2-1-r1i1p1_rcp45",
## "GFDL-CM2-1-r2i1p1_rcp45",
## "GFDL-CM2-1-r3i1p1_rcp45",
## "GFDL-CM2-1-r4i1p1_rcp45",
## "GFDL-CM2-1-r5i1p1_rcp45",
## "GFDL-CM2-1-r6i1p1_rcp45",
## "GFDL-CM2-1-r7i1p1_rcp45",
## "GFDL-CM2-1-r8i1p1_rcp45",
## "GFDL-CM2-1-r9i1p1_rcp45",
"GFDL-CM3-r1i1p1_rcp45",
"GFDL-ESM2G-r1i1p1_rcp45",
"GFDL-ESM2M-r1i1p1_rcp45",
"GISS-E2-H-r1i1p1_rcp45",
"GISS-E2-H-r1i1p2_rcp45",
"GISS-E2-H-r1i1p3_rcp45",
"GISS-E2-H-r2i1p1_rcp45",
"GISS-E2-H-r2i1p2_rcp45",
"GISS-E2-H-r2i1p3_rcp45",
"GISS-E2-H-r3i1p1_rcp45",
"GISS-E2-H-r3i1p2_rcp45",
"GISS-E2-H-r3i1p3_rcp45",
"GISS-E2-H-r4i1p1_rcp45",
"GISS-E2-H-r4i1p2_rcp45",
"GISS-E2-H-r4i1p3_rcp45",
"GISS-E2-H-r5i1p1_rcp45",
"GISS-E2-H-r5i1p2_rcp45",
"GISS-E2-H-r5i1p3_rcp45",
"GISS-E2-H-r6i1p3_rcp45",
"GISS-E2-H-CC-r1i1p1_rcp45",
"GISS-E2-R-r1i1p1_rcp45",
"GISS-E2-R-r1i1p2_rcp45",
"GISS-E2-R-r1i1p3_rcp45",
"GISS-E2-R-r2i1p1_rcp45",
"GISS-E2-R-r2i1p2_rcp45",
"GISS-E2-R-r2i1p3_rcp45",
"GISS-E2-R-r3i1p1_rcp45",
"GISS-E2-R-r3i1p2_rcp45",
"GISS-E2-R-r3i1p3_rcp45",
"GISS-E2-R-r4i1p1_rcp45",
"GISS-E2-R-r4i1p2_rcp45",
"GISS-E2-R-r4i1p3_rcp45",
"GISS-E2-R-r5i1p1_rcp45",
"GISS-E2-R-r5i1p2_rcp45",
"GISS-E2-R-r5i1p3_rcp45",
"GISS-E2-R-r6i1p1_rcp45",
"GISS-E2-R-r6i1p3_rcp45",
"GISS-E2-R-CC-r1i1p1_rcp45",
## "HadCM3-r10i1p1_rcp45", too short timeseries...
## "HadCM3-r1i1p1_rcp45",
## "HadCM3-r2i1p1_rcp45",
## "HadCM3-r3i1p1_rcp45",
## "HadCM3-r4i1p1_rcp45",
## "HadCM3-r5i1p1_rcp45",
## "HadCM3-r6i1p1_rcp45",
## "HadCM3-r7i1p1_rcp45",
## "HadCM3-r8i1p1_rcp45",
## "HadCM3-r9i1p1_rcp45",
"HadGEM2-AO-r1i1p1_rcp45",
"HadGEM2-CC-r1i1p1_rcp45",
"HadGEM2-ES-r1i1p1_rcp45",
## "HadGEM2-ES-r2i1p1_rcp45", making touble with dates in NetCDF file
## "HadGEM2-ES-r3i1p1_rcp45", making touble with dates in NetCDF file
"HadGEM2-ES-r4i1p1_rcp45",
"INM-CM4-r1i1p1_rcp45",
"IPSL-CM5A-LR-r1i1p1_rcp45",
"IPSL-CM5A-LR-r2i1p1_rcp45",
"IPSL-CM5A-LR-r3i1p1_rcp45",
"IPSL-CM5A-LR-r4i1p1_rcp45",
"IPSL-CM5A-MR-r1i1p1_rcp45",
"IPSL-CM5B-LR-r1i1p1_rcp45",
## "MIROC4h-r1i1p1_rcp45", too short timeseries...
## "MIROC4h-r2i1p1_rcp45",
## "MIROC4h-r3i1p1_rcp45",
"MIROC5-r1i1p1_rcp45",
"MIROC5-r2i1p1_rcp45",
"MIROC5-r3i1p1_rcp45",
## "MIROC5-r4i1p1_rcp45", too short timeseries...
## "MIROC5-r5i1p1_rcp45",
"MIROC-ESM-r1i1p1_rcp45",
"MIROC-ESM-CHEM-r1i1p1_rcp45",
"MPI-ESM-LR-r1i1p1_rcp45",
"MPI-ESM-LR-r2i1p1_rcp45",
"MPI-ESM-LR-r3i1p1_rcp45",
"MPI-ESM-MR-r1i1p1_rcp45",
"MPI-ESM-MR-r2i1p1_rcp45",
"MPI-ESM-MR-r3i1p1_rcp45",
"MRI-CGCM3-r1i1p1_rcp45",
"NorESM1-M-r1i1p1_rcp45",
"NorESM1-ME-r1i1p1_rcp45"
## "CSIRO-Mk3L-1-2-r1i2p1_rcp45", ## missing historical runs
## "CSIRO-Mk3L-1-2-r2i2p1_rcp45",
## "CSIRO-Mk3L-1-2-r3i2p1_rcp45",
)
## rcp45
cmip5.rcp45.shortperiods.labels <- c("cmip5-rcp45-shortperiods", "CMIP5-RCP45-SHORTPERIODS")
cmip5.rcp45.shortperiods.modelnames <- c("ACCESS1-0-r1i1p1_rcp45",
"ACCESS1-3-r1i1p1_rcp45",
"BCC-CSM1-1-r1i1p1_rcp45",
"BCC-CSM1-1-m-r1i1p1_rcp45",
"BNU-ESM-r1i1p1_rcp45",
"CanCM4-r10i1p1_rcp45",
"CanCM4-r1i1p1_rcp45",
"CanCM4-r2i1p1_rcp45",
"CanCM4-r3i1p1_rcp45",
"CanCM4-r4i1p1_rcp45",
"CanCM4-r5i1p1_rcp45",
"CanCM4-r6i1p1_rcp45",
"CanCM4-r7i1p1_rcp45",
"CanCM4-r8i1p1_rcp45",
"CanCM4-r9i1p1_rcp45",
"CanESM2-r1i1p1_rcp45",
"CanESM2-r2i1p1_rcp45",
"CanESM2-r3i1p1_rcp45",
"CanESM2-r4i1p1_rcp45",
"CanESM2-r5i1p1_rcp45",
"CCSM4-r1i1p1_rcp45",
"CCSM4-r2i1p1_rcp45",
"CCSM4-r3i1p1_rcp45",
"CCSM4-r4i1p1_rcp45",
"CCSM4-r5i1p1_rcp45",
"CCSM4-r6i1p1_rcp45",
"CESM1-BGC-r1i1p1_rcp45",
"CESM1-CAM5-r1i1p1_rcp45",
"CESM1-CAM5-r2i1p1_rcp45",
"CESM1-CAM5-r3i1p1_rcp45",
"CESM1-WACCM-r2i1p1_rcp45",
"CESM1-WACCM-r3i1p1_rcp45",
"CESM1-WACCM-r4i1p1_rcp45",
"CMCC-CM-r1i1p1_rcp45",
"CMCC-CMS-r1i1p1_rcp45",
"CNRM-CM5-r1i1p1_rcp45",
"CSIRO-Mk3-6-0-r10i1p1_rcp45",
"CSIRO-Mk3-6-0-r1i1p1_rcp45",
"CSIRO-Mk3-6-0-r2i1p1_rcp45",
"CSIRO-Mk3-6-0-r3i1p1_rcp45",
"CSIRO-Mk3-6-0-r4i1p1_rcp45",
"CSIRO-Mk3-6-0-r5i1p1_rcp45",
"CSIRO-Mk3-6-0-r6i1p1_rcp45",
"CSIRO-Mk3-6-0-r7i1p1_rcp45",
"CSIRO-Mk3-6-0-r8i1p1_rcp45",
"CSIRO-Mk3-6-0-r9i1p1_rcp45",
"EC-EARTH-r12i1p1_rcp45",
"EC-EARTH-r13i1p1_rcp45",
"EC-EARTH-r14i1p1_rcp45",
"EC-EARTH-r1i1p1_rcp45",
"EC-EARTH-r2i1p1_rcp45",
"EC-EARTH-r6i1p1_rcp45",
## "EC-EARTH-r7i1p1_rcp45", ## missing historical runs
"EC-EARTH-r8i1p1_rcp45",
"EC-EARTH-r9i1p1_rcp45",
"FGOALS-g2-r1i1p1_rcp45",
"FIO-ESM-r1i1p1_rcp45",
"FIO-ESM-r2i1p1_rcp45",
"FIO-ESM-r3i1p1_rcp45",
"GFDL-CM2-1-r10i1p1_rcp45",
"GFDL-CM2-1-r1i1p1_rcp45",
"GFDL-CM2-1-r2i1p1_rcp45",
"GFDL-CM2-1-r3i1p1_rcp45",
"GFDL-CM2-1-r4i1p1_rcp45",
"GFDL-CM2-1-r5i1p1_rcp45",
"GFDL-CM2-1-r6i1p1_rcp45",
"GFDL-CM2-1-r7i1p1_rcp45",
"GFDL-CM2-1-r8i1p1_rcp45",
"GFDL-CM2-1-r9i1p1_rcp45",
"GFDL-CM3-r1i1p1_rcp45",
"GFDL-ESM2G-r1i1p1_rcp45",
"GFDL-ESM2M-r1i1p1_rcp45",
"GISS-E2-H-r1i1p1_rcp45",
"GISS-E2-H-r1i1p2_rcp45",
"GISS-E2-H-r1i1p3_rcp45",
"GISS-E2-H-r2i1p1_rcp45",
"GISS-E2-H-r2i1p2_rcp45",
"GISS-E2-H-r2i1p3_rcp45",
"GISS-E2-H-r3i1p1_rcp45",
"GISS-E2-H-r3i1p2_rcp45",
"GISS-E2-H-r3i1p3_rcp45",
"GISS-E2-H-r4i1p1_rcp45",
"GISS-E2-H-r4i1p2_rcp45",
"GISS-E2-H-r4i1p3_rcp45",
"GISS-E2-H-r5i1p1_rcp45",
"GISS-E2-H-r5i1p2_rcp45",
"GISS-E2-H-r5i1p3_rcp45",
"GISS-E2-H-r6i1p3_rcp45",
"GISS-E2-H-CC-r1i1p1_rcp45",
"GISS-E2-R-r1i1p1_rcp45",
"GISS-E2-R-r1i1p2_rcp45",
"GISS-E2-R-r1i1p3_rcp45",
"GISS-E2-R-r2i1p1_rcp45",
"GISS-E2-R-r2i1p2_rcp45",
"GISS-E2-R-r2i1p3_rcp45",
"GISS-E2-R-r3i1p1_rcp45",
"GISS-E2-R-r3i1p2_rcp45",
"GISS-E2-R-r3i1p3_rcp45",
"GISS-E2-R-r4i1p1_rcp45",
"GISS-E2-R-r4i1p2_rcp45",
"GISS-E2-R-r4i1p3_rcp45",
"GISS-E2-R-r5i1p1_rcp45",
"GISS-E2-R-r5i1p2_rcp45",
"GISS-E2-R-r5i1p3_rcp45",
"GISS-E2-R-r6i1p1_rcp45",
"GISS-E2-R-r6i1p3_rcp45",
"GISS-E2-R-CC-r1i1p1_rcp45",
"HadCM3-r10i1p1_rcp45",
"HadCM3-r1i1p1_rcp45",
"HadCM3-r2i1p1_rcp45",
"HadCM3-r3i1p1_rcp45",
"HadCM3-r4i1p1_rcp45",
"HadCM3-r5i1p1_rcp45",
"HadCM3-r6i1p1_rcp45",
"HadCM3-r7i1p1_rcp45",
"HadCM3-r8i1p1_rcp45",
"HadCM3-r9i1p1_rcp45",
"HadGEM2-AO-r1i1p1_rcp45",
"HadGEM2-CC-r1i1p1_rcp45",
"HadGEM2-ES-r1i1p1_rcp45",
"HadGEM2-ES-r2i1p1_rcp45",
"HadGEM2-ES-r3i1p1_rcp45",
"HadGEM2-ES-r4i1p1_rcp45",
"INM-CM4-r1i1p1_rcp45",
"IPSL-CM5A-LR-r1i1p1_rcp45",
"IPSL-CM5A-LR-r2i1p1_rcp45",
"IPSL-CM5A-LR-r3i1p1_rcp45",
"IPSL-CM5A-LR-r4i1p1_rcp45",
"IPSL-CM5A-MR-r1i1p1_rcp45",
"IPSL-CM5B-LR-r1i1p1_rcp45",
"MIROC4h-r1i1p1_rcp45",
"MIROC4h-r2i1p1_rcp45",
"MIROC4h-r3i1p1_rcp45",
"MIROC5-r1i1p1_rcp45",
"MIROC5-r2i1p1_rcp45",
"MIROC5-r3i1p1_rcp45",
"MIROC5-r4i1p1_rcp45",
"MIROC5-r5i1p1_rcp45",
"MIROC-ESM-r1i1p1_rcp45",
"MIROC-ESM-CHEM-r1i1p1_rcp45",
"MPI-ESM-LR-r1i1p1_rcp45",
"MPI-ESM-LR-r2i1p1_rcp45",
"MPI-ESM-LR-r3i1p1_rcp45",
"MPI-ESM-MR-r1i1p1_rcp45",
"MPI-ESM-MR-r2i1p1_rcp45",
"MPI-ESM-MR-r3i1p1_rcp45",
"MRI-CGCM3-r1i1p1_rcp45",
"NorESM1-M-r1i1p1_rcp45",
"NorESM1-ME-r1i1p1_rcp45"
## "CSIRO-Mk3L-1-2-r1i2p1_rcp45", ## missing historical runs
## "CSIRO-Mk3L-1-2-r2i2p1_rcp45",
## "CSIRO-Mk3L-1-2-r3i2p1_rcp45",
)
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## rcp60
cmip5.rcp60.labels <- c("cmip5-rcp60", "CMIP5-RCP60")
cmip5.rcp60.modelnames <- c("BCC-CSM1-1-r1i1p1_rcp60",
"BCC-CSM1-1-m-r1i1p1_rcp60",
"CCSM4-r1i1p1_rcp60",
"CCSM4-r2i1p1_rcp60",
"CCSM4-r3i1p1_rcp60",
"CCSM4-r4i1p1_rcp60",
"CCSM4-r5i1p1_rcp60",
"CCSM4-r6i1p1_rcp60",
"CESM1-CAM5-r1i1p1_rcp60",
"CESM1-CAM5-r2i1p1_rcp60",
"CESM1-CAM5-r3i1p1_rcp60",
"CSIRO-Mk3-6-0-r10i1p1_rcp60",
"CSIRO-Mk3-6-0-r1i1p1_rcp60",
"CSIRO-Mk3-6-0-r2i1p1_rcp60",
"CSIRO-Mk3-6-0-r3i1p1_rcp60",
"CSIRO-Mk3-6-0-r4i1p1_rcp60",
"CSIRO-Mk3-6-0-r5i1p1_rcp60",
"CSIRO-Mk3-6-0-r6i1p1_rcp60",
"CSIRO-Mk3-6-0-r7i1p1_rcp60",
"CSIRO-Mk3-6-0-r8i1p1_rcp60",
"CSIRO-Mk3-6-0-r9i1p1_rcp60",
"FGOALS-s2-r1i1p1_rcp60",
"FIO-ESM-r1i1p1_rcp60",
"FIO-ESM-r2i1p1_rcp60",
"FIO-ESM-r3i1p1_rcp60",
"GFDL-CM3-r1i1p1_rcp60",
"GFDL-ESM2G-r1i1p1_rcp60",
"GFDL-ESM2M-r1i1p1_rcp60",
"GISS-E2-H-r1i1p1_rcp60",
"GISS-E2-H-r1i1p2_rcp60",
"GISS-E2-H-r1i1p3_rcp60",
"GISS-E2-R-r1i1p1_rcp60",
"GISS-E2-R-r1i1p2_rcp60",
"GISS-E2-R-r1i1p3_rcp60",
"HadGEM2-AO-r1i1p1_rcp60",
"HadGEM2-ES-r1i1p1_rcp60",
## "HadGEM2-ES-r2i1p1_rcp60", making touble with dates in NetCDF file
## "HadGEM2-ES-r3i1p1_rcp60", making touble with dates in NetCDF file
"HadGEM2-ES-r4i1p1_rcp60",
"IPSL-CM5A-LR-r1i1p1_rcp60",
"IPSL-CM5A-MR-r1i1p1_rcp60",
"MIROC5-r1i1p1_rcp60",
"MIROC5-r2i1p1_rcp60",
"MIROC5-r3i1p1_rcp60",
## "MIROC5-r4i1p1_rcp60", too short timeseries...
## "MIROC5-r5i1p1_rcp60",
"MIROC-ESM-r1i1p1_rcp60",
"MIROC-ESM-CHEM-r1i1p1_rcp60",
"MRI-CGCM3-r1i1p1_rcp60",
"NorESM1-M-r1i1p1_rcp60",
"NorESM1-ME-r1i1p1_rcp60")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## rcp60 including all siulations with shorter periods
cmip5.rcp60.shortperiods.labels <- c("cmip5-rcp60-shortperiods", "CMIP5-RCP60-SHORTPERIODS")
cmip5.rcp60.shortperiods.modelnames <- c("BCC-CSM1-1-r1i1p1_rcp60",
"BCC-CSM1-1-m-r1i1p1_rcp60",
"CCSM4-r1i1p1_rcp60",
"CCSM4-r2i1p1_rcp60",
"CCSM4-r3i1p1_rcp60",
"CCSM4-r4i1p1_rcp60",
"CCSM4-r5i1p1_rcp60",
"CCSM4-r6i1p1_rcp60",
"CESM1-CAM5-r1i1p1_rcp60",
"CESM1-CAM5-r2i1p1_rcp60",
"CESM1-CAM5-r3i1p1_rcp60",
"CSIRO-Mk3-6-0-r10i1p1_rcp60",
"CSIRO-Mk3-6-0-r1i1p1_rcp60",
"CSIRO-Mk3-6-0-r2i1p1_rcp60",
"CSIRO-Mk3-6-0-r3i1p1_rcp60",
"CSIRO-Mk3-6-0-r4i1p1_rcp60",
"CSIRO-Mk3-6-0-r5i1p1_rcp60",
"CSIRO-Mk3-6-0-r6i1p1_rcp60",
"CSIRO-Mk3-6-0-r7i1p1_rcp60",
"CSIRO-Mk3-6-0-r8i1p1_rcp60",
"CSIRO-Mk3-6-0-r9i1p1_rcp60",
"FGOALS-s2-r1i1p1_rcp60",
"FIO-ESM-r1i1p1_rcp60",
"FIO-ESM-r2i1p1_rcp60",
"FIO-ESM-r3i1p1_rcp60",
"GFDL-CM3-r1i1p1_rcp60",
"GFDL-ESM2G-r1i1p1_rcp60",
"GFDL-ESM2M-r1i1p1_rcp60",
"GISS-E2-H-r1i1p1_rcp60",
"GISS-E2-H-r1i1p2_rcp60",
"GISS-E2-H-r1i1p3_rcp60",
"GISS-E2-R-r1i1p1_rcp60",
"GISS-E2-R-r1i1p2_rcp60",
"GISS-E2-R-r1i1p3_rcp60",
"HadGEM2-AO-r1i1p1_rcp60",
"HadGEM2-ES-r1i1p1_rcp60",
"HadGEM2-ES-r2i1p1_rcp60",
"HadGEM2-ES-r3i1p1_rcp60",
"HadGEM2-ES-r4i1p1_rcp60",
"IPSL-CM5A-LR-r1i1p1_rcp60",
"IPSL-CM5A-MR-r1i1p1_rcp60",
"MIROC5-r1i1p1_rcp60",
"MIROC5-r2i1p1_rcp60",
"MIROC5-r3i1p1_rcp60",
"MIROC5-r4i1p1_rcp60",
"MIROC5-r5i1p1_rcp60",
"MIROC-ESM-r1i1p1_rcp60",
"MIROC-ESM-CHEM-r1i1p1_rcp60",
"MRI-CGCM3-r1i1p1_rcp60",
"NorESM1-M-r1i1p1_rcp60",
"NorESM1-ME-r1i1p1_rcp60")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## rcp85
cmip5.rcp85.labels <- c("cmip5-rcp85", "CMIP5-RCP85")
cmip5.rcp85.modelnames <- c("ACCESS1-0-r1i1p1_rcp85",
"ACCESS1-3-r1i1p1_rcp85",
"BCC-CSM1-1-r1i1p1_rcp85",
"BCC-CSM1-1-m-r1i1p1_rcp85",
"BNU-ESM-r1i1p1_rcp85",
"CanESM2-r1i1p1_rcp85",
"CanESM2-r2i1p1_rcp85",
"CanESM2-r3i1p1_rcp85",
"CanESM2-r4i1p1_rcp85",
"CanESM2-r5i1p1_rcp85",
"CCSM4-r1i1p1_rcp85",
"CCSM4-r2i1p1_rcp85",
"CCSM4-r3i1p1_rcp85",
"CCSM4-r4i1p1_rcp85",
"CCSM4-r5i1p1_rcp85",
"CCSM4-r6i1p1_rcp85",
"CESM1-BGC-r1i1p1_rcp85",
"CESM1-CAM5-r1i1p1_rcp85",
"CESM1-CAM5-r2i1p1_rcp85",
"CESM1-CAM5-r3i1p1_rcp85",
"CESM1-WACCM-r2i1p1_rcp85",
## "CESM1-WACCM-r3i1p1_rcp85",too short timeseries
## "CESM1-WACCM-r4i1p1_rcp85",
"CMCC-CESM-r1i1p1_rcp85",
"CMCC-CM-r1i1p1_rcp85",
"CMCC-CMS-r1i1p1_rcp85",
"CNRM-CM5-r10i1p1_rcp85",
"CNRM-CM5-r1i1p1_rcp85",
"CNRM-CM5-r2i1p1_rcp85",
"CNRM-CM5-r4i1p1_rcp85",
"CNRM-CM5-r6i1p1_rcp85",
"CSIRO-Mk3-6-0-r10i1p1_rcp85",
"CSIRO-Mk3-6-0-r1i1p1_rcp85",
"CSIRO-Mk3-6-0-r2i1p1_rcp85",
"CSIRO-Mk3-6-0-r3i1p1_rcp85",
"CSIRO-Mk3-6-0-r4i1p1_rcp85",
"CSIRO-Mk3-6-0-r5i1p1_rcp85",
"CSIRO-Mk3-6-0-r6i1p1_rcp85",
"CSIRO-Mk3-6-0-r7i1p1_rcp85",
"CSIRO-Mk3-6-0-r8i1p1_rcp85",
"CSIRO-Mk3-6-0-r9i1p1_rcp85",
"EC-EARTH-r12i1p1_rcp85",
"EC-EARTH-r13i1p1_rcp85",
"EC-EARTH-r14i1p1_rcp85",
## "EC-EARTH-r1i1p1_rcp85", ## corrupt scen tas files
"EC-EARTH-r2i1p1_rcp85",
"EC-EARTH-r6i1p1_rcp85",
"EC-EARTH-r8i1p1_rcp85",
"EC-EARTH-r9i1p1_rcp85",
"FGOALS-g2-r1i1p1_rcp85",
"FIO-ESM-r1i1p1_rcp85",
"FIO-ESM-r2i1p1_rcp85",
"FIO-ESM-r3i1p1_rcp85",
"GFDL-CM3-r1i1p1_rcp85",
"GFDL-ESM2G-r1i1p1_rcp85",
"GFDL-ESM2M-r1i1p1_rcp85",
"GISS-E2-H-r1i1p1_rcp85",
"GISS-E2-H-r1i1p2_rcp85",
"GISS-E2-H-r1i1p3_rcp85",
"GISS-E2-H-r2i1p1_rcp85",
"GISS-E2-H-r2i1p3_rcp85",
"GISS-E2-H-CC-r1i1p1_rcp85",
"GISS-E2-R-r1i1p1_rcp85",
"GISS-E2-R-r1i1p2_rcp85",
"GISS-E2-R-r1i1p3_rcp85",
"GISS-E2-R-r2i1p1_rcp85",
"GISS-E2-R-r2i1p3_rcp85",
"GISS-E2-R-CC-r1i1p1_rcp85",
"HadGEM2-AO-r1i1p1_rcp85",
"HadGEM2-CC-r1i1p1_rcp85",
## "HadGEM2-CC-r2i1p1_rcp85", making touble with dates in NetCDF file
## "HadGEM2-CC-r3i1p1_rcp85", making touble with dates in NetCDF file
"HadGEM2-ES-r1i1p1_rcp85",
## "HadGEM2-ES-r2i1p1_rcp85", making touble with dates in NetCDF file
## "HadGEM2-ES-r3i1p1_rcp85", making touble with dates in NetCDF file
"HadGEM2-ES-r4i1p1_rcp85",
"INM-CM4-r1i1p1_rcp85",
"IPSL-CM5A-LR-r1i1p1_rcp85",
"IPSL-CM5A-LR-r2i1p1_rcp85",
"IPSL-CM5A-LR-r3i1p1_rcp85",
"IPSL-CM5A-LR-r4i1p1_rcp85",
"IPSL-CM5A-MR-r1i1p1_rcp85",
"IPSL-CM5B-LR-r1i1p1_rcp85",
"MIROC5-r1i1p1_rcp85",
"MIROC5-r2i1p1_rcp85",
"MIROC5-r3i1p1_rcp85",
## "MIROC5-r4i1p1_rcp85", too short timeseries...
## "MIROC5-r5i1p1_rcp85",
"MIROC-ESM-r1i1p1_rcp85",
"MIROC-ESM-CHEM-r1i1p1_rcp85",
"MPI-ESM-LR-r1i1p1_rcp85",
"MPI-ESM-LR-r2i1p1_rcp85",
"MPI-ESM-LR-r3i1p1_rcp85",
"MPI-ESM-MR-r1i1p1_rcp85",
"MRI-CGCM3-r1i1p1_rcp85",
"NorESM1-M-r1i1p1_rcp85",
"NorESM1-ME-r1i1p1_rcp85",
"MRI-ESM1-r1i1p1_rcp85")
##-------------------------------------------------------------------
## rcp85
cmip5.rcp85.shortperiods.labels <- c("cmip5-rcp85-shortperiods", "CMIP5-RCP85-SHORTPERIODS")
cmip5.rcp85.shortperiods.modelnames <- c("ACCESS1-0-r1i1p1_rcp85",
"ACCESS1-3-r1i1p1_rcp85",
"BCC-CSM1-1-r1i1p1_rcp85",
"BCC-CSM1-1-m-r1i1p1_rcp85",
"BNU-ESM-r1i1p1_rcp85",
"CanESM2-r1i1p1_rcp85",
"CanESM2-r2i1p1_rcp85",
"CanESM2-r3i1p1_rcp85",
"CanESM2-r4i1p1_rcp85",
"CanESM2-r5i1p1_rcp85",
"CCSM4-r1i1p1_rcp85",
"CCSM4-r2i1p1_rcp85",
"CCSM4-r3i1p1_rcp85",
"CCSM4-r4i1p1_rcp85",
"CCSM4-r5i1p1_rcp85",
"CCSM4-r6i1p1_rcp85",
"CESM1-BGC-r1i1p1_rcp85",
"CESM1-CAM5-r1i1p1_rcp85",
"CESM1-CAM5-r2i1p1_rcp85",
"CESM1-CAM5-r3i1p1_rcp85",
"CESM1-WACCM-r2i1p1_rcp85",
"CESM1-WACCM-r3i1p1_rcp85",
"CESM1-WACCM-r4i1p1_rcp85",
"CMCC-CESM-r1i1p1_rcp85",
"CMCC-CM-r1i1p1_rcp85",
"CMCC-CMS-r1i1p1_rcp85",
"CNRM-CM5-r10i1p1_rcp85",
"CNRM-CM5-r1i1p1_rcp85",
"CNRM-CM5-r2i1p1_rcp85",
"CNRM-CM5-r4i1p1_rcp85",
"CNRM-CM5-r6i1p1_rcp85",
"CSIRO-Mk3-6-0-r10i1p1_rcp85",
"CSIRO-Mk3-6-0-r1i1p1_rcp85",
"CSIRO-Mk3-6-0-r2i1p1_rcp85",
"CSIRO-Mk3-6-0-r3i1p1_rcp85",
"CSIRO-Mk3-6-0-r4i1p1_rcp85",
"CSIRO-Mk3-6-0-r5i1p1_rcp85",
"CSIRO-Mk3-6-0-r6i1p1_rcp85",
"CSIRO-Mk3-6-0-r7i1p1_rcp85",
"CSIRO-Mk3-6-0-r8i1p1_rcp85",
"CSIRO-Mk3-6-0-r9i1p1_rcp85",
"EC-EARTH-r12i1p1_rcp85",
"EC-EARTH-r13i1p1_rcp85",
"EC-EARTH-r14i1p1_rcp85",
## "EC-EARTH-r1i1p1_rcp85", ## corrupt scen tas files
"EC-EARTH-r2i1p1_rcp85",
"EC-EARTH-r6i1p1_rcp85",
"EC-EARTH-r8i1p1_rcp85",
"EC-EARTH-r9i1p1_rcp85",
"FGOALS-g2-r1i1p1_rcp85",
"FIO-ESM-r1i1p1_rcp85",
"FIO-ESM-r2i1p1_rcp85",
"FIO-ESM-r3i1p1_rcp85",
"GFDL-CM3-r1i1p1_rcp85",
"GFDL-ESM2G-r1i1p1_rcp85",
"GFDL-ESM2M-r1i1p1_rcp85",
"GISS-E2-H-r1i1p1_rcp85",
"GISS-E2-H-r1i1p2_rcp85",
"GISS-E2-H-r1i1p3_rcp85",
"GISS-E2-H-r2i1p1_rcp85",
"GISS-E2-H-r2i1p3_rcp85",
"GISS-E2-H-CC-r1i1p1_rcp85",
"GISS-E2-R-r1i1p1_rcp85",
"GISS-E2-R-r1i1p2_rcp85",
"GISS-E2-R-r1i1p3_rcp85",
"GISS-E2-R-r2i1p1_rcp85",
"GISS-E2-R-r2i1p3_rcp85",
"GISS-E2-R-CC-r1i1p1_rcp85",
"HadGEM2-AO-r1i1p1_rcp85",
"HadGEM2-CC-r1i1p1_rcp85",
"HadGEM2-CC-r2i1p1_rcp85",
"HadGEM2-CC-r3i1p1_rcp85",
"HadGEM2-ES-r1i1p1_rcp85",
"HadGEM2-ES-r2i1p1_rcp85",
"HadGEM2-ES-r3i1p1_rcp85",
"HadGEM2-ES-r4i1p1_rcp85",
"INM-CM4-r1i1p1_rcp85",
"IPSL-CM5A-LR-r1i1p1_rcp85",
"IPSL-CM5A-LR-r2i1p1_rcp85",
"IPSL-CM5A-LR-r3i1p1_rcp85",
"IPSL-CM5A-LR-r4i1p1_rcp85",
"IPSL-CM5A-MR-r1i1p1_rcp85",
"IPSL-CM5B-LR-r1i1p1_rcp85",
"MIROC5-r1i1p1_rcp85",
"MIROC5-r2i1p1_rcp85",
"MIROC5-r3i1p1_rcp85",
"MIROC5-r4i1p1_rcp85",
"MIROC5-r5i1p1_rcp85",
"MIROC-ESM-r1i1p1_rcp85",
"MIROC-ESM-CHEM-r1i1p1_rcp85",
"MPI-ESM-LR-r1i1p1_rcp85",
"MPI-ESM-LR-r2i1p1_rcp85",
"MPI-ESM-LR-r3i1p1_rcp85",
"MPI-ESM-MR-r1i1p1_rcp85",
"MRI-CGCM3-r1i1p1_rcp85",
"NorESM1-M-r1i1p1_rcp85",
"NorESM1-ME-r1i1p1_rcp85",
"MRI-ESM1-r1i1p1_rcp85")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## ENSEMBLES RCMs
ensembles.labels <- c("ENSEMBLES", "ensembles")
ensembles.modelnames <- c("METO-HC_HadRM3Q0",
"METO-HC_HadRM3Q16",
"METO-HC_HadRM3Q3",
"ETHZ-CLM",
"METNOHIRHAM_HadCM3Q0",
"METNOHIRHAM_BCM",
"ICTP-REGCM3",
"MPI-M-REMO",
"C4IRCA3",
"CNRM-RM5.1",
"CNRM-RM4.5",
"DMI-HIRHAM5_ARPEGE",
"DMI-HIRHAM5_ECHAM5",
"DMI-HIRHAM5_BCM",
"GKSS-CCLM4.8",
"KNMI-RACMO2",
"OURANOSMRCC4.2.1",
"SMHIRCA_BCM",
"SMHIRCA_ECHAM5-r3",
"SMHIRCA_HadCM3Q3",
"UCLM-PROMES",
"VMGO-RRCM")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## ENSEMBLES RCMs QM corrected Schoener
ensembles.qmschoener.labels <- c("ENSEMBLES_QM_SCHOENER",
"ensembles_qm_schoener")
ensembles.qmschoener.modelnames <- c('METO-HC_HadRM3Q0_QM_SCHOENER',
'METO-HC_HadRM3Q16_QM_SCHOENER',
'METO-HC_HadRM3Q3_QM_SCHOENER',
'ETHZ-CLM_QM_SCHOENER',
'METNOHIRHAM_HadCM3Q0_QM_SCHOENER',
'METNOHIRHAM_BCM_QM_SCHOENER',
'ICTP-REGCM3_QM_SCHOENER',
'MPI-M-REMO_QM_SCHOENER',
'C4IRCA3_QM_SCHOENER',
'CNRM-RM5.1_QM_SCHOENER',
'CNRM-RM4.5_QM_SCHOENER',
'DMI-HIRHAM5_ARPEGE_QM_SCHOENER',
'DMI-HIRHAM5_ECHAM5_QM_SCHOENER',
'DMI-HIRHAM5_BCM_QM_SCHOENER',
'GKSS-CCLM4.8_QM_SCHOENER',
'KNMI-RACMO2_QM_SCHOENER',
'OURANOSMRCC4.2.1_QM_SCHOENER',
'SMHIRCA_BCM_QM_SCHOENER',
'SMHIRCA_ECHAM5-r3_QM_SCHOENER',
'SMHIRCA_HadCM3Q3_QM_SCHOENER',
'UCLM-PROMES_QM_SCHOENER',
'VMGO-RRCM_QM_SCHOENER',
'AIT-CCLM_QM_SCHOENER',
'WEGC-CCLM_QM_SCHOENER')
##-------------------------------------------------------------------
## extract all known modelnames from dictionary
dictionary.names <- names(model.input)
## check whether modelnames are unique, give error else
if (length(unique(dictionary.names)) != length(model.input)){
msg1 <- "MODELNAMES IN DICTIONARY MUST BE UNIQUE! "
msg2 <- "THINK ABOUT A CREATIVE MODELNAME ;)"
stop(msg1, msg2)
}
## models matching dictnames
model.input[modelnames[modelnames %in% dictionary.names]]
unknown.modelnames <- NULL
unknown.modelnames <- modelnames[! modelnames %in% dictionary.names]
known.modelnames <- modelnames[ modelnames %in% dictionary.names]
## not a single modelname is found in dictionary, set list to length 0, else
## read the funky shit!
match.modelnames <- match(known.modelnames,modelnames)
model.list <- model.input[modelnames[match.modelnames]]
## if there is at least one model not found in InitModelsDictionary then
## look it up in labels defined previously in this function
if (length(unknown.modelnames) > 0) {
if (any(unknown.modelnames %in% cmip3.sresa2.labels)) {
model.list <- c(model.list, model.input[cmip3.sresa2.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip3.sresa2.labels]
}
if (any(unknown.modelnames %in% cmip3.sresb1.labels)) {
model.list <- c(model.list, model.input[cmip3.sresb1.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip3.sresb1.labels]
}
if (any(unknown.modelnames %in% cmip3.sresa1b.labels)) {
model.list <- c(model.list, model.input[cmip3.sresa1b.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip3.sresa1b.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp26.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp26.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp26.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp26.shortperiods.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp26.shortperiods.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp26.shortperiods.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp45.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp45.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp45.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp45.shortperiods.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp45.shortperiods.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp45.shortperiods.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp60.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp60.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp60.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp60.shortperiods.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp60.shortperiods.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp60.shortperiods.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp85.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp85.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp85.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp85.shortperiods.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp85.shortperiods.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp85.shortperiods.labels]
}
if (any(unknown.modelnames %in% ensembles.labels)) {
model.list <- c(model.list, model.input[ensembles.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% ensembles.labels]
}
if (any(unknown.modelnames %in% ensembles.qmschoener.labels)) {
model.list <- c(model.list, model.input[ensembles.qmschoener.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% ensembles.qmschoener.labels]
}
}
## if there is still at least one model not found in the dictionary
if (length(unknown.modelnames) > 0) {
msg1 <- paste("MODEL(S) \"", paste(unknown.modelnames, collapse="\" \""),
"\" UNKNOWN\n", sep="")
msg2 <- "ADD THE MODEL TO DICTIONARY FILE, "
msg3 <- "OR ADD AS A NEW ENSEMBLE INTO ReadFromModelDictionary."
stop(msg1, msg2, msg3)
}
## there's at least one NA -name in model.list, thats wrong for sure!
if (any(is.na(names(model.list))))
stop("THERE IS AT LEAST ONE MODEL CALLED \"NA\" IN THE MODEL.INPUT.")
## empty list...
if (length(model.list) == 0)
stop("NO MODELS HAVE BEEN SELECTED")
return(model.list)
}
###################### handling WUX dataset ############################
MakeModelDataFrame <- function(model.name, institute.name,
rcm.name, global.model.name,
gcm.run,
is.reference.period,
time.step,
season = "none",
is.time.series,
subregion = "no", is.corrected = NULL,
emission.scenario = "A1B", resolution,
data, parameter.name) {
## Merges aggregated model data ("data") with all the information provided for
## the particular climate model and returns a data.frame. This is a crucial
## function where loads can go wrong, so pay EXTRA attention when changing
## this function!
##
## Args:
## model.name: Name the model shall be called
## institute.name: Name of the institute running the RCM
## rcm.name: RCM name
## global.model.name: GCM name
## is.reference.period: Is given period the reference period? (yes/no)
## season: string vector with second statistic timestamps
## e.g. seasons c("DJF", "MAM", "JJA", "SON")
## subregion: string vector with subregion names
## is.corrected: Do we have error corrected data?
## emission.scenario: string with emmission scenario,
## data: list of spatially aggregated data. It has to have EXACTLY the form:
## list of.... subregions containing named seasonal lists with
## containing aggregated data
##
## Returns:
## Data.Frame with model information and spatio-temporal aggregated data.
##
## History:
## 2010-10-20 | Original code (thm)
## 2010-11-03 | renaming variables and set default is.corrected
## "resolution" added
## 2010-11-18 | sorting arguments added (thm)
## 2010-12-15 | gcm.run added
## 2011-04-29 | restructure from MakeModelInfo to MakeModelDataFrame
## 2011-10-20 | NA case added for rcm name
## 2014-11-20 | NA case added for gcm.run and resolution
if (is.null(data))
stop("NO AGGREGATED DATA SPECIFIED")
if (is.null(parameter.name))
stop("PARAMETER NAME NOT SPECIFIED")
## setting default
##"not error corrected data"
if (is.null(is.corrected))
is.corrected <- "no"
## no resolution given
if (is.null(resolution) || !is.null(resolution) && resolution == "")
resolution <- NA
## no gcm.run given
if (is.null(gcm.run) || !is.null(gcm.run) && gcm.run == "")
gcm.run <- NA
## if no RCMs has been passed
if (is.null(rcm.name) || !is.null(rcm.name) && rcm.name == "")
rcm.name <- NA
## sort arguments to get sorted factor levels
emission.scenario <- emission.scenario[order(emission.scenario)]
subregion <- subregion[order(subregion)]
season.order <- season[order(season)]
## generating full data.frame by expanding all possibilities
WUX.df <- list(acronym = model.name,
institute = institute.name,
gcm = global.model.name,
gcm.run = gcm.run,
rcm = rcm.name,
em.scn = emission.scenario,
subreg = subregion,
period = time.step,
ref.per = is.reference.period,
season = season.order,
resolution = resolution,
corrected = is.corrected)
WUX.df <- expand.grid(WUX.df)
## aggregated data to dataframe,
data.df <- reshape::melt(data)
## these 3 dims identify the current data: the data itself, the seasons
## and the subregions
if (length(data.df) != 3) {
msg1 <- "OBVIOUSLY YOU HAVE CHANGED THE AGGREGATION DIMENSION, "
msg2 <- "WATCH OUT WITH MERGING IN MakeModelDataFrame"
stop(msg1, msg2)
}
names(data.df) <- c(parameter.name, "season", "subreg")
## merge dataframe-construct and aggregated parameter data
WUX.df <- merge(WUX.df, data.df)
## if dealing with time series split "season" into year and period
if (is.time.series) {
## extract year
WUX.df[["year"]] <-
factor(sapply(strsplit(as.character(WUX.df[["season"]]), " "), "[", 1))
## extract period
WUX.df[["season"]] <-
factor(sapply(strsplit(as.character(WUX.df[["season"]]), " "), "[", 2))
## rearrange data frame that "year" is beside "season"
season.pos <- which(names(WUX.df) == "season")
year.pos <- which(names(WUX.df) == "year")
WUX.df <- data.frame(WUX.df[1:(season.pos - 1)], year = WUX.df[year.pos],
WUX.df[season.pos:(length(names(WUX.df)) - 1)])
}
return(WUX.df)
}
MergeParameterDataframes <- function(wux.df.list, parameter.names) {
## Merges list of data.frames of different parameters of the same model
## to a single data.frame.
##
## Args:
## wux.df.list: Named list containing data.frames. Each list entry
## corresponds to one climate parameter with its name
## being the CF-convention parameter name.
## parameter.names: Character vector containing CF-convention parameter
## names to be extracted from wux.df.list.
## parameter.names have to match names(wux.df.list).
##
## Returns:
## Single data.frame with model.info columns and one column for each
## parameter.
##
## History:
## 2010-10-27 | Original code (thm)
## 2011-11-24 | added missing list entry error message (thm)
## 2011-12-21 | added case for models with empty wux.df.list (thm)
## if any list entry is NULL
if (any(sapply(wux.df.list, is.null))){
msg1 <- "AT LEAST ONE DATA.FRAME FOR ONE PARAMETER IS MISSING. "
msg2 <- "THIS REALLY SHOULD NOT HAPPPEN."
stop(msg1, msg2)
}
## number of parameters
n.of.pars <- length(parameter.names)
## sort length of parameters, longest parameter first
## --> important for merging
par.length <- c(1:n.of.pars)
for (ii in c(1:n.of.pars)) {
if (length(wux.df.list[[ii]])== 0)
par.length[ii] <- 0
else
par.length[ii] <- length(wux.df.list[[ii]][[1]])
}
sort.indices <- sort(par.length, decreasing = TRUE, index.return = TRUE)
parameter.names <- parameter.names[sort.indices$ix]
par.length <- par.length[sort.indices$ix]
## initializing dataframe with first element of list
data <- wux.df.list[[parameter.names[1]]]
## always merge pairwise
ii <- 2
while (ii <= n.of.pars) {
## case of empty dataframe (i.e. this parameter doesnt exist for this model)
if (length(wux.df.list[[parameter.names[ii]]]) == 0){
data <- cbind(data, NA)
names(data)[length(data)] <- parameter.names[ii]
} else {
data <- merge(data, wux.df.list[[parameter.names[ii]]], all.x = TRUE)
}
ii <- ii + 1
}
return(data)
}
WuxDataDiff <- function(wux.df, parameters) {
## Calculating differences between scenario period and reference period
## data values within 'wux.df' data.frame.
##
## Args:
## wux.df: WUX dataframe with ref.per values yes and no.
## user.input: input file
##
## Returns:
## WUX data.frame with cliamte change signals.
##
## History:
## 2010-10-27 | Original code (thm)
## 2010-11-24 | Only one reference period taken (thm)
## 2011-11-24 | aestethic corrections (thm)
## 2014-11-20 | all subset commands changed to '[' operation (thm)
## sort data frame by season, then by time, and at
## last by modelname (acronym)
sortby <- c("acronym", "period", "season")
wux.df <-
wux.df[order(wux.df$acronym, wux.df$period, wux.df$season),]
## initialize diffs dataset, take all columns except the parameter
## data colummns
col.noparam <- which(! names(wux.df) %in% parameters)
wux.diff.df <- wux.df[wux.df[["ref.per"]] == "no", col.noparam]
for (param in parameters) {
## initializing vector obtaining all parameter-diff values and
## percentage changes
diff.column <- NULL
perc.column <- NULL
## get times of reference and scenarios
timesteps.ref <- unique(wux.df[wux.df[["ref.per"]] == "yes", ]$period)
timesteps.scn <- unique(wux.df[wux.df[["ref.per"]] == "no" , ]$period)
## always take the refernce period as one single ref period,
## regardless of length(timesteps.ref)
number.of.timesteps.ref <- 1
number.of.timesteps.scn <- length(timesteps.scn)
## so far we can only handle one reference period....
stopifnot(number.of.timesteps.ref == 1)
## we have as many data for in the reference period as in the scenario
## period
x <- number.of.timesteps.ref / number.of.timesteps.scn
## we have x- times more scenario data than reference period data, where
## x has to be integer.
## x == round(x) tests whether we deal with integers or not (the R solution)
if(x == round(x)){
## calculate difference for every scenario timestep and the
## corresponding reference period
for (scn.period in timesteps.scn) {
scn <- wux.df[wux.df[["ref.per"]] == "no" &
wux.df[["period"]] == scn.period
, c("acronym", "season", param)]
ref <- wux.df[wux.df[["ref.per"]] == "yes", c("acronym", "season", param)]
## calculating differences / percentual change
diff <- scn[[param]] - ref[[param]]
perc <- (scn[[param]] / ref[[param]] - 1) * 100
## appending to previous timesteps
diff.column <- c(diff.column, diff)
perc.column <- c(perc.column, perc)
}
} else {
## else x was no integer (having x-times more scenario data than
## reference period data)
stop("WuxDataDiff, SOMETHING WENT SERIOUSLY WRONG")
}
## append diff column to diff dataset
wux.diff.df <- cbind(wux.diff.df, diff.column)
diff.cols <- which(names(wux.diff.df) == "diff.column")
names(wux.diff.df)[diff.cols] <- paste("delta.", param, sep="")
## get percentual change for precipitation parameter
if (param == "precipitation_amount") {
wux.diff.df <- cbind(wux.diff.df, perc.column)
perc.cols <- which(names(wux.diff.df) == "perc.column")
names(wux.diff.df)[perc.cols] <- paste("perc.delta.", param, sep="")
}
}
return(wux.diff.df)
}
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# ----------------------------------------------------------------
# $Author: thm $
# $Date: 2015-04-03 12:24:30 +0200 (Fri, 03 Apr 2015) $
# $Rev: 338 $
# ----------------------------------------------------------------
## ----------------------------------------------------------------
## Process of reading in climate data and storing them to a data.frame
## object will take place here.
##
## - models2wux, the 'main' WUX data input routine.
## - Helperfunctions for models2wux.
## ----------------------------------------------------------------
models2wux <- function(userinput,
modelinput = NULL
) {
## Creates a dataframe containing climate change signals of climate models
## listed in user.input.
##
## Args:
## userinput: Filename ot list object from input created by the user.
## The file contains a
## general.input section with tuning parameters and
## a model.input section listing the climate models
## to be processed.
## modelinput: filepath to model.input file. default is NULL,
## which reads in the internal IniModelsDictionary file.
##
## Returns:
## Dataframe containing climate change signals for each model.
##
## History:
## 2010-10-20 | Original code (thm)
## 2010-11-03 | user.input sourced from Models2Wux
## model.input inserted in user.input
## renaming of several variables (thm)
## 2010-11-17 | model.info read in after reading in parameter data (thm)
## 2010-11-29 | some additional user.input - model.input keywords added
## 2011-02-11 | user.input handling changes, renaming from 'Modes2Wux'
## to 'models2wux'
## 2011-04-29 | structural changes for generating WUX dataframe
## 2011-09-14 | added display.subregions keyword to models2wux function
## 2011-09-22 | added case filenames = NA (thus not reading in any data)
## | plus: return value added (it got lost somewhere...)
## 2014-11-12 | alternative model.input pathway introduced (thm)
## 2014-11-14 | rename argument "input" to "userinput" (thm)
## 2014-11-19 | now omitting argument does.plot.subregions (thm)
## 2014-11-21 | platform independent (thm)
## 2015-03-31 | new handling of subregion plotting
cat("\n")
## get user.input list from 'input.filename'
if (!is.list(userinput)) {
user.input.sourced <- source(userinput, local = TRUE)
user.input <- user.input.sourced$value
} else {
user.input <- userinput
}
## -------------------------------------------
## ++++++++++ READING GENERAL INPUT ++++++++++
## -------------------------------------------
############## EXTRACTING INFORMATION FROM user.input FILE ##############
## create outdir for data if path not existing
save.as.data <- user.input$save.as.data
if ( !file.exists(dirname(save.as.data)) )
dir.create(dirname(save.as.data), recursive = TRUE)
## create outdir for plots if path not existing
if ( !is.null(user.input$plot.subregions$save.subregions.plots) ) {
save.subregions.plots <- user.input$plot.subregions$save.subregions.plots
if ( !file.exists(save.subregions.plots) )
dir.create(save.subregions.plots, recursive = TRUE)
} else {
save.subregions.plots <- NULL
}
## user input for area fraction
if ( !is.null(user.input$area.fraction) ) {
does.area.fraction <- user.input$area.fraction
} else {
does.area.fraction <- FALSE
}
## user input for land mask
if ( !is.null(user.input$use.land.mask) ) {
use.land.mask <- user.input$use.land.mask
print("USING LAND-SEA MASK")
} else {
use.land.mask <- FALSE
}
## user input for area weighting
if ( !is.null(user.input$spatial.weighting) ) {
does.spatial.weighting <- user.input$spatial.weighting
} else {
does.spatial.weighting <- FALSE
}
reference.period <- user.input$reference.period
scenario.period <- user.input$scenario.period
periods <- c("reference.period" = reference.period,
"scenario.period" = scenario.period)
## user input for area weighting
if ( !is.null(user.input$na.rm) ) {
na.rm <- user.input$na.rm
} else {
na.rm <- FALSE
}
## subregion specification
subregions <- user.input$subregions
## temporal aggregation specification
temporal.aggr <- user.input$temporal.aggregation
## generate list for dataframes for different parameters
parameter.names <- user.input$parameter.names
wux.df.list <- vector("list", length(parameter.names))
names(wux.df.list) <- parameter.names
## -------------------------------------------
## +++++++++++ READING MODEL DATA ++++++++++++
## -------------------------------------------
############## LOOP OVER PARMS, MODELS, PERIODS AND EXTRACT DATA ##############
## first loop over specified parameters
for ( parameter.longname in parameter.names ) {
cat("\n******************************************************************")
cat("****\n", "PROCESSING PARAMETER \"", parameter.longname,
"\"\n", sep = "")
cat("******************************************************************")
cat("****\n")
## initialize WUX dataset
wux.df <- data.frame()
## climate model names from user.input
user.model.names <- user.input$climate.models
## load Model Dictionary, i.e. a named list containing all the information
## for known models as filenames, directories and further metainformation
model.input <- ReadFromModelDictionary(user.model.names, modelinput)
model.names <- names(model.input)
## second loop over specified models
for (modelname.counter in model.names) {
## Need parameter short name (i.e. variable name from NetCDF file)
## to extract data.
## If "parameters" tag is provided in InitModelDictionary, use its value
## as NetCDF variable name.
model.pars <- model.input[[modelname.counter]]$parameters
parameter.shortname <- GetParameterShortName(parameter.longname,
model.pars)
## getting general model info for data to be read in
institute.name <- model.input[[modelname.counter]]$institute
global.model.name <- model.input[[modelname.counter]]$gcm
gcm.run <- model.input[[modelname.counter]]$gcm.run
rcm.name <- model.input[[modelname.counter]]$rcm
emission.scenario <- model.input[[modelname.counter]]$emission.scenario
is.corrected <- model.input[[modelname.counter]]$corrected.data
resolution <- model.input[[modelname.counter]]$resolution
what.timesteps <- model.input[[modelname.counter]]$what.timesteps
## optional input for models having other periods
model.ref.per <- model.input[[modelname.counter]]$reference.period
model.scn.per <- model.input[[modelname.counter]]$scenario.period
## optional input for model having different period lengths per file
years.in.ref.per.files <-
model.input[[modelname.counter]]$years.in.ref.per.files
years.in.scn.per.files <-
model.input[[modelname.counter]]$years.in.scn.per.files
## optional input for respecification of NetCDF entries
time.units <- model.input[[modelname.counter]]$time.units
calendar <- model.input[[modelname.counter]]$calendar
count.first.time.value <-
model.input[[modelname.counter]]$count.first.time.value
lonlat.var.name <-
model.input[[modelname.counter]]$lonlat.var.name
## location of gridfile
## model grid
grid.filename <-
file.path(model.input[[modelname.counter]]$gridfile.path,
model.input[[modelname.counter]]$gridfile.filename)
## model subregion
subregion.filename <-
file.path(model.input[[modelname.counter]]$subregion.path,
model.input[[modelname.counter]]$subregion.filename)
## in case no subregionfiles are passed, we flag filename with "FALSE"
if (length(subregion.filename) == 0)
subregion.filename <- FALSE
## land mask
land.mask <- model.input[[modelname.counter]]$land.mask
land.mask.name <- model.input[[modelname.counter]]$land.mask.name
## third loop over periods (reference period and scenario period)
for (periods.counter in periods) {
cat("\n-------------------------------------------------------------")
cat("---------\n\n PROCESSING MODEL \"", modelname.counter,
"\" FOR PERIOD \"", periods.counter, "\"\n", sep = "")
## convert periods to POSIXct dates
period.begin <- ISOdate(strsplit(periods.counter, "-")[[1]][1],
1, 1, 0)
period.end <- ISOdate(strsplit(periods.counter, "-")[[1]][2],
12, 31, 23)
## flag whether in reference period or not
current.period.char = names(which(periods == periods.counter))
if (current.period.char == "reference.period") {
is.refperiod <- "yes"
## in case model has specific reference period, use it here
if (!is.null(model.ref.per))
periods.counter <- model.ref.per
} else {
is.refperiod <- "no"
## in case model has specific scenario period, use it here
if (!is.null(model.scn.per))
periods.counter <- model.scn.per
}
## retrieving filenames whose data will be read in and processed
## get required files to cover the period
cat(" GETTING REQUIRED FILENAMES FOR PERIOD", "\n", sep = "")
current.model.input <- model.input[[modelname.counter]]
filenames <- GetFileNames(current.model.input,
period.begin, period.end,
parameter.shortname,
period = current.period.char,
calendar = calendar,
count.first.time.value =
count.first.time.value,
time.units = time.units,
what.timesteps = what.timesteps)
cat(paste(" ", filenames, "\n", sep=""))
## read in data, corresponding grids and aggregate over time to
## get corresponding seasonal values
if ( !all(is.na(filenames)) ) {
data.aggr.list <-
GetAggregatedSpatioTemporalData(filenames,
model.name = modelname.counter,
plot.subregion = user.input$plot.subregion,
grid.filenames = grid.filename,
subregions = subregions,
parameter.name = parameter.shortname,
interpolate.to.grid = FALSE,
temporal.aggr = temporal.aggr,
startdate = period.begin,
enddate = period.end,
## '...' for ReadNetCdfTimeData
time.units = time.units,
calendar = calendar,
count.first.time.value =
count.first.time.value,
lonlat.var.name = lonlat.var.name,
## for AggregateTemporal(...)
what.timesteps = what.timesteps,
na.rm = na.rm,
## '...' for GetSubregionShapes
area.fraction = does.area.fraction,
spatial.weighting = does.spatial.weighting,
## '...' for GetAgg***Data.nointerpol
use.land.mask = use.land.mask,
land.mask = land.mask,
land.mask.name = land.mask.name)
## get number of datapoints read in each subregion (not used further)
n.of.pixels <-
lapply(data.aggr.list, function(z) {
lapply(z, function(x) {lapply(x, function(y) length(!is.na(y)))})})
## aggregate over spatial component
data.aggr.list <- lapply(data.aggr.list,
function(x)
lapply(x$data,
AggregateWeightedSubregions,
weight=x$weight)
)
## check if dealing with a time series
if (sum(which(unlist(sapply(temporal.aggr,
"[", "time.series")) == FALSE)) == 0) {
is.time.series <- TRUE
} else {
is.time.series <- FALSE
}
## get dataframe for particular model & period & parameter
model.data <- MakeModelDataFrame(modelname.counter,
institute.name,
rcm.name,
global.model.name,
gcm.run = gcm.run,
is.reference.period = is.refperiod,
time.step = periods.counter,
subregion = names(data.aggr.list),
is.corrected = is.corrected,
resolution = resolution,
season = sort(names(data.aggr.list[[1]])),
is.time.series = is.time.series,
data = data.aggr.list,
parameter.name = parameter.longname,
emission.scenario = emission.scenario)
## append to dataset of previos models
wux.df <- rbind(wux.df, model.data)
}
}
}
## now we have all models read in for this specific parameter, so we plug
## the data.frame to a list to merged to a dataframe again later
wux.df.list[[parameter.longname]] <- wux.df
}
############## MERGING ALL CREATED data.frames AND SAVE THEM ON DISK ##########
## merge datasets with different parmers to single data.frame
wux.df <- MergeParameterDataframes(wux.df.list, parameter.names)
## getting climate change signal calculating the diff of scen.per and ref.per.
if (!is.time.series) {
## wux.df has still seperated data for ref.per and scen.per. To get the
## climate change signal we calculate the difference of these two periods
## (and for precipitation the perentual change as well)
wux.diff.df <- WuxDataDiff(wux.df, parameter.names)
write.table(wux.diff.df,
file = paste(save.as.data, "_diff.csv", sep = ""), sep=";")
}
## storing WUX dataset to harddisk
cat("\n-------------------------------------------------------------------")
cat("---\n")
cat("\n", "SAVING DATA", "\n", sep = "")
write.table(wux.df, file = paste(save.as.data, ".csv", sep = ""), sep=";")
## time to say goodbye
cat("\n*******************************************************************")
cat("***\n")
cat(" YOUR WUX DATA FRAME HAS BEEN CREATED SUCCESSFULLY :-) ")
cat(" \n")
cat("********************************************************************")
cat("**\n")
if (!is.time.series) {
class(wux.df) <- append("wux.df", class(wux.df))
return(wux.diff.df)
} else {
class(wux.df) <- append("wux.ts.df", class(wux.df))
return(wux.df)
}
}
ReadFromModelDictionary <- function(modelnames, modelinput = NULL){
## Extracts model information from InitModelDictionary for given models.
##
## Args:
## modelnames: Character vector of model names (acronyms) to be extracted
## from InitModelDictionary, or model ensemble name for multiple
## climate models, which then has to be defined here
## (eg. "ensembles").
## modelinput: filepath to model.input file. default is NULL,
## which reads in the internal IniModelsDictionary file.
##
## Returns:
## Named list of model information needed for models2wux.
##
## History:
## 2010-10-27 | Original code (thm)
## ... | many things have happened
## 2012-08-29 | extended CMIP5 ensemble, alphabetized vectors (msu)
## 2014-04-14 | extended CMIP5 ensemble. up to date ensemble! (thm)
## 2014-11-12 | alternative model.input possible (thm)
## check wheteher user specified a specific model input (NULL means no)
if (is.null(modelinput)){
## read in whole InitModelDictionary model.list
model.input <- InitModelDictionary()
} else {
cat(is.list(modelinput))
## check whether modelinput is a directory or a list object
if (is.list(modelinput)) {
## this is a list object
model.input <- modelinput
} else {
## this is a file to be read in
model.input.sourced <- source(modelinput, local = TRUE)
model.input <- model.input.sourced$value
}
if (!is.list(model.input))
stop("YOUR model.input FILE IS WRONG. BE SURE YOU HAVE SPECIFIED A LIST CALLED \"model.input\". SEE ?models2wux FOR DETAILS. ")
}
## known model ensembles from the CMIP3 project and the "ENSEMBLES" project
##-------------------------------------------------------------------
## CMIP3 A1B scenario:
cmip3.sresa1b.labels <- c("cmip3-sresa1b", "CMIP3-SRESA1B")
cmip3.sresa1b.modelnames <- c("bccr_bcm2_0-r1",
"cccma_cgcm3_1-r1",
"cccma_cgcm3_1-r2",
"cccma_cgcm3_1-r3",
"cccma_cgcm3_1-r4",
"cccma_cgcm3_1-r5",
"cccma_cgcm3_1_t63-r1",
"cnrm_cm3-r1",
"csiro_mk3_0-r1",
"csiro_mk3_5-r1",
"gfdl_cm2_0-r1",
"gfdl_cm2_1-r1",
"giss_aom-r1",
"giss_aom-r2",
"giss_model_e_h-r1",
"giss_model_e_h-r2",
"giss_model_e_h-r3",
"giss_model_e_r-r1",
"giss_model_e_r-r2",
"giss_model_e_r-r3",
"giss_model_e_r-r4",
"giss_model_e_r-r5",
"iap_fgoals1_0_g-r1",
"iap_fgoals1_0_g-r2",
"iap_fgoals1_0_g-r3",
"ingv_echam4-r1",
"inmcm3_0-r1",
"ipsl_cm4-r1",
"miroc3_2_hires-r1",
"miroc3_2_medres-r1",
"miroc3_2_medres-r2",
"miroc3_2_medres-r3",
"miub_echo_g-r1",
"miub_echo_g-r2",
"miub_echo_g-r3",
"mpi_echam5-r1",
"mpi_echam5-r2",
"mpi_echam5-r3",
"mpi_echam5-r4",
"mri_cgcm2_3_2a-r1",
"mri_cgcm2_3_2a-r2",
"mri_cgcm2_3_2a-r3",
"mri_cgcm2_3_2a-r4",
"mri_cgcm2_3_2a-r5",
"ncar_ccsm3_0-r1",
"ncar_ccsm3_0-r2",
"ncar_ccsm3_0-r3",
"ncar_ccsm3_0-r5",
"ncar_ccsm3_0-r6",
"ncar_ccsm3_0-r7",
"ncar_ccsm3_0-r9",
"ukmo_hadcm3-r1",
"ukmo_hadgem1-r1")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## CMIP3 A2 scenario:
cmip3.sresa2.labels <- c("cmip3-sresa2", "CMIP3-SRESA2")
cmip3.sresa2.modelnames <- c("bccr_bcm2_0-sresa2-r1",
"cccma_cgcm3_1-sresa2-r1",
"cccma_cgcm3_1-sresa2-r2",
"cccma_cgcm3_1-sresa2-r3",
"cccma_cgcm3_1-sresa2-r4",
"cccma_cgcm3_1-sresa2-r5",
"cnrm_cm3-sresa2-r1",
"csiro_mk3_0-sresa2-r1",
"csiro_mk3_5-sresa2-r1",
"gfdl_cm2_0-sresa2-r1",
"gfdl_cm2_1-sresa2-r1",
"giss_model_e_r-sresa2-r1",
"ingv_echam4-sresa2-r1",
"inmcm3_0-sresa2-r1",
"ipsl_cm4-sresa2-r1",
"miroc3_2_medres-sresa2-r1",
"miroc3_2_medres-sresa2-r2",
"miroc3_2_medres-sresa2-r3",
"miub_echo_g-sresa2-r1",
"miub_echo_g-sresa2-r2",
"miub_echo_g-sresa2-r3",
"mpi_echam5-sresa2-r1",
"mpi_echam5-sresa2-r2",
"mpi_echam5-sresa2-r3",
"mri_cgcm2_3_2a-sresa2-r1",
"mri_cgcm2_3_2a-sresa2-r2",
"mri_cgcm2_3_2a-sresa2-r3",
"mri_cgcm2_3_2a-sresa2-r4",
"mri_cgcm2_3_2a-sresa2-r5",
"ncar_ccsm3_0-sresa2-r1",
"ncar_ccsm3_0-sresa2-r2",
"ncar_ccsm3_0-sresa2-r3",
"ncar_ccsm3_0-sresa2-r4",
"ncar_ccsm3_0-sresa2-r5",
"ukmo_hadcm3-sresa2-r1",
"ukmo_hadgem1-sresa2-r1")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## CMIP3 B1 scenario:
cmip3.sresb1.labels <- c("cmip3-sresb1", "CMIP3-SRESB1")
cmip3.sresb1.modelnames <- c("bccr_bcm2_0-sresb1-r1",
"cccma_cgcm3_1-sresb1-r1",
"cccma_cgcm3_1-sresb1-r2",
"cccma_cgcm3_1-sresb1-r3",
"cccma_cgcm3_1-sresb1-r4",
"cccma_cgcm3_1-sresb1-r5",
"cccma_cgcm3_1_t63-sresb1-r1",
"cnrm_cm3-sresb1-r1",
"csiro_mk3_0-sresb1-r1",
"csiro_mk3_5-sresb1-r1",
"gfdl_cm2_0-sresb1-r1",
"gfdl_cm2_1-sresb1-r1",
"giss_aom-sresb1-r1",
"giss_aom-sresb1-r2",
"giss_model_e_r-sresb1-r1" ,
"iap_fgoals1_0_g-sresb1-r1",
"iap_fgoals1_0_g-sresb1-r2",
"iap_fgoals1_0_g-sresb1-r3",
"inmcm3_0-sresb1-r1",
"ipsl_cm4-sresb1-r1",
"miroc3_2_hires-sresb1-r1",
"miroc3_2_medres-sresb1-r1",
"miroc3_2_medres-sresb1-r2",
"miroc3_2_medres-sresb1-r3",
"miub_echo_g-sresb1-r1",
"miub_echo_g-sresb1-r2",
"miub_echo_g-sresb1-r3",
"mpi_echam5-sresb1-r1",
"mpi_echam5-sresb1-r2",
"mpi_echam5-sresb1-r3",
"mri_cgcm2_3_2a-sresb1-r1",
"mri_cgcm2_3_2a-sresb1-r2",
"mri_cgcm2_3_2a-sresb1-r3",
"mri_cgcm2_3_2a-sresb1-r4",
"mri_cgcm2_3_2a-sresb1-r5",
"ncar_ccsm3_0-sresb1-r1",
"ncar_ccsm3_0-sresb1-r2",
"ncar_ccsm3_0-sresb1-r3",
"ncar_ccsm3_0-sresb1-r4",
"ncar_ccsm3_0-sresb1-r5",
"ncar_ccsm3_0-sresb1-r6",
"ncar_ccsm3_0-sresb1-r7",
"ncar_ccsm3_0-sresb1-r9",
"ukmo_hadcm3-sresb1-r1")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## CMIP5
## rcp26
cmip5.rcp26.labels <- c("cmip5-rcp26", "CMIP5-RCP26")
cmip5.rcp26.modelnames <- c("BCC-CSM1-1-r1i1p1_rcp26",
"BCC-CSM1-1-m-r1i1p1_rcp26",
"BNU-ESM-r1i1p1_rcp26",
"CanESM2-r1i1p1_rcp26",
"CanESM2-r2i1p1_rcp26",
"CanESM2-r3i1p1_rcp26",
"CanESM2-r4i1p1_rcp26",
"CanESM2-r5i1p1_rcp26",
"CCSM4-r1i1p1_rcp26",
"CCSM4-r2i1p1_rcp26",
"CCSM4-r3i1p1_rcp26",
"CCSM4-r4i1p1_rcp26",
"CCSM4-r5i1p1_rcp26",
"CCSM4-r6i1p1_rcp26",
"CESM1-CAM5-r1i1p1_rcp26",
"CESM1-CAM5-r2i1p1_rcp26",
"CESM1-CAM5-r3i1p1_rcp26",
"CNRM-CM5-r1i1p1_rcp26",
"CSIRO-Mk3-6-0-r10i1p1_rcp26",
"CSIRO-Mk3-6-0-r1i1p1_rcp26",
"CSIRO-Mk3-6-0-r2i1p1_rcp26",
"CSIRO-Mk3-6-0-r3i1p1_rcp26",
"CSIRO-Mk3-6-0-r4i1p1_rcp26",
"CSIRO-Mk3-6-0-r5i1p1_rcp26",
"CSIRO-Mk3-6-0-r6i1p1_rcp26",
"CSIRO-Mk3-6-0-r7i1p1_rcp26",
"CSIRO-Mk3-6-0-r8i1p1_rcp26",
"CSIRO-Mk3-6-0-r9i1p1_rcp26",
"EC-EARTH-r12i1p1_rcp26",
"EC-EARTH-r8i1p1_rcp26",
"FGOALS-g2-r1i1p1_rcp26",
"FGOALS-s2-r1i1p1_rcp26",
"FIO-ESM-r1i1p1_rcp26",
"FIO-ESM-r2i1p1_rcp26",
"FIO-ESM-r3i1p1_rcp26",
"GFDL-CM3-r1i1p1_rcp26",
"GFDL-ESM2G-r1i1p1_rcp26",
"GFDL-ESM2M-r1i1p1_rcp26",
"GISS-E2-H-r1i1p1_rcp26",
"GISS-E2-H-r1i1p2_rcp26",
"GISS-E2-H-r1i1p3_rcp26",
"GISS-E2-R-r1i1p1_rcp26",
"GISS-E2-R-r1i1p2_rcp26",
"GISS-E2-R-r1i1p3_rcp26",
"HadGEM2-AO-r1i1p1_rcp26",
"HadGEM2-ES-r1i1p1_rcp26",
## "HadGEM2-ES-r2i1p1_rcp26", making touble with dates in NetCDF file
## "HadGEM2-ES-r3i1p1_rcp26", making touble with dates in NetCDF file
"HadGEM2-ES-r4i1p1_rcp26",
"IPSL-CM5A-LR-r1i1p1_rcp26",
"IPSL-CM5A-LR-r2i1p1_rcp26",
"IPSL-CM5A-LR-r3i1p1_rcp26",
"IPSL-CM5A-LR-r4i1p1_rcp26",
"IPSL-CM5A-MR-r1i1p1_rcp26",
"MIROC5-r1i1p1_rcp26",
"MIROC5-r2i1p1_rcp26",
"MIROC5-r3i1p1_rcp26",
"MIROC-ESM-r1i1p1_rcp26",
"MIROC-ESM-CHEM-r1i1p1_rcp26",
"MPI-ESM-LR-r1i1p1_rcp26",
"MPI-ESM-LR-r2i1p1_rcp26",
"MPI-ESM-LR-r3i1p1_rcp26",
"MPI-ESM-MR-r1i1p1_rcp26",
"MRI-CGCM3-r1i1p1_rcp26",
"NorESM1-M-r1i1p1_rcp26",
"NorESM1-ME-r1i1p1_rcp26")
##-------------------------------------------------------------------
## rcp26 including models with shorter simulations (usually until 2035)
## with MIROC5_r4i1p1_rcp26
## MIROC5_r5i1p1_rcp26
cmip5.rcp26.shortperiods.labels <- c("cmip5-rcp26-shortperiods", "CMIP5-RCP26-SHORTPERIODS")
cmip5.rcp26.shortperiods.modelnames <- c("BCC-CSM1-1-r1i1p1_rcp26",
"BCC-CSM1-1-m-r1i1p1_rcp26",
"BNU-ESM-r1i1p1_rcp26",
"CanESM2-r1i1p1_rcp26",
"CanESM2-r2i1p1_rcp26",
"CanESM2-r3i1p1_rcp26",
"CanESM2-r4i1p1_rcp26",
"CanESM2-r5i1p1_rcp26",
"CCSM4-r1i1p1_rcp26",
"CCSM4-r2i1p1_rcp26",
"CCSM4-r3i1p1_rcp26",
"CCSM4-r4i1p1_rcp26",
"CCSM4-r5i1p1_rcp26",
"CCSM4-r6i1p1_rcp26",
"CESM1-CAM5-r1i1p1_rcp26",
"CESM1-CAM5-r2i1p1_rcp26",
"CESM1-CAM5-r3i1p1_rcp26",
"CNRM-CM5-r1i1p1_rcp26",
"CSIRO-Mk3-6-0-r10i1p1_rcp26",
"CSIRO-Mk3-6-0-r1i1p1_rcp26",
"CSIRO-Mk3-6-0-r2i1p1_rcp26",
"CSIRO-Mk3-6-0-r3i1p1_rcp26",
"CSIRO-Mk3-6-0-r4i1p1_rcp26",
"CSIRO-Mk3-6-0-r5i1p1_rcp26",
"CSIRO-Mk3-6-0-r6i1p1_rcp26",
"CSIRO-Mk3-6-0-r7i1p1_rcp26",
"CSIRO-Mk3-6-0-r8i1p1_rcp26",
"CSIRO-Mk3-6-0-r9i1p1_rcp26",
"EC-EARTH-r12i1p1_rcp26",
"EC-EARTH-r8i1p1_rcp26",
"FGOALS-g2-r1i1p1_rcp26",
"FGOALS-s2-r1i1p1_rcp26",
"FIO-ESM-r1i1p1_rcp26",
"FIO-ESM-r2i1p1_rcp26",
"FIO-ESM-r3i1p1_rcp26",
"GFDL-CM3-r1i1p1_rcp26",
"GFDL-ESM2G-r1i1p1_rcp26",
"GFDL-ESM2M-r1i1p1_rcp26",
"GISS-E2-H-r1i1p1_rcp26",
"GISS-E2-H-r1i1p2_rcp26",
"GISS-E2-H-r1i1p3_rcp26",
"GISS-E2-R-r1i1p1_rcp26",
"GISS-E2-R-r1i1p2_rcp26",
"GISS-E2-R-r1i1p3_rcp26",
"HadGEM2-AO-r1i1p1_rcp26",
"HadGEM2-ES-r1i1p1_rcp26",
"HadGEM2-ES-r2i1p1_rcp26",
"HadGEM2-ES-r3i1p1_rcp26",
"HadGEM2-ES-r4i1p1_rcp26",
"IPSL-CM5A-LR-r1i1p1_rcp26",
"IPSL-CM5A-LR-r2i1p1_rcp26",
"IPSL-CM5A-LR-r3i1p1_rcp26",
"IPSL-CM5A-LR-r4i1p1_rcp26",
"IPSL-CM5A-MR-r1i1p1_rcp26",
"MIROC5-r1i1p1_rcp26",
"MIROC5-r2i1p1_rcp26",
"MIROC5-r3i1p1_rcp26",
"MIROC5-r4i1p1_rcp26",
"MIROC5-r5i1p1_rcp26",
"MIROC-ESM-r1i1p1_rcp26",
"MIROC-ESM-CHEM-r1i1p1_rcp26",
"MPI-ESM-LR-r1i1p1_rcp26",
"MPI-ESM-LR-r2i1p1_rcp26",
"MPI-ESM-LR-r3i1p1_rcp26",
"MPI-ESM-MR-r1i1p1_rcp26",
"MRI-CGCM3-r1i1p1_rcp26",
"NorESM1-M-r1i1p1_rcp26",
"NorESM1-ME-r1i1p1_rcp26")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## rcp45
cmip5.rcp45.labels <- c("cmip5-rcp45", "CMIP5-RCP45")
cmip5.rcp45.modelnames <- c("ACCESS1-0-r1i1p1_rcp45",
"ACCESS1-3-r1i1p1_rcp45",
"BCC-CSM1-1-r1i1p1_rcp45",
"BCC-CSM1-1-m-r1i1p1_rcp45",
"BNU-ESM-r1i1p1_rcp45",
## "CanCM4-r10i1p1_rcp45", too short timeseries...
## "CanCM4-r1i1p1_rcp45",
## "CanCM4-r2i1p1_rcp45",
## "CanCM4-r3i1p1_rcp45",
## "CanCM4-r4i1p1_rcp45",
## "CanCM4-r5i1p1_rcp45",
## "CanCM4-r6i1p1_rcp45",
## "CanCM4-r7i1p1_rcp45",
## "CanCM4-r8i1p1_rcp45",
## "CanCM4-r9i1p1_rcp45",
"CanESM2-r1i1p1_rcp45",
"CanESM2-r2i1p1_rcp45",
"CanESM2-r3i1p1_rcp45",
"CanESM2-r4i1p1_rcp45",
"CanESM2-r5i1p1_rcp45",
"CCSM4-r1i1p1_rcp45",
"CCSM4-r2i1p1_rcp45",
"CCSM4-r3i1p1_rcp45",
"CCSM4-r4i1p1_rcp45",
"CCSM4-r5i1p1_rcp45",
"CCSM4-r6i1p1_rcp45",
"CESM1-BGC-r1i1p1_rcp45",
"CESM1-CAM5-r1i1p1_rcp45",
"CESM1-CAM5-r2i1p1_rcp45",
"CESM1-CAM5-r3i1p1_rcp45",
"CESM1-WACCM-r2i1p1_rcp45",
## "CESM1-WACCM-r3i1p1_rcp45", too short timeseries...
## "CESM1-WACCM-r4i1p1_rcp45",
"CMCC-CM-r1i1p1_rcp45",
"CMCC-CMS-r1i1p1_rcp45",
"CNRM-CM5-r1i1p1_rcp45",
"CSIRO-Mk3-6-0-r10i1p1_rcp45",
"CSIRO-Mk3-6-0-r1i1p1_rcp45",
"CSIRO-Mk3-6-0-r2i1p1_rcp45",
"CSIRO-Mk3-6-0-r3i1p1_rcp45",
"CSIRO-Mk3-6-0-r4i1p1_rcp45",
"CSIRO-Mk3-6-0-r5i1p1_rcp45",
"CSIRO-Mk3-6-0-r6i1p1_rcp45",
"CSIRO-Mk3-6-0-r7i1p1_rcp45",
"CSIRO-Mk3-6-0-r8i1p1_rcp45",
"CSIRO-Mk3-6-0-r9i1p1_rcp45",
"EC-EARTH-r12i1p1_rcp45",
"EC-EARTH-r13i1p1_rcp45",
"EC-EARTH-r14i1p1_rcp45",
## "EC-EARTH-r1i1p1_rcp45", too short timeseries...
"EC-EARTH-r2i1p1_rcp45",
"EC-EARTH-r6i1p1_rcp45",
## "EC-EARTH-r7i1p1_rcp45", ## missing historical runs
"EC-EARTH-r8i1p1_rcp45",
"EC-EARTH-r9i1p1_rcp45",
"FGOALS-g2-r1i1p1_rcp45",
"FIO-ESM-r1i1p1_rcp45",
"FIO-ESM-r2i1p1_rcp45",
"FIO-ESM-r3i1p1_rcp45",
## "GFDL-CM2-1-r10i1p1_rcp45", too short timeseries...
## "GFDL-CM2-1-r1i1p1_rcp45",
## "GFDL-CM2-1-r2i1p1_rcp45",
## "GFDL-CM2-1-r3i1p1_rcp45",
## "GFDL-CM2-1-r4i1p1_rcp45",
## "GFDL-CM2-1-r5i1p1_rcp45",
## "GFDL-CM2-1-r6i1p1_rcp45",
## "GFDL-CM2-1-r7i1p1_rcp45",
## "GFDL-CM2-1-r8i1p1_rcp45",
## "GFDL-CM2-1-r9i1p1_rcp45",
"GFDL-CM3-r1i1p1_rcp45",
"GFDL-ESM2G-r1i1p1_rcp45",
"GFDL-ESM2M-r1i1p1_rcp45",
"GISS-E2-H-r1i1p1_rcp45",
"GISS-E2-H-r1i1p2_rcp45",
"GISS-E2-H-r1i1p3_rcp45",
"GISS-E2-H-r2i1p1_rcp45",
"GISS-E2-H-r2i1p2_rcp45",
"GISS-E2-H-r2i1p3_rcp45",
"GISS-E2-H-r3i1p1_rcp45",
"GISS-E2-H-r3i1p2_rcp45",
"GISS-E2-H-r3i1p3_rcp45",
"GISS-E2-H-r4i1p1_rcp45",
"GISS-E2-H-r4i1p2_rcp45",
"GISS-E2-H-r4i1p3_rcp45",
"GISS-E2-H-r5i1p1_rcp45",
"GISS-E2-H-r5i1p2_rcp45",
"GISS-E2-H-r5i1p3_rcp45",
"GISS-E2-H-r6i1p3_rcp45",
"GISS-E2-H-CC-r1i1p1_rcp45",
"GISS-E2-R-r1i1p1_rcp45",
"GISS-E2-R-r1i1p2_rcp45",
"GISS-E2-R-r1i1p3_rcp45",
"GISS-E2-R-r2i1p1_rcp45",
"GISS-E2-R-r2i1p2_rcp45",
"GISS-E2-R-r2i1p3_rcp45",
"GISS-E2-R-r3i1p1_rcp45",
"GISS-E2-R-r3i1p2_rcp45",
"GISS-E2-R-r3i1p3_rcp45",
"GISS-E2-R-r4i1p1_rcp45",
"GISS-E2-R-r4i1p2_rcp45",
"GISS-E2-R-r4i1p3_rcp45",
"GISS-E2-R-r5i1p1_rcp45",
"GISS-E2-R-r5i1p2_rcp45",
"GISS-E2-R-r5i1p3_rcp45",
"GISS-E2-R-r6i1p1_rcp45",
"GISS-E2-R-r6i1p3_rcp45",
"GISS-E2-R-CC-r1i1p1_rcp45",
## "HadCM3-r10i1p1_rcp45", too short timeseries...
## "HadCM3-r1i1p1_rcp45",
## "HadCM3-r2i1p1_rcp45",
## "HadCM3-r3i1p1_rcp45",
## "HadCM3-r4i1p1_rcp45",
## "HadCM3-r5i1p1_rcp45",
## "HadCM3-r6i1p1_rcp45",
## "HadCM3-r7i1p1_rcp45",
## "HadCM3-r8i1p1_rcp45",
## "HadCM3-r9i1p1_rcp45",
"HadGEM2-AO-r1i1p1_rcp45",
"HadGEM2-CC-r1i1p1_rcp45",
"HadGEM2-ES-r1i1p1_rcp45",
## "HadGEM2-ES-r2i1p1_rcp45", making touble with dates in NetCDF file
## "HadGEM2-ES-r3i1p1_rcp45", making touble with dates in NetCDF file
"HadGEM2-ES-r4i1p1_rcp45",
"INM-CM4-r1i1p1_rcp45",
"IPSL-CM5A-LR-r1i1p1_rcp45",
"IPSL-CM5A-LR-r2i1p1_rcp45",
"IPSL-CM5A-LR-r3i1p1_rcp45",
"IPSL-CM5A-LR-r4i1p1_rcp45",
"IPSL-CM5A-MR-r1i1p1_rcp45",
"IPSL-CM5B-LR-r1i1p1_rcp45",
## "MIROC4h-r1i1p1_rcp45", too short timeseries...
## "MIROC4h-r2i1p1_rcp45",
## "MIROC4h-r3i1p1_rcp45",
"MIROC5-r1i1p1_rcp45",
"MIROC5-r2i1p1_rcp45",
"MIROC5-r3i1p1_rcp45",
## "MIROC5-r4i1p1_rcp45", too short timeseries...
## "MIROC5-r5i1p1_rcp45",
"MIROC-ESM-r1i1p1_rcp45",
"MIROC-ESM-CHEM-r1i1p1_rcp45",
"MPI-ESM-LR-r1i1p1_rcp45",
"MPI-ESM-LR-r2i1p1_rcp45",
"MPI-ESM-LR-r3i1p1_rcp45",
"MPI-ESM-MR-r1i1p1_rcp45",
"MPI-ESM-MR-r2i1p1_rcp45",
"MPI-ESM-MR-r3i1p1_rcp45",
"MRI-CGCM3-r1i1p1_rcp45",
"NorESM1-M-r1i1p1_rcp45",
"NorESM1-ME-r1i1p1_rcp45"
## "CSIRO-Mk3L-1-2-r1i2p1_rcp45", ## missing historical runs
## "CSIRO-Mk3L-1-2-r2i2p1_rcp45",
## "CSIRO-Mk3L-1-2-r3i2p1_rcp45",
)
## rcp45
cmip5.rcp45.shortperiods.labels <- c("cmip5-rcp45-shortperiods", "CMIP5-RCP45-SHORTPERIODS")
cmip5.rcp45.shortperiods.modelnames <- c("ACCESS1-0-r1i1p1_rcp45",
"ACCESS1-3-r1i1p1_rcp45",
"BCC-CSM1-1-r1i1p1_rcp45",
"BCC-CSM1-1-m-r1i1p1_rcp45",
"BNU-ESM-r1i1p1_rcp45",
"CanCM4-r10i1p1_rcp45",
"CanCM4-r1i1p1_rcp45",
"CanCM4-r2i1p1_rcp45",
"CanCM4-r3i1p1_rcp45",
"CanCM4-r4i1p1_rcp45",
"CanCM4-r5i1p1_rcp45",
"CanCM4-r6i1p1_rcp45",
"CanCM4-r7i1p1_rcp45",
"CanCM4-r8i1p1_rcp45",
"CanCM4-r9i1p1_rcp45",
"CanESM2-r1i1p1_rcp45",
"CanESM2-r2i1p1_rcp45",
"CanESM2-r3i1p1_rcp45",
"CanESM2-r4i1p1_rcp45",
"CanESM2-r5i1p1_rcp45",
"CCSM4-r1i1p1_rcp45",
"CCSM4-r2i1p1_rcp45",
"CCSM4-r3i1p1_rcp45",
"CCSM4-r4i1p1_rcp45",
"CCSM4-r5i1p1_rcp45",
"CCSM4-r6i1p1_rcp45",
"CESM1-BGC-r1i1p1_rcp45",
"CESM1-CAM5-r1i1p1_rcp45",
"CESM1-CAM5-r2i1p1_rcp45",
"CESM1-CAM5-r3i1p1_rcp45",
"CESM1-WACCM-r2i1p1_rcp45",
"CESM1-WACCM-r3i1p1_rcp45",
"CESM1-WACCM-r4i1p1_rcp45",
"CMCC-CM-r1i1p1_rcp45",
"CMCC-CMS-r1i1p1_rcp45",
"CNRM-CM5-r1i1p1_rcp45",
"CSIRO-Mk3-6-0-r10i1p1_rcp45",
"CSIRO-Mk3-6-0-r1i1p1_rcp45",
"CSIRO-Mk3-6-0-r2i1p1_rcp45",
"CSIRO-Mk3-6-0-r3i1p1_rcp45",
"CSIRO-Mk3-6-0-r4i1p1_rcp45",
"CSIRO-Mk3-6-0-r5i1p1_rcp45",
"CSIRO-Mk3-6-0-r6i1p1_rcp45",
"CSIRO-Mk3-6-0-r7i1p1_rcp45",
"CSIRO-Mk3-6-0-r8i1p1_rcp45",
"CSIRO-Mk3-6-0-r9i1p1_rcp45",
"EC-EARTH-r12i1p1_rcp45",
"EC-EARTH-r13i1p1_rcp45",
"EC-EARTH-r14i1p1_rcp45",
"EC-EARTH-r1i1p1_rcp45",
"EC-EARTH-r2i1p1_rcp45",
"EC-EARTH-r6i1p1_rcp45",
## "EC-EARTH-r7i1p1_rcp45", ## missing historical runs
"EC-EARTH-r8i1p1_rcp45",
"EC-EARTH-r9i1p1_rcp45",
"FGOALS-g2-r1i1p1_rcp45",
"FIO-ESM-r1i1p1_rcp45",
"FIO-ESM-r2i1p1_rcp45",
"FIO-ESM-r3i1p1_rcp45",
"GFDL-CM2-1-r10i1p1_rcp45",
"GFDL-CM2-1-r1i1p1_rcp45",
"GFDL-CM2-1-r2i1p1_rcp45",
"GFDL-CM2-1-r3i1p1_rcp45",
"GFDL-CM2-1-r4i1p1_rcp45",
"GFDL-CM2-1-r5i1p1_rcp45",
"GFDL-CM2-1-r6i1p1_rcp45",
"GFDL-CM2-1-r7i1p1_rcp45",
"GFDL-CM2-1-r8i1p1_rcp45",
"GFDL-CM2-1-r9i1p1_rcp45",
"GFDL-CM3-r1i1p1_rcp45",
"GFDL-ESM2G-r1i1p1_rcp45",
"GFDL-ESM2M-r1i1p1_rcp45",
"GISS-E2-H-r1i1p1_rcp45",
"GISS-E2-H-r1i1p2_rcp45",
"GISS-E2-H-r1i1p3_rcp45",
"GISS-E2-H-r2i1p1_rcp45",
"GISS-E2-H-r2i1p2_rcp45",
"GISS-E2-H-r2i1p3_rcp45",
"GISS-E2-H-r3i1p1_rcp45",
"GISS-E2-H-r3i1p2_rcp45",
"GISS-E2-H-r3i1p3_rcp45",
"GISS-E2-H-r4i1p1_rcp45",
"GISS-E2-H-r4i1p2_rcp45",
"GISS-E2-H-r4i1p3_rcp45",
"GISS-E2-H-r5i1p1_rcp45",
"GISS-E2-H-r5i1p2_rcp45",
"GISS-E2-H-r5i1p3_rcp45",
"GISS-E2-H-r6i1p3_rcp45",
"GISS-E2-H-CC-r1i1p1_rcp45",
"GISS-E2-R-r1i1p1_rcp45",
"GISS-E2-R-r1i1p2_rcp45",
"GISS-E2-R-r1i1p3_rcp45",
"GISS-E2-R-r2i1p1_rcp45",
"GISS-E2-R-r2i1p2_rcp45",
"GISS-E2-R-r2i1p3_rcp45",
"GISS-E2-R-r3i1p1_rcp45",
"GISS-E2-R-r3i1p2_rcp45",
"GISS-E2-R-r3i1p3_rcp45",
"GISS-E2-R-r4i1p1_rcp45",
"GISS-E2-R-r4i1p2_rcp45",
"GISS-E2-R-r4i1p3_rcp45",
"GISS-E2-R-r5i1p1_rcp45",
"GISS-E2-R-r5i1p2_rcp45",
"GISS-E2-R-r5i1p3_rcp45",
"GISS-E2-R-r6i1p1_rcp45",
"GISS-E2-R-r6i1p3_rcp45",
"GISS-E2-R-CC-r1i1p1_rcp45",
"HadCM3-r10i1p1_rcp45",
"HadCM3-r1i1p1_rcp45",
"HadCM3-r2i1p1_rcp45",
"HadCM3-r3i1p1_rcp45",
"HadCM3-r4i1p1_rcp45",
"HadCM3-r5i1p1_rcp45",
"HadCM3-r6i1p1_rcp45",
"HadCM3-r7i1p1_rcp45",
"HadCM3-r8i1p1_rcp45",
"HadCM3-r9i1p1_rcp45",
"HadGEM2-AO-r1i1p1_rcp45",
"HadGEM2-CC-r1i1p1_rcp45",
"HadGEM2-ES-r1i1p1_rcp45",
"HadGEM2-ES-r2i1p1_rcp45",
"HadGEM2-ES-r3i1p1_rcp45",
"HadGEM2-ES-r4i1p1_rcp45",
"INM-CM4-r1i1p1_rcp45",
"IPSL-CM5A-LR-r1i1p1_rcp45",
"IPSL-CM5A-LR-r2i1p1_rcp45",
"IPSL-CM5A-LR-r3i1p1_rcp45",
"IPSL-CM5A-LR-r4i1p1_rcp45",
"IPSL-CM5A-MR-r1i1p1_rcp45",
"IPSL-CM5B-LR-r1i1p1_rcp45",
"MIROC4h-r1i1p1_rcp45",
"MIROC4h-r2i1p1_rcp45",
"MIROC4h-r3i1p1_rcp45",
"MIROC5-r1i1p1_rcp45",
"MIROC5-r2i1p1_rcp45",
"MIROC5-r3i1p1_rcp45",
"MIROC5-r4i1p1_rcp45",
"MIROC5-r5i1p1_rcp45",
"MIROC-ESM-r1i1p1_rcp45",
"MIROC-ESM-CHEM-r1i1p1_rcp45",
"MPI-ESM-LR-r1i1p1_rcp45",
"MPI-ESM-LR-r2i1p1_rcp45",
"MPI-ESM-LR-r3i1p1_rcp45",
"MPI-ESM-MR-r1i1p1_rcp45",
"MPI-ESM-MR-r2i1p1_rcp45",
"MPI-ESM-MR-r3i1p1_rcp45",
"MRI-CGCM3-r1i1p1_rcp45",
"NorESM1-M-r1i1p1_rcp45",
"NorESM1-ME-r1i1p1_rcp45"
## "CSIRO-Mk3L-1-2-r1i2p1_rcp45", ## missing historical runs
## "CSIRO-Mk3L-1-2-r2i2p1_rcp45",
## "CSIRO-Mk3L-1-2-r3i2p1_rcp45",
)
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## rcp60
cmip5.rcp60.labels <- c("cmip5-rcp60", "CMIP5-RCP60")
cmip5.rcp60.modelnames <- c("BCC-CSM1-1-r1i1p1_rcp60",
"BCC-CSM1-1-m-r1i1p1_rcp60",
"CCSM4-r1i1p1_rcp60",
"CCSM4-r2i1p1_rcp60",
"CCSM4-r3i1p1_rcp60",
"CCSM4-r4i1p1_rcp60",
"CCSM4-r5i1p1_rcp60",
"CCSM4-r6i1p1_rcp60",
"CESM1-CAM5-r1i1p1_rcp60",
"CESM1-CAM5-r2i1p1_rcp60",
"CESM1-CAM5-r3i1p1_rcp60",
"CSIRO-Mk3-6-0-r10i1p1_rcp60",
"CSIRO-Mk3-6-0-r1i1p1_rcp60",
"CSIRO-Mk3-6-0-r2i1p1_rcp60",
"CSIRO-Mk3-6-0-r3i1p1_rcp60",
"CSIRO-Mk3-6-0-r4i1p1_rcp60",
"CSIRO-Mk3-6-0-r5i1p1_rcp60",
"CSIRO-Mk3-6-0-r6i1p1_rcp60",
"CSIRO-Mk3-6-0-r7i1p1_rcp60",
"CSIRO-Mk3-6-0-r8i1p1_rcp60",
"CSIRO-Mk3-6-0-r9i1p1_rcp60",
"FGOALS-s2-r1i1p1_rcp60",
"FIO-ESM-r1i1p1_rcp60",
"FIO-ESM-r2i1p1_rcp60",
"FIO-ESM-r3i1p1_rcp60",
"GFDL-CM3-r1i1p1_rcp60",
"GFDL-ESM2G-r1i1p1_rcp60",
"GFDL-ESM2M-r1i1p1_rcp60",
"GISS-E2-H-r1i1p1_rcp60",
"GISS-E2-H-r1i1p2_rcp60",
"GISS-E2-H-r1i1p3_rcp60",
"GISS-E2-R-r1i1p1_rcp60",
"GISS-E2-R-r1i1p2_rcp60",
"GISS-E2-R-r1i1p3_rcp60",
"HadGEM2-AO-r1i1p1_rcp60",
"HadGEM2-ES-r1i1p1_rcp60",
## "HadGEM2-ES-r2i1p1_rcp60", making touble with dates in NetCDF file
## "HadGEM2-ES-r3i1p1_rcp60", making touble with dates in NetCDF file
"HadGEM2-ES-r4i1p1_rcp60",
"IPSL-CM5A-LR-r1i1p1_rcp60",
"IPSL-CM5A-MR-r1i1p1_rcp60",
"MIROC5-r1i1p1_rcp60",
"MIROC5-r2i1p1_rcp60",
"MIROC5-r3i1p1_rcp60",
## "MIROC5-r4i1p1_rcp60", too short timeseries...
## "MIROC5-r5i1p1_rcp60",
"MIROC-ESM-r1i1p1_rcp60",
"MIROC-ESM-CHEM-r1i1p1_rcp60",
"MRI-CGCM3-r1i1p1_rcp60",
"NorESM1-M-r1i1p1_rcp60",
"NorESM1-ME-r1i1p1_rcp60")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## rcp60 including all siulations with shorter periods
cmip5.rcp60.shortperiods.labels <- c("cmip5-rcp60-shortperiods", "CMIP5-RCP60-SHORTPERIODS")
cmip5.rcp60.shortperiods.modelnames <- c("BCC-CSM1-1-r1i1p1_rcp60",
"BCC-CSM1-1-m-r1i1p1_rcp60",
"CCSM4-r1i1p1_rcp60",
"CCSM4-r2i1p1_rcp60",
"CCSM4-r3i1p1_rcp60",
"CCSM4-r4i1p1_rcp60",
"CCSM4-r5i1p1_rcp60",
"CCSM4-r6i1p1_rcp60",
"CESM1-CAM5-r1i1p1_rcp60",
"CESM1-CAM5-r2i1p1_rcp60",
"CESM1-CAM5-r3i1p1_rcp60",
"CSIRO-Mk3-6-0-r10i1p1_rcp60",
"CSIRO-Mk3-6-0-r1i1p1_rcp60",
"CSIRO-Mk3-6-0-r2i1p1_rcp60",
"CSIRO-Mk3-6-0-r3i1p1_rcp60",
"CSIRO-Mk3-6-0-r4i1p1_rcp60",
"CSIRO-Mk3-6-0-r5i1p1_rcp60",
"CSIRO-Mk3-6-0-r6i1p1_rcp60",
"CSIRO-Mk3-6-0-r7i1p1_rcp60",
"CSIRO-Mk3-6-0-r8i1p1_rcp60",
"CSIRO-Mk3-6-0-r9i1p1_rcp60",
"FGOALS-s2-r1i1p1_rcp60",
"FIO-ESM-r1i1p1_rcp60",
"FIO-ESM-r2i1p1_rcp60",
"FIO-ESM-r3i1p1_rcp60",
"GFDL-CM3-r1i1p1_rcp60",
"GFDL-ESM2G-r1i1p1_rcp60",
"GFDL-ESM2M-r1i1p1_rcp60",
"GISS-E2-H-r1i1p1_rcp60",
"GISS-E2-H-r1i1p2_rcp60",
"GISS-E2-H-r1i1p3_rcp60",
"GISS-E2-R-r1i1p1_rcp60",
"GISS-E2-R-r1i1p2_rcp60",
"GISS-E2-R-r1i1p3_rcp60",
"HadGEM2-AO-r1i1p1_rcp60",
"HadGEM2-ES-r1i1p1_rcp60",
"HadGEM2-ES-r2i1p1_rcp60",
"HadGEM2-ES-r3i1p1_rcp60",
"HadGEM2-ES-r4i1p1_rcp60",
"IPSL-CM5A-LR-r1i1p1_rcp60",
"IPSL-CM5A-MR-r1i1p1_rcp60",
"MIROC5-r1i1p1_rcp60",
"MIROC5-r2i1p1_rcp60",
"MIROC5-r3i1p1_rcp60",
"MIROC5-r4i1p1_rcp60",
"MIROC5-r5i1p1_rcp60",
"MIROC-ESM-r1i1p1_rcp60",
"MIROC-ESM-CHEM-r1i1p1_rcp60",
"MRI-CGCM3-r1i1p1_rcp60",
"NorESM1-M-r1i1p1_rcp60",
"NorESM1-ME-r1i1p1_rcp60")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## rcp85
cmip5.rcp85.labels <- c("cmip5-rcp85", "CMIP5-RCP85")
cmip5.rcp85.modelnames <- c("ACCESS1-0-r1i1p1_rcp85",
"ACCESS1-3-r1i1p1_rcp85",
"BCC-CSM1-1-r1i1p1_rcp85",
"BCC-CSM1-1-m-r1i1p1_rcp85",
"BNU-ESM-r1i1p1_rcp85",
"CanESM2-r1i1p1_rcp85",
"CanESM2-r2i1p1_rcp85",
"CanESM2-r3i1p1_rcp85",
"CanESM2-r4i1p1_rcp85",
"CanESM2-r5i1p1_rcp85",
"CCSM4-r1i1p1_rcp85",
"CCSM4-r2i1p1_rcp85",
"CCSM4-r3i1p1_rcp85",
"CCSM4-r4i1p1_rcp85",
"CCSM4-r5i1p1_rcp85",
"CCSM4-r6i1p1_rcp85",
"CESM1-BGC-r1i1p1_rcp85",
"CESM1-CAM5-r1i1p1_rcp85",
"CESM1-CAM5-r2i1p1_rcp85",
"CESM1-CAM5-r3i1p1_rcp85",
"CESM1-WACCM-r2i1p1_rcp85",
## "CESM1-WACCM-r3i1p1_rcp85",too short timeseries
## "CESM1-WACCM-r4i1p1_rcp85",
"CMCC-CESM-r1i1p1_rcp85",
"CMCC-CM-r1i1p1_rcp85",
"CMCC-CMS-r1i1p1_rcp85",
"CNRM-CM5-r10i1p1_rcp85",
"CNRM-CM5-r1i1p1_rcp85",
"CNRM-CM5-r2i1p1_rcp85",
"CNRM-CM5-r4i1p1_rcp85",
"CNRM-CM5-r6i1p1_rcp85",
"CSIRO-Mk3-6-0-r10i1p1_rcp85",
"CSIRO-Mk3-6-0-r1i1p1_rcp85",
"CSIRO-Mk3-6-0-r2i1p1_rcp85",
"CSIRO-Mk3-6-0-r3i1p1_rcp85",
"CSIRO-Mk3-6-0-r4i1p1_rcp85",
"CSIRO-Mk3-6-0-r5i1p1_rcp85",
"CSIRO-Mk3-6-0-r6i1p1_rcp85",
"CSIRO-Mk3-6-0-r7i1p1_rcp85",
"CSIRO-Mk3-6-0-r8i1p1_rcp85",
"CSIRO-Mk3-6-0-r9i1p1_rcp85",
"EC-EARTH-r12i1p1_rcp85",
"EC-EARTH-r13i1p1_rcp85",
"EC-EARTH-r14i1p1_rcp85",
## "EC-EARTH-r1i1p1_rcp85", ## corrupt scen tas files
"EC-EARTH-r2i1p1_rcp85",
"EC-EARTH-r6i1p1_rcp85",
"EC-EARTH-r8i1p1_rcp85",
"EC-EARTH-r9i1p1_rcp85",
"FGOALS-g2-r1i1p1_rcp85",
"FIO-ESM-r1i1p1_rcp85",
"FIO-ESM-r2i1p1_rcp85",
"FIO-ESM-r3i1p1_rcp85",
"GFDL-CM3-r1i1p1_rcp85",
"GFDL-ESM2G-r1i1p1_rcp85",
"GFDL-ESM2M-r1i1p1_rcp85",
"GISS-E2-H-r1i1p1_rcp85",
"GISS-E2-H-r1i1p2_rcp85",
"GISS-E2-H-r1i1p3_rcp85",
"GISS-E2-H-r2i1p1_rcp85",
"GISS-E2-H-r2i1p3_rcp85",
"GISS-E2-H-CC-r1i1p1_rcp85",
"GISS-E2-R-r1i1p1_rcp85",
"GISS-E2-R-r1i1p2_rcp85",
"GISS-E2-R-r1i1p3_rcp85",
"GISS-E2-R-r2i1p1_rcp85",
"GISS-E2-R-r2i1p3_rcp85",
"GISS-E2-R-CC-r1i1p1_rcp85",
"HadGEM2-AO-r1i1p1_rcp85",
"HadGEM2-CC-r1i1p1_rcp85",
## "HadGEM2-CC-r2i1p1_rcp85", making touble with dates in NetCDF file
## "HadGEM2-CC-r3i1p1_rcp85", making touble with dates in NetCDF file
"HadGEM2-ES-r1i1p1_rcp85",
## "HadGEM2-ES-r2i1p1_rcp85", making touble with dates in NetCDF file
## "HadGEM2-ES-r3i1p1_rcp85", making touble with dates in NetCDF file
"HadGEM2-ES-r4i1p1_rcp85",
"INM-CM4-r1i1p1_rcp85",
"IPSL-CM5A-LR-r1i1p1_rcp85",
"IPSL-CM5A-LR-r2i1p1_rcp85",
"IPSL-CM5A-LR-r3i1p1_rcp85",
"IPSL-CM5A-LR-r4i1p1_rcp85",
"IPSL-CM5A-MR-r1i1p1_rcp85",
"IPSL-CM5B-LR-r1i1p1_rcp85",
"MIROC5-r1i1p1_rcp85",
"MIROC5-r2i1p1_rcp85",
"MIROC5-r3i1p1_rcp85",
## "MIROC5-r4i1p1_rcp85", too short timeseries...
## "MIROC5-r5i1p1_rcp85",
"MIROC-ESM-r1i1p1_rcp85",
"MIROC-ESM-CHEM-r1i1p1_rcp85",
"MPI-ESM-LR-r1i1p1_rcp85",
"MPI-ESM-LR-r2i1p1_rcp85",
"MPI-ESM-LR-r3i1p1_rcp85",
"MPI-ESM-MR-r1i1p1_rcp85",
"MRI-CGCM3-r1i1p1_rcp85",
"NorESM1-M-r1i1p1_rcp85",
"NorESM1-ME-r1i1p1_rcp85",
"MRI-ESM1-r1i1p1_rcp85")
##-------------------------------------------------------------------
## rcp85
cmip5.rcp85.shortperiods.labels <- c("cmip5-rcp85-shortperiods", "CMIP5-RCP85-SHORTPERIODS")
cmip5.rcp85.shortperiods.modelnames <- c("ACCESS1-0-r1i1p1_rcp85",
"ACCESS1-3-r1i1p1_rcp85",
"BCC-CSM1-1-r1i1p1_rcp85",
"BCC-CSM1-1-m-r1i1p1_rcp85",
"BNU-ESM-r1i1p1_rcp85",
"CanESM2-r1i1p1_rcp85",
"CanESM2-r2i1p1_rcp85",
"CanESM2-r3i1p1_rcp85",
"CanESM2-r4i1p1_rcp85",
"CanESM2-r5i1p1_rcp85",
"CCSM4-r1i1p1_rcp85",
"CCSM4-r2i1p1_rcp85",
"CCSM4-r3i1p1_rcp85",
"CCSM4-r4i1p1_rcp85",
"CCSM4-r5i1p1_rcp85",
"CCSM4-r6i1p1_rcp85",
"CESM1-BGC-r1i1p1_rcp85",
"CESM1-CAM5-r1i1p1_rcp85",
"CESM1-CAM5-r2i1p1_rcp85",
"CESM1-CAM5-r3i1p1_rcp85",
"CESM1-WACCM-r2i1p1_rcp85",
"CESM1-WACCM-r3i1p1_rcp85",
"CESM1-WACCM-r4i1p1_rcp85",
"CMCC-CESM-r1i1p1_rcp85",
"CMCC-CM-r1i1p1_rcp85",
"CMCC-CMS-r1i1p1_rcp85",
"CNRM-CM5-r10i1p1_rcp85",
"CNRM-CM5-r1i1p1_rcp85",
"CNRM-CM5-r2i1p1_rcp85",
"CNRM-CM5-r4i1p1_rcp85",
"CNRM-CM5-r6i1p1_rcp85",
"CSIRO-Mk3-6-0-r10i1p1_rcp85",
"CSIRO-Mk3-6-0-r1i1p1_rcp85",
"CSIRO-Mk3-6-0-r2i1p1_rcp85",
"CSIRO-Mk3-6-0-r3i1p1_rcp85",
"CSIRO-Mk3-6-0-r4i1p1_rcp85",
"CSIRO-Mk3-6-0-r5i1p1_rcp85",
"CSIRO-Mk3-6-0-r6i1p1_rcp85",
"CSIRO-Mk3-6-0-r7i1p1_rcp85",
"CSIRO-Mk3-6-0-r8i1p1_rcp85",
"CSIRO-Mk3-6-0-r9i1p1_rcp85",
"EC-EARTH-r12i1p1_rcp85",
"EC-EARTH-r13i1p1_rcp85",
"EC-EARTH-r14i1p1_rcp85",
## "EC-EARTH-r1i1p1_rcp85", ## corrupt scen tas files
"EC-EARTH-r2i1p1_rcp85",
"EC-EARTH-r6i1p1_rcp85",
"EC-EARTH-r8i1p1_rcp85",
"EC-EARTH-r9i1p1_rcp85",
"FGOALS-g2-r1i1p1_rcp85",
"FIO-ESM-r1i1p1_rcp85",
"FIO-ESM-r2i1p1_rcp85",
"FIO-ESM-r3i1p1_rcp85",
"GFDL-CM3-r1i1p1_rcp85",
"GFDL-ESM2G-r1i1p1_rcp85",
"GFDL-ESM2M-r1i1p1_rcp85",
"GISS-E2-H-r1i1p1_rcp85",
"GISS-E2-H-r1i1p2_rcp85",
"GISS-E2-H-r1i1p3_rcp85",
"GISS-E2-H-r2i1p1_rcp85",
"GISS-E2-H-r2i1p3_rcp85",
"GISS-E2-H-CC-r1i1p1_rcp85",
"GISS-E2-R-r1i1p1_rcp85",
"GISS-E2-R-r1i1p2_rcp85",
"GISS-E2-R-r1i1p3_rcp85",
"GISS-E2-R-r2i1p1_rcp85",
"GISS-E2-R-r2i1p3_rcp85",
"GISS-E2-R-CC-r1i1p1_rcp85",
"HadGEM2-AO-r1i1p1_rcp85",
"HadGEM2-CC-r1i1p1_rcp85",
"HadGEM2-CC-r2i1p1_rcp85",
"HadGEM2-CC-r3i1p1_rcp85",
"HadGEM2-ES-r1i1p1_rcp85",
"HadGEM2-ES-r2i1p1_rcp85",
"HadGEM2-ES-r3i1p1_rcp85",
"HadGEM2-ES-r4i1p1_rcp85",
"INM-CM4-r1i1p1_rcp85",
"IPSL-CM5A-LR-r1i1p1_rcp85",
"IPSL-CM5A-LR-r2i1p1_rcp85",
"IPSL-CM5A-LR-r3i1p1_rcp85",
"IPSL-CM5A-LR-r4i1p1_rcp85",
"IPSL-CM5A-MR-r1i1p1_rcp85",
"IPSL-CM5B-LR-r1i1p1_rcp85",
"MIROC5-r1i1p1_rcp85",
"MIROC5-r2i1p1_rcp85",
"MIROC5-r3i1p1_rcp85",
"MIROC5-r4i1p1_rcp85",
"MIROC5-r5i1p1_rcp85",
"MIROC-ESM-r1i1p1_rcp85",
"MIROC-ESM-CHEM-r1i1p1_rcp85",
"MPI-ESM-LR-r1i1p1_rcp85",
"MPI-ESM-LR-r2i1p1_rcp85",
"MPI-ESM-LR-r3i1p1_rcp85",
"MPI-ESM-MR-r1i1p1_rcp85",
"MRI-CGCM3-r1i1p1_rcp85",
"NorESM1-M-r1i1p1_rcp85",
"NorESM1-ME-r1i1p1_rcp85",
"MRI-ESM1-r1i1p1_rcp85")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## ENSEMBLES RCMs
ensembles.labels <- c("ENSEMBLES", "ensembles")
ensembles.modelnames <- c("METO-HC_HadRM3Q0",
"METO-HC_HadRM3Q16",
"METO-HC_HadRM3Q3",
"ETHZ-CLM",
"METNOHIRHAM_HadCM3Q0",
"METNOHIRHAM_BCM",
"ICTP-REGCM3",
"MPI-M-REMO",
"C4IRCA3",
"CNRM-RM5.1",
"CNRM-RM4.5",
"DMI-HIRHAM5_ARPEGE",
"DMI-HIRHAM5_ECHAM5",
"DMI-HIRHAM5_BCM",
"GKSS-CCLM4.8",
"KNMI-RACMO2",
"OURANOSMRCC4.2.1",
"SMHIRCA_BCM",
"SMHIRCA_ECHAM5-r3",
"SMHIRCA_HadCM3Q3",
"UCLM-PROMES",
"VMGO-RRCM")
##-------------------------------------------------------------------
##-------------------------------------------------------------------
## ENSEMBLES RCMs QM corrected Schoener
ensembles.qmschoener.labels <- c("ENSEMBLES_QM_SCHOENER",
"ensembles_qm_schoener")
ensembles.qmschoener.modelnames <- c('METO-HC_HadRM3Q0_QM_SCHOENER',
'METO-HC_HadRM3Q16_QM_SCHOENER',
'METO-HC_HadRM3Q3_QM_SCHOENER',
'ETHZ-CLM_QM_SCHOENER',
'METNOHIRHAM_HadCM3Q0_QM_SCHOENER',
'METNOHIRHAM_BCM_QM_SCHOENER',
'ICTP-REGCM3_QM_SCHOENER',
'MPI-M-REMO_QM_SCHOENER',
'C4IRCA3_QM_SCHOENER',
'CNRM-RM5.1_QM_SCHOENER',
'CNRM-RM4.5_QM_SCHOENER',
'DMI-HIRHAM5_ARPEGE_QM_SCHOENER',
'DMI-HIRHAM5_ECHAM5_QM_SCHOENER',
'DMI-HIRHAM5_BCM_QM_SCHOENER',
'GKSS-CCLM4.8_QM_SCHOENER',
'KNMI-RACMO2_QM_SCHOENER',
'OURANOSMRCC4.2.1_QM_SCHOENER',
'SMHIRCA_BCM_QM_SCHOENER',
'SMHIRCA_ECHAM5-r3_QM_SCHOENER',
'SMHIRCA_HadCM3Q3_QM_SCHOENER',
'UCLM-PROMES_QM_SCHOENER',
'VMGO-RRCM_QM_SCHOENER',
'AIT-CCLM_QM_SCHOENER',
'WEGC-CCLM_QM_SCHOENER')
##-------------------------------------------------------------------
## extract all known modelnames from dictionary
dictionary.names <- names(model.input)
## check whether modelnames are unique, give error else
if (length(unique(dictionary.names)) != length(model.input)){
msg1 <- "MODELNAMES IN DICTIONARY MUST BE UNIQUE! "
msg2 <- "THINK ABOUT A CREATIVE MODELNAME ;)"
stop(msg1, msg2)
}
## models matching dictnames
model.input[modelnames[modelnames %in% dictionary.names]]
unknown.modelnames <- NULL
unknown.modelnames <- modelnames[! modelnames %in% dictionary.names]
known.modelnames <- modelnames[ modelnames %in% dictionary.names]
## not a single modelname is found in dictionary, set list to length 0, else
## read the funky shit!
match.modelnames <- match(known.modelnames,modelnames)
model.list <- model.input[modelnames[match.modelnames]]
## if there is at least one model not found in InitModelsDictionary then
## look it up in labels defined previously in this function
if (length(unknown.modelnames) > 0) {
if (any(unknown.modelnames %in% cmip3.sresa2.labels)) {
model.list <- c(model.list, model.input[cmip3.sresa2.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip3.sresa2.labels]
}
if (any(unknown.modelnames %in% cmip3.sresb1.labels)) {
model.list <- c(model.list, model.input[cmip3.sresb1.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip3.sresb1.labels]
}
if (any(unknown.modelnames %in% cmip3.sresa1b.labels)) {
model.list <- c(model.list, model.input[cmip3.sresa1b.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip3.sresa1b.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp26.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp26.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp26.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp26.shortperiods.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp26.shortperiods.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp26.shortperiods.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp45.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp45.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp45.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp45.shortperiods.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp45.shortperiods.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp45.shortperiods.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp60.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp60.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp60.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp60.shortperiods.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp60.shortperiods.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp60.shortperiods.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp85.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp85.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp85.labels]
}
if (any(unknown.modelnames %in% cmip5.rcp85.shortperiods.labels)) {
model.list <- c(model.list, model.input[cmip5.rcp85.shortperiods.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% cmip5.rcp85.shortperiods.labels]
}
if (any(unknown.modelnames %in% ensembles.labels)) {
model.list <- c(model.list, model.input[ensembles.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% ensembles.labels]
}
if (any(unknown.modelnames %in% ensembles.qmschoener.labels)) {
model.list <- c(model.list, model.input[ensembles.qmschoener.modelnames])
unknown.modelnames <-
unknown.modelnames[!unknown.modelnames %in% ensembles.qmschoener.labels]
}
}
## if there is still at least one model not found in the dictionary
if (length(unknown.modelnames) > 0) {
msg1 <- paste("MODEL(S) \"", paste(unknown.modelnames, collapse="\" \""),
"\" UNKNOWN\n", sep="")
msg2 <- "ADD THE MODEL TO DICTIONARY FILE, "
msg3 <- "OR ADD AS A NEW ENSEMBLE INTO ReadFromModelDictionary."
stop(msg1, msg2, msg3)
}
## there's at least one NA -name in model.list, thats wrong for sure!
if (any(is.na(names(model.list))))
stop("THERE IS AT LEAST ONE MODEL CALLED \"NA\" IN THE MODEL.INPUT.")
## empty list...
if (length(model.list) == 0)
stop("NO MODELS HAVE BEEN SELECTED")
return(model.list)
}
###################### handling WUX dataset ############################
MakeModelDataFrame <- function(model.name, institute.name,
rcm.name, global.model.name,
gcm.run,
is.reference.period,
time.step,
season = "none",
is.time.series,
subregion = "no", is.corrected = NULL,
emission.scenario = "A1B", resolution,
data, parameter.name) {
## Merges aggregated model data ("data") with all the information provided for
## the particular climate model and returns a data.frame. This is a crucial
## function where loads can go wrong, so pay EXTRA attention when changing
## this function!
##
## Args:
## model.name: Name the model shall be called
## institute.name: Name of the institute running the RCM
## rcm.name: RCM name
## global.model.name: GCM name
## is.reference.period: Is given period the reference period? (yes/no)
## season: string vector with second statistic timestamps
## e.g. seasons c("DJF", "MAM", "JJA", "SON")
## subregion: string vector with subregion names
## is.corrected: Do we have error corrected data?
## emission.scenario: string with emmission scenario,
## data: list of spatially aggregated data. It has to have EXACTLY the form:
## list of.... subregions containing named seasonal lists with
## containing aggregated data
##
## Returns:
## Data.Frame with model information and spatio-temporal aggregated data.
##
## History:
## 2010-10-20 | Original code (thm)
## 2010-11-03 | renaming variables and set default is.corrected
## "resolution" added
## 2010-11-18 | sorting arguments added (thm)
## 2010-12-15 | gcm.run added
## 2011-04-29 | restructure from MakeModelInfo to MakeModelDataFrame
## 2011-10-20 | NA case added for rcm name
## 2014-11-20 | NA case added for gcm.run and resolution
if (is.null(data))
stop("NO AGGREGATED DATA SPECIFIED")
if (is.null(parameter.name))
stop("PARAMETER NAME NOT SPECIFIED")
## setting default
##"not error corrected data"
if (is.null(is.corrected))
is.corrected <- "no"
## no resolution given
if (is.null(resolution) || !is.null(resolution) && resolution == "")
resolution <- NA
## no gcm.run given
if (is.null(gcm.run) || !is.null(gcm.run) && gcm.run == "")
gcm.run <- NA
## if no RCMs has been passed
if (is.null(rcm.name) || !is.null(rcm.name) && rcm.name == "")
rcm.name <- NA
## sort arguments to get sorted factor levels
emission.scenario <- emission.scenario[order(emission.scenario)]
subregion <- subregion[order(subregion)]
season.order <- season[order(season)]
## generating full data.frame by expanding all possibilities
WUX.df <- list(acronym = model.name,
institute = institute.name,
gcm = global.model.name,
gcm.run = gcm.run,
rcm = rcm.name,
em.scn = emission.scenario,
subreg = subregion,
period = time.step,
ref.per = is.reference.period,
season = season.order,
resolution = resolution,
corrected = is.corrected)
WUX.df <- expand.grid(WUX.df)
## aggregated data to dataframe,
data.df <- reshape::melt(data)
## these 3 dims identify the current data: the data itself, the seasons
## and the subregions
if (length(data.df) != 3) {
msg1 <- "OBVIOUSLY YOU HAVE CHANGED THE AGGREGATION DIMENSION, "
msg2 <- "WATCH OUT WITH MERGING IN MakeModelDataFrame"
stop(msg1, msg2)
}
names(data.df) <- c(parameter.name, "season", "subreg")
## merge dataframe-construct and aggregated parameter data
WUX.df <- merge(WUX.df, data.df)
## if dealing with time series split "season" into year and period
if (is.time.series) {
## extract year
WUX.df[["year"]] <-
factor(sapply(strsplit(as.character(WUX.df[["season"]]), " "), "[", 1))
## extract period
WUX.df[["season"]] <-
factor(sapply(strsplit(as.character(WUX.df[["season"]]), " "), "[", 2))
## rearrange data frame that "year" is beside "season"
season.pos <- which(names(WUX.df) == "season")
year.pos <- which(names(WUX.df) == "year")
WUX.df <- data.frame(WUX.df[1:(season.pos - 1)], year = WUX.df[year.pos],
WUX.df[season.pos:(length(names(WUX.df)) - 1)])
}
return(WUX.df)
}
MergeParameterDataframes <- function(wux.df.list, parameter.names) {
## Merges list of data.frames of different parameters of the same model
## to a single data.frame.
##
## Args:
## wux.df.list: Named list containing data.frames. Each list entry
## corresponds to one climate parameter with its name
## being the CF-convention parameter name.
## parameter.names: Character vector containing CF-convention parameter
## names to be extracted from wux.df.list.
## parameter.names have to match names(wux.df.list).
##
## Returns:
## Single data.frame with model.info columns and one column for each
## parameter.
##
## History:
## 2010-10-27 | Original code (thm)
## 2011-11-24 | added missing list entry error message (thm)
## 2011-12-21 | added case for models with empty wux.df.list (thm)
## if any list entry is NULL
if (any(sapply(wux.df.list, is.null))){
msg1 <- "AT LEAST ONE DATA.FRAME FOR ONE PARAMETER IS MISSING. "
msg2 <- "THIS REALLY SHOULD NOT HAPPPEN."
stop(msg1, msg2)
}
## number of parameters
n.of.pars <- length(parameter.names)
## sort length of parameters, longest parameter first
## --> important for merging
par.length <- c(1:n.of.pars)
for (ii in c(1:n.of.pars)) {
if (length(wux.df.list[[ii]])== 0)
par.length[ii] <- 0
else
par.length[ii] <- length(wux.df.list[[ii]][[1]])
}
sort.indices <- sort(par.length, decreasing = TRUE, index.return = TRUE)
parameter.names <- parameter.names[sort.indices$ix]
par.length <- par.length[sort.indices$ix]
## initializing dataframe with first element of list
data <- wux.df.list[[parameter.names[1]]]
## always merge pairwise
ii <- 2
while (ii <= n.of.pars) {
## case of empty dataframe (i.e. this parameter doesnt exist for this model)
if (length(wux.df.list[[parameter.names[ii]]]) == 0){
data <- cbind(data, NA)
names(data)[length(data)] <- parameter.names[ii]
} else {
data <- merge(data, wux.df.list[[parameter.names[ii]]], all.x = TRUE)
}
ii <- ii + 1
}
return(data)
}
WuxDataDiff <- function(wux.df, parameters) {
## Calculating differences between scenario period and reference period
## data values within 'wux.df' data.frame.
##
## Args:
## wux.df: WUX dataframe with ref.per values yes and no.
## user.input: input file
##
## Returns:
## WUX data.frame with cliamte change signals.
##
## History:
## 2010-10-27 | Original code (thm)
## 2010-11-24 | Only one reference period taken (thm)
## 2011-11-24 | aestethic corrections (thm)
## 2014-11-20 | all subset commands changed to '[' operation (thm)
## sort data frame by season, then by time, and at
## last by modelname (acronym)
sortby <- c("acronym", "period", "season")
wux.df <-
wux.df[order(wux.df$acronym, wux.df$period, wux.df$season),]
## initialize diffs dataset, take all columns except the parameter
## data colummns
col.noparam <- which(! names(wux.df) %in% parameters)
wux.diff.df <- wux.df[wux.df[["ref.per"]] == "no", col.noparam]
for (param in parameters) {
## initializing vector obtaining all parameter-diff values and
## percentage changes
diff.column <- NULL
perc.column <- NULL
## get times of reference and scenarios
timesteps.ref <- unique(wux.df[wux.df[["ref.per"]] == "yes", ]$period)
timesteps.scn <- unique(wux.df[wux.df[["ref.per"]] == "no" , ]$period)
## always take the refernce period as one single ref period,
## regardless of length(timesteps.ref)
number.of.timesteps.ref <- 1
number.of.timesteps.scn <- length(timesteps.scn)
## so far we can only handle one reference period....
stopifnot(number.of.timesteps.ref == 1)
## we have as many data for in the reference period as in the scenario
## period
x <- number.of.timesteps.ref / number.of.timesteps.scn
## we have x- times more scenario data than reference period data, where
## x has to be integer.
## x == round(x) tests whether we deal with integers or not (the R solution)
if(x == round(x)){
## calculate difference for every scenario timestep and the
## corresponding reference period
for (scn.period in timesteps.scn) {
scn <- wux.df[wux.df[["ref.per"]] == "no" &
wux.df[["period"]] == scn.period
, c("acronym", "season", param)]
ref <- wux.df[wux.df[["ref.per"]] == "yes", c("acronym", "season", param)]
## calculating differences / percentual change
diff <- scn[[param]] - ref[[param]]
perc <- (scn[[param]] / ref[[param]] - 1) * 100
## appending to previous timesteps
diff.column <- c(diff.column, diff)
perc.column <- c(perc.column, perc)
}
} else {
## else x was no integer (having x-times more scenario data than
## reference period data)
stop("WuxDataDiff, SOMETHING WENT SERIOUSLY WRONG")
}
## append diff column to diff dataset
wux.diff.df <- cbind(wux.diff.df, diff.column)
diff.cols <- which(names(wux.diff.df) == "diff.column")
names(wux.diff.df)[diff.cols] <- paste("delta.", param, sep="")
## get percentual change for precipitation parameter
if (param == "precipitation_amount") {
wux.diff.df <- cbind(wux.diff.df, perc.column)
perc.cols <- which(names(wux.diff.df) == "perc.column")
names(wux.diff.df)[perc.cols] <- paste("perc.delta.", param, sep="")
}
}
return(wux.diff.df)
}
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