Last updated: 2020-12-14

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Rmd	923aa7f	jens-daniel-mueller	2020-12-14	rebuild with new path and auto folder creation
html	984697e	jens-daniel-mueller	2020-12-12	Build site.
html	3ebff89	jens-daniel-mueller	2020-12-12	Build site.
Rmd	a1aaa71	jens-daniel-mueller	2020-12-12	selectable basinmask, rebuild
html	7788175	jens-daniel-mueller	2020-12-09	Build site.
Rmd	64b795c	jens-daniel-mueller	2020-12-09	added histograms after data preparation
html	b01a367	jens-daniel-mueller	2020-12-09	Build site.
Rmd	71c63b0	jens-daniel-mueller	2020-12-09	rerun with variable predictor assignment
html	24a632f	jens-daniel-mueller	2020-12-07	Build site.
html	92dca91	jens-daniel-mueller	2020-12-07	Build site.
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html	143d6fa	jens-daniel-mueller	2020-12-07	Build site.
html	3c8a83c	jens-daniel-mueller	2020-12-04	Build site.
Rmd	e11f455	jens-daniel-mueller	2020-12-04	improved output plots by using stat_2d instead of points
html	abc6818	jens-daniel-mueller	2020-12-03	Build site.
Rmd	992ba15	jens-daniel-mueller	2020-12-03	rebuild with variable inventory depth
html	c8c2e7b	jens-daniel-mueller	2020-12-03	Build site.
Rmd	83203db	jens-daniel-mueller	2020-12-03	calculate cant with variable inventory depth
html	090e4d5	jens-daniel-mueller	2020-12-02	Build site.
html	7c25f7a	jens-daniel-mueller	2020-12-02	Build site.
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Rmd	1d71aff	jens-daniel-mueller	2020-12-02	revised plots in mapping steps
html	f8358f8	jens-daniel-mueller	2020-12-02	Build site.
Rmd	3ba472c	jens-daniel-mueller	2020-12-02	revised plots in mapping steps
html	b03ddb8	jens-daniel-mueller	2020-12-02	Build site.
Rmd	9183e8f	jens-daniel-mueller	2020-12-02	revised assignment of era to eras
html	22d0127	jens-daniel-mueller	2020-12-01	Build site.
html	0ff728b	jens-daniel-mueller	2020-12-01	Build site.
html	f8f449c	jens-daniel-mueller	2020-12-01	Build site.
Rmd	bcfa1a9	jens-daniel-mueller	2020-12-01	auto eras naming
html	cf19652	jens-daniel-mueller	2020-11-30	Build site.
Rmd	2842970	jens-daniel-mueller	2020-11-30	cleaned for eMLR part only
html	196be51	jens-daniel-mueller	2020-11-30	Build site.
Rmd	7a4b015	jens-daniel-mueller	2020-11-30	first rebuild on ETH server
Rmd	bc61ce3	Jens Müller	2020-11-30	Initial commit
html	bc61ce3	Jens Müller	2020-11-30	Initial commit

1 Predictor fields

Currently, we use combined predictor fields:

WOA18: S, T, and derived variables
GLODAP16: Oxygen, PO4, NO3, Silicate, and derived variables

predictors <-
  read_csv(paste(path_version_data,
                 "W18_st_G16_opsn.csv",
                 sep = ""))

predictors_surface <-
  read_csv(paste(path_version_data,
                 "W18_st_G16_opsn_surface.csv",
                 sep = ""))

2 Atm. pCO2

co2_atm_tref <-
  read_csv(paste(path_version_data,
                 "co2_atm_tref.csv",
                 sep = ""))

3 Load MLR models

lm_all_wide <-
  read_csv(paste(path_version_data,
                 "lm_all_cant.csv",
                 sep = ""))

4 Merge MLRs + climatologies

lm_all_wide <- lm_all_wide %>% 
  mutate(model = str_remove(model, "Cstar ~ "))
   
cant <- full_join(predictors, lm_all_wide)

rm(predictors, lm_all_wide)

5 Map cant

5.1 Deep water

5.2 Apply MLRs to predictor

cant <- b_cant(cant)

5.2.1 Sections by model

Zonal sections plots are produced for every 20° longitude, each era and for all models individually and can be downloaded here.

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

for (i_eras in unique(cant$eras)) {
  # i_eras <- unique(cant$eras)[2]
  cant_eras <- cant %>%
    filter(eras == i_eras)
  
  for (i_lon in params_global$longitude_sections_regular) {
    # i_lon <- params_global$longitude_sections_regular[7]
    cant_eras_lon <- cant_eras %>%
      filter(lon == i_lon)
    
    limits = max(abs(cant_eras_lon$cant)) * c(-1,1)
    
    cant_eras_lon %>%
      ggplot(aes(lat, depth, z = cant)) +
      stat_summary_2d(fun = "mean",
                      na.rm = TRUE,
                      bins = 20,
                      col = "grey") +
      scale_fill_scico(
        name = "Cant",
        palette = "vik",
        limit = limits
      ) +
      scale_y_reverse(limits = c(params_global$plotting_depth,NA)) +
      scale_x_continuous(limits = c(-85, 85)) +
      labs(title = paste("eras:", i_eras,
                         "| lon:", i_lon,
                         "|", params_local$Version_ID)) +
      facet_wrap( ~ model, ncol = 5)
    
    ggsave(
      paste(
        path_version_figures,
        "Cant_model_sections/",
        paste("Cant_model",
              i_eras,
              "lon",
              i_lon,
              "section.png",
              sep = "_"),
        sep = ""
      ),
      width = 17,
      height = 9
    )
    
  }
}
}

5.3 Surface water

As outlined in Gruber et al. (2019), a transient equilibrium approach was applied to estimate cant in surface waters, assuming that the CO₂ system in these waters has followed the increase in atmospheric CO₂ closely.

Using eq 10.2.16 from OBD, the change in anthropogenic CO₂ in the upper ocean was computed as:

_tC_ant,eq(t2,ref − t1,ref) = 1∕γ ⋅ DIC/pCO2 ⋅ (pCO2atm (t2ref)− pCO2atm (t1ref))

, where DIC and pCO₂ are the in situ values, where γ is the buffer (Revelle) factor and where we evaluated the right-hand side using seacarb employing the Mehrbach constants as refitted by Dickson and Millero using the climatological values for temperature, salinity, DIC and Alk.

5.3.1 pCO₂ climatology

# calculate pCO2 from talk and tco2 climatology
predictors_surface <- predictors_surface %>% 
  mutate(pCO2 = carb(flag = 15,
                     var1 = TAlk*1e-6,
                     var2 = TCO2*1e-6,
                     S = sal,
                     T = temp,
                     P = depth/10,
                     Pt = phosphate*1e-6,
                     Sit = silicate*1e-6,
                     k1k2 = "l")$pCO2)

p_map_climatology(
  df = predictors_surface,
  var = "pCO2")

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
143d6fa	jens-daniel-mueller	2020-12-07
abc6818	jens-daniel-mueller	2020-12-03
b03ddb8	jens-daniel-mueller	2020-12-02
22d0127	jens-daniel-mueller	2020-12-01
f8f449c	jens-daniel-mueller	2020-12-01
cf19652	jens-daniel-mueller	2020-11-30
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

p_section_climatology_regular(
  df = predictors_surface,
  var = "pCO2")

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
143d6fa	jens-daniel-mueller	2020-12-07
abc6818	jens-daniel-mueller	2020-12-03
f8358f8	jens-daniel-mueller	2020-12-02
b03ddb8	jens-daniel-mueller	2020-12-02
22d0127	jens-daniel-mueller	2020-12-01
f8f449c	jens-daniel-mueller	2020-12-01
cf19652	jens-daniel-mueller	2020-11-30
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

5.3.2 Revelle factor

predictors_surface <- predictors_surface %>% 
  mutate(rev_fac = buffer(flag = 15,
                     var1 = TAlk*1e-6,
                     var2 = TCO2*1e-6,
                     S = sal,
                     T = temp,
                     P = depth/10,
                     Pt = phosphate*1e-6,
                     Sit = silicate*1e-6,
                     k1k2 = "l")$BetaD)

p_map_climatology(
  df = predictors_surface,
  var = "rev_fac")

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
143d6fa	jens-daniel-mueller	2020-12-07
abc6818	jens-daniel-mueller	2020-12-03
b03ddb8	jens-daniel-mueller	2020-12-02
22d0127	jens-daniel-mueller	2020-12-01
f8f449c	jens-daniel-mueller	2020-12-01
cf19652	jens-daniel-mueller	2020-11-30
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

p_section_climatology_regular(
  df = predictors_surface,
  var = "rev_fac")

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
143d6fa	jens-daniel-mueller	2020-12-07
abc6818	jens-daniel-mueller	2020-12-03
f8358f8	jens-daniel-mueller	2020-12-02
b03ddb8	jens-daniel-mueller	2020-12-02
22d0127	jens-daniel-mueller	2020-12-01
f8f449c	jens-daniel-mueller	2020-12-01
cf19652	jens-daniel-mueller	2020-11-30
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

5.3.3 C_ant

# calculate increase in atm pCO2 between eras
co2_atm_tref <- co2_atm_tref %>% 
  arrange(pCO2_tref) %>% 
  mutate(d_pCO2_tref = pCO2_tref - lag(pCO2_tref),
         eras = paste(lag(era), era, sep = " --> ")) %>% 
  drop_na() %>% 
  select(eras, d_pCO2_tref)

cant_surface <- full_join(predictors_surface, co2_atm_tref,
                          by = character())

# calculate cant
cant_surface <- cant_surface %>% 
  mutate(cant = (1 / rev_fac) * (TCO2 / pCO2) * d_pCO2_tref)

# calculate positive cant
cant_surface <- cant_surface %>%
  mutate(cant_pos = if_else(cant < 0, 0, cant))

# i_eras <- unique(cant_average$eras)[2]

for (i_eras in unique(cant_surface$eras)) {
  print(
    p_map_climatology(df = cant_surface %>% filter(eras == i_eras),
                      var = "cant")  +
      labs(subtitle = paste("era:", i_eras))
  )
  
}

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
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
abc6818	jens-daniel-mueller	2020-12-03
7c25f7a	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
f8358f8	jens-daniel-mueller	2020-12-02
b03ddb8	jens-daniel-mueller	2020-12-02
22d0127	jens-daniel-mueller	2020-12-01
0ff728b	jens-daniel-mueller	2020-12-01
f8f449c	jens-daniel-mueller	2020-12-01
cf19652	jens-daniel-mueller	2020-11-30
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
7788175	jens-daniel-mueller	2020-12-09
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
abc6818	jens-daniel-mueller	2020-12-03
7c25f7a	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02

# i_eras <- unique(cant_surface$eras)[2]

for (i_eras in unique(cant_surface$eras)) {
  print(
    p_section_climatology_regular(df = cant_surface %>% filter(eras == i_eras),
                                  var = "cant") +
      labs(subtitle = paste("era:", i_eras))
  )
  
}

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
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
abc6818	jens-daniel-mueller	2020-12-03
7c25f7a	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
f8358f8	jens-daniel-mueller	2020-12-02
b03ddb8	jens-daniel-mueller	2020-12-02
22d0127	jens-daniel-mueller	2020-12-01
0ff728b	jens-daniel-mueller	2020-12-01
f8f449c	jens-daniel-mueller	2020-12-01
cf19652	jens-daniel-mueller	2020-11-30
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
3ebff89	jens-daniel-mueller	2020-12-12
7788175	jens-daniel-mueller	2020-12-09
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
abc6818	jens-daniel-mueller	2020-12-03
7c25f7a	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02

5.4 Mean Cant fields

Mean and sd are calculated for cant in each grid cell (XYZ), basin and era combination. Calculations are performed for all cant values vs positive values only. This averaging step summarizes the information derived from ten best fitting MLRs.

5.4.1 Deep water averaging

cant_average <- m_cant_model_average(cant)
cant_average <- m_cut_gamma(cant_average, "gamma")

# split data set for individual predictor contributions and total cant
cant_predictor_average <- cant_average %>% 
  select(-c("cant", "cant_pos", ends_with("_sd")))

cant_average <- cant_average %>% 
  select(lon, lat, depth, eras, basin, basin_AIP,
            cant, cant_pos, cant_sd, cant_pos_sd,
            gamma, gamma_sd, gamma_slab)

# i_eras <- unique(cant_average$eras)[2]

for (i_eras in unique(cant_surface$eras)) {
  print(
    p_map_climatology(
      df = cant_average %>% filter(eras == i_eras),
      var = "cant_pos",
      subtitle_text = paste("era:", i_eras)
    )
  )
  
}

Version	Author	Date
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
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
abc6818	jens-daniel-mueller	2020-12-03
090e4d5	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02

Version	Author	Date
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
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
abc6818	jens-daniel-mueller	2020-12-03
090e4d5	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02

# i_eras <- unique(cant_surface$eras)[2]

for (i_eras in unique(cant_surface$eras)) {
  print(
    p_section_climatology_regular(
      df = cant_average %>% filter(eras == i_eras),
      var = "cant_pos",
      subtitle_text = paste("era:", i_eras)
    )
  )
}

Version	Author	Date
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
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
abc6818	jens-daniel-mueller	2020-12-03
090e4d5	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02

Version	Author	Date
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
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
abc6818	jens-daniel-mueller	2020-12-03
090e4d5	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02

5.4.2 Surface water averaging

cant_surface_average <- m_cant_model_average(cant_surface)
cant_surface_average <- m_cut_gamma(cant_surface_average, "gamma")
rm(cant_surface)

5.4.3 Join surface and deep water

cant_average <- full_join(cant_average, cant_surface_average)
rm(cant_surface_average)

5.5 Mean cant sections

For each basin and era combination, the zonal mean cant is calculated, again for all vs positive only values. Likewise, sd is calculated for the averaging of the mean basin fields.

cant_average_zonal <- m_cant_zonal_mean(cant_average)
cant_average_zonal <- m_cut_gamma(cant_average_zonal, "gamma_mean")

5.6 Mean cant sections by coefficient

For each basin and era combination, the zonal mean cant is calculated by model coefficient.

cant_predictor_average_zonal <-
  m_cant_predictor_zonal_mean(cant_predictor_average)

cant_predictor_average_zonal <-
  m_cut_gamma(cant_predictor_average_zonal, "gamma")

5.7 Inventory calculation

To calculate cant column inventories, we:

Multiple layer thickness with cant concentration to get a layer inventory
For each horizontal grid cell and era, sum cant layer inventories from 150 - 3000 m

Step 2 is performed again for all cant and positive cant values only

cant_inv <- m_cant_inv(cant_average)

p_map_cant_inv(
  df = cant_inv,
  var = "cant_pos_inv",
  subtitle_text = "for predefined integration depths"
) +
  facet_grid(inv_depth ~ eras)

Version	Author	Date
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
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
abc6818	jens-daniel-mueller	2020-12-03

6 Write csv

cant_average %>%
  write_csv(paste(path_version_data,
                  "cant_3d.csv", sep = ""))

cant_predictor_average %>%
  write_csv(paste(path_version_data,
                  "cant_predictor_3d.csv", sep = ""))

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

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

cant_inv %>%
  write_csv(paste(path_version_data,
                  "cant_inv.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] seacarb_3.2.14  oce_1.2-0       gsw_1.0-5       testthat_3.0.0 
 [5] metR_0.9.0      scico_1.2.0     patchwork_1.1.0 collapse_1.4.2 
 [9] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
[13] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.2  
[17] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               jsonlite_1.7.1           viridisLite_0.3.0       
 [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] promises_1.1.1           checkmate_2.0.0          rvest_0.3.6             
[19] colorspace_2.0-0         htmltools_0.5.0          httpuv_1.5.4            
[22] Matrix_1.2-18            pkgconfig_2.0.3          broom_0.7.2             
[25] haven_2.3.1              scales_1.1.1             whisker_0.4             
[28] later_1.1.0.1            git2r_0.27.1             farver_2.0.3            
[31] generics_0.0.2           ellipsis_0.3.1           withr_2.3.0             
[34] cli_2.2.0                magrittr_2.0.1           crayon_1.3.4            
[37] readxl_1.3.1             evaluate_0.14            fs_1.5.0                
[40] fansi_0.4.1              xml2_1.3.2               RcppArmadillo_0.10.1.2.0
[43] tools_4.0.3              data.table_1.13.2        hms_0.5.3               
[46] lifecycle_0.2.0          munsell_0.5.0            reprex_0.3.0            
[49] isoband_0.2.2            compiler_4.0.3           rlang_0.4.9             
[52] grid_4.0.3               rstudioapi_0.13          labeling_0.4.2          
[55] rmarkdown_2.5            gtable_0.3.0             DBI_1.1.0               
[58] R6_2.5.0                 lubridate_1.7.9          knitr_1.30              
[61] rprojroot_2.0.2          stringi_1.5.3            parallel_4.0.3          
[64] Rcpp_1.0.5               vctrs_0.3.5              dbplyr_1.4.4            
[67] tidyselect_1.1.0         xfun_0.18

Mapping cant

Jens Daniel Müller

14 December, 2020

1 Predictor fields

2 Atm. pCO2

3 Load MLR models

4 Merge MLRs + climatologies

5 Map cant

5.1 Deep water

5.2 Apply MLRs to predictor

5.2.1 Sections by model

5.3 Surface water

5.3.1 pCO₂ climatology

5.3.2 Revelle factor

5.3.3 C_ant

5.4 Mean Cant fields

5.4.1 Deep water averaging

5.4.2 Surface water averaging

5.4.3 Join surface and deep water

5.5 Mean cant sections

5.6 Mean cant sections by coefficient

5.7 Inventory calculation

6 Write csv

Mapping cant

Jens Daniel Müller

14 December, 2020

1 Predictor fields

2 Atm. pCO2

3 Load MLR models

4 Merge MLRs + climatologies

5 Map cant

5.1 Deep water

5.2 Apply MLRs to predictor

5.2.1 Sections by model

5.3 Surface water

5.3.1 pCO2 climatology

5.3.2 Revelle factor

5.3.3 Cant

5.4 Mean Cant fields

5.4.1 Deep water averaging

5.4.2 Surface water averaging

5.4.3 Join surface and deep water

5.5 Mean cant sections

5.6 Mean cant sections by coefficient

5.7 Inventory calculation

6 Write csv

5.3.1 pCO₂ climatology

5.3.3 C_ant