GLODAPv2_2021 halogenated tracers
Jens Daniel Müller
06 December, 2021
Last updated: 2021-12-06
Checks: 7 0
Knit directory: emlr_obs_v_XXX/
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1 Version ID
The results displayed on this site correspond to the Version_ID:
params$Version_ID[1] "v_XXX"2 Read files
Main data source for this project is the preprocessed version of GLODAPv2:
params_local$GLODAPv2_version[1] "2021"GLODAP <-
read_csv(
paste0(
path_preprocessing,
"GLODAPv2.",
params_local$GLODAPv2_version,
"_preprocessed_tracer.csv"),
guess_max = 1e5
)pCFC_12_3d <-
read_csv(paste(path_preprocessing,
"K04_pCFC_12_3d.csv", sep = ""))dcant_3d <-
read_csv(paste(path_version_data,
"dcant_3d.csv",
sep = ""))tref <-
read_csv(paste(path_version_data,
"tref.csv",
sep = ""))3 Data preparation
3.1 Filter eras
# create labels for era
era_labels <- bind_cols(
start = params_local$era_start,
end = params_local$era_end)
era_labels <- era_labels %>%
mutate(start = if_else(start == -Inf, max(GLODAP$year), start),
end = if_else(end == Inf, max(GLODAP$year), end),
era = as.factor(paste(start, end, sep = "-")))
# filter GLODAP data within eras
GLODAP <- expand_grid(
GLODAP,
era_labels
)
# select data within each era
GLODAP <- GLODAP %>%
filter(year >= start & year <= end)
GLODAP <- GLODAP %>%
select(-c(start, end))3.2 Spatial boundaries
3.2.1 Basin mask
The basin mask from the World Ocean Atlas was used. For details consult the data base subsection for WOA18 data.
Please note that some GLODAP observations were made outside the WOA18 basin mask (i.e. in marginal seas) and will be removed for further analysis.
# use only data inside basinmask
GLODAP <- inner_join(GLODAP, basinmask)3.3 Flags and NA
Only rows (samples) for which all relevant parameters are available were selected, ie NA’s were removed.
According to Olsen et al (2020), flags within the merged master file identify:
f:
- 2: Acceptable
- 0: Interpolated (nutrients/oxygen) or calculated (CO[2] variables)
- 9: Data not used (so, only NA data should have this flag)
qc:
- 1: Adjusted or unadjusted data
- 0: Data appear of good quality but have not been subjected to full secondary QC
- data with poor or uncertain quality are excluded.
Following flagging criteria were taken into account:
- flag_f: 2, 9, 0
- flag_qc: 1, 0
The cleaning process was performed successively and the maps below represent the data coverage at various cleaning levels.
Summary statistics were calculated during cleaning process.
3.3.1 NA
GLODAP_NA <- GLODAP %>%
select(lon, lat, era, pcfc11, pcfc12, pcfc113, pccl4, psf6) %>%
pivot_longer(pcfc11:psf6,
names_to = "parameter",
values_to = "value") %>%
mutate(NA_flag = if_else(is.na(value), "NA", "available"),
parameter = fct_inorder(as.factor(parameter)))
GLODAP_NA_stats <- GLODAP_NA %>%
count(era, parameter, NA_flag)
GLODAP_NA <- GLODAP_NA %>%
count(lat, lon, era, parameter, NA_flag)3.3.1.1 Maps
GLODAP_NA %>%
group_split(NA_flag) %>%
# head(1) %>%
map(
~ map +
geom_raster(data = .x,
aes(lon, lat, fill = n)) +
scale_fill_viridis_c(
option = "magma",
direction = -1,
trans = "log10"
) +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = paste("Flag:", unique(.x$NA_flag))) +
facet_grid(parameter ~ era)
)[[1]]
[[2]]
rm(GLODAP_NA) 3.3.1.2 Stats
GLODAP_NA_stats %>%
ggplot(aes(parameter, n, fill = NA_flag)) +
coord_flip() +
scale_x_discrete(limits = rev) +
geom_col() +
facet_grid(era~.) +
scale_fill_brewer(palette = "Dark2")
rm(GLODAP_NA_stats)3.3.2 f flag
GLODAP_f_flags <- GLODAP %>%
select(lon, lat, era, ends_with("f")) %>%
pivot_longer(cfc11f:sf6f,
names_to = "parameter",
values_to = "value") %>%
mutate(parameter = fct_inorder(as.factor(parameter)))
GLODAP_f_flags_stats <- GLODAP_f_flags %>%
count(era, parameter, value)
GLODAP_f_flags <- GLODAP_f_flags %>%
count(lat, lon, era, parameter, value)3.3.2.1 Maps
GLODAP_f_flags %>%
group_split(value) %>%
# head(1) %>%
map(
~ map +
geom_raster(data = .x,
aes(lon, lat, fill = n)) +
scale_fill_viridis_c(
option = "magma",
direction = -1,
trans = "log10"
) +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = paste("f flag:", unique(.x$value))) +
facet_grid(parameter ~ era)
)[[1]]
[[2]]
rm(GLODAP_f_flags)3.3.2.2 Stats
GLODAP_f_flags_stats %>%
mutate(value = as.factor(value)) %>%
ggplot(aes(parameter, n, fill = value)) +
coord_flip() +
scale_x_discrete(limits = rev) +
geom_col() +
facet_grid(era~.) +
scale_fill_brewer(palette = "Dark2")
rm(GLODAP_f_flags_stats)3.3.3 qc flag
GLODAP_qc_flags <- GLODAP %>%
select(lon, lat, era, ends_with("qc")) %>%
pivot_longer(cfc11qc:ccl4qc,
names_to = "parameter",
values_to = "value") %>%
mutate(parameter = fct_inorder(as.factor(parameter))) %>%
count(lat, lon, era, parameter, value)
GLODAP_qc_flags_stats <- GLODAP_qc_flags %>%
count(era, parameter, value)
GLODAP_qc_flags <- GLODAP_qc_flags %>%
count(lat, lon, era, parameter, value)3.3.3.1 Maps
GLODAP_qc_flags %>%
group_split(value) %>%
# head(1) %>%
map(
~ map +
geom_raster(data = .x,
aes(lon, lat, fill = n)) +
scale_fill_viridis_c(
option = "magma",
direction = -1,
trans = "log10"
) +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = paste("qc flag:", unique(.x$value))) +
facet_grid(parameter ~ era)
)[[1]]
[[2]]
rm(GLODAP_qc_flags)3.3.3.2 Stats
GLODAP_qc_flags_stats %>%
mutate(value = as.factor(value)) %>%
ggplot(aes(parameter, n, fill = value)) +
coord_flip() +
scale_x_discrete(limits = rev) +
geom_col() +
facet_grid(era~.) +
scale_fill_brewer(palette = "Dark2")
rm(GLODAP_qc_flags_stats)3.3.4 Apply filter
GLODAP <- GLODAP %>%
filter(
if_all(
c(tco2, talk, params_local$MLR_predictors, depth, gamma),
~ !is.na(.)
),
if_all(ends_with("f"), ~ . %in% params_local$flag_f),
if_all(ends_with("qc"), ~ . %in% params_local$flag_qc)
)4 Indian Ocean
exist_IO_1990 <- GLODAP %>%
filter(between(year, 1989, 1999) &
basin_AIP == "Indian") %>%
nrow() > 04.1 NS cruise
4.1.1 Absolute
GLODAP_grid_Indian <- GLODAP %>%
filter(basin_AIP == "Indian",
lon > 70,
lon < 100,
cruise %in% c(249, 250, 352, 353)) %>%
mutate(cruise = as.factor(cruise)) %>%
distinct(era, lon, lat, cruise, year = as.factor(year(date)))
map +
geom_tile(data = GLODAP_grid_Indian,
aes(lon, lat, fill = year)) +
facet_grid(era ~ .)
IO_NS <- GLODAP %>%
filter(cruise %in% c(249, 250, 352, 353),
!is.na(pcfc12))
IO_NS %>%
ggplot(aes(pcfc12)) +
geom_histogram() +
facet_grid(era ~ .)
IO_NS %>%
filter(pcfc12 < 2) %>%
ggplot(aes(pcfc12)) +
geom_histogram() +
facet_grid(era ~ .)
IO_NS %>%
ggplot(aes(lat , depth, col = pcfc12)) +
geom_point() +
scale_color_viridis_c(trans = "pseudo_log",
breaks = c(0,10,100)) +
scale_y_reverse() +
facet_grid(era ~ .)
IO_NS_grid <- IO_NS %>%
select(lat, lon, depth, era, basin_AIP, pcfc12) %>%
group_by(era) %>%
nest() %>%
mutate(zonal = map(.x = data, ~m_zonal_mean_sd_bottle(.x))) %>%
select(-data) %>%
unnest(zonal)
IO_NS_grid %>%
ggplot(aes(lat , depth, fill = pcfc12_mean)) +
geom_raster() +
scale_fill_viridis_c(trans = "pseudo_log",
breaks = c(0,10,100)) +
scale_y_reverse() +
coord_cartesian(expand = 0) +
facet_grid(era ~ .)4.1.2 Change
IO_NS_grid_offset <- IO_NS_grid %>%
select(-pcfc12_sd) %>%
pivot_wider(names_from = era,
values_from = pcfc12_mean) %>%
mutate(delta_pcfc12_mean := !!sym(tref$era[2]) - !!sym(tref$era[1]))
IO_NS_grid_offset %>%
ggplot(aes(lat , depth, fill = delta_pcfc12_mean)) +
geom_raster() +
scale_fill_divergent(
mid = "grey80",
na.value = "black",
trans = "pseudo_log",
breaks = c(-100, -10, 0, 10, 100)
) +
scale_y_reverse() +
coord_cartesian(expand = 0)
IO_NS_grid_offset %>%
mutate(delta_pcfc12_mean = cut(delta_pcfc12_mean, c(-Inf, 2, 5, 20, Inf))) %>%
ggplot(aes(lat , depth, fill = delta_pcfc12_mean)) +
geom_raster() +
scale_fill_viridis_d(na.value = "grey") +
scale_y_reverse() +
coord_cartesian(expand = 0)
rm(IO_NS, IO_NS_grid, IO_NS_grid_offset)4.2 EW cruise
4.2.1 Absolute
GLODAP_grid_Indian <- GLODAP %>%
filter(basin_AIP == "Indian",
lat > -25,
lat < -15,
cruise %in% c(252, 488)
) %>%
mutate(cruise = as.factor(cruise)) %>%
distinct(era, lon, lat, cruise, year = as.factor(year(date)))
map +
geom_tile(data = GLODAP_grid_Indian,
aes(lon, lat, fill = year)) +
facet_grid(era ~ .)
IO_EW <- GLODAP %>%
filter(cruise %in% c(252, 488),
!is.na(pcfc12))
IO_EW %>%
ggplot(aes(pcfc12)) +
geom_histogram() +
facet_grid(era ~ .)
IO_EW %>%
filter(pcfc12 < 2) %>%
ggplot(aes(pcfc12)) +
geom_histogram() +
facet_grid(era ~ .)
IO_EW %>%
ggplot(aes(lon , depth, col = pcfc12)) +
geom_point() +
scale_fill_viridis_c(trans = "pseudo_log",
breaks = c(0,10,100)) +
scale_y_reverse() +
facet_grid(era ~ .)
IO_EW_grid <- IO_EW %>%
mutate(depth = cut(depth,
seq(0,1e4,200),
seq(100,1e4,200)),
depth = as.numeric(as.character(depth)),
lon_grid = cut(lon,
seq(-100,200,2),
seq(-99,200,2)),
lon_grid = as.numeric(as.character(lon_grid))) %>%
group_by(lon_grid, depth, era) %>%
summarise(pcfc12 = mean(pcfc12, na.rm = TRUE)) %>%
ungroup()
IO_EW_grid %>%
ggplot(aes(lon_grid , depth, fill = pcfc12)) +
geom_tile() +
scale_fill_viridis_c(trans = "pseudo_log",
breaks = c(0,10,100)) +
scale_y_reverse() +
coord_cartesian(expand = 0) +
facet_grid(era ~ .)4.2.2 Change
IO_EW_grid_offset <- IO_EW_grid %>%
pivot_wider(names_from = era,
values_from = pcfc12) %>%
mutate(delta_pcfc12_mean := !!sym(tref$era[2]) - !!sym(tref$era[1]))
IO_EW_grid_offset %>%
ggplot(aes(lon_grid , depth, fill = delta_pcfc12_mean)) +
geom_raster() +
scale_fill_divergent(
mid = "grey80",
na.value = "black",
trans = "pseudo_log",
breaks = c(-100, -10, 0, 10, 100)
) +
scale_y_reverse() +
coord_cartesian(expand = 0)
IO_EW_grid_offset %>%
mutate(delta_pcfc12_mean = cut(delta_pcfc12_mean, c(-Inf, 2, 5, 20, Inf))) %>%
ggplot(aes(lon_grid , depth, fill = delta_pcfc12_mean)) +
geom_raster() +
scale_fill_viridis_d(na.value = "grey") +
scale_y_reverse() +
coord_cartesian(expand = 0)
rm(IO_EW, IO_EW_grid, IO_EW_grid_offset, GLODAP_grid_Indian)5 Zonal sections
5.1 Absolute
zonal_sections <- GLODAP %>%
select(lat, lon, depth, era, basin_AIP, pcfc12) %>%
group_by(era) %>%
nest() %>%
mutate(zonal = map(.x = data, ~m_zonal_mean_sd_bottle(.x))) %>%
select(-data) %>%
unnest(zonal)
zonal_sections %>%
ggplot(aes(lat , depth, fill = pcfc12_mean)) +
geom_tile() +
scale_fill_viridis_c(trans = "pseudo_log",
breaks = c(0,10,100)) +
scale_y_reverse() +
labs(x = "Latitude (°N)", y = "Depth (m)") +
coord_fixed(ratio = 1e-2, expand = 0) +
facet_grid(basin_AIP ~ era) +
theme(legend.position = "left")
5.2 Change
zonal_sections_offset <- zonal_sections %>%
select(-pcfc12_sd) %>%
pivot_wider(names_from = era,
values_from = pcfc12_mean) %>%
mutate(delta_pcfc12_mean := !!sym(tref$era[2]) - !!sym(tref$era[1]))
zonal_sections_offset %>%
ggplot(aes(delta_pcfc12_mean)) +
geom_histogram() +
scale_y_log10() +
coord_cartesian(expand = 0) +
facet_grid(basin_AIP ~ .)
zonal_sections_offset %>%
ggplot(aes(delta_pcfc12_mean)) +
geom_histogram() +
coord_cartesian(expand = 0) +
scale_x_continuous(trans = "pseudo_log",
breaks = c(-100, -10, 0, 10, 100)) +
scale_y_log10() +
facet_grid(basin_AIP ~ .)
zonal_sections_offset %>%
ggplot(aes(lat , depth, fill = delta_pcfc12_mean)) +
geom_raster() +
scale_fill_divergent(
mid = "grey80",
na.value = "black",
trans = "pseudo_log",
breaks = c(-100, -10, 0, 10, 100)
) +
labs(x = "Latitude (°N)", y = "Depth (m)") +
scale_y_reverse() +
coord_fixed(ratio = 1e-2, expand = 0) +
facet_grid(basin_AIP ~ .) +
theme(axis.text.y = element_blank(),
axis.title.y = element_blank())
zonal_sections_offset %>%
mutate(delta_pcfc12_mean = cut(
delta_pcfc12_mean,
c(-Inf, 0.2, 1, 2, 5, 20, Inf))) %>%
ggplot(aes(lat , depth, fill = delta_pcfc12_mean)) +
geom_raster() +
scale_fill_viridis_d(na.value = "grey") +
scale_y_reverse() +
facet_grid(basin_AIP ~ .) +
coord_fixed(ratio = 1e-2, expand = 0)
6 Interior relation to dCant
6.1 Distributions
CFC_dcant <- inner_join(
dcant_3d %>%
filter(data_source == "obs") %>%
select(lon, lat, depth, basin_AIP, dcant, gamma, gamma_slab),
pCFC_12_3d
)
CFC_dcant <- CFC_dcant %>%
mutate(depth_grid = cut(depth, seq(0, 1e4, 1000), right = FALSE))CFC_dcant %>%
ggplot(aes(pCFC_12, dcant)) +
geom_hline(yintercept = 0) +
geom_bin2d() +
geom_smooth(col = "red", se = FALSE) +
scale_fill_viridis_c(trans = "log10") +
facet_grid(gamma_slab ~ basin_AIP,
scales = "free_y") +
coord_cartesian(ylim = c(-5,15))
| Version | Author | Date |
|---|---|---|
| 3c60929 | jens-daniel-mueller | 2021-12-06 |
| 3f76ee3 | jens-daniel-mueller | 2021-12-06 |
| 2ca1313 | jens-daniel-mueller | 2021-12-05 |
| 605b380 | jens-daniel-mueller | 2021-12-02 |
| a83a09b | jens-daniel-mueller | 2021-11-29 |
| 72c1041 | jens-daniel-mueller | 2021-11-23 |
| 3eba8ac | jens-daniel-mueller | 2021-11-23 |
| ec18ee5 | jens-daniel-mueller | 2021-11-23 |
| 59cdf58 | jens-daniel-mueller | 2021-11-22 |
| 3ae2dd1 | jens-daniel-mueller | 2021-11-21 |
| 5b46219 | jens-daniel-mueller | 2021-11-21 |
| 5016fc9 | jens-daniel-mueller | 2021-11-19 |
| 6562075 | jens-daniel-mueller | 2021-11-19 |
| 6b80483 | jens-daniel-mueller | 2021-11-19 |
| 98d9e33 | jens-daniel-mueller | 2021-11-11 |
| 38cad1d | jens-daniel-mueller | 2021-11-11 |
| 4e4c0b7 | jens-daniel-mueller | 2021-11-11 |
CFC_dcant %>%
filter(pCFC_12 < 5) %>%
ggplot(aes(dcant, col = basin_AIP, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_density(alpha = 0.3) +
facet_grid(gamma_slab ~ .) +
labs(title = "pCFC-12 < 5")
| Version | Author | Date |
|---|---|---|
| 3c60929 | jens-daniel-mueller | 2021-12-06 |
| 3f76ee3 | jens-daniel-mueller | 2021-12-06 |
| 2ca1313 | jens-daniel-mueller | 2021-12-05 |
| 605b380 | jens-daniel-mueller | 2021-12-02 |
| a83a09b | jens-daniel-mueller | 2021-11-29 |
| 72c1041 | jens-daniel-mueller | 2021-11-23 |
| 3eba8ac | jens-daniel-mueller | 2021-11-23 |
| ec18ee5 | jens-daniel-mueller | 2021-11-23 |
| 59cdf58 | jens-daniel-mueller | 2021-11-22 |
| 3ae2dd1 | jens-daniel-mueller | 2021-11-21 |
| 5b46219 | jens-daniel-mueller | 2021-11-21 |
| 5016fc9 | jens-daniel-mueller | 2021-11-19 |
| 6562075 | jens-daniel-mueller | 2021-11-19 |
| 6b80483 | jens-daniel-mueller | 2021-11-19 |
| 98d9e33 | jens-daniel-mueller | 2021-11-11 |
| 38cad1d | jens-daniel-mueller | 2021-11-11 |
CFC_dcant %>%
filter(gamma_slab %in% params_local$plot_slabs) %>%
group_split(gamma_slab) %>%
map(
~ ggplot(data = .x,
aes(pCFC_12, dcant)) +
geom_hline(yintercept = 0) +
geom_bin2d() +
geom_smooth(col = "red", se=FALSE) +
scale_fill_viridis_c(trans = "log10") +
labs(title = paste("gamma_slab", unique(.x$gamma_slab))) +
facet_grid(. ~ basin_AIP)
)[[1]]
| Version | Author | Date |
|---|---|---|
| 3c60929 | jens-daniel-mueller | 2021-12-06 |
| 3f76ee3 | jens-daniel-mueller | 2021-12-06 |
| 2ca1313 | jens-daniel-mueller | 2021-12-05 |
| d258523 | jens-daniel-mueller | 2021-12-02 |
| 605b380 | jens-daniel-mueller | 2021-12-02 |
| a83a09b | jens-daniel-mueller | 2021-11-29 |
| 72c1041 | jens-daniel-mueller | 2021-11-23 |
| 3eba8ac | jens-daniel-mueller | 2021-11-23 |
| ec18ee5 | jens-daniel-mueller | 2021-11-23 |
| 59cdf58 | jens-daniel-mueller | 2021-11-22 |
| 3ae2dd1 | jens-daniel-mueller | 2021-11-21 |
| 5b46219 | jens-daniel-mueller | 2021-11-21 |
| 5016fc9 | jens-daniel-mueller | 2021-11-19 |
| 6562075 | jens-daniel-mueller | 2021-11-19 |
| 6b80483 | jens-daniel-mueller | 2021-11-19 |
| b7d656b | jens-daniel-mueller | 2021-11-16 |
[[2]]
| Version | Author | Date |
|---|---|---|
| 3c60929 | jens-daniel-mueller | 2021-12-06 |
| 3f76ee3 | jens-daniel-mueller | 2021-12-06 |
| 2ca1313 | jens-daniel-mueller | 2021-12-05 |
| 605b380 | jens-daniel-mueller | 2021-12-02 |
| a83a09b | jens-daniel-mueller | 2021-11-29 |
| 72c1041 | jens-daniel-mueller | 2021-11-23 |
| 3eba8ac | jens-daniel-mueller | 2021-11-23 |
| ec18ee5 | jens-daniel-mueller | 2021-11-23 |
| 59cdf58 | jens-daniel-mueller | 2021-11-22 |
| 3ae2dd1 | jens-daniel-mueller | 2021-11-21 |
| 5b46219 | jens-daniel-mueller | 2021-11-21 |
| 5016fc9 | jens-daniel-mueller | 2021-11-19 |
| 6562075 | jens-daniel-mueller | 2021-11-19 |
| 6b80483 | jens-daniel-mueller | 2021-11-19 |
| b7d656b | jens-daniel-mueller | 2021-11-16 |
CFC_dcant %>%
filter(pCFC_12 < 5) %>%
group_split(gamma_slab) %>%
# head(1) %>%
map(~ ggplot(data = .x,
aes(dcant, col = basin_AIP, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_density(alpha = 0.3) +
labs(title = paste("pCFC-12 < 5 | gamma_slab", unique(.x$gamma_slab))) +
coord_cartesian(xlim = c(-4,11.5))
)6.2 Zonal sections
CFC_dcant_zonal <- m_zonal_mean_sd(
CFC_dcant %>%
select(lat, lon, depth, basin_AIP, gamma, dcant, pCFC_12)
)
CFC_dcant_zonal <- CFC_dcant_zonal %>%
mutate(
dcant_mean_pos = if_else(dcant_mean < 0, 0, dcant_mean),
dcant_per_CFC = dcant_mean_pos / pCFC_12_mean,
dcant_per_CFC_log = log10(dcant_per_CFC)
)
# dcant_per_CFC_log_min <- CFC_dcant_zonal %>%
# filter(dcant_per_CFC_log != -Inf) %>%
# slice_min(dcant_per_CFC_log) %>%
# pull(dcant_per_CFC_log)
#
# dcant_per_CFC_log_max <- CFC_dcant_zonal %>%
# filter(dcant_per_CFC_log != Inf) %>%
# slice_max(dcant_per_CFC_log) %>%
# pull(dcant_per_CFC_log)
#
#
# CFC_dcant_zonal <- CFC_dcant_zonal %>%
# mutate(
# dcant_per_CFC_log = if_else(dcant_mean_pos == 0,
# dcant_per_CFC_log_min,
# dcant_per_CFC_log),
# dcant_per_CFC_log = if_else(
# pCFC_12_mean == 0,
# dcant_per_CFC_log_max,
# dcant_per_CFC_log
# )
# )
CFC_dcant_zonal %>%
p_section_zonal_continous_depth(
var = "dcant_mean_pos",
title_text = NULL) +
facet_grid(basin_AIP ~ .)
| Version | Author | Date |
|---|---|---|
| 3c60929 | jens-daniel-mueller | 2021-12-06 |
| 3f76ee3 | jens-daniel-mueller | 2021-12-06 |
| 2ca1313 | jens-daniel-mueller | 2021-12-05 |
| 605b380 | jens-daniel-mueller | 2021-12-02 |
| a83a09b | jens-daniel-mueller | 2021-11-29 |
| 72c1041 | jens-daniel-mueller | 2021-11-23 |
| 3eba8ac | jens-daniel-mueller | 2021-11-23 |
| ec18ee5 | jens-daniel-mueller | 2021-11-23 |
| 59cdf58 | jens-daniel-mueller | 2021-11-22 |
| 3ae2dd1 | jens-daniel-mueller | 2021-11-21 |
| 5b46219 | jens-daniel-mueller | 2021-11-21 |
| 5016fc9 | jens-daniel-mueller | 2021-11-19 |
| 6562075 | jens-daniel-mueller | 2021-11-19 |
| 6b80483 | jens-daniel-mueller | 2021-11-19 |
| 029c50a | jens-daniel-mueller | 2021-11-12 |
| 20e7603 | jens-daniel-mueller | 2021-11-12 |
CFC_dcant_zonal %>%
p_section_zonal_continous_depth(
var = "pCFC_12_mean",
breaks = NULL,
legend_title = "pCFC_12",
title_text = NULL
) +
facet_grid(basin_AIP ~ .)
CFC_dcant_zonal %>%
mutate(lg_pCFC_12_mean = log10(pCFC_12_mean)) %>%
p_section_zonal_continous_depth(
var = "lg_pCFC_12_mean",
breaks = NULL,
legend_title = "pCFC_12",
title_text = NULL
) +
facet_grid(basin_AIP ~ .)
CFC_dcant_zonal %>%
# filter(dcant_per_CFC_log > -4) %>%
p_section_zonal_continous_depth(
var = "dcant_per_CFC_log",
breaks = NULL,
legend_title = "log10\ndcant /\npCFC_12",
title_text = NULL
) +
facet_grid(basin_AIP ~ .)
| Version | Author | Date |
|---|---|---|
| 3c60929 | jens-daniel-mueller | 2021-12-06 |
| 3f76ee3 | jens-daniel-mueller | 2021-12-06 |
| 2ca1313 | jens-daniel-mueller | 2021-12-05 |
| 605b380 | jens-daniel-mueller | 2021-12-02 |
| a83a09b | jens-daniel-mueller | 2021-11-29 |
| 72c1041 | jens-daniel-mueller | 2021-11-23 |
| 3eba8ac | jens-daniel-mueller | 2021-11-23 |
| ec18ee5 | jens-daniel-mueller | 2021-11-23 |
| 59cdf58 | jens-daniel-mueller | 2021-11-22 |
| 3ae2dd1 | jens-daniel-mueller | 2021-11-21 |
| 5b46219 | jens-daniel-mueller | 2021-11-21 |
| 5016fc9 | jens-daniel-mueller | 2021-11-19 |
| 6562075 | jens-daniel-mueller | 2021-11-19 |
| 6b80483 | jens-daniel-mueller | 2021-11-19 |
| b7d656b | jens-daniel-mueller | 2021-11-16 |
| 8a3e867 | jens-daniel-mueller | 2021-11-12 |
| e5a0a8f | jens-daniel-mueller | 2021-11-12 |
| 1b58f8e | jens-daniel-mueller | 2021-11-12 |
| 029c50a | jens-daniel-mueller | 2021-11-12 |
| 20e7603 | jens-daniel-mueller | 2021-11-12 |
CFC_dcant_zonal %>%
# filter(dcant_per_CFC_log > -4) %>%
p_section_zonal_continous_depth(
var = "dcant_per_CFC_log",
breaks = NULL,
legend_title = "log10\ndcant /\npCFC_12",
title_text = NULL
) +
scale_y_reverse(
limits = c(1000, 0),
breaks = seq(0, 900, 200),
name = "Depth (m)"
) +
facet_grid(basin_AIP ~ .)
| Version | Author | Date |
|---|---|---|
| 3c60929 | jens-daniel-mueller | 2021-12-06 |
| 3f76ee3 | jens-daniel-mueller | 2021-12-06 |
| 2ca1313 | jens-daniel-mueller | 2021-12-05 |
| 605b380 | jens-daniel-mueller | 2021-12-02 |
| a83a09b | jens-daniel-mueller | 2021-11-29 |
| 72c1041 | jens-daniel-mueller | 2021-11-23 |
| 3eba8ac | jens-daniel-mueller | 2021-11-23 |
| ec18ee5 | jens-daniel-mueller | 2021-11-23 |
| 59cdf58 | jens-daniel-mueller | 2021-11-22 |
| 3ae2dd1 | jens-daniel-mueller | 2021-11-21 |
| 5b46219 | jens-daniel-mueller | 2021-11-21 |
| 5016fc9 | jens-daniel-mueller | 2021-11-19 |
| 6562075 | jens-daniel-mueller | 2021-11-19 |
| 6b80483 | jens-daniel-mueller | 2021-11-19 |
| b7d656b | jens-daniel-mueller | 2021-11-16 |
| 8a3e867 | jens-daniel-mueller | 2021-11-12 |
| e5a0a8f | jens-daniel-mueller | 2021-11-12 |
| 1b58f8e | jens-daniel-mueller | 2021-11-12 |
| 029c50a | jens-daniel-mueller | 2021-11-12 |
| 20e7603 | jens-daniel-mueller | 2021-11-12 |
7 Overview plots
7.1 Number of overservations
GLODAP %>%
filter(!is.na(pcfc12)) %>%
group_by(era, basin_AIP) %>%
count() %>%
ggplot(aes(basin_AIP, n, fill = era)) +
geom_col() +
scale_fill_brewer(palette = "Dark2")
7.2 Coverage maps by era
7.2.1 Final input data
The following plots show the remaining data density in each grid cell after all cleaning steps, separately for each era.
map +
geom_bin2d(data = GLODAP %>% filter(!is.na(pcfc12)),
aes(lon, lat),
binwidth = c(1,1)) +
scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10") +
facet_wrap(~era, ncol = 1) +
labs(title = "GLODAP observations",
subtitle = paste("Version:", params_local$Version_ID)) +
theme(axis.title = element_blank())
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] lubridate_1.7.9 ggforce_0.3.3 metR_0.9.0 scico_1.2.0
[5] patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0 stringr_1.4.0
[9] dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[13] tibble_3.1.3 ggplot2_3.3.5 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-149 fs_1.5.0 RColorBrewer_1.1-2
[4] httr_1.4.2 rprojroot_2.0.2 tools_4.0.3
[7] backports_1.1.10 bslib_0.2.5.1 utf8_1.1.4
[10] R6_2.5.0 mgcv_1.8-33 DBI_1.1.0
[13] colorspace_2.0-2 withr_2.3.0 tidyselect_1.1.0
[16] compiler_4.0.3 git2r_0.27.1 cli_3.0.1
[19] rvest_0.3.6 xml2_1.3.2 isoband_0.2.2
[22] labeling_0.4.2 sass_0.4.0 scales_1.1.1
[25] checkmate_2.0.0 digest_0.6.27 rmarkdown_2.10
[28] pkgconfig_2.0.3 htmltools_0.5.1.1 dbplyr_1.4.4
[31] highr_0.8 rlang_0.4.11 readxl_1.3.1
[34] rstudioapi_0.13 jquerylib_0.1.4 generics_0.1.0
[37] farver_2.0.3 jsonlite_1.7.1 magrittr_1.5
[40] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
[43] fansi_0.4.1 lifecycle_1.0.0 stringi_1.5.3
[46] whisker_0.4 yaml_2.2.1 MASS_7.3-53
[49] plyr_1.8.6 grid_4.0.3 blob_1.2.1
[52] parallel_4.0.3 promises_1.1.1 crayon_1.3.4
[55] lattice_0.20-41 splines_4.0.3 haven_2.3.1
[58] hms_0.5.3 knitr_1.33 pillar_1.6.2
[61] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[64] RcppArmadillo_0.10.1.2.0 data.table_1.14.0 modelr_0.1.8
[67] vctrs_0.3.8 tweenr_1.0.2 httpuv_1.5.4
[70] cellranger_1.1.0 gtable_0.3.0 polyclip_1.10-0
[73] assertthat_0.2.1 xfun_0.25 broom_0.7.9
[76] RcppEigen_0.3.3.7.0 later_1.2.0 viridisLite_0.3.0
[79] memoise_1.1.0 ellipsis_0.3.2 here_0.1