Last updated: 2022-05-12
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Knit directory: bgc_argo_r_argodata/
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html | 710edd4 | jens-daniel-mueller | 2022-05-11 | Build site. |
Rmd | 2f20a76 | jens-daniel-mueller | 2022-05-11 | rebuild all after subsetting AB profiles and code cleaning |
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Rmd | 86144c6 | jens-daniel-mueller | 2022-05-11 | rerun with flag A and B subset |
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Rmd | 3bde57b | pasqualina-vonlanthendinenna | 2022-05-05 | added argo profile locations |
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Rmd | d21c526 | pasqualina-vonlanthendinenna | 2022-04-11 | cleaned up code |
Rmd | f3ca885 | pasqualina-vonlanthendinenna | 2022-04-07 | added OceanSODA-Argo SST comparison |
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Rmd | 72a65a7 | pasqualina-vonlanthendinenna | 2022-04-05 | added new biomes to extreme pH |
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Rmd | c4d4031 | pasqualina-vonlanthendinenna | 2022-03-31 | extended OceanSODA to 1995 for extreme detection |
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Rmd | 25d5eed | pasqualina-vonlanthendinenna | 2022-03-30 | updated figure aspects |
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Rmd | 5432acf | pasqualina-vonlanthendinenna | 2022-03-29 | added january pH climatology |
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Rmd | b9a42f9 | pasqualina-vonlanthendinenna | 2022-03-29 | added january plots and changed pH anomaly detection to mean |
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Rmd | 5b93849 | pasqualina-vonlanthendinenna | 2022-03-25 | added climatology pages |
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Rmd | e4d1d1e | pasqualina-vonlanthendinenna | 2022-03-15 | updated to new only flag A data |
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Rmd | f0fde29 | pasqualina-vonlanthendinenna | 2022-03-11 | changed anomaly detection to 1x1 grid with old data |
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Rmd | 8ef1277 | pasqualina-vonlanthendinenna | 2022-03-01 | plotted Atlantic mean seasonal profiles |
html | 8ef1277 | pasqualina-vonlanthendinenna | 2022-03-01 | plotted Atlantic mean seasonal profiles |
Rmd | 73463cc | pasqualina-vonlanthendinenna | 2022-03-01 | changed line thickness for H and L raw profiles |
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Rmd | aad1df4 | pasqualina-vonlanthendinenna | 2022-02-28 | plotted specific profiles |
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Rmd | fe0b970 | pasqualina-vonlanthendinenna | 2022-02-25 | plotted line profiles and changed HNL colors |
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Rmd | 64c2c71 | pasqualina-vonlanthendinenna | 2022-02-25 | plotted line profiles and changed HNL colors |
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Rmd | 4557a6e | pasqualina-vonlanthendinenna | 2022-02-24 | added st dev for pH profiles |
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Rmd | 7517b78 | pasqualina-vonlanthendinenna | 2022-02-23 | updated regression and merging for extreme_pH |
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html | 25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 | Build site. |
Rmd | ecf2f74 | pasqualina-vonlanthendinenna | 2022-02-03 | corrected surface mean pH |
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html | 71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 | Build site. |
Rmd | 3f38f15 | pasqualina-vonlanthendinenna | 2022-02-03 | corrected mean argo surface pH |
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Rmd | a73c7cf | pasqualina-vonlanthendinenna | 2022-02-02 | changed to log scale and mean surface argo ph |
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Rmd | b45a03e | pasqualina-vonlanthendinenna | 2022-02-01 | added sigma maps and log transform depth |
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Rmd | 3851824 | pasqualina-vonlanthendinenna | 2022-01-25 | added basin-mean profiles |
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Rmd | 3f8e824 | pasqualina-vonlanthendinenna | 2022-01-24 | updated 24/01 |
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Rmd | 054f8a6 | pasqualina-vonlanthendinenna | 2022-01-03 | added Argo profiles |
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")
HNL_colors_map <- c('H' = 'red3',
'N' = 'gray90',
'L' = 'blue3')
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) %>%
# mutate(coast = as.character(coast))
# load in new Mayot biomes
nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))
# WOA 18 basin mask
basinmask <-
read_csv(
paste(path_emlr_utilities,
"basin_mask_WOA18.csv",
sep = ""),
col_types = cols("MLR_basins" = col_character())
)
basinmask <- basinmask %>%
filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>%
select(-c(MLR_basins, basin))
# OceanSODA
OceanSODA <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA.rds"))
OceanSODA <- OceanSODA %>%
mutate(year = year(date),
month = month(date))
# argo pH data (flag A only)
full_argo <-
read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_flag_AB.rds")) %>%
select(-c(temp_adjusted:temp_adjusted_error,
profile_temp_qc))
# change the date format for compatibility with OceanSODA pH data
full_argo <- full_argo %>%
mutate(year = year(date),
month = month(date)) %>%
mutate(date = ymd(format(date, "%Y-%m-15")))
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))
map +
geom_tile(data = nm_biomes,
aes(x = lon,
y = lat,
fill = biome_name))+
lims(y = c(-85, -30))+
scale_fill_brewer(palette = 'Dark2')+
labs(title = 'Mayot biomes pre-grid reduction')
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
d14b7f1 | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
nm_biomes_2x2 <- nm_biomes %>%
mutate(lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
lon = as.numeric(as.character(lon)),
lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
lat = as.numeric(as.character(lat)))
nm_biomes_2x2 <- nm_biomes_2x2 %>%
count(lon, lat, biome_name) %>%
group_by(lon, lat) %>%
slice_max(n, with_ties = FALSE) %>%
ungroup()
rm(nm_biomes)
map+
geom_tile(data = nm_biomes_2x2,
aes(x = lon,
y = lat,
fill = biome_name))+
lims(y = c(-85, -30))+
scale_fill_brewer(palette = 'Dark2')+
labs(title = 'Mayot biomes post-grid reduction')
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
d14b7f1 | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
basinmask <- basinmask %>%
filter(lat < -30)
map +
geom_tile(data = basinmask,
aes(x = lon,
y = lat,
fill = basin_AIP))+
lims(y = c(-85, -30))+
scale_fill_brewer(palette = 'Dark2')
basinmask_2x2 <- basinmask %>%
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
#
# # assign basins from each pixel to to each 2 Lon x Lat pixel, based on the majority of basins in each 2x2 grid
#
basinmask_2x2 <- basinmask_2x2 %>%
count(lon, lat, basin_AIP) %>%
group_by(lon, lat) %>%
slice_max(n, with_ties = FALSE) %>%
ungroup() %>%
select(-n)
#
rm(basinmask)
map+
geom_tile(data = basinmask_2x2 %>% filter(lat < -30),
aes(x = lon,
y = lat,
fill = basin_AIP))+
lims(y = c(-85, -30))+
scale_fill_brewer(palette = 'Dark2')
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
d14b7f1 | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
OceanSODA <- OceanSODA %>%
group_by(lon, lat, month) %>%
mutate(clim_ph = mean(ph_total, na.rm = TRUE),
clim_diff = ph_total - clim_ph,
.after = ph_total) %>%
ungroup()
# Note: While reducing lon x lat grid,
# we keep the original number of observations
OceanSODA_2x2 <- OceanSODA %>%
mutate(
lat_raw = lat,
lon_raw = lon,
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
rm(OceanSODA)
# keep only Southern Ocean data
OceanSODA_2x2_SO <- inner_join(OceanSODA_2x2, nm_biomes_2x2)
# add in basin separations
OceanSODA_2x2_SO <- inner_join(OceanSODA_2x2_SO, basinmask_2x2)
# expected number of rows from -30 to -70º latitude, 360º longitude, for 12 months, 8 years:
# 40 lat x 360 lon x 12 months x 8 years = 1 382 400 rows
# actual number of rows: 919 768
OceanSODA_2x2_SO <- OceanSODA_2x2_SO %>%
filter(!is.na(ph_total))
OceanSODA_2x2_SO %>%
filter(year == 2020) %>%
ggplot(aes(lon_raw, lat_raw, fill = clim_ph)) +
geom_tile() +
scale_fill_viridis_c() +
facet_wrap(~ month, ncol = 2) +
coord_quickmap(expand = 0)
Version | Author | Date |
---|---|---|
710edd4 | jens-daniel-mueller | 2022-05-11 |
# map of climatological OceanSODA pH
OceanSODA_2x2_SO %>%
group_split(month) %>%
#head(1) %>%
map(
~map+
geom_tile(data = .x,
aes(x = lon_raw,
y = lat_raw,
fill = clim_ph))+
scale_fill_viridis_c()+
lims(y = c(-80, -30))+
labs(title = paste('climatological OceanSODA pH (1995-2020) month:', unique(.x$month)))
)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
# map of monthly anomaly relative to climatology
OceanSODA_2x2_SO %>%
filter(year >= 2013) %>%
group_split(month) %>%
#head(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(
x = lon_raw,
y = lat_raw,
fill = clim_diff
)) +
scale_fill_divergent(mid = 'grey80') +
lims(y = c(-80, -30)) +
facet_wrap( ~ year, ncol = 2) +
labs(
title = paste(
'in-situ OceanSODA pH - clim OceanSODA pH, month:',
unique(.x$month)
)
) +
theme(legend.position = 'right')
)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
# fit a linear regression of OceanSODA pH against time (temporal trend)
# in each lat/lon/month grid
OceanSODA_regression <- OceanSODA_2x2_SO %>%
# filter(basin_AIP == "Indian",
# biome_name == "SPSS",
# lon < 40) %>%
nest(data = -c(lon, lat, month)) %>%
mutate(fit = map(.x = data,
.f = ~ lm(clim_diff ~ year, data = .x)),
tidied = map(.x = fit, .f = tidy),
glanced = map(.x = fit, .f = glance),
augmented = map(.x = fit, .f = augment))
OceanSODA_regression_tidied <- OceanSODA_regression %>%
select(-c(data, fit, augmented, glanced)) %>%
unnest(tidied)
OceanSODA_regression_tidied <- OceanSODA_regression_tidied %>%
select(lat:estimate) %>%
pivot_wider(names_from = term,
values_from = estimate) %>%
rename(intercept = `(Intercept)`,
slope = year)
OceanSODA_regression_data <- OceanSODA_regression %>%
select(-c(fit, tidied, glanced, augmented)) %>%
unnest(data)
OceanSODA_regression_augmented <- OceanSODA_regression %>%
select(-c(fit, tidied, glanced, data)) %>%
unnest(augmented) %>%
select(lat:year, .resid)
OceanSODA_regression_augmented <- bind_cols(
OceanSODA_regression_augmented,
OceanSODA_regression_data %>% select(
date, basin_AIP, biome_name,
clim_ph, ph_total,
lon_raw, lat_raw))
OceanSODA_regression_glanced <- OceanSODA_regression %>%
select(-c(data, fit, tidied, augmented)) %>%
unnest(glanced)
# identify the mean value
# in each lat/lon/month grid
OceanSODA_regression_tidied <- OceanSODA_2x2_SO %>%
# filter(basin_AIP == "Indian",
# biome_name == "SPSS",
# lon < 40) %>%
group_by(lon, lat, month) %>%
summarise(slope = 0,
intercept = mean(clim_diff, na.rm = TRUE)) %>%
ungroup()
OceanSODA_regression_glanced <- OceanSODA_2x2_SO %>%
# filter(basin_AIP == "Indian",
# biome_name == "SPSS",
# lon < 40) %>%
group_by(lon, lat, month) %>%
summarise(sigma = sd(clim_diff, na.rm = TRUE)) %>%
ungroup()
OceanSODA_regression_augmented <- OceanSODA_2x2_SO %>%
# filter(basin_AIP == "Indian",
# biome_name == "SPSS",
# lon < 40) %>%
mutate(.resid = clim_diff)
map+
geom_tile(data = OceanSODA_regression_tidied,
aes(x = lon,
y = lat,
fill = slope))+
scale_fill_scico(palette = 'vik', midpoint = 0)+
lims(y = c(-85, -30))+
facet_wrap(~month, ncol = 2)
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
31e4d42 | pasqualina-vonlanthendinenna | 2022-02-02 |
de183c6 | pasqualina-vonlanthendinenna | 2022-02-01 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
map+
geom_tile(data = OceanSODA_regression_glanced,
aes(x = lon,
y = lat,
fill = sigma))+
scale_fill_viridis_c()+
lims(y = c(-85, -30))+
facet_wrap(~month, ncol = 2)+
labs(fill = '1 residual \nst. dev.')
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
31e4d42 | pasqualina-vonlanthendinenna | 2022-02-02 |
de183c6 | pasqualina-vonlanthendinenna | 2022-02-01 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
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
# combine observations and regression statistics
OceanSODA_2x2_SO_extreme_grid <-
full_join(
OceanSODA_regression_augmented,
OceanSODA_regression_glanced %>%
select(lat:month, sigma)
)
# identify observations in anomaly classes
OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
mutate(
ph_extreme = case_when(
.resid < -sigma*2 ~ 'L',
.resid > sigma*2 ~ 'H',
TRUE ~ 'N'
)
)
OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
mutate(ph_extreme = fct_relevel(ph_extreme, "H", "N", "L"))
# combine with regression coefficients
OceanSODA_2x2_SO_extreme_grid <-
full_join(OceanSODA_2x2_SO_extreme_grid,
OceanSODA_regression_tidied)
OceanSODA_2x2_SO_extreme_grid %>%
write_rds(file = paste0(path = path_argo_preprocessed, "/OceanSODA_pH_anomaly_field.rds"))
OceanSODA_2x2_SO_extreme_grid %>%
group_split(lon, lat, month) %>%
head(8) %>%
map(~ ggplot(data = .x) +
geom_point(aes(x = year,
y = clim_diff,
col = ph_extreme)) +
geom_abline(data = .x, aes(slope = slope,
intercept = intercept)) +
geom_abline(data = .x, aes(slope = slope,
intercept = intercept + 2*sigma),
linetype = 2) +
geom_abline(data = .x, aes(slope = slope,
intercept = intercept - 2*sigma),
linetype = 2) +
labs(title = paste(fititle = paste(
"lon:", unique(.x$lon),
"| lat:", unique(.x$lat),
"| month:", unique(.x$month)
))) +
scale_color_manual(values = HNL_colors))
[[1]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
486c9c8 | jens-daniel-mueller | 2022-01-07 |
[[2]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
486c9c8 | jens-daniel-mueller | 2022-01-07 |
[[3]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
486c9c8 | jens-daniel-mueller | 2022-01-07 |
[[4]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
486c9c8 | jens-daniel-mueller | 2022-01-07 |
[[5]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
486c9c8 | jens-daniel-mueller | 2022-01-07 |
[[6]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
486c9c8 | jens-daniel-mueller | 2022-01-07 |
[[7]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
[[8]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
#use the original lat/lon grid to plot the extreme on a 1x1
#(anomalies detected with 1995-2020 data but mapped only from 2013 to 2020)
OceanSODA_2x2_SO_extreme_grid %>%
filter(year >= 2013) %>%
group_split(month) %>%
#head(1) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon_raw,
y = lat_raw,
fill = ph_extreme))+
scale_fill_manual(values = HNL_colors_map)+
facet_wrap(~year, ncol = 2)+
lims(y = c(-85, -30))+
labs(title = paste('month:', unique(.x$month)),
fill = 'pH')
)
[[1]]
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
da665ab | pasqualina-vonlanthendinenna | 2022-03-01 |
[[2]]
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
da665ab | pasqualina-vonlanthendinenna | 2022-03-01 |
[[3]]
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
da665ab | pasqualina-vonlanthendinenna | 2022-03-01 |
[[4]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
e4188d2 | pasqualina-vonlanthendinenna | 2022-03-01 |
[[5]]
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
e4188d2 | pasqualina-vonlanthendinenna | 2022-03-01 |
[[6]]
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
e4188d2 | pasqualina-vonlanthendinenna | 2022-03-01 |
[[7]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
e4188d2 | pasqualina-vonlanthendinenna | 2022-03-01 |
[[8]]
Version | Author | Date |
---|---|---|
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
e4188d2 | pasqualina-vonlanthendinenna | 2022-03-01 |
[[9]]
[[10]]
[[11]]
[[12]]
# map the anomalies on the original 1x1 grid
# 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(year, biome_name, basin_AIP, ph_extreme) %>%
summarise(ph_regional = mean(ph_total, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = year, y = ph_regional, col = ph_extreme))+
geom_point(size = 0.3)+
geom_line()+
scale_color_manual(values = HNL_colors) +
facet_grid(basin_AIP~biome_name)+
theme(legend.position = 'bottom')
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
587755e | pasqualina-vonlanthendinenna | 2022-01-21 |
c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
486c9c8 | jens-daniel-mueller | 2022-01-07 |
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_name) +
coord_cartesian(xlim = c(8, 8.2)) +
labs(x = 'value',
y = 'density',
col = 'pH anomaly') +
theme(legend.position = 'bottom')
Version | Author | Date |
---|---|---|
8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
962cdb9 | pasqualina-vonlanthendinenna | 2022-01-25 |
6b22341 | pasqualina-vonlanthendinenna | 2022-01-21 |
587755e | pasqualina-vonlanthendinenna | 2022-01-21 |
c96ad5e | pasqualina-vonlanthendinenna | 2022-01-21 |
486c9c8 | jens-daniel-mueller | 2022-01-07 |
# Note: While reducing lon x lat grid,
# we keep the original number of observations
full_argo_2x2 <- full_argo %>%
mutate(
lat_raw = lat,
lon_raw = lon,
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
# keep only Southern Ocean argo data
full_argo_2x2_SO <- inner_join(full_argo_2x2, nm_biomes_2x2)
# add in basin separations
full_argo_2x2_SO <- inner_join(full_argo_2x2_SO, basinmask_2x2)
# rename OceanSODA columns
OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
select(-c(lon, lat)) %>%
rename(OceanSODA_ph = ph_total,
lon = lon_raw,
lat = lat_raw) %>%
filter(year >= 2013)
# combine the argo profile data to the surface extreme data
profile_extreme <- inner_join(
full_argo %>%
select(year, month, date, lon, lat, depth,
ph_in_situ_total_adjusted,
platform_number,
cycle_number),
OceanSODA_2x2_SO_extreme_grid %>%
select(year, month, date, lon, lat,
OceanSODA_ph, ph_extreme,
clim_ph, clim_diff,
biome_name, basin_AIP))
profile_extreme <- profile_extreme %>%
unite('platform_cycle', platform_number:cycle_number, sep = '_', remove = FALSE)
OceanSODA_2x2_SO_extreme_grid %>%
group_split(month) %>%
# head(1) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = ph_extreme),
alpha = 0.5)+
geom_tile(data = profile_extreme %>%
filter(depth < 10),
aes(x = lon,
y = lat,
fill = 'black',
height = 1,
width = 1),
alpha = 0.3)+
scale_fill_manual(values = HNL_colors_map)+
facet_wrap(~year, ncol = 1)+
lims(y = c(-85, -30))+
labs(title = paste('month:', unique(.x$month)),
fill = 'pH')
)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
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
profile_extreme %>%
group_split(biome_name, basin_AIP, year) %>%
head(6) %>%
map(
~ ggplot(
data = .x,
aes(
x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme
)
) +
geom_path(data = .x %>% filter(ph_extreme == 'N'),
aes(x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.3) +
geom_path(data = .x %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
aes(x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.5)+
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_name)
),
col = 'OceanSODA pH \nanomaly'
)
)
[[1]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
8ef1277 | pasqualina-vonlanthendinenna | 2022-03-01 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
[[2]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
8ef1277 | pasqualina-vonlanthendinenna | 2022-03-01 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
[[3]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
8ef1277 | pasqualina-vonlanthendinenna | 2022-03-01 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
[[4]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
8ef1277 | pasqualina-vonlanthendinenna | 2022-03-01 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
[[5]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
8ef1277 | pasqualina-vonlanthendinenna | 2022-03-01 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
[[6]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
8ef1277 | pasqualina-vonlanthendinenna | 2022-03-01 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
# plot profiles for the Atlantic basin, biome 1, month 08, 2017
OceanSODA_SO_extreme_grid_2017 <-
OceanSODA_2x2_SO_extreme_grid %>%
filter(date == '2017-08-15')
map+
geom_tile(data = OceanSODA_SO_extreme_grid_2017,
aes(x = lon,
y = lat,
fill = ph_extreme))+
scale_fill_manual(values = HNL_colors_map)+
lims(y = c(-85, -30))+
labs(title = 'August 2017',
fill = 'OceanSODA pH \nextreme')
profile_extreme_Atl_2017 <- profile_extreme %>%
filter(date == '2017-08-15',
basin_AIP == 'Atlantic',
biome_name == 'STSS')
profile_extreme_Atl_2017 %>%
ggplot(aes(y = depth,
x = ph_in_situ_total_adjusted,
group = platform_cycle,
col = ph_extreme))+
geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'N'),
aes(x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.3)+
geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'H'| ph_extreme == 'L'),
aes(x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.5)+
scale_y_reverse()+
scale_color_manual(values = HNL_colors)+
labs(title = 'Atlantic basin, STSS biome, August 2017',
col = 'OceanSODA\npH anomaly',
x = 'Argo pH')
rm(OceanSODA_SO_extreme_grid_2017, profile_extreme_Atl_2017)
# Plot profiles for the Pacific basin, biome 3, months 12, 2017
OceanSODA_SO_extreme_grid_2017 <-
OceanSODA_2x2_SO_extreme_grid %>%
filter(date == '2017-12-15')
map+
geom_tile(data = OceanSODA_SO_extreme_grid_2017,
aes(x = lon,
y = lat,
fill = ph_extreme))+
scale_fill_manual(values = HNL_colors_map)+
lims(y = c(-85, -30))+
labs(title = 'December 2017',
fill = 'OceanSODA pH \nextreme')
profile_extreme_Atl_2017 <- profile_extreme %>%
filter(date == '2017-12-15',
basin_AIP == 'Atlantic',
biome_name == 'STSS')
profile_extreme_Atl_2017 %>%
ggplot(aes(y = depth,
x = ph_in_situ_total_adjusted,
group = platform_cycle,
col = ph_extreme))+
geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'N'),
aes(x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.3)+
geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
aes(x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.5)+
scale_y_reverse()+
scale_color_manual(values = HNL_colors)+
labs(title = 'Atlantic basin, STSS biome, December 2017',
col = 'OceanSODA\npH anomaly',
x = 'Argo pH')
rm(OceanSODA_SO_extreme_grid_2017, profile_extreme_Atl_2017)
OceanSODA_SO_extreme_grid_2018 <-
OceanSODA_2x2_SO_extreme_grid %>%
filter(date == '2018-01-15')
map+
geom_tile(data = OceanSODA_SO_extreme_grid_2018,
aes(x = lon,
y = lat,
fill = ph_extreme))+
scale_fill_manual(values = HNL_colors_map)+
lims(y = c(-85, -30))+
labs(title = 'January 2018',
fill = 'OceanSODA pH \nextreme')
profile_extreme_Atl_2018 <- profile_extreme %>%
filter(date == '2018-01-15',
basin_AIP == 'Atlantic',
biome_name == 'STSS')
profile_extreme_Atl_2018 %>%
ggplot(aes(y = depth,
x = ph_in_situ_total_adjusted,
group = platform_cycle,
col = ph_extreme))+
geom_path(data = profile_extreme_Atl_2018 %>% filter(ph_extreme == 'N'),
aes(x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.3)+
geom_path(data = profile_extreme_Atl_2018 %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
aes(x = ph_in_situ_total_adjusted,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.5)+
scale_y_reverse()+
scale_color_manual(values = HNL_colors)+
labs(title = 'Atlantic basin, STSS biome, January 2018',
col = 'OceanSODA\npH anomaly',
x = 'Argo pH')
rm(OceanSODA_SO_extreme_grid_2018, profile_extreme_Atl_2018)
# 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 <- 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
)
profile_extreme_mean <- profile_extreme %>%
group_by(ph_extreme, depth) %>%
summarise(ph_mean = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
ph_std = sd(ph_in_situ_total_adjusted, na.rm = TRUE)) %>%
ungroup()
profile_extreme_mean %>%
arrange(depth) %>%
ggplot(aes(
x = ph_mean,
y = depth,
group = ph_extreme,
col = ph_extreme
)) +
geom_ribbon(aes(xmin = ph_mean - ph_std,
xmax = ph_mean + ph_std,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(title = "Overall mean",
col = 'OceanSODA\npH anomaly \n(mean ± st dev)',
fill = 'OceanSODA\npH anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'Argo mean pH') +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
daa0a8f | pasqualina-vonlanthendinenna | 2022-02-24 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
44fcfb6 | pasqualina-vonlanthendinenna | 2022-02-01 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
rm(profile_extreme_mean)
Number of profiles
profile_count_mean <- profile_extreme %>%
distinct(ph_extreme, platform_number, cycle_number) %>%
count(ph_extreme)
profile_count_mean %>%
ggplot(aes(x = ph_extreme, y = n, fill = ph_extreme))+
geom_col(width = 0.5)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = 'Number of profiles')
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
# rm(profile_count_mean)
Surface Argo pH vs surface OceanSODA pH (20 m)
# calculate surface-mean argo pH, for each profile
surface_ph_mean <- profile_extreme %>%
filter(depth <= 20) %>%
group_by(ph_extreme, platform_number, cycle_number) %>%
summarise(argo_surf_ph = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
OceanSODA_surf_ph = mean(OceanSODA_ph, na.rm = TRUE))
surface_ph_mean %>%
group_by(ph_extreme) %>%
group_split(ph_extreme) %>%
map(
~ggplot(data = .x, aes(x = OceanSODA_surf_ph,
y = argo_surf_ph))+
geom_bin2d(data = .x, aes(x = OceanSODA_surf_ph,
y = argo_surf_ph)) +
scale_fill_viridis_c()+
geom_abline(slope = 1, intercept = 0)+
coord_fixed(ratio = 1,
xlim = c(7.9, 8.21),
ylim = c(7.9, 8.21))+
# facet_grid(basin_AIP ~ biome) +
labs(title = paste('pH extreme:', unique(.x$ph_extreme)),
x = 'OceanSODA pH',
y = 'Argo pH')
)
[[1]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[2]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[3]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
rm(surface_ph_mean)
profile_extreme_mean_jan <- profile_extreme %>%
filter(month == 1) %>%
group_by(ph_extreme, depth) %>%
summarise(ph_mean = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
ph_std = sd(ph_in_situ_total_adjusted, na.rm = TRUE)) %>%
ungroup()
profile_extreme_mean_jan %>%
arrange(depth) %>%
ggplot(aes(
x = ph_mean,
y = depth,
group = ph_extreme,
col = ph_extreme
)) +
geom_ribbon(aes(xmin = ph_mean - ph_std,
xmax = ph_mean + ph_std,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(title = "Overall mean January profiles",
col = 'OceanSODA\npH anomaly \n(mean ± st dev)',
fill = 'OceanSODA\npH anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'Argo mean pH') +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))
rm(profile_extreme_mean_jan)
profile_extreme_biome <- profile_extreme %>%
group_by(season, biome_name, ph_extreme, depth) %>%
summarise(ph_biome = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
ph_biome_std = sd(ph_in_situ_total_adjusted, na.rm = TRUE)) %>%
ungroup()
profile_extreme_biome %>%
ggplot(aes(
x = ph_biome,
y = depth,
group = ph_extreme,
col = ph_extreme
)) +
geom_ribbon(aes(xmin = ph_biome - ph_biome_std,
xmax = ph_biome + ph_biome_std,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(col = 'OceanSODA\npH anomaly \n(mean ± st dev)',
fill = 'OceanSODA\npH anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'Biome mean Argo pH',
title = 'Biome-mean Argo profiles') +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
facet_grid(season ~ biome_name)
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
daa0a8f | pasqualina-vonlanthendinenna | 2022-02-24 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
44fcfb6 | pasqualina-vonlanthendinenna | 2022-02-01 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
rm(profile_extreme_biome)
Number of profiles season x biome
profile_count_biome <- profile_extreme %>%
distinct(season, biome_name, ph_extreme, platform_number, cycle_number) %>%
group_by(season, biome_name, ph_extreme) %>%
count(ph_extreme)
profile_count_biome %>%
ggplot(aes(x = ph_extreme, y = n, fill = ph_extreme))+
geom_col(width = 0.5)+
facet_grid(season ~ biome_name)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = 'Number of profiles season x biome')
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
# rm(profile_count_biome)
Surface Argo vs surface OceanSODA pH (20 m) season x biome
surface_ph_biome <- profile_extreme %>%
filter(depth <= 20) %>%
group_by(season, biome_name, ph_extreme, platform_number, cycle_number) %>%
summarise(argo_surf_ph = mean(ph_in_situ_total_adjusted, na.rm=TRUE),
OceanSODA_surf_ph = mean(OceanSODA_ph, na.rm = TRUE))
surface_ph_biome %>%
group_by(ph_extreme) %>%
group_split(ph_extreme) %>%
map(
~ggplot(data = .x, aes(x = OceanSODA_surf_ph,
y = argo_surf_ph))+
geom_bin2d(data = .x, aes(x = OceanSODA_surf_ph,
y = argo_surf_ph)) +
scale_fill_viridis_c()+
geom_abline(slope = 1, intercept = 0)+
coord_fixed(ratio = 1,
xlim = c(7.94, 8.21),
ylim = c(7.94, 8.21))+
facet_grid(season~biome_name) +
labs(title = paste( 'pH extreme:', unique(.x$ph_extreme)),
x = 'OceanSODA pH',
y = 'Argo pH')
)
[[1]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[2]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[3]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
rm(surface_ph_biome)
profile_extreme_basin <- profile_extreme %>%
group_by(season, basin_AIP, ph_extreme, depth) %>%
summarise(ph_basin = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
ph_basin_std = sd(ph_in_situ_total_adjusted, na.rm = TRUE)) %>%
ungroup()
profile_extreme_basin %>%
ggplot(aes(x = ph_basin,
y = depth,
group = ph_extreme,
col = ph_extreme))+
geom_ribbon(aes(xmax = ph_basin + ph_basin_std,
xmin = ph_basin - ph_basin_std,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.3)+
geom_path()+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
labs(col = 'OceanSODA\npH anomaly\n(mean ± st dev)',
fill = 'OceanSODA\npH anomaly\n(mean ± st dev)',
y = 'depth (m)',
x = 'Basin mean Argo pH',
title = 'Basin-mean Argo profiles')+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
facet_grid(season~basin_AIP)
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
daa0a8f | pasqualina-vonlanthendinenna | 2022-02-24 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
44fcfb6 | pasqualina-vonlanthendinenna | 2022-02-01 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
cfd734c | jens-daniel-mueller | 2022-01-28 |
c44ff0f | pasqualina-vonlanthendinenna | 2022-01-25 |
rm(profile_extreme_basin)
Number of profiles season x basin
profile_count_basin <- profile_extreme %>%
distinct(season, basin_AIP, ph_extreme, platform_number, cycle_number) %>%
group_by(season, basin_AIP, ph_extreme) %>%
count(ph_extreme)
profile_count_basin %>%
ggplot(aes(x = ph_extreme, y = n, fill = ph_extreme))+
geom_col(width = 0.5)+
facet_grid(season~basin_AIP)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = 'Number of profiles season x basin')
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
# rm(profile_count_basin)
Surface Argo vs surface OceanSODA pH (20 m) season x basin
# calculate surface-mean argo pH to compare against OceanSODA surface pH (one value)
surface_ph_basin <- profile_extreme %>%
filter(depth <= 20) %>%
group_by(season, basin_AIP, ph_extreme, platform_number, cycle_number) %>%
summarise(surf_argo_ph = mean(ph_in_situ_total_adjusted, na.rm=TRUE),
surf_OceanSODA_ph = mean(OceanSODA_ph, na.rm = TRUE))
surface_ph_basin %>%
group_by(ph_extreme) %>%
group_split(ph_extreme) %>%
map(
~ggplot(data = .x, aes(x = surf_OceanSODA_ph,
y = surf_argo_ph))+
geom_bin2d(data = .x, aes(x = surf_OceanSODA_ph,
y = surf_argo_ph)) +
scale_fill_viridis_c()+
geom_abline(slope = 1, intercept = 0)+
coord_fixed(ratio = 1,
xlim = c(7.94, 8.21),
ylim = c(7.94, 8.21))+
facet_grid(season~basin_AIP) +
labs(title = paste('pH extreme:', unique(.x$ph_extreme)),
x = 'OceanSODA pH',
y = 'Argo pH')
)
[[1]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
2d1bdae | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[2]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
2d1bdae | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[3]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
2d1bdae | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
rm(surface_ph_basin)
profile_extreme_biome_basin_jan <- profile_extreme %>%
filter(month == 1) %>%
group_by(biome_name, basin_AIP, ph_extreme, depth) %>%
summarise(ph_mean = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
ph_std = sd(ph_in_situ_total_adjusted, na.rm = TRUE)) %>%
ungroup()
profile_extreme_biome_basin_jan %>%
arrange(depth) %>%
ggplot(aes(x = ph_mean,
y = depth)) +
geom_ribbon(aes(xmin = ph_mean - ph_std,
xmax = ph_mean + ph_std,
fill = ph_extreme),
alpha = 0.2)+
geom_path(aes(x = ph_mean,
col = ph_extreme))+
facet_grid(basin_AIP~biome_name)+
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(title = "Basin-biome-mean January profiles",
col = 'OceanSODA\nph anomaly \n(mean ± st dev)',
fill = 'OceanSODA\nph anomaly \n(mean ± st dev)',
y = 'sqrt(depth)',
x = 'mean Argo pH)') +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))
rm(profile_extreme_biome_basin_jan)
profile_extreme_season <- profile_extreme %>%
group_by(season, biome_name, basin_AIP, ph_extreme, depth) %>%
summarise(ph_mean = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
ph_std = sd(ph_in_situ_total_adjusted, na.rm = TRUE)) %>%
ungroup()
profile_extreme_season %>%
arrange(depth) %>%
group_split(season) %>%
# head(1) %>%
map(
~ ggplot(
data = .x,
aes(
x = ph_mean,
y = depth,
group = ph_extreme,
col = ph_extreme
)
) +
geom_ribbon(aes(xmax = ph_mean + ph_std,
xmin = ph_mean - ph_std,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(title = paste("season:", unique(.x$season)),
col = 'OceanSODA\npH anomaly\n(mean ± st dev)',
fill = 'OceanSODA\npH anomaly\n(mean ± st dev)',
y = 'sqrt(depth)',
x = 'Argo pH') +
scale_y_continuous(
trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))
) +
facet_grid(basin_AIP ~ biome_name)
)
[[1]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
daa0a8f | pasqualina-vonlanthendinenna | 2022-02-24 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
44fcfb6 | pasqualina-vonlanthendinenna | 2022-02-01 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
[[2]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
daa0a8f | pasqualina-vonlanthendinenna | 2022-02-24 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
44fcfb6 | pasqualina-vonlanthendinenna | 2022-02-01 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
[[3]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
daa0a8f | pasqualina-vonlanthendinenna | 2022-02-24 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
44fcfb6 | pasqualina-vonlanthendinenna | 2022-02-01 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
[[4]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
daa0a8f | pasqualina-vonlanthendinenna | 2022-02-24 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
44fcfb6 | pasqualina-vonlanthendinenna | 2022-02-01 |
28c8d17 | jens-daniel-mueller | 2022-01-29 |
Number of profiles per season x biome x basin x pH extreme
profile_count_season <- profile_extreme %>%
distinct(season, biome_name, basin_AIP,
ph_extreme, platform_number, cycle_number) %>%
group_by(season, biome_name, basin_AIP, ph_extreme) %>%
count(ph_extreme)
profile_count_season %>%
group_by(season) %>%
group_split(season) %>%
map(
~ggplot()+
geom_col(data =.x,
aes(x = ph_extreme,
y = n,
fill = ph_extreme),
width = 0.5)+
facet_grid(basin_AIP ~ biome_name)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = paste('season:', unique(.x$season)))
)
[[1]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
2d1bdae | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
[[2]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
2d1bdae | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
[[3]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
2d1bdae | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
[[4]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
2d1bdae | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
# rm(profile_count_season)
Surface OceanSODA pH vs surface Argo pH (20 m)
# calculate surface-mean argo pH, for each season x biome x basin x ph extreme
surface_ph_season <- profile_extreme %>%
filter(depth <= 20) %>%
group_by(season,
basin_AIP,
biome_name,
ph_extreme,
platform_number,
cycle_number) %>%
summarise(surf_argo_ph = mean(ph_in_situ_total_adjusted, na.rm=TRUE),
surf_OceanSODA_ph = mean(OceanSODA_ph, na.rm = TRUE))
surface_ph_season %>%
group_by(season, ph_extreme) %>%
group_split(season, ph_extreme) %>%
map(
~ggplot(data = .x, aes(x = surf_OceanSODA_ph,
y = surf_argo_ph))+
geom_bin2d(data = .x, aes(x = surf_OceanSODA_ph,
y = surf_argo_ph)) +
scale_fill_viridis_c()+
geom_abline(slope = 1, intercept = 0)+
coord_fixed(ratio = 1,
xlim = c(7.94, 8.21),
ylim = c(7.94, 8.21))+
facet_grid(basin_AIP ~ biome_name) +
labs(title = paste('season:', unique(.x$season),
'| pH extreme:', unique(.x$ph_extreme)),
x = 'OceanSODA pH',
y = 'Argo pH')
)
[[1]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
de183c6 | pasqualina-vonlanthendinenna | 2022-02-01 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
[[2]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
de183c6 | pasqualina-vonlanthendinenna | 2022-02-01 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
[[3]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
de183c6 | pasqualina-vonlanthendinenna | 2022-02-01 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
[[4]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
de183c6 | pasqualina-vonlanthendinenna | 2022-02-01 |
44a2ec3 | pasqualina-vonlanthendinenna | 2022-02-01 |
[[5]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[6]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[7]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[8]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[9]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[10]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[11]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
[[12]]
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
71ced5f | pasqualina-vonlanthendinenna | 2022-02-23 |
25c9e6b | pasqualina-vonlanthendinenna | 2022-02-03 |
71958c4 | pasqualina-vonlanthendinenna | 2022-02-03 |
d7debab | pasqualina-vonlanthendinenna | 2022-02-02 |
7376be6 | pasqualina-vonlanthendinenna | 2022-02-02 |
rm(surface_ph_season)
profile_extreme_season %>%
filter(basin_AIP == 'Atlantic',
season == 'winter',
biome_name == 'SPSS') %>%
arrange(depth) %>%
ggplot(aes(x = ph_mean,
y = depth,
group = ph_extreme,
col = ph_extreme)) +
geom_ribbon(aes(xmax = ph_mean + ph_std,
xmin = ph_mean - ph_std,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(title = 'Atlantic basin, SPSS biome, winter',
col = 'OceanSODA\npH anomaly\n(mean ± st dev)',
fill = 'OceanSODA\npH anomaly\n(mean ± st dev)',
y = 'sqrt(depth)',
x = 'Argo pH') +
scale_y_continuous(
trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
a2271df | pasqualina-vonlanthendinenna | 2022-03-30 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
e4188d2 | pasqualina-vonlanthendinenna | 2022-03-01 |
profile_extreme_season %>%
filter(basin_AIP == 'Atlantic',
season == 'summer',
biome_name == 'STSS') %>%
arrange(depth) %>%
ggplot(aes(x = ph_mean,
y = depth,
group = ph_extreme,
col = ph_extreme)) +
geom_ribbon(aes(xmax = ph_mean + ph_std,
xmin = ph_mean - ph_std,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(title = 'Atlantic basin, STSS biome, summer',
col = 'OceanSODA\npH anomaly\n(mean ± st dev)',
fill = 'OceanSODA\npH anomaly\n(mean ± st dev)',
y = 'sqrt(depth)',
x = 'Argo pH') +
scale_y_continuous(
trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))
Version | Author | Date |
---|---|---|
b917bd0 | jens-daniel-mueller | 2022-05-11 |
9875dd0 | pasqualina-vonlanthendinenna | 2022-04-05 |
eb8e3be | pasqualina-vonlanthendinenna | 2022-03-31 |
a2271df | pasqualina-vonlanthendinenna | 2022-03-30 |
968ad85 | pasqualina-vonlanthendinenna | 2022-03-29 |
dfd75e9 | pasqualina-vonlanthendinenna | 2022-03-29 |
e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
1ffe07f | pasqualina-vonlanthendinenna | 2022-03-11 |
7540ae4 | pasqualina-vonlanthendinenna | 2022-03-08 |
e4188d2 | pasqualina-vonlanthendinenna | 2022-03-01 |
profile_extreme_binned <- profile_extreme %>%
group_by(lon, lat, year, month, platform_cycle,
biome_name, basin_AIP, ph_extreme,
depth, season) %>%
summarise(ph_adjusted_binned = mean(ph_in_situ_total_adjusted, na.rm = TRUE)) %>%
ungroup()
broullon_clim <- read_rds(file = paste0(path_argo_preprocessed,
'/broullon_TA_DIC_clim_SO_pH.rds'))
# compatibility with profile_extreme
broullon_clim <- broullon_clim %>%
mutate(depth_broullon = depth)
# grid average climatological temp into the argo depth bins
broullon_clim <- broullon_clim %>%
mutate(
depth = cut(
depth_broullon,
breaks = c(0, 10, 20, 30, 50, 70, 100, 300, 500, 800, 1000, 1500, 2000),
include.lowest = TRUE,
labels = as.factor(unique(profile_extreme$depth))[1:12]
),
depth = as.numeric(as.character(depth))
)
# calculate mean climatological pH per depth bin
broullon_clim_binned <- broullon_clim %>%
group_by(lon, lat, depth, month, basin_AIP, biome_name) %>%
summarise(clim_ph_binned = mean(pH, na.rm = TRUE)) %>%
ungroup()
# join climatology and ARGO profiles
remove_clim <- inner_join(profile_extreme_binned,
broullon_clim_binned)
remove_clim %>%
group_split(biome_name, basin_AIP, year) %>%
head(6) %>%
map(
~ ggplot() +
geom_path(
data = .x %>%
filter(ph_extreme == 'N'),
aes(
x = ph_adjusted_binned,
y = depth,
group = platform_cycle,
col = ph_extreme
),
size = 0.3
) +
geom_path(
data = .x %>%
filter(ph_extreme == 'H' | ph_extreme == 'L'),
aes(
x = ph_adjusted_binned,
y = depth,
group = platform_cycle,
col = ph_extreme
),
size = 0.5
) +
geom_point(
data = .x,
aes(x = clim_ph_binned,
y = depth,
col = ph_extreme),
size = 0.5
) +
scale_y_reverse() +
scale_color_manual(values = HNL_colors) +
labs(
x = 'Argo pH',
y = 'depth (m)',
title = paste(
"Biome:",
unique(.x$biome_name),
"| basin:",
unique(.x$basin_AIP),
" | ",
unique(.x$year)
),
col = 'OceanSODA pH \nanomaly'
)
)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
remove_clim <- remove_clim %>%
mutate(argo_ph_anomaly = ph_adjusted_binned - clim_ph_binned)
remove_clim %>%
group_split(month) %>%
#head(1) %>%
map(
~ggplot()+
geom_path(data = .x %>% filter(ph_extreme == 'N'),
aes(x = argo_ph_anomaly,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.2)+
geom_path(data = .x %>% filter(ph_extreme == 'H'| ph_extreme == 'L'),
aes(x = argo_ph_anomaly,
y = depth,
group = platform_cycle,
col = ph_extreme),
size = 0.3)+
geom_vline(xintercept = 0)+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
facet_grid(basin_AIP~biome_name)+
labs(title = paste('month:', unique(.x$month)))
)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
remove_clim_overall_mean <- remove_clim %>%
group_by(ph_extreme, depth) %>%
summarise(ph_anomaly_mean = mean(argo_ph_anomaly, na.rm = TRUE),
ph_anomaly_sd = sd(argo_ph_anomaly, na.rm = TRUE))
remove_clim_overall_mean %>%
ggplot()+
geom_path(aes(x = ph_anomaly_mean,
y = depth,
group = ph_extreme,
col = ph_extreme))+
geom_ribbon(aes(xmax = ph_anomaly_mean + ph_anomaly_sd,
xmin = ph_anomaly_mean - ph_anomaly_sd,
y = depth,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_vline(xintercept = 0)+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
geom_text_repel(data = profile_count_mean,
aes(x = -0.075,
y = 1500,
label = paste0(n),
col = ph_extreme),
size = 7,
segment.color = 'transparent')+
labs(title = 'Overall Mean Anomaly Profiles')
rm(remove_clim_overall_mean, profile_count_mean)
remove_clim_biome <- remove_clim %>%
group_by(depth, ph_extreme, biome_name, season) %>%
summarise(ph_anomaly_mean = mean(argo_ph_anomaly, na.rm = TRUE),
ph_anomaly_sd = sd(argo_ph_anomaly, na.rm = TRUE))
remove_clim_biome %>%
ggplot()+
geom_path(aes(x = ph_anomaly_mean,
y = depth,
group = ph_extreme,
col = ph_extreme))+
geom_ribbon(aes(xmax = ph_anomaly_mean + ph_anomaly_sd,
xmin = ph_anomaly_mean - ph_anomaly_sd,
y = depth,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_vline(xintercept = 0)+
scale_y_continuous(trans = trans_reverser('sqrt'),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
facet_grid(season~biome_name)+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
geom_text_repel(data = profile_count_biome,
aes(x = -0.085,
y = 1500,
label = paste0(n),
col = ph_extreme),
size = 4,
segment.color = 'transparent')+
labs(title = 'Biome-mean anomaly profiles')
rm(remove_clim_biome, profile_count_biome)
remove_clim_basin <- remove_clim %>%
group_by(depth, ph_extreme, basin_AIP, season) %>%
summarise(ph_anomaly_mean = mean(argo_ph_anomaly, na.rm = TRUE),
ph_anomaly_sd = sd(argo_ph_anomaly, na.rm = TRUE))
remove_clim_basin %>%
ggplot()+
geom_path(aes(x = ph_anomaly_mean,
y = depth,
group = ph_extreme,
col = ph_extreme))+
geom_ribbon(aes(xmax = ph_anomaly_mean + ph_anomaly_sd,
xmin = ph_anomaly_mean - ph_anomaly_sd,
y = depth,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
scale_y_continuous(trans = trans_reverser('sqrt'),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
geom_vline(xintercept = 0)+
facet_grid(season~basin_AIP)+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
geom_text_repel(data = profile_count_basin,
aes(x = -0.1,
y = 1500,
label = paste0(n),
col = ph_extreme),
size = 4,
segment.color = 'transparent')+
labs(title = 'Basin-mean anomaly profiles')
rm(remove_clim_basin, profile_count_basin)
remove_clim_basin_biome <- remove_clim %>%
group_by(depth, ph_extreme, season, basin_AIP, biome_name) %>%
summarise(ph_anomaly_mean = mean(argo_ph_anomaly, na.rm=TRUE),
ph_anomaly_sd = sd(argo_ph_anomaly, na.rm = TRUE))
remove_clim_basin_biome %>%
group_by(season) %>%
group_split(season) %>%
map(
~ggplot()+
geom_path(data = .x,
aes(x = ph_anomaly_mean,
y = depth,
group = ph_extreme,
col = ph_extreme))+
geom_ribbon(data = .x,
aes(xmax = ph_anomaly_mean+ph_anomaly_sd,
xmin = ph_anomaly_mean-ph_anomaly_sd,
y = depth,
group = ph_extreme,
fill = ph_extreme),
col = NA,
alpha = 0.2)+
geom_vline(xintercept = 0)+
scale_y_continuous(trans = trans_reverser('sqrt'),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
facet_grid(biome_name~basin_AIP)+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
labs(title = paste0('basin-biome-mean profiles ', unique(.x$season)))
)
[[1]]
[[2]]
[[3]]
[[4]]
rm(remove_clim_basin_biome)
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] ggrepel_0.9.1 ggforce_0.3.3 metR_0.11.0 scico_1.3.0
[5] ggOceanMaps_1.2.6 ggspatial_1.1.5 broom_0.7.11 lubridate_1.8.0
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[17] 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 polyclip_1.10-0
[19] Formula_1.2-4 jsonlite_1.7.3 cluster_2.1.2
[22] dbplyr_2.1.1 png_0.1-7 rgeos_0.5-9
[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 tweenr_1.0.2
[34] htmltools_0.5.2 tools_4.1.2 gtable_0.3.0
[37] glue_1.6.0 Rcpp_1.0.8 cellranger_1.1.0
[40] jquerylib_0.1.4 raster_3.5-11 vctrs_0.3.8
[43] xfun_0.29 ps_1.6.0 rvest_1.0.2
[46] lifecycle_1.0.1 terra_1.5-12 getPass_0.2-2
[49] MASS_7.3-55 scales_1.1.1 vroom_1.5.7
[52] hms_1.1.1 promises_1.2.0.1 parallel_4.1.2
[55] RColorBrewer_1.1-2 yaml_2.2.1 gridExtra_2.3
[58] sass_0.4.0 rpart_4.1-15 latticeExtra_0.6-29
[61] stringi_1.7.6 highr_0.9 e1071_1.7-9
[64] checkmate_2.0.0 rlang_1.0.2 pkgconfig_2.0.3
[67] evaluate_0.14 lattice_0.20-45 sf_1.0-5
[70] htmlwidgets_1.5.4 labeling_0.4.2 bit_4.0.4
[73] processx_3.5.2 tidyselect_1.1.1 magrittr_2.0.1
[76] R6_2.5.1 generics_0.1.1 Hmisc_4.6-0
[79] DBI_1.1.2 foreign_0.8-82 pillar_1.6.4
[82] haven_2.4.3 whisker_0.4 withr_2.4.3
[85] units_0.7-2 nnet_7.3-17 survival_3.2-13
[88] sp_1.4-6 modelr_0.1.8 crayon_1.4.2
[91] KernSmooth_2.23-20 utf8_1.2.2 tzdb_0.2.0
[94] rmarkdown_2.11 jpeg_0.1-9 grid_4.1.2
[97] readxl_1.3.1 data.table_1.14.2 callr_3.7.0
[100] git2r_0.29.0 reprex_2.0.1 digest_0.6.29
[103] classInt_0.4-3 httpuv_1.6.5 munsell_0.5.0
[106] viridisLite_0.4.0 bslib_0.3.1