Last updated: 2020-05-25

Checks: 7 0

Knit directory: BloomSail/

This reproducible R Markdown analysis was created with workflowr (version 1.6.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20191021)

The command set.seed(20191021) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 3414c23

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 3414c23. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/Finnmaid_2018/
    Ignored:    data/GETM/
    Ignored:    data/Maps/
    Ignored:    data/Ostergarnsholm/
    Ignored:    data/TinaV/
    Ignored:    data/_merged_data_files/
    Ignored:    data/_summarized_data_files/
    Ignored:    data/backup/
    Ignored:    output/Plots/Figures_publication/.tmp.drivedownload/

Untracked files:
    Untracked:  code/BloomSail_surface.Rmd
    Untracked:  code/Finnmaid+GETM.Rmd
    Untracked:  output/Plots/Figures_publication/Appendix/Phytoplankton_mean_total_biomass.pdf
    Untracked:  output/Plots/Figures_publication/Appendix/TPD_CPD_cumulative.pdf
    Untracked:  output/Plots/Figures_publication/Appendix/tb_profiles.pdf
    Untracked:  output/Plots/Figures_publication/Article/reconstruction_NCP_timeseries.pdf
    Untracked:  output/Plots/Figures_publication/Article/reconstruction_iCT_timeseries.pdf

Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   output/Plots/Figures_publication/Appendix/pCO2_RT_corrected_profile_example.pdf
    Modified:   output/Plots/Figures_publication/Appendix/pCO2_RT_corrected_profile_mean_abs_offset.pdf
    Modified:   output/Plots/Figures_publication/Appendix/tau_fit_example.pdf
    Modified:   output/Plots/Figures_publication/Article/Hov_abs_profiles_cum.pdf
    Modified:   output/Plots/Figures_publication/Article/atm_water_timeseries.pdf
    Modified:   output/Plots/Figures_publication/Article/data_coverage.pdf
    Modified:   output/Plots/Figures_publication/Article/profiles_all.pdf
    Modified:   output/Plots/Figures_publication/Article/station_map.pdf

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/NCP_reconstruction.Rmd) and HTML (docs/NCP_reconstruction.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html 3414c23 jens-daniel-mueller 2020-05-25 Build site.
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
html 6e3d899 jens-daniel-mueller 2020-05-19 Build site.
Rmd 0ba33ec jens-daniel-mueller 2020-05-19 cleaned NCP reconstruction IV
html 7f4066e jens-daniel-mueller 2020-05-19 Build site.
Rmd 536cb1a jens-daniel-mueller 2020-05-19 cleaned NCP reconstruction III
html dd7e745 jens-daniel-mueller 2020-05-19 Build site.
Rmd 33c4313 jens-daniel-mueller 2020-05-19 cleaned NCP reconstruction II
html 57b5f60 jens-daniel-mueller 2020-05-19 Build site.
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 Approach

In order to test how (and how well) the depth-integrated CT estimates can be reproduced if only surface CO2 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
  • CT profile reconstruction: Vertical reconstruction of incremental CT changes based on profiles of incremental changes in temperature
  • Temperature penetration depth: Integration of surface observation across the temperature penetration depth, assuming similar vertical extension as for CT drawdown.

Note: The reconstruction of CT profiles and the integration across the temperature penetration depth should produce very similar results. However, the latter avoids to create misinterpretable information about the vertical distribution of CT.

max_dep <- 25
surface_dep <- 6
integration_dep <- 12

date_CT_min <- ymd_hms("2018-07-24 07:58:29")
date_tem_max <- ymd_hms("2018-08-04 00:00:00")

2 BloomSail data

1m gridded, downcast profiles were used. Mean CO2 data from upper 6 metres were used as surface values.

tm_profiles_ID <-
  read_csv(here::here("Data/_merged_data_files/CT_dynamics", "tm_profiles_ID.csv"))

tm_profiles_ID <- tm_profiles_ID %>% 
  select(-c(date_ID))
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()

2.1 CT and tem penetration depth

2.1.1 Cumulative on July 23

tm_profiles_ID_long_180723 <- tm_profiles_ID_long %>% 
  filter(ID == 180723)

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 < 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)
p_tm_profiles_ID_long <- tm_profiles_ID_long_180723 %>% 
  arrange(dep) %>% 
  ggplot(aes(value_cum, dep))+
  geom_hline(aes(yintercept = integration_dep, 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 = integration_dep, 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
# A tibble: 2 x 4
  var   value_cum_i value_surface   TPD
  <chr>       <dbl>         <dbl> <dbl>
1 nCT        -935.         -89.5   10.4
2 tem          71.0          6.10  11.6
rm(tm_profiles_ID_long_180723_dep,
   p_tm_profiles_ID_long,
   p_tm_profiles_ID_long_rel)
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_cum<0),
           aes(x=value_cum, y=dep, fill="Integrated change"),
           width = 1, alpha=0.3)+
  geom_vline(xintercept = 0)+
  scale_y_reverse(expand = c(0,0))+
  geom_point(aes(value_cum, dep))+
  geom_path(aes(value_cum, dep))+
  geom_hline(data = TPD %>% filter(var == "nCT"),
             aes(yintercept = TPD, col="Penetration depth"))+
  geom_vline(data = TPD %>% filter(var == "nCT"),
             aes(xintercept = value_surface, col="Surface change"))+
  scale_fill_manual(values = "black")+
  scale_color_brewer(palette = "Dark2")+
  labs(y = "Depth (m)", x = expression(nC[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_cum>0),
           aes(x=value_cum, y=dep, fill="Integrated change"),
           width = 1, alpha=0.3)+
  geom_vline(xintercept = 0)+
  scale_y_reverse(expand = c(0,0))+
  geom_point(aes(value_cum, dep))+
  geom_path(aes(value_cum, dep))+
  geom_hline(data = TPD %>% filter(var == "tem"),
             aes(yintercept = TPD, col="Penetration depth"))+
  geom_vline(data = TPD %>% filter(var == "tem"),
             aes(xintercept = value_surface, col="Surface change"))+
  scale_fill_manual(values = "black")+
  scale_color_brewer(palette = "Dark2")+
  labs(y = "Depth (m)", x = "Temperature (\u00B0C)")+
  theme(legend.title = element_blank())

p_tem + p_nCT +
  plot_layout(guides = 'collect')

ggsave(here::here("output/Plots/Figures_publication/appendix",
                  "TPD_CPD_cumulative.pdf"),
       width = 210, height = 220, dpi = 300, units = "mm")

rm(TPD, tm_profiles_ID_long_180723, p_tem, p_nCT)

2.1.2 Daily

# surface values
diff_surface <- tm_profiles_ID_long %>% 
  filter(dep < 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()
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
# A tibble: 2 x 3
  var   i_dep_mean i_dep_sd
  <chr>      <dbl>    <dbl>
1 nCT         10.1     1.11
2 tem         11.5     2.46
CPD <- TPD %>% 
  filter(var == "nCT") %>% 
  drop_na()

rm(p_surface, p_integrated, p_pen_dep)
rm(TPD, TPD_mean, tm_profiles_ID_long)

3 GETM

3.1 Subsetting criteria

# route
select_route <- "E"

# latitude limits
low_lat <- 57.3
high_lat <- 57.5

# date limits
start_date <- "2018-06-20"
end_date <- "2018-08-25"

fixed_values <- 
  read_csv(here::here("Data/_summarized_data_files", "tb_fix.csv"))

3.2 Read netcdf file

nc <- nc_open(here::here("data/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 > low_lat, lat < high_lat,
               dep <= 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 >= start_date & date_time <= 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/_summarized_data_files", file = "gt_3d_long.csv"))


rm(gt_3d, gt_3d_long, i, lat, d, t, var_3d)

3.3 Sal and tem profiles

gt_3d_long <- 
  read_csv(here::here("data/_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)

rm(select_route, high_lat, low_lat)
gt_3d_long <- gt_3d %>% 
  pivot_longer(c(sal, tem), values_to = "value", names_to = "var")

gt_3d_long %>% 
  ggplot(aes(value, dep,
             col=date_time,
             group=date_time))+
  geom_path()+
  scale_y_reverse(expand = c(0,0))+
  scale_color_viridis_c(name="Date", trans = "time")+
  facet_wrap(~var, scales = "free_x", ncol = 2)

rm(gt_3d_long)

3.4 Comparison BloomSail

Vertical, 1m-gridded BloomSail CTD profiles were used for comparison with GETM results. Note that the sampling location does match exactly.

GETM results were linearly interpolated to the mean BloomSail time stamp.

3.4.1 Interpolate gt dep grid

gt_3d_int <- gt_3d %>% 
  mutate(dep_int = dep + 0.5) %>% 
  group_by(date_time) %>% 
  mutate(sal_int = approxfun(dep, sal)(dep_int),
         tem_int = approxfun(dep, tem)(dep_int)) %>% 
  ungroup() %>% 
  select(date_time, dep=dep_int, sal=sal_int, tem=tem_int) %>% 
  drop_na()

rm(gt_3d)
tm_gt_3d <- full_join(gt_3d_int,
                      tm_profiles_ID %>% select(date_time = date_time_ID,
                                                dep, sal, tem),
                      by = c("date_time", "dep"), suffix=c("_gt", "_tm"))

tm_gt_3d <- tm_gt_3d  %>% 
  mutate(rho_gt = swSigma(salinity = sal_gt,
                          temperature = tem_gt,
                          pressure = dep/10),
         rho_tm = swSigma(salinity = sal_tm,
                          temperature = tem_tm,
                          pressure = dep/10))

tm_gt_3d <- tm_gt_3d %>% 
  arrange(date_time) %>% 
  group_by(dep) %>% 
  mutate(tem_gt = approxfun(date_time, tem_gt)(date_time),
         sal_gt = approxfun(date_time, sal_gt)(date_time),
         rho_gt = approxfun(date_time, rho_gt)(date_time)) %>% 
  ungroup() %>% 
  drop_na()
tm_gt_3d_long <- tm_gt_3d %>% 
  pivot_longer(sal_gt:rho_tm,
               values_to = "value",
               names_to = c("var", "source"),
               names_sep = "_")

tm_gt_3d_long %>% 
  ggplot(aes(value, dep,
             col=date_time,
             group=date_time))+
  geom_path()+
  scale_y_reverse(expand = c(0,0), name="Depth (m)")+
  scale_color_viridis_c(name="Date", trans = "time")+
  facet_grid(source~var, scales = "free_x")
STD profiles modeled with GETM (upper panels, gt) and measured during BloomSail campaign (lower panels, ts)

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

tm_gt_3d <- tm_gt_3d_long %>% 
  pivot_wider(values_from = "value", names_from = "source") %>% 
  mutate(value_diff = gt - tm)

tm_gt_3d %>% 
  ggplot(aes(value_diff, dep,
             col=date_time,
             group=date_time))+
  geom_vline(xintercept = 0, col="red")+
  geom_path()+
  scale_y_reverse(expand = c(0,0), name="Depth (m)")+
  scale_color_viridis_c(name="Date", trans = "time")+
  facet_grid(.~var, scales = "free_x")+
  labs(x="Difference GETM (gt) - Bloomsail (ts)")

rm(tm_gt_3d, tm_gt_3d_long)

4 Finnmaid

4.1 Data preparation

Finnmaid data, including reconstructed data during LICOS operation failure.

fm <-
 read_csv(here::here("Data/_summarized_data_files",
                      "fm_bloomsail.csv"))

fm <- fm %>% 
  filter(date_time > start_date,
         date_time < end_date) %>% 
  select(ID, date_time, sensor, sal, tem, pCO2) %>% 
  mutate(ID = as.factor(ID))

4.1.1 CT calculation

Calculate nCT based on fixed AT and salinity mean values.

fm <- fm %>% 
  mutate(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)

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, 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)

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/_merged_data_files/CT_dynamics", "tm_profiles.csv"))

tm_profiles_ID_long_surface <- tm_profiles %>% 
  filter(dep < 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()

4.1.4 Timeseries

fm_ID %>% 
  ggplot()+
  geom_rect(data = fixed_values, aes(xmin=start, xmax=end, ymin=-Inf, ymax=Inf), alpha=0.2)+
  geom_path(aes(x=date_time_ID, y=value))+
  geom_ribbon(aes(x=date_time_ID, y=value, ymax=value+sd, ymin=value-sd, fill="Finnmaid"), alpha=0.3)+
  geom_ribbon(data = tm_profiles_ID_long_surface,
             aes(x=date_time_ID, ymin=mean-sd, ymax=mean+sd, fill="BloomSail"), alpha=0.3)+
  geom_point(aes(x=date_time_ID, y=value, col=sensor))+
  geom_point(data = tm_profiles_ID_long_surface,
             aes(x=date_time_ID, y=mean, col="BloomSail"))+
  geom_line(data = tm_profiles_ID_long_surface,
             aes(x=date_time_ID, y=mean, col="BloomSail"))+
  facet_grid(var~., scales = "free_y")+
  scale_color_brewer(palette = "Set1")+
  scale_fill_brewer(palette = "Set1", name="+/- SD")+
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d")+
  theme(axis.title.x = element_blank())

4.2 Missing observations

The observational gaps in the Finnmaid SST and CT time series were filled with:

  • two BloomSail observations
  • an interpolated finnmaid value to match the starting date

The time series was restricted to the period where BloomSail observations are available.

tm_start_date <- tm_profiles_ID_long_surface %>% 
  filter(ID %in% c("180705"),
         var %in% c("tem", "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)
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)
fm_tm_ID %>% 
  ggplot()+
  geom_path(aes(date_time_ID, value))+
  geom_point(aes(date_time_ID, value, col=sensor))+
  facet_grid(var~., scales = "free_y")+
  scale_color_brewer(palette = "Set1")+
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d")+
  theme(axis.title.x = element_blank())

5 Merge all data sets

5.1 Merge fm and gt

fm_tm_ID_wide <- fm_tm_ID %>% 
  filter(var %in% c("nCT")) %>% 
  select(date_time_ID, var, value) %>% 
  pivot_wider(values_from = value, names_from = var)


fm_gt <- expand_grid(fm_tm_ID_wide, dep = unique(gt_3d_int$dep))

fm_gt <- full_join(fm_gt,
                   gt_3d_int %>% rename(date_time_ID = date_time)) %>% 
  arrange(date_time_ID)

rm(fm_tm_ID_wide, fm_tm_ID, gt_3d_int)

5.2 Interpolate gt time stamp

fm_gt <- fm_gt %>% 
  arrange(date_time_ID) %>% 
  group_by(dep) %>% 
  mutate(tem = approxfun(date_time_ID, tem)(date_time_ID),
         sal = approxfun(date_time_ID, sal)(date_time_ID)) %>% 
  ungroup() %>% 
  arrange(dep) %>% 
  filter(!is.na(nCT))

5.3 Bind tm and fm_gt

tm_profiles_ID <- tm_profiles_ID %>% 
  select(-c(ID, pCO2)) %>% 
  mutate(source = "tm")

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

tm_fm_gt <- bind_rows(tm_profiles_ID, fm_gt)


rm(fm_gt, tm_profiles_ID)
tm_fm_gt_long <- tm_fm_gt %>% 
  pivot_longer(sal: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())

bin <- 2

tm_fm_gt %>% 
  ggplot(aes(date_time_ID, dep, z=tem))+
  geom_contour_fill(breaks = MakeBreaks(bin))+
  geom_vline(aes(xintercept = date_time_ID),
             col="white", linetype = "1f")+
  scale_fill_viridis_c(name="Tem (°C)", option = "B",
                       guide = "colorstrip", breaks = MakeBreaks(bin))+
  scale_y_reverse()+
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d")+
  coord_cartesian(expand = 0)+
  labs(y="Depth (m)")+
  theme(axis.title.x = element_blank())+
  facet_grid(source~.)

rm(bin)

6 Integration depths

6.1 MLD

6.1.1 Density

tm_fm_gt <- tm_fm_gt %>% 
  mutate(rho = swSigma(salinity = sal, temperature = tem, pressure = dep/10))
bin <- 0.5

tm_fm_gt %>% 
  ggplot()+
  geom_contour_fill(aes(date_time_ID, dep, z=rho),
                    breaks = MakeBreaks(bin))+
  geom_vline(aes(xintercept = date_time_ID),
             col="white", linetype = "1f")+
  scale_fill_viridis_c(name="Rho", option = "B",
                       guide = "colorstrip", breaks = MakeBreaks(bin),
                       direction = -1)+
  scale_y_reverse()+
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d")+
  coord_cartesian(expand = 0)+
  labs(y="Depth (m)")+
  theme(axis.title.x = element_blank())+
  facet_grid(source~.)

rm(bin)

6.1.2 MLD

tm_fm_gt_MLD <- expand_grid(tm_fm_gt, rho_lim = seq(0.1,0.5,0.1))

tm_fm_gt_MLD <- tm_fm_gt_MLD %>% 
  arrange(dep) %>% 
  group_by(date_time_ID, source, rho_lim) %>% 
  mutate(d_rho = rho - first(rho)) %>% 
  filter(d_rho > rho_lim) %>% 
  summarise(MLD = min(dep)) %>% 
  ungroup() %>% 
  mutate(rho_lim = as.factor(rho_lim))
bin <- 2

tm_fm_gt %>% 
  ggplot()+
  geom_contour_fill(aes(date_time_ID, dep, z=tem),
                    breaks = MakeBreaks(bin))+
  geom_path(data = tm_fm_gt_MLD, aes(date_time_ID, MLD, col=rho_lim))+
  scale_fill_gradient(name="Tem (°C)",
                      guide = "colorstrip", breaks = MakeBreaks(bin),
                      high = "grey80",
                      low = "grey5")+
  scale_color_viridis_d()+
  scale_y_reverse()+
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d")+
  coord_cartesian(expand = 0)+
  labs(y="Depth (m)")+
  theme(axis.title.x = element_blank())+
  facet_grid(source~.)

rm(bin)
rho_lim_value <- 0.1

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

rm(tm_fm_gt_MLD)
MLD_mean <- MLD %>% 
  filter(date_time_ID <= date_tem_max) %>% 
  group_by(source) %>% 
  summarise(i_dep = mean(i_dep, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(i_method = "MLD", i_res = "mean")

MLD_dates <- MLD %>% 
  select(source, date_time_ID)

MLD_mean <- full_join(MLD_dates, MLD_mean)
MLD <- full_join(MLD, MLD_mean)

rm(MLD_mean)

6.2 Penetration depth

tm_fm_gt_long <- tm_fm_gt %>%
  select(-c(sal)) %>% 
  pivot_longer(c("tem", "nCT"), values_to = "value", names_to = "var") %>% 
  group_by(source, var, dep) %>%
  arrange(date_time_ID) %>%
  mutate(date_time_ID_diff = as.numeric(date_time_ID - lag(date_time_ID)),
         date_time_ID_ref  = date_time_ID - (date_time_ID - lag(date_time_ID))/2,
         value_diff = value     - lag(value, default = first(value)),
         value_diff_daily = value_diff / date_time_ID_diff,
         value_cum = cumsum(value_diff)) %>% 
  ungroup()

tm_fm_gt_long <- tm_fm_gt_long %>% 
  filter(var == "tem") %>% 
  select(-var)
gt_i_dep <- 19
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 = gt_i_dep)+
  geom_point()+
  geom_path()+
  scale_y_reverse()+
  labs(y = "Depth (m)", x="Cumulative change")+
  theme(legend.position = "left")+
  facet_grid(.~source, scales = "free_x")

6.2.1 Cumulative changes

tm_fm_gt_long_180723 <- tm_fm_gt_long %>% 
  filter(date_time_ID == date_CT_min) %>% 
    mutate(value_cum = if_else(value_cum < 0,
                             NaN, value_cum),
         value_cum = if_else(source == "fm" & dep > gt_i_dep,
                             NaN, value_cum))

tm_fm_gt_long_180723_dep <- tm_fm_gt_long_180723 %>% 
  select(source, dep, value_cum) %>% 
  group_by(source) %>% 
  arrange(dep) %>% 
  mutate(value_cum_i = sum(value_cum, na.rm = TRUE),
         value_cum_dep = cumsum(value_cum),
         value_cum_i_rel = value_cum_dep/value_cum_i*100) %>% 
  ungroup()

value_cum <- tm_fm_gt_long_180723_dep %>% 
  group_by(source) %>% 
  summarise(value_cum_i = mean(value_cum_i)) %>% 
  ungroup()

value_surface <- tm_fm_gt_long_180723 %>% 
  select(source, dep, value_cum) %>% 
  filter(dep < surface_dep) %>%
  group_by(source) %>% 
  summarise(value_surface = mean(value_cum)) %>% 
  ungroup()

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

6.2.2 Cumulative on July 23

p_tm_fm_gt_long <- tm_fm_gt_long_180723 %>% 
  arrange(dep) %>% 
  ggplot(aes(value_cum, dep))+
  geom_hline(aes(yintercept = integration_dep, col="integration"))+
  geom_hline(data = TPD, aes(yintercept = i_dep, col="penetration"))+
  geom_vline(xintercept = 0)+
  geom_point()+
  geom_path()+
  scale_y_reverse()+
  scale_color_brewer(palette = "Dark2", guide = FALSE)+
  labs(y = "Depth (m)", x="Cumulative change")+
  theme(legend.position = "left")+
  facet_wrap(.~source, ncol=1, scales = "free_x")

p_tm_fm_gt_long_rel <- tm_fm_gt_long_180723_dep %>% 
  ggplot(aes(value_cum_i_rel, dep))+
  geom_hline(aes(yintercept = integration_dep, col="integration"))+
  geom_hline(data = TPD, aes(yintercept = i_dep, col="penetration"))+
  geom_vline(xintercept = 90)+
  geom_point()+
  geom_line()+
  scale_y_reverse(limits = c(25,0))+
  scale_color_brewer(palette = "Dark2", name = "Depth")+
  scale_x_continuous(limits = c(0, NA))+
  labs(x = "Relative contribution (%)")+
  facet_wrap(.~source, ncol = 1, scales = "free_x")+
  theme(axis.title.y = element_blank())

p_tm_fm_gt_long + p_tm_fm_gt_long_rel

rm(tm_fm_gt_long_180723,
   tm_fm_gt_long_180723_dep,
   p_tm_fm_gt_long,
   p_tm_fm_gt_long_rel)

TPD_cum <- TPD

rm(TPD)

6.2.3 Daily

# surface values
diff_surface <- tm_fm_gt_long %>% 
  filter(dep < surface_dep) %>% 
  group_by(date_time_ID, source) %>% 
  summarise(value_diff_surface = mean(value_diff, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(value_diff_surface = if_else(value_diff_surface < 0,
                                      NaN, value_diff_surface))

tm_fm_gt_long <- full_join(tm_fm_gt_long, diff_surface) 
rm(diff_surface)

# calculate penetration depths

TPD <- tm_fm_gt_long %>% 
  mutate(value_diff = if_else(value_diff < 0,
                                      NaN, value_diff),
         value_diff = if_else(source == "fm" & dep > 19,
                              NaN, value_diff)) %>% 
  group_by(date_time_ID, source) %>% 
  summarise(value_diff_int = sum(value_diff, na.rm = TRUE),
            value_diff_surface = mean(value_diff_surface, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(i_dep = value_diff_int / value_diff_surface)

TPD_mean <- TPD %>% 
  filter(date_time_ID <= date_CT_min) %>% 
  group_by(source) %>% 
  summarise(i_dep_sd = sd(i_dep, na.rm=TRUE),
            i_dep = mean(i_dep, na.rm=TRUE)) %>% 
  ungroup()

p_surface <- TPD %>% 
  ggplot(aes(date_time_ID, value_diff_surface, col=source))+
  geom_hline(yintercept = 0)+
  geom_line()+
  geom_point()+
  scale_y_reverse(name = "Change surface value")+
  scale_x_datetime(breaks = "week", date_labels = "%d %b")+
  scale_color_brewer(palette = "Set1", direction = -1)+
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_integrated <- TPD %>% 
  ggplot(aes(date_time_ID, value_diff_int, col=source))+
  geom_hline(yintercept = 0)+
  geom_line()+
  geom_point()+
  scale_y_reverse(name = "Change integrated value")+
  scale_x_datetime(breaks = "week", date_labels = "%d %b")+
  scale_color_brewer(palette = "Set1", direction = -1)+
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_TPD <- TPD %>% 
  ggplot(aes(date_time_ID, i_dep, col=source))+
  geom_hline(yintercept = 0)+
  geom_hline(data = TPD_mean,
             aes(yintercept = i_dep,
                 col=source, linetype = "mean"))+
  geom_line(aes(linetype="cruise"))+
  geom_point()+
  scale_y_reverse(name = "Penetration depth (m)", breaks = seq(0,20,5))+
  scale_x_datetime(breaks = "week", date_labels = "%d %b")+
  scale_color_brewer(palette = "Set1", direction = -1)+
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())


p_surface + p_integrated + p_TPD + 
  plot_layout(ncol = 1)

TPD_mean
# A tibble: 2 x 3
  source i_dep_sd i_dep
  <chr>     <dbl> <dbl>
1 fm         2.31  11.4
2 tm         2.46  12.3
rm(p_surface, p_integrated, p_TPD)
TPD <- TPD %>% 
  select(date_time_ID, source, i_dep) %>% 
  mutate(i_method = "TPD", i_res = "daily") %>% 
  filter(date_time_ID < date_tem_max) %>% 
  mutate(i_dep = if_else(is.na(i_dep), 0, i_dep))

TPD_cum <- TPD_cum %>% 
  select(source, i_dep) %>% 
  mutate(i_method = "TPD", i_res = "cum")

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, MLD_mean)
i_dep <- full_join(MLD, TPD)
rm(MLD, TPD)
bin <- 2

CPD <- CPD %>% 
  mutate(source = "tm")

p_hov_dep <- 
  tm_fm_gt %>% 
  ggplot()+
  geom_contour_fill(aes(date_time_ID, dep, z=tem),
                    breaks = MakeBreaks(bin))+
  geom_path(data = CPD,
            aes(date_time_ID, i_dep),
            col="white", linetype = 2)+
  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="Tem (°C)",
                      guide = "colorstrip", breaks = MakeBreaks(bin),
                      high = "grey90",
                      low = "grey20")+
  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())+
  facet_wrap(~source)

p_hov_dep

rm(bin)

7 Surface obs + integration depths

tm_fm_gt_surface <- tm_fm_gt %>% 
  filter(dep < surface_dep) %>% 
  select(source, date_time_ID, nCT) %>% 
  group_by(source, date_time_ID) %>% 
  summarise(nCT = mean(nCT, na.rm = TRUE)) %>% 
  ungroup()

tm_fm_gt_surface <- tm_fm_gt_surface %>%
  group_by(source) %>%
  arrange(date_time_ID) %>%
  mutate(date_time_ID_diff = as.numeric(date_time_ID - lag(date_time_ID)),
         date_time_ID_ref  = date_time_ID - (date_time_ID - lag(date_time_ID))/2,
         nCT_diff = nCT - lag(nCT, default = first(nCT)),
         nCT_cum = cumsum(nCT_diff)) %>% 
  ungroup()
iCT <- full_join(tm_fm_gt_surface, i_dep)
rm(tm_fm_gt_surface)

8 iCT

iCT <- iCT %>% 
  mutate(CT_i_diff = nCT_diff * i_dep)

iCT <- iCT %>% 
  group_by(source, i_method, i_res) %>%
  arrange(date_time_ID) %>%
  mutate(nCT_i_cum = cumsum(CT_i_diff/1000)) %>% 
  ungroup()
tm_NCP_cum <- read_csv(here::here("Data/_merged_data_files/CT_dynamics",
                                  "tm_NCP_cum.csv"))
tm_NCP_cum_flux <- tm_NCP_cum %>% 
  select(date_time, flux_cum)

tm_NCP_cum_flux <-
  expand_grid(tm_NCP_cum_flux, 
              source = unique(iCT$source),
              i_method = unique(iCT$i_method),
              i_res = unique(iCT$i_res))

NCP_flux <- full_join(iCT %>% rename(date_time=date_time_ID),
                      tm_NCP_cum_flux) %>% 
  arrange(date_time)

# linear interpolation of cumulative changes to frequency of the flux estimates estimates 
NCP_flux_int <- NCP_flux %>% 
  filter(!(i_method == "MLD" & i_res == "cum")) %>% 
  group_by(source, i_method, i_res) %>% 
  mutate(nCT_i_cum = approxfun(date_time, nCT_i_cum)(date_time),
         flux_cum = approxfun(date_time, flux_cum)(date_time)) %>% 
    fill(flux_cum) %>% 
  mutate(nCT_i_flux_cum = nCT_i_cum + flux_cum) %>% 
  ungroup()
p_nCT <- iCT %>% 
  ggplot(aes(date_time_ID, nCT))+
  geom_point()+
  geom_path()+
  scale_color_discrete(name = "Reconstruction")+
  scale_x_datetime(breaks = "week", date_labels = "%d %b",
                   expand = c(0,0))+
  scale_linetype(name="Resolution")+
  facet_wrap(~source)+
  labs(y = expression(atop(nC[T], (mu*mol~kg^{-1}))))+
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank())

iCT <- iCT %>% 
  mutate(i_res = fct_recode(i_res, `cumulative` = "cum")) %>% 
  mutate(i_res = factor(i_res, c("mean", "cumulative", "daily")))

p_iCT <- iCT %>% 
  ggplot()+
  geom_hline(yintercept = 0)+
  geom_path(data=tm_NCP_cum, aes(date_time, nCT_i_cum), col="black")+
  geom_path(aes(date_time_ID, nCT_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_nCT / p_hov_dep / p_iCT

ggsave(here::here("output/Plots/Figures_publication/article", "reconstruction_iCT_timeseries.pdf"),
       width = 210, height = 220, dpi = 300, units = "mm")
NCP_flux <- NCP_flux %>% 
  mutate(i_res = fct_recode(i_res, `cumulative` = "cum")) %>% 
  mutate(i_res = factor(i_res, c("mean", "cumulative", "daily")))

p_NCP <- NCP_flux_int %>% 
  ggplot()+
  geom_hline(yintercept = 0)+
  geom_path(data=tm_NCP_cum, aes(date_time, nCT_i_flux_mix_cum), col="black")+
  geom_path(aes(date_time, nCT_i_flux_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_nCT / p_hov_dep / p_NCP

ggsave(here::here("output/Plots/Figures_publication/article", "reconstruction_NCP_timeseries.pdf"),
       width = 210, height = 220, dpi = 300, units = "mm")

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-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_0.6.0      lubridate_1.7.4 patchwork_1.0.0 seacarb_3.2.13 
 [5] oce_1.2-0       gsw_1.0-5       testthat_2.3.2  ncdf4_1.17     
 [9] forcats_0.5.0   stringr_1.4.0   dplyr_0.8.5     purrr_0.3.4    
[13] readr_1.3.1     tidyr_1.0.2     tibble_3.0.1    ggplot2_3.3.0  
[17] tidyverse_1.3.0 workflowr_1.6.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6       whisker_0.4        knitr_1.28         xml2_1.3.0        
 [5] magrittr_1.5       hms_0.5.3          rvest_0.3.5        tidyselect_1.1.0  
 [9] viridisLite_0.3.0  here_0.1           colorspace_1.4-1   lattice_0.20-41   
[13] R6_2.4.1           rlang_0.4.6        fansi_0.4.1        broom_0.5.5       
[17] xfun_0.12          dbplyr_1.4.2       modelr_0.1.6       withr_2.1.2       
[21] git2r_0.26.1       ellipsis_0.3.1     htmltools_0.4.0    assertthat_0.2.1  
[25] rprojroot_1.3-2    digest_0.6.25      lifecycle_0.2.0    haven_2.2.0       
[29] rmarkdown_2.1      sp_1.4-1           compiler_3.6.3     cellranger_1.1.0  
[33] pillar_1.4.4       scales_1.1.0       backports_1.1.5    generics_0.0.2    
[37] jsonlite_1.6.1     httpuv_1.5.2       pkgconfig_2.0.3    rstudioapi_0.11   
[41] munsell_0.5.0      plyr_1.8.6         highr_0.8          httr_1.4.1        
[45] tools_3.6.3        grid_3.6.3         nlme_3.1-145       data.table_1.12.8 
[49] gtable_0.3.0       checkmate_2.0.0    utf8_1.1.4         DBI_1.1.0         
[53] cli_2.0.2          readxl_1.3.1       yaml_2.2.1         crayon_1.3.4      
[57] later_1.0.0        farver_2.0.3       RColorBrewer_1.1-2 promises_1.1.0    
[61] fs_1.4.0           vctrs_0.3.0        memoise_1.1.0      glue_1.4.1        
[65] evaluate_0.14      labeling_0.3       reprex_0.3.0       stringi_1.4.6