Analysis of Cant estimates
Jens Daniel Müller
10 November, 2020
Last updated: 2020-11-10
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Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)
library(marelac)
library(kableExtra)1 Data sources
1.1 Sabine 2004
Cant_94 <- read_csv(here::here("data/GLODAPv1_1/_summarized_files",
"Cant_94.csv"))
Cant_94_inv <-
read_csv(here::here("data/GLODAPv1_1/_summarized_files",
"Cant_94_inv.csv"))1.2 Gruber 2019
Cant_07 <- read_csv(here::here("data/Gruber_2019/_summarized_files",
"G19_cant_3d.csv"))
Cant_07_inv <-
read_csv(here::here("data/Gruber_2019/_summarized_files",
"G19_cant_inv.csv"))2 Comparison of previous estimates
Cant inventory estimates of S04 (Sabine et al, 2004) and G19 (Gruber et al, 2019) were compared.
2.1 Merge data sets
Cant_94_inv <- Cant_94_inv %>%
select(-c(cant_inv_incl_neg, basin))
Cant_07_inv <- Cant_07_inv %>%
select(-c(cant_pos_inv, eras))
Cant_inv <- full_join(Cant_07_inv %>% mutate(estimate = "G19"),
Cant_94_inv %>% mutate(estimate = "S04"))
rm(Cant_07_inv, Cant_94_inv)2.2 Inventory maps
Spanning different time periods, the Cant inventories differ in magnitude.
Cant_inv %>%
ggplot() +
geom_raster(data = landmask,
aes(lon, lat),
fill = "grey80") +
geom_raster(aes(lon, lat, fill = cant_inv)) +
coord_quickmap(expand = 0) +
scale_fill_viridis_c() +
facet_wrap( ~ estimate, ncol = 1) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
2.3 Cant budgets
Global Cant inventories were estimated in Pg-C. Please note that here we only added positive Cant values in the upper 3000m and do not apply additional corrections for areas not covered.
Cant_inv <- Cant_inv %>%
mutate(surface_area = earth_surf(lat, lon),
cant_inv_grid = cant_inv*surface_area)
Cant_inv_budget <- Cant_inv %>%
group_by(estimate, basin_AIP) %>%
summarise(cant_total = sum(cant_inv_grid)*12*1e-15,
cant_total = round(cant_total,1)) %>%
ungroup() %>%
pivot_wider(values_from = cant_total, names_from = basin_AIP) %>%
mutate(total = Atlantic + Indian + Pacific)
Cant_inv_budget %>%
kableExtra::kable() %>%
add_header_above() %>%
kable_styling(full_width = FALSE)| estimate | Atlantic | Indian | Pacific | total |
|---|---|---|---|---|
| G19 | 10.8 | 5.6 | 12.5 | 28.9 |
| S04 | 38.6 | 22.9 | 40.2 | 101.7 |
2.4 Relative inventories
Cant_inv_wide <- Cant_inv %>%
pivot_wider(values_from = c(cant_inv, cant_inv_grid),
names_from = estimate)
Cant_inv_wide <- Cant_inv_wide %>%
drop_na() %>%
mutate(G19_rel = cant_inv_grid_G19 / sum(cant_inv_grid_G19),
S04_rel = cant_inv_grid_S04 / sum(cant_inv_grid_S04),
cant_ratio_rel = G19_rel / S04_rel)
Cant_inv_rel <- Cant_inv_wide %>%
pivot_longer(cols = c(G19_rel, S04_rel),
names_to = "estimate",
values_to = "cant_inv_rel")Cant_inv_rel %>%
ggplot() +
geom_raster(data = landmask,
aes(lon, lat),
fill = "grey80") +
geom_raster(aes(lon, lat, fill = cant_inv_rel*100)) +
coord_quickmap(expand = 0) +
scale_fill_viridis_c() +
facet_wrap( ~ estimate, ncol = 1) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
2.5 Relative inventory ratios
Cant_inv_wide %>%
filter(cant_ratio_rel < 10,
cant_ratio_rel > 0.1) %>%
ggplot() +
geom_raster(data = landmask,
aes(lon, lat),
fill = "grey80") +
geom_contour_filled(aes(lon, lat, z = log10(cant_ratio_rel))) +
coord_quickmap(expand = 0) +
scale_fill_brewer(palette = "RdBu", direction = -1) +
labs(title = "Cant inventory distribution | 1994-2007 vs preind-1994",
subtitle = "Log ratio of relative contributions to total inventory") +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
legend.title = element_blank()
)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=English_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] kableExtra_1.1.0 marelac_2.1.10 shape_1.4.4 lubridate_1.7.9
[5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4
[9] readr_1.3.1 tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
[13] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 here_0.1 assertthat_0.2.1 rprojroot_1.3-2
[5] digest_0.6.25 R6_2.4.1 cellranger_1.1.0 backports_1.1.8
[9] reprex_0.3.0 evaluate_0.14 highr_0.8 httr_1.4.2
[13] pillar_1.4.6 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
[17] whisker_0.4 blob_1.2.1 rmarkdown_2.3 labeling_0.3
[21] webshot_0.5.2 munsell_0.5.0 broom_0.7.0 compiler_4.0.2
[25] httpuv_1.5.4 modelr_0.1.8 xfun_0.16 pkgconfig_2.0.3
[29] htmltools_0.5.0 tidyselect_1.1.0 viridisLite_0.3.0 fansi_0.4.1
[33] crayon_1.3.4 dbplyr_1.4.4 withr_2.2.0 later_1.1.0.1
[37] gsw_1.0-5 grid_4.0.2 jsonlite_1.7.0 gtable_0.3.0
[41] lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1 magrittr_1.5
[45] seacarb_3.2.13 scales_1.1.1 cli_2.0.2 stringi_1.4.6
[49] oce_1.2-0 farver_2.0.3 fs_1.4.2 promises_1.1.1
[53] testthat_2.3.2 xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2
[57] vctrs_0.3.2 RColorBrewer_1.1-2 tools_4.0.2 glue_1.4.1
[61] hms_0.5.3 yaml_2.2.1 colorspace_1.4-1 isoband_0.2.2
[65] rvest_0.3.6 knitr_1.30 haven_2.3.1