UCSD Argo pH Climatology
Pasqualina Vonlanthen, David Stappard & Jens Daniel Müller
13 December, 2023
Last updated: 2023-12-13
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Knit directory: bgc_argo_r_argodata/
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| Rmd | 4fcef97 | ds2n19 | 2023-11-01 | vertical alignment of bgc and core profiles and then cluster analysis. |
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Task
This script loads the pH climatology as described in Mazloff et al. (2023). The climatology netCDF has previously been downloaded. The lat and lon fields are harmonised to our requirements, i.e -75.5 ≥ lat ≤ -30.5 and 20.5 ≥ lon ≤ 379.5.
Mazloff, M. R., A. Verdy, S. T. Gille, K. S. Johnson, B. D. Cornuelle, and J. Sarmiento (2023), Southern Ocean Acidification Revealed by Biogeochemical-Argo Floats, Journal of Geophysical Research: Oceans, 128(5), e2022JC019530, doi:https://doi.org/10.1029/2022JC019530.
Dependencies
pH climatology - /nfs/kryo/work/datasets/gridded/ocean/interior/observation/ph/mazloff_2023/PH-QCv3-v10r1.nc
Outputs (in BGC preprocessed folder)
ucsd_ph_clim.rds – the pH climatology for the Southern Ocean (30.5° S and south) by 1°x1°, depths (2.1, 6.7…. 1800) and month.
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.4 ✔ purrr 1.0.2
✔ tibble 3.2.1 ✔ dplyr 1.1.3
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()#library(ggOceanMaps)
library(oce)Loading required package: gsw#library(ncdf4)
library(stars)Loading required package: abind
Loading required package: sf
Linking to GEOS 3.11.1, GDAL 3.4.1, PROJ 7.2.1; sf_use_s2() is TRUE
WARNING: different compile-time and runtime versions for GEOS found:
Linked against: 3.11.1-CAPI-1.17.1 compiled against: 3.9.1-CAPI-1.14.2
It is probably a good idea to reinstall sf, and maybe rgeos and rgdal toopath_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_mazloff_ph <-"/nfs/kryo/work/datasets/gridded/ocean/interior/observation/ph/mazloff_2023"
fn_mazloff_ph <- "PH-QCv3-v10r1.nc"
fn_mazloff_ph <- paste0(path_mazloff_ph, "/", fn_mazloff_ph)
theme_set(theme_bw())Read data
# # read pH, position and depth data
# nc_pH <- read_ncdf(fn_mazloff_ph, var = c("pH"))
# nc_pH <- as_tibble(nc_pH)
#
# nc_lon <- read_ncdf(fn_mazloff_ph, var = c("longitude"))
# nc_lon <- as_tibble(nc_lon)
#
# nc_lat <- read_ncdf(fn_mazloff_ph, var = c("latitude"))
# nc_lat <- as_tibble(nc_lat)
#
# nc_depth <- read_ncdf(fn_mazloff_ph, var = c("depth"))
# nc_depth <- as_tibble(nc_depth)
#
# nc_pH <- nc_pH %>%
# mutate(ny = ny - 0.5,
# nx = nx - 0.5,
# t = t + 0.5)
#
# # Join each attribute in turn to pH data
# clim_argo_ph <- full_join(nc_pH, nc_lat)
# clim_argo_ph <- full_join(clim_argo_ph, nc_lon)
# clim_argo_ph <- full_join(clim_argo_ph, nc_depth)
#
# clim_argo_ph <- clim_argo_ph %>%
# select(-c(starts_with("n")))
#
# clim_argo_ph <- clim_argo_ph %>%
# filter(pH != 0)
#
# # harmonise data
# clim_argo_ph <- clim_argo_ph %>%
# rename(lat = latitude,
# lon = longitude,
# month = t,
# clim_pH = pH) %>%
# mutate(lat = lat -0.5,
# lon = if_else(lon < 20, lon + 360, lon))
#
# read pH, position and depth data
nc_pH <- read_stars(fn_mazloff_ph) %>%
as_tibble()Warning in CPL_read_gdal(as.character(x), as.character(options),
as.character(driver), : GDAL Message 1: The dataset has several variables
that could be identified as vector fields, but not all share the same primary
dimension. Consequently they will be ignored.
Warning in CPL_read_gdal(as.character(x), as.character(options),
as.character(driver), : GDAL Message 1: The dataset has several variables
that could be identified as vector fields, but not all share the same primary
dimension. Consequently they will be ignored.nc_lat <- read_ncdf(fn_mazloff_ph, var = c("latitude")) %>% as_tibble()Will return stars object with 46 cells.Warning in .get_nc_projection(meta$attribute, rep_var, cv): No projection
information found in nc file.nc_pH <- full_join(nc_pH %>% rename(ny = y),
nc_lat)Joining with `by = join_by(ny)`# harmonise data
clim_argo_ph <- nc_pH %>%
select(-ny) %>%
rename(lat = latitude,
lon = x,
depth = nz,
month = t,
clim_pH = "PH-QCv3-v10r1.nc") %>%
mutate(depth = round(depth, 2),
lon = if_else(lon < 20, lon + 360, lon),
lat = lat - 0.5)Maps
clim_argo_ph %>%
filter(depth < 30) %>%
ggplot() +
geom_tile(aes(lon, lat, fill = clim_pH)) +
facet_wrap(~depth) +
scale_fill_viridis_c() +
coord_quickmap()
clim_argo_ph %>%
ggplot(aes(clim_pH)) +
geom_histogram() +
facet_wrap(~depth) +
scale_y_log10() +
geom_vline(xintercept = 7.5)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 1319033 rows containing non-finite values (`stat_bin()`).Warning: Transformation introduced infinite values in continuous y-axisWarning: Removed 655 rows containing missing values (`geom_bar()`).
Write data to file
clim_argo_ph %>%
drop_na() %>%
write_rds(file = paste0(path_argo_preprocessed, "/ucsd_ph_clim.rds"))
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] stars_0.6-0 sf_1.0-9 abind_1.4-5 oce_1.7-10
[5] gsw_1.1-1 forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3
[9] purrr_1.0.2 readr_2.1.3 tidyr_1.3.0 tibble_3.2.1
[13] ggplot2_3.4.4 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] fs_1.5.2 lubridate_1.9.0 httr_1.4.4
[4] rprojroot_2.0.3 tools_4.2.2 backports_1.4.1
[7] bslib_0.4.1 utf8_1.2.2 R6_2.5.1
[10] KernSmooth_2.23-20 DBI_1.1.3 colorspace_2.0-3
[13] withr_2.5.0 tidyselect_1.2.0 compiler_4.2.2
[16] git2r_0.30.1 cli_3.6.1 rvest_1.0.3
[19] RNetCDF_2.6-1 xml2_1.3.3 labeling_0.4.2
[22] sass_0.4.4 scales_1.2.1 classInt_0.4-8
[25] proxy_0.4-27 digest_0.6.30 rmarkdown_2.18
[28] pkgconfig_2.0.3 htmltools_0.5.3 highr_0.9
[31] dbplyr_2.2.1 fastmap_1.1.0 rlang_1.1.1
[34] readxl_1.4.1 rstudioapi_0.15.0 jquerylib_0.1.4
[37] generics_0.1.3 farver_2.1.1 jsonlite_1.8.3
[40] googlesheets4_1.0.1 magrittr_2.0.3 ncmeta_0.3.5
[43] Rcpp_1.0.10 munsell_0.5.0 fansi_1.0.3
[46] lifecycle_1.0.3 stringi_1.7.8 whisker_0.4
[49] yaml_2.3.6 grid_4.2.2 parallel_4.2.2
[52] promises_1.2.0.1 crayon_1.5.2 haven_2.5.1
[55] hms_1.1.2 knitr_1.41 pillar_1.9.0
[58] reprex_2.0.2 glue_1.6.2 evaluate_0.18
[61] modelr_0.1.10 vctrs_0.6.4 tzdb_0.3.0
[64] httpuv_1.6.6 cellranger_1.1.0 gtable_0.3.1
[67] assertthat_0.2.1 cachem_1.0.6 xfun_0.35
[70] lwgeom_0.2-10 broom_1.0.5 e1071_1.7-12
[73] later_1.3.0 class_7.3-20 googledrive_2.0.0
[76] viridisLite_0.4.1 gargle_1.2.1 workflowr_1.7.0
[79] units_0.8-0 timechange_0.1.1 ellipsis_0.3.2