GLODAPv2_2016: Mapped Climatologies
Jens Daniel Müller
12 May, 2021
Last updated: 2021-05-12
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Knit directory: emlr_obs_preprocessing/
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1 Read source files
Data source: Globally mapped climatologies from Lauvset et al. (2016) downloaded in June 2020 from glodap.info.
file_list <- c(
"GLODAPv2.2016b.Cant.nc",
"GLODAPv2.2016b.NO3.nc",
"GLODAPv2.2016b.oxygen.nc",
"GLODAPv2.2016b.PO4.nc",
"GLODAPv2.2016b.salinity.nc",
"GLODAPv2.2016b.silicate.nc",
"GLODAPv2.2016b.TAlk.nc",
"GLODAPv2.2016b.TCO2.nc",
"GLODAPv2.2016b.temperature.nc"
)# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>%
filter(MLR_basins == "2") %>%
select(lat, lon, basin_AIP)2 Plot data and write csv
Below, subsets of the climatologies are plotted. For all relevant parameters, the plots show:
- maps at depth levels
- concentration along global section
The global section path is indicated as white line in maps.
Please note that NA values in the climatologies were filled with neighbouring values on the longitudinal axis.
# file <- file_list[2]
for (file in file_list) {
print(file)
# open file
clim <-
read_stars(paste(path_glodapv2_2016b, file, sep = ""),
quiet = TRUE)
# extract parameter name
parameter <-
str_split(file, pattern = "GLODAPv2.2016b.", simplify = TRUE)[2]
parameter <-
str_split(parameter, pattern = ".nc", simplify = TRUE)[1]
# extract parameter
clim <- clim %>% select(all_of(parameter))
#convert to table
clim_tibble <- clim %>%
as_tibble()
# harmonize column names
clim_tibble <- clim_tibble %>%
rename(lat = y,
lon = x,
depth = depth_surface)
# join with basin mask and remove data outside basin mask
clim_tibble <- inner_join(clim_tibble, basinmask)
# determine bottom depth
bottom_depth <- clim_tibble %>%
filter(!is.na(!!sym(parameter))) %>%
group_by(lon, lat) %>%
summarise(bottom_depth = max(depth)) %>%
ungroup()
# remove data below bottom depth
clim_tibble <- left_join(clim_tibble, bottom_depth)
rm(bottom_depth)
clim_tibble <- clim_tibble %>%
filter(depth <= bottom_depth) %>%
select(-bottom_depth)
# fill NAs with closest value along longitude
clim_tibble <- clim_tibble %>%
group_by(lat, depth, basin_AIP) %>%
arrange(lon) %>%
fill(!!sym(parameter), .direction = "downup") %>%
ungroup()
# # plot column NA inventory
#
# clim_tibble_grid <- clim_tibble %>%
# filter(!is.na(!!sym(parameter))) %>%
# group_by(lat, lon) %>%
# summarise(depth_max = max(depth)) %>%
# ungroup()
#
# clim_tibble_grid <- full_join(clim_tibble, clim_tibble_grid)
#
# clim_tibble_grid <- clim_tibble_grid %>%
# filter(depth <= depth_max) %>%
# select(-depth_max)
#
# clim_tibble_grid <- clim_tibble_grid %>%
# filter(is.na(!!sym(parameter)),
# depth <= params_global$inventory_depth) %>%
# count(lat, lon)
#
# # plot NA map
# print(
#
# map +
# geom_raster(data = clim_tibble_grid,
# aes(lon, lat, fill = n)) +
# scale_fill_viridis_c() +
# theme(axis.title = element_blank()) +
# labs(title = paste(parameter, "colum NA"))
#
# )
#
# rm(clim_tibble_grid)
# remove NAs
clim_tibble <- clim_tibble %>%
drop_na()
# plot maps
print(p_map_climatology(df = clim_tibble,
var = parameter))
# plot sections
print(
p_section_global(
df = clim_tibble,
var = parameter,
title_text = "GLODAPv2_2016_Mapped_Climatology"
)
)
# write csv file
clim_tibble %>%
write_csv(paste(
path_preprocessing,
paste("GLODAPv2_2016_MappedClimatology_",
parameter,
".csv",
sep = ""),
sep = ""
))
}[1] "GLODAPv2.2016b.Cant.nc"
[1] "GLODAPv2.2016b.NO3.nc"
[1] "GLODAPv2.2016b.oxygen.nc"
[1] "GLODAPv2.2016b.PO4.nc"
[1] "GLODAPv2.2016b.salinity.nc"
[1] "GLODAPv2.2016b.silicate.nc"
[1] "GLODAPv2.2016b.TAlk.nc"
[1] "GLODAPv2.2016b.TCO2.nc"
[1] "GLODAPv2.2016b.temperature.nc"
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] stars_0.5-2 sf_0.9-8 abind_1.4-5 metR_0.9.0
[5] scico_1.2.0 patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 viridisLite_0.3.0 jsonlite_1.7.1
[4] modelr_0.1.8 assertthat_0.2.1 blob_1.2.1
[7] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.7
[10] backports_1.1.10 lattice_0.20-41 glue_1.4.2
[13] RcppEigen_0.3.3.7.0 digest_0.6.27 promises_1.1.1
[16] checkmate_2.0.0 rvest_0.3.6 colorspace_1.4-1
[19] htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18
[22] pkgconfig_2.0.3 broom_0.7.5 haven_2.3.1
[25] scales_1.1.1 whisker_0.4 later_1.1.0.1
[28] git2r_0.27.1 farver_2.0.3 generics_0.0.2
[31] ellipsis_0.3.1 withr_2.3.0 cli_2.1.0
[34] magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[37] evaluate_0.14 fs_1.5.0 fansi_0.4.1
[40] lwgeom_0.2-5 xml2_1.3.2 RcppArmadillo_0.10.1.2.0
[43] class_7.3-17 tools_4.0.3 data.table_1.13.2
[46] hms_0.5.3 lifecycle_1.0.0 munsell_0.5.0
[49] reprex_0.3.0 isoband_0.2.2 compiler_4.0.3
[52] e1071_1.7-4 rlang_0.4.10 units_0.6-7
[55] classInt_0.4-3 grid_4.0.3 rstudioapi_0.13
[58] labeling_0.4.2 rmarkdown_2.5 gtable_0.3.0
[61] DBI_1.1.0 R6_2.5.0 lubridate_1.7.9
[64] knitr_1.30 rprojroot_2.0.2 KernSmooth_2.23-17
[67] stringi_1.5.3 parallel_4.0.3 Rcpp_1.0.5
[70] vctrs_0.3.5 dbplyr_1.4.4 tidyselect_1.1.0
[73] xfun_0.18