Extreme pH Profiles
Pasqualina Vonlanthen & Jens Daniel Müller
07 January, 2022
Last updated: 2022-01-07
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Knit directory: bgc_argo_r_argodata/
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| Rmd | f53cc2d | pasqualina-vonlanthendinenna | 2022-01-06 | updated profile page |
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| 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_1x1 <- read_rds(file = paste0(path_argo_preprocessed,
"/region_masks_all_1x1.rds"))
region_masks_all_1x1 <- region_masks_all_1x1 %>%
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 <- basinmask %>%
filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>%
select(lon, lat, basin_AIP)
# OceanSODA
OceanSODA <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA.rds"))
OceanSODA <- OceanSODA %>%
mutate(month = month(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 <- full_argo %>%
mutate(year = year(date),
month = month(date)) %>%
mutate(date = ymd(format(date, "%Y-%m-15")))Regions
Biomes
basemap(limits = -32) +
geom_spatial_tile(
data = region_masks_all_1x1,
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_1x1,
aes(x = lon,
y = lat,
fill = coast),
col = 'transparent'
) +
scale_fill_brewer(palette = "Dark2")
Apply region masks
region_masks_all_1x1 <- region_masks_all_1x1 %>%
filter(coast == "0")# keep only Southern Ocean data
OceanSODA_SO <- inner_join(region_masks_all_1x1, OceanSODA)
# add in basin separations
OceanSODA_SO <- inner_join(OceanSODA_SO, basinmask)
# keep only Southern Ocean argo data
full_argo_SO <- inner_join(full_argo, region_masks_all_1x1)
# add in basin separations
full_argo_SO <- inner_join(full_argo_SO, basinmask)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
# calculate climatological average OceanSODA pH
# and the 95th percentile of the monthly OceanSODA pH
OceanSODA_SO_clim_grid <- OceanSODA_SO %>%
group_by(lon, lat, month) %>%
summarise(
ph_mean = mean(ph_total, na.rm = TRUE),
ph_threshold_high = quantile(ph_total, 0.95, na.rm = TRUE),
ph_threshold_low = quantile(ph_total, 0.05, na.rm = TRUE)
) %>%
ungroup()
OceanSODA_SO_extreme_grid <- inner_join(OceanSODA_SO, OceanSODA_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, 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_SO_extreme_grid <- OceanSODA_SO_extreme_grid %>%
mutate(
ph_extreme = case_when(
ph_total < ph_threshold_low ~ 'L',
ph_total > ph_threshold_high ~ 'H',
TRUE ~ 'N'
)
) %>%
drop_na()
OceanSODA_SO_extreme_grid <- OceanSODA_SO_extreme_grid %>%
mutate(ph_extreme = fct_relevel(ph_extreme, "H", "N", "L"))OceanSODA_SO_extreme_grid %>%
group_split(lon, lat, month) %>%
head(6) %>%
map(~ ggplot(data = .x) +
geom_hline(aes(yintercept = ph_threshold_high), linetype = 2) +
geom_hline(aes(yintercept = ph_threshold_low), linetype = 2) +
geom_hline(aes(yintercept = ph_mean)) +
geom_point(
aes(x = year, y = ph_total, col = ph_extreme)) +
labs(title = paste(
"lon:", unique(.x$lon),
"| lat:", unique(.x$lat),
"| month:", unique(.x$month)
)) +
scale_color_manual(values = HNL_colors))[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
Anomaly maps
Location of OceanSODA pH extremes
OceanSODA_SO_extreme_grid %>%
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]]
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[[8]]
Anomaly time series
# calculate a regional mean pH for each biome, basin, and ph extreme (H/L/N) and plot a timeseries
OceanSODA_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_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 = 'date',
y = 'regional mean pH',
col = 'pH anomaly') +
theme(legend.position = 'bottom')
Threshold histogram
OceanSODA_SO_clim_grid %>%
pivot_longer(starts_with("ph_"),
names_to = "level",
values_to = "value") %>%
ggplot(aes(value, col = level)) +
geom_density() +
coord_cartesian(xlim = c(8, 8.2)) +
theme(legend.position = 'bottom')
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 climatological average OceanSODA pH
# and the 95th percentile of the monthly OceanSODA pH
OceanSODA_SO_clim_biome <- OceanSODA_SO %>%
group_by(biome, basin_AIP, month) %>%
summarise(
ph_mean = mean(ph_total, na.rm = TRUE),
ph_threshold_high = quantile(ph_total, 0.95, na.rm = TRUE),
ph_threshold_low = quantile(ph_total, 0.05, na.rm = TRUE)
) %>%
ungroup()
OceanSODA_SO_extreme_biome <- inner_join(OceanSODA_SO, OceanSODA_SO_clim_biome)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_SO_extreme_biome <- OceanSODA_SO_extreme_biome %>%
mutate(
ph_extreme = case_when(
ph_total < ph_threshold_low ~ 'L',
ph_total > ph_threshold_high ~ 'H',
TRUE ~ 'N'
)
) %>%
drop_na()
OceanSODA_SO_extreme_biome <- OceanSODA_SO_extreme_biome %>%
mutate(ph_extreme = fct_relevel(ph_extreme, "H", "N", "L"))OceanSODA_SO_extreme_biome %>%
group_split(biome, basin_AIP, month) %>%
head(6) %>%
map(~ ggplot(data = .x) +
geom_hline(aes(yintercept = ph_threshold_high), linetype = 2) +
geom_hline(aes(yintercept = ph_threshold_low), linetype = 2) +
geom_hline(aes(yintercept = ph_mean)) +
geom_point(
aes(x = year, y = ph_total, col = ph_extreme)) +
labs(title = paste(
"lon:", unique(.x$biome),
"| lat:", unique(.x$basin_AIP),
"| month:", unique(.x$month)
)) +
scale_color_manual(values = HNL_colors))[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
Anomaly maps
Location of OceanSODA pH extremes
OceanSODA_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_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_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 = 'date',
y = 'regional mean pH',
col = 'pH anomaly') +
theme(legend.position = 'bottom')
Threshold histogram
OceanSODA_SO_clim_biome %>%
pivot_longer(starts_with("ph_"),
names_to = "level",
values_to = "value") %>%
ggplot(aes(value, col = level)) +
geom_density() +
coord_cartesian(xlim = c(8, 8.2)) +
theme(legend.position = 'bottom')
Argo
Join OceanSODA
# rename OceanSODA columns
OceanSODA_SO_extreme <- OceanSODA_SO_extreme_biome %>%
rename(OceanSODA_ph_uncert = ph_total_uncert,
OceanSODA_ph = ph_total)
# combine the argo profile data to the surface extreme data
profile_extreme <- inner_join(full_argo_SO, OceanSODA_SO_extreme)Mean monthly profiles
# calculate mean profiles in each basin and biome, for each month between 2014 and 2021
profile_extreme_monthly <- profile_extreme %>%
mutate(depth = cut(depth, seq(0,2000,10), seq(5,2000,10)),
depth = as.numeric(as.character(depth))) %>%
group_by(month, biome, basin_AIP, ph_extreme, depth) %>%
summarise(mean_ph = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
mean_temp = mean(temp_adjusted, na.rm = TRUE)) %>%
ungroup()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(1) %>%
map(
~ ggplot(
data = .x,
aes(
x = ph_in_situ_total_adjusted,
y = depth,
group = ph_extreme,
col = ph_extreme
)
) +
geom_point(pch = 19, size = 0.5) +
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)
),
col = 'OceanSODA pH \nanomaly'
)
)[[1]]
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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$biome)
),
col = 'OceanSODA\npH\nanomaly'
)
)[[1]]
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Plot monthly profiles
pH
profile_extreme_monthly %>%
arrange(depth) %>%
group_split(month) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(
x = mean_ph,
y = depth,
group = ph_extreme,
col = ph_extreme
)) +
geom_path() +
scale_color_manual(values = HNL_colors) +
labs(title = paste("month:", unique(.x$month)),
col = 'OceanSODA\npH\nanomaly') +
scale_y_reverse() +
facet_grid(basin_AIP ~ biome)
)[[1]]
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sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggOceanMaps_0.4.3 ggspatial_1.1.5 lubridate_1.7.9 forcats_0.5.0
[5] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4 readr_1.4.0
[9] tidyr_1.1.3 tibble_3.1.3 ggplot2_3.3.5 tidyverse_1.3.0
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] smoothr_0.1.2 fs_1.5.0 sf_0.9-8
[4] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2
[7] tools_4.0.3 backports_1.1.10 bslib_0.2.5.1
[10] utf8_1.1.4 rgdal_1.5-18 R6_2.5.0
[13] KernSmooth_2.23-17 rgeos_0.5-5 DBI_1.1.0
[16] colorspace_2.0-2 raster_3.4-5 withr_2.3.0
[19] sp_1.4-4 tidyselect_1.1.0 compiler_4.0.3
[22] git2r_0.27.1 cli_3.0.1 rvest_0.3.6
[25] xml2_1.3.2 labeling_0.4.2 sass_0.4.0
[28] scales_1.1.1 classInt_0.4-3 ggOceanMapsData_1.0.1
[31] digest_0.6.27 rmarkdown_2.10 pkgconfig_2.0.3
[34] htmltools_0.5.1.1 highr_0.8 dbplyr_1.4.4
[37] rlang_0.4.11 readxl_1.3.1 rstudioapi_0.13
[40] jquerylib_0.1.4 generics_0.1.0 farver_2.0.3
[43] jsonlite_1.7.1 magrittr_1.5 Rcpp_1.0.5
[46] munsell_0.5.0 fansi_0.4.1 abind_1.4-5
[49] lifecycle_1.0.0 stringi_1.5.3 whisker_0.4
[52] yaml_2.2.1 grid_4.0.3 blob_1.2.1
[55] parallel_4.0.3 promises_1.1.1 crayon_1.3.4
[58] lattice_0.20-41 haven_2.3.1 stars_0.5-2
[61] hms_0.5.3 knitr_1.33 pillar_1.6.2
[64] codetools_0.2-16 reprex_0.3.0 glue_1.4.2
[67] evaluate_0.14 modelr_0.1.8 vctrs_0.3.8
[70] httpuv_1.5.4 cellranger_1.1.0 gtable_0.3.0
[73] assertthat_0.2.1 xfun_0.25 lwgeom_0.2-5
[76] broom_0.7.9 e1071_1.7-4 later_1.2.0
[79] class_7.3-17 units_0.6-7 ellipsis_0.3.2