Comparison of cant estimates to results of Gruber et al 2019
Jens Daniel Müller
21 December, 2020
Last updated: 2020-12-21
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Knit directory: emlr_mod_v_XXX/
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1 Data sources
Following Cant estimates are used:
- Zonal mean (basin, lat, depth)
- Inventories (lat, lon)
1.1 This study
Results from this study are referred to as JDM.
cant_zonal_JDM <-
read_csv(paste(path_version_data,
"cant_zonal.csv",
sep = ""))
cant_zonal_JDM <- cant_zonal_JDM %>%
filter(eras == unique(cant_zonal_JDM$eras)[1]) %>%
select(lat,
depth,
basin_AIP,
cant_mean,
cant_pos_mean,
cant_sd,
cant_pos_sd)
cant_inv_JDM <-
read_csv(paste(path_version_data,
"cant_inv.csv",
sep = ""))
cant_inv_JDM <- cant_inv_JDM %>%
filter(eras == unique(cant_inv_JDM$eras)[1],
inv_depth == params_global$inventory_depth_standard) %>%
select(-c(eras))1.2 Gruber 2019
Results from Gruber et al 2019 are referred to as G19.
cant_inv_G19 <-
read_csv(paste(path_preprocessing,
"G19_cant_inv.csv",
sep = ""))
cant_inv_G19 <- cant_inv_G19 %>%
select(-eras)
cant_zonal_G19 <-
read_csv(paste(path_preprocessing,
"G19_cant_zonal.csv",
sep = ""))
cant_zonal_G19 <- cant_zonal_G19 %>%
filter(eras == "JGOFS_GO") %>%
select(lat,
depth,
basin_AIP,
cant_mean,
cant_pos_mean,
cant_sd,
cant_pos_sd)1.3 Join data sets
Inventories and zonal sections are merged, and differences calculate per grid cell.
# add estimate label
cant_inv_long <- bind_rows(
cant_inv_JDM %>% mutate(estimate = "JDM"),
cant_inv_G19 %>% mutate(estimate = "G19")
)
# pivot to wide format
cant_inv_wide <- cant_inv_long %>%
pivot_wider(names_from = estimate, values_from = cant_pos_inv:cant_inv) %>%
drop_na()
# calculate offset
cant_inv_wide <- cant_inv_wide %>%
mutate(cant_pos_inv_offset = cant_pos_inv_JDM - cant_pos_inv_G19,
cant_inv_offset = cant_inv_JDM - cant_inv_G19,
estimate = "JDM - G19")# add estimate label
cant_zonal_long <- bind_rows(
cant_zonal_JDM %>% mutate(estimate = "JDM"),
cant_zonal_G19 %>% mutate(estimate = "G19")
)
# pivot to wide format
cant_zonal_wide <- cant_zonal_long %>%
pivot_wider(names_from = estimate, values_from = cant_mean:cant_pos_sd) %>%
drop_na()
# calculate offset
cant_zonal_wide <- cant_zonal_wide %>%
mutate(cant_pos_mean_offset = cant_pos_mean_JDM - cant_pos_mean_G19,
cant_mean_offset = cant_mean_JDM - cant_mean_G19,
estimate = "JDM - G19")2 Cant budgets
Global Cant inventories budget were estimated for different ocean basins in units of Pg C, based on all vs positive only Cant estimates. Please note that here we only added Cant values for the standard inventory depth (3000 m) and do not apply additional corrections for areas not covered.
# calculate budgets
cant_inv_budget <- cant_inv_long %>%
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, estimate) %>%
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()
# print budget table
cant_inv_budget %>%
gt(rowname_col = "basin_AIP",
groupname_col = c("estimate")) %>%
summary_rows(
groups = TRUE,
fns = list(total = "sum")
)| cant_total | cant_pos_total | |
|---|---|---|
| G19 | ||
| Atlantic | 10.8 | 11.0 |
| Indian | 5.9 | 7.1 |
| Pacific | 12.8 | 13.4 |
| total | 29.50 | 31.50 |
| JDM | ||
| Atlantic | 6.6 | 7.0 |
| Indian | 7.2 | 8.0 |
| Pacific | 9.5 | 12.0 |
| total | 23.30 | 27.00 |
rm(cant_inv_budget)3 Cant - positive only
In a first series of plots we explore the distribution of Cant, taking only positive estimates into account (positive here refers to the mean cant estimate across the MLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.
3.1 Inventory maps
3.1.1 Absolute values
Column inventory of positive Cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).
# i_estimate <- unique(cant_inv_long$estimate)[1]
for (i_estimate in unique(cant_inv_long$estimate)) {
print(
p_map_cant_inv(
cant_inv_long %>% filter(estimate == i_estimate),
subtitle_text = paste("Estimate:", i_estimate))
)
}
| Version | Author | Date |
|---|---|---|
| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
| Version | Author | Date |
|---|---|---|
| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
3.1.2 Offset
Column inventory of positive cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).
p_map_cant_inv_offset(cant_inv_wide,
"cant_pos_inv_offset",
subtitle_text = "Estimate JDM - G19")
| Version | Author | Date |
|---|---|---|
| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
3.2 Zonal mean sections
3.2.1 Absolute values
# i_basin_AIP <- unique(cant_zonal_long$basin_AIP)[1]
# i_estimate <- unique(cant_zonal_long$estimate)[1]
for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
for (i_estimate in unique(cant_zonal_long$estimate)) {
print(
p_section_zonal(
df = cant_zonal_long %>%
filter(basin_AIP == i_basin_AIP,
estimate == i_estimate),
var = "cant_pos_mean",
plot_slabs = "n",
subtitle_text =
paste("Basin:", i_basin_AIP, "| estimate:", i_estimate)
)
)
}
}
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3.2.2 Offset
# i_basin_AIP <- unique(cant_zonal_wide$basin_AIP)[1]
for (i_basin_AIP in unique(cant_zonal_wide$basin_AIP)) {
print(
p_section_zonal(
df = cant_zonal_wide %>%
filter(basin_AIP == i_basin_AIP),
var = "cant_pos_mean_offset",
breaks = params_global$breaks_cant_offset,
plot_slabs = "n",
col = "divergent",
subtitle_text =
paste("Basin:", i_basin_AIP, "| estimate: JDM-G19")
)
)
}
| Version | Author | Date |
|---|---|---|
| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
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| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
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| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
4 Cant - all
In a second series of plots we explore the distribution of Cant, taking positive and negative estimates into account (positive here refers to the mean cant estimate across MLR model predictions available for each grid cell).
4.1 Inventory maps
4.1.1 Absolute values
Column inventory of Cant (including positive and negative values) between the surface and 3000m water depth per horizontal grid cell (lat x lon).
# i_estimate <- unique(cant_inv_long$estimate)[1]
for (i_estimate in unique(cant_inv_long$estimate)) {
print(
p_map_cant_inv(
cant_inv_long %>% filter(estimate == i_estimate),
subtitle_text = paste("Estimate:", i_estimate),
col = "divergent")
)
}
| Version | Author | Date |
|---|---|---|
| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
| Version | Author | Date |
|---|---|---|
| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
4.1.2 Offset
p_map_cant_inv_offset(
df = cant_inv_wide,
var = "cant_inv_offset",
subtitle_text = "Estimate: JDM - G19")
| Version | Author | Date |
|---|---|---|
| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
4.2 Zonal mean sections
4.2.1 Absolute values
# i_basin_AIP <- unique(df$basin_AIP)[1]
# i_estimate <- unique(df$estimate)[1]
for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
for (i_estimate in unique(cant_zonal_long$estimate)) {
print(
p_section_zonal(
df = cant_zonal_long %>%
filter(basin_AIP == i_basin_AIP,
estimate == i_estimate),
var = "cant_mean",
col = "divergent",
breaks = params_global$breaks_cant,
plot_slabs = "n",
legend_title = expression(atop(Delta * C[ant],
(mu * mol ~ kg ^ {-1}))),
subtitle_text =
paste("Basin:", i_basin_AIP, "| estimate:", i_estimate)
)
)
}
}
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| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
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| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
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4.2.2 Offset
# i_basin_AIP <- unique(cant_zonal_wide$basin_AIP)[1]
for (i_basin_AIP in unique(cant_zonal_wide$basin_AIP)) {
print(
p_section_zonal(
df = cant_zonal_wide %>%
filter(basin_AIP == i_basin_AIP),
var = "cant_mean_offset",
plot_slabs = "n",
col = "divergent",
breaks = params_global$breaks_cant_offset,
subtitle_text =
paste("Basin:", i_basin_AIP, "| estimate: JDM - G19")
)
)
}
| Version | Author | Date |
|---|---|---|
| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
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| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
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| c8b76b3 | jens-daniel-mueller | 2020-12-19 |
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.1
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_0.8.0 scico_1.2.0 patchwork_1.1.0 collapse_1.4.2
[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_1.7.2
[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_1.2.1 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_3.2.14 haven_2.3.1 whisker_0.4
[28] later_1.1.0.1 git2r_0.27.1 farver_2.0.3
[31] generics_0.1.0 ellipsis_0.3.1 withr_2.3.0
[34] cli_2.2.0 magrittr_2.0.1 crayon_1.3.4
[37] readxl_1.3.1 evaluate_0.14 fs_1.5.0
[40] fansi_0.4.1 xml2_1.3.2 RcppArmadillo_0.10.1.2.0
[43] oce_1.2-0 tools_4.0.3 data.table_1.13.4
[46] hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
[49] reprex_0.3.0 gsw_1.0-5 isoband_0.2.3
[52] compiler_4.0.3 rlang_0.4.9 grid_4.0.3
[55] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.5
[58] testthat_3.0.1 gtable_0.3.0 DBI_1.1.0
[61] R6_2.5.0 lubridate_1.7.9 knitr_1.30
[64] rprojroot_2.0.2 stringi_1.5.3 parallel_4.0.3
[67] Rcpp_1.0.5 vctrs_0.3.6 dbplyr_1.4.4
[70] tidyselect_1.1.0 xfun_0.19