Analysis of Cant estimates
Jens Daniel Müller
18 September, 2020
Last updated: 2020-09-18
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Knit directory: Cant_eMLR/
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library(tidyverse)
library(patchwork)
library(scico)
library(scales)
library(metR)
library(marelac)
library(kableExtra)1 Data sources
Cant estimates from this study:
- Mean and SD per grid cell (lat, lon, depth)
- Zonal mean and SD (basin, lat, depth)
- Inventories (lat, lon)
Cant_average <-
read_csv(here::here("data/mapping/_summarized_files",
"Cant_average.csv"))
Cant_average_zonal <-
read_csv(here::here("data/mapping/_summarized_files",
"Cant_average_zonal.csv"))
Cant_predictor_average_zonal <-
read_csv(here::here("data/mapping/_summarized_files",
"Cant_predictor_average_zonal.csv"))
Cant_inv <-
read_csv(here::here("data/mapping/_summarized_files",
"Cant_inv.csv"))C* estimates from this study:
- Mean and SD per grid cell (lat, lon, depth)
- Zonal mean and SD (basin, lat, depth)
Cstar_average <-
read_csv(here::here("data/mapping/_summarized_files",
"Cstar_average.csv"))
Cstar_average_zonal <-
read_csv(here::here("data/mapping/_summarized_files",
"Cstar_average_zonal.csv"))Cleaned GLODAPv2_2020 file as used in this study
GLODAP <-
read_csv(
here::here(
"data/GLODAPv2_2020/_summarized_data_files",
"GLODAP_MLR_fitting_ready.csv"
)
)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. 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 <- left_join(Cant_inv, basinmask_AIP)
Cant_inv_budget <- Cant_inv %>%
mutate(surface_area = earth_surf(lat, lon),
cant_inv_grid = cant_inv*surface_area) %>%
group_by(eras, basin_AIP) %>%
summarise(cant_total = sum(cant_inv_grid)*12*1e-15,
cant_total = round(cant_total,1)) %>%
ungroup() %>%
arrange(desc(eras)) %>%
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)| eras | Atlantic | Indian | Pacific | total |
|---|---|---|---|---|
| JGOFS_GO | 7.6 | 8.9 | 15.3 | 31.8 |
| GO_new | 5.6 | 3.5 | 12.8 | 21.9 |
4 Cant - positive
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 Zonal mean sections
breaks <- c(seq(0,18,1),Inf)
breaks_n <- length(breaks) - 1
Cant_average_zonal <- Cant_average_zonal %>%
mutate(Cant_pos_mean_int = cut(Cant_pos_mean,
breaks,
right = FALSE))
zonal_section <- function(df, i_basin_AIP, i_eras, var) {
name_var <- var
var <- sym(var)
lat_max <- max(df$lat)
lat_min <- min(df$lat)
df_sub <- df %>%
filter(eras == i_eras,
basin_AIP == i_basin_AIP)
surface <- df_sub %>%
ggplot(aes(lat, depth, z = !!var)) +
geom_contour_filled(breaks = breaks) +
scale_fill_manual(values = Gruber_rainbow(breaks_n),
name = "Cant") +
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, "| eras:", i_eras))
deep <- df_sub %>%
ggplot(aes(lat, depth, z = !!var)) +
geom_contour_filled(breaks = breaks) +
scale_fill_manual(values = Gruber_rainbow(breaks_n),
name = "Cant") +
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_average_zonal$basin_AIP)) {
for (i_eras in rev(unique(Cant_average_zonal$eras))) {
print(zonal_section(Cant_average_zonal,
i_basin_AIP = i_basin_AIP,
i_eras = i_eras,
"Cant_pos_mean"))
}
}
rm(breaks, breaks_n)4.2 Isoneutral slab distribution
Mean of positive Cant within each horizontal grid cell (lon x lat) per isoneutral slab.
Please note that:
- density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
- gaps in the maps represent areas where (thin) density layers fit between discrete depth levels used for mapping
Cant_gamma_maps <- Cant_average %>%
group_by(lat, lon, gamma_slab, eras) %>%
summarise(Cant_pos = mean(Cant_pos, na.rm = TRUE)) %>%
ungroup()
breaks <- c(seq(0,16,2),Inf)
breaks_n <- length(breaks) - 1
Cant_gamma_maps <- Cant_gamma_maps %>%
mutate(Cant_pos_int = cut(Cant_pos,
breaks,
right = FALSE))
ggplot() +
geom_raster(data = landmask,
aes(lon, lat),
fill = "grey30") +
geom_raster(data = Cant_gamma_maps,
aes(lon, lat, fill = Cant_pos_int)) +
scale_fill_manual(values = Gruber_rainbow(breaks_n)) +
facet_grid(gamma_slab ~ eras) +
coord_quickmap(expand = 0) +
theme(axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "top")
rm(Cant_gamma_maps, breaks, breaks_n)4.3 Inventory map
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_inv <- Cant_inv %>%
mutate(cant_inv_pos_int = cut(cant_inv_pos,
breaks,
right = FALSE))
Cant_inv %>%
ggplot() +
geom_raster(data = landmask,
aes(lon, lat), fill = "grey30") +
geom_raster(aes(lon, lat, fill = cant_inv_pos_int)) +
coord_quickmap(expand = 0) +
scale_fill_manual(values = Gruber_rainbow(breaks_n)) +
facet_wrap( ~ eras, ncol = 1) +
theme(axis.title = element_blank())
rm(breaks, breaks_n)5 Global section
5.1 JGOFS_GO
section_global(Cant_average %>% filter(eras == "JGOFS_GO"),
"Cant_pos")
5.2 GO_new
section_global(Cant_average %>% filter(eras == "GO_new"),
"Cant_pos")
6 Cant - all
In a second series of plots we explore the distribution of Cant, taking positive and negative estimates into account.
6.1 Zonal mean sections
section_zonal_average_divergent(Cant_average_zonal,
"Cant_mean",
"gamma_mean")
6.2 Isoneutral slab distribution
Mean of Cant within each horizontal grid cell (lon x lat) per isoneutral slab.
Please note that:
- density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
- gaps in the maps represent areas where (thin) density layers fit between discrete depth levels used for mapping
Cant_gamma_maps <- Cant_average %>%
group_by(lat, lon, gamma_slab, eras) %>%
summarise(Cant = mean(Cant, na.rm = TRUE)) %>%
ungroup()
breaks <- c(-Inf, seq(-16, 16, 4), Inf)
breaks_n <- length(breaks) - 1
Cant_gamma_maps <- Cant_gamma_maps %>%
mutate(Cant_int = cut(Cant,
breaks,
right = FALSE))
ggplot() +
geom_raster(data = landmask,
aes(lon, lat),
fill = "grey30") +
geom_raster(data = Cant_gamma_maps,
aes(lon, lat, fill = Cant_int)) +
scale_fill_brewer(palette = "RdBu",
direction = -1) +
facet_grid(gamma_slab ~ eras) +
coord_quickmap(expand = 0) +
theme(
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "top"
)
rm(Cant_gamma_maps, breaks, breaks_n)6.3 Inventory map
Column inventory of positive Cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).
breaks <- c(-Inf, seq(-16,16,4),Inf)
breaks_n <- length(breaks) - 1
Cant_inv <- Cant_inv %>%
mutate(cant_inv_int = cut(cant_inv,
breaks,
right = FALSE))
Cant_inv %>%
ggplot() +
geom_raster(data = landmask,
aes(lon, lat), fill = "grey80") +
geom_raster(aes(lon, lat, fill = cant_inv_int)) +
coord_quickmap(expand = 0) +
scale_fill_brewer(palette = "RdBu",
direction = -1) +
facet_wrap(~eras, ncol = 1) +
theme(
axis.title = element_blank()
)
rm(breaks, breaks_n)7 Cant - standard deviation
7.1 Across models
Standard deviation across Cant from all MLR models was calculate for each grid cell (XYZ). The zonal mean of this standard deviation should reflect the uncertainty associated to the predictor selection within each slab and era.
section_zonal_average_continous(Cant_average_zonal,
"Cant_sd_mean",
"gamma_mean")
7.2 Across basins
Standard deviation of mean Cant values was calculate across all longitudes. This standard deviation should reflect the zonal variability of Cant within the basin and era.
section_zonal_average_continous(Cant_average_zonal,
"Cant_sd",
"gamma_mean")
7.3 Correlation
7.3.1 Cant vs model SD
Cant_average %>%
ggplot(aes(Cant, Cant_sd)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 10) +
geom_bin2d() +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10",
name = "log10(n)") +
facet_grid(basin_AIP ~ eras)
Cant_average %>%
ggplot(aes(Cant, Cant_sd)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 10) +
geom_bin2d() +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10",
name = "log10(n)") +
facet_grid(gamma_slab ~ basin_AIP)
7.3.2 Cant vs regional SD
Cant_average_zonal %>%
ggplot(aes(Cant_mean, Cant_sd)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 10) +
geom_bin2d() +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10",
name = "log10(n)") +
facet_grid(basin_AIP ~ eras)
Cant_average_zonal %>%
ggplot(aes(Cant_mean, Cant_sd)) +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 10) +
geom_bin2d() +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10",
name = "log10(n)") +
facet_grid(gamma_slab ~ basin_AIP)
8 Cant - predictor contribution
for (variable in c(
"Cant_intercept",
"Cant_aou",
"Cant_oxygen",
"Cant_phosphate",
"Cant_phosphate_star",
"Cant_silicate",
"Cant_tem",
"Cant_sal")) {
print(
section_zonal_average_continous(Cant_predictor_average_zonal,
variable,
"gamma")
)
}
rm(variable)9 Neutral density
9.1 Slab depth
Please note that:
- density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
- predictor density slabs are only shown for the upper 3000m as used for the mapping, whereas GLODAP observations are displayed for the entire water column as used for fitting eMLRs (in both cases shallow waters are excluded at low density)
gamma_maps <- Cant_average %>%
group_by(lat, lon, gamma_slab) %>%
summarise(depth_max = max(depth, na.rm = TRUE)) %>%
ungroup()
GLODAP_obs_coverage <- GLODAP %>%
count(lat, lon, gamma_slab, era)
ggplot() +
geom_raster(data = landmask,
aes(lon, lat),
fill = "grey80") +
geom_raster(data = gamma_maps,
aes(lon, lat, fill = depth_max)) +
geom_raster(data = GLODAP_obs_coverage,
aes(lon, lat), fill = "red") +
facet_grid(gamma_slab ~ era) +
coord_quickmap(expand = 0) +
scale_fill_viridis_c(direction = -1) +
theme(axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "top")
rm(gamma_maps, GLODAP_obs_coverage)9.2 Surface maps
map_climatology(Cant_average, "gamma")
9.3 Zonal sections
The mean zonal distribution of neutral densities was calculated. CAVEAT: Due to practical reasons, binning here does not include the two highest isoneutral density slabs in the Atlantic, yet.
9.3.1 Mean
Cant_average_zonal %>%
filter(eras == "JGOFS_GO") %>%
ggplot(aes(lat, depth, z = gamma_mean)) +
geom_contour_filled(breaks = slab_breaks) +
geom_contour(breaks = slab_breaks,
col = "white") +
geom_text_contour(breaks = slab_breaks,
col = "white",
skip = 1) +
scale_fill_viridis_d(name = "Gamma",
direction = -1) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20)) +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP~.)
9.3.2 SD
Higher SD of gamma in shallow, subtropical waters results from a more pronounced longitudinal variability.
Cant_average_zonal %>%
filter(eras == "JGOFS_GO") %>%
ggplot(aes(lat, depth, z = gamma_sd)) +
geom_contour_filled() +
scale_fill_viridis_d(name = "Gamma SD",
direction = -1) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20)) +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP~.)
10 Cstar
10.1 Zonal mean sections
Cstar_average_zonal <- Cstar_average_zonal %>%
mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))
Cstar_average_zonal %>%
ggplot(aes(lat, depth, z = Cstar_mean_mean)) +
geom_contour_filled(bins = 11) +
scale_fill_viridis_d(name = "Cstar") +
geom_contour(aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "white",
skip = 1
) +
scale_y_reverse() +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP ~ era)
11 Sections by model
Zonal sections plots are produced for every 20° longitude, each era and for all models individually and can be downloaded here.
12 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] kableExtra_1.1.0 marelac_2.1.10 shape_1.4.4 metR_0.7.0
[5] scales_1.1.1 scico_1.2.0 patchwork_1.0.1 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[13] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[17] 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] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_1.3-2 tools_4.0.2
[9] backports_1.1.8 R6_2.4.1 DBI_1.1.0 colorspace_1.4-1
[13] withr_2.2.0 sp_1.4-2 tidyselect_1.1.0 compiler_4.0.2
[17] git2r_0.27.1 cli_2.0.2 rvest_0.3.6 xml2_1.3.2
[21] isoband_0.2.2 labeling_0.3 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 highr_0.8 rlang_0.4.7 readxl_1.3.1
[33] rstudioapi_0.11 generics_0.0.2 farver_2.0.3 jsonlite_1.7.0
[37] magrittr_1.5 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
[41] lifecycle_0.2.0 stringi_1.4.6 whisker_0.4 yaml_2.2.1
[45] plyr_1.8.6 grid_4.0.2 blob_1.2.1 promises_1.1.1
[49] crayon_1.3.4 lattice_0.20-41 haven_2.3.1 hms_0.5.3
[53] seacarb_3.2.13 knitr_1.29 pillar_1.4.6 reprex_0.3.0
[57] glue_1.4.1 evaluate_0.14 data.table_1.13.0 modelr_0.1.8
[61] vctrs_0.3.2 httpuv_1.5.4 testthat_2.3.2 cellranger_1.1.0
[65] gtable_0.3.0 assertthat_0.2.1 xfun_0.16 broom_0.7.0
[69] later_1.1.0.1 viridisLite_0.3.0 memoise_1.1.0 ellipsis_0.3.1
[73] here_0.1