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Environment_MARGO.R
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###################################
# N.B. this is not the code I used in the LDG paper, that's in the MARGO project code folder
###################################
## 26 / 3 / 2015
## Isabel Fenton
## Collecting the Environmental data from the ldg analysis for the MARGO points
##
## Based on 1311 LDGPaper/Code/Environment.R
##
## Input:
## 150326_Environment.RData - the environmental data already downloaded for the bfd
## BFD_SST.txt (BFD/Environmental/SST_4km) temperature at 4km res
## files of mean and sd of 1deg temperature at various depths (BFD/Environmental/SST_1deg_Mean & SST_1deg_SD)
## MLD_dec_ed.txt (BFD/Environmental/MLD) mixed layer depth yearly values
## monthly_chl.txt (BFD/Environmental/Chl_4km) chlorophyll data at 4km resolution
## files of Chla_1deg_means.csv (BFD/Environmental/Chl_1deg) chlorophyll data at 1 degree resolution
## s00mn01.csv (BFD/Environment/Salinity) mean salinity with depth data at 1 degree resolution
## s00sd01.csv (BFD/Environment/Salinity) sd salinity with depth data at 1 degree resolution
##
## Output:
## distrib.maps of all files
## temperature sections with depth for range of stratification measures
## 140522_ldg_env.Rdata - environmental data for BFD sites
## 140522_Environment_ws.Rdata - Complete workspace
##
source("C:/Documents/Science/PhD/Code/maps.R")
source("C:/Documents/Science/PhD/Code/palettes.R")
source("C:/Documents/Science/PhD/Code/sp_mat_2_df.R") # for section 6i.
library(sp) # point in polygon, section 6ii.
load("C:/Documents/Science/PhD/Work/1311\ LDGPaper/Output/150326_Environment.RData")
## 1. SST at 4km resolution ------------------------------------------------
setwd("C:/Documents/Science/PhD/Project/BFD/Environmental/SST_4km/")
## 1i. load in the data ----------------------------------------------------
# This data has been obtained from NOAA. See temperature.docx and SSTmeta.txt for more info
SST.4km.bfd <- read.table("BFD_SST.txt", header = T)
## convert to SST
# scale*pixel_Value + offset
# scale = 0.075 and offset = - 3.0
SST.4km.bfd[, 4:15] <- SST.4km.bfd[, 4:15] * 0.075 - 3
## 1ii. calculate mean and SD ----------------------------------------------
# add columns of mean and SD
SST.4km.bfd$meanSST <- rowMeans(SST.4km.bfd[, 4:15])
SST.4km.bfd$sdSST <- apply(SST.4km.bfd[, 4:15], 1, sd)
## 1iii. output images ------------------------------------------------------
# notice there is one point in the tropical Atlantic which is coming out very low T
order(SST.4km.bfd$meanSST)[1]
(sort(SST.4km.bfd$meanSST)[1] + 3) / 0.075
which(SST.4km.bfd$sdSST == 0)
# for some reason this hasn't worked, so set it to NA
SST.4km.bfd$meanSST[SST.4km.bfd$meanSST == min(SST.4km.bfd$meanSST)] <- NA
SST.4km.bfd$sdSST[SST.4km.bfd$sdSST == 0] <- NA
png(file = "meanSST_4km.png", width = 800, height = 500)
with(SST.4km.bfd[!is.na(SST.4km.bfd$meanSST), ], distrib.filled(interp(LONG, LAT, meanSST + 0.5, duplicate = "mean"), nlevels = 100))
dev.off()
png(file = "sdSST_4km.png", width = 800, height = 500)
with(SST.4km.bfd[!is.na(SST.4km.bfd$sdSST), ], distrib.filled(interp(LONG, LAT, sdSST, duplicate = "mean"), nlevels = 100))
dev.off()
## 1iv. put values for mean and sd in a dataframe ----------------------------------------
ldg.env <- ldg.data[, 1:6]
# add that data to ldg.data
ldg.env$meanSST.4km <- SST.4km.bfd$meanSST
ldg.env$sdSST.4km <- SST.4km.bfd$sdSST
## 2. Temperature with depth at 1deg resolution ----------------------------
## 2i. Create a function for loading data and one for images -------------------
# actual depths
WOA.depths <- c(0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500)
## function to load the data in for mean / sd and to produce a dataframe
load.depths <- function(value){
# value - either "mn" or "sd"
# for each depth
for (i in 1:33) {
# load in the file
if (i < 10) {
tmp <- read.table(paste("t00", value, "1.00", i, sep = ""), header = T)
} else {
tmp <- read.table(paste("t00", value, "1.0", i, sep = ""), header = T)
}
# for the first load the entire file, for the rest, only add the value column
if (i == 1) {
t1 <- tmp
colnames(t1)[1:2] <- c("Lat", "Long")
} else {
t1 <- cbind(t1, tmp$WOA_VALUE)
}
# change column names to relevant depth
colnames(t1)[i + 2] <- paste("depth", WOA.depths[i], "m", sep = "")
# change missing values to NA
t1[,i + 2][t1[,i + 2] == -99.999] <- NA
}
return(t1)
}
# function to produce image files for each depth
data.img <- function(data, name) {
# data - dataframe of temp data with depth
# name - either "mn" or "sd"
for (i in 1:33) {
# produce .png images in the same file
png(paste("t", name, i, ".png", sep = ""), width = 800, height = 500)
# n.b. the scale is offset by 3 (so currently no key)
with(data, distrib.map(Long, Lat, data[,i + 2], pch = 15, maintitle = colnames(data)[i + 2], cex = 0.4))
dev.off()
}
}
## 2ii. Run function for mean T ----------------------------------------------
setwd("C:/Documents/Science/PhD/Project/BFD/Environmental/SST_1deg_Mean")
mean.t.depth <- load.depths("mn")
data.img(mean.t.depth, "mn")
## 2iii. Run function for SD --------------------------------------------------
setwd("C:/Documents/Science/PhD/Project/BFD/Environmental/SST_1deg_SD")
sd.t.depth <- load.depths("sd")
data.img(sd.t.depth, "sd")
## 2iv. Identifying environmental conditions for BFD points ----------------------
# write a function that will resolve a value to a given accuracy
matching <- function(coord, to.match) {
# coord - the latitude or longitude of the site (single number)
# to.match - vector of the new resolutions
# give it data to a certain resolution, return data to a new resolution
# what is the new resolution?
new.res <- unique(to.match %% 1)
round.coord <- coord %% 1
# set up the resolution comparisons
new.res <- c(max(new.res) - 1, new.res, min(new.res) + 1)
# return the new resolution ending
new.coord <- new.res[which(abs(new.res - round.coord) == min(abs(new.res - round.coord)))][1]
new.coord + coord %/% 1
}
# a function that will return the temperature data for a given set of coordinates
WOA.temp <- function(bfd.row, T.data, T.cols) {
# bfd.row - a row from a sites dataframe, with columns 'Lat' and 'Long
# T.data - an environmental dataframe, with columns 'Lat' and 'Long'
# T.data - the columns of the environmental dataframe which relate to the environmental variable
# calculate the possible lats and longs
long <- matching(bfd.row$Long, T.data$Long)
lat <- matching(bfd.row$Lat, T.data$Lat)
# which row has both
row <- which(T.data$Long == long & T.data$Lat == lat)
# if there is no data throw an error
# return either a set of values or single value
if (!is.null(dim(T.cols))) {
if (sum(!is.na(T.cols[row,])) == 0) stop ("All data are NAs")
return(T.cols[row,])
} else {
if (is.na(T.cols[row])) stop ("Data is NA")
return(T.cols[row])
}
}
# generate a dataframe containing the coordinates to calculate temperature at those points.
temp.bfd <- ldg.data[,1:6]
# create columns for mean t
for (i in 3:ncol(mean.t.depth)){
temp.bfd <- cbind(temp.bfd,NA)
colnames(temp.bfd)[4 + i] <- as.character(paste("mean", colnames(mean.t.depth[i]), sep = "."))
}
# and for sd
for (i in 3:ncol(sd.t.depth)){
temp.bfd <- cbind(temp.bfd,NA)
colnames(temp.bfd)[37 + i] <- as.character(paste("sd", colnames(sd.t.depth[i]), sep = "."))
}
# add the temperature data (mean and sd) for the whole of the dataset
for(i in 1:nrow(temp.bfd))
{
tryCatch(temp.bfd[i, grep("mean", colnames(temp.bfd))] <- WOA.temp(temp.bfd[i,], mean.t.depth, mean.t.depth[, 3:ncol(mean.t.depth)]), error = function(e) print(i))
tryCatch(temp.bfd[i, grep("sd", colnames(temp.bfd))] <- WOA.temp(temp.bfd[i,], sd.t.depth, sd.t.depth[, 3:ncol(sd.t.depth)]), error = function(e) print(i))
}
# identify where there is no data
which(is.na(temp.bfd$mean.depth0m))
which(is.na(temp.bfd$sd.depth0m))
# for those sites average the neighbouring sites
WOA.na.temp <- function(bfd.row, T.data, T.cols) {
# bfd.row - a row from a sites dataframe, with columns 'Lat' and 'Long
# T.data - an environmental dataframe, with columns 'Lat' and 'Long'
# T.data - the columns of the environmental dataframe which relate to the environmental variable
# calculate the possible lats and longs
long <- matching(bfd.row$Long, T.data$Long)
lat <- matching(bfd.row$Lat, T.data$Lat)
# generate a data frame of nearest points
nearest <- data.frame(long = c(long, long + 1, long - 1, long, long), lat = c(rep(lat, 3), lat + 1, lat - 1))
# cycle through those points to look for data
tmp.values <- NULL
if (!is.null(dim(T.cols))) {
for (i in 1:nrow(nearest)) {
row <- which(T.data$Long == nearest$long[i] & T.data$Lat == nearest$lat[i])
tmp.values <- rbind(tmp.values, T.cols[row, ])
}
# calculate the mean values
avg.value <- apply(tmp.values, 2, mean, na.rm = T)
avg.value[is.na(avg.value)] <- NA
# if all data is NA, throw an error
if (sum(!is.na(avg.value)) == 0) stop ("All data are NAs")
return(avg.value)
} else {
for (i in 1:nrow(nearest)) {
row <- which(T.data$Long == nearest$long[i] & T.data$Lat == nearest$lat[i])
tmp.values <- c(tmp.values, T.cols[row])
}
return(mean(tmp.values, na.rm = T))
}
}
na.sites <- sort(unique(c(which(is.na(temp.bfd$mean.depth0m)), which(is.na(temp.bfd$sd.depth0m)))))
# add a column to the dataframe showing which are exact
temp.bfd$exact <- "Y"
temp.bfd$exact[na.sites] <- "N"
# add the data (mean and sd) for those missing values by averaging surrounding sites
for(i in na.sites)
{
tryCatch(temp.bfd[i, grep("mean", colnames(temp.bfd))] <- WOA.na.temp(temp.bfd[i,], mean.t.depth, mean.t.depth[, 3:ncol(mean.t.depth)]), error = function(e) print(i))
tryCatch(temp.bfd[i, grep("sd", colnames(temp.bfd))] <- WOA.na.temp(temp.bfd[i,], sd.t.depth, sd.t.depth[, 3:ncol(sd.t.depth)]), error = function(e) print(i))
}
rm(na.sites)
# check whether this has worked
which(is.na(temp.bfd$mean.depth0m)) # there now aren't any
which(is.na(temp.bfd$sd.depth0m)) # or of these
# add the data to the ldg.env dataframe
ldg.env$meanSST.1deg <- temp.bfd$mean.depth0m
ldg.env$sdSST.1deg <- temp.bfd$sd.depth0m
ldg.env$SST.1deg.exact <- temp.bfd$exact
## 3. Calculating MLD data -------------------------------------------------
## 3i. Load in the MLD data ------------------------------------------------
setwd("C:/Documents/Science/PhD/Project/BFD/Environmental/MLD/")
mld.2 <- read.table("MLD_dec_ed.txt", header = T)
## 3ii. Calculate the mean and SD ------------------------------------------
# generate a dataframe with a row for each site (MLD_Apr is arbitrary choice)
names(mld.2)
mld.ann <- mld.2[mld.2$Cruise == "MLD_Apr", 2:8]
dim(mld.ann)
dim(mld.2)
# rename colnames in mld.ann to "Lat" and "Long"
names(mld.ann)
names(mld.ann) <- gsub("Latitude", "Lat", names(mld.ann))
names(mld.ann) <- gsub("Longitude", "Long", names(mld.ann))
names(mld.ann)
# add mean values for the year for potential temperature
# n.b. Station is unique for each site
mld.ann$mean.pt <- unlist(lapply(mld.ann$Station, function (x) mean(mld.2$MLD.pt.m.[mld.2$Station == x])))
# there are 110 NAs, but these are from sites that have no MLD data
sum(is.na(mld.2$MLD.pt.m.))/12
# add sd values for the year
mld.ann$sd.pt <- unlist(lapply(mld.ann$Station, function (x) sd(mld.2$MLD.pt.m.[mld.2$Station == x])))
# add for the other measures of mld (potential density and variable density)
mld.ann$mean.pd <- unlist(lapply(mld.ann$Station, function (x) mean(mld.2$MLD.pd.m.[mld.2$Station == x])))
mld.ann$sd.pd <- unlist(lapply(mld.ann$Station, function (x) sd(mld.2$MLD.pd.m.[mld.2$Station == x])))
mld.ann$mean.vd <- unlist(lapply(mld.ann$Station, function (x) mean(mld.2$MLD.vd.m.[mld.2$Station == x])))
mld.ann$sd.vd <- unlist(lapply(mld.ann$Station, function (x) sd(mld.2$MLD.vd.m.[mld.2$Station == x])))
# check these are giving what I think they do
head(mld.ann)
mld.2[mld.2$Station == 1,]
mean(mld.2$MLD.pt.m.[mld.2$Station == 1]) == mld.ann$mean.pt[mld.ann$Station == 1]
sd(mld.2$MLD.pt.m.[mld.2$Station == 1]) == mld.ann$sd.pt[mld.ann$Station == 1]
mean(mld.2$MLD.pd.m.[mld.2$Station == 1]) == mld.ann$mean.pd[mld.ann$Station == 1]
sd(mld.2$MLD.pd.m.[mld.2$Station == 1]) == mld.ann$sd.pd[mld.ann$Station == 1]
mean(mld.2$MLD.vd.m.[mld.2$Station == 1]) == mld.ann$mean.vd[mld.ann$Station == 1]
sd(mld.2$MLD.vd.m.[mld.2$Station == 1]) == mld.ann$sd.vd[mld.ann$Station == 1]
# these are all true, so that has worked
## 3iii. Plot the data -----------------------------------------------------
tmp <- c("mean.pt", "sd.pt", "mean.pd", "sd.pd", "mean.vd", "sd.vd")
# plot the environmental data
for (i in tmp) {
png(file = paste(i, ".png", sep = ""), width = 800, height = 500)
with(mld.ann[!is.na(mld.ann[,i]), ], distrib.map(Long, Lat, mld.ann[,i][!is.na(mld.ann[,i]) ], pch = 15, cex = 0.4))
dev.off()
}
rm(i, tmp)
# but note that the max values are not the same as the max on the scale
max(mld.ann$mean.pt, na.rm = T)
plot(sort(mld.ann$mean.pt))
plot(sort(log(mld.ann$mean.pt)))
# so log the data and then replot
for (i in tmp) {
png(file = paste(i, "_log.png", sep = ""), width = 800, height = 500)
with(mld.ann[!is.na(mld.ann[,i]), ], distrib.map(Long, Lat, log(mld.ann[,i][!is.na(mld.ann[,i]) ] + 1), pch = 15, cex = 0.4))
dev.off()
}
rm(i, tmp)
## 3iv. Obtain values for BFD points ---------------------------------------
# create a dataframe for the data
mld.bfd <- ldg.data[,1:6]
# create columns for mld data
for (i in 8:ncol(mld.ann)){
mld.bfd <- cbind(mld.bfd,NA)
colnames(mld.bfd)[i - 1] <- colnames(mld.ann[i])
}
head(mld.bfd)
# not all rows are present, so obtain data for those that do and identify those that don't
for(i in 1:nrow(mld.bfd))
{
tryCatch(mld.bfd[i, 7:ncol(mld.bfd)] <- WOA.temp(mld.bfd[i,], mld.ann, mld.ann[, 8:ncol(mld.ann)]), error = function(e) print(i))
}
# check that this function works for the first one
mld.bfd[1,]
mld.ann[mld.ann$Lat == 27.5 & mld.ann$Long == -38.5, ]
# seems only three don't have data
which(is.na(mld.bfd$mean.pt))
# create a list of those sites where it hasn't worked
na.sites <- which(is.na(mld.bfd$mean.pt))
# add a column to the dataframe showing which are exact
mld.bfd$exact <- "Y"
mld.bfd$exact[na.sites] <- "N"
table(mld.bfd$exact)
# add the salinity data (mean and sd) for the whole of the dataset
for(i in na.sites)
{
tryCatch(mld.bfd[i, 7:(ncol(mld.bfd) - 1)] <- WOA.na.temp(mld.bfd[i,], mld.ann, mld.ann[, 8:ncol(mld.ann)]), error = function(e) print(i))
}
rm(na.sites)
# check there are now no na's
which(is.na(mld.bfd$mean.pt))
# compare this with the mld.ann to see if it looks reasonable
with(mld.bfd, distrib.filled(interp(Long, Lat, mean.pt, duplicate = "mean")))
with(mld.ann[!is.na(mld.ann$mean.pt), ], distrib.filled(interp(Long, Lat, mean.pt, duplicate = "mean")))
ldg.env <- cbind(ldg.env, mld.bfd[,7:ncol(mld.bfd)])
names(ldg.env)[names(ldg.env) == "exact"] <- "mld.exact"
## 4. Stratification -------------------------------------------------------
## 4i. A function for plotting depth gradients ------------------------------
depth.grad <- function(env.data, depth.data, x.axis = "", main = "", ...)
{
# env.data - data.frame row, the environmental data
# depth.data - vector, the depths
# x.axis - string
# main - string
# plot a temperature by depth graph
plot(as.numeric(env.data), -depth.data, bty = "l", xlab = x.axis, ...)
}
depth.grad(temp.bfd[1, grep("mean", names(temp.bfd))], WOA.depths)
## 4ii. plotting latitude depth profiles -----------------------------------
# currently plotting at 0.5 deg Long
filled.contour(mean.t.depth$Lat[1:180], WOA.depths, as.matrix(mean.t.depth[1:180, 3:ncol(mean.t.depth)]), ylim = rev(range(WOA.depths)), zlim = range(mean.t.depth[1:180,3:ncol(mean.t.depth)], na.rm = T), color.palette = log.heat)
# for the top 1500m
filled.contour(mean.t.depth$Lat[1:180], WOA.depths, as.matrix(mean.t.depth[1:180, 3:ncol(mean.t.depth)]), ylim = c(1500, 0), zlim = range(mean.t.depth[1:180,3:ncol(mean.t.depth)], na.rm = T), color.palette = log.heat)
## 4iii. Calculate 10deg contour for temp.bfd -------------------------------
# create a function for identifying the 10 degree contour for a given longitudinal transect
calc.10deg <- function(row, data, cols) {
# row - the row of the dataset
# data - dataframe of temperature with depth
# cols - a list of the columns containing the environmental data
WOA.depths <- c(0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500)
tmp <- data[row, cols]
# check row has data
if(sum(!is.na(tmp)) > 0) {
# check it includes the 10degree contour
if(max(tmp, na.rm = T) > 10 & min(tmp, na.rm = T) < 10) {
# if it does calculate the depth closest to 10
return(WOA.depths[which(abs(tmp - 10) == min(abs(tmp - 10), na.rm = T))[1]])
}
}
return(NA)
}
# test this for Long = 0.5
tmp.depth <- rep(NA, 180)
tmp.lat <- rep(NA, 180)
for(i in 1:180) {
tmp.depth[i] <- calc.10deg(i, mean.t.depth, 3:ncol(mean.t.depth))
tmp.lat[i] <- mean.t.depth$Lat[i]
}
plot(tmp.lat, tmp.depth, xlim = c(-90, 90), ylim = c(2000, 0), pch = 20)
rm(tmp.depth, tmp.lat)
# generate a dataframe for adding stratification variables
strat <- mean.t.depth[, 1:2]
# calculate the 10 deg depth for all locations
strat$depth10deg <- NA
# for each line of longitude
for (i in 1:length(unique(mean.t.depth$Long))) {
# for each degree of latitude
for(j in 1:180) {
# check that the T range includes 10
strat$depth10deg[strat$Long == unique(mean.t.depth$Long)[i] & strat$Lat == mean.t.depth$Lat[j]] <- calc.10deg(j + 180 * (i - 1), mean.t.depth, 3:ncol(mean.t.depth))
}
}
# check this has worked
plot(strat$Lat[1:180], strat$depth10deg[1:180], xlim = c(-90, 90), ylim = c(2000, 0), pch = 20)
# cycle through these with the contour plot
for (i in 1:length(unique(strat$Long))) {
# matrix for the contour background
tmp <- as.matrix(mean.t.depth[mean.t.depth$Long == unique(mean.t.depth$Long)[i], 3:ncol(mean.t.depth)])
with(strat[strat$Long == unique(strat$Long)[i], ], filled.contour(Lat, WOA.depths, tmp, ylim = c(1500, 0), zlim = range(tmp, na.rm = T), color.palette = log.heat, main.title = title(paste("Longitude =", unique(strat$Long)[i])), plot.axes = { axis(1); axis(2); points(Lat, depth10deg, pch = 20) }))
}
rm(i, tmp)
# Look at the spatial variation of this
png("depth10deg.png", width = 800, height = 500)
with(strat, distrib.map(Long, Lat, depth10deg, palette = "rev.log.heat", pch = 15, cex = 0.4))
dev.off()
# calculate the BFD values
temp.bfd$depth10deg <- rep(NA, nrow(temp.bfd))
for (i in 1:nrow(temp.bfd)) {
tryCatch(temp.bfd$depth10deg[i] <- WOA.temp(temp.bfd[i,], strat, strat$depth10deg), error = function(e) print(i))
}
summary(temp.bfd$depth10deg)
with(temp.bfd,distrib.map(Long, Lat, depth10deg))
with(temp.bfd,distrib.map(Long, Lat, depth10deg, palette = "rev.log.heat"))
# where are the NAs
with(temp.bfd[is.na(temp.bfd$depth10deg),], distrib.map(Long, Lat, 2))
ldg.env$depth10deg <- temp.bfd$depth10deg
## 4iv. Thermocline as fastest rate of change below MLD ------------------------------------
# create a function to calculate this
calc.thermo.rate <- function(num) {
WOA.depths <- c(0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500)
lat <- mean.t.depth$Lat[num]
long <- mean.t.depth$Long[num]
env <- mean.t.depth[num, 3:ncol(mean.t.depth)]
# calculate thermocline depth for all these
# thermocline is maximum rate of change of Temp with depth below the mld
depth <- NA
# remove depths shallower than MLD
mld.depth <- mld.ann$mean.pt[mld.ann$Lat == lat & mld.ann$Long == long]
# if no mld data for that site
if (length(mld.depth) == 0 || is.na(mld.depth))
return(depth)
env.diff <- diff(as.numeric(env[WOA.depths > mld.depth]))
short.depths <- WOA.depths[WOA.depths > mld.depth]
# calculate thermocline depth
if (sum(!is.na(env.diff)) > 0) {
# sometimes get two depths with the same rate of change - select the first
num <- which(env.diff / diff(short.depths) == min(env.diff / diff(short.depths), na.rm = T))[1]
# as we are calculating diff, should return an average of the two depths
depth <- mean(c(short.depths[num], short.depths[num + 1]))
}
return(depth)
}
# calculate this for all points
strat$thermo.rate <- NA
# for each line of longitude
for (i in 1:length(unique(mean.t.depth$Long))) {
# for each degree of latitude
for(j in 1:180) {
# check that the T range includes 10
strat$thermo.rate[strat$Long == unique(mean.t.depth$Long)[i] & strat$Lat == mean.t.depth$Lat[j]] <- calc.thermo.rate(j + 180 * (i - 1))
}
}
# check this
summary(strat$thermo.rate)
# cycle through these with the contour plot
for (i in 1:length(unique(strat$Long))) {
# matrix for the contour background
tmp <- as.matrix(mean.t.depth[mean.t.depth$Long == unique(mean.t.depth$Long)[i], 3:ncol(mean.t.depth)])
with(strat[strat$Long == unique(strat$Long)[i], ], filled.contour(Lat, WOA.depths, tmp, ylim = c(1500, 0), zlim = range(tmp, na.rm = T), color.palette = log.heat, main.title = title(paste("Longitude =", unique(strat$Long)[i])), plot.axes = { axis(1); axis(2); points(Lat, thermo.rate, pch = 20) }))
}
rm(i, tmp)
# Look at the spatial variation of this
with(strat, distrib.map(Long, Lat, thermo.rate, palette = "rev.log.heat"))
# calculate the BFD values
temp.bfd$thermo.rate <- rep(NA, nrow(temp.bfd))
for (i in 1:nrow(temp.bfd)) {
tryCatch(temp.bfd$thermo.rate[i] <- WOA.temp(temp.bfd[i,], strat, strat$thermo.rate), error = function(e) print(i))
}
# which have no values
which(is.na(temp.bfd$thermo.rate))
# create a list of those sites
na.sites <- which(is.na(temp.bfd$thermo.rate))
# add a column to the dataframe showing which are exact
temp.bfd$thra.exact <- "Y"
temp.bfd$thra.exact[na.sites] <- "N"
# calculate means for sites where there are NA's.
for(i in na.sites)
{
tryCatch(temp.bfd$thermo.rate[i] <- WOA.na.temp(temp.bfd[i,], strat, strat$thermo.rate), error = function(e) print(i))
}
rm(na.sites)
# look at this
summary(temp.bfd$thermo.rate)
with(temp.bfd,distrib.map(Long, Lat, temp.bfd$thermo.rate))
with(temp.bfd,distrib.map(Long, Lat, temp.bfd$thermo.rate, palette = "rev.log.heat"))
# add this data to the bfd dataframe
ldg.env$thermo.rate <- temp.bfd$thermo.rate
ldg.env$thra.exact <- temp.bfd$thra.exact
# points should be below MLD (as forced), but check
plot(temp.bfd$thermo.rate, ldg.env$mean.pt)
abline(1,1)
# also how where it is most different from MLD
with(ldg.env, distrib.map(Long, Lat, thermo.rate - mean.pt, palette = "rev.log.heat"))
# look at those which are deepest
rev(order(temp.bfd$thermo.rate))[1:10]
plot(-WOA.depths ~ as.numeric(temp.bfd[954, 7:39]), type = "l")
abline(h = -ldg.env$mean.pt[954])
abline(h = -temp.bfd$thermo.rate[954])
# can check multiple of these plots
par(ask = T)
for (i in 1:nrow(temp.bfd)) {
plot(-WOA.depths ~ as.numeric(temp.bfd[i, 7:39]), type = "l", main = i)
abline(h = -ldg.env$mean.pt[i])
abline(h = -temp.bfd$thermo.rate[i])
}
par(ask = F)
## 4v. Depth of temp halfway between MLD and zero (mid-thermocline) -------
# create a function to calculate this
calc.thermo.depth <- function(num) {
WOA.depths <- c(0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500)
lat <- mean.t.depth$Lat[num]
long <- mean.t.depth$Long[num]
env <- mean.t.depth[num, 3:ncol(mean.t.depth)]
# calculate thermocline depth for all these
# thermocline is maximum rate of change of Temp with depth below the mld
depth <- NA
# remove depths shallower than MLD
mld.depth <- mld.ann$mean.pt[mld.ann$Lat == lat & mld.ann$Long == long]
# if no mld data for that site
if (length(mld.depth) == 0 || is.na(mld.depth))
return(depth)
env.mld <- as.numeric(env[WOA.depths > mld.depth])
short.depths <- WOA.depths[WOA.depths > mld.depth]
# calculate thermocline depth
if (sum(!is.na(env.mld)) > 0 & !is.na(env.mld[1])) {
# identify the temperature at the mld
# assume bottom water at 0 degrees, so calculate depth of halfway between mld and 0
mid.temp <- env.mld[1]/2
# stop if mid.temp isn't within the temp range
if (sum(env.mld - mid.temp < 0, na.rm = T) != 0) {
# find midpoint of thermocline
mid.val <- which(abs(env.mld - mid.temp) == min(abs(env.mld - mid.temp), na.rm = T))
# calc mean so that if there are multiple (i.e. mid is exactly between 2), then get the average depth
depth <- mean(short.depths[mid.val])
}
}
return(depth)
}
# calculate this for all points
strat$thermo.depth <- NA
# for each line of longitude
for (i in 1:length(unique(mean.t.depth$Long))) {
# for each degree of latitude
for(j in 1:180) {
# check that the T range includes 10
strat$thermo.depth[strat$Long == unique(mean.t.depth$Long)[i] & strat$Lat == mean.t.depth$Lat[j]] <- calc.thermo.depth(j + 180 * (i - 1))
}
}
# check this
summary(strat$thermo.depth)
# cycle through these with the contour plot
for (i in 1:length(unique(strat$Long))) {
# matrix for the contour background
tmp <- as.matrix(mean.t.depth[mean.t.depth$Long == unique(mean.t.depth$Long)[i], 3:ncol(mean.t.depth)])
with(strat[strat$Long == unique(strat$Long)[i], ], filled.contour(Lat, WOA.depths, tmp, ylim = c(1500, 0), zlim = range(tmp, na.rm = T), color.palette = log.heat, main.title = title(paste("Longitude =", unique(strat$Long)[i])), plot.axes = { axis(1); axis(2); points(Lat, thermo.depth, pch = 20) }))
}
rm(i, tmp)
# Look at the spatial variation of this
with(strat, distrib.map(Long, Lat, thermo.depth, palette = "rev.log.heat"))
# calculate the BFD values
temp.bfd$thermo.depth <- rep(NA, nrow(temp.bfd))
for (i in 1:nrow(temp.bfd)) {
tryCatch(temp.bfd$thermo.depth[i] <- WOA.temp(temp.bfd[i,], strat, strat$thermo.depth), error = function(e) print(i))
}
# which have no values
which(is.na(temp.bfd$thermo.depth))
# create a list of those sites
na.sites <- which(is.na(temp.bfd$thermo.depth))
# add a column to the dataframe showing which are exact
temp.bfd$thde.exact <- "Y"
temp.bfd$thde.exact[na.sites] <- "N"
# calculate the mean values for those sites which are NAs
for(i in na.sites)
{
tryCatch(temp.bfd$thermo.depth[i] <- WOA.na.temp(temp.bfd[i,], strat, strat$thermo.depth), error = function(e) print(i))
}
rm(na.sites)
# look at this
summary(temp.bfd$thermo.depth)
with(temp.bfd,distrib.map(Long, Lat, temp.bfd$thermo.depth))
with(temp.bfd,distrib.map(Long, Lat, temp.bfd$thermo.depth, palette = "rev.log.heat"))
# add these rows to the bfd dataset
ldg.env$thermo.depth <- temp.bfd$thermo.depth
ldg.env$thde.exact <- temp.bfd$thde.exact
# points should be below MLD (as forced), but check
plot(temp.bfd$thermo.depth, ldg.env$mean.pt)
abline(1,1)
# also how where it is most different from MLD
with(ldg.env, distrib.map(Long, Lat, thermo.depth - mean.pt + 10, palette = "rev.log.heat"))
## 4vi. Mid, width, top and bottom of thermocline -------------------------
# create a function to calculate this
calc.thermocline.val <- function(num) {
WOA.depths <- c(0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1750, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500)
lat <- mean.t.depth$Lat[num]
long <- mean.t.depth$Long[num]
env <- mean.t.depth[num, 3:ncol(mean.t.depth)]
# calculate thermocline depth for all these
# thermocline is maximum rate of change of Temp with depth below the mld
mid.depth <- NA
width.thermo <- NA
top.depth <- NA
bot.depth <- NA
# remove depths shallower than MLD
mld.depth <- mld.ann$mean.pt[mld.ann$Lat == lat & mld.ann$Long == long]
# if no mld data for that site
if (length(mld.depth) == 0 || is.na(mld.depth))
return(c(mid.depth, width.thermo, top.depth, bot.depth))
env.mld <- as.numeric(env[WOA.depths > mld.depth])
short.depths <- WOA.depths[WOA.depths > mld.depth]
# calculate thermocline depth
if (sum(!is.na(env.mld)) > 0) {
# identify the maximum temperature
top.temp <- max(env.mld, na.rm = T)
# identify the deepest temperature
deep.temp <- as.numeric(rev(env.mld[!is.na(env.mld)])[1])
# stop if data too incomplete
if (top.temp != deep.temp & deep.temp < 10) {
# calculate the mid temperature
mid.temp <- (top.temp + deep.temp)/2
# calculate the temp range
t.range <- top.temp - deep.temp
# top of thermocline is MLD
top.depth <- short.depths[1]
# find bottom of thermocline (10% warmer than min)
bot.val <- max(which(env.mld - deep.temp > t.range/10), na.rm = T)
bot.depth <- short.depths[bot.val]
# width of thermocline
width.thermo <- bot.depth - top.depth
# find midpoint of thermocline
mid.val <- which(abs(env.mld - mid.temp) == min(abs(env.mld - mid.temp), na.rm = T))
mid.depth <- short.depths[mid.val][1]
}
}
return(c(mid.depth, width.thermo, top.depth, bot.depth))
}
# check this for some random numbers
calc.thermocline.val(runif(1, 1, 360*180))
# add columns to the dataset with mid and width of the thermocline as well as top and bottom temperatures
strat$thcl.mid <- NA
strat$thcl.width <- NA
strat$thcl.top <- NA
strat$thcl.bottom <- NA
# for each line of longitude
for (i in 1:length(unique(mean.t.depth$Long))) {
# for each degree of latitude
for(j in 1:180) {
tmp <- calc.thermocline.val(j + 180 * (i - 1))
strat$thcl.mid[strat$Long == unique(mean.t.depth$Long)[i] & strat$Lat == mean.t.depth$Lat[j]] <- tmp[1]
strat$thcl.width[strat$Long == unique(mean.t.depth$Long)[i] & strat$Lat == mean.t.depth$Lat[j]] <- tmp[2]
strat$thcl.top[strat$Long == unique(mean.t.depth$Long)[i] & strat$Lat == mean.t.depth$Lat[j]] <- tmp[3]
strat$thcl.bottom[strat$Long == unique(mean.t.depth$Long)[i] & strat$Lat == mean.t.depth$Lat[j]] <- tmp[4]
}
}
rm(i, j, tmp)
# check for NAs and overall spread of data
summary(strat$thcl.mid)
summary(strat$thcl.width)
summary(strat$thcl.top)
summary(strat$thcl.bottom)
# cycle through these with the contour plot
# mid
for (i in 1:length(unique(strat$Long))) {
# matrix for the contour background
tmp <- as.matrix(mean.t.depth[mean.t.depth$Long == unique(mean.t.depth$Long)[i], 3:ncol(mean.t.depth)])
with(strat[strat$Long == unique(strat$Long)[i], ], filled.contour(Lat, WOA.depths, tmp, ylim = c(1500, 0), zlim = range(tmp, na.rm = T), color.palette = log.heat, main.title = title(paste("Mid : Longitude =", unique(strat$Long)[i])), plot.axes = { axis(1); axis(2); points(Lat, thcl.mid, pch = 20) }))
}
rm(i, tmp)
# width
for (i in 1:length(unique(strat$Long))) {
# matrix for the contour background
tmp <- as.matrix(mean.t.depth[mean.t.depth$Long == unique(mean.t.depth$Long)[i], 3:ncol(mean.t.depth)])
with(strat[strat$Long == unique(strat$Long)[i], ], filled.contour(Lat, WOA.depths, tmp, ylim = c(1500, 0), zlim = range(tmp, na.rm = T), color.palette = log.heat, main.title = title(paste("Width : Longitude =", unique(strat$Long)[i])), plot.axes = { axis(1); axis(2); points(Lat, thcl.width, pch = 20) }))
}
rm(i, tmp)
# MLD
for (i in 1:length(unique(strat$Long))) {
# matrix for the contour background
tmp <- as.matrix(mean.t.depth[mean.t.depth$Long == unique(mean.t.depth$Long)[i], 3:ncol(mean.t.depth)])
with(strat[strat$Long == unique(strat$Long)[i], ], filled.contour(Lat, WOA.depths, tmp, ylim = c(1500, 0), zlim = range(tmp, na.rm = T), color.palette = log.heat, main.title = title(paste("MLD : Longitude =", unique(strat$Long)[i])), plot.axes = { axis(1); axis(2); points(Lat, thcl.top, pch = 20) }))
}
rm(i, tmp)
# bottom
for (i in 1:length(unique(strat$Long))) {
# matrix for the contour background
tmp <- as.matrix(mean.t.depth[mean.t.depth$Long == unique(mean.t.depth$Long)[i], 3:ncol(mean.t.depth)])
with(strat[strat$Long == unique(strat$Long)[i], ], filled.contour(Lat, WOA.depths, tmp, ylim = c(1500, 0), zlim = range(tmp, na.rm = T), color.palette = log.heat, main.title = title(paste("Bottom : Longitude =", unique(strat$Long)[i])), plot.axes = { axis(1); axis(2); points(Lat, thcl.bottom, pch = 20) }))
}
rm(i, tmp)
# Look at the spatial variation of this
with(strat, distrib.map(Long, Lat, thcl.mid, palette = "rev.log.heat"))
with(strat, distrib.map(Long, Lat, thcl.width, palette = "rev.log.heat"))
with(strat, distrib.map(Long, Lat, thcl.top, palette = "rev.log.heat"))
with(strat, distrib.map(Long, Lat, thcl.bottom, palette = "rev.log.heat"))
temp.bfd$thcl.mid <- NA
temp.bfd$thcl.width <- NA
temp.bfd$thcl.top <- NA
temp.bfd$thcl.bottom <- NA
# test this
WOA.temp(temp.bfd[runif(1, 1, 1265),], strat, strat[, grep("thcl", names(strat))])
# identify values for bfd
for (i in 1:nrow(temp.bfd)) {
tryCatch(temp.bfd[i, grep("thcl", names(temp.bfd))] <- WOA.temp(temp.bfd[i,], strat, strat[, grep("thcl", names(strat))]), error = function(e) print(i))
}
# which have no values
which(is.na(temp.bfd$thcl.mid))
which(is.na(temp.bfd$thcl.width))
which(is.na(temp.bfd$thcl.top))
which(is.na(temp.bfd$thcl.bottom))
# create a list of those sites
na.sites <- unique(c(which(is.na(temp.bfd$thcl.mid)), which(is.na(temp.bfd$thcl.width)), which(is.na(temp.bfd$thcl.top)), which(is.na(temp.bfd$thcl.bottom))))
# add a column to the dataframe showing which are exact
temp.bfd$thercl.exact <- "Y"
temp.bfd$thercl.exact[na.sites] <- "N"
# calculate the mean values for those sites which are NAs
for(i in na.sites) {
tryCatch(temp.bfd[i, grep("thcl", names(temp.bfd))] <- WOA.na.temp(temp.bfd[i,], strat, strat[, grep("thcl", names(strat))]), error = function(e) print(i))
}
rm(na.sites)
# look at these
summary(temp.bfd$thcl.mid)
summary(temp.bfd$thcl.width)
summary(temp.bfd$thcl.top)
summary(temp.bfd$thcl.bottom)
with(temp.bfd,distrib.map(Long, Lat, thcl.mid))
with(temp.bfd,distrib.map(Long, Lat, thcl.mid, palette = "rev.log.heat"))
with(temp.bfd,distrib.map(Long, Lat, thcl.width))
with(temp.bfd,distrib.map(Long, Lat, thcl.width, palette = "rev.log.heat"))
with(temp.bfd,distrib.map(Long, Lat, thcl.top))
with(temp.bfd,distrib.map(Long, Lat, thcl.top, palette = "rev.log.heat"))
with(temp.bfd,distrib.map(Long, Lat, thcl.bottom))
with(temp.bfd,distrib.map(Long, Lat, thcl.bottom, palette = "rev.log.heat"))
# add these rows to the bfd dataset
ldg.env$mid <- temp.bfd$thcl.mid
ldg.env$width <- temp.bfd$thcl.width
ldg.env$top <- temp.bfd$thcl.top
ldg.env$bottom <- temp.bfd$thcl.bottom
ldg.env$thcl.exact <- temp.bfd$thercl.exact
# points should be below MLD (as forced), but check, n.b. this doesn't apply to width as it is relative
with(ldg.env, plot(top, mean.pt, xlim = c(0, 1000), pch = 20, col = 1))
with(ldg.env, points(mid, mean.pt, pch = 20, col = 2))
with(ldg.env, points(bottom, mean.pt, pch = 20, col = 3))
abline(1,1)
## 4vii. plotting images for stratification --------------------------------
setwd("C:/Documents/Science/PhD/Project/BFD/Environmental/Stratification")
tmp.names <- names(strat)[3:ncol(strat)]
# plot distrib.maps for all the possible strat variables
for (i in tmp.names) {
png(file = paste(i, ".png", sep = ""), width = 800, height = 500)
with(strat, distrib.map(Long, Lat, strat[,i], pch = 15, maintitle = i, cex = 0.4, palette = "rev.log.heat"))
dev.off()
}
# plot contour plots at Longitude = -179.5, -33.5 for all the different strat variables
for (i in tmp.names) {
# -179.5
png(file = paste(i, "_cont_179.5W.png", sep = ""), width = 800, height = 500)
# matrix for the contour background
tmp.mat <- as.matrix(mean.t.depth[mean.t.depth$Long == -179.5, 3:ncol(mean.t.depth)])
with(strat[strat$Long == -179.5, ], filled.contour(Lat, WOA.depths, tmp.mat, ylim = c(1500, 0), zlim = range(tmp.mat, na.rm = T), color.palette = log.heat, main.title = title(paste(i, ": Longitude =", Long[1])), xlab = "Latitude / degrees", ylab = "Depth / m", plot.axes = { axis(1); axis(2); points(Lat, strat[strat$Long == -179.5, i], pch = 20) }))
dev.off()
# -33.5
png(file = paste(i, "_cont_33.5W.png", sep = ""), width = 800, height = 500)
tmp.mat <- as.matrix(mean.t.depth[mean.t.depth$Long == -33.5, 3:ncol(mean.t.depth)])
with(strat[strat$Long == -33.5, ], filled.contour(Lat, WOA.depths, tmp.mat, ylim = c(1500, 0), zlim = range(tmp.mat, na.rm = T), color.palette = log.heat, main.title = title(paste(i, ": Longitude =", Long[1])), xlab = "Latitude / degrees", ylab = "Depth / m", plot.axes = { axis(1); axis(2); points(Lat, strat[strat$Long == -33.5, i], pch = 20) }))
dev.off()
}
# plot contour plots at Longitude = -179.5, -33.5 for all the different strat variables
for (i in tmp.names) {
# -179.5
png(file = paste(i, "_cont_179.5W_bw.png", sep = ""), width = 800, height = 500)
# matrix for the contour background
tmp.mat <- as.matrix(mean.t.depth[mean.t.depth$Long == -179.5, 3:ncol(mean.t.depth)])
with(strat[strat$Long == -179.5, ], filled.contour(Lat, WOA.depths, tmp.mat, ylim = c(1500, 0), zlim = range(tmp.mat, na.rm = T), color.palette = gray.palette, main.title = title(paste(i, ": Longitude =", Long[1])), xlab = "Latitude / degrees", ylab = "Depth / m", plot.axes = { axis(1); axis(2); points(Lat, strat[strat$Long == -179.5, i], pch = 20) }))
dev.off()
# -33.5
png(file = paste(i, "_cont_33.5W_bw.png", sep = ""), width = 800, height = 500)
tmp.mat <- as.matrix(mean.t.depth[mean.t.depth$Long == -33.5, 3:ncol(mean.t.depth)])
with(strat[strat$Long == -33.5, ], filled.contour(Lat, WOA.depths, tmp.mat, ylim = c(1500, 0), zlim = range(tmp.mat, na.rm = T), color.palette = gray.palette, main.title = title(paste(i, ": Longitude =", Long[1])), xlab = "Latitude / degrees", ylab = "Depth / m", plot.axes = { axis(1); axis(2); points(Lat, strat[strat$Long == -33.5, i], pch = 20) }))
dev.off()
}
rm(tmp.names, tmp.mat)
## 5. Calculating Chla (4km) ------------------------------------------------
setwd("C:/Documents/Science/PhD/Project/BFD/Environmental/Chl_4km")
## 5i. Load in the data ----------------------------------------------------
chla <- read.table("monthly_chl.txt",header = T)
head(chla)
# have a look at the data
summary(chla)
## set the -32767 to NA for each month
for (i in 4:15){
chla[which(chla[,i] == -32767),i] <- NA
}
summary(chla)
## 5ii. Save images of monthly chla ----------------------------------------
tmp <- names(chla)[4:15]
for (i in tmp) {
png(file = paste(i, ".png", sep = ""), width = 800, height = 500)
with(chla[!is.na(chla[,i]), ], distrib.filled(interp(LONG, LAT, chla[,i][!is.na(chla[,i]) ], duplicate = "mean"), nlevels = 100, palette = "rev.log.heat", maintitle = i))
dev.off()
}
rm(i, tmp)
## 5iii. calculate mean and sd ---------------------------------------------
chla$meanChla <- rowMeans(chla[, 4:15], na.rm = TRUE)
chla$sdChla <- apply(chla[, 4:15], 1, sd, na.rm = TRUE)
# need na.rm = TRUE or many points are missed
summary(chla$meanChla)
summary(chla$sdChla)
# there is one site which has no data
which(is.na(chla$sdChla))
chla[262, ]
with(chla[262, ], distrib.map(LONG, LAT, 2))
# this is in the Indian Ocean - no particularly good reason for it
## 5iv. plot the mean and sd -----------------------------------------------
png(file = "meanChla.png", width = 800, height = 500)
with(chla[!is.na(chla$meanChla), ], distrib.filled(interp(LONG, LAT, meanChla, duplicate = "mean"), nlevels = 100, palette = "rev.log.heat"))
dev.off()
png(file = "sdChla.png", width = 800, height = 500)
with(chla[!is.na(chla$sdChla), ], distrib.filled(interp(LONG, LAT, sdChla, duplicate = "mean"), nlevels = 100, palette = "rev.log.heat"))
dev.off()
## 5v. add the columns to ldg.env ------------------------------------------
ldg.env$meanChla.4km <- chla$meanChla
ldg.env$sdChla.4km <- chla$sdChla
# 6. Chla for 1deg --------------------------------------------------------
setwd("C:/Documents/Science/PhD/Project/BFD/Environmental/Chl_1deg")
# 6i. Read in data for chla months ----------------------------------------
chla.1deg <- as.data.frame(cbind(rep(-179.5:179.5, 180), rep(89.5:-89.5, each = 360)))
names(chla.1deg) <- c("Long", "Lat")
for (i in 1:12) {
if(i < 10){
tmp <- read.csv(paste("Chla_1deg_means_0",i, ".csv", sep = ""), header = FALSE)
} else {
tmp <- read.csv(paste("Chla_1deg_means_",i, ".csv", sep = ""), header = FALSE)
}
chla.1deg <- cbind(chla.1deg, sp.mat.2.df(-179.5:179.5, 89.5:-89.5, tmp)[3])
names(chla.1deg)[ncol(chla.1deg)] <- paste("Chla", i, sep = ".")
print(paste("Completed", i))
}
rm(i, tmp)
head(chla.1deg)
tail(chla.1deg)
dim(chla.1deg)
# 6ii. Plot these chlorophyll data ----------------------------------------
for (i in 1:12) {
# produce .png images in the same file
if (i < 10) {
png(paste("log_chla_0", i, ".png", sep = ""), width = 800, height = 500)
} else {
png(paste("log_chla_", i, ".png", sep = ""), width = 800, height = 500)
}
with(chla.1deg[chla.1deg[, i + 2] < 15, ], distrib.map(Long, Lat, log(chla.1deg[chla.1deg[, i + 2] < 15, i + 2] + 1), pch = 15, maintitle = colnames(chla.1deg)[i + 2], cex = 0.4))
dev.off()
}