Comparison of cant estimates to results of Gruber et al 2019
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(patchwork)
library(scico)
library(scales)
library(metR)
library(kableExtra)
library(collapse)
library(marelac)
library(gt)1 Data sources
Following Cant estimates from this study (JDM) and Gruber 2019 (G19) are used:
- Zonal mean (basin, lat, depth)
- Inventories (lat, lon)
1.1 This study
# cant_3d_JDM <-
# read_csv(here::here("data/mapping/_summarized_files",
# "cant_3d.csv"))
#
# cant_3d_JDM <- cant_3d_JDM %>%
# filter(eras == "JGOFS_GO") %>%
# select(lon,
# lat,
# depth,
# basin_AIP,
# cant,
# cant_pos) %>%
# mutate(estimate = "JDM")
cant_zonal_JDM <-
read_csv(here::here("data/mapping/_summarized_files",
"cant_zonal.csv"))
cant_zonal_JDM <- cant_zonal_JDM %>%
filter(eras == "JGOFS_GO") %>%
select(lat,
depth,
basin_AIP,
cant_mean,
cant_pos_mean,
cant_sd,
cant_pos_sd)
cant_inventory_JDM <-
read_csv(here::here("data/mapping/_summarized_files",
"cant_inv.csv"))
cant_inventory_JDM <- cant_inventory_JDM %>%
rename(cant_pos_inv = cant_inv_pos)
cant_inventory_JDM <- cant_inventory_JDM %>%
filter(eras == "JGOFS_GO") %>%
select(-c(eras, basin))1.2 Gruber 2019
# cant_3d_G19 <-
# read_csv(here::here("data/Gruber_2019/_summarized_files",
# "G19_cant_3d.csv"))
#
# cant_3d_G19 <- cant_3d_G19 %>%
# mutate(estimate = "G19") %>%
# select(-eras)
cant_inventory_G19 <-
read_csv(here::here("data/Gruber_2019/_summarized_files",
"G19_cant_inv.csv"))
cant_inventory_G19 <- cant_inventory_G19 %>%
select(-eras)
cant_zonal_G19 <-
read_csv(here::here("data/Gruber_2019/_summarized_files",
"G19_cant_zonal.csv"))
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
cant_inventory_long <- bind_rows(
cant_inventory_JDM %>% mutate(estimate = "JDM"),
cant_inventory_G19 %>% mutate(estimate = "G19")
)
cant_inventory_wide <- cant_inventory_long %>%
pivot_wider(names_from = estimate, values_from = cant_pos_inv:cant_inv) %>%
drop_na()
cant_inventory_wide <- cant_inventory_wide %>%
mutate(cant_pos_inv_delta = cant_pos_inv_JDM - cant_pos_inv_G19,
cant_inv_delta = cant_inv_JDM - cant_inv_G19)cant_zonal_long <- bind_rows(
cant_zonal_JDM %>% mutate(estimate = "JDM"),
cant_zonal_G19 %>% mutate(estimate = "G19")
)
cant_zonal_wide <- cant_zonal_long %>%
pivot_wider(names_from = estimate, values_from = cant_mean:cant_pos_sd) %>%
drop_na()
cant_zonal_wide <- cant_zonal_wide %>%
mutate(cant_pos_mean_delta = cant_pos_mean_JDM - cant_pos_mean_G19,
cant_mean_delta = cant_mean_JDM - cant_mean_G19,
estimate = "JDM - G19")2 Color scale
For ease of comparison with Gruber et al (2019) we adapt their color scale, including the ranges and breaks applied in various types of visualizations.
rgb2hex <- function(r, g, b)
rgb(r, g, b, maxColorValue = 100)
cols = c(rgb2hex(95, 95, 95),
rgb2hex(0, 0, 95),
rgb2hex(100, 0, 0),
rgb2hex(100, 100, 0))
Gruber_rainbow <- colorRampPalette(cols)
rm(rgb2hex, cols)3 Cant budgets
Global Cant inventories were estimated in Pg-C, based on all vs positive only Cant estimates. Please note that here we only added cant values in the upper 3000m and do not apply additional corrections for areas not covered.
cant_inventory_budget <- cant_inventory_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()
cant_inventory_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 | 5.6 | 6.9 |
| Pacific | 12.5 | 13.2 |
| total | 28.90 | 31.10 |
| JDM | ||
| Atlantic | 9.9 | 10.3 |
| Indian | 10.1 | 10.3 |
| Pacific | 17.5 | 18.1 |
| total | 37.50 | 38.70 |
rm(cant_inventory_budget)4 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 10 eMLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.
4.1 Inventory map
4.1.1 Absolute values
Column inventory of positive cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).
breaks <- c(seq(0, 16, 2), Inf)
breaks_n <- length(breaks) - 1
cant_inventory_long <- cant_inventory_long %>%
mutate(cant_pos_inv_int = cut(cant_pos_inv,
breaks,
right = FALSE))
map +
geom_raster(data = cant_inventory_long,
aes(lon, lat, fill = cant_pos_inv_int)) +
scale_fill_manual(values = Gruber_rainbow(breaks_n),
name = expression(atop(Delta~C[ant],
(mol~m^{-2})))) +
# scale_fill_scico_d(palette = "lapaz", direction = -1) +
facet_wrap(~ estimate, ncol = 1)
4.1.2 Offset
Column inventory of positive cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).
breaks <- c(-Inf, seq(-15, 15, 3), Inf)
map +
geom_raster(data = cant_inventory_wide,
aes(lon, lat, fill = cut(cant_pos_inv_delta, breaks))) +
scale_fill_scico_d(palette = "vik", drop = FALSE,
name = expression(atop(Delta~Delta~C[ant],
(mol~m^{-2})))) +
facet_wrap(~ "JDM - G19", ncol = 1)
4.2 Zonal mean sections
4.2.1 Absolute values
df <- cant_zonal_long
breaks <- c(seq(0, 18, 1), Inf)
var <- "cant_pos_mean"
legend_title <- expression(atop(Delta * C[ant],
(mu * mol ~ kg ^ {-1})))
# i_basin_AIP <- unique(df$basin_AIP)[1]
# i_estimate <- unique(df$estimate)[1]
for (i_basin_AIP in unique(df$basin_AIP)) {
for (i_estimate in unique(df$estimate)) {
print(
section_zonal_layered_continous(
df,
i_basin_AIP = i_basin_AIP,
i_estimate = i_estimate,
var,
breaks,
legend_title
)
)
}
}
rm(df, var, breaks, legend_title, breaks_n)4.2.2 Offset
df <- cant_zonal_wide
breaks <- c(-Inf,seq(-16,16,2),Inf)
var <- "cant_pos_mean_delta"
legend_title <- expression(atop(Offset~Delta * C[ant],
(mu * mol ~ kg ^ {-1})))
i_basin_AIP <- unique(df$basin_AIP)[1]
i_estimate <- unique(df$estimate)[1]
for (i_basin_AIP in unique(df$basin_AIP)) {
print(
section_zonal_layered_divergent(
df,
i_basin_AIP = i_basin_AIP,
i_estimate = i_estimate,
var,
breaks,
legend_title
)
)
}
rm(df, var, breaks, legend_title)5 Cant - all
In a first series of plots we explore the distribution of cant, taking only positive estimates into account (positive here refers to the mean cant estimate across 10 eMLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.
5.1 Inventory map
5.1.1 Absolute values
Column inventory of Cant (including positive and negative values) between the surface and 3000m water depth per horizontal grid cell (lat x lon).
breaks <- c(-Inf,seq(-16,16,2),Inf)
map +
geom_raster(data = cant_inventory_long,
aes(lon, lat, fill = cut(cant_inv, breaks))) +
scale_fill_scico_d(palette = "vik", drop = FALSE) +
facet_wrap(~ estimate, labeller = label_both, ncol = 1)
5.1.2 Offset
breaks <- c(-Inf,seq(-15,15,3),Inf)
map +
geom_raster(data = cant_inventory_wide,
aes(lon, lat, fill = cut(cant_inv_delta, breaks))) +
scale_fill_scico_d(palette = "vik", drop = FALSE,
name = expression(atop(Delta~C[ant],
"JDM-G19")))
5.2 Zonal mean sections
5.2.1 Absolute values
breaks <- c(-Inf,seq(-16,16,2),Inf)
breaks_n <- length(breaks) - 1
zonal_section <- function(df, i_basin_AIP, i_estimate, var) {
# df <- cant_zonal_long
# i_basin_AIP <- unique(cant_zonal_long$basin_AIP)[1]
# i_estimate <- unique(cant_zonal_long$estimate)[1]
# var <- "cant_mean"
name_var <- var
var <- sym(var)
lat_max <- max(df$lat)
lat_min <- min(df$lat)
df_sub <- df %>%
filter(basin_AIP == i_basin_AIP,
estimate == i_estimate)
surface <- df_sub %>%
ggplot(aes(lat, depth, z = !!var)) +
geom_contour_filled(breaks = breaks) +
scale_fill_scico_d(palette = "vik", drop = FALSE) +
coord_cartesian(expand = 0,
ylim = c(500, 0),
xlim = c(lat_min, lat_max)) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100,100,20)) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()
) +
labs(y = "Depth (m)",
title = paste("Basin:", i_basin_AIP))
deep <- df_sub %>%
ggplot(aes(lat, depth, z = !!var)) +
geom_contour_filled(breaks = breaks) +
scale_fill_scico_d(palette = "vik", drop = FALSE) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100,100,20)) +
coord_cartesian(expand = 0,
ylim = c(3000, 500),
xlim = c(lat_min, lat_max)) +
labs(x = "latitude (°N)", y = "Depth (m)")
surface / deep +
plot_layout(guides = "collect")
}
for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
for (i_estimate in unique(cant_zonal_long$estimate)) {
print(zonal_section(cant_zonal_long,
i_basin_AIP = i_basin_AIP,
i_estimate = i_estimate,
"cant_mean"))
}
}
rm(breaks, breaks_n)5.2.2 Offset
breaks <- c(-Inf,seq(-15,15,3),Inf)
breaks_n <- length(breaks) - 1
zonal_section <- function(df, i_basin_AIP, i_estimate, var) {
df <- cant_zonal_wide
i_basin_AIP <- unique(cant_zonal_long$basin_AIP)[1]
var <- "cant_mean_delta"
name_var <- var
var <- sym(var)
lat_max <- max(df$lat)
lat_min <- min(df$lat)
df_sub <- df %>%
filter(basin_AIP == i_basin_AIP)
surface <- df_sub %>%
ggplot(aes(lat, depth, z = !!var)) +
geom_contour_filled(breaks = breaks) +
scale_fill_scico_d(palette = "vik", drop = FALSE) +
coord_cartesian(expand = 0,
ylim = c(500, 0),
xlim = c(lat_min, lat_max)) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100,100,20)) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()
) +
labs(y = "Depth (m)",
title = paste("Basin:", i_basin_AIP))
deep <- df_sub %>%
ggplot(aes(lat, depth, z = !!var)) +
geom_contour_filled(breaks = breaks) +
scale_fill_scico_d(palette = "vik", drop = FALSE) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100,100,20)) +
coord_cartesian(expand = 0,
ylim = c(3000, 500),
xlim = c(lat_min, lat_max)) +
labs(x = "latitude (°N)", y = "Depth (m)")
surface / deep +
plot_layout(guides = "collect")
}
for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
print(zonal_section(cant_zonal_wide,
i_basin_AIP = i_basin_AIP,
"cant_mean_delta"))
}
rm(breaks, breaks_n)6 Known issues
Deviations between this study and the results by Gruber et al (2019), short G19, for the same period, might be attributable to following known differences in the implementation of the eMLR(C*) method:
- GLODAPv2_2020 here vs an extended version of GLODAPv2 in G19
- flagging: Here, we accept f flags 0 and 2 (except for tco2, where only 0 is accepted). G19 claim to use 0 throughout, yet have a high coverage of talk observations in the SE Pacific
- Neutral density calculation: Here and in GLODAPv2_2020 a polynomial approximation is used, whereas G19 uses the original Matlab code
- Predictor climatology: Here we used WOA18, whereas G19 used WOA13
- Missing data in the GLODAP mapped climatology, eg NO3 at surface, where not filled in this study
- cant on neutral density levels calculate as slab mean, rather than on one surface
- Here, surface delta cant were calculated based on Luecker constants, rather than Mehrbach as in G19
- Here, pCO2 was calculated from DIC/TA Climatology
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] gt_0.2.2 marelac_2.1.10 shape_1.4.4 collapse_1.3.2
[5] kableExtra_1.1.0 metR_0.7.0 scales_1.1.1 scico_1.2.0
[9] patchwork_1.0.1 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[13] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[17] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.4.2 lubridate_1.7.9 gsw_1.0-5 webshot_0.5.2
[5] httr_1.4.2 rprojroot_1.3-2 tools_4.0.2 backports_1.1.8
[9] R6_2.4.1 DBI_1.1.0 colorspace_1.4-1 withr_2.2.0
[13] tidyselect_1.1.0 compiler_4.0.2 git2r_0.27.1 cli_2.0.2
[17] rvest_0.3.6 xml2_1.3.2 isoband_0.2.2 sandwich_2.5-1
[21] labeling_0.3 sass_0.2.0 checkmate_2.0.0 digest_0.6.25
[25] rmarkdown_2.3 oce_1.2-0 pkgconfig_2.0.3 htmltools_0.5.0
[29] dbplyr_1.4.4 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
[33] generics_0.0.2 farver_2.0.3 zoo_1.8-8 jsonlite_1.7.0
[37] magrittr_1.5 Formula_1.2-3 Matrix_1.2-18 Rcpp_1.0.5
[41] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0 stringi_1.4.6
[45] whisker_0.4 yaml_2.2.1 grid_4.0.2 blob_1.2.1
[49] parallel_4.0.2 promises_1.1.1 crayon_1.3.4 lattice_0.20-41
[53] haven_2.3.1 hms_0.5.3 seacarb_3.2.13 knitr_1.30
[57] pillar_1.4.6 reprex_0.3.0 glue_1.4.1 evaluate_0.14
[61] data.table_1.13.0 modelr_0.1.8 vctrs_0.3.2 httpuv_1.5.4
[65] testthat_2.3.2 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[69] xfun_0.16 xtable_1.8-4 broom_0.7.0 lfe_2.8-5.1
[73] later_1.1.0.1 viridisLite_0.3.0 ellipsis_0.3.1 here_0.1