Analysis of Cant estimates
Jens Daniel Müller
26 August, 2020
Last updated: 2020-08-26
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Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)
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"))2 Neutral density section
2.1 Mean values
The mean zonal distribution of neutral densities was calculated. CAVEAT: Binning here does not include two highest isoneutral density slabs in the Atlantic, yet.
slab_breaks <- c(parameters$slabs_Atl[1:12],Inf)
Cant_average_zonal %>%
filter(depth <= parameters$inventory_depth) %>%
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~eras)
3 Cant sections
3.1 By model
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)
}
}3.2 Mean values
Cant_average_zonal %>%
filter(depth <= parameters$inventory_depth) %>%
ggplot(aes(lat, depth, z = Cant_mean_mean)) +
geom_contour_fill(breaks = MakeBreaks(5),
na.fill = TRUE) +
scale_fill_divergent(guide = "colorstrip",
breaks = MakeBreaks(5),
name = "Cant") +
geom_contour(aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "black") +
geom_text_contour(
aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "black",
skip = 1
) +
scale_y_reverse() +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP ~ eras)
3.3 Mean zonal standard deviation across MLR models
Standard deviation across Cant from all MLR models was calculate for each grid cell (XYZ). The zonal mean of this standard deviation should reflect the uncertainty associated to the predictor selection within each slab and era.
Cant_average_zonal %>%
filter(depth <= parameters$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)
3.4 Zonal standard deviation of mean Cant estimates
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$inventory_depth) %>%
ggplot(aes(lat, depth, z = Cant_mean_sd)) +
geom_contour_filled() +
geom_contour(aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean_mean),
breaks = slab_breaks,
col = "white",
skip = 1
) +
scale_fill_viridis_d(name = "Cant") +
scale_y_reverse() +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(basin_AIP~eras)
3.5 Mean positive values
Cant_average_zonal %>%
filter(depth <= parameters$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)
3.6 Sections
3.6.1 JGOFS_GO
section_climatology(Cant_average %>% filter(eras == "JGOFS_GO"),
"Cant_mean")
3.6.2 GO_new
section_climatology(Cant_average %>% filter(eras == "GO_new"),
"Cant_mean")
4 Cant maps
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 Inventory calculation
To calculate Cant column inventories, we:
- Multiple layer thickness with Cant concentration to get a layer inventory
- For each horizontal grid cell and era, sum Cant layer inventories from 150 - 3000 m
Step 2 is performed again for all Cant and positive Cant values only
depth_level_volume <- tibble(depth = unique(Cant_average$depth))
depth_level_volume <- depth_level_volume %>%
mutate(
layer_thickness_above = replace_na((depth - lag(depth)) / 2, 0),
layer_thickness_below = replace_na((lead(depth) - depth) / 2, 0),
layer_thickness = layer_thickness_above + layer_thickness_below
) %>%
select(-c(layer_thickness_above,
layer_thickness_below))
Cant_average <-
full_join(Cant_average, depth_level_volume)
Cant_average <- Cant_average %>%
mutate(layer_inv = Cant_mean * layer_thickness) %>%
mutate(layer_inv_pos = if_else(layer_inv < 0, 0, layer_inv)) %>%
select(-layer_thickness)
Cant_inv <- Cant_average %>%
filter(depth <= parameters$inventory_depth) %>%
group_by(lon, lat, basin, eras) %>%
summarise(
cant_inv_pos = sum(layer_inv_pos, na.rm = TRUE) / 1000,
cant_inv = sum(layer_inv, na.rm = TRUE) / 1000
) %>%
ungroup()4.3 Inventories
4.3.1 All Cant estimates
library(scales)
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()
)
4.3.2 Positive Cant estimates
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()
)
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] scales_1.1.1 metR_0.7.0 lubridate_1.7.9 forcats_0.5.0
[5] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[9] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 lattice_0.20-41 here_0.1 assertthat_0.2.1
[5] rprojroot_1.3-2 digest_0.6.25 R6_2.4.1 cellranger_1.1.0
[9] plyr_1.8.6 backports_1.1.8 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.2 pillar_1.4.6 rlang_0.4.7 readxl_1.3.1
[17] rstudioapi_0.11 data.table_1.13.0 whisker_0.4 blob_1.2.1
[21] checkmate_2.0.0 rmarkdown_2.3 labeling_0.3 munsell_0.5.0
[25] broom_0.7.0 compiler_4.0.2 httpuv_1.5.4 modelr_0.1.8
[29] xfun_0.16 pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0
[33] viridisLite_0.3.0 fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4
[37] withr_2.2.0 later_1.1.0.1 grid_4.0.2 jsonlite_1.7.0
[41] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1
[45] magrittr_1.5 cli_2.0.2 stringi_1.4.6 farver_2.0.3
[49] fs_1.4.2 promises_1.1.1 sp_1.4-2 xml2_1.3.2
[53] ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.2 tools_4.0.2
[57] glue_1.4.1 hms_0.5.3 yaml_2.2.1 colorspace_1.4-1
[61] rvest_0.3.6 isoband_0.2.2 memoise_1.1.0 knitr_1.29
[65] haven_2.3.1