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library(tidyverse)
library(stars)
library(patchwork)

1 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"

2 Plot data and write csv

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

  • number of NA between surface and inventory depth
  • maps at depth levels -concentration along global section

The global section path is indicated as white line in maps.

# 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()

map +
  geom_raster(data = bottom_depth,
              aes(lon, lat, fill=bottom_depth)) +
  scale_fill_viridis_c(direction = -1) +
  labs(title = parameter)

# 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(
map_climatology(clim_tibble,
                parameter)
)


# plot sections

print(
section_global(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] patchwork_1.0.1 stars_0.4-3     sf_0.9-5        abind_1.4-5    
 [5] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4    
 [9] readr_1.3.1     tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2  
[13] tidyverse_1.3.0 workflowr_1.6.2

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