Last updated: 2020-08-24

Checks: 7 0

Knit directory: Cant_eMLR/

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R Markdown file: up-to-date

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Environment: empty

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Seed: set.seed(20200707)

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Session information: recorded

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Cache: none

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File paths: relative

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Repository version: a804955

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Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    data/GLODAPv1_1/
    Ignored:    data/GLODAPv2_2016b_MappedClimatologies/
    Ignored:    data/GLODAPv2_2020/
    Ignored:    data/Gruber_2019/
    Ignored:    data/WOCE/
    Ignored:    data/World_Ocean_Atlas_2013_Clement/
    Ignored:    data/World_Ocean_Atlas_2018/
    Ignored:    data/eMLR/
    Ignored:    data/mapping/
    Ignored:    data/pCO2_atmosphere/
    Ignored:    dump/

Untracked files:
    Untracked:  output/figure/mapping/

Unstaged changes:
    Modified:   analysis/_site.yml
    Deleted:    analysis/mapping.Rmd
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/mapping_predictor_preparation.Rmd) and HTML (docs/mapping_predictor_preparation.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	a804955	jens-daniel-mueller	2020-08-24	split mapping into 2 rmds, po4star selection in parameters, use po4star nitrate

library(tidyverse)
library(metR)
library(marelac)
# library(lubridate)
# library(oce)
# library(reticulate)

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"))

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:

Salinity
Temperature
Phosphate (+Phosphate*)
Silicate
Oxygen (+AOU)

variables <-
  c("salinity", "temperature", "oxygen", "PO4", "silicate", "NO3")

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()

GLODAP_predictors <- GLODAP_predictors %>%
  rename(sal = salinity,
         tem = temperature)

1.2 World Ocean Atlas 2018

Neutral density
Basin mask

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

1.3 WOA13

Neutral densities and the basin mask based on WOA13 and provided by Dominic Clement are 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)

2 Join predictor climatologies

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

2.1 Control plots

Maps of number of observations per horizontal grid cell, which reflects the number of depth levels.

2.1.1 GLODAP climatology

GLODAP_predictors %>% 
  ggplot(aes(lon, lat)) +
  geom_bin2d(binwidth = 1) +
  scale_fill_viridis_c(direction = -1) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "bottom")

2.1.2 WOA18 climatology

WOA18_predictors <- WOA18_predictors %>% 
  filter(depth %in% parameters$depth_levels_33)

WOA18_predictors %>% 
  ggplot(aes(lon, lat)) +
  geom_bin2d(binwidth = 1) +
  scale_fill_viridis_c(direction = -1) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "bottom")

2.1.3 Neutral density zonal mean section

WOA18_predictors_zonal <- WOA18_predictors %>%
  group_by(lat, depth, basin) %>%
  summarise(gamma_mean = mean(gamma)) %>%
  ungroup()

WOA18_predictors_zonal_Atl <- WOA18_predictors_zonal %>%
  filter(basin == "Atlantic") %>%
  mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Atl))

WOA18_predictors_zonal_Ind_Pac <- WOA18_predictors_zonal %>%
  filter(basin == "Indo-Pacific") %>%
  mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Ind_Pac))

WOA18_predictors_zonal <- bind_rows(WOA18_predictors_zonal_Atl, WOA18_predictors_zonal_Ind_Pac)

rm(WOA18_predictors_zonal_Atl, WOA18_predictors_zonal_Ind_Pac)

slab_breaks <- c(parameters$slabs_Atl[1:12], Inf)

WOA18_predictors_zonal %>%
  filter(depth <= parameters$inventory_depth) %>%
  ggplot(aes(lat, depth, z = gamma_mean)) +
  geom_contour_filled(breaks = slab_breaks) +
  geom_contour(breaks = slab_breaks,
               col = "white") +
  geom_text_contour(breaks = slab_breaks,
                    col = "white",
                    skip = 1) +
  scale_fill_viridis_d(name = "Gamma",
                       direction = -1) +
  scale_y_reverse() +
  coord_cartesian(expand = 0) +
  guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
  facet_grid(basin ~ .)

rm(WOA18_predictors_zonal, slab_breaks)

2.2 WOA18 + GLODAP

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

predictors <- full_join(
  GLODAP_predictors %>% select(-c(sal,tem)),
  WOA18_predictors)

predictors <- predictors %>% 
  drop_na()

# rm(GLODAP_predictors, WOA18_predictors)

2.2.1 Control plots

2.2.1.1 Maps

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

map_climatology(predictors, "PO4")

map_climatology(predictors, "tem")

predictors %>%
  ggplot(aes(lon, lat)) +
  geom_bin2d(binwidth = c(1,1)) +
  scale_fill_viridis_c(direction = -1) +
  coord_quickmap(expand = 0) +
  theme(legend.position = "bottom")

2.2.1.2 Predictor profiles

Likewise, predictor profiles for the North Atlantic 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)

2.2.1.3 Neutral density zonal mean section

predictors_zonal <- predictors %>%
  group_by(lat, depth, basin) %>%
  summarise(gamma_mean = mean(gamma)) %>%
  ungroup()

predictors_zonal_Atl <- predictors_zonal %>%
  filter(basin == "Atlantic") %>%
  mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Atl))

predictors_zonal_Ind_Pac <- predictors_zonal %>%
  filter(basin == "Indo-Pacific") %>%
  mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Ind_Pac))

predictors_zonal <- bind_rows(predictors_zonal_Atl, predictors_zonal_Ind_Pac)

rm(predictors_zonal_Atl, predictors_zonal_Ind_Pac)

slab_breaks <- c(parameters$slabs_Atl[1:12], Inf)

predictors_zonal %>%
  filter(depth <= parameters$inventory_depth) %>%
  ggplot(aes(lat, depth, z = gamma_mean)) +
  geom_contour_filled(breaks = slab_breaks) +
  geom_contour(breaks = slab_breaks,
               col = "white") +
  geom_text_contour(breaks = slab_breaks,
                    col = "white",
                    skip = 1) +
  scale_fill_viridis_d(name = "Gamma",
                       direction = -1) +
  scale_y_reverse() +
  coord_cartesian(expand = 0) +
  guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
  facet_grid(basin ~ .)

rm(predictors_zonal, slab_breaks)

3 Prepare predictor fields

3.1 PO₄*

3.1.1 Calculation

Currently, the predictor PO₄* is calculated according to Clement and Gruber (2018), ie based on oxygen rather than nitrate.

predictors <- predictors %>% 
  rename(phosphate = PO4) %>% 
  mutate(phosphate_star_oxy = phosphate + (oxygen / 170)  - 1.95,
         phosphate_star_nit = phosphate - NO3 / 16  + 2.9)

3.1.2 Maps

map_climatology(predictors, "phosphate_star_nit")

map_climatology(predictors, "phosphate_star_oxy")

3.1.3 Sections

section_climatology(predictors, "phosphate_star_nit")

section_climatology(predictors, "phosphate_star_oxy")

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 = gas_satconc(S = sal,
                                  t = tem,
                                  P = 1.013253,
                                  species = "O2"),
         aou = oxygen_sat - oxygen) %>% 
  select(-oxygen_sat)

3.2.2 Maps

map_climatology(predictors, "aou")

3.2.3 Sections

section_climatology(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 Write csv

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

4.1 WOA13 + GLODAP

Currently not used.

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()

5 Open tasks

Check PO4* calculation
Harmonize AOU calculation in fitting and mapping

6 Open questions

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] marelac_2.1.10  shape_1.4.4     metR_0.7.0      forcats_0.5.0  
 [5] stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4     readr_1.3.1    
 [9] tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0
[13] 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     plyr_1.8.6        R6_2.4.1         
 [9] cellranger_1.1.0  backports_1.1.8   reprex_0.3.0      evaluate_0.14    
[13] httr_1.4.2        pillar_1.4.6      rlang_0.4.7       readxl_1.3.1     
[17] rstudioapi_0.11   data.table_1.13.0 whisker_0.4       blob_1.2.1       
[21] checkmate_2.0.0   rmarkdown_2.3     labeling_0.3      munsell_0.5.0    
[25] broom_0.7.0       compiler_4.0.2    httpuv_1.5.4      modelr_0.1.8     
[29] xfun_0.16         pkgconfig_2.0.3   htmltools_0.5.0   tidyselect_1.1.0 
[33] viridisLite_0.3.0 fansi_0.4.1       crayon_1.3.4      dbplyr_1.4.4     
[37] withr_2.2.0       later_1.1.0.1     gsw_1.0-5         grid_4.0.2       
[41] jsonlite_1.7.0    gtable_0.3.0      lifecycle_0.2.0   DBI_1.1.0        
[45] git2r_0.27.1      magrittr_1.5      seacarb_3.2.13    scales_1.1.1     
[49] oce_1.2-0         cli_2.0.2         stringi_1.4.6     farver_2.0.3     
[53] fs_1.4.2          promises_1.1.1    testthat_2.3.2    xml2_1.3.2       
[57] ellipsis_0.3.1    generics_0.0.2    vctrs_0.3.2       tools_4.0.2      
[61] glue_1.4.1        hms_0.5.3         yaml_2.2.1        colorspace_1.4-1 
[65] isoband_0.2.2     rvest_0.3.6       memoise_1.1.0     knitr_1.29       
[69] haven_2.3.1

Mapping

Jens Daniel Müller

24 August, 2020

1 Required data

1.1 GLODAPv2_2016b_MappedClimatologies

1.2 World Ocean Atlas 2018

1.3 WOA13

2 Join predictor climatologies

2.1 Control plots

2.1.1 GLODAP climatology

2.1.2 WOA18 climatology

2.1.3 Neutral density zonal mean section

2.2 WOA18 + GLODAP

2.2.1 Control plots

2.2.1.1 Maps

2.2.1.2 Predictor profiles

2.2.1.3 Neutral density zonal mean section

3 Prepare predictor fields

3.1 PO₄*

3.1.1 Calculation

3.1.2 Maps

3.1.3 Sections

3.2 AOU

3.2.1 Calculation

3.2.2 Maps

3.2.3 Sections

3.3 Isoneutral slabs

4 Write csv

4.1 WOA13 + GLODAP

5 Open tasks

6 Open questions

Mapping

Jens Daniel Müller

24 August, 2020

1 Required data

1.1 GLODAPv2_2016b_MappedClimatologies

1.2 World Ocean Atlas 2018

1.3 WOA13

2 Join predictor climatologies

2.1 Control plots

2.1.1 GLODAP climatology

2.1.2 WOA18 climatology

2.1.3 Neutral density zonal mean section

2.2 WOA18 + GLODAP

2.2.1 Control plots

2.2.1.1 Maps

2.2.1.2 Predictor profiles

2.2.1.3 Neutral density zonal mean section

3 Prepare predictor fields

3.1 PO4*

3.1.1 Calculation

3.1.2 Maps

3.1.3 Sections

3.2 AOU

3.2.1 Calculation

3.2.2 Maps

3.2.3 Sections

3.3 Isoneutral slabs

4 Write csv

4.1 WOA13 + GLODAP

5 Open tasks

6 Open questions

3.1 PO₄*