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CT_dynamics.Rmd
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CT_dynamics.Rmd
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---
title: "CT dynamics"
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(patchwork)
library(seacarb)
library(marelac)
library(metR)
library(scico)
library(lubridate)
library(zoo)
```
```{r ggplot_theme, include = FALSE}
theme_set(theme_bw())
```
# Sensor data
Profile data are prepared by:
- Ignoring those made on June 16 (pCO2 sensor not in operation)
- Removing HydroC Flush and Zeroing periods
- Selecting only continous downcast periods
- Gridding profiles to 1m depth intervals
- Discarding profiles with 3 or more observation missing within upper 20m
- assigning mean date_time_ID value to all profiles belonging to one cruise
- discarding "coastal" station P01, P13, P14
- Restricting profiles to upper 25m
Please note that:
- The label ID represents the start date of the cruise ("YYMMDD"), not the exact mean sampling date
## pCO~2~ profile overview
```{r read_prepare_sensor_data, fig.cap="Overview pCO2 profiles at stations (P02-P12) and cruise dates (ID). y-axis restricted to displayed range.", fig.asp = 1.5}
ts <-
read_csv(here::here("data/_merged_data_files",
"BloomSail_CTD_HydroC_track_RT.csv"),
col_types = cols(ID = col_character(),
pCO2_analog = col_double(),
pCO2 = col_double(),
Zero = col_character(),
Flush = col_character(),
mixing = col_character(),
Zero_ID = col_integer(),
deployment = col_integer(),
lon = col_double(),
lat = col_double(),
pCO2_RT = col_double()))
# Filter relevant rows and columns
ts_profiles <- ts %>%
filter(type == "P",
Flush == "0",
Zero == "0",
!ID %in% c("180616","180820"),
!(station %in% c("PX1", "PX2", "P14", "P13", "P01"))) %>%
select(date_time, ID, station, lat, lon, dep, sal, tem, pCO2_raw = pCO2, pCO2 = pCO2_RT_mean, duration)
# Assign meta information
ts_profiles <- ts_profiles %>%
group_by(ID, station) %>%
mutate(duration = as.numeric(date_time - min(date_time))) %>%
arrange(date_time) %>%
ungroup()
meta <- read_csv(here::here("Data/_summarized_data_files",
"Tina_V_Sensor_meta.csv"),
col_types = cols(ID = col_character()))
meta <- meta %>%
filter(!ID %in% c("180616","180820"),
!(station %in% c("PX1", "PX2", "P14", "P13", "P01")))
ts_profiles <- full_join(ts_profiles, meta)
rm(meta)
# creating descriptive variables
ts_profiles <- ts_profiles %>%
mutate(phase = "standby",
phase = if_else(duration >= start & duration < down & !is.na(down) & !is.na(start),
"down", phase),
phase = if_else(duration >= down & duration < lift & !is.na(lift) & !is.na(down ),
"low", phase),
phase = if_else(duration >= lift & duration < up & !is.na(up ) & !is.na(lift ),
"mid", phase),
phase = if_else(duration >= up & duration < end & !is.na(end ) & !is.na(up ),
"up", phase))
ts_profiles <- ts_profiles %>%
select(-c(start, down, lift, up, end, comment, p_type, duration))
# select downcasst only
ts_profiles <- ts_profiles %>%
filter(phase == "down") %>%
select(-phase)
# ts_profiles_highres <- ts_profiles
# grid observation to 1m depth intervals
ts_profiles <- ts_profiles %>%
mutate(dep_grid = as.numeric(as.character( cut(dep, seq(0,40,1), seq(0.5,39.5,1))))) %>%
group_by(ID, station, dep_grid) %>%
summarise_all("mean", na.rm = TRUE) %>%
ungroup() %>%
select(-dep, dep=dep_grid)
# subset complete profiles
profiles_in <- ts_profiles %>%
filter(dep < 20) %>%
group_by(ID, station) %>%
summarise(nr = n()) %>%
mutate(select = if_else(nr > 18 | station == "P14", "in", "out")) %>%
select(-nr) %>%
ungroup()
ts_profiles <- full_join(ts_profiles, profiles_in)
rm(profiles_in)
ts_profiles %>%
arrange(date_time) %>%
ggplot(aes(pCO2, dep, col=select))+
geom_hline(yintercept = 25)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_x_continuous(breaks = c(0, 600), labels = c(0, 600))+
scale_color_brewer(palette = "Set1", direction = -1)+
coord_cartesian(xlim = c(0,600))+
facet_grid(ID~station)
ts_profiles <- ts_profiles %>%
filter(select == "in") %>%
select(-select) %>%
filter(dep < 25)
# assign mean date_time stamp
cruise_dates <- ts_profiles %>%
group_by(ID) %>%
summarise(date_time_ID = mean(date_time)) %>%
ungroup()
# inner_join remove P14 observations lacking date_time_ID
ts_profiles <- inner_join(cruise_dates, ts_profiles)
```
## Station map
```{r map, fig.cap="Location of stations sampled between the east coast of Gotland and Gotland deep."}
map <- read_csv(here::here("data/Maps","Bathymetry_Gotland_east_small.csv"))
# ggplot()+
# geom_contour_fill(data=map, aes(x=lon, y=lat, z=-elev), na.fill = TRUE)+
# coord_quickmap(expand = 0, xlim = c(18.7, 19.9), ylim = c(57.25,57.6))+
# theme_bw()+
# theme(legend.position="bottom")
ts_profiles %>%
group_by(station) %>%
summarise(lat = mean(lat),
lon = mean(lon)) %>%
ungroup() %>%
ggplot()+
geom_raster(data=map, aes(lon, lat, fill=-elev))+
scale_fill_scico(palette = "oslo", na.value = "grey",
name="Depth [m]", direction = -1)+
geom_label(aes(lon, lat, label=station))+
coord_quickmap(expand = 0, xlim = c(18.7, 19.9), ylim = c(57.25,57.6))+
theme_bw()
rm(map)
```
## Data coverage
```{r data_coverage, fig.cap="Spatio-temporal data coverage, indicated as station visits over time. ID (color) refers to the starting date of the cruise, except for P14, which was visited twice during each cruise."}
cover <- ts_profiles %>%
group_by(ID, station) %>%
summarise(date = mean(date_time),
date_time_ID = mean(date_time_ID)) %>%
ungroup()
cover %>%
ggplot(aes(date, station, fill=ID))+
geom_vline(aes(xintercept = date_time_ID, col=ID))+
geom_point(shape=21)+
scale_color_viridis_d()+
scale_fill_viridis_d()
rm(cover)
```
# Bottle C~T~ and A~T~
At stations P07 and P10 discrete samples for lab measurments of C~T~ and A~T~ were collected. Please note that - in contrast to the pCO~2~ profiles - samples were taken on June 16, but removed here for harmonization of results.
```{r read_bottle_CO2}
tb <-
read_csv(here::here("Data/_summarized_data_files", "Tina_V_Bottle_CO2_lab.csv"),
col_types = cols(ID = col_character()))
tb <- tb %>%
filter(station %in% c("P07", "P10")) %>%
select(-pH_Mosley) %>%
mutate(CT_AT_ratio = CT/AT)
tb <- inner_join(tb, cruise_dates)
```
## Vertical profiles
```{r plot_bottle_CO2_profiles}
tb_long <- tb %>%
pivot_longer(4:7, names_to = "var", values_to = "value")
tb_long %>%
ggplot(aes(value, dep))+
geom_path(aes(col=ID))+
geom_point(aes(fill=ID), shape=21)+
scale_y_reverse()+
scale_fill_viridis_d()+
scale_color_viridis_d()+
facet_grid(station~var, scales = "free_x")+
theme(legend.position = "bottom")
```
Important notes:
- Spatio-temporal variation of AT is small, which jusitfies conversion of pCO~2~ to C~T~ based on a fixed mean AT - On July 30 we see a drop in surface salinity, associated with a rise in A~T~, clearly pointing at exchange of water masses, presumably later
## Surface time series
```{r plot_bottle_CO2_surface_timeseries, fig.cap="Time series of bottle data. Shown are mean values of samples collected at water depths < 10m (usually collected at 0 and 5 m).", fig.asp=1.2}
tb_surface <- tb_long %>%
filter(dep<10) %>%
group_by(ID, date_time_ID, var, station) %>%
summarise(value = mean(value, na.rm = TRUE)) %>%
ungroup()
rm(tb_long)
tb_surface %>%
ggplot(aes(date_time_ID, value, col=station))+
#geom_point(aes(lubridate::ymd(ID), value, col=station))+
geom_point()+
geom_path()+
scale_fill_viridis_d()+
scale_color_brewer(palette = "Set1")+
facet_grid(var~., scales = "free_y")+
labs(x="Mean transect date")
```
```{r plot_bottle_CT_surface_timeseries, fig.cap="CT timeseries, derived by multiplying the CT-AT-ratio with mean AT", fig.asp=0.4}
AT_mean <- tb_surface %>%
filter(var == "AT") %>%
summarise(AT = mean(value, na.rm = TRUE)) %>%
pull()
tb_surface %>%
filter(var == "CT_AT_ratio") %>%
ggplot(aes(lubridate::ymd(ID), value*AT_mean, col=station))+
geom_point()+
geom_path()+
scale_fill_viridis_d()+
scale_color_brewer(palette = "Set1")+
labs(x="Mean transect date", y="CT-AT-ratio * mean AT")
```
Important notes:
- C~T~ drop and temporal patterns observed in the C~T~/A~T~ time series agrees well with those found in the C~T~ time series derived from pCO~2~ measurements
## Mean alkalinity
In order to derive C~T~ from measured pCO~2~ profiles, the mean alkalinity in the upper 20 m and both stations was calculated as:
```{r bottle_AT}
AT_mean <- tb %>%
filter(dep <= 20) %>%
summarise(AT = mean(AT, na.rm = TRUE)) %>%
pull()
AT_mean
```
Likewise, the mean salinity amounts to:
```{r bottle_sal}
sal_mean <- tb %>%
filter(dep <= 20) %>%
summarise(sal = mean(sal, na.rm = TRUE)) %>%
pull()
sal_mean
```
```{r write_fixed_values}
bind_cols(start = min(ts_profiles$date_time),
end = max(ts_profiles$date_time),
AT = AT_mean,
sal = sal_mean) %>%
write_csv(here::here("Data/_summarized_data_files", "tb_fix.csv"))
rm(tb, tb_surface)
```
# C~T~ profiles
## Calculation from pCO~2~
C~T~ profiles were calculated from sensor pCO~2~ and T profiles, and constant salinity and alkalinity values. Note that the impact of fixed vs. measured salinity has only a negligible impact on C~T~ profiles.
```{r CT_calculation}
ts_profiles <- ts_profiles %>%
drop_na()
ts_profiles <- ts_profiles %>%
filter(pCO2 > 0)
ts_profiles <- ts_profiles %>%
mutate(CT = carb(24, var1=pCO2, var2=1720*1e-6,
S=sal_mean, T=tem, P=dep/10, k1k2="m10", kf="dg", ks="d",
gas="insitu")[,16]*1e6)
rm(sal_mean, AT_mean)
ts_profiles %>%
write_csv(here::here("Data/_merged_data_files", "ts_profiles.csv"))
```
## Mean profiles
Mean vertical profiles were calculated for each cruise day (ID).
```{r plot_CT_profiles_all, fig.cap="Mean vertical profiles per cruise day across all stations."}
ts_profiles_ID_mean <- ts_profiles %>%
select(-c(station,lat, lon, pCO2_raw, date_time)) %>%
group_by(ID, date_time_ID, dep) %>%
summarise_all(list(mean), na.rm=TRUE) %>%
ungroup()
ts_profiles_ID_sd <- ts_profiles %>%
select(-c(station,lat, lon, pCO2_raw, date_time)) %>%
group_by(ID, date_time_ID, dep) %>%
summarise_all(list(sd), na.rm=TRUE) %>%
ungroup()
ts_profiles_ID_sd_long <- ts_profiles_ID_sd %>%
pivot_longer(4:7, names_to = "var", values_to = "sd")
ts_profiles_ID_mean_long <- ts_profiles_ID_mean %>%
pivot_longer(4:7, names_to = "var", values_to = "value")
ts_profiles_ID_long <- inner_join(ts_profiles_ID_mean_long, ts_profiles_ID_sd_long)
ts_profiles_ID_mean %>%
write_csv(here::here("Data/_merged_data_files", "ts_profiles_ID.csv"))
rm(ts_profiles_ID_sd_long, ts_profiles_ID_sd, ts_profiles_ID_mean_long, ts_profiles_ID_mean)
ts_profiles_ID_long %>%
ggplot(aes(value, dep, col=ID))+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~var, scales = "free_x")
```
```{r plot_CT_profiles_individual, fig.cap="Mean vertical profiles per cruise day across all stations plotted indivdually. Ribbons indicate the standard deviation observed across all profiles at each depth and transect.", fig.asp=1.5}
all <- ts_profiles_ID_long %>%
filter(var %in% c("CT", "tem")) %>%
rename(group = ID)
ts_profiles_ID_long %>%
filter(var %in% c("CT", "tem")) %>%
ggplot()+
geom_path(data=all, aes(value, dep, group=group))+
geom_ribbon(aes(xmin = value-sd, xmax=value+sd, y=dep, fill=ID), alpha=0.5)+
geom_path(aes(value, dep, col=ID))+
scale_y_reverse()+
scale_color_viridis_d()+
scale_fill_viridis_d()+
facet_grid(ID~var, scales = "free_x")
rm(all)
```
Important notes:
- the standard deviation of C~T~ in the upper 10m increases on June 30.
## Individual profiles
C~T~, pCO~2~, S, and T profiles were plotted individually
[pdf here](https://github.com/jens-daniel-mueller/BloomSail/tree/master/output/Plots/CT_dynamics/ts_profiles_pCO2_tem_sal_CT.pdf){target="_blank"}
and grouped by ID
[pdf here](https://github.com/jens-daniel-mueller/BloomSail/tree/master/output/Plots/CT_dynamics/ts_profiles_ID_pCO2_tem_sal_CT.pdf){target="_blank"}. The later gives an idea of the differences between stations at one point in time.
```{r plot_all_discretized_profiles_individual, eval=FALSE}
pdf(file=here::here("output/Plots/CT_dynamics",
"ts_profiles_pCO2_tem_sal_CT.pdf"), onefile = TRUE, width = 9, height = 5)
for(i_ID in unique(ts_profiles$ID)){
for(i_station in unique(ts_profiles$station)){
if (nrow(ts_profiles %>% filter(ID == i_ID, station == i_station)) > 0){
# i_ID <- unique(ts_profiles$ID)[1]
# i_station <- unique(ts_profiles$station)[1]
p_pCO2 <-
ts_profiles %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(pCO2, dep, col="grid_RT"))+
geom_point(data = ts_profiles_highres %>% arrange(date_time) %>% filter(ID == i_ID, station == i_station),
aes(pCO2_raw, dep, col="raw"))+
geom_point(data = ts_profiles_highres %>% arrange(date_time) %>% filter(ID == i_ID, station == i_station),
aes(pCO2, dep, col="raw_RT"))+
geom_point(aes(pCO2_raw, dep, col="grid"))+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_brewer(palette = "Set1")+
labs(y="Depth [m]", x="pCO2 [µatm]", title = str_c(i_ID," | ",i_station))+
coord_cartesian(xlim = c(0,200), ylim = c(30,0))+
theme_bw()+
theme(legend.position = "left")
p_tem <-
ts_profiles %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(tem, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="Tem [°C]")+
coord_cartesian(xlim = c(14,26), ylim = c(30,0))+
theme_bw()
p_sal <-
ts_profiles %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(sal, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="Tem [°C]")+
coord_cartesian(xlim = c(6.5,7.5), ylim = c(30,0))+
theme_bw()
p_CT <-
ts_profiles %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(CT, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="CT* [µmol/kg]")+
coord_cartesian(xlim = c(1400,1700), ylim = c(30,0))+
theme_bw()
print(
p_pCO2 + p_tem + p_sal + p_CT
)
rm(p_pCO2, p_sal, p_tem, p_CT)
}
}
}
dev.off()
rm(i_ID, i_station, ts_profiles_highres)
```
```{r plot_all_discretized_profiles_by_ID, eval=FALSE}
ts_profiles_long <- ts_profiles %>%
select(-c(lat, lon, pCO2_raw)) %>%
pivot_longer(6:9, values_to = "value", names_to = "var")
pdf(file=here::here("output/Plots/CT_dynamics",
"ts_profiles_ID_pCO2_tem_sal_CT.pdf"), onefile = TRUE, width = 9, height = 5)
for(i_ID in unique(ts_profiles$ID)){
#i_ID <- unique(ts_profiles$ID)[1]
sub_ts_profiles_long <- ts_profiles_long %>%
arrange(date_time) %>%
filter(ID == i_ID)
print(
sub_ts_profiles_long %>%
ggplot()+
geom_path(data = ts_profiles_long, aes(value, dep, group=interaction(station, ID)), col="grey")+
geom_path(aes(value, dep, col=station))+
scale_y_reverse()+
labs(y="Depth [m]", title = str_c("ID: ", i_ID))+
theme_bw()+
facet_wrap(~var, scales = "free_x")
)
rm(sub_ts_profiles_long)
}
dev.off()
rm(i_ID, ts_profiles_long)
```
## Profiles of incremental changes
Changes of seawater vars at each depth are calculated from one cruise day to the next and divided by the number of days inbetween.
```{r incremental_changes_profiles}
ts_profiles_ID_long <- ts_profiles_ID_long %>%
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()
ts_profiles_ID_long %>%
arrange(dep) %>%
ggplot(aes(value_diff_daily, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~var, scales = "free_x")+
labs(x="Change of value inbetween cruises per day")
```
## Profiles of cumulative changes
Cumulative changes of seawater vars were calculated at each depth relative to the first cruise day on July 5.
```{r cumulative_changes_profiles}
ts_profiles_ID_long %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~var, scales = "free_x")+
labs(x="Cumulative change of value")
```
Important notes:
- Salinity in the upper 10m decreases by >0.1 on June 30, and returns to average conditions already on Aug 02.
Cumulative positive and negative changes of seawater vars were calculated separately at each depth relative to the first cruise day on July 5.
```{r cumulative_directional_changes_profiles}
ts_profiles_ID_long <- ts_profiles_ID_long %>%
mutate(sign = if_else(value_diff < 0, "neg", "pos")) %>%
group_by(var, dep, sign) %>%
arrange(date_time_ID) %>%
mutate(value_cum_sign = cumsum(value_diff)) %>%
ungroup()
ts_profiles_ID_long %>%
arrange(dep) %>%
ggplot(aes(value_cum_sign, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
scale_fill_viridis_d()+
facet_wrap(~interaction(sign, var), scales = "free_x", ncol=4)+
labs(x="Cumulative directional change of value")
ts_profiles_ID_long %>%
write_csv(here::here("Data/_merged_data_files", "ts_profiles_ID_long_cum.csv"))
```
# Timeseries
## Timeseries depth intervals
Mean seawater parameters were calculated for 5m depth intervals.
```{r observed_timeseries, fig.asp=1.3}
ts_profiles_ID_long_grid <- ts_profiles_ID_long %>%
mutate(dep = cut(dep, seq(0,30,5))) %>%
group_by(ID, date_time_ID, dep, var) %>%
summarise_all(list(mean), na.rm=TRUE)
ts_profiles_ID_long_grid %>%
ggplot(aes(date_time_ID, value, col=as.factor(dep)))+
geom_path()+
#geom_errorbar(aes(date_time_ID, ymax=value+sd, ymin=value-sd, col=as.factor(dep)))+
geom_point()+
scale_color_viridis_d(name="Depth [m]")+
facet_wrap(~var, scales = "free_y", ncol=1)
rm(ts_profiles_ID_long_grid)
```
## Hovmoeller plots
### Absolute values
```{r plot_hovmoeller_absolut, fig.cap="Hovmoeller plots of absolute changes in C~T~ and temperature."}
bin_CT <- 20
CT_hov <- ts_profiles_ID_long %>%
filter(var == "CT") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=value),
breaks = MakeBreaks(bin_CT),
col="black")+
geom_point(aes(x=date_time_ID, y=c(24.5)), size=3, shape=24, fill="white")+
scale_fill_viridis_c(breaks = MakeBreaks(bin_CT),
guide = "colorstrip",
name="CT (µmol/kg)")+
scale_y_reverse()+
theme_bw()+
labs(y="Depth (m)")+
coord_cartesian(expand = 0)+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
bin_Tem <- 2
Tem_hov <- ts_profiles_ID_long %>%
filter(var == "tem") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=value),
breaks = MakeBreaks(bin_Tem),
col="black")+
geom_point(aes(x=date_time_ID, y=c(24.5)), size=3, shape=24, fill="white")+
scale_fill_viridis_c(breaks = MakeBreaks(bin_Tem),
guide = "colorstrip",
name="Tem (°C)",
option = "inferno")+
scale_y_reverse()+
theme_bw()+
labs(x="",y="Depth (m)")+
coord_cartesian(expand = 0)
CT_hov / Tem_hov
rm(CT_hov, bin_CT, Tem_hov, bin_Tem)
```
### Incremental changes
```{r plot_hovmoeller_daily, fig.cap="Hovmoeller plots of daily changes in C~T~ and temperature. Note that calculated value of change (in contrast to absolute and cumulative values) are referred to the mean dates inbetween cruise, and are not extrapolated to the full observational period."}
bin_CT <- 2.5
CT_hov <- ts_profiles_ID_long %>%
filter(var == "CT") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID_ref, y=dep, z=value_diff_daily),
breaks = MakeBreaks(bin_CT),
col="black")+
geom_point(aes(x=date_time_ID, y=c(24.5)), size=3, shape=24, fill="white")+
scale_fill_divergent(breaks = MakeBreaks(bin_CT),
guide = "colorstrip",
name="CT (µmol/kg)")+
scale_y_reverse()+
theme_bw()+
labs(y="Depth (m)")+
coord_cartesian(expand = 0)+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
bin_Tem <- 0.1
Tem_hov <- ts_profiles_ID_long %>%
filter(var == "tem") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID_ref, y=dep, z=value_diff_daily),
breaks = MakeBreaks(bin_Tem),
col="black")+
geom_point(aes(x=date_time_ID, y=c(24.5)), size=3, shape=24, fill="white")+
scale_fill_divergent(breaks = MakeBreaks(bin_Tem),
guide = "colorstrip",
name="Tem (°C)")+
scale_y_reverse()+
theme_bw()+
labs(x="",y="Depth (m)")+
coord_cartesian(expand = 0)
CT_hov / Tem_hov
rm(CT_hov, bin_CT, Tem_hov, bin_Tem)
```
### Cumulative changes
```{r plot_hovmoeller_cumulative, fig.cap="Hovmoeller plots of cumulative changes in C~T~ and temperature."}
bin_CT <- 20
CT_hov <- ts_profiles_ID_long %>%
filter(var == "CT") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=value_cum),
breaks = MakeBreaks(bin_CT),
col="black")+
geom_point(aes(x=date_time_ID, y=c(24.5)), size=3, shape=24, fill="white")+
scale_fill_divergent(breaks = MakeBreaks(bin_CT),
guide = "colorstrip",
name="CT (µmol/kg)")+
scale_y_reverse()+
theme_bw()+
labs(y="Depth (m)")+
coord_cartesian(expand = 0)+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
bin_Tem <- 2
Tem_hov <- ts_profiles_ID_long %>%
filter(var == "tem") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=value_cum),
breaks = MakeBreaks(bin_Tem),
col="black")+
geom_point(aes(x=date_time_ID, y=c(24.5)), size=3, shape=24, fill="white")+
scale_fill_divergent(breaks = MakeBreaks(bin_Tem),
guide = "colorstrip",
name="Tem (°C)")+
scale_y_reverse()+
theme_bw()+
labs(x="",y="Depth (m)")+
coord_cartesian(expand = 0)
CT_hov / Tem_hov
rm(CT_hov, bin_CT, Tem_hov, bin_Tem)
```
# Depth-integration C~T~
A critical first step for the determination of net community production (NCP) is the integration of observed changes in C~T~ over depth to derive iC~T~. Two approaches were tested:
- Integration of changes in CT over a predefined, fixed water depth
- Integration of changes in CT over a mixed layer depth (MLD)
Both aproaches deliver depth-integrated, incremental changes of C~T~ inbetween cruise dates. Those were summed up to derive a trajectory of cummulative iC~T~ changes.
## Fixed depths approach
Incremental and cumulative C~T~ changes inbetween cruise dates were integrated across the water colums down to predefined depth limits. This was done separately for observed positive/negative changes in C~T~, as well as for the total observed changes.
```{r integrated_iCT_fixed_depth, fig.asp=1.1}
iCT_grid_sign <- ts_profiles_ID_long %>%
select(ID, date_time_ID, date_time_ID_ref) %>%
unique() %>%
expand_grid(sign = c("pos", "neg"))
iCT_grid_total <- ts_profiles_ID_long %>%
select(ID, date_time_ID, date_time_ID_ref) %>%
unique() %>%
expand_grid(sign = c("total"))
# dep_i <- 10
#rm(iCT, dep_i)
for (dep_i in seq(9,13,1)) {
iCT_sign_temp <- ts_profiles_ID_long %>%
filter(var == "CT", dep < dep_i) %>%
mutate(sign = if_else(ID == "180705" & dep == 0.5, "neg", sign)) %>%
group_by(ID, date_time_ID, date_time_ID_ref, sign) %>%
summarise(CT_i_diff = sum(value_diff)/1000) %>%
ungroup()
iCT_sign_temp <- iCT_sign_temp %>%
group_by(sign) %>%
arrange(date_time_ID) %>%
mutate(CT_i_cum = cumsum(CT_i_diff)) %>%
ungroup()
iCT_sign_temp <- full_join(iCT_sign_temp, iCT_grid_sign) %>%
arrange(sign, date_time_ID) %>%
fill(CT_i_cum)
iCT_total_temp <- ts_profiles_ID_long %>%
filter(var == "CT", dep < dep_i) %>%
group_by(ID, date_time_ID, date_time_ID_ref) %>%
summarise(CT_i_diff = sum(value_diff)/1000) %>%
ungroup()
iCT_total_temp <- iCT_total_temp %>%
arrange(date_time_ID) %>%
mutate(CT_i_cum = cumsum(CT_i_diff)) %>%
ungroup() %>%
mutate(sign = "total")
iCT_total_temp <- full_join(iCT_total_temp, iCT_grid_total) %>%
arrange(sign, date_time_ID) %>%
fill(CT_i_cum)
iCT_temp <- bind_rows(iCT_sign_temp, iCT_total_temp) %>%
mutate(dep_i = dep_i)
if (exists("iCT")) {
iCT <- bind_rows(iCT, iCT_temp)
} else {iCT <- iCT_temp}
rm(iCT_temp, iCT_sign_temp, iCT_total_temp)
}
rm(iCT_grid_sign, iCT_grid_total)
iCT <- iCT %>%
mutate(dep_i = as.factor(dep_i))
iCT %>%
ggplot()+
geom_point(data = cruise_dates, aes(date_time_ID, 0), shape=21)+
geom_col(aes(date_time_ID_ref, CT_i_diff, fill=dep_i),
position = "dodge", alpha=0.3)+
geom_line(aes(date_time_ID, CT_i_cum, col=dep_i))+
scale_color_viridis_d(name="Depth limit (m)")+
scale_fill_viridis_d(name="Depth limit (m)")+
labs(y="iCT [mol/m2]", x="")+
facet_grid(sign~., scales = "free_y", space = "free_y")+
theme_bw()
iCT_fixed_dep <- iCT
rm(iCT, dep_i)
# iCT %>%
# write_csv(here::here("Data/_merged_data_files", "iCT_dep_limits.csv"))
```
## MLD approach
As an alternative to fixed depth levels, vertical integration as low as the mixed layer depth was tested.
### Density Calculation
Seawater density Rho was determined from S, T, and p according to TEOS-10.
```{r calulcate_seawater_density}
ts_profiles <- ts_profiles %>%
mutate(rho = swSigma(salinity = sal, temperature = tem, pressure = dep/10))
```
### Density profiles
```{r plot_hydrographical_profiles_all, fig.cap="Mean vertical profiles per cruise day across all stations."}
ts_profiles_ID_mean_hydro <- ts_profiles %>%
select(-c(station,lat, lon, pCO2_raw, pCO2, CT, date_time)) %>%
group_by(ID, date_time_ID, dep) %>%
summarise_all(list(mean), na.rm=TRUE) %>%
ungroup()
ts_profiles_ID_sd_hydro <- ts_profiles %>%
select(-c(station,lat, lon, pCO2_raw, pCO2, CT, date_time)) %>%
group_by(ID, date_time_ID, dep) %>%
summarise_all(list(sd), na.rm=TRUE) %>%
ungroup()
ts_profiles_ID_sd_hydro_long <- ts_profiles_ID_sd_hydro %>%
pivot_longer(4:6, names_to = "var", values_to = "sd")
ts_profiles_ID_mean_hydro_long <- ts_profiles_ID_mean_hydro %>%
pivot_longer(4:6, names_to = "var", values_to = "value")
ts_profiles_ID_hydro_long <- inner_join(ts_profiles_ID_mean_hydro_long, ts_profiles_ID_sd_hydro_long)
ts_profiles_ID_hydro <- ts_profiles_ID_mean_hydro
rm(ts_profiles_ID_mean_hydro_long,
ts_profiles_ID_mean_hydro,
ts_profiles_ID_sd_hydro_long,
ts_profiles_ID_sd_hydro)
ts_profiles_ID_hydro_long %>%
ggplot(aes(value, dep, col=ID))+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+