pCO2 prediction skill
Jens Daniel Müller
20 September, 2024
Last updated: 2024-09-20
Checks: 7 0
Knit directory:
heatwave_co2_flux_2023/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 59d4ded | jens-daniel-mueller | 2024-09-20 | minor edits |
| html | d6d6440 | jens-daniel-mueller | 2024-09-12 | Build site. |
| Rmd | 3bb52db | jens-daniel-mueller | 2024-09-12 | maps created |
| html | 756fa19 | jens-daniel-mueller | 2024-07-20 | Build site. |
| Rmd | de2e9c2 | jens-daniel-mueller | 2024-07-20 | label v2 and fco2 products |
| html | 0b5ffe4 | jens-daniel-mueller | 2024-07-11 | Build site. |
| Rmd | 7c286b2 | jens-daniel-mueller | 2024-07-11 | rebuild all products |
| html | 430e926 | jens-daniel-mueller | 2024-07-10 | manual commit |
| Rmd | b4a08ea | jens-daniel-mueller | 2024-06-26 | high res analysis |
| html | d2a80a9 | jens-daniel-mueller | 2024-06-14 | Build site. |
| Rmd | fe5eaa5 | jens-daniel-mueller | 2024-06-14 | testrun on sea |
| html | f03b1d8 | jens-daniel-mueller | 2024-06-12 | Build site. |
| html | d46002d | jens-daniel-mueller | 2024-06-12 | manual commit |
| html | e5cb4e9 | jens-daniel-mueller | 2024-06-12 | Build site. |
| html | 5261667 | jens-daniel-mueller | 2024-06-11 | manual commit |
| Rmd | dfc33c9 | jens-daniel-mueller | 2024-06-11 | manual commit |
| html | 6954c65 | jens-daniel-mueller | 2024-06-06 | Build site. |
| html | 6a16b4c | jens-daniel-mueller | 2024-06-05 | Build site. |
| Rmd | 8ff0fa3 | jens-daniel-mueller | 2024-06-05 | SOCATv2024 comparison |
center <- -160
boundary <- center + 180
target_crs <- paste0("+proj=robin +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +lon_0=", center)
# target_crs <- paste0("+proj=igh_o +lon_0=", center)
worldmap <- ne_countries(scale = 'small',
type = 'map_units',
returnclass = 'sf')
worldmap <- worldmap %>% st_break_antimeridian(lon_0 = center)
worldmap_trans <- st_transform(worldmap, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans)
coastline <- ne_coastline(scale = 'small', returnclass = "sf")
coastline <- st_break_antimeridian(coastline, lon_0 = 200)
coastline_trans <- st_transform(coastline, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
# geom_sf(data = coastline_trans)
bbox <- st_bbox(c(xmin = -180, xmax = 180, ymax = 65, ymin = -78), crs = st_crs(4326))
bbox <- st_as_sfc(bbox)
bbox_trans <- st_break_antimeridian(bbox, lon_0 = center)
bbox_graticules <- st_graticule(
x = bbox_trans,
crs = st_crs(bbox_trans),
datum = st_crs(bbox_trans),
lon = c(20, 20.001),
lat = c(-78,65),
ndiscr = 1e3,
margin = 0.001
)
bbox_graticules_trans <- st_transform(bbox_graticules, crs = target_crs)
rm(worldmap, coastline, bbox, bbox_trans)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans)
lat_lim <- ext(bbox_graticules_trans)[c(3,4)]*1.002
lon_lim <- ext(bbox_graticules_trans)[c(1,2)]*1.005
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans, linewidth = 1) +
# coord_sf(crs = target_crs,
# ylim = lat_lim,
# xlim = lon_lim,
# expand = FALSE) +
# theme(
# panel.border = element_blank(),
# axis.text = element_blank(),
# axis.ticks = element_blank()
# )
latitude_graticules <- st_graticule(
x = bbox_graticules,
crs = st_crs(bbox_graticules),
datum = st_crs(bbox_graticules),
lon = c(20, 20.001),
lat = c(-60,-30,0,30,60),
ndiscr = 1e3,
margin = 0.001
)
latitude_graticules_trans <- st_transform(latitude_graticules, crs = target_crs)
latitude_labels <- data.frame(lat_label = c("60°N","30°N","Eq.","30°S","60°S"),
lat = c(60,30,0,-30,-60)-4, lon = c(35)-c(0,2,4,2,0))
latitude_labels <- st_as_sf(x = latitude_labels,
coords = c("lon", "lat"),
crs = "+proj=longlat")
latitude_labels_trans <- st_transform(latitude_labels, crs = target_crs)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col = "grey") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans) +
# geom_sf(data = latitude_graticules_trans,
# col = "grey60",
# linewidth = 0.2) +
# geom_sf_text(data = latitude_labels_trans,
# aes(label = lat_label),
# size = 3,
# col = "grey60")library(lubridate)
library(scales)
library(RColorBrewer)
library(ggtext)
library(ggnewscale)files <- list.files(here::here("data/"),
pattern = paste0(2023,"_map_monthly_anomaly.csv"),
full.names = TRUE)
pco2_product_coarse_monthly_regression <-
read_csv(files,
id = "product")
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly_regression %>%
mutate(product = str_extract(product, "OceanSODAv2|SOM-FFN|CMEMS|fCO2-Residual|ETHZ-CESM|FESOM-REcoM"))
all_product_list <- c("OceanSODAv2",
"SOM-FFN",
"fCO2-Residual",
"CMEMS",
"ETHZ-CESM",
"FESOM-REcoM")
pco2_product_coarse_monthly_regression <-
pco2_product_coarse_monthly_regression %>%
mutate(
product = factor(product, levels = all_product_list)
) %>%
filter(!is.na(name))
pco2_product_list <- c("OceanSODAv2", "SOM-FFN", "fCO2-Residual", "CMEMS")map <-
read_rds(here::here("data/map.rds"))
key_biomes <-
read_rds(here::here("data/key_biomes.rds"))
biome_mask <-
read_rds(here::here("data/biome_mask.rds"))
map +
geom_tile(data = biome_mask,
aes(fill = biome))
| Version | Author | Date |
|---|---|---|
| d6d6440 | jens-daniel-mueller | 2024-09-12 |
biome_mask <-
bind_rows(biome_mask,
biome_mask %>%
filter(!str_detect(biome, "SO-SPSS|SO-ICE|Arctic")) %>%
mutate(biome = "Global non-polar"))SOCAT_gridded_monthly <-
read_csv(
here::here(
"data/SOCATv2024_prelim/SOCATv2024_tracks_gridded_monthly.csv"
),
skip = 241
)
SOCAT_gridded_monthly <-
SOCAT_gridded_monthly %>%
mutate(year = year(DATE),
month = month(DATE)) %>%
select(year,
month,
lat = LAT,
lon = LON,
sfco2_obs = FCO2_AVE_WEIGHTED_YEAR) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
SOCAT_gridded_monthly <-
inner_join(
SOCAT_gridded_monthly,
biome_mask %>%
filter(!str_detect(biome, "SO-SPSS|SO-ICE|Arctic|Global")) %>%
select(lon, lat))
map +
geom_tile(data = SOCAT_gridded_monthly %>% filter(year == 2023),
aes(lon, lat, fill = sfco2_obs)) +
scale_fill_viridis_c() +
facet_wrap(~ month)
map +
geom_tile(data = SOCAT_gridded_monthly %>% count(lat, lon),
aes(lon, lat, fill = n)) +
scale_fill_scico(palette = "grayC", trans = "log10")
| Version | Author | Date |
|---|---|---|
| d6d6440 | jens-daniel-mueller | 2024-09-12 |
| 0b5ffe4 | jens-daniel-mueller | 2024-07-11 |
| d2a80a9 | jens-daniel-mueller | 2024-06-14 |
| f03b1d8 | jens-daniel-mueller | 2024-06-12 |
| de65385 | jens-daniel-mueller | 2024-06-12 |
| e5cb4e9 | jens-daniel-mueller | 2024-06-12 |
| 0a7394b | jens-daniel-mueller | 2024-06-11 |
| 6a16b4c | jens-daniel-mueller | 2024-06-05 |
SOCAT_gridded_monthly_raster <- rast(SOCAT_gridded_monthly %>%
count(lon, lat), crs = "+proj=longlat")
SOCAT_gridded_monthly_raster <- project(SOCAT_gridded_monthly_raster, target_crs, method = "near")
SOCAT_gridded_monthly_tibble <- SOCAT_gridded_monthly_raster %>%
as.data.frame(xy = TRUE, na.rm = FALSE) %>%
as_tibble() %>%
rename(lon = x, lat = y) %>%
drop_na()
SOCAT_gridded_monthly_raster_2023 <- rast(SOCAT_gridded_monthly %>%
filter(year == 2023) %>%
count(lon, lat), crs = "+proj=longlat")
SOCAT_gridded_monthly_raster_2023 <- project(SOCAT_gridded_monthly_raster_2023, target_crs, method = "near")
SOCAT_gridded_monthly_tibble_2023 <- SOCAT_gridded_monthly_raster_2023 %>%
as.data.frame(xy = TRUE, na.rm = FALSE) %>%
as_tibble() %>%
rename(lon = x, lat = y) %>%
drop_na()
bbox <- st_bbox(c(xmin = -180, xmax = 180, ymax = 76, ymin = -54), crs = st_crs(4326))
bbox <- st_as_sfc(bbox)
bbox_trans <- st_break_antimeridian(bbox, lon_0 = center)
bbox_graticules <- st_graticule(
x = bbox_trans,
crs = st_crs(bbox_trans),
datum = st_crs(bbox_trans),
lon = c(20, 20.001),
lat = c(-54,76),
ndiscr = 1e3,
margin = 0.001
)
bbox_graticules_trans <- st_transform(bbox_graticules, crs = target_crs)
rm(bbox, bbox_trans)
# ggplot() +
# geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
# geom_sf(data = coastline_trans) +
# geom_sf(data = bbox_graticules_trans)
lat_lim <- ext(bbox_graticules_trans)[c(3,4)]*1.002
lon_lim <- ext(bbox_graticules_trans)[c(1,2)]*1.005
stations <-
tibble(
lon = c(-64, -170, -12),
lat = c(32, 0, 30)
# label = c(1, 2, 3)
)
stations <-
stations %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
stations <-
stations %>% as.matrix()
# pts = matrix(1:10, , 2)
# st_multipoint(pts)
stations <- st_multipoint(stations)
stations <- st_sfc(stations, crs = "+proj=longlat")
stations_trans <- st_transform(stations, crs = target_crs)
stations_tibble <- do.call(rbind, st_geometry(stations_trans)) %>%
as_tibble() %>% setNames(c("lon","lat"))
stations_tibble <-
bind_cols(stations_tibble, label = c(1,2,3))
# Bats: 31.5 - 32.5°N / 63.5 – 64.5°W
# Mid Pacific: 0.5°S-0.5°N/170.5°W
# VOS-Line: 29°N/13°W , 30°N/12°W , 31°N/11°W
ggplot() +
geom_tile(data = SOCAT_gridded_monthly_tibble, aes(x = lon, y = lat, fill = n)) +
scale_fill_scico(palette = "grayC",
begin = 0.2,
trans = "log10",
"Months with\nobservations\n1990-2022") +
new_scale_fill() +
geom_tile(data = SOCAT_gridded_monthly_tibble_2023, aes(x = lon, y = lat, fill = n)) +
scale_fill_scico(palette = "buda",
trans = "log10",
"Months with\nobservations\n2023") +
geom_sf(data = worldmap_trans,
fill = "grey90",
col = "grey90") +
geom_sf(data = coastline_trans, linewidth = 0.3) +
geom_sf(data = bbox_graticules_trans, linewidth = 0.5) +
geom_label(data = stations_tibble, aes(x = lon, y = lat, label = label)) +
coord_sf(
crs = target_crs,
ylim = lat_lim,
xlim = lon_lim,
expand = FALSE
) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.border = element_rect(colour = "transparent"),
strip.background = element_blank(),
legend.position = "top"
)
| Version | Author | Date |
|---|---|---|
| d6d6440 | jens-daniel-mueller | 2024-09-12 |
| 0b5ffe4 | jens-daniel-mueller | 2024-07-11 |
| d2a80a9 | jens-daniel-mueller | 2024-06-14 |
| f03b1d8 | jens-daniel-mueller | 2024-06-12 |
| de65385 | jens-daniel-mueller | 2024-06-12 |
| e5cb4e9 | jens-daniel-mueller | 2024-06-12 |
| 0a7394b | jens-daniel-mueller | 2024-06-11 |
| 6a16b4c | jens-daniel-mueller | 2024-06-05 |
ggsave(width = 8,
height = 4.5,
dpi = 600,
filename = "../output/SOCAT_observations_map.jpg")
biome_mask_robin_raster <- rast(
biome_mask %>%
filter(!str_detect(biome, "SO-SPSS|SO-ICE|Arctic|Global")) %>%
mutate(dist = 1) %>%
mutate(biome = case_when(
str_detect(biome, "SPSS") ~ "SPSS",
str_detect(biome, "STSS") ~ "STSS",
str_detect(biome, "STPS") ~ "STPS",
TRUE ~ "Tropics"
)) %>%
pivot_wider(names_from = biome, values_from = dist),
crs = "+proj=longlat"
)
biome_mask_robin_raster <- project(biome_mask_robin_raster, target_crs, method = "near")
biome_mask_robin_tibble <- biome_mask_robin_raster %>%
as.data.frame(xy = TRUE, na.rm = FALSE) %>%
as_tibble() %>%
rename(lon = x, lat = y) %>%
pivot_longer(cols = -c("lon", "lat"),
names_to = "biome",
values_to = "dist") %>%
drop_na() %>%
mutate(biome = as.character(biome))
ggplot() +
geom_raster(
data = biome_mask_robin_tibble %>%
mutate(biome = fct_inorder(biome)),
aes(
x = lon,
y = lat,
fill = biome
)
) +
geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
geom_sf(data = coastline_trans, linewidth = 0.3) +
geom_sf(data = bbox_graticules_trans, linewidth = 0.5) +
coord_sf(
crs = target_crs,
ylim = lat_lim,
xlim = lon_lim,
expand = FALSE
) +
scale_fill_viridis_d(option = "magma", begin = 0.1, end = 0.9) +
guides(fill = guide_legend(byrow = TRUE)) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.border = element_rect(colour = "transparent"),
strip.background = element_blank(),
legend.position = "top",
legend.title = element_blank()
)
ggsave(width = 8,
height = 4.5,
dpi = 600,
filename = "../output/biome_map.jpg")
# SOCAT_gridded_monthly <-
# m_grid_horizontal_coarse(SOCAT_gridded_monthly)
#
# SOCAT_gridded_monthly <-
# SOCAT_gridded_monthly %>%
# mutate(area = earth_surf(lat, lon)) %>%
# group_by(year, month, lon_grid, lat_grid) %>%
# summarise(across(sfco2_obs,
# ~ weighted.mean(., area, na.rm = TRUE))) %>%
# ungroup() %>%
# rename(lon = lon_grid, lat = lat_grid)
#
# map +
# geom_tile(data = SOCAT_gridded_monthly %>% filter(year == 2023),
# aes(lon, lat, fill = sfco2_obs)) +
# scale_fill_viridis_c() +
# facet_wrap(~ month)
SOCAT_pCO2_product <-
left_join(
pco2_product_coarse_monthly_regression %>%
filter(name == "sfco2"),
SOCAT_gridded_monthly
)
map +
geom_tile(data = SOCAT_pCO2_product %>% filter(year == 2023),
aes(lon, lat, fill = sfco2_obs)) +
scale_fill_viridis_c() +
facet_wrap(~ month)
| Version | Author | Date |
|---|---|---|
| d6d6440 | jens-daniel-mueller | 2024-09-12 |
SOCAT_pCO2_product <-
SOCAT_pCO2_product %>%
group_by(lon, lat, month, year) %>%
mutate(n = n()) %>%
ungroup() %>%
filter(n == 6) %>%
select(-n)
SOCAT_pCO2_product <-
SOCAT_pCO2_product %>%
mutate(sfco2_bias = value - sfco2_obs)
map +
geom_tile(
data = SOCAT_pCO2_product %>% filter(year == 2023,
product %in% pco2_product_list) %>%
group_by(lon, lat, month) %>%
summarise(sfco2_bias = mean(sfco2_bias)) %>%
ungroup(),
aes(lon, lat, fill = sfco2_bias)
) +
scale_fill_gradientn(
colours = rev(brewer.pal(7, "Spectral")),
rescaler = ~ scales::rescale_mid(.x, mid = 0),
name = "fCO<sub>2,ocean</sub> bias<br>pred. - obs.<br>(µatm)",
# limits = c(
# quantile(SOCAT_pCO2_product$sfco2_bias, .01),
# quantile(SOCAT_pCO2_product$sfco2_bias, .99)
# ),
oob = squish
) +
guides(
fill = guide_colorbar(
barheight = unit(0.3, "cm"),
barwidth = unit(6, "cm"),
ticks = TRUE,
ticks.colour = "grey20",
frame.colour = "grey20",
label.position = "top",
direction = "horizontal"
)
) +
theme(legend.title = element_markdown(),
legend.position = "top") +
facet_wrap(~ month)
ggsave(width = 7,
height = 4,
dpi = 600,
filename = "../output/SOCAT_fco2_bias_map_2023_ensemble_mean.jpg")
map +
geom_tile(data = SOCAT_pCO2_product %>% filter(year == 2023),
aes(lon, lat, fill = sfco2_bias)) +
scale_fill_gradientn(
colours = rev(brewer.pal(7, "Spectral")),
rescaler = ~ scales::rescale_mid(.x, mid = 0),
name = "fCO<sub>2,ocean</sub> bias<br>pred. - obs.<br>(µatm)",
# limits = c(quantile(SOCAT_pCO2_product$sfco2_bias, .01),
# quantile(SOCAT_pCO2_product$sfco2_bias, .99)),
oob = squish
) +
guides(
fill = guide_colorbar(
barheight = unit(0.3, "cm"),
barwidth = unit(6, "cm"),
ticks = TRUE,
ticks.colour = "grey20",
frame.colour = "grey20",
label.position = "top",
direction = "horizontal"
)
) +
theme(legend.title = element_markdown(),
legend.position = "top") +
facet_grid(month ~ product)
ggsave(width = 8,
height = 7,
dpi = 600,
filename = "../output/SOCAT_fco2_bias_map_2023.jpg")SOCAT_pCO2_product_biome <-
inner_join(SOCAT_pCO2_product,
biome_mask,
relationship = "many-to-many")
SOCAT_pCO2_product_biome <-
SOCAT_pCO2_product_biome %>%
group_by(year, month, product, biome) %>%
summarise(
sfco2_bias = mean(sfco2_bias, na.rm = TRUE),
sfco2_resid = mean(resid, na.rm = TRUE)
) %>%
ungroup()
SOCAT_pCO2_product_biome %>%
filter(biome %in% c("Global", key_biomes),
year >= 2020,
product %in% pco2_product_list) %>%
group_by(biome, year, month) %>%
summarise(sfco2_bias = mean(sfco2_bias, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(year + month / 12, sfco2_bias))+
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(year != 2023),
aes(fill = "2020-2022"),
size = 1) +
scale_fill_manual(values = "grey70",name = "") +
new_scale_fill() +
geom_point(data = . %>% filter(year == 2023),
aes(fill = month),
shape = 21) +
scale_fill_scico(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
coord_cartesian(ylim = c(-50,50)) +
scale_x_continuous(breaks = seq(1980, 2022, 2)) +
labs(y = "fCO<sub>2,ocean</sub> bias<br>pred. - obs.<br>(µatm)",) +
theme(axis.title.x = element_blank(),
axis.title.y = element_markdown()) +
facet_wrap(~ biome)
| Version | Author | Date |
|---|---|---|
| d6d6440 | jens-daniel-mueller | 2024-09-12 |
| 0b5ffe4 | jens-daniel-mueller | 2024-07-11 |
| d2a80a9 | jens-daniel-mueller | 2024-06-14 |
| f03b1d8 | jens-daniel-mueller | 2024-06-12 |
| de65385 | jens-daniel-mueller | 2024-06-12 |
| e5cb4e9 | jens-daniel-mueller | 2024-06-12 |
| 0a7394b | jens-daniel-mueller | 2024-06-11 |
| 6a16b4c | jens-daniel-mueller | 2024-06-05 |
ggsave(width = 7,
height = 4,
dpi = 600,
filename = "../output/SOCAT_fco2_bias_timeseries_2018_2023_ensemble_mean.jpg")
SOCAT_pCO2_product_biome %>%
filter(biome %in% c("Global", key_biomes),
year >= 2020) %>%
ggplot(aes(year + month/12, sfco2_bias)) +
geom_hline(yintercept = 0) +
geom_path() +
geom_point(data = . %>% filter(year != 2023),
aes(fill = "2020-2022"),
size = 1) +
scale_fill_manual(values = "grey70",name = "") +
new_scale_fill() +
geom_point(data = . %>% filter(year == 2023),
aes(fill = month),
shape = 21) +
scale_fill_scico(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
coord_cartesian(ylim = c(-50,50)) +
scale_x_continuous(breaks = seq(1980, 2022, 2)) +
labs(y = "fCO<sub>2,ocean</sub> bias<br>pred. - obs.<br>(µatm)") +
theme(axis.title.x = element_blank(),
axis.title.y = element_markdown()) +
facet_grid(biome ~ product)
ggsave(width = 10,
height = 7,
dpi = 600,
filename = "../output/SOCAT_fco2_bias_timeseries_2018_2023.jpg")SOCAT_pCO2_product_biome %>%
mutate(month = as.factor(month)) %>%
filter(biome %in% c("Global", key_biomes),
product %in% pco2_product_list) %>%
group_by(biome, year, month) %>%
summarise(sfco2_bias = mean(sfco2_bias, na.rm = TRUE),
sfco2_resid = mean(sfco2_resid, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(sfco2_bias, sfco2_resid)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_point(data = . %>% filter(year != 2023),
alpha = 0.1) +
geom_path(data = . %>% filter(year == 2023)) +
geom_point(
data = . %>% filter(year == 2023),
aes(fill = month),
shape = 21,
size = 2
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
coord_fixed(ylim = c(-50, 50),
xlim = c(-50, 50)) +
labs(x = "fCO<sub>2,ocean</sub> bias<br>pred. - obs.<br>(µatm)",
y = "fCO<sub>2,ocean</sub><br>anomaly<br>(µatm)") +
facet_wrap(~ biome) +
theme(
axis.title.x = element_markdown(),
axis.title.y = element_markdown()
)
| Version | Author | Date |
|---|---|---|
| d6d6440 | jens-daniel-mueller | 2024-09-12 |
| 0b5ffe4 | jens-daniel-mueller | 2024-07-11 |
| d2a80a9 | jens-daniel-mueller | 2024-06-14 |
| f03b1d8 | jens-daniel-mueller | 2024-06-12 |
| de65385 | jens-daniel-mueller | 2024-06-12 |
| e5cb4e9 | jens-daniel-mueller | 2024-06-12 |
| 0a7394b | jens-daniel-mueller | 2024-06-11 |
| 6a16b4c | jens-daniel-mueller | 2024-06-05 |
ggsave(width = 7,
height = 4,
dpi = 600,
filename = "../output/SOCAT_fco2_anomaly_vs_bias_ensemble_mean.jpg")
SOCAT_pCO2_product_biome %>%
mutate(month = as.factor(month)) %>%
filter(biome %in% c("Global", key_biomes)) %>%
ggplot(aes(sfco2_bias, sfco2_resid)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_point(data = . %>% filter(year != 2023),
alpha = 0.1) +
geom_path(data = . %>% filter(year == 2023)) +
geom_point(
data = . %>% filter(year == 2023),
aes(fill = month),
shape = 21,
size = 2
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
coord_fixed(ylim = c(-50, 50),
xlim = c(-50, 50)) +
labs(x = "fCO<sub>2,ocean</sub> bias<br>pred. - obs.<br>(µatm)",
y = "fCO<sub>2,ocean</sub><br>anomaly<br>(µatm)") +
facet_grid(biome ~ product) +
theme(
axis.title.x = element_markdown(),
axis.title.y = element_markdown()
)
| Version | Author | Date |
|---|---|---|
| d6d6440 | jens-daniel-mueller | 2024-09-12 |
| 756fa19 | jens-daniel-mueller | 2024-07-20 |
| 0b5ffe4 | jens-daniel-mueller | 2024-07-11 |
| d2a80a9 | jens-daniel-mueller | 2024-06-14 |
| f03b1d8 | jens-daniel-mueller | 2024-06-12 |
| de65385 | jens-daniel-mueller | 2024-06-12 |
| e5cb4e9 | jens-daniel-mueller | 2024-06-12 |
| 0a7394b | jens-daniel-mueller | 2024-06-11 |
| 6a16b4c | jens-daniel-mueller | 2024-06-05 |
ggsave(width = 10,
height = 7,
dpi = 600,
filename = "../output/SOCAT_fco2_anomaly_vs_bias.jpg")
SOCAT_pCO2_product_biome %>%
mutate(month = as.factor(month)) %>%
filter(biome %in% c("Global", key_biomes)) %>%
ggplot(aes(sfco2_bias, sfco2_resid)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_point(data = . %>% filter(year != 2023),
alpha = 0.1) +
geom_path(data = . %>% filter(year == 2023)) +
geom_point(
data = . %>% filter(year == 2023),
aes(fill = month),
shape = 21,
size = 2
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
coord_fixed(ylim = c(-20, 20),
xlim = c(-20, 20)) +
labs(x = "fCO<sub>2,ocean</sub> bias<br>pred. - obs.<br>(µatm)",
y = "fCO<sub>2,ocean</sub><br>anomaly<br>(µatm)") +
facet_grid(biome ~ product) +
theme(
axis.title.x = element_markdown(),
axis.title.y = element_markdown()
)
ggsave(width = 10,
height = 7,
dpi = 600,
filename = "../output/SOCAT_fco2_anomaly_vs_bias_zoom.jpg")SOCAT_pCO2_product_biome %>%
filter(biome %in% c("Global", key_biomes)) %>%
mutate(month = as.factor(month)) %>%
ggplot(aes(sfco2_bias, product)) +
geom_vline(xintercept = 0) +
geom_jitter(data = . %>% filter(year != 2023),
alpha = 0.1) +
geom_jitter(
data = . %>% filter(year == 2023),
aes(fill = month),
shape = 21,
size = 2
) +
scale_fill_scico_d(
palette = "buda",
guide = guide_legend(reverse = TRUE,
order = 1),
name = paste("Month\nof", 2023)
) +
scale_y_discrete(limits = rev) +
coord_cartesian(xlim = c(-50, 50)) +
labs(x = "fCO<sub>2,ocean</sub><br>bias<br>(µatm)") +
facet_wrap( ~ biome) +
theme(
axis.title.x = element_markdown(),
axis.title.y = element_markdown()
)
| Version | Author | Date |
|---|---|---|
| d6d6440 | jens-daniel-mueller | 2024-09-12 |
| 756fa19 | jens-daniel-mueller | 2024-07-20 |
| 0b5ffe4 | jens-daniel-mueller | 2024-07-11 |
| d2a80a9 | jens-daniel-mueller | 2024-06-14 |
| f03b1d8 | jens-daniel-mueller | 2024-06-12 |
| de65385 | jens-daniel-mueller | 2024-06-12 |
| e5cb4e9 | jens-daniel-mueller | 2024-06-12 |
| 0a7394b | jens-daniel-mueller | 2024-06-11 |
| 6a16b4c | jens-daniel-mueller | 2024-06-05 |
ggsave(width = 10,
height = 10,
dpi = 600,
filename = "../output/SOCAT_fco2_anomaly.jpg")
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5
Matrix products: default
BLAS: /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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] ggnewscale_0.4.8 ggtext_0.1.2 RColorBrewer_1.1-3
[4] scales_1.2.1 lubridate_1.9.0 timechange_0.1.1
[7] terra_1.7-65 sf_1.0-9 rnaturalearth_0.1.0
[10] geomtextpath_0.1.1 colorspace_2.0-3 marelac_2.1.10
[13] shape_1.4.6 ggforce_0.4.1 metR_0.13.0
[16] scico_1.3.1 patchwork_1.1.2 collapse_1.8.9
[19] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3
[22] purrr_1.0.2 readr_2.1.3 tidyr_1.3.0
[25] tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2
[28] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 ellipsis_0.3.2 class_7.3-20
[4] rprojroot_2.0.3 markdown_1.4 fs_1.5.2
[7] gridtext_0.1.5 rstudioapi_0.15.0 proxy_0.4-27
[10] farver_2.1.1 bit64_4.0.5 fansi_1.0.3
[13] xml2_1.3.3 codetools_0.2-18 cachem_1.0.6
[16] knitr_1.41 polyclip_1.10-4 jsonlite_1.8.3
[19] gsw_1.1-1 broom_1.0.5 dbplyr_2.2.1
[22] compiler_4.2.2 httr_1.4.4 backports_1.4.1
[25] assertthat_0.2.1 fastmap_1.1.0 gargle_1.2.1
[28] cli_3.6.1 later_1.3.0 tweenr_2.0.2
[31] htmltools_0.5.3 tools_4.2.2 rnaturalearthdata_0.1.0
[34] gtable_0.3.1 glue_1.6.2 Rcpp_1.0.11
[37] cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.6.4
[40] xfun_0.35 ps_1.7.2 rvest_1.0.3
[43] lifecycle_1.0.3 googlesheets4_1.0.1 oce_1.7-10
[46] getPass_0.2-2 MASS_7.3-58.1 vroom_1.6.0
[49] ragg_1.2.4 hms_1.1.2 promises_1.2.0.1
[52] parallel_4.2.2 yaml_2.3.6 memoise_2.0.1
[55] sass_0.4.4 stringi_1.7.8 highr_0.9
[58] e1071_1.7-12 checkmate_2.1.0 commonmark_1.8.1
[61] rlang_1.1.1 pkgconfig_2.0.3 systemfonts_1.0.4
[64] evaluate_0.18 lattice_0.20-45 SolveSAPHE_2.1.0
[67] labeling_0.4.2 bit_4.0.5 processx_3.8.0
[70] tidyselect_1.2.0 here_1.0.1 seacarb_3.3.1
[73] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[76] DBI_1.1.3 pillar_1.9.0 haven_2.5.1
[79] whisker_0.4 withr_2.5.0 units_0.8-0
[82] sp_1.5-1 modelr_0.1.10 crayon_1.5.2
[85] KernSmooth_2.23-20 utf8_1.2.2 tzdb_0.3.0
[88] rmarkdown_2.18 grid_4.2.2 readxl_1.4.1
[91] data.table_1.14.6 callr_3.7.3 git2r_0.30.1
[94] reprex_2.0.2 digest_0.6.30 classInt_0.4-8
[97] httpuv_1.6.6 textshaping_0.3.6 munsell_0.5.0
[100] viridisLite_0.4.1 bslib_0.4.1