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html	078df6a	jens-daniel-mueller	2020-08-21	Build site.
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Rmd	525cb52	jens-daniel-mueller	2020-08-12	WOA 18 gamma calculation, new lon values in mapping
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Rmd	eb838d4	jens-daniel-mueller	2020-08-04	Read, plot, write data from D Clement
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Rmd	864a6e3	jens-daniel-mueller	2020-07-24	merged predictor data sets
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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
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Rmd	87a4680	jens-daniel-mueller	2020-07-18	added WOA blank script

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

1 Data source

Data source: World Ocean Atlas 2018

2 Masks

2.1 Land

2.1.1 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"))

2.1.2 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)

2.2 Basins

2.2.1 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)

2.2.2 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))

2.2.3 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())

2.3 Global section

To plot sections from the North Atlantic south to the Southern Ocean, around Antartica and back North across the Pacific Ocean, corresponding coordinates were subsetted from the basin mask and distances between coordinate grid points calculated.

section <- basinmask_01 %>%
  select(lon, lat)

Atl_NS <- section %>%
  filter(
    lon == parameters$lon_Atl_section,
    lat <= parameters$lat_section_N,
    lat >= parameters$lat_section_S
  ) %>%
  arrange(-lat)

Atl_SO <- section %>%
  filter(lon > parameters$lon_Atl_section,
         lat == parameters$lat_section_S) %>%
  arrange(lon)

Pac_SO <- section %>%
  filter(lon < parameters$lon_Pac_section,
         lat == parameters$lat_section_S) %>%
  arrange(lon)

Pac_SN <- section %>%
  filter(
    lon == parameters$lon_Pac_section,
    lat <= parameters$lat_section_N,
    lat >= parameters$lat_section_S
  ) %>%
  arrange(lat)

section_global_coordinates <- bind_rows(Atl_NS,
                     Atl_SO,
                     Pac_SO,
                     Pac_SN)

section_global_coordinates <- section_global_coordinates %>%
  mutate(lon_180 = if_else(lon > 180, lon - 360, lon))

section_global_coordinates <- section_global_coordinates %>%
  mutate(dist_int = distGeo(cbind(lon_180, lat)) / 1e6) %>%
  mutate(dist = cumsum(dist_int))


section_global_coordinates <- section_global_coordinates %>%
  select(lon, lat, dist) %>% 
  drop_na()

rm(Atl_NS, Atl_SO, Pac_SN, Pac_SO, section)

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey80") +
  geom_point(data = section_global_coordinates,
             aes(lon, lat, col = dist)) +
  scale_fill_grey() +
  scale_colour_viridis_b(name = "Distance (Mm)") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        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"))

section_global_coordinates %>%
  write_csv(here::here("data",
                       "section_global_coordinates.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")

3.4.3 Section

section_global_coordinates <-
  read_csv(here::here("data",
                      "section_global_coordinates.csv"))

section_global(WOA18_predictors, "THETA")

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)

rm(WOA18_predictors_gamma_calc, WOA18_predictors_gamma_prep)

3.5.2 Apply density threshold

The predictor field was split into two parts:

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

WOA18_predictors_surface <- WOA18_predictors %>%
  filter(depth < parameters$depth_min,
         gamma < parameters$gamma_min)

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"))

WOA18_predictors_surface %>% 
  write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
                       "WOA18_predictors_surface.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 Section

section_global(WOA18_predictors, "tem")

3.7 Salinity plots

3.7.1 Surface map

map_climatology(WOA18_predictors, "sal")

3.7.2 Section

section_global(WOA18_predictors, "sal")

3.8 Neutral density plots

3.8.1 Surface map

map_climatology(WOA18_predictors, "gamma")

3.8.2 Section

section_global(WOA18_predictors, "gamma")

3.8.3 Surface map shallow

map_climatology(WOA18_predictors_surface, "gamma")

3.8.4 Section shallow

section_global(WOA18_predictors_surface, "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] patchwork_1.0.1  geosphere_1.5-10 oce_1.2-0        gsw_1.0-5       
 [5] testthat_2.3.2   reticulate_1.16  tidync_0.2.4     forcats_0.5.0   
 [9] stringr_1.4.0    dplyr_1.0.0      purrr_0.3.4      readr_1.3.1     
[13] tidyr_1.1.0      tibble_3.0.3     ggplot2_3.3.2    tidyverse_1.3.0 
[17] workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] httr_1.4.2         viridisLite_0.3.0  jsonlite_1.7.0     here_0.1          
 [5] modelr_0.1.8       assertthat_0.2.1   sp_1.4-2           blob_1.2.1        
 [9] cellranger_1.1.0   yaml_2.2.1         pillar_1.4.6       backports_1.1.8   
[13] lattice_0.20-41    glue_1.4.1         digest_0.6.25      RColorBrewer_1.1-2
[17] promises_1.1.1     rvest_0.3.6        colorspace_1.4-1   htmltools_0.5.0   
[21] httpuv_1.5.4       Matrix_1.2-18      pkgconfig_2.0.3    broom_0.7.0       
[25] haven_2.3.1        scales_1.1.1       whisker_0.4        later_1.1.0.1     
[29] git2r_0.27.1       farver_2.0.3       generics_0.0.2     ellipsis_0.3.1    
[33] withr_2.2.0        cli_2.0.2          magrittr_1.5       crayon_1.3.4      
[37] readxl_1.3.1       evaluate_0.14      fs_1.4.2           ncdf4_1.17        
[41] fansi_0.4.1        xml2_1.3.2         tools_4.0.2        hms_0.5.3         
[45] lifecycle_0.2.0    munsell_0.5.0      reprex_0.3.0       isoband_0.2.2     
[49] compiler_4.0.2     RNetCDF_2.3-1      rlang_0.4.7        grid_4.0.2        
[53] rstudioapi_0.11    rappdirs_0.3.1     labeling_0.3       rmarkdown_2.3     
[57] gtable_0.3.0       DBI_1.1.0          R6_2.4.1           ncmeta_0.2.5      
[61] lubridate_1.7.9    knitr_1.29         rprojroot_1.3-2    stringi_1.4.6     
[65] Rcpp_1.0.5         vctrs_0.3.2        dbplyr_1.4.4       tidyselect_1.1.0  
[69] xfun_0.16

World Ocean Atlas 2018

Jens Daniel Müller

19 September, 2020