Last updated: 2021-04-16

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1 Data sources

Cant estimates from this sensitivity case:

  • Mean and SD per grid cell (lat, lon, depth)
  • Zonal mean and SD (basin, lat, depth)
  • Inventories (lat, lon)
cant_inv <-
  read_csv(paste(path_version_data,
                 "cant_inv.csv",
                 sep = ""))

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

cant_inv <- bind_rows(cant_inv, cant_inv_mod_truth)

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

2 Time periods

tref
# A tibble: 2 x 2
  era       median_year
  <chr>           <dbl>
1 2000-2009        2006
2 2010-2019        2014
duration <- sort(tref$median_year)[2] - sort(tref$median_year)[1]
duration
[1] 8

3 Cant budgets

Global Cant inventories were estimated in units of Pg C. Please note that here we added Cant (all vs postitive only) values and do not apply additional corrections for areas not covered.

cant_inv_budget <- cant_inv %>% 
  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, data_source, inv_depth) %>% 
  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 %>%  
  group_by(data_source, inv_depth) %>% 
  summarise(cant_total = sum(cant_total),
            cant_total = round(cant_total,1),
            cant_pos_total = sum(cant_pos_total),
            cant_pos_total = round(cant_pos_total,1),
            cant_total_rate = cant_total / duration,
            cant_pos_total_rate = cant_pos_total / duration) %>% 
  ungroup()
# A tibble: 15 x 6
   data_source inv_depth cant_total cant_pos_total cant_total_rate
   <chr>           <dbl>      <dbl>          <dbl>           <dbl>
 1 mod               100        2.9            3             0.362
 2 mod               500       10.4           10.4           1.3  
 3 mod              1000       14             14.1           1.75 
 4 mod              3000       17.1           17.4           2.14 
 5 mod             10000       17.3           17.8           2.16 
 6 mod_truth         100        2.9            2.9           0.362
 7 mod_truth         500       10.8           10.9           1.35 
 8 mod_truth        1000       14.8           14.8           1.85 
 9 mod_truth        3000       16.6           16.8           2.08 
10 mod_truth       10000       17             17.1           2.12 
11 obs               100        3.4            3.4           0.425
12 obs               500       11.5           11.6           1.44 
13 obs              1000       16.2           16.2           2.02 
14 obs              3000       21.7           22.2           2.71 
15 obs             10000       24             24.9           3    
# … with 1 more variable: cant_pos_total_rate <dbl>

3.1 Standard depth

Results integrated over the upper 3000 m

cant_inv_budget %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  pivot_longer(cols = c(cant_total, cant_pos_total), names_to = "estimate", values_to = "cant_total") %>% 
  ggplot(aes(data_source, cant_total, fill = basin_AIP)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_wrap(~ estimate)

Version Author Date
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
a00ec94 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
81b7c6d jens-daniel-mueller 2021-04-16
0a6099c jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
099d566 jens-daniel-mueller 2021-04-14
bb44686 jens-daniel-mueller 2021-04-14
bf40480 jens-daniel-mueller 2021-04-13
9f31fe3 jens-daniel-mueller 2021-04-13
f6ed7de jens-daniel-mueller 2021-04-13 
cant_inv_budget %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  gt(rowname_col = "basin_AIP",
     groupname_col = c("data_source", "inv_depth"),
     row_group.sep = " | Depth: ") %>% 
  summary_rows(
    groups = TRUE,
    fns = list(total = "sum")
  )
cant_total cant_pos_total
mod | Depth: 3000
Atlantic 4.5 4.6
Indian 4.3 4.4
Pacific 8.3 8.4
total 17.10 17.40
mod_truth | Depth: 3000
Atlantic 4.1 4.2
Indian 4.5 4.5
Pacific 8.0 8.1
total 16.60 16.80
obs | Depth: 3000
Atlantic 6.3 6.7
Indian 4.0 4.1
Pacific 11.4 11.4
total 21.70 22.20 
cant_inv_bias <- cant_inv_budget %>%
  filter(data_source %in% c("mod", "mod_truth")) %>%
  select(data_source, basin_AIP, inv_depth, cant_pos_total, cant_total) %>%
  pivot_longer(cols = c(cant_total, cant_pos_total), names_to = "estimate", values_to = "cant_total") %>% 
  pivot_wider(names_from = data_source,
              values_from = cant_total) %>%
  mutate(cant_bias = mod - mod_truth,
         cant_bias_rel = cant_bias / mod_truth)

cant_inv_bias %>% 
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  ggplot(aes(cant_bias, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2")

Version Author Date
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
a00ec94 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
81b7c6d jens-daniel-mueller 2021-04-16
0a6099c jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
099d566 jens-daniel-mueller 2021-04-14
bb44686 jens-daniel-mueller 2021-04-14
bf40480 jens-daniel-mueller 2021-04-13
9f31fe3 jens-daniel-mueller 2021-04-13
6eecb54 jens-daniel-mueller 2021-04-13

3.2 Other depths

Results integrated over the upper 100, 500, 1000, 3000, 10^{4} m

cant_inv_budget %>%
  filter(inv_depth != params_global$inventory_depth_standard) %>% 
  pivot_longer(cols = c(cant_total, cant_pos_total), names_to = "estimate", values_to = "cant_total") %>% 
  ggplot(aes(data_source, cant_total, fill = basin_AIP)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_grid(inv_depth ~ estimate, scales = "free_y")

Version Author Date
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
a00ec94 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
81b7c6d jens-daniel-mueller 2021-04-16
0a6099c jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
099d566 jens-daniel-mueller 2021-04-14
bb44686 jens-daniel-mueller 2021-04-14
bf40480 jens-daniel-mueller 2021-04-13
9f31fe3 jens-daniel-mueller 2021-04-13
f6ed7de jens-daniel-mueller 2021-04-13 
cant_inv_budget %>%
  filter(inv_depth != params_global$inventory_depth_standard) %>% 
  gt(rowname_col = "basin_AIP",
     groupname_col = c("data_source", "inv_depth"),
     row_group.sep = " | Depth: ") %>% 
  summary_rows(
    groups = TRUE,
    fns = list(total = "sum")
  )
cant_total cant_pos_total
mod | Depth: 100
Atlantic 0.7 0.8
Indian 0.6 0.6
Pacific 1.6 1.6
total 2.90 3.00
mod | Depth: 500
Atlantic 2.6 2.6
Indian 2.4 2.4
Pacific 5.4 5.4
total 10.40 10.40
mod | Depth: 1000
Atlantic 3.5 3.5
Indian 3.5 3.5
Pacific 7.0 7.1
total 14.00 14.10
mod | Depth: 10000
Atlantic 4.6 4.7
Indian 4.2 4.4
Pacific 8.5 8.7
total 17.30 17.80
mod_truth | Depth: 100
Atlantic 0.7 0.7
Indian 0.6 0.6
Pacific 1.6 1.6
total 2.90 2.90
mod_truth | Depth: 500
Atlantic 2.7 2.7
Indian 2.5 2.5
Pacific 5.6 5.7
total 10.80 10.90
mod_truth | Depth: 1000
Atlantic 3.7 3.7
Indian 3.8 3.8
Pacific 7.3 7.3
total 14.80 14.80
mod_truth | Depth: 10000
Atlantic 4.2 4.2
Indian 4.6 4.6
Pacific 8.2 8.3
total 17.00 17.10
obs | Depth: 100
Atlantic 0.9 0.9
Indian 0.7 0.7
Pacific 1.8 1.8
total 3.40 3.40
obs | Depth: 500
Atlantic 3.1 3.2
Indian 2.4 2.4
Pacific 6.0 6.0
total 11.50 11.60
obs | Depth: 1000
Atlantic 4.7 4.7
Indian 3.4 3.4
Pacific 8.1 8.1
total 16.20 16.20
obs | Depth: 10000
Atlantic 6.3 7.0
Indian 4.3 4.4
Pacific 13.4 13.5
total 24.00 24.90 
rm(cant_inv_budget)

cant_inv_bias %>% 
  filter(inv_depth != params_global$inventory_depth_standard) %>% 
  ggplot(aes(cant_bias, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_wrap(~ inv_depth)

Version Author Date
c0a47df jens-daniel-mueller 2021-04-16
50290e8 jens-daniel-mueller 2021-04-16
a00ec94 jens-daniel-mueller 2021-04-16
b6fe355 jens-daniel-mueller 2021-04-16
81b7c6d jens-daniel-mueller 2021-04-16
0a6099c jens-daniel-mueller 2021-04-16
ddec5b7 jens-daniel-mueller 2021-04-15
29edae5 jens-daniel-mueller 2021-04-14
099d566 jens-daniel-mueller 2021-04-14
bb44686 jens-daniel-mueller 2021-04-14
bf40480 jens-daniel-mueller 2021-04-13
9f31fe3 jens-daniel-mueller 2021-04-13
6eecb54 jens-daniel-mueller 2021-04-13 

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         kableExtra_1.3.1 marelac_2.1.10   shape_1.4.5     
 [5] scales_1.1.1     metR_0.9.0       scico_1.2.0      patchwork_1.1.1 
 [9] collapse_1.5.0   forcats_0.5.0    stringr_1.4.0    dplyr_1.0.5     
[13] purrr_0.3.4      readr_1.4.0      tidyr_1.1.2      tibble_3.0.4    
[17] ggplot2_3.3.3    tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 lubridate_1.7.9          gsw_1.0-5               
 [4] RColorBrewer_1.1-2       webshot_0.5.2            httr_1.4.2              
 [7] rprojroot_2.0.2          tools_4.0.3              backports_1.1.10        
[10] utf8_1.1.4               R6_2.5.0                 DBI_1.1.0               
[13] colorspace_1.4-1         withr_2.3.0              tidyselect_1.1.0        
[16] compiler_4.0.3           git2r_0.27.1             cli_2.1.0               
[19] rvest_0.3.6              xml2_1.3.2               sass_0.2.0              
[22] labeling_0.4.2           checkmate_2.0.0          digest_0.6.27           
[25] rmarkdown_2.5            oce_1.2-0                pkgconfig_2.0.3         
[28] htmltools_0.5.0          dbplyr_1.4.4             rlang_0.4.10            
[31] readxl_1.3.1             rstudioapi_0.13          farver_2.0.3            
[34] generics_0.0.2           jsonlite_1.7.1           magrittr_1.5            
[37] Matrix_1.2-18            Rcpp_1.0.5               munsell_0.5.0           
[40] fansi_0.4.1              lifecycle_1.0.0          stringi_1.5.3           
[43] whisker_0.4              yaml_2.2.1               grid_4.0.3              
[46] blob_1.2.1               parallel_4.0.3           promises_1.1.1          
[49] crayon_1.3.4             lattice_0.20-41          haven_2.3.1             
[52] hms_0.5.3                seacarb_3.2.14           knitr_1.30              
[55] pillar_1.4.7             reprex_0.3.0             glue_1.4.2              
[58] evaluate_0.14            RcppArmadillo_0.10.1.2.0 data.table_1.13.2       
[61] modelr_0.1.8             vctrs_0.3.5              httpuv_1.5.4            
[64] testthat_2.3.2           cellranger_1.1.0         gtable_0.3.0            
[67] assertthat_0.2.1         xfun_0.18                broom_0.7.5             
[70] RcppEigen_0.3.3.7.0      later_1.1.0.1            viridisLite_0.3.0       
[73] ellipsis_0.3.1           here_0.1