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Script#22.4_OVERSEE_CMIP5_ensemble_prepare.R
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Script#22.4_OVERSEE_CMIP5_ensemble_prepare.R
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##### 11/11/2019 - ETHZ - Fabio Benedetti © UP Group+ IBP+ ETH Zürich
##### Script for :
# - extracting the CMIP5 models outputs from the netCDF files prepared by C. Laufkötter
# - preparing the baseline (to be defined) and future (2081-2100) climatologies from the monthly climatologies
# - derive the difference (delta) to apply to the in situ climatologies
# - plot and map outputs along the way
# - do the same for O2 and O2 from the various sorts of netCDF...
### Last update: 18/11/2019
# --------------------------------------------------------------------------------------------------------------------------------
library("tidyverse")
library("ncdf4")
library("raster")
library("reshape2")
library("scales")
library("maps")
library("RColorBrewer")
library("viridis")
# Coastline
world2 <- map_data("world2")
# WD
WD <- getwd()
# Vector of months names
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
# --------------------------------------------------------------------------------------------------------------------------------
##### 1°) Start with ALL MODELS by LOTTE (SST, Si, NO3, PO4, CHL (nano+diat))
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/oxygen_files_separate_esm")
### 1.A) Extract and format SST monthly outputs
# sst1.nc
nc <- nc_open("sst1.nc")
nc # units: days since 2006-01-01 00:00:00
names(nc$var) # DATA_pisces + DATA_CNRM # ok
# And with raster ?
ras <- raster::stack("sst1.nc", varname = "DATA_pisces") # By default --> DATA_pisces
ras
# dimensions : 180, 360, 64800, 1068 (nrow, ncol, ncell, nlayers)
# nlayers ? = 2880/12 = 89 years --> one layer = one month
r <- as.data.frame(ras, xy = T)
dim(r) # 64800 1070
head(r[,c(1:10)])
summary(r[,c(1:10)]) # x = 0-360°, y = -90°/+90°
colnames(r) # 3 to 1068 = 1068 months and years
# Ok, let's go :-) Extract and make data.frames out of the .nc files
### Choose ESM
ras <- raster::stack("sst1.nc", varname = "DATA_pisces")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract SST data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
# dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
# cols[c(1:240)] # for baseline
# cols[c(829:length(cols))] # for future conditions
sst_baseline <- r[,c(1,2,3:242)]
sst_fut <- r[,c(1,2,831:length(r))]
# dim(sst_baseline); dim(sst_fut)
head(sst_baseline); head(sst_fut)
# Add an id
sst_baseline$id <- factor(paste(sst_baseline$x, sst_baseline$y, sep = "_"))
sst_fut$id <- factor(paste(sst_fut$x, sst_fut$y, sep = "_"))
head(sst_baseline$id); head(sst_fut$id)
# Melt, and no need to convert to SST !
m_sst_base <- melt(sst_baseline, id.vars = c("id","x","y"))
m_sst_fut <- melt(sst_fut, id.vars = c("id","x","y"))
colnames(m_sst_base)[c(4,5)] <- c("month","SST")
colnames(m_sst_fut)[c(4,5)] <- c("month","SST")
# dim(m_sst_base); dim(m_sst_fut)
# head(m_sst_base); head(m_sst_fut)
summary(m_sst_base)
summary(m_sst_fut) # +4°C in max temperatures, ok guys
# head(m_sst_base)
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_sst_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_sst_base$month), "_") ))[,1]
m_sst_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_sst_fut$month), "_") ))[,1]
# head(m_sst_base); head(m_sst_fut)
# Clean some stuff
rm(sst_fut, sst_baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_sst_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), SST = mean(SST) ) )
clims_fut <- data.frame( m_sst_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), SST = mean(SST) ) )
# dim(clims_base); dim(clims_fut)
# head(clims_base); head(clims_fut)
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "SST")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "SST")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
#save(deltas, file = "clims_mon_diff_sst_rcp85_CNRM-PISCES_2031-2100.Rdata")
save(clims_base, file = "clims_mon_sst_rcp85_IPSL-PISCES_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_sst_rcp85_IPSL-PISCES_2100-2081.Rdata")
#
# # maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta SST (°C)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_sst_rcp85_CNRM-PISCES_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
# sst2.nc
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/Monthly forecast files by Lotte (ALL MODELS)")
nc <- nc_open("sst2.nc")
nc # units: days since 2008-01-01 00:00:00
names(nc$var) # DATA_GFDL + DATA_PELAGOS + DATA_nemuro # ok, skip PELAGOS though
### Choose ESL
ras <- raster::stack("sst2.nc", varname = "DATA_nemuro")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract SST data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
sst_baseline <- r[,c(1,2,3:242)]
sst_fut <- r[,c(1,2,831:length(r))]
# dim(sst_baseline); dim(sst_fut)
head(sst_baseline); head(sst_fut)
# Add an id
sst_baseline$id <- factor(paste(sst_baseline$x, sst_baseline$y, sep = "_"))
sst_fut$id <- factor(paste(sst_fut$x, sst_fut$y, sep = "_"))
head(sst_baseline$id); head(sst_fut$id)
# Melt, and no need to convert to SST !
m_sst_base <- melt(sst_baseline, id.vars = c("id","x","y"))
m_sst_fut <- melt(sst_fut, id.vars = c("id","x","y"))
colnames(m_sst_base)[c(4,5)] <- c("month","SST")
colnames(m_sst_fut)[c(4,5)] <- c("month","SST")
summary(m_sst_base)
summary(m_sst_fut) # +4°C in max temperatures, ok guys
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_sst_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_sst_base$month), "_") ))[,1]
m_sst_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_sst_fut$month), "_") ))[,1]
head(m_sst_base); head(m_sst_fut)
# Clean some stuff
rm(sst_fut, sst_baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_sst_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), SST = mean(SST) ) )
clims_fut <- data.frame( m_sst_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), SST = mean(SST) ) )
# dim(clims_base); dim(clims_fut)
# head(clims_base); head(clims_fut)
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "SST")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "SST")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
save(deltas, file = "clims_mon_diff_sst_rcp85_MRI-NEMURO_2031-2100.Rdata")
# And monthly means
save(clims_base, file = "clims_mon_sst_rcp85_MRI-NEMURO_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_sst_rcp85_MRI-NEMURO_2100-2081.Rdata")
# # maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta SST (°C)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_sst_rcp85_MRI-NEMURO_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
# sst3.nc
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/Monthly forecast files by Lotte (ALL MODELS)")
nc <- nc_open("sst3.nc")
nc # units: days since 2008-01-01 00:00:00
names(nc$var) # DATA_Hadgem + DATA_BEC # ok, skip DATA_Hadgem though
### Choose ESL
ras <- raster::stack("sst3.nc", varname = "DATA_BEC")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract SST data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
sst_baseline <- r[,c(1,2,3:242)]
sst_fut <- r[,c(1,2,831:length(r))]
# dim(sst_baseline); dim(sst_fut)
head(sst_baseline); head(sst_fut)
# Add an id
sst_baseline$id <- factor(paste(sst_baseline$x, sst_baseline$y, sep = "_"))
sst_fut$id <- factor(paste(sst_fut$x, sst_fut$y, sep = "_"))
head(sst_baseline$id); head(sst_fut$id)
# Melt, and no need to convert to SST !
m_sst_base <- melt(sst_baseline, id.vars = c("id","x","y"))
m_sst_fut <- melt(sst_fut, id.vars = c("id","x","y"))
colnames(m_sst_base)[c(4,5)] <- c("month","SST")
colnames(m_sst_fut)[c(4,5)] <- c("month","SST")
summary(m_sst_base)
summary(m_sst_fut)
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_sst_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_sst_base$month), "_") ))[,1]
m_sst_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_sst_fut$month), "_") ))[,1]
head(m_sst_base); head(m_sst_fut)
# Clean some stuff
rm(sst_fut, sst_baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_sst_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), SST = mean(SST) ) )
clims_fut <- data.frame( m_sst_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), SST = mean(SST) ) )
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "SST")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "SST")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
save(deltas, file = "clims_mon_diff_sst_rcp85_CESM-BEC_2031-2100.Rdata")
save(clims_base, file = "clims_mon_sst_rcp85_CESM-BEC_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_sst_rcp85_CESM-BEC_2100-2081.Rdata")
# maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta SST (°C)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_sst_rcp85_CESM-BEC_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
#
### 1.B) Extract and format NO3 monthly outputs ----------------------------------------------------------------------
# no31.nc
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/Monthly forecast files by Lotte (ALL MODELS)")
nc <- nc_open("no31.nc")
nc # units = mol m-3 --> will need to convert to µmol
names(nc$var) # DATA_pisces + DATA_CNRM # ok, use both
### Choose ESM
ras <- raster::stack("no31.nc", varname = "DATA_pisces")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract SST data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
baseline <- r[,c(1,2,3:242)]
fut <- r[,c(1,2,831:length(r))]
# dim(baseline); dim(fut)
head(baseline); head(fut)
# Add an id
baseline$id <- factor(paste(baseline$x, baseline$y, sep = "_"))
fut$id <- factor(paste(fut$x, fut$y, sep = "_"))
head(baseline$id); head(fut$id)
# Melt, and no need to convert to SST !
m_base <- melt(baseline, id.vars = c("id","x","y"))
m_fut <- melt(fut, id.vars = c("id","x","y"))
colnames(m_base)[c(4,5)] <- c("month","NO3")
colnames(m_fut)[c(4,5)] <- c("month","NO3")
summary(m_base)
summary(m_fut)
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_base$month), "_") ))[,1]
m_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_fut$month), "_") ))[,1]
head(m_base); head(m_fut)
# Clean some stuff
rm(fut, baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), NO3 = mean(NO3) ) )
clims_fut <- data.frame( m_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), NO3 = mean(NO3) ) )
head(clims_base); head(clims_fut)
# Convert to µmol --> *1000
# summary(log1p((clims_base$NO3)*1000)); summary(log1p((clims_fut$NO3)*1000))
clims_base$NO3 <- (clims_base$NO3)*1000
clims_fut$NO3 <- (clims_fut$NO3)*1000
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "NO3")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "NO3")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
save(deltas, file = "clims_mon_diff_no3_rcp85_CNRM-PISCES_2031-2100.Rdata")
save(clims_base, file = "clims_mon_no3_rcp85_IPSL-PISCES_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_no3_rcp85_IPSL-PISCES_2100-2081.Rdata")
# maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta NO3 (µM)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_no3_rcp85_CNRM-PISCES_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
# no32.nc
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/")
nc <- nc_open("no32.nc")
nc # units = mol m-3 --> will need to convert to µmol
names(nc$var) # DATA_GFDL + DATA_nemuro # ok, use both
### Choose ESM
ras <- raster::stack("no32.nc", varname = "DATA_nemuro")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract SST data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
baseline <- r[,c(1,2,3:242)]
fut <- r[,c(1,2,831:length(r))]
# dim(baseline); dim(fut)
head(baseline); head(fut)
# Add an id
baseline$id <- factor(paste(baseline$x, baseline$y, sep = "_"))
fut$id <- factor(paste(fut$x, fut$y, sep = "_"))
head(baseline$id); head(fut$id)
# Melt, and no need to convert to SST !
m_base <- melt(baseline, id.vars = c("id","x","y"))
m_fut <- melt(fut, id.vars = c("id","x","y"))
colnames(m_base)[c(4,5)] <- c("month","NO3")
colnames(m_fut)[c(4,5)] <- c("month","NO3")
summary(m_base)
summary(m_fut)
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_base$month), "_") ))[,1]
m_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_fut$month), "_") ))[,1]
head(m_base); head(m_fut)
# Clean some stuff
rm(fut, baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), NO3 = mean(NO3) ) )
clims_fut <- data.frame( m_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), NO3 = mean(NO3) ) )
head(clims_base); head(clims_fut)
# Convert to µmol --> *1000
# summary(log1p((clims_base$NO3)*1000)); summary(log1p((clims_fut$NO3)*1000))
clims_base$NO3 <- (clims_base$NO3)*1000
clims_fut$NO3 <- (clims_fut$NO3)*1000
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "NO3")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "NO3")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
save(clims_base, file = "clims_mon_no3_rcp85_MRI-NEMURO_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_no3_rcp85_MRI-NEMURO_2100-2081.Rdata")
save(deltas, file = "clims_mon_diff_no3_rcp85_MRI-NEMURO_2031-2100.Rdata")
# maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta NO3 (µM)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_no3_rcp85_MRI-NEMURO_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
# no33.nc
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/")
nc <- nc_open("no33.nc")
nc # units = mol m-3 --> will need to convert to µmol
names(nc$var) # DATA_Hadgem + DATA_BEC # ok, use both
### Choose ESM
ras <- raster::stack("no33.nc", varname = "DATA_BEC")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract SST data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
baseline <- r[,c(1,2,3:242)]
fut <- r[,c(1,2,831:length(r))]
# dim(baseline); dim(fut)
head(baseline); head(fut)
# Add an id
baseline$id <- factor(paste(baseline$x, baseline$y, sep = "_"))
fut$id <- factor(paste(fut$x, fut$y, sep = "_"))
head(baseline$id); head(fut$id)
# Melt, and no need to convert to SST !
m_base <- melt(baseline, id.vars = c("id","x","y"))
m_fut <- melt(fut, id.vars = c("id","x","y"))
colnames(m_base)[c(4,5)] <- c("month","NO3")
colnames(m_fut)[c(4,5)] <- c("month","NO3")
summary(m_base)
summary(m_fut)
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_base$month), "_") ))[,1]
m_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_fut$month), "_") ))[,1]
head(m_base); head(m_fut)
# Clean some stuff
rm(fut, baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), NO3 = mean(NO3) ) )
clims_fut <- data.frame( m_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), NO3 = mean(NO3) ) )
head(clims_base); head(clims_fut)
# Convert to µmol --> *1000
# summary(log1p((clims_base$NO3)*1000)); summary(log1p((clims_fut$NO3)*1000))
clims_base$NO3 <- (clims_base$NO3)*1000
clims_fut$NO3 <- (clims_fut$NO3)*1000
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "NO3")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "NO3")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
save(clims_base, file = "clims_mon_no3_rcp85_CESM-BEC_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_no3_rcp85_CESM-BEC_2100-2081.Rdata")
#save(deltas, file = "clims_mon_diff_no3_rcp85_CESM-BEC_2031-2100.Rdata")
# maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta NO3 (µM)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_no3_rcp85_CESM-BEC_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
### 1.C) Extract and format PO4 monthly outputs ----------------------------------------------------------------------
# po41.nc
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/")
nc <- nc_open("po41.nc")
nc # units = mol m-3 --> will need to convert to µmol
names(nc$var) # DATA_pisces + DATA_CNRM # ok, use both
### Choose ESM
ras <- raster::stack("po41.nc", varname = "DATA_CNRM")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
baseline <- r[,c(1,2,3:242)]
fut <- r[,c(1,2,831:length(r))]
# dim(baseline); dim(fut)
head(baseline); head(fut)
# Add an id
baseline$id <- factor(paste(baseline$x, baseline$y, sep = "_"))
fut$id <- factor(paste(fut$x, fut$y, sep = "_"))
head(baseline$id); head(fut$id)
# Melt, and no need to convert to SST !
m_base <- melt(baseline, id.vars = c("id","x","y"))
m_fut <- melt(fut, id.vars = c("id","x","y"))
colnames(m_base)[c(4,5)] <- c("month","PO4")
colnames(m_fut)[c(4,5)] <- c("month","PO4")
summary(m_base)
summary(m_fut)
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_base$month), "_") ))[,1]
m_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_fut$month), "_") ))[,1]
head(m_base); head(m_fut)
# Clean some stuff
rm(fut, baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), PO4 = mean(PO4) ) )
clims_fut <- data.frame( m_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), PO4 = mean(PO4) ) )
head(clims_base); head(clims_fut)
# Convert to µmol --> *1000
# summary(log1p((clims_base$PO4)*1000)); summary(log1p((clims_fut$PO4)*1000))
clims_base$PO4 <- (clims_base$PO4)*1000
clims_fut$PO4 <- (clims_fut$PO4)*1000
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "PO4")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "PO4")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
#save(deltas, file = "clims_mon_diff_po4_rcp85_CNRM-PISCES_2031-2100.Rdata")
save(clims_base, file = "clims_mon_po4_rcp85_CNRM-PISCES_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_po4_rcp85_CNRM-PISCES_2100-2081.Rdata")
# maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta PO4 (µM)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_po4_rcp85_CNRM-PISCES_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
# po42.nc
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/")
nc <- nc_open("po42.nc")
nc # units = mol m-3 --> will need to convert to µmol
names(nc$var) # DATA_pisces + DATA_CNRM # ok, use both
### Choose ESM
ras <- raster::stack("po42.nc", varname = "DATA_GFDL")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
baseline <- r[,c(1,2,3:242)]
fut <- r[,c(1,2,831:length(r))]
# dim(baseline); dim(fut)
head(baseline); head(fut)
# Add an id
baseline$id <- factor(paste(baseline$x, baseline$y, sep = "_"))
fut$id <- factor(paste(fut$x, fut$y, sep = "_"))
head(baseline$id); head(fut$id)
# Melt, and no need to convert to SST !
m_base <- melt(baseline, id.vars = c("id","x","y"))
m_fut <- melt(fut, id.vars = c("id","x","y"))
colnames(m_base)[c(4,5)] <- c("month","PO4")
colnames(m_fut)[c(4,5)] <- c("month","PO4")
summary(m_base)
summary(m_fut)
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_base$month), "_") ))[,1]
m_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_fut$month), "_") ))[,1]
head(m_base); head(m_fut)
# Clean some stuff
rm(fut, baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), PO4 = mean(PO4) ) )
clims_fut <- data.frame( m_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), PO4 = mean(PO4) ) )
head(clims_base); head(clims_fut)
# Convert to µmol --> *1000
# summary(log1p((clims_base$PO4)*1000)); summary(log1p((clims_fut$PO4)*1000))
clims_base$PO4 <- (clims_base$PO4)*1000
clims_fut$PO4 <- (clims_fut$PO4)*1000
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "PO4")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "PO4")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
#save(deltas, file = "clims_mon_diff_po4_rcp85_GFDL-TOPAZ_2031-2100.Rdata")
save(clims_base, file = "clims_mon_po4_rcp85_GFDL-TOPAZ_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_po4_rcp85_GFDL-TOPAZ_2100-2081.Rdata")
# maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta PO4 (µM)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_po4_rcp85_GFDL-TOPAZ_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
# po43.nc
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/")
nc <- nc_open("po43.nc")
nc # units = mol m-3 --> will need to convert to µmol
names(nc$var) # DATA_Hadgem + DATA_BEC # ok, use DATA_BEC
### Choose ESM
ras <- raster::stack("po43.nc", varname = "DATA_BEC")
ras
months <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
length(rep(months, times = 89)) # ok, now add the years: vector of same length but with years repeating 12 times
length(rep(2012:2100, each=12))
# Ok, seems like a good start
m <- rep(months, times = 89)
l <- rep(2012:2100, each= 12)
# paste(m,l, sep = "_") # yeah
cols <- paste(m,l, sep = "_")
# Extract data for RCP8.5 and provide colnames
r <- as.data.frame(ras, xy = T)
dim(r)
colnames(r)[c(3:length(r))] <- cols
head(r[,c(1:25)])
summary(r[,c(1:10)])
# Ok, select the columns you are not interested in
baseline <- r[,c(1,2,3:242)]
fut <- r[,c(1,2,831:length(r))]
# dim(baseline); dim(fut)
head(baseline); head(fut)
# Add an id
baseline$id <- factor(paste(baseline$x, baseline$y, sep = "_"))
fut$id <- factor(paste(fut$x, fut$y, sep = "_"))
head(baseline$id); head(fut$id)
# Melt, and no need to convert to SST !
m_base <- melt(baseline, id.vars = c("id","x","y"))
m_fut <- melt(fut, id.vars = c("id","x","y"))
colnames(m_base)[c(4,5)] <- c("month","PO4")
colnames(m_fut)[c(4,5)] <- c("month","PO4")
summary(m_base)
summary(m_fut)
# Add a column with just months to compute the 12 monthly climatologies with dplyr
m_base$m <- data.frame(do.call(rbind, strsplit(as.character(m_base$month), "_") ))[,1]
m_fut$m <- data.frame(do.call(rbind, strsplit(as.character(m_fut$month), "_") ))[,1]
head(m_base); head(m_fut)
# Clean some stuff
rm(fut, baseline, r, ras); gc()
# Compute the 12 monthly clims
require("dplyr")
clims_base <- data.frame( m_base %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), PO4 = mean(PO4) ) )
clims_fut <- data.frame( m_fut %>% group_by(id,m) %>% summarise(x = unique(x), y = unique(y), PO4 = mean(PO4) ) )
head(clims_base); head(clims_fut)
# Convert to µmol --> *1000
# summary(log1p((clims_base$PO4)*1000)); summary(log1p((clims_fut$PO4)*1000))
clims_base$PO4 <- (clims_base$PO4)*1000
clims_fut$PO4 <- (clims_fut$PO4)*1000
### dcast to put the 12 months and coordinates as columns
clims_base <- dcast(data = clims_base, id + x + y ~ m, value.var = "PO4")
clims_fut <- dcast(data = clims_fut, id + x + y ~ m, value.var = "PO4")
dim(clims_base); dim(clims_fut)
head(clims_base); head(clims_fut)
summary(clims_base); summary(clims_fut)
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base$id, x = clims_base$x, y = clims_base$y,
Jan = (clims_fut$Jan) - (clims_base$Jan), Feb = (clims_fut$Feb) - (clims_base$Feb), Mar = (clims_fut$Mar) - (clims_base$Mar),
Apr = (clims_fut$Apr) - (clims_base$Apr), May = (clims_fut$May) - (clims_base$May), Jun = (clims_fut$Jun) - (clims_base$Jun),
Jul = (clims_fut$Jul) - (clims_base$Jul), Aug = (clims_fut$Aug) - (clims_base$Aug), Sep = (clims_fut$Sep) - (clims_base$Sep),
Oct = (clims_fut$Oct) - (clims_base$Oct), Nov = (clims_fut$Nov) - (clims_base$Nov), Dec = (clims_fut$Dec) - (clims_base$Dec)
) # eo ddf
summary(deltas)
### Save on your dir and plot from your computer because something is obviously wrong with the graphics device
setwd("/net/kryo/work/fabioben/OVERSEE/data/future/MAREMIP_data/mon_clims/")
dir()
#save(deltas, file = "clims_mon_diff_po4_rcp85_CESM-BEC_2031-2100.Rdata")
save(clims_base, file = "clims_mon_po4_rcp85_CESM-BEC_2031-2012.Rdata")
save(clims_fut, file = "clims_mon_po4_rcp85_CESM-BEC_2100-2081.Rdata")
# maps
# for(momo in months) {
#
# d <- deltas[,c("x","y",momo)]
# colnames(d)[c(3)] <- "delta"
#
# map <- ggplot() + geom_raster(aes(x = x, y = y, fill = delta), data = na.omit(d) ) +
# scale_fill_gradient2(name = "Delta PO4 (µM)\n(2100-2031)", low = "blue", high = "red", mid = "white") +
# geom_polygon(aes(x = long, y = lat, group = group), data = world2, fill = "grey70", colour = "black", size = 0.3) +
# coord_quickmap() + scale_x_continuous(name = "Longitude", breaks = c(0,60,120,180,240,300,360),
# labels = c("0°W","60°W","120°W","180°W","-120°W","-60°W","0°W"), expand = c(0,0)) +
# scale_y_continuous(name = "Latitude", breaks = c(-90,-60,-30,0,30,60,90),
# labels = c("-90°N","-60°N","-30°N","0°N","30°N","60°N","90°N"), expand = c(0,0)) +
# theme(panel.background = element_rect(fill = "white"),legend.key = element_rect(fill = "grey50"),
# panel.grid.major = element_line(colour = "grey70",linetype = "dashed") )
#
# ggsave(plot = map, filename = paste("map_delta_po4_rcp85_CESM-BEC_",momo,".pdf", sep = ""), height = 3, width = 6, dpi = 300)
# rm(d);gc()
# } # eo for loop
### 12/11/19: For MRI-NEMURO, compute monthly climatologies and diff of PO4 based on NO3 and the Redfield C:N:P ratio
### (106:16:1) --> divide average NO3 by 16 to get average PO4
clims_base_no3 <- get(load("clims_mon_no3_rcp85_MRI-NEMURO_2031-2012.Rdata"))
clims_fut_no3 <- get(load("clims_mon_no3_rcp85_MRI-NEMURO_2100-2081.Rdata"))
dim(clims_base_no3); dim(clims_fut_no3)
# Convert to PO4
clims_base_po4 <- (clims_base_no3[,c(4:15)])/16
clims_fut_po4 <- (clims_fut_no3[,c(4:15)])/16
summary(clims_base_po4)
summary(clims_fut_po4)
summary(log1p(clims_base_po4$Jan))
summary(log1p(clims_fut_po4$Oct))
# Compute the 12 monthly deltas SST and map
deltas <- data.frame(id = clims_base_po4$id, x = clims_base_po4$x, y = clims_base_po4$y,
Jan = (clims_fut_po4$Jan) - (clims_base_po4$Jan), Feb = (clims_fut_po4$Feb) - (clims_base_po4$Feb),
Mar = (clims_fut_po4$Mar) - (clims_base_po4$Mar),
Apr = (clims_fut_po4$Apr) - (clims_base_po4$Apr), May = (clims_fut_po4$May) - (clims_base_po4$May),
Jun = (clims_fut_po4$Jun) - (clims_base_po4$Jun),
Jul = (clims_fut_po4$Jul) - (clims_base_po4$Jul), Aug = (clims_fut_po4$Aug) - (clims_base_po4$Aug),
Sep = (clims_fut_po4$Sep) - (clims_base_po4$Sep),
Oct = (clims_fut_po4$Oct) - (clims_base_po4$Oct), Nov = (clims_fut_po4$Nov) - (clims_base_po4$Nov),