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Following Cant column inventories are used:
Results from this study are referred to as JDM.
cant_inv_JDM <-
read_csv(paste(path_version_data,
"cant_inv.csv",
sep = ""))“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 = ""))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
)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 | 1.3 | 1.2 | 1.4 | 1.2 | 0.1 | 0.2 |
| Indian | 1.1 | 1.0 | 1.1 | 1.0 | 0.1 | 0.1 |
| Pacific | 2.7 | 2.5 | 2.7 | 2.5 | 0.2 | 0.2 |
| total | 5.10 | 4.70 | 5.20 | 4.70 | 0.40 | 0.50 |
| 1982-1999 --> 2000-2012 | Depth: 500 | ||||||
| Atlantic | 4.6 | 4.3 | 4.6 | 4.3 | 0.3 | 0.3 |
| Indian | 4.2 | 4.0 | 4.2 | 4.0 | 0.2 | 0.2 |
| Pacific | 9.4 | 8.9 | 9.4 | 8.9 | 0.4 | 0.4 |
| total | 18.20 | 17.20 | 18.20 | 17.20 | 0.90 | 0.90 |
| 1982-1999 --> 2000-2012 | Depth: 1000 | ||||||
| Atlantic | 6.1 | 5.7 | 6.1 | 5.7 | 0.3 | 0.4 |
| Indian | 6.3 | 6.0 | 6.3 | 6.0 | 0.3 | 0.3 |
| Pacific | 12.4 | 11.4 | 12.4 | 11.4 | 0.9 | 0.9 |
| total | 24.80 | 23.10 | 24.80 | 23.10 | 1.50 | 1.60 |
| 1982-1999 --> 2000-2012 | Depth: 3000 | ||||||
| Atlantic | 6.6 | 6.5 | 6.9 | 6.5 | 0.0 | 0.3 |
| Indian | 7.6 | 7.0 | 7.6 | 7.0 | 0.6 | 0.6 |
| Pacific | 13.7 | 12.3 | 13.8 | 12.4 | 1.4 | 1.4 |
| total | 27.90 | 25.80 | 28.30 | 25.90 | 2.00 | 2.30 |
| 1982-1999 --> 2000-2012 | Depth: 10000 | ||||||
| Atlantic | 6.6 | 6.5 | 6.9 | 6.6 | 0.0 | 0.4 |
| Indian | 7.7 | 7.0 | 7.8 | 7.0 | 0.8 | 0.7 |
| Pacific | 13.8 | 12.3 | 14.0 | 12.5 | 1.5 | 1.5 |
| total | 28.10 | 25.80 | 28.70 | 26.10 | 2.30 | 2.60 |
| 2000-2012 --> 2013-2019 | Depth: 100 | ||||||
| Atlantic | 1.2 | 0.9 | 1.2 | 0.9 | 0.2 | 0.2 |
| Indian | 0.9 | 0.8 | 0.9 | 0.8 | 0.1 | 0.1 |
| Pacific | 2.2 | 1.9 | 2.2 | 1.9 | 0.3 | 0.3 |
| total | 4.30 | 3.60 | 4.30 | 3.60 | 0.60 | 0.60 |
| 2000-2012 --> 2013-2019 | Depth: 500 | ||||||
| Atlantic | 3.7 | 3.4 | 3.7 | 3.4 | 0.3 | 0.3 |
| Indian | 3.3 | 3.2 | 3.3 | 3.2 | 0.2 | 0.2 |
| Pacific | 7.5 | 7.0 | 7.5 | 7.0 | 0.4 | 0.4 |
| total | 14.50 | 13.60 | 14.50 | 13.60 | 0.90 | 0.90 |
| 2000-2012 --> 2013-2019 | Depth: 1000 | ||||||
| Atlantic | 4.9 | 4.6 | 4.9 | 4.6 | 0.3 | 0.3 |
| Indian | 5.0 | 4.8 | 5.0 | 4.8 | 0.2 | 0.2 |
| Pacific | 9.8 | 9.2 | 9.8 | 9.2 | 0.7 | 0.7 |
| total | 19.70 | 18.60 | 19.70 | 18.60 | 1.20 | 1.20 |
| 2000-2012 --> 2013-2019 | Depth: 3000 | ||||||
| Atlantic | 5.3 | 5.3 | 5.5 | 5.3 | 0.0 | 0.2 |
| Indian | 5.9 | 5.6 | 6.0 | 5.7 | 0.3 | 0.3 |
| Pacific | 10.9 | 10.0 | 11.0 | 10.1 | 0.9 | 1.0 |
| total | 22.10 | 20.90 | 22.50 | 21.10 | 1.20 | 1.50 |
| 2000-2012 --> 2013-2019 | Depth: 10000 | ||||||
| Atlantic | 5.3 | 5.3 | 5.6 | 5.3 | 0.0 | 0.3 |
| Indian | 6.0 | 5.7 | 6.1 | 5.7 | 0.3 | 0.4 |
| Pacific | 11.0 | 10.0 | 11.2 | 10.1 | 1.0 | 1.1 |
| total | 22.30 | 21.00 | 22.90 | 21.10 | 1.30 | 1.80 |
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_1.0.2 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] httr_1.4.2 sass_0.2.0 jsonlite_1.7.2
[4] here_1.0.1 modelr_0.1.8 assertthat_0.2.1
[7] blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[10] pillar_1.4.7 backports_1.1.10 lattice_0.20-41
[13] glue_1.4.2 RcppEigen_0.3.3.9.1 digest_0.6.27
[16] promises_1.1.1 checkmate_2.0.0 rvest_0.3.6
[19] colorspace_2.0-0 htmltools_0.5.0 httpuv_1.5.4
[22] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.3
[25] seacarb_3.2.15 haven_2.3.1 whisker_0.4
[28] later_1.1.0.1 git2r_0.27.1 generics_0.1.0
[31] ellipsis_0.3.1 withr_2.3.0 cli_2.2.0
[34] magrittr_2.0.1 crayon_1.3.4 readxl_1.3.1
[37] evaluate_0.14 fs_1.5.0 fansi_0.4.1
[40] xml2_1.3.2 RcppArmadillo_0.10.1.2.2 oce_1.2-0
[43] tools_4.0.3 data.table_1.13.6 hms_0.5.3
[46] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0
[49] gsw_1.0-5 compiler_4.0.3 rlang_0.4.10
[52] grid_4.0.3 rstudioapi_0.13 rmarkdown_2.5
[55] testthat_3.0.1 gtable_0.3.0 DBI_1.1.0
[58] R6_2.5.0 lubridate_1.7.9 knitr_1.30
[61] rprojroot_2.0.2 stringi_1.5.3 parallel_4.0.3
[64] Rcpp_1.0.5 vctrs_0.3.6 dbplyr_1.4.4
[67] tidyselect_1.1.0 xfun_0.20