Analysis - zonal mean sections
Jens Daniel Müller and Donghe Zhu
14 January, 2021
Last updated: 2021-01-14
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Knit directory: emlr_mod_v_XXX/
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1 Data sources
Following Cant zonal mean sections are used:
1.1 This study
Results from this study are referred to as JDM.
cant_zonal_JDM <-
read_csv(paste(path_version_data,
"cant_zonal.csv",
sep = ""))
cant_zonal_JDM <- cant_zonal_JDM %>%
select(lat,
depth,
eras,
basin_AIP,
gamma_mean,
cant_mean,
cant_pos_mean,
cant_sd,
cant_pos_sd)1.2 Modeled Cant
“True” Cant fields directly inferred from the model output are referred to as M.
tref <-
read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
cant_tref_1 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[1]),
".csv",
sep = ""
)
)
cant_tref_1 <- cant_tref_1 %>%
rename(cant_tref_1 = cant_total) %>%
select(-year)
cant_tref_2 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[2]),
".csv",
sep = ""
)
)
cant_tref_2 <- cant_tref_2 %>%
rename(cant_tref_2 = cant_total) %>%
select(-year)
cant_tref_3 <-
read_csv(
paste(
path_preprocessing,
"cant_annual_field_",
params_local$model_runs,
"/cant_",
unique(tref$year[3]),
".csv",
sep = ""
)
)
cant_tref_3 <- cant_tref_3 %>%
rename(cant_tref_3 = cant_total) %>%
select(-year)cant_M_1 <- left_join(cant_tref_1, cant_tref_2) %>%
mutate(cant = cant_tref_2 - cant_tref_1,
eras = unique(cant_zonal_JDM$eras)[1]) %>%
select(-c(cant_tref_1, cant_tref_2))
cant_M_2 <- left_join(cant_tref_2, cant_tref_3) %>%
mutate(cant = cant_tref_3 - cant_tref_2,
eras = unique(cant_zonal_JDM$eras)[2]) %>%
select(-c(cant_tref_2, cant_tref_3))
cant_M <- full_join(cant_M_1, cant_M_2) %>%
arrange(lon, lat, depth, basin_AIP)
cant_M <- cant_M %>%
mutate(cant_pos = if_else(cant <= 0, 0, cant))
rm(cant_tref_1, cant_tref_2, cant_tref_3, cant_M_1, cant_M_2)cant_zonal_M <- m_zonal_mean_section(cant_M)cant_zonal_JDM_gamma <- cant_zonal_JDM %>%
select(lat, depth, eras, basin_AIP, gamma_mean)
cant_zonal_M <- left_join(cant_zonal_JDM_gamma, cant_zonal_M)
rm(cant_zonal_JDM_gamma)1.3 Join data sets
Zonal sections are merged, and differences calculate per grid cell and per eras.
# add estimate label
cant_zonal_long <- bind_rows(
cant_zonal_JDM %>% mutate(estimate = "JDM"),
cant_zonal_M %>% mutate(estimate = "M")
)
# pivot to wide format
cant_zonal_wide <- cant_zonal_long %>%
pivot_wider(names_from = estimate, values_from = cant_mean:cant_pos_sd) %>%
drop_na()
# calculate offset
cant_zonal_wide <- cant_zonal_wide %>%
mutate(cant_pos_mean_offset = cant_pos_mean_JDM - cant_pos_mean_M,
cant_mean_offset = cant_mean_JDM - cant_mean_M,
estimate = "JDM - M")2 Zonal mean sections
2.1 Cant - positive only
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 MLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections.
2.1.1 Absolute values
for (i_eras in unique(cant_zonal_long$eras)) {
for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
for (i_estimate in unique(cant_zonal_long$estimate)) {
print(
p_section_zonal(
df = cant_zonal_long %>%
filter(
basin_AIP == i_basin_AIP,
estimate == i_estimate,
eras == i_eras
),
var = "cant_pos_mean",
subtitle_text =
paste(
"Basin:",
i_basin_AIP,
"| estimate:",
i_estimate,
" | eras:",
i_eras
)
)
)
}
print(
p_section_zonal(
df = cant_zonal_wide %>%
filter(basin_AIP == i_basin_AIP,
eras == i_eras),
var = "cant_pos_mean_offset",
breaks = params_global$breaks_cant_offset,
col = "divergent",
title_text = "Zonal mean section - offset",
subtitle_text =
paste("Basin:", i_basin_AIP, "| estimate: JDM-M | eras:", i_eras)
)
)
}
}
2.1.2 Offset
for (i_eras in unique(cant_zonal_wide$eras)) {
for (i_basin_AIP in unique(cant_zonal_wide$basin_AIP)) {
print(
p_section_zonal(
df = cant_zonal_wide %>%
filter(basin_AIP == i_basin_AIP,
eras == i_eras),
var = "cant_pos_mean_offset",
breaks = params_global$breaks_cant_offset,
col = "divergent",
subtitle_text =
paste("Basin:", i_basin_AIP, "| estimate: JDM-M | eras:", i_eras)
)
)
}
}
2.2 Cant - all
In a second series of plots we explore the distribution of all Cant, taking positive and negative estimates into account.
2.2.1 Absolute values
for (i_eras in unique(cant_zonal_long$eras)) {
for (i_basin_AIP in unique(cant_zonal_long$basin_AIP)) {
for (i_estimate in unique(cant_zonal_long$estimate)) {
print(
p_section_zonal(
df = cant_zonal_long %>%
filter(
basin_AIP == i_basin_AIP,
estimate == i_estimate,
eras == i_eras
),
var = "cant_mean",
col = "divergent",
breaks = params_global$breaks_cant,
legend_title = expression(atop(Delta * C[ant],
(mu * mol ~ kg ^ {
-1
}))),
subtitle_text =
paste(
"Basin:",
i_basin_AIP,
"| estimate:",
i_estimate,
" | eras:",
i_eras
)
)
)
}
}
}
2.2.2 Offset
for (i_eras in unique(cant_zonal_wide$eras)) {
for (i_basin_AIP in unique(cant_zonal_wide$basin_AIP)) {
print(
p_section_zonal(
df = cant_zonal_wide %>%
filter(basin_AIP == i_basin_AIP,
eras == i_eras),
var = "cant_mean_offset",
col = "divergent",
breaks = params_global$breaks_cant_offset,
subtitle_text =
paste("Basin:", i_basin_AIP, "| estimate: JDM - M | eras:", i_eras)
)
)
}
}
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 marelac_2.1.10 shape_1.4.5 scales_1.1.1
[5] metR_0.9.0 scico_1.2.0 patchwork_1.1.1 collapse_1.5.0
[9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
[17] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.7.1 here_0.1
[4] modelr_0.1.8 assertthat_0.2.1 blob_1.2.1
[7] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.7
[10] backports_1.1.10 lattice_0.20-41 glue_1.4.2
[13] RcppEigen_0.3.3.7.0 digest_0.6.27 promises_1.1.1
[16] checkmate_2.0.0 rvest_0.3.6 colorspace_1.4-1
[19] plyr_1.8.6 htmltools_0.5.0 httpuv_1.5.4
[22] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.2
[25] seacarb_3.2.14 haven_2.3.1 whisker_0.4
[28] later_1.1.0.1 git2r_0.27.1 farver_2.0.3
[31] generics_0.0.2 ellipsis_0.3.1 withr_2.3.0
[34] cli_2.1.0 magrittr_1.5 crayon_1.3.4
[37] readxl_1.3.1 memoise_1.1.0 evaluate_0.14
[40] fs_1.5.0 fansi_0.4.1 xml2_1.3.2
[43] RcppArmadillo_0.10.1.2.0 oce_1.2-0 tools_4.0.3
[46] data.table_1.13.2 hms_0.5.3 lifecycle_0.2.0
[49] munsell_0.5.0 reprex_0.3.0 gsw_1.0-5
[52] isoband_0.2.2 compiler_4.0.3 rlang_0.4.9
[55] grid_4.0.3 rstudioapi_0.13 labeling_0.4.2
[58] rmarkdown_2.5 testthat_2.3.2 gtable_0.3.0
[61] DBI_1.1.0 R6_2.5.0 lubridate_1.7.9
[64] knitr_1.30 rprojroot_2.0.2 stringi_1.5.3
[67] parallel_4.0.3 Rcpp_1.0.5 vctrs_0.3.5
[70] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.18