File:Beringia npp ipsl ds 2.svg

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Captions

Captions

NPP Beringia LGM kg/ha C

Summary

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Description
English: NPP in Beringia at Last Glacial Maximum, kg/ha pure carbon (kg ha-1 C)
Date
Source Own work
Author Merikanto
SVG development
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Camera location65° 00′ 00″ N, 150° 00′ 00″ W  Heading=1° Kartographer map based on OpenStreetMap.View this and other nearby images on: OpenStreetMapinfo

This image is geneterad from IPSL PMIP data. Downscaling against Chelsa CCSM3 data Processing with R scripts. Visualizing with NASA Panoply

Chelsa data

library(raster)

indirrax1="D:/datav4/"

indirrax2="D:/data_processed/beringia_chelsa_ccsm4_lgm/" indirrax3="D:/data_processed/beringia_chelsa_bio_lgm/"

  1. indirrax2="D:/data_processed/beringia_chelsa_current/"
  2. indirrax3="D:/data_processed/beringia_chelsa_bio_current/"
    1. inpath_etopo1<-"D:/datavarasto/etopo1.nc"

swename1="swe.nc" shname1="snowheight.nc" swiname1="chelsa_lgm_swi.nc"

simuname1="CCSM4" varname1="prec" varname2="tmean"

    1. East Beringia

lon1=-180 lon2=-120 lat1=50.0 lat2=80.0

ext11a<- extent(lon1,lon2,lat1,lat2)

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")

}

chelsa_pmipname1<-function(indirrax1, simuname1, varnamx1,month1) { 

 retus1=""
 numus1=sprintf("%02i",month1)
 
 retus1=paste0(indirrax1,"CHELSA_PMIP_",simuname1,"_",varnamx1,"_",numus1,".tif")	

 return(retus1)

}

chelsa_pmipname2<-function(indirrax1, simuname1, varnamx1,month1) { 

 retus1=""
 numus1=sprintf("%i",month1)

 retus1=paste0(indirrax1,"CHELSA_PMIP_",simuname1,"_",varnamx1,"_",numus1,"_1.tif")	

 return(retus1)

}

chelsa_pmipbioname1<-function(indirrax1, simuname1, varnamx1,month1) { 

 retus1=""
 numus1=sprintf("%02i",month1)
 #"CHELSA_PMIP_CCSM4_BIO_01"
 
 
 retus1=paste0(indirrax1,"CHELSA_PMIP_",simuname1,"_BIO_",numus1,".tif")	

 return(retus1)

}

acquire_chelsa_lgm_monthly_files<-function(indirrax1,indirrax2) { dir.create(indirrax2) for (montho1 in 1:12) { finame1=chelsa_pmipname2(indirrax1, simuname1, varname1,montho1) finame2=chelsa_pmipname1(indirrax1, simuname1, varname2,montho1) foname1=paste0(indirrax2,varname1,montho1) foname2=paste0(indirrax2,varname2,montho1) print(finame1) print(finame2) preci0<-raster(finame1) tempe0<-raster(finame2) prec1<-crop(preci0, ext11a) temp1<-crop(tempe0, ext11a) writeout(prec1,foname1,varname1, "mm/month", "IceAgePrecip") writeout(temp1,foname2,varname2, "tempunits", "IceAgeTemp") }

}

acquire_chelsa_lgm_bio_files<-function(indirrax1,indirrax2) { dir.create(indirrax3) for (montho1 in 1:19) { finame1=chelsa_pmipbioname1(indirrax1, simuname1, varname1,montho1) foname1=paste0(indirrax3,"bio",montho1) biona1=paste0("bio",montho1) print(finame1) bio0<-raster(finame1) bio1<-crop(bio0, ext11a) writeout(bio1,foname1,biona1, "mm/month", biona1) }

}

calculate_lgm_snow_swe<-function(indirrax2) {

foname2=paste0(indirrax2,varname2,1,".nc") print(foname2) swe0<-raster(foname2) 

   swe0<-swe0*0.0

for (montho1 in 1:12) { foname1=paste0(indirrax2,varname1,montho1,".nc") foname2=paste0(indirrax2,varname2,montho1,".nc") print(foname1) print(foname2) preci00<-raster(foname1) tempe0<-raster(foname2) preci0<-resample(preci00, tempe0) tempe0[tempe0<2730]<-1.0 tempe0[tempe0>2729]<-0.0 swe0=swe0+((tempe0*preci0)/10) }

writeout(swe0,swename1,"SWE", "mm", "swe") }

chelsa_current_temp_name<-function(indirrax1,month1) { 

 retus1=""
 numus1=sprintf("%02i",month1)
 #CHELSA_temp10_01_1979-2013_V1.2_land
 
 retus1=paste0(indirrax1,"CHELSA_temp10_",numus1,"_1979-2013_V1.2_land.tif")	

 return(retus1)

}

chelsa_current_precip_name<-function(indirrax1,month1) { 

 retus1=""
 numus1=sprintf("%02i",month1)

 #CHELSA_prec_01_V1.2_land



 retus1=paste0(indirrax1,"CHELSA_prec_",numus1,"_V1.2_land.tif")	

 return(retus1)

}

chelsa_current_bio_name<-function(indirrax1,month1) { 

 retus1=""
 numus1=sprintf("%02i",month1)
  1. CHELSA_bio10_01


 retus1=paste0(indirrax1,"CHELSA_bio10_",numus1,".tif")	

 return(retus1)

}

acquire_chelsa_current_monthly_files<-function(indirrax1,indirrax2) { dir.create(indirrax2) for (montho1 in 1:12) { #finame1=chelsa_pmipname2(indirrax1, simuname1, varname1,montho1) #finame2=chelsa_pmipname1(indirrax1, simuname1, varname2,montho1) finame2=chelsa_current_temp_name(indirrax1,montho1) finame1=chelsa_current_precip_name(indirrax1,montho1) foname1=paste0(indirrax2,varname1,montho1) foname2=paste0(indirrax2,varname2,montho1) print(finame1) print(finame2) preci0<-raster(finame1) tempe0<-raster(finame2) prec1<-crop(preci0, ext11a) temp1<-crop(tempe0, ext11a) writeout(prec1,foname1,varname1, "mm/month", "Precip") writeout(temp1,foname2,varname2, "Kmul0", "AgeTemp") }

}

acquire_chelsa_current_bio_files<-function(indirrax1,indirrax2) { dir.create(indirrax3) for (montho1 in 1:19) { #finame1=chelsa_pmipbioname1(indirrax1, simuname1, varname1,montho1) finame1<-chelsa_current_bio_name(indirrax1,montho1) foname1=paste0(indirrax3,"bio",montho1) biona1=paste0("bio",montho1) print(finame1) bio0<-raster(finame1) bio1<-crop(bio0, ext11a) writeout(bio1,foname1,biona1, "mm/month", biona1) }

}

calculate_current_snow_swe<-function(indirrax2) {

foname2=paste0(indirrax2,varname2,1,".nc") print(foname2) swe0<-raster(foname2) 

   swe0<-swe0*0.0

for (montho1 in 1:12) { foname1=paste0(indirrax2,varname1,montho1,".nc") foname2=paste0(indirrax2,varname2,montho1,".nc") print(foname1) print(foname2) preci00<-raster(foname1) tempe0<-raster(foname2) preci0<-resample(preci00, tempe0) tempe0[tempe0<2730]<-1.0 tempe0[tempe0>2729]<-0.0 swe0=swe0+((tempe0*preci0)/10) }

writeout(swe0,swename1,"SWE", "mm", "swe") }

calculate_snow_depth_test<-function() { swi0<-raster(swename1) # 25% #sh0=swi0*4 # umiat ## fairbanks test only sh0=swi0*4/3

writeout(sh0,shname1,"SNOWH", "cm", "showh") }

calculate_lgm_swi<-function(indirrax2) {

foname2=paste0(indirrax2,varname2,1,".nc") print(foname2) swi0<-raster(foname2) 

   swi0<-swi0*0.0

for (montho1 in 1:12) { #foname1=paste0(indirrax2,varname1,montho1,".nc") foname2=paste0(indirrax2,varname2,montho1,".nc") #print(foname1) print(foname2) #preci00<-raster(foname1) tempe0<-raster(foname2) #preci0<-resample(preci00, tempe0) tempe0=(tempe0-2730)/10 tempe0[tempe0<0.0]<-0.0 swi0=swi0+tempe0 }

writeout(swi0,swiname1,"SWI", "monthC", "Sumner Warmth Index") }

    1. lgm
  2. acquire_chelsa_lgm_bio_files(indirrax1,indirrax3)
  3. acquire_chelsa_lgm_monthly_files(indirrax1, indirrax2)
  4. calculate_lgm_snow_swe(indirrax2)
    1. current
  2. acquire_chelsa_current_bio_files(indirrax1,indirrax3)
  3. acquire_chelsa_current_monthly_files(indirrax1, indirrax2)
  4. calculate_current_snow_swe(indirrax2)
  1. calculate_snow_depth_test()

calculate_lgm_swi(indirrax2)

NPP extract

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

ipsl_npp_load_ds <- function(dirra2, posit, numyears) { 

   print("IPSL NPP ...")
   
   
   nppname1="d:/datav3/npp_Lmon_MPI-ESM-P_lgm_r1i1p1_185001-194912.nc"
  #nppname1="d:/datav3/npp_Lmon_IPSL-CM5A-LR_lgm_r1i1p1_260101-280012.nc"
  
  ## nppname1="d:/datav3/b40.lgm21ka.1deg.003M.clm2.h0.AGNPP.187001-190012.nc"
# nppname1="d:/datav3/b40.lgm21ka.1deg.003M.clm2.h0.NPP.187001-190012.nc"

nppin1 <- nc_open(nppname1)

  1. snow0 <- ncvar_get( snin1, varid='snd',start=c(1,1,lokat1), count=c(-1,-1,nummero1) )

vext1<-c(0,360,-90,90)

    1. !! note: we take snow of month 3, March
  lok1=posit*12
  mara=numyears*12
  stacksnow1<-stack()
  
  invarname1="npp"

  for(n in 1:mara)
   {

# print (".") snow00 <- ncvar_get( nppin1, varid=invarname1,start=c(1,1,n), 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+1

} 

   rasnow0<-mean(stacksnow1)

   ## g m-2 s -1
   rasnow1=rasnow0*12*30.5*24*3600  # g m-2 yr : mm dd hh minsec

	crs(stacksnow1) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

writeRaster(stacksnow1, "nppmany_ipsl.nc", overwrite=TRUE, format="CDF", xname="lon", yname="lat", varname="NPP", varunit="gm2", 

 longname="NPP")     
       
  	crs(rasnow1) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

writeRaster(rasnow1, "npp_ipsl.nc", overwrite=TRUE, format="CDF", xname="lon", yname="lat", varname="NPP", varunit="gm2", 

 longname="NPP")

print("Downscaling NPP ...") 


   bio5<-raster(bioname_5)
   bio10<-raster(bioname_10)
  1. names(bio10)<-"Elevation"
  1. names(bio5)<-"bio5"


#  rastafari1<-stack(bio10, bio5)

 # swi1<-raster("./wanha1/summerwarmth_beringia.nc")
    swi1<-raster("./wanha1/chelsa_lgm_swi.nc")

#print(names(rastafari1))

out3<-downscale_raster(rasnow1, swi1,2)

#out3<<-downscale_dissever(rasnow1, rastafari1,"glm",1.0)

## kg/m2 to kg/ha C out4<<-out3*10000

writeout(out3,"./npp_mpiesm_ds.nc","NPP", "g/m2_C", "NPP") writeout(out4,"./npp_mpiesm_c_kgha.nc","NPP", "kg/ha_C", "NPP")


    1. loadipslnpp
 }
    1. program init

ipsl_npp_load_ds(dirra2, 0,40)

Licensing

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I, the copyright holder of this work, hereby publish it under the following license:
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