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NCP_reconstruction.Rmd
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NCP_reconstruction.Rmd
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---
title: "NCP reconstruction"
author: "Jens Daniel Müller"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
workflowr::wflow_html:
number_sections: true
toc_depth: 3
toc_float:
collapsed: false
editor_options:
chunk_output_type: console
---
```{r global_options, include = FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
```{r packages}
library(tidyverse)
library(ncdf4)
library(seacarb)
library(oce)
library(patchwork)
library(lubridate)
library(metR)
```
```{r ggplot_theme, include = FALSE}
geom_text_size <- (25.4 / 72.27) * 9
theme_set(theme_bw(base_size = 9))
```
```{r read_parameters, include = FALSE}
parameters <-
read_rds(here::here("data",
"parameters.rds"))
```
# Approach
In order to test how (and how well) the depth-integrated C~T~ estimates can be reproduced if only surface CO~2~ data were available, the BloomSail observations were restricted to those made in surface water and following reconstruction approaches were tested:
- Mixed layer depth: Integration of surface observation across the MLD, assuming homogenious vertical patterns
- C~T~ profile reconstruction: Vertical reconstruction of incremental C~T~ 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 C~T~ drawdown.
Note: The reconstruction of C~T~ 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 C~T~.
```{r subsetting criteria}
date_CT_min <- ymd_hms("2018-07-24 07:58:29")
date_tem_max <- ymd_hms("2018-08-04 00:00:00")
```
# BloomSail data
1m gridded, downcast profiles were used.
Mean CO~2~ data from upper 6 metres were used as surface values.
```{r read_tm}
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))
```
```{r calculate_cumulative_changes_tm}
tm_profiles_ID_long <- tm_profiles_ID %>%
select(-c(pCO2, sal)) %>%
pivot_longer(c("tem", "nCT"), 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()
```
## C~T~ and tem penetration depth
### Cumulative on July 09
```{r calculate_TPD_cumulative_tm}
tm_profiles_ID_long_180723 <- tm_profiles_ID_long %>%
filter(ID == 180709)
tm_profiles_ID_long_180723_dep <- tm_profiles_ID_long_180723 %>%
select(var, dep, value_cum) %>%
mutate(
value_cum = if_else(value_cum > 0 & var == "nCT",
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()
value_cum <- tm_profiles_ID_long_180723_dep %>%
group_by(var) %>%
summarise(value_cum_i = mean(value_cum_i)) %>%
ungroup()
value_surface <- tm_profiles_ID_long_180723 %>%
select(var, dep, value_cum) %>%
filter(dep < parameters$surface_dep) %>%
group_by(var) %>%
summarise(value_surface = mean(value_cum)) %>%
ungroup()
TPD <- full_join(value_cum, value_surface)
TPD <- TPD %>%
mutate(TPD = value_cum_i / value_surface)
rm(value_cum, value_surface)
```
```{r TPD_cumulative_tm_profiles}
p_tm_profiles_ID_long <- tm_profiles_ID_long_180723 %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep)) +
geom_hline(aes(yintercept = parameters$i_dep_lim, col = "integration")) +
geom_hline(data = TPD, aes(yintercept = TPD, col = "penetration")) +
geom_vline(xintercept = 0) +
geom_point() +
geom_path() +
scale_y_reverse() +
scale_color_brewer(palette = "Dark2", guide = FALSE) +
labs(y = "Depth (m)", x = "Cumulative change") +
theme(legend.position = "left") +
facet_wrap(var ~ ., ncol = 1, scales = "free_x")
p_tm_profiles_ID_long_rel <- tm_profiles_ID_long_180723_dep %>%
ggplot(aes(value_cum_i_rel, dep)) +
geom_hline(aes(yintercept = parameters$i_dep_lim, col = "integration")) +
geom_hline(data = TPD, aes(yintercept = TPD, 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
TPD
rm(tm_profiles_ID_long_180723_dep,
p_tm_profiles_ID_long,
p_tm_profiles_ID_long_rel)
```
```{r TPD_CPD_cumulative_tm_profiles_example}
col_value <- "red"
p_nCT <-
tm_profiles_ID_long_180723 %>%
filter(var == "nCT") %>%
arrange(dep) %>%
ggplot() +
geom_col(
data = tm_profiles_ID_long_180723 %>%
filter(var == "nCT", 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
) +
annotate(
"text",
x = -3.5,
y = 2.5,
label = "Integrated change"
) +
geom_point(aes(value_diff_daily, dep)) +
geom_path(aes(value_diff_daily, dep)) +
geom_hline(data = TPD %>% filter(var == "nCT"),
aes(yintercept = TPD),
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_180723 %>%
filter(var == "tem") %>%
arrange(dep) %>%
ggplot() +
geom_col(
data = tm_profiles_ID_long_180723 %>%
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 = "TPD",
col = col_value
) +
annotate(
"text",
x = 0.2,
y = 2.5,
label = "Integrated change"
) +
geom_point(aes(value_diff_daily, dep)) +
geom_path(aes(value_diff_daily, dep)) +
geom_hline(data = TPD %>% filter(var == "tem"),
aes(yintercept = TPD),
col = col_value) +
labs(y = "Depth (m)", x = expression(Delta ~ Temperature ~ (degree * C))) +
theme(legend.title = element_blank())
p_tem + p_nCT +
plot_layout(guides = 'collect') +
plot_annotation(tag_levels = 'a')
ggsave(
here::here("output/Plots/Figures_publication/appendix",
"Fig_A8.pdf"),
width = 150,
height = 150,
dpi = 300,
units = "mm"
)
ggsave(
here::here("output/Plots/Figures_publication/appendix",
"Fig_A8.png"),
width = 150,
height = 150,
dpi = 300,
units = "mm"
)
rm(TPD, tm_profiles_ID_long_180723, p_tem, p_nCT)
```
### Daily
```{r calculate_TPD_daily_tm}
# 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 == "nCT",
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
TPD <- tm_profiles_ID_long %>%
mutate(
value_diff = if_else(value_diff > 0 & var == "nCT",
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)
TPD_mean <- TPD %>%
group_by(var) %>%
summarise(i_dep_mean = mean(i_dep, na.rm = TRUE),
i_dep_sd = sd(i_dep, na.rm = TRUE)) %>%
ungroup()
```
```{r TPD_daily_tm_time_series}
p_surface <- TPD %>%
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 <- TPD %>%
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 <- TPD %>%
ggplot(aes(date_time_ID, i_dep, col = var)) +
geom_hline(yintercept = 0) +
geom_hline(data = TPD_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)
TPD_mean
CPD <- TPD %>%
filter(var == "nCT") %>%
drop_na()
rm(p_surface, p_integrated, p_pen_dep)
rm(TPD, TPD_mean, tm_profiles_ID_long)
```
# GETM
## Subsetting criteria
```{r load_bottle_data}
fixed_values <-
read_csv(here::here("data/intermediate/_summarized_data_files", "tb_fix.csv"))
```
## Read netcdf file
```{r read_gt_3d_sal_tem_age, eval=FALSE}
nc <- nc_open(here::here("data/input/GETM", "Finnmaid.E.3d.2018.nc"))
#print(nc$var)
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)
```
## Sal and tem profiles
```{r re-read_gt_3d}
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)
```
```{r gt_profiles_sal_tem}
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)
```
## 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.
### Interpolate gt dep grid
```{r interpolate_gt_3d_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)
```
```{r merge_gt_ts_profiles}
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()
```
```{r gt_ts_profiles_salt_temp_rho, fig.asp=0.8, fig.cap="STD profiles modeled with GETM (upper panels, gt) and measured during BloomSail campaign (lower panels, ts)"}
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")
```
```{r gt_ts_profiles_diff_salt_temp_rho, fig.asp=0.5}
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)
```
# Finnmaid
## Data preparation
Finnmaid data, including reconstructed data during LICOS operation failure.
```{r read_Finnmaid}
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$getm_end_date) %>%
select(ID, date_time, sensor, sal, tem, pCO2) %>%
mutate(ID = as.factor(ID))
```
### C~T~ calculation
Calculate nC~T~ based on fixed A~T~ and salinity mean values.
```{r CT_calculation}
fm <- fm %>%
mutate(
nCT = carb(
24,
var1 = pCO2,
var2 = fixed_values$AT * 1e-6,
S = fixed_values$sal,
T = tem,
k1k2 = "m10",
kf = "dg",
ks = "d",
gas = "insitu"
)[, 16] * 1e6
)
```
### Regional averaging
Calculate regional mean and sd values for each crossing of the area.
```{r timeseries_of_regional_averages}
fm_ID <- fm %>%
pivot_longer(c(pCO2, sal, tem, nCT),
values_to = "value",
names_to = "var") %>%
group_by(ID) %>%
mutate(date_time_ID = mean(date_time)) %>%
ungroup() %>%
select(-date_time) %>%
group_by(ID, date_time_ID, sensor, var) %>%
summarise_all(list( ~ mean(.), ~ sd(.)), na.rm = TRUE) %>%
ungroup() %>%
rename(value = mean)
```
### Read tm profile data
Read original profile data and calculate surface mean and sd values.
```{r tm_long_surface}
tm_profiles <-
read_csv(
here::here(
"data/intermediate/_merged_data_files/CT_dynamics",
"tm_profiles.csv"
)
)
tm_profiles_ID_long_surface <- tm_profiles %>%
filter(dep < parameters$surface_dep) %>%
select(-c(dep, date_ID, station, date_time, lat, lon, pCO2_corr)) %>%
mutate(ID = as.factor(ID)) %>%
pivot_longer(sal:nCT, values_to = "value", names_to = "var") %>%
group_by(ID, date_time_ID, var) %>%
summarise_all(list( ~ mean(.), ~ sd(.)), na.rm = TRUE) %>%
ungroup()
```
### Timeseries
```{r fm_tm_time_series, fig.asp=1.2}
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())
```
## Missing observations
The observational gaps in the Finnmaid SST and C~T~ 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.
```{r fm_interpolate_starting_values}
tm_start_date <- tm_profiles_ID_long_surface %>%
filter(ID %in% c("180705"),
var %in% c("tem", "nCT")) %>%
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", "nCT"))
fm_tm_ID <- fm_tm_ID %>%
group_by(var) %>%
mutate(value = approxfun(date_time_ID, value)(date_time_ID)) %>%
ungroup()
rm(tm_start_date)
```
```{r fm_fill_gap_with_tm}
tm_gap <- tm_profiles_ID_long_surface %>%
filter(ID %in% c("180718", "180723"),
var %in% c("tem", "nCT")) %>%
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", "nCT")) %>%
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)
```
```{r fm_time_series_filled}
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())
```
# Merge all data sets
## Merge fm and gt
```{r fm_gt_merge}
fm_tm_ID_wide <- fm_tm_ID %>%
filter(var %in% c("nCT")) %>%
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)
```
## Interpolate gt time stamp
```{r fm_gt_interpolate_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(nCT))
```
## Bind tm and fm_gt
```{r tm_fm_gt_bind}
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)
```
```{r tm_fm_gt_time_series}
tm_fm_gt_long <- tm_fm_gt %>%
pivot_longer(sal:nCT, 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())
```
```{r tm_fm_gt_hovmoeller_tem}
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)
```
# Integration depths
## MLD
### Density
```{r calculate_density}
tm_fm_gt <- tm_fm_gt %>%
mutate(rho = swSigma(
salinity = sal,
temperature = tem,
pressure = dep / 10
))
```
```{r tm_fm_gt_hovmoeller_rho}
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)
```
### MLD
```{r calculate_MLD}