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library(tidyverse)
library(lubridate)
library(stars)
library(metR)
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/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

depth_surface_selection <- c(0,100,500,2000)
Atl_lon <- 335.5
Pac_lon <- 190.5

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

  • Horizontal planes at 0, 100, 500, 2000m
  • Meridional sections at longitudes:
    • Atlantic: 335.5
    • Pacific: 190.5

Section locations are indicated as white lines in maps.

Please note that longitudes in the climatologies range from 20.5 - 379.5. For later merging with GLODAP observational data this will be projected to -179.5 - 179.5.

#file <- file_list[1]

for (file in file_list) {
 
clim_stars <- read_stars(here::here("data/GLODAPv2_2016b_Mappedclimatologies",
                          file))

# extract parameter name

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

# extract parameter of interest and write csv

map_clim_stars <- clim_stars %>% select(all_of(parameter))

map_clim_stars %>% 
  as_tibble() %>% 
  rename(lat = y, lon = x, depth = depth_surface) %>% 
  mutate(lon = if_else(lon > 180, lon - 360, lon)) %>% 
  write_csv(here::here("data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
                       paste(parameter,".csv", sep = "")))

# subset depth horizons for overview map plots

map_clim_stars_horizons <- map_clim_stars %>% 
  filter(depth_surface %in% depth_surface_selection)

# plot maps

print(
ggplot() +
  geom_stars(data = map_clim_stars_horizons) +
  geom_vline(xintercept = c(Atl_lon, Pac_lon), col = "white") +
  scale_fill_viridis_b(n.breaks = 8) +
  labs(title = file) +
  facet_wrap(~depth_surface, ncol = 2) +
  coord_quickmap(expand = 0)
)

# plot sections

Atl_section <- map_clim_stars %>% 
  as_tibble() %>% 
  rename(lat = y, lon = x, depth = depth_surface, parameter = 4) %>% 
  filter(lon == Atl_lon)

Atl_section_bathy <- Atl_section %>% 
  filter(is.na(parameter)) %>% 
  group_by(lat) %>% 
  summarise(bottom_depth = min(depth)) %>% 
  ungroup()

p_Atl_section <-
ggplot() +
  geom_contour_fill(data = Atl_section,
                    aes(lat, depth, z = parameter),
                    na.fill = TRUE) +
  geom_ribbon(data = Atl_section_bathy,
              aes(x = lat, ymin = bottom_depth, ymax = 5500),
              fill = "grey80",
              col = "black") +
  labs(title = "Atlantic ocean N-S section") +
  scale_y_reverse() +
  scale_fill_viridis_b(name = parameter,
                       n.breaks = 8) +
  coord_cartesian(expand = 0)

Pac_section <- map_clim_stars %>% 
  as_tibble() %>% 
  rename(lat = y, lon = x, depth = depth_surface, parameter = 4) %>% 
  filter(lon == Pac_lon)

Pac_section_bathy <- Pac_section %>% 
  filter(is.na(parameter)) %>% 
  group_by(lat) %>% 
  summarise(bottom_depth = min(depth)) %>% 
  ungroup()

p_Pac_section <-
ggplot() +
  geom_contour_fill(data = Pac_section,
                    aes(lat, depth, z = parameter),
                    na.fill = TRUE) +
  geom_ribbon(data = Pac_section_bathy,
              aes(x = lat, ymin = bottom_depth, ymax = 5500),
              fill = "grey80",
              col = "black") +
  labs(title = "Pacific ocean N-S section") +
  scale_y_reverse() +
  scale_fill_viridis_b(name = parameter,
                       n.breaks = 8) +
  coord_cartesian(expand = 0)

print(
p_Atl_section / p_Pac_section
)

}
Cant, Cant_error, Input_mean, Input_std, Input_N, Cant_relerr, 

NO3, NO3_error, Input_mean, Input_std, Input_N, NO3_relerr, 

oxygen, oxygen_error, Input_mean, Input_std, Input_N, oxygen_relerr, 

PO4, PO4_error, Input_mean, Input_std, Input_N, PO4_relerr, 

salinity, salinity_error, Input_mean, Input_std, Input_N, salinity_relerr, 

silicate, silicate_error, Input_mean, Input_std, Input_N, silicate_relerr, 

TAlk, TAlk_error, Input_mean, Input_std, Input_N, TAlk_relerr, 

TCO2, TCO2_error, Input_mean, Input_std, Input_N, TCO2_relerr, 

temperature, temperature_error, Input_mean, Input_std, Input_N, temperature_relerr,

3 Open tasks

  • none

4 Questions

  • none

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 metR_0.7.0      stars_0.4-3     sf_0.9-5       
 [5] abind_1.4-5     lubridate_1.7.9 forcats_0.5.0   stringr_1.4.0  
 [9] dplyr_1.0.0     purrr_0.3.4     readr_1.3.1     tidyr_1.1.0    
[13] tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2         jsonlite_1.7.0     viridisLite_0.3.0  here_0.1          
 [5] modelr_0.1.8       assertthat_0.2.1   sp_1.4-2           blob_1.2.1        
 [9] cellranger_1.1.0   yaml_2.2.1         lattice_0.20-41    pillar_1.4.6      
[13] backports_1.1.8    glue_1.4.1         digest_0.6.25      promises_1.1.1    
[17] checkmate_2.0.0    rvest_0.3.6        colorspace_1.4-1   plyr_1.8.6        
[21] htmltools_0.5.0    httpuv_1.5.4       pkgconfig_2.0.3    broom_0.7.0       
[25] haven_2.3.1        scales_1.1.1       whisker_0.4        later_1.1.0.1     
[29] git2r_0.27.1       farver_2.0.3       generics_0.0.2     ellipsis_0.3.1    
[33] withr_2.2.0        cli_2.0.2          magrittr_1.5       crayon_1.3.4      
[37] readxl_1.3.1       memoise_1.1.0      evaluate_0.14      fs_1.4.2          
[41] fansi_0.4.1        xml2_1.3.2         lwgeom_0.2-5       class_7.3-17      
[45] tools_4.0.2        data.table_1.13.0  hms_0.5.3          lifecycle_0.2.0   
[49] munsell_0.5.0      reprex_0.3.0       compiler_4.0.2     e1071_1.7-3       
[53] rlang_0.4.7        classInt_0.4-3     units_0.6-7        grid_4.0.2        
[57] rstudioapi_0.11    cubelyr_1.0.0      labeling_0.3       rmarkdown_2.3     
[61] gtable_0.3.0       DBI_1.1.0          R6_2.4.1           knitr_1.29        
[65] rprojroot_1.3-2    KernSmooth_2.23-17 stringi_1.4.6      parallel_4.0.2    
[69] Rcpp_1.0.5         vctrs_0.3.2        dbplyr_1.4.4       tidyselect_1.1.0  
[73] xfun_0.16