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1 Data sources

Following Cant estimates from this study (JDM) and Gruber 2019 (G19) are used:

Zonal mean (basin, lat, depth)
Inventories (lat, lon)

1.1 This study

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

cant_zonal_JDM <- cant_zonal_JDM %>%
  filter(eras == unique(cant_zonal_JDM$eras)[1]) %>%
  select(lat,
         depth,
         basin_AIP,
         cant_mean,
         cant_pos_mean,
         cant_sd,
         cant_pos_sd)


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

cant_inv_JDM <- cant_inv_JDM %>%
  filter(eras == unique(cant_inv_JDM$eras)[1],
         inv_depth == params_global$inventory_depth_standard) %>%
  select(-c(eras))

1.2 Gruber 2019

cant_inv_G19 <-
  read_csv(paste(path_preprocessing,
                 "G19_cant_inv.csv",
                 sep = ""))

cant_inv_G19 <- cant_inv_G19 %>%
  select(-eras)

cant_zonal_G19 <-
  read_csv(paste(path_preprocessing,
                 "G19_cant_zonal.csv",
                 sep = ""))

cant_zonal_G19 <- cant_zonal_G19 %>%
  filter(eras == "JGOFS_GO") %>%
  select(lat,
         depth,
         basin_AIP,
         cant_mean,
         cant_pos_mean,
         cant_sd,
         cant_pos_sd)

1.3 Join data sets

Inventories and zonal sections are merged, and differences calculate per grid cell.

cant_inv_long <- bind_rows(
  cant_inv_JDM %>%  mutate(estimate = "JDM"),
  cant_inv_G19 %>%  mutate(estimate = "G19")
  )

cant_inv_wide <- cant_inv_long %>% 
  pivot_wider(names_from = estimate, values_from = cant_pos_inv:cant_inv) %>% 
  drop_na()

cant_inv_wide <- cant_inv_wide %>% 
  mutate(cant_pos_inv_offset = cant_pos_inv_JDM - cant_pos_inv_G19,
         cant_inv_offset = cant_inv_JDM - cant_inv_G19,
         estimate = "JDM - G19")

cant_zonal_long <- bind_rows(
  cant_zonal_JDM %>%  mutate(estimate = "JDM"),
  cant_zonal_G19 %>%  mutate(estimate = "G19")
  )

cant_zonal_wide <- cant_zonal_long %>% 
  pivot_wider(names_from = estimate, values_from = cant_mean:cant_pos_sd) %>% 
  drop_na()

cant_zonal_wide <- cant_zonal_wide %>% 
  mutate(cant_pos_mean_offset = cant_pos_mean_JDM - cant_pos_mean_G19,
         cant_mean_offset = cant_mean_JDM - cant_mean_G19,
         estimate = "JDM - G19")

2 Cant budgets

Global Cant inventories were estimated in units of Pg C, based on all vs positive only Cant estimates. Please note that here we only added cant values in the upper m and do not apply additional corrections for areas not covered.

cant_inv_budget <- cant_inv_long %>% 
  mutate(surface_area = earth_surf(lat, lon),
         cant_inv_grid = cant_inv*surface_area,
         cant_pos_inv_grid = cant_pos_inv*surface_area) %>% 
  group_by(basin_AIP, estimate) %>% 
  summarise(cant_total = sum(cant_inv_grid)*12*1e-15,
            cant_total = round(cant_total,1),
            cant_pos_total = sum(cant_pos_inv_grid)*12*1e-15,
            cant_pos_total = round(cant_pos_total,1)) %>% 
  ungroup()


cant_inv_budget %>%
  gt(rowname_col = "basin_AIP",
     groupname_col = c("estimate")) %>% 
  summary_rows(
    groups = TRUE,
    fns = list(total = "sum")
  )

	cant_total	cant_pos_total
G19
Atlantic	10.8	11.0
Indian	5.9	7.1
Pacific	12.8	13.4
total	29.50	31.50
JDM
Atlantic	9.9	10.5
Indian	13.2	13.4
Pacific	12.6	13.4
total	35.70	37.30

rm(cant_inv_budget)

3 Cant - positive only

In a first series of plots we explore the distribution of Cant, taking only positive estimates into account (positive here refers to the mean cant estimate across 10 eMLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.

3.1 Inventory map

3.1.1 Absolute values

Column inventory of positive cant between the surface and m water depth per horizontal grid cell (lat x lon).

# i_estimate <- unique(cant_inv_long$estimate)[1]

for (i_estimate in unique(cant_inv_long$estimate)) {
  
  print(
    p_map_cant_inv(
      cant_inv_long %>% filter(estimate == i_estimate),
      subtitle_text = paste("Estimate:", i_estimate))
    )
  
}

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
dfde8b7	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
ec8dc38	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

3.1.2 Offset

Column inventory of positive cant between the surface and m water depth per horizontal grid cell (lat x lon).

p_map_cant_inv_offset(cant_inv_wide,
                      "cant_pos_inv_offset",
                      subtitle_text = "JDM - G19")

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
ec8dc38	jens-daniel-mueller	2020-12-02
dfde8b7	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

3.2 Zonal mean sections

3.2.1 Absolute values

# i_basin_AIP <- unique(cant_zonal_long$basin_AIP)[1]
# i_estimate <- unique(cant_zonal_long$estimate)[1]

for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
  for (i_estimate in unique(cant_zonal_long$estimate)) {
   
     print(
      p_section_zonal(
        df = cant_zonal_long %>%
          filter(basin_AIP == i_basin_AIP,
                 estimate == i_estimate),
        var = "cant_pos_mean",
        plot_slabs = "n",
        subtitle_text =
          paste("Basin:", i_basin_AIP, "| estimate:", i_estimate)
      )
      
    )
    
  }
}

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

3.2.2 Offset

# i_basin_AIP <- unique(cant_zonal_wide$basin_AIP)[1]
# i_estimate <- unique(cant_zonal_wide$estimate)[1]

for (i_basin_AIP in unique(cant_zonal_wide$basin_AIP)) {
    print(
      p_section_zonal(
        df = cant_zonal_wide %>%
          filter(basin_AIP == i_basin_AIP),
        var = "cant_pos_mean_offset",
        breaks = params_global$breaks_cant_offset,
        plot_slabs = "n",
        col = "divergent",
        subtitle_text =
          paste("Basin:", i_basin_AIP)
      )
    )
  }

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

4 Cant - all

In a first series of plots we explore the distribution of cant, taking only positive estimates into account (positive here refers to the mean cant estimate across 10 eMLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.

4.1 Inventory map

4.1.1 Absolute values

Column inventory of Cant (including positive and negative values) between the surface and m water depth per horizontal grid cell (lat x lon).

# i_estimate <- unique(cant_inv_long$estimate)[1] 

for (i_estimate in unique(cant_inv_long$estimate)) {
  
  print(
    p_map_cant_inv(
    cant_inv_long %>% filter(estimate == i_estimate),
    subtitle_text = paste("Estimate:", i_estimate),
    col = "divergent")
  )
  
}

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
dfde8b7	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
ec8dc38	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

4.1.2 Offset

p_map_cant_inv_offset(
  df = cant_inv_wide,
  var = "cant_inv_offset",
  subtitle_text = "JDM - G19")

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
ec8dc38	jens-daniel-mueller	2020-12-02
dfde8b7	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

4.2 Zonal mean sections

4.2.1 Absolute values

# i_basin_AIP <- unique(df$basin_AIP)[1]
# i_estimate <- unique(df$estimate)[1]

for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
  for (i_estimate in unique(cant_zonal_long$estimate)) {
   
     print(
      p_section_zonal(
        df = cant_zonal_long %>%
          filter(basin_AIP == i_basin_AIP,
                 estimate == i_estimate),
        var = "cant_mean",
        col = "divergent",
        breaks = params_global$breaks_cant,
        plot_slabs = "n",
        legend_title = expression(atop(Delta * C[ant],
                                          (mu * mol ~ kg ^ {-1}))),
        subtitle_text =
          paste("Basin:", i_basin_AIP, "| estimate:", i_estimate)
      )
      
    )
    
  }
}

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

4.2.2 Offset

# i_basin_AIP <- unique(cant_zonal_wide$basin_AIP)[1]
# i_estimate <- unique(cant_zonal_wide$estimate)[1]

for (i_basin_AIP in unique(cant_zonal_wide$basin_AIP)) {

     print(
      p_section_zonal(
        df = cant_zonal_wide %>%
          filter(basin_AIP == i_basin_AIP),
        var = "cant_mean_offset",
        plot_slabs = "n",
        col = "divergent",
        breaks = params_global$breaks_cant_offset,
        subtitle_text =
          paste("Basin:", i_basin_AIP, "| estimate:", i_estimate)
      )
      
    )
}

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
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
090e4d5	jens-daniel-mueller	2020-12-02
902f65a	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
92edddb	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

5 Known issues

Deviations between this study and the results by Gruber et al (2019), short G19, for the same period, might be attributable to following known differences in the implementation of the eMLR(C*) method:

GLODAPv2_2020 here vs an extended version of GLODAPv2 in G19
flagging: Here, we accept f flags 0 and 2 (except for tco2, where only 0 is accepted). G19 claim to use 0 throughout, yet have a high coverage of talk observations in the SE Pacific
Neutral density calculation: Here and in GLODAPv2_2020 a polynomial approximation is used, whereas G19 uses the original Matlab code
Predictor climatology: Here we used WOA18, whereas G19 used WOA13
Missing data in the GLODAP mapped climatology, eg NO3 at surface, where not filled in this study
cant on neutral density levels calculate as slab mean, rather than on one surface
Here, surface delta cant were calculated based on Luecker constants, rather than Mehrbach as in G19
Here, pCO2 was calculated from DIC/TA Climatology

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] gt_0.2.2        marelac_2.1.10  shape_1.4.5     scales_1.1.1   
 [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               sass_0.2.0               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] 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] seacarb_3.2.14           haven_2.3.1              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] oce_1.2-0                tools_4.0.3              data.table_1.13.2       
[46] hms_0.5.3                lifecycle_0.2.0          munsell_0.5.0           
[49] reprex_0.3.0             gsw_1.0-5                isoband_0.2.2           
[52] compiler_4.0.3           rlang_0.4.9              grid_4.0.3              
[55] rstudioapi_0.13          labeling_0.4.2           rmarkdown_2.5           
[58] testthat_3.0.0           gtable_0.3.0             DBI_1.1.0               
[61] R6_2.5.0                 lubridate_1.7.9          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

Comparison of cant estimates to results of Gruber et al 2019

Jens Daniel Müller

17 December, 2020