Finnmaid and GETM
Jens Daniel Müller und Lara Sophie Burchardt
30 March, 2020
Last updated: 2020-03-30
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Knit directory: BloomSail/
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library(tidyverse)
library(ncdf4)
library(vroom)
library(lubridate)
library(here)
library(seacarb)
library(oce)
library(patchwork)
library(zoo)# route
select_route <- "E"
# latitude limits
low_lat <- 57.3
high_lat <- 57.5
#depth range to subset GETM 3d files
d1_shallow <- 0
d1_deep <- 30
# date limits
start_date <- "2018-06-20"
end_date <- "2018-08-25"
fixed_values <-
read_csv(here::here("Data/_summarized_data_files", "tb_fix.csv"))1 GETM Data preparation
1.1 Salinity and temperature profiles
Mean salinity and temperature profiles within the BloomSail area were extracted from daily GETM transects beneath the Finnmaid track.
nc <- nc_open(here::here("data/GETM", "Finnmaid.E.3d.2018.nc"))
lat <- ncvar_get(nc, "latc")
time_units <- nc$dim$time$units %>% #we read the time unit from the netcdf file to calibrate the time
substr(start = 15, stop = 33) %>% #calculation, we take the relevant information from the string
ymd_hms() # and transform it to the right format
t <- time_units + ncvar_get(nc, "time") # read time vector
rm(time_units)
d <- ncvar_get(nc, "zax") # read depths vector
for (var_3d in c("salt", "temp")) {
array <- ncvar_get(nc, var_3d) # store the data in a 3-dimensional array
#dim(array) # should be 3d with dimensions: 544 coordinates, 51 depths, and number of days of month
fillvalue <- ncatt_get(nc, var_3d, "_FillValue")
# Working with the data
array[array == fillvalue$value] <- NA
for (i in seq(1,length(t),1)){
# i <- 3
array_slice <- array[, , i] # slices data from one day
array_slice_df <- as.data.frame(t(array_slice))
array_slice_df <- as_tibble(array_slice_df)
gt_3d_part <- array_slice_df %>%
set_names(as.character(lat)) %>%
mutate(dep = -d) %>%
gather("lat", "value", 1:length(lat)) %>%
mutate(lat = as.numeric(lat)) %>%
filter(lat > low_lat, lat < high_lat,
dep >= d1_shallow, dep <= d1_deep) %>%
#summarise_all("mean") %>%
mutate(var = var_3d,
date_time=t[i]) %>%
dplyr::select(date_time, dep, value, var) #%>%
#filter(date_time >= start_date, date_time <= end_date)
if (exists("gt_3d")) {
gt_3d <- bind_rows(gt_3d, gt_3d_part)
} else {gt_3d <- gt_3d_part}
rm(array_slice, array_slice_df, gt_3d_part)
}
rm(array, fillvalue)
}
nc_close(nc)
rm(nc)
gt_3d_jun_aug <- gt_3d %>%
filter(date_time >= start_date & date_time <= end_date) %>%
group_by(dep,var,date_time ) %>%
summarise_all(list(value=~mean(.,na.rm=TRUE))) %>%
ungroup()
gt_3d_jun_aug %>%
vroom_write((here::here("data/_summarized_data_files", file = "gt_3d_jun_aug_2018.csv")))
rm(gt_3d, gt_3d_jun_aug, d1_deep, d1_shallow, i, lat, d, t, var_3d)1.2 Mixed layer depth
Regional mean mixed layer depth estimates based on sewater density and windspeed within the BloomSail area were extracted from 3h GETM surface data along the Finnmaid track.
nc_2d <- nc_open(here("data/GETM", "Finnmaid.E.2d.2018.nc"))
#print(nc_2d)
lat <- ncvar_get(nc_2d, "latc")
time_units <- nc_2d$dim$time$units %>% #we read the time unit from the netcdf file to calibrate the time
substr(start = 15, stop = 33) %>% #calculation, we take the relevant information from the string
ymd_hms() # and transform it to the right format
t <- time_units + ncvar_get(nc_2d, "time") # read time vector
rm(time_units)
for (var in names(nc_2d$var)[c(3,4,6:12)]) {
#var <- "mld_rho"
array <- ncvar_get(nc_2d, var) # store the data in a 3-dimensional array
fillvalue <- ncatt_get(nc_2d, var, "_FillValue")
array[array == fillvalue$value] <- NA
array <- as.data.frame(t(array), xy=TRUE)
array <- as_tibble(array)
gt_2d_part <- array %>%
set_names(as.character(lat)) %>%
mutate(date_time = t) %>%
filter(date_time >= start_date & date_time <= end_date) %>%
gather("lat", "value", 1:length(lat)) %>%
mutate(lat = as.numeric(lat)) %>%
filter(lat > low_lat, lat<high_lat) %>%
select(-lat) %>%
group_by(date_time) %>%
summarise_all(list(value=~mean(.,na.rm=TRUE))) %>%
ungroup() %>%
mutate(var = var)
if (exists("gt_2d")) {
gt_2d <- bind_rows(gt_2d, gt_2d_part)
} else {gt_2d <- gt_2d_part}
rm(array, fillvalue, gt_2d_part)
}
nc_close(nc_2d)
rm(nc_2d)
gt_2d <- gt_2d %>%
mutate(value = round(value, 3)) %>%
pivot_wider(values_from = value, names_from = var) %>%
mutate(U_10 = round(sqrt(u10^2 + v10^2), 3)) %>%
select(-c(u10, v10))
gt_2d %>%
vroom_write((here::here("data/_summarized_data_files", file = "gt_2d_jun_aug_2018.csv")))
rm(t, var, gt_2d, lat)2 Hydrography
2.1 Read summary files
gt_3d_jun_aug <-
read_tsv((here::here("data/_summarized_data_files", file = "gt_3d_jun_aug_2018.csv")))
gt_2d_jun_aug <-
read_tsv((here::here("data/_summarized_data_files", file = "gt_2d_jun_aug_2018.csv")))
gt_3d_jun_aug <- gt_3d_jun_aug %>%
pivot_wider(values_from = value, names_from = var) %>%
mutate(rho = swSigma(salinity = salt, temperature = temp, pressure = dep/10))
gt_2d_jun_aug_daily <- gt_2d_jun_aug %>%
mutate(day = yday(date_time)) %>%
group_by(day) %>%
summarise_all(list(~mean(.,na.rm=TRUE))) %>%
ungroup() %>%
select(-day)2.2 Hovmoeller Plots
p_sal <- gt_3d_jun_aug %>%
ggplot()+
geom_raster(aes(date_time, dep, fill=salt))+
geom_vline(data=fixed_values, aes(xintercept = start))+
geom_vline(data=fixed_values, aes(xintercept = end))+
scale_fill_viridis_c(name="Salinity ", direction = -1)+
scale_y_reverse()+
coord_cartesian(expand = 0)+
labs(y="Depth [m]")+
theme_bw()+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())+
geom_line(data= gt_2d_jun_aug_daily,
aes(x = date_time, y = mld_rho), color = "white")+
scale_color_discrete(name = "Legend", labels = c("MLD Rho"))
p_tem <- gt_3d_jun_aug %>%
ggplot()+
geom_raster(aes(date_time, dep, fill=temp))+
geom_vline(data=fixed_values, aes(xintercept = start))+
geom_vline(data=fixed_values, aes(xintercept = end))+
scale_fill_viridis_c(name="Temperature (°C)", option = "B")+
scale_y_reverse()+
coord_cartesian(expand = 0)+
labs(y="Depth [m]", x="")+
theme_bw()+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())+
geom_line(data= gt_2d_jun_aug_daily,
aes(x = date_time, y = mld_rho), color = "white")+
scale_color_discrete(name = "Legend", labels = c("MLD Rho"))
p_rho <- gt_3d_jun_aug %>%
ggplot()+
geom_raster(aes(date_time, dep, fill=rho))+
geom_vline(data=fixed_values, aes(xintercept = start))+
geom_vline(data=fixed_values, aes(xintercept = end))+
scale_fill_viridis_c(name="d Rho (kg/m^3)", direction = -1)+
scale_y_reverse()+
coord_cartesian(expand = 0)+
labs(y="Depth [m]", x="")+
theme_bw()+
theme(axis.title.x = element_blank())+
geom_line(data= gt_2d_jun_aug_daily,
aes(x = date_time, y = mld_rho), color = "white")+
scale_color_discrete(name = "Legend", labels = c("MLD Rho"))
p_sal / p_tem / p_rho
rm(p_sal, p_tem, p_rho)2.3 Profiles
gt_3d_jun_aug_long <- gt_3d_jun_aug %>%
pivot_longer(3:5, values_to = "value", names_to = "parameter")
#
lab_dates <- pretty(gt_3d_jun_aug_long$date_time)
gt_3d_jun_aug_long %>%
ggplot(aes(value, dep,
col=as.numeric(date_time),
group=as.factor(date_time)))+
geom_path()+
scale_y_reverse(expand = c(0,0))+
scale_color_viridis_c(breaks = as.numeric(lab_dates),
labels = lab_dates,
name="Date")+
theme_bw()+
facet_grid(~parameter, scales = "free_x")
3 Metrology
3.1 Windspeeds
gt_2d_jun_aug %>%
ggplot()+
geom_rect(data = fixed_values, aes(xmin=start, xmax=end, ymin=-Inf, ymax=Inf), alpha=0.2)+
geom_line(aes(x= date_time, y = U_10, col="3-hourly"))+
geom_line(data = gt_2d_jun_aug_daily,
aes(x= date_time, y = U_10, col="Daily mean"))+
labs(y="U (m/s)", x = "Date")+
scale_color_brewer(palette = "Set1", name="", direction = -1)+
theme_bw()
4 Finnmaid
4.1 Read data
Finnmaid data, including reconstructed data during LICOS operation failure.
df <-
read_csv(here::here("Data/_summarized_data_files",
"Finnmaid.csv"))
df <- df %>%
filter(Area == "BS",
date > start_date,
date < end_date) %>%
select(-c(Lon, Lat, patm, Teq, xCO2, route, Area)) %>%
mutate(ID = as.factor(ID)) %>%
rename(tem=Tem,
sal=Sal)4.2 CT calculation
Calculate based on fixed AT and salinity mean values.
df <- df %>%
mutate(CT = carb(24, var1=pCO2, var2=1720*1e-6,
S=6.9, T=tem, k1k2="m10", kf="dg", ks="d",
gas="insitu")[,16]*1e6)4.3 Timeseries
FM_ts_mean <- df %>%
arrange(date) %>%
group_by(ID, sensor) %>%
summarise_all(list(~mean(.)), na.rm=TRUE) %>%
ungroup() %>%
pivot_longer(4:8, values_to = "mean", names_to = "parameter")
FM_ts_sd <- df %>%
arrange(date) %>%
group_by(ID, sensor) %>%
summarise_all(list(~sd(.)), na.rm=TRUE) %>%
ungroup() %>%
pivot_longer(4:8, values_to = "sd", names_to = "parameter") %>%
select(-date)
FM_ts <- inner_join(FM_ts_mean, FM_ts_sd)
rm(FM_ts_mean, FM_ts_sd)FM_ts %>%
ggplot()+
geom_rect(data = fixed_values, aes(xmin=start, xmax=end, ymin=-Inf, ymax=Inf), alpha=0.2)+
geom_path(aes(x=date, y=mean, ymax=mean+sd, ymin=mean-sd))+
geom_ribbon(aes(x=date, y=mean, ymax=mean+sd, ymin=mean-sd), alpha=0.5)+
geom_point(aes(x=date, y=mean, ymax=mean+sd, ymin=mean-sd, col=sensor))+
facet_grid(parameter~., scales = "free_y")+
scale_color_brewer(palette = "Set1", direction=-1)+
theme_bw()
5 CT vs temperature
5.1 SST time series
FM_ts %>%
filter(parameter == "tem") %>%
ggplot()+
geom_rect(data = fixed_values, aes(xmin=start, xmax=end, ymin=-Inf, ymax=Inf), alpha=0.2)+
geom_path(aes(x=date, y=mean, ymax=mean+sd, ymin=mean-sd))+
geom_ribbon(aes(x=date, y=mean, ymax=mean+sd, ymin=mean-sd), alpha=0.5)+
geom_point(aes(x=date, y=mean, col="Finnmaid"))+
geom_path(data = gt_2d_jun_aug, aes(x=date_time, y=SST, col="GETM, 3h"))+
geom_path(data = gt_2d_jun_aug_daily, aes(x=date_time, y=SST, col="GETM, daily"))+
scale_color_brewer(palette = "Set1", name="")+
labs(x="", y="SST (°C)")+
theme_bw()
5.2 dCT vs dSST
As primary production (negative changes in CT) and increase in seawater temperature have a common driver (light), the relation between both changes was investigated.
The following analysis is restricted to the BloomSail period.
CT_tem <- FM_ts %>%
filter(date > fixed_values$start,
date < fixed_values$end) %>%
filter(parameter %in% c("tem", "CT")) %>%
select(date, parameter, mean) %>%
pivot_wider(values_from = mean, names_from = parameter) %>%
set_names(c("date_time", "FM", "CT"))
CT_tem <- full_join(CT_tem,
gt_2d_jun_aug_daily %>% select(date_time, gt = SST))
CT_tem <- CT_tem %>%
arrange(date_time) %>%
mutate(gt = na.approx(gt)) %>%
drop_na()
CT_tem <- CT_tem %>%
pivot_longer(cols = c(FM, gt), values_to = "SST", names_to = "obs")
CT_tem <- CT_tem %>%
group_by(obs) %>%
arrange(date_time) %>%
mutate(dCT = CT - lag(CT),
dSST = SST - lag(SST),
sign = if_else(dCT < 0, "neg", "pos")) %>%
ungroup()
lab_dates <- pretty(CT_tem$date_time)
CT_tem %>%
ggplot()+
geom_hline(yintercept = 0)+
geom_vline(xintercept = 0)+
geom_smooth(aes(dSST, dCT), method = "lm", se=FALSE)+
geom_point(aes(dSST, dCT, fill=as.numeric(date_time)), shape=21)+
scale_fill_viridis_c(breaks = as.numeric(lab_dates),
labels = lab_dates,
name="Date")+
guides(fill = guide_legend(override.aes=list(shape=21)))+
facet_wrap(~obs)
6 NCP
6.1 MLD approach
Use dCT/dtem from Finnmaid, MLD from GETM
NCP_MLD <- FM_ts %>%
filter(date > fixed_values$start,
date < fixed_values$end) %>%
filter(parameter %in% c("tem", "CT")) %>%
select(date, parameter, mean) %>%
pivot_wider(values_from = mean, names_from = parameter) %>%
set_names(c("date_time", "SST", "CT"))
NCP_MLD <- full_join(NCP_MLD,
gt_2d_jun_aug_daily %>% select(-c(SSS, SST, U_10)))
NCP_MLD <- NCP_MLD %>%
pivot_longer(cols = 4:8, values_to = "MLD", names_to = "Parameter") %>%
arrange(date_time) %>%
group_by(Parameter) %>%
mutate(MLD = na.approx(MLD)) %>%
ungroup()
NCP_MLD %>%
ggplot()+
geom_rect(data = fixed_values, aes(xmin=start, xmax=end, ymin=-Inf, ymax=Inf), alpha=0.2)+
geom_line(aes(date_time, MLD, col=Parameter))+
geom_point(data = NCP_MLD %>% drop_na, aes(date_time, MLD, col=Parameter))+
scale_color_brewer(palette = "Set1")+
labs(x="", y="MLD (m)")+
theme_bw()
NCP_MLD <- NCP_MLD %>%
drop_na() %>%
group_by(Parameter) %>%
arrange(date_time) %>%
mutate(dCT = CT - lag(CT),
dSST = SST - lag(SST),
dNCP = dCT * MLD / 1000,
NCP_cum = cumsum(replace_na(dNCP, 0))) %>%
ungroup()6.1.1 Incremental and cumulative timeseries
Total incremental and cumulative CT changes inbetween cruise dates were calculated.
p_iNCP <- NCP_MLD %>%
ggplot(aes(date_time, dNCP, fill= Parameter))+
geom_hline(yintercept = 0)+
geom_col(col="black", position = "dodge")+
scale_y_continuous(breaks = seq(-100, 100, 0.2))+
labs(y="integrated, directional CT changes [mol/m2]", x="date")
p_iNCPcum <- NCP_MLD %>%
ggplot(aes(date_time, NCP_cum,
fill= Parameter))+
geom_hline(yintercept = -0.4)+
geom_area(position = "identity", col="black")+
geom_hline(yintercept = 0)+
scale_y_continuous(breaks = seq(-100, 100, 0.2))+
facet_grid(Parameter~., scales = "free_y", space = "free_y")+
theme(strip.background = element_blank(),
strip.text = element_blank())+
labs(y="integrated, cumulative, directional CT changes [mol/m2]", x="date")
(p_iNCP / p_iNCPcum)+
plot_layout(guides = 'collect', heights = c(1, 2))
rm(p_iNCP, p_iNCPcum)6.2 delta T appraoch
CT_tem <- CT_tem %>%
filter(obs == "FM") %>%
mutate(factor = dCT/dSST,
factor = if_else(is.na(factor), 0, factor)) %>%
select(date_time, factor)
CT_tem <- expand_grid(CT_tem, dep = unique(gt_3d_jun_aug$dep))
NCP_dT <- full_join(gt_3d_jun_aug %>% select(date_time, dep, temp),
CT_tem)
NCP_dT <- NCP_dT %>%
arrange(dep, date_time) %>%
group_by(dep) %>%
mutate(temp = na.approx(temp)) %>%
ungroup() %>%
drop_na()
NCP_dT <- NCP_dT %>%
group_by(dep) %>%
arrange(date_time) %>%
mutate(dtem = temp - lag(temp)) %>%
ungroup() %>%
mutate(diff_value = dtem * factor) %>%
select(-factor)The reconstructed incremental changes are added up to derive cummulative CT changes throughout the water column.
NCP_dT <- NCP_dT %>%
group_by(dep) %>%
arrange(date_time) %>%
mutate(diff_time = as.numeric(date_time - lag(date_time)),
diff_value_daily = diff_value / diff_time,
cum_value = replace_na(diff_value, 0))6.3 Profiles of incremental changes
Changes of seawater parameters at each depth were reconstructed from one cruise day to the next and divided by the number of days inbetween.
NCP_dT %>%
arrange(dep) %>%
ggplot(aes(diff_value_daily, dep, col=as.factor(date_time)))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
#scale_color_viridis_d()+
#facet_wrap(~parameter, scales = "free_x")+
labs(x="Change of value inbetween cruises per day")
6.4 Profiles of cumulative changes
Cumulative changes of seawater parameters were calculated at each depth relative to the first cruise day on July 5.
NCP_dT %>%
arrange(dep) %>%
ggplot(aes(cum_value, dep, col=as.factor(date_time)))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
#scale_color_viridis_d()+
labs(x="Cumulative change of value")
Reconstructed cumulative positive and negative changes of seawater parameters were calculated separately at each depth relative to the first cruise day on July 5.
NCP_dT <- NCP_dT %>%
mutate(sign = if_else(diff_value < 0, "neg", "pos")) %>%
group_by(dep, sign) %>%
arrange(date_time) %>%
mutate(cum_value_sign = replace_na(diff_value, 0)) %>%
ungroup()
NCP_dT %>%
arrange(dep) %>%
ggplot(aes(cum_value_sign, dep, col=as.factor(date_time)))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
scale_fill_viridis_d()+
facet_wrap(~sign, scales = "free_x", ncol=4)+
labs(x="Cumulative directional change of value")
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_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] zoo_1.8-6 patchwork_1.0.0 seacarb_3.2.12 oce_1.2-0
[5] gsw_1.0-5 testthat_2.3.1 here_0.1 lubridate_1.7.4
[9] vroom_1.2.0 ncdf4_1.17 forcats_0.4.0 stringr_1.4.0
[13] dplyr_0.8.3 purrr_0.3.3 readr_1.3.1 tidyr_1.0.0
[17] tibble_2.1.3 ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 lattice_0.20-35 assertthat_0.2.1 zeallot_0.1.0
[5] rprojroot_1.3-2 digest_0.6.22 R6_2.4.0 cellranger_1.1.0
[9] backports_1.1.5 reprex_0.3.0 evaluate_0.14 httr_1.4.1
[13] pillar_1.4.2 rlang_0.4.5 readxl_1.3.1 rstudioapi_0.10
[17] Matrix_1.2-14 rmarkdown_2.0 labeling_0.3 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 mgcv_1.8-23
[29] htmltools_0.4.0 tidyselect_0.2.5 workflowr_1.6.0 viridisLite_0.3.0
[33] crayon_1.3.4 dbplyr_1.4.2 withr_2.1.2 later_1.0.0
[37] grid_3.5.0 nlme_3.1-137 jsonlite_1.6 gtable_0.3.0
[41] lifecycle_0.1.0 DBI_1.0.0 git2r_0.26.1 magrittr_1.5
[45] scales_1.0.0 cli_1.1.0 stringi_1.4.3 fs_1.3.1
[49] promises_1.1.0 xml2_1.2.2 ellipsis_0.3.0 generics_0.0.2
[53] vctrs_0.2.0 RColorBrewer_1.1-2 tools_3.5.0 bit64_0.9-7
[57] glue_1.3.1 hms_0.5.2 yaml_2.2.0 colorspace_1.4-1
[61] rvest_0.3.5 knitr_1.26 haven_2.2.0