In RS-eco/rISIMIP: R Package for processing ISIMIP data in R

knitr::opts_chunk$set(echo = TRUE, fig.width=14, fig.height=8, warning=FALSE, comment=NA, message=FALSE, eval=F)

Data Set-Up

Load rISIMIP package

library(rISIMIP)

First, we specify the file path, where the ISIMIP data is located.

# Specify path of file directory
filedir <- "/media/matt/Data/Documents/Wissenschaft/Data"

Data Pre-processing

Climate data

Read climate files

# Install processNC package
remotes::install_github("RS-eco/processNC")

Load processNC package

library(processNC)

We want to extract data for a certain region (30°W-75°E and 30°N - 90°N), but also include ocean data.

# Crop data by given extent
hurs_ns <- lapply(hurs, FUN=function(x) crop(x, y=c(-30,75,30,90)))
huss_ns <- lapply(huss, FUN=function(x) crop(x, y=c(-30,75,30,90)))
tas_ns <- lapply(tas, FUN=function(x) crop(x, y=c(-30,75,30,90)))
tmin_ns <- lapply(tmin, FUN=function(x) crop(x, y=c(-30,75,30,90)))
tmax_ns <- lapply(tmax, FUN=function(x) crop(x, y=c(-30,75,30,90)))
prec_ns <- lapply(prec, FUN=function(x) crop(x, y=c(-30,75,30,90)))
bioclim_ns <- lapply(bioclim, FUN=function(x) crop(x, y=c(-30,75,30,90)))

# Change name from .grd to _NorthSea.grd
hurs_ns_names <- gsub(x = hurs_names, pattern = "\\.grd", replacement = "_NorthSea.grd")  
huss_ns_names <- gsub(x = huss_names, pattern = "\\.grd", replacement = "_NorthSea.grd")
tas_ns_names <- gsub(x = tas_names, pattern = "\\.grd", replacement = "_NorthSea.grd")
tmin_ns_names <- gsub(x = tmin_names, pattern = "\\.grd", replacement = "_NorthSea.grd")
tmax_ns_names <- gsub(x = tmax_names, pattern = "\\.grd", replacement = "_NorthSea.grd")
prec_ns_names <- gsub(x = prec_names, pattern = "\\.grd", replacement = "_NorthSea.grd")
bioclim_ns_names <- gsub(x = bioclim_names, pattern = "\\.grd", replacement = "_NorthSea.grd")

# Save to file into NorthSea subfolder
mapply(FUN=function(x,y) writeRaster(x, filename=paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/", y)), x=hurs_ns, y=hurs_ns_names)
mapply(FUN=function(x,y) writeRaster(x, filename=paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/", y)), x=huss_ns, y=huss_ns_names)
mapply(FUN=function(x,y) writeRaster(x, filename=paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/", y)), x=tas_ns, y=tas_ns_names)
mapply(FUN=function(x,y) writeRaster(x, filename=paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/", y)), x=tmin_ns, y=tmin_ns_names)
mapply(FUN=function(x,y) writeRaster(x, filename=paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/", y)), x=tmax_ns, y=tmax_ns_names)
mapply(FUN=function(x,y) writeRaster(x, filename=paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/", y)), x=prec_ns, y=prec_ns_names)
mapply(FUN=function(x,y) writeRaster(x, filename=paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/", y)), x=bioclim_ns, y=bioclim_ns_names)

For the North Sea, we also want to have data on wind speed:

# Read and crop wind data and save to file in NorthSea subfolder
sfcWind_ns <- lapply(list.files(paste0(filedir, "/ISIMIP2b/ProcessedData/global"), 
                                pattern="monthly_sfcWind_.*\\.grd", full.names=TRUE), 
                     FUN=function(x){
                       data <- stack(x)
                       data <- crop(data, y=c(-30,75,30,90))
                       names <- basename(x)
                       name <- gsub(x = names, pattern = "\\.grd", 
                                    replacement = "_NorthSea.grd")
                       writeRaster(data, filename=paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/", name))
                     })

Data visualisation

ggmap2 package

For plotting our data with the createMap function, we need to install the ggmap2 package.

# Install ggmap2 package from Github
devtools::install_github("RS-eco/ggmap2")

Load ggmap2 package

library(ggmap2)

North Sea, hurs, 1985, ...

# Read huss NorthSea data files
hurs <- lapply(list.files(paste0(filedir, "/ISIMIP2b/ProcessedData/NorthSea/historical"), 
                                pattern="monthly_hurs_.*\\.grd", full.names=TRUE), stack)
hurs[[1]]

# Create Map
createMap(hurs[[1]], name="hurs", subnames=month.abb, split=FALSE, ncol=4, width=12, height=8, units="in", dpi=100)

Marine data

Get marine data

Currently ony uses z_annual for historical data and zall_annual for rcp2.6. z, zb, zs, zs2, zint, zint100, v3, zall stand for different depth versions.

# Climate variables
vars <- c("to", "o2", "intpp", "phy", "zooc", "so", "pic", "prsn")

# Climate models
models <- c("gfdl-esm2m", "ipsl-cm5a-lr")

# Timeframes (Current, Horizon 2050, 2080, 2100, 2150)
timeframes <- c("ref", "2050","2080","2100","2150")
startyears <- c(1970,2036,2066,2086,2136)
endyears <- c(1999,2065,2095,2115,2165)
counter <- 1

# Run summariseNC for all combinations
for(a in 1:length(vars)){
  for(b in 1:length(models)){
    for(c in 1:length(timeframes)){
      files <- listISIMIP(path=filedir, version="ISIMIP2a", type="ocean", var=vars[a],
                          model=models[b], startyear=startyears[c],
                          endyear=endyears[c])
      filename1 <- paste0(filedir, "ISIMIP2a/ProcessedData/monthly_", vars[a],"_", 
                         timeframes[c], "_", models[b], ".tif")
      filename2 <- paste0(filedir, "ISIMIP2a/ProcessedData/monthly_cv_", vars[a],"_", 
                         timeframes[c], "_", models[b], ".tif")
      if(unique(!is.na(files))){
        if(!file.exists(filename1)){
          data_sub <- summariseNC(files=files, startyear=startyears[c], tres="month", 
                                  endyear=endyears[c], filename1=filename1, 
                                  filename2=filename2, format="GTiff", 
                                  overwrite=FALSE)
        }
      }
      d <- round((counter*100/(length(vars)*length(models)*length(timeframes))), 
                 digits = 2)
      print(paste(d,"% done"))
      counter <- counter + 1
    }
  }
}