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Rmd	0c8eed6	jens-daniel-mueller	2020-09-23	included postprocessed cleaned data
html	e4797a2	jens-daniel-mueller	2020-07-01	Build site.
Rmd	857208b	jens-daniel-mueller	2020-07-01	update NCP recon figure
html	c919fb7	jens-daniel-mueller	2020-06-29	Build site.
Rmd	1461cb6	jens-daniel-mueller	2020-06-29	Fig update for talk
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html	9eb7215	jens-daniel-mueller	2020-05-25	Build site.
Rmd	80a7e08	jens-daniel-mueller	2020-05-25	Removed separate BloomSail and fm+gt reconstruction
html	c5cf8de	jens-daniel-mueller	2020-05-25	Build site.
Rmd	2b97ae3	jens-daniel-mueller	2020-05-25	added gas flux to reconstructed iCT
html	4dd0f4f	jens-daniel-mueller	2020-05-25	Build site.
Rmd	f7f0983	jens-daniel-mueller	2020-05-25	added gas flux to reconstructed iCT
html	1b366c4	jens-daniel-mueller	2020-05-25	Build site.
Rmd	eef261f	jens-daniel-mueller	2020-05-25	TPD CPD appendix plots
html	9166c1d	jens-daniel-mueller	2020-05-20	Build site.
Rmd	ef99640	jens-daniel-mueller	2020-05-20	finalized reconstruction approach
html	1e15837	jens-daniel-mueller	2020-05-20	Build site.
Rmd	2378108	jens-daniel-mueller	2020-05-20	finalized iCT reconstruction
html	6aad8d7	jens-daniel-mueller	2020-05-19	Build site.
Rmd	d7aa227	jens-daniel-mueller	2020-05-19	finalized integration depth estimates
html	ae5779d	jens-daniel-mueller	2020-05-19	Build site.
Rmd	5651fe5	jens-daniel-mueller	2020-05-19	removed deep warming signal for z_pen determination
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Rmd	0ba33ec	jens-daniel-mueller	2020-05-19	cleaned NCP reconstruction IV
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Rmd	536cb1a	jens-daniel-mueller	2020-05-19	cleaned NCP reconstruction III
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Rmd	33c4313	jens-daniel-mueller	2020-05-19	cleaned NCP reconstruction II
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Rmd	fa8ce00	jens-daniel-mueller	2020-05-19	cleaned NCP reconstruction
html	6fcea7b	jens-daniel-mueller	2020-05-18	Build site.
Rmd	09ccf10	jens-daniel-mueller	2020-05-18	merged tm and gt NCP reconstruction

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 C_T* 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
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*.

The integration depth parameterizations were tested on two data sets, namely:

BloomSail observations, restricted to C_T* 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/NCP_best_guess",
      "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)),
         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 C_T* penetration depth (CPD) were investigated based on the BloomSail data.

2.1.1 Cumulative profile July 9

# 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.         -28.5  10.1 
2 tem            16.3          1.65  9.90

rm(tm_profiles_ID_long_day_dep,
   p_tm_profiles_ID_long,
   p_tm_profiles_ID_long_rel)

2.1.2 Incremental profile July 9

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, col_value)

2.1.3 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 / C_T*, 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       10.3     1.03
2 tem           11.5     2.46

rm(p_surface, p_integrated, p_pen_dep)
rm(PD, PD_mean, tm_profiles_ID_long)

3 GETM model

3.1 Read netcdf file

# read netcdf file
nc <- 
  nc_open(here::here("data/input/GETM", "Finnmaid.E.3d.2018.nc"))

# extract latitude vector
lat <- ncvar_get(nc, "latc")

# extract start time
time_start <- nc$dim$time$units %>%
  substr(start = 15, stop = 33) %>%
  ymd_hms()

# create time vecotr
t <- time_start + ncvar_get(nc, "time")
rm(time_start)

# extract depths vector
d <- ncvar_get(nc, "zax")

# read model data
for (var_3d in c("salt", "temp")) {
  
  # store the data in a 3-dimensional array
  array <-
    ncvar_get(nc, var_3d) 
  
  # find NA value
  fillvalue <- ncatt_get(nc, var_3d, "_FillValue")
  
  # replace NA value with NA
  array[array == fillvalue$value] <- NA
  
  for (i in seq(1, length(t), 1)) {
    # i <- 3
    array_slice <- array[, , i] # slices data from one day
    
    # convert to tibble
    array_slice_df <- as.data.frame(t(array_slice))
    array_slice_df <- as_tibble(array_slice_df)
    
    # rename, format and subset data
    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)

# subset time period
# calculate daily, regional mean profile in study area
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 <- gt_3d_long %>%
  pivot_wider(values_from = value, names_from = var) %>%
  rename(sal = salt, tem = temp)


rm(i, lat, d, t, var_3d)

3.2 Sal and tem profiles

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)

3.3 Comparison BloomSail

Vertical, 1m-gridded BloomSail CTD profiles were used for comparison with GETM results. Note that the sampling location does not match exactly.

3.3.1 Interpolate GETM to BloomSail

GETM results were linearly interpolated to the BloomSail depth levels and the mean cruise dates.

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)

# join GETM and BloomSail 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")
)

# calculate density profiles
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
    )
  )

# interpolate to BloomSail time stamp
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()

3.3.2 S, T, rho profiles

# convert to long format
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)

3.3.3 S, T, rho offset profiles

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)")

Offset STD profiles comparing modeled with GETM (upper panels, gt) and measured during BloomSail campaign (lower panels, ts)

rm(tm_gt_3d, tm_gt_3d_long)

4 SOOP Finnmaid

4.1 Data preparation

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 C_T* calculation

Calculate C_T* based on fixed A_T and salinity mean values.

# read mean bottle data
fixed_values <- 
  read_csv(here::here("data/intermediate/_summarized_data_files", "tb_fix.csv"))

# calculate CT*
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/NCP_best_guess",
      "tm_profiles.csv"
    )
  )

# surface mean calculation
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())

4.1.5 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.

# create data frame with start dates
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")

# add start dates to Finnmaid data
fm_tm_ID <- full_join(fm_ID, tm_start_date) %>% 
  arrange(date_time_ID) %>% 
  filter(var %in% c("tem", "CT_star"))

# interpolate Finnmaid to BloomSail start date
fm_tm_ID <- fm_tm_ID %>% 
  group_by(var) %>% 
  mutate(value = approxfun(date_time_ID, value)(date_time_ID)) %>% 
  ungroup()

rm(tm_start_date)

# subset BloomSail data in Finnmaid gap
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")

# add data to fill Finnmaid gap
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)
  )

# filter only Finnmaid data within 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, fixed_values)

4.1.6 Final time series

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

# convert to wide format
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)

# extend surface data with depth grid
fm_gt <- expand_grid(fm_tm_ID_wide, dep = unique(gt_3d_int$dep))

# join Finnmaid and GETM data
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 to fm

GETM data are interpolated to the time of Finnmaid observations.

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

Here, we merge the in-situ sensor data from SV Tina V with the Finnmaid+GETM data set, in order to perform following computations only once.

# subset relevant columns and assign source label
tm_profiles_ID <- tm_profiles_ID %>%
  select(-c(ID, pCO2)) %>%
  mutate(source = "tm",
         sensor = "BloomSail")

fm_gt <- fm_gt %>%
  mutate(source = "fm")

# Merge data sets
tm_fm_gt <- bind_rows(tm_profiles_ID, fm_gt)

rm(fm_gt, tm_profiles_ID)

5.4 Comparison

5.4.1 Surface time series

# convert to long format
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())

5.4.2 Hovmoeller temperature

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",
    option = "B",
    guide = "colorstrip",
    breaks = MakeBreaks(bin)
  ) +
  scale_y_reverse() +
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d") +
  coord_cartesian(expand = 0) +
  theme(axis.title.x = element_blank()) +
  facet_grid(source ~ .)

rm(bin)

6 Integration depths

6.1 MLD

6.1.1 Density calculation

tm_fm_gt <- tm_fm_gt %>%
  mutate(rho = swSigma(
    salinity = sal,
    temperature = tem,
    pressure = dep / 10
  ))

6.1.2 Hovmoeller density

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) +
  theme(axis.title.x = element_blank()) +
  facet_grid(source ~ .)

rm(bin)

6.1.3 MLD calculation

tm_fm_gt_MLD <- expand_grid(tm_fm_gt, rho_lim = parameters$rho_lim_integration_depths)

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))

6.1.4 Hovmoeller MLD

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)

6.1.5 Select rho criterion

MLD <- tm_fm_gt_MLD %>%
  filter(rho_lim == parameters$rho_lim) %>%
  select(-rho_lim) %>%
  rename(i_dep = MLD) %>%
  mutate(i_method = "MLD", i_res = "daily")

rm(tm_fm_gt_MLD)

6.1.6 Mean MLD

# Filter data before temperature peak, and calculate mean
MLD_mean <- 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()

MLD_mean

# A tibble: 2 x 3
  source MLD_mean MLD_sd
  <chr>     <dbl>  <dbl>
1 fm          5.5   1.18
2 tm          6     1.87

# format mean MLD data frame
MLD_mean <- MLD_mean %>%
  select(-MLD_sd) %>% 
  rename(i_dep = MLD_mean) %>%
  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 TPD

6.2.1 Cumulative changes

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)),
    value_diff = value - lag(value, default = first(value)),
    value_diff_daily = value_diff / date_time_ID_diff,
    value_cum = cumsum(value_diff)
  ) %>%
  ungroup()

# select only temperature data
tm_fm_gt_long <- tm_fm_gt_long %>%
  filter(var == "tem") %>%
  select(-var)

6.2.2 Temperature profiles

Please note that the cumulative temperature profile of GETM shows a deep maximum below 20m, which must be attributed to water mass exchange rather than surface warming to radiation or heat uptake, which we want to capture here. Therefore, the integration depth of the GETM data was manually restricted to 19m.

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(x = "Cumulative tem change",
       title = as.Date(date_CT_min)) +
  theme(legend.position = "left") +
  facet_grid(. ~ source, scales = "free_x")

Profiles of cumulative temperature changes from the GETM model (label fm) and as measured in the field (label tm).

6.2.3 Cumulative TPD

# subset data at CT* minimum
tm_fm_gt_long_day <- 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)
  )

# integrate cumulative changes
tm_fm_gt_long_day_dep <- tm_fm_gt_long_day %>%
  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()

# extract integrated cumulative value
value_cum <- tm_fm_gt_long_day_dep %>%
  group_by(source) %>%
  summarise(value_cum_i = mean(value_cum_i)) %>%
  ungroup()

# extract surface cumulative value
value_surface <- tm_fm_gt_long_day %>%
  select(source, dep, value_cum) %>%
  filter(dep < parameters$surface_dep) %>%
  group_by(source) %>%
  summarise(value_surface = mean(value_cum)) %>%
  ungroup()

# calculate TPD
TPD <- full_join(value_cum, value_surface)
TPD <- TPD %>%
  mutate(i_dep = value_cum_i / value_surface)

rm(value_cum, value_surface)

p_tm_fm_gt_long <- tm_fm_gt_long_day %>%
  arrange(dep) %>%
  ggplot(aes(value_cum, dep)) +
  geom_hline(aes(yintercept = parameters$i_dep_lim, col = "fixed\nintegration\ndepth")) +
  geom_hline(data = TPD, aes(yintercept = i_dep, col = "TPD\nestimate")) +
  geom_vline(xintercept = 0) +
  geom_point() +
  geom_path() +
  scale_y_reverse() +
  scale_color_brewer(palette = "Dark2", guide = FALSE) +
  labs(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_day_dep %>%
  ggplot(aes(value_cum_i_rel, dep)) +
  geom_hline(aes(yintercept = parameters$i_dep_lim, col = "fixed\nintegration\ndepth")) +
  geom_hline(data = TPD, aes(yintercept = i_dep, col = "TPD\nestimate")) +
  geom_vline(xintercept = 90) +
  geom_point() +
  geom_line() +
  scale_y_reverse(limits = c(25, 0)) +
  scale_color_brewer(palette = "Dark2", name = "") +
  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_day,
  tm_fm_gt_long_day_dep,
  p_tm_fm_gt_long,
  p_tm_fm_gt_long_rel
)

TPD_cum <- TPD

rm(TPD)

6.2.4 Incremental TPD

# incremental changes of 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)

# calculate mean TPD across bloom period
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  12.3

rm(p_surface, p_integrated, p_TPD)

6.2.5 Join TPD estimates

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)

6.3 Join TPD and MLD

i_dep <- full_join(MLD, TPD)
rm(MLD, TPD)

6.4 Hovmoeller + MLD/TPD

bin <- 2

# prepare data sets
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 data + integr. depth

Here, we restrict all C_T* observations to surface waters, and merge with the integrations depth estimates.

# calculate surface water time series
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()

# calculate incremental and cumulative changes
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)),
    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 NCP reconstruction

8.1 Integration of C_T*

Incremental depth-integrated changes of C_T* are calculated as the product of incremental changes of surface C_T* and the integration depth parameter.

# integrate incremental CT* changes across depth
iCT <- iCT %>% 
  mutate(CT_i_diff = CT_star_diff * i_dep)

# calculate cumulative changes
iCT <- iCT %>% 
  group_by(source, i_method, i_res) %>%
  arrange(date_time_ID) %>%
  mutate(CT_star_i_cum = cumsum(CT_i_diff/1000)) %>% 
  ungroup()

8.2 Read best-guess NCP

tm_NCP_cum <- read_csv(here::here("data/intermediate/_merged_data_files/NCP_best_guess",
                                  "tm_NCP_cum.csv"))

8.3 Adapt air sea flux

The air-sea fluxes of CO₂ previously calculated for the NCP best-guess were also applied to all reconstructed estimates

# prepare air sea flux data from best-guess approach
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()

8.4 Time series

8.4.1 Without mixing corrrection

Below, we compare all derived NCP reconstructions to the best-guess time series of integrated C_T* changes, which were only corrected for air-sea fluxes but not for mixing.

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")
  )

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

8.4.2 With mixing corrrection

Below, we compare four NCP reconstructions based on daily incremental integration depth estimates to the best-guess NCP time series, ie the integrated C_T* changes, which were corrected for air-sea fluxes and for mixing.

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_0.7.5        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

NCP reconstruction

Jens Daniel Müller

30 March, 2021