Extreme pH Profiles
Pasqualina Vonlanthen & Jens Daniel Müller
25 January, 2022
Last updated: 2022-01-25
Checks: 7 0
Knit directory: bgc_argo_r_argodata/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3851824 | pasqualina-vonlanthendinenna | 2022-01-25 | added basin-mean profiles |
| html | 962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 | Build site. |
| Rmd | 825a50a | pasqualina-vonlanthendinenna | 2022-01-25 | added seasonal and biome profiles |
| html | 3ae43e4 | pasqualina-vonlanthendinenna | 2022-01-24 | Build site. |
| Rmd | 3f8e824 | pasqualina-vonlanthendinenna | 2022-01-24 | updated 24/01 |
| html | 6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 | Build site. |
| Rmd | e72d7ca | pasqualina-vonlanthendinenna | 2022-01-21 | updated linear regression to monthly |
| html | 587755e | pasqualina-vonlanthendinenna | 2022-01-21 | Build site. |
| Rmd | 7a9209b | pasqualina-vonlanthendinenna | 2022-01-21 | updated threshold calculation 2 |
| html | c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 | Build site. |
| Rmd | 58b3b3b | pasqualina-vonlanthendinenna | 2022-01-21 | updated threshold calculation |
| html | ed3fef2 | jens-daniel-mueller | 2022-01-07 | Build site. |
| Rmd | 3d2f8fc | jens-daniel-mueller | 2022-01-07 | code review |
| html | 486c9c8 | jens-daniel-mueller | 2022-01-07 | Build site. |
| Rmd | e9ad067 | jens-daniel-mueller | 2022-01-07 | code review |
| html | 343689f | pasqualina-vonlanthendinenna | 2022-01-06 | Build site. |
| Rmd | f53cc2d | pasqualina-vonlanthendinenna | 2022-01-06 | updated profile page |
| html | b8a6482 | pasqualina-vonlanthendinenna | 2022-01-03 | Build site. |
| Rmd | 054f8a6 | pasqualina-vonlanthendinenna | 2022-01-03 | added Argo profiles |
Task
Compare depth profiles of normal pH and of extreme pH, as identified in the surface OceanSODA pH data product
theme_set(theme_bw())
HNL_colors <- c("H" = "#b2182b",
"N" = "#636363",
"L" = "#2166ac")Load data
path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"# RECCAP2-ocean region mask
region_masks_all_2x2 <- read_rds(file = paste0(path_argo_preprocessed,
"/region_masks_all_2x2.rds"))
region_masks_all_2x2 <- region_masks_all_2x2 %>%
rename(biome = value) %>%
filter(region == 'southern',
biome != 0) %>%
select(-region) %>%
mutate(coast = as.character(coast))
# WOA 18 basin mask
basinmask <-
read_csv(
paste(path_emlr_utilities,
"basin_mask_WOA18.csv",
sep = ""),
col_types = cols("MLR_basins" = col_character())
)
basinmask_2x2 <- basinmask %>%
filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>%
select(lon, lat, basin_AIP) %>%
mutate(
lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
lon = as.numeric(as.character(lon))
) # regrid into 2x2º grid
# OceanSODA
OceanSODA <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA.rds"))
OceanSODA <- OceanSODA %>%
mutate(month = month(date))
OceanSODA_2x2 <- OceanSODA %>%
mutate(
lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
lon = as.numeric(as.character(lon))) # regrid into 2x2º grid
# group_by(lon, lat, date, year, month) %>%
# summarise(ph_month = mean(ph_total, na.rm = TRUE)) %>%
# ungroup()
# calculate mean pH for each 2x2 grid at each date
# load in the full argo data
full_argo <- read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_pH_qc_1.rds"))
# change the date format for compatibility with OceanSODA pH data
full_argo_2x2 <- full_argo %>%
mutate(year = year(date),
month = month(date)) %>%
mutate(date = ymd(format(date, "%Y-%m-15"))) %>%
mutate(
lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
lon = as.numeric(as.character(lon))) # re-grid to 2x2
# group_by(lon, lat, date, year, month, depth, platform_number, cycle_number) %>%
# summarise(ph_adjusted_month = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
# temp_adjusted_month = mean(temp_adjusted, na.rm = TRUE)) %>%
# ungroup()
# calculate mean pH and temperature for each 2x2 grid for each dateRegions
Biomes
basemap(limits = -32) +
geom_spatial_tile(
data = region_masks_all_2x2,
aes(x = lon,
y = lat,
fill = biome),
col = 'transparent'
) +
scale_fill_brewer(palette = "Dark2")
Coast
basemap(limits = -32) +
geom_spatial_tile(
data = region_masks_all_2x2,
aes(x = lon,
y = lat,
fill = coast),
col = 'transparent'
) +
scale_fill_brewer(palette = "Dark2")
Apply region masks
region_masks_all_2x2 <- region_masks_all_2x2 %>%
filter(coast == "0")# keep only Southern Ocean data
OceanSODA_2x2_SO <- inner_join(OceanSODA_2x2, region_masks_all_2x2)
# add in basin separations
OceanSODA_2x2_SO <- inner_join(OceanSODA_2x2_SO, basinmask_2x2)
# (adds in duplicate rows?
# OceanSODA_2x2 -> 4234672 rows
# OceanSODA_2x2_SO -> 13988280 rows)
# remove the duplicate rows created by inner_join
OceanSODA_2x2_SO <- OceanSODA_2x2_SO %>%
distinct()
# (results in 988956 rows)
# keep only Southern Ocean argo data
full_argo_2x2_SO <- inner_join(full_argo_2x2, region_masks_all_2x2)
# add in basin separations
full_argo_2x2_SO <- inner_join(full_argo_2x2_SO, basinmask_2x2)
# remove duplicate rows (keep only distinct rows)
full_argo_2x2_SO <- full_argo_2x2_SO %>%
distinct()OceanSODA pH anomalies
Grid level
Climatological thresholds
Climatological monthly OceanSODA pH and the 5th and 95th percentiles, calculated for 2013-2021, with the full spatial OceanSODA data
# fit a linear regression of OceanSODA pH against time (temporal trend) in each lat/lon grid
OceanSODA_regression <- OceanSODA_2x2_SO %>%
drop_na() %>%
nest(data = -c(lon, lat, month)) %>% # group by lon, lat
mutate(fit = map(.x = data,
.f = ~ lm(ph_total ~ date, data = .x)),
tidied = map(.x = fit, .f = tidy),
augmented = map(.x = fit, .f = augment))
OceanSODA_regression <- OceanSODA_regression %>%
unnest(tidied) %>%
unnest(augmented)# join the regression estimates to OceanSODA and remove duplicate rows
OceanSODA_2x2_SO_extreme_grid <- inner_join(OceanSODA_2x2_SO, OceanSODA_regression) %>%
distinct()
# sigma is the residual standard deviation
# .fitted are the predicted pH values by the linear model
# calculate H and L pH thresholds for climatological monthly pH
OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
mutate(ph_L = .fitted - 2*(.sigma),
ph_H = .fitted + 2*(.sigma))# calculate climatological average OceanSODA pH
# and the 95th percentile of the monthly OceanSODA pH
#
# OceanSODA_2x2_SO_clim_grid <- OceanSODA_2x2_SO %>%
# group_by(lon, lat, month) %>%
# summarise(
# ph_N = mean(ph_total, na.rm = TRUE),
# ph_H = quantile(ph_total, 0.95, na.rm = TRUE),
# ph_L = quantile(ph_total, 0.05, na.rm = TRUE)
# ) %>%
# ungroup()
#
# OceanSODA_2x2_SO_extreme_grid <- inner_join(OceanSODA_2x2_SO, OceanSODA_2x2_SO_clim_grid)Anomaly identification
Calculate OceanSODA pH anomalies: L for abnormally low, H for abnormally high, N for normal pH
# when the in-situ OceanSODA pH is lower than the 5th percentile (predicted - 2*residual.st.dev), assign 'L' for low extreme
# when the in-situ OceanSODA pH exceeds the 95th percentile (predicted + 2*residual.st.dev), assign 'H' for high extreme
# when the in-situ OceanSODA pH is within 95% of the range, then assign 'N' for normal pH
OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
mutate(
ph_extreme = case_when(
ph_total < ph_L ~ 'L',
ph_total > ph_H ~ 'H',
TRUE ~ 'N'
)
) %>%
drop_na()
OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
mutate(ph_extreme = fct_relevel(ph_extreme, "H", "N", "L"))
# pivot_wider two columns (slope and intercept), values_from = estimate, names_from = terms, names.repair = 'unique'
# gives a slope and intercept column
# rename date...26 = slope and date...2 = date
OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
pivot_wider(names_from = term,
values_from = estimate,
names_repair = 'unique') %>%
rename(date = date...2,
regression_slope = date...26,
regression_intercept = `(Intercept)`)
# fill in NAs in the slope and intercept columns (values from above for regression_slope and values from below for regression_intercept) (creates duplicate rows) and remove duplicate rows
# OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
# group_by(lon, lat, date, year, month) %>%
# fill(regression_slope, .direction = 'up') %>%
# fill(regression_intercept, .direction = 'down') %>%
# distinct()OceanSODA_2x2_SO_extreme_grid %>%
group_split(lon, lat, month) %>%
head(12) %>%
map(~ ggplot(data = .x) +
geom_point(aes(x = year,
y = ph_total,
col = ph_extreme)) +
geom_abline(data = .x, aes(slope = regression_slope,
intercept = regression_intercept - 2*.sigma),
linetype = 2) +
geom_abline(data = .x, aes(slope = regression_slope,
intercept = regression_intercept + 2*.sigma),
linetype = 2) +
geom_abline(data = .x, aes(intercept = regression_intercept,
slope = regression_slope)) +
labs(title = paste(fititle = paste(
"lon:", unique(.x$lon),
"| lat:", unique(.x$lat),
"| month:", unique(.x$month)
))) +
scale_color_manual(values = HNL_colors))[[1]]
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Anomaly maps
Location of OceanSODA pH extremes
OceanSODA_2x2_SO_extreme_grid %>%
group_split(year) %>%
#head(3) %>%
map(
~ basemap(limits = -32, data = .x)+
geom_spatial_tile(data = .x,
aes(x = lon,
y = lat,
fill = ph_extreme),
linejoin = 'mitre',
col = 'transparent',
detail = 60
) +
scale_fill_manual(values = HNL_colors) +
facet_wrap(~month, ncol = 2)+
labs(title = paste("Year:", unique(.x$year)),
fill = 'pH')
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Anomaly time series
# calculate a regional mean pH for each biome, basin, and ph extreme (H/L/N) and plot a timeseries
OceanSODA_2x2_SO_extreme_grid %>%
group_by(date, biome, basin_AIP, ph_extreme) %>%
summarise(ph_regional = mean(ph_total, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = date, y = ph_regional, col = ph_extreme))+
geom_point(size = 0.3)+
geom_line()+
scale_color_manual(values = HNL_colors) +
facet_grid(basin_AIP~biome)+
labs(x = 'date',
y = 'regional mean pH',
col = 'pH anomaly') +
theme(legend.position = 'bottom')
Anomaly histogram
OceanSODA_2x2_SO_extreme_grid %>%
ggplot(aes(ph_total, col = ph_extreme)) +
geom_density() +
scale_color_manual(values = HNL_colors) +
facet_grid(basin_AIP ~ biome) +
coord_cartesian(xlim = c(8, 8.2)) +
labs(x = 'value',
y = 'density',
col = 'pH anomaly') +
theme(legend.position = 'bottom')
Threshold histogram
OceanSODA_2x2_SO_extreme_grid %>%
mutate(ph_extreme = as.double(ph_extreme)) %>%
pivot_longer(starts_with("ph_"),
names_to = "level",
values_to = "value",
names_prefix = "ph_") %>%
distinct() %>%
ggplot(aes(value, col = level)) +
geom_density() +
scale_color_manual(values = HNL_colors, name = "threshold") +
coord_cartesian(xlim = c(8, 8.2)) +
lims(y = c(0, 230))+
theme(legend.position = 'bottom')
| Version | Author | Date |
|---|---|---|
| 962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
| 3ae43e4 | pasqualina-vonlanthendinenna | 2022-01-24 |
| 6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
| 587755e | pasqualina-vonlanthendinenna | 2022-01-21 |
| c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
| ed3fef2 | jens-daniel-mueller | 2022-01-07 |
| 486c9c8 | jens-daniel-mueller | 2022-01-07 |
Biome level
Climatological thresholds
Climatological monthly OceanSODA pH and the 5th and 95th percentiles, calculated for 2013-2021, with the full spatial OceanSODA data
# calculate biome-mean pH for each date
OceanSODA_biome <- OceanSODA_2x2_SO %>%
group_by(date, year, month, biome, basin_AIP) %>%
summarise(biome_ph = mean(ph_total, na.rm = TRUE))# fit a linear regression of biome-mean pH against time (temporal trend) in each biome/basin area
OceanSODA_biome_regression <- OceanSODA_biome %>%
drop_na() %>%
nest(data = -c(biome, basin_AIP, month)) %>% # group by biome, basin
mutate(fit = map(.x = data,
.f = ~ lm(biome_ph ~ date, data = .x)),
tidied = map(.x = fit, .f = tidy),
augmented = map(.x = fit, .f = augment))
OceanSODA_biome_regression <- OceanSODA_biome_regression %>%
unnest(tidied) %>%
unnest(augmented)# calculate climatological average OceanSODA pH
# and the 95th percentile of the monthly OceanSODA pH
# OceanSODA_2x2_SO_clim_biome <- OceanSODA_SO %>%
# group_by(biome, basin_AIP, month) %>%
# summarise(
# ph_N = mean(ph_total, na.rm = TRUE),
# ph_H = quantile(ph_total, 0.95, na.rm = TRUE),
# ph_L = quantile(ph_total, 0.05, na.rm = TRUE)
# ) %>%
# ungroup()
#
# OceanSODA_2x2_SO_extreme_biome <- inner_join(OceanSODA_SO, OceanSODA_2x2_SO_clim_biome)# join the regression estimates to OceanSODA and remove duplicate rows
OceanSODA_2x2_SO_extreme_biome <- inner_join(OceanSODA_2x2_SO, OceanSODA_biome_regression) %>%
distinct()
# sigma is the residual standard deviation
# .fitted are the predicted pH values by the linear model
# calculate H and L pH thresholds for climatological monthly pH
OceanSODA_2x2_SO_extreme_biome <- OceanSODA_2x2_SO_extreme_biome %>%
mutate(ph_L = .fitted - 2*(.sigma),
ph_H = .fitted + 2*(.sigma))Anomaly identification
Calculate OceanSODA pH anomalies: L for abnormally low, H for abnormally high, N for normal pH
# when the in-situ OceanSODA pH is lower than the 5th percentile, assign 'L' for low extreme
# when the in-situ OceanSODA pH exceeds the 95th percentile, assign 'H' for high extreme
# when the in-situ OceanSODA pH is within 95% of the range, then assign 'N' for normal pH
OceanSODA_2x2_SO_extreme_biome <- OceanSODA_2x2_SO_extreme_biome %>%
mutate(
ph_extreme = case_when(
ph_total < ph_L ~ 'L',
ph_total > ph_H ~ 'H',
TRUE ~ 'N'
)
) %>%
drop_na()
OceanSODA_2x2_SO_extreme_biome <- OceanSODA_2x2_SO_extreme_biome %>%
mutate(ph_extreme = fct_relevel(ph_extreme, "H", "N", "L"))
# pivot_wider two columns (slope and intercept), values_from = estimate, names_from = terms, names.repair = 'unique'
# gives a slope and intercept column
# rename date...27 = slope and date...2 = date
OceanSODA_2x2_SO_extreme_biome <- OceanSODA_2x2_SO_extreme_biome %>%
pivot_wider(names_from = term,
values_from = estimate,
names_repair = 'unique') %>%
rename(date = date...2,
regression_slope = date...27,
regression_intercept = `(Intercept)`)#
# OceanSODA_2x2_SO_extreme_biome %>%
# group_split(biome, basin_AIP, month) %>%
# head(6) %>%
# map(~ ggplot(data = .x) +
# geom_hline(aes(yintercept = ph_H), linetype = 2) +
# geom_hline(aes(yintercept = ph_L), linetype = 2) +
# geom_abline(aes(slope = regression_slope,
# intercept = regression_intercept)) +
# geom_point(
# aes(x = year, y = ph_month, col = ph_extreme)) +
# labs(title = paste(
# "biome:", unique(.x$biome),
# "| basin:", unique(.x$basin_AIP),
# "| month:", unique(.x$month)
# )) +
# scale_color_manual(values = HNL_colors))Anomaly maps
Location of OceanSODA pH extremes
OceanSODA_2x2_SO_extreme_biome %>%
group_split(year) %>%
#head(1) %>%
map(
~ basemap(limits = -32, data = .x)+
geom_spatial_tile(data = .x,
aes(x = lon,
y = lat,
fill = ph_extreme),
linejoin = 'mitre',
col = 'transparent',
detail = 60
) +
scale_fill_manual(values = HNL_colors) +
facet_wrap(~month, ncol = 2)+
labs(title = paste("Year:", unique(.x$year)),
fill = 'pH')
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
Anomaly time series
# calculate a regional mean pH for each biome, basin, and ph extreme (H/L/N) and plot a timeseries
OceanSODA_2x2_SO_extreme_biome %>%
group_by(date, biome, basin_AIP, ph_extreme) %>%
summarise(ph_regional = mean(ph_total, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = date, y = ph_regional, col = ph_extreme))+
geom_point(size = 0.3)+
geom_line()+
scale_color_manual(values = HNL_colors) +
facet_grid(basin_AIP~biome)+
labs(x = 'date',
y = 'regional mean pH',
col = 'pH anomaly') +
theme(legend.position = 'bottom')
Anomaly histogram
OceanSODA_2x2_SO_extreme_biome %>%
ggplot(aes(ph_total, col = ph_extreme)) +
geom_density() +
scale_color_manual(values = HNL_colors) +
facet_grid(basin_AIP ~ biome) +
coord_cartesian(xlim = c(8, 8.2)) +
labs(x = 'value',
y = 'density',
col = 'pH anomaly') +
theme(legend.position = 'bottom')
Threshold histogram
OceanSODA_2x2_SO_extreme_biome %>%
mutate(ph_extreme = as.double(ph_extreme)) %>%
pivot_longer(starts_with("ph_"),
names_to = "level",
values_to = "value",
names_prefix = "ph_") %>%
ggplot(aes(value, col = level)) +
geom_density() +
scale_color_manual(values = HNL_colors, name = "threshold") +
coord_cartesian(xlim = c(8, 8.2)) +
lims(y = c(0, 400))+
theme(legend.position = 'bottom')
Argo
Join OceanSODA
# rename OceanSODA columns
OceanSODA_2x2_SO_extreme <- OceanSODA_2x2_SO_extreme_grid %>%
rename(OceanSODA_ph = ph_total,
OceanSODA_ph_uncert = ph_total_uncert)
# combine the argo profile data to the surface extreme data
profile_extreme <- inner_join(full_argo_2x2_SO, OceanSODA_2x2_SO_extreme)Plot profiles
Argo profiles plotted according to the surface OceanSODA pH
L profiles correspond to a surface acidification event (low pH), as recorded in OceanSODA
H profiles correspond to an event of high surface pH, as recorded in OceanSODA
N profiles correspond to normal surface OceanSODA pH
pH
profile_extreme %>%
group_split(biome, basin_AIP, year) %>%
# head(5) %>%
map(
~ ggplot(
data = .x,
aes(
x = ph_in_situ_total_adjusted,
y = depth,
group = ph_extreme,
col = ph_extreme
)
) +
geom_point(pch = 19, size = 0.3) +
scale_y_reverse() +
scale_color_manual(values = HNL_colors) +
facet_wrap(~ month, ncol = 6) +
labs(
x = 'Argo pH (total scale)',
y = 'depth (m)',
title = paste(
unique(.x$basin_AIP),
"|",
unique(.x$year),
"| biome:",
unique(.x$biome)
),
col = 'OceanSODA pH \nanomaly'
)
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Temperature
# # plot temperature profiles for the Atlantic
# profile_extreme %>%
# group_split(biome, basin_AIP, year) %>%
# # head(1) %>%
# map(
# ~ ggplot(
# data = .x,
# aes(
# x = temp_adjusted,
# y = depth,
# group = ph_extreme,
# col = ph_extreme
# )
# ) +
# geom_point(pch = 19, size = 0.5) +
# scale_y_reverse() +
# scale_color_manual(values = HNL_colors) +
# facet_wrap( ~ month, ncol = 6) +
# labs(
# x = 'Argo temperature (°C)',
# y = 'depth (m)',
# title = paste(
# unique(.x$basin_AIP),
# "|",
# unique(.x$year),
# "| biome:",
# unique(.x$biome)
# ),
# col = 'OceanSODA\npH\nanomaly'
# )
# )Plot monthly profiles
# calculate mean profiles in each basin and biome, for each month between 2014 and 2021
# cut depth levels at 10, 20, .... etc m
# add seasons
# Dec, Jan, Feb <- summer
# Mar, Apr, May <- autumn
# Jun, Jul, Aug <- winter
# Sep, Oct, Nov <- spring
profile_extreme_monthly <- profile_extreme %>%
mutate(depth = Hmisc::cut2(depth,
cuts = c(10, 20, 30, 50, 70, 100, 300, 500, 800, 1000, 1500, 2000, 2500),
m = 5,
levels.mean = TRUE),
depth = as.numeric(as.character(depth))) %>%
mutate(season = case_when(
between(month, 3, 5) ~ 'autumn',
between(month, 6, 8) ~ 'winter',
between(month, 9, 11) ~ 'spring',
month == 12 | 1 | 2 ~ 'summer'),
.after = date
) %>%
group_by(season, biome, basin_AIP, ph_extreme, depth) %>%
summarise(ph_mean = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
temp_mean = mean(temp_adjusted, na.rm = TRUE)) %>%
ungroup()pH
By season
profile_extreme_monthly %>%
arrange(depth) %>%
group_split(season) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(
x = ph_mean,
y = depth,
group = ph_extreme,
col = ph_extreme
)) +
geom_path() +
scale_color_manual(values = HNL_colors) +
labs(title = paste("season:", unique(.x$season)),
col = 'OceanSODA\npH\nanomaly') +
scale_y_reverse() +
facet_grid(basin_AIP ~ biome)
)[[1]]
| Version | Author | Date |
|---|---|---|
| 962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
[[2]]
| Version | Author | Date |
|---|---|---|
| 962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
[[3]]
| Version | Author | Date |
|---|---|---|
| 962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
[[4]]
| Version | Author | Date |
|---|---|---|
| 962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
Averaged over biomes
profile_extreme_biome <- profile_extreme_monthly %>%
group_by(season, biome, ph_extreme, depth) %>%
summarise(ph_biome = mean(ph_mean, na.rm = TRUE)) %>%
ungroup()
profile_extreme_biome %>%
ggplot(aes(x = ph_biome,
y = depth,
group = ph_extreme,
col = ph_extreme))+
geom_path()+
scale_color_manual(values = HNL_colors)+
labs(col = 'OceanSODA\npH\nanomaly')+
scale_y_reverse()+
facet_grid(season ~ biome)
| Version | Author | Date |
|---|---|---|
| 962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
Averaged over ocean basins
profile_extreme_basin <- profile_extreme_monthly %>%
group_by(season, basin_AIP, ph_extreme, depth) %>%
summarise(ph_basin = mean(ph_mean, na.rm = TRUE)) %>%
ungroup()
profile_extreme_basin %>%
ggplot(aes(x = ph_basin,
y = depth,
group = ph_extreme,
col = ph_extreme))+
geom_path()+
scale_color_manual(values = HNL_colors)+
labs(col = 'OceanSODA\npH\nanomaly')+
scale_y_reverse()+
facet_grid(season~basin_AIP)
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggOceanMaps_1.2.6 ggspatial_1.1.5 broom_0.7.11 lubridate_1.8.0
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[9] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[13] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ellipsis_0.3.2 class_7.3-20
[4] rgdal_1.5-28 rprojroot_2.0.2 htmlTable_2.4.0
[7] base64enc_0.1-3 fs_1.5.2 rstudioapi_0.13
[10] proxy_0.4-26 farver_2.1.0 bit64_4.0.5
[13] fansi_1.0.2 xml2_1.3.3 codetools_0.2-18
[16] splines_4.1.2 knitr_1.37 Formula_1.2-4
[19] jsonlite_1.7.3 cluster_2.1.2 dbplyr_2.1.1
[22] png_0.1-7 rgeos_0.5-9 ggOceanMapsData_1.0.1
[25] compiler_4.1.2 httr_1.4.2 backports_1.4.1
[28] assertthat_0.2.1 Matrix_1.4-0 fastmap_1.1.0
[31] cli_3.1.1 later_1.3.0 htmltools_0.5.2
[34] tools_4.1.2 gtable_0.3.0 glue_1.6.0
[37] Rcpp_1.0.8 cellranger_1.1.0 jquerylib_0.1.4
[40] raster_3.5-11 vctrs_0.3.8 xfun_0.29
[43] ps_1.6.0 rvest_1.0.2 lifecycle_1.0.1
[46] terra_1.5-12 getPass_0.2-2 scales_1.1.1
[49] vroom_1.5.7 hms_1.1.1 promises_1.2.0.1
[52] parallel_4.1.2 RColorBrewer_1.1-2 yaml_2.2.1
[55] gridExtra_2.3 sass_0.4.0 rpart_4.1-15
[58] latticeExtra_0.6-29 stringi_1.7.6 highr_0.9
[61] checkmate_2.0.0 e1071_1.7-9 rlang_0.4.12
[64] pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-45
[67] sf_1.0-5 htmlwidgets_1.5.4 labeling_0.4.2
[70] bit_4.0.4 processx_3.5.2 tidyselect_1.1.1
[73] magrittr_2.0.1 R6_2.5.1 generics_0.1.1
[76] Hmisc_4.6-0 DBI_1.1.2 pillar_1.6.4
[79] haven_2.4.3 whisker_0.4 foreign_0.8-82
[82] withr_2.4.3 units_0.7-2 survival_3.2-13
[85] sp_1.4-6 nnet_7.3-17 modelr_0.1.8
[88] crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2
[91] tzdb_0.2.0 rmarkdown_2.11 jpeg_0.1-9
[94] grid_4.1.2 readxl_1.3.1 data.table_1.14.2
[97] callr_3.7.0 git2r_0.29.0 reprex_2.0.1
[100] digest_0.6.29 classInt_0.4-3 httpuv_1.6.5
[103] munsell_0.5.0 bslib_0.3.1