Materials for publication
Jens Daniel Müller
27 May, 2021
Last updated: 2021-05-27
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Knit directory: emlr_obs_analysis/
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|---|---|---|---|---|
| html | 17e7230 | jens-daniel-mueller | 2021-04-23 | Build site. |
| Rmd | 63e2c99 | jens-daniel-mueller | 2021-04-23 | implement UP feedback |
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| Rmd | b6e9179 | jens-daniel-mueller | 2021-04-23 | implement UP feedback |
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| Rmd | 8e3e166 | jens-daniel-mueller | 2021-04-22 | model truth bias added |
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| Rmd | c86fcdd | jens-daniel-mueller | 2021-04-22 | model truth bias added |
| html | 0a9f7a7 | jens-daniel-mueller | 2021-04-22 | Build site. |
| Rmd | 9a89eb9 | jens-daniel-mueller | 2021-04-22 | model truth bias added |
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| Rmd | 656d1e2 | jens-daniel-mueller | 2021-04-22 | model truth bias added |
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| Rmd | 1d621cb | jens-daniel-mueller | 2021-04-22 | model truth bias added |
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| Rmd | 26c1939 | jens-daniel-mueller | 2021-04-22 | model truth bias added |
| html | 3bb60c7 | jens-daniel-mueller | 2021-04-22 | Build site. |
| Rmd | 9da882f | jens-daniel-mueller | 2021-04-22 | zonal mean section added |
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| Rmd | 728b5c5 | jens-daniel-mueller | 2021-04-21 | revised histogram |
| html | e336d01 | jens-daniel-mueller | 2021-04-19 | Build site. |
| Rmd | 5f76a71 | jens-daniel-mueller | 2021-04-19 | zonal mean sections added |
| html | 0e9a91d | jens-daniel-mueller | 2021-04-16 | Build site. |
| Rmd | d4af2c2 | jens-daniel-mueller | 2021-04-16 | refined inventory map |
| html | 2ab1b25 | jens-daniel-mueller | 2021-04-15 | Build site. |
| Rmd | 974c6c1 | jens-daniel-mueller | 2021-04-15 | refined inventory map |
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| Rmd | 71e6c6f | jens-daniel-mueller | 2021-04-15 | add publication figures |
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| Rmd | 56f179d | jens-daniel-mueller | 2021-04-14 | add publication figures |
1 Data sources
Following Cant estimates are used:
- Zonal mean (basin, lat, depth)
- Inventories (lat, lon)
cant_inv <-
read_csv(paste(path_version_data,
"cant_inv.csv",
sep = ""))
cant_inv_mod_truth <-
read_csv(paste(path_version_data,
"cant_inv_mod_truth.csv",
sep = ""))
cant_inv <- bind_rows(cant_inv, cant_inv_mod_truth)
cant_zonal <-
read_csv(paste(path_version_data,
"cant_zonal.csv",
sep = ""))
cant_zonal_mod_truth <-
read_csv(paste(path_version_data,
"cant_zonal_mod_truth.csv",
sep = ""))
cant_zonal <- bind_rows(cant_zonal,
cant_zonal_mod_truth)
GLODAP_clean <-
read_csv(paste(path_version_data,
"GLODAPv2.2020_clean.csv",
sep = ""))
GLODAP_preprocessed <-
read_csv(
paste(
path_preprocessing_model,
"GLODAPv2.2020_preprocessed_model_runA_both.csv",
sep = ""
)
)
GLODAP_grid_dup <-
read_csv(paste(path_version_data,
"GLODAPv2.2020_clean_obs_grid_duplicates.csv",
sep = ""))
tref <-
read_csv(paste(path_version_data,
"tref.csv",
sep = ""))cant_inv <- cant_inv %>%
filter(inv_depth == params_global$inventory_depth_standard)2 Observations
2.1 Inventory map
# coastlines and worldmap
coastlines <- ne_coastline(scale = "small", returnclass = "sf")
coastlines_re <- ne_coastline(scale = "small", returnclass = "sf")
worldmap <- ne_countries(scale = "small", returnclass = "sf")
worldmap_re <- ne_countries(scale = "small", returnclass = "sf")
crs <- st_crs(coastlines)
st_geometry(worldmap_re) <- st_geometry(worldmap_re) + c(360, 0)
st_crs(worldmap_re) <- crs
worldmap <- rbind(worldmap, worldmap_re)
rm(worldmap_re)
st_geometry(coastlines_re) <- st_geometry(coastlines_re) + c(360, 0)
st_crs(coastlines_re) <- crs
coastlines <- rbind(coastlines, coastlines_re)
rm(coastlines_re)
# coastlines_buffer <- st_buffer(coastlines, dist = 1)
# coastlines_re_buffer <- st_buffer(coastlines_re, dist = 1)
# coastline_raster <- stars::st_rasterize(coastlines, options = "ALL_TOUCHED=TRUE") %>%
# as.tibble()
# unmapped regions shape files
for (i_file in list.files("data/iho_marginal_seas")) {
iho <- st_read(paste0("data/iho_marginal_seas/", i_file, "/iho.shp"))
if (exists("marine_polys")) {
marine_polys <- rbind(marine_polys, iho)
} else {
marine_polys <- iho
}
}Reading layer `iho' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/iho_marginal_seas/baltic_sea/iho.shp' using driver `ESRI Shapefile'
Simple feature collection with 5 features and 10 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 9.365596 ymin: 52.65352 xmax: 37.46891 ymax: 67.08059
Geodetic CRS: WGS 84
Reading layer `iho' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/iho_marginal_seas/caribbean_sea/iho.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 10 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -89.41293 ymin: 7.709799 xmax: -59.4216 ymax: 22.70652
Geodetic CRS: WGS 84
Reading layer `iho' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/iho_marginal_seas/gulf_of_mexico/iho.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 10 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -98.05392 ymin: 17.40681 xmax: -80.43304 ymax: 31.46484
Geodetic CRS: WGS 84
Reading layer `iho' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/iho_marginal_seas/hudson_bay/iho.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 10 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -95.34617 ymin: 51.14359 xmax: -75.88438 ymax: 66.02643
Geodetic CRS: WGS 84
Reading layer `iho' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/iho_marginal_seas/mediterranean_sea/iho.shp' using driver `ESRI Shapefile'
Simple feature collection with 10 features and 10 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: -6.032549 ymin: 30.06809 xmax: 36.21573 ymax: 45.80891
Geodetic CRS: WGS 84
Reading layer `iho' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/iho_marginal_seas/persian_gulf/iho.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 10 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 47.70244 ymin: 23.95901 xmax: 57.33998 ymax: 31.18586
Geodetic CRS: WGS 84
Reading layer `iho' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/iho_marginal_seas/red_sea/iho.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 10 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 33.63937 ymin: 12.45626 xmax: 43.50552 ymax: 28.13954
Geodetic CRS: WGS 84marine_polys_re <- marine_polys
st_geometry(marine_polys_re) <- st_geometry(marine_polys) + c(360, 0)
st_crs(marine_polys) <- crs
st_crs(marine_polys_re) <- crs
marine_polys <- rbind(marine_polys, marine_polys_re)
rm(marine_polys_re)
# plot(st_geometry(marine_polys))
# ggplot() +
# geom_sf(data = st_geometry(marine_polys), fill = "white")
# marine_polys_simple <- st_simplify(marine_polys, dTolerance = 0.5)
# ggplot() +
# geom_sf(data = marine_polys, fill = "red") +
# geom_sf(data = marine_polys_simple, fill = "white")
black_sea <- st_read("data/black_sea/provinces.shp")Reading layer `provinces' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/black_sea/provinces.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 7 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 27.44959 ymin: 40.91028 xmax: 41.77609 ymax: 47.28054
Geodetic CRS: WGS 84black_sea_re <- black_sea
st_geometry(black_sea_re) <- st_geometry(black_sea) + c(360, 0)
st_crs(black_sea) <- crs
st_crs(black_sea_re) <- crs
black_sea <- rbind(black_sea, black_sea_re)
rm(black_sea_re)
hudson_bay <- st_read("data/hudson_bay/lme.shp")Reading layer `lme' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/hudson_bay/lme.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 16 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -94.99611 ymin: 50.72388 xmax: -64.5891 ymax: 70.68027
Geodetic CRS: WGS 84hudson_bay_re <- hudson_bay
st_geometry(hudson_bay_re) <- st_geometry(hudson_bay) + c(360, 0)
st_crs(hudson_bay) <- crs
st_crs(hudson_bay_re) <- crs
hudson_bay <- rbind(hudson_bay, hudson_bay_re)
rm(hudson_bay_re)
caspian_sea <- st_read("data/caspian_sea/seavox_v17.shp")Reading layer `seavox_v17' from data source `/UP_home/jenmueller/Projects/emlr_cant/observations/emlr_obs_analysis/data/caspian_sea/seavox_v17.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 20 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 46.6797 ymin: 36.5801 xmax: 54.7659 ymax: 47.1153
Geodetic CRS: WGS 84caspian_sea_re <- caspian_sea
st_geometry(caspian_sea_re) <- st_geometry(caspian_sea) + c(360, 0)
st_crs(caspian_sea) <- crs
st_crs(caspian_sea_re) <- crs
caspian_sea <- rbind(caspian_sea, caspian_sea_re)
rm(caspian_sea_re)
# ggplot() +
# geom_sf(data = marine_polys, fill = "white") +
# geom_sf(data = black_sea, fill = "white") +
# geom_sf(data = caspian_sea, fill = "white") +
# geom_sf(data = hudson_bay, fill = "white")set_breaks <- c(-Inf, seq(0, 10, 2), Inf)
color_land <- "grey80"
color_unmapped <- "grey90"
var_name <- expression(atop(Delta * C["ant"],
(mol ~ m ^ {
-2
})))
GLODAP_grid_both <- GLODAP_grid_dup %>%
filter(duplicate == "no") %>%
distinct(lon, lat, era)
p_inv_map <- ggplot() +
geom_contour_fill(
data = cant_inv %>% filter(data_source == "obs"),
aes(lon, lat, z = cant_inv, fill = stat(level)),
breaks = set_breaks,
na.fill = TRUE
) +
scale_fill_viridis_d(option = "D", name = var_name,
guide = guide_colorsteps(barheight = 10)) +
new_scale_fill() +
geom_tile(data = GLODAP_grid_both,
aes(x = lon, y = lat, height = 0.7, width = 0.7, fill=era)) +
scale_fill_manual(values = c("Deeppink4", "Deeppink"),
name = "Decade", guide = FALSE) +
geom_sf(data = marine_polys, fill = color_unmapped, col="transparent") +
geom_sf(data = black_sea, fill = color_unmapped, col="transparent") +
geom_sf(data = caspian_sea, fill = color_unmapped, col="transparent") +
geom_sf(data = hudson_bay, fill = "white", col="white") +
geom_sf(data = worldmap, fill = color_land, col="transparent") +
geom_sf(data = coastlines, col = "black") +
coord_sf(ylim = c(-77.5,64.5), xlim = c(20.5,379.5), expand = 0) +
labs(title = expression("Column inventory (0 - 3000m) of the change in anthropogenic CO"[2]~
"from 2006 to 2014")) +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.key = element_rect(colour = "black"))
ggsave(plot = p_inv_map,
path = "output/publication",
filename = "dCant_inventory_map.png",
height = 4,
width = 10)2.2 Time series histogram
time_histo <- GLODAP_preprocessed %>%
filter(!is.na(tco2)) %>%
count(year)
p_time_histo <-
ggplot() +
geom_col(data = time_histo %>% filter(year < 2000),
aes(year, n, fill = "era1"),
col = "grey20") +
geom_col(
data = time_histo %>% filter(year >= 2000, year < 2010),
aes(year, n, fill = "era2"),
col = "grey20"
) +
geom_col(data = time_histo %>% filter(year >= 2010),
aes(year, n, fill = "era3"),
col = "grey20") +
scale_fill_manual(values = c("grey", "Deeppink4", "Deeppink"),
name = "Decade", guide = FALSE) +
scale_x_continuous(breaks = seq(1900, 2100, 5)) +
scale_y_continuous(limits = c(0, max(time_histo$n)+500)) +
coord_cartesian(expand = 0) +
labs(title = "GLODAPv2.2020 | Observations per year") +
theme_classic() +
theme(axis.title = element_blank())
p_time_histo
ggsave(plot = p_time_histo,
path = "output/publication",
filename = "time_histo.png",
height = 2,
width = 10)2.3 Spatial time coverage
time_histo <- GLODAP_preprocessed %>%
filter(year >= 2000) %>%
distinct(lat, lon, year)
p_coverage_maps <-
map +
geom_raster(data = time_histo, aes(lon, lat)) +
facet_wrap( ~ year) +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank()
)
p_coverage_maps
| Version | Author | Date |
|---|---|---|
| 6cebef7 | jens-daniel-mueller | 2021-04-22 |
ggsave(plot = p_coverage_maps,
path = "output/publication",
filename = "data_coverage_maps_by_year.png",
height = 7,
width = 16)time_histo <- GLODAP_preprocessed %>%
filter(year >= 2000) %>%
distinct(lat, lon, year)
GLODAP_grid_both <- GLODAP_grid_dup %>%
filter(duplicate == "no") %>%
count(lon, lat) %>%
mutate(n = as.factor(n))
p_coverage_maps <-
ggplot() +
geom_raster(data = GLODAP_grid_both,
aes(x = lon, y = lat, fill = n)) +
scale_fill_brewer(palette = "Set1",
name = "Decades\noccupied",
direction = -1) +
geom_sf(data = worldmap, fill = color_land, col = "transparent") +
geom_sf(data = coastlines, col = "black") +
coord_sf(
ylim = c(-77.5, 64.5),
xlim = c(20.5, 379.5),
expand = 0
) +
labs(title = "GLODAPv2.2020 | Data coverage in the post-2000 era") +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.key = element_rect(colour = "black"),
panel.grid = element_blank()
)
p_coverage_maps
ggsave(plot = p_coverage_maps,
path = "output/publication",
filename = "data_coverage_map_post_2000.png",
height = 4,
width = 10)p_coverage_maps <-
ggplot() +
geom_raster(data = GLODAP_grid_dup,
aes(x = lon, y = lat, fill = duplicate)) +
scale_fill_brewer(palette = "Set1",
name = "Duplicated\ndata",
direction = -1) +
geom_sf(data = worldmap, fill = color_land, col = "transparent") +
geom_sf(data = coastlines, col = "black") +
coord_sf(
ylim = c(-77.5, 64.5),
xlim = c(20.5, 379.5),
expand = 0
) +
facet_wrap(~ era, ncol = 1) +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.key = element_rect(colour = "black"),
panel.grid = element_blank()
)
p_coverage_maps
ggsave(plot = p_coverage_maps,
path = "output/publication",
filename = "data_coverage_map_duplicated_data.png",
height = 6,
width = 7)color_land <- "white"
color_unmapped <- "white"
p_inv_map <- ggplot() +
geom_contour_fill(
data = cant_inv %>% filter(data_source == "obs"),
aes(lon, lat, z = cant_inv, fill = stat(level)),
breaks = set_breaks,
na.fill = TRUE
) +
scale_fill_viridis_d(option = "D", name = var_name,
guide = FALSE) +
geom_sf(data = marine_polys, fill = color_unmapped, col="transparent") +
geom_sf(data = black_sea, fill = color_unmapped, col="transparent") +
geom_sf(data = caspian_sea, fill = color_unmapped, col="transparent") +
geom_sf(data = hudson_bay, fill = "white", col="white") +
geom_sf(data = worldmap, fill = color_land, col="transparent") +
geom_sf(data = coastlines, col = "white") +
coord_sf(ylim = c(-77.5,64.5), xlim = c(20.5,379.5), expand = 0) +
theme_void()
ggsave(plot = p_inv_map,
path = "output/publication",
filename = "dCant_inventory_map_color_only.png",
height = 4,
width = 10)2.4 Uptake rate comparison
cant_inv_budget <- cant_inv %>%
mutate(surface_area = earth_surf(lat, lon),
cant_inv_grid = cant_inv*surface_area,
cant_pos_inv_grid = cant_pos_inv*surface_area) %>%
group_by(basin_AIP, data_source, inv_depth) %>%
summarise(cant_total = sum(cant_inv_grid)*12*1e-15,
cant_total = round(cant_total,1),
cant_pos_total = sum(cant_pos_inv_grid)*12*1e-15,
cant_pos_total = round(cant_pos_total,1)) %>%
ungroup()
duration <- sort(tref$median_year)[2] - sort(tref$median_year)[1]
cant_inv_budget_obs <- cant_inv_budget %>%
filter(data_source == "obs",
inv_depth == 3000) %>%
summarise(cant_uptake_rate = sum(cant_total)/duration) %>%
mutate(source = "Interior\nstorage",
period = "This study",
uncertainty = 0.3)
cant_inv_budget_lit <-
bind_cols(
cant_uptake_rate = c(2.37, 2.18 + 0.61),
source = c("Ocean\nmodels", "Observed\nfluxes"),
period = c("Global\nCarbon Budget", "Global\nCarbon Budget"),
uncertainty = c(0.6, 0.6)
)
cant_inv_budget_all <- bind_rows(
cant_inv_budget_obs,
cant_inv_budget_lit
)
p_budget <-
cant_inv_budget_all %>%
ggplot() +
geom_col(aes(source, cant_uptake_rate),
fill = "grey80",
col = "grey20") +
geom_errorbar(
aes(
x = source,
ymin = cant_uptake_rate - uncertainty,
ymax = cant_uptake_rate + uncertainty
),
width = .2
) +
geom_text(
aes(
x = source,
y = 0.8,
label = period,
angle = 90
)
) +
scale_y_continuous(limits = c(
0,
max(
cant_inv_budget_all$cant_uptake_rate +
cant_inv_budget_all$uncertainty
) + 0.1
),
expand = c(0, 0)) +
labs(title = "Ocean carbon sink 2006 - 2014",
y = expression(Average~rate ~ (PgC ~ yr ^ {-1}))) +
theme_classic() +
theme(axis.title.x = element_blank(),
panel.grid = element_blank())
p_budget
ggsave(plot = p_budget,
path = "output/publication",
filename = "uptake_rate_comparison.png",
height = 3.5,
width = 3.5)2.5 Zonal mean sections
breaks <- c(-Inf, seq(0, 10, 1), Inf)
breaks_n <- length(breaks) - 1
legend_title = expression(atop(Delta * C[ant, pos],
(mu * mol ~ kg ^ {
-1
})))
i_basin_AIP <- "Atlantic"
slab_breaks <- params_local$slabs_Atl
i_data_source <- "obs"
# plot base section
section <-
cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
data_source == i_data_source) %>%
ggplot() +
guides(fill = FALSE) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_mean),
breaks = breaks) +
scale_fill_viridis_d(name = legend_title) +
geom_hline(yintercept = params_local$depth_min,
col = "white",
linetype = 2) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white",
skip = 2
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)",
title =paste(i_basin_AIP, "Ocean")) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()
)
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)")
# combine surface and deep water section
section_combined_Atl <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Atl
i_basin_AIP <- "Pacific"
slab_breaks <- params_local$slabs_Ind_Pac
# plot base section
section <-
cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
data_source == i_data_source) %>%
ggplot() +
guides(fill = FALSE) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_mean),
breaks = breaks) +
scale_fill_viridis_d(name = legend_title) +
geom_hline(yintercept = params_local$depth_min,
col = "white",
linetype = 2) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white",
skip = 2
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)",
title =paste(i_basin_AIP, "Ocean")) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)") +
theme(
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
)
# combine surface and deep water section
section_combined_Pac <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Pac
| Version | Author | Date |
|---|---|---|
| 5758680 | jens-daniel-mueller | 2021-04-22 |
i_basin_AIP <- "Indian"
slab_breaks <- params_local$slabs_Ind_Pac
# plot base section
section <-
cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
data_source == i_data_source) %>%
ggplot() +
guides(fill = guide_colorsteps(barheight = unit(6, "cm"))) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_mean),
breaks = breaks) +
scale_fill_viridis_d(name = legend_title) +
geom_hline(yintercept = params_local$depth_min,
col = "white",
linetype = 2) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white",
skip = 2
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)",
title =paste(i_basin_AIP, "Ocean")) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)") +
theme(
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
)
# combine surface and deep water section
section_combined_Ind <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Ind
| Version | Author | Date |
|---|---|---|
| 5758680 | jens-daniel-mueller | 2021-04-22 |
section_combined <-
section_combined_Atl | section_combined_Pac | section_combined_Ind
ggsave(plot = section_combined,
path = "output/publication",
filename = "zonal_mean_section_obs.png",
height = 4,
width = 12)3 Synthetic data
3.1 Inventory map
set_breaks <- c(-Inf, seq(0, 12, 2), Inf)
color_land <- "grey80"
color_unmapped <- "grey90"
var_name <- expression(atop(Delta * C["ant"],
(mol ~ m ^ {
-2
})))
p_inv_map <- ggplot() +
geom_contour_fill(
data = cant_inv %>% filter(data_source == "mod"),
aes(lon, lat, z = cant_inv, fill = stat(level)),
breaks = set_breaks,
na.fill = TRUE
) +
scale_fill_viridis_d(option = "D", name = var_name,
guide = guide_colorsteps()) +
new_scale_fill() +
geom_tile(data = GLODAP_grid_both,
aes(x = lon, y = lat, height = 0.7, width = 0.7, fill=n)) +
scale_fill_manual(values = c("Deeppink4", "Deeppink"),
name = "Eras\noccupied") +
geom_sf(data = marine_polys, fill = color_unmapped, col="transparent") +
geom_sf(data = black_sea, fill = color_unmapped, col="transparent") +
geom_sf(data = caspian_sea, fill = color_unmapped, col="transparent") +
geom_sf(data = hudson_bay, fill = "white", col="white") +
geom_sf(data = worldmap, fill = color_land, col="transparent") +
geom_sf(data = coastlines, col = "black") +
coord_sf(ylim = c(-77.5,64.5), xlim = c(20.5,379.5), expand = 0) +
labs(title = expression("Column inventory (0 - 3000m) of the change in anthropogenic CO"[2]~
"from 2006 to 2014"),
subtitle = "eMLR(C*) reconstruction") +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.key = element_rect(colour = "black"))
# bias map
set_breaks <- c(-Inf, seq(-4, 4, 1), Inf)
var_name <- expression(atop(Delta * C["ant"]~bias,
(mol ~ m ^ {
-2
})))
cant_inv_bias <- cant_inv %>%
filter(data_source %in% c("mod", "mod_truth")) %>%
select(lat, lon, data_source, cant_inv) %>%
pivot_wider(names_from = data_source,
values_from = cant_inv) %>%
mutate(cant_inv_bias = mod - mod_truth) %>%
drop_na()
p_inv_map_bias <- ggplot() +
geom_contour_fill(
data = cant_inv_bias,
aes(lon, lat, z = cant_inv_bias, fill = stat(level)),
breaks = set_breaks,
na.fill = TRUE
) +
scale_fill_brewer(
palette = "RdBu",
direction = -1,
drop = FALSE,
name = var_name,
guide = guide_colorsteps(barheight = unit(5, "cm"))
) +
geom_sf(data = marine_polys, fill = color_unmapped, col = "transparent") +
geom_sf(data = black_sea, fill = color_unmapped, col = "transparent") +
geom_sf(data = caspian_sea, fill = color_unmapped, col = "transparent") +
geom_sf(data = hudson_bay, fill = "white", col = "white") +
geom_sf(data = worldmap, fill = color_land, col = "transparent") +
geom_sf(data = coastlines, col = "black") +
coord_sf(
ylim = c(-77.5, 64.5),
xlim = c(20.5, 379.5),
expand = 0
) +
labs(subtitle = "eMLR(C*) reconstruction - model truth")+
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.key = element_rect(colour = "black")
)
p_inv_map_mod <-
p_inv_map / p_inv_map_bias
ggsave(plot = p_inv_map_mod,
path = "output/publication",
filename = "dCant_inventory_map_mod_bias.png",
height = 7,
width = 9)3.2 Budgets
cant_inv_budget_mod <- cant_inv_budget %>%
filter(
inv_depth == params_global$inventory_depth_standard,
data_source %in% c("mod", "mod_truth")
) %>%
select(-c(cant_pos_total, inv_depth))
cant_inv_budget_mod %>%
pivot_wider(names_from = data_source,
values_from = cant_total) %>%
mutate(abs_bias = mod - mod_truth,
rel_bias = ((mod / mod_truth) - 1) * 100)# A tibble: 3 x 5
basin_AIP mod mod_truth abs_bias rel_bias
<chr> <dbl> <dbl> <dbl> <dbl>
1 Atlantic 4.6 4.1 0.5 12.2
2 Indian 2.6 4.5 -1.9 -42.2
3 Pacific 7.1 8 -0.9 -11.3cant_inv_budget_mod %>%
group_by(data_source) %>%
summarise(cant_total = sum(cant_total)) %>%
ungroup() %>%
pivot_wider(names_from = data_source,
values_from = cant_total) %>%
mutate(abs_bias = mod - mod_truth,
rel_bias = ((mod / mod_truth) - 1) * 100)# A tibble: 1 x 4
mod mod_truth abs_bias rel_bias
<dbl> <dbl> <dbl> <dbl>
1 14.3 16.6 -2.3 -13.9p_budget_comparison <-
cant_inv_budget_mod %>%
mutate(
data_source = recode(data_source,
mod = "eMLR(C*)\nreconstruction",
mod_truth = "Model\ntruth")
) %>%
ggplot(aes(data_source, cant_total, fill = basin_AIP)) +
scale_fill_brewer(palette = "Greys", name = "Ocean basin",
direction = -1) +
scale_y_continuous(limits = c(0, 18), expand = c(0, 0)) +
labs(y = expression(Delta * C["ant"] ~ (PgC)),
title = "Regionally integrated budgets") +
geom_col(col = "grey20") +
theme_bw() +
theme(axis.title.x = element_blank())
p_budget_comparison
| Version | Author | Date |
|---|---|---|
| 8a0c4d6 | jens-daniel-mueller | 2021-04-22 |
ggsave(plot = p_budget_comparison,
path = "output/publication",
filename = "budget_comparison.png",
height = 3.5,
width = 4)3.3 Zonal mean sections
3.3.1 eMLR
breaks <- c(-Inf, seq(0, 10, 1), Inf)
breaks_n <- length(breaks) - 1
legend_title = expression(atop(Delta * C[ant, pos],
(mu * mol ~ kg ^ {
-1
})))
i_basin_AIP <- "Atlantic"
slab_breaks <- params_local$slabs_Atl
i_data_source <- "mod"
# plot base section
section <-
cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
data_source == i_data_source) %>%
ggplot() +
guides(fill = FALSE) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_mean),
breaks = breaks) +
scale_fill_viridis_d(name = legend_title) +
geom_hline(yintercept = params_local$depth_min,
col = "white",
linetype = 2) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white",
skip = 2
) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)",
title =paste(i_basin_AIP, "Ocean"))
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)")
# combine surface and deep water section
section_combined_Atl <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Atl
i_basin_AIP <- "Pacific"
slab_breaks <- params_local$slabs_Ind_Pac
# plot base section
section <-
cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
data_source == i_data_source) %>%
ggplot() +
guides(fill = FALSE) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_mean),
breaks = breaks) +
scale_fill_viridis_d(name = legend_title) +
geom_hline(yintercept = params_local$depth_min,
col = "white",
linetype = 2) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white",
skip = 2
) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)",
title =paste(i_basin_AIP, "Ocean"))
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)") +
theme(
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
)
# combine surface and deep water section
section_combined_Pac <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Pac
i_basin_AIP <- "Indian"
slab_breaks <- params_local$slabs_Ind_Pac
# plot base section
section <-
cant_zonal %>%
filter(basin_AIP == i_basin_AIP,
data_source == i_data_source) %>%
ggplot() +
guides(fill = guide_colorsteps(barheight = unit(5, "cm"))) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_mean),
breaks = breaks) +
scale_fill_viridis_d(name = legend_title) +
geom_hline(yintercept = params_local$depth_min,
col = "white",
linetype = 2) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white",
skip = 2
) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)",
title =paste(i_basin_AIP, "Ocean")) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)") +
theme(
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
)
# combine surface and deep water section
section_combined_Ind <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Ind
section_combined <-
section_combined_Atl | section_combined_Pac | section_combined_Ind
section_combined
| Version | Author | Date |
|---|---|---|
| 0a9f7a7 | jens-daniel-mueller | 2021-04-22 |
3.3.2 Bias
breaks <- c(-Inf, seq(-6, 6, 2), Inf)
breaks_n <- length(breaks) - 1
legend_title = expression(atop(Delta * C[ant]~bias,
(mu * mol ~ kg ^ {
-1
})))
cant_zonal_bias <- cant_zonal %>%
filter(data_source %in% c("mod", "mod_truth")) %>%
select(lat, depth, basin_AIP, data_source, cant_mean) %>%
pivot_wider(names_from = data_source,
values_from = cant_mean) %>%
mutate(cant_bias = mod - mod_truth)
cant_zonal_bias <- full_join(
cant_zonal_bias,
cant_zonal_mod_truth %>% select(lat, depth, basin_AIP, gamma_mean)
)
i_basin_AIP <- "Atlantic"
slab_breaks <- params_local$slabs_Atl
# plot base section
section <-
cant_zonal_bias %>%
filter(basin_AIP == i_basin_AIP) %>%
ggplot() +
guides(fill = FALSE) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_bias),
breaks = breaks) +
scale_fill_brewer(
palette = "RdBu",
direction = -1,
drop = FALSE,
name = legend_title,
guide = guide_colorsteps(barheight = unit(5, "cm"))
) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "black") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "black",
skip = 2
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)") +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank()
)
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)")
# combine surface and deep water section
section_combined_Atl <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Atl
i_basin_AIP <- "Pacific"
slab_breaks <- params_local$slabs_Ind_Pac
# plot base section
section <-
cant_zonal_bias %>%
filter(basin_AIP == i_basin_AIP) %>%
ggplot() +
guides(fill = FALSE) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_bias),
breaks = breaks) +
scale_fill_brewer(
palette = "RdBu",
direction = -1,
drop = FALSE,
name = legend_title,
guide = guide_colorsteps(barheight = unit(4, "cm"))
) +
geom_hline(yintercept = params_local$depth_min,
col = "black",
linetype = 2) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "black") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "white",
skip = 2
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)") +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)") +
theme(
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
)
# combine surface and deep water section
section_combined_Pac <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Pac
i_basin_AIP <- "Indian"
slab_breaks <- params_local$slabs_Ind_Pac
# plot base section
section <-
cant_zonal_bias %>%
filter(basin_AIP == i_basin_AIP) %>%
ggplot() +
guides(fill = guide_colorsteps(barheight = unit(6, "cm"))) +
scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 20),
limits = c(min(cant_zonal$lat), max(cant_zonal$lat))) +
geom_contour_filled(aes(lat, depth, z = cant_bias),
breaks = breaks) +
scale_fill_brewer(
palette = "RdBu",
direction = -1,
drop = FALSE,
name = legend_title,
guide = guide_colorsteps(barheight = unit(4, "cm"))
) +
geom_hline(yintercept = params_local$depth_min,
col = "white",
linetype = 2) +
geom_contour(aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "black") +
geom_text_contour(
aes(lat, depth, z = gamma_mean),
breaks = slab_breaks,
col = "black",
skip = 2
)
# cut surface water section
surface <-
section +
coord_cartesian(expand = 0,
ylim = c(500, 0)) +
labs(y = "Depth (m)") +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
# cut deep water section
deep <-
section +
coord_cartesian(expand = 0,
ylim = c(params_global$inventory_depth_standard, 500)) +
labs(x = expression(latitude ~ (degree * N)), y = "Depth (m)") +
theme(
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
)
# combine surface and deep water section
section_combined_Ind <-
surface / deep +
plot_layout(guides = "collect")
section_combined_Ind
section_combined_bias <-
section_combined_Atl | section_combined_Pac | section_combined_Ind
section_mod_bias <-
section_combined / section_combined_bias
ggsave(plot = section_mod_bias,
path = "output/publication",
filename = "zonal_mean_section_mod_bias.png",
height = 7,
width = 14)
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] marelac_2.1.10 shape_1.4.5 ggnewscale_0.4.5
[4] rnaturalearth_0.1.0 sf_0.9-8 metR_0.9.0
[7] scico_1.2.0 patchwork_1.1.1 collapse_1.5.0
[10] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[16] tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0
[19] workflowr_1.6.2
loaded via a namespace (and not attached):
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[4] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2
[7] tools_4.0.3 backports_1.1.10 utf8_1.1.4
[10] R6_2.5.0 KernSmooth_2.23-17 rgeos_0.5-5
[13] DBI_1.1.0 colorspace_1.4-1 withr_2.3.0
[16] sp_1.4-4 rnaturalearthdata_0.1.0 tidyselect_1.1.0
[19] compiler_4.0.3 git2r_0.27.1 cli_2.1.0
[22] rvest_0.3.6 xml2_1.3.2 isoband_0.2.2
[25] labeling_0.4.2 scales_1.1.1 checkmate_2.0.0
[28] classInt_0.4-3 digest_0.6.27 rmarkdown_2.5
[31] oce_1.2-0 pkgconfig_2.0.3 htmltools_0.5.0
[34] dbplyr_1.4.4 rlang_0.4.10 readxl_1.3.1
[37] rstudioapi_0.11 farver_2.0.3 generics_0.0.2
[40] jsonlite_1.7.1 magrittr_1.5 Matrix_1.2-18
[43] Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
[46] lifecycle_1.0.0 stringi_1.5.3 whisker_0.4
[49] yaml_2.2.1 plyr_1.8.6 grid_4.0.3
[52] blob_1.2.1 parallel_4.0.3 promises_1.1.1
[55] crayon_1.3.4 lattice_0.20-41 haven_2.3.1
[58] hms_0.5.3 seacarb_3.2.14 knitr_1.30
[61] pillar_1.4.7 reprex_0.3.0 glue_1.4.2
[64] evaluate_0.14 RcppArmadillo_0.10.1.2.0 data.table_1.13.2
[67] modelr_0.1.8 vctrs_0.3.5 httpuv_1.5.4
[70] testthat_2.3.2 cellranger_1.1.0 gtable_0.3.0
[73] assertthat_0.2.1 xfun_0.18 broom_0.7.5
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[82] units_0.6-7 ellipsis_0.3.1