Last updated: 2021-01-22

Checks: 7 0

Knit directory: BloomSail/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


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.

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.

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.

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

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

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

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 f656a73. 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/input/
    Ignored:    data/intermediate/
    Ignored:    data/output_submission/
    Ignored:    output/Plots/Figures_publication/.tmp.drivedownload/

Untracked files:
    Untracked:  output/Plots/Figures_publication/Appendix/

Unstaged changes:
    Modified:   code/Workflowr_project_managment.R
    Deleted:    output/Plots/Figures_publication/Article/Fig_1.pdf
    Deleted:    output/Plots/Figures_publication/Article/Fig_1.png
    Deleted:    output/Plots/Figures_publication/Article/Fig_2.pdf
    Deleted:    output/Plots/Figures_publication/Article/Fig_2.png
    Deleted:    output/Plots/Figures_publication/Article/Fig_3.pdf
    Deleted:    output/Plots/Figures_publication/Article/Fig_3.png
    Deleted:    output/Plots/Figures_publication/Article/Fig_4.pdf
    Deleted:    output/Plots/Figures_publication/Article/Fig_4.png
    Deleted:    output/Plots/Figures_publication/Article/Fig_5.pdf
    Deleted:    output/Plots/Figures_publication/Article/Fig_5.png
    Deleted:    output/Plots/Figures_publication/Article/Fig_6.pdf
    Deleted:    output/Plots/Figures_publication/Article/Fig_6.png
    Modified:   output/Plots/merging_interpolation/Zero_time_synchronization.pdf
    Modified:   output/Plots/response_time/tau_determination_pCO2_corr_flushperiods_nls.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/CT_dynamics.Rmd) and HTML (docs/CT_dynamics.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
Rmd f656a73 jens-daniel-mueller 2021-01-22 all figs revised
html a7950fd jens-daniel-mueller 2021-01-22 Build site.
Rmd 88fcb00 jens-daniel-mueller 2021-01-22 modified figs
html 6b031b2 jens-daniel-mueller 2021-01-21 Build site.
Rmd fd08dfe jens-daniel-mueller 2021-01-21 modified figs
html 0a46411 jens-daniel-mueller 2021-01-05 Build site.
Rmd c5d47f7 jens-daniel-mueller 2021-01-05 use only V2 of Fig S5
html e55d103 jens-daniel-mueller 2021-01-05 Build site.
Rmd f31d3e2 jens-daniel-mueller 2021-01-05 revised figure 4 and 6
html 4277235 jens-daniel-mueller 2021-01-05 Build site.
Rmd 58c1637 jens-daniel-mueller 2021-01-05 new Fig_AX names, A5 added
html 0f737ea jens-daniel-mueller 2021-01-04 Build site.
Rmd 36ace38 jens-daniel-mueller 2021-01-04 revised figures
html f4eb429 jens-daniel-mueller 2020-11-16 Build site.
Rmd 24ebe53 jens-daniel-mueller 2020-11-16 corrected link to and recreated pdf files
html 41e3c63 jens-daniel-mueller 2020-11-06 Build site.
Rmd 6f7ada2 jens-daniel-mueller 2020-11-06 Included count of all profiles
html c80d0cd jens-daniel-mueller 2020-11-04 Build site.
Rmd d12afad jens-daniel-mueller 2020-11-04 changed output plot size
html 68a1fad jens-daniel-mueller 2020-11-04 Build site.
Rmd ce737d3 jens-daniel-mueller 2020-11-04 added panel annotation
html 101cf30 jens-daniel-mueller 2020-11-04 Build site.
Rmd 9ba19ce jens-daniel-mueller 2020-11-04 added panel annotation
html de80063 jens-daniel-mueller 2020-11-04 Build site.
Rmd 3f05b29 jens-daniel-mueller 2020-11-04 included satellite image
html 3880b4a jens-daniel-mueller 2020-11-04 Build site.
Rmd 399a6bb jens-daniel-mueller 2020-11-04 included satellite image
html 17cf505 jens-daniel-mueller 2020-11-04 Build site.
Rmd 64f5375 jens-daniel-mueller 2020-11-04 included satellite image
html 7e29c30 jens-daniel-mueller 2020-11-02 Build site.
Rmd 7e5a700 jens-daniel-mueller 2020-11-02 renamed and revised figures for publication
html caf4db0 jens-daniel-mueller 2020-10-30 Build site.
Rmd bf80977 jens-daniel-mueller 2020-10-30 updated and renamed figures
html 952fb91 jens-daniel-mueller 2020-10-30 Build site.
Rmd 56716e9 jens-daniel-mueller 2020-10-30 updated hovmoeller
html bc4d01c jens-daniel-mueller 2020-10-30 Build site.
Rmd dd5b61f jens-daniel-mueller 2020-10-30 updated map and coverage plot
html 9a8c78b jens-daniel-mueller 2020-10-30 Build site.
Rmd c8908b4 jens-daniel-mueller 2020-10-30 updated map
html 1b8abf5 jens-daniel-mueller 2020-10-26 Build site.
Rmd 6749581 jens-daniel-mueller 2020-10-26 vertical entrainment plot generated
html f8895fe jens-daniel-mueller 2020-10-26 Build site.
Rmd 7ee1f34 jens-daniel-mueller 2020-10-26 vertical entrainment plot generated
html 203c2c8 jens-daniel-mueller 2020-10-24 Build site.
Rmd 2ddeb47 jens-daniel-mueller 2020-10-24 mixing plot started
html 9a3f42a jens-daniel-mueller 2020-10-24 Build site.
html 05248bf jens-daniel-mueller 2020-10-20 Build site.
Rmd 7d02517 jens-daniel-mueller 2020-10-20 rebuild all
html b465a28 jens-daniel-mueller 2020-10-20 Build site.
Rmd 9462207 jens-daniel-mueller 2020-10-20 table with time series in depth intervals added
html 102828d jens-daniel-mueller 2020-10-20 Build site.
Rmd 1c4fe8e jens-daniel-mueller 2020-10-20 table with time series in depth intervals added
html 1c4fe8e jens-daniel-mueller 2020-10-20 table with time series in depth intervals added
html ea57f5a jens-daniel-mueller 2020-10-20 Build site.
Rmd b83e97e jens-daniel-mueller 2020-10-20 table of timeseries in depth intervals added
html f8a9f90 jens-daniel-mueller 2020-10-19 Build site.
Rmd 26101c9 jens-daniel-mueller 2020-10-19 CT* sensitivity to AT
html d0ae81b jens-daniel-mueller 2020-10-19 Build site.
Rmd 2c6f290 jens-daniel-mueller 2020-10-19 relative CT* sensitivity to AT bias
html fba2b06 jens-daniel-mueller 2020-10-15 Build site.
Rmd dcfd745 jens-daniel-mueller 2020-10-15 included AT sensitivity analysis
html 6896725 jens-daniel-mueller 2020-10-01 Build site.
html 9f66019 jens-daniel-mueller 2020-10-01 Build site.
Rmd d6dd205 jens-daniel-mueller 2020-10-01 wind speed tower converted to 10m
html 27c5431 jens-daniel-mueller 2020-09-29 Build site.
Rmd 2e0f902 jens-daniel-mueller 2020-09-29 all parameters separate, rebuild
html 1d01685 jens-daniel-mueller 2020-09-28 Build site.
html 1278900 jens-daniel-mueller 2020-09-25 Build site.
html 904f0f7 jens-daniel-mueller 2020-09-23 Build site.
html e97109a jens-daniel-mueller 2020-07-01 Build site.
Rmd f38a1ab jens-daniel-mueller 2020-07-01 rearranged Hovmoeller plots
html c919fb7 jens-daniel-mueller 2020-06-29 Build site.
Rmd 1461cb6 jens-daniel-mueller 2020-06-29 Fig update for talk
html 603af23 jens-daniel-mueller 2020-05-25 Build site.
html 3414c23 jens-daniel-mueller 2020-05-25 Build site.
html 9ccd9a3 jens-daniel-mueller 2020-05-25 Build site.
Rmd 9bedac5 jens-daniel-mueller 2020-05-25 revised pp time series plot
html c6f6553 jens-daniel-mueller 2020-05-25 Build site.
Rmd 7e708cc jens-daniel-mueller 2020-05-25 tb mean profiles plot
html f44c4e3 jens-daniel-mueller 2020-05-18 Build site.
Rmd eefd9d1 jens-daniel-mueller 2020-05-18 merged tm and gt NCP reconstruction
html adfc1fe jens-daniel-mueller 2020-05-16 Build site.
Rmd 93cb4d3 jens-daniel-mueller 2020-05-16 mixing from inventory redistribution approach
html 2f00b27 jens-daniel-mueller 2020-05-15 Build site.
Rmd 9e32c7a jens-daniel-mueller 2020-05-15 MLD line in profiles plots
html 6cbf9a4 jens-daniel-mueller 2020-05-15 Build site.
Rmd 99e6bfa jens-daniel-mueller 2020-05-15 Fentr calculated from concentration gradient
html 01daf06 jens-daniel-mueller 2020-05-11 Build site.
Rmd 316d86c jens-daniel-mueller 2020-05-11 finalized mixing correction
html 5e016a6 jens-daniel-mueller 2020-05-11 Build site.
Rmd 3a9d977 jens-daniel-mueller 2020-05-11 clean until comparison
html 03dccf3 jens-daniel-mueller 2020-05-11 Build site.
Rmd 26b4810 jens-daniel-mueller 2020-05-11 clean until integration
html 66aaac3 jens-daniel-mueller 2020-05-11 Build site.
Rmd 4433c58 jens-daniel-mueller 2020-05-11 flux plots vertical
html 337dad1 jens-daniel-mueller 2020-05-11 Build site.
Rmd 23d67e3 jens-daniel-mueller 2020-05-11 mean + sd nCT discrete values in time series + pdf eval true
html 9046ec0 jens-daniel-mueller 2020-05-11 Build site.
Rmd dfd507f jens-daniel-mueller 2020-05-11 mean + sd nCT discrete values in time series
html 3fb704d jens-daniel-mueller 2020-05-08 Build site.
Rmd b604fbb jens-daniel-mueller 2020-05-08 integration depth revised
html 4c4a849 jens-daniel-mueller 2020-05-08 Build site.
Rmd 85241e5 jens-daniel-mueller 2020-05-08 replaced CT by nCT
html 612dfc6 jens-daniel-mueller 2020-05-08 Build site.
Rmd 7fe598e jens-daniel-mueller 2020-05-08 map update and finnmaid subsetting in area
html dd3bd89 jens-daniel-mueller 2020-05-07 Build site.
Rmd ad98da2 jens-daniel-mueller 2020-05-07 harmonized parameter labeling
html b5722a7 jens-daniel-mueller 2020-04-28 Build site.
Rmd 058c709 jens-daniel-mueller 2020-04-28 Moved nomenlacture to seperate Rmd
html d2036b0 jens-daniel-mueller 2020-04-24 Build site.
Rmd c28b943 jens-daniel-mueller 2020-04-24 discrete data in CT timeseries plot
html b004af3 jens-daniel-mueller 2020-04-24 Build site.
Rmd e07781a jens-daniel-mueller 2020-04-24 discrete surface CT in timeseries
html a075635 jens-daniel-mueller 2020-04-24 Build site.
Rmd 72f9a86 jens-daniel-mueller 2020-04-24 Refined depth for discrete surface time series
html 472c2b4 jens-daniel-mueller 2020-04-21 Build site.
html 69c301c jens-daniel-mueller 2020-04-21 Build site.
html c9549ee jens-daniel-mueller 2020-04-19 Build site.
Rmd f8fcf50 jens-daniel-mueller 2020-04-19 created pub figures for time series
html f8fcf50 jens-daniel-mueller 2020-04-19 created pub figures for time series
html 6810175 jens-daniel-mueller 2020-04-17 Build site.
Rmd 864596a jens-daniel-mueller 2020-04-17 plotted all profiles
html 4054ba1 jens-daniel-mueller 2020-04-17 Build site.
Rmd acc1379 jens-daniel-mueller 2020-04-17 calculate AT sd
html 729b4c6 jens-daniel-mueller 2020-04-17 Build site.
Rmd 2edd18d jens-daniel-mueller 2020-04-17 included bottle CT AT from 180723
html bf6384a jens-daniel-mueller 2020-04-17 Build site.
Rmd d0eb264 jens-daniel-mueller 2020-04-17 all stations on map
html cc2baf3 jens-daniel-mueller 2020-04-16 Build site.
Rmd 13436a3 jens-daniel-mueller 2020-04-16 worked on map
html 5e8f8e1 jens-daniel-mueller 2020-04-16 Build site.
Rmd 86b0833 jens-daniel-mueller 2020-04-16 New fixed integration depth 12m
html 4ac8782 jens-daniel-mueller 2020-04-16 Build site.
Rmd 95380d4 jens-daniel-mueller 2020-04-16 Cumulative temperature distribution on July 23
html 48631ee jens-daniel-mueller 2020-04-09 Build site.
Rmd 4e9464f jens-daniel-mueller 2020-04-09 corrected na approx function
html 849e990 jens-daniel-mueller 2020-04-01 Build site.
Rmd c199200 jens-daniel-mueller 2020-04-01 included BloomSail data to Finnmaid analysis
html f4a27b8 jens-daniel-mueller 2020-04-01 Build site.
Rmd b1613b7 jens-daniel-mueller 2020-04-01 re-calculated MLD, renamed objects and structured site
html 6302994 jens-daniel-mueller 2020-03-31 Build site.
Rmd 50ab313 jens-daniel-mueller 2020-03-31 implemented temperature reconstruction
html a6c4c22 jens-daniel-mueller 2020-03-30 Build site.
Rmd d8120b3 jens-daniel-mueller 2020-03-30 reconstruction BloomSail surface started, merging MLD and DT approach
html 80c78b3 jens-daniel-mueller 2020-03-30 Build site.
html 70dbfbe jens-daniel-mueller 2020-03-30 Build site.
Rmd e69d1f0 jens-daniel-mueller 2020-03-30 cleaned object names
html 431a56a jens-daniel-mueller 2020-03-30 Build site.
Rmd 9edf20d jens-daniel-mueller 2020-03-30 flux and mixing correction revised
html f8ad4ff jens-daniel-mueller 2020-03-30 Build site.
Rmd 265e568 jens-daniel-mueller 2020-03-30 NCP calculation finished
html 2ade511 jens-daniel-mueller 2020-03-27 Build site.
Rmd 858e01f jens-daniel-mueller 2020-03-27 iCT flux correction applied
html a22daa8 jens-daniel-mueller 2020-03-27 Build site.
Rmd 9118b70 jens-daniel-mueller 2020-03-27 iCT flux correction applied
html 2d358fb jens-daniel-mueller 2020-03-27 Build site.
Rmd d17a2b0 jens-daniel-mueller 2020-03-27 Added air sea CO2 fluxes
html 43da055 jens-daniel-mueller 2020-03-26 Build site.
Rmd 6afdea9 jens-daniel-mueller 2020-03-26 selected iCT time series for NCP
html 1d7eebc jens-daniel-mueller 2020-03-26 Build site.
Rmd 4d734a1 jens-daniel-mueller 2020-03-26 Started NCP estimation
html 57e3e73 jens-daniel-mueller 2020-03-26 Build site.
Rmd 275b061 jens-daniel-mueller 2020-03-26 renamed NCP correctly als iCT
html 30d5b10 jens-daniel-mueller 2020-03-26 Build site.
Rmd 0405651 jens-daniel-mueller 2020-03-26 Restructure MLD iCT chapter
html 90633b8 jens-daniel-mueller 2020-03-26 Build site.
Rmd baa81d6 jens-daniel-mueller 2020-03-26 heigth surface timeseries reduced
html f139cbd jens-daniel-mueller 2020-03-26 Build site.
Rmd 1b8a11e jens-daniel-mueller 2020-03-26 restructured NCP chapter, and renamed as iCT
html c2b128e jens-daniel-mueller 2020-03-26 Build site.
Rmd 6ec4005 jens-daniel-mueller 2020-03-26 added interpretation notes
html 63909fc jens-daniel-mueller 2020-03-26 Build site.
Rmd 069600c jens-daniel-mueller 2020-03-26 theme_bw
html 5011448 jens-daniel-mueller 2020-03-26 Build site.
Rmd 69ec53e jens-daniel-mueller 2020-03-26 Comparison iCT estimates
html b6e6117 jens-daniel-mueller 2020-03-25 Build site.
Rmd 07690b6 jens-daniel-mueller 2020-03-25 NCP MLD approach implmented
html a667be1 jens-daniel-mueller 2020-03-25 Build site.
Rmd 93800e0 jens-daniel-mueller 2020-03-25 NCP MLD approach implmented
html b8d7014 jens-daniel-mueller 2020-03-25 Build site.
Rmd a13c901 jens-daniel-mueller 2020-03-25 NCP fixed depth, new variable names, ref dates introduced
html b589daf jens-daniel-mueller 2020-03-24 Build site.
Rmd 90979bb jens-daniel-mueller 2020-03-24 nameing convention and NCP approaches list
html d0d5c9e jens-daniel-mueller 2020-03-24 Build site.
Rmd 1e2508a jens-daniel-mueller 2020-03-24 harmonized starting dates
html 5f8ca30 jens-daniel-mueller 2020-03-20 Build site.
html 2a20453 jens-daniel-mueller 2020-03-20 Build site.
html 473ab25 jens-daniel-mueller 2020-03-19 Build site.
html e9d33a7 jens-daniel-mueller 2020-03-19 Build site.
Rmd ff79dbe jens-daniel-mueller 2020-03-19 remoced errorbars in ts plot
html 4766353 jens-daniel-mueller 2020-03-19 Build site.
Rmd 0d90486 jens-daniel-mueller 2020-03-19 Hovmoeller daily changes
html 592f3b5 jens-daniel-mueller 2020-03-19 Build site.
Rmd 4103279 jens-daniel-mueller 2020-03-19 CT: removed coastal, added errorbars and hovmoeller
html 81f022e jens-daniel-mueller 2020-03-18 Build site.
html 18a74d1 jens-daniel-mueller 2020-03-18 Build site.
Rmd b839b18 jens-daniel-mueller 2020-03-18 CT vs tem changes implemented
html 1e39d85 jens-daniel-mueller 2020-03-18 Build site.
html 2105236 jens-daniel-mueller 2020-03-18 Build site.
html 4858097 jens-daniel-mueller 2020-03-18 Build site.
Rmd f0233c2 jens-daniel-mueller 2020-03-18 MLD and NCP penetration depth
html 05b9bdc jens-daniel-mueller 2020-03-17 Build site.
html 943cd6b jens-daniel-mueller 2020-03-17 Build site.
Rmd 859c4a4 jens-daniel-mueller 2020-03-17 corrected gas exchange calculation
html 26bc407 jens-daniel-mueller 2020-03-17 Build site.
Rmd 7be14e4 jens-daniel-mueller 2020-03-17 corrected CT cum timeseries, used exact mean dates
html cb196d8 jens-daniel-mueller 2020-03-17 Build site.
Rmd 7c10336 jens-daniel-mueller 2020-03-17 corrected CT cum timeseries, used exact mean dates
html 0202742 jens-daniel-mueller 2020-03-16 Build site.
html 7508d11 jens-daniel-mueller 2020-03-16 Build site.
Rmd 53ee423 jens-daniel-mueller 2020-03-16 gas exchange calculation completed
html 9f0c30b jens-daniel-mueller 2020-03-16 Build site.
Rmd 1c60add jens-daniel-mueller 2020-03-16 incremental CT changes timeseries + raw pCO2 profiles plotted
html 4150817 jens-daniel-mueller 2020-03-13 Build site.
Rmd 94e12d8 jens-daniel-mueller 2020-03-13 final cleaning
html 443d9a1 jens-daniel-mueller 2020-03-13 Build site.
Rmd 39b841d jens-daniel-mueller 2020-03-13 all profiles pdfs included
html ff22d6f jens-daniel-mueller 2020-03-13 Build site.
Rmd f49ce78 jens-daniel-mueller 2020-03-13 cumulative changes per depth
html e404359 jens-daniel-mueller 2020-03-12 Build site.
Rmd e9725fe jens-daniel-mueller 2020-03-12 Clean CT dynamics
html 8e83afd jens-daniel-mueller 2020-03-12 Build site.
Rmd 3c17c46 jens-daniel-mueller 2020-03-12 update CT cynamics
html a3ddea4 jens-daniel-mueller 2020-03-12 Build site.
Rmd 97355fa jens-daniel-mueller 2020-03-12 CT calculations and plots

library(tidyverse)
library(patchwork)
library(seacarb)
library(marelac)
library(metR)
library(scico)
library(lubridate)
library(zoo)
library(tibbletime)
library(sp)
library(kableExtra)
library(LakeMetabolizer)
library(rgdal)
library(ggnewscale)

1 Sensor data

1.1 Data preparation

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
  • removing grids with pCO2 < 0 µatm (presumably RT correction artefact after zeroing)
  • Discarding profiles with 20 or more observation missing within upper 25m
  • 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 representm the start date of the cruise (“YYMMDD”), not the exact mean sampling date
tm <-
 read_csv(here::here("data/intermediate/_merged_data_files/response_time",
                      "tm_RT_all.csv"),
               col_types = cols(ID = col_character(),
                                pCO2_analog = col_double(),
                                pCO2_corr = col_double(),
                                Zero = col_character(),
                                Flush = col_character(),
                                mixing = col_character(),
                                Zero_counter = col_integer(),
                                deployment = col_integer(),
                                lon = col_double(),
                                lat = col_double(),
                                pCO2 = col_double()))


# Filter relevant rows and columns
tm_profiles <- tm %>% 
  filter(type == "P",
         Flush == "0",
         Zero == "0",
         !ID %in% parameters$dates_out,
         !(station %in% c("PX1", "PX2"))) %>% 
  select(date_time, ID, station, lat, lon, dep, sal, tem, pCO2_corr, pCO2, duration)

#calculate mean location of stations
stations <- tm_profiles %>% 
  group_by(station) %>% 
  summarise(lat = mean(lat),
            lon = mean(lon)) %>% 
  ungroup() %>% 
  mutate(station = str_sub(station, 2, 3))

tm_profiles <- tm_profiles %>% 
  filter(!(station %in% c("P14", "P13", "P01")))

# Assign meta information
tm_profiles <- tm_profiles %>% 
  group_by(ID, station) %>% 
  mutate(duration = as.numeric(date_time - min(date_time))) %>%
  arrange(date_time) %>% 
  ungroup()

meta <- read_csv(here::here("data/input/TinaV/Sensor",
                            "Sensor_meta.csv"),
                 col_types = cols(ID = col_character()))

meta <- meta %>% 
    filter(!ID %in% parameters$dates_out,
           !(station %in% parameters$stations_out))

tm_profiles <- full_join(tm_profiles, meta)
rm(meta)


# creating descriptive variables
tm_profiles <- tm_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))

tm_profiles <- tm_profiles %>% 
  select(-c(start, down, lift, up, end, comment, p_type, duration))


# select downcasts only
tm_profiles <- tm_profiles %>% 
  filter(phase %in% parameters$phases_in)

# grid observation to 1m depth intervals
tm_profiles <- tm_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, phase) %>%
  summarise_all("mean", na.rm = TRUE) %>%
  ungroup() %>%
  select(-dep, dep = dep_grid)

# Remove zero pCO2 data
tm_profiles <- tm_profiles %>%
  filter(pCO2 >= 0)

# subset complete profiles
profiles_in <- tm_profiles %>% 
  filter(dep < parameters$max_dep_gap,
         phase == "down") %>% 
  group_by(ID, station) %>% 
  summarise(nr_na = parameters$max_dep_gap/parameters$dep_grid - n()) %>% 
  mutate(select = if_else(nr_na < parameters$max_gap,
                          "in", "out")) %>% 
  select(-nr_na) %>% 
  ungroup()

tm_profiles <- full_join(tm_profiles, profiles_in)
rm(profiles_in)

1.2 pCO2 profile overview

tm_profiles %>%
  arrange(date_time) %>%
  ggplot(aes(pCO2, dep, col = select, linetype = phase)) +
  geom_hline(yintercept = 25) +
  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)
Overview pCO~2~ profiles at stations (P02-P12) and cruise dates (ID). y-axis restricted to displayed range.

Overview pCO2 profiles at stations (P02-P12) and cruise dates (ID). y-axis restricted to displayed range.

1.3 Subset

tm_profiles <- tm_profiles %>%
  filter(select == "in",
         phase == "down") %>%
  select(-c(select, phase)) %>% 
  filter(dep < parameters$max_dep)

1.4 Cruise dates

# assign mean date_time stamp

cruise_dates <- tm_profiles %>% 
  group_by(ID) %>% 
  summarise(date_time_ID = mean(date_time),
            date_ID = format(as.Date(date_time_ID), "%b %d")) %>% 
  ungroup()

# inner_join remove P14 observations lacking date_time_ID 
tm_profiles <- inner_join(cruise_dates, tm_profiles)

cruise_dates %>% 
    write_csv(here::here("data/intermediate/_summarized_data_files",
                       "cruise_date.csv"))

1.5 Station map

fm <-
  read_csv(here::here("data/intermediate/_summarized_data_files",
                       "fm.csv"))

fm <- fm %>% 
  filter(lat <= parameters$map_lat_hi, lat >= parameters$map_lat_lo, lon >= parameters$map_lon_lo)

fm <- fm %>% 
  mutate(Area = point.in.polygon(point.x = lon,
                                 point.y = lat,
                                 pol.x = parameters$fm_box_lon,
                                 pol.y = parameters$fm_box_lat),
         Area = as.character(Area),
         Area = if_else(Area == "1", "utilized", "sampled"))

fm %>% 
  filter(Area == "utilized") %>% 
  select(-Area) %>% 
  write_csv(here::here("data/intermediate/_summarized_data_files",
                       "fm_bloomsail.csv"))
# https://shekeine.github.io/visualization/2014/09/27/sfcc_rgb_in_R

EGS  <-
  raster::stack(here::here("data/input/Maps",
                   "MODIS_2018_07_26_EGS.tiff"))

EGS <- raster::as.data.frame(EGS, xy = T)

EGS <- as_tibble(EGS)

EGS <- EGS %>%
  rename(lat = y,
         lon = x) %>% 
  filter(lat >= 56.4, lat <= 58.3)

# hist(EGS$MODIS_2018_07_26_EGS.1)
# hist(EGS$MODIS_2018_07_26_EGS.2)
# hist(EGS$MODIS_2018_07_26_EGS.3)

EGS <- EGS %>%
  mutate(
    MODIS_2018_07_26_EGS.1_s = MODIS_2018_07_26_EGS.1 * 2.5,
    MODIS_2018_07_26_EGS.2_s = MODIS_2018_07_26_EGS.2 * 2.5,
    MODIS_2018_07_26_EGS.3_s = MODIS_2018_07_26_EGS.3 * 2.5
  ) %>%
  mutate(
    MODIS_2018_07_26_EGS.1_s =
      if_else(MODIS_2018_07_26_EGS.1_s > 255,
              255,
              MODIS_2018_07_26_EGS.1_s),
    MODIS_2018_07_26_EGS.2_s =
      if_else(MODIS_2018_07_26_EGS.2_s > 255,
              255,
              MODIS_2018_07_26_EGS.2_s),
    MODIS_2018_07_26_EGS.3_s =
      if_else(MODIS_2018_07_26_EGS.3_s > 255,
              255,
              MODIS_2018_07_26_EGS.3_s)) %>%
      mutate(
        RGB = rgb(
          MODIS_2018_07_26_EGS.1_s,
          MODIS_2018_07_26_EGS.2_s,
          MODIS_2018_07_26_EGS.3_s,
          maxColorValue = 255
        )
      )

EGS <- EGS %>%
  dplyr::select(-c(
    MODIS_2018_07_26_EGS.1,
    MODIS_2018_07_26_EGS.2,
    MODIS_2018_07_26_EGS.3
  )) %>% 
  dplyr::select(-c(
    MODIS_2018_07_26_EGS.1_s,
    MODIS_2018_07_26_EGS.2_s,
    MODIS_2018_07_26_EGS.3_s
  ))

p_MODIS <-
  ggplot(data = EGS,
         aes(lon, lat, fill = RGB)) +
  coord_quickmap(expand = 0) +
  geom_raster() +
  scale_fill_identity() +
  annotate(
    "rect",
    ymax = parameters$map_lat_hi,
    ymin = parameters$map_lat_lo,
    xmax = parameters$map_lon_hi,
    xmin = parameters$map_lon_lo,
    fill = NA,
    color = "orangered",
    size = 1.5
  ) +
  scale_x_continuous(breaks = seq(10,30,1)) +
  labs(x = "Longitude (°E)", y = "Latitude (°N)")
map <-
  read_csv(here::here("data/input/Maps", "Bathymetry_Gotland_east_small.csv"))

map <- map %>%
  filter(
    lat < parameters$map_lat_hi,
    lat > parameters$map_lat_lo,
    lon < parameters$map_lon_hi,
    lon > parameters$map_lon_lo
  )

map_low_res <- map %>%
  mutate(
    lat = cut(
      lat,
      breaks = seq(57, 58, 0.01),
      labels = seq(57.005, 57.995, 0.01)
    ),
    lon = cut(
      lon,
      breaks = seq(18, 22, 0.01),
      labels = seq(18.005, 21.995, 0.01)
    )
  ) %>%
  group_by(lat, lon) %>%
  summarise_all(mean, na.rm = TRUE) %>%
  ungroup() %>%
  mutate(lat = as.numeric(as.character(lat)),
         lon = as.numeric(as.character(lon)))

tm_track <- tm %>%
  arrange(date_time) %>%
  slice(which(row_number() %% 50 == 1))


p_map <-
  ggplot() +
  geom_contour_fill(
    data = map_low_res,
    aes(x = lon, y = lat, z = -elev),
    na.fill = TRUE,
    breaks = seq(0, 300, 30)
  ) +
  geom_raster(data = map %>% filter(is.na(elev)),
              aes(lon, lat),
              fill = "darkgrey") +
  geom_path(data = tm_track, aes(lon, lat, group = ID, col = "Data\nused")) +
  scale_color_manual(values = c("orangered"),
                     name = "") +
  new_scale_color() +
  geom_path(data = tm_track, aes(lon, lat, group = ID, col = "sampled")) +
  geom_path(data = fm, aes(lon, lat, group = ID, col = Area)) +
  geom_label(
    data = stations %>% filter(!(station %in% c("14", "13", "01"))),
    aes(lon, lat, label = station, col = "utilized"),
    size = geom_text_size
  ) +
  geom_label(
    data = stations %>% filter(station %in% c("14", "13", "01")),
    aes(lon, lat, label = station, col = "sampled_station"),
    size = geom_text_size
  ) +
  geom_point(aes(parameters$herrvik_lon, parameters$herrvik_lat)) +
  geom_text(
    aes(parameters$herrvik_lon, parameters$herrvik_lat, label = "Herrvik"),
    nudge_x = -0.05,
    size = geom_text_size
  ) +
  geom_point(aes(parameters$ostergarn_lon, parameters$ostergarn_lat)) +
  geom_text(
    aes(parameters$ostergarn_lon, parameters$ostergarn_lat, label = "Flux tower"),
    nudge_x = -0.07,
    nudge_y = 0.01,
    size = geom_text_size
  ) +
  geom_text(aes(19.26, 57.57, label = "VOS Finnmaid"),
            col = "white",
            size = geom_text_size) +
  geom_text(aes(19.54, 57.29, label = "SV Tina V"),
            col = "white",
            size = geom_text_size) +
  coord_quickmap(
    expand = 0,
    ylim = c(parameters$map_lat_lo + 0.01, parameters$map_lat_hi - 0.01)
  ) +
  scale_x_continuous(breaks = seq(10, 30, 0.1)) +
  labs(x = "Longitude (°E)", y = "Latitude (°N)") +
  scale_fill_gradient(
    low = "lightsteelblue1",
    high = "dodgerblue4",
    name = "Depth (m)\n",
    breaks = seq(30, 150, 30),
    limits = c(0, 180),
    guide = "colorstrip"
  ) +
  guides(
    fill = guide_colorsteps(
      barheight = unit(4.5, "cm"),
      show.limits = TRUE,
      frame.colour = "black",
      ticks = TRUE,
      ticks.colour = "black"
    )
  ) +
  scale_color_manual(values = c("white", "darkgrey", "orangered"),
                     guide = FALSE)

p_MODIS + p_map +
  plot_layout(ncol = 1) +
  plot_annotation(tag_levels = 'a')
Location of stations sampled between the east coast of Gotland and Gotland deep.

Location of stations sampled between the east coast of Gotland and Gotland deep.

ggsave(
  here::here("output/Plots/Figures_publication/article",
             "Fig_1.pdf"),
  width = 175,
  height = 200,
  dpi = 300,
  units = "mm"
)

ggsave(
  here::here("output/Plots/Figures_publication/article",
             "Fig_1.png"),
  width = 160,
  height = 170,
  dpi = 300,
  units = "mm"
)

rm(map, map_low_res,
   fm, tm_track)

rm(tm)

1.6 Data coverage

cover <- tm_profiles %>%
  group_by(ID, station) %>%
  summarise(date = mean(date_time),
            date_time_ID = mean(date_time_ID)) %>%
  ungroup() %>%
  mutate(station = str_sub(station, 2, 3))

cover %>%
  ggplot(aes(date, station, fill = ID)) +
  geom_vline(aes(xintercept = date_time_ID, col = ID)) +
  geom_point(shape = 21) +
  scale_fill_viridis_d(labels = cruise_dates$date_ID,
                       name = "Mean\ncruise date") +
  scale_color_viridis_d(labels = cruise_dates$date_ID,
                        name = "Mean\ncruise date") +
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d") +
  labs(y = "Station") +
  theme(axis.title.x = element_blank())
Spatio-temporal data coverage, indicated as station visits over time.

Spatio-temporal data coverage, indicated as station visits over time.

ggsave(
  here::here(
    "output/Plots/Figures_publication/article",
    "Fig_2.pdf"
  ),
  width = 100,
  height = 65,
  dpi = 300,
  units = "mm"
)

ggsave(
  here::here(
    "output/Plots/Figures_publication/article",
    "Fig_2.png"
  ),
  width = 100,
  height = 65,
  dpi = 300,
  units = "mm"
)

rm(cover)

2 Bottle CT and AT

At stations P07 and P10 discrete samples for lab measurmentm of CT and AT were collected. Please note that - in contrast to the pCO2 profiles - samples were taken on June 16, but removed here for harmonization of results.

tb <-
  read_csv(here::here("data/intermediate/_summarized_data_files", "tb.csv"),
           col_types = cols(ID = col_character()))

tb <- tb %>% 
  filter(station %in% c("P07", "P10"),
         dep <= parameters$max_dep) %>% 
  mutate(ID = if_else(ID == "180722", "180723", ID))

tb <- inner_join(tb, cruise_dates)

2.1 Mean alkalinity

In order to derive CT from measured pCO2 profiles, the alkalinity mean + sd in the upper 25m and both stations was calculated as:

AT_mean <- tb %>% 
  filter(dep <= parameters$max_dep) %>% 
  summarise(AT = mean(AT, na.rm = TRUE)) %>%
  pull()

AT_mean
[1] 1719.706
AT_sd <- tb %>% 
  filter(dep <= parameters$max_dep) %>% 
  summarise(AT = sd(AT, na.rm = TRUE)) %>%
  pull()

AT_sd
[1] 26.95771

Likewise, the mean salinity amounts to:

sal_mean <- tb %>% 
  filter(dep <= parameters$max_dep) %>% 
  summarise(sal = mean(sal, na.rm = TRUE)) %>%
  pull()

sal_mean
[1] 6.908356
tb_fix <- bind_cols(start = min(tm_profiles$date_time), 
          end = max(tm_profiles$date_time),
          AT = AT_mean,
          AT_sd = AT_sd,
          sal = sal_mean)

tb_fix %>% 
  write_csv(here::here("data/intermediate/_summarized_data_files", "tb_fix.csv"))

2.2 nCT calculation

The alkalinity-normalized CT, nCT, was calculated.

tb <- tb %>% 
  mutate(nCT = CT/AT * AT_mean)

2.3 Vertical profiles

2.3.1 Stations

tb_long <- tb %>%
  pivot_longer(c(sal:AT, nCT), 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(labels = cruise_dates$date_ID) +
  scale_color_viridis_d(labels = cruise_dates$date_ID) +
  facet_grid(station ~ var, scales = "free_x") +
  theme(legend.position = "bottom",
        legend.title = element_blank())

2.3.2 Mean

tb_long_mean <- tb_long %>%
  mutate(dep_grid = as.numeric(as.character(cut(
    dep,
    breaks = seq(-2.5, 30, 5),
    labels = seq(0, 25, 5)
  )))) %>%
  group_by(ID, date_time_ID, date_ID, dep_grid, var) %>%
  summarise(value = mean(value, na.rm = TRUE)) %>%
  ungroup()

p_AT <- tb_long_mean %>%
  filter(dep_grid < parameters$max_dep, var == "AT") %>%
  ggplot(aes(value, dep_grid)) +
  annotate(
    "rect",
    xmin = AT_mean - AT_sd,
    xmax = AT_mean + AT_sd,
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.3
  ) +
  geom_vline(data = tb_fix, aes(xintercept = AT), linetype = 2) +
  geom_path(aes(col = ID)) +
  geom_point(aes(fill = ID), shape = 21) +
  scale_y_reverse(sec.axis = dup_axis()) +
  labs(x = expression(A[T] ~ (µmol ~ kg ^ {
    -1
  })),
  y = "Depth (m)") +
  scale_fill_viridis_d(guide = FALSE) +
  scale_color_viridis_d(guide = FALSE) +
  theme(axis.text.y.right = element_blank(),
        axis.title.y.right = element_blank())

p_CT <- tb_long_mean %>%
  filter(dep_grid < parameters$max_dep, var == "CT") %>%
  ggplot(aes(value, dep_grid)) +
  geom_path(aes(col = ID)) +
  geom_point(aes(fill = ID), shape = 21) +
  scale_y_reverse(sec.axis = dup_axis()) +
  labs(x = expression(C[T] ~ (µmol ~ kg ^ {
    -1
  })),
  y = "Depth (m)") +
  scale_fill_viridis_d(guide = FALSE) +
  scale_color_viridis_d(guide = FALSE) +
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank())

p_nCT <- tb_long_mean %>%
  filter(dep_grid < parameters$max_dep, var == "nCT") %>%
  ggplot(aes(value, dep_grid)) +
  geom_path(aes(col = ID)) +
  geom_point(aes(fill = ID), shape = 21) +
  scale_y_reverse(sec.axis = dup_axis()) +
  labs(x = expression(paste(C[T], "*", ~ (µmol ~ kg ^ {-1}))),
       y = "Depth (m)") +
  scale_fill_viridis_d(labels = cruise_dates$date_ID,
                        name = "Mean\ncruise date") +
  scale_color_viridis_d(labels = cruise_dates$date_ID,
                        name = "Mean\ncruise date") +
  theme(
    axis.text.y = element_blank(),
    axis.title.y = element_blank()
  )

p_AT + p_CT + p_nCT +
  plot_annotation(tag_levels = 'a')

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A3.pdf"
  ),
  width = 150,
  height = 80,
  dpi = 300,
  units = "mm"
)

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A3.png"
  ),
  width = 150,
  height = 80,
  dpi = 300,
  units = "mm"
)

rm(tb_long_mean, p_AT, p_CT, p_nCT, tb_fix)

2.4 Surface time series

tb_surface <- tb_long %>%
  filter(dep < parameters$surface_dep) %>%
  group_by(ID, date_time_ID, var, station) %>%
  summarise(value = mean(value, na.rm = TRUE)) %>%
  ungroup()

tb_surface_station_mean <- tb_long %>%
  filter(dep < parameters$surface_dep) %>%
  group_by(ID, date_time_ID, var) %>%
  summarise(value_mean = mean(value, na.rm = TRUE),
            value_sd = sd(value, na.rm = TRUE)) %>%
  ungroup()

tb_long %>%
  filter(dep < 11) %>%
  ggplot() +
  geom_line(data = tb_surface, aes(date_time_ID, value, col = "Individual")) +
  geom_line(data = tb_surface_station_mean, aes(date_time_ID, value_mean, col =
                                                  "Both (mean)")) +
  geom_point(aes(date_time_ID, value, fill = dep), shape = 21) +
  scale_fill_scico(palette = "oslo",
                   direction = -1,
                   name = "Depth (m)") +
  scale_color_brewer(palette = "Set1", name = "Station surface mean") +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  facet_grid(var ~ station, scales = "free_y") +
  labs(x = "Mean transect date")
Time series of bottle data. Shown are mean values of samples collected at water depths < 10m (usually collected at 0 and 5 m).

Time series of bottle data. Shown are mean values of samples collected at water depths < 10m (usually collected at 0 and 5 m).

rm(tb_long, tb_surface, tb)

Important notes: - nCT drop and temporal patterns agree well with those found in the nCT time series derived from pCO2 measurements (below).

3 nCT profiles

3.1 Calculation from pCO2

Alkalinity normalized CT (nCT) profiles were calculated from sensor pCO2 and T profiles, and constant salinity and alkalinity values. Note that the impact of fixed vs. measured salinity has only a negligible impact on nCT profiles.

tm_profiles <- tm_profiles %>%
  mutate(
    nCT = carb(
      24,
      var1 = pCO2,
      var2 = AT_mean * 1e-6,
      S = sal_mean,
      T = tem,
      P = dep / 10,
      k1k2 = "m10",
      kf = "dg",
      ks = "d",
      gas = "insitu"
    )[, 16] * 1e6
  )



tm_profiles %>%
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/CT_dynamics",
      "tm_profiles.csv"
    )
  )

tm_profiles <- tm_profiles %>%
  mutate(
    nCT_test = carb(
      24,
      var1 = pCO2,
      var2 = (AT_mean + 2*AT_sd) * 1e-6,
      S = sal_mean,
      T = tem,
      P = dep / 10,
      k1k2 = "m10",
      kf = "dg",
      ks = "d",
      gas = "insitu"
    )[, 16] * 1e6
  )

3.2 Plot all profiles

tm_profiles <-  tm_profiles %>%
  arrange(date_time_ID)

p_tem <-
  tm_profiles %>%
  ggplot(aes(tem, dep, col = ID, group = interaction(station, ID))) +
  geom_path() +
  scale_y_reverse(expand = c(0, 0)) +
  labs(x = "Temperature (\u00B0C)",
       y = "Depth (m)") +
  scale_color_viridis_d(guide = FALSE)

p_pCO2 <-
  tm_profiles %>%
  ggplot(aes(pCO2, dep, col = ID, group = interaction(station, ID))) +
  geom_path() +
  scale_y_reverse(expand = c(0, 0)) +
  labs(x = expression(pCO[2] ~ (µatm))) +
  scale_color_viridis_d(guide = FALSE) +
  theme(
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks.y = element_blank()
  )


p_nCT <-
  tm_profiles %>%
  ggplot(aes(nCT, dep, col = ID, group = interaction(station, ID))) +
  geom_path() +
  scale_y_reverse(expand = c(0, 0)) +
  labs(x = expression(paste(C[T], "*", ~ (µmol ~ kg ^ {-1})))) +
  scale_color_viridis_d(labels = cruise_dates$date_ID,
                        name = "Mean\ncruise date") +
  theme(
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank()
  )

p_tem + p_pCO2 + p_nCT +
  plot_annotation(tag_levels = 'a')

ggsave(
  here::here(
    "output/Plots/Figures_publication/article",
    "Fig_3.pdf"
  ),
  width = 150,
  height = 80,
  dpi = 300,
  units = "mm"
)

ggsave(
  here::here(
    "output/Plots/Figures_publication/article",
    "Fig_3.png"
  ),
  width = 150,
  height = 80,
  dpi = 300,
  units = "mm"
)

rm(p_tem, p_pCO2, p_nCT)

Number of profiles:

tm_profiles %>% 
  count(date_ID, station) %>% 
  nrow()
[1] 78

3.3 Mean profiles

Mean vertical profiles were calculated for each cruise day (ID).

tm_profiles_ID_mean <- tm_profiles %>%
  select(-c(station, lat, lon, pCO2_corr, date_time)) %>%
  group_by(ID, date_time_ID, dep) %>%
  summarise_all(list(mean), na.rm = TRUE) %>%
  ungroup()

tm_profiles_ID_sd <- tm_profiles %>%
  select(-c(station, lat, lon, pCO2_corr, date_time)) %>%
  group_by(ID, date_time_ID, dep) %>%
  summarise_all(list(sd), na.rm = TRUE) %>%
  ungroup()

tm_profiles_ID_sd_long <- tm_profiles_ID_sd %>%
  pivot_longer(sal:nCT_test, names_to = "var", values_to = "sd")

tm_profiles_ID_mean_long <- tm_profiles_ID_mean %>%
  pivot_longer(sal:nCT_test, names_to = "var", values_to = "value")

tm_profiles_ID_long_test <-
  inner_join(tm_profiles_ID_mean_long, tm_profiles_ID_sd_long)

tm_profiles_ID_long <- tm_profiles_ID_long_test %>% 
  filter(var != "nCT_test")

tm_profiles_ID_mean_test <- tm_profiles_ID_mean

tm_profiles_ID_mean_test <- tm_profiles_ID_mean_test %>% 
  mutate(nCT_delta = nCT - nCT_test)

tm_profiles_ID_mean <- tm_profiles_ID_mean %>%
  select(-nCT_test)

tm_profiles_ID_mean %>%
  write_csv(here::here("data/intermediate/_merged_data_files/CT_dynamics", "tm_profiles_ID.csv"))

rm(
  tm_profiles_ID_sd_long,
  tm_profiles_ID_sd,
  tm_profiles_ID_mean_long
)
tm_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")
Mean vertical profiles per cruise day across all stations.

Mean vertical profiles per cruise day across all stations.

3.3.1 nCT sensitivity to AT

tm_profiles_ID_mean_test %>%
  ggplot(aes(nCT_delta - mean(nCT_delta), dep, col = ID)) +
  geom_point() +
  geom_path() +
  scale_y_reverse() +
  scale_color_viridis_d()
Mean vertical profiles per cruise day across all stations.

Mean vertical profiles per cruise day across all stations.

profiles_min_max <- tm_profiles %>%
  group_by(dep) %>%
  summarise(max_CT = max(nCT),
            min_CT = min(nCT),
            max_tem = max(tem),
            min_tem = min(tem)) %>%
  ungroup()


p_CT <-
  tm_profiles_ID_long %>%
  filter(var %in% c("nCT")) %>%
  ggplot() +
  geom_ribbon(data = profiles_min_max,
              aes(xmin = min_CT,
                  xmax = max_CT,
                  y = dep),
              alpha = 0.2) +
  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(labels = cruise_dates$date_ID,
                        name = "Cruise mean +/- SD") +
  scale_fill_viridis_d(labels = cruise_dates$date_ID,
                       name = "Cruise mean +/- SD") +
  facet_grid(ID ~ .) +
  labs(x = expression(paste(C[T], "*", ~ (µmol ~ kg ^ {-1}))),
  y = "Depth (m)") +
  theme(strip.background = element_blank(),
        strip.text = element_blank(),
        legend.position = "none")

p_tem <-
  tm_profiles_ID_long %>%
  filter(var %in% c("tem")) %>%
  ggplot() +
  geom_ribbon(data = profiles_min_max,
              aes(xmin = min_tem,
                  xmax = max_tem,
                  y = dep),
              alpha = 0.2) +
  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(labels = cruise_dates$date_ID,
                        name = "Cruise mean +/- SD") +
  scale_fill_viridis_d(labels = cruise_dates$date_ID,
                       name = "Cruise mean +/- SD") +
  facet_grid(ID ~ .) +
  labs(x = "Temperature (\u00B0C)",
       y = "Depth (m)") +
  theme(strip.background = element_blank(),
        strip.text = element_blank(),
        axis.title.y = element_blank(),
        axis.text.y = element_blank())

p_CT | p_tem
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.

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.

# ggsave(
#   here::here(
#     "output/Plots/Figures_publication/appendix",
#     "Fig_A5_V1.pdf"
#   ),
#   width = 140,
#   height = 250,
#   dpi = 300,
#   units = "mm"
# )
# 
# ggsave(
#   here::here(
#     "output/Plots/Figures_publication/appendix",
#     "Fig_A5_V1.png"
#   ),
#   width = 140,
#   height = 250,
#   dpi = 300,
#   units = "mm"
# )
p_CT <-
  tm_profiles %>%
  ggplot() +
  geom_ribbon(data = profiles_min_max,
              aes(xmin = min_CT,
                  xmax = max_CT,
                  y = dep),
              alpha = 0.2) +
   geom_path(aes(nCT, dep, col = station)) +
  scale_y_reverse() +
  facet_grid(ID ~ .) +
  labs(x = expression(paste(C[T], "*", ~ (µmol ~ kg ^ {-1}))),
  y = "Depth (m)") +
  theme(strip.background = element_blank(),
        strip.text = element_blank(),
        legend.position = "none")

cruise_labels <- c(
  `180705` = cruise_dates$date_ID[1],
  `180709` = cruise_dates$date_ID[2],
  `180718` = cruise_dates$date_ID[3],
  `180723` = cruise_dates$date_ID[4],
  `180730` = cruise_dates$date_ID[5],
  `180802` = cruise_dates$date_ID[6],
  `180806` = cruise_dates$date_ID[7],
  `180815` = cruise_dates$date_ID[8]
)

p_tem <-
  tm_profiles %>%
  ggplot() +
  geom_ribbon(data = profiles_min_max,
              aes(xmin = min_tem,
                  xmax = max_tem,
                  y = dep),
              alpha = 0.2) +
  geom_path(aes(tem, dep, col = station)) +
  scale_y_reverse() +
  scale_color_discrete(name = "Station") +
  facet_grid(ID ~ .,
             labeller = labeller(ID = cruise_labels)) +
  labs(x = "Temperature (\u00B0C)",
       y = "Depth (m)") +
  theme(axis.title.y = element_blank(),
        axis.text.y = element_blank())

p_CT | p_tem
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.

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.

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A4.pdf"
  ),
  width = 120,
  height = 200,
  dpi = 300,
  units = "mm"
)

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A4.png"
  ),
  width = 120,
  height = 200,
  dpi = 300,
  units = "mm"
)

rm(p_nCT, p_tem, cruise_labels, profiles_min_max)

Important notes:

  • the standard deviation of CT in the upper 10m increases on June 30.

3.4 Individual profiles

CT, pCO2, S, and T profiles were plotted individually pdf here and grouped by ID pdf here. The later gives an idea of the differences between stations at one point in time.

# tm_profiles_highres <- tm_profiles_highres %>% 
#   filter(phase == "down")

pdf(file=here::here("output/Plots/CT_dynamics",
    "tm_profiles_pCO2_tem_sal_CT.pdf"), onefile = TRUE, width = 9, height = 5)

for(i_ID in unique(tm_profiles$ID)){
  for(i_station in unique(tm_profiles$station)){

    if (nrow(tm_profiles %>% filter(ID == i_ID, station == i_station)) > 0){
      
      # i_ID      <-      unique(tm_profiles$ID)[1]
      # i_station <- unique(tm_profiles$station)[1]

      p_pCO2 <- 
        tm_profiles %>%
        arrange(date_time) %>% 
        filter(ID == i_ID,
               station == i_station) %>%
        ggplot(aes(pCO2, dep, col="grid_RT"))+
        geom_point(aes(pCO2_corr, 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 <- 
        tm_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 <- 
        tm_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_nCT <- 
        tm_profiles %>%
        arrange(date_time) %>% 
        filter(ID == i_ID,
               station == i_station) %>%
        ggplot(aes(nCT, dep))+
        geom_point()+
        geom_path()+
        scale_y_reverse()+
        labs(y="Depth [m]", x="nCT* [µmol/kg]")+
        coord_cartesian(xlim = c(1400,1700), ylim = c(30,0))+
        theme_bw()
      

      print(
            p_pCO2 + p_tem + p_sal + p_nCT
            )
      
      rm(p_pCO2, p_sal, p_tem, p_nCT)

      
    }
  }
}

dev.off()

rm(i_ID, i_station)
tm_profiles_long <- tm_profiles %>%
  select(-c(lat, lon, pCO2_corr)) %>% 
  pivot_longer(sal:nCT, values_to = "value", names_to = "var")


pdf(file=here::here("output/Plots/CT_dynamics",
    "tm_profiles_ID_pCO2_tem_sal_CT.pdf"), onefile = TRUE, width = 9, height = 5)

for(i_ID in unique(tm_profiles$ID)){

  #i_ID <- unique(tm_profiles$ID)[1]
  
  sub_tm_profiles_long <- tm_profiles_long %>% 
        arrange(date_time) %>% 
        filter(ID == i_ID)
 
  print(
  
  sub_tm_profiles_long %>% 
    ggplot()+
    geom_path(data = tm_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_tm_profiles_long)
}

dev.off()

rm(i_ID, tm_profiles_long)

3.5 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.

tm_profiles_ID_long <- tm_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()
tm_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")

3.6 Profiles of cumulative changes

Cumulative changes of seawater vars were calculated at each depth relative to the first cruise day on July 5.

tm_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.

tm_profiles_ID_long <- tm_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()
tm_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")

4 Timeseries

4.1 Timeseries depth intervals

Mean seawater parameters were calculated for 5m depth intervals.

tm_profiles_ID_long_grid <- tm_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) %>% 
  ungroup()

tm_profiles_ID_long_grid %>%
  ggplot(aes(date_time_ID, value, col = as.factor(dep))) +
  geom_path() +
  geom_point() +
  scale_color_viridis_d(name = "Depth (m)") +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  facet_wrap( ~ var, scales = "free_y", ncol = 1) +
  theme(axis.title.x = element_blank())

tm_profiles_ID_long_grid %>%
  mutate(value = round(value, 1),
         date_ID = as.Date(date_time_ID)) %>%
  select(date_ID, dep, var, value) %>%
  pivot_wider(values_from = value, names_from = var) %>%
  kable() %>%
  add_header_above() %>%
  kable_styling(full_width = FALSE) %>% 
  scroll_box(height = "400px")
date_ID dep nCT pCO2 sal tem
2018-07-06 (0,5] 1528.4 98.3 6.9 15.4
2018-07-06 (5,10] 1541.9 106.0 6.9 14.7
2018-07-06 (10,15] 1562.9 123.3 6.9 14.1
2018-07-06 (15,20] 1575.0 136.5 7.0 13.9
2018-07-06 (20,25] 1589.1 153.5 7.0 13.7
2018-07-10 (0,5] 1500.3 86.1 6.9 17.0
2018-07-10 (5,10] 1517.0 93.4 6.9 15.9
2018-07-10 (10,15] 1561.0 124.6 6.9 14.4
2018-07-10 (15,20] 1584.4 148.5 6.9 14.0
2018-07-10 (20,25] 1596.1 163.8 7.0 13.5
2018-07-19 (0,5] 1466.8 79.1 6.9 20.5
2018-07-19 (5,10] 1479.3 81.5 7.0 19.0
2018-07-19 (10,15] 1553.9 124.4 7.0 15.5
2018-07-19 (15,20] 1586.9 155.0 7.1 14.4
2018-07-19 (20,25] 1597.8 168.3 7.2 13.9
2018-07-24 (0,5] 1439.9 69.0 7.0 21.5
2018-07-24 (5,10] 1453.4 73.2 7.0 20.7
2018-07-24 (10,15] 1565.5 141.8 7.0 15.8
2018-07-24 (15,20] 1609.3 190.8 7.1 14.3
2018-07-24 (20,25] 1618.7 206.1 7.1 13.6
2018-07-31 (0,5] 1474.7 100.3 6.8 24.3
2018-07-31 (5,10] 1484.4 99.2 6.8 22.3
2018-07-31 (10,15] 1582.6 165.6 6.9 16.0
2018-07-31 (15,20] 1626.7 226.6 7.0 14.0
2018-07-31 (20,25] 1645.5 277.0 7.1 13.0
2018-08-03 (0,5] 1458.2 91.1 6.9 24.9
2018-08-03 (5,10] 1471.5 93.4 6.9 23.2
2018-08-03 (10,15] 1590.1 177.3 6.9 15.9
2018-08-03 (15,20] 1634.9 246.1 6.9 13.9
2018-08-03 (20,25] 1651.5 290.6 7.0 12.8
2018-08-07 (0,5] 1473.1 92.1 6.9 23.0
2018-08-07 (5,10] 1483.4 98.6 6.9 22.5
2018-08-07 (10,15] 1605.2 200.1 6.9 15.5
2018-08-07 (15,20] 1638.8 257.3 7.0 14.0
2018-08-07 (20,25] 1650.7 291.8 7.1 12.8
2018-08-16 (0,5] 1555.9 140.6 7.0 18.6
2018-08-16 (5,10] 1561.3 146.8 7.0 18.5
2018-08-16 (10,15] 1581.8 173.5 7.0 17.7
2018-08-16 (15,20] 1638.9 276.9 7.0 14.8
2018-08-16 (20,25] 1679.2 404.0 7.1 11.6
rm(tm_profiles_ID_long_grid)

4.1.1 Test AT sensitivity

Mean seawater CT were calculated for 5m depth intervals based on two AT values.

tm_profiles_ID_long_grid <- tm_profiles_ID_long_test %>%
  mutate(dep = cut(dep, seq(0, 30, 5))) %>%
  group_by(ID, date_time_ID, dep, var)  %>%
  summarise_all(list(mean), na.rm = TRUE)

tm_profiles_ID_long_grid %>%
  filter(var %in% c("nCT", "nCT_test")) %>% 
  ggplot(aes(date_time_ID, value, col = as.factor(dep))) +
  geom_path() +
  geom_point() +
  scale_color_viridis_d(name = "Depth (m)") +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  facet_wrap( ~ var, scales = "free_y", ncol = 1) +
  theme(axis.title.x = element_blank())

rm(tm_profiles_ID_long_grid)

Calculate CT* changes for range of AT errors

nCT_sens <- tm_profiles %>%
  filter(dep < parameters$surface_dep,
         date_ID %in% c("Jul 06", "Jul 24")) %>%
  select(date_ID, tem, pCO2) %>%
  group_by(date_ID) %>%
  summarise_all(mean, na.rm = TRUE) %>%
  ungroup()

nCT_sens <- expand_grid(nCT_sens, factor = seq(-3, 3, 0.2))

nCT_sens <- nCT_sens %>%
  mutate(AT = (AT_mean + factor * AT_sd) * 1e-6)

nCT_sens <- nCT_sens %>%
  mutate(
    nCT = carb(
      24,
      var1 = pCO2,
      var2 = AT,
      S = sal_mean,
      T = tem,
      k1k2 = "m10",
      kf = "dg",
      ks = "d",
      gas = "insitu"
    )[, 16] * 1e6
  )

nCT_sens <- nCT_sens %>%
  mutate(AT = AT * 1e6) %>%
  select(date_ID, factor, AT, nCT) %>%
  pivot_wider(names_from = "date_ID",
              values_from = c("nCT"))

nCT_sens <- nCT_sens %>%
  mutate(nCT_delta = `Jul 24` - `Jul 06`) %>%
  select(factor, AT, nCT_delta)

nCT_delta_mean <- nCT_sens %>%
  filter(factor == 0) %>%
  pull(nCT_delta)

nCT_sens <- nCT_sens %>%
  mutate(nCT_delta_offset = nCT_delta - nCT_delta_mean,
         nCT_delta_offset_rel = nCT_delta / nCT_delta_mean *100,
         AT_offset = AT - AT_mean)

nCT_delta_sd <- nCT_sens %>%
  filter(factor == 1) %>% 
  pull(nCT_delta_offset)


nCT_sens %>%
  ggplot(aes(AT_offset, nCT_delta_offset)) +
  annotate(
    "rect",
    xmin = -AT_sd,
    xmax = +AT_sd,
    ymin = -Inf,
    ymax = Inf,
    alpha = 0.3
  ) +
  annotate(
    "rect",
    xmin = -Inf,
    xmax = Inf,
    ymin = -nCT_delta_sd,
    ymax = +nCT_delta_sd,
    alpha = 0.3
  ) +
  geom_vline(xintercept = 0, linetype = 2) +
  geom_hline(yintercept = 0, linetype = 2) +
  geom_line(col="red") +
  scale_y_continuous(
    expression(paste(
      "Absolute bias ", Delta ~ C[T], "*",  ~ (µmol ~ kg ^ {
        -1
      })
    )),
    sec.axis = sec_axis(
      ~ . / nCT_delta_mean * 100,
      name = expression(paste("Relative bias ", Delta ~ C[T], "* (%)")),
      breaks = seq(-10, 10, 1)
    )
  ) +
  scale_x_continuous(expression(paste("Absolute bias ", A[T]  ~ (µmol ~ kg ^ {
    -1
  }))),
  sec.axis = sec_axis(~ . / AT_mean * 100,
                      name = expression(paste(
                        "Relative bias ", A[T], " (%)")),
      breaks = seq(-10, 10, 1)))

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A6.pdf"
  ),
  width = 83,
  height = 60,
  dpi = 300,
  units = "mm"
)

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A6.png"
  ),
  width = 83,
  height = 60,
  dpi = 300,
  units = "mm"
)

4.2 Hovmoeller plots

4.2.1 Absolute values

bin_nCT <- 30

p_nCT_hov <- tm_profiles_ID_long %>%
  filter(var == "nCT") %>%
  ggplot() +
  geom_contour_fill(aes(x = date_time_ID, y = dep, z = value),
                    breaks = MakeBreaks(bin_nCT),
                    col = "black") +
  geom_point(
    aes(x = date_time_ID, y = c(24.5)),
    size = 3,
    shape = 24,
    fill = "white"
  ) +
  scale_fill_scico(
    breaks = MakeBreaks(bin_nCT),
    guide = "colorstrip",
    name = "nCT (µmol/kg)",
    palette = "davos",
    direction = -1
  ) +
  scale_y_reverse() +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  theme_bw() +
  labs(y = "Depth (m)") +
  coord_cartesian(expand = 0) +
  theme(axis.title.x = element_blank(),
        legend.position = "left")

bin_Tem <- 2

p_tem_hov <- tm_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() +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  labs(y = "Depth (m)") +
  coord_cartesian(expand = 0) +
  theme(axis.title.x = element_blank(),
        legend.position = "left")

p_nCT_hov / p_tem_hov
Hovmoeller plotm of absolute changes in C~T~ and temperature.

Hovmoeller plotm of absolute changes in CT and temperature.

rm(p_nCT_hov, bin_nCT, p_tem_hov, bin_Tem)

4.2.2 Incremental changes

bin_nCT <- 2.5

nCT_hov <- tm_profiles_ID_long %>%
  filter(var == "nCT") %>%
  ggplot() +
  geom_contour_fill(
    aes(x = date_time_ID_ref, y = dep, z = value_diff_daily),
    breaks = MakeBreaks(bin_nCT),
    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_nCT),
                       guide = "colorstrip",
                       name = "nCT (µmol/kg)") +
  scale_y_reverse() +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  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 <- tm_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() +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  theme_bw() +
  labs(x = "", y = "Depth (m)") +
  coord_cartesian(expand = 0)

nCT_hov / Tem_hov
Hovmoeller plotm 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.

Hovmoeller plotm of daily changes in CT 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.

rm(nCT_hov, bin_nCT, Tem_hov, bin_Tem)

4.2.3 Cumulative changes

bin_nCT <- 20

nCT_hov <- tm_profiles_ID_long %>%
  filter(var == "nCT") %>%
  ggplot() +
  geom_contour_fill(
    aes(x = date_time_ID, y = dep, z = value_cum),
    breaks = MakeBreaks(bin_nCT),
    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_nCT),
                       guide = "colorstrip",
                       name = "nCT (µmol/kg)") +
  scale_y_reverse() +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  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 <- tm_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() +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  theme_bw() +
  labs(x = "", y = "Depth (m)") +
  coord_cartesian(expand = 0)

nCT_hov / Tem_hov
Hovmoeller plotm of cumulative changes in C~T~ and temperature.

Hovmoeller plotm of cumulative changes in CT and temperature.

rm(nCT_hov, bin_nCT, Tem_hov, bin_Tem)

5 Depth-integration CT

A critical first step for the determination of net community production (NCP) is the integration of observed changes in nCT over depth. Two approaches were tested:

  • Integration of changes in nCT over a predefined, fixed water depth
  • Integration of changes in nCT over a mixed layer depth (MLD)

Both aproaches deliver depth-integrated, incremental changes of CT inbetween cruise dates. Those were summed up to derive a trajectory of cummulative integrated nCT changes.

5.1 Fixed depths approach

Incremental and cumulative nCT 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 CT, as well as for the total observed changes.

Predefined integration depth levels in metres are: 9, 10, 11, 12, 13

5.1.1 Calculate inCT

inCT_grid_sign <- tm_profiles_ID_long %>% 
  select(ID, date_time_ID, date_time_ID_ref) %>% 
  unique() %>% 
  expand_grid(sign = c("pos", "neg"))

inCT_grid_total <- tm_profiles_ID_long %>% 
  select(ID, date_time_ID, date_time_ID_ref) %>% 
  unique() %>% 
  expand_grid(sign = c("total"))

for (i_dep in parameters$fixed_integration_depths) {

inCT_sign_temp <- tm_profiles_ID_long %>% 
  filter(var == "nCT", dep < i_dep) %>% 
  mutate(sign = if_else(ID == "180705" & dep == 0.5, "neg", sign)) %>% 
  group_by(ID, date_time_ID, date_time_ID_ref, sign) %>% 
  summarise(nCT_i_diff = sum(value_diff)/1000) %>% 
  ungroup()

inCT_sign_temp <- inCT_sign_temp %>% 
  group_by(sign) %>%
  arrange(date_time_ID) %>% 
  mutate(nCT_i_cum = cumsum(nCT_i_diff)) %>% 
  ungroup()

inCT_sign_temp <- full_join(inCT_sign_temp, inCT_grid_sign) %>% 
  arrange(sign, date_time_ID) %>% 
  fill(nCT_i_cum)


inCT_total_temp <- tm_profiles_ID_long %>% 
  filter(var == "nCT", dep < i_dep) %>% 
  group_by(ID, date_time_ID, date_time_ID_ref) %>% 
  summarise(nCT_i_diff = sum(value_diff)/1000) %>% 
  ungroup()

inCT_total_temp <- inCT_total_temp %>% 
  arrange(date_time_ID) %>% 
  mutate(nCT_i_cum = cumsum(nCT_i_diff)) %>% 
  ungroup() %>% 
  mutate(sign = "total")

inCT_total_temp <- full_join(inCT_total_temp, inCT_grid_total) %>% 
  arrange(sign, date_time_ID) %>% 
  fill(nCT_i_cum)

inCT_temp <- bind_rows(inCT_sign_temp, inCT_total_temp) %>% 
    mutate(i_dep = i_dep)


if (exists("inCT")) {
  inCT <- bind_rows(inCT, inCT_temp)
  } else {inCT <- inCT_temp}

rm(inCT_temp, inCT_sign_temp, inCT_total_temp)

}

rm(inCT_grid_sign, inCT_grid_total)

inCT <- inCT %>% 
  mutate(i_dep = as.factor(i_dep))

inCT_fixed_dep <- inCT
rm(inCT, i_dep)

5.1.2 Time series

inCT_fixed_dep %>%
  ggplot() +
  geom_point(data = cruise_dates, aes(date_time_ID, 0), shape = 21) +
  geom_col(
    aes(date_time_ID_ref, nCT_i_diff, fill = i_dep),
    position = "dodge",
    alpha = 0.3
  ) +
  geom_line(aes(date_time_ID, nCT_i_cum, col = i_dep)) +
  scale_color_viridis_d(name = "Depth limit (m)") +
  scale_fill_viridis_d(name = "Depth limit (m)") +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  labs(y = "inCT (mol/m2)", x = "") +
  facet_grid(sign ~ ., scales = "free_y", space = "free_y") +
  theme_bw()

5.2 MLD approach

As an alternative to fixed depth levels, vertical integration as low as the mixed layer depth was tested.

5.2.1 Density calculation

Seawater density Rho was determined from S, T, and p according to TEOS-10.

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

5.2.2 Density profiles

tm_profiles_ID_mean_hydro <- tm_profiles %>%
  select(-c(station, lat, lon, pCO2_corr, pCO2, nCT, date_time)) %>%
  group_by(ID, date_time_ID, date_ID, dep) %>%
  summarise_all(list(mean), na.rm = TRUE) %>%
  ungroup()

tm_profiles_ID_sd_hydro <- tm_profiles %>%
  select(-c(station, lat, lon, pCO2_corr, pCO2, nCT, date_time)) %>%
  group_by(ID, date_time_ID, date_ID, dep) %>%
  summarise_all(list(sd), na.rm = TRUE) %>%
  ungroup()

tm_profiles_ID_sd_hydro_long <- tm_profiles_ID_sd_hydro %>%
  pivot_longer(sal:rho, names_to = "var", values_to = "sd")

tm_profiles_ID_mean_hydro_long <- tm_profiles_ID_mean_hydro %>%
  pivot_longer(sal:rho, names_to = "var", values_to = "value")

tm_profiles_ID_hydro_long <-
  inner_join(tm_profiles_ID_mean_hydro_long,
             tm_profiles_ID_sd_hydro_long)
tm_profiles_ID_hydro <- tm_profiles_ID_mean_hydro

rm(
  tm_profiles_ID_mean_hydro_long,
  tm_profiles_ID_mean_hydro,
  tm_profiles_ID_sd_hydro_long,
  tm_profiles_ID_sd_hydro
)
tm_profiles_ID_hydro_long %>%
  ggplot(aes(value, dep, col = ID)) +
  geom_point() +
  geom_path() +
  scale_y_reverse() +
  scale_color_viridis_d() +
  facet_wrap( ~ var, scales = "free_x")
Mean vertical profiles per cruise day across all stations.

Mean vertical profiles per cruise day across all stations.

5.2.3 MLD calculation

Mixed layer depth (MLD) was determined based on the difference between density at the surface and at depth, for a range of density criteria

tm_profiles_ID_hydro <- expand_grid(tm_profiles_ID_hydro, rho_lim = c(0.1,0.2,0.5))

MLD <- tm_profiles_ID_hydro  %>% 
  arrange(dep) %>% 
  group_by(ID, date_time_ID, rho_lim) %>% 
  mutate(d_rho = rho - first(rho)) %>% 
  filter(d_rho > rho_lim) %>% 
  summarise(MLD = min(dep)) %>% 
  ungroup()

5.2.4 Daily density profiles

tm_profiles_ID_hydro <-
  full_join(tm_profiles_ID_hydro, MLD)

tm_profiles_ID_hydro %>%
  arrange(dep) %>%
  ggplot(aes(rho, dep)) +
  geom_hline(aes(yintercept = MLD, col = as.factor(rho_lim))) +
  geom_path() +
  scale_y_reverse() +
  scale_color_brewer(palette = "Set1", name = "Rho limit") +
  facet_wrap( ~ ID) +
  theme_bw()
Mean density profiles and MLD per cruise dates (ID).

Mean density profiles and MLD per cruise dates (ID).

5.2.5 MLD timeseries

MLD %>%
  ggplot(aes(date_time_ID, MLD, col = as.factor(rho_lim))) +
  geom_hline(yintercept = 0) +
  geom_point() +
  geom_path() +
  scale_color_brewer(palette = "Set1", name = "Rho limit") +
  scale_y_reverse() +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  labs(x = "")

5.2.6 inCT calculation

inCT <- tm_profiles_ID_long %>% 
  filter(var == "nCT")

inCT <- full_join(inCT, MLD)

inCT <- inCT %>% 
  filter(dep <= MLD)

inCT <- inCT %>% 
  group_by(ID, date_time_ID, date_time_ID_ref, rho_lim) %>% 
  summarise(nCT_i_diff = sum(value_diff)/1000) %>% 
  ungroup()

inCT <- inCT %>% 
  group_by(rho_lim) %>% 
  arrange(date_time_ID) %>% 
  mutate(nCT_i_cum = cumsum(nCT_i_diff)) %>% 
  ungroup()

inCT <- inCT %>% 
  mutate(rho_lim = as.factor(rho_lim))

inCT_MLD <- inCT

rm(inCT, MLD, tm_profiles_ID_hydro, tm_profiles_ID_hydro_long)

5.2.7 Time series

inCT_MLD %>%
  ggplot() +
  geom_point(data = cruise_dates, aes(date_time_ID, 0), shape = 21) +
  geom_col(
    aes(date_time_ID_ref, nCT_i_diff, fill = rho_lim),
    position = "dodge",
    alpha = 0.3
  ) +
  geom_line(aes(date_time_ID, nCT_i_cum, col = rho_lim)) +
  scale_color_viridis_d(name = "Rho limit") +
  scale_fill_viridis_d(name = "Rho limit") +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  labs(y = "inCT [mol/m2]", x = "") +
  theme_bw()

5.3 Comparison of approaches

In the following, all cummulative iCT trajectories are displayed. Highlighted are those obtained for the fixed depth approach with 10 m limit, and the MLD approach with a high density threshold of 0.5 kg/m3.

inCT <- full_join(inCT_fixed_dep, inCT_MLD)

inCT <- inCT %>%
  mutate(group = paste(
    as.character(sign),
    as.character(i_dep),
    as.character(rho_lim)
  ))

inCT %>%
  arrange(date_time_ID) %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_point(data = cruise_dates, aes(date_time_ID, 0), shape = 21) +
  geom_line(aes(date_time_ID, nCT_i_cum,
                group = group), col = "grey") +
  geom_line(
    data = inCT_fixed_dep %>% filter(i_dep == 12, sign == "total"),
    aes(date_time_ID, nCT_i_cum, col = "12m - total")
  ) +
  geom_line(data = inCT_MLD %>% filter(rho_lim == 0.1),
            aes(date_time_ID, nCT_i_cum, col = "MLD - 0.1")) +
  scale_color_brewer(palette = "Set1", name = "") +
  scale_x_datetime(breaks = "week", date_labels = "%d %b") +
  labs(y = "inCT [mol/m2]", x = "")

rm(inCT, inCT_MLD)

6 NCP determination

In order to derive an estimate of the net community production NCP (which is equivalent to the formed organic matter that can be exported from the investigated surface layer), two steps are required:

  • decision about the most appropiate iCT trajectory
  • correction of quantifyable CO2 fluxes in and out of the investigated water volume during the period of interest, this includes:
    • Air-sea CO2 fluxes
    • CO2 fluxes due to vertical mixing
    • CO2 fluxes due to lateral transport of water masses (not corrected here)

6.1 Best iCT estimate

To determine the optimum depth for the nCT integration we investigated the vertical distribution of cumulative temperature and nCT changes on the peak of the productivity signal on June 23:

tm_profiles_ID_long_180723 <- tm_profiles_ID_long %>%
  filter(ID == 180723,
         var == "nCT")

p_tm_profiles_ID_long <- tm_profiles_ID_long_180723 %>%
  arrange(dep) %>%
  ggplot(aes(value_cum, dep)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 12, col = "red") +
  geom_point() +
  geom_path() +
  scale_y_reverse() +
  labs(x = "Cumulative change of nCT on July 23 (180723)") +
  theme(legend.position = "left")

tm_profiles_ID_long_180723_dep <- tm_profiles_ID_long_180723 %>%
  select(dep, value_cum) %>%
  filter(value_cum < 0) %>%
  arrange(dep) %>%
  mutate(
    value_cum_i = sum(value_cum),
    value_cum_dep = cumsum(value_cum),
    value_cum_i_rel = value_cum_dep / value_cum_i * 100
  )

p_tm_profiles_ID_long_rel <- tm_profiles_ID_long_180723_dep %>%
  ggplot(aes(value_cum_i_rel, dep)) +
  geom_hline(yintercept = 12, col = "red") +
  geom_vline(xintercept = 90) +
  geom_point() +
  geom_line() +
  scale_y_reverse(limits = c(25, 0)) +
  scale_x_continuous(breaks = seq(0, 100, 10)) +
  labs(y = "Depth (m)", x = "Relative contribution on July 23") +
  theme_bw()

p_tm_profiles_ID_long + p_tm_profiles_ID_long_rel

rm(
  tm_profiles_ID_long_180723,
  tm_profiles_ID_long_180723_dep,
  p_tm_profiles_ID_long,
  p_tm_profiles_ID_long_rel
)
tm_profiles_ID_long_180723 <- tm_profiles_ID_long %>%
  filter(ID == 180723,
         var == "tem")

p_tm_profiles_ID_long <- tm_profiles_ID_long_180723 %>%
  arrange(dep) %>%
  ggplot(aes(value_cum, dep)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 12, col = "red") +
  geom_point() +
  geom_path() +
  scale_y_reverse() +
  labs(x = "Cumulative change of Temp on July 23") +
  theme(legend.position = "left")

tm_profiles_ID_long_180723_dep <- tm_profiles_ID_long_180723 %>%
  select(dep, value_cum) %>%
  filter(value_cum > 0) %>%
  arrange(dep) %>%
  mutate(
    value_cum_i = sum(value_cum),
    value_cum_dep = cumsum(value_cum),
    value_cum_i_rel = value_cum_dep / value_cum_i * 100
  )

p_tm_profiles_ID_long_rel <- tm_profiles_ID_long_180723_dep %>%
  ggplot(aes(value_cum_i_rel, dep)) +
  geom_hline(yintercept = 12, col = "red") +
  geom_vline(xintercept = 90) +
  geom_point() +
  geom_line() +
  scale_y_reverse(limits = c(25, 0)) +
  scale_x_continuous(breaks = seq(0, 100, 10)) +
  labs(y = "Depth (m)", x = "Relative contribution on July 23") +
  theme_bw()

p_tm_profiles_ID_long + p_tm_profiles_ID_long_rel

rm(
  tm_profiles_ID_long_180723,
  tm_profiles_ID_long_180723_dep,
  p_tm_profiles_ID_long,
  p_tm_profiles_ID_long_rel
)

The cummulative iCT trajectory determined by integration of CT to a fixed water depth of 12 m was used for NCP calculation for the following reasons:

  • During the first productivity pulse that lasted until July 23:
    • no negative nCT changes were detected below that depth
    • cumulative nCT switch sign at that depth
    • 95% of the cumulative warming signal appears across that depth
  • MLD were too shallow to cover all observed negative CT changes

6.2 Air-Sea CO2 flux

6.2.1 Surface water data

The cruise mean pCO2 recorded in profiling-mode (stations only) and depths < 6m was used for gas exchange calcualtions.

tm_profiles_surface_long <- tm_profiles %>%
  filter(dep < parameters$surface_dep) %>%
  select(date_time = date_time_ID, ID, tem, pCO2 = pCO2, nCT) %>%
  pivot_longer(tem:nCT, values_to = "value", names_to = "var")

tm_profiles_surface_long_ID <- tm_profiles_surface_long %>%
  group_by(ID, date_time, var) %>%
  summarise_all(list( ~ mean(.), ~ sd(.), ~ min(.), ~ max(.))) %>%
  ungroup()

rm(tm_profiles_surface_long)

p_pCO2_surf <- tm_profiles_surface_long_ID %>%
  filter(var == "pCO2") %>%
  ggplot(aes(x = date_time)) +
  geom_ribbon(aes(ymin = mean - sd, ymax = mean + sd), alpha = 0.2) +
  geom_path(aes(y = mean)) +
  geom_point(aes(y = mean)) +
  scale_fill_discrete(guide = FALSE) +
  scale_x_datetime(date_breaks = "week",
                   sec.axis = dup_axis()) +
  labs(y = expression(atop(pCO[2], (mu * atm))),
       title = "Surface water observations") +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank())

p_tem_surf <- tm_profiles_surface_long_ID %>%
  filter(var == "tem") %>%
  ggplot(aes(x = date_time)) +
  geom_ribbon(aes(ymin = mean - sd, ymax = mean + sd), alpha = 0.2) +
  geom_path(aes(y = mean)) +
  geom_point(aes(y = mean)) +
  scale_fill_discrete(guide = FALSE) +
  scale_x_datetime(date_breaks = "week",
                   sec.axis = dup_axis()) +
  labs(y = expression(atop("Temperature","(\u00B0C)"))) +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank())

p_nCT_surf <-
  tm_profiles_surface_long_ID %>%
  filter(var == "nCT") %>%
  ggplot() +
  geom_point(data = tb_surface_station_mean %>%
               filter(var == "nCT"),
             aes(x = date_time_ID,
                 y = value_mean,
                 color = "discrete")) +
  geom_linerange(
    data = tb_surface_station_mean %>%
      filter(var == "nCT"),
    aes(
      x = date_time_ID,
      ymin = value_mean - value_sd,
      ymax = value_mean + value_sd,
      color = "discrete"
    )
  ) +
  geom_ribbon(aes(
    x = date_time,
    ymin = mean - sd,
    ymax = mean + sd
  ), alpha = 0.2) +
  geom_path(aes(x = date_time, y = mean)) +
  geom_point(aes(x = date_time, y = mean)) +
  scale_color_manual(values = "red") +
  scale_x_datetime(date_breaks = "week",
                   sec.axis = dup_axis()) +
  labs(y = expression(atop(paste(C[T],"*"),
                           (mu * mol ~ kg ^ {
                             -1
                           })))) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    legend.position = c(0.35, 0.75),
    legend.title = element_blank(),
    legend.direction = "horizontal",
    legend.background = element_rect(fill = "transparent"),
    legend.key = element_rect(colour = "black", fill = "white"),
    legend.key.height = unit(4, "mm"),
    legend.key.width = unit(4,"mm") 
  )


p_pCO2_surf + p_tem_surf + p_nCT_surf +
  plot_layout(ncol = 1)

start <- min(tm_profiles_surface_long_ID$date_time)
end   <- max(tm_profiles_surface_long_ID$date_time)

6.2.2 Wind and atm. pCO2

Metrological data were recorded on the flux tower located on Ostergarnsholm island.

og <-
  read_csv(here::here("data/intermediate/_summarized_data_files",
                      "og.csv"))

og <- og %>%
  filter(date_time > start,
         date_time < end)

rm(end, start)

6.2.3 Conversion to U10

Wind speed was determined at 12 and converted to 10 m above sea level, to be used for gas exchange calculation.

og <- og %>%
  mutate(wind = wind.scale.base(wnd = wind, wnd.z = 12))

Data sets for atmospheric and seawater observations were merged and interpolated to a common time stamp.

tm_profiles_surface_ID <- tm_profiles_surface_long_ID %>%
  filter(var %in% c("pCO2", "tem")) %>%
  select(date_time:mean) %>%
  pivot_wider(names_from = "var", values_from = "mean")

rm(tm_profiles_surface_long_ID)

tm_og <- full_join(og, tm_profiles_surface_ID) %>%
  arrange(date_time)

tm_og <- tm_og %>%
  mutate(
    pCO2 = approxfun(date_time, pCO2)(date_time),
    tem = approxfun(date_time, tem)(date_time),
    wind = approxfun(date_time, wind)(date_time)
  ) %>%
  filter(!is.na(pCO2_atm))

rm(tm_profiles_surface_ID, og)
rolling_mean   <- rollify( ~ mean(.x, na.rm = TRUE), window = 48)

tm_og <- tm_og %>%
  mutate(wind_daily = rolling_mean(wind),
         pCO2_atm_daily = rolling_mean(pCO2_atm))
p_pCO2_atm <- tm_og %>%
  ggplot(aes(x = date_time)) +
  geom_path(aes(y = pCO2_atm)) +
  scale_x_datetime(date_breaks = "week",
                   sec.axis = dup_axis()) +
  labs(y = expression(atop(pCO["2,atm"], (mu * atm))),
       title = "Atmospheric observations") +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank())

p_wind <- tm_og %>%
  ggplot(aes(x = date_time)) +
  geom_path(aes(y = wind)) +
  scale_x_datetime(date_breaks = "week",
                   sec.axis = dup_axis()) +
  labs(y = expression(atop(Wind~speed, (m ~ s ^ {
    -1
  })))) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    legend.title = element_blank()
  )

p_pCO2_atm + p_wind +
  plot_layout(ncol = 1) +
  plot_layout(guides = 'collect')

6.2.4 Air-sea fluxes

F = k * dCO2

with

dCO2 = K0 * dpCO2 and

k = coeff * U^2 * (660/Sc)^0.5

Unitm used here are:

  • dpCO2: µatm

  • K0: mol atm-1 kg-1

  • dCO2: µmol kg-1

  • wind speed U: m s-1

  • coeff for k calculation (eg 0.251 in W14): cm hr-1 (m s-1)-2

  • gas transfer velocities k: cm hr-1 (= 60 x 60 x 100 m s-1)

  • air sea CO2 flux F: mol m–2 d–1

  • conversion between the unit of k * dCO2 and F requires a factor of 10-5 * 24

Sc_W14 <- function(tem) {
  2116.8 - 136.25 * tem + 4.7353 * tem ^ 2 - 0.092307 * tem ^ 3 + 0.0007555 * tem ^
    4
}

# Sc_W14(20)

tm_og <- tm_og %>%
  mutate(
    dpCO2 = pCO2 - pCO2_atm,
    dCO2  = dpCO2 * K0(S = 6.92, T = tem),
    # W92 = gas_transfer(t = tem, u10 = wind, species = "CO2",
    #                      method = "Wanninkhof1")* 60^2 * 100,
    #k_SM18 = 0.24 * wind^2 * ((1943-119.6*tem + 3.488*tem^2 - 0.0417*tem^3) / 660)^(-0.5),
    k = 0.251 * wind ^ 2 * (Sc_W14(tem) / 660) ^ (-0.5)
  )
# pivot_longer(9:10, names_to = "k_para", values_to = "k_value")

# calculate flux F [mol m–2 d–1]

tm_og <- tm_og %>%
  mutate(flux = k * dCO2 * 1e-5 * 24)
#         flux_daily = rolling_mean(flux))

rm(Sc_W14)
p_flux_daily <- tm_og %>%
  ggplot(aes(x = date_time)) +
  geom_path(aes(y = flux)) +
  # geom_path(aes(y=flux_daily, col="24h"))+
  # scale_color_brewer(palette = "Set1")+
  scale_x_datetime(date_breaks = "week",
                   sec.axis = dup_axis()) +
  labs(y = expression(atop(F["daily"], (mol ~ m ^ {
    -2
  } ~ d ^ {
    -1
  }))),
  title = "Air-sea fluxes") +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    legend.title = element_blank()
  )
# scale flux to time interval

tm_og <- tm_og %>%
  mutate(scale = 24 * 2) %>%
  mutate(flux_scale = flux / scale) %>%
  arrange(date_time) %>%
  mutate(flux_cum = cumsum(flux_scale)) %>%
  ungroup()

p_flux_cum <- tm_og %>%
  ggplot(aes(x = date_time)) +
  geom_path(aes(y = flux_cum)) +
  scale_fill_discrete(guide = FALSE) +
  scale_x_datetime(date_breaks = "week",
                   sec.axis = dup_axis()) +
  labs(y = expression(atop(F["cumulative"],
                           (mol ~ m ^ {
                             -2
                           })))) +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank())

p_flux_daily + p_flux_cum +
  plot_layout(ncol = 1)

6.3 iCT correction

The cumulative integrated nCT (inCT) time series obtained through integration across the upper 12m of the water column was used for further calculations of NCP.

Correction of inCT for air-sea CO2 fluxes will be based on estimates derived from observation with 30min measurement interval and calculation according to Wanninkhof (2014).

To derive an integrated NCP estimated, the observed change in inCT must be corrected for the air-sea flux of CO2. inCT was determined for the upper 12m of the water column. The MLD was always shallower 12m, except for the last cruise day. Therefore:

  • Cumulative air-sea fluxes can be added completely to inCT before Aug 7.
  • Between Aug 7 and the last cruise on Aug 15 it was assumed, that the CO2 flux was homogenously mixed down to the deepend thermocline at 17m. The flux correction applied to the upper 12m can therefore be scaled with a factor 12/17.

During the last cruise, deeper mixing up to 17m water depth was observed, resulting in increased inCT at 0-12 m and a decrease of inCT in 12-17m. The loss of nCT in 12-17m can be assumed to be entirely cause by mixing with low-nCT surface water. However, some of the observed nCT loss is balanced through nCT input attributable to the air-sea flux. Therefore, the observed loss, corrected for 5/17 of the air-sea-flux, was added to the integrated nCT changes in 0-12m.

# extract CT data for fixed depth approach, depth limit 10m
NCP <- inCT_fixed_dep %>%
  filter(i_dep == parameters$i_dep_lim, sign == "total") %>%
  select(-c(sign, i_dep))

rm(inCT_fixed_dep)

NCP <- NCP %>%
  select(ID, date_time = date_time_ID, date_time_ID_ref, nCT_i_diff, nCT_i_cum)

# date of the second last cruise
date_180806 <- unique(NCP$date_time)[7]

6.3.1 Air-sea fluxes

# calculate cumulative air-sea fluxes affecting surface water column
tm_og_flux <- tm_og %>%
  mutate(
    flux_scale = if_else(
      date_time > date_180806,
      parameters$i_dep_lim / parameters$i_dep_mix_lim * flux_scale,
      flux_scale
    )
  ) %>%
  arrange(date_time) %>%
  mutate(flux_cum = cumsum(flux_scale)) %>%
  select(date_time, flux_cum)

# calculate cumulative air-sea fluxes affecting deepened mixed layer
tm_og_flux_dep <- tm_og %>%
  filter(date_time > date_180806) %>%
  mutate(
    flux_scale =
      (parameters$i_dep_mix_lim - parameters$i_dep_lim) / parameters$i_dep_mix_lim * flux_scale
  ) %>%
  arrange(date_time) %>%
  mutate(flux_cum = cumsum(flux_scale)) %>%
  select(date_time, flux_cum)

NCP_flux <- full_join(NCP, tm_og_flux) %>%
  arrange(date_time)

rm(tm_og_flux, NCP, tm_og)


# linear interpolation of cumulative changes to frequency of the flux estimates estimates
NCP_flux <- NCP_flux %>%
  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)


# calculate cumulative fluxes inbetween cruises
NCP_flux_diff <- NCP_flux %>%
  filter(!is.na(date_time_ID_ref)) %>%
  mutate(flux_diff = flux_cum - lag(flux_cum, default = 0)) %>%
  select(ID, date_time_ID_ref, observed = nCT_i_diff, flux = flux_diff) %>%
  pivot_longer(cols = "observed":"flux",
               names_to = "var",
               values_to = "value_diff")

6.3.2 Vertical mixing

The aim is to approximate the CT entrainment flux between Aug 06 and 15. The relevant profiles are:

CT_mix <- tm_profiles_ID_long %>%
      filter(ID %in% c("180806"),
             var %in% c("nCT"),
             dep < 17) %>% 
  summarise(mean(value)) %>% 
  pull()

CT_profile <- tm_profiles_ID_mean %>%
  filter(ID %in% c("180806"))

p_nCT <- CT_profile %>% 
  ggplot() +
  geom_rect(
    data = CT_profile %>% filter(dep > 12, dep < 17),
    aes(
      xmax = nCT,
      xmin = CT_mix,
      ymax = dep + 0.5,
      ymin = dep - 0.5
    )
  ) +
  geom_hline(yintercept = c(12, 17)) +
  geom_segment(aes(x = CT_mix, xend = CT_mix,
              y = -Inf, yend = 17),
              linetype = 2) +
  annotate("text", label = as.character(expression(paste(C[T],"*",mix))),
           parse = TRUE, x = 1560, y = 3) +
  annotate("text", label = as.character(expression(paste(C[T],"*")~flux)),
           parse = TRUE, x = 1580, y = 14.5, col = "white") +
  geom_point(aes(nCT, dep, col = ID)) +
  geom_path(aes(nCT, dep, col = ID)) +
  scale_y_reverse() +
  scale_color_viridis_d() +
    labs(y = "Depth (m)", x = expression(paste(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(),
    legend.position = "none"
  )

p_tem <- tm_profiles_ID_long %>%
  filter(ID %in% c("180806", "180815"),
         var %in% c("tem")) %>%
  ggplot(aes(value, dep, col = ID)) +
  geom_hline(yintercept = c(12, 17)) +
  geom_point() +
  geom_path() +
  scale_y_reverse() +
  scale_color_viridis_d() +
    labs(y = "Depth (m)", x = expression(paste(Temperature ~ (degree*C)))) +
  theme(
    legend.title = element_blank()
  )

p_tem + p_nCT +
  plot_layout(guides = 'collect') +
  plot_annotation(tag_levels = 'a')

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A7.pdf"
  ),
  width = 150,
  height = 140,
  dpi = 300,
  units = "mm"
)

ggsave(
  here::here(
    "output/Plots/Figures_publication/appendix",
    "Fig_A7.png"
  ),
  width = 150,
  height = 140,
  dpi = 300,
  units = "mm"
)


rm(p_tem, p_nCT, CT_mix, CT_profile)

The effect of mixing was derived from the mean concentration difference on Aug 06.

# calculate mixing with deep waters, corrected for air sea fluxes
nCT_surface <- tm_profiles_ID_long %>%
  filter(ID == "180806",
         var == "nCT",
         dep < parameters$i_dep_lim) %>%
  group_by(ID) %>%
  summarise(nCT_surface = mean(value)) %>%
  ungroup()

nCT_ML <- tm_profiles_ID_long %>%
  filter(ID == "180806",
         var == "nCT",
         dep < parameters$i_dep_mix_lim,
         dep > parameters$i_dep_lim) %>%
  group_by(ID) %>%
  summarise(nCT_ML = mean(value)) %>%
  ungroup()

NCP_mix <- full_join(nCT_surface, nCT_ML)

NCP_mix <- NCP_mix %>%
  mutate(
    value_diff = (nCT_surface - nCT_ML) * 1e-3 * parameters$i_dep_lim * (parameters$i_dep_mix_lim - parameters$i_dep_lim) / parameters$i_dep_mix_lim,
    ID = "180815"
  ) %>%
  select(-c(nCT_surface, nCT_ML))

rm(tm_og_flux_dep)
rm(nCT_ML, nCT_surface)

NCP_mix_diff <- NCP_mix %>%
  mutate(var = "mixing")

NCP_flux_mix_diff <-
  full_join(NCP_flux_diff, NCP_mix_diff) %>% #
  arrange(ID) %>%
  fill(date_time_ID_ref)

NCP_mix <- NCP_mix %>%
  rename(mix_cum = value_diff) %>%
  select(ID, mix_cum)

NCP_flux_mix <-
  full_join(NCP_flux,
            NCP_mix)

rm(NCP_mix, NCP_mix_diff, NCP_flux, NCP_flux_diff, date_180806)

NCP_flux_mix <- NCP_flux_mix %>%
  arrange(date_time) %>%
  fill(ID) %>%
  mutate(
    mix_cum = if_else(ID %in% c("180806", 180815), mix_cum, 0),
    mix_cum = na.approx(mix_cum),
    nCT_i_flux_mix_cum = nCT_i_flux_cum + mix_cum
  )

# reorder factors for plotting
NCP_flux_mix_diff <- NCP_flux_mix_diff %>%
  mutate(var = factor(var, c("observed", "flux correction", "mixing correction")))

NCP_flux_mix_long <- NCP_flux_mix %>%
  select(date_time, nCT_i_cum, nCT_i_flux_cum, nCT_i_flux_mix_cum) %>%
  pivot_longer(nCT_i_cum:nCT_i_flux_mix_cum,
               values_to = "value",
               names_to = "var") %>%
  mutate(
    var = fct_recode(
      var,
      observed = "nCT_i_cum",
      `flux corrected` = "nCT_i_flux_cum",
      `flux + mixing corrected (NCP)` = "nCT_i_flux_mix_cum"
    )
  )


p_inCT <- NCP_flux_mix_long %>%
  arrange(date_time) %>%
  ggplot() +
  geom_col(
    data = NCP_flux_mix_diff,
    aes(date_time_ID_ref, value_diff, fill = var),
    position = position_dodge2(preserve = "single"),
    alpha = 0.5
  ) +
  geom_hline(yintercept = 0) +
  geom_point(data = cruise_dates, aes(date_time_ID, 0), shape = 21) +
  geom_line(aes(date_time, value, col = var)) +
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d",
                   sec.axis = dup_axis()) +
  scale_fill_brewer(palette = "Dark2", name = "Incremental changes") +
  scale_color_brewer(palette = "Dark2", name = "Cumulative changes") +
  labs(y = expression(atop(Integrated ~ paste(C[T],"*"), (mol ~ m ^ {
    -2
  }))),
  title = "Water column inventory changes") +
  guides(guide_colourbar(order = 1)) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x.top = element_blank(),
    legend.position = "bottom",
    legend.direction = "vertical"
  )

p_inCT

NCP_flux_mix %>%
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/CT_dynamics",
      "tm_NCP_cum.csv"
    )
  )

NCP_flux_mix_diff %>%
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/CT_dynamics",
      "tm_NCP_inc.csv"
    )
  )
# calculate mixing with deep waters, corrected for air sea fluxes
nCT_inventory_mean <- tm_profiles_ID_long %>%
  filter(ID == "180806",
         var == "nCT",
         dep < parameters$i_dep_mix_lim) %>%
  summarise(nCT_surface = sum(value) / parameters$i_dep_mix_lim) %>%
  pull()

nCT_delta_mix <- tm_profiles_ID_long %>%
  filter(ID == "180806",
         var == "nCT",
         dep < parameters$i_dep_mix_lim) %>%
  mutate(nCT_delta_mix = nCT_inventory_mean - value)

NCP_mix_deep <- nCT_delta_mix %>%
  filter(dep < parameters$i_dep_mix_lim,
         dep > parameters$i_dep_lim) %>%
  summarise(value_diff = sum(nCT_delta_mix) / 1000) %>%
  mutate(ID = "180815")

NCP_mix_shallow <- nCT_delta_mix %>%
  filter(dep < parameters$i_dep_lim) %>%
  summarise(value_diff = sum(nCT_delta_mix) / 1000) %>%
  mutate(ID = "180815")

rm(tm_og_flux_dep)

NCP_mix_deep_diff <- NCP_mix_deep %>%
  mutate(var = "mixing")

NCP_flux_mix_diff <-
  full_join(NCP_flux_diff, NCP_mix_deep_diff) %>% #
  arrange(ID) %>%
  fill(date_time_ID_ref)

NCP_mix_deep <- NCP_mix_deep %>%
  rename(mix_cum = value_diff) %>%
  select(ID, mix_cum)

NCP_flux_mix <-
  full_join(NCP_flux,
            NCP_mix_deep)

rm(NCP_mix_deep,
   NCP_mix_deep_diff,
   NCP_flux,
   NCP_flux_diff,
   date_180806)

NCP_flux_mix <- NCP_flux_mix %>%
  arrange(date_time) %>%
  fill(ID) %>%
  mutate(
    mix_cum = if_else(ID %in% c("180806", 180815), mix_cum, 0),
    mix_cum = na.approx(mix_cum),
    nCT_i_flux_mix_cum = nCT_i_flux_cum + mix_cum
  )

# reorder factors for plotting
NCP_flux_mix_diff <- NCP_flux_mix_diff %>%
  mutate(var = factor(var, c("observed", "flux", "mixing")))

NCP_flux_mix_long <- NCP_flux_mix %>%
  select(date_time, nCT_i_cum, nCT_i_flux_cum, nCT_i_flux_mix_cum) %>%
  pivot_longer(nCT_i_cum:nCT_i_flux_mix_cum,
               values_to = "value",
               names_to = "var") %>%
  mutate(
    var = fct_recode(
      var,
      observed = "nCT_i_cum",
      `flux corrected` = "nCT_i_flux_cum",
      `flux + mixing corrected (NCP)` = "nCT_i_flux_mix_cum"
    )
  )


p_inCT <- NCP_flux_mix_long %>%
  arrange(date_time) %>%
  ggplot() +
  geom_col(
    data = NCP_flux_mix_diff,
    aes(date_time_ID_ref, value_diff, fill = var),
    position = position_dodge2(preserve = "single"),
    alpha = 0.5
  ) +
  geom_hline(yintercept = 0) +
  geom_point(data = cruise_dates, aes(date_time_ID, 0), shape = 21) +
  geom_line(aes(date_time, value, col = var)) +
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d",
                   sec.axis = dup_axis()) +
  scale_fill_brewer(palette = "Dark2", name = "incremental changes") +
  scale_color_brewer(palette = "Dark2", name = "cumulative changes") +
  labs(y = expression(atop(integrated ~ nC[T], (mol ~ m ^ {
    -2
  }))),
  title = "Water column inventory changes") +
  guides(guide_colourbar(order = 1)) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x.top = element_blank(),
    legend.position = "bottom",
    legend.direction = "vertical"
  )

p_inCT

NCP_flux_mix %>%
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/CT_dynamics",
      "tm_NCP_cum.csv"
    )
  )

NCP_flux_mix_diff %>%
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/CT_dynamics",
      "tm_NCP_inc.csv"
    )
  )
# calculate mixing with deep waters, corrected for air sea fluxes
NCP_mix <- tm_profiles_ID_long %>%
  filter(ID == "180815",
         var == "nCT",
         dep < parameters$i_dep_mix_lim,
         dep > parameters$i_dep_lim) %>%
  group_by(ID, date_time_ID, date_time_ID_ref) %>%
  summarise(value_diff =
              sum(value_diff) / 1000 + min(tm_og_flux_dep$flux_cum)) %>%
  ungroup()

rm(tm_og_flux_dep)

NCP_mix_diff <- NCP_mix %>%
  select(date_time_ID_ref, value_diff) %>%
  mutate(var = "mixing")

NCP_flux_mix_diff <-
  full_join(NCP_flux_diff, NCP_mix_diff)

NCP_flux_mix <-
  full_join(NCP_flux,
            NCP_mix %>% rename(mix_cum = value_diff))

rm(NCP_mix, NCP_mix_diff, NCP_flux, NCP_flux_diff, date_180806)

NCP_flux_mix <- NCP_flux_mix %>%
  arrange(date_time) %>%
  fill(ID) %>%
  mutate(
    mix_cum = if_else(ID %in% c("180806", 180815), mix_cum, 0),
    mix_cum = na.approx(mix_cum),
    nCT_i_flux_mix_cum = nCT_i_flux_cum + mix_cum
  )

# reorder factors for plotting
NCP_flux_mix_diff <- NCP_flux_mix_diff %>%
  mutate(var = factor(var, c("observed", "flux", "mixing")))

NCP_flux_mix_long <- NCP_flux_mix %>%
  select(date_time, nCT_i_cum, nCT_i_flux_cum, nCT_i_flux_mix_cum) %>%
  pivot_longer(nCT_i_cum:nCT_i_flux_mix_cum,
               values_to = "value",
               names_to = "var") %>%
  mutate(
    var = fct_recode(
      var,
      observed = "nCT_i_cum",
      `flux corrected` = "nCT_i_flux_cum",
      `flux + mixing corrected (NCP)` = "nCT_i_flux_mix_cum"
    )
  )


p_inCT <- NCP_flux_mix_long %>%
  arrange(date_time) %>%
  ggplot() +
  geom_col(
    data = NCP_flux_mix_diff,
    aes(date_time_ID_ref, value_diff, fill = var),
    position = position_dodge2(preserve = "single"),
    alpha = 0.5
  ) +
  geom_hline(yintercept = 0) +
  geom_point(data = cruise_dates, aes(date_time_ID, 0), shape = 21) +
  geom_line(aes(date_time, value, col = var)) +
  scale_x_datetime(date_breaks = "week",
                   date_labels = "%b %d",
                   sec.axis = dup_axis()) +
  scale_fill_brewer(palette = "Dark2", name = "Incremental changes") +
  scale_color_brewer(palette = "Dark2", name = "Cumulative changes") +
  labs(y = expression(atop(Integrated ~ paste(C[T],"*"), (mol ~ m ^ {
    -2
  }))),
  title = "Water column inventory changes") +
  guides(guide_colourbar(order = 1)) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x.top = element_blank(),
    legend.position = "bottom",
    legend.direction = "vertical"
  )

p_inCT

NCP_flux_mix %>%
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/CT_dynamics",
      "tm_NCP_cum.csv"
    )
  )

NCP_flux_mix_diff %>%
  write_csv(
    here::here(
      "data/intermediate/_merged_data_files/CT_dynamics",
      "tm_NCP_inc.csv"
    )
  )

7 Open tasks / questions

  • clean and harmonize chunk labeling (label: plot, 1 plot per chunk, etc)
  • included removed stations in coverage plot
  • Significance of changes in AT for calculated nCT changes
    • Calculate AT-S ratios, reconstruct AT profiles, calculate true nCT profiles, normalize nCT profiles to mean AT
  • demonstrate strong permanent thermocline at around 25 m
  • calculate oxygen demand for mineralization (4.68*1091.2 / (300e-6103) / 10^9)

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] ggnewscale_0.4.5       rgdal_1.5-18           LakeMetabolizer_1.5.0 
 [4] rLakeAnalyzer_1.11.4.1 kableExtra_1.3.1       sp_1.4-4              
 [7] tibbletime_0.1.6       zoo_1.8-8              lubridate_1.7.9.2     
[10] scico_1.2.0            metR_0.9.0             marelac_2.1.10        
[13] shape_1.4.5            seacarb_3.2.14         oce_1.2-0             
[16] gsw_1.0-5              testthat_3.0.1         patchwork_1.1.1       
[19] forcats_0.5.0          stringr_1.4.0          dplyr_1.0.2           
[22] purrr_0.3.4            readr_1.4.0            tidyr_1.1.2           
[25] tibble_3.0.4           ggplot2_3.3.3          tidyverse_1.3.0       
[28] workflowr_1.6.2       

loaded via a namespace (and not attached):
 [1] fs_1.5.0           RColorBrewer_1.1-2 webshot_0.5.2      httr_1.4.2        
 [5] rprojroot_2.0.2    tools_4.0.3        backports_1.2.1    R6_2.5.0          
 [9] DBI_1.1.0          colorspace_2.0-0   raster_3.4-5       withr_2.3.0       
[13] tidyselect_1.1.0   compiler_4.0.3     git2r_0.27.1       cli_2.2.0         
[17] rvest_0.3.6        xml2_1.3.2         isoband_0.2.3      labeling_0.4.2    
[21] scales_1.1.1       checkmate_2.0.0    digest_0.6.27      rmarkdown_2.6     
[25] pkgconfig_2.0.3    htmltools_0.5.0    highr_0.8          dbplyr_2.0.0      
[29] rlang_0.4.10       readxl_1.3.1       rstudioapi_0.13    farver_2.0.3      
[33] generics_0.1.0     jsonlite_1.7.2     magrittr_2.0.1     Rcpp_1.0.5        
[37] munsell_0.5.0      fansi_0.4.1        lifecycle_0.2.0    stringi_1.5.3     
[41] whisker_0.4        yaml_2.2.1         plyr_1.8.6         grid_4.0.3        
[45] promises_1.1.1     crayon_1.3.4       lattice_0.20-41    haven_2.3.1       
[49] hms_0.5.3          knitr_1.30         ps_1.5.0           pillar_1.4.7      
[53] codetools_0.2-16   reprex_0.3.0       glue_1.4.2         evaluate_0.14     
[57] data.table_1.13.6  modelr_0.1.8       vctrs_0.3.6        httpuv_1.5.4      
[61] cellranger_1.1.0   gtable_0.3.0       assertthat_0.2.1   xfun_0.19         
[65] broom_0.7.3        later_1.1.0.1      viridisLite_0.3.0  ellipsis_0.3.1    
[69] here_1.0.1