World Ocean Atlas 2018
Jens Daniel Müller
11 August, 2020
Last updated: 2020-08-11
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Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)
library(tidync)
library(stars)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)
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) %>%
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"))
landmask <- landsea_01 %>%
filter(Bottom_Standard_Level == 1) %>%
select(-Bottom_Standard_Level)
landmask <- landmask %>%
rename(lat = Latitude,
lon = Longitude) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
rm(landsea_01)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.
2.3 Plot map
ggplot() +
geom_raster(data = landmask,
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"))
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(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.
3.2 Temperature plots
3.2.1 Surface map
source(here::here("code",
"plotting_functions.R"))
map_climatology(WOA_tem_tibble, "tem")
3.2.2 Sections
section_climatology_Atl(WOA_tem_tibble, "tem")
section_climatology_Pac(WOA_tem_tibble, "tem")
3.3 Salinity plots
3.3.1 Surface map
map_climatology(WOA_sal_tibble, "sal")
3.3.2 Sections
section_climatology_Atl(WOA_sal_tibble, "sal")
section_climatology_Pac(WOA_sal_tibble, "sal")
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 <- 190.5Below, 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
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 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 glue_1.4.1
[13] digest_0.6.25 RColorBrewer_1.1-2 promises_1.1.1 rvest_0.3.6
[17] colorspace_1.4-1 htmltools_0.5.0 httpuv_1.5.4 pkgconfig_2.0.3
[21] broom_0.7.0 haven_2.3.1 scales_1.1.1 whisker_0.4
[25] later_1.1.0.1 git2r_0.27.1 generics_0.0.2 farver_2.0.3
[29] ellipsis_0.3.1 withr_2.2.0 cli_2.0.2 magrittr_1.5
[33] crayon_1.3.4 readxl_1.3.1 evaluate_0.14 fs_1.4.2
[37] ncdf4_1.17 fansi_0.4.1 xml2_1.3.2 lwgeom_0.2-5
[41] class_7.3-17 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] e1071_1.7-3 RNetCDF_2.3-1 rlang_0.4.7 classInt_0.4-3
[53] units_0.6-7 grid_4.0.2 rstudioapi_0.11 labeling_0.3
[57] rmarkdown_2.3 gtable_0.3.0 DBI_1.1.0 R6_2.4.1
[61] ncmeta_0.2.5 knitr_1.29 rprojroot_1.3-2 KernSmooth_2.23-17
[65] stringi_1.4.6 parallel_4.0.2 Rcpp_1.0.5 vctrs_0.3.2
[69] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.16