Results publication
Jens Daniel Müller
31 January, 2022
Last updated: 2022-01-31
Checks: 7 0
Knit directory: emlr_obs_analysis/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5c948ae | jens-daniel-mueller | 2022-01-31 | added time series vs atm pco2 |
| html | de557de | jens-daniel-mueller | 2022-01-28 | Build site. |
| html | 5f2aed0 | jens-daniel-mueller | 2022-01-27 | Build site. |
| Rmd | 54c9e26 | jens-daniel-mueller | 2022-01-27 | added layer budget profiles |
| html | eccd82b | jens-daniel-mueller | 2022-01-26 | Build site. |
| Rmd | c5577d3 | jens-daniel-mueller | 2022-01-26 | added meand sd to offset mean concentrations profiles |
| html | c6fe495 | jens-daniel-mueller | 2022-01-26 | Build site. |
| Rmd | e0e7974 | jens-daniel-mueller | 2022-01-26 | added offset mean concentrations profiles |
| html | 9753eb8 | jens-daniel-mueller | 2022-01-26 | Build site. |
| html | b1d7720 | jens-daniel-mueller | 2022-01-21 | Build site. |
| Rmd | 0210ed5 | jens-daniel-mueller | 2022-01-21 | added mean concentrations profiles per 5 basins |
| html | d6b399a | jens-daniel-mueller | 2022-01-21 | Build site. |
| Rmd | da17a07 | jens-daniel-mueller | 2022-01-21 | added mean concentrations profiles |
| html | c499be8 | jens-daniel-mueller | 2022-01-21 | Build site. |
| Rmd | d941871 | jens-daniel-mueller | 2022-01-21 | run color map test |
| html | e572075 | jens-daniel-mueller | 2022-01-21 | Build site. |
| Rmd | 99b6c92 | jens-daniel-mueller | 2022-01-21 | run color map test |
| html | 4fe7150 | jens-daniel-mueller | 2022-01-21 | Build site. |
| Rmd | 0379e99 | jens-daniel-mueller | 2022-01-21 | script cleaning |
| html | 49b41cf | jens-daniel-mueller | 2022-01-21 | Build site. |
| Rmd | 2c82651 | jens-daniel-mueller | 2022-01-21 | added map of scaled absolute change |
| html | c0807e8 | jens-daniel-mueller | 2022-01-21 | Build site. |
| Rmd | 5dd3d7a | jens-daniel-mueller | 2022-01-21 | added map of scaled relative change |
| html | 22b421f | jens-daniel-mueller | 2022-01-21 | Build site. |
| Rmd | 2c3fa75 | jens-daniel-mueller | 2022-01-21 | cleaned alluvial plots |
| html | 1a35f1f | jens-daniel-mueller | 2022-01-20 | Build site. |
| Rmd | e58f510 | jens-daniel-mueller | 2022-01-20 | added relative changes to alluvial plots |
| html | b503ae1 | jens-daniel-mueller | 2022-01-20 | Build site. |
| Rmd | 2eb2567 | jens-daniel-mueller | 2022-01-20 | added relative changes to alluvial plots |
| html | cc31f4b | jens-daniel-mueller | 2022-01-20 | Build site. |
| Rmd | 416e107 | jens-daniel-mueller | 2022-01-20 | added delta dcant map |
| html | 11a800b | jens-daniel-mueller | 2022-01-20 | Build site. |
| Rmd | 81a40d5 | jens-daniel-mueller | 2022-01-20 | updated alluvial plots |
| html | 3087804 | jens-daniel-mueller | 2022-01-20 | Build site. |
| Rmd | 2ae5966 | jens-daniel-mueller | 2022-01-20 | updated alluvial plots |
| html | 6d566d5 | jens-daniel-mueller | 2022-01-20 | Build site. |
| Rmd | 4901b0f | jens-daniel-mueller | 2022-01-20 | updated alluvial plots |
| html | 44796b1 | jens-daniel-mueller | 2022-01-20 | Build site. |
| Rmd | cdbd92c | jens-daniel-mueller | 2022-01-20 | created alluvial plots |
| html | 48ec4c6 | jens-daniel-mueller | 2022-01-19 | Build site. |
| Rmd | 0fb2ae5 | jens-daniel-mueller | 2022-01-19 | printed column inv from AIP standard runs |
| html | f347cd7 | jens-daniel-mueller | 2022-01-18 | Build site. |
| Rmd | 86b711c | jens-daniel-mueller | 2022-01-18 | plot hemisphere budgets and publication results |
1 Libraries
2 Read files
2.1 Paths and Versions
version_id_pattern <- "103"
# identify required version IDs
Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_1", version_id_pattern))
Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_2", version_id_pattern))
Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = paste0("v_3", version_id_pattern))
Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
print(Version_IDs)[1] "v_1103" "v_2103" "v_3103"2.2 Parameters
for (i_Version_IDs in Version_IDs) {
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
params_local <-
read_rds(paste(path_version_data,
"params_local.rds",
sep = ""))
params_local <- bind_cols(
Version_ID = i_Version_IDs,
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("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}
rm(params_local,
tref)
params_local_all <- params_local_all %>%
select(Version_ID, period, tref2)2.3 Budgets
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
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)
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
)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
}
}[1] "v_1103"
[1] "v_2103"
[1] "v_3103"rm(
dcant_budget_basin_MLR,
dcant_budget_basin_MLR_mod_truth
)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_MLR_all <- dcant_budget_basin_MLR_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
filter(inv_depth == params_global$inventory_depth_standard)2.4 Inventories
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_inv <-
read_csv(paste(path_version_data,
"dcant_inv.csv",
sep = ""))
dcant_inv_mod_truth <-
read_csv(paste(path_version_data,
"dcant_inv_mod_truth.csv",
sep = "")) %>%
filter(method == "total") %>%
select(-method)
dcant_inv_bias <-
read_csv(paste(path_version_data,
"dcant_inv_bias.csv",
sep = "")) %>%
mutate(Version_ID = i_Version_IDs)
dcant_inv <- bind_rows(dcant_inv,
dcant_inv_mod_truth) %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_lat_grid <-
read_csv(paste(path_version_data,
"dcant_budget_lat_grid.csv",
sep = "")) %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_lon_grid <-
read_csv(paste(path_version_data,
"dcant_budget_lon_grid.csv",
sep = "")) %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_inv_all")) {
dcant_inv_all <- bind_rows(dcant_inv_all, dcant_inv)
}
if (!exists("dcant_inv_all")) {
dcant_inv_all <- dcant_inv
}
if (exists("dcant_inv_bias_all")) {
dcant_inv_bias_all <- bind_rows(dcant_inv_bias_all, dcant_inv_bias)
}
if (!exists("dcant_inv_bias_all")) {
dcant_inv_bias_all <- dcant_inv_bias
}
if (exists("dcant_budget_lat_grid_all")) {
dcant_budget_lat_grid_all <- bind_rows(dcant_budget_lat_grid_all, dcant_budget_lat_grid)
}
if (!exists("dcant_budget_lat_grid_all")) {
dcant_budget_lat_grid_all <- dcant_budget_lat_grid
}
if (exists("dcant_budget_lon_grid_all")) {
dcant_budget_lon_grid_all <- bind_rows(dcant_budget_lon_grid_all, dcant_budget_lon_grid)
}
if (!exists("dcant_budget_lon_grid_all")) {
dcant_budget_lon_grid_all <- dcant_budget_lon_grid
}
}
rm(dcant_inv,
dcant_inv_bias,
dcant_inv_mod_truth,
dcant_budget_lat_grid,
dcant_budget_lon_grid)dcant_inv_all <- dcant_inv_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_lat_grid_all <- dcant_budget_lat_grid_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_lon_grid_all <- dcant_budget_lon_grid_all %>%
filter(inv_depth == params_global$inventory_depth_standard)dcant_budget_lat_grid_all <- dcant_budget_lat_grid_all %>%
pivot_wider(names_from = estimate,
values_from = value) %>%
filter(period != "1994 - 2014",
method == "total")
dcant_budget_lon_grid_all <- dcant_budget_lon_grid_all %>%
pivot_wider(names_from = estimate,
values_from = value) %>%
filter(period != "1994 - 2014",
method == "total")2.5 Zonal sections
for (i_Version_IDs in Version_IDs) {
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_zonal <-
read_csv(paste(path_version_data,
"dcant_zonal.csv",
sep = ""))
dcant_zonal_mod_truth <-
read_csv(paste(path_version_data,
"dcant_zonal_mod_truth.csv",
sep = ""))
dcant_zonal <- bind_rows(dcant_zonal,
dcant_zonal_mod_truth)
dcant_profile <-
read_csv(paste(path_version_data,
"dcant_profile.csv",
sep = ""))
dcant_profile_mod_truth <-
read_csv(paste(path_version_data,
"dcant_profile_mod_truth.csv",
sep = ""))
dcant_profile_basin_MLR <-
read_csv(paste(path_version_data,
"dcant_profile_basin_MLR.csv",
sep = ""))
dcant_profile <- bind_rows(dcant_profile,
dcant_profile_mod_truth)
dcant_budget_basin_AIP_layer <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP_layer.csv",
sep = ""))
dcant_budget_basin_MLR_layer <-
read_csv(paste(path_version_data,
"dcant_budget_basin_MLR_layer.csv",
sep = ""))
dcant_zonal_bias <-
read_csv(paste(path_version_data,
"dcant_zonal_bias.csv",
sep = ""))
dcant_zonal <- dcant_zonal %>%
mutate(Version_ID = i_Version_IDs)
dcant_profile <- dcant_profile %>%
mutate(Version_ID = i_Version_IDs)
dcant_profile_basin_MLR <- dcant_profile_basin_MLR %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_basin_AIP_layer <- dcant_budget_basin_AIP_layer %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_basin_MLR_layer <- dcant_budget_basin_MLR_layer %>%
mutate(Version_ID = i_Version_IDs)
dcant_zonal_bias <- dcant_zonal_bias %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_zonal_all")) {
dcant_zonal_all <- bind_rows(dcant_zonal_all, dcant_zonal)
}
if (!exists("dcant_zonal_all")) {
dcant_zonal_all <- dcant_zonal
}
if (exists("dcant_profile_all")) {
dcant_profile_all <- bind_rows(dcant_profile_all, dcant_profile)
}
if (!exists("dcant_profile_all")) {
dcant_profile_all <- dcant_profile
}
if (exists("dcant_profile_basin_MLR_all")) {
dcant_profile_basin_MLR_all <- bind_rows(dcant_profile_basin_MLR_all, dcant_profile_basin_MLR)
}
if (!exists("dcant_profile_basin_MLR_all")) {
dcant_profile_basin_MLR_all <- dcant_profile_basin_MLR
}
if (exists("dcant_budget_basin_AIP_layer_all")) {
dcant_budget_basin_AIP_layer_all <-
bind_rows(dcant_budget_basin_AIP_layer_all,
dcant_budget_basin_AIP_layer)
}
if (!exists("dcant_budget_basin_AIP_layer_all")) {
dcant_budget_basin_AIP_layer_all <- dcant_budget_basin_AIP_layer
}
if (exists("dcant_budget_basin_MLR_layer_all")) {
dcant_budget_basin_MLR_layer_all <-
bind_rows(dcant_budget_basin_MLR_layer_all,
dcant_budget_basin_MLR_layer)
}
if (!exists("dcant_budget_basin_MLR_layer_all")) {
dcant_budget_basin_MLR_layer_all <- dcant_budget_basin_MLR_layer
}
if (exists("dcant_zonal_bias_all")) {
dcant_zonal_bias_all <- bind_rows(dcant_zonal_bias_all, dcant_zonal_bias)
}
if (!exists("dcant_zonal_bias_all")) {
dcant_zonal_bias_all <- dcant_zonal_bias
}
}
rm(dcant_zonal, dcant_zonal_bias, dcant_zonal_mod_truth,
dcant_budget_basin_AIP_layer, dcant_budget_basin_MLR_layer)2.6 Atm CO2
co2_atm <-
read_csv(paste(path_preprocessing,
"co2_atm.csv",
sep = ""))3 Define labels
dcant_pgc_label <- expression(Delta * C["ant"]~(PgC))
dcant_umol_label <- expression(Delta * C[ant]~(mu * mol ~ kg ^ {-1}))
dcant_layer_budget_label <-
expression(Delta ~ C[ant] ~ "budget per 500m depth layer (PgC)")4 Inventory maps
4.1 Absoulte values
4.1.1 dcant - absolute
dcant_inv_all %>%
filter(data_source %in% c("mod", "obs"),
period != "1994 - 2014") %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_map_cant_inv(df = .x,
var = "dcant",
subtitle_text = paste("data_source:",
unique(.x$data_source))) +
facet_grid(period ~ .) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
)[[1]]
[[2]]
4.1.1.1 test color palettes
breaks <- c(-Inf, seq(0, 16, 2), Inf)
legend_title <- expression(atop(Delta * C["ant"],
(mol ~ m ^ {
-2
})))
breaks_n <- length(breaks) - 1
dcant_inv_all_color_test <- dcant_inv_all %>%
filter(data_source %in% c("obs"),
period == "2004 - 2014") %>%
mutate(dcant_int = cut(dcant,
breaks,
right = FALSE))
scico_continous_palettes <- c(
"acton",
"bamako",
"batlow",
"bilbao",
"buda",
"davos",
"devon",
"grayC",
"hawaii",
"imola",
"lajolla",
"lapaz",
"nuuk",
"oslo",
"tokyo",
"turku"
)
for (i_palette in scico_continous_palettes) {
p_reg <- map +
geom_tile(data = dcant_inv_all_color_test,
aes(lon, lat, fill = dcant_int)) +
scale_fill_scico_d(
palette = i_palette,
drop = FALSE,
name = legend_title,
guide = "none"
) +
# guides(fill = guide_colorsteps(barheight = unit(3, "cm"))) +
labs(title = i_palette)
p_rev <- map +
geom_tile(data = dcant_inv_all_color_test,
aes(lon, lat, fill = dcant_int)) +
scale_fill_scico_d(
palette = i_palette,
drop = FALSE,
name = legend_title,
direction = -1,
guide = "none"
) +
# guides(fill = guide_colorsteps(barheight = unit(3, "cm"))) +
labs(title = paste(i_palette, "rev"))
print(p_reg | p_rev)
}
viridis_continous_palettes <- c(
"civides",
"magma",
"inferno",
"plasma"
)
for (i_palette in viridis_continous_palettes) {
p_reg <- map +
geom_tile(data = dcant_inv_all_color_test,
aes(lon, lat, fill = dcant_int)) +
scale_fill_viridis_d(
option = i_palette,
drop = FALSE,
name = legend_title,
guide = "none"
) +
# guides(fill = guide_colorsteps(barheight = unit(3, "cm"))) +
labs(title = i_palette)
p_rev <- map +
geom_tile(data = dcant_inv_all_color_test,
aes(lon, lat, fill = dcant_int)) +
scale_fill_viridis_d(
option = i_palette,
drop = FALSE,
name = legend_title,
direction = -1,
guide = "none"
) +
# guides(fill = guide_colorsteps(barheight = unit(3, "cm"))) +
labs(title = paste(i_palette, "rev"))
print(p_reg | p_rev)
}
4.1.2 dcant - absolute delta
dcant_inv_all %>%
filter(data_source %in% c("mod", "obs"),
period != "1994 - 2014") %>%
select(data_source, lon, lat, basin_AIP, period, dcant) %>%
pivot_wider(names_from = period,
values_from = dcant) %>%
mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_map_cant_inv(df = .x,
var = "delta_dcant",
subtitle_text = paste("data_source:",
unique(.x$data_source)),
col = "divergent") +
# facet_grid(period ~ .) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
)[[1]]
[[2]]
4.1.3 dcant - absolute delta scaled
dcant_budget_scaling <- dcant_budget_global_all %>%
filter(#data_source %in% c("mod", "obs"),
period != "1994 - 2014") %>%
select(data_source, period, dcant) %>%
pivot_wider(names_from = period,
values_from = dcant) %>%
mutate(dcant_scaling = `2004 - 2014` / `1994 - 2004`) %>%
select(data_source, dcant_scaling)
left_join(dcant_inv_all,
dcant_budget_scaling) %>%
filter(#data_source %in% c("mod", "obs"),
period != "1994 - 2014") %>%
select(data_source, lon, lat, basin_AIP, period, dcant, dcant_scaling) %>%
pivot_wider(names_from = period,
values_from = dcant) %>%
mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`*dcant_scaling) %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_map_cant_inv(df = .x,
var = "delta_dcant",
subtitle_text = paste("data_source:",
unique(.x$data_source)),
col = "divergent") +
# facet_grid(period ~ .) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
)[[1]]
[[2]]
| Version | Author | Date |
|---|---|---|
| 4fe7150 | jens-daniel-mueller | 2022-01-21 |
[[3]]
4.1.4 dcant pos - absolute
dcant_inv_all %>%
filter(data_source %in% c("mod", "obs"),
period != "1994 - 2014") %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_map_cant_inv(df = .x,
var = "dcant_pos",
subtitle_text = paste("data_source:",
unique(.x$data_source))) +
facet_grid(period ~ .) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
)[[1]]
[[2]]
5 Zonal sections
5.1 dcant - absolut
dcant_zonal_all %>%
filter(data_source == "obs",
period != "1994 - 2014",
depth <= params_global$inventory_depth_standard) %>%
p_section_zonal_continous_depth(var = "dcant",
plot_slabs = "n",
title_text = NULL) +
facet_grid(basin_AIP ~ period)
5.2 dcant - absolute delta
dcant_zonal_all %>%
filter(data_source %in% c("mod", "obs"),
period != "1994 - 2014") %>%
select(data_source, lat, depth, basin_AIP, period, dcant) %>%
pivot_wider(names_from = period,
values_from = dcant) %>%
mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "delta_dcant",
plot_slabs = "n",
title_text = NULL,
col = "bias"
) +
facet_grid(basin_AIP ~ .)
)[[1]]
[[2]]
6 Concentration profiles
6.1 dcant - absolute
dcant_profile_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014") %>%
group_split(data_source) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant,
depth)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_ribbon(
aes(
xmin = dcant - dcant_sd,
xmax = dcant + dcant_sd,
fill = period
),
alpha = 0.3
) +
geom_path(aes(col = period)) +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
coord_cartesian(expand = 0) +
scale_color_brewer(palette = "Set1", name = "mean \u00B1 sd") +
scale_fill_brewer(palette = "Set1", name = "mean \u00B1 sd") +
labs(
title = paste("data_source", unique(.x$data_source)),
y = "Depth (m)",
x = dcant_umol_label
) +
facet_wrap(~ basin_AIP,
ncol = 2)
)[[1]]
[[2]]
[[3]]
dcant_profile_basin_MLR_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014",
MLR_basins == "5") %>%
group_split(data_source) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant,
depth)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_ribbon(
aes(
xmin = dcant - dcant_sd,
xmax = dcant + dcant_sd,
fill = period
),
alpha = 0.3
) +
geom_path(aes(col = period)) +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
coord_cartesian(expand = 0) +
scale_color_brewer(palette = "Set1", name = "mean \u00B1 sd") +
scale_fill_brewer(palette = "Set1", name = "mean \u00B1 sd") +
labs(
title = paste("data_source", unique(.x$data_source)),
y = "Depth (m)",
x = dcant_umol_label
) +
facet_wrap( ~ basin,
ncol = 2)
)[[1]]
[[2]]
6.2 dcant - absolute delta
delta <- dcant_profile_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014") %>%
select(data_source, depth, basin_AIP, period, dcant) %>%
pivot_wider(names_from = period,
values_from = dcant) %>%
mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
select(-c(`2004 - 2014`, `1994 - 2004`))
delta_sd <- dcant_profile_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014") %>%
select(data_source, depth, basin_AIP, period, dcant_sd) %>%
pivot_wider(names_from = period,
values_from = dcant_sd) %>%
mutate(delta_dcant_sd = (`2004 - 2014` + `1994 - 2004`) / 2) %>%
select(-c(`2004 - 2014`, `1994 - 2004`))
dcant_profile_all_delta <- full_join(delta, delta_sd)
rm(delta, delta_sd)
dcant_profile_all_delta %>%
group_split(data_source) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(delta_dcant,
depth)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_ribbon(
aes(
xmin = delta_dcant - delta_dcant_sd,
xmax = delta_dcant + delta_dcant_sd
),
alpha = 0.3
) +
geom_path() +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
coord_cartesian(expand = 0) +
scale_color_brewer(palette = "Set1", name = "mean \u00B1 sd") +
scale_fill_brewer(palette = "Set1", name = "mean \u00B1 sd") +
labs(title = paste("data_source", unique(.x$data_source)),
y = "Depth (m)",
x = dcant_umol_label) +
facet_wrap( ~ basin_AIP,
ncol = 2))[[1]]
[[2]]
[[3]]
delta <- dcant_profile_basin_MLR_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014",
MLR_basins == "5") %>%
select(data_source, depth, basin, period, dcant) %>%
pivot_wider(names_from = period,
values_from = dcant) %>%
mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
select(-c(`2004 - 2014`, `1994 - 2004`))
delta_sd <- dcant_profile_basin_MLR_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014",
MLR_basins == "5") %>%
select(data_source, depth, basin, period, dcant_sd) %>%
pivot_wider(names_from = period,
values_from = dcant_sd) %>%
mutate(delta_dcant_sd = (`2004 - 2014` + `1994 - 2004`) / 2) %>%
select(-c(`2004 - 2014`, `1994 - 2004`))
dcant_profile_basin_MLR_all_delta <- full_join(delta, delta_sd)
rm(delta, delta_sd)
dcant_profile_basin_MLR_all_delta %>%
group_split(data_source) %>%
# head(3) %>%
map(
~ ggplot(data = .x,
aes(delta_dcant,
depth)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_ribbon(aes(
xmin = delta_dcant - delta_dcant_sd,
xmax = delta_dcant + delta_dcant_sd
),
alpha = 0.3)+
geom_path() +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
coord_cartesian(expand = 0) +
scale_color_brewer(palette = "Set1", name = "mean \u00B1 sd") +
scale_fill_brewer(palette = "Set1", name = "mean \u00B1 sd") +
labs(
title = paste("data_source", unique(.x$data_source)),
y = "Depth (m)",
x = dcant_umol_label
) +
facet_wrap( ~ basin,
ncol = 2)
)[[1]]
[[2]]
7 Budgets
7.1 Basin & hemisphere
dcant_budget_basin_MLR_all_plot <- dcant_budget_basin_MLR_all %>%
filter(period != "1994 - 2014",
data_source == "obs") %>%
mutate(
basin = str_replace(basin, "_", ". "),
basin = fct_relevel(
basin,
"N. Pacific",
"S. Pacific",
"N. Atlantic",
"S. Atlantic",
"Indian"
)
)g1 <- dcant_budget_basin_MLR_all_plot %>%
ggplot(aes(
y = dcant,
x = period,
alluvium = basin,
fill = basin,
stratum = basin
)) +
stat_alluvium(decreasing = FALSE) +
stat_stratum(decreasing = FALSE) +
stat_stratum(geom = "text",
decreasing = FALSE,
aes(label = paste(
round(after_stat(max-min),1)
# 100*round(after_stat(prop), 2), "%"
))) +
ggrepel::geom_label_repel(
data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
stat = "stratum",
size = 4,
nudge_x = .5,
point.padding = 3,
aes(fill = basin, label = basin),
decreasing = FALSE
)+
scale_fill_brewer(palette = "Paired", guide = "none") +
scale_y_continuous(limits = c(0, 32), expand = c(0, 0)) +
labs(y = dcant_pgc_label) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank()) +
theme_classic()
newdat <- tibble(layer_data(g1))
change <-
newdat %>%
select(x, alluvium, count) %>%
pivot_wider(names_from = x,
values_from = count) %>%
mutate(dcant_change = round(100*(`2` - `1`) / `1`)) %>%
select(alluvium, dcant_change)
coord <- newdat %>%
filter(x == 2) %>%
select(x, y, alluvium)
new_layer <- full_join(
change,
coord
)
new_layer <- new_layer %>%
mutate(dcant_change = as.character(dcant_change),
dcant_change = if_else(str_detect(dcant_change, "-"),
dcant_change,
paste0("+", dcant_change)),
dcant_change = paste(dcant_change, "%"))
g1 +
geom_text(data = new_layer,
aes(
x = x - 0.3,
y = y,
label = dcant_change
),
inherit.aes = FALSE)
g2 <- dcant_budget_basin_MLR_all_plot %>%
ggplot(aes(
y = dcant,
x = period,
alluvium = basin,
fill = basin,
stratum = basin,
label = basin
)) +
geom_alluvium() +
geom_stratum() +
stat_stratum(geom = "text",
aes(label = paste(
round(after_stat(count),1)
# 100*round(after_stat(prop), 2), "%"
))) +
ggrepel::geom_label_repel(
data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
stat = "stratum",
size = 4,
nudge_x = .5,
point.padding = 3,
aes(fill = basin)
)+
scale_fill_brewer(palette = "Paired", guide = "none") +
scale_color_brewer(palette = "Paired", guide = "none") +
scale_y_continuous(limits = c(0, 33), expand = c(0, 0)) +
guides(y = "none") +
labs(title = dcant_pgc_label) +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
plot.title = element_text(hjust = 0.5))
newdat <- tibble(layer_data(g2))
change_basin <-
newdat %>%
select(x, alluvium, count) %>%
pivot_wider(names_from = x,
values_from = count) %>%
mutate(dcant_change = round(100*(`2` - `1`) / `1`)) %>%
select(alluvium, dcant_change)
coord_basin <- newdat %>%
filter(x == 2) %>%
select(x, y, alluvium)
new_layer_basin <- full_join(
change_basin,
coord_basin
)
new_layer_basin <- new_layer_basin %>%
mutate(dcant_change = as.character(dcant_change),
dcant_change = if_else(str_detect(dcant_change, "-"),
dcant_change,
paste0("+", dcant_change)),
dcant_change = paste(dcant_change, "%"))
new_layer_total <-
newdat %>%
select(x, alluvium, count) %>%
group_by(x) %>%
summarise(dcant_change = sum(count)) %>%
ungroup()
new_layer_total <- new_layer_total %>%
mutate(y = dcant_change,
dcant_change = as.character(round(dcant_change,1)),
dcant_change = paste("global:",dcant_change))
g2 +
geom_text(data = new_layer_basin,
aes(
x = x - 0.3,
y = y,
label = dcant_change
),
inherit.aes = FALSE) +
geom_label(data = new_layer_total,
aes(
x = x,
y = y + 1,
label = dcant_change
),
inherit.aes = FALSE)
7.2 Depth layer
7.2.1 Basin AIP
dcant_budget_basin_AIP_layer_all %>%
filter(estimate == "dcant",
period != "1994 - 2014",
inv_depth <= 3000) %>%
rename(dcant = value) %>%
ggplot(aes(dcant, inv_depth, col=period)) +
geom_vline(xintercept = 0) +
geom_path() +
geom_point() +
scale_y_reverse(breaks = seq(250, 3000, 500)) +
scale_color_brewer(palette = "Dark2") +
facet_grid(basin_AIP ~ data_source) +
labs(y = dcant_layer_budget_label)
| Version | Author | Date |
|---|---|---|
| 5f2aed0 | jens-daniel-mueller | 2022-01-27 |
7.2.2 5 basins
dcant_budget_basin_MLR_layer_all %>%
filter(estimate == "dcant",
period != "1994 - 2014",
inv_depth <= 3000) %>%
rename(dcant = value) %>%
ggplot(aes(dcant, inv_depth, col=period)) +
geom_vline(xintercept = 0) +
geom_path() +
geom_point() +
scale_y_reverse(breaks = seq(250, 3000, 500)) +
scale_color_brewer(palette = "Dark2") +
facet_grid(basin ~ data_source) +
labs(y = dcant_layer_budget_label)
| Version | Author | Date |
|---|---|---|
| 5f2aed0 | jens-daniel-mueller | 2022-01-27 |
dcant_budget_basin_MLR_layer_all %>%
filter(estimate == "dcant",
period != "1994 - 2014",
inv_depth <= 3000) %>%
rename(dcant = value) %>%
arrange(inv_depth) %>%
group_by(data_source, basin, period) %>%
mutate(dcant_cum = cumsum(dcant)) %>%
ungroup() %>%
ggplot(aes(dcant_cum, inv_depth, col=period)) +
geom_vline(xintercept = 0) +
geom_path() +
geom_point() +
scale_y_reverse(breaks = seq(250, 3000, 500)) +
scale_color_brewer(palette = "Dark2") +
facet_grid(basin ~ data_source) +
labs(y = dcant_layer_budget_label)
| Version | Author | Date |
|---|---|---|
| 5f2aed0 | jens-daniel-mueller | 2022-01-27 |
8 Time series
dcant_budget_global_ts <- dcant_budget_global_all %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
select(year = tref2, dcant_mean = dcant) %>%
mutate(dcant_sd = 3)
tcant_S04 <- bind_cols(year = 1994, dcant_mean = 118, dcant_sd = 19)
tcant_ts <- full_join(dcant_budget_global_ts, tcant_S04)
tcant_ts <- left_join(tcant_ts, co2_atm)
co2_atm_pi <- bind_cols(pCO2 = 280, dcant_mean = 0, year = 1750, dcant_sd = 0)
tcant_ts <- full_join(tcant_ts, co2_atm_pi)
tcant_ts <- tcant_ts %>%
arrange(year) %>%
mutate(tcant = cumsum(dcant_mean),
tcant_sd = cumsum(dcant_sd))
tcant_ts %>%
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)
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] ggalluvial_0.12.3 ggforce_0.3.3 metR_0.11.0 scico_1.3.0
[5] patchwork_1.1.1 collapse_1.7.0 forcats_0.5.1 stringr_1.4.0
[9] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[13] 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 lubridate_1.8.0 bit64_4.0.5 RColorBrewer_1.1-2
[5] httr_1.4.2 rprojroot_2.0.2 tools_4.1.2 backports_1.4.1
[9] bslib_0.3.1 utf8_1.2.2 R6_2.5.1 DBI_1.1.2
[13] colorspace_2.0-2 withr_2.4.3 tidyselect_1.1.1 processx_3.5.2
[17] bit_4.0.4 compiler_4.1.2 git2r_0.29.0 cli_3.1.1
[21] rvest_1.0.2 xml2_1.3.3 isoband_0.2.5 labeling_0.4.2
[25] sass_0.4.0 scales_1.1.1 checkmate_2.0.0 callr_3.7.0
[29] digest_0.6.29 rmarkdown_2.11 pkgconfig_2.0.3 htmltools_0.5.2
[33] highr_0.9 dbplyr_2.1.1 fastmap_1.1.0 rlang_0.4.12
[37] readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.4 generics_0.1.1
[41] farver_2.1.0 jsonlite_1.7.3 vroom_1.5.7 magrittr_2.0.1
[45] Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2 lifecycle_1.0.1
[49] stringi_1.7.6 whisker_0.4 yaml_2.2.1 MASS_7.3-55
[53] grid_4.1.2 ggrepel_0.9.1 parallel_4.1.2 promises_1.2.0.1
[57] crayon_1.4.2 haven_2.4.3 hms_1.1.1 knitr_1.37
[61] ps_1.6.0 pillar_1.6.4 reprex_2.0.1 glue_1.6.0
[65] evaluate_0.14 getPass_0.2-2 data.table_1.14.2 modelr_0.1.8
[69] vctrs_0.3.8 tzdb_0.2.0 tweenr_1.0.2 httpuv_1.6.5
[73] cellranger_1.1.0 gtable_0.3.0 polyclip_1.10-0 assertthat_0.2.1
[77] xfun_0.29 broom_0.7.11 later_1.3.0 viridisLite_0.4.0
[81] ellipsis_0.3.2