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1 Libraries

Loading libraries specific to the the analysis performed in this section.

library(stars)

2 Read source files

Data source: Globally mapped climatologies from Lauvset et al. (2016) downloaded in June 2020 from glodap.info.

Following files were used:

file_list <- list.files(path = "data/input/GLODAPv2_2016b_Mappedclimatologies", pattern = "*.nc")
print(file_list)
[1] "GLODAPv2.2016b.Cant.nc"        "GLODAPv2.2016b.NO3.nc"        
[3] "GLODAPv2.2016b.oxygen.nc"      "GLODAPv2.2016b.PO4.nc"        
[5] "GLODAPv2.2016b.salinity.nc"    "GLODAPv2.2016b.silicate.nc"   
[7] "GLODAPv2.2016b.TAlk.nc"        "GLODAPv2.2016b.TCO2.nc"       
[9] "GLODAPv2.2016b.temperature.nc"

3 Plot data and write csv

Below, subsets of the climatologies are plotted, which display for all relevant parameters:

  • 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) {
 
clim <- read_stars(here::here("data/input/GLODAPv2_2016b_Mappedclimatologies",
                          file),
                   quiet = TRUE)

# extract parameter name

parameter <- str_split(file, pattern = "6b.", simplify = TRUE)[2]
parameter <- str_split(parameter, pattern = ".nc", simplify = TRUE)[1]
print(parameter)

# 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 bottomdepth

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, 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 <= parameters$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_continous(clim_tibble,
                                  parameter))

# plot sections

print(
  p_section_global_continous(clim_tibble,
                             parameter,
                             "GLODAPv2_2016_Mapped_Climatology")
)


# write csv file

clim_tibble %>% 
  write_csv(here::here("data/interim",
                       paste("GLODAPv2_2016_MappedClimatology",parameter,".csv", sep = "")))

}
[1] "Cant"

[1] "NO3"

[1] "oxygen"

[1] "PO4"

[1] "salinity"

[1] "silicate"

[1] "TAlk"

[1] "TCO2"

[1] "temperature"


sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] stars_0.4-3     sf_0.9-5        abind_1.4-5     metR_0.7.0     
 [5] scico_1.2.0     patchwork_1.0.1 collapse_1.3.2  forcats_0.5.0  
 [9] stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4     readr_1.3.1    
[13] tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0
[17] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] fs_1.4.2           lubridate_1.7.9    httr_1.4.2         rprojroot_1.3-2   
 [5] tools_4.0.2        backports_1.1.8    R6_2.4.1           KernSmooth_2.23-17
 [9] DBI_1.1.0          colorspace_1.4-1   withr_2.2.0        tidyselect_1.1.0  
[13] compiler_4.0.2     git2r_0.27.1       cli_2.0.2          rvest_0.3.6       
[17] xml2_1.3.2         isoband_0.2.2      sandwich_2.5-1     labeling_0.3      
[21] scales_1.1.1       checkmate_2.0.0    classInt_0.4-3     digest_0.6.25     
[25] rmarkdown_2.3      pkgconfig_2.0.3    htmltools_0.5.0    dbplyr_1.4.4      
[29] rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11    generics_0.0.2    
[33] farver_2.0.3       zoo_1.8-8          jsonlite_1.7.0     magrittr_1.5      
[37] Formula_1.2-3      Matrix_1.2-18      Rcpp_1.0.5         munsell_0.5.0     
[41] fansi_0.4.1        lifecycle_0.2.0    stringi_1.4.6      whisker_0.4       
[45] yaml_2.2.1         grid_4.0.2         blob_1.2.1         parallel_4.0.2    
[49] promises_1.1.1     crayon_1.3.4       lattice_0.20-41    haven_2.3.1       
[53] hms_0.5.3          knitr_1.30         pillar_1.4.6       reprex_0.3.0      
[57] glue_1.4.1         evaluate_0.14      data.table_1.13.0  modelr_0.1.8      
[61] vctrs_0.3.2        httpuv_1.5.4       cellranger_1.1.0   gtable_0.3.0      
[65] assertthat_0.2.1   xfun_0.16          lwgeom_0.2-5       xtable_1.8-4      
[69] broom_0.7.0        e1071_1.7-3        lfe_2.8-5.1        later_1.1.0.1     
[73] class_7.3-17       viridisLite_0.3.0  units_0.6-7        ellipsis_0.3.1    
[77] here_0.1