Analysis of cant budgets
Jens Daniel Müller
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
Following Cant estimates are used:
- Inventories for this study and model truth (lat, lon, eras)
1.1 This study
Results from this study are referred to as JDM.
cant_inv_JDM <-
read_csv(paste(path_version_data,
"cant_inv.csv",
sep = ""))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_inv_JDM$eras)[1]) %>%
select(-c(cant_tref_1, cant_tref_2))
cant_M_1 <- cant_M_1 %>%
mutate(cant_pos = if_else(cant <= 0, 0, cant))
cant_M_2 <- left_join(cant_tref_2, cant_tref_3) %>%
mutate(cant = cant_tref_3 - cant_tref_2,
eras = unique(cant_inv_JDM$eras)[2]) %>%
select(-c(cant_tref_2, cant_tref_3))
cant_M_2 <- cant_M_2 %>%
mutate(cant_pos = if_else(cant <= 0, 0, cant))
cant_M <- full_join(cant_M_1, cant_M_2) %>%
arrange(lon, lat, depth, basin_AIP)
rm(cant_tref_1, cant_tref_2, cant_tref_3, cant_M_1, cant_M_2)cant_inv_M <- m_cant_inv(cant_M)1.3 Join data sets
Inventories are merged, and differences calculate per grid cell and per eras.
# add estimate label
cant_inv_long <- bind_rows(
cant_inv_JDM %>% mutate(estimate = "JDM"),
cant_inv_M %>% mutate(estimate = "M")
)
# pivot to wide format
cant_inv_wide <- cant_inv_long %>%
pivot_wider(names_from = estimate, values_from = cant_pos_inv:cant_inv) %>%
drop_na()
# calculate offset
cant_inv_wide <- cant_inv_wide %>%
mutate(cant_pos_inv_offset = cant_pos_inv_JDM - cant_pos_inv_M,
cant_inv_offset = cant_inv_JDM - cant_inv_M)
# restrict to the standard inventory depth
cant_inv_long <- cant_inv_long %>%
filter(inv_depth == params_global$inventory_depth_standard)
cant_inv_wide <- cant_inv_wide %>%
filter(inv_depth == params_global$inventory_depth_standard)2 Inventory maps
This analysis is restricted to the standard inventory depth of 3000 m.
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 the MLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.
2.1.1 Absolute values
Column inventory of positive Cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).
for (i_estimate in unique(cant_inv_long$estimate)) {
for (i_eras in unique(cant_inv_long$eras)) {
print(p_map_cant_inv(
cant_inv_long %>% filter(estimate == i_estimate, eras == i_eras),
subtitle_text = paste("Estimate:", i_estimate, " | Eras:", i_eras)
))
}
}
2.1.2 Offset
Column inventory offset of positive cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).
for (i_eras in unique(cant_inv_wide$eras)) {
print(
p_map_cant_inv_offset(
cant_inv_wide %>% filter(eras == i_eras),
"cant_pos_inv_offset",
subtitle_text = paste("Estimate JDM - M | Eras:", i_eras)
)
)
}
2.2 Cant - all
In a second series of plots we explore the distribution of Cant, taking positive and negative estimates into account (positive here refers to the mean cant estimate across MLR model predictions available for each grid cell).
2.2.1 Absolute values
Column inventory of Cant (including positive and negative values) between the surface and 3000m water depth per horizontal grid cell (lat x lon).
for (i_estimate in unique(cant_inv_long$estimate)) {
for (i_eras in unique(cant_inv_long$eras)) {
print(
p_map_cant_inv(
cant_inv_long %>% filter(estimate == i_estimate, eras == i_eras),
subtitle_text = paste("Estimate:", i_estimate, " | Eras:", i_eras),
col = "divergent"
)
)
}
}
2.2.2 Offset
Column inventory offset of Cant (including positive and negative values) between the surface and 3000m water depth per horizontal grid cell (lat x lon).
for (i_eras in unique(cant_inv_wide$eras)) {
print(
p_map_cant_inv_offset(
df = cant_inv_wide %>% filter(eras == i_eras),
var = "cant_inv_offset",
subtitle_text = paste("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] htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18
[22] pkgconfig_2.0.3 broom_0.7.2 seacarb_3.2.14
[25] haven_2.3.1 whisker_0.4 later_1.1.0.1
[28] git2r_0.27.1 farver_2.0.3 generics_0.0.2
[31] ellipsis_0.3.1 withr_2.3.0 cli_2.1.0
[34] magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[37] evaluate_0.14 fs_1.5.0 fansi_0.4.1
[40] xml2_1.3.2 RcppArmadillo_0.10.1.2.0 oce_1.2-0
[43] tools_4.0.3 data.table_1.13.2 hms_0.5.3
[46] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0
[49] gsw_1.0-5 compiler_4.0.3 rlang_0.4.9
[52] grid_4.0.3 rstudioapi_0.13 labeling_0.4.2
[55] rmarkdown_2.5 testthat_2.3.2 gtable_0.3.0
[58] DBI_1.1.0 R6_2.5.0 lubridate_1.7.9
[61] knitr_1.30 rprojroot_2.0.2 stringi_1.5.3
[64] parallel_4.0.3 Rcpp_1.0.5 vctrs_0.3.5
[67] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.18