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1 Data sources

Following Cant column inventories are used:

1.1 This study

Results from this study are referred to as JDM.

# path_version_data <- "/nfs/kryo/work/jenmueller/emlr_cant/model/v_XXX_118_Gruber_atm_incr_gamma/data/"

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

#cant_inv_JDM <- cant_inv

1.2 Model Cant

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

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

1.3 Join data sets

Inventories 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)

# 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
  )

2 Cant budgets

Global Cant inventories budget were estimated separately for ocean basins in units of Pg C, based on all vs positive only Cant estimates.

Results were integrated over the upper 100, 500, 1000, 3000, 10^{4} m of the water column.

# calculate budgets
cant_inv_budget <- cant_inv_wide %>%
  mutate(
    surface_area = earth_surf(lat, lon),
    cant_inv_grid_JDM = cant_inv_JDM * surface_area,
    cant_inv_grid_M = cant_inv_M * surface_area,
    cant_pos_inv_grid_JDM = cant_pos_inv_JDM * surface_area,
    cant_pos_inv_grid_M = cant_pos_inv_M * surface_area,
    cant_inv_offset_grid = cant_inv_offset * surface_area,
    cant_pos_inv_offset_grid = cant_pos_inv_offset * surface_area
  ) %>%
  group_by(basin_AIP, eras, inv_depth) %>%
  summarise(
    cant_JDM = sum(cant_inv_grid_JDM) * 12 * 1e-15,
    cant_JDM = round(cant_JDM, 1),
    cant_M = sum(cant_inv_grid_M) * 12 * 1e-15,
    cant_M = round(cant_M, 1),
    cant_pos_JDM = sum(cant_pos_inv_grid_JDM) * 12 * 1e-15,
    cant_pos_JDM = round(cant_pos_JDM, 1),
    cant_pos_M = sum(cant_pos_inv_grid_M) * 12 * 1e-15,
    cant_pos_M = round(cant_pos_M, 1),
    cant_inv_offset = sum(cant_inv_offset_grid) * 12 * 1e-15,
    cant_inv_offset = round(cant_inv_offset, 1),
    cant_pos_inv_offset = sum(cant_pos_inv_offset_grid) * 12 * 1e-15,
    cant_pos_inv_offset = round(cant_pos_inv_offset, 1)
  ) %>%
  ungroup()

# print budget table
cant_inv_budget %>%
  gt(
    rowname_col = "basin_AIP",
    groupname_col = c("eras", "inv_depth"),
    row_group.sep = " | Depth: "
  ) %>%
  summary_rows(groups = TRUE,
               fns = list(total = "sum"))
cant_JDM cant_M cant_pos_JDM cant_pos_M cant_inv_offset cant_pos_inv_offset
1982-1999 --> 2000-2012 | Depth: 100
Atlantic 1.1 1.0 1.1 1.0 0.1 0.1
Indian 1.0 0.9 1.0 0.9 0.1 0.1
Pacific 2.4 2.1 2.4 2.1 0.3 0.3
total 4.50 4.00 4.50 4.00 0.50 0.50
1982-1999 --> 2000-2012 | Depth: 500
Atlantic 4.0 3.7 4.0 3.7 0.3 0.3
Indian 3.1 3.4 3.1 3.4 -0.3 -0.3
Pacific 6.7 7.5 6.8 7.5 -0.7 -0.7
total 13.80 14.60 13.90 14.60 −0.70 −0.70
1982-1999 --> 2000-2012 | Depth: 1000
Atlantic 5.2 4.8 5.3 4.8 0.3 0.5
Indian 4.1 5.0 4.4 5.0 -1.0 -0.6
Pacific 7.5 9.6 8.4 9.6 -2.1 -1.2
total 16.80 19.40 18.10 19.40 −2.80 −1.30
1982-1999 --> 2000-2012 | Depth: 3000
Atlantic 5.9 5.5 6.3 5.5 0.4 0.8
Indian 3.6 5.9 5.0 5.9 -2.3 -0.9
Pacific 8.6 10.3 10.2 10.4 -1.7 -0.2
total 18.10 21.70 21.50 21.80 −3.60 −0.30
1982-1999 --> 2000-2012 | Depth: 10000
Atlantic 5.9 5.5 6.4 5.5 0.4 0.9
Indian 3.5 5.9 5.2 5.9 -2.3 -0.7
Pacific 9.1 10.4 10.9 10.5 -1.2 0.4
total 18.50 21.80 22.50 21.90 −3.10 0.60
2000-2012 --> 2008-2019 | Depth: 100
Atlantic 0.7 0.5 0.7 0.5 0.1 0.1
Indian 0.6 0.5 0.6 0.5 0.1 0.1
Pacific 1.3 1.1 1.3 1.1 0.2 0.2
total 2.60 2.10 2.60 2.10 0.40 0.40
2000-2012 --> 2008-2019 | Depth: 500
Atlantic 2.1 2.0 2.1 2.0 0.1 0.1
Indian 2.0 1.9 2.0 1.9 0.1 0.1
Pacific 4.2 4.1 4.2 4.1 0.1 0.1
total 8.30 8.00 8.30 8.00 0.30 0.30
2000-2012 --> 2008-2019 | Depth: 1000
Atlantic 2.9 2.7 2.9 2.7 0.2 0.2
Indian 2.5 2.8 2.7 2.8 -0.3 -0.1
Pacific 5.2 5.4 5.4 5.4 -0.1 0.0
total 10.60 10.90 11.00 10.90 −0.20 0.10
2000-2012 --> 2008-2019 | Depth: 3000
Atlantic 3.8 3.1 3.8 3.1 0.7 0.7
Indian 2.3 3.3 3.2 3.3 -1.1 -0.1
Pacific 5.6 5.8 6.2 5.9 -0.2 0.4
total 11.70 12.20 13.20 12.30 −0.60 1.00
2000-2012 --> 2008-2019 | Depth: 10000
Atlantic 3.9 3.1 4.0 3.1 0.8 0.9
Indian 2.2 3.3 3.3 3.3 -1.1 0.0
Pacific 5.9 5.8 6.6 5.9 0.1 0.7
total 12.00 12.20 13.90 12.30 −0.20 1.60 
# cant_inv_budget %>%
#   write_csv("~/Thesis/emlr_cant/model/emlr_mod_preprocessing/budget_118_Gruber.csv")

rm(cant_inv_budget)

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_1.0.5     purrr_0.3.4    
[13] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.3  
[17] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 sf_0.9-8                 lubridate_1.7.9         
 [4] gsw_1.0-5                httr_1.4.2               rprojroot_2.0.2         
 [7] tools_4.0.3              backports_1.1.10         R6_2.5.0                
[10] KernSmooth_2.23-18       DBI_1.1.0                colorspace_2.0-0        
[13] withr_2.3.0              tidyselect_1.1.0         compiler_4.0.3          
[16] git2r_0.27.1             cli_2.2.0                rvest_0.3.6             
[19] xml2_1.3.2               sass_0.2.0               checkmate_2.0.0         
[22] classInt_0.4-3           digest_0.6.27            rmarkdown_2.5           
[25] oce_1.3-0                pkgconfig_2.0.3          htmltools_0.5.0         
[28] dbplyr_1.4.4             rlang_0.4.10             readxl_1.3.1            
[31] rstudioapi_0.13          generics_0.1.0           jsonlite_1.7.2          
[34] magrittr_2.0.1           Matrix_1.2-18            Rcpp_1.0.5              
[37] munsell_0.5.0            fansi_0.4.1              lifecycle_1.0.0         
[40] stringi_1.5.3            whisker_0.4              yaml_2.2.1              
[43] grid_4.0.3               blob_1.2.1               parallel_4.0.3          
[46] promises_1.1.1           crayon_1.3.4             lattice_0.20-41         
[49] haven_2.3.1              hms_0.5.3                seacarb_3.2.15          
[52] knitr_1.30               pillar_1.4.7             reprex_0.3.0            
[55] glue_1.4.2               evaluate_0.14            RcppArmadillo_0.10.1.2.2
[58] data.table_1.13.6        modelr_0.1.8             vctrs_0.3.6             
[61] httpuv_1.5.4             testthat_3.0.1           cellranger_1.1.0        
[64] gtable_0.3.0             assertthat_0.2.1         xfun_0.20               
[67] broom_0.7.5              RcppEigen_0.3.3.9.1      e1071_1.7-4             
[70] later_1.1.0.1            class_7.3-17             units_0.6-7             
[73] ellipsis_0.3.1           here_1.0.1