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Rmd	449195a	jens-daniel-mueller	2020-12-15	rebuild without subsetting nitrate
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Rmd	d234226	jens-daniel-mueller	2020-12-15	rebuild with cstar_tref
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Rmd	6069c23	jens-daniel-mueller	2020-12-11	selectable basinmask, try 5
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Rmd	7a4b015	jens-daniel-mueller	2020-11-30	first rebuild on ETH server
Rmd	bc61ce3	Jens Müller	2020-11-30	Initial commit
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1 Required data

Required are:

GLODAPv2.2020
- cleaned data file
- horizontal grid of sampling coordinates
Cant from Sabine 2004
annual mean atmospheric pCO₂

GLODAP <-
  read_csv(paste(path_version_data,
                 "GLODAPv2.2020_clean.csv",
                 sep = ""))

GLODAP_obs_grid <-
  read_csv(paste(path_version_data,
                 "GLODAPv2.2020_clean_obs_grid.csv",
                 sep = ""))

S04_cant_3d <-
  read_csv(paste(path_preprocessing,
                 "S04_cant_3d.csv",
                 sep = ""))

co2_atm <-
  read_csv(paste(path_preprocessing,
                 "co2_atm.csv",
                 sep = ""))

2 PO₄*

2.1 Calculation

The predictor PO₄* was be calculated according to Clement and Gruber (2018), ie based on oxygen. Please note that an erroneous equations for PO4* calculation is given in the supplement of Gruber et al (2019), based on nitrate.

Here we use following equation:

print(b_phosphate_star)

function (phosphate, oxygen) 
{
    phosphate_star = phosphate + (oxygen/params_local$rPO) - 
        params_local$rPO_offset
    return(phosphate_star)
}

GLODAP <- GLODAP %>% 
  mutate(phosphate_star = b_phosphate_star(phosphate, oxygen))

3 C*

C* serves as a conservative tracer of anthropogenic CO₂ uptake. It is derived from measured DIC by removing the impact of

organic matter formation and respiration
calcification and calcium carbonate dissolution

Contributions of those processes are estimated from phosphate and alkalinity concentrations.

3.1 Stoichiometric ratios

The stoichiometric nutrient ratios for the production and mineralization of organic matter were set to:

C/P: 117
N/P: 16

3.2 Calculation

C* was calculated as:

print(b_cstar)

function (tco2, phosphate, talk) 
{
    cstar = tco2 - (params_local$rCP * phosphate) - 0.5 * (talk - 
        (params_local$rNP * phosphate))
    return(cstar)
}

GLODAP <- GLODAP %>% 
  mutate(rCP_phosphate = -params_local$rCP * phosphate,
         talk_05 = -0.5 * talk,
         rNP_phosphate_05 = -0.5 * params_local$rNP * phosphate,
         cstar = b_cstar(tco2, phosphate, talk))

3.3 Reference year adjustment

The reference year adjustment relies on an apriori estimate of Cant at a given location and depth, which is used as a scaling factor for the concurrent change in atmospheric CO₂. The underlying assumption is a transient steady state for the oceanic Cant uptake. Here, Cant from the GLODAP mapped Climatology was used.

Note that eq. 6 in Clement and Gruber (2018) misses pCO₂ pre-industrial in the denominator. Here we use the equation published in Gruber et al. (2019).

3.3.1 Combine GLODAP + Cant

S04_cant_3d_obs <- left_join(
  GLODAP_obs_grid,
  S04_cant_3d %>% select(-c(cant, eras))
  )

# calculate number of cant data points per grid cell
S04_cant_3d_obs <- S04_cant_3d_obs %>%
  group_by(lon, lat) %>% 
  mutate(n = n()) %>% 
  ungroup()

# S04_cant_3d_obs %>%
#   filter(n <= 1) %>%
#   ggplot(aes(lon,lat)) +
#   geom_point(data = GLODAP_obs_grid, aes(lon, lat)) +
#   geom_point(col = "red")

rm(S04_cant_3d, GLODAP_obs_grid)

GLODAP_cant_obs <- full_join(GLODAP, S04_cant_3d_obs)

rm(GLODAP, S04_cant_3d_obs)

# fill number of cant data points per grid cell to all observations
GLODAP_cant_obs <- GLODAP_cant_obs %>%
  group_by(lon, lat) %>% 
  fill(n, .direction = "updown") %>% 
  ungroup()

The mapped Cant product was merged with GLODAP observation by:

using an identical 1x1° horizontal grid
linear interpolation of C_ant from standard to sampling depth

# interpolate cant to observation depth
GLODAP_cant_obs_int <- GLODAP_cant_obs %>%
  filter(n > 1) %>% 
  group_by(lat, lon) %>%
  arrange(depth) %>%
  mutate(cant_pos_int = approxfun(depth, cant_pos, rule = 2)(depth)) %>%
  ungroup()

# set cant for observation depth if only one cant available
GLODAP_cant_obs_set <- GLODAP_cant_obs %>%
  filter(n == 1) %>%
  group_by(lat, lon) %>%
  mutate(cant_pos_int = mean(cant_pos, na.rm = TRUE)) %>%
  ungroup()

# bin data sets with interpolated and set cant
GLODAP_cant_obs <- bind_rows(GLODAP_cant_obs_int, GLODAP_cant_obs_set)
rm(GLODAP_cant_obs_int, GLODAP_cant_obs_set)


ggplot() +
  geom_path(
    data = GLODAP_cant_obs %>%
      filter(lat == 48.5, lon == 165.5,!is.na(cant_pos)) %>%
      arrange(depth),
    aes(cant_pos, depth, col = "mapped")
  ) +
  geom_point(
    data = GLODAP_cant_obs %>%
      filter(lat == 48.5, lon == 165.5,!is.na(cant_pos)) %>%
      arrange(depth),
    aes(cant_pos, depth, col = "mapped")
  ) +
  geom_point(
    data = GLODAP_cant_obs %>%
      filter(lat == 48.5, lon == 165.5, date == ymd("2018-06-27")),
    aes(cant_pos_int, depth, col = "interpolated")
  ) +
  scale_y_reverse() +
  scale_color_brewer(palette = "Dark2", name = "") +
  labs(title = "Cant interpolation to sampling depth - example profile")

Version	Author	Date
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

# remove cant data at grid cells without observations
GLODAP <- GLODAP_cant_obs %>%
  filter(!is.na(cstar)) %>%
  mutate(cant_pos = cant_pos_int) %>%
  select(-cant_pos_int, n)

rm(GLODAP_cant_obs)

3.3.2 Merge GLODAP + atm. pCO₂

GLODAP observations were merged with mean annual atmospheric pCO₂ levels by year.

GLODAP <- left_join(GLODAP, co2_atm)

3.3.3 Calculation

C_ant for median year of each era was calculated by applying alpha = 0.28/13 * (median year - 1994) to the estimate of Sabine et al. (2004).

# assign reference year
GLODAP <- GLODAP %>% 
  group_by(era) %>% 
  mutate(tref = median(year)) %>% 
  ungroup()

# calculate reference year
tref <- GLODAP %>% 
  group_by(era) %>% 
  summarise(year = median(year)) %>% 
  ungroup()

# extract atm pCO2 at reference year
co2_atm_tref <- right_join(co2_atm, tref) %>% 
  select(-year) %>% 
  rename(pCO2_tref = pCO2)

# merge atm pCO2 at tref with GLODAP
GLODAP <- full_join(GLODAP, co2_atm_tref)
rm(co2_atm, tref)

# scale cant to reference year
GLODAP <- GLODAP %>%
  mutate(alpha = (tref - 1994) * (0.28 / 13),
         cant_pos = cant_pos * (1 + alpha))

# calculate cstar for reference year
GLODAP <- GLODAP %>%
  mutate(
    cstar_tref_delta =
      ((pCO2 - pCO2_tref) / (pCO2_tref - params_local$preind_atm_pCO2)) * cant_pos,
    cstar_tref = cstar - cstar_tref_delta)

3.4 Control plots

GLODAP %>% 
  ggplot(aes(cstar_tref_delta)) +
  geom_histogram(binwidth = 1) +
  labs(title = "Histogramm with binwidth = 1")

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

GLODAP %>% 
  sample_n(1e4) %>% 
  ggplot(aes(year, cstar_tref_delta, col = cant_pos)) +
  geom_point() +
  scale_color_viridis_c() +
  labs(title = "Time series of random subsample 1e4")

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

GLODAP %>% 
  ggplot(aes(year, cstar_tref_delta)) +
  geom_bin2d(binwidth = 1) +
  scale_fill_viridis_c(trans = "log10") +
  labs(title = "Heatmap with binwidth = 1")

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
090e4d5	jens-daniel-mueller	2020-12-02
37e9dac	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

4 Selected section plots

A selected section is plotted to demonstrate the magnitude of various parameters and corrections relevant to C*.

GLODAP_cruise <- GLODAP %>% 
  filter(cruise %in% params_global$cruises_meridional)

map +
  geom_path(data = GLODAP_cruise %>%
              arrange(date),
            aes(lon, lat)) +
  geom_point(data = GLODAP_cruise %>%
              arrange(date),
             aes(lon, lat, col = date)) +
  scale_color_viridis_c(trans = "date") +
  labs(title = paste("Cruise year:", mean(GLODAP_cruise$year)))

Version	Author	Date
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
090e4d5	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

lat_section <- 
GLODAP_cruise %>%
  ggplot(aes(lat, depth)) +
  scale_y_reverse() +
  scale_fill_viridis_c() +
  theme(axis.title.x = element_blank())

for (i_var in c("tco2",
                "rCP_phosphate",
                "talk_05",
                "rNP_phosphate_05",
                "cstar",
                "cstar_tref")) {
  print(lat_section +
          stat_summary_2d(aes(z = !!sym(i_var))) +
          scale_fill_viridis_c(name = i_var)
        )
  
}

Version	Author	Date
7d82772	jens-daniel-mueller	2020-12-11
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
3c8a83c	jens-daniel-mueller	2020-12-04
090e4d5	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
7a9a4cb	jens-daniel-mueller	2020-12-15
7d82772	jens-daniel-mueller	2020-12-11
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
3c8a83c	jens-daniel-mueller	2020-12-04
090e4d5	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
7a9a4cb	jens-daniel-mueller	2020-12-15
7d82772	jens-daniel-mueller	2020-12-11
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
3c8a83c	jens-daniel-mueller	2020-12-04
090e4d5	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
7a9a4cb	jens-daniel-mueller	2020-12-15
7d82772	jens-daniel-mueller	2020-12-11
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
3c8a83c	jens-daniel-mueller	2020-12-04
090e4d5	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
7a9a4cb	jens-daniel-mueller	2020-12-15
7d82772	jens-daniel-mueller	2020-12-11
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
3c8a83c	jens-daniel-mueller	2020-12-04
090e4d5	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
7a9a4cb	jens-daniel-mueller	2020-12-15
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09
6a8004b	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
3c8a83c	jens-daniel-mueller	2020-12-04
090e4d5	jens-daniel-mueller	2020-12-02
37e9dac	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

rm(lat_section, GLODAP_cruise)

5 Isoneutral slabs

The following boundaries for isoneutral slabs were defined:

Atlantic: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1, 28.15, 28.2,
Indo-Pacific: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1,

Continuous neutral densities (gamma) values from GLODAP are grouped into isoneutral slabs.

GLODAP <- m_cut_gamma(GLODAP, "gamma")

GLODAP_cruise <- GLODAP %>% 
  filter(cruise %in% params_global$cruises_meridional)

lat_section <- 
GLODAP_cruise %>%
  ggplot(aes(lat, depth)) +
  scale_y_reverse() +
  theme(legend.position = "bottom")

lat_section +
  geom_point(aes(col = gamma_slab)) +
  scale_color_viridis_d()

Version	Author	Date
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

rm(lat_section, GLODAP_cruise)

# this section was only used to calculate gamma locally, and compare it to the value provided in GLODAP data set

GLODAP_cruise <- GLODAP %>% 
  filter(cruise %in% params_global$cruises_meridional)

library(oce)
library(gsw)
# calculate pressure from depth

GLODAP_cruise <- GLODAP_cruise %>% 
  mutate(CTDPRS = gsw_p_from_z(-depth,
                               lat))

GLODAP_cruise <- GLODAP_cruise %>% 
  mutate(THETA = swTheta(salinity = sal,
                         temperature = temp,
                         pressure = CTDPRS,
                         referencePressure = 0,
                         longitude = lon-180,
                         latitude = lat))

GLODAP_cruise <- GLODAP_cruise %>% 
  rename(LATITUDE = lat,
         LONGITUDE = lon,
         SALNTY = sal,
         gamma_provided = gamma)

library(reticulate)
source_python(here::here("code/python_scripts",
                         "Gamma_GLODAP_python.py"))

GLODAP_cruise <- calculate_gamma(GLODAP_cruise)

GLODAP_cruise <- GLODAP_cruise %>% 
  mutate(gamma_delta = gamma_provided - GAMMA)

lat_section <- 
GLODAP_cruise %>%
  ggplot(aes(LATITUDE, CTDPRS)) +
  scale_y_reverse() +
  theme(legend.position = "bottom")

lat_section +
  stat_summary_2d(aes(z = gamma_delta)) +
  scale_color_viridis_c()

GLODAP_cruise %>% 
  ggplot(aes(gamma_delta))+
  geom_histogram()

rm(lat_section, GLODAP_cruise, cruises_meridional)

6 Observations coverage

GLODAP <- GLODAP %>% 
  mutate(gamma_slab = factor(gamma_slab), 
         gamma_slab = factor(gamma_slab, levels = rev(levels(gamma_slab))))

for (i_basin in unique(GLODAP$basin)) {
  # i_basin <- unique(GLODAP$basin)[3]
  
  print(
    GLODAP %>%
      filter(basin == i_basin) %>%
      ggplot(aes(lat, gamma_slab)) +
      geom_bin2d(binwidth = 5) +
      scale_fill_viridis_c(
        option = "magma",
        direction = -1,
        trans = "log10"
      ) +
      scale_x_continuous(breaks = seq(-100, 100, 20),
                         limits = c(params_global$lat_min,
                                    params_global$lat_max)) +
      facet_grid(era ~ .) +
      labs(title = paste("MLR region: ", i_basin))
  )
  
}

Version	Author	Date
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11

Version	Author	Date
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11

Version	Author	Date
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11

6.1 Histograms

GLODAP_vars <- GLODAP %>% 
  select(cstar_tref,
         sal,
         temp,
         oxygen,
         aou,
         silicate,
         phosphate,
         phosphate_star)

GLODAP_vars_long <- GLODAP_vars %>% 
  pivot_longer(cstar_tref:phosphate_star, names_to = "variable", values_to = "value")

GLODAP_vars_long %>% 
  ggplot(aes(value)) +
  geom_histogram() +
  facet_wrap(~ variable,
             ncol = 2,
             scales = "free")

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09

rm(GLODAP_vars, GLODAP_vars_long)

7 Individual cruise sections

Zonal and meridional section plots are produce for each cruise individually and can be downloaded here.

if (params_local$plot_all_figures == "y") {

cruises <- GLODAP %>% 
  group_by(cruise) %>% 
  summarise(date_mean = mean(date, na.rm = TRUE),
            n = n()) %>% 
  ungroup() %>% 
  arrange(date_mean)

GLODAP <- full_join(GLODAP, cruises)

n <- 0
for (i_cruise in unique(cruises$cruise)) {

# i_cruise <- unique(cruises$cruise)[1]
# n <- n + 1
# print(n)  
  
GLODAP_cruise <- GLODAP %>%
  filter(cruise == i_cruise) %>% 
  arrange(date)

cruises_cruise <- cruises %>%
  filter(cruise == i_cruise)
  
map_plot <- 
  map +
  geom_point(data = GLODAP_cruise,
             aes(lon, lat, col = date)) +
  scale_color_viridis_c(trans = "date") +
  labs(title = paste("Mean date:", cruises_cruise$date_mean,
                     "| cruise:", cruises_cruise$cruise,
                     "| n(samples):", cruises_cruise$n))


lon_section <- GLODAP_cruise %>%
  ggplot(aes(lon, depth)) +
  scale_y_reverse() +
  scale_fill_viridis_c()

lon_tco2 <- lon_section+
  stat_summary_2d(aes(z=tco2))

lon_talk <- lon_section+
  stat_summary_2d(aes(z=talk))

lon_phosphate <- lon_section+
  stat_summary_2d(aes(z=phosphate))

lon_oxygen <- lon_section+
  stat_summary_2d(aes(z=oxygen))

lon_aou <- lon_section+
  stat_summary_2d(aes(z=aou))

lon_phosphate_star <- lon_section+
  stat_summary_2d(aes(z=phosphate_star))

lon_nitrate <- lon_section+
  stat_summary_2d(aes(z=nitrate))

lon_cstar <- lon_section+
  stat_summary_2d(aes(z=cstar_tref))


lat_section <- GLODAP_cruise %>%
  ggplot(aes(lat, depth)) +
  scale_y_reverse() +
  scale_fill_viridis_c()

lat_tco2 <- lat_section+
  stat_summary_2d(aes(z=tco2))

lat_talk <- lat_section+
  stat_summary_2d(aes(z=talk))

lat_phosphate <- lat_section+
  stat_summary_2d(aes(z=phosphate))

lat_oxygen <- lat_section+
  stat_summary_2d(aes(z=oxygen))

lat_aou <- lat_section+
  stat_summary_2d(aes(z=aou))

lat_phosphate_star <- lat_section+
  stat_summary_2d(aes(z=phosphate_star))

lat_nitrate <- lat_section+
  stat_summary_2d(aes(z=nitrate))

lat_cstar <- lat_section+
  stat_summary_2d(aes(z=cstar_tref))

hist_tco2 <- GLODAP_cruise %>%
  ggplot(aes(tco2)) +
  geom_histogram()

hist_talk <- GLODAP_cruise %>%
  ggplot(aes(talk)) +
  geom_histogram()

hist_phosphate <- GLODAP_cruise %>%
  ggplot(aes(phosphate)) +
  geom_histogram()

hist_oxygen <- GLODAP_cruise %>%
  ggplot(aes(oxygen)) +
  geom_histogram()

hist_aou <- GLODAP_cruise %>%
  ggplot(aes(aou)) +
  geom_histogram()

hist_phosphate_star <- GLODAP_cruise %>%
  ggplot(aes(phosphate_star)) +
  geom_histogram()

hist_nitrate <- GLODAP_cruise %>%
  ggplot(aes(nitrate)) +
  geom_histogram()

hist_cstar <- GLODAP_cruise %>%
  ggplot(aes(cstar_tref)) +
  geom_histogram()

(map_plot /
    ((hist_tco2 / hist_talk / hist_phosphate / hist_cstar) |
       (hist_oxygen / hist_phosphate_star / hist_nitrate / hist_aou)
    )) |
  ((lat_tco2 / lat_talk / lat_phosphate / lat_oxygen / lat_aou / lat_phosphate_star / lat_nitrate / lat_cstar) |
     (lon_tco2 / lon_talk / lon_phosphate / lon_oxygen /  lon_aou /lon_phosphate_star / lon_nitrate / lon_cstar))    

ggsave(
  path = paste(path_version_figures, "Cruise_sections_histograms/", sep = ""),
  filename = paste(
    "Cruise_date",
    cruises_cruise$date_mean,
    "count",
    cruises_cruise$n,
    "cruiseID",
    cruises_cruise$cruise,
    ".png",
    sep = "_"
  ),
width = 20, height = 12)

rm(map_plot,
   lon_section, lat_section,
   lat_tco2, lat_talk, lat_phosphate, lon_tco2, lon_talk, lon_phosphate,
   GLODAP_cruise, cruises_cruise)

}

}

8 Write files

# select relevant columns
GLODAP <- GLODAP %>%
  select(
    year,
    date,
    era,
    basin,
    basin_AIP,
    lat,
    lon,
    depth,
    gamma,
    gamma_slab,
    params_local$MLR_predictors,
    params_local$MLR_target
  )

GLODAP %>% write_csv(paste(
  path_version_data,
  "GLODAPv2.2020_MLR_fitting_ready.csv",
  sep = ""
))

co2_atm_tref %>%  write_csv(paste(path_version_data,
                                  "co2_atm_tref.csv",
                                  sep = ""))

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.1

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.7.9 marelac_2.1.10  shape_1.4.5     metR_0.9.0     
 [5] scico_1.2.0     patchwork_1.1.0 collapse_1.4.2  forcats_0.5.0  
 [9] stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4     readr_1.4.0    
[13] tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.2   tidyverse_1.3.0
[17] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               viridisLite_0.3.0        jsonlite_1.7.1          
 [4] here_0.1                 modelr_0.1.8             assertthat_0.2.1        
 [7] blob_1.2.1               cellranger_1.1.0         yaml_2.2.1              
[10] pillar_1.4.7             backports_1.1.10         lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.7.0      digest_0.6.27           
[16] RColorBrewer_1.1-2       promises_1.1.1           checkmate_2.0.0         
[19] rvest_0.3.6              colorspace_2.0-0         htmltools_0.5.0         
[22] httpuv_1.5.4             Matrix_1.2-18            pkgconfig_2.0.3         
[25] broom_0.7.2              seacarb_3.2.14           haven_2.3.1             
[28] scales_1.1.1             whisker_0.4              later_1.1.0.1           
[31] git2r_0.27.1             farver_2.0.3             generics_0.0.2          
[34] ellipsis_0.3.1           withr_2.3.0              cli_2.2.0               
[37] magrittr_2.0.1           crayon_1.3.4             readxl_1.3.1            
[40] evaluate_0.14            fs_1.5.0                 fansi_0.4.1             
[43] xml2_1.3.2               RcppArmadillo_0.10.1.2.0 oce_1.2-0               
[46] tools_4.0.3              data.table_1.13.2        hms_0.5.3               
[49] lifecycle_0.2.0          munsell_0.5.0            reprex_0.3.0            
[52] gsw_1.0-5                compiler_4.0.3           rlang_0.4.9             
[55] grid_4.0.3               rstudioapi_0.13          labeling_0.4.2          
[58] rmarkdown_2.5            testthat_3.0.0           gtable_0.3.0            
[61] DBI_1.1.0                R6_2.5.0                 knitr_1.30              
[64] rprojroot_2.0.2          stringi_1.5.3            parallel_4.0.3          
[67] Rcpp_1.0.5               vctrs_0.3.5              dbplyr_1.4.4            
[70] tidyselect_1.1.0         xfun_0.18

eMLR - data preparation

Jens Daniel Müller

15 December, 2020

1 Required data

2 PO₄*

2.1 Calculation

3 C*

3.1 Stoichiometric ratios

3.2 Calculation

3.3 Reference year adjustment

3.3.1 Combine GLODAP + Cant

3.3.2 Merge GLODAP + atm. pCO₂

3.3.3 Calculation

3.4 Control plots

4 Selected section plots

5 Isoneutral slabs

6 Observations coverage

6.1 Histograms

7 Individual cruise sections

8 Write files

eMLR - data preparation

Jens Daniel Müller

15 December, 2020

1 Required data

2 PO4*

2.1 Calculation

3 C*

3.1 Stoichiometric ratios

3.2 Calculation

3.3 Reference year adjustment

3.3.1 Combine GLODAP + Cant

3.3.2 Merge GLODAP + atm. pCO2

3.3.3 Calculation

3.4 Control plots

4 Selected section plots

5 Isoneutral slabs

6 Observations coverage

6.1 Histograms

7 Individual cruise sections

8 Write files

2 PO₄*

3.3.2 Merge GLODAP + atm. pCO₂