World Ocean Atlas 2018
Jens Daniel Müller
07 July, 2021
Last updated: 2021-07-07
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Knit directory: emlr_obs_preprocessing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 6312bd4 | jens-daniel-mueller | 2021-07-07 | Build site. |
| html | 58bc706 | jens-daniel-mueller | 2021-07-06 | Build site. |
| Rmd | 0db89e1 | jens-daniel-mueller | 2021-07-06 | rerun with revised variable names |
| html | f600971 | jens-daniel-mueller | 2021-07-02 | Build site. |
| html | 265c4ef | jens-daniel-mueller | 2021-06-04 | Build site. |
| Rmd | 00065c8 | jens-daniel-mueller | 2021-06-04 | included OceanSODA |
| html | c79346a | jens-daniel-mueller | 2021-06-03 | Build site. |
| html | 0ef91d5 | jens-daniel-mueller | 2021-05-12 | Build site. |
| Rmd | f091aa5 | jens-daniel-mueller | 2021-05-12 | removed Sea of Japan from basin mask |
| html | ace484d | jens-daniel-mueller | 2021-05-12 | Build site. |
| Rmd | 149c948 | jens-daniel-mueller | 2021-05-12 | removed Sea of Japan from basin mask |
| html | 6ff516e | jens-daniel-mueller | 2021-03-30 | Build site. |
| html | 86aee67 | jens-daniel-mueller | 2021-03-26 | Build site. |
| Rmd | 96f4b3a | jens-daniel-mueller | 2021-03-26 | darker grey for landmask |
| html | a5846c5 | jens-daniel-mueller | 2020-12-18 | Build site. |
| Rmd | 5538466 | jens-daniel-mueller | 2020-12-18 | subsetted WOA depth levels to 33 standard depths |
| html | 88967c0 | jens-daniel-mueller | 2020-12-16 | Build site. |
| html | fd1a2c9 | jens-daniel-mueller | 2020-12-15 | cleaned for copying |
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| Rmd | 325e1fa | jens-daniel-mueller | 2020-12-14 | rebuild with new root folder |
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| Rmd | a09b95f | jens-daniel-mueller | 2020-12-14 | rebuild with new root folder |
| html | 474daac | jens-daniel-mueller | 2020-12-12 | Build site. |
| Rmd | 53d8356 | jens-daniel-mueller | 2020-12-12 | removed basinmask issues |
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| html | ae7aa2e | jens-daniel-mueller | 2020-12-11 | Build site. |
| 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 |
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| Rmd | 8a8db38 | jens-daniel-mueller | 2020-12-11 | renamed tem to temp |
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| 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 |
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 Basins for budgets
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; currently not included)
# assign basin labels
basinmask_01 <- basinmask_01 %>%
filter(Basin_0m %in% c("1", "2", "3", "10", "11", "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"
)
) %>%
select(-Basin_0m)
# apply northern latitude boundary
basinmask_01 <- basinmask_01 %>%
filter(lat <= params_global$lat_max)
# harmonize lon scale
basinmask_01 <- basinmask_01 %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))# generate base map, which is further used throughout the project
map <-
ggplot() +
geom_raster(data = landmask,
aes(lon, lat), fill = "grey80") +
coord_quickmap(expand = 0) +
theme(axis.title = element_blank())
# plot basin_AIP map
map +
geom_raster(data = basinmask_01,
aes(lon, lat, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2")
2.2.3 Basins for MLR fitting
For the MLR fitting, ocean basins are further split up, as plotted below.
# 2 basins
basinmask_01 <- basinmask_01 %>%
mutate(basin = if_else(basin_AIP == "Atlantic",
"Atlantic",
"Indo-Pacific"),
MLR_basins = "2")
# 5 basins
basinmask_5 <- basinmask_01 %>%
mutate(
basin = case_when(
basin_AIP == "Atlantic" & lat > params_global$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")
# SO basin separate, with others being AIP
basinmask_SO_AIP <- basinmask_01 %>%
mutate(
basin = if_else(
lat < params_global$lat_min_SO, "SO", basin_AIP)
) %>%
mutate(MLR_basins = "SO_AIP")
# 3 basins
basinmask_AIP <- basinmask_01 %>%
mutate(
basin = basin_AIP) %>%
mutate(MLR_basins = "AIP")
# join basin masks into one file
basinmask_01 <- bind_rows(basinmask_01, basinmask_5, basinmask_SO, basinmask_SO_AIP, basinmask_AIP)for (i_MLR_basins in unique(basinmask_01$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 |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| 64cfdd6 | jens-daniel-mueller | 2020-12-15 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
| Version | Author | Date |
|---|---|---|
| 58bc706 | jens-daniel-mueller | 2021-07-06 |
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| 64cfdd6 | jens-daniel-mueller | 2020-12-15 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| Version | Author | Date |
|---|---|---|
| 58bc706 | jens-daniel-mueller | 2021-07-06 |
2.3 Global section
To plot sections from the North Atlantic south to the Southern Ocean, around Antarctica 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)
# subset individual section parts
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)
# join individual section parts
section_global_coordinates <- bind_rows(Atl_NS,
Atl_SO,
Pac_SO,
Pac_SN)
# convert to regular lon coordinates for distance calculation
section_global_coordinates <- section_global_coordinates %>%
mutate(lon_180 = if_else(lon > 180, lon - 360, lon))
# calculate distance along section
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)")
| Version | Author | Date |
|---|---|---|
| 58bc706 | jens-daniel-mueller | 2021-07-06 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| 64cfdd6 | jens-daniel-mueller | 2020-12-15 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
2.4 Write files
# basin mask
basinmask_01 %>%
write_csv(paste(path_files,
"basin_mask_WOA18.csv",
sep = ""))
# global section
section_global_coordinates %>%
write_csv(paste(path_files,
"section_global_coordinates.csv",
sep = ""))
# base map ggplot
map %>%
write_rds(paste(path_files,
"map_landmask_WOA18.rds",
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 nc files
# 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))
# 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))
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 Apply basin mask
# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask_01 <- basinmask_01 %>%
filter(MLR_basins == "2") %>%
select(lat, lon, basin_AIP)
# restrict predictor fields to basin mask grid
WOA18_sal_temp <- inner_join(WOA18_sal_temp, basinmask_01)3.4 Subset depth levels
WOA18_sal_temp <- WOA18_sal_temp %>%
filter(depth %in% params_global$depth_levels_33)3.5 Potential temperature
Potential temperature is calculated as in input variable for the neutral density calculation.
3.5.1 Calculation
WOA18_sal_temp <- WOA18_sal_temp %>%
mutate(THETA = swTheta(salinity = sal,
temperature = temp,
pressure = depth,
referencePressure = 0,
longitude = lon - 180,
latitude = lat))3.5.2 Profile
Example profile from North Atlantic Ocean.
WOA18_sal_temp %>%
filter(lat == params_global$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")
| Version | Author | Date |
|---|---|---|
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| a5846c5 | jens-daniel-mueller | 2020-12-18 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| ae7aa2e | jens-daniel-mueller | 2020-12-11 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 9c5d86d | jens-daniel-mueller | 2020-12-11 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
3.5.3 Section
p_section_global(
df = WOA18_sal_temp,
var = "THETA")
3.6 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.
3.6.1 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)3.7 Write file
WOA18_sal_temp %>%
write_csv(paste(path_preprocessing,
"WOA18_sal_temp.csv",
sep = ""))3.8 Temperature plots
Below, following subsets of the climatologies are plotted for all relevant parameters:
- Horizontal planes at 0, 5, 150, 155, 483, 500, 2000, 1969m
- Global section as defined above and indicated as white lines in maps.
3.8.1 Surface map
p_map_climatology(
df = WOA18_sal_temp,
var = "temp")
| Version | Author | Date |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| cc07d29 | jens-daniel-mueller | 2020-12-14 |
| ae7aa2e | jens-daniel-mueller | 2020-12-11 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 9c5d86d | jens-daniel-mueller | 2020-12-11 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
3.8.2 Section
p_section_global(
df = WOA18_sal_temp,
var = "temp")
| Version | Author | Date |
|---|---|---|
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| a5846c5 | jens-daniel-mueller | 2020-12-18 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 9c5d86d | jens-daniel-mueller | 2020-12-11 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
3.9 Salinity plots
3.9.1 Surface map
p_map_climatology(
df = WOA18_sal_temp,
var = "sal")
| Version | Author | Date |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| cc07d29 | jens-daniel-mueller | 2020-12-14 |
| ae7aa2e | jens-daniel-mueller | 2020-12-11 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
3.9.2 Section
p_section_global(
df = WOA18_sal_temp,
var = "sal")
3.10 Neutral density plots
3.10.1 Surface map
p_map_climatology(
df = WOA18_sal_temp,
var = "gamma")
| Version | Author | Date |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| cc07d29 | jens-daniel-mueller | 2020-12-14 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
3.10.2 Section
p_section_global(
df = WOA18_sal_temp,
var = "gamma")
4 Climatology Nuts and O2
4.1 Read nc files
Data are read-in looping over all relevant files, thereby reproducing the same subsetting steps as applied above to the salintity and temperature fields.
# 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 = "")
)
# read, plot and join data sets while looping over file list
for (file in file_list) {
# file <- file_list[1]
# 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(file)
WOA18_tibble <- WOA18_tibble %>%
select(all_of(parameter),
lon, lat, depth) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# apply general basin mask
WOA18_tibble <- inner_join(WOA18_tibble, basinmask_01)
# subset depth levels
WOA18_tibble <- WOA18_tibble %>%
filter(depth %in% params_global$depth_levels_33)
# 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] "/nfs/kryo/work/updata/woa2018/phosphate/all/1.00/woa18_all_p00_01.nc"
| Version | Author | Date |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| cc07d29 | jens-daniel-mueller | 2020-12-14 |
| ae7aa2e | jens-daniel-mueller | 2020-12-11 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 825309e | jens-daniel-mueller | 2020-11-27 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
[1] "/nfs/kryo/work/updata/woa2018/nitrate/all/1.00/woa18_all_n00_01.nc"
| Version | Author | Date |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| cc07d29 | jens-daniel-mueller | 2020-12-14 |
| ae7aa2e | jens-daniel-mueller | 2020-12-11 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 825309e | jens-daniel-mueller | 2020-11-27 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
[1] "/nfs/kryo/work/updata/woa2018/silicate/all/1.00/woa18_all_i00_01.nc"
| Version | Author | Date |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| cc07d29 | jens-daniel-mueller | 2020-12-14 |
| ae7aa2e | jens-daniel-mueller | 2020-12-11 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 825309e | jens-daniel-mueller | 2020-11-27 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
[1] "/nfs/kryo/work/updata/woa2018/oxygen/all/1.00/woa18_all_o00_01.nc"
| Version | Author | Date |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| cc07d29 | jens-daniel-mueller | 2020-12-14 |
| ae7aa2e | jens-daniel-mueller | 2020-12-11 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 825309e | jens-daniel-mueller | 2020-11-27 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
[1] "/nfs/kryo/work/updata/woa2018/AOU/all/1.00/woa18_all_A00_01.nc"
| Version | Author | Date |
|---|---|---|
| 0ef91d5 | jens-daniel-mueller | 2021-05-12 |
| ace484d | jens-daniel-mueller | 2021-05-12 |
| 86aee67 | jens-daniel-mueller | 2021-03-26 |
| 88967c0 | jens-daniel-mueller | 2020-12-16 |
| fd1a2c9 | jens-daniel-mueller | 2020-12-15 |
| cc07d29 | jens-daniel-mueller | 2020-12-14 |
| ae7aa2e | jens-daniel-mueller | 2020-12-11 |
| 914159f | jens-daniel-mueller | 2020-12-11 |
| 825309e | jens-daniel-mueller | 2020-11-27 |
| 58359ac | jens-daniel-mueller | 2020-11-27 |
| 92e10aa | Jens Müller | 2020-11-27 |
4.2 Write file
WOA18_nuts_O2 %>%
rename(phosphate = p_an,
nitrate = n_an,
silicate = i_an,
oxygen = o_an,
aou = A_an) %>%
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 15.2
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_1.1.1 geosphere_1.5-10 oce_1.2-0 gsw_1.0-5
[5] testthat_2.3.2 reticulate_1.18 tidync_0.2.4 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3 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_1.4-1 htmltools_0.5.0 httpuv_1.5.4
[21] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.5 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_2.1.0 magrittr_1.5 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_1.0.0
[45] munsell_0.5.0 reprex_0.3.0 isoband_0.2.2 compiler_4.0.3
[49] RNetCDF_2.4-2 rlang_0.4.10 grid_4.0.3 rstudioapi_0.11
[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