# Case 19: Control with smaller T-amplitude
# Set boundary conditions
Td <- seq(1,12*365,1) # Create timeline of 12 years in days
Ty <- Td/365 # Convert to years
MAT<-20 # Set mean annual temperature
Amp<-5 # Set seasonal amplitude
Sext<-2*Amp # Calculate extent of seasonal variability
TSD<-1.5 # Set the degree of random non-seasonal noise on the SST curve ("weather")
SST<-rnorm(length(Ty),MAT+Amp*sin(2*pi*Ty),TSD) # Create virtual daily SST data
GR<-rep(10/365,length(Ty)) # Set growth rate to 10 mm/yr, create daily GR vector
DSD<-0.6 # Set the degree of random non-seasonal noise on the d18Osw curve ("salinity fluctuations")
d18Osw<-rnorm(length(Ty),rep(0,length(Ty)),DSD) # Set d18Osw to 0 permille VSMOW, create daily d18Osw vector
SR<-as.vector(c(0.1,0.2,0.45,0.75,1.55,3.25)) # Set sampling resolutions at 3.3 mm (~3 yr-1), 1.55 mm (~6 yr-1; bimonthly), 0.75 mm (~12 yr-1; monthly), 0.45 mm (~25 yr-1), 0.2 mm (~50 yr-1) and 0.1 mm (~100 yr-1, maximum isotope sampling)
# Sampling resolutions for courser sampling are deliberately chosen as non-multiples of the growth rate (irregular numbers) to prevent bias against some months
# Loop through vector and calculate D, d18Oc and D47 data for all sampling densities
Case19 <- data.frame(column = rep(NA, sum(GR) / SR[1]))
for(i in 1:length(SR)){
# Create vector for all samples along entire shell length by applying constant sampling resolution
D <- seq(SR[i], sum(GR), SR[i])
# Calculate virtual data
newdata <- carbmodel(Ty, SST, GR, d18Osw, D, AV=TRUE)
# Increase length of new data to match the storage dataframe
if(nrow(newdata) < nrow(Case19)){
newdata <- rbind(newdata, matrix(NA, ncol = ncol(newdata), nrow = nrow(Case19) - nrow(newdata)))
}
newdata <- cbind(Case19$column, newdata)
# Add the new data to the storage dataframe
Case19 <- cbind(Case19, newdata)
}
Case19$column <- NULL
colnames(Case19)[seq(1, 26, 5)] <- paste("SR_", SR)
save(Case19, file = "Case19.rda")
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