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Rmd	864a6e3	jens-daniel-mueller	2020-07-24	merged predictor data sets
html	eb716ce	jens-daniel-mueller	2020-07-23	Build site.
Rmd	ffd46da	jens-daniel-mueller	2020-07-23	WOA18 read in
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Rmd	09f3f73	jens-daniel-mueller	2020-07-20	basin masks read in
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library(tidyverse)
library(tidync)
library(reticulate)
library(oce)

1 Data source

Data source: World Ocean Atlas 2018

2 Masks

2.1 Basins

2.1.1 Read mask

The surface mask (0m) with 1x1° resolution from the file basinmask_01.msk was used.

basinmask_01 <- read_csv(here::here("data/World_Ocean_Atlas_2018",
                                    "basinmask_01.msk"),
                         skip = 1,
                         col_types = list(.default = "d"))

basinmask_01 <- basinmask_01 %>% 
  select(Latitude:Basin_0m) %>% 
  mutate(Basin_0m = as.factor(Basin_0m)) %>% 
  rename(lat = Latitude, lon = Longitude)

2.1.2 Labels

According to WOA FAQ website and WOA18 documentation, number codes in the mask files were used to assign ocean basins as follows:

Atlantic Ocean:

1: Atlantic Ocean (with 10 - Southern Ocean, between 63°W and 20°E)
11: Arctic Ocean (restricted by northern latitude limit)

Indo-Pacific:

2: Pacific Ocean (with 10 - Southern Ocean, between 147°E and 63°W)
3: Indian Ocean (with 10 - Southern Ocean, between 20°E and 147°E)
12: Sea of Japan
56: Bay of Bengal

basinmask_01 <- basinmask_01 %>% 
  filter(Basin_0m %in% c("1", "2", "3", "10", "11", "12", "56"),
         lat <= parameters$lat_max) %>% 
  mutate(basin = if_else(Basin_0m == "10" & lon >= -63 & lon < 20,
                         "Atlantic", "Indo-Pacific"),
         basin = if_else(Basin_0m == "11",
                         "Atlantic", basin),
         basin = if_else(Basin_0m == "1",
                         "Atlantic", basin)) %>% 
  select(-Basin_0m) %>% 
  mutate(lon = if_else(lon < 20, lon + 360, lon))

2.2 Land

2.2.1 Read mask

The land sea mask with 1x1° resolution from the file landsea_01.msk was used.

landsea_01 <- read_csv(here::here("data/World_Ocean_Atlas_2018",
                                  "landsea_01.msk"),
                         skip = 1,
                         col_types = list(.default = "d"))

2.2.2 Label

According to the WOA18 documentation document:

“The landsea_XX.msk contains the standard depth level number at which the bottom of the ocean is first encountered at each quarter-degree or one-degree square for the entire world. Land will have a value of 1, corresponding to the surface.”

The landmask was derived as coordinates with value 1.

landmask <- landsea_01 %>% 
  mutate(region = if_else(Bottom_Standard_Level == "1",
                         "land", "ocean")) %>% 
  select(-Bottom_Standard_Level)

landmask <- landmask %>% 
  rename(lat = Latitude,
         lon = Longitude) %>% 
  mutate(lon = if_else(lon < 20, lon + 360, lon))

rm(landsea_01)

2.3 Map

ggplot() +
  geom_raster(data = landmask %>% filter(region == "land"),
              aes(lon, lat), fill = "grey80") +
  geom_raster(data = basinmask_01,
              aes(lon, lat, fill = basin)) +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

2.4 Write files

basinmask_01 %>% 
  write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
                       "basin_mask_WOA18.csv"))

landmask %>% 
  write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
                       "land_mask_WOA18.csv"))

3 Climatologies

Copied from the WOA FAQ website, the file naming conventions is:

PREF_DDDD_VTTFFGG.EXT, where:

PREF: prefix
DDDD: decade
V: variable
TT: time period
FF: field type
GG: grid (5deg- 5°, 01- 1°, 04 - 1/4°)
EXT: file extention

Short description of two statistical fields in WOA

Objectively analyzed climatologies are the objectively interpolated mean fields for oceanographic variables at standard - depth levels for the World Ocean.
The statistical mean is the average of all unflagged interpolated values at each standard depth level for each variable - in each 1° square which contains at least one measurement for the given oceanographic variable.

Here, we use

Fields: objectively analyzed mean
Decades: all decades
Grid: 1 deg resolution

According to the WOA18 documentation document:

What are the units for temperature and salinity in the WOA18?

In situ temperatures used for WOA18 are not converted from their original scale, so there is a mix of IPTS-48, IPTS-68, and ITS-90 (and pre IPTS-48 temperatures). The differences between scales are small (on the order of 0.01°C) and should not have much effect on the climatological means, except, possibly at very deep depths. Values for salinity are on the Practical salinity scale (PSS-78). Pre-1978 salinity values converted from conductivity may have used a different salinity scale. Pre-conductivity salinities use the Knudsen method.

3.1 Read ncdfs

# temperature

WOA_tem <- tidync(here::here("data/World_Ocean_Atlas_2018",
                                 "woa18_decav_t00_01.nc"))

WOA_tem_tibble <- WOA_tem %>% hyper_tibble()

WOA_tem_tibble <- WOA_tem_tibble  %>% 
  select(tem = t_an, lon, lat, depth) %>% 
  drop_na() %>% 
  mutate(lon = if_else(lon < 20, lon + 360, lon))

# salinity

WOA_sal <- tidync(here::here("data/World_Ocean_Atlas_2018",
                                 "woa18_decav_s00_01.nc"))

WOA_sal_tibble <- WOA_sal %>% hyper_tibble()

WOA_sal_tibble <- WOA_sal_tibble  %>% 
  select(sal = s_an, lon, lat, depth) %>% 
  drop_na() %>% 
  mutate(lon = if_else(lon < 20, lon + 360, lon))

rm(WOA_sal, WOA_tem)

WOA18_predictors <- full_join(WOA_sal_tibble, WOA_tem_tibble)
rm(WOA_sal_tibble, WOA_tem_tibble)

3.2 Apply basin mask

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

WOA18_predictors <- inner_join(WOA18_predictors, basinmask_01)
rm(basinmask_01)

3.3 Potential temperature

3.3.1 Calculation

WOA18_predictors <- WOA18_predictors %>% 
  mutate(THETA = swTheta(salinity = sal,
                         temperature = tem,
                         pressure = depth,
                         referencePressure = 0,
                         longitude = lon - 180,
                         latitude = lat))

3.3.2 Profile

Example profile from North Atlantic Ocean.

WOA18_predictors %>%
  filter(lat == parameters$lat_Atl_profile,
         lon == parameters$lon_Atl_section) %>%
  ggplot() +
  geom_line(aes(tem, depth, col = "insitu")) +
  geom_point(aes(tem, depth, col = "insitu")) +
  geom_line(aes(THETA, depth, col = "theta")) +
  geom_point(aes(THETA, depth, col = "theta")) +
  scale_y_reverse() +
  scale_color_brewer(palette = "Dark2", name = "Scale")

3.4 Neutral density

Neutral density gamma was calculated with a Python script provided by Serazin et al (2011), which performs a polynomial approximation of the original gamma calculation.

3.4.1 Calculation

WOA18_predictors_gamma_prep <- WOA18_predictors %>% 
  rename(LATITUDE = lat,
         LONGITUDE = lon,
         SALNTY = sal,
         CTDPRS = depth)

source_python(here::here("code/python_scripts",
                         "Gamma_GLODAP_python.py"))

WOA18_predictors_gamma_calc <- calculate_gamma(WOA18_predictors_gamma_prep)

3.4.2 Write/open file

WOA18_predictors <- WOA18_predictors_gamma_calc %>% 
  rename(lat = LATITUDE,
         lon = LONGITUDE,
         sal = SALNTY,
         depth = CTDPRS,
         gamma  = GAMMA)

WOA18_predictors %>% 
  write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
                       "WOA18_predictors.csv"))

rm(list = setdiff(ls(), c("landmask", "parameters")))

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

3.5 Temperature plots

Below, following subsets of the climatologies are plotted for all relevant parameters:

Horizontal planes at 0, 100, 500, 2000m
Meridional sections at longitudes:
- Atlantic: 335.5
- Pacific: 190.5

Section locations are indicated as white lines in maps.

3.5.1 Surface map

source(here::here("code",
                  "plotting_functions.R"))

map_climatology(WOA18_predictors, "tem")

3.5.2 Sections

section_climatology(WOA18_predictors, "tem")

3.5.3 Sections shallow

section_climatology_shallow(WOA18_predictors, "tem")

3.6 Salinity plots

3.6.1 Surface map

map_climatology(WOA18_predictors, "sal")

3.6.2 Sections

section_climatology(WOA18_predictors, "sal")

3.6.3 Sections shallow

section_climatology_shallow(WOA18_predictors, "sal")

3.7 Neutral density plots

3.7.1 Surface map

map_climatology(WOA18_predictors, "gamma")

3.7.2 Sections

section_climatology(WOA18_predictors, "gamma")

3.7.3 Sections shallow

section_climatology_shallow(WOA18_predictors, "gamma")

4 Open tasks

How to convert depth from WOA18 to CTD pressure (dbars) for neutral density calculation?

5 Questions

Which version of the WOA to be used
- Fields (currently: objectively analyzed mean)
- Decades (currently: all decades)
- Grid (currently: 1 deg resolution)

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] oce_1.2-0       gsw_1.0-5       testthat_2.3.2  reticulate_1.16
 [5] tidync_0.2.4    forcats_0.5.0   stringr_1.4.0   dplyr_1.0.0    
 [9] purrr_0.3.4     readr_1.3.1     tidyr_1.1.0     tibble_3.0.3   
[13] ggplot2_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2         jsonlite_1.7.0     viridisLite_0.3.0  here_0.1          
 [5] modelr_0.1.8       assertthat_0.2.1   blob_1.2.1         cellranger_1.1.0  
 [9] yaml_2.2.1         pillar_1.4.6       backports_1.1.8    lattice_0.20-41   
[13] glue_1.4.1         digest_0.6.25      RColorBrewer_1.1-2 promises_1.1.1    
[17] rvest_0.3.6        colorspace_1.4-1   htmltools_0.5.0    httpuv_1.5.4      
[21] Matrix_1.2-18      pkgconfig_2.0.3    broom_0.7.0        haven_2.3.1       
[25] scales_1.1.1       whisker_0.4        later_1.1.0.1      git2r_0.27.1      
[29] generics_0.0.2     farver_2.0.3       ellipsis_0.3.1     withr_2.2.0       
[33] cli_2.0.2          magrittr_1.5       crayon_1.3.4       readxl_1.3.1      
[37] evaluate_0.14      fs_1.4.2           ncdf4_1.17         fansi_0.4.1       
[41] xml2_1.3.2         tools_4.0.2        hms_0.5.3          lifecycle_0.2.0   
[45] munsell_0.5.0      reprex_0.3.0       isoband_0.2.2      compiler_4.0.2    
[49] RNetCDF_2.3-1      rlang_0.4.7        grid_4.0.2         rstudioapi_0.11   
[53] labeling_0.3       rmarkdown_2.3      gtable_0.3.0       DBI_1.1.0         
[57] R6_2.4.1           ncmeta_0.2.5       lubridate_1.7.9    knitr_1.29        
[61] rprojroot_1.3-2    stringi_1.4.6      Rcpp_1.0.5         vctrs_0.3.2       
[65] dbplyr_1.4.4       tidyselect_1.1.0   xfun_0.16

World Ocean Atlas 2018

Jens Daniel Müller

20 August, 2020