World Ocean Atlas 2018
Jens Daniel Müller
11 December, 2020
Last updated: 2020-12-11
Checks: 7 0
Knit directory: emlr_obs_preprocessing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ec6f803 | jens-daniel-mueller | 2020-12-11 | apply only lat max but not basinmak |
| html | 914159f | jens-daniel-mueller | 2020-12-11 | Build site. |
| Rmd | 30eb59a | jens-daniel-mueller | 2020-12-11 | created basin masks for analysis |
| html | 9c5d86d | jens-daniel-mueller | 2020-12-11 | Build site. |
| Rmd | 8a8db38 | jens-daniel-mueller | 2020-12-11 | renamed tem to temp |
| html | 999cd9d | jens-daniel-mueller | 2020-12-02 | Build site. |
| html | e28cc90 | jens-daniel-mueller | 2020-11-30 | Build site. |
| Rmd | cd676e8 | jens-daniel-mueller | 2020-11-30 | created global parameterization file params_global.rds |
| html | 825309e | jens-daniel-mueller | 2020-11-27 | Build site. |
| Rmd | 4dd5bda | jens-daniel-mueller | 2020-11-27 | correct source link, basinmask and plotting function |
| html | 58359ac | jens-daniel-mueller | 2020-11-27 | Build site. |
| Rmd | 2f37595 | jens-daniel-mueller | 2020-11-27 | first rebuild after splitting the preprocessing part |
| Rmd | cb7a9ca | jens-daniel-mueller | 2020-11-27 | linked to local paths on server |
| Rmd | 92e10aa | Jens Müller | 2020-11-27 | Initial commit |
| html | 92e10aa | Jens Müller | 2020-11-27 | Initial commit |
library(tidyverse)
library(tidync)
library(reticulate)
library(oce)
library(gsw)
library(geosphere)
library(patchwork)path_functions <- "/nfs/kryo/work/updata/emlr_cant/utilities/functions/"
path_files <- "/nfs/kryo/work/updata/emlr_cant/utilities/files/"
path_woa2018 <- "/nfs/kryo/work/updata/woa2018/"
path_preprocessing <- "/nfs/kryo/work/updata/emlr_cant/observations/preprocessing/"CAVEAT: Please note that neutral density should be calculated in this script, but is currently not, because the required Python code is not yet running on the IAC server. Therefore, the desired output file was manually replaced with a locally generated version.
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(
paste(
path_woa2018,
"masks/landsea_01.msk",
sep = ""),
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 >= params_global$lat_min,
lat <= params_global$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(
paste(
path_woa2018,
"masks/basinmask_01.msk",
sep = ""),
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
- 56: Bay of Bengal
Pacific Ocean:
- 2: Pacific Ocean
- 10: Southern Ocean between 147°E and 63°W
- 12: Sea of Japan
For eMLR model fitting and mapping, Indian and Pacific Ocean were combined as Indo-Pacific.
# assign basin labels
basinmask_01 <- basinmask_01 %>%
filter(Basin_0m %in% c("1", "2", "3", "10", "11", "12", "56")) %>%
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 == "56" ~ "Indian",
Basin_0m == "10" & lon >= 20 & lon < 147 ~ "Indian",
Basin_0m == "2" ~ "Pacific",
Basin_0m == "12" ~ "Pacific",
Basin_0m == "10" &
lon >= 147 | lon < -63 ~ "Pacific"
)
) %>%
mutate(basin = if_else(basin_AIP == "Atlantic",
"Atlantic",
"Indo-Pacific"),
MLR_basins = "2") %>%
select(-Basin_0m)
# apply northern latitude boundary
basinmask_01 <- basinmask_01 %>%
filter(lat <= params_global$lat_max)
# harmonize lon scale
basinmask_01 <- basinmask_01 %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))# 5 basins
basinmask_5 <- basinmask_01 %>%
mutate(
basin = case_when(
basin_AIP == "Atlantic" & lat > params_global$lat_equator ~ "N_Atlantic",
basin_AIP == "Atlantic" & lat < params_global$lat_equator ~ "S_Atlantic",
basin_AIP == "Pacific" & lat > params_global$lat_equator ~ "N_Pacific",
basin_AIP == "Pacific" & lat < params_global$lat_equator ~ "S_Pacific",
basin_AIP == "Indian" ~ "Indian"
)
) %>%
mutate(MLR_basins = "5")
# SO basin separate
basinmask_SO <- basinmask_01 %>%
mutate(
basin = if_else(
lat < params_global$lat_min_SO, "SO", basin)
) %>%
mutate(MLR_basins = "SO")
# 3 basins
basinmask_AIP <- basinmask_01 %>%
mutate(
basin = basin_AIP) %>%
mutate(MLR_basins = "AIP")
basinmask_01 <- bind_rows(basinmask_01, basinmask_5, basinmask_SO, basinmask_AIP)2.2.3 Maps
2.2.3.1 Basins for budgets
map <-
ggplot() +
geom_raster(data = landmask,
aes(lon, lat), fill = "grey80") +
coord_quickmap(expand = 0) +
theme(axis.title = element_blank())
map +
geom_raster(data = basinmask_01,
aes(lon, lat, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2")
| Version | Author | Date |
|---|---|---|
| 914159f | jens-daniel-mueller | 2020-12-11 |
2.2.3.2 Basins for MLR fitting
for (i_MLR_basins in unique(basinmask_01$MLR_basins)) {
print(
map +
geom_raster(
data = basinmask_01 %>% filter(MLR_basins == i_MLR_basins),
aes(lon, lat, fill = basin)
) +
scale_fill_brewer(palette = "Dark2") +
labs(title = paste("MLR basin label:", i_MLR_basins))
)
}
| Version | Author | Date |
|---|---|---|
| 914159f | jens-daniel-mueller | 2020-12-11 |
| Version | Author | Date |
|---|---|---|
| 914159f | jens-daniel-mueller | 2020-12-11 |
| Version | Author | Date |
|---|---|---|
| 914159f | jens-daniel-mueller | 2020-12-11 |
map %>%
write_rds(
paste(
path_files,
"map_landmask_WOA18.rds",
sep = ""
)
)2.3 Global section
To plot sections from the North Atlantic south to the Southern Ocean, around Antartica and back North across the Pacific Ocean, corresponding coordinates were subsetted from the basin mask and distances between coordinate grid points calculated.
section <- basinmask_01 %>%
select(lon, lat)
Atl_NS <- section %>%
filter(
lon == params_global$lon_Atl_section,
lat <= params_global$lat_section_N,
lat >= params_global$lat_section_S
) %>%
arrange(-lat)
Atl_SO <- section %>%
filter(lon > params_global$lon_Atl_section,
lat == params_global$lat_section_S) %>%
arrange(lon)
Pac_SO <- section %>%
filter(lon < params_global$lon_Pac_section,
lat == params_global$lat_section_S) %>%
arrange(lon)
Pac_SN <- section %>%
filter(
lon == params_global$lon_Pac_section,
lat <= params_global$lat_section_N,
lat >= params_global$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)map +
geom_point(data = section_global_coordinates,
aes(lon, lat, col = dist)) +
scale_colour_viridis_b(name = "Distance (Mm)") +
theme(legend.position = "top")
2.4 Write files
basinmask_01 %>%
write_csv(paste(path_files,
"basin_mask_WOA18.csv",
sep = ""))
section_global_coordinates %>%
write_csv(paste(path_files,
"section_global_coordinates.csv",
sep = ""))3 Climatology S and T
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
WOA18_temp <- tidync(
paste(
path_woa2018,
"temperature/decav/1.00/woa18_decav_t00_01.nc",
sep = ""
)
)
WOA18_temp_tibble <- WOA18_temp %>% hyper_tibble()
WOA18_temp_tibble <- WOA18_temp_tibble %>%
select(temp = t_an, lon, lat, depth) %>%
drop_na() %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
filter(lat <= params_global$lat_max)
# salinity
WOA18_sal <- tidync(
paste(
path_woa2018,
"salinity/decav/1.00/woa18_decav_s00_01.nc",
sep = ""
)
)
WOA18_sal_tibble <- WOA18_sal %>% hyper_tibble()
WOA18_sal_tibble <- WOA18_sal_tibble %>%
select(sal = s_an, lon, lat, depth) %>%
drop_na() %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
filter(lat <= params_global$lat_max)
rm(WOA18_sal, WOA18_temp)3.2 Join predictors
WOA18_sal_temp <- full_join(WOA18_sal_tibble, WOA18_temp_tibble)
rm(WOA18_sal_tibble, WOA18_temp_tibble)3.3 Potential temperature
Potential temperature is calculated as in input variable for the neutral density calculation.
3.3.1 Calculation
WOA18_sal_temp <- WOA18_sal_temp %>%
mutate(THETA = swTheta(salinity = sal,
temperature = temp,
pressure = depth,
referencePressure = 0,
longitude = lon - 180,
latitude = lat))3.3.2 Profile
Example profile from North Atlantic Ocean.
WOA18_sal_temp %>%
filter(lat == params_global$lat_Atl_profile,
lon == params_global$lon_Atl_section) %>%
ggplot() +
geom_line(aes(temp, depth, col = "insitu")) +
geom_point(aes(temp, 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 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.
3.4.1 Calculation
# calculate pressure from depth
WOA18_sal_temp <- WOA18_sal_temp %>%
mutate(CTDPRS = gsw_p_from_z(-depth,
lat))
# rename variables according to python script
WOA18_sal_temp_gamma_prep <- WOA18_sal_temp %>%
rename(LATITUDE = lat,
LONGITUDE = lon,
SALNTY = sal)
# load python scripts
source_python(paste(
path_functions,
"python_scripts/Gamma_GLODAP_python.py",
sep = ""
))
# calculate gamma
WOA18_sal_temp_gamma_calc <- calculate_gamma(WOA18_sal_temp_gamma_prep)
# reverse variable naming
WOA18_sal_temp <- WOA18_sal_temp_gamma_calc %>%
select(-c(CTDPRS, THETA)) %>%
rename(lat = LATITUDE,
lon = LONGITUDE,
sal = SALNTY,
gamma = GAMMA)
WOA18_sal_temp <- as_tibble(WOA18_sal_temp)
rm(WOA18_sal_temp_gamma_calc, WOA18_sal_temp_gamma_prep)3.4.2 Write file
WOA18_sal_temp %>%
write_csv(paste(path_preprocessing,
"WOA18_sal_temp.csv",
sep = ""))3.5 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.
3.5.1 Surface map
p_map_climatology(
df = WOA18_sal_temp,
var = "temp")
3.6 Salinity plots
3.6.1 Surface map
p_map_climatology(
df = WOA18_sal_temp,
var = "sal")
3.7 Neutral density plots
3.7.1 Surface map
p_map_climatology(
df = WOA18_sal_temp,
var = "gamma")3.7.2 Section
p_section_global(
df = WOA18_sal_temp,
var = "gamma")4 Climatology Nuts and O2
4.1 Read ncdfs
# Keep grid cells of WOA18 sal/temp data set, to join with
WOA18_nuts_O2 <-
WOA18_sal_temp %>%
select(lon, lat, depth)
rm(WOA18_sal_temp)
# create file list
file_list <- c(
paste(path_woa2018, "phosphate/all/1.00/woa18_all_p00_01.nc", sep = ""),
paste(path_woa2018, "nitrate/all/1.00/woa18_all_n00_01.nc", sep = ""),
paste(path_woa2018, "silicate/all/1.00/woa18_all_i00_01.nc", sep = ""),
paste(path_woa2018, "oxygen/all/1.00/woa18_all_o00_01.nc", sep = ""),
paste(path_woa2018, "AOU/all/1.00/woa18_all_A00_01.nc", sep = "")
)
# print(file_list)
# file <- file_list[1]
# read, plot and join data sets while looping over file list
for (file in file_list) {
# open file
WOA18 <- tidync(file)
WOA18_tibble <- WOA18 %>% hyper_tibble()
# extract parameter name
parameter <- str_split(file, pattern = "00_", simplify = TRUE)[1]
parameter <- str_split(parameter, pattern = "all_", simplify = TRUE)[2]
parameter <- paste(parameter, "_an", sep = "")
print(parameter)
WOA18_tibble <- WOA18_tibble %>%
select(all_of(parameter),
lon, lat, depth) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
filter(lat <= params_global$lat_max)
# join with previous WOA data and keep only rows in existing data frame
# this is equal to applying the basinmask
WOA18_nuts_O2 <- left_join(
x = WOA18_nuts_O2,
y = WOA18_tibble)
# plot maps
print(
p_map_climatology(
df = WOA18_nuts_O2,
var = parameter)
)
# plot sections
print(p_section_global(
df = WOA18_nuts_O2,
var = parameter
))
}[1] "p_an"
[1] "n_an"
[1] "i_an"
[1] "o_an"
[1] "A_an"
4.2 Write file
WOA18_nuts_O2 %>%
write_csv(paste(path_preprocessing,
"WOA18_nuts_O2.csv",
sep = ""))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.1
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.1.0 geosphere_1.5-10 oce_1.2-0 gsw_1.0-5
[5] testthat_3.0.0 reticulate_1.18 tidync_0.2.4 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 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_1.7.1 modelr_0.1.8
[5] assertthat_0.2.1 sp_1.4-4 blob_1.2.1 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.4.7 backports_1.1.10 lattice_0.20-41
[13] glue_1.4.2 digest_0.6.27 RColorBrewer_1.1-2 promises_1.1.1
[17] rvest_0.3.6 colorspace_2.0-0 htmltools_0.5.0 httpuv_1.5.4
[21] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.2 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_2.3.0
[33] cli_2.2.0 magrittr_2.0.1 crayon_1.3.4 readxl_1.3.1
[37] evaluate_0.14 fs_1.5.0 ncdf4_1.17 fansi_0.4.1
[41] xml2_1.3.2 tools_4.0.3 hms_0.5.3 lifecycle_0.2.0
[45] munsell_0.5.0 reprex_0.3.0 isoband_0.2.2 compiler_4.0.3
[49] RNetCDF_2.4-2 rlang_0.4.9 grid_4.0.3 rstudioapi_0.13
[53] rappdirs_0.3.1 labeling_0.4.2 rmarkdown_2.5 gtable_0.3.0
[57] DBI_1.1.0 R6_2.5.0 ncmeta_0.3.0 lubridate_1.7.9
[61] knitr_1.30 rprojroot_2.0.2 stringi_1.5.3 Rcpp_1.0.5
[65] vctrs_0.3.5 dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.18