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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.
tref <-
read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
cant_tref_1 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[1]),
".csv",
sep = ""
)
)
cant_tref_1 <- cant_tref_1 %>%
rename(cant_tref_1 = cant_total) %>%
select(-year)
cant_tref_2 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[2]),
".csv",
sep = ""
)
)
cant_tref_2 <- cant_tref_2 %>%
rename(cant_tref_2 = cant_total) %>%
select(-year)
cant_tref_3 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[3]),
".csv",
sep = ""
)
)
cant_tref_3 <- cant_tref_3 %>%
rename(cant_tref_3 = cant_total) %>%
select(-year)cant_M_1 <- left_join(cant_tref_1, cant_tref_2) %>%
mutate(cant = cant_tref_2 - cant_tref_1,
eras = unique(cant_inv_JDM$eras)[1]) %>%
select(-c(cant_tref_1, cant_tref_2))
cant_M_2 <- left_join(cant_tref_2, cant_tref_3) %>%
mutate(cant = cant_tref_3 - cant_tref_2,
eras = unique(cant_inv_JDM$eras)[2]) %>%
select(-c(cant_tref_2, cant_tref_3))
cant_M <- full_join(cant_M_1, cant_M_2) %>%
arrange(lon, lat, depth, basin_AIP)
cant_M <- cant_M %>%
mutate(cant_pos = if_else(cant <= 0, 0, cant))
rm(cant_tref_1, cant_tref_2, cant_tref_3, cant_M_1, cant_M_2)cant_inv_M <- m_cant_inv(cant_M)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 | 1.2 | 1.1 | 1.2 | 1.1 | 0.1 | 0.1 |
| Indian | 1.0 | 0.9 | 1.0 | 0.9 | 0.1 | 0.1 |
| Pacific | 2.3 | 2.2 | 2.4 | 2.2 | 0.1 | 0.2 |
| total | 4.50 | 4.20 | 4.60 | 4.20 | 0.30 | 0.40 |
| 1982-1999 --> 2000-2012 | Depth: 500 | ||||||
| Atlantic | 4.0 | 3.7 | 4.0 | 3.7 | 0.3 | 0.3 |
| Indian | 3.1 | 3.4 | 3.2 | 3.4 | -0.3 | -0.2 |
| Pacific | 7.3 | 7.7 | 7.5 | 7.7 | -0.4 | -0.2 |
| total | 14.40 | 14.80 | 14.70 | 14.80 | −0.40 | −0.10 |
| 1982-1999 --> 2000-2012 | Depth: 1000 | ||||||
| Atlantic | 5.3 | 4.9 | 5.4 | 4.9 | 0.5 | 0.5 |
| Indian | 3.9 | 5.1 | 4.3 | 5.1 | -1.2 | -0.8 |
| Pacific | 8.8 | 9.9 | 9.5 | 9.9 | -1.0 | -0.4 |
| total | 18.00 | 19.90 | 19.20 | 19.90 | −1.70 | −0.70 |
| 1982-1999 --> 2000-2012 | Depth: 3000 | ||||||
| Atlantic | 6.2 | 5.5 | 6.5 | 5.5 | 0.6 | 0.9 |
| Indian | 3.8 | 6.0 | 5.1 | 6.1 | -2.2 | -1.0 |
| Pacific | 11.3 | 10.7 | 12.7 | 10.8 | 0.6 | 1.9 |
| total | 21.30 | 22.20 | 24.30 | 22.40 | −1.00 | 1.80 |
| 1982-1999 --> 2000-2012 | Depth: 10000 | ||||||
| Atlantic | 6.2 | 5.6 | 6.8 | 5.6 | 0.7 | 1.2 |
| Indian | 4.1 | 6.1 | 5.6 | 6.1 | -2.0 | -0.5 |
| Pacific | 12.2 | 10.9 | 13.8 | 11.0 | 1.3 | 2.7 |
| total | 22.50 | 22.60 | 26.20 | 22.70 | 0.00 | 3.40 |
| 2000-2012 --> 2013-2019 | Depth: 100 | ||||||
| Atlantic | 0.9 | 0.7 | 1.1 | 0.7 | 0.3 | 0.4 |
| Indian | 0.8 | 0.6 | 0.8 | 0.6 | 0.1 | 0.2 |
| Pacific | 1.9 | 1.6 | 1.9 | 1.6 | 0.3 | 0.3 |
| total | 3.60 | 2.90 | 3.80 | 2.90 | 0.70 | 0.90 |
| 2000-2012 --> 2013-2019 | Depth: 500 | ||||||
| Atlantic | 2.7 | 2.7 | 3.0 | 2.7 | -0.1 | 0.2 |
| Indian | 2.1 | 2.5 | 2.2 | 2.5 | -0.4 | -0.3 |
| Pacific | 6.2 | 5.7 | 6.2 | 5.7 | 0.5 | 0.5 |
| total | 11.00 | 10.90 | 11.40 | 10.90 | −0.00 | 0.40 |
| 2000-2012 --> 2013-2019 | Depth: 1000 | ||||||
| Atlantic | 3.6 | 3.7 | 3.9 | 3.7 | 0.0 | 0.3 |
| Indian | 2.7 | 3.8 | 3.1 | 3.8 | -1.1 | -0.6 |
| Pacific | 8.3 | 7.4 | 8.3 | 7.4 | 0.9 | 0.9 |
| total | 14.60 | 14.90 | 15.30 | 14.90 | −0.20 | 0.60 |
| 2000-2012 --> 2013-2019 | Depth: 3000 | ||||||
| Atlantic | 4.5 | 4.1 | 5.0 | 4.2 | 0.4 | 0.8 |
| Indian | 3.0 | 4.5 | 4.1 | 4.5 | -1.6 | -0.4 |
| Pacific | 10.7 | 8.1 | 10.9 | 8.3 | 2.6 | 2.6 |
| total | 18.20 | 16.70 | 20.00 | 17.00 | 1.40 | 3.00 |
| 2000-2012 --> 2013-2019 | Depth: 10000 | ||||||
| Atlantic | 4.4 | 4.2 | 5.3 | 4.2 | 0.2 | 1.1 |
| Indian | 2.8 | 4.6 | 4.2 | 4.6 | -1.8 | -0.4 |
| Pacific | 11.2 | 8.3 | 11.4 | 8.4 | 2.9 | 3.0 |
| total | 18.40 | 17.10 | 20.90 | 17.20 | 1.30 | 3.70 |
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_3.3.2
[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_1.7.1
[4] here_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_0.3.3.7.0 digest_0.6.27
[16] promises_1.1.1 checkmate_2.0.0 rvest_0.3.6
[19] colorspace_1.4-1 htmltools_0.5.0 httpuv_1.5.4
[22] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.2
[25] seacarb_3.2.14 haven_2.3.1 whisker_0.4
[28] later_1.1.0.1 git2r_0.27.1 generics_0.0.2
[31] ellipsis_0.3.1 withr_2.3.0 cli_2.1.0
[34] magrittr_1.5 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_0.10.1.2.0 oce_1.2-0
[43] tools_4.0.3 data.table_1.13.2 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_0.4.9
[52] grid_4.0.3 rstudioapi_0.13 rmarkdown_2.5
[55] testthat_2.3.2 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_0.3.5 dbplyr_1.4.4
[67] tidyselect_1.1.0 xfun_0.18