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library(tidyverse)
library(patchwork)
library(metR)
library(marelac)
library(gsw)

basinmask <-
  read_csv(
    here::here(
      "data/World_Ocean_Atlas_2018/_summarized_files",
      "basin_mask_WOA18.csv"
    )
  )

landmask <-
  read_csv(
    here::here(
      "data/World_Ocean_Atlas_2018/_summarized_files",
      "land_mask_WOA18.csv"
    )
  )

section_global_coordinates <-
  read_csv(here::here("data",
                       "section_global_coordinates.csv"))

1 Required data

All required data sets were subsetted spatially in the read-in section Data base. Currently, following data sets are used for mapping:

1.1 GLODAPv2_2016b_MappedClimatologies

Following variables are currently used:

Phosphate (+Phosphate*)
Silicate
Oxygen (+AOU)
TAlk (surface only)
TCO2 (surface only)

variables <-
  c("oxygen", "PO4", "silicate")

for (i_variable in variables) {
  temp <- read_csv(
    here::here(
      "data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
      paste(i_variable, ".csv", 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()

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

for (i_variable in variables) {
  temp <- read_csv(
    here::here(
      "data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
      paste(i_variable, ".csv", sep = "")
    )
  )
  
  if (exists("GLODAP_predictors_CO2")) {
    GLODAP_predictors_CO2 <- full_join(GLODAP_predictors_CO2, temp)
  }
  
  if (!exists("GLODAP_predictors_CO2")) {
    GLODAP_predictors_CO2 <- temp
  }
}

rm(temp, i_variable, variables)

# removed na's attributable to slightly different coverage of predictor fields
GLODAP_predictors_CO2 <- GLODAP_predictors_CO2 %>%
  drop_na()

1.2 World Ocean Atlas 2018

Salinity
Temperature
Neutral density
Basin mask

WOA18_predictors <-
  read_csv(
    here::here(
      "data/World_Ocean_Atlas_2018/_summarized_files",
      "WOA18_predictors.csv"
    )
  )

WOA18_predictors_surface <-
  read_csv(
    here::here(
      "data/World_Ocean_Atlas_2018/_summarized_files",
      "WOA18_predictors_surface.csv"
    )
  )

2 Join WOA18 + GLODAP

WOA18 and GLODAP predictor climatologies are merged. Only horizontal grid cells with observations from both predictor fields are kept.

CAVEAT: Coverage of GLODAP climatologies differs slightly for parameters (some are NA in some regions)

predictors <- full_join(
  GLODAP_predictors,
  WOA18_predictors)

predictors <- predictors %>% 
  drop_na()

rm(GLODAP_predictors, WOA18_predictors)

predictors_surface <- full_join(
  GLODAP_predictors_CO2,
  WOA18_predictors_surface)

predictors_surface <- predictors_surface %>% 
  drop_na()

rm(GLODAP_predictors_CO2, WOA18_predictors_surface)

2.1 Control plots

2.1.1 Maps

Three maps are generated to control successful merging of data sets.

map_climatology(predictors, "PO4")

map_climatology(predictors, "tem")

2.1.2 Maps surface

Three maps are generated to control successful merging of data sets.

map_climatology(predictors_surface, "TAlk")

map_climatology(predictors_surface, "TCO2")

map_climatology(predictors_surface, "sal")

map_climatology(predictors_surface, "tem")

2.1.3 Predictor profiles

Likewise, predictor profiles for the North Atlantic (40.5 / 335.5) are plotted to control successful merging of the data sets.

N_Atl <- predictors %>% 
  filter(lat == parameters$lat_Atl_profile,
         lon == parameters$lon_Atl_section)

N_Atl <- N_Atl %>% 
  select(-basin) %>% 
  pivot_longer(oxygen:gamma, names_to = "parameter", values_to = "value")

N_Atl %>% 
  ggplot(aes(value, depth)) +
  geom_path() +
  geom_point() +
  scale_y_reverse() +
  facet_wrap(~parameter,
             scales = "free_x",
             ncol = 2)

rm(N_Atl)

3 Prepare predictor fields

3.1 PO₄* calculation

The predictor PO₄* was be calculated according to Clement and Gruber (2018), ie based on oxygen. Please note that an errornous equations for PO4* calculation is given in the supplement of Gruber et al (2019), based on nitrate.

predictors <- predictors %>% 
  rename(phosphate = PO4) %>% 
  mutate(phosphate_star = phosphate + (oxygen / 170)  - 1.95)

3.1.1 Maps

map_climatology(predictors, "phosphate_star")

3.1.2 Sections

section_global(predictors, "phosphate_star")

3.2 AOU

3.2.1 Calculation

AOU was calculated as the difference between saturation concentration and observed concentration.

CAVEAT: Algorithms used to calculate oxygen saturation concentration are not yet identical in GLODAP data set (fitting) and predictor climatologies (mapping).

predictors <- predictors %>% 
  mutate(oxygen_sat_m3 = gas_satconc(S = sal,
                                  t = tem,
                                  P = 1.013253,
                                  species = "O2"),
         rho = gsw_pot_rho_t_exact(SA = sal, t = tem, p = depth, p_ref = 10.1325),
         oxygen_sat_kg = oxygen_sat_m3 * (1000/rho),
         aou = oxygen_sat_kg - oxygen) %>% 
  select(-c(oxygen_sat_m3, rho, oxygen_sat_kg))

3.2.2 Maps

map_climatology(predictors, "aou")

3.2.3 Sections

section_global(predictors, "aou")

3.3 Isoneutral slabs

The following boundaries for isoneutral slabs were defined:

Atlantic: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1, 28.15, 28.2,
Indo-Pacific: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1,

Continuous neutral density (gamma) values based on WOA18 are grouped into isoneutral slabs.

predictors_Atl <- predictors %>% 
  filter(basin == "Atlantic") %>% 
  mutate(gamma_slab = cut(gamma, parameters$slabs_Atl))

predictors_Ind_Pac <- predictors %>% 
  filter(basin == "Indo-Pacific") %>% 
  mutate(gamma_slab = cut(gamma, parameters$slabs_Ind_Pac))

predictors <- bind_rows(predictors_Atl, predictors_Ind_Pac)

rm(predictors_Atl, predictors_Ind_Pac)

4 Plot al predictor sections

4.1 Deep waters

Predictor sections along with lines are shown below for each (potential) predictor variable.

predictors %>%
  ggplot(aes(lon, lat)) +
      geom_raster(data = landmask,
                aes(lon, lat), fill = "grey80") +
  geom_bin2d(binwidth = c(1,1)) +
  geom_vline(xintercept = parameters$longitude_sections_regular,
             col = "white") +
  scale_fill_viridis_c(direction = -1) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "bottom")

vars <-
  c(
    "gamma",
    "sal",
    "tem",
    "phosphate",
    "phosphate_star",
    "oxygen",
    "aou",
    "silicate"
  )

for (i_var in vars) {
  print(section_climatology_regular(predictors, i_var))
}

4.2 Surface waters

Predictor sections along with lines are shown below for each (potential) predictor variable.

predictors_surface %>%
  ggplot(aes(lon, lat)) +
  geom_raster(data = landmask,
                aes(lon, lat), fill = "grey80") +
  geom_bin2d(binwidth = c(1,1)) +
  geom_vline(xintercept = parameters$longitude_sections_regular,
             col = "white") +
  scale_fill_viridis_c(direction = -1) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "bottom")

vars <-
  c(
    "gamma",
    "sal",
    "tem",
    "TCO2",
    "TAlk"
  )

for (i_var in vars) {
  print(section_climatology_regular(predictors_surface, i_var))
}

5 Write csv

predictors %>%
    write_csv(here::here("data/mapping/predictor_fields",
                         "W18_st_G16_opsn.csv"))

predictors_surface %>%
    write_csv(here::here("data/mapping/predictor_fields",
                         "W18_st_G16_opsn_surface.csv"))

6 Alternative predictors

6.1 WOA13 + GLODAP

Currently not used.

WOA13 <-
  read_csv(
    here::here(
      "data/World_Ocean_Atlas_2013_Clement/_summarized_files",
      "WOA13_mask_gamma.csv"
    )
  )

WOA13_gamma <- WOA13 %>%
  select(-mask)

rm(WOA13)

predictors <- full_join(GLODAP_predictors, WOA13_gamma)
GLODAP_depths <- unique(GLODAP_predictors$depth)

rm(GLODAP_predictors, WOA13_gamma)

predictors <- predictors %>% 
  group_by(lat, lon) %>% 
  mutate(n_oxygen = sum(!is.na(oxygen)),
         n_PO4 = sum(!is.na(PO4)),
         n_silicate = sum(!is.na(silicate)),
         n_sal = sum(!is.na(sal)),
         n_tem = sum(!is.na(tem)),
         n_gamma = sum(!is.na(gamma))) %>% 
  ungroup()

predictors <- predictors %>% 
  filter(n_oxygen > 0,
         n_PO4 > 0,
         n_silicate > 0, 
         n_sal > 0, 
         n_tem > 0, 
         n_gamma > 0) %>% 
  select(-c(n_oxygen, 
            n_PO4, 
            n_silicate, 
            n_sal, 
            n_tem,
            n_gamma))

predictors <- predictors %>% 
  drop_na()

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] gsw_1.0-5       testthat_2.3.2  marelac_2.1.10  shape_1.4.4    
 [5] metR_0.7.0      patchwork_1.0.1 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] Rcpp_1.0.5        here_0.1          lubridate_1.7.9   assertthat_0.2.1 
 [5] rprojroot_1.3-2   digest_0.6.25     R6_2.4.1          cellranger_1.1.0 
 [9] backports_1.1.8   reprex_0.3.0      evaluate_0.14     httr_1.4.2       
[13] pillar_1.4.6      rlang_0.4.7       readxl_1.3.1      rstudioapi_0.11  
[17] data.table_1.13.0 whisker_0.4       blob_1.2.1        checkmate_2.0.0  
[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        jsonlite_1.7.0    gtable_0.3.0     
[41] lifecycle_0.2.0   DBI_1.1.0         seacarb_3.2.13    git2r_0.27.1     
[45] magrittr_1.5      scales_1.1.1      oce_1.2-0         cli_2.0.2        
[49] stringi_1.4.6     farver_2.0.3      fs_1.4.2          promises_1.1.1   
[53] xml2_1.3.2        ellipsis_0.3.1    generics_0.0.2    vctrs_0.3.2      
[57] tools_4.0.2       glue_1.4.1        hms_0.5.3         yaml_2.2.1       
[61] colorspace_1.4-1  isoband_0.2.2     rvest_0.3.6       knitr_1.30       
[65] haven_2.3.1

Mapping

Jens Daniel Müller

10 November, 2020

1 Required data

1.1 GLODAPv2_2016b_MappedClimatologies

1.2 World Ocean Atlas 2018

2 Join WOA18 + GLODAP

2.1 Control plots

2.1.1 Maps

2.1.2 Maps surface

2.1.3 Predictor profiles

3 Prepare predictor fields

3.1 PO₄* calculation

3.1.1 Maps

3.1.2 Sections

3.2 AOU

3.2.1 Calculation

3.2.2 Maps

3.2.3 Sections

3.3 Isoneutral slabs

4 Plot al predictor sections

4.1 Deep waters

4.2 Surface waters

5 Write csv

6 Alternative predictors

6.1 WOA13 + GLODAP

Mapping

Jens Daniel Müller

10 November, 2020

1 Required data

1.1 GLODAPv2_2016b_MappedClimatologies

1.2 World Ocean Atlas 2018

2 Join WOA18 + GLODAP

2.1 Control plots

2.1.1 Maps

2.1.2 Maps surface

2.1.3 Predictor profiles

3 Prepare predictor fields

3.1 PO4* calculation

3.1.1 Maps

3.1.2 Sections

3.2 AOU

3.2.1 Calculation

3.2.2 Maps

3.2.3 Sections

3.3 Isoneutral slabs

4 Plot al predictor sections

4.1 Deep waters

4.2 Surface waters

5 Write csv

6 Alternative predictors

6.1 WOA13 + GLODAP

3.1 PO₄* calculation