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Task

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

library(tidyverse)
library(lubridate)
library(stars)
library(seacarb)
library(gsw)
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_emlr_preprocessing <- "/nfs/kryo/work/jenmueller/emlr_cant/observations/preprocessing/"
path_updata <- '/nfs/kryo/work/updata'
path_broullon_clim <- paste0(path_updata, "/broullon_co2_monthly_climatology")
path_woa13_temp <- paste0(path_updata, "/woa2013/temperature/decav/1.00/")
path_woa13_sal <- paste0(path_updata, "/woa2013/salinity/decav/1.00/")
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 Broullon 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 

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 
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)

Join data

broullon_clim <- full_join(DIC_clim, TA_clim)
# broullon_clim <- full_join(broullon_clim, pco2_clim)

rm(DIC_clim, TA_clim)

Load WOA13 data

months <- sprintf("%02d", seq(1,12,1))

for (i_month in months) {
  # i_month <- months[1]

  
  # read temperature climatology
  woa13_temp <-
    read_ncdf(
      paste0(path_woa13_temp, "woa13_decav_t", i_month, "_01.nc"),
      var = "t_an",
      make_units = FALSE,
      make_time = FALSE
    )
  
  
  woa13_temp <- woa13_temp %>%
    as_tibble()
  
  woa13_temp <- woa13_temp %>%
    mutate(month = i_month) %>%
    select(-time) %>%
    rename(temp = t_an) %>%
    drop_na()
  
  
  # read salinity climatology
  woa13_sal <-
    read_ncdf(
      paste0(path_woa13_sal, "woa13_decav_s", i_month, "_01.nc"),
      var = "s_an",
      make_units = FALSE,
      make_time = FALSE
    )
  
  woa13_sal <- woa13_sal %>%
    as_tibble()
  
  woa13_sal <- woa13_sal %>%
    mutate(month = i_month) %>%
    select(-time) %>%
    rename(sal = s_an) %>%
    drop_na()
  
  
  # join temperature and salinity climatology
  woa13_temp <- full_join(woa13_temp,
                           woa13_sal)
  
  # bind months into joined data frame
  if (exists("woa13")) {
    woa13 <- bind_rows(woa13, woa13_temp)
  }
  
  if (!exists("woa13")) {
    woa13 <- woa13_temp
  }
  
}


woa13 <- woa13 %>% 
  mutate(month = as.numeric(month))

rm(woa13_temp, woa13_sal, months, i_month)

Harmonise data

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

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

broullon_clim <- broullon_clim %>% 
  drop_na()
# put longitude and latitude labels to the center of the grid (.5º)

woa13 <- woa13 %>% 
  mutate(lon = if_else(lon < 20, lon + 360, lon))

Join Broullon/WOA13

broullon_clim <- full_join(broullon_clim,
                                   woa13)

# remove grid cells with only one sal value to allow for interpolation
broullon_clim <- broullon_clim %>%
  group_by(month, lat, lon) %>%
  mutate(n = sum(!is.na(sal))) %>%
  ungroup()

broullon_clim <- broullon_clim %>%
  filter(n > 1) %>%
  select(-n)

# interpolate sal/temp to broullon depth levels
broullon_clim <- broullon_clim %>%
  group_by(lon, lat, month) %>%
  arrange(depth) %>%
  mutate(sal := approxfun(depth, sal, rule = 2)(depth),
         temp := approxfun(depth, temp, rule = 2)(depth)) %>%
  ungroup()

# remove sal/temp data on original woa13 depth levels
broullon_clim <- broullon_clim %>% 
  filter(!is.na(DIC))

Apply basin mask

# subset Southerh Ocean data
broullon_clim_SO <- broullon_clim %>% 
  filter(lat <= -30,
         depth <= 2000)

# join regional separations 

broullon_clim_SO <- inner_join(broullon_clim_SO, nm_biomes)

broullon_clim_SO <- inner_join(broullon_clim_SO, basinmask)

broullon_clim <- inner_join(broullon_clim, basinmask)

Write all data

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

Load Gruber 2019

G19_dcant_3d <-
  read_csv(paste0(path_emlr_preprocessing,
                  "G19_dcant_3d.csv"))


G19_dcant_3d <- G19_dcant_3d %>% 
  select(lon, lat, depth, dcant = dcant_pos)


G19_dcant_3d <- inner_join(G19_dcant_3d,
                           nm_biomes %>% select(lon, lat))

Scale DIC to 2016

# unique(G19_dcant_3d$depth)
# unique(broullon_clim_SO$depth)

broullon_clim_SO <-
  full_join(broullon_clim_SO,
            G19_dcant_3d %>% filter(depth <= 2000))

# remove grid cells with only one sal value to allow for interpolation
broullon_clim_SO <- broullon_clim_SO %>%
  group_by(month, lat, lon) %>%
  mutate(n = sum(!is.na(dcant))) %>%
  ungroup()

broullon_clim_SO <- broullon_clim_SO %>%
  filter(n > 1) %>%
  select(-n)

# interpolate dcant to Broullon clim depth levels
broullon_clim_SO <- broullon_clim_SO %>%
  group_by(lon, lat, month) %>%
  arrange(depth) %>%
  mutate(dcant := approxfun(depth, dcant, rule = 2)(depth)) %>%
  ungroup()

# remove sal/temp data on original woa13 depth levels
broullon_clim_SO <- broullon_clim_SO %>% 
  filter(!is.na(DIC))

broullon_clim_SO <- broullon_clim_SO %>% 
  mutate(DIC = DIC + dcant * ((2019-1995)/(2007-1994))) %>% 
  select(-dcant)

Calculate pH

rm(broullon_clim, woa13)

# calculate pressure from depth
broullon_clim_SO <- broullon_clim_SO %>% 
  mutate(pressure = gsw_p_from_z(z = -depth,
                                 latitude = lat))

# broullon_clim_SO <- broullon_clim_SO %>% 
#   arrange(lon, lat)
# 
# # calculate pHT from DIC, TA and ancillary parameters
# for (i_lon in unique(broullon_clim_SO$lon)) {
#   print("***")
#   print(i_lon)
#   
#   broullon_clim_SO_lon <- broullon_clim_SO %>%
#     filter(lon == i_lon)
#   
#   for (i_lat in unique(broullon_clim_SO_lon$lat)) {
#     print(i_lat)
#     
#     broullon_clim_SO_lon_lat <- 
#       broullon_clim_SO_lon %>%
#       filter(lat == i_lat) %>%
#       mutate(
#         pH = carb(
#           flag = 15,
#           var1 = TA * 1e-6,
#           var2 = DIC * 1e-6,
#           S = sal,
#           T = temp,
#           P = pressure / 10,
#           Pt = phosphate * 1e-6,
#           Sit = silicate * 1e-6,
#           k1k2 = "l"
#         )[,6]
#       )
#     
#       # bind months into joined data frame
#   if (exists("broullon_clim_SO_pH")) {
#     broullon_clim_SO_pH <- bind_rows(broullon_clim_SO_pH, broullon_clim_SO_lon_lat)
#   }
#   
#   if (!exists("broullon_clim_SO_pH")) {
#     broullon_clim_SO_pH <- broullon_clim_SO_lon_lat
#   }
#     
#   }
# }
#
# rm(broullon_clim_SO_lon_lat, broullon_clim_SO_lon)

broullon_clim_SO_pH <-
  broullon_clim_SO %>%
  mutate(
    pH = carb(
      flag = 15,
      var1 = TA * 1e-6,
      var2 = DIC * 1e-6,
      S = sal,
      T = temp,
      P = pressure / 10,
      Pt = phosphate * 1e-6,
      Sit = silicate * 1e-6,
      k1k2 = "l"
    )[, 6]
  )

Write SO+pH data

broullon_clim_SO_pH %>% 
  write_rds(file = paste0(path_argo_preprocessed, "/broullon_TA_DIC_clim_SO_pH.rds"))

Plot data

pH

broullon_clim_SO_pH %>% 
  group_split(depth) %>% 
  head(2) %>% 
  map(
    ~map +
      geom_tile(data = .x, 
                aes(x = lon, 
                    y = lat, 
                    fill = pH))+
      scale_fill_viridis_c()+
      lims(y = c(-85, -28))+
      facet_wrap(~month, ncol = 2)+
      labs(title = paste0('Broullon et al. (2020) pH clim, depth: ', unique(.x$depth)))
  )
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broullon_clim_SO_pH %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  head(2) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = pH,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = pH,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim pH, month: ', unique(.x$month)),
           x = 'pH')
  )
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DIC

broullon_clim_SO_pH %>% 
  group_split(depth) %>% 
  head(2) %>% 
  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$depth)))
  )
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broullon_clim_SO_pH %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  head(2) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = DIC,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = DIC,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim DIC, month: ', unique(.x$month)),
           x = 'DIC')
  )
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TA

broullon_clim_SO_pH %>% 
  group_split(depth) %>% 
  head(2) %>% 
  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$depth)))
  )
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broullon_clim_SO_pH %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  head(2) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = TA,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = TA,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim TA, month: ', unique(.x$month)))
  )
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Oxygen

broullon_clim_SO_pH %>% 
  group_split(depth) %>% 
  head(2) %>% 
  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$depth)))
  )
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broullon_clim_SO_pH %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  head(2) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = oxygen,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = oxygen,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim oxygen, month: ', unique(.x$month)))
  )
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Nitrate

broullon_clim_SO_pH %>% 
  group_split(depth) %>% 
  head(2) %>% 
  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$depth)))
  )
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broullon_clim_SO_pH %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  head(2) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = nitrate,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = nitrate,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim nitrate, month: ', unique(.x$month)))
  )
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Phosphate

broullon_clim_SO_pH %>% 
  group_split(depth) %>% 
  head(2) %>% 
  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$depth)))
  )
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broullon_clim_SO_pH %>% 
  # filter(depth <= 1500) %>% 
  group_split(month) %>% 
  head(2) %>% 
  map(
    ~ ggplot(data = .x,
             aes(x = phosphate,
                 y = depth))+
      geom_point(data = .x, 
                 aes(x = phosphate,
                     y = depth),
                 size = 0.2,
                 pch = 1)+
      scale_y_reverse()+
      facet_grid(biome_name~basin_AIP)+
      labs(title = paste0('Broullon et al. clim phosphate, month: ', unique(.x$month)))
  )
[[1]]

Version Author Date
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11

[[2]]

Version Author Date
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11

sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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_3.3.1    SolveSAPHE_2.1.0 oce_1.7-10       gsw_1.1-1       
 [5] stars_0.6-0      sf_1.0-9         abind_1.4-5      lubridate_1.9.0 
 [9] timechange_0.1.1 forcats_0.5.2    stringr_1.5.0    dplyr_1.1.3     
[13] purrr_1.0.2      readr_2.1.3      tidyr_1.3.0      tibble_3.2.1    
[17] ggplot2_3.4.4    tidyverse_1.3.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.2            bit64_4.0.5         httr_1.4.4         
 [4] rprojroot_2.0.3     tools_4.2.2         backports_1.4.1    
 [7] bslib_0.4.1         utf8_1.2.2          R6_2.5.1           
[10] KernSmooth_2.23-20  DBI_1.1.3           colorspace_2.0-3   
[13] withr_2.5.0         tidyselect_1.2.0    bit_4.0.5          
[16] compiler_4.2.2      git2r_0.30.1        cli_3.6.1          
[19] rvest_1.0.3         RNetCDF_2.6-1       xml2_1.3.3         
[22] labeling_0.4.2      sass_0.4.4          scales_1.2.1       
[25] classInt_0.4-8      proxy_0.4-27        digest_0.6.30      
[28] rmarkdown_2.18      pkgconfig_2.0.3     htmltools_0.5.3    
[31] highr_0.9           dbplyr_2.2.1        fastmap_1.1.0      
[34] rlang_1.1.1         tidync_0.3.0        readxl_1.4.1       
[37] rstudioapi_0.15.0   farver_2.1.1        jquerylib_0.1.4    
[40] generics_0.1.3      jsonlite_1.8.3      vroom_1.6.0        
[43] googlesheets4_1.0.1 magrittr_2.0.3      ncmeta_0.3.5       
[46] Rcpp_1.0.10         munsell_0.5.0       fansi_1.0.3        
[49] lifecycle_1.0.3     stringi_1.7.8       whisker_0.4        
[52] yaml_2.3.6          grid_4.2.2          parallel_4.2.2     
[55] promises_1.2.0.1    crayon_1.5.2        haven_2.5.1        
[58] hms_1.1.2           knitr_1.41          pillar_1.9.0       
[61] reprex_2.0.2        glue_1.6.2          evaluate_0.18      
[64] modelr_0.1.10       vctrs_0.6.4         tzdb_0.3.0         
[67] httpuv_1.6.6        cellranger_1.1.0    gtable_0.3.1       
[70] assertthat_0.2.1    cachem_1.0.6        xfun_0.35          
[73] lwgeom_0.2-10       broom_1.0.5         e1071_1.7-12       
[76] later_1.3.0         viridisLite_0.4.1   ncdf4_1.19         
[79] class_7.3-20        googledrive_2.0.0   gargle_1.2.1       
[82] workflowr_1.7.0     units_0.8-0         ellipsis_0.3.2