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Rmd	7a4b015	jens-daniel-mueller	2020-11-30	first rebuild on ETH server
Rmd	bc61ce3	Jens Müller	2020-11-30	Initial commit
html	bc61ce3	Jens Müller	2020-11-30	Initial commit

1 Required data

Currently, following data sets are used for mapping:

GLODAPv2_2016b_MappedClimatologies, with variables
- Phosphate (-> phosphate_star)
- Silicate
- Oxygen (-> AOU)
- TAlk (surface only)
- TCO2 (surface only)

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

# i_variable <- variables[1]

for (i_variable in variables) {
  temp <- read_csv(paste(
    path_preprocessing,
    paste("GLODAPv2_2016_MappedClimatology_", i_variable, ".csv", sep = ""),
    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,
         nitrate = NO3,
         talk = TAlk)

# 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(paste(
    path_preprocessing,
    paste("GLODAPv2_2016_MappedClimatology_", i_variable, ".csv", sep = ""),
    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()

World Ocean Atlas 2018
- Salinity
- Temperature
- Neutral density

WOA18_predictors <-
  read_csv(paste(path_preprocessing,
                 "WOA18_sal_temp.csv",
                 sep = ""))

2 Join WOA18 + GLODAP

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

# join deep water predictors
predictors <- full_join(
  GLODAP_predictors,
  WOA18_predictors)

rm(GLODAP_predictors)

predictors <- predictors %>% 
  drop_na()

# join surface water predictors
predictors_surface <- full_join(
  GLODAP_predictors_CO2,
  WOA18_predictors)

predictors_surface <- predictors_surface %>% 
  drop_na()

2.1 Apply density threshold

The predictor field was split into two parts:

Deep water: neutral densities >= 26 and depth >= 150m
Shallow water: rest

# predictors for deep waters
predictors <- predictors %>%
  filter(depth >= params_local$depth_min | gamma >= params_local$gamma_min)

# predictors for surface waters
predictors_surface <- predictors_surface %>%
  filter(depth < params_local$depth_min,
         gamma < params_local$gamma_min)

2.2 Apply basin mask

Data outside the WOA18 basin mask were removed for further analysis.

predictors <- inner_join(predictors, basinmask)
predictors_surface <- inner_join(predictors_surface, basinmask)

2.3 Control plots

Plots below are generated to control successful merging of data sets.

2.3.1 Maps interior

p_map_climatology(
  df = predictors, 
  var = "phosphate")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

p_map_climatology(
  df = predictors, 
  var = "temp")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12

2.3.2 Maps surface

p_map_climatology(
  df = predictors_surface, 
  var = "TAlk")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

p_map_climatology(
  df = predictors_surface, 
  var = "TCO2")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

p_map_climatology(
  df = predictors_surface, 
  var = "sal")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

p_map_climatology(
  df = predictors_surface, 
  var = "temp")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12

2.3.3 Predictor profiles

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

# subset data
N_Atl <- predictors %>% 
  filter(lat == params_global$lat_Atl_profile,
         lon == params_global$lon_Atl_section)

# pivot table to long format
N_Atl <- N_Atl %>% 
  select(-c(basin, basin_AIP)) %>% 
  pivot_longer(c(any_of(params_local$MLR_predictors), gamma),
               names_to = "parameter", values_to = "value")

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

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

rm(N_Atl)

3 Prepare predictor fields

Additional predictor fields need to calculated from available climatologies

3.1 PO₄* calculation

The predictor PO₄* was be calculated according to Clement and Gruber (2018), ie based on oxygen. Please note that an erroneous 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))

3.1.1 Maps

p_map_climatology(
  df = predictors,
  var = "phosphate_star",
  col = "divergent")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

3.1.2 Global section

p_section_global(
  df = predictors,
  var = "phosphate_star",
  col = "divergent")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

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

3.2.2 Maps

p_map_climatology(
  df = predictors,
  var = "aou",
  col = "divergent")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

3.2.3 Global section

p_section_global(
  df = predictors,
  var = "aou",
  col = "divergent")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

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 <- m_cut_gamma(predictors, "gamma")

4 Plot al predictor sections

4.1 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 = params_global$longitude_sections_regular,
             col = "white") +
  scale_fill_viridis_c(direction = -1)

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

for (i_var in params_local$MLR_predictors) {
  print(
    p_section_climatology_regular(
      df = predictors,
      var = i_var)
    )
}

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

4.2 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 = params_global$longitude_sections_regular,
             col = "white") +
  scale_fill_viridis_c(direction = -1) +
  theme(legend.position = "bottom")

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

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

# i_var <- vars[1]

for (i_var in vars) {
  print(
    p_section_climatology_regular(
      df = predictors_surface,
      var = i_var,
      surface = "y")
    )
}

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

Version	Author	Date
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
ae8463c	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
3ebff89	jens-daniel-mueller	2020-12-12
f8358f8	jens-daniel-mueller	2020-12-02
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

5 Write csv

predictors %>%
  write_csv(paste(path_version_data,
                  "W18_st_G16_opsn.csv",
                  sep = ""))

predictors_surface %>%
  write_csv(paste(path_version_data,
                  "W18_st_G16_opsn_surface.csv",
                  sep = ""))

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] gsw_1.0-5       testthat_2.3.2  marelac_2.1.10  shape_1.4.5    
 [5] metR_0.9.0      scico_1.2.0     patchwork_1.1.1 collapse_1.5.0 
 [9] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
[13] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    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_1.7.1          
 [4] here_0.1                 modelr_0.1.8             assertthat_0.2.1        
 [7] blob_1.2.1               cellranger_1.1.0         yaml_2.2.1              
[10] pillar_1.4.7             backports_1.1.10         lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.7.0      digest_0.6.27           
[16] promises_1.1.1           checkmate_2.0.0          rvest_0.3.6             
[19] colorspace_1.4-1         htmltools_0.5.0          httpuv_1.5.4            
[22] Matrix_1.2-18            pkgconfig_2.0.3          broom_0.7.2             
[25] seacarb_3.2.14           haven_2.3.1              scales_1.1.1            
[28] whisker_0.4              later_1.1.0.1            git2r_0.27.1            
[31] farver_2.0.3             generics_0.0.2           ellipsis_0.3.1          
[34] withr_2.3.0              cli_2.1.0                magrittr_1.5            
[37] crayon_1.3.4             readxl_1.3.1             evaluate_0.14           
[40] fs_1.5.0                 fansi_0.4.1              xml2_1.3.2              
[43] RcppArmadillo_0.10.1.2.0 oce_1.2-0                tools_4.0.3             
[46] data.table_1.13.2        hms_0.5.3                lifecycle_0.2.0         
[49] munsell_0.5.0            reprex_0.3.0             isoband_0.2.2           
[52] compiler_4.0.3           rlang_0.4.9              grid_4.0.3              
[55] rstudioapi_0.13          labeling_0.4.2           rmarkdown_2.5           
[58] gtable_0.3.0             DBI_1.1.0                R6_2.5.0                
[61] lubridate_1.7.9          knitr_1.30               rprojroot_2.0.2         
[64] stringi_1.5.3            parallel_4.0.3           Rcpp_1.0.5              
[67] vctrs_0.3.5              dbplyr_1.4.4             tidyselect_1.1.0        
[70] xfun_0.18

Mapping

Jens Daniel Müller

23 March, 2021

1 Required data

2 Join WOA18 + GLODAP

2.1 Apply density threshold

2.2 Apply basin mask

2.3 Control plots

2.3.1 Maps interior

2.3.2 Maps surface

2.3.3 Predictor profiles

3 Prepare predictor fields

3.1 PO₄* calculation

3.1.1 Maps

3.1.2 Global section

3.2 AOU

3.2.1 Calculation

3.2.2 Maps

3.2.3 Global section

3.3 Isoneutral slabs

4 Plot al predictor sections

4.1 Deep waters

4.2 Surface waters

5 Write csv

Mapping

Jens Daniel Müller

23 March, 2021

1 Required data

2 Join WOA18 + GLODAP

2.1 Apply density threshold

2.2 Apply basin mask

2.3 Control plots

2.3.1 Maps interior

2.3.2 Maps surface

2.3.3 Predictor profiles

3 Prepare predictor fields

3.1 PO4* calculation

3.1.1 Maps

3.1.2 Global section

3.2 AOU

3.2.1 Calculation

3.2.2 Maps

3.2.3 Global section

3.3 Isoneutral slabs

4 Plot al predictor sections

4.1 Deep waters

4.2 Surface waters

5 Write csv

3.1 PO₄* calculation