OceanSODA-Argo pH
Pasqualina Vonlanthen, David Stappard & Jens Daniel Müller
01 January, 2024
Last updated: 2024-01-01
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Knit directory:
bgc_argo_r_argodata/analysis/
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Task
Compare BGC-Argo pH data to pH from the OceanSODA surface data product
Dependencies
OceanSODA.rds - bgc preprocessed folder, created by load_OceanSODA.
pH_bgc_observed.rds - bgc preprocessed folder, created by ph_align_climatology. Not this file is written BEFORE the vertical alignment stage.
Outputs
argo_OceanSODA.rds - bgc preprocessed folder
theme_set(theme_bw())Load data
Load in surface Argo pH and the OceanSODA pH, gridded to 1x1º
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/"
path_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
# /nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo/preprocessed_bgc_data
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")# load in OceanSODA data and Argo pH
OceanSODA <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA.rds"))
argo <- read_rds(file = paste0(path_argo_preprocessed, '/pH_bgc_observed.rds')) %>%
filter(between(depth, 0, 20))
# argo <-
# read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_flag_AB.rds")) %>%
# filter(between(depth, 0, 20)) %>%
# mutate(year = year(date),
# month = month(date)) %>%
# select(-c(
# temp_adjusted,
# temp_adjusted_qc,
# temp_adjusted_error,
# profile_temp_qc
# ))
# for plotting later, load in region and coastline information
# region_masks_all_seamask_2x2 <- read_rds(file = paste0(
# path_argo_preprocessed, "/region_masks_all_seamask_2x2.rds"))
#
# region_masks_all_2x2 <- read_rds(file = paste0(path_argo_preprocessed, "/region_masks_all_2x2.rds"))
#
# region_masks_all_1x1 <- read_rds(file = paste0(path_argo_preprocessed, "/region_masks_all_1x1.rds"))
nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))# read in the map from updata
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))Harmonise the two datasets
Calculate monthly average pH for Argo pH for each lon/lat grid, centered on the 15th of each month, to match the format of OceanSODA
argo_monthly <- argo %>%
mutate(year_month = format_ISO8601(date, precision = "ym"), .after = 'date') %>%
group_by(year, month, year_month, date, lat, lon) %>%
summarise(
argo_ph_month = mean(ph_in_situ_total_adjusted, na.rm = TRUE)
) %>%
ungroup() %>%
select(
date,
year_month,
year,
month,
lon,
lat,
argo_ph_month
)Join the two datasets
OceanSODA <- OceanSODA %>%
mutate(year_month = format_ISO8601(date, precision = "ym")) %>%
rename(date_OceanSODA = date)
# change date format in OceanSODA to match argo date (yyyy-mm)
argo_OceanSODA <- left_join(argo_monthly, OceanSODA) %>%
rename(OceanSODA_ph = ph_total,
OceanSODA_ph_error = ph_total_uncert) argo_OceanSODA %>%
write_rds(file = paste0(path_argo_preprocessed, "/argo_OceanSODA.rds"))Southern Ocean surface pH
The focus here is on Southern Ocean surface pH, south of 30ºS, as defined in the RECCAP biome regions
# region_masks_all_1x1_SO <- region_masks_all_1x1 %>%
# filter(region == 'southern',
# value != 0)
# keep only Southern Ocean data
argo_OceanSODA_SO <- inner_join(argo_OceanSODA, nm_biomes)Monthly climatological OceanSODA pH
Map monthly mean pH from the OceanSODA data product
Climatological OceanSODA pH
# calculate average monthly pH between April 2014 and August 2021
argo_OceanSODA_SO_clim <- argo_OceanSODA_SO %>%
group_by(lon, lat, month) %>%
summarise(
clim_OceanSODA_ph = mean(OceanSODA_ph, na.rm = TRUE),
clim_argo_ph = mean(argo_ph_month, na.rm = TRUE),
offset_clim = clim_OceanSODA_ph - clim_argo_ph
) %>%
ungroup()
# regrid to a 2x2 grid for mapping
argo_OceanSODA_SO_clim_2x2 <- argo_OceanSODA_SO_clim %>%
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))
) %>%
group_by(lon, lat, month) %>%
summarise(
clim_OceanSODA_ph = mean(clim_OceanSODA_ph, na.rm = TRUE),
clim_argo_ph = mean(clim_argo_ph, na.rm = TRUE),
offset_clim = mean(offset_clim, na.rm = TRUE)
) %>%
ungroup()
map +
geom_tile(data = argo_OceanSODA_SO_clim_2x2,
aes(lon, lat, fill = clim_OceanSODA_ph)) +
lims(y = c(-85, -25)) +
scale_fill_viridis_c() +
labs(x = 'lon',
y = 'lat',
fill = 'pH',
title = 'Monthly climatological \nOceanSODA pH') +
theme(legend.position = 'right') +
facet_wrap(~month, ncol = 2)
# plot the climatological monthly OceanSODA pH on a polar projection
basemap(limits = -32, data = argo_OceanSODA_SO_clim_2x2) + # change to polar projection
geom_spatial_tile(data = argo_OceanSODA_SO_clim_2x2,
aes(x = lon,
y = lat,
fill = clim_OceanSODA_ph),
linejoin = 'mitre',
col = 'transparent',
detail = 60)+
scale_fill_viridis_c()+
theme(legend.position = 'right')+
labs(x = 'lon',
y = 'lat',
fill = 'pH',
title = 'monthly climatological \nOceanSODA pH')+
facet_wrap(~month, ncol = 2)Climatological monthly Argo pH
Climatological Argo pH
map +
geom_tile(data = argo_OceanSODA_SO_clim_2x2,
aes(lon, lat, fill = clim_argo_ph)) +
lims(y = c(-85, -25)) +
scale_fill_viridis_c() +
labs(x = 'lon',
y = 'lat',
fill = 'pH',
title = 'Monthly climatological \nArgo pH') +
theme(legend.position = 'right') +
facet_wrap(~month, ncol = 2)
basemap(limits = -32, data = argo_OceanSODA_SO_clim_2x2) + # change to polar projection
geom_spatial_tile(data = argo_OceanSODA_SO_clim_2x2,
aes(x = lon,
y = lat,
fill = clim_argo_ph),
linejoin = 'mitre',
col = 'transparent',
detail = 60)+
scale_fill_viridis_c()+
theme(legend.position = 'right')+
labs(x = 'lon',
y = 'lat',
fill = 'pH',
title = 'monthly climatological \nArgo pH')+
facet_wrap(~month, ncol = 2)Timeseries of monthly OceanSODA pH
Evolution of monthly surface pH, for the three Southern Ocean RECCAP regions
map +
geom_raster(data = nm_biomes,
aes(x = lon,
y = lat,
fill = biome_name)) +
labs(title = 'Southern Ocean Mayot biomes',
fill = 'biome')
| Version | Author | Date |
|---|---|---|
| 710edd4 | jens-daniel-mueller | 2022-05-11 |
# plot timeseries of monthly OceanSODA pH
argo_OceanSODA_SO_clim_regional <- argo_OceanSODA_SO %>%
select(year, month, biome_name, OceanSODA_ph, argo_ph_month) %>%
pivot_longer(c(OceanSODA_ph,argo_ph_month),
values_to = "ph",
names_to = "data_source") %>%
group_by(year, month, biome_name, data_source) %>% # compute regional mean OceanSODA pH for the three biomes
summarise(ph = mean(ph, na.rm = TRUE)) %>%
ungroup()argo_OceanSODA_SO_clim_regional %>%
ggplot(aes(x = year,
y = ph,
col = biome_name)) +
facet_grid(month ~ data_source) +
geom_line() +
geom_point() +
labs(x = 'year',
y = 'pH in situ (total scale)',
title = 'monthly mean pH (Southern Ocean)',
col = 'region')argo_OceanSODA_SO_clim_regional %>%
# filter(year != 2014,
# year != 2021) %>%
ggplot(aes(x = month,
y = ph,
group = year,
col = as.character(year)))+
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 2))+
facet_grid(biome_name~data_source)+
labs(x = 'month',
y = 'pH in situ (total scale)',
title = 'monthly mean OceanSODA pH (Southern Ocean)',
col = 'year')
Comparison between Argo and OceanSODA pH
Calculate the difference between Argo and OceanSODA pH values
Offset between in-situ monthly pH:
argo_OceanSODA_SO <- argo_OceanSODA_SO %>%
mutate(offset = OceanSODA_ph - argo_ph_month)
argo_OceanSODA_SO %>%
drop_na() %>%
ggplot() +
geom_hline(yintercept = 0, size = 1)+
geom_point(aes(x = year_month, y = offset, col = biome_name), size = 0.7, pch = 19) +
scale_x_discrete(breaks = c('2014-01', '2015-01', '2016-01', '2017-01', '2018-01', '2019-01', '2020-01', '2021-01', '2022-01', '2023-01'))+
labs(title = 'oceanSODA pH - Argo pH',
x = 'date',
y = 'offset (pH units)',
col = 'region')
argo_OceanSODA_SO %>%
drop_na() %>%
ggplot(aes(x = OceanSODA_ph, y = argo_ph_month))+
# geom_point(pch = 19, size = 0.7)+
geom_bin2d(aes(x = OceanSODA_ph, y = argo_ph_month), size = 0.3, bins = 60)+
scale_fill_viridis_c()+
lims(x = c(7.8, 8.25),
y = c(7.8, 8.25)) +
geom_abline(slope = 1, intercept = 0)+
facet_wrap(~biome_name)+
labs(x = 'OceanSODA pH (total scale)',
y = 'Argo pH (total scale)',
title = 'Southern Ocean regional pH')
Mean offset between in-situ OceanSODA pH and in-situ Argo pH
mean_insitu_offset <- argo_OceanSODA_SO %>%
group_by(year_month, biome_name) %>%
summarise(mean_offset = mean(offset, na.rm = TRUE),
std_offset = sd(offset, na.rm = TRUE))
mean_insitu_offset %>%
drop_na() %>%
ggplot() +
geom_hline(yintercept = 0, size = 1, col = 'red')+
geom_point(aes(x = year_month, y = mean_offset, group = biome_name, col = biome_name),
size = 0.7, pch = 19) +
geom_line(aes(x = year_month, y = mean_offset, group = biome_name, col = biome_name))+
geom_ribbon(aes(x = year_month,
ymin = mean_offset-std_offset,
ymax = mean_offset+std_offset,
group = biome_name,
fill = biome_name),
alpha = 0.2)+
scale_x_discrete(breaks = c('2014-01', '2015-01', '2016-01', '2017-01', '2018-01', '2019-01', '2020-01', '2021-01', '2022-01', '2023-01'))+
# facet_wrap(~year)+
labs(title = 'Mean offset (in situ oceanSODA pH - in situ Argo pH)',
x = 'date',
y = 'offset (pH units)',
col = 'region',
fill = '± 1 std')
Offset between climatological Argo and climatological OceanSODA pH:
# Offset between climatological argo and climatological OceanSODA pH
argo_OceanSODA_SO_clim <- inner_join(argo_OceanSODA_SO_clim, nm_biomes)argo_OceanSODA_SO_clim %>%
drop_na() %>%
ggplot() +
geom_point(aes(x = month, y = offset_clim, col = biome_name), size = 0.7, pch = 19) +
geom_hline(yintercept = 0, size = 1, col = 'red')+
scale_x_continuous(breaks = seq(1, 12, 1))+
labs(title = 'clim oceanSODA pH - clim Argo pH',
x = 'month',
y = 'offset (pH units)',
col = 'region')Mean offset between climatological OceanSODA pH and climatological Argo pH
mean_clim_offset <- argo_OceanSODA_SO_clim %>%
group_by(month, biome_name) %>%
summarise(mean_offset_clim = mean(offset_clim, na.rm = TRUE),
std_offset_clim = sd(offset_clim, na.rm = TRUE))
mean_clim_offset %>%
ggplot()+
geom_point(aes(x = month, y = mean_offset_clim, col = biome_name))+
geom_line(aes(x = month, y = mean_offset_clim, col = biome_name))+
geom_hline(yintercept = 0, col = 'red') +
geom_ribbon(aes(x = month,
ymin = mean_offset_clim - std_offset_clim,
ymax = mean_offset_clim + std_offset_clim,
group = biome_name,
fill = biome_name),
alpha = 0.2) +
scale_x_continuous(breaks = seq(1, 12, 1)) +
labs(x = 'month',
y = 'mean offset (pH units)',
title = 'Mean offset (clim OceanSODA pH - clim Argo pH)',
col = 'region',
fill = '± 1 std') 
Mapped offset between climatological OceanSODA pH and climatological Argo pH
# bin the offsets for better plotting
# plot the offsets on a map of the Southern Ocean
argo_OceanSODA_SO_clim_2x2 <- argo_OceanSODA_SO_clim_2x2 %>%
mutate(offset_clim_binned =
cut(offset_clim,
breaks = c(-Inf, -0.025, -0.005, 0.000, 0.005, 0.025, 0.035, 0.05, Inf))) %>% # bin the offsets into intervals (create a discrete variable instead of continuous)
# offset_clim_binned = as.factor(as.character(offset_clim_binned))) %>%
drop_na()
map +
geom_tile(data = argo_OceanSODA_SO_clim_2x2,
aes(lon, lat, fill = offset_clim_binned)) +
lims(y = c(-85, -30)) +
scale_fill_brewer(palette = 'RdBu', drop = FALSE) +
labs(x = 'lon',
y = 'lat',
fill = 'offset (pH units)',
title = 'clim OceanSODA ph - clim Argo pH') +
theme(legend.position = 'right')+
facet_wrap(~month, ncol = 2)
basemap(limits = -32, data = argo_OceanSODA_SO_clim_2x2) + # change to polar projection
geom_spatial_tile(data = argo_OceanSODA_SO_clim_2x2,
aes(x = lon,
y = lat,
fill = offset_clim_binned),
linejoin = 'mitre',
col = 'transparent',
detail = 60)+
scale_fill_brewer(palette = 'RdBu', drop = FALSE)+
theme(legend.position = 'right')+
labs(x = 'lon',
y = 'lat',
fill = 'offset (pH units)',
title = 'clim Ocean SODA pH - clim Argo pH')+
facet_wrap(~month, ncol = 2)Basin separation
Using full OceanSODA data (even where there is no Argo data) Each RECCAP biome (1, 2, 3) is separated into basins (Atlantic, Pacific, Indian)
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_SO <- inner_join(OceanSODA, nm_biomes)
OceanSODA_SO <- inner_join(OceanSODA_SO, basinmask) %>%
mutate(year = year(date_OceanSODA),
month = month(date_OceanSODA)) %>%
mutate(date = format_ISO8601(date_OceanSODA, precision = 'ym')) %>%
filter(year >= 2013)# plot timeseries of monthly OceanSODA pH
OceanSODA_SO_clim_subregional <- OceanSODA_SO %>%
group_by(year, month, biome_name, basin_AIP) %>% # compute regional mean OceanSODA pH for the three biomes
summarise(ph = mean(ph_total, na.rm = TRUE)) %>%
ungroup()
# plot a timeseries of monthly average OceanSODA pH, per region and per basin
OceanSODA_SO_clim_subregional %>%
ggplot(aes(x = month,
y = ph,
group = year,
col = as.character(year)))+
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 2))+
facet_grid(biome_name~basin_AIP)+
labs(x = 'month',
y = 'pH in situ (total scale)',
title = 'monthly mean OceanSODA pH (Southern Ocean basins)',
col = 'year')
OceanSODA_SO_clim_subregional %>%
ggplot(aes(x = year,
y = ph,
col = biome_name)) +
facet_grid(month ~ basin_AIP) +
geom_line() +
geom_point() +
labs(x = 'year',
y = 'pH in situ (total scale)',
title = 'monthly mean pH (Southern Ocean basins)',
col = 'region')
Longitudinal separation
Bin the pH data into 20º longitude bins (20º - 380º)
OceanSODA_SO_lon_binned <- OceanSODA_SO %>%
mutate(lon = cut(lon, seq(20, 380, 20), seq(30, 370, 20)),
lon = as.numeric(as.character(lon))
) %>%
group_by(lon, year, month, biome_name) %>%
summarise(
OceanSODA_ph_binned = mean(ph_total, na.rm = TRUE)
) %>%
ungroup()OceanSODA_SO_lon_binned %>%
drop_na() %>%
ggplot(aes(x = month, y = OceanSODA_ph_binned, group = lon, col = as.factor(lon))) +
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 2))+
facet_grid(year~biome_name)+
labs(x = 'month',
y = 'OceanSODA pH',
col = 'longitude bin')
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5
Matrix products: default
BLAS: /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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] metR_0.13.0 ggOceanMaps_1.3.4 ggspatial_1.1.7 lubridate_1.9.0
[5] timechange_0.1.1 argodata_0.1.0 forcats_0.5.2 stringr_1.5.0
[9] dplyr_1.1.3 purrr_1.0.2 readr_2.1.3 tidyr_1.3.0
[13] tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] fs_1.5.2 sf_1.0-9 bit64_4.0.5
[4] RColorBrewer_1.1-3 httr_1.4.4 rprojroot_2.0.3
[7] tools_4.2.2 backports_1.4.1 bslib_0.4.1
[10] utf8_1.2.2 R6_2.5.1 KernSmooth_2.23-20
[13] rgeos_0.5-9 DBI_1.1.3 colorspace_2.0-3
[16] raster_3.6-11 withr_2.5.0 sp_1.5-1
[19] tidyselect_1.2.0 bit_4.0.5 compiler_4.2.2
[22] git2r_0.30.1 cli_3.6.1 rvest_1.0.3
[25] RNetCDF_2.6-1 xml2_1.3.3 labeling_0.4.2
[28] sass_0.4.4 checkmate_2.1.0 scales_1.2.1
[31] classInt_0.4-8 proxy_0.4-27 digest_0.6.30
[34] rmarkdown_2.18 pkgconfig_2.0.3 htmltools_0.5.3
[37] highr_0.9 dbplyr_2.2.1 fastmap_1.1.0
[40] rlang_1.1.1 readxl_1.4.1 rstudioapi_0.15.0
[43] farver_2.1.1 jquerylib_0.1.4 generics_0.1.3
[46] jsonlite_1.8.3 vroom_1.6.0 googlesheets4_1.0.1
[49] magrittr_2.0.3 Rcpp_1.0.10 munsell_0.5.0
[52] fansi_1.0.3 lifecycle_1.0.3 terra_1.7-39
[55] stringi_1.7.8 whisker_0.4 yaml_2.3.6
[58] grid_4.2.2 parallel_4.2.2 promises_1.2.0.1
[61] crayon_1.5.2 lattice_0.20-45 haven_2.5.1
[64] hms_1.1.2 knitr_1.41 pillar_1.9.0
[67] codetools_0.2-18 reprex_2.0.2 glue_1.6.2
[70] evaluate_0.18 data.table_1.14.6 modelr_0.1.10
[73] vctrs_0.6.4 tzdb_0.3.0 httpuv_1.6.6
[76] cellranger_1.1.0 gtable_0.3.1 assertthat_0.2.1
[79] cachem_1.0.6 xfun_0.35 broom_1.0.5
[82] e1071_1.7-12 later_1.3.0 viridisLite_0.4.1
[85] class_7.3-20 googledrive_2.0.0 gargle_1.2.1
[88] memoise_2.0.1 workflowr_1.7.0 units_0.8-0
[91] ellipsis_0.3.2