vignettes/global-means.R
In RS-eco/rISIMIP: R Package for processing ISIMIP data in R
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo=T, warning=F, comment=NA, message=F, eval=F)
## -----------------------------------------------------------------------------
# library(rISIMIP)
# library(ggplot2)
# library(dplyr)
## ----global_options-----------------------------------------------------------
# # Specify path of file directory
# filedir <- "/media/matt/Data/Documents/Wissenschaft/Data/"
## ---- eval=FALSE--------------------------------------------------------------
# data(yearmean_tas, package="rISIMIP")
# data(runmean31_tas, package="rISIMIP")
## ---- tasmean_EWEMBI, fig.width=9, fig.height=6, dpi=600----------------------
# # Load temperature files
# tas_ewembi_files <- list.files(
# paste0(filedir, "/ISIMIP2b/EWEMBI"),
# pattern="^tas_", full.names=TRUE, recursive=T)
# outfiles <- sub("tas_", "yearmean_tas_", sub("EWEMBI", "DerivedInputData/globalmeans/EWEMBI", tas_ewembi_files))
#
# landseamask <- paste0(filedir, "ISIMIP2b/InputData/landseamask/ISIMIP2b_landseamask_generic.nc4")
# mapply(FUN=function(x,y) {
# if(!file.exists(y)){system(paste0('cdo fldmean -yearmean -ifthen ', landseamask, " ", x, ' ', y))}}, x=tas_ewembi_files, y=outfiles); rm(tas_ewembi_files, landseamask)
#
# # Read outfiles into one data.frame
# tas <- lapply(outfiles, function(x){
# nc <- ncdf4::nc_open(x)
# tas <- ncdf4::ncvar_get(nc)
# tas <- as.data.frame(tas)
# timeref <- as.Date(strsplit(nc$dim[[1]]$units, " ")[[1]][3])
# if(ncdf4::ncvar_get(nc, nc$dim$time)[1] == 0){
# tas$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time)
# } else if (ncdf4::ncvar_get(nc, nc$dim$time)[1] != 0){
# tas$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time) - 1
# }
# ncdf4::nc_close(nc)
# return(tas)
# })
# tas <- dplyr::bind_rows(tas); rm(outfiles)
#
# # Create plot
# tas$year <- as.Date(tas$year)
# library(ggplot2)
# ggplot(data=tas, aes(x=year, y=tas-273.15)) + geom_line() +
# labs(x="Year", y="Mean global annual mean temperature (°C)") +
# scale_x_date() + theme_bw() + theme(strip.background= element_blank(),
# strip.placement= "outside",
# legend.position = c(0.9,0.905),
# legend.title = element_blank(),
# legend.background = element_rect(fill = NA),
# panel.spacing.x=unit(0.25, "lines"),
# panel.spacing.y=unit(0.25, "lines"))
## -----------------------------------------------------------------------------
#
# # Compare mean 31-yr average temperature for 2050, 2080 among RCPs
# library(dplyr)
# runsub <- runmean31_tas %>% filter(scenario %in% c("rcp26", "rcp60")) %>%
# filter(year %in% c("2050", "2080")) %>% group_by(year, scenario, model)
# colnames(runsub)[2] <- "tas"
# runsub$tas <- runsub$tas - 286.7891
#
# # Calculate 31yr running mean from EWEMBI data
# tas$year <- lubridate::year(tas$year)
# runmean31_tas <- zoo::rollapply(zoo::as.zoo(tas[,c("tas", "year")]), 31, mean, na.rm=TRUE, partial=TRUE)
# runmean31_tas <- as.data.frame(runmean31_tas)
# runmean31_tas[runmean31_tas$year == 1995,]
## -----------------------------------------------------------------------------
# # List tasmax files
# tasmax_files <- list.files(
# paste0(filedir, "/ISIMIP2b/InputData/landonly"),
# pattern="^tasmax_.*.nc4", full.names=TRUE, recursive=T)
# # Create outfile names
# outfiles <- sub("tasmax_day", "yearmax_tas", sub("InputData/landonly", "DerivedInputData/globalmeans/", tasmax_files))
#
# # Load landsea mask
# landseamask <- paste0(filedir, "ISIMIP2b/InputData/landseamask/ISIMIP2b_landseamask_generic.nc4")
#
# # Calculate global mean annual maximum temperature
# mapply(FUN=function(x,y) {if(!file.exists(y)){system(paste0('cdo fldmean -yearmax -ifthen ', landseamask, " ", x, ' ', y))}}, x=tasmax_files, y=outfiles)
# # Without landsea subset
# mapply(FUN=function(x,y) {if(!file.exists(y)){system(paste0('cdo fldmean -yearmax ', x, ' ', y))}}, x=tasmax_files, y=outfiles)
#
# # Save outfiles in csv file
# yearmax_tas <- lapply(outfiles, function(x){
# nc <- ncdf4::nc_open(x)
# tasmax <- ncdf4::ncvar_get(nc)
# tasmax <- as.data.frame(tasmax)
# timeref <- as.Date(strsplit(nc$dim[[1]]$units, " ")[[1]][3])
# if(ncdf4::ncvar_get(nc, nc$dim$time)[1] == 0){
# tasmax$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time)
# } else if (ncdf4::ncvar_get(nc, nc$dim$time)[1] != 0){
# tasmax$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time) - 1
# }
# ncdf4::nc_close(nc)
# tasmax$model <- strsplit(x, split="_")[[1]][3]
# tasmax$scenario <- strsplit(x, split="_")[[1]][4]
# return(tasmax)
# })
# yearmax_tas <- do.call(rbind, tasmax)
## -----------------------------------------------------------------------------
# pr_files <- list.files(
# paste0(filedir, "/ISIMIP2b/InputData/landonly"),
# pattern="^pr_.*.nc4", full.names=TRUE, recursive=T)
# outfiles <- sub("pr_day", "yearsum_pr", sub("InputData/landonly", "DerivedInputData/globalmeans", pr_files))
#
# landseamask <- paste0(filedir, "ISIMIP2b/InputData/landseamask/ISIMIP2b_landseamask_generic.nc4")
#
# mapply(FUN=function(x,y) {if(!file.exists(y)){system(paste0('cdo fldmean -yearsum -ifthen ', landseamask, " ", x, ' ', y))}}, x=pr_files, y=outfiles)
# # Without landsea subset
# mapply(FUN=function(x,y) {if(!file.exists(y)){system(paste0('cdo fldmean -yearsum ', x, ' ', y))}}, x=pr_files, y=outfiles)
#
# # Save outfiles in csv file
# yearsum_pr <- lapply(outfiles, function(x){
# nc <- ncdf4::nc_open(x)
# pr <- ncdf4::ncvar_get(nc)
# pr <- as.data.frame(pr)
# timeref <- as.Date(strsplit(nc$dim[[1]]$units, " ")[[1]][3])
# if(ncdf4::ncvar_get(nc, nc$dim$time)[1] == 0){
# pr$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time)
# } else if (ncdf4::ncvar_get(nc, nc$dim$time)[1] != 0){
# pr$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time) - 1
# }
# ncdf4::nc_close(nc)
# pr$model <- strsplit(x, split="_")[[1]][3]
# pr$scenario <- strsplit(x, split="_")[[1]][4]
# return(pr)
# })
# yearsum_pr <- do.call(rbind, yearsum_pr)
## ----runmean31----------------------------------------------------------------
# # Calculate 31yr running mean
# runmean31_tasmax <- lapply(unique(yearmax_tas$model), function(x){
# data_agg <- lapply(unique(yearmax_tas$scenario), function(y){
# data <- yearmax_tas[yearmax_tas$model == x,]
# data <- data[data$scenario == y,]
# data$year <- lubridate::year(data$year)
# data_mean <- zoo::rollapply(zoo::as.zoo(data[,c("tasmax", "year")]), 31, mean, na.rm=TRUE, partial=TRUE)
# data_mean <- as.data.frame(data_mean)
# data_mean$scenario <- y
# return(data_mean)
# })
# data_agg <- do.call("rbind", data_agg)
# data_agg$model <- x
# return(data_agg)
# })
# runmean31_tasmax <- do.call("rbind", runmean31_tasmax)
#
# # Calculate 31yr running mean
# runmean31_prsum <- lapply(unique(yearsum_pr$model), function(x){
# data_agg <- lapply(unique(yearsum_pr$scenario), function(y){
# data <- yearsum_pr[yearsum_pr$model == x,]
# data <- data[data$scenario == y,]
# data$year <- lubridate::year(data$year)
# data_mean <- zoo::rollapply(zoo::as.zoo(data[,c("pr", "year")]), 31, mean, na.rm=TRUE, partial=TRUE)
# data_mean <- as.data.frame(data_mean)
# data_mean$scenario <- y
# return(data_mean)
# })
# data_agg <- do.call("rbind", data_agg)
# data_agg$model <- x
# return(data_agg)
# })
# runmean31_prsum <- do.call("rbind", runmean31_prsum)
## ----tasmax_change, fig.width=9, fig.height=6, dpi=600------------------------
# # Create simple plot
# yearmax_tas$year <- lubridate::year(yearmax_tas$year)
# ggplot(data=yearmax_tas, aes(x=year, y=tasmax-273.15, colour=scenario)) + geom_line() +
# facet_wrap(~ model, scales="free_y", ncol=1) +
# #facet_grid(var~model, scales="free_y", switch="y") +
# labs(x="Year", y="Mean global annual maximum temperature (°C)") +
# scale_x_continuous(limits=c(1650, 2310), expand=c(0,0),
# breaks=c(1700, 1800, 1900, 2000, 2100, 2200, 2300)) +
# #scale_colour_manual(values=c("black", "darkgrey", "blue", "yellow")) +
# theme_bw() + theme(strip.background= element_blank(),
# strip.placement= "outside",
# legend.position = c(0.9,0.905),
# legend.title = element_blank(),
# legend.background = element_rect(fill = NA),
# panel.spacing.x=unit(0.25, "lines"),
# panel.spacing.y=unit(0.25, "lines"))
## ----prsum_change, fig.width=9, fig.height=6, dpi=600-------------------------
# # Create simple plot
# yearsum_pr$year <- lubridate::year(yearsum_pr$year)
# ggplot(data=yearsum_pr, aes(x=year, y=pr*86400, colour=scenario)) + geom_line() +
# facet_wrap(~ model, scales="free_y", ncol=1) +
# labs(x="Year", y="Mean global annual total precipitation (mm)") +
# #scale_x_date() +
# scale_x_continuous(limits=c(1650, 2310), expand=c(0,0),
# breaks=c(1700, 1800, 1900, 2000, 2100, 2200, 2300)) +
# #scale_colour_manual(values=c("black", "darkgrey", "blue", "yellow")) +
# theme_bw() + theme(strip.background= element_blank(),
# strip.placement= "outside",
# legend.position = c(0.9,0.905),
# legend.title = element_blank(),
# legend.background = element_rect(fill = NA),
# panel.spacing.x=unit(0.25, "lines"),
# panel.spacing.y=unit(0.25, "lines"))
## ----pr_tasmax_ipsl-cm5a-lr, width=9, height=6, dpi=600-----------------------
# library(dplyr)
# yearsum_ipsl <- yearsum_pr %>% filter(model == "IPSL-CM5A-LR")
# yearsum_ipsl$var <- "pr"
# colnames(yearsum_ipsl)[1] <- "value"
# yearsum_ipsl$value <- yearsum_ipsl$value*86400
# yearmax_ipsl <- yearmax_tas %>% filter(model == "IPSL-CM5A-LR")
# yearmax_ipsl$var <- "tasmax"
# colnames(yearmax_ipsl)[1] <- "value"
# yearmax_ipsl$value <- yearmax_ipsl$value-273.15
# yearvar_ipsl <- bind_rows(yearsum_ipsl, yearmax_ipsl)
# ggplot(data=yearvar_ipsl, aes(x=year, y=value, colour=scenario)) +
# geom_line() + facet_wrap(~ var, scales="free_y", ncol=1,
# strip.position="left", labeller= as_labeller(c(pr="Mean global annual total precipitation (mm)", tasmax="Mean global annual maximum temperature (°C)"))) +
# labs(x="Year", y="") +
# scale_x_date() +
# #scale_colour_manual(values=c("black", "darkgrey", "blue", "yellow")) +
# theme_bw() + theme(strip.background= element_blank(),
# strip.placement= "outside",
# legend.position = c(0.06,0.9),
# legend.title = element_blank(),
# legend.background = element_rect(fill = NA),
# panel.spacing.x=unit(0.25, "lines"),
# panel.spacing.y=unit(0.25, "lines"))
RS-eco/rISIMIP documentation built on Oct. 31, 2022, 2:26 a.m.
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## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo=T, warning=F, comment=NA, message=F, eval=F)
## -----------------------------------------------------------------------------
# library(rISIMIP)
# library(ggplot2)
# library(dplyr)
## ----global_options-----------------------------------------------------------
# # Specify path of file directory
# filedir <- "/media/matt/Data/Documents/Wissenschaft/Data/"
## ---- eval=FALSE--------------------------------------------------------------
# data(yearmean_tas, package="rISIMIP")
# data(runmean31_tas, package="rISIMIP")
## ---- tasmean_EWEMBI, fig.width=9, fig.height=6, dpi=600----------------------
# # Load temperature files
# tas_ewembi_files <- list.files(
# paste0(filedir, "/ISIMIP2b/EWEMBI"),
# pattern="^tas_", full.names=TRUE, recursive=T)
# outfiles <- sub("tas_", "yearmean_tas_", sub("EWEMBI", "DerivedInputData/globalmeans/EWEMBI", tas_ewembi_files))
#
# landseamask <- paste0(filedir, "ISIMIP2b/InputData/landseamask/ISIMIP2b_landseamask_generic.nc4")
# mapply(FUN=function(x,y) {
# if(!file.exists(y)){system(paste0('cdo fldmean -yearmean -ifthen ', landseamask, " ", x, ' ', y))}}, x=tas_ewembi_files, y=outfiles); rm(tas_ewembi_files, landseamask)
#
# # Read outfiles into one data.frame
# tas <- lapply(outfiles, function(x){
# nc <- ncdf4::nc_open(x)
# tas <- ncdf4::ncvar_get(nc)
# tas <- as.data.frame(tas)
# timeref <- as.Date(strsplit(nc$dim[[1]]$units, " ")[[1]][3])
# if(ncdf4::ncvar_get(nc, nc$dim$time)[1] == 0){
# tas$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time)
# } else if (ncdf4::ncvar_get(nc, nc$dim$time)[1] != 0){
# tas$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time) - 1
# }
# ncdf4::nc_close(nc)
# return(tas)
# })
# tas <- dplyr::bind_rows(tas); rm(outfiles)
#
# # Create plot
# tas$year <- as.Date(tas$year)
# library(ggplot2)
# ggplot(data=tas, aes(x=year, y=tas-273.15)) + geom_line() +
# labs(x="Year", y="Mean global annual mean temperature (°C)") +
# scale_x_date() + theme_bw() + theme(strip.background= element_blank(),
# strip.placement= "outside",
# legend.position = c(0.9,0.905),
# legend.title = element_blank(),
# legend.background = element_rect(fill = NA),
# panel.spacing.x=unit(0.25, "lines"),
# panel.spacing.y=unit(0.25, "lines"))
## -----------------------------------------------------------------------------
#
# # Compare mean 31-yr average temperature for 2050, 2080 among RCPs
# library(dplyr)
# runsub <- runmean31_tas %>% filter(scenario %in% c("rcp26", "rcp60")) %>%
# filter(year %in% c("2050", "2080")) %>% group_by(year, scenario, model)
# colnames(runsub)[2] <- "tas"
# runsub$tas <- runsub$tas - 286.7891
#
# # Calculate 31yr running mean from EWEMBI data
# tas$year <- lubridate::year(tas$year)
# runmean31_tas <- zoo::rollapply(zoo::as.zoo(tas[,c("tas", "year")]), 31, mean, na.rm=TRUE, partial=TRUE)
# runmean31_tas <- as.data.frame(runmean31_tas)
# runmean31_tas[runmean31_tas$year == 1995,]
## -----------------------------------------------------------------------------
# # List tasmax files
# tasmax_files <- list.files(
# paste0(filedir, "/ISIMIP2b/InputData/landonly"),
# pattern="^tasmax_.*.nc4", full.names=TRUE, recursive=T)
# # Create outfile names
# outfiles <- sub("tasmax_day", "yearmax_tas", sub("InputData/landonly", "DerivedInputData/globalmeans/", tasmax_files))
#
# # Load landsea mask
# landseamask <- paste0(filedir, "ISIMIP2b/InputData/landseamask/ISIMIP2b_landseamask_generic.nc4")
#
# # Calculate global mean annual maximum temperature
# mapply(FUN=function(x,y) {if(!file.exists(y)){system(paste0('cdo fldmean -yearmax -ifthen ', landseamask, " ", x, ' ', y))}}, x=tasmax_files, y=outfiles)
# # Without landsea subset
# mapply(FUN=function(x,y) {if(!file.exists(y)){system(paste0('cdo fldmean -yearmax ', x, ' ', y))}}, x=tasmax_files, y=outfiles)
#
# # Save outfiles in csv file
# yearmax_tas <- lapply(outfiles, function(x){
# nc <- ncdf4::nc_open(x)
# tasmax <- ncdf4::ncvar_get(nc)
# tasmax <- as.data.frame(tasmax)
# timeref <- as.Date(strsplit(nc$dim[[1]]$units, " ")[[1]][3])
# if(ncdf4::ncvar_get(nc, nc$dim$time)[1] == 0){
# tasmax$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time)
# } else if (ncdf4::ncvar_get(nc, nc$dim$time)[1] != 0){
# tasmax$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time) - 1
# }
# ncdf4::nc_close(nc)
# tasmax$model <- strsplit(x, split="_")[[1]][3]
# tasmax$scenario <- strsplit(x, split="_")[[1]][4]
# return(tasmax)
# })
# yearmax_tas <- do.call(rbind, tasmax)
## -----------------------------------------------------------------------------
# pr_files <- list.files(
# paste0(filedir, "/ISIMIP2b/InputData/landonly"),
# pattern="^pr_.*.nc4", full.names=TRUE, recursive=T)
# outfiles <- sub("pr_day", "yearsum_pr", sub("InputData/landonly", "DerivedInputData/globalmeans", pr_files))
#
# landseamask <- paste0(filedir, "ISIMIP2b/InputData/landseamask/ISIMIP2b_landseamask_generic.nc4")
#
# mapply(FUN=function(x,y) {if(!file.exists(y)){system(paste0('cdo fldmean -yearsum -ifthen ', landseamask, " ", x, ' ', y))}}, x=pr_files, y=outfiles)
# # Without landsea subset
# mapply(FUN=function(x,y) {if(!file.exists(y)){system(paste0('cdo fldmean -yearsum ', x, ' ', y))}}, x=pr_files, y=outfiles)
#
# # Save outfiles in csv file
# yearsum_pr <- lapply(outfiles, function(x){
# nc <- ncdf4::nc_open(x)
# pr <- ncdf4::ncvar_get(nc)
# pr <- as.data.frame(pr)
# timeref <- as.Date(strsplit(nc$dim[[1]]$units, " ")[[1]][3])
# if(ncdf4::ncvar_get(nc, nc$dim$time)[1] == 0){
# pr$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time)
# } else if (ncdf4::ncvar_get(nc, nc$dim$time)[1] != 0){
# pr$year <- timeref + ncdf4::ncvar_get(nc, nc$dim$time) - 1
# }
# ncdf4::nc_close(nc)
# pr$model <- strsplit(x, split="_")[[1]][3]
# pr$scenario <- strsplit(x, split="_")[[1]][4]
# return(pr)
# })
# yearsum_pr <- do.call(rbind, yearsum_pr)
## ----runmean31----------------------------------------------------------------
# # Calculate 31yr running mean
# runmean31_tasmax <- lapply(unique(yearmax_tas$model), function(x){
# data_agg <- lapply(unique(yearmax_tas$scenario), function(y){
# data <- yearmax_tas[yearmax_tas$model == x,]
# data <- data[data$scenario == y,]
# data$year <- lubridate::year(data$year)
# data_mean <- zoo::rollapply(zoo::as.zoo(data[,c("tasmax", "year")]), 31, mean, na.rm=TRUE, partial=TRUE)
# data_mean <- as.data.frame(data_mean)
# data_mean$scenario <- y
# return(data_mean)
# })
# data_agg <- do.call("rbind", data_agg)
# data_agg$model <- x
# return(data_agg)
# })
# runmean31_tasmax <- do.call("rbind", runmean31_tasmax)
#
# # Calculate 31yr running mean
# runmean31_prsum <- lapply(unique(yearsum_pr$model), function(x){
# data_agg <- lapply(unique(yearsum_pr$scenario), function(y){
# data <- yearsum_pr[yearsum_pr$model == x,]
# data <- data[data$scenario == y,]
# data$year <- lubridate::year(data$year)
# data_mean <- zoo::rollapply(zoo::as.zoo(data[,c("pr", "year")]), 31, mean, na.rm=TRUE, partial=TRUE)
# data_mean <- as.data.frame(data_mean)
# data_mean$scenario <- y
# return(data_mean)
# })
# data_agg <- do.call("rbind", data_agg)
# data_agg$model <- x
# return(data_agg)
# })
# runmean31_prsum <- do.call("rbind", runmean31_prsum)
## ----tasmax_change, fig.width=9, fig.height=6, dpi=600------------------------
# # Create simple plot
# yearmax_tas$year <- lubridate::year(yearmax_tas$year)
# ggplot(data=yearmax_tas, aes(x=year, y=tasmax-273.15, colour=scenario)) + geom_line() +
# facet_wrap(~ model, scales="free_y", ncol=1) +
# #facet_grid(var~model, scales="free_y", switch="y") +
# labs(x="Year", y="Mean global annual maximum temperature (°C)") +
# scale_x_continuous(limits=c(1650, 2310), expand=c(0,0),
# breaks=c(1700, 1800, 1900, 2000, 2100, 2200, 2300)) +
# #scale_colour_manual(values=c("black", "darkgrey", "blue", "yellow")) +
# theme_bw() + theme(strip.background= element_blank(),
# strip.placement= "outside",
# legend.position = c(0.9,0.905),
# legend.title = element_blank(),
# legend.background = element_rect(fill = NA),
# panel.spacing.x=unit(0.25, "lines"),
# panel.spacing.y=unit(0.25, "lines"))
## ----prsum_change, fig.width=9, fig.height=6, dpi=600-------------------------
# # Create simple plot
# yearsum_pr$year <- lubridate::year(yearsum_pr$year)
# ggplot(data=yearsum_pr, aes(x=year, y=pr*86400, colour=scenario)) + geom_line() +
# facet_wrap(~ model, scales="free_y", ncol=1) +
# labs(x="Year", y="Mean global annual total precipitation (mm)") +
# #scale_x_date() +
# scale_x_continuous(limits=c(1650, 2310), expand=c(0,0),
# breaks=c(1700, 1800, 1900, 2000, 2100, 2200, 2300)) +
# #scale_colour_manual(values=c("black", "darkgrey", "blue", "yellow")) +
# theme_bw() + theme(strip.background= element_blank(),
# strip.placement= "outside",
# legend.position = c(0.9,0.905),
# legend.title = element_blank(),
# legend.background = element_rect(fill = NA),
# panel.spacing.x=unit(0.25, "lines"),
# panel.spacing.y=unit(0.25, "lines"))
## ----pr_tasmax_ipsl-cm5a-lr, width=9, height=6, dpi=600-----------------------
# library(dplyr)
# yearsum_ipsl <- yearsum_pr %>% filter(model == "IPSL-CM5A-LR")
# yearsum_ipsl$var <- "pr"
# colnames(yearsum_ipsl)[1] <- "value"
# yearsum_ipsl$value <- yearsum_ipsl$value*86400
# yearmax_ipsl <- yearmax_tas %>% filter(model == "IPSL-CM5A-LR")
# yearmax_ipsl$var <- "tasmax"
# colnames(yearmax_ipsl)[1] <- "value"
# yearmax_ipsl$value <- yearmax_ipsl$value-273.15
# yearvar_ipsl <- bind_rows(yearsum_ipsl, yearmax_ipsl)
# ggplot(data=yearvar_ipsl, aes(x=year, y=value, colour=scenario)) +
# geom_line() + facet_wrap(~ var, scales="free_y", ncol=1,
# strip.position="left", labeller= as_labeller(c(pr="Mean global annual total precipitation (mm)", tasmax="Mean global annual maximum temperature (°C)"))) +
# labs(x="Year", y="") +
# scale_x_date() +
# #scale_colour_manual(values=c("black", "darkgrey", "blue", "yellow")) +
# theme_bw() + theme(strip.background= element_blank(),
# strip.placement= "outside",
# legend.position = c(0.06,0.9),
# legend.title = element_blank(),
# legend.background = element_rect(fill = NA),
# panel.spacing.x=unit(0.25, "lines"),
# panel.spacing.y=unit(0.25, "lines"))
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