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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(lubridate)
library(tidync)
library(stars)
library(reticulate)

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, number codes in the mask file refer to Ocean basins as follows:

1: Atlantic Ocean (with 10 - Southern Ocean, between 63°W and 20°E)
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)
56: Bay of Bengal (included in Indo-Pacifc)

From this, the Atlantic and the Indo-Pacific were labeled.

basinmask_01 <- basinmask_01 %>% 
  filter(Basin_0m %in% c("1", "2", "3", "10", "56")) %>% 
  mutate(basin = if_else(Basin_0m == "10" & lon >= -63 & lon < 20,
                         "Atlantic", "Indo-Pacific"),
         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 Plot 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 WOA18 data

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 the 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.
The number of observations of each variable in each 1° square of the World Ocean at each standard depth level.
The standard deviation about the statistical mean of each variable in each 1° square at each standard depth level.
The standard error of the mean of each variable in each 1° square at each standard depth level.
The seasonal or monthly climatology minus the annual climatology at each 1° square at each standard depth.
The statistical mean minus the climatological mean at each 1° square at each standard depth. This value is used as an estimate of interpolation and smoothing error.
The number of 1° squares within the smallest radius of influence around each 1° square which contain a statistical mean.

Here, we use

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

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)

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.

Please note that longitudes in the climatologies range from -179.5 - 179.5, which is different from GLODAP mapped climatologies.

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 Neutral density calculation

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

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

WOA18_predictors_gamma_calc <- calculate_gamma(WOA18_predictors_gamma_prep)

3.3.1 Write file

WOA18_predictors <- WOA18_predictors_gamma_calc %>% 
  rename(lat = LATITUDE,
         lon = LONGITUDE,
         sal = SALNTY,
         tem = THETA,
         depth = CTDPRS,
         gamma  = GAMMA) %>% 
  mutate(depth = depth*10)

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

3.3.2 Open file

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

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

3.4 Temperature plots

3.4.1 Surface map

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

map_climatology(WOA18_predictors, "tem")

3.4.2 Sections

section_climatology(WOA18_predictors, "tem")

3.5 Salinity plots

3.5.1 Surface map

map_climatology(WOA18_predictors, "sal")

3.5.2 Sections

section_climatology(WOA18_predictors, "sal")

3.6 Neutral density plots

3.6.1 Surface map

map_climatology(WOA18_predictors, "gamma")

3.6.2 Sections

section_climatology(WOA18_predictors, "gamma")

3.6.3 Sections shallow

section_climatology_shallow(WOA18_predictors, "gamma")

4 WOA13 from D. Clement

Dominic Clement provided a netcdf file with the basin mask and neutral densities used in Clement and Gruber (2018), both derived from the World Ocean Atlas 2013.

4.1 Read ncdfs

# temperature

nd_mask <- tidync(here::here("data/dclement",
                          "nd_mask.nc"))
nd_mask_tibble <- nd_mask %>% hyper_tibble()

nd_mask_tibble <- nd_mask_tibble %>% 
  mutate(gamma = if_else(gamma == -999, NaN, gamma))

4.2 Plots

depth_surface_selection <- c(0)
Atl_lon <- 335.5
Pac_lon <- 190.5

Below, following subsets of the climatologies are plotted:

Basin mask at 0m
Meridional sections of neutral density at longitudes:
- Atlantic: 335.5
- Pacific: 190.5

Section locations are indicated as white lines in the basin map.

4.3 Basin map

nd_mask_tibble %>% 
  filter(depth == 0) %>% 
  ggplot(aes(longitude, latitude, fill = as.factor(mask))) +
  geom_raster() +
  geom_vline(xintercept = c(Atl_lon, Pac_lon), col = "white") +
  coord_quickmap(expand = 0) +
  scale_fill_brewer(palette = "Set1",
                    name = "basin mask") +
  theme(legend.position = "top")

4.4 Neutral density sections

4.4.1 Atlantic

nd_mask_tibble %>% 
  filter(longitude == Atl_lon) %>% 
  ggplot(aes(latitude, depth, z = gamma)) +
  geom_contour_filled() +
  scale_y_reverse() +
  coord_cartesian(expand = 0) +
  theme(legend.position = "top")

4.4.2 Pacific

nd_mask_tibble %>% 
  filter(longitude == Pac_lon) %>% 
  ggplot(aes(latitude, depth, z = gamma)) +
  geom_contour_filled() +
  scale_y_reverse() +
  coord_cartesian(expand = 0) +
  theme(legend.position = "top")

4.5 Write file

nd_mask_tibble %>%
  write_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
                       "WOA13_mask_gamma.csv"))

rm(nd_mask, nd_mask_tibble, Atl_lon, Pac_lon, depth_surface_selection)

5 Open tasks

apply basin mask to WOA18 data
add Indian ocean section plot -calculate neutral density

6 Questions

Which version of the WOA to be used
- Fields (currently: objectively analyzed mean)
- Decades (currently: oall decades)
- Grid (currently: o1 deg resolution)
How to merge with GLODAP climatology (currently interpolated to GLODAP depth)

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] reticulate_1.16 stars_0.4-3     sf_0.9-5        abind_1.4-5    
 [5] tidync_0.2.4    lubridate_1.7.9 forcats_0.5.0   stringr_1.4.0  
 [9] dplyr_1.0.0     purrr_0.3.4     readr_1.3.1     tidyr_1.1.0    
[13] tibble_3.0.3    ggplot2_3.3.2   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_1.7.0     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] farver_2.0.3       generics_0.0.2     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         lwgeom_0.2-5       class_7.3-17       tools_4.0.2       
[45] hms_0.5.3          lifecycle_0.2.0    munsell_0.5.0      reprex_0.3.0      
[49] isoband_0.2.2      compiler_4.0.2     e1071_1.7-3        RNetCDF_2.3-1     
[53] rlang_0.4.7        classInt_0.4-3     units_0.6-7        grid_4.0.2        
[57] rstudioapi_0.11    labeling_0.3       rmarkdown_2.3      gtable_0.3.0      
[61] DBI_1.1.0          R6_2.4.1           ncmeta_0.2.5       knitr_1.29        
[65] rprojroot_1.3-2    KernSmooth_2.23-17 stringi_1.4.6      parallel_4.0.2    
[69] Rcpp_1.0.5         vctrs_0.3.2        dbplyr_1.4.4       tidyselect_1.1.0  
[73] xfun_0.16

World Ocean Atlas 2018

Jens Daniel Müller

12 August, 2020