Last updated: 2020-09-08

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Rmd	864a6e3	jens-daniel-mueller	2020-07-24	merged predictor data sets
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Rmd	ffd46da	jens-daniel-mueller	2020-07-23	WOA18 read in
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Rmd	d2ed0f8	jens-daniel-mueller	2020-07-21	harmonied lat lon labeling
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Rmd	5def7e8	jens-daniel-mueller	2020-07-21	create csvs for each parameter
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Rmd	68e4615	jens-daniel-mueller	2020-07-20	assigned ocean basins and plotted world map
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Rmd	918f8c8	jens-daniel-mueller	2020-07-20	assigned ocean basins and plotted world map
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Rmd	23220a8	jens-daniel-mueller	2020-07-20	assigned ocean basins and plotted world map
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Rmd	09f3f73	jens-daniel-mueller	2020-07-20	basin masks read in
html	22b588c	jens-daniel-mueller	2020-07-18	Build site.
Rmd	87a4680	jens-daniel-mueller	2020-07-18	added WOA blank script

library(tidyverse)
library(tidync)
library(reticulate)
library(oce)
library(gsw)

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
10: Southern Ocean between 63°W and 20°E
11: Arctic Ocean (restricted by northern latitude limit 65N)

Indian Ocean:

3: Indian Ocean
10: Southern Ocean between 20°E and 147°E

Pacific Ocean:

2: Pacific Ocean
10: Southern Ocean between 147°E and 63°W
12: Sea of Japan
56: Bay of Bengal

For eMLR model fitting and mapping, Indian and Pacific Ocean were combined as Indo-Pacific.

basinmask_01 <- basinmask_01 %>% 
  filter(Basin_0m %in% c("1", "2", "3", "10", "11", "12", "56"),
         lat <= parameters$lat_max) %>% 
  mutate(basin_AIP = "none",
         basin_AIP = case_when(
           Basin_0m == "1" ~ "Atlantic",
           Basin_0m == "10" & lon >= -63 & lon < 20 ~ "Atlantic",
           Basin_0m == "11" ~ "Atlantic",
           Basin_0m == "3" ~ "Indian",
           Basin_0m == "10" & lon >= 20 & lon < 147 ~ "Indian",
           Basin_0m == "2" ~ "Pacific",
           Basin_0m == "12" ~ "Pacific",
           Basin_0m == "56" ~ "Pacific",
           Basin_0m == "10" & lon >= 147 | lon < -63 ~ "Pacific")) %>% 
  mutate(basin = if_else(basin_AIP == "Atlantic",
                         "Atlantic",
                         "Indo-Pacific")) %>% 
  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))

landmask <- landmask %>%
  filter(region == "land", 
         lat >= parameters$lat_min,
         lat <= parameters$lat_max) %>% 
  select(-region)

rm(landsea_01)

2.3 Map

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  geom_raster(data = basinmask_01,
              aes(lon, lat, fill = basin_AIP)) +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.title = element_blank())

2.4 Write files

basinmask_01 %>% 
  write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
                       "basin_mask_WOA18_AIP.csv"))

basinmask_01 <- basinmask_01 %>% 
  select(-basin_AIP)

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)

3.3 Apply spatial boundaries

Only predictors were taken into consideration with:

minimum bottom depth: 500m

WOA18_predictors_grid <- WOA18_predictors %>% 
  group_by(lat, lon) %>% 
  summarise(bottomdepth = max(depth)) %>% 
  ungroup()

WOA18_predictors_grid <- WOA18_predictors_grid %>% 
  filter(bottomdepth >= parameters$bottomdepth_min) %>% 
  select(-bottomdepth)

WOA18_predictors <- left_join(WOA18_predictors_grid, WOA18_predictors)

Only predictors were taken into consideration with:

maximum depth: 3000m

WOA18_predictors <- WOA18_predictors %>% 
  filter(depth <= parameters$inventory_depth)

3.4 Potential temperature

3.4.1 Calculation

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

3.4.2 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")

3.5 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.

3.5.1 Calculation

# calculate pressure from depth

WOA18_predictors <- WOA18_predictors %>% 
  mutate(CTDPRS = gsw_p_from_z(-depth,
                               lat))
# rename variables according to python script

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

# load python scripts

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

# calculate gamma

WOA18_predictors_gamma_calc <- calculate_gamma(WOA18_predictors_gamma_prep)

# reverse variabel naming

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

WOA18_predictors <- as_tibble(WOA18_predictors)

3.5.2 Apply density threshold

For water masses with neutral densities below 26, only predictor fields with a water depth higher than 150m were taken into account.

WOA18_predictors <- WOA18_predictors %>%
  filter(depth >= parameters$depth_min | gamma >= parameters$gamma_min)

3.5.3 Write/open file

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

rm(list = setdiff(ls(), c("landmask", "parameters")))
source(here::here("code", "plotting_functions.R"))

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

3.6 Temperature plots

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

Horizontal planes at 0, 150, 500, 2000m
Meridional sections at longitudes: 335.5, 190.5, 70.5

Section locations are indicated as white lines in maps.

3.6.1 Surface map

map_climatology(WOA18_predictors, "tem")

3.6.2 Sections

section_climatology(WOA18_predictors, "tem")

3.6.3 Sections shallow

section_climatology_shallow(WOA18_predictors, "tem")

3.7 Salinity plots

3.7.1 Surface map

map_climatology(WOA18_predictors, "sal")

3.7.2 Sections

section_climatology(WOA18_predictors, "sal")

3.7.3 Sections shallow

section_climatology_shallow(WOA18_predictors, "sal")

3.8 Neutral density plots

3.8.1 Surface map

map_climatology(WOA18_predictors, "gamma")

3.8.2 Sections

section_climatology(WOA18_predictors, "gamma")

3.8.3 Sections shallow

section_climatology_shallow(WOA18_predictors, "gamma")

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] rappdirs_0.3.1     labeling_0.3       rmarkdown_2.3      gtable_0.3.0      
[57] DBI_1.1.0          R6_2.4.1           ncmeta_0.2.5       lubridate_1.7.9   
[61] knitr_1.29         rprojroot_1.3-2    stringi_1.4.6      Rcpp_1.0.5        
[65] vctrs_0.3.2        dbplyr_1.4.4       tidyselect_1.1.0   xfun_0.16

World Ocean Atlas 2018

Jens Daniel Müller

08 September, 2020