vignettes/isimip2b-isimip3b_comparison.R
In RS-eco/rISIMIP: R Package for processing ISIMIP data in R
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo=T, warning=F, comment=NA, message=F, eval=T,
fig.width=9, fig.height=10, dpi=82)
## ---- eval=FALSE--------------------------------------------------------------
# # Install remotes if not previously installed
# if(!"remotes" %in% installed.packages()[,"Package"]) install.packages("remotes")
#
# # Install rISIMIP from Github if not previously installed
# if(!"rISIMIP" %in% installed.packages()[,"Package"]) remotes::install_github("RS-eco/rISIMIP")
## -----------------------------------------------------------------------------
library(rISIMIP)
## ---- fig.cap="Map of temperature for a) RCP2.6 (ISIMIP2b), b) SSP126 (ISIMIP3b) and c) the difference between RCP2.6 and SSP126 for the year 2080."----
# Load packages
library(dplyr); library(sf); library(ggplot2); library(patchwork)
#map for ISIMIP2b RCP2.6 - 2080
data(outline, package="ggmap2")
outline <- sf::st_as_sf(outline)
#### rcp26 - ssp126 -2080 ####
data("bioclim_gfdl-esm2m_rcp26_2080_landonly")
data("bioclim_hadgem2-es_rcp26_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp26_2080_landonly")
data("bioclim_miroc5_rcp26_2080_landonly")
bioclim_rcp26_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2080_landonly`,
`bioclim_hadgem2-es_rcp26_2080_landonly`,
`bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`,
`bioclim_miroc5_rcp26_2080_landonly`) %>%
select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val1 <- scales::rescale(unique(c(seq(min(bioclim_rcp26_2080_landonly$bio1), 0, length=5),
seq(0, max(bioclim_rcp26_2080_landonly$bio1), length=5))))
data("bioclim_gfdl-esm4_ssp126_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp126_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp126_2080_landonly")
data("bioclim_mri-esm2-0_ssp126_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp126_2080_landonly")
bioclim_ssp126_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp126_2080_landonly`,
`bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`,
`bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`,
`bioclim_mri-esm2-0_ssp126_2080_landonly`,
`bioclim_ukesm1-0-ll_ssp126_2080_landonly`) %>%
select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val2 <- scales::rescale(unique(c(seq(min(bioclim_ssp126_2080_landonly$bio1), 0, length=5),
seq(0, max(bioclim_ssp126_2080_landonly$bio1), length=5))))
p1 <- ggplot() + geom_tile(data=bioclim_rcp26_2080_landonly, aes(x=x, y=y, fill=bio1)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val1,
limits=c(min(bioclim_rcp26_2080_landonly$bio1)-2,
max(bioclim_rcp26_2080_landonly$bio1)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp26_2080_landonly$x),
max(bioclim_rcp26_2080_landonly$x)),
ylim=c(min(bioclim_rcp26_2080_landonly$y),
max(bioclim_rcp26_2080_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p2 <- ggplot() + geom_tile(data=bioclim_ssp126_2080_landonly, aes(x=x, y=y, fill=bio1)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val2,
limits=c(min(bioclim_ssp126_2080_landonly$bio1)-2,
max(bioclim_ssp126_2080_landonly$bio1)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_ssp126_2080_landonly$x),
max(bioclim_ssp126_2080_landonly$x)),
ylim=c(min(bioclim_ssp126_2080_landonly$y),
max(bioclim_ssp126_2080_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
bioclim_rcp26_ssp126 <- full_join(bioclim_rcp26_2080_landonly, bioclim_ssp126_2080_landonly,
by=c("x", "y"))
bioclim_rcp26_ssp126$dif <- bioclim_rcp26_ssp126$bio1.x - bioclim_rcp26_ssp126$bio1.y
col_val3 <- scales::rescale(unique(c(seq(min(bioclim_rcp26_ssp126$dif), 0, length=5),
seq(0, max(bioclim_rcp26_ssp126$dif), length=5))))
p3 <- ggplot() + geom_tile(data=bioclim_rcp26_ssp126, aes(x=x, y=y, fill=dif)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val3,
limits=c(min(bioclim_rcp26_ssp126$dif)-2,
max(bioclim_rcp26_ssp126$dif )+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp26_ssp126$x),
max(bioclim_rcp26_ssp126$x)),
ylim=c(min(bioclim_rcp26_ssp126$y),
max(bioclim_rcp26_ssp126$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p1 / p2 / p3
## ---- fig.cap="Map of precipitation for a) RCP6.0 (ISIMIP2b), b) SSP370 (ISIMIP3b) and c) the difference between RCP6.0 and SSP370 for the year 2080."----
#### rcp6.0 - ssp370 - 2080 ####
data("bioclim_gfdl-esm2m_rcp60_2080_landonly")
data("bioclim_hadgem2-es_rcp60_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp60_2080_landonly")
data("bioclim_miroc5_rcp60_2080_landonly")
bioclim_rcp60_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp60_2080_landonly`,
`bioclim_hadgem2-es_rcp60_2080_landonly`,
`bioclim_ipsl-cm5a-lr_rcp60_2080_landonly`,
`bioclim_miroc5_rcp60_2080_landonly`) %>%
select(x,y,bio12) %>% group_by(x,y) %>% summarise(bio12=mean(bio12, na.rm=T))
col_val1 <- scales::rescale(unique(seq(0, max(bioclim_rcp60_2080_landonly$bio12), length=9)))
p1 <- ggplot() + geom_tile(data=bioclim_rcp60_2080_landonly, aes(x=x, y=y, fill=bio12)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val1,
limits=c(0, max(bioclim_rcp60_2080_landonly$bio12)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp60_2080_landonly$x),
max(bioclim_rcp60_2080_landonly$x)),
ylim=c(min(bioclim_rcp60_2080_landonly$y),
max(bioclim_rcp60_2080_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
data("bioclim_gfdl-esm4_ssp370_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp370_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp370_2080_landonly")
data("bioclim_mri-esm2-0_ssp370_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp370_2080_landonly")
bioclim_ssp370_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp370_2080_landonly`,
`bioclim_ipsl-cm6a-lr_ssp370_2080_landonly`,
`bioclim_mpi-esm1-2-hr_ssp370_2080_landonly`,
`bioclim_mri-esm2-0_ssp370_2080_landonly`,
`bioclim_ukesm1-0-ll_ssp370_2080_landonly`) %>%
select(x,y,bio12) %>% group_by(x,y) %>% summarise(bio12=mean(bio12, na.rm=T))
col_val2 <- scales::rescale(unique(seq(0, max(bioclim_ssp370_2080_landonly$bio12), length=9)))
p2 <- ggplot() + geom_tile(data=bioclim_ssp370_2080_landonly, aes(x=x, y=y, fill=bio12)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val2,
limits=c(0, max(bioclim_ssp370_2080_landonly$bio12)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_ssp370_2080_landonly$x),
max(bioclim_ssp370_2080_landonly$x)),
ylim=c(min(bioclim_ssp370_2080_landonly$y),
max(bioclim_ssp370_2080_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
bioclim_rcp60_ssp370 <- full_join(bioclim_rcp60_2080_landonly, bioclim_ssp370_2080_landonly,
by=c("x", "y"))
bioclim_rcp60_ssp370$dif <- bioclim_rcp60_ssp370$bio12.x - bioclim_rcp60_ssp370$bio12.y
col_val3 <- scales::rescale(unique(c(seq(min(bioclim_rcp60_ssp370$dif), 0, length=5),
seq(0, max(bioclim_rcp60_ssp370$dif), length=5))))
p3 <- ggplot() + geom_tile(data=bioclim_rcp60_ssp370, aes(x=x, y=y, fill=dif)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val3,
limits=c(min(bioclim_rcp60_ssp370$dif)-2,
max(bioclim_rcp60_ssp370$dif )+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp60_ssp370$x),
max(bioclim_rcp60_ssp370$x)),
ylim=c(min(bioclim_rcp60_ssp370$y),
max(bioclim_rcp60_ssp370$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p1 / p2 / p3
## ---- fig.cap="Map of precipitation for a) RCP6.0 (ISIMIP2b), b) SSP370 (ISIMIP3b) and c) the difference between RCP6.0 and SSP370 for the year 2050."----
#### rcp6.0 - ssp370 - 2050 ####
data("bioclim_gfdl-esm2m_rcp60_2050_landonly")
data("bioclim_hadgem2-es_rcp60_2050_landonly")
data("bioclim_ipsl-cm5a-lr_rcp60_2050_landonly")
data("bioclim_miroc5_rcp60_2050_landonly")
bioclim_rcp60_2050_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp60_2050_landonly`,
`bioclim_hadgem2-es_rcp60_2050_landonly`,
`bioclim_ipsl-cm5a-lr_rcp60_2050_landonly`,
`bioclim_miroc5_rcp60_2050_landonly`) %>%
select(x,y,bio12) %>% group_by(x,y) %>% summarise(bio12=mean(bio12, na.rm=T))
col_val1 <- scales::rescale(unique(c(seq(min(bioclim_rcp60_2050_landonly$bio12), 0, length=5),
seq(0, max(bioclim_rcp60_2050_landonly$bio12), length=5))))
p1 <- ggplot() + geom_tile(data=bioclim_rcp60_2050_landonly, aes(x=x, y=y, fill=bio12)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val1,
limits=c(min(bioclim_rcp60_2050_landonly$bio12)-2,
max(bioclim_rcp60_2050_landonly$bio12)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp60_2050_landonly$x),
max(bioclim_rcp60_2050_landonly$x)),
ylim=c(min(bioclim_rcp60_2050_landonly$y),
max(bioclim_rcp60_2050_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
data("bioclim_gfdl-esm4_ssp370_2050_landonly")
data("bioclim_ipsl-cm6a-lr_ssp370_2050_landonly")
data("bioclim_mpi-esm1-2-hr_ssp370_2050_landonly")
data("bioclim_mri-esm2-0_ssp370_2050_landonly")
data("bioclim_ukesm1-0-ll_ssp370_2050_landonly")
bioclim_ssp370_2050_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp370_2050_landonly`,
`bioclim_ipsl-cm6a-lr_ssp370_2050_landonly`,
`bioclim_mpi-esm1-2-hr_ssp370_2050_landonly`,
`bioclim_mri-esm2-0_ssp370_2050_landonly`,
`bioclim_ukesm1-0-ll_ssp370_2050_landonly`) %>%
select(x,y,bio12) %>% group_by(x,y) %>% summarise(bio12=mean(bio12, na.rm=T))
col_val2 <- scales::rescale(unique(c(seq(min(bioclim_ssp370_2050_landonly$bio12), 0, length=5),
seq(0, max(bioclim_ssp370_2050_landonly$bio12), length=5))))
p2 <- ggplot() + geom_tile(data=bioclim_ssp370_2050_landonly, aes(x=x, y=y, fill=bio12)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val2,
limits=c(min(bioclim_ssp370_2050_landonly$bio12)-2,
max(bioclim_ssp370_2050_landonly$bio12)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_ssp370_2050_landonly$x),
max(bioclim_ssp370_2050_landonly$x)),
ylim=c(min(bioclim_ssp370_2050_landonly$y),
max(bioclim_ssp370_2050_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
bioclim_rcp60_ssp370 <- full_join(bioclim_rcp60_2050_landonly, bioclim_ssp370_2050_landonly,
by=c("x", "y"))
bioclim_rcp60_ssp370$dif <- bioclim_rcp60_ssp370$bio12.x - bioclim_rcp60_ssp370$bio12.y
col_val3 <- scales::rescale(unique(c(seq(min(bioclim_rcp60_ssp370$dif), 0, length=5),
seq(0, max(bioclim_rcp60_ssp370$dif), length=5))))
p3 <- ggplot() + geom_tile(data=bioclim_rcp60_ssp370, aes(x=x, y=y, fill=dif)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val3,
limits=c(min(bioclim_rcp60_ssp370$dif)-2,
max(bioclim_rcp60_ssp370$dif )+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp60_ssp370$x),
max(bioclim_rcp60_ssp370$x)),
ylim=c(min(bioclim_rcp60_ssp370$y),
max(bioclim_rcp60_ssp370$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p1 / p2 / p3
## ---- fig.cap="Map of precipitation seasonality for a) RCP8.5 (ISIMIP2b), b) SSP585 (ISIMIP3b) and c) the difference between RCP8.5 and SSP585 for the year 2050."----
#### rcp8.5 - ssp585 - 2050 ####
data("bioclim_gfdl-esm2m_rcp85_2050_landonly")
data("bioclim_hadgem2-es_rcp85_2050_landonly")
data("bioclim_ipsl-cm5a-lr_rcp85_2050_landonly")
data("bioclim_miroc5_rcp85_2050_landonly")
bioclim_rcp85_2050_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp85_2050_landonly`,
`bioclim_hadgem2-es_rcp85_2050_landonly`,
`bioclim_ipsl-cm5a-lr_rcp85_2050_landonly`,
`bioclim_miroc5_rcp85_2050_landonly`) %>%
select(x,y,bio15) %>% group_by(x,y) %>% summarise(bio15=mean(bio15, na.rm=T))
col_val1 <- scales::rescale(unique(seq(0, max(bioclim_rcp85_2050_landonly$bio15), length=9)))
p1 <- ggplot() + geom_tile(data=bioclim_rcp85_2050_landonly, aes(x=x, y=y, fill=bio15)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="bio15", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val1,
limits=c(0, max(bioclim_rcp85_2050_landonly$bio15)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp85_2050_landonly$x),
max(bioclim_rcp85_2050_landonly$x)),
ylim=c(min(bioclim_rcp85_2050_landonly$y),
max(bioclim_rcp85_2050_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
data("bioclim_gfdl-esm4_ssp585_2050_landonly")
data("bioclim_ipsl-cm6a-lr_ssp585_2050_landonly")
data("bioclim_mpi-esm1-2-hr_ssp585_2050_landonly")
data("bioclim_mri-esm2-0_ssp585_2050_landonly")
data("bioclim_ukesm1-0-ll_ssp585_2050_landonly")
bioclim_ssp585_2050_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp585_2050_landonly`,
`bioclim_ipsl-cm6a-lr_ssp585_2050_landonly`,
`bioclim_mpi-esm1-2-hr_ssp585_2050_landonly`,
`bioclim_mri-esm2-0_ssp585_2050_landonly`,
`bioclim_ukesm1-0-ll_ssp585_2050_landonly`) %>%
select(x,y,bio15) %>% group_by(x,y) %>% summarise(bio15=mean(bio15, na.rm=T))
col_val2 <- scales::rescale(unique(seq(0, max(bioclim_ssp585_2050_landonly$bio15), length=9)))
p2 <- ggplot() + geom_tile(data=bioclim_ssp585_2050_landonly, aes(x=x, y=y, fill=bio15)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="bio15", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val2,
limits=c(0, max(bioclim_ssp585_2050_landonly$bio15)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_ssp585_2050_landonly$x),
max(bioclim_ssp585_2050_landonly$x)),
ylim=c(min(bioclim_ssp585_2050_landonly$y),
max(bioclim_ssp585_2050_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
bioclim_rcp85_ssp585 <- full_join(bioclim_rcp85_2050_landonly, bioclim_ssp585_2050_landonly,
by=c("x", "y"))
bioclim_rcp85_ssp585$dif <- bioclim_rcp85_ssp585$bio15.x - bioclim_rcp85_ssp585$bio15.y
col_val3 <- scales::rescale(unique(c(seq(min(bioclim_rcp85_ssp585$dif), 0, length=5),
seq(0, max(bioclim_rcp85_ssp585$dif), length=5))))
p3 <- ggplot() + geom_tile(data=bioclim_rcp85_ssp585, aes(x=x, y=y, fill=dif)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="bio15", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val3,
limits=c(min(bioclim_rcp85_ssp585$dif)-2,
max(bioclim_rcp85_ssp585$dif )+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp85_ssp585$x),
max(bioclim_rcp85_ssp585$x)),
ylim=c(min(bioclim_rcp85_ssp585$y),
max(bioclim_rcp85_ssp585$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p1 / p2 / p3
rm(list=ls()); invisible(gc())
## ---- fig.cap="Correlation of each bioclimatic variable of RCP2.6 (ISIMIP2b) and SSP126 (ISIMIP3b) data for the year 2080."----
#data_names <- data(package = "rISIMIP")$results[,3]
#bioclim_names <- data_names[grepl(data_names, pattern="bioclim")]
# Get RCP data
#rcp_names <- bioclim_names[grepl(bioclim_names, pattern="rcp")]
#ssp_names <- bioclim_names[grepl(bioclim_names, pattern="ssp")]
#data(list=rcp_names, package="rISIMIP")
#data(list=ssp_names, package="rISIMIP")
#### rcp26 - ssp126 -2080 ####
data("bioclim_gfdl-esm2m_rcp26_2080_landonly")
data("bioclim_hadgem2-es_rcp26_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp26_2080_landonly")
data("bioclim_miroc5_rcp26_2080_landonly")
bioclim_rcp26_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2080_landonly`,
`bioclim_hadgem2-es_rcp26_2080_landonly`,
`bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`,
`bioclim_miroc5_rcp26_2080_landonly`) %>%
group_by(x,y) %>% summarise_all(mean)
data("bioclim_gfdl-esm4_ssp126_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp126_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp126_2080_landonly")
data("bioclim_mri-esm2-0_ssp126_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp126_2080_landonly")
bioclim_ssp126_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp126_2080_landonly`,
`bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`,
`bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`,
`bioclim_mri-esm2-0_ssp126_2080_landonly`,
`bioclim_ukesm1-0-ll_ssp126_2080_landonly`) %>%
group_by(x,y) %>% summarise_all(mean)
library(tidyr)
bioclim_rcp26_ssp126_2080 <- full_join(bioclim_rcp26_2080_landonly,
bioclim_ssp126_2080_landonly, by=c("x", "y")) %>%
pivot_longer(!c(x,y), names_to="var", values_to="values") %>%
separate(var, c("var", "scenario"))
bioclim_rcp26_ssp126_2080$scenario <- factor(bioclim_rcp26_ssp126_2080$scenario, labels=c("rcp26", "ssp126"))
bioclim_rcp26_ssp126_2080 <- bioclim_rcp26_ssp126_2080 %>% pivot_wider(names_from="scenario", values_from="values")
head(bioclim_rcp26_ssp126_2080)
ggplot(data=bioclim_rcp26_ssp126_2080, aes(x=rcp26, y=ssp126)) + geom_point() +
facet_wrap(.~var, scales="free") + geom_smooth(method="lm") +
theme_bw() + labs(x="ISIMIP2b RCP26 2080", y="ISIMIP3b SSP126 2080") +
theme(strip.background=element_blank(), strip.text = element_text(size=12, face="bold"))
rm(list=ls()); invisible(gc())
## ---- fig.cap="Global variation in selected bioclimatic variables (bio1, bio12 and bio15) of RCP2.6 (ISIMIP2b) and SSP126 (ISIMIP3b) data across all years."----
#Compare ISIMIP2b & ISIMIP3b global distribution of the different scenarios and time spans to check if they actually mirror an increase in temperature and if it's within the expectations
library(ggsci)
#### ISIMIP2b data ####
data("bioclim_ewembi_1995_landonly")
data("bioclim_gfdl-esm2m_rcp26_2050_landonly")
`bioclim_gfdl-esm2m_rcp26_2050_landonly`$gcm <- "gfdl-esm2m"
data("bioclim_hadgem2-es_rcp26_2050_landonly")
`bioclim_hadgem2-es_rcp26_2050_landonly`$gcm <- "hadgem2-es"
data("bioclim_ipsl-cm5a-lr_rcp26_2050_landonly")
`bioclim_ipsl-cm5a-lr_rcp26_2050_landonly`$gcm <- "ipsl-cm5a-lr"
data("bioclim_miroc5_rcp26_2050_landonly")
`bioclim_miroc5_rcp26_2050_landonly`$gcm <- "miroc5"
data("bioclim_gfdl-esm2m_rcp26_2080_landonly")
`bioclim_gfdl-esm2m_rcp26_2080_landonly`$gcm <- "gfdl-esm2m"
data("bioclim_hadgem2-es_rcp26_2080_landonly")
`bioclim_hadgem2-es_rcp26_2080_landonly`$gcm <- "hadgem2-es"
data("bioclim_ipsl-cm5a-lr_rcp26_2080_landonly")
`bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`$gcm <- "ipsl-cm5a-lr"
data("bioclim_miroc5_rcp26_2080_landonly")
`bioclim_miroc5_rcp26_2080_landonly`$gcm <- "miroc5"
bioclim_ewembi_1995_landonly$year <- 1995
bioclim_ewembi_1995_landonly$gcm <- "EWEMBI"
bioclim_rcp26_2050 <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2050_landonly`,
`bioclim_hadgem2-es_rcp26_2050_landonly`,
`bioclim_ipsl-cm5a-lr_rcp26_2050_landonly`,
`bioclim_miroc5_rcp26_2050_landonly`)
bioclim_rcp26_2050$year <- 2050
bioclim_rcp26_2080 <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2080_landonly`,
`bioclim_hadgem2-es_rcp26_2080_landonly`,
`bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`,
`bioclim_miroc5_rcp26_2080_landonly`)
bioclim_rcp26_2080$year <- 2080
bioclim_rcp26 <- bind_rows(list(bioclim_ewembi_1995_landonly, bioclim_rcp26_2050,
bioclim_rcp26_2080)) %>%
tidyr::pivot_longer(names_to="var", values_to="value", -c(x,y,gcm,year))
p1 <- bioclim_rcp26 %>% filter(var %in% c("bio1", "bio12", "bio15")) %>%
ggplot() + geom_violin(aes(x=as.factor(year), y=value, fill=gcm)) +
facet_wrap(.~var, scales="free_y", strip.position="left", ncol=1) +
ggtitle("ISIMIP2b") + labs(x="Year", y="") +
scale_fill_manual(name="GCM", values=pal_d3("category20")(11)[1:5]) +
theme_bw() + theme(strip.background = element_blank(), strip.placement = "outside",
strip.text = element_text(size=12, face="bold"),
plot.title = element_text(hjust=0.5))
# ISIMIP3b data
data("bioclim_gswp3-w5e5_obsclim_1995_landonly")
data("bioclim_gfdl-esm4_ssp126_2050_landonly")
`bioclim_gfdl-esm4_ssp126_2050_landonly`$gcm <- "gfdl-esm4"
data("bioclim_ipsl-cm6a-lr_ssp126_2050_landonly")
`bioclim_ipsl-cm6a-lr_ssp126_2050_landonly`$gcm <- "ipsl-cm6a-lr"
data("bioclim_mpi-esm1-2-hr_ssp126_2050_landonly")
`bioclim_mpi-esm1-2-hr_ssp126_2050_landonly`$gcm <- "mpi-esm1-2-hr"
data("bioclim_mri-esm2-0_ssp126_2050_landonly")
`bioclim_mri-esm2-0_ssp126_2050_landonly`$gcm <- "mri-esm2-0"
data("bioclim_ukesm1-0-ll_ssp126_2050_landonly")
`bioclim_ukesm1-0-ll_ssp126_2050_landonly`$gcm <- "ukesm1-0-ll"
data("bioclim_gfdl-esm4_ssp126_2080_landonly")
`bioclim_gfdl-esm4_ssp126_2080_landonly`$gcm <- "gfdl-esm4"
data("bioclim_ipsl-cm6a-lr_ssp126_2080_landonly")
`bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`$gcm <- "ipsl-cm6a-lr"
data("bioclim_mpi-esm1-2-hr_ssp126_2080_landonly")
`bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`$gcm <- "mpi-esm1-2-hr"
data("bioclim_mri-esm2-0_ssp126_2080_landonly")
`bioclim_mri-esm2-0_ssp126_2080_landonly`$gcm <- "mri-esm2-0"
data("bioclim_ukesm1-0-ll_ssp126_2080_landonly")
`bioclim_ukesm1-0-ll_ssp126_2080_landonly`$gcm <- "ukesm1-0-ll"
# Merge data
`bioclim_gswp3-w5e5_obsclim_1995_landonly`$year <- 1995
`bioclim_gswp3-w5e5_obsclim_1995_landonly`$gcm <- "gswp3-w5e5"
bioclim_ssp126_2050 <- bind_rows(`bioclim_gfdl-esm4_ssp126_2050_landonly`,
`bioclim_ipsl-cm6a-lr_ssp126_2050_landonly`,
`bioclim_mpi-esm1-2-hr_ssp126_2050_landonly`,
`bioclim_mri-esm2-0_ssp126_2050_landonly`,
`bioclim_ukesm1-0-ll_ssp126_2050_landonly`)
bioclim_ssp126_2050$year <- 2050
bioclim_ssp126_2080 <- bind_rows(`bioclim_gfdl-esm4_ssp126_2080_landonly`,
`bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`,
`bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`,
`bioclim_mri-esm2-0_ssp126_2080_landonly`,
`bioclim_ukesm1-0-ll_ssp126_2080_landonly`)
bioclim_ssp126_2080$year <- 2080
bioclim_ssp126 <- bind_rows(list(`bioclim_gswp3-w5e5_obsclim_1995_landonly`,
bioclim_ssp126_2050, bioclim_ssp126_2080)) %>%
tidyr::pivot_longer(names_to="var", values_to="value", -c(x,y,gcm,year))
p2 <- bioclim_ssp126 %>% filter(var %in% c("bio1", "bio12", "bio15")) %>%
ggplot() + geom_violin(aes(x=as.factor(year), y=value, fill=gcm)) +
facet_wrap(.~var, scales="free_y", strip.position="left", ncol=1) +
ggtitle("ISIMIP3b") + labs(x="Year", y="") +
scale_fill_manual(name="", values=pal_d3("category20")(11)[6:11]) +
theme_bw() + theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text = element_blank(),
plot.title = element_text(hjust=0.5))
p1 + p2 + plot_layout(guides="collect")
RS-eco/rISIMIP documentation built on Oct. 31, 2022, 2:26 a.m.
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## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo=T, warning=F, comment=NA, message=F, eval=T,
fig.width=9, fig.height=10, dpi=82)
## ---- eval=FALSE--------------------------------------------------------------
# # Install remotes if not previously installed
# if(!"remotes" %in% installed.packages()[,"Package"]) install.packages("remotes")
#
# # Install rISIMIP from Github if not previously installed
# if(!"rISIMIP" %in% installed.packages()[,"Package"]) remotes::install_github("RS-eco/rISIMIP")
## -----------------------------------------------------------------------------
library(rISIMIP)
## ---- fig.cap="Map of temperature for a) RCP2.6 (ISIMIP2b), b) SSP126 (ISIMIP3b) and c) the difference between RCP2.6 and SSP126 for the year 2080."----
# Load packages
library(dplyr); library(sf); library(ggplot2); library(patchwork)
#map for ISIMIP2b RCP2.6 - 2080
data(outline, package="ggmap2")
outline <- sf::st_as_sf(outline)
#### rcp26 - ssp126 -2080 ####
data("bioclim_gfdl-esm2m_rcp26_2080_landonly")
data("bioclim_hadgem2-es_rcp26_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp26_2080_landonly")
data("bioclim_miroc5_rcp26_2080_landonly")
bioclim_rcp26_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2080_landonly`,
`bioclim_hadgem2-es_rcp26_2080_landonly`,
`bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`,
`bioclim_miroc5_rcp26_2080_landonly`) %>%
select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val1 <- scales::rescale(unique(c(seq(min(bioclim_rcp26_2080_landonly$bio1), 0, length=5),
seq(0, max(bioclim_rcp26_2080_landonly$bio1), length=5))))
data("bioclim_gfdl-esm4_ssp126_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp126_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp126_2080_landonly")
data("bioclim_mri-esm2-0_ssp126_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp126_2080_landonly")
bioclim_ssp126_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp126_2080_landonly`,
`bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`,
`bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`,
`bioclim_mri-esm2-0_ssp126_2080_landonly`,
`bioclim_ukesm1-0-ll_ssp126_2080_landonly`) %>%
select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val2 <- scales::rescale(unique(c(seq(min(bioclim_ssp126_2080_landonly$bio1), 0, length=5),
seq(0, max(bioclim_ssp126_2080_landonly$bio1), length=5))))
p1 <- ggplot() + geom_tile(data=bioclim_rcp26_2080_landonly, aes(x=x, y=y, fill=bio1)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val1,
limits=c(min(bioclim_rcp26_2080_landonly$bio1)-2,
max(bioclim_rcp26_2080_landonly$bio1)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp26_2080_landonly$x),
max(bioclim_rcp26_2080_landonly$x)),
ylim=c(min(bioclim_rcp26_2080_landonly$y),
max(bioclim_rcp26_2080_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p2 <- ggplot() + geom_tile(data=bioclim_ssp126_2080_landonly, aes(x=x, y=y, fill=bio1)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val2,
limits=c(min(bioclim_ssp126_2080_landonly$bio1)-2,
max(bioclim_ssp126_2080_landonly$bio1)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_ssp126_2080_landonly$x),
max(bioclim_ssp126_2080_landonly$x)),
ylim=c(min(bioclim_ssp126_2080_landonly$y),
max(bioclim_ssp126_2080_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
bioclim_rcp26_ssp126 <- full_join(bioclim_rcp26_2080_landonly, bioclim_ssp126_2080_landonly,
by=c("x", "y"))
bioclim_rcp26_ssp126$dif <- bioclim_rcp26_ssp126$bio1.x - bioclim_rcp26_ssp126$bio1.y
col_val3 <- scales::rescale(unique(c(seq(min(bioclim_rcp26_ssp126$dif), 0, length=5),
seq(0, max(bioclim_rcp26_ssp126$dif), length=5))))
p3 <- ggplot() + geom_tile(data=bioclim_rcp26_ssp126, aes(x=x, y=y, fill=dif)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val3,
limits=c(min(bioclim_rcp26_ssp126$dif)-2,
max(bioclim_rcp26_ssp126$dif )+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp26_ssp126$x),
max(bioclim_rcp26_ssp126$x)),
ylim=c(min(bioclim_rcp26_ssp126$y),
max(bioclim_rcp26_ssp126$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p1 / p2 / p3
## ---- fig.cap="Map of precipitation for a) RCP6.0 (ISIMIP2b), b) SSP370 (ISIMIP3b) and c) the difference between RCP6.0 and SSP370 for the year 2080."----
#### rcp6.0 - ssp370 - 2080 ####
data("bioclim_gfdl-esm2m_rcp60_2080_landonly")
data("bioclim_hadgem2-es_rcp60_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp60_2080_landonly")
data("bioclim_miroc5_rcp60_2080_landonly")
bioclim_rcp60_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp60_2080_landonly`,
`bioclim_hadgem2-es_rcp60_2080_landonly`,
`bioclim_ipsl-cm5a-lr_rcp60_2080_landonly`,
`bioclim_miroc5_rcp60_2080_landonly`) %>%
select(x,y,bio12) %>% group_by(x,y) %>% summarise(bio12=mean(bio12, na.rm=T))
col_val1 <- scales::rescale(unique(seq(0, max(bioclim_rcp60_2080_landonly$bio12), length=9)))
p1 <- ggplot() + geom_tile(data=bioclim_rcp60_2080_landonly, aes(x=x, y=y, fill=bio12)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val1,
limits=c(0, max(bioclim_rcp60_2080_landonly$bio12)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp60_2080_landonly$x),
max(bioclim_rcp60_2080_landonly$x)),
ylim=c(min(bioclim_rcp60_2080_landonly$y),
max(bioclim_rcp60_2080_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
data("bioclim_gfdl-esm4_ssp370_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp370_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp370_2080_landonly")
data("bioclim_mri-esm2-0_ssp370_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp370_2080_landonly")
bioclim_ssp370_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp370_2080_landonly`,
`bioclim_ipsl-cm6a-lr_ssp370_2080_landonly`,
`bioclim_mpi-esm1-2-hr_ssp370_2080_landonly`,
`bioclim_mri-esm2-0_ssp370_2080_landonly`,
`bioclim_ukesm1-0-ll_ssp370_2080_landonly`) %>%
select(x,y,bio12) %>% group_by(x,y) %>% summarise(bio12=mean(bio12, na.rm=T))
col_val2 <- scales::rescale(unique(seq(0, max(bioclim_ssp370_2080_landonly$bio12), length=9)))
p2 <- ggplot() + geom_tile(data=bioclim_ssp370_2080_landonly, aes(x=x, y=y, fill=bio12)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val2,
limits=c(0, max(bioclim_ssp370_2080_landonly$bio12)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_ssp370_2080_landonly$x),
max(bioclim_ssp370_2080_landonly$x)),
ylim=c(min(bioclim_ssp370_2080_landonly$y),
max(bioclim_ssp370_2080_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
bioclim_rcp60_ssp370 <- full_join(bioclim_rcp60_2080_landonly, bioclim_ssp370_2080_landonly,
by=c("x", "y"))
bioclim_rcp60_ssp370$dif <- bioclim_rcp60_ssp370$bio12.x - bioclim_rcp60_ssp370$bio12.y
col_val3 <- scales::rescale(unique(c(seq(min(bioclim_rcp60_ssp370$dif), 0, length=5),
seq(0, max(bioclim_rcp60_ssp370$dif), length=5))))
p3 <- ggplot() + geom_tile(data=bioclim_rcp60_ssp370, aes(x=x, y=y, fill=dif)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val3,
limits=c(min(bioclim_rcp60_ssp370$dif)-2,
max(bioclim_rcp60_ssp370$dif )+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp60_ssp370$x),
max(bioclim_rcp60_ssp370$x)),
ylim=c(min(bioclim_rcp60_ssp370$y),
max(bioclim_rcp60_ssp370$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p1 / p2 / p3
## ---- fig.cap="Map of precipitation for a) RCP6.0 (ISIMIP2b), b) SSP370 (ISIMIP3b) and c) the difference between RCP6.0 and SSP370 for the year 2050."----
#### rcp6.0 - ssp370 - 2050 ####
data("bioclim_gfdl-esm2m_rcp60_2050_landonly")
data("bioclim_hadgem2-es_rcp60_2050_landonly")
data("bioclim_ipsl-cm5a-lr_rcp60_2050_landonly")
data("bioclim_miroc5_rcp60_2050_landonly")
bioclim_rcp60_2050_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp60_2050_landonly`,
`bioclim_hadgem2-es_rcp60_2050_landonly`,
`bioclim_ipsl-cm5a-lr_rcp60_2050_landonly`,
`bioclim_miroc5_rcp60_2050_landonly`) %>%
select(x,y,bio12) %>% group_by(x,y) %>% summarise(bio12=mean(bio12, na.rm=T))
col_val1 <- scales::rescale(unique(c(seq(min(bioclim_rcp60_2050_landonly$bio12), 0, length=5),
seq(0, max(bioclim_rcp60_2050_landonly$bio12), length=5))))
p1 <- ggplot() + geom_tile(data=bioclim_rcp60_2050_landonly, aes(x=x, y=y, fill=bio12)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val1,
limits=c(min(bioclim_rcp60_2050_landonly$bio12)-2,
max(bioclim_rcp60_2050_landonly$bio12)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp60_2050_landonly$x),
max(bioclim_rcp60_2050_landonly$x)),
ylim=c(min(bioclim_rcp60_2050_landonly$y),
max(bioclim_rcp60_2050_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
data("bioclim_gfdl-esm4_ssp370_2050_landonly")
data("bioclim_ipsl-cm6a-lr_ssp370_2050_landonly")
data("bioclim_mpi-esm1-2-hr_ssp370_2050_landonly")
data("bioclim_mri-esm2-0_ssp370_2050_landonly")
data("bioclim_ukesm1-0-ll_ssp370_2050_landonly")
bioclim_ssp370_2050_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp370_2050_landonly`,
`bioclim_ipsl-cm6a-lr_ssp370_2050_landonly`,
`bioclim_mpi-esm1-2-hr_ssp370_2050_landonly`,
`bioclim_mri-esm2-0_ssp370_2050_landonly`,
`bioclim_ukesm1-0-ll_ssp370_2050_landonly`) %>%
select(x,y,bio12) %>% group_by(x,y) %>% summarise(bio12=mean(bio12, na.rm=T))
col_val2 <- scales::rescale(unique(c(seq(min(bioclim_ssp370_2050_landonly$bio12), 0, length=5),
seq(0, max(bioclim_ssp370_2050_landonly$bio12), length=5))))
p2 <- ggplot() + geom_tile(data=bioclim_ssp370_2050_landonly, aes(x=x, y=y, fill=bio12)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val2,
limits=c(min(bioclim_ssp370_2050_landonly$bio12)-2,
max(bioclim_ssp370_2050_landonly$bio12)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_ssp370_2050_landonly$x),
max(bioclim_ssp370_2050_landonly$x)),
ylim=c(min(bioclim_ssp370_2050_landonly$y),
max(bioclim_ssp370_2050_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
bioclim_rcp60_ssp370 <- full_join(bioclim_rcp60_2050_landonly, bioclim_ssp370_2050_landonly,
by=c("x", "y"))
bioclim_rcp60_ssp370$dif <- bioclim_rcp60_ssp370$bio12.x - bioclim_rcp60_ssp370$bio12.y
col_val3 <- scales::rescale(unique(c(seq(min(bioclim_rcp60_ssp370$dif), 0, length=5),
seq(0, max(bioclim_rcp60_ssp370$dif), length=5))))
p3 <- ggplot() + geom_tile(data=bioclim_rcp60_ssp370, aes(x=x, y=y, fill=dif)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="prec (mm)", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val3,
limits=c(min(bioclim_rcp60_ssp370$dif)-2,
max(bioclim_rcp60_ssp370$dif )+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp60_ssp370$x),
max(bioclim_rcp60_ssp370$x)),
ylim=c(min(bioclim_rcp60_ssp370$y),
max(bioclim_rcp60_ssp370$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p1 / p2 / p3
## ---- fig.cap="Map of precipitation seasonality for a) RCP8.5 (ISIMIP2b), b) SSP585 (ISIMIP3b) and c) the difference between RCP8.5 and SSP585 for the year 2050."----
#### rcp8.5 - ssp585 - 2050 ####
data("bioclim_gfdl-esm2m_rcp85_2050_landonly")
data("bioclim_hadgem2-es_rcp85_2050_landonly")
data("bioclim_ipsl-cm5a-lr_rcp85_2050_landonly")
data("bioclim_miroc5_rcp85_2050_landonly")
bioclim_rcp85_2050_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp85_2050_landonly`,
`bioclim_hadgem2-es_rcp85_2050_landonly`,
`bioclim_ipsl-cm5a-lr_rcp85_2050_landonly`,
`bioclim_miroc5_rcp85_2050_landonly`) %>%
select(x,y,bio15) %>% group_by(x,y) %>% summarise(bio15=mean(bio15, na.rm=T))
col_val1 <- scales::rescale(unique(seq(0, max(bioclim_rcp85_2050_landonly$bio15), length=9)))
p1 <- ggplot() + geom_tile(data=bioclim_rcp85_2050_landonly, aes(x=x, y=y, fill=bio15)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="bio15", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val1,
limits=c(0, max(bioclim_rcp85_2050_landonly$bio15)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp85_2050_landonly$x),
max(bioclim_rcp85_2050_landonly$x)),
ylim=c(min(bioclim_rcp85_2050_landonly$y),
max(bioclim_rcp85_2050_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
data("bioclim_gfdl-esm4_ssp585_2050_landonly")
data("bioclim_ipsl-cm6a-lr_ssp585_2050_landonly")
data("bioclim_mpi-esm1-2-hr_ssp585_2050_landonly")
data("bioclim_mri-esm2-0_ssp585_2050_landonly")
data("bioclim_ukesm1-0-ll_ssp585_2050_landonly")
bioclim_ssp585_2050_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp585_2050_landonly`,
`bioclim_ipsl-cm6a-lr_ssp585_2050_landonly`,
`bioclim_mpi-esm1-2-hr_ssp585_2050_landonly`,
`bioclim_mri-esm2-0_ssp585_2050_landonly`,
`bioclim_ukesm1-0-ll_ssp585_2050_landonly`) %>%
select(x,y,bio15) %>% group_by(x,y) %>% summarise(bio15=mean(bio15, na.rm=T))
col_val2 <- scales::rescale(unique(seq(0, max(bioclim_ssp585_2050_landonly$bio15), length=9)))
p2 <- ggplot() + geom_tile(data=bioclim_ssp585_2050_landonly, aes(x=x, y=y, fill=bio15)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="bio15", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val2,
limits=c(0, max(bioclim_ssp585_2050_landonly$bio15)+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_ssp585_2050_landonly$x),
max(bioclim_ssp585_2050_landonly$x)),
ylim=c(min(bioclim_ssp585_2050_landonly$y),
max(bioclim_ssp585_2050_landonly$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
bioclim_rcp85_ssp585 <- full_join(bioclim_rcp85_2050_landonly, bioclim_ssp585_2050_landonly,
by=c("x", "y"))
bioclim_rcp85_ssp585$dif <- bioclim_rcp85_ssp585$bio15.x - bioclim_rcp85_ssp585$bio15.y
col_val3 <- scales::rescale(unique(c(seq(min(bioclim_rcp85_ssp585$dif), 0, length=5),
seq(0, max(bioclim_rcp85_ssp585$dif), length=5))))
p3 <- ggplot() + geom_tile(data=bioclim_rcp85_ssp585, aes(x=x, y=y, fill=dif)) +
geom_sf(data=outline, fill="transparent", colour="black") +
scale_fill_gradientn(name="bio15", colours=rev(colorRampPalette(
c("#00007F", "blue", "#007FFF", "cyan",
"white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
na.value="transparent", values=col_val3,
limits=c(min(bioclim_rcp85_ssp585$dif)-2,
max(bioclim_rcp85_ssp585$dif )+2)) +
coord_sf(expand=F,
xlim=c(min(bioclim_rcp85_ssp585$x),
max(bioclim_rcp85_ssp585$x)),
ylim=c(min(bioclim_rcp85_ssp585$y),
max(bioclim_rcp85_ssp585$y)),
ndiscr=0) + theme_classic() +
theme(axis.title = element_blank(), axis.line = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
plot.background = element_rect(fill = "transparent"),
legend.background = element_rect(fill = "transparent"),
legend.box.background = element_rect(fill = "transparent", colour=NA))
p1 / p2 / p3
rm(list=ls()); invisible(gc())
## ---- fig.cap="Correlation of each bioclimatic variable of RCP2.6 (ISIMIP2b) and SSP126 (ISIMIP3b) data for the year 2080."----
#data_names <- data(package = "rISIMIP")$results[,3]
#bioclim_names <- data_names[grepl(data_names, pattern="bioclim")]
# Get RCP data
#rcp_names <- bioclim_names[grepl(bioclim_names, pattern="rcp")]
#ssp_names <- bioclim_names[grepl(bioclim_names, pattern="ssp")]
#data(list=rcp_names, package="rISIMIP")
#data(list=ssp_names, package="rISIMIP")
#### rcp26 - ssp126 -2080 ####
data("bioclim_gfdl-esm2m_rcp26_2080_landonly")
data("bioclim_hadgem2-es_rcp26_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp26_2080_landonly")
data("bioclim_miroc5_rcp26_2080_landonly")
bioclim_rcp26_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2080_landonly`,
`bioclim_hadgem2-es_rcp26_2080_landonly`,
`bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`,
`bioclim_miroc5_rcp26_2080_landonly`) %>%
group_by(x,y) %>% summarise_all(mean)
data("bioclim_gfdl-esm4_ssp126_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp126_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp126_2080_landonly")
data("bioclim_mri-esm2-0_ssp126_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp126_2080_landonly")
bioclim_ssp126_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp126_2080_landonly`,
`bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`,
`bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`,
`bioclim_mri-esm2-0_ssp126_2080_landonly`,
`bioclim_ukesm1-0-ll_ssp126_2080_landonly`) %>%
group_by(x,y) %>% summarise_all(mean)
library(tidyr)
bioclim_rcp26_ssp126_2080 <- full_join(bioclim_rcp26_2080_landonly,
bioclim_ssp126_2080_landonly, by=c("x", "y")) %>%
pivot_longer(!c(x,y), names_to="var", values_to="values") %>%
separate(var, c("var", "scenario"))
bioclim_rcp26_ssp126_2080$scenario <- factor(bioclim_rcp26_ssp126_2080$scenario, labels=c("rcp26", "ssp126"))
bioclim_rcp26_ssp126_2080 <- bioclim_rcp26_ssp126_2080 %>% pivot_wider(names_from="scenario", values_from="values")
head(bioclim_rcp26_ssp126_2080)
ggplot(data=bioclim_rcp26_ssp126_2080, aes(x=rcp26, y=ssp126)) + geom_point() +
facet_wrap(.~var, scales="free") + geom_smooth(method="lm") +
theme_bw() + labs(x="ISIMIP2b RCP26 2080", y="ISIMIP3b SSP126 2080") +
theme(strip.background=element_blank(), strip.text = element_text(size=12, face="bold"))
rm(list=ls()); invisible(gc())
## ---- fig.cap="Global variation in selected bioclimatic variables (bio1, bio12 and bio15) of RCP2.6 (ISIMIP2b) and SSP126 (ISIMIP3b) data across all years."----
#Compare ISIMIP2b & ISIMIP3b global distribution of the different scenarios and time spans to check if they actually mirror an increase in temperature and if it's within the expectations
library(ggsci)
#### ISIMIP2b data ####
data("bioclim_ewembi_1995_landonly")
data("bioclim_gfdl-esm2m_rcp26_2050_landonly")
`bioclim_gfdl-esm2m_rcp26_2050_landonly`$gcm <- "gfdl-esm2m"
data("bioclim_hadgem2-es_rcp26_2050_landonly")
`bioclim_hadgem2-es_rcp26_2050_landonly`$gcm <- "hadgem2-es"
data("bioclim_ipsl-cm5a-lr_rcp26_2050_landonly")
`bioclim_ipsl-cm5a-lr_rcp26_2050_landonly`$gcm <- "ipsl-cm5a-lr"
data("bioclim_miroc5_rcp26_2050_landonly")
`bioclim_miroc5_rcp26_2050_landonly`$gcm <- "miroc5"
data("bioclim_gfdl-esm2m_rcp26_2080_landonly")
`bioclim_gfdl-esm2m_rcp26_2080_landonly`$gcm <- "gfdl-esm2m"
data("bioclim_hadgem2-es_rcp26_2080_landonly")
`bioclim_hadgem2-es_rcp26_2080_landonly`$gcm <- "hadgem2-es"
data("bioclim_ipsl-cm5a-lr_rcp26_2080_landonly")
`bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`$gcm <- "ipsl-cm5a-lr"
data("bioclim_miroc5_rcp26_2080_landonly")
`bioclim_miroc5_rcp26_2080_landonly`$gcm <- "miroc5"
bioclim_ewembi_1995_landonly$year <- 1995
bioclim_ewembi_1995_landonly$gcm <- "EWEMBI"
bioclim_rcp26_2050 <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2050_landonly`,
`bioclim_hadgem2-es_rcp26_2050_landonly`,
`bioclim_ipsl-cm5a-lr_rcp26_2050_landonly`,
`bioclim_miroc5_rcp26_2050_landonly`)
bioclim_rcp26_2050$year <- 2050
bioclim_rcp26_2080 <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2080_landonly`,
`bioclim_hadgem2-es_rcp26_2080_landonly`,
`bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`,
`bioclim_miroc5_rcp26_2080_landonly`)
bioclim_rcp26_2080$year <- 2080
bioclim_rcp26 <- bind_rows(list(bioclim_ewembi_1995_landonly, bioclim_rcp26_2050,
bioclim_rcp26_2080)) %>%
tidyr::pivot_longer(names_to="var", values_to="value", -c(x,y,gcm,year))
p1 <- bioclim_rcp26 %>% filter(var %in% c("bio1", "bio12", "bio15")) %>%
ggplot() + geom_violin(aes(x=as.factor(year), y=value, fill=gcm)) +
facet_wrap(.~var, scales="free_y", strip.position="left", ncol=1) +
ggtitle("ISIMIP2b") + labs(x="Year", y="") +
scale_fill_manual(name="GCM", values=pal_d3("category20")(11)[1:5]) +
theme_bw() + theme(strip.background = element_blank(), strip.placement = "outside",
strip.text = element_text(size=12, face="bold"),
plot.title = element_text(hjust=0.5))
# ISIMIP3b data
data("bioclim_gswp3-w5e5_obsclim_1995_landonly")
data("bioclim_gfdl-esm4_ssp126_2050_landonly")
`bioclim_gfdl-esm4_ssp126_2050_landonly`$gcm <- "gfdl-esm4"
data("bioclim_ipsl-cm6a-lr_ssp126_2050_landonly")
`bioclim_ipsl-cm6a-lr_ssp126_2050_landonly`$gcm <- "ipsl-cm6a-lr"
data("bioclim_mpi-esm1-2-hr_ssp126_2050_landonly")
`bioclim_mpi-esm1-2-hr_ssp126_2050_landonly`$gcm <- "mpi-esm1-2-hr"
data("bioclim_mri-esm2-0_ssp126_2050_landonly")
`bioclim_mri-esm2-0_ssp126_2050_landonly`$gcm <- "mri-esm2-0"
data("bioclim_ukesm1-0-ll_ssp126_2050_landonly")
`bioclim_ukesm1-0-ll_ssp126_2050_landonly`$gcm <- "ukesm1-0-ll"
data("bioclim_gfdl-esm4_ssp126_2080_landonly")
`bioclim_gfdl-esm4_ssp126_2080_landonly`$gcm <- "gfdl-esm4"
data("bioclim_ipsl-cm6a-lr_ssp126_2080_landonly")
`bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`$gcm <- "ipsl-cm6a-lr"
data("bioclim_mpi-esm1-2-hr_ssp126_2080_landonly")
`bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`$gcm <- "mpi-esm1-2-hr"
data("bioclim_mri-esm2-0_ssp126_2080_landonly")
`bioclim_mri-esm2-0_ssp126_2080_landonly`$gcm <- "mri-esm2-0"
data("bioclim_ukesm1-0-ll_ssp126_2080_landonly")
`bioclim_ukesm1-0-ll_ssp126_2080_landonly`$gcm <- "ukesm1-0-ll"
# Merge data
`bioclim_gswp3-w5e5_obsclim_1995_landonly`$year <- 1995
`bioclim_gswp3-w5e5_obsclim_1995_landonly`$gcm <- "gswp3-w5e5"
bioclim_ssp126_2050 <- bind_rows(`bioclim_gfdl-esm4_ssp126_2050_landonly`,
`bioclim_ipsl-cm6a-lr_ssp126_2050_landonly`,
`bioclim_mpi-esm1-2-hr_ssp126_2050_landonly`,
`bioclim_mri-esm2-0_ssp126_2050_landonly`,
`bioclim_ukesm1-0-ll_ssp126_2050_landonly`)
bioclim_ssp126_2050$year <- 2050
bioclim_ssp126_2080 <- bind_rows(`bioclim_gfdl-esm4_ssp126_2080_landonly`,
`bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`,
`bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`,
`bioclim_mri-esm2-0_ssp126_2080_landonly`,
`bioclim_ukesm1-0-ll_ssp126_2080_landonly`)
bioclim_ssp126_2080$year <- 2080
bioclim_ssp126 <- bind_rows(list(`bioclim_gswp3-w5e5_obsclim_1995_landonly`,
bioclim_ssp126_2050, bioclim_ssp126_2080)) %>%
tidyr::pivot_longer(names_to="var", values_to="value", -c(x,y,gcm,year))
p2 <- bioclim_ssp126 %>% filter(var %in% c("bio1", "bio12", "bio15")) %>%
ggplot() + geom_violin(aes(x=as.factor(year), y=value, fill=gcm)) +
facet_wrap(.~var, scales="free_y", strip.position="left", ncol=1) +
ggtitle("ISIMIP3b") + labs(x="Year", y="") +
scale_fill_manual(name="", values=pal_d3("category20")(11)[6:11]) +
theme_bw() + theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text = element_blank(),
plot.title = element_text(hjust=0.5))
p1 + p2 + plot_layout(guides="collect")
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