Last updated: 2020-12-21

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1 Required data

Required are:

GLODAPv2.2020
- cleaned data file
Cant from Sabine 2004 (S04)
Cant from Gruber 2019 (G19)
annual mean atmospheric pCO₂

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

cant_1994 <-
  read_csv(paste(path_preprocessing,
                 "cant_annual_field/cant_1994.csv",
                 sep = ""))

cant_2007 <-
  read_csv(paste(path_preprocessing,
                 "cant_annual_field/cant_2007.csv",
                 sep = ""))

cant_2015 <-
  read_csv(paste(path_preprocessing,
                 "cant_annual_field/cant_2015.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

To adjust C* values to the reference year of each observation period, we assume a transient steady state change of cant between the time of sampling the reference year. The adjustment requires an approximation of the cant concentration at the reference year. We approximate this concentration by adding the delta cant signal estimated by Gruber et al (2019) to the “base line” total cant concentration determined for 1994 by Sabine et al (2004):

Cant(tref) = S04 + (tref-1994)/13 * G19

This way, we use exactly S04+G19 for tref=2007. For all other tref we scale Cant with the observed anomalous change over the 1994-2007 period, rather than assuming a transient steady state. However, one assumes a linear behaviour of the anomalous change over time, which might be wrong in particular for the years past 2007.

3.3.1 Cant at tref

Calculate Cant at tref by adding G19, scaled for the time since 1994.

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

# join cant with tref
cant_3d <- bind_rows(cant_1994, cant_2007, cant_2015)

cant_3d <- left_join(cant_3d, tref) %>%
  arrange(lon, lat, depth) %>% 
  select(lon, lat, depth, era, cant)

rm(cant_1994, cant_2007, cant_2015)

3.3.2 Combine GLODAP + Cant

# observations grid per era
GLODAP_obs_grid_era <- GLODAP %>% 
  distinct(lat, lon, era)

# cant data at observations grid
cant_3d_obs <- left_join(
  GLODAP_obs_grid_era,
  cant_3d)

# calculate number of cant data points per grid cell
cant_3d_obs <- cant_3d_obs %>%
  group_by(lon, lat, era) %>% 
  mutate(n = n()) %>% 
  ungroup()

cant_3d_obs %>%
  filter(n <= 1) %>%
  ggplot(aes(lon,lat)) +
  geom_point(data = GLODAP_obs_grid_era, aes(lon, lat)) +
  geom_point(col = "red") +
  facet_wrap(~era)

Version	Author	Date
c8b76b3	jens-daniel-mueller	2020-12-19

rm(cant_3d, GLODAP_obs_grid_era)

GLODAP_cant_obs <- full_join(GLODAP, cant_3d_obs)

rm(GLODAP, 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, era) %>% 
  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, era) %>%
  arrange(depth) %>%
  mutate(cant_int = approxfun(depth, cant, 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, era) %>%
  mutate(cant_int = mean(cant, 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)) %>%
      arrange(depth),
    aes(cant, depth, col = "mapped")
  ) +
  geom_point(
    data = GLODAP_cant_obs %>%
      filter(lat == 48.5, lon == 165.5,!is.na(cant)) %>%
      arrange(depth),
    aes(cant, depth, col = "mapped")
  ) +
  geom_point(
    data = GLODAP_cant_obs %>%
      filter(lat == 48.5, lon == 165.5, date == ymd("2018-06-27")),
    aes(cant_int, depth, col = "interpolated")
  ) +
  scale_y_reverse() +
  facet_wrap(~era) +
  scale_color_brewer(palette = "Dark2", name = "") +
  labs(title = "Cant interpolation to sampling depth - example profile")

Version	Author	Date
c8b76b3	jens-daniel-mueller	2020-12-19

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

rm(GLODAP_cant_obs)

3.3.3 Merge GLODAP + atm. pCO₂

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

GLODAP <- left_join(GLODAP, co2_atm)

3.3.4 Calculation

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

# calculate cstar for reference year
GLODAP <- GLODAP %>%
  mutate(
    cstar_tref_delta =
      ((pCO2 - pCO2_tref) / (pCO2_tref - params_local$preind_atm_pCO2)) * cant,
    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
c8b76b3	jens-daniel-mueller	2020-12-19

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

Version	Author	Date
c8b76b3	jens-daniel-mueller	2020-12-19

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
c8b76b3	jens-daniel-mueller	2020-12-19

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
c8b76b3	jens-daniel-mueller	2020-12-19

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)
        )
  
}

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c8b76b3	jens-daniel-mueller	2020-12-19

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c8b76b3	jens-daniel-mueller	2020-12-19

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c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
c8b76b3	jens-daniel-mueller	2020-12-19

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
c8b76b3	jens-daniel-mueller	2020-12-19

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(
  paste(
    path_root,
    "/utilities/functions/python_scripts/",
    "Gamma_GLODAP_python.py",
    sep = ""
  )
)

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
c8b76b3	jens-daniel-mueller	2020-12-19

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c8b76b3	jens-daniel-mueller	2020-12-19

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c8b76b3	jens-daniel-mueller	2020-12-19

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
c8b76b3	jens-daniel-mueller	2020-12-19

rm(GLODAP_vars, GLODAP_vars_long)

7 Individual cruise sections

Zonal and meridional section plots are produce for each cruise individually and are available under:

/nfs/kryo/work/jenmueller/emlr_cant/model/v_XXX/figures/Cruise_sections_histograms/

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_0.8.0     
 [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_1.7.2          
 [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_1.2.1          lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.9.1      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_0.7.3              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_0.1.0          
[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_1.13.4        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_3.0.1           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_0.3.6              dbplyr_1.4.4            
[70] tidyselect_1.1.0         xfun_0.19

eMLR data preparation

Jens Daniel Müller

21 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 Cant at tref

3.3.2 Combine GLODAP + Cant

3.3.3 Merge GLODAP + atm. pCO₂

3.3.4 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

21 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 Cant at tref

3.3.2 Combine GLODAP + Cant

3.3.3 Merge GLODAP + atm. pCO2

3.3.4 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*

3.3.3 Merge GLODAP + atm. pCO₂