UCSD Argo pH Climatology
Pasqualina Vonlanthen, David Stappard & Jens Daniel Müller
19 December, 2023
Last updated: 2023-12-19
Checks: 7 0
Knit directory:
bgc_argo_r_argodata/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20211008) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version f895ac9. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: output/
Unstaged changes:
Modified: code/start_background_job.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown
(analysis/load_argo_clim_pH_ucsd.Rmd) and HTML
(docs/load_argo_clim_pH_ucsd.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | fa6cf38 | ds2n19 | 2023-12-14 | Build site. |
| html | f110b74 | ds2n19 | 2023-12-13 | Build site. |
| Rmd | acb6523 | ds2n19 | 2023-12-12 | Added documentation added to tasks section at start of each script. |
| html | e60ebd2 | ds2n19 | 2023-12-07 | Build site. |
| Rmd | fa1083d | ds2n19 | 2023-12-01 | Additional analysis to cluster process. |
| html | 80c16c2 | ds2n19 | 2023-11-15 | Build site. |
| Rmd | e07ffb5 | ds2n19 | 2023-11-11 | Updates after code reviewand additional documentation. |
| Rmd | b18136a | jens-daniel-mueller | 2023-11-09 | code review |
| Rmd | 4fcef97 | ds2n19 | 2023-11-01 | vertical alignment of bgc and core profiles and then cluster analysis. |
| html | 7b3d8c5 | pasqualina-vonlanthendinenna | 2022-08-29 | Build site. |
| html | bdd516d | pasqualina-vonlanthendinenna | 2022-05-23 | Build site. |
| html | dfe89d7 | jens-daniel-mueller | 2022-05-12 | Build site. |
| 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 |
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