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1 Libraries

Loading libraries specific to the the analysis performed in this section.

library(metR)
library(marelac)
library(gsw)

2 Required data

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

2.1 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/interim",
      paste("GLODAPv2_2016_MappedClimatology",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)

GLODAP_predictors <- GLODAP_predictors %>%
  rename(phosphate = PO4)

# 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/interim",
      paste("GLODAPv2_2016_MappedClimatology",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)


GLODAP_predictors_CO2 <- GLODAP_predictors_CO2 %>%
  rename(phosphate = PO4)

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

2.2 World Ocean Atlas 2018

Salinity
Temperature
Neutral density

WOA18_predictors <-
  read_csv(
    here::here(
      "data/interim",
      "WOA18_sal_tem.csv"
    )
  )

WOA18_predictors_surface <-
  read_csv(
    here::here(
      "data/interim",
      "WOA18_sal_tem_surface.csv"
    )
  )

3 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)

3.1 Control plots

3.1.1 Maps

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

p_map_climatology_continous(predictors, "phosphate")

p_map_climatology_continous(predictors, "tem")

3.1.2 Maps surface

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

p_map_climatology_continous(predictors_surface, "TAlk")

p_map_climatology_continous(predictors_surface, "TCO2")

p_map_climatology_continous(predictors_surface, "sal")

p_map_climatology_continous(predictors_surface, "tem")

3.1.3 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(-c(basin, basin_AIP)) %>% 
  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)

4 Prepare predictor fields

4.1 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 %>% 
  mutate(phosphate_star = b_phosphate_star(phosphate, oxygen))

4.1.1 Maps

p_map_climatology_divergent(predictors, "phosphate_star")

4.1.2 Sections

p_section_global_divergent(predictors, "phosphate_star")

4.2 AOU

4.2.1 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(aou = b_aou(sal, tem, depth, oxygen))

4.2.2 Maps

p_map_climatology_continous(predictors, "aou")

4.2.3 Sections

p_section_global_divergent(predictors, "aou")

4.3 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 <- m_cut_gamma(predictors, "gamma")

5 Plot al predictor sections

5.1 Deep waters

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

map +
  geom_bin2d(data = predictors,
             aes(lon, lat),
             binwidth = c(1,1)) +
  geom_vline(xintercept = parameters$longitude_sections_regular,
             col = "white") +
  scale_fill_viridis_c(direction = -1) +
  theme(legend.position = "bottom")

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

# i_var <- vars[1]

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

5.2 Surface waters

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

map +
  geom_bin2d(data = predictors_surface,
             aes(lon, lat),
             binwidth = c(1,1)) +
  geom_vline(xintercept = parameters$longitude_sections_regular,
             col = "white") +
  scale_fill_viridis_c(direction = -1) +
  theme(legend.position = "bottom")

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

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

6 Write csv

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

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

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      scico_1.2.0     patchwork_1.0.1 collapse_1.3.2 
 [9] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4    
[13] readr_1.3.1     tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2  
[17] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2        viridisLite_0.3.0 jsonlite_1.7.0    here_0.1         
 [5] modelr_0.1.8      Formula_1.2-3     assertthat_0.2.1  blob_1.2.1       
 [9] cellranger_1.1.0  yaml_2.2.1        pillar_1.4.6      backports_1.1.8  
[13] lattice_0.20-41   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  sandwich_2.5-1   
[21] htmltools_0.5.0   httpuv_1.5.4      Matrix_1.2-18     pkgconfig_2.0.3  
[25] broom_0.7.0       seacarb_3.2.13    haven_2.3.1       xtable_1.8-4     
[29] scales_1.1.1      whisker_0.4       later_1.1.0.1     git2r_0.27.1     
[33] farver_2.0.3      generics_0.0.2    ellipsis_0.3.1    withr_2.2.0      
[37] cli_2.0.2         magrittr_1.5      crayon_1.3.4      readxl_1.3.1     
[41] evaluate_0.14     fs_1.4.2          fansi_0.4.1       xml2_1.3.2       
[45] oce_1.2-0         tools_4.0.2       data.table_1.13.0 hms_0.5.3        
[49] lifecycle_0.2.0   munsell_0.5.0     reprex_0.3.0      isoband_0.2.2    
[53] compiler_4.0.2    lfe_2.8-5.1       rlang_0.4.7       grid_4.0.2       
[57] rstudioapi_0.11   labeling_0.3      rmarkdown_2.3     gtable_0.3.0     
[61] DBI_1.1.0         R6_2.4.1          zoo_1.8-8         lubridate_1.7.9  
[65] knitr_1.30        rprojroot_1.3-2   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

Mapping

Jens Daniel Müller

25 November, 2020

1 Libraries

2 Required data

2.1 GLODAPv2_2016b_MappedClimatologies

2.2 World Ocean Atlas 2018

3 Join WOA18 + GLODAP

3.1 Control plots

3.1.1 Maps

3.1.2 Maps surface

3.1.3 Predictor profiles

4 Prepare predictor fields

4.1 PO₄* calculation

4.1.1 Maps

4.1.2 Sections

4.2 AOU

4.2.1 Calculation

4.2.2 Maps

4.2.3 Sections

4.3 Isoneutral slabs

5 Plot al predictor sections

5.1 Deep waters

5.2 Surface waters

6 Write csv

Mapping

Jens Daniel Müller

25 November, 2020

1 Libraries

2 Required data

2.1 GLODAPv2_2016b_MappedClimatologies

2.2 World Ocean Atlas 2018

3 Join WOA18 + GLODAP

3.1 Control plots

3.1.1 Maps

3.1.2 Maps surface

3.1.3 Predictor profiles

4 Prepare predictor fields

4.1 PO4* calculation

4.1.1 Maps

4.1.2 Sections

4.2 AOU

4.2.1 Calculation

4.2.2 Maps

4.2.3 Sections

4.3 Isoneutral slabs

5 Plot al predictor sections

5.1 Deep waters

5.2 Surface waters

6 Write csv

4.1 PO₄* calculation