Last updated: 2021-04-16

Checks: 7 0

Knit directory: emlr_obs_v_XXX/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200707)

The command set.seed(20200707) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 0b33708

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 0b33708. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Unstaged changes:
    Modified:   code/Workflowr_project_managment.R
    Modified:   data/auxillary/params_local.rds

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/analysis_budgets.Rmd) and HTML (docs/analysis_budgets.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html 0a6099c jens-daniel-mueller 2021-04-16 Build site.
html ddec5b7 jens-daniel-mueller 2021-04-15 Build site.
html 29edae5 jens-daniel-mueller 2021-04-14 Build site.
html 099d566 jens-daniel-mueller 2021-04-14 Build site.
html bb44686 jens-daniel-mueller 2021-04-14 Build site.
html bf40480 jens-daniel-mueller 2021-04-13 Build site.
html 9f31fe3 jens-daniel-mueller 2021-04-13 Build site.
html 6eecb54 jens-daniel-mueller 2021-04-13 Build site.
Rmd b9c18c8 jens-daniel-mueller 2021-04-13 included model data
html f6ed7de jens-daniel-mueller 2021-04-13 Build site.
Rmd 3efd03e jens-daniel-mueller 2021-04-13 included model data
html c98be84 jens-daniel-mueller 2021-04-13 Build site.
html 338dd3c jens-daniel-mueller 2021-04-09 Build site.
html a79ca2c jens-daniel-mueller 2021-04-09 included model data
html 7831fd3 jens-daniel-mueller 2021-04-09 Build site.
html 20b26d4 jens-daniel-mueller 2021-04-08 Build site.
html c0895f8 jens-daniel-mueller 2021-04-07 Build site.
html 156d5b7 jens-daniel-mueller 2021-04-07 Build site.
html d21a452 jens-daniel-mueller 2021-04-07 Build site.
Rmd 296db5c jens-daniel-mueller 2021-04-07 included model data

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)

2 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()

2.1 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
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 2.6 2.9
Indian 2.7 2.7
Pacific 5.5 5.7
total 10.80 11.30
mod_truth | Depth: 3000
Atlantic 2.6 2.6
Indian 2.8 2.8
Pacific 4.9 5.0
total 10.30 10.40
obs | Depth: 3000
Atlantic 3.6 4.0
Indian 4.4 4.6
Pacific 6.5 7.2
total 14.50 15.80 
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
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

2.2 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
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 0.3 0.5
Indian 0.4 0.4
Pacific 1.1 1.1
total 1.80 2.00
mod | Depth: 500
Atlantic 1.5 1.6
Indian 1.4 1.4
Pacific 3.5 3.6
total 6.40 6.60
mod | Depth: 1000
Atlantic 2.1 2.2
Indian 2.1 2.1
Pacific 4.6 4.7
total 8.80 9.00
mod | Depth: 10000
Atlantic 2.7 3.0
Indian 2.7 2.8
Pacific 5.6 5.8
total 11.00 11.60
mod_truth | Depth: 100
Atlantic 0.4 0.4
Indian 0.4 0.4
Pacific 0.9 0.9
total 1.70 1.70
mod_truth | Depth: 500
Atlantic 1.7 1.7
Indian 1.6 1.6
Pacific 3.4 3.4
total 6.70 6.70
mod_truth | Depth: 1000
Atlantic 2.3 2.3
Indian 2.3 2.3
Pacific 4.4 4.5
total 9.00 9.10
mod_truth | Depth: 10000
Atlantic 2.6 2.6
Indian 2.8 2.8
Pacific 5.0 5.1
total 10.40 10.50
obs | Depth: 100
Atlantic 0.5 0.5
Indian 0.4 0.4
Pacific 1.0 1.1
total 1.90 2.00
obs | Depth: 500
Atlantic 1.8 1.8
Indian 1.7 1.8
Pacific 3.5 3.6
total 7.00 7.20
obs | Depth: 1000
Atlantic 2.6 2.6
Indian 2.9 2.9
Pacific 4.7 4.9
total 10.20 10.40
obs | Depth: 10000
Atlantic 3.6 4.3
Indian 5.2 5.4
Pacific 7.1 8.1
total 15.90 17.80 
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
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] webshot_0.5.2            RColorBrewer_1.1-2       httr_1.4.2              
 [7] rprojroot_2.0.2          tools_4.0.3              backports_1.1.10        
[10] R6_2.5.0                 DBI_1.1.0                colorspace_1.4-1        
[13] withr_2.3.0              tidyselect_1.1.0         compiler_4.0.3          
[16] git2r_0.27.1             cli_2.1.0                rvest_0.3.6             
[19] xml2_1.3.2               sass_0.2.0               labeling_0.4.2          
[22] checkmate_2.0.0          digest_0.6.27            rmarkdown_2.5           
[25] oce_1.2-0                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          farver_2.0.3             generics_0.0.2          
[34] jsonlite_1.7.1           magrittr_1.5             Matrix_1.2-18           
[37] Rcpp_1.0.5               munsell_0.5.0            fansi_0.4.1             
[40] lifecycle_1.0.0          stringi_1.5.3            whisker_0.4             
[43] yaml_2.2.1               grid_4.0.3               blob_1.2.1              
[46] parallel_4.0.3           promises_1.1.1           crayon_1.3.4            
[49] lattice_0.20-41          haven_2.3.1              hms_0.5.3               
[52] seacarb_3.2.14           knitr_1.30               pillar_1.4.7            
[55] reprex_0.3.0             glue_1.4.2               evaluate_0.14           
[58] RcppArmadillo_0.10.1.2.0 data.table_1.13.2        modelr_0.1.8            
[61] vctrs_0.3.5              httpuv_1.5.4             testthat_2.3.2          
[64] cellranger_1.1.0         gtable_0.3.0             assertthat_0.2.1        
[67] xfun_0.18                broom_0.7.5              RcppEigen_0.3.3.7.0     
[70] later_1.1.0.1            viridisLite_0.3.0        ellipsis_0.3.1          
[73] here_0.1