Analysis of Cant estimates
Jens Daniel Müller
27 August, 2020
Last updated: 2020-08-27
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Knit directory: Cant_eMLR/
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library(tidyverse)
library(scales)
library(metR)1 Data sources
Cant estimates from this study:
- Raw results by each of 10 MLR models per density slab
- Mean and SD per grid cell (lat, lon, depth)
- Zonal mean and SD (basin, lat, depth)
- Inventories (lat, lon)
Cant <-
read_csv(here::here("data/mapping/_summarized_files",
"Cant.csv"))
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_inv <-
read_csv(here::here("data/mapping/_summarized_files",
"Cant_inv.csv"))All following analysis are restricted to the inventory depth of 3000m.
Cant <- Cant %>%
filter(depth <= parameters$inventory_depth)
Cant_average <- Cant_average %>%
filter(depth <= parameters$inventory_depth)
Cant_average_zonal <- Cant_average_zonal %>%
filter(depth <= parameters$inventory_depth)2 Zonal mean sections
2.1 Neutral density
The mean zonal distribution of neutral densities was calculated. CAVEAT: Due to practical reasons, binning here does not include the two highest isoneutral density slabs in the Atlantic, yet.
slab_breaks <- c(parameters$slabs_Atl[1:12],Inf)
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() +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP~.)
2.2 Cant
2.2.1 Mean
Cant_average_zonal %>%
ggplot(aes(lat, depth, z = Cant_mean_mean)) +
geom_contour_fill(breaks = MakeBreaks(5),
na.fill = TRUE) +
scale_fill_divergent(guide = "colorstrip",
breaks = MakeBreaks(5),
name = "Cant") +
geom_contour(aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "black") +
geom_text_contour(
aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "black",
skip = 1
) +
scale_y_reverse() +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP ~ eras)
2.2.2 Mean positive values
Cant_average_zonal %>%
filter(depth <= parameters$inventory_depth) %>%
ggplot(aes(lat, depth, z = Cant_pos_mean_mean)) +
geom_contour_filled() +
geom_contour(aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "white",
skip = 1
) +
scale_fill_viridis_d(name = "Cant") +
scale_y_reverse() +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP~eras)
2.2.3 Mean standard deviation across MLR models
Standard deviation across Cant from all MLR models was calculate for each grid cell (XYZ). The zonal mean of this standard deviation should reflect the uncertainty associated to the predictor selection within each slab and era.
Cant_average_zonal %>%
filter(depth <= parameters$inventory_depth) %>%
ggplot(aes(lat, depth, z = Cant_sd_mean)) +
geom_contour_filled() +
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_fill_viridis_d(name = "Cant") +
scale_y_reverse() +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP ~ eras)
2.2.4 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.
Cant_average_zonal %>%
filter(depth <= parameters$inventory_depth) %>%
ggplot(aes(lat, depth, z = Cant_mean_sd)) +
geom_contour_filled() +
geom_contour(aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "white",
skip = 1
) +
scale_fill_viridis_d(name = "Cant") +
scale_y_reverse() +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP~eras)
3 Sections at longitudes
3.1 JGOFS_GO
section_climatology(Cant_average %>% filter(eras == "JGOFS_GO"),
"Cant_mean")
3.2 GO_new
section_climatology(Cant_average %>% filter(eras == "GO_new"),
"Cant_mean")
3.3 By model
Zonal sections plots are produced for every 20° longitude, each era and for all models individually and can be downloaded here.
for (i_eras in unique(Cant$eras)) {
#i_eras <- unique(Cant$eras)[2]
Cant_eras <- Cant %>%
filter(eras == i_eras)
for (i_lon in seq(20.5, 360, 20)) {
#i_lon <- seq(20.5, 360, 20)[7]
Cant_eras_lon <- Cant_eras %>%
filter(lon == i_lon)
Cant_eras_lon %>%
ggplot(aes(lat, depth, col = Cant)) +
geom_point() +
scale_color_divergent(
guide = "colorstrip",
breaks = MakeBreaks(5),
name = "Cant",
mid = "grey"
) +
scale_y_reverse(limits = c(3000,0)) +
scale_x_continuous(limits = c(-75,65)) +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
labs(title = paste("eras:", i_eras, "| lon:", i_lon)) +
facet_wrap(~ model, ncol = 5)
ggsave(here::here("output/figure/mapping",
paste(i_eras,
"lon",
i_lon,
"model_Cant.png",
sep = "_")),
width = 17, height = 9)
}
}4 Maps of Cant
4.1 Depth layers
Cant concentration for selected depth levels at which mapping was performed.
Cant_average %>%
filter(depth %in% c(150, 500, 1000, 3000)) %>%
ggplot(aes(lon, lat, fill = Cant_mean)) +
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)
4.2 Inventories
4.2.1 All Cant
Cant_inv %>%
ggplot() +
geom_raster(data = landmask %>% filter(region == "land"),
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(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
4.2.2 Positive Cant
Cant_inv %>%
ggplot() +
geom_raster(data = landmask %>% filter(region == "land"),
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(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
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)
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)
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 forcats_0.5.0 stringr_1.4.0
[5] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[9] tibble_3.0.3 ggplot2_3.3.2 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 R6_2.4.1
[9] cellranger_1.1.0 plyr_1.8.6 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 rvest_0.3.6 isoband_0.2.2 memoise_1.1.0
[65] knitr_1.29 haven_2.3.1