Broullon DIC/TA climatology

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Last updated: 2022-04-29

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Knit directory: bgc_argo_r_argodata/

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---

Task

Explore the Broullón et al. (2020) DIC / TA climatology

library(tidyverse)
library(lubridate)
library(stars)

path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_updata <- '/nfs/kryo/work/updata'
path_broullon_clim <- paste0(path_updata, "/broullon_co2_monthly_climatology")

theme_set(theme_bw())

map <- map <-
  read_rds(paste(path_emlr_utilities,
                 "map_landmask_WOA18.rds",
                 sep = ""))

nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))


# WOA 18 basin mask

basinmask <-
  read_csv(
    paste(path_emlr_utilities,
          "basin_mask_WOA18.csv",
          sep = ""),
    col_types = cols("MLR_basins" = col_character())
  )

basinmask <- basinmask %>%
  filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>% 
  select(-c(MLR_basins, basin))

Load Data

DIC

DIC_clim <- tidync::hyper_tibble(paste0(path_broullon_clim, "/TCO2_NNGv2LDEO_climatology.nc"))

nc_depth <- read_ncdf(paste0(path_broullon_clim, "/TCO2_NNGv2LDEO_climatology.nc"),
                             var = c('depth'))
nc_depth <- as_tibble(nc_depth)

nc_depth <- nc_depth %>% 
  mutate(depth_level = depth_level+0.5)

DIC_clim <- full_join(DIC_clim, nc_depth)

DIC_clim <- DIC_clim %>% 
  rename(DIC = TCO2_NNGv2LDEO,
         month = time)

rm(nc_depth)
# table(unique(DIC_clim$latitude))
# table(unique(DIC_clim$longitude))
# table(unique(DIC_clim$time))
# table(unique(DIC_clim$depth))

# text <- read_file(paste0(path_broullon_clim, "/README_global_monthly_2020.txt"))

# Depth goes down to 5500 m, but below 1500 m DIC is an annual climatological value, rather than a monthly climatological value 

pCO2

pco2_clim <- tidync::hyper_tibble(paste0(path_broullon_clim, "/pCO2_NNGv2LDEO_climatology.nc"))

pco2_clim <- pco2_clim %>% 
  mutate(depth_level = 1) %>% 
  rename(pco2 = pCO2_NNGv2LDEO,
         month = time)

TA

TA_clim <- tidync::hyper_tibble(paste0(path_broullon_clim, "/AT_NNGv2_climatology.nc"))

nc_depth <- read_ncdf(paste0(path_broullon_clim, "/AT_NNGv2_climatology.nc"),
                   var = c('depth'))
nc_depth <- as_tibble(nc_depth)

nc_depth <- nc_depth %>% 
  mutate(depth_level = depth_level+0.5)

TA_clim <- full_join(TA_clim, nc_depth)

rm(nc_depth)

# read_file(paste0(path_broullon_clim, "/README_Global_monthly_2019.txt"))

TA_clim <- TA_clim %>% 
  rename(TA = AT_NNGv2,
         month = time)

# Depth goes down to 5500 m, but below 1500 m TA is an annual climatological value, rather than a monthly climatological value 

Join data

broullon_clim <- full_join(DIC_clim, TA_clim)

broullon_clim <- full_join(broullon_clim, pco2_clim)

rm(DIC_clim, pco2_clim, TA_clim)

Harmonise data

# put longitude and latitude labels to the center of the grid (.5º)

broullon_clim <- broullon_clim %>% 
  rename(lon = longitude, 
         lat = latitude) %>% 
  mutate(lon = if_else(lon < 20, lon + 360, lon))

broullon_clim_SO <- broullon_clim %>% 
  filter(lat <= -30)

Write data

broullon_clim %>% 
  write_rds(file = paste0(path_argo_preprocessed, "/broullon_TA_DIC_clim.rds"))

broullon_clim_SO %>% 
  write_rds(file = paste0(path_argo_preprocessed, "/broullon_TA_DIC_clim_SO.rds"))

Plot data

# join regional separations 

broullon_clim_SO <- inner_join(broullon_clim_SO, nm_biomes)

broullon_clim_SO <- inner_join(broullon_clim_SO, basinmask)

DIC

broullon_clim_SO %>% 
  group_split(depth) %>% 
  map(
    ~map +
      geom_tile(data = .x, 
                aes(x = lon, 
                    y = lat, 
                    fill = DIC))+
      scale_fill_viridis_c()+
      lims(y = c(-85, -28))+
      facet_wrap(~month, ncol = 2)+
      labs(title = paste0('Broullon et al. (2020) DIC clim, depth: ', unique(.x$depth)))
  )

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broullon_clim_SO %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = DIC,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = DIC,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim DIC, month: ', unique(.x$month)),
           x = 'DIC')
  )

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TA

broullon_clim_SO %>% 
  group_split(depth) %>% 
  map(
    ~map +
      geom_tile(data = .x, 
                aes(x = lon, 
                    y = lat, 
                    fill = TA))+
      scale_fill_viridis_c()+
      lims(y = c(-85, -28))+
      facet_wrap(~month, ncol = 2)+
      labs(title = paste0('Broullon et al. (2020) TA clim, depth: ', unique(.x$depth)))
  )

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broullon_clim_SO %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = TA,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = TA,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim TA, month: ', unique(.x$month)))
  )

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pCO2

map+
  geom_tile(data = broullon_clim_SO %>% filter(depth_level == 1),
            aes(x = lon,
                y = lat,
                fill = pco2))+
  scale_fill_viridis_c()+
  lims(y = c(-85, -28))+
  facet_wrap(~month, ncol = 2)+
  labs(title = 'Broullon et al. pCO2 clim')

Oxygen

broullon_clim_SO %>% 
  group_split(depth) %>% 
  map(
    ~map +
      geom_tile(data = .x, 
                aes(x = lon, 
                    y = lat, 
                    fill = oxygen))+
      scale_fill_viridis_c()+
      lims(y = c(-85, -28))+
      facet_wrap(~month, ncol = 2)+
      labs(title = paste0('Broullon et al. (2020) oxygen clim, depth: ', unique(.x$depth)))
  )

broullon_clim_SO %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = oxygen,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = oxygen,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim oxygen, month: ', unique(.x$month)))
  )

Nitrate

broullon_clim_SO %>% 
  group_split(depth) %>% 
  map(
    ~map +
      geom_tile(data = .x, 
                aes(x = lon, 
                    y = lat, 
                    fill = nitrate))+
      scale_fill_viridis_c()+
      lims(y = c(-85, -28))+
      facet_wrap(~month, ncol = 2)+
      labs(title = paste0('Broullon et al. (2020) nitrate clim, depth: ', unique(.x$depth)))
  )

broullon_clim_SO %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = nitrate,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = nitrate,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim nitrate, month: ', unique(.x$month)))
  )

Phosphate

broullon_clim_SO %>% 
  group_split(depth) %>% 
  map(
    ~map +
      geom_tile(data = .x, 
                aes(x = lon, 
                    y = lat, 
                    fill = phosphate))+
      scale_fill_viridis_c()+
      lims(y = c(-85, -28))+
      facet_wrap(~month, ncol = 2)+
      labs(title = paste0('Broullon et al. (2020) phosphate clim, depth: ', unique(.x$depth)))
  )

broullon_clim_SO %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = phosphate,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = phosphate,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim phosphate, month: ', unique(.x$month)))
  )

Session information

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] stars_0.5-5     sf_1.0-5        abind_1.4-5     lubridate_1.8.0
 [5] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7     purrr_0.3.4    
 [9] readr_2.1.1     tidyr_1.1.4     tibble_3.1.6    ggplot2_3.3.5  
[13] tidyverse_1.3.1 workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] fs_1.5.2           bit64_4.0.5        httr_1.4.2         rprojroot_2.0.2   
 [5] tools_4.1.2        backports_1.4.1    bslib_0.3.1        utf8_1.2.2        
 [9] R6_2.5.1           KernSmooth_2.23-20 DBI_1.1.2          colorspace_2.0-2  
[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       cli_3.1.1          rvest_1.0.2       
[21] RNetCDF_2.5-2      xml2_1.3.3         labeling_0.4.2     sass_0.4.0        
[25] scales_1.1.1       classInt_0.4-3     callr_3.7.0        proxy_0.4-26      
[29] digest_0.6.29      rmarkdown_2.11     pkgconfig_2.0.3    htmltools_0.5.2   
[33] highr_0.9          dbplyr_2.1.1       fastmap_1.1.0      rlang_1.0.2       
[37] tidync_0.2.4       readxl_1.3.1       rstudioapi_0.13    farver_2.1.0      
[41] jquerylib_0.1.4    generics_0.1.1     jsonlite_1.7.3     vroom_1.5.7       
[45] magrittr_2.0.1     ncmeta_0.3.0       Rcpp_1.0.8         munsell_0.5.0     
[49] fansi_1.0.2        lifecycle_1.0.1    stringi_1.7.6      whisker_0.4       
[53] yaml_2.2.1         grid_4.1.2         parallel_4.1.2     promises_1.2.0.1  
[57] crayon_1.4.2       haven_2.4.3        hms_1.1.1          knitr_1.37        
[61] ps_1.6.0           pillar_1.6.4       reprex_2.0.1       glue_1.6.0        
[65] evaluate_0.14      getPass_0.2-2      modelr_0.1.8       vctrs_0.3.8       
[69] tzdb_0.2.0         httpuv_1.6.5       cellranger_1.1.0   gtable_0.3.0      
[73] assertthat_0.2.1   xfun_0.29          lwgeom_0.2-8       broom_0.7.11      
[77] e1071_1.7-9        later_1.3.0        class_7.3-20       ncdf4_1.19        
[81] viridisLite_0.4.0  units_0.7-2        ellipsis_0.3.2