File:Beringia Baresoil mpiesm envirem lgm 25m 1.svg
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Summary
[edit]DescriptionBeringia Baresoil mpiesm envirem lgm 25m 1.svg |
English: Baresoil fraction in Beringia, during Last Glacial Maximum. |
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Date | ||||
Source | Own work | |||
Author | Merikanto | |||
SVG development InfoField |
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This image is based on PMIP MPI-ESM "baresoilFrac" data.
Data io dowsnscaled against wotldclim and envirem variables and derivatives.
Visualized with NASA Panoply.
install_libraries=FALSE
if(install_libraries==TRUE)
{
install.packages("raster")
install.packages("rgdal")
install.packages("sp")
install.packages("spatialEco")
install.packages("ncdf4")
install.packages("dissever")
install.packages("viridis")
install.packages("dplyr")
install.packages("lattice")
install.packages("RColorBrewer")
install.packages("rgeos")
install.packages("sp")
install.packages("reshape2")
install.packages("data.table")
install.packages("stringr")
install.packages("rlist")
install.packages("pipeR")
install.packages("maptools")
install.packages("gdata", dependencies=TRUE)
install.packages("abind")
install.packages("Cairo")
install.packages("pals")
install.packages("REdaS")
install.packages("easyNCDF")
install.packages("numbers")
install.packages("rasterVis")
install.packages("OceanView")
install.packages("rainfarmr")
}
library(raster)
library(rgdal)
library(ncdf4)
library(lattice)
library(maptools)
library(rgeos)
library(spatialEco)
library(dissever)
library(rainfarmr)
library(RColorBrewer)
library(viridis)
library(pals)
library(data.table)
library(stringr)
library(rlist)
library(pipeR)
library(rasterVis)
- library(OceanView)
library(sp)
library(reshape2)
library(dplyr)
library(REdaS)
library(easyNCDF)
library(numbers)
- library(gdata)
library(abind)
- bioname_11="D:/datav3/CHELSA_PMIP_CCSM4_BIO_11.tif" # temperature of coldest 3 month
- bioname_19="D:/datav3/CHELSA_PMIP_CCSM4_BIO_19.tif" ## precip of coldest 3 month
- bioname_10="D:/datav3/CHELSA_PMIP_CCSM4_BIO_11.tif"
bioname_10="D:/data_processed/beringia_chelsa_bio_lgm/bio10.nc"
bioname_5="D:/data_processed/beringia_chelsa_bio_lgm/bio5.nc"
downscale_dissever <- function (coarse_rastera, fine_stack, dismethod, samplerate)
{
print ("Dissever()")
names(fine_stack)
coarse_raster<-coarse_rastera
p1<-fine_stack$Elevation
- plot(p1)
- return(0)
coarseoro<- resample(p1, coarse_raster)
coarseoro_big<-resample(coarseoro, p1)
orodelta<-(p1-coarseoro_big)
baset1 <- resample(coarse_raster, p1)
raster_stack <- fine_stack
min_iter <- 5 # Minimum number of iterations
max_iter <- 10 # Maximum number of iterations
p_train <- samplerate # Subsampling of the initial data
oma_juttu <- dissever(coarse = coarse_raster, fine = raster_stack, method = dismethod, p = p_train, min_iter = min_iter,max_iter = max_iter, verbose=1)
orotemp<-oma_juttu$map
#tempiso<-baset1+oma_juttu$map+biassi
coarseorotemp<- resample(orotemp, coarse_raster)
coarseorotemp_big<-resample(coarseorotemp, p1)
orotempdelta<-orotemp-coarseorotemp_big
outtemp<-baset1+orotempdelta
- plot(outtemp, col=rev(rainbow(256)) )
- outtempr<-rotate(outtemp)
#plot(outtempr)
return(outtemp)
}
downscale_raster <- function (coarse_rastera, fine_rastera, method)
{
## methods: 0 delta, 1 spatialeco, 2 dissever, 3 temperature lapse 6.5 C/1 km lm
print ("Downscaler()")
coarse_raster<-coarse_rastera
fine_raster<-fine_rastera
p1<-fine_raster
p2<-fine_raster
- plot(fine_raster)
- plot(coarse_raster, col=viridis(200))
coarseoro<- resample(p1, coarse_raster)
coarseoro_big<-resample(coarseoro, p1)
orodelta<-(p1-coarseoro_big)
baset1 <- resample(coarse_raster, p1)
raster_stack <- stack(p1,p2)
min_iter <- 5 # Minimum number of iterations
max_iter <- 20 # Maximum number of iterations
p_train <- 1.0 # Subsampling of the initial data
if(method>9999)
{
method=2
}
## dissever run
if(method==2)
{
oma_juttu <- dissever(coarse = coarse_raster, fine = raster_stack, method = "glm", p = p_train, min_iter = min_iter,max_iter = max_iter, verbose=1)
orotemp<-oma_juttu$map
}
## spatialeco downscale
if(method==1)
{
oma_juttu2 <- raster.downscale(p1, coarse_raster)
orotemp<-oma_juttu2$downscale
}
- delta regression 1,1
if(method==0)
{
orotemp<-orodelta
}
- delta regression by lapse rate
if(method==3)
{
orotemp<-orodelta*0.0065*-1
}
#biassi=4
#tempiso<-baset1+oma_juttu$map+biassi
coarseorotemp<- resample(orotemp, coarse_raster)
coarseorotemp_big<-resample(coarseorotemp, p1)
orotempdelta<-orotemp-coarseorotemp_big
outtemp<-baset1+orotempdelta
- plot(outtemp, col=rev(rainbow(256)) )
- outtempr<-rotate(outtemp)
#plot(outtempr)
return(outtemp)
}
downscale_dissever <- function (coarse_rastera, fine_stack, dismethod, samplerate)
{
print ("Dissever()")
names(fine_stack)
coarse_raster<-coarse_rastera
p1<-fine_stack$Elevation
- plot(p1)
- return(0)
coarseoro<- resample(p1, coarse_raster)
coarseoro_big<-resample(coarseoro, p1)
orodelta<-(p1-coarseoro_big)
baset1 <- resample(coarse_raster, p1)
raster_stack <- fine_stack
min_iter <- 5 # Minimum number of iterations
max_iter <- 10 # Maximum number of iterations
p_train <- samplerate # Subsampling of the initial data
oma_juttu <- dissever(coarse = coarse_raster, fine = raster_stack, method = dismethod, p = p_train, min_iter = min_iter,max_iter = max_iter, verbose=1)
orotemp<-oma_juttu$map
#tempiso<-baset1+oma_juttu$map+biassi
coarseorotemp<- resample(orotemp, coarse_raster)
coarseorotemp_big<-resample(coarseorotemp, p1)
orotempdelta<-orotemp-coarseorotemp_big
outtemp<-baset1+orotempdelta
- plot(outtemp, col=rev(rainbow(256)) )
- outtempr<-rotate(outtemp)
#plot(outtempr)
return(outtemp)
}
writeout<-function(oras, outn, varnamex, varunitx, longnamex)
{
crs(oras) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
writeRaster(oras, filename=outn, overwrite=TRUE, format="CDF", varname=varnamex, varunit=varunitx,
longname=longnamex, xname="lon", yname="lat")
}
- snow
downscale_cmip5_variable <- function(dataname1, invarname1, instak1, posit, numyears, month1, methodi1, submethodi1, subaccuracu1)
{
print("Loading data ...")
nppin1 <- nc_open(dataname1)
vext1<-c(0,360,-90,90)
lok1=posit*12+month1
mara=numyears*12
stacksnow1<-stack()
for(n in 1:mara)
{
# print (".")
snow00 <- ncvar_get( nppin1, varid=invarname1,start=c(1,1,lok1), count=c(-1,-1,1) )
snow01=t(snow00)
snow02<-apply(snow01,2,rev)
snow0=raster(snow02)
extent(snow0)<-vext1
names(snow0)<-invarname1
snow2=rotate(snow0)
crs(snow2) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
stacksnow1 <- stack( stacksnow1 , snow2 )
lok1=lok1+12
}
rasnow0<-mean(stacksnow1)
print (rasnow0)
rasnow1=rasnow0
rasnow1[is.na(rasnow1)] <- 0
print("Downscaling ...")
#methodi1, submethodi1, subaccuracu1
if(methodi1==2)
{
out3<-downscale_dissever(rasnow1, instak1, submethodi1, subaccuracu1)
}
return(out3)
- loadipslnpp
}
create_stack_variables_1<-function(rext1)
{
ptopet0<-raster("d:/data_worldclim/ccsm4_lgm_nc/world_lgm_ccsm4_ptopet_25m.nc")
annprecip0<-raster("d:/data_worldclim/ccsm4_lgm_base/cclgmbi12.tif")
anntemp0<-raster("d:/data_worldclim/ccsm4_lgm_base/cclgmbi1.tif")
warmprecip0<-raster("d:/data_worldclim/ccsm4_lgm_base/cclgmbi18.tif")
warmtemp0<-raster("d:/data_worldclim/ccsm4_lgm_base/cclgmbi10.tif")
topowet0<-raster("d:/data_worldclim/ccsm4_lgm_base/lgm_2-5arcmin_topoWet.tif")
gdd00<-raster("d:/data_worldclim/ccsm4_lgm_base/lgm_ccsm4_2-5arcmin_growingDegDays0.tif")
## bio 18 warmest precip
## bio 10 warmest temp
ptopet1<<-crop(ptopet0, rext1)
annprecip1<<-crop(annprecip0, rext1)
anntemp1<<-crop(anntemp0, rext1)
warmprecip1<<-crop(warmprecip0, rext1)
warmtemp1<<-crop(warmtemp0, rext1)
topowet1<<-crop(topowet0, rext1)
gdd01<<-crop(gdd00, rext1)
names(ptopet1)<<-"PTOPET"
names(annprecip1)<<-"PrecipAnn"
names(anntemp1)<<-"TempAnn"
names(warmprecip1)<<-"PrecipWarm"
names(warmtemp1)<<-"TempWarm"
names(topowet1)<<-"Topowet"
names(gdd01)<<-"GDD0"
- NOTE first raster must be nameed "Elevation" , due to subroutine implementation
names(anntemp1)<-"Elevation"
dstak1<-stack(anntemp1, annprecip1, ptopet1,topowet1)
## note remove NA
# dstak1[is.na(dstak1)] <- 0
return(dstak1)
}
- program init
infilname1<-"d:/datav3/lai_Lmon_MPI-ESM-P_lgm_r1i1p2_185001-194912.nc"
invarname1<-"lai"
posit=0
numyears=8
month1=7
rext1<-c(-180,-120,50,80) # beringia
methodi1=2
submethodi1="lm"
subaccuracu1=1.0
instak1<-create_stack_variables_1(rext1)
rds1<-downscale_cmip5_variable(infilname1, invarname1, instak1, posit, numyears, month1, methodi1, submethodi1, subaccuracu1)
rds2<-rds1
rds2[rds2<0] <- 0
writeout(rds2,"./lai_mipesm_ds.nc","LAI (LGM MPI-ESM)", "MPI-ESM", "Leaf Area Index, Last Glacial Maximum, Beringia")
infilname2<-"d:/datav3/baresoilFrac_Lmon_MPI-ESM-P_lgm_r1i1p1_185001-194912.nc"
invarname2<-"baresoilFrac"
posit=0
numyears=8
month1=7
rext2<-c(-180,-120,50,80) # beringia
methodi1=2
submethodi1="glm"
subaccuracu1=1.0
- instak1<-create_stack_variables_1(rext1)
- ptopet1<<-crop(ptopet0, rext1)
- names(rds2)<-"Elevation"
- instak2<-stack(anntemp1, warmprecip1, ptopet1)
print (instak1)
rds3<-downscale_cmip5_variable(infilname2, invarname2, instak1, posit, numyears, month1, methodi1, submethodi1, subaccuracu1)
rds4<-rds3
rds4[rds4<0] <- 0
writeout(rds4,"./baresoilfrac_mipesm_ds.nc","baresoilfrac (LGM MPI-ESM)", "MPI-ESM", "Fraction of Bare Soil, Last Glacial Maximum, Beringia")
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- Under the following conditions:
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