World Ocean Atlas 2018

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Last updated: 2021-11-19

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Knit directory: emlr_obs_preprocessing/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/read_World_Ocean_Atlas_2018.Rmd) and HTML (docs/read_World_Ocean_Atlas_2018.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	0908ee5	jens-daniel-mueller	2021-11-15	Build site.
html	6d6a23e	jens-daniel-mueller	2021-11-01	Build site.
Rmd	2f36786	jens-daniel-mueller	2021-11-01	preprocess adjustment table, create new basinmaps
html	2a50fa9	jens-daniel-mueller	2021-10-28	Build site.
Rmd	67de9ab	jens-daniel-mueller	2021-10-28	preprocess tracers
html	fb9c1e9	jens-daniel-mueller	2021-09-28	Build site.
Rmd	67d6850	jens-daniel-mueller	2021-09-28	SO_2 basin mask created
html	90b0670	jens-daniel-mueller	2021-08-06	Build site.
Rmd	1a3fb91	jens-daniel-mueller	2021-08-06	added 1 basin mask, included reccap2 atm co2
html	217cf5e	jens-daniel-mueller	2021-07-16	Build site.
Rmd	da1b770	jens-daniel-mueller	2021-07-16	added SO_5 basin mask
html	e49875a	jens-daniel-mueller	2021-07-07	Build site.
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
html	ed3e64c	jens-daniel-mueller	2020-12-15	Build site.
html	64cfdd6	jens-daniel-mueller	2020-12-15	Build site.
Rmd	6f2a669	jens-daniel-mueller	2020-12-15	code and output cleaning
html	2fdd2a7	jens-daniel-mueller	2020-12-14	Build site.
html	cc07d29	jens-daniel-mueller	2020-12-14	Build site.
Rmd	325e1fa	jens-daniel-mueller	2020-12-14	rebuild with new root folder
html	4ded3cd	jens-daniel-mueller	2020-12-14	Build site.
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
html	5c773fa	jens-daniel-mueller	2020-12-11	Build site.
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
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

---

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")

Past versions of basin_AIP_masks_WOA18_map-1.png

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

2.2.3 Basins for MLR fitting

For the MLR fitting, ocean basins are further split up, as plotted below.

# 1 basins
basinmask_01 <- basinmask_01 %>%
  mutate(basin = "global",
         MLR_basins = "1")

# 2 basins
basinmask_2 <- 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_2 basin separate
basinmask_SO_2 <- basinmask_01 %>%
  mutate(
    basin = if_else(basin_AIP == "Atlantic",
                         "Atlantic",
                         "Indo-Pacific"),
    basin = if_else(
      lat < params_global$lat_min_SO, "SO", basin)
  ) %>%
  mutate(MLR_basins = "SO_2")


# SO_5 basin separate
basinmask_SO_5 <- basinmask_01 %>%
  mutate(
    basin = case_when(
      basin_AIP ==  "Atlantic" & lat > 35 ~ "N_Atlantic",
      basin_AIP ==  "Atlantic" & lat < 35 ~ "Atlantic",
      basin_AIP ==  "Pacific" & lat > 35 ~ "N_Pacific",
      basin_AIP ==  "Pacific" & lat < 35 ~ "Pacific",
      basin_AIP ==  "Indian" ~ "Indian"
    ),
    basin = if_else(lat < params_global$lat_min_SO, "SO", basin)
  ) %>%
  mutate(MLR_basins = "SO_5")

# 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")

# Indian ocean lat bands test
basinmask_IO_test_lat <- basinmask_01 %>%
  mutate(
    basin = case_when(
      basin_AIP ==  "Indian" & lat > 0 ~ "Indian1",
      basin_AIP ==  "Indian" & lat <= 0 & lat > -20 ~ "Indian2",
      basin_AIP ==  "Indian" & lat <= -20 & lat > -40 ~ "Indian3",
      basin_AIP ==  "Indian" & lat <= -40 & lat > -60 ~ "Indian4",
      basin_AIP ==  "Indian" & lat <= -60 ~ "Indian5",
      basin_AIP ==  "Atlantic" ~ "Atlantic",
      basin_AIP ==  "Pacific" ~ "Pacific"
    ),
    MLR_basins = "IO_test_lat"
  )

# Indian ocean lon bands test
basinmask_IO_test_lon <- basinmask_01 %>%
  mutate(
    basin = case_when(
      basin_AIP ==  "Indian" & lon <= 50 & lon > 20 ~ "Indian1",
      basin_AIP ==  "Indian" & lon <= 80 & lon > 50 ~ "Indian2",
      basin_AIP ==  "Indian" & lon <= 110 & lon > 80 ~ "Indian3",
      basin_AIP ==  "Indian" & lon > 110 ~ "Indian4",
      basin_AIP ==  "Atlantic" ~ "Atlantic",
      basin_AIP ==  "Pacific" ~ "Pacific"
    ),
    MLR_basins = "IO_test_lon"
  )


# join basin masks into one file
basinmask_01 <- bind_rows(basinmask_01, basinmask_2, basinmask_5,
                          basinmask_SO, basinmask_SO_2, basinmask_SO_5,
                          basinmask_SO_AIP, basinmask_AIP,
                          basinmask_IO_test_lat, basinmask_IO_test_lon)

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))
  )
  
}

Past versions of basin_masks_WOA18_map-1.png

Version	Author	Date
90b0670	jens-daniel-mueller	2021-08-06
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
58359ac	jens-daniel-mueller	2020-11-27
92e10aa	Jens Müller	2020-11-27

Past versions of basin_masks_WOA18_map-2.png

Version	Author	Date
90b0670	jens-daniel-mueller	2021-08-06
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

Past versions of basin_masks_WOA18_map-3.png

Version	Author	Date
90b0670	jens-daniel-mueller	2021-08-06
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

Past versions of basin_masks_WOA18_map-4.png

Version	Author	Date
90b0670	jens-daniel-mueller	2021-08-06
217cf5e	jens-daniel-mueller	2021-07-16
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

Past versions of basin_masks_WOA18_map-5.png

Version	Author	Date
fb9c1e9	jens-daniel-mueller	2021-09-28
90b0670	jens-daniel-mueller	2021-08-06
217cf5e	jens-daniel-mueller	2021-07-16
58bc706	jens-daniel-mueller	2021-07-06

Past versions of basin_masks_WOA18_map-6.png

Version	Author	Date
fb9c1e9	jens-daniel-mueller	2021-09-28
90b0670	jens-daniel-mueller	2021-08-06
217cf5e	jens-daniel-mueller	2021-07-16

Past versions of basin_masks_WOA18_map-7.png

Version	Author	Date
fb9c1e9	jens-daniel-mueller	2021-09-28
90b0670	jens-daniel-mueller	2021-08-06

Past versions of basin_masks_WOA18_map-8.png

Version	Author	Date
fb9c1e9	jens-daniel-mueller	2021-09-28

Past versions of basin_masks_WOA18_map-9.png

Version	Author	Date
6d6a23e	jens-daniel-mueller	2021-11-01
2a50fa9	jens-daniel-mueller	2021-10-28

Past versions of basin_masks_WOA18_map-10.png

Version	Author	Date
6d6a23e	jens-daniel-mueller	2021-11-01
2a50fa9	jens-daniel-mueller	2021-10-28

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)")

Past versions of section_global_map-1.png

Version	Author	Date
2a50fa9	jens-daniel-mueller	2021-10-28
fb9c1e9	jens-daniel-mueller	2021-09-28
90b0670	jens-daniel-mueller	2021-08-06
217cf5e	jens-daniel-mueller	2021-07-16
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")

Past versions of WOA18_potential_temperature_profile-1.png

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")

Past versions of WOA18_pot_temperature_section-1.png

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58359ac	jens-daniel-mueller	2020-11-27
92e10aa	Jens Müller	2020-11-27

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, 150, 500, 2000, 5, 155, 483, 1969, 3, 160, 534, 2054m
• 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")

Past versions of temperature_surface_WOA18_map-1.png

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3.8.2 Section

p_section_global(
  df = WOA18_sal_temp,
  var = "temp")

Past versions of temperature_sections_WOA18-1.png

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58359ac	jens-daniel-mueller	2020-11-27
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3.9 Salinity plots

3.9.1 Surface map

p_map_climatology(
  df = WOA18_sal_temp,
  var = "sal")

Past versions of salinity_surface_WOA18_map-1.png

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3.9.2 Section

p_section_global(
  df = WOA18_sal_temp,
  var = "sal")

Past versions of salinity_sections_WOA18-1.png

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3.10 Neutral density plots

3.10.1 Surface map

p_map_climatology(
  df = WOA18_sal_temp,
  var = "gamma")

Past versions of neutral_density_surface_WOA18_map-1.png

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3.10.2 Section

p_section_global(
  df = WOA18_sal_temp,
  var = "gamma")

Past versions of neutral_density_sections_WOA18-1.png

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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"

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[1] "/nfs/kryo/work/updata/woa2018/nitrate/all/1.00/woa18_all_n00_01.nc"

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Past versions of read_WOA18_nuts_O2_files-4.png

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[1] "/nfs/kryo/work/updata/woa2018/silicate/all/1.00/woa18_all_i00_01.nc"

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[1] "/nfs/kryo/work/updata/woa2018/oxygen/all/1.00/woa18_all_o00_01.nc"

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[1] "/nfs/kryo/work/updata/woa2018/AOU/all/1.00/woa18_all_A00_01.nc"

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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 = ""))

Session information

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_1.1.3      tibble_3.1.3     ggplot2_3.3.5    tidyverse_1.3.0 
[17] workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] httr_1.4.2         sass_0.4.0         viridisLite_0.3.0  jsonlite_1.7.1    
 [5] modelr_0.1.8       bslib_0.2.5.1      assertthat_0.2.1   highr_0.8         
 [9] sp_1.4-4           blob_1.2.1         cellranger_1.1.0   yaml_2.2.1        
[13] pillar_1.6.2       backports_1.1.10   lattice_0.20-41    glue_1.4.2        
[17] digest_0.6.27      RColorBrewer_1.1-2 promises_1.1.1     rvest_0.3.6       
[21] colorspace_2.0-2   htmltools_0.5.1.1  httpuv_1.5.4       Matrix_1.2-18     
[25] pkgconfig_2.0.3    broom_0.7.9        haven_2.3.1        scales_1.1.1      
[29] whisker_0.4        later_1.2.0        git2r_0.27.1       farver_2.0.3      
[33] generics_0.1.0     ellipsis_0.3.2     withr_2.3.0        cli_3.0.1         
[37] magrittr_1.5       crayon_1.3.4       readxl_1.3.1       evaluate_0.14     
[41] fs_1.5.0           ncdf4_1.17         fansi_0.4.1        xml2_1.3.2        
[45] tools_4.0.3        hms_0.5.3          lifecycle_1.0.0    munsell_0.5.0     
[49] reprex_0.3.0       isoband_0.2.2      compiler_4.0.3     jquerylib_0.1.4   
[53] RNetCDF_2.4-2      rlang_0.4.11       grid_4.0.3         rstudioapi_0.13   
[57] rappdirs_0.3.1     labeling_0.4.2     rmarkdown_2.10     gtable_0.3.0      
[61] DBI_1.1.0          R6_2.5.0           ncmeta_0.3.0       lubridate_1.7.9   
[65] knitr_1.33         utf8_1.1.4         rprojroot_2.0.2    stringi_1.5.3     
[69] Rcpp_1.0.5         vctrs_0.3.8        dbplyr_1.4.4       tidyselect_1.1.0  
[73] xfun_0.25