NCP reconstruction
Jens Daniel Müller
18 February, 2021
Last updated: 2021-02-18
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Knit directory: BloomSail/
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library(tidyverse)
library(ncdf4)
library(seacarb)
library(oce)
library(patchwork)
library(lubridate)
library(metR)1 Scope of this script
In order to test how well the depth-integrated CT* estimates can be reproduced if only surface CO2 data were available, the following reconstruction approaches were tested:
- Mixed layer depth: Integration of surface observation across the MLD, assuming homogeneous vertical patterns
- CT profile reconstruction: Vertical reconstruction of incremental CT changes based on profiles of incremental changes in temperature
- Temperature penetration depth: Integration of surface observation across the temperature penetration depth, assuming similar vertical extension as for CT drawdown.
Note: The reconstruction of CT* profiles and the integration across the temperature penetration depth should produce very similar results. However, the latter avoids to create misinterpretable information about the vertical distribution of CT*.
The integration depth parameterizations were tested on two data sets, namely:
- BloomSail observations, restricted to CT* data in surface water
- SOOP Finnmaid pCO2 + vertical hydrographical data from GETM model
date_CT_min <- ymd_hms("2018-07-24 07:58:29")
date_tem_max <- ymd_hms("2018-08-04 00:00:00")2 BloomSail
1m gridded, downcast profiles were used.
Mean CO2 data from upper 6 meters were used as surface values.
# read data
tm_profiles_ID <-
read_csv(here::here("data/intermediate/_merged_data_files/CT_dynamics", "tm_profiles_ID.csv"))
tm_profiles_ID <- tm_profiles_ID %>%
select(-c(date_ID))
# calculate cumulative changes
tm_profiles_ID_long <- tm_profiles_ID %>%
select(-c(pCO2, sal)) %>%
pivot_longer(c("tem", "CT_star"), values_to = "value", names_to = "var") %>%
group_by(var, dep) %>%
arrange(date_time_ID) %>%
mutate(date_time_ID_diff = as.numeric(date_time_ID - lag(date_time_ID)),
date_time_ID_ref = date_time_ID - (date_time_ID - lag(date_time_ID))/2,
value_diff = value - lag(value, default = first(value)),
value_diff_daily = value_diff / date_time_ID_diff,
value_cum = cumsum(value_diff)) %>%
ungroup()2.1 TPD and CPD
Temperature penetration depth (TPD) and CT* penetration depth (CPD) were investigated based on the BloomSail data.
2.1.1 Cumulative on July 09
# subset date for cumulative assesment
tm_profiles_ID_long_day <- tm_profiles_ID_long %>%
filter(ID == 180709)
# calculate integrated value of pos/neg changes for Temp/CT*
# and relative contribution with increasing water depth
tm_profiles_ID_long_day_dep <- tm_profiles_ID_long_day %>%
select(var, dep, value_cum) %>%
mutate(
value_cum = if_else(value_cum > 0 & var == "CT_star",
NaN, value_cum),
value_cum = if_else(value_cum < 0 & var == "tem",
NaN, value_cum)
) %>%
group_by(var) %>%
arrange(dep) %>%
mutate(
value_cum_i = sum(value_cum, na.rm = TRUE),
value_cum_dep = cumsum(value_cum),
value_cum_i_rel = value_cum_dep / value_cum_i * 100
) %>%
ungroup()
# cumulative integrated values
value_cum <- tm_profiles_ID_long_day_dep %>%
group_by(var) %>%
summarise(value_cum_i = mean(value_cum_i)) %>%
ungroup()
# cumulative surface values
value_surface <- tm_profiles_ID_long_day %>%
select(var, dep, value_cum) %>%
filter(dep < parameters$surface_dep) %>%
group_by(var) %>%
summarise(value_surface = mean(value_cum)) %>%
ungroup()
# calculate penentration depth for T and C
PD <- full_join(value_cum, value_surface)
PD <- PD %>%
mutate(PD = value_cum_i / value_surface)
rm(value_cum, value_surface)p_tm_profiles_ID_long <- tm_profiles_ID_long_day %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep)) +
geom_hline(aes(yintercept = parameters$i_dep_lim, col = "integration")) +
geom_hline(data = PD, aes(yintercept = PD, col = "penetration")) +
geom_vline(xintercept = 0) +
geom_point() +
geom_path() +
scale_y_reverse() +
scale_color_brewer(palette = "Dark2", guide = FALSE) +
labs(y = "Depth (m)", x = "Cumulative change") +
theme(legend.position = "left") +
facet_wrap(var ~ ., ncol = 1, scales = "free_x")
p_tm_profiles_ID_long_rel <- tm_profiles_ID_long_day_dep %>%
ggplot(aes(value_cum_i_rel, dep)) +
geom_hline(aes(yintercept = parameters$i_dep_lim, col = "integration")) +
geom_hline(data = PD, aes(yintercept = PD, col = "penetration")) +
geom_vline(xintercept = 90) +
geom_point() +
geom_line() +
scale_y_reverse(limits = c(25, 0)) +
scale_color_brewer(palette = "Dark2", name = "Depth") +
scale_x_continuous(limits = c(0, NA)) +
labs(x = "Relative contribution (%)") +
facet_wrap(var ~ ., ncol = 1, scales = "free_x") +
theme(axis.title.y = element_blank())
p_tm_profiles_ID_long + p_tm_profiles_ID_long_rel
PD# A tibble: 2 x 4
var value_cum_i value_surface PD
<chr> <dbl> <dbl> <dbl>
1 CT_star -286. -28.4 10.1
2 tem 16.3 1.65 9.89rm(tm_profiles_ID_long_day_dep,
p_tm_profiles_ID_long,
p_tm_profiles_ID_long_rel)2.1.2 Daily profile on July 09
col_value <- "red"
p_CT_star <-
tm_profiles_ID_long_day %>%
filter(var == "CT_star") %>%
arrange(dep) %>%
ggplot() +
geom_col(
data = tm_profiles_ID_long_day %>%
filter(var == "CT_star", value_diff_daily < 0),
aes(x = value_diff_daily, y = dep),
width = 1,
alpha = 0.2,
orientation = "y"
) +
geom_vline(xintercept = 0) +
scale_y_reverse(expand = c(0, 0)) +
annotate(
"text",
x = -6,
y = 11,
label = "CPD",
col = col_value,
size = geom_text_size
) +
annotate(
"text",
x = -3.5,
y = 2.5,
label = "Integrated\nchange",
size = geom_text_size
) +
geom_point(aes(value_diff_daily, dep)) +
geom_path(aes(value_diff_daily, dep)) +
geom_hline(data = PD %>% filter(var == "CT_star"),
aes(yintercept = PD),
col = col_value) +
labs(y = "Depth (m)", x = expression(paste(Delta ~ C[T], "*") ~ (µmol ~ kg ^ {
-1
}))) +
theme(
legend.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank()
)
p_tem <-
tm_profiles_ID_long_day %>%
filter(var == "tem") %>%
arrange(dep) %>%
ggplot() +
geom_col(
data = tm_profiles_ID_long_day %>%
filter(var == "tem", value_diff_daily > 0),
aes(x = value_diff_daily, y = dep),
width = 1,
alpha = 0.2,
orientation = "y"
) +
geom_vline(xintercept = 0) +
scale_y_reverse(expand = c(0, 0)) +
annotate(
"text",
x = 0.4,
y = 11,
label = "PD",
col = col_value,
size = geom_text_size
) +
annotate(
"text",
x = 0.2,
y = 2.5,
label = "Integrated\nchange",
size = geom_text_size
) +
geom_point(aes(value_diff_daily, dep)) +
geom_path(aes(value_diff_daily, dep)) +
geom_hline(data = PD %>% filter(var == "tem"),
aes(yintercept = PD),
col = col_value) +
labs(y = "Depth (m)", x = expression(Delta ~ Temperature ~ (degree * C))) +
theme(legend.title = element_blank())
p_tem + p_CT_star +
plot_layout(guides = 'collect') +
plot_annotation(tag_levels = 'a')
ggsave(
here::here("output/Plots/Figures_publication/appendix",
"Fig_C4.pdf"),
width = 120,
height = 110,
dpi = 300,
units = "mm"
)
ggsave(
here::here("output/Plots/Figures_publication/appendix",
"Fig_C4.png"),
width = 120,
height = 110,
dpi = 300,
units = "mm"
)
rm(PD, tm_profiles_ID_long_day, p_tem, p_CT_star)2.1.3 Daily, incremental time series
A time series of TPD and CPD was calculated, based on the incremental (ie from cruise day to cruise day) changes of temperature / CT*, taking only pos. / neg. changes of both parameters into account.
# surface values
diff_surface <- tm_profiles_ID_long %>%
filter(dep < parameters$surface_dep) %>%
group_by(ID, var) %>%
summarise(value_diff_surface = mean(value_diff, na.rm = TRUE)) %>%
ungroup() %>%
mutate(
value_diff_surface = if_else(value_diff_surface > 0 & var == "CT_star",
NaN, value_diff_surface),
value_diff_surface = if_else(value_diff_surface < 0 &
var == "tem",
NaN, value_diff_surface)
)
tm_profiles_ID_long <- full_join(tm_profiles_ID_long, diff_surface)
rm(diff_surface)
# calculate penetration depths for T and C
PD <- tm_profiles_ID_long %>%
mutate(
value_diff = if_else(value_diff > 0 & var == "CT_star",
NaN, value_diff),
value_diff = if_else(value_diff < 0 & var == "tem",
NaN, value_diff)
) %>%
group_by(var, ID, date_time_ID) %>%
summarise(
value_diff_int = sum(value_diff, na.rm = TRUE),
value_diff_surface = mean(value_diff_surface, na.rm = TRUE)
) %>%
ungroup() %>%
mutate(i_dep = value_diff_int / value_diff_surface)
# calculate temporal mean
PD_mean <- PD %>%
group_by(var) %>%
summarise(i_dep_mean = mean(i_dep, na.rm = TRUE),
i_dep_sd = sd(i_dep, na.rm = TRUE)) %>%
ungroup()p_surface <- PD %>%
ggplot(aes(date_time_ID, value_diff_surface)) +
geom_hline(yintercept = 0) +
geom_line() +
geom_point() +
scale_y_reverse(name = "Change surface value") +
scale_x_datetime(breaks = "week", date_labels = "%d %b") +
scale_color_brewer(palette = "Set1", direction = -1) +
theme(axis.title.x = element_blank(),
legend.title = element_blank()) +
facet_grid(var ~ ., scales = "free_y")
p_integrated <- PD %>%
ggplot(aes(date_time_ID, value_diff_int)) +
geom_hline(yintercept = 0) +
geom_line() +
geom_point() +
scale_y_reverse(name = "Change integrated value") +
scale_x_datetime(breaks = "week", date_labels = "%d %b") +
scale_color_brewer(palette = "Set1", direction = -1) +
theme(axis.title.x = element_blank(),
legend.title = element_blank()) +
facet_grid(var ~ ., scales = "free_y")
p_pen_dep <- PD %>%
ggplot(aes(date_time_ID, i_dep, col = var)) +
geom_hline(yintercept = 0) +
geom_hline(data = PD_mean,
aes(
yintercept = i_dep_mean,
col = var,
linetype = "mean"
)) +
geom_line(aes(linetype = "cruise")) +
geom_point() +
scale_y_reverse(name = "Penetration depth (m)", breaks = seq(0, 20, 5)) +
scale_x_datetime(breaks = "week", date_labels = "%d %b") +
scale_color_brewer(palette = "Set1", direction = -1) +
theme(axis.title.x = element_blank(),
legend.title = element_blank())
p_surface + p_integrated + p_pen_dep +
plot_layout(ncol = 1)
PD_mean# A tibble: 2 x 3
var i_dep_mean i_dep_sd
<chr> <dbl> <dbl>
1 CT_star 10.2 1.04
2 tem 11.5 2.46CPD <- PD %>%
filter(var == "CT_star") %>%
drop_na()
rm(p_surface, p_integrated, p_pen_dep)
rm(PD, PD_mean, tm_profiles_ID_long)3 GETM model
3.1 Subsetting criteria
fixed_values <-
read_csv(here::here("data/intermediate/_summarized_data_files", "tb_fix.csv"))3.2 Read netcdf file
nc <- nc_open(here::here("data/input/GETM", "Finnmaid.E.3d.2018.nc"))
lat <- ncvar_get(nc, "latc")
time_units <- nc$dim$time$units %>% #we read the time unit from the netcdf file to calibrate the time
substr(start = 15, stop = 33) %>% #calculation, we take the relevant information from the string
ymd_hms() # and transform it to the right format
t <- time_units + ncvar_get(nc, "time") # read time vector
rm(time_units)
d <- ncvar_get(nc, "zax") # read depths vector
for (var_3d in c("salt", "temp", "SurfaceAge")) {
array <- ncvar_get(nc, var_3d) # store the data in a 3-dimensional array
#dim(array) # should be 3d with dimensions: 544 coordinates, 51 depths, and number of days of month
fillvalue <- ncatt_get(nc, var_3d, "_FillValue")
# Working with the data
array[array == fillvalue$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 > parameters$getm_low_lat, lat < parameters$getm_high_lat,
dep <= parameters$max_dep) %>%
mutate(var = var_3d,
date_time=t[i]) %>%
select(date_time, dep, value, var)
if (exists("gt_3d")) {
gt_3d <- bind_rows(gt_3d, gt_3d_part)
} else {gt_3d <- gt_3d_part}
rm(array_slice, array_slice_df, gt_3d_part)
}
rm(array, fillvalue)
}
nc_close(nc)
rm(nc)
gt_3d_long <- gt_3d %>%
filter(date_time >= parameters$getm_start_date & date_time <= parameters$getm_end_date) %>%
group_by(date_time, var, dep) %>%
summarise_all(list(value=~mean(.,na.rm=TRUE))) %>% # regional averaging
ungroup()
gt_3d_long %>%
write_csv(here::here("data/intermediate/_summarized_data_files",
file = "gt_3d_long.csv"))
rm(gt_3d, gt_3d_long, i, lat, d, t, var_3d)3.3 Sal and tem profiles
gt_3d_long <-
read_csv(here::here(
"data/intermediate/_summarized_data_files",
"gt_3d_long.csv"
))
gt_3d <- gt_3d_long %>%
pivot_wider(values_from = value, names_from = var) %>%
select(-SurfaceAge) %>%
rename(sal = salt, tem = temp)gt_3d_long <- gt_3d %>%
pivot_longer(c(sal, tem), values_to = "value", names_to = "var")
gt_3d_long %>%
ggplot(aes(value, dep,
col = date_time,
group = date_time)) +
geom_path() +
scale_y_reverse(expand = c(0, 0)) +
scale_color_viridis_c(name = "Date", trans = "time") +
facet_wrap( ~ var, scales = "free_x", ncol = 2)
rm(gt_3d_long)3.4 Comparison BloomSail
Vertical, 1m-gridded BloomSail CTD profiles were used for comparison with GETM results. Note that the sampling location does match exactly.
GETM results were linearly interpolated to the mean BloomSail time stamp.
3.4.1 Interpolate gt dep grid
gt_3d_int <- gt_3d %>%
mutate(dep_int = dep + 0.5) %>%
group_by(date_time) %>%
mutate(sal_int = approxfun(dep, sal)(dep_int),
tem_int = approxfun(dep, tem)(dep_int)) %>%
ungroup() %>%
select(date_time,
dep = dep_int,
sal = sal_int,
tem = tem_int) %>%
drop_na()
rm(gt_3d)tm_gt_3d <- full_join(
gt_3d_int,
tm_profiles_ID %>% select(date_time = date_time_ID,
dep, sal, tem),
by = c("date_time", "dep"),
suffix = c("_gt", "_tm")
)
tm_gt_3d <- tm_gt_3d %>%
mutate(
rho_gt = swSigma(
salinity = sal_gt,
temperature = tem_gt,
pressure = dep / 10
),
rho_tm = swSigma(
salinity = sal_tm,
temperature = tem_tm,
pressure = dep / 10
)
)
tm_gt_3d <- tm_gt_3d %>%
arrange(date_time) %>%
group_by(dep) %>%
mutate(
tem_gt = approxfun(date_time, tem_gt)(date_time),
sal_gt = approxfun(date_time, sal_gt)(date_time),
rho_gt = approxfun(date_time, rho_gt)(date_time)
) %>%
ungroup() %>%
drop_na()tm_gt_3d_long <- tm_gt_3d %>%
pivot_longer(
sal_gt:rho_tm,
values_to = "value",
names_to = c("var", "source"),
names_sep = "_"
)
tm_gt_3d_long %>%
ggplot(aes(value, dep,
col = date_time,
group = date_time)) +
geom_path() +
scale_y_reverse(expand = c(0, 0), name = "Depth (m)") +
scale_color_viridis_c(name = "Date", trans = "time") +
facet_grid(source ~ var, scales = "free_x")
STD profiles modeled with GETM (upper panels, gt) and measured during BloomSail campaign (lower panels, ts)
tm_gt_3d <- tm_gt_3d_long %>%
pivot_wider(values_from = "value", names_from = "source") %>%
mutate(value_diff = gt - tm)
tm_gt_3d %>%
ggplot(aes(value_diff, dep,
col = date_time,
group = date_time)) +
geom_vline(xintercept = 0, col = "red") +
geom_path() +
scale_y_reverse(expand = c(0, 0), name = "Depth (m)") +
scale_color_viridis_c(name = "Date", trans = "time") +
facet_grid(. ~ var, scales = "free_x") +
labs(x = "Difference GETM (gt) - Bloomsail (ts)")
rm(tm_gt_3d, tm_gt_3d_long)4 Finnmaid
4.1 Data preparation
Finnmaid data, including reconstructed data during LICOS operation failure.
fm <-
read_csv(here::here("data/intermediate/_summarized_data_files",
"fm_bloomsail.csv"))
fm <- fm %>%
filter(date_time > parameters$getm_start_date,
date_time < parameters$getm_end_date) %>%
select(ID, date_time, sensor, sal, tem, pCO2) %>%
mutate(ID = as.factor(ID))4.1.1 CT calculation
Calculate nCT based on fixed AT and salinity mean values.
fm <- fm %>%
mutate(
CT_star = carb(
24,
var1 = pCO2,
var2 = fixed_values$AT * 1e-6,
S = fixed_values$sal,
T = tem,
k1k2 = "m10",
kf = "dg",
ks = "d",
gas = "insitu"
)[, 16] * 1e6
)4.1.2 Regional averaging
Calculate regional mean and sd values for each crossing of the area.
fm_ID <- fm %>%
pivot_longer(c(pCO2, sal, tem, CT_star),
values_to = "value",
names_to = "var") %>%
group_by(ID) %>%
mutate(date_time_ID = mean(date_time)) %>%
ungroup() %>%
select(-date_time) %>%
group_by(ID, date_time_ID, sensor, var) %>%
summarise_all(list( ~ mean(.), ~ sd(.)), na.rm = TRUE) %>%
ungroup() %>%
rename(value = mean)4.1.3 Read tm profile data
Read original profile data and calculate surface mean and sd values.
tm_profiles <-
read_csv(
here::here(
"data/intermediate/_merged_data_files/CT_dynamics",
"tm_profiles.csv"
)
)
tm_profiles_ID_long_surface <- tm_profiles %>%
filter(dep < parameters$surface_dep) %>%
select(-c(dep, date_ID, station, date_time, lat, lon, pCO2_corr)) %>%
mutate(ID = as.factor(ID)) %>%
pivot_longer(sal:CT_star, values_to = "value", names_to = "var") %>%
group_by(ID, date_time_ID, var) %>%
summarise_all(list( ~ mean(.), ~ sd(.)), na.rm = TRUE) %>%
ungroup()4.1.4 Timeseries
fm_ID %>%
ggplot() +
geom_rect(data = fixed_values,
aes(
xmin = start,
xmax = end,
ymin = -Inf,
ymax = Inf
),
alpha = 0.2) +
geom_path(aes(x = date_time_ID, y = value)) +
geom_ribbon(aes(
x = date_time_ID,
y = value,
ymax = value + sd,
ymin = value - sd,
fill = "Finnmaid"
),
alpha = 0.3) +
geom_ribbon(
data = tm_profiles_ID_long_surface,
aes(
x = date_time_ID,
ymin = mean - sd,
ymax = mean + sd,
fill = "BloomSail"
),
alpha = 0.3
) +
geom_point(aes(x = date_time_ID, y = value, col = sensor)) +
geom_point(data = tm_profiles_ID_long_surface,
aes(x = date_time_ID, y = mean, col = "BloomSail")) +
geom_line(data = tm_profiles_ID_long_surface,
aes(x = date_time_ID, y = mean, col = "BloomSail")) +
facet_grid(var ~ ., scales = "free_y") +
scale_color_brewer(palette = "Set1") +
scale_fill_brewer(palette = "Set1", name = "+/- SD") +
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d") +
theme(axis.title.x = element_blank())
4.2 Missing observations
The observational gaps in the Finnmaid SST and CT time series were filled with:
- two BloomSail observations
- an interpolated finnmaid value to match the starting date
The time series was restricted to the period where BloomSail observations are available.
tm_start_date <- tm_profiles_ID_long_surface %>%
filter(ID %in% c("180705"),
var %in% c("tem", "CT_star")) %>%
select(date_time_ID, ID, var) %>%
mutate(sensor = "interpolated")
fm_tm_ID <- full_join(fm_ID, tm_start_date) %>%
arrange(date_time_ID) %>%
filter(var %in% c("tem", "CT_star"))
fm_tm_ID <- fm_tm_ID %>%
group_by(var) %>%
mutate(value = approxfun(date_time_ID, value)(date_time_ID)) %>%
ungroup()
rm(tm_start_date)tm_gap <- tm_profiles_ID_long_surface %>%
filter(ID %in% c("180718", "180723"),
var %in% c("tem", "CT_star")) %>%
select(date_time_ID, ID, var, value = mean) %>%
mutate(sensor = "BloomSail")
fm_tm_ID <- full_join(fm_tm_ID, tm_gap) %>%
arrange(date_time_ID) %>%
select(-sd) %>%
filter(var %in% c("tem", "CT_star")) %>%
mutate(
period = "BloomSail",
period = if_else(date_time_ID < fixed_values$start, "pre-BloomSail", period),
period = if_else(date_time_ID > fixed_values$end, "post-BloomSail", period)
)
fm_tm_ID <- fm_tm_ID %>%
filter(period == "BloomSail") %>%
select(-period)
rm(fm_ID, fm, tm_gap, tm_profiles_ID_long_surface, tm_profiles)fm_tm_ID %>%
ggplot() +
geom_path(aes(date_time_ID, value)) +
geom_point(aes(date_time_ID, value, col = sensor)) +
facet_grid(var ~ ., scales = "free_y") +
scale_color_brewer(palette = "Set1") +
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d") +
theme(axis.title.x = element_blank())
5 Merge all data sets
5.1 Merge fm and gt
fm_tm_ID_wide <- fm_tm_ID %>%
filter(var %in% c("CT_star")) %>%
select(date_time_ID, var, sensor, value) %>%
pivot_wider(values_from = value, names_from = var)
fm_gt <- expand_grid(fm_tm_ID_wide, dep = unique(gt_3d_int$dep))
fm_gt <- full_join(fm_gt,
gt_3d_int %>% rename(date_time_ID = date_time)) %>%
arrange(date_time_ID)
rm(fm_tm_ID_wide, fm_tm_ID, gt_3d_int)5.2 Interpolate gt time stamp
fm_gt <- fm_gt %>%
arrange(date_time_ID) %>%
group_by(dep) %>%
mutate(
tem = approxfun(date_time_ID, tem)(date_time_ID),
sal = approxfun(date_time_ID, sal)(date_time_ID)
) %>%
ungroup() %>%
arrange(dep) %>%
filter(!is.na(CT_star))5.3 Bind tm and fm_gt
tm_profiles_ID <- tm_profiles_ID %>%
select(-c(ID, pCO2)) %>%
mutate(source = "tm")
fm_gt <- fm_gt %>%
mutate(source = "fm")
tm_profiles_ID <- tm_profiles_ID %>%
mutate(sensor = "BloomSail")
tm_fm_gt <- bind_rows(tm_profiles_ID, fm_gt)
rm(fm_gt, tm_profiles_ID)tm_fm_gt_long <- tm_fm_gt %>%
pivot_longer(sal:CT_star, values_to = "value", names_to = "var")
tm_fm_gt_long %>%
filter(dep == 3.5) %>%
ggplot(aes(date_time_ID, value, col = source)) +
geom_path() +
geom_point() +
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d") +
facet_grid(var ~ ., scales = "free_y") +
labs(title = "Time series at 3.5 m") +
theme(axis.title.x = element_blank())
bin <- 2
tm_fm_gt %>%
ggplot(aes(date_time_ID, dep, z = tem)) +
geom_contour_fill(breaks = MakeBreaks(bin)) +
geom_vline(aes(xintercept = date_time_ID),
col = "white",
linetype = "1f") +
scale_fill_viridis_c(
name = "Tem (°C)",
option = "B",
guide = "colorstrip",
breaks = MakeBreaks(bin)
) +
scale_y_reverse() +
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d") +
coord_cartesian(expand = 0) +
labs(y = "Depth (m)") +
theme(axis.title.x = element_blank()) +
facet_grid(source ~ .)
rm(bin)6 Integration depths
6.1 MLD
6.1.1 Density
tm_fm_gt <- tm_fm_gt %>%
mutate(rho = swSigma(
salinity = sal,
temperature = tem,
pressure = dep / 10
))bin <- 0.5
tm_fm_gt %>%
ggplot() +
geom_contour_fill(aes(date_time_ID, dep, z = rho),
breaks = MakeBreaks(bin)) +
geom_vline(aes(xintercept = date_time_ID),
col = "white",
linetype = "1f") +
scale_fill_viridis_c(
name = "Rho",
option = "B",
guide = "colorstrip",
breaks = MakeBreaks(bin),
direction = -1
) +
scale_y_reverse() +
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d") +
coord_cartesian(expand = 0) +
labs(y = "Depth (m)") +
theme(axis.title.x = element_blank()) +
facet_grid(source ~ .)
rm(bin)6.1.2 MLD
tm_fm_gt_MLD <- expand_grid(tm_fm_gt, rho_lim = seq(0.1, 0.5, 0.1))
tm_fm_gt_MLD <- tm_fm_gt_MLD %>%
arrange(dep) %>%
group_by(date_time_ID, source, rho_lim) %>%
mutate(d_rho = rho - first(rho)) %>%
filter(d_rho > rho_lim) %>%
summarise(MLD = min(dep)) %>%
ungroup() %>%
mutate(rho_lim = as.factor(rho_lim))bin <- 2
tm_fm_gt %>%
ggplot() +
geom_contour_fill(aes(date_time_ID, dep, z = tem),
breaks = MakeBreaks(bin)) +
geom_path(data = tm_fm_gt_MLD, aes(date_time_ID, MLD, col = rho_lim)) +
scale_fill_gradient(
name = "Tem (°C)",
guide = "colorstrip",
breaks = MakeBreaks(bin),
high = "grey80",
low = "grey5"
) +
scale_color_viridis_d() +
scale_y_reverse() +
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d") +
coord_cartesian(expand = 0) +
labs(y = "Depth (m)") +
theme(axis.title.x = element_blank()) +
facet_grid(source ~ .)
rm(bin)rho_lim_value <- 0.1
MLD <- tm_fm_gt_MLD %>%
filter(rho_lim == rho_lim_value) %>%
select(-rho_lim) %>%
rename(i_dep = MLD) %>%
mutate(i_method = "MLD", i_res = "daily")
rm(tm_fm_gt_MLD)MLD %>%
filter(date_time_ID <= date_tem_max) %>%
group_by(source) %>%
summarise(MLD_mean = mean(i_dep, na.rm = TRUE),
MLD_sd = sd(i_dep, na.rm = TRUE)) %>%
ungroup()# A tibble: 2 x 3
source MLD_mean MLD_sd
<chr> <dbl> <dbl>
1 fm 5.5 1.18
2 tm 6 1.87MLD_mean <- MLD %>%
filter(date_time_ID <= date_tem_max) %>%
group_by(source) %>%
summarise(i_dep = mean(i_dep, na.rm = TRUE)) %>%
ungroup() %>%
mutate(i_method = "MLD", i_res = "mean")
MLD_dates <- MLD %>%
select(source, date_time_ID)
MLD_mean <- full_join(MLD_dates, MLD_mean)
MLD <- full_join(MLD, MLD_mean)
rm(MLD_mean)6.2 Penetration depth
tm_fm_gt_long <- tm_fm_gt %>%
select(-c(sal)) %>%
pivot_longer(c("tem", "CT_star"), values_to = "value", names_to = "var") %>%
group_by(source, var, dep) %>%
arrange(date_time_ID) %>%
mutate(
date_time_ID_diff = as.numeric(date_time_ID - lag(date_time_ID)),
date_time_ID_ref = date_time_ID - (date_time_ID - lag(date_time_ID)) /
2,
value_diff = value - lag(value, default = first(value)),
value_diff_daily = value_diff / date_time_ID_diff,
value_cum = cumsum(value_diff)
) %>%
ungroup()
tm_fm_gt_long <- tm_fm_gt_long %>%
filter(var == "tem") %>%
select(-var)tm_fm_gt_long %>%
filter(date_time_ID == date_CT_min) %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = parameters$getm_i_dep) +
geom_point() +
geom_path() +
scale_y_reverse() +
labs(y = "Depth (m)", x = "Cumulative change") +
theme(legend.position = "left") +
facet_grid(. ~ source, scales = "free_x")
6.2.1 Cumulative changes
tm_fm_gt_long_180723 <- tm_fm_gt_long %>%
filter(date_time_ID == date_CT_min) %>%
mutate(
value_cum = if_else(value_cum < 0,
NaN, value_cum),
value_cum = if_else(source == "fm" & dep > parameters$getm_i_dep,
NaN, value_cum)
)
tm_fm_gt_long_180723_dep <- tm_fm_gt_long_180723 %>%
select(source, dep, value_cum) %>%
group_by(source) %>%
arrange(dep) %>%
mutate(
value_cum_i = sum(value_cum, na.rm = TRUE),
value_cum_dep = cumsum(value_cum),
value_cum_i_rel = value_cum_dep / value_cum_i * 100
) %>%
ungroup()
value_cum <- tm_fm_gt_long_180723_dep %>%
group_by(source) %>%
summarise(value_cum_i = mean(value_cum_i)) %>%
ungroup()
value_surface <- tm_fm_gt_long_180723 %>%
select(source, dep, value_cum) %>%
filter(dep < parameters$surface_dep) %>%
group_by(source) %>%
summarise(value_surface = mean(value_cum)) %>%
ungroup()
TPD <- full_join(value_cum, value_surface)
TPD <- TPD %>%
mutate(i_dep = value_cum_i / value_surface)
rm(value_cum, value_surface)6.2.2 Cumulative on July 23
p_tm_fm_gt_long <- tm_fm_gt_long_180723 %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep)) +
geom_hline(aes(yintercept = parameters$i_dep_lim, col = "integration")) +
geom_hline(data = TPD, aes(yintercept = i_dep, col = "penetration")) +
geom_vline(xintercept = 0) +
geom_point() +
geom_path() +
scale_y_reverse() +
scale_color_brewer(palette = "Dark2", guide = FALSE) +
labs(y = "Depth (m)", x = "Cumulative change") +
theme(legend.position = "left") +
facet_wrap(. ~ source, ncol = 1, scales = "free_x")
p_tm_fm_gt_long_rel <- tm_fm_gt_long_180723_dep %>%
ggplot(aes(value_cum_i_rel, dep)) +
geom_hline(aes(yintercept = parameters$i_dep_lim, col = "integration")) +
geom_hline(data = TPD, aes(yintercept = i_dep, col = "penetration")) +
geom_vline(xintercept = 90) +
geom_point() +
geom_line() +
scale_y_reverse(limits = c(25, 0)) +
scale_color_brewer(palette = "Dark2", name = "Depth") +
scale_x_continuous(limits = c(0, NA)) +
labs(x = "Relative contribution (%)") +
facet_wrap(. ~ source, ncol = 1, scales = "free_x") +
theme(axis.title.y = element_blank())
p_tm_fm_gt_long + p_tm_fm_gt_long_rel
rm(
tm_fm_gt_long_180723,
tm_fm_gt_long_180723_dep,
p_tm_fm_gt_long,
p_tm_fm_gt_long_rel
)
TPD_cum <- TPD
rm(TPD)6.2.3 Daily
# surface values
diff_surface <- tm_fm_gt_long %>%
filter(dep < parameters$surface_dep) %>%
group_by(date_time_ID, source) %>%
summarise(value_diff_surface = mean(value_diff, na.rm = TRUE)) %>%
ungroup() %>%
mutate(value_diff_surface = if_else(value_diff_surface < 0,
NaN, value_diff_surface))
tm_fm_gt_long <- full_join(tm_fm_gt_long, diff_surface)
rm(diff_surface)
# calculate penetration depths
TPD <- tm_fm_gt_long %>%
mutate(
value_diff = if_else(value_diff < 0,
NaN, value_diff),
value_diff = if_else(source == "fm" & dep > 19,
NaN, value_diff)
) %>%
group_by(date_time_ID, source) %>%
summarise(
value_diff_int = sum(value_diff, na.rm = TRUE),
value_diff_surface = mean(value_diff_surface, na.rm = TRUE)
) %>%
ungroup() %>%
mutate(i_dep = value_diff_int / value_diff_surface)
TPD_mean <- TPD %>%
filter(date_time_ID <= date_CT_min) %>%
group_by(source) %>%
summarise(i_dep_sd = sd(i_dep, na.rm = TRUE),
i_dep = mean(i_dep, na.rm = TRUE)) %>%
ungroup()
p_surface <- TPD %>%
ggplot(aes(date_time_ID, value_diff_surface, col = source)) +
geom_hline(yintercept = 0) +
geom_line() +
geom_point() +
scale_y_reverse(name = "Change surface value") +
scale_x_datetime(breaks = "week", date_labels = "%d %b") +
scale_color_brewer(palette = "Set1", direction = -1) +
theme(axis.title.x = element_blank(),
legend.title = element_blank())
p_integrated <- TPD %>%
ggplot(aes(date_time_ID, value_diff_int, col = source)) +
geom_hline(yintercept = 0) +
geom_line() +
geom_point() +
scale_y_reverse(name = "Change integrated value") +
scale_x_datetime(breaks = "week", date_labels = "%d %b") +
scale_color_brewer(palette = "Set1", direction = -1) +
theme(axis.title.x = element_blank(),
legend.title = element_blank())
p_TPD <- TPD %>%
ggplot(aes(date_time_ID, i_dep, col = source)) +
geom_hline(yintercept = 0) +
geom_hline(data = TPD_mean,
aes(
yintercept = i_dep,
col = source,
linetype = "mean"
)) +
geom_line(aes(linetype = "cruise")) +
geom_point() +
scale_y_reverse(name = "Penetration depth (m)", breaks = seq(0, 20, 5)) +
scale_x_datetime(breaks = "week", date_labels = "%d %b") +
scale_color_brewer(palette = "Set1", direction = -1) +
theme(axis.title.x = element_blank(),
legend.title = element_blank())
p_surface + p_integrated + p_TPD +
plot_layout(ncol = 1)
TPD_mean# A tibble: 2 x 3
source i_dep_sd i_dep
<chr> <dbl> <dbl>
1 fm 2.31 11.4
2 tm 2.46 12.3rm(p_surface, p_integrated, p_TPD)TPD <- TPD %>%
select(date_time_ID, source, i_dep) %>%
mutate(i_method = "TPD", i_res = "daily") %>%
filter(date_time_ID < date_tem_max) %>%
mutate(i_dep = if_else(is.na(i_dep), 0, i_dep))
TPD_cum <- TPD_cum %>%
select(source, i_dep) %>%
mutate(i_method = "TPD", i_res = "cumulative")
TPD_cum <- full_join(MLD_dates, TPD_cum)
TPD_mean <- TPD_mean %>%
select(source, i_dep) %>%
mutate(i_method = "TPD", i_res = "mean")
TPD_mean <- full_join(MLD_dates, TPD_mean)
TPD <- full_join(TPD, TPD_cum)
TPD <- full_join(TPD, TPD_mean)
rm(TPD_cum, TPD_mean)i_dep <- full_join(MLD, TPD)
rm(MLD, TPD)bin <- 2
CPD <- CPD %>%
mutate(source = "tm")
CPD <- CPD %>%
mutate(source = factor(source, c("tm", "fm"))) %>%
mutate(source = fct_recode(
source,
`SOOP Finnmaid + GETM model` = "fm",
`SV Tina V (surface only)` = "tm"
))
i_dep <- i_dep %>%
mutate(source = factor(source, c("tm", "fm"))) %>%
mutate(source = fct_recode(
source,
`SOOP Finnmaid + GETM model` = "fm",
`SV Tina V (surface only)` = "tm"
))
tm_fm_gt <- tm_fm_gt %>%
mutate(source = factor(source, c("tm", "fm"))) %>%
mutate(source = fct_recode(
source,
`SOOP Finnmaid + GETM model` = "fm",
`SV Tina V (surface only)` = "tm"
))
p_hov_dep <-
tm_fm_gt %>%
ggplot() +
geom_contour_fill(aes(date_time_ID, dep, z = tem),
breaks = MakeBreaks(bin),
col = "black",
size = 0.1) +
geom_path(data = i_dep %>% filter(i_res == "daily" & i_dep != 0),
aes(date_time_ID, i_dep, col = i_method)) +
scale_fill_gradient(
name = "Temperature\n(\u00B0C)",
guide = "colorstrip",
breaks = MakeBreaks(bin),
high = "grey90",
low = "grey20"
) +
guides(fill = guide_colorsteps(barheight = unit(35, "mm"),
barwidth = unit(4, "mm"),
frame.colour = "black",
ticks = TRUE,
ticks.colour = "black")) +
scale_color_discrete(name = "Reconstruction", guide = FALSE) +
scale_y_reverse() +
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d",
sec.axis = dup_axis()) +
coord_cartesian(expand = 0) +
labs(y = expression(atop(Depth, (m)))) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text.x.top = element_blank(),
plot.margin = margin(0, 0, 0, 0, "cm")
) +
facet_wrap( ~ source)
p_hov_dep
rm(bin)7 Surface obs + integration depths
tm_fm_gt_surface <- tm_fm_gt %>%
filter(dep < parameters$surface_dep) %>%
select(source, date_time_ID, sensor, CT_star) %>%
group_by(source, date_time_ID, sensor) %>%
summarise(CT_star = mean(CT_star, na.rm = TRUE)) %>%
ungroup()
tm_fm_gt_surface <- tm_fm_gt_surface %>%
group_by(source) %>%
arrange(date_time_ID) %>%
mutate(
date_time_ID_diff = as.numeric(date_time_ID - lag(date_time_ID)),
date_time_ID_ref = date_time_ID - (date_time_ID - lag(date_time_ID)) /
2,
CT_star_diff = CT_star - lag(CT_star, default = first(CT_star)),
CT_star_cum = cumsum(CT_star_diff)
) %>%
ungroup()iCT <- full_join(tm_fm_gt_surface, i_dep)
rm(tm_fm_gt_surface)8 iCT
iCT <- iCT %>%
mutate(CT_i_diff = CT_star_diff * i_dep)
iCT <- iCT %>%
group_by(source, i_method, i_res) %>%
arrange(date_time_ID) %>%
mutate(CT_star_i_cum = cumsum(CT_i_diff/1000)) %>%
ungroup()tm_NCP_cum <- read_csv(here::here("data/intermediate/_merged_data_files/CT_dynamics",
"tm_NCP_cum.csv"))tm_NCP_cum_flux <- tm_NCP_cum %>%
select(date_time, flux_cum)
tm_NCP_cum_flux <-
expand_grid(
tm_NCP_cum_flux,
source = unique(iCT$source),
i_method = unique(iCT$i_method),
i_res = unique(iCT$i_res)
)
NCP_flux <- full_join(iCT %>% rename(date_time = date_time_ID),
tm_NCP_cum_flux) %>%
arrange(date_time)
# linear interpolation of cumulative changes to frequency of the flux estimates estimates
NCP_flux_int <- NCP_flux %>%
filter(!(i_method == "MLD" & i_res == "cumulative")) %>%
group_by(source, i_method, i_res) %>%
mutate(
CT_star_i_cum = approxfun(date_time, CT_star_i_cum)(date_time),
flux_cum = approxfun(date_time, flux_cum)(date_time)
) %>%
fill(flux_cum) %>%
mutate(CT_star_i_flux_cum = CT_star_i_cum + flux_cum) %>%
ungroup()iCT <- iCT %>%
mutate(sensor = if_else(sensor == "BloomSail", sensor, "VOS"))
p_CT_star <- iCT %>%
ggplot() +
geom_path(aes(date_time_ID, CT_star)) +
geom_point(aes(date_time_ID, CT_star, fill = sensor), shape = 21) +
scale_fill_manual(values = c("white", "black"),
guide = FALSE) +
scale_x_datetime(breaks = "week",
date_labels = "%d %b",
expand = c(0, 0)) +
facet_wrap(~ source) +
labs(y = expression(atop(paste(C[T], "*"), (mu * mol ~ kg ^ {
-1
})))) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
plot.margin = margin(0, 0, 0, 0, "cm")
)
# iCT <- iCT %>%
# mutate(i_res = fct_recode(i_res, `cumulative` = "cum")) %>%
# mutate(i_res = factor(i_res, c("mean", "cumulative", "daily")))
p_iCT <- iCT %>%
ggplot() +
geom_hline(yintercept = 0) +
geom_path(data = tm_NCP_cum, aes(date_time, CT_star_i_cum), col = "black") +
geom_path(aes(date_time_ID, CT_star_i_cum, col = i_method, linetype = i_res)) +
scale_color_discrete(name = "Reconstruction") +
scale_x_datetime(
breaks = "week",
date_labels = "%d %b",
sec.axis = dup_axis(),
expand = c(0, 0)
) +
scale_linetype(name = "Resolution") +
facet_wrap( ~ source) +
labs(y = expression(atop(Integrated ~ nC[T], (mol ~ m ^ {
-2
})))) +
guides(color = guide_legend(order = 1)) +
theme(
axis.title.x = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text.x.top = element_blank()
)
p_CT_star / p_hov_dep / p_iCT
# ggsave(
# here::here(
# "output/Plots/Figures_publication/article",
# "reconstruction_iCT_timeseries.pdf"
# ),
# width = 190,
# height = 200,
# dpi = 300,
# units = "mm"
# )
#
# ggsave(
# here::here(
# "output/Plots/Figures_publication/article",
# "reconstruction_iCT_timeseries.png"
# ),
# width = 190,
# height = 200,
# dpi = 300,
# units = "mm"
# )NCP_flux <- NCP_flux %>%
mutate(i_res = factor(i_res, c("mean", "cumulative", "daily")))
p_NCP <- NCP_flux_int %>%
filter(i_res %in% c("daily")) %>%
ggplot() +
geom_hline(yintercept = 0) +
geom_path(data = tm_NCP_cum,
aes(date_time, CT_star_i_flux_mix_cum, linetype = "best \U2013 guess"),
col = "black") +
geom_path(aes(
date_time,
CT_star_i_flux_cum,
col = i_method,
linetype = "reconstruction"
)) +
scale_color_discrete(name = "Integration depth") +
scale_x_datetime(
breaks = "week",
date_labels = "%d %b",
sec.axis = dup_axis(),
expand = c(0, 0)
) +
scale_linetype_manual(name = "NCP estimate",
values = c(2,1)) +
facet_wrap( ~ source) +
labs(y = expression(atop(NCP, (mol ~ m ^ {
-2
})))) +
guides(color = guide_legend(order = 1)) +
theme(
axis.title.x = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text.x.top = element_blank(),
plot.margin = margin(0, 0, 0, 0, "cm")
)
p_CT_star / p_hov_dep / p_NCP +
plot_annotation(tag_levels = 'a')
ggsave(
here::here(
"output/Plots/Figures_publication/article",
"Fig_6.pdf"
),
width = 160,
height = 155,
dpi = 300,
units = "mm"
)
ggsave(
here::here(
"output/Plots/Figures_publication/article",
"Fig_6.png"
),
width = 160,
height = 155,
dpi = 300,
units = "mm"
)
sessionInfo()R version 4.0.3 (2020-10-10)
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] metR_0.9.0 lubridate_1.7.9.2 patchwork_1.1.1 seacarb_3.2.14
[5] oce_1.2-0 gsw_1.0-5 testthat_3.0.1 ncdf4_1.17
[9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3
[17] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.7.2 viridisLite_0.3.0 here_1.0.1
[5] modelr_0.1.8 assertthat_0.2.1 highr_0.8 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.4.7 backports_1.2.1 glue_1.4.2
[13] digest_0.6.27 RColorBrewer_1.1-2 promises_1.1.1 checkmate_2.0.0
[17] rvest_0.3.6 colorspace_2.0-0 plyr_1.8.6 htmltools_0.5.0
[21] httpuv_1.5.4 pkgconfig_2.0.3 broom_0.7.3 haven_2.3.1
[25] scales_1.1.1 whisker_0.4 later_1.1.0.1 git2r_0.27.1
[29] generics_0.1.0 farver_2.0.3 ellipsis_0.3.1 withr_2.3.0
[33] cli_2.2.0 magrittr_2.0.1 crayon_1.3.4 readxl_1.3.1
[37] evaluate_0.14 ps_1.5.0 fs_1.5.0 fansi_0.4.1
[41] xml2_1.3.2 tools_4.0.3 data.table_1.13.6 hms_0.5.3
[45] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 isoband_0.2.3
[49] compiler_4.0.3 rlang_0.4.10 grid_4.0.3 rstudioapi_0.13
[53] labeling_0.4.2 rmarkdown_2.6 gtable_0.3.0 DBI_1.1.0
[57] R6_2.5.0 knitr_1.30 utf8_1.1.4 rprojroot_2.0.2
[61] stringi_1.5.3 Rcpp_1.0.5 vctrs_0.3.6 dbplyr_2.0.0
[65] tidyselect_1.1.0 xfun_0.19