GLODAPv2_2021 halogenated tracers
Jens Daniel Müller
12 November, 2021
Last updated: 2021-11-12
Checks: 7 0
Knit directory: emlr_obs_v_XXX/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 13a030c | jens-daniel-mueller | 2021-11-12 | add zonal mean section of gridded dcant per CFC |
| html | 20e7603 | jens-daniel-mueller | 2021-11-12 | Build site. |
| Rmd | 6b9a242 | jens-daniel-mueller | 2021-11-12 | add zonal mean section of gridded dcant and CFC |
| html | 654ab1b | jens-daniel-mueller | 2021-11-12 | Build site. |
| Rmd | b0cf2af | jens-daniel-mueller | 2021-11-12 | add pseudo-log transformation and zonal mean mapping function |
| html | 950d8e7 | jens-daniel-mueller | 2021-11-12 | Build site. |
| Rmd | 2127c9d | jens-daniel-mueller | 2021-11-12 | add pseudo-log transformation and zonal mean mapping function |
| html | 98d9e33 | jens-daniel-mueller | 2021-11-11 | Build site. |
| html | 38cad1d | jens-daniel-mueller | 2021-11-11 | Build site. |
| Rmd | 2b7d6d5 | jens-daniel-mueller | 2021-11-11 | update plot correlation of dCant to cfc12 data |
| html | 4e4c0b7 | jens-daniel-mueller | 2021-11-11 | Build site. |
| Rmd | e12c27c | jens-daniel-mueller | 2021-11-11 | plot correlation of dCant to cfc12 data |
| html | 07ccee8 | jens-daniel-mueller | 2021-11-11 | Build site. |
| Rmd | 4e79444 | jens-daniel-mueller | 2021-11-11 | read gridded cfc12 data |
| html | d3cb92d | jens-daniel-mueller | 2021-11-08 | Build site. |
| html | 3879a6d | jens-daniel-mueller | 2021-11-08 | Build site. |
| html | dfdb778 | jens-daniel-mueller | 2021-11-04 | Build site. |
| Rmd | f88f11c | jens-daniel-mueller | 2021-11-04 | zonal mean section plots modified |
| html | e3faf2f | jens-daniel-mueller | 2021-11-04 | Build site. |
| Rmd | f8331c3 | jens-daniel-mueller | 2021-11-04 | zonal mean section plots modified |
| html | 1d4c657 | jens-daniel-mueller | 2021-11-04 | Build site. |
| Rmd | e3b9785 | jens-daniel-mueller | 2021-11-04 | zonal mean section plots modified |
| html | 0e032dc | jens-daniel-mueller | 2021-11-04 | Build site. |
| Rmd | 5ca91b7 | jens-daniel-mueller | 2021-11-04 | rerun standard |
| html | abcd28f | jens-daniel-mueller | 2021-11-02 | Build site. |
| html | 290c8fc | jens-daniel-mueller | 2021-11-02 | Build site. |
| html | e02acc9 | jens-daniel-mueller | 2021-11-01 | Build site. |
| html | ceea35b | jens-daniel-mueller | 2021-11-01 | Build site. |
| Rmd | a4179fc | jens-daniel-mueller | 2021-11-01 | zonal mean section plots |
| html | 58da811 | jens-daniel-mueller | 2021-11-01 | Build site. |
| html | 2781a97 | jens-daniel-mueller | 2021-10-29 | Build site. |
| html | 973192c | jens-daniel-mueller | 2021-10-28 | Build site. |
| Rmd | 0b14406 | jens-daniel-mueller | 2021-10-28 | added tracer plots and test without O2 data |
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$value))) +
facet_grid(parameter ~ era)
)[[1]]
[[2]]
rm(GLODAP_f_flags)3.3.2.2 Stats
GLODAP_f_flags_stats %>%
mutate(value = as.factor(value)) %>%
ggplot(aes(parameter, n, fill = value)) +
coord_flip() +
scale_x_discrete(limits = rev) +
geom_col() +
facet_grid(era~.) +
scale_fill_brewer(palette = "Dark2")
| Version | Author | Date |
|---|---|---|
| 973192c | jens-daniel-mueller | 2021-10-28 |
rm(GLODAP_f_flags_stats)3.3.3 qc flag
GLODAP_qc_flags <- GLODAP %>%
select(lon, lat, era, ends_with("qc")) %>%
pivot_longer(cfc11qc:ccl4qc,
names_to = "parameter",
values_to = "value") %>%
mutate(parameter = fct_inorder(as.factor(parameter))) %>%
count(lat, lon, era, parameter, value)
GLODAP_qc_flags_stats <- GLODAP_qc_flags %>%
count(era, parameter, value)
GLODAP_qc_flags <- GLODAP_qc_flags %>%
count(lat, lon, era, parameter, value)3.3.3.1 Maps
GLODAP_qc_flags %>%
group_split(value) %>%
# head(1) %>%
map(
~ map +
geom_raster(data = .x,
aes(lon, lat, fill = n)) +
scale_fill_viridis_c(
option = "magma",
direction = -1,
trans = "log10"
) +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = paste("qc flag:", unique(.x$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")
| Version | Author | Date |
|---|---|---|
| 973192c | jens-daniel-mueller | 2021-10-28 |
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
4.1 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 = `2000-2009` - `1989-1999`)
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 ~ .)
| Version | Author | Date |
|---|---|---|
| 07ccee8 | jens-daniel-mueller | 2021-11-11 |
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 ~ .)
| Version | Author | Date |
|---|---|---|
| 950d8e7 | jens-daniel-mueller | 2021-11-12 |
4.2.2 Change
IO_EW_grid_offset <- IO_EW_grid %>%
pivot_wider(names_from = era,
values_from = pcfc12) %>%
mutate(delta_pcfc12_mean = `2000-2009` - `1989-1999`)
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)
| Version | Author | Date |
|---|---|---|
| 654ab1b | jens-daniel-mueller | 2021-11-12 |
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))
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")
CFC_dcant %>%
group_split(gamma_slab, depth_grid) %>%
# head(1) %>%
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),
"| depth", unique(.x$depth_grid))) +
facet_grid(. ~ basin_AIP)
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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(pCFC_12_mean_pos = if_else(pCFC_12_mean < 0, 0, pCFC_12_mean),
dcant_mean_pos = if_else(dcant_mean < 0, 0, dcant_mean),
dcant_per_CFC = dcant_mean_pos / pCFC_12_mean_pos,
dcant_per_CFC_log = log10(dcant_per_CFC))
CFC_dcant_zonal %>%
p_section_zonal_continous_depth(
var = "dcant_mean_pos",
title_text = NULL) +
facet_grid(basin_AIP ~ .)
| Version | Author | Date |
|---|---|---|
| 20e7603 | jens-daniel-mueller | 2021-11-12 |
CFC_dcant_zonal %>%
p_section_zonal_continous_depth(
var = "pCFC_12_mean_pos",
breaks = NULL,
legend_title = "pCFC_12",
title_text = NULL
) +
facet_grid(basin_AIP ~ .)
| Version | Author | Date |
|---|---|---|
| 20e7603 | jens-daniel-mueller | 2021-11-12 |
CFC_dcant_zonal %>%
ggplot(aes(dcant_per_CFC)) +
geom_histogram() +
scale_y_log10() +
facet_grid(.~ basin_AIP)
| Version | Author | Date |
|---|---|---|
| 20e7603 | jens-daniel-mueller | 2021-11-12 |
CFC_dcant_zonal %>%
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 |
|---|---|---|
| 20e7603 | jens-daniel-mueller | 2021-11-12 |
7 Overview plots
7.1 Number of overservations
GLODAP %>%
group_by(era, basin_AIP) %>%
count() %>%
ggplot(aes(basin_AIP, n, fill = era)) +
geom_col() +
scale_fill_brewer(palette = "Dark2")
| Version | Author | Date |
|---|---|---|
| 973192c | jens-daniel-mueller | 2021-10-28 |
7.2 Assign coarse spatial grid
For the following plots, the cleaned data set was re-opened and observations were gridded spatially to intervals of:
- 5° x 5°
GLODAP <- m_grid_horizontal_coarse(GLODAP)7.3 Histogram Zonal coverage
GLODAP_histogram_lat <- GLODAP %>%
group_by(era, lat, basin_AIP) %>%
tally() %>%
ungroup()
GLODAP_histogram_lat %>%
ggplot(aes(lat, n, fill = era)) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_wrap( ~ basin_AIP) +
coord_flip(expand = 0) +
theme(legend.position = "top",
legend.title = element_blank())
rm(GLODAP_histogram_lat)7.4 Histogram temporal coverage
GLODAP_histogram_year <- GLODAP %>%
group_by(year, basin_AIP) %>%
tally() %>%
ungroup()
GLODAP_histogram_year %>%
ggplot() +
geom_vline(xintercept = sort(params_local$era_end)[1] + 0.5) +
geom_col(aes(year, n,
fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2") +
scale_shape_manual(values = 24, name = "") +
scale_y_continuous() +
coord_cartesian(expand = 0) +
theme(
legend.position = "top",
legend.direction = "vertical",
legend.title = element_blank(),
axis.title.x = element_blank()
)
| Version | Author | Date |
|---|---|---|
| 973192c | jens-daniel-mueller | 2021-10-28 |
rm(GLODAP_histogram_year,
era_median_year)7.5 Zonal temporal coverage (Hovmoeller)
GLODAP_hovmoeller_year <- GLODAP %>%
group_by(year, lat, basin_AIP) %>%
tally() %>%
ungroup()
GLODAP_hovmoeller_year %>%
ggplot(aes(year, lat, fill = n)) +
geom_tile() +
geom_vline(xintercept = sort(params_local$era_end)[1] + 0.5) +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10") +
coord_cartesian(expand = 0) +
facet_wrap( ~ basin_AIP, ncol = 1) +
theme(legend.position = "top",
axis.title.x = element_blank())
rm(GLODAP_hovmoeller_year)7.6 Coverage maps by era
7.6.1 Final input data
The following plots show the remaining data density in each grid cell after all cleaning steps, separately for each era.
GLODAP_tco2_grid <- GLODAP %>%
count(lat, lon)
map +
geom_bin2d(data = GLODAP,
aes(lon, lat),
binwidth = c(1,1)) +
scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10") +
facet_wrap(~era, ncol = 1) +
labs(title = "Cleaned GLODAP observations",
subtitle = paste("Version:", params_local$Version_ID)) +
theme(axis.title = element_blank())
| Version | Author | Date |
|---|---|---|
| 973192c | jens-daniel-mueller | 2021-10-28 |
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