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library(tidyverse)
library(lubridate)
library(tidync)
library(stars)

1 Data source

2 Basin mask

2.1 Read data

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)

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

2.2 Basin 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)

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

basinmask_01 <- basinmask_01 %>% 
  filter(Basin_0m %in% c("1", "2", "3", "10")) %>% 
  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)

2.3 Plot map

mapWorld <- borders("world", col = "gray60", fill = "gray60")

basinmask_01 %>% 
  ggplot(aes(lon, lat, fill = basin)) +
  mapWorld +
  geom_raster() +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top")

rm(mapWorld)

2.4 Write file

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

rm(basinmask_01)

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(t_an, lon, lat, depth) %>% 
  drop_na()

# 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(s_an, lon, lat, depth) %>% 
  drop_na()
depth_surface_selection <- c(0)
Atl_lon <- 335.5 - 360 # subtract 360 from value used for GLODAP climatology
Pac_lon <- 190.5 - 360 # subtract 360 from value used for GLODAP climatology

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

  • Horizontal planes at 0m
  • Meridional sections at longitudes:
    • Atlantic: -24.5
    • Pacific: -169.5

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.

3.2 Temperature plots

3.2.1 Surface map

WOA_tem_tibble %>% 
  filter(depth == 0) %>% 
  ggplot(aes(lon, lat, fill = t_an)) +
  geom_raster() +
  geom_vline(xintercept = c(Atl_lon, Pac_lon), col = "white") +
  coord_quickmap(expand = 0) +
  scale_fill_viridis_c() +
  theme(legend.position = "top")

3.2.2 Section

WOA_tem_tibble %>% 
  filter(lon == Atl_lon) %>% 
  ggplot(aes(lat, depth, z = t_an)) +
  geom_contour_filled() +
  scale_y_reverse() +
  coord_cartesian(expand = 0) +
  theme(legend.position = "top")

3.3 Salinity plots

3.3.1 Surface map

WOA_sal_tibble %>% 
  filter(depth == 0) %>% 
  ggplot(aes(lon, lat, fill = s_an)) +
  geom_raster() +
  geom_vline(xintercept = c(Atl_lon, Pac_lon), col = "white") +
  coord_quickmap(expand = 0) +
  scale_fill_viridis_c() +
  theme(legend.position = "top")

3.3.2 Section

WOA_sal_tibble %>% 
  filter(lon == Pac_lon) %>% 
  ggplot(aes(lat, depth, z = s_an)) +
  geom_contour_filled() +
  scale_y_reverse() +
  coord_cartesian(expand = 0) +
  theme(legend.position = "top")

3.4 Write file

WOA18_predictors <- full_join(WOA_sal_tibble, WOA_tem_tibble)

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

rm(WOA18_predictors, WOA_sal, WOA_sal_tibble, WOA_tem, WOA_tem_tibble)

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

  • basin mask not yet applied to WOA18 data

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] stars_0.4-3     sf_0.9-5        abind_1.4-5     tidync_0.2.4   
 [5] lubridate_1.7.9 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         maps_3.3.0         viridisLite_0.3.0  jsonlite_1.7.0    
 [5] here_0.1           modelr_0.1.8       assertthat_0.2.1   blob_1.2.1        
 [9] cellranger_1.1.0   yaml_2.2.1         pillar_1.4.6       backports_1.1.8   
[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] pkgconfig_2.0.3    broom_0.7.0        haven_2.3.1        scales_1.1.1      
[25] whisker_0.4        later_1.1.0.1      git2r_0.27.1       farver_2.0.3      
[29] generics_0.0.2     ellipsis_0.3.1     withr_2.2.0        cli_2.0.2         
[33] magrittr_1.5       crayon_1.3.4       readxl_1.3.1       evaluate_0.14     
[37] fs_1.4.2           ncdf4_1.17         fansi_0.4.1        xml2_1.3.2        
[41] lwgeom_0.2-5       class_7.3-17       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     e1071_1.7-3        RNetCDF_2.3-1      rlang_0.4.7       
[53] classInt_0.4-3     units_0.6-7        grid_4.0.2         rstudioapi_0.11   
[57] labeling_0.3       rmarkdown_2.3      gtable_0.3.0       DBI_1.1.0         
[61] R6_2.4.1           ncmeta_0.2.5       knitr_1.29         rprojroot_1.3-2   
[65] KernSmooth_2.23-17 stringi_1.4.6      parallel_4.0.2     Rcpp_1.0.5        
[69] vctrs_0.3.2        dbplyr_1.4.4       tidyselect_1.1.0   xfun_0.16