File:Beringia Baresoil mpiesm envirem lgm 25m 1.svg

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Captions

Captions

Baresoil fraction percent in Beringia, Last Glacial Maximum

Summary

[edit]
Description
English: Baresoil fraction in Beringia, during Last Glacial Maximum.
Date
Source Own work
Author Merikanto
SVG development
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This map was created with Adobe Illustrator by Merikanto.
This map is not a real SVG file and is just a wrapper containing one or more raster graphics without vector coding.
Using SVG as just a wrapper is undesirable.
Note: This template should be used if the SVG file contains only raster graphics. If the SVG file contains both vector and raster graphics, then use {{BadSVG}}.

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)

  1. library(OceanView)

library(sp) library(reshape2)

library(dplyr) library(REdaS) library(easyNCDF) library(numbers)

  1. library(gdata)

library(abind)

  1. bioname_11="D:/datav3/CHELSA_PMIP_CCSM4_BIO_11.tif" # temperature of coldest 3 month
  2. bioname_19="D:/datav3/CHELSA_PMIP_CCSM4_BIO_19.tif" ## precip of coldest 3 month
  1. 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


  1. plot(p1)
  1. 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

  1. plot(outtemp, col=rev(rainbow(256)) )
  1. 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

  1. plot(fine_raster)
  2. 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 }

    1. delta regression 1,1

if(method==0) {

orotemp<-orodelta 

   	}
    1. 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

  1. plot(outtemp, col=rev(rainbow(256)) )
  1. 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


  1. plot(p1)
  1. 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

  1. plot(outtemp, col=rev(rainbow(256)) )
  1. 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")

}

    1. 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)
    1. 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"
    1. 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)

}

    1. 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

    1. instak1<-create_stack_variables_1(rext1)
  1. ptopet1<<-crop(ptopet0, rext1)
  1. names(rds2)<-"Elevation"
  1. 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")

Licensing

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I, the copyright holder of this work, hereby publish it under the following license:
w:en:Creative Commons
attribution share alike
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You are free:
  • to share – to copy, distribute and transmit the work
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Under the following conditions:
  • attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.

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