Comparison of cant estimates to results of Gruber et al 2019

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Last updated: 2021-01-13

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---

1 Data sources

Following Cant estimates are used:

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

1.1 This study

Results from this study are referred to as JDM.

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

cant_zonal_JDM <- cant_zonal_JDM %>%
  select(lat,
         depth,
         eras,
         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(inv_depth == params_global$inventory_depth_standard)

1.2 Modeled Cant

“True” Cant fields directly inferred from the model output are referred to as M.

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

cant_tref_1 <-
  read_csv(paste(
    path_preprocessing,
    "cant_annual_field_", params_local$model_runs, "/cant_",
    unique(tref$year[1]),
    ".csv",
    sep = ""
  ))

cant_tref_1 <- cant_tref_1 %>%
  rename(cant_tref_1 = cant_total) %>%
  select(-year)

cant_tref_2 <-
  read_csv(paste(
    path_preprocessing,
    "cant_annual_field_", params_local$model_runs, "/cant_",
    unique(tref$year[2]),
    ".csv",
    sep = ""
  ))

cant_tref_2 <- cant_tref_2 %>%
  rename(cant_tref_2 = cant_total) %>%
  select(-year)

cant_tref_3 <-
  read_csv(paste(
    path_preprocessing,
    "cant_annual_field_", params_local$model_runs, "/cant_",
    unique(tref$year[3]),
    ".csv",
    sep = ""
  ))

cant_tref_3 <- cant_tref_3 %>%
  rename(cant_tref_3 = cant_total) %>%
  select(-year)

cant_M_1 <- left_join(cant_tref_1, cant_tref_2) %>%
  mutate(cant = cant_tref_2 - cant_tref_1,
         eras = unique(cant_inv_JDM$eras)[1]) %>% 
  select(-c(cant_tref_1, cant_tref_2))

cant_M_1 <- cant_M_1 %>% 
   mutate(cant_pos = if_else(cant <= 0, 0, cant))

cant_M_2 <- left_join(cant_tref_2, cant_tref_3) %>%
  mutate(cant = cant_tref_3 - cant_tref_2,
         eras = unique(cant_inv_JDM$eras)[2]) %>% 
  select(-c(cant_tref_2, cant_tref_3))

cant_M_2 <- cant_M_2 %>% 
   mutate(cant_pos = if_else(cant <= 0, 0, cant))

cant_M <- full_join(cant_M_1, cant_M_2) %>%
  arrange(lon, lat, depth, basin_AIP)

rm(cant_tref_1, cant_tref_2, cant_tref_3, cant_M_1, cant_M_2)

cant_zonal_M <- m_zonal_mean_section(cant_M)

cant_inv_M <- m_cant_inv(cant_M)

cant_inv_M <- cant_inv_M %>%
  filter(inv_depth == params_global$inventory_depth_standard)

1.3 Join data sets

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

# add estimate label
cant_inv_long <- bind_rows(
  cant_inv_JDM %>%  mutate(estimate = "JDM"),
  cant_inv_M %>%  mutate(estimate = "M")
  )

# pivot to wide format
cant_inv_wide <- cant_inv_long %>% 
  pivot_wider(names_from = estimate, values_from = cant_pos_inv:cant_inv) %>% 
  drop_na()

# calculate offset
cant_inv_wide <- cant_inv_wide %>% 
  mutate(cant_pos_inv_offset = cant_pos_inv_JDM - cant_pos_inv_M,
         cant_inv_offset = cant_inv_JDM - cant_inv_M,
         estimate = "JDM - M")

# add estimate label
cant_zonal_long <- bind_rows(
  cant_zonal_JDM %>%  mutate(estimate = "JDM"),
  cant_zonal_M %>%  mutate(estimate = "M")
  )

# pivot to wide format
cant_zonal_wide <- cant_zonal_long %>% 
  pivot_wider(names_from = estimate, values_from = cant_mean:cant_pos_sd) %>% 
  drop_na()

# calculate offset
cant_zonal_wide <- cant_zonal_wide %>% 
  mutate(cant_pos_mean_offset = cant_pos_mean_JDM - cant_pos_mean_M,
         cant_mean_offset = cant_mean_JDM - cant_mean_M,
         estimate = "JDM - M")

2 Cant budgets

Global Cant inventories budget were estimated for different ocean basins in units of Pg C, based on all vs positive only Cant estimates. Please note that here we only added Cant values for the standard inventory depth (3000 m) and do not apply additional corrections for areas not covered.

# calculate budgets
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, eras) %>% 
  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()

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

	cant_total	cant_pos_total
1982-1999 --> 2000-2012 - JDM
Atlantic	6.2	6.5
Indian	3.8	5.1
Pacific	11.3	12.7
total	21.30	24.30
2000-2012 --> 2013-2019 - JDM
Atlantic	4.5	5.0
Indian	3.0	4.1
Pacific	10.7	10.9
total	18.20	20.00
1982-1999 --> 2000-2012 - M
Atlantic	5.5	5.5
Indian	6.0	6.1
Pacific	10.7	10.8
total	22.20	22.40
2000-2012 --> 2013-2019 - M
Atlantic	4.1	4.2
Indian	4.5	4.5
Pacific	8.1	8.3
total	16.70	17.00

rm(cant_inv_budget)

3 Inventory maps

3.1 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 the MLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.

3.1.1 Absolute values

Column inventory of positive Cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).

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

Past versions of cant_pos_inventory_map-1.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_inventory_map-2.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_inventory_map-3.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_pos_inventory_map-4.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

3.1.2 Offset

Column inventory of positive cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).

for (i_eras in unique(cant_inv_wide$eras)) {
  
  print(
    p_map_cant_inv_offset(
      cant_inv_wide %>% filter(eras == i_eras),
      "cant_pos_inv_offset",
      subtitle_text = paste("Estimate JDM - M | Eras:", i_eras)
    )
  )
  
}

Past versions of cant_pos_offset_inventory_map-1.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_offset_inventory_map-2.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

3.2 Cant - all

In a second series of plots we explore the distribution of Cant, taking positive and negative estimates into account (positive here refers to the mean cant estimate across MLR model predictions available for each grid cell).

3.2.1 Absolute values

Column inventory of Cant (including positive and negative values) between the surface and 3000m water depth per horizontal grid cell (lat x lon).

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

Past versions of cant_inventory_map-1.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_inventory_map-2.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_inventory_map-3.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_inventory_map-4.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

3.2.2 Offset

for (i_eras in unique(cant_inv_wide$eras)) {
  
  print(
    p_map_cant_inv_offset(
      df = cant_inv_wide %>% filter(eras == i_eras),
      var = "cant_inv_offset",
      subtitle_text = paste("Estimate JDM - M | Eras:", i_eras)
    )
  )
  
}

Past versions of cant_offset_inventory_map-1.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_offset_inventory_map-2.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

4 Zonal mean sections

4.1 Cant - positive only

4.1.1 Absolute values

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

Past versions of cant_pos_zonal_mean_section-1.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_zonal_mean_section-2.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_zonal_mean_section-3.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_zonal_mean_section-4.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_zonal_mean_section-5.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_zonal_mean_section-6.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_zonal_mean_section-7.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_pos_zonal_mean_section-8.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_pos_zonal_mean_section-9.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_pos_zonal_mean_section-10.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_pos_zonal_mean_section-11.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_pos_zonal_mean_section-12.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

4.1.2 Offset

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

Past versions of cant_pos_offset_zonal_section-1.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_offset_zonal_section-2.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_offset_zonal_section-3.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_pos_offset_zonal_section-4.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_pos_offset_zonal_section-5.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_pos_offset_zonal_section-6.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

4.2 Cant - all

4.2.1 Absolute values

for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
  for (i_estimate in unique(cant_zonal_long$estimate)) {
    for (i_eras in unique(cant_zonal_long$eras)) {
      
      print(
        p_section_zonal(
          df = cant_zonal_long %>%
            filter(
              basin_AIP == i_basin_AIP,
              estimate == i_estimate,
              eras == i_eras
            ),
          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,
              " | Eras:",
              i_eras
            )
        )
        
      )
      
    }
  }
}

Past versions of cant_zonal_mean_section-1.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_zonal_mean_section-2.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_zonal_mean_section-3.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_zonal_mean_section-4.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_zonal_mean_section-5.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_zonal_mean_section-6.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_zonal_mean_section-7.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_zonal_mean_section-8.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_zonal_mean_section-9.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_zonal_mean_section-10.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_zonal_mean_section-11.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_zonal_mean_section-12.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

4.2.2 Offset

for (i_basin_AIP in unique(cant_zonal_wide$basin_AIP)) {
  for (i_eras in unique(cant_zonal_wide$eras)) {
    
    print(
      p_section_zonal(
        df = cant_zonal_wide %>%
          filter(basin_AIP == i_basin_AIP,
                 eras == i_eras),
        var = "cant_mean_offset",
        plot_slabs = "n",
        col = "divergent",
        breaks = params_global$breaks_cant_offset,
        subtitle_text =
          paste("Basin:", i_basin_AIP, "| estimate: JDM - M | Eras:", i_eras)
      )
      
    )
  }
}

Past versions of cant_offset_zonal_mean_section-1.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_offset_zonal_mean_section-2.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_offset_zonal_mean_section-3.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11
7cdea0c	jens-daniel-mueller	2021-01-06

Past versions of cant_offset_zonal_mean_section-4.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_offset_zonal_mean_section-5.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Past versions of cant_offset_zonal_mean_section-6.png

Version	Author	Date
a076226	Donghe-Zhu	2021-01-11

Session information

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2

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_1.1.1 collapse_1.5.0 
 [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_1.4-1         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_2.1.0                magrittr_1.5             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_2.3.2           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