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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.

for (file in file_list) {
  # file <- file_list[2]
  
  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()
  
  # 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"

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[1] "GLODAPv2.2016b.NO3.nc"

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[1] "GLODAPv2.2016b.oxygen.nc"

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[1] "GLODAPv2.2016b.PO4.nc"

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[1] "GLODAPv2.2016b.salinity.nc"

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[1] "GLODAPv2.2016b.silicate.nc"

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[1] "GLODAPv2.2016b.TAlk.nc"

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[1] "GLODAPv2.2016b.TCO2.nc"

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[1] "GLODAPv2.2016b.temperature.nc"

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3 Revelle factor

variables <-
  c("salinity", "temperature", "PO4", "silicate", "TAlk", "TCO2")

# i_variable <- variables[1]

for (i_variable in variables) {
  temp <- read_csv(paste(
    path_preprocessing,
    paste("GLODAPv2_2016_MappedClimatology_", i_variable, ".csv", sep = ""),
    sep = ""
  ))
  
  if (exists("GLODAP_predictors")) {
    GLODAP_predictors <- full_join(GLODAP_predictors, temp)
  }
  
  if (!exists("GLODAP_predictors")) {
    GLODAP_predictors <- temp
  }
}

rm(temp, i_variable, variables)

# removed na's attributable to slightly different coverage of predictor fields
GLODAP_predictors <- GLODAP_predictors %>%
  drop_na()

GLODAP_predictors <- GLODAP_predictors %>%
  mutate(
    rev_fac = buffer(
      flag = 15,
      var1 = TAlk * 1e-6,
      var2 = TCO2 * 1e-6,
      S = salinity,
      T = temperature,
      P = depth / 10,
      Pt = PO4 * 1e-6,
      Sit = silicate * 1e-6,
      k1k2 = "l"
    )$BetaD
  )

p_map_climatology(df = GLODAP_predictors,
                  var = "rev_fac")

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p_section_global(df = GLODAP_predictors,
                 var = "rev_fac",
                 title_text = "GLODAPv2_2016_Mapped_Climatology")

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GLODAP_predictors %>%
  select(lon, lat, depth, basin_AIP, rev_fac) %>%
  write_csv(paste(
    path_preprocessing,
    paste("GLODAPv2_2016_MappedClimatology_",
          "rev_fac",
          ".csv",
          sep = ""),
    sep = ""
  ))

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3

Matrix products: default
BLAS:   /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] seacarb_3.3.0      SolveSAPHE_2.1.0   oce_1.5-0          gsw_1.0-6         
 [5] stars_0.5-5        sf_1.0-5           abind_1.4-5        geomtextpath_0.1.0
 [9] colorspace_2.0-2   marelac_2.1.10     shape_1.4.6        ggforce_0.3.3     
[13] metR_0.11.0        scico_1.3.0        patchwork_1.1.1    collapse_1.7.0    
[17] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.7        purrr_0.3.4       
[21] readr_2.1.1        tidyr_1.1.4        tibble_3.1.6       ggplot2_3.3.5     
[25] tidyverse_1.3.1    workflowr_1.7.0   

loaded via a namespace (and not attached):
 [1] fs_1.5.2           bit64_4.0.5        lubridate_1.8.0    httr_1.4.2        
 [5] rprojroot_2.0.2    tools_4.1.2        backports_1.4.1    bslib_0.3.1       
 [9] utf8_1.2.2         R6_2.5.1           KernSmooth_2.23-20 DBI_1.1.2         
[13] withr_2.4.3        tidyselect_1.1.1   processx_3.5.2     bit_4.0.4         
[17] compiler_4.1.2     git2r_0.29.0       textshaping_0.3.6  cli_3.1.1         
[21] rvest_1.0.2        xml2_1.3.3         isoband_0.2.5      labeling_0.4.2    
[25] sass_0.4.0         scales_1.1.1       checkmate_2.0.0    classInt_0.4-3    
[29] proxy_0.4-26       callr_3.7.0        systemfonts_1.0.3  digest_0.6.29     
[33] rmarkdown_2.11     pkgconfig_2.0.3    htmltools_0.5.2    highr_0.9         
[37] dbplyr_2.1.1       fastmap_1.1.0      rlang_1.0.2        readxl_1.3.1      
[41] rstudioapi_0.13    jquerylib_0.1.4    generics_0.1.1     farver_2.1.0      
[45] jsonlite_1.7.3     vroom_1.5.7        magrittr_2.0.1     Rcpp_1.0.8        
[49] munsell_0.5.0      fansi_1.0.2        lifecycle_1.0.1    stringi_1.7.6     
[53] whisker_0.4        yaml_2.2.1         MASS_7.3-55        grid_4.1.2        
[57] parallel_4.1.2     promises_1.2.0.1   crayon_1.4.2       haven_2.4.3       
[61] hms_1.1.1          knitr_1.37         ps_1.6.0           pillar_1.6.4      
[65] reprex_2.0.1       glue_1.6.0         evaluate_0.14      getPass_0.2-2     
[69] data.table_1.14.2  modelr_0.1.8       vctrs_0.3.8        tzdb_0.2.0        
[73] tweenr_1.0.2       httpuv_1.6.5       cellranger_1.1.0   gtable_0.3.0      
[77] polyclip_1.10-0    assertthat_0.2.1   xfun_0.29          lwgeom_0.2-8      
[81] broom_0.7.11       e1071_1.7-9        later_1.3.0        viridisLite_0.4.0 
[85] class_7.3-20       units_0.7-2        ellipsis_0.3.2