Last updated: 2021-01-27

Checks: 7 0

Knit directory: emlr_mod_v_XXX/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200707)

The command set.seed(20200707) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 9655866

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 9655866. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Unstaged changes:
    Modified:   data/auxillary/params_local.rds

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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
html	ceed31b	Donghe-Zhu	2021-01-27	Build site.
html	342402d	Donghe-Zhu	2021-01-27	Build site.
html	5bad5c2	Donghe-Zhu	2021-01-27	Build site.
html	61efb56	Donghe-Zhu	2021-01-25	Build site.
html	48f638e	Donghe-Zhu	2021-01-25	Build site.
html	c1cec47	Donghe-Zhu	2021-01-25	Build site.
Rmd	560dcf7	Donghe-Zhu	2021-01-25	no temp
html	05ffb0c	Donghe-Zhu	2021-01-25	Build site.
html	8b97165	Donghe-Zhu	2021-01-25	Build site.
Rmd	c41c01f	Donghe-Zhu	2021-01-25	OXYGEN REPLACE AOU
html	c569946	Donghe-Zhu	2021-01-24	Build site.
html	a2f0d56	Donghe-Zhu	2021-01-23	Build site.
html	28509fc	Donghe-Zhu	2021-01-23	Build site.
html	4c28e4a	Donghe-Zhu	2021-01-22	Build site.
html	24cc264	jens-daniel-mueller	2021-01-22	cleaned /docs before creating copies
html	88eb28f	Donghe-Zhu	2021-01-21	Build site.
html	2679490	Donghe-Zhu	2021-01-21	Build site.
html	7891955	Donghe-Zhu	2021-01-21	Build site.
html	d4cf1cb	Donghe-Zhu	2021-01-21	Build site.
html	1f3e5b6	jens-daniel-mueller	2021-01-20	Build site.
Rmd	30afcd9	jens-daniel-mueller	2021-01-20	local rebuild after revision
html	0e7bdf1	jens-daniel-mueller	2021-01-15	cleaning template repository
html	73cbef3	jens-daniel-mueller	2021-01-15	Build site.
html	4571843	jens-daniel-mueller	2021-01-14	revision and html deleted for template copying
html	23151cd	jens-daniel-mueller	2021-01-14	Build site.
html	b3564aa	jens-daniel-mueller	2021-01-14	Build site.
html	8d032c3	jens-daniel-mueller	2021-01-14	Build site.
html	022871c	Donghe-Zhu	2021-01-13	Build site.
Rmd	d44f36f	Donghe-Zhu	2021-01-13	reorder analysis final
html	17dee1d	jens-daniel-mueller	2021-01-13	Build site.
html	a076226	Donghe-Zhu	2021-01-11	Build site.
Rmd	52eff18	Donghe-Zhu	2021-01-09	Implemet model_run and subsetting
html	7cdea0c	jens-daniel-mueller	2021-01-06	Build site.
Rmd	b5934dd	jens-daniel-mueller	2021-01-06	local rebuild after revision
html	fa85b93	jens-daniel-mueller	2021-01-06	Build site.
html	e5cb81a	Donghe-Zhu	2021-01-05	Build site.
Rmd	608cc45	Donghe-Zhu	2021-01-05	modification of analysis
html	a499f10	Donghe-Zhu	2021-01-05	Build site.
Rmd	715bdb4	Donghe-Zhu	2021-01-02	model modification
html	fb8a752	Donghe-Zhu	2020-12-23	Build site.
Rmd	82e3c9c	Donghe-Zhu	2020-12-23	first build after creating model template
html	8fae0b2	Donghe-Zhu	2020-12-21	Build site.
Rmd	00a1322	Donghe-Zhu	2020-12-21	first build after creating model template
html	c8b76b3	jens-daniel-mueller	2020-12-19	Build site.
Rmd	b5fedce	jens-daniel-mueller	2020-12-19	first build after creating model template
Rmd	8e8abf5	Jens Müller	2020-12-18	Initial commit

1 Required data

Currently, following data sets are used for mapping:

Cmorized model runA climatologies in 2007, with variables
- Phosphate (-> phosphate_star)
- Silicate
- Oxygen
- AOU (already calculated)
- TAlk (surface only)
- TCO2 (surface only)

if (params_local$model_runs == "AD") {
  
  climatology <- read_csv(paste(path_preprocessing, "climatology_runA_2007.csv", sep = ""))
  
}

if (params_local$model_runs == "CB") {
  
  climatology <- read_csv(paste(path_preprocessing, "climatology_runC_2007.csv", sep = ""))
  
}

climatology <- climatology %>%
  rename(aou = AOU, TCO2 = tco2, TAlk = talk)

predictors <- climatology %>%
  select(lon, lat, depth, basin_AIP, gamma, 
         phosphate, oxygen, temp,
         any_of(params_local$MLR_predictors))

predictors_surface <- climatology %>%
  select(lon, lat, depth, basin_AIP, gamma, sal, temp, TCO2, TAlk, phosphate, silicate)

1.1 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)

1.2 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)

1.3 Control plots

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

1.3.1 Maps interior

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

1.3.2 Maps surface

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

1.3.3 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
48f638e	Donghe-Zhu	2021-01-25
c1cec47	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

rm(N_Atl)

2 Prepare predictor fields

Additional predictor fields need to calculated from available climatologies

2.1 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))

2.1.1 Maps

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

2.1.2 Global section

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

2.2 AOU

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).

2.2.1 Maps

if ("aou" %in% params_local$MLR_predictors){
p_map_climatology(
  df = predictors,
  var = "aou",
  col = "divergent")
}

Version	Author	Date
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

2.2.2 Global section

if ("aou" %in% params_local$MLR_predictors){
p_section_global(
  df = predictors,
  var = "aou",
  col = "divergent")
}

Version	Author	Date
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

2.3 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")

3 Plot all predictor sections

3.1 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,
                       name = "Number of depth levels") +
  theme(legend.position = "bottom")

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
7cdea0c	jens-daniel-mueller	2021-01-06
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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

Version	Author	Date
c1cec47	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
48f638e	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
48f638e	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
48f638e	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
48f638e	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
48f638e	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
48f638e	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
48f638e	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

3.2 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, name = "Number of depth levels") +
  theme(legend.position = "bottom")

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
7cdea0c	jens-daniel-mueller	2021-01-06
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

4 Write csv

predictors %>%
  write_csv(paste(path_version_data,
                  "predictors_M2007.csv",
                  sep = ""))

predictors_surface %>%
  write_csv(paste(path_version_data,
                  "predictors_surface_M2007.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_3.0.1  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_3.3.3  
[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_1.7.2          
 [4] here_1.0.1               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_0.3.3.9.1      digest_0.6.27           
[16] promises_1.1.1           checkmate_2.0.0          rvest_0.3.6             
[19] colorspace_2.0-0         htmltools_0.5.0          httpuv_1.5.4            
[22] Matrix_1.2-18            pkgconfig_2.0.3          broom_0.7.3             
[25] seacarb_3.2.15           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_0.1.0           ellipsis_0.3.1          
[34] withr_2.3.0              cli_2.2.0                magrittr_2.0.1          
[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_0.10.1.2.2 oce_1.2-0                tools_4.0.3             
[46] data.table_1.13.6        hms_0.5.3                lifecycle_0.2.0         
[49] munsell_0.5.0            reprex_0.3.0             isoband_0.2.3           
[52] compiler_4.0.3           rlang_0.4.10             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_0.3.6              dbplyr_1.4.4             tidyselect_1.1.0        
[70] xfun_0.20

Mapping - predictor preparation

Jens Daniel Müller and Donghe Zhu

27 January, 2021

1 Required data

1.1 Apply density threshold

1.2 Apply basin mask

1.3 Control plots

1.3.1 Maps interior

1.3.2 Maps surface

1.3.3 Predictor profiles

2 Prepare predictor fields

2.1 PO₄* calculation

2.1.1 Maps

2.1.2 Global section

2.2 AOU

2.2.1 Maps

2.2.2 Global section

2.3 Isoneutral slabs

3 Plot all predictor sections

3.1 Deep waters

3.2 Surface waters

4 Write csv

Mapping - predictor preparation

Jens Daniel Müller and Donghe Zhu

27 January, 2021

1 Required data

1.1 Apply density threshold

1.2 Apply basin mask

1.3 Control plots

1.3.1 Maps interior

1.3.2 Maps surface

1.3.3 Predictor profiles

2 Prepare predictor fields

2.1 PO4* calculation

2.1.1 Maps

2.1.2 Global section

2.2 AOU

2.2.1 Maps

2.2.2 Global section

2.3 Isoneutral slabs

3 Plot all predictor sections

3.1 Deep waters

3.2 Surface waters

4 Write csv

2.1 PO₄* calculation