Budgets
Jens Daniel Müller
14 November, 2022
Last updated: 2022-11-14
Checks: 7 0
Knit directory:
emlr_obs_analysis/analysis/
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version_id_pattern <- "x"
config <- "MLR_basins"1 Read files
print(version_id_pattern)[1] "x"# identify required version IDs
Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_1", "x"))
Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_2", "x"))
Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_3", "x"))
Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
# print(Version_IDs)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
}
}
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
}
}
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 <- dcant_budget_basin_MLR %>%
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
}
}
rm(
dcant_budget_basin_MLR,
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
}
}
rm(dcant_obs_budget)1.5 Atm CO2
co2_atm <-
read_csv(paste(path_preprocessing,
"co2_atm.csv",
sep = ""))all_predictors <- c("saltempaouoxygenphosphatenitratesilicate")
params_local_all <- params_local_all %>%
mutate(MLR_predictors = str_remove_all(all_predictors,
MLR_predictors))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 <- 10
global_bias_rel_max[1] 10regional_bias_rel_max <- 20
regional_bias_rel_max[1] 203 Individual cases
3.1 Global
3.1.1 Absoulte values
legend_title = expression(atop(Delta * C[ant],
(mu * mol ~ kg ^ {
-1
})))
dcant_budget_global_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_jitter(width = 0.05, height = 0) +
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())
3.1.2 Biases
dcant_budget_global_bias_all %>%
ggplot(aes(period, dcant_bias, col=MLR_basins)) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(atop(Delta * C[ant] ~ bias,
(PgC)))) +
geom_point() +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank())
p_global_bias <-
dcant_budget_global_bias_all %>%
ggplot() +
geom_hline(yintercept = global_bias_rel_max * c(-1, 1),
linetype = 2) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
title = "Model-based assesment") +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
alpha = 0.7) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank())
p_global_bias
dcant_budget_global_bias_all %>%
group_by(period) %>%
summarise(
dcant_bias_sd = sd(dcant_bias),
dcant_bias = mean(dcant_bias),
dcant_bias_rel_sd = sd(dcant_bias_rel),
dcant_bias_rel = mean(dcant_bias_rel)
) %>%
ungroup() %>%
kable() %>%
kable_styling() %>%
scroll_box(height = "300px")| period | dcant_bias_sd | dcant_bias | dcant_bias_rel_sd | dcant_bias_rel |
|---|---|---|---|---|
| 1994 - 2004 | 1.5306958 | 3.2125000 | 8.677414 | 18.211451 |
| 1994 - 2014 | 1.7037876 | 3.9873333 | 4.429104 | 10.365325 |
| 2004 - 2014 | 0.8276216 | 0.9133333 | 3.973601 | 4.385123 |
3.2 Basins
3.2.1 Absoulte values
dcant_budget_basin_AIP_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_jitter(width = 0.05, height = 0) +
scale_color_brewer(palette = "Dark2") +
facet_grid(basin_AIP ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
3.2.2 Biases
dcant_budget_basin_AIP_bias_all %>%
ggplot(aes(period, dcant_bias, col=MLR_basins)) +
geom_hline(yintercept = 0) +
geom_point() +
facet_grid(basin_AIP ~ .)
dcant_budget_basin_AIP_bias_all %>%
ggplot() +
geom_tile(aes(y = 0, height = regional_bias_rel_max * 2,
x = "2004 - 2014", width = Inf,
fill = "bias\nthreshold"), alpha = 0.5) +
geom_hline(yintercept = 0) +
scale_fill_manual(values = "grey70", name = "") +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta ~ C[ant] ~ bias)) +
theme(axis.title.x = element_blank()) +
geom_jitter(aes(period, dcant_bias_rel, col = MLR_basins),
width = 0.05, height = 0) +
facet_grid(. ~ basin_AIP)
p_regional_bias <-
dcant_budget_basin_AIP_bias_all %>%
ggplot() +
geom_hline(yintercept = regional_bias_rel_max * c(-1,1),
linetype = 2) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
title = "Model-based assesment") +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
alpha = 0.7) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank()) +
facet_grid(. ~ basin_AIP) +
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)
p_regional_bias
3.3 Slab budgets
3.3.1 Absolute values
dcant_slab_budget_all %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
ggplot(aes(MLR_basins, dcant, fill = gamma_slab)) +
geom_hline(yintercept = 0, col = "red") +
geom_col() +
scale_fill_scico_d(direction = -1) +
facet_grid(basin_AIP ~ period)
dcant_slab_budget_all %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
group_by(basin_AIP) %>%
group_split() %>%
map(
~ ggplot(data = .x,
aes(MLR_basins, dcant, fill = gamma_slab)) +
geom_hline(yintercept = 0) +
geom_col() +
scale_fill_scico_d(direction = -1) +
labs(title = paste("data_source:", unique(.x$basin_AIP))) +
facet_grid(gamma_slab ~ period)
)[[1]]
[[2]]
[[3]]
3.3.2 Bias
dcant_slab_budget_bias_all %>%
filter(period != "1994 - 2014") %>%
group_by(basin_AIP) %>%
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(period ~ MLR_basins) +
labs(title = paste("data_source:", unique(.x$basin_AIP)))
)[[1]]Warning: Removed 40 rows containing missing values (position_stack).
[[2]]Warning: Removed 24 rows containing missing values (position_stack).
[[3]]Warning: Removed 108 rows containing missing values (position_stack).
3.3.3 Spread
dcant_slab_budget_all %>%
filter(period != "1994 - 2014",
data_source != "mod_truth") %>%
group_by(data_source, basin_AIP, gamma_slab, period) %>%
summarise(dcant_range = max(dcant) - min(dcant)) %>%
ungroup() %>%
group_split(basin_AIP) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(gamma_slab, dcant_range, fill = gamma_slab)) +
geom_col() +
coord_flip() +
scale_x_discrete(limits = rev) +
scale_fill_scico_d(direction = -1) +
facet_grid(period ~ data_source) +
labs(title = paste("data_source:", unique(.x$basin_AIP)))
)`summarise()` has grouped output by 'data_source', 'basin_AIP', 'gamma_slab'.
You can override using the `.groups` argument.[[1]]
[[2]]
[[3]]
3.4 Basins hemisphere
3.4.1 Absoulte values
dcant_budget_basin_MLR_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_jitter(width = 0.05, height = 0) +
scale_color_brewer(palette = "Dark2") +
facet_grid(basin ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
3.4.2 Biases
dcant_budget_basin_MLR_bias_all <-
dcant_budget_basin_MLR_all %>%
filter(data_source %in% c("mod", "mod_truth")) %>%
pivot_wider(names_from = data_source,
values_from = dcant) %>%
mutate(dcant_bias = mod - mod_truth,
dcant_bias_rel = 100*(mod - mod_truth)/mod_truth)
dcant_budget_basin_MLR_bias_all %>%
ggplot(aes(period, dcant_bias, col=MLR_basins)) +
geom_hline(yintercept = 0) +
geom_point() +
facet_grid(basin ~ .)
dcant_budget_basin_MLR_bias_all %>%
ggplot() +
geom_tile(aes(y = 0, height = regional_bias_rel_max * 2,
x = "2004 - 2014", width = Inf,
fill = "bias\nthreshold"), alpha = 0.5) +
geom_hline(yintercept = 0) +
scale_fill_manual(values = "grey70", name = "") +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta ~ C[ant] ~ bias)) +
theme(axis.title.x = element_blank()) +
geom_jitter(aes(period, dcant_bias_rel, col = MLR_basins),
width = 0.05, height = 0) +
facet_grid(. ~ basin)
p_regional_bias <-
dcant_budget_basin_MLR_bias_all %>%
ggplot() +
geom_hline(yintercept = regional_bias_rel_max * c(-1,1),
linetype = 2) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2") +
labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
title = "Model-based assesment") +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
alpha = 0.7) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank()) +
facet_grid(. ~ basin) +
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)
p_regional_bias
dcant_budget_basin_MLR_bias_all %>%
group_by(period, basin) %>%
summarise(
dcant_bias_sd = sd(dcant_bias),
dcant_bias = mean(dcant_bias),
dcant_bias_rel_sd = sd(dcant_bias_rel),
dcant_bias_rel = mean(dcant_bias_rel)
) %>%
ungroup() %>%
kable() %>%
kable_styling() %>%
scroll_box(height = "300px")`summarise()` has grouped output by 'period'. You can override using the
`.groups` argument.| period | basin | dcant_bias_sd | dcant_bias | dcant_bias_rel_sd | dcant_bias_rel |
|---|---|---|---|---|---|
| 1994 - 2004 | Indian | 1.0369086 | 0.7295000 | 21.508165 | 15.1317154 |
| 1994 - 2004 | N_Atlantic | 0.2018521 | -0.1666667 | 10.346084 | -8.5426277 |
| 1994 - 2004 | N_Pacific | 0.2068791 | 0.6858333 | 7.617051 | 25.2515955 |
| 1994 - 2004 | S_Atlantic | 0.4171838 | 0.5455000 | 16.687351 | 21.8200000 |
| 1994 - 2004 | S_Pacific | 0.8320883 | 1.4181667 | 14.722015 | 25.0914131 |
| 1994 - 2014 | Indian | 1.3419170 | 1.7741667 | 12.843770 | 16.9809214 |
| 1994 - 2014 | N_Atlantic | 0.3085525 | -0.6293333 | 7.182322 | -14.6492862 |
| 1994 - 2014 | N_Pacific | 0.2491238 | 0.6016667 | 4.267280 | 10.3060409 |
| 1994 - 2014 | S_Atlantic | 0.3050478 | 0.4898333 | 5.767589 | 9.2613601 |
| 1994 - 2014 | S_Pacific | 1.2739643 | 1.7508333 | 10.113236 | 13.8988119 |
| 2004 - 2014 | Indian | 0.6226199 | 1.0030000 | 11.066832 | 17.8279417 |
| 2004 - 2014 | N_Atlantic | 0.1936527 | -0.3431667 | 8.258111 | -14.6339730 |
| 2004 - 2014 | N_Pacific | 0.1591713 | -0.0435000 | 5.098376 | -1.3933376 |
| 2004 - 2014 | S_Atlantic | 0.2628600 | -0.0148333 | 9.424883 | -0.5318513 |
| 2004 - 2014 | S_Pacific | 0.4982227 | 0.3125000 | 7.173834 | 4.4996400 |
4 Ensemble
4.1 Global
dcant_budget_global_all_in <- dcant_budget_global_all %>%
filter(data_source %in% c("mod", "obs"))
dcant_budget_global_ensemble <- dcant_budget_global_all_in %>%
group_by(data_source, period, tref2) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()`summarise()` has grouped output by 'data_source', 'period'. You can override
using the `.groups` argument.4.1.1 Mean
legend_title = expression(Delta * C[ant]~(PgC))
ggplot() +
geom_col(data = dcant_budget_global_ensemble,
aes(x = period,
y = dcant_mean),
fill = "darkgrey") +
geom_errorbar(
data = dcant_budget_global_ensemble,
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
geom_point(
data = dcant_budget_global_all,
aes(period, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0.2, height = 0)
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2") +
facet_grid(. ~ data_source) +
labs(y = legend_title) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
p_global_dcant <- ggplot() +
geom_col(data = dcant_budget_global_ensemble %>%
filter(data_source == "obs"),
aes(x = period,
y = dcant_mean),
fill = "darkgrey") +
geom_point(
data = dcant_budget_global_all %>%
filter(data_source == "obs"),
aes(period, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0.1, height = 0)
) +
geom_errorbar(
data = dcant_budget_global_ensemble %>%
filter(data_source == "obs"),
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2") +
labs(y = legend_title,
title = "Observation-based results") +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank())
p_global_dcant_bias <-
p_global_dcant / p_global_bias +
plot_layout(guides = 'collect',
heights = c(2,1))
p_global_dcant_bias
# ggsave(plot = p_global_dcant_bias,
# path = here::here("output/publication"),
# filename = "Fig_global_dcant_budget.png",
# height = 5,
# width = 5)
rm(p_global_bias, p_global_dcant, p_global_dcant_bias)4.1.2 Mean vs atm CO2
dcant_ensemble <- dcant_budget_global_ensemble %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
select(year = tref2, dcant_mean, dcant_sd)
tcant_S04 <- bind_cols(year = 1994, dcant_mean = 118, dcant_sd = 19)
tcant_ensemble <- full_join(dcant_ensemble, tcant_S04)Joining, by = c("year", "dcant_mean", "dcant_sd")tcant_ensemble <- left_join(tcant_ensemble, co2_atm)Joining, by = "year"co2_atm_pi <- bind_cols(pCO2 = 280, dcant_mean = 0, year = 1750, dcant_sd = 0)
tcant_ensemble <- full_join(tcant_ensemble, co2_atm_pi)Joining, by = c("year", "dcant_mean", "dcant_sd", "pCO2")tcant_ensemble <- tcant_ensemble %>%
arrange(year) %>%
mutate(tcant = cumsum(dcant_mean),
tcant_sd = cumsum(dcant_sd))
tcant_ensemble %>%
ggplot(aes(pCO2, tcant, ymin = tcant - tcant_sd, ymax = tcant + tcant_sd)) +
geom_ribbon(fill = "grey80") +
geom_point() +
geom_line() +
scale_x_continuous(breaks = seq(280, 400, 30),
sec.axis = dup_axis(labels = c(1750, 1940, 1980, 2000, 2015),
name = "Year")) +
geom_text(aes(label = year), nudge_x = -5, nudge_y = 5) +
labs(x = expression(Atmospheric~pCO[2]~(µatm)),
y = expression(Total~oceanic~C[ant]~(PgC)))
# ggsave(path = "output/publication",
# filename = "Fig_global_dcant_budget_vs_atm_pCO2.png",
# height = 4,
# width = 7)4.1.3 Sum decades
dcant_budget_global_all_in_sum <-
dcant_budget_global_all_in %>%
filter(period != "1994 - 2014") %>%
arrange(tref1) %>%
group_by(data_source, MLR_basins) %>%
mutate(dcant = dcant + lag(dcant)) %>%
ungroup() %>%
drop_na() %>%
mutate(estimate = "sum")
dcant_budget_global_all_in_sum <-
bind_rows(
dcant_budget_global_all_in_sum,
dcant_budget_global_all_in %>%
filter(period == "1994 - 2014") %>%
mutate(estimate = "direct")
)
ggplot() +
geom_point(
data = dcant_budget_global_all_in_sum,
aes(estimate, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0, height = 0)
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2") +
facet_grid(. ~ data_source) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
4.1.4 Mean bias
dcant_budget_global_ensemble_bias <- full_join(
dcant_budget_global_ensemble %>%
filter(data_source == "mod") %>%
select(period, dcant_mean, dcant_sd),
dcant_budget_global_all %>%
filter(data_source == "mod_truth",
MLR_basins == unique(dcant_budget_global_all$MLR_basins)[1]) %>%
select(period, dcant)
)Joining, by = "period"dcant_budget_global_ensemble_bias <- dcant_budget_global_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant,
dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)
dcant_budget_global_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_global_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias_rel)) +
geom_hline(yintercept = 0) +
geom_point()
4.1.5 Vertical patterns
4.1.5.1 Absoulte values
dcant_budget_global_all_depth %>%
filter(data_source != "mod_truth") %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, MLR_basins, fill=period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .) +
labs(title = paste("data_source:", unique(.x$data_source)))
)[[1]]
[[2]]
4.1.5.2 Biases
dcant_budget_global_bias_all_depth %>%
ggplot(aes(dcant_bias, MLR_basins, fill = period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .)
dcant_budget_global_bias_all_depth %>%
ggplot(aes(dcant_bias_rel, MLR_basins, fill = period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .)
rm(dcant_budget_global_all,
dcant_budget_global_all_depth,
dcant_budget_global_bias_all,
dcant_budget_global_bias_all_depth,
dcant_budget_global_ensemble,
dcant_budget_global_ensemble_bias)4.2 Basins
dcant_budget_basin_AIP_ensemble <- dcant_budget_basin_AIP_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(basin_AIP, data_source, period) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()`summarise()` has grouped output by 'basin_AIP', 'data_source'. You can override
using the `.groups` argument.4.2.1 Mean
dcant_budget_basin_AIP_ensemble %>%
ggplot(aes(period, dcant_mean, col=basin_AIP)) +
geom_pointrange(aes(ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd),
shape = 21) +
facet_grid(. ~ data_source)
p_regional_dcant <- ggplot() +
geom_col(
data = dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "obs"),
aes(x = period,
y = dcant_mean),
fill = "darkgrey"
) +
geom_point(
data = dcant_budget_basin_AIP_all %>%
filter(data_source == "obs"),
aes(period, dcant, col = MLR_basins),
position = position_jitter(width = 0.1, height = 0),
alpha = 0.7
) +
geom_errorbar(
data = dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "obs"),
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
scale_y_continuous(limits = c(0, 35), expand = c(0, 0)) +
scale_color_brewer(palette = "Dark2") +
labs(y = legend_title,
title = "Observation-based results") +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank()) +
facet_grid(. ~ basin_AIP)
p_regional_dcant_bias <-
p_regional_dcant / p_regional_bias +
plot_layout(guides = 'collect',
heights = c(2,1))
p_regional_dcant_bias
# ggsave(plot = p_regional_dcant_bias,
# path = "output/publication",
# filename = "Fig_regional_dcant_budget.png",
# height = 5,
# width = 10)
rm(p_regional_bias, p_regional_dcant, p_regional_dcant_bias)4.2.2 Mean bias
dcant_budget_basin_AIP_ensemble_bias <- full_join(
dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "mod") %>%
select(basin_AIP, period, dcant_mean, dcant_sd),
dcant_budget_basin_AIP_all %>%
filter(data_source == "mod_truth",
MLR_basins == unique(dcant_budget_basin_AIP_all$MLR_basins)[1]) %>%
select(basin_AIP, period, dcant)
)Joining, by = c("basin_AIP", "period")dcant_budget_basin_AIP_ensemble_bias <- dcant_budget_basin_AIP_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant,
dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias_rel, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_point()
4.2.3 Vertical patterns
4.2.3.1 Absoulte values
dcant_budget_basin_AIP_all_depth %>%
filter(data_source != "mod_truth") %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period) +
labs(title = paste("data_source:", unique(.x$data_source)))
)[[1]]
[[2]]
4.2.3.2 Biases
dcant_budget_basin_AIP_bias_all_depth %>%
ggplot(aes(dcant_bias, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
dcant_budget_basin_AIP_bias_all_depth %>%
ggplot(aes(dcant_bias_rel, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
5 Steady state
dcant_obs_budget_all %>%
group_by(inv_depth) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(estimate, dcant_pos, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2") +
geom_col() +
facet_grid(MLR_basins ~ period) +
labs(title = paste("inventory depth:",unique(.x$inv_depth)))
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
6 Predictor analysis
lm_best_predictor_counts_all <-
full_join(lm_best_predictor_counts_all,
params_local_all)Joining, by = "Version_ID"lm_best_predictor_counts_all <- lm_best_predictor_counts_all %>%
mutate(n_predictors_total = rowSums(across(aou:temp), na.rm = TRUE)/10)
lm_best_predictor_counts_all %>%
ggplot(aes(x = MLR_basins, y = n_predictors_total)) +
# ggdist::stat_halfeye(
# adjust = .5,
# width = .6,
# .width = 0,
# justification = -.2,
# point_colour = NA
# ) +
geom_boxplot(width = 0.5,
outlier.shape = NA) +
gghalves::geom_half_point(
side = "l",
range_scale = .4,
alpha = .5,
aes(col = gamma_slab)
) +
scale_color_viridis_d() +
facet_grid(basin ~ data_source)
lm_best_predictor_counts_all %>%
pivot_longer(aou:temp,
names_to = "predictor",
values_to = "count") %>%
group_split(predictor) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(MLR_basins, count, color = gamma_slab)) +
geom_jitter(alpha = 0.5) +
scale_color_viridis_d() +
labs(title = paste0("predictor:", unique(.x$predictor))) +
coord_cartesian(ylim = c(0, 10)) +
facet_grid(basin ~ data_source)
)[[1]]Warning: Removed 2 rows containing missing values (geom_point).
[[2]]Warning: Removed 39 rows containing missing values (geom_point).
[[3]]Warning: Removed 3 rows containing missing values (geom_point).
[[4]]Warning: Removed 6 rows containing missing values (geom_point).
[[5]]
[[6]]Warning: Removed 3 rows containing missing values (geom_point).
[[7]]Warning: Removed 2 rows containing missing values (geom_point).
lm_best_dcant_all <-
full_join(lm_best_dcant_all,
params_local_all)Joining, by = "Version_ID"lm_best_dcant_all %>%
count(basin, data_source, gamma_slab, MLR_basins, period) %>%
ggplot(aes(MLR_basins, n)) +
geom_jitter(height = 0, alpha = 0.3) +
facet_grid(basin ~ data_source)
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] kableExtra_1.3.4 geomtextpath_0.1.0 colorspace_2.0-2 marelac_2.1.10
[5] shape_1.4.6 ggforce_0.3.3 metR_0.11.0 scico_1.3.0
[9] patchwork_1.1.1 collapse_1.7.0 forcats_0.5.1 stringr_1.4.0
[13] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[17] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 gghalves_0.1.1 bit64_4.0.5 lubridate_1.8.0
[5] gsw_1.0-6 RColorBrewer_1.1-2 webshot_0.5.2 httr_1.4.2
[9] rprojroot_2.0.2 tools_4.1.2 backports_1.4.1 bslib_0.3.1
[13] utf8_1.2.2 R6_2.5.1 DBI_1.1.2 withr_2.4.3
[17] tidyselect_1.1.1 processx_3.5.2 bit_4.0.4 compiler_4.1.2
[21] git2r_0.29.0 textshaping_0.3.6 cli_3.1.1 rvest_1.0.2
[25] xml2_1.3.3 labeling_0.4.2 sass_0.4.0 scales_1.1.1
[29] checkmate_2.0.0 SolveSAPHE_2.1.0 callr_3.7.0 systemfonts_1.0.3
[33] digest_0.6.29 svglite_2.0.0 rmarkdown_2.11 oce_1.5-0
[37] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[41] fastmap_1.1.0 rlang_1.0.2 readxl_1.3.1 rstudioapi_0.13
[45] jquerylib_0.1.4 generics_0.1.1 farver_2.1.0 jsonlite_1.7.3
[49] vroom_1.5.7 magrittr_2.0.1 Rcpp_1.0.8 munsell_0.5.0
[53] fansi_1.0.2 lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[57] yaml_2.2.1 MASS_7.3-55 grid_4.1.2 parallel_4.1.2
[61] promises_1.2.0.1 crayon_1.4.2 haven_2.4.3 hms_1.1.1
[65] seacarb_3.3.0 knitr_1.37 ps_1.6.0 pillar_1.6.4
[69] reprex_2.0.1 glue_1.6.0 evaluate_0.14 getPass_0.2-2
[73] data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0
[77] tweenr_1.0.2 httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[81] polyclip_1.10-0 assertthat_0.2.1 xfun_0.29 broom_0.7.11
[85] later_1.3.0 viridisLite_0.4.0 ellipsis_0.3.2 here_1.0.1