Budgets
Jens Daniel Müller
08 March, 2022
Last updated: 2022-03-08
Checks: 7 0
Knit directory: emlr_obs_analysis/analysis/
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|---|---|---|---|---|
| Rmd | 775eb4f | jens-daniel-mueller | 2022-03-08 | moving eras analysis implemented |
version_id_pattern <- "m"
config <- "MLR_basins"1 Read files
# identify required version IDs
Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_1", "m"))
Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_2", "m"))
Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_3", "m"))
Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
print(Version_IDs) [1] "v_1m00" "v_1m01" "v_1m02" "v_1m03" "v_1m04" "v_1m05" "v_1m06" "v_1m07"
[9] "v_1m08" "v_1m09" "v_1m10"1.1 Global
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_global <-
read_csv(paste(path_version_data,
"dcant_budget_global.csv",
sep = ""))
dcant_budget_global_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_global_mod_truth.csv",
sep = ""
))
dcant_budget_global_bias <-
read_csv(paste(path_version_data,
"dcant_budget_global_bias.csv",
sep = ""))
lm_best_predictor_counts <-
read_csv(paste(path_version_data,
"lm_best_predictor_counts.csv",
sep = ""))
lm_best_dcant <-
read_csv(paste(path_version_data,
"lm_best_dcant.csv",
sep = ""))
dcant_budget_global <- bind_rows(dcant_budget_global,
dcant_budget_global_mod_truth)
dcant_budget_global <- dcant_budget_global %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_global_bias <- dcant_budget_global_bias %>%
mutate(Version_ID = i_Version_IDs)
lm_best_predictor_counts <- lm_best_predictor_counts %>%
mutate(Version_ID = i_Version_IDs)
lm_best_dcant <- lm_best_dcant %>%
mutate(Version_ID = i_Version_IDs)
params_local <-
read_rds(paste(path_version_data,
"params_local.rds",
sep = ""))
params_local <- bind_cols(
Version_ID = i_Version_IDs,
MLR_basins := str_c(params_local$MLR_basins, collapse = "|"),
tref1 = params_local$tref1,
tref2 = params_local$tref2)
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
params_local <- params_local %>%
mutate(
median_year_1 = sort(tref$median_year)[1],
median_year_2 = sort(tref$median_year)[2],
duration = median_year_2 - median_year_1,
period = paste(median_year_1, "-", median_year_2)
)
if (exists("dcant_budget_global_all")) {
dcant_budget_global_all <-
bind_rows(dcant_budget_global_all, dcant_budget_global)
}
if (!exists("dcant_budget_global_all")) {
dcant_budget_global_all <- dcant_budget_global
}
if (exists("dcant_budget_global_bias_all")) {
dcant_budget_global_bias_all <-
bind_rows(dcant_budget_global_bias_all,
dcant_budget_global_bias)
}
if (!exists("dcant_budget_global_bias_all")) {
dcant_budget_global_bias_all <- dcant_budget_global_bias
}
if (exists("lm_best_predictor_counts_all")) {
lm_best_predictor_counts_all <-
bind_rows(lm_best_predictor_counts_all, lm_best_predictor_counts)
}
if (!exists("lm_best_predictor_counts_all")) {
lm_best_predictor_counts_all <- lm_best_predictor_counts
}
if (exists("lm_best_dcant_all")) {
lm_best_dcant_all <-
bind_rows(lm_best_dcant_all, lm_best_dcant)
}
if (!exists("lm_best_dcant_all")) {
lm_best_dcant_all <- lm_best_dcant
}
if (exists("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}[1] "v_1m00"
[1] "v_1m01"
[1] "v_1m02"
[1] "v_1m03"
[1] "v_1m04"
[1] "v_1m05"
[1] "v_1m06"
[1] "v_1m07"
[1] "v_1m08"
[1] "v_1m09"
[1] "v_1m10"rm(
dcant_budget_global,
dcant_budget_global_bias,
dcant_budget_global_mod_truth,
lm_best_predictor_counts,
lm_best_dcant,
params_local,
tref
)1.2 Basins
# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_basin_AIP <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP.csv",
sep = ""))
dcant_budget_basin_AIP_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_basin_AIP_mod_truth.csv",
sep = ""
))
dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
dcant_budget_basin_AIP_mod_truth)
dcant_budget_basin_AIP_bias <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP_bias.csv",
sep = ""))
dcant_slab_budget_bias <-
read_csv(paste0(path_version_data,
"dcant_slab_budget_bias.csv"))
dcant_slab_budget <-
read_csv(paste0(path_version_data,
"dcant_slab_budget.csv"))
dcant_budget_basin_AIP <- dcant_budget_basin_AIP %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_basin_AIP_bias <- dcant_budget_basin_AIP_bias %>%
mutate(Version_ID = i_Version_IDs)
dcant_slab_budget <- dcant_slab_budget %>%
mutate(Version_ID = i_Version_IDs)
dcant_slab_budget_bias <- dcant_slab_budget_bias %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_budget_basin_AIP_all")) {
dcant_budget_basin_AIP_all <-
bind_rows(dcant_budget_basin_AIP_all, dcant_budget_basin_AIP)
}
if (!exists("dcant_budget_basin_AIP_all")) {
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP
}
if (exists("dcant_budget_basin_AIP_bias_all")) {
dcant_budget_basin_AIP_bias_all <-
bind_rows(dcant_budget_basin_AIP_bias_all,
dcant_budget_basin_AIP_bias)
}
if (!exists("dcant_budget_basin_AIP_bias_all")) {
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias
}
if (exists("dcant_slab_budget_all")) {
dcant_slab_budget_all <-
bind_rows(dcant_slab_budget_all, dcant_slab_budget)
}
if (!exists("dcant_slab_budget_all")) {
dcant_slab_budget_all <- dcant_slab_budget
}
if (exists("dcant_slab_budget_bias_all")) {
dcant_slab_budget_bias_all <-
bind_rows(dcant_slab_budget_bias_all,
dcant_slab_budget_bias)
}
if (!exists("dcant_slab_budget_bias_all")) {
dcant_slab_budget_bias_all <- dcant_slab_budget_bias
}
}[1] "v_1m00"
[1] "v_1m01"
[1] "v_1m02"
[1] "v_1m03"
[1] "v_1m04"
[1] "v_1m05"
[1] "v_1m06"
[1] "v_1m07"
[1] "v_1m08"
[1] "v_1m09"
[1] "v_1m10"rm(
dcant_budget_basin_AIP,
dcant_budget_basin_AIP_bias,
dcant_budget_basin_AIP_mod_truth,
dcant_slab_budget,
dcant_slab_budget_bias
)1.3 Basins hemisphere
# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_basin_MLR <-
read_csv(paste(path_version_data,
"dcant_budget_basin_MLR.csv",
sep = ""))
dcant_budget_basin_MLR_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_basin_MLR_mod_truth.csv",
sep = ""
))
dcant_budget_basin_MLR <- bind_rows(dcant_budget_basin_MLR,
dcant_budget_basin_MLR_mod_truth)
# dcant_budget_basin_MLR_bias <-
# read_csv(paste(path_version_data,
# "dcant_budget_basin_MLR_bias.csv",
# sep = ""))
dcant_budget_basin_MLR <- dcant_budget_basin_MLR %>%
mutate(Version_ID = i_Version_IDs)
# dcant_budget_basin_MLR_bias <- dcant_budget_basin_MLR_bias %>%
# mutate(Version_ID = i_Version_IDs)
if (exists("dcant_budget_basin_MLR_all")) {
dcant_budget_basin_MLR_all <-
bind_rows(dcant_budget_basin_MLR_all, dcant_budget_basin_MLR)
}
if (!exists("dcant_budget_basin_MLR_all")) {
dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR
}
# if (exists("dcant_budget_basin_MLR_bias_all")) {
# dcant_budget_basin_MLR_bias_all <-
# bind_rows(dcant_budget_basin_MLR_bias_all,
# dcant_budget_basin_MLR_bias)
# }
#
# if (!exists("dcant_budget_basin_MLR_bias_all")) {
# dcant_budget_basin_MLR_bias_all <- dcant_budget_basin_MLR_bias
# }
}[1] "v_1m00"
[1] "v_1m01"
[1] "v_1m02"
[1] "v_1m03"
[1] "v_1m04"
[1] "v_1m05"
[1] "v_1m06"
[1] "v_1m07"
[1] "v_1m08"
[1] "v_1m09"
[1] "v_1m10"rm(
dcant_budget_basin_MLR,
# dcant_budget_basin_MLR_bias,
dcant_budget_basin_MLR_mod_truth
)1.4 Steady state
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_obs_budget <-
read_csv(paste0(path_version_data,
"anom_dcant_obs_budget.csv"))
dcant_obs_budget <- dcant_obs_budget %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_obs_budget_all")) {
dcant_obs_budget_all <-
bind_rows(dcant_obs_budget_all, dcant_obs_budget)
}
if (!exists("dcant_obs_budget_all")) {
dcant_obs_budget_all <- dcant_obs_budget
}
}[1] "v_1m00"
[1] "v_1m01"
[1] "v_1m02"
[1] "v_1m03"
[1] "v_1m04"
[1] "v_1m05"
[1] "v_1m06"
[1] "v_1m07"
[1] "v_1m08"
[1] "v_1m09"
[1] "v_1m10"rm(dcant_obs_budget)1.5 Atm CO2
co2_atm <-
read_csv(paste(path_preprocessing,
"co2_atm.csv",
sep = ""))params_local_all <- params_local_all %>%
mutate(vif_max = fct_inorder(vif_max))dcant_budget_global_all <- dcant_budget_global_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_global_all_depth <- dcant_budget_global_all
dcant_budget_global_all <- dcant_budget_global_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(estimate == "dcant") %>%
select(-c(estimate))
dcant_budget_global_bias_all_depth <- dcant_budget_global_bias_all
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(inv_depth == params_global$inventory_depth_standard)dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_basin_AIP_all_depth <- dcant_budget_basin_AIP_all
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(estimate == "dcant") %>%
select(-c(estimate))
dcant_budget_basin_AIP_bias_all_depth <- dcant_budget_basin_AIP_bias_all
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(inv_depth == params_global$inventory_depth_standard)dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
# dcant_budget_basin_MLR_all_depth <- dcant_budget_basin_MLR_all
dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
# dcant_budget_basin_MLR_bias_all <- dcant_budget_basin_MLR_bias_all %>%
# filter(estimate == "dcant") %>%
# select(-c(estimate))
#
# dcant_budget_basin_MLR_bias_all_depth <- dcant_budget_basin_MLR_bias_all
#
# dcant_budget_basin_MLR_bias_all <- dcant_budget_basin_MLR_bias_all %>%
# filter(inv_depth == params_global$inventory_depth_standard)2 Bias thresholds
global_bias_rel_max <- 12.5
global_bias_rel_max[1] 12.5regional_bias_rel_max <- 30
regional_bias_rel_max[1] 303 Global
3.1 Individual cases
3.1.1 Absoulte values
legend_title = expression(atop(Delta * C[ant],
(mu * mol ~ kg ^ {
-1
})))
dcant_budget_global_all %>%
ggplot(aes(tref1, dcant)) +
geom_line() +
geom_point() +
facet_grid(. ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
4 Basins
4.1 Individual cases
4.1.1 Absoulte values
dcant_budget_basin_AIP_all %>%
ggplot(aes(tref1, dcant, col = basin_AIP)) +
geom_line() +
geom_point() +
scale_color_brewer(palette = "Dark2") +
facet_grid(. ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
4.1.2 Biases
dcant_budget_basin_AIP_bias_all %>%
ggplot(aes(tref1, dcant_bias, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_line() +
geom_point()
4.2 Slab budgets
4.2.1 Absolute values
dcant_slab_budget_all %>%
filter(data_source == "obs") %>%
mutate(tref1 = as.factor(tref1)) %>%
ggplot(aes(tref1, dcant, fill = gamma_slab)) +
geom_hline(yintercept = 0, col = "red") +
geom_col() +
scale_fill_scico_d(direction = -1) +
facet_grid(basin_AIP ~ .)
4.2.2 Bias
dcant_slab_budget_bias_all %>%
group_by(period) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(gamma_slab, dcant_bias, fill = gamma_slab)) +
geom_col() +
coord_flip() +
scale_x_discrete(limits = rev) +
scale_fill_scico_d(direction = -1) +
facet_grid(basin_AIP ~ .) +
labs(title = paste("period:", unique(.x$period)))
)[[1]]Warning: Removed 18 rows containing missing values (position_stack).
[[2]]Warning: Removed 8 rows containing missing values (position_stack).
[[3]]Warning: Removed 16 rows containing missing values (position_stack).
[[4]]Warning: Removed 14 rows containing missing values (position_stack).
[[5]]Warning: Removed 12 rows containing missing values (position_stack).
[[6]]Warning: Removed 18 rows containing missing values (position_stack).
[[7]]Warning: Removed 8 rows containing missing values (position_stack).
[[8]]Warning: Removed 10 rows containing missing values (position_stack).
[[9]]Warning: Removed 14 rows containing missing values (position_stack).
[[10]]Warning: Removed 10 rows containing missing values (position_stack).
[[11]]Warning: Removed 16 rows containing missing values (position_stack).
5 Basins hemisphere
5.1 Individual cases
5.1.1 Absoulte values
dcant_budget_basin_MLR_all %>%
mutate(
basin = str_replace(basin, "_", ". "),
basin = fct_relevel(
basin,
"N. Pacific",
"S. Pacific",
"N. Atlantic",
"S. Atlantic",
"Indian"
)
) %>%
ggplot(aes(tref1, dcant)) +
geom_line(aes(col = basin)) +
geom_point(aes(fill = basin),
shape = 21) +
scale_color_brewer(palette = "Paired") +
scale_fill_brewer(palette = "Paired") +
facet_grid(. ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] marelac_2.1.10 shape_1.4.6 ggforce_0.3.3 metR_0.11.0
[5] scico_1.3.0 patchwork_1.1.1 collapse_1.7.0 forcats_0.5.1
[9] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[13] tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[17] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 gsw_1.0-6 lubridate_1.8.0
[5] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2 tools_4.1.2
[9] backports_1.4.1 bslib_0.3.1 utf8_1.2.2 R6_2.5.1
[13] DBI_1.1.2 colorspace_2.0-2 withr_2.4.3 tidyselect_1.1.1
[17] processx_3.5.2 bit_4.0.4 compiler_4.1.2 git2r_0.29.0
[21] cli_3.1.1 rvest_1.0.2 xml2_1.3.3 labeling_0.4.2
[25] sass_0.4.0 scales_1.1.1 checkmate_2.0.0 SolveSAPHE_2.1.0
[29] callr_3.7.0 digest_0.6.29 rmarkdown_2.11 oce_1.5-0
[33] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[37] fastmap_1.1.0 rlang_0.4.12 readxl_1.3.1 rstudioapi_0.13
[41] jquerylib_0.1.4 generics_0.1.1 farver_2.1.0 jsonlite_1.7.3
[45] vroom_1.5.7 magrittr_2.0.1 Rcpp_1.0.8 munsell_0.5.0
[49] fansi_1.0.2 lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[53] yaml_2.2.1 MASS_7.3-55 grid_4.1.2 parallel_4.1.2
[57] promises_1.2.0.1 crayon_1.4.2 haven_2.4.3 hms_1.1.1
[61] seacarb_3.3.0 knitr_1.37 ps_1.6.0 pillar_1.6.4
[65] reprex_2.0.1 glue_1.6.0 evaluate_0.14 getPass_0.2-2
[69] data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0
[73] tweenr_1.0.2 httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[77] polyclip_1.10-0 assertthat_0.2.1 xfun_0.29 broom_0.7.11
[81] later_1.3.0 ellipsis_0.3.2 here_1.0.1