BGC-Argo pH Data
Pasqualina Vonlanthen & Jens Daniel Müller
12 November, 2021
Last updated: 2021-11-12
Checks: 7 0
Knit directory: bgc_argo_r_argodata/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 06b6be7 | pasqualina-vonlanthendinenna | 2021-11-12 | added NE Pacific oxygen |
| html | 284003d | pasqualina-vonlanthendinenna | 2021-11-11 | Build site. |
| Rmd | f7807db | pasqualina-vonlanthendinenna | 2021-11-11 | added oxygen data page |
| html | 6276d6c | pasqualina-vonlanthendinenna | 2021-11-11 | Build site. |
| Rmd | 8baed53 | pasqualina-vonlanthendinenna | 2021-11-11 | added pH data page |
Task
Explore BGC-Argo pH data through timeseries and monthly climatological maps
path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"Load pH data
Load in delayed-mode adjusted pH values from the BGC-Argo synthetic profile files
# set cache directory
argo_set_cache_dir(cache_dir = path_argo)
# periodically update the cached files
argo_update_global(max_global_cache_age = Inf)
argo_update_data(max_data_cache_age = Inf)
# load synthetic Argo files containing delayed-mode data between 2013 and now
bgc_subset <- argo_global_synthetic_prof() %>%
argo_filter_data_mode(data_mode = 'delayed') %>% # load in delayed-mode data
argo_filter_date(date_min = '2013-01-01',
date_max = Sys.time())Loading argo_global_synthetic_prof()# read in the pH data (with corresponding CTD data)
ph_data <- argo_prof_levels(
path = bgc_subset,
vars =
c(
'PRES_ADJUSTED',
'PRES_ADJUSTED_QC',
'PRES_ADJUSTED_ERROR',
'PSAL_ADJUSTED',
'PSAL_ADJUSTED_QC',
'PSAL_ADJUSTED_ERROR',
'TEMP_ADJUSTED',
'TEMP_ADJUSTED_QC',
'TEMP_ADJUSTED_ERROR',
'PH_IN_SITU_TOTAL_ADJUSTED',
'PH_IN_SITU_TOTAL_ADJUSTED_QC',
'PH_IN_SITU_TOTAL_ADJUSTED_ERROR'
),
quiet = TRUE
)
# read in corresponding metadata
ph_metadata <- argo_prof_prof(path = bgc_subset)Extracting from 138158 files# merge the data and metadata
ph_merge <-
full_join(ph_data, ph_metadata)Joining, by = c("file", "n_prof")# harmonize lat and lon variables and remove columns with no data
ph_merge <- ph_merge %>%
rename(lon = longitude,
lat = latitude) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(
lat = cut(lat, seq(-90, 90, 2), seq(-89.5, 89.5, 2)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 2), seq(20.5, 379.5, 2)), # change to 2x2º grid
lon = as.numeric(as.character(lon))
) %>%
select(
-c(profile_doxy_qc:profile_cdom_qc),
-c(profile_cndc_qc:profile_up_radiance555_qc)
) # remove columns which don't contain data Southern Ocean surface pH
The focus here is on surface pH (in the top 10 m of the watercolumn), in the region south of 30ºS
# select only best pH data (with QC flag 1) below 30ºS, for the top 10 m of the watercolumn
ph_surface <- ph_merge %>%
mutate(depth = swDepth(pres_adjusted, latitude = lat), .before = pres_adjusted) %>%
filter(ph_in_situ_total_adjusted_qc == '1', # keep only 'good' data
lat <= -30, # keep only data at or south of 30ºS
depth <= 10) %>% # keep only data above or at 10 m depth
mutate(
year = year(date), # separate the year and month from the date column
month = month(date), .after = n_prof
)
# check the correct latitudes, QC flags, and depth levels have been filtered
# max(ph_surface$lat)
# min(ph_surface$lat)
# table(ph_surface$ph_in_situ_total_adjusted_qc)
# max(ph_surface$depth)Monthly climatological map
Create a climatological monthly map of surface pH, in a 2x2º longitude/latitude grid, for the region south of 30ºS (monthly pH averaged over April 2014-August 2021)
# average pH values in the top 10 m for each month in each 2 x 2º longitude/latitude grid
ph_mean <- ph_surface %>%
group_by(lat, lon, month) %>%
summarise(ph_ave_month = mean(ph_in_situ_total_adjusted))`summarise()` has grouped output by 'lat', 'lon'. You can override using the `.groups` argument.# read in the map from updata
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))
# map a monthly climatology of pH (April 2014 - August 2021)
map +
geom_tile(data = ph_mean,
aes(lon, lat, fill = ph_ave_month)) +
lims(y = c(-85, -25)) +
scale_fill_gradientn(colors = oceColorsJet(n = ph_mean$ph_ave_month)) +
labs(x = 'lon',
y = 'lat',
fill = 'pH',
title = 'Monthly average pH values (Apr 2014 - Aug 2021)') +
facet_wrap(~month)Warning in seq.int(0, 1, length.out = n): first element used of 'length.out'
argumentWarning: Raster pixels are placed at uneven vertical intervals and will be
shifted. Consider using geom_tile() instead.Warning: Removed 153708 rows containing missing values (geom_raster).
| Version | Author | Date |
|---|---|---|
| 6276d6c | pasqualina-vonlanthendinenna | 2021-11-11 |
Monthly timeseries
Plot timeseries of monthly pH values, averaged over the whole region south of 30ºS
# plot a timeseries of monthly values over the whole southern ocean south of 30ºS
ph_month <- ph_surface %>%
group_by(year, month) %>%
summarise(ph_ave = mean(ph_in_situ_total_adjusted))`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.# timeseries of monthly pH values over 2014-2021 (separate panels for each month)
ph_month %>%
ggplot(aes(x = year, y = ph_ave)) +
facet_wrap(~month) +
geom_line() +
geom_point() +
labs(x = 'year',
y = 'pH in situ (total scale)',
title = 'monthly mean pH (Apr 2014-Aug 2021, south of 30ºS)')
| Version | Author | Date |
|---|---|---|
| 284003d | pasqualina-vonlanthendinenna | 2021-11-11 |
#all months on one plot in different colors (not very nice plot)
# ph_month %>%
# ggplot(aes(x = year, y = ph_ave, group = month, col = as.character(month))) +
# geom_line() +
# geom_point() +
# labs(x = 'year', y = 'pH in situ (total scale)', title = 'monthly mean pH (Apr 2014-Aug 2021)')Plot the monthly average pH, per year (from Jan 2015 - Dec 2020), over the whole region south of 30ºS
# timeseries of monthly pH values for each year (separate years on the same plot)
ph_month %>%
filter(year != 2014,
year != 2021) %>% # remove the two years that are missing data (keep only data for full years)
ggplot(aes(x = month, y = ph_ave, group = year, col = as.character(year)))+
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 1))+
labs(x = 'month',
y = 'pH in situ (total scale)',
title = 'monthly mean pH (Jan 2015-Dec 2020, south of 30ºS)',
col = 'year')
| Version | Author | Date |
|---|---|---|
| 6276d6c | pasqualina-vonlanthendinenna | 2021-11-11 |
# calculate a yearly average ph (one ph value per year, for the whole domain)
ph_year <- ph_surface %>%
group_by(year) %>%
summarise(ph_ave = mean(ph_in_situ_total_adjusted))
# plot a timeseries of the yearly average pH value (one value per year)
ph_year %>%
ggplot(aes(x = year, y = ph_ave))+
lims(y = c(8.03, 8.06))+
geom_line()+
geom_point()+
labs(x = 'year',
y = 'pH in situ (total scale)',
title = 'yearly mean pH (Apr 2014-Aug 2021, south of 30ºS)')Northeast Pacific surface pH
Focus on surface pH in the northeast Pacific Ocean (10ºN - 70ºN, -190ºE - -140ºE)
# select only best oxygen data (with QC flag 1) between 10 and 70ºN, and 190 and 140ºW, for the top 10 m of the watercolumn
ph_nepacific <- ph_merge %>%
mutate(depth = swDepth(pres_adjusted, latitude = lat), .before = pres_adjusted) %>%
filter(ph_in_situ_total_adjusted_qc == '1', # keep only 'good' data
between(lat, 10, 70),
between(lon, 190, 240), # NE Pacific
depth <= 10) %>% # keep only data above or at 10 m depth
mutate(
year = year(date), # separate the year and month from the date column
month = month(date), .after = n_prof
)
# longitudes larger than -180ºE are lon-380Monthly climatological map
Create a map of climatological monthly surface pH values, in the north-west Pacific ocean (10ºN - 70ºN, -190ºE, -140ºE), for
# average oxygen values in the top 10 m for each month in each 2 x 2º longitude/latitude grid
ph_mean_nepacific <- ph_nepacific %>%
group_by(lat, lon, month) %>%
summarise(ph_ave_month = mean(ph_in_situ_total_adjusted))`summarise()` has grouped output by 'lat', 'lon'. You can override using the `.groups` argument.# map a monthly climatology of surface oxygen (Jan 2013 - August 2021)
map +
geom_tile(data = ph_mean_nepacific,
aes(lon, lat, fill = ph_ave_month)) +
lims(y = c(5, 60),
x = c(180, 250)) +
scale_fill_gradientn(colors = oceColorsJet(n = ph_mean_nepacific$ph_ave_month)) +
labs(x = 'lon',
y = 'lat',
fill = 'pH',
title = 'Monthly average pH (Jan 2013-Aug 2021)') +
theme(legend.position = 'right')+
facet_wrap(~month)Warning in seq.int(0, 1, length.out = n): first element used of 'length.out'
argumentWarning: Removed 219516 rows containing missing values (geom_raster).
Monthly timeseries
Timeseries of monthly mean pH, averaged over the whole NE-Pacific region (10ºN - 70ºN, -190ºE - -140ºE), in the upper 10 m of the watercolumn.
# plot a timeseries of monthly values over the whole southern ocean south of 30ºS
ph_month_nepacific <- ph_nepacific %>%
group_by(year, month) %>%
summarise(ph_ave = mean(ph_in_situ_total_adjusted))`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.# timeseries of monthly pH values over 2014-2021 (separate panels for each month)
ph_month_nepacific %>%
ggplot(aes(x = year, y = ph_ave)) +
facet_wrap(~month) +
scale_x_continuous(breaks = seq(2013, 2021, 2)) +
geom_line() +
geom_point() +
labs(x = 'year',
y = 'pH in situ (total scale)',
title = 'monthly mean pH (Jan 2013-Aug 2021, NE Pacific)')
| Version | Author | Date |
|---|---|---|
| 284003d | pasqualina-vonlanthendinenna | 2021-11-11 |
Monthly average pH, per year, over the NE Pacific region
# timeseries of monthly pH values for each year (separate years on the same plot)
ph_month_nepacific %>%
filter(year != 2016,
year != 2021) %>% # remove the two years that are missing data (keep only data for full years)
ggplot(aes(x = month, y = ph_ave, group = year, col = as.character(year)))+
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 1))+
labs(x = 'month',
y = 'pH in situ (total scale)',
title = 'monthly mean pH (Jan 2013-Dec 2020, NE Pacific)',
col = 'year')
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] oce_1.4-0 testthat_3.0.4 sf_1.0-2
[4] gsw_1.0-6 lubridate_1.7.9 argodata_0.0.0.9000
[7] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[10] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[13] tibble_3.1.3 ggplot2_3.3.5 tidyverse_1.3.0
[16] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 bit64_4.0.5 progress_1.2.2 httr_1.4.2
[5] rprojroot_2.0.2 tools_4.0.3 backports_1.1.10 bslib_0.2.5.1
[9] utf8_1.2.2 R6_2.5.1 KernSmooth_2.23-17 DBI_1.1.1
[13] colorspace_2.0-2 withr_2.4.2 tidyselect_1.1.0 prettyunits_1.1.1
[17] bit_4.0.4 compiler_4.0.3 git2r_0.27.1 cli_3.0.1
[21] rvest_0.3.6 RNetCDF_2.4-2 xml2_1.3.2 labeling_0.4.2
[25] sass_0.4.0 scales_1.1.1 classInt_0.4-3 proxy_0.4-26
[29] digest_0.6.27 rmarkdown_2.10 pkgconfig_2.0.3 htmltools_0.5.1.1
[33] dbplyr_1.4.4 highr_0.8 rlang_0.4.11 readxl_1.3.1
[37] rstudioapi_0.13 jquerylib_0.1.4 generics_0.1.0 farver_2.1.0
[41] jsonlite_1.7.2 vroom_1.5.5 magrittr_2.0.1 Rcpp_1.0.7
[45] munsell_0.5.0 fansi_0.5.0 lifecycle_1.0.0 stringi_1.5.3
[49] whisker_0.4 yaml_2.2.1 grid_4.0.3 blob_1.2.1
[53] parallel_4.0.3 promises_1.2.0.1 crayon_1.4.1 haven_2.3.1
[57] hms_0.5.3 knitr_1.33 pillar_1.6.2 reprex_0.3.0
[61] glue_1.4.2 evaluate_0.14 modelr_0.1.8 vctrs_0.3.8
[65] tzdb_0.1.2 httpuv_1.6.2 cellranger_1.1.0 gtable_0.3.0
[69] assertthat_0.2.1 xfun_0.25 broom_0.7.9 e1071_1.7-8
[73] later_1.3.0 class_7.3-17 units_0.7-2 ellipsis_0.3.2