Last updated: 2021-07-22

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Knit directory: emlr_obs_v_XXX/

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1 Version ID

The results displayed on this site correspond to the Version_ID: v_XXX

2 Data sources

dcant estimates from this sensitivity case:

  • Mean and SD per grid cell (lat, lon, depth)
  • Zonal mean and SD (basin, lat, depth)
  • Inventories (lat, lon)
dcant_budget_basin_AIP <-
  read_csv(paste(path_version_data,
                 "dcant_budget_basin_AIP.csv",
                 sep = "")) %>% 
  filter(method == "total") %>% 
  select(-method)

dcant_budget_basin_AIP_mod_truth <-
  read_csv(paste(
    path_version_data,
    "dcant_budget_basin_AIP_mod_truth.csv",
    sep = ""
  )) %>% 
  filter(method == "total") %>% 
  select(-method)

dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
                                    dcant_budget_basin_AIP_mod_truth)

rm(dcant_budget_basin_AIP_mod_truth)

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

3 Time periods

tref
# A tibble: 2 x 2
  era       median_year
  <chr>           <dbl>
1 2000-2009        2004
2 2010-2019        2014
duration <- sort(tref$median_year)[2] - sort(tref$median_year)[1]
duration
[1] 10

4 dcant budgets

4.1 Standard depth

Results integrated over the upper 3000 m

dcant_budget_basin_AIP %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  ggplot(aes(estimate, value, fill=basin_AIP)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_grid(~data_source)

Version Author Date
426b2df jens-daniel-mueller 2021-07-21
971ce87 jens-daniel-mueller 2021-07-13 
dcant_budget_basin_AIP %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>% 
  gt(
    rowname_col = "basin_AIP",
    groupname_col = c("data_source", "inv_depth"),
    row_group.sep = " | Depth: "
  ) %>%
  summary_rows(groups = TRUE,
               fns = list(total = "sum"))
dcant dcant_pos
mod | Depth: 3000
Atlantic 5.590 5.796
Indian 6.571 6.644
Pacific 10.255 10.703
total 22.42 23.14
obs | Depth: 3000
Atlantic 7.824 8.054
Indian 6.612 6.819
Pacific 14.739 15.279
total 29.18 30.15
mod_truth | Depth: 3000
Atlantic 5.108 5.126
Indian 5.604 5.621
Pacific 10.011 10.115
total 20.72 20.86 
dcant_budget_basin_AIP_bias <- dcant_budget_basin_AIP %>%
  filter(data_source %in% c("mod", "mod_truth")) %>%
  select(data_source, basin_AIP, inv_depth, estimate, value) %>%
  pivot_wider(names_from = data_source,
              values_from = value) %>%
  mutate(dcant_bias = mod - mod_truth,
         dcant_bias_rel = dcant_bias / mod_truth * 100)

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

Version Author Date
426b2df jens-daniel-mueller 2021-07-21
971ce87 jens-daniel-mueller 2021-07-13 
dcant_budget_basin_AIP_bias %>% 
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  ggplot(aes(dcant_bias_rel, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2")

Version Author Date
426b2df jens-daniel-mueller 2021-07-21
971ce87 jens-daniel-mueller 2021-07-13

4.2 Other depths

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

dcant_budget_basin_AIP %>%
  ggplot(aes(data_source, value, fill=basin_AIP)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_grid(inv_depth ~ estimate, scales = "free_y")

Version Author Date
426b2df jens-daniel-mueller 2021-07-21
971ce87 jens-daniel-mueller 2021-07-13
910d64a jens-daniel-mueller 2021-07-02
1cbf907 jens-daniel-mueller 2021-07-02
955f5dc jens-daniel-mueller 2021-06-29
d054c39 jens-daniel-mueller 2021-06-24
4fcd066 jens-daniel-mueller 2021-06-24
7a8d5f8 jens-daniel-mueller 2021-06-15
e0660fe jens-daniel-mueller 2021-06-15
c6b3da6 jens-daniel-mueller 2021-06-14
7e1f407 jens-daniel-mueller 2021-06-10
2cbe18c jens-daniel-mueller 2021-06-10
69c79d0 jens-daniel-mueller 2021-06-08
1772903 jens-daniel-mueller 2021-06-07
594ed9a jens-daniel-mueller 2021-06-04
db7df0e jens-daniel-mueller 2021-06-04
2edc791 jens-daniel-mueller 2021-06-04
207339d jens-daniel-mueller 2021-06-03
315710b jens-daniel-mueller 2021-06-03
be90356 jens-daniel-mueller 2021-06-02
d37a85d jens-daniel-mueller 2021-05-31
4b7a5ee jens-daniel-mueller 2021-05-28
25bd183 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
52e7583 jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
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 
dcant_budget_basin_AIP %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>% 
  gt(
    rowname_col = "basin_AIP",
    groupname_col = c("data_source", "inv_depth"),
    row_group.sep = " | Depth: "
  ) %>%
  summary_rows(groups = TRUE,
               fns = list(total = "sum"))
dcant dcant_pos
mod | Depth: 100
Atlantic 1.061 1.061
Indian 0.834 0.834
Pacific 2.083 2.084
total 3.98 3.98
mod | Depth: 500
Atlantic 3.522 3.524
Indian 3.035 3.041
Pacific 6.966 6.969
total 13.52 13.53
mod | Depth: 1000
Atlantic 4.593 4.599
Indian 4.714 4.744
Pacific 9.159 9.177
total 18.47 18.52
mod | Depth: 3000
Atlantic 5.590 5.796
Indian 6.571 6.644
Pacific 10.255 10.703
total 22.42 23.14
mod | Depth: 10000
Atlantic 5.638 5.928
Indian 6.601 6.762
Pacific 10.414 11.156
total 22.65 23.85
obs | Depth: 100
Atlantic 1.074 1.079
Indian 0.894 0.903
Pacific 2.237 2.246
total 4.21 4.23
obs | Depth: 500
Atlantic 3.932 3.959
Indian 3.333 3.360
Pacific 7.661 7.706
total 14.93 15.03
obs | Depth: 1000
Atlantic 5.797 5.835
Indian 4.752 4.857
Pacific 10.732 10.812
total 21.28 21.50
obs | Depth: 3000
Atlantic 7.824 8.054
Indian 6.612 6.819
Pacific 14.739 15.279
total 29.18 30.15
obs | Depth: 10000
Atlantic 8.142 8.756
Indian 7.445 7.725
Pacific 16.119 16.983
total 31.71 33.46
mod_truth | Depth: 100
Atlantic 0.930 0.930
Indian 0.796 0.796
Pacific 1.983 1.983
total 3.71 3.71
mod_truth | Depth: 500
Atlantic 3.409 3.410
Indian 3.118 3.118
Pacific 7.061 7.067
total 13.59 13.60
mod_truth | Depth: 1000
Atlantic 4.523 4.530
Indian 4.652 4.652
Pacific 9.146 9.156
total 18.32 18.34
mod_truth | Depth: 3000
Atlantic 5.108 5.126
Indian 5.604 5.621
Pacific 10.011 10.115
total 20.72 20.86
mod_truth | Depth: 10000
Atlantic 5.151 5.180
Indian 5.678 5.700
Pacific 10.227 10.341
total 21.06 21.22 
dcant_budget_basin_AIP_bias %>% 
  ggplot(aes(dcant_bias, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Version Author Date
426b2df jens-daniel-mueller 2021-07-21
971ce87 jens-daniel-mueller 2021-07-13
910d64a jens-daniel-mueller 2021-07-02
1cbf907 jens-daniel-mueller 2021-07-02
955f5dc jens-daniel-mueller 2021-06-29
d054c39 jens-daniel-mueller 2021-06-24
4fcd066 jens-daniel-mueller 2021-06-24
7a8d5f8 jens-daniel-mueller 2021-06-15
e0660fe jens-daniel-mueller 2021-06-15
c6b3da6 jens-daniel-mueller 2021-06-14
7e1f407 jens-daniel-mueller 2021-06-10
2cbe18c jens-daniel-mueller 2021-06-10
69c79d0 jens-daniel-mueller 2021-06-08
1772903 jens-daniel-mueller 2021-06-07
594ed9a jens-daniel-mueller 2021-06-04
db7df0e jens-daniel-mueller 2021-06-04
2edc791 jens-daniel-mueller 2021-06-04
207339d jens-daniel-mueller 2021-06-03
315710b jens-daniel-mueller 2021-06-03
be90356 jens-daniel-mueller 2021-06-02
d37a85d jens-daniel-mueller 2021-05-31
4b7a5ee jens-daniel-mueller 2021-05-28
25bd183 jens-daniel-mueller 2021-05-26
62bd574 jens-daniel-mueller 2021-05-20
7c56c39 jens-daniel-mueller 2021-05-19
52e7583 jens-daniel-mueller 2021-05-12
969e631 jens-daniel-mueller 2021-05-12
d2a83bc jens-daniel-mueller 2021-04-16
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 
dcant_budget_basin_AIP_bias %>% 
  ggplot(aes(dcant_bias_rel, estimate, fill=basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Version Author Date
426b2df jens-daniel-mueller 2021-07-21
971ce87 jens-daniel-mueller 2021-07-13

5 Write csv

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

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     ggforce_0.3.3    metR_0.9.0       scico_1.2.0     
 [9] patchwork_1.1.1  collapse_1.5.0   forcats_0.5.0    stringr_1.4.0   
[13] dplyr_1.0.5      purrr_0.3.4      readr_1.4.0      tidyr_1.1.2     
[17] tibble_3.0.4     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_0.11          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               MASS_7.3-53             
[46] grid_4.0.3               blob_1.2.1               parallel_4.0.3          
[49] promises_1.1.1           crayon_1.3.4             lattice_0.20-41         
[52] haven_2.3.1              hms_0.5.3                seacarb_3.2.14          
[55] knitr_1.30               pillar_1.4.7             reprex_0.3.0            
[58] glue_1.4.2               evaluate_0.14            RcppArmadillo_0.10.1.2.0
[61] data.table_1.13.2        modelr_0.1.8             vctrs_0.3.5             
[64] tweenr_1.0.2             httpuv_1.5.4             testthat_2.3.2          
[67] cellranger_1.1.0         gtable_0.3.0             polyclip_1.10-0         
[70] assertthat_0.2.1         xfun_0.18                broom_0.7.5             
[73] RcppEigen_0.3.3.7.0      later_1.1.0.1            viridisLite_0.3.0       
[76] ellipsis_0.3.1           here_0.1