Analysis of dcant budgets

workflowr

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Last updated: 2021-08-03

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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)

# integrated per basin_AIP

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)

# globally integrated

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

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

dcant_budget_global <- bind_rows(dcant_budget_global,
                                    dcant_budget_global_mod_truth)

rm(dcant_budget_global_mod_truth)

# reference year

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 Global

4.1 Standard depth

Results integrated over the upper 3000 m

dcant_budget_global %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  ggplot(aes(estimate, value, fill = data_source)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col(position = position_dodge2())

dcant_budget_global %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  select(-inv_depth) %>% 
  pivot_wider(names_from = estimate,
              values_from = value) %>% 
  gt(
    groupname_col = c("data_source")
  ) %>%
  summary_rows(groups = TRUE,
               fns = list(total = "sum"))

dcant	dcant_pos
mod
22.684	23.421
obs
29.181	30.199
mod_truth
20.722	20.861

dcant_budget_global_bias <- dcant_budget_global %>%
  filter(data_source %in% c("mod", "mod_truth")) %>%
  select(data_source, 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_global_bias %>% 
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  ggplot(aes(dcant_bias, estimate)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2")

dcant_budget_global_bias %>% 
  filter(inv_depth == params_global$inventory_depth_standard) %>% 
  ggplot(aes(dcant_bias_rel, estimate)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2")

dcant_budget_global_bias %>%
  filter(inv_depth == params_global$inventory_depth_standard) %>%
  ggplot() +
  geom_col(aes(estimate, mod,
               fill = "eMLR")) +
  geom_col(aes(estimate, mod_truth,
               col = "truth"),
           fill = "transparent") +
  scale_fill_manual(values = "grey60") +
  scale_color_manual(values = c("red", "red")) +
  theme(legend.title = element_blank()) +
  labs(y = "value")

  # geom_segment(aes(
  #   x = estimate,
  #   y = mod,
  #   xend = estimate,
  #   yend = mod_truth,
  #   col = "truth"
  # )) +
  # geom_point(aes(x = estimate,
  #                y = mod_truth,
  #                col = "truth"))

4.2 Other depths

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

dcant_budget_global <- dcant_budget_global %>%
  mutate(inv_depth = as.factor(inv_depth))

dcant_budget_global %>%
  ggplot() +
  scale_fill_viridis_d() +
  geom_col(aes(data_source, value, fill = inv_depth),
           position = position_dodge2()) +
  facet_grid(. ~ estimate, scales = "free_y")

dcant_budget_global %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>% 
  gt(
    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
3.983	3.984
mod | Depth: 500
13.559	13.575
mod | Depth: 1000
18.593	18.654
mod | Depth: 3000
22.684	23.421
mod | Depth: 10000
22.936	24.101
obs | Depth: 100
4.188	4.217
obs | Depth: 500
14.914	15.020
obs | Depth: 1000
21.219	21.452
obs | Depth: 3000
29.181	30.199
obs | Depth: 10000
31.840	33.627
mod_truth | Depth: 100
3.710	3.710
mod_truth | Depth: 500
13.589	13.596
mod_truth | Depth: 1000
18.322	18.338
mod_truth | Depth: 3000
20.722	20.861
mod_truth | Depth: 10000
21.055	21.220

dcant_budget_global_bias %>% 
  ggplot(aes(dcant_bias, estimate)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

dcant_budget_global_bias %>% 
  ggplot(aes(dcant_bias_rel, estimate)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

dcant_budget_global_bias %>%
  ggplot() +
  geom_vline(xintercept = 0) +
  geom_col(aes(mod, estimate, fill = "eMLR"),
           position = "dodge") +
  geom_col(aes(mod_truth, estimate,
               col = "truth"),
           fill = "transparent") +
  scale_fill_manual(values = "grey60") +
  scale_color_manual(values = c("red", "red")) +
  theme(legend.title = element_blank()) +
  labs(x = "value") +
  facet_grid(inv_depth ~ .)

5 Basin AIP

5.1 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)

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	5.595	5.768
Indian	6.697	6.792
Pacific	10.392	10.861
total	22.68	23.42
obs | Depth: 3000
Atlantic	7.788	8.029
Indian	6.584	6.821
Pacific	14.810	15.349
total	29.18	30.20
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")

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")

5.2 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")

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	1.052	1.053
Indian	0.837	0.837
Pacific	2.093	2.094
total	3.98	3.98
mod | Depth: 500
Atlantic	3.499	3.500
Indian	3.057	3.068
Pacific	7.003	7.007
total	13.56	13.57
mod | Depth: 1000
Atlantic	4.575	4.583
Indian	4.745	4.781
Pacific	9.273	9.290
total	18.59	18.65
mod | Depth: 3000
Atlantic	5.595	5.768
Indian	6.697	6.792
Pacific	10.392	10.861
total	22.68	23.42
mod | Depth: 10000
Atlantic	5.662	5.905
Indian	6.742	6.924
Pacific	10.532	11.272
total	22.94	24.10
obs | Depth: 100
Atlantic	1.072	1.078
Indian	0.886	0.895
Pacific	2.230	2.243
total	4.19	4.22
obs | Depth: 500
Atlantic	3.951	3.976
Indian	3.352	3.377
Pacific	7.612	7.666
total	14.91	15.02
obs | Depth: 1000
Atlantic	5.810	5.846
Indian	4.780	4.882
Pacific	10.628	10.724
total	21.22	21.45
obs | Depth: 3000
Atlantic	7.788	8.029
Indian	6.584	6.821
Pacific	14.810	15.349
total	29.18	30.20
obs | Depth: 10000
Atlantic	8.047	8.705
Indian	7.407	7.711
Pacific	16.385	17.212
total	31.84	33.63
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 ~ .)

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 ~ .)

6 Write files

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

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

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         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