Last updated: 2020-03-31
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Knit directory: Baltic_Productivity/
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Ignored: data/ARGO/
Ignored: data/Finnmaid/
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Ignored: data/_merged_data_files/
Ignored: data/_merged_data_files_2019V1/
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library(tidyverse)
library(ncdf4)
library(vroom)
library(lubridate)
library(geosphere)
library(dygraphs)
library(xts)
library(here)
library(seacarb)
library(zoo)# route
select_route <- c("E", "F", "G", "W", "X")
# variable names in 2d and 3d GETM files
var <- "SSS_east"
# latitude limits
low_lat <- 57.5
high_lat <- 58.8The mean salinity was calculated across all measurments made between march - september in the NGS subregion.
nc <- nc_open(paste("data/Finnmaid/", "FM_all_2019_on_standard_tracks.nc", sep = ""))
# read required vectors from netcdf file
route <- ncvar_get(nc, "route")
route <- unlist(strsplit(route, ""))
date_time <- ncvar_get(nc, "time")
latitude_east <- ncvar_get(nc, "latitude_east")
array <- ncvar_get(nc, var) # store the data in a 2-dimensional array
#dim(array) # should have 2 dimensions: 544 coordinate, 2089 time steps
fillvalue <- ncatt_get(nc, var, "_FillValue")
array[array == fillvalue$value] <- NA
rm(fillvalue)
#i <- 5
for (i in seq(1,length(route),1)){
if(route[i] %in% select_route) {
slice <- array[i,]
value <- mean(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
sd <- sd(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
date <- ymd("2000-01-01") + date_time[i]
temp <- bind_cols(date = date, var=var, value = value, sd = sd)
if (exists("timeseries", inherits = FALSE)){
timeseries <- bind_rows(timeseries, temp)
} else{timeseries <- temp}
rm(temp, value, date, sd)
}
}
nc_close(nc)
fm_sss__ngs <- timeseries %>%
mutate(sss = value,
year = year(date),
month = month(date))
fm_sss_ngs_monthlymean <- fm_sss__ngs %>%
filter(month >=3 , month <=9) %>%
summarise(sss_mean = mean(sss, na.rm = TRUE))
rm(array,fm_sss__ngs,nc, timeseries, date_time,
i, latitude_east, route, slice, var)The mean salinity between March and September for the NGS subregion for all years is 6.67.
pCO2 and SST observations in NGS were extracted for all crossings.
nc <- nc_open(paste("data/Finnmaid/", "FM_all_2019_on_standard_tracks.nc", sep = ""))
# read required vectors from netcdf file
route <- ncvar_get(nc, "route")
route <- unlist(strsplit(route, ""))
date_time <- ncvar_get(nc, "time")
latitude_east <- ncvar_get(nc, "latitude_east")
for (var in c("SST_east", "pCO2_east")) {
#print(var)
array <- ncvar_get(nc, var) # store the data in a 2-dimensional array
#dim(array) # should have 2 dimensions: 544 coordinate, 2089 time steps
fillvalue <- ncatt_get(nc, var, "_FillValue")
array[array == fillvalue$value] <- NA
rm(fillvalue)
for (i in seq(1,length(route),1)){
if(route[i] %in% select_route) {
slice <- array[i,]
value <- mean(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
sd <- sd(slice[latitude_east > low_lat & latitude_east < high_lat], na.rm = TRUE)
date <- ymd("2000-01-01") + date_time[i]
fm_ngs_all_routes_part <- bind_cols(date = date, var=var, value = value, sd = sd, route=route[i])
if (exists("fm_ngs_all_routes", inherits = FALSE)){
fm_ngs_all_routes <- bind_rows(fm_ngs_all_routes, fm_ngs_all_routes_part)
} else{fm_ngs_all_routes <- fm_ngs_all_routes_part}
rm(fm_ngs_all_routes_part, value, date, sd, slice)
}
}
rm(array, var,i)
}
nc_close(nc)
fm_ngs_all_routes %>%
vroom_write(here::here("data/_summarized_data_files/", file = "fm_ngs_all_routes.csv"))
rm(nc, fm_ngs_all_routes, latitude_east, route,date_time)Reanalysis windspeed data as used in the GETM model run were used.
var_all <- c("u10", "v10")
filesList_2d <- list.files(path= "data", pattern = "Finnmaid.E.2d.20", recursive = TRUE)
file <- filesList_2d[1]
nc <- nc_open(paste("data/", file, sep = ""))
lon <- ncvar_get(nc, "lonc")
lat <- ncvar_get(nc, "latc", verbose = F)
nc_close(nc)
rm(file, nc)
for (var in var_all){
for (n in 1:length(filesList_2d)) {
file <- filesList_2d[n]
nc <- nc_open(paste("data/", file, sep = ""))
time_units <- nc$dim$time$units %>% #we read the time unit from the netcdf file to calibrate the time
substr(start = 15, stop = 33) %>% #calculation, we take the relevant information from the string
ymd_hms() # and transform it to the right format
t <- time_units + ncvar_get(nc, "time")
array <- ncvar_get(nc, var) # store the data in a 2-dimensional array
dim(array) # should be 2d with dimensions: 544 coordinate, 31d*(24h/d/3h)=248 time steps
array <- as.data.frame(t(array), xy=TRUE)
array <- as_tibble(array)
gt_windspeed_ngs_part <- array %>%
set_names(as.character(lat)) %>%
mutate(date_time = t) %>%
gather("lat", "value", 1:length(lat)) %>%
mutate(lat = as.numeric(lat)) %>%
filter(lat > low_lat, lat<high_lat) %>%
group_by(date_time) %>%
summarise_all("mean") %>%
ungroup() %>%
mutate(var = var)
if (exists("gt_windspeed_ngs")) {gt_windspeed_ngs <- bind_rows(gt_windspeed_ngs, gt_windspeed_ngs_part)
}else {gt_windspeed_ngs <- gt_windspeed_ngs_part}
nc_close(nc)
rm(array, nc, t, gt_windspeed_ngs_part)
print(n) # to see working progress
}
print(paste("gt_", var, "_ngs.csv", sep = "")) # to see working progress
rm(n, file, time_units)
}
rm(filesList_2d, var, var_all, lat, lon)
gt_windspeed_ngs <- gt_windspeed_ngs %>%
group_by(date_time, var) %>%
summarise(mean_value= mean(value)) %>%
pivot_wider(values_from = mean_value, names_from = var) %>%
mutate(U_10 = round(sqrt(u10^2 + v10^2), 3)) %>%
select(-c(u10, v10))
gt_windspeed_ngs %>%
vroom_write(here::here("data/_summarized_data_files/", file = paste("gt_windspeed_ngs.csv", sep = "")))
rm(gt_windspeed_ngs)CT was calculated from measured pCO2 based on a fixed mean alkalinity value of 1650 µmol kg-1.
df <- vroom::vroom(here::here("data/_summarized_data_files/", file = "fm_ngs_all_routes.csv"))
df <- df %>%
select(date, var, value, route)
df <- df %>%
pivot_wider(values_from = value, names_from = var) %>%
drop_na()
#df <- df%>%
# mutate(sal = SSS_east,
# tem = SST_east,
# pCO2 = pCO2_east) %>%
# select(pCO2,tem, sal, date)
# not enough RAM for pivot_wider
# alternative with "filter"
# df_sst <- df %>%
# filter( var == "SST_east") %>%
# mutate( tem = value) %>%
# select (tem)
#
# df_sss <- df %>%
# filter( var == "SSS_east") %>%
# mutate( sal = value) %>%
# select( sal)
#
# df_pco2 <- df %>%
# filter( var == "pCO2_east") %>%
# mutate (pCO2 = value) %>%
# select( pCO2, date)
#
# df <- cbind(df_pco2, df_sst, df_sss) %>%
# select(date, tem, sal, pCO2)
pull(fm_sss_ngs_monthlymean)
df <- df %>%
rename(SST = SST_east,
pCO2 = pCO2_east) %>%
mutate(CT = carb(24,
var1=pCO2,
var2=1650*1e-6,
S=pull(fm_sss_ngs_monthlymean),
T=SST,
k1k2="m10", kf="dg", ks="d", gas="insitu")[,16]*1e6)
df %>%
vroom_write(here::here("data/_summarized_data_files/", file = "fm_CT_ngs.csv"))
rm(df)The CO2 flux across the sea surface was calculated according to Wanninkhof (2014).
df_1 <- vroom::vroom(here::here("data/_summarized_data_files/", file = "fm_CT_ngs.csv"))
df_2 <- vroom::vroom(here::here("data/_summarized_data_files/", file = "gt_windspeed_ngs.csv"))
df_2$date_time <- round_date(df_2$date_time, unit = "day")
df_2 <- df_2 %>%
mutate(date = as.Date(date_time)) %>%
select(date, U_10) %>%
group_by(date) %>%
summarise_all("mean") %>%
ungroup()
df <- full_join(df_1, df_2, by = "date") %>%
arrange(date)
rm(df_1,df_2)
df <- df %>%
mutate(year = year(date),
pCO2_int = na.approx(pCO2, na.rm = FALSE),
SST_int = na.approx(SST, na.rm = FALSE)) %>%
filter(!is.na(pCO2_int))
Sc_W14 <- function(tem) {
2116.8 - 136.25 * tem + 4.7353 * tem^2 - 0.092307 * tem^3 + 0.0007555 * tem^4
}
Sc_W14(20)
# calculate flux F [mol m–2 d–1]
df <- df %>%
mutate(pCO2_air = 400 - 2*(2015-year),
dpCO2 = pCO2_int - pCO2_air,
dCO2 = dpCO2 * K0(S=pull(fm_sss_ngs_monthlymean), T=SST_int),
k = 0.251 * U_10^2 * (Sc_W14(SST_int)/660)^(-0.5),
flux_daily = k*dCO2*1e-5*24)
df %>%
vroom_write(here::here("data/_merged_data_files/", file = paste("gt_fm_flux_ngs.csv", sep = "")))
rm(df, Sc_W14)# read CT and flux data
gt_fm_flux_ngs <- vroom::vroom(here::here("data/_merged_data_files/", file = "gt_fm_flux_ngs.csv"))
ts_xts_CT <- xts(gt_fm_flux_ngs$CT, order.by = gt_fm_flux_ngs$date)
names(ts_xts_CT) <- "CT"
ts_xts_SST <- xts(gt_fm_flux_ngs$SST, order.by = gt_fm_flux_ngs$date)
names(ts_xts_SST) <- "SST"
ts_xts_windspeed <- xts(gt_fm_flux_ngs$U_10, order.by = gt_fm_flux_ngs$date)
names(ts_xts_windspeed) <- "Windspeed"
ts_xts_flux <- xts(gt_fm_flux_ngs$flux_daily, order.by = gt_fm_flux_ngs$date)
names(ts_xts_flux) <- "Daily Flux"
# read MLD data
gt_mld_fm_pco2_ngs <-
vroom::vroom(here::here("data/_merged_data_files/", file = "gt_mld_fm_pco2_ngs.csv"))
ts_xts_mld5 <- xts(gt_mld_fm_pco2_ngs$value_mld5, order.by = gt_mld_fm_pco2_ngs$date)
names(ts_xts_mld5) <- "mld_age_5"
ts_xts_CT %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("CT") %>%
dyAxis("y", label = "CT") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)ts_xts_SST %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("SST") %>%
dyAxis("y", label = "SST") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)ts_xts_mld5 %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("mld_age_5") %>%
dyAxis("y", label = "mld_age_5") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)ts_xts_windspeed %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("Windspeed") %>%
dyAxis("y", label = "Windspeed [m/s]") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5,
drawAxesAtZero=TRUE)ts_xts_flux %>%
dygraph(group = "Fluxes") %>%
dyRangeSelector(dateWindow = c("2014-01-01", "2016-12-31")) %>%
dySeries("Daily Flux") %>%
dyAxis("y", label = "Daily Flux") %>%
dyOptions(drawPoints = TRUE, pointSize = 1.5, connectSeparatedPoints=TRUE, strokeWidth=0.5)rm(gt_fm_flux_ngs, gt_windspeed_ngs, fm_CT_ngs, ts_xts_CT, ts_xts_flux, ts_xts_windspeed)
sessionInfo()R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=English_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] seacarb_3.2.12 oce_1.2-0 gsw_1.0-5 testthat_2.3.1
[5] here_0.1 xts_0.11-2 zoo_1.8-6 dygraphs_1.1.1.6
[9] geosphere_1.5-10 lubridate_1.7.4 vroom_1.2.0 ncdf4_1.17
[13] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[17] readr_1.3.1 tidyr_1.0.0 tibble_2.1.3 ggplot2_3.3.0
[21] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 lattice_0.20-35 utf8_1.1.4 assertthat_0.2.1
[5] zeallot_0.1.0 rprojroot_1.3-2 digest_0.6.22 R6_2.4.0
[9] cellranger_1.1.0 backports_1.1.5 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.1 pillar_1.4.2 rlang_0.4.5 readxl_1.3.1
[17] rstudioapi_0.10 rmarkdown_2.0 htmlwidgets_1.5.1 bit_1.1-14
[21] munsell_0.5.0 broom_0.5.3 compiler_3.5.0 httpuv_1.5.2
[25] modelr_0.1.5 xfun_0.10 pkgconfig_2.0.3 htmltools_0.4.0
[29] tidyselect_0.2.5 workflowr_1.6.0 fansi_0.4.0 crayon_1.3.4
[33] dbplyr_1.4.2 withr_2.1.2 later_1.0.0 grid_3.5.0
[37] nlme_3.1-137 jsonlite_1.6 gtable_0.3.0 lifecycle_0.1.0
[41] DBI_1.0.0 git2r_0.26.1 magrittr_1.5 scales_1.0.0
[45] cli_1.1.0 stringi_1.4.3 fs_1.3.1 promises_1.1.0
[49] sp_1.3-2 xml2_1.2.2 generics_0.0.2 vctrs_0.2.0
[53] tools_3.5.0 bit64_0.9-7 glue_1.3.1 hms_0.5.2
[57] parallel_3.5.0 yaml_2.2.0 colorspace_1.4-1 rvest_0.3.5
[61] knitr_1.26 haven_2.2.0