Analysis of Cant estimates
Jens Daniel Müller
16 September, 2020
Last updated: 2020-09-16
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Knit directory: Cant_eMLR/
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library(tidyverse)
library(patchwork)
library(scico)
library(scales)
library(metR)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 Neutral density
2.1 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)2.2 Surface maps
map_climatology(Cant_average, "gamma_mean")
2.3 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.
2.3.1 Mean
Cant_average_zonal %>%
filter(eras == "JGOFS_GO") %>%
ggplot(aes(lat, depth, z = gamma_mean_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~.)
2.3.2 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_mean_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~.)
3 Cant
3.1 Zonal sections
3.1.1 Mean
section_zonal_average_divergent(Cant_average_zonal,
"Cant_mean_mean",
"gamma_mean_mean")
3.1.2 Mean positive values
section_zonal_average_continous(Cant_average_zonal,
"Cant_pos_mean_mean",
"gamma_mean_mean")
3.1.3 Mean standard deviation across MLR 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_mean")
3.1.4 Standard deviation across basin
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_mean_sd",
"gamma_mean_mean")
3.1.5 Predictor contribution
for (variable in c(
"Cant_intercept_mean",
"Cant_aou_mean",
"Cant_oxygen_mean",
"Cant_phosphate_mean",
"Cant_phosphate_star_mean",
"Cant_silicate_mean",
"Cant_tem_mean",
"Cant_sal_mean")) {
print(
section_zonal_average_continous(Cant_predictor_average_zonal,
variable,
"gamma_mean_mean")
)
}
rm(variable)3.2 Sections along path
3.2.1 JGOFS_GO
section_global(Cant_average %>% filter(eras == "JGOFS_GO"),
"Cant_mean")
3.2.2 GO_new
section_global(Cant_average %>% filter(eras == "GO_new"),
"Cant_mean")
3.3 Sections by model
Zonal sections plots are produced for every 20° longitude, each era and for all models individually and can be downloaded here.
3.4 Maps
3.4.1 Surfaces
Cant concentration for selected depth levels at which mapping was performed.
Cant_average %>%
filter(depth %in% parameters$depth_levels) %>%
ggplot(aes(lon, lat, fill = Cant_mean)) +
geom_raster(data = landmask,
aes(lon, lat), fill = "grey80") +
geom_raster() +
scale_fill_divergent(guide = "colorstrip",
breaks = MakeBreaks(5),
name = "Cant") +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
coord_quickmap(expand = 0) +
facet_grid(depth ~ eras)
3.4.2 Inventories
3.4.2.1 All Cant
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_gradient2(high = muted("red"),
mid = "white",
low = muted("blue"),
midpoint = 0,
space = "Lab",
na.value = "green") +
facet_wrap(~eras, ncol = 1) +
theme(
axis.title = element_blank()
)
3.4.2.2 Positive Cant
Cant_inv %>%
ggplot() +
geom_raster(data = landmask,
aes(lon, lat), fill = "grey80") +
geom_raster(aes(lon, lat, fill = cant_inv_pos)) +
coord_quickmap(expand = 0) +
scale_fill_viridis_c() +
facet_wrap(~eras, ncol = 1) +
theme(
axis.title = element_blank()
)
3.5 Cant vs model SD
Cant_average %>%
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 %>%
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)
3.6 Cant vs regional SD
Cant_average_zonal %>%
ggplot(aes(Cant_mean_mean, Cant_mean_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_mean, Cant_mean_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)
4 Cstar
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)
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] metR_0.7.0 scales_1.1.1 scico_1.2.0 patchwork_1.0.1
[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 lattice_0.20-41 lubridate_1.7.9 here_0.1
[5] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25 plyr_1.8.6
[9] R6_2.4.1 cellranger_1.1.0 backports_1.1.8 reprex_0.3.0
[13] evaluate_0.14 httr_1.4.2 pillar_1.4.6 rlang_0.4.7
[17] readxl_1.3.1 rstudioapi_0.11 data.table_1.13.0 whisker_0.4
[21] blob_1.2.1 checkmate_2.0.0 rmarkdown_2.3 labeling_0.3
[25] munsell_0.5.0 broom_0.7.0 compiler_4.0.2 httpuv_1.5.4
[29] modelr_0.1.8 xfun_0.16 pkgconfig_2.0.3 htmltools_0.5.0
[33] tidyselect_1.1.0 viridisLite_0.3.0 fansi_0.4.1 crayon_1.3.4
[37] dbplyr_1.4.4 withr_2.2.0 later_1.1.0.1 grid_4.0.2
[41] jsonlite_1.7.0 gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0
[45] git2r_0.27.1 magrittr_1.5 cli_2.0.2 stringi_1.4.6
[49] farver_2.0.3 fs_1.4.2 promises_1.1.1 sp_1.4-2
[53] xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.2
[57] tools_4.0.2 glue_1.4.1 hms_0.5.3 yaml_2.2.1
[61] colorspace_1.4-1 isoband_0.2.2 rvest_0.3.6 memoise_1.1.0
[65] knitr_1.29 haven_2.3.1