Analysis - others
Jens Daniel Müller and Donghe Zhu
13 January, 2021
Last updated: 2021-01-13
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Knit directory: emlr_mod_v_XXX/
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1 Data sources
Cant estimates from this sensitivity case:
- Mean and SD per grid cell (lat, lon, depth)
- Zonal mean and SD (basin, lat, depth)
cant_3d <-
read_csv(paste(path_version_data,
"cant_3d.csv",
sep = ""))
cant_zonal <-
read_csv(paste(path_version_data,
"cant_zonal.csv",
sep = ""))
cant_predictor_zonal <-
read_csv(paste(path_version_data,
"cant_predictor_zonal.csv",
sep = ""))Target variable (cstar_tref) estimates from this sensitivity case:
- Mean and SD per grid cell (lat, lon, depth)
- Zonal mean and SD (basin, lat, depth)
target_3d <-
read_csv(paste(path_version_data,
"target_3d.csv",
sep = ""))
target_zonal <-
read_csv(paste(path_version_data,
"target_zonal.csv",
sep = ""))Cleaned GLODAP-based synthetic model subsetting file as used in this sensitivity case
GLODAP <-
read_csv(paste(
path_version_data,
"GLODAPv2.2020_MLR_fitting_ready.csv",
sep = ""
))2 Calculate gamma slab maps
cant_gamma_maps <- m_cant_slab(cant_3d)
cant_gamma_maps <- cant_gamma_maps %>%
arrange(gamma_slab, eras)3 Cant variability
3.1 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.
for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
for (i_eras in unique(cant_zonal$eras)) {
print(
p_section_zonal(
df = cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
eras == i_eras),
var = "cant_sd_mean",
gamma = "gamma_mean",
legend_title = "sd",
title_text = "Zonal mean section of SD across models",
subtitle_text =
paste("Basin:", i_basin_AIP, "| eras:", i_eras)
)
)
}
}
3.2 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.
for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
for (i_eras in unique(cant_zonal$eras)) {
print(
p_section_zonal(
df = cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
eras == i_eras),
var = "cant_sd",
gamma = "gamma_mean",
legend_title = "sd",
title_text = "Zonal mean section of Cant SD",
subtitle_text =
paste("Basin:", i_basin_AIP, "| eras:", i_eras)
)
)
}
}
3.3 Correlation
3.3.1 Cant vs model SD
3.3.1.1 Era vs basin
cant_3d %>%
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)
3.3.1.2 Basin vs gamma
cant_3d %>%
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)
3.3.2 Cant vs regional SD
3.3.2.1 Era vs basin
cant_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)
3.3.2.2 Era vs basin
cant_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)
4 Cant - predictor contribution
for (i_var in paste("cant",
c("intercept", params_local$MLR_predictors),
sep = "_")) {
print(
p_section_zonal_divergent_gamma_eras_basin(df = cant_predictor_zonal,
var = i_var,
gamma = "gamma")
)
}
rm(i_var)5 Neutral density
5.1 Slab depth
The plot below shows the depths of individual gamma slabs (color) together with the synthetic subsetting available in the respective slab.
Please note that:
- density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
GLODAP_obs_coverage <- GLODAP %>%
count(lat, lon, gamma_slab, era)
map +
geom_raster(data = cant_gamma_maps,
aes(lon, lat, fill = depth_max)) +
geom_raster(data = GLODAP_obs_coverage,
aes(lon, lat), fill = "red") +
facet_grid(gamma_slab ~ era) +
scale_fill_viridis_c(direction = -1) +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
legend.position = "top")
rm(GLODAP_obs_coverage)6 Target variable
The predicted target variable (cstar_tref in this sensitivity case) is based on fitted MLRs and climatological fields of predictor variables, and calculated for each era.
6.1 Zonal mean sections
slab_breaks <- c(params_local$slabs_Atl[1:12], Inf)
for (i_basin_AIP in unique(target_zonal$basin_AIP)) {
print(
target_zonal %>%
filter(basin_AIP == i_basin_AIP) %>%
ggplot(aes(lat, depth,
z = !!sym(
paste(params_local$MLR_target, "mean", sep = "_")
))) +
geom_contour_filled(bins = 11) +
scale_fill_viridis_d(name = params_local$MLR_target) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white",
skip = 1
) +
scale_y_reverse() +
coord_cartesian(expand = 0,
ylim = c(params_global$plotting_depth, 0)) +
scale_x_continuous(breaks = seq(-100, 100, 20)) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
facet_grid(era ~ eras,
labeller = labeller(.default = label_both)) +
labs(title = i_basin_AIP)
)
}
rm(slab_breaks)
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gt_0.2.2 kableExtra_1.3.1 marelac_2.1.10 shape_1.4.5
[5] scales_1.1.1 metR_0.9.0 scico_1.2.0 patchwork_1.1.1
[9] collapse_1.5.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[17] ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9 gsw_1.0-5
[4] webshot_0.5.2 httr_1.4.2 rprojroot_2.0.2
[7] tools_4.0.3 backports_1.1.10 R6_2.5.0
[10] DBI_1.1.0 colorspace_2.0-0 withr_2.3.0
[13] tidyselect_1.1.0 compiler_4.0.3 git2r_0.27.1
[16] cli_2.2.0 rvest_0.3.6 xml2_1.3.2
[19] isoband_0.2.3 labeling_0.4.2 checkmate_2.0.0
[22] digest_0.6.27 rmarkdown_2.5 oce_1.2-0
[25] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
[28] rlang_0.4.10 readxl_1.3.1 rstudioapi_0.13
[31] farver_2.0.3 generics_0.1.0 jsonlite_1.7.2
[34] magrittr_2.0.1 Matrix_1.2-18 Rcpp_1.0.5
[37] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0
[40] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[43] plyr_1.8.6 grid_4.0.3 blob_1.2.1
[46] parallel_4.0.3 promises_1.1.1 crayon_1.3.4
[49] lattice_0.20-41 haven_2.3.1 hms_0.5.3
[52] seacarb_3.2.14 knitr_1.30 pillar_1.4.7
[55] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[58] RcppArmadillo_0.10.1.2.2 data.table_1.13.6 modelr_0.1.8
[61] vctrs_0.3.6 httpuv_1.5.4 testthat_3.0.1
[64] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[67] xfun_0.20 broom_0.7.3 RcppEigen_0.3.3.9.1
[70] later_1.1.0.1 viridisLite_0.3.0 memoise_1.1.0
[73] ellipsis_0.3.1 here_1.0.1