Ocean regions
Jens Daniel Müller
01 April, 2022
Last updated: 2022-04-01
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Knit directory: emlr_obs_preprocessing/
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| Rmd | d23e425 | jens-daniel-mueller | 2022-04-01 | rerun all including arctic and North Atlantic biome |
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| Rmd | a1ea47d | jens-daniel-mueller | 2022-04-01 | rerun all including arctic and North Atlantic biome |
1 Data source
- Data source: World Ocean Atlas 2018
region_masks_all <-
read_ncdf(paste(path_reccap2, "RECCAP2_region_masks_all_v20210412.nc", sep = "")) %>%
as_tibble()
# region_masks_all_seamask <- region_masks_all %>%
# select(lat, lon, seamask)
region_masks_all <- region_masks_all %>%
select(-seamask)
region_masks_all <- region_masks_all %>%
mutate(arctic = if_else(arctic != 0 & atlantic != 0, 0, arctic),
southern = if_else(southern != 0 & atlantic != 0, 0, southern),
southern = if_else(southern != 0 & pacific != 0, 0, southern),
southern = if_else(southern != 0 & indian != 0, 0, southern))
region_masks_all <- region_masks_all %>%
pivot_longer(open_ocean:southern,
names_to = "region",
values_to = "value") %>%
mutate(value = as.factor(value)) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
region_masks_all %>%
filter(value != 0,
region != "open_ocean") %>%
ggplot(aes(lon, lat, fill = region)) +
geom_raster() +
scale_fill_brewer(palette = "Dark2") +
coord_quickmap(expand = 0)
| Version | Author | Date |
|---|---|---|
| dde77eb | jens-daniel-mueller | 2022-04-01 |
reccap2_region_mask <- region_masks_all %>%
filter(value != 0,
region != "open_ocean") %>%
select(lon, lat, region)
rm(region_masks_all)2 WOA 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)) %>%
filter(lat >= params_global$lat_min,
lat <= params_global$lat_max
)
landseamask <- landmask
landmask <- landmask %>%
filter(region == "land") %>%
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 90N)
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$lat_max)
# harmonize lon scale
basinmask_01 <- basinmask_01 %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))basinmask_01 <- inner_join(basinmask_01,reccap2_region_mask)
basinmask_01 <- basinmask_01 %>%
mutate(basin_AIP = if_else(region == "arctic", "Arctic", basin_AIP))# generate base map, which is further used throughout the project
map <-
ggplot() +
geom_tile(data = landmask,
aes(lon, lat), fill = "grey80") +
coord_quickmap(expand = 0) +
theme(axis.title = element_blank())
# plot basin_AIP map
map +
geom_raster(data = basinmask_01,
aes(lon, lat, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2")
| Version | Author | Date |
|---|---|---|
| dde77eb | jens-daniel-mueller | 2022-04-01 |
# generate base map, which is further used throughout the project
ggplot() +
geom_raster(data = landseamask,
aes(lon, lat, fill = region)) +
coord_quickmap(expand = 0) +
scale_fill_brewer(palette = "Paired") +
theme(axis.title = element_blank())
| Version | Author | Date |
|---|---|---|
| dde77eb | jens-daniel-mueller | 2022-04-01 |
2.2.3 Basins for MLR fitting
For the MLR fitting, ocean basins are further split up, as plotted below.
basinmask_01 <- basinmask_01 %>%
select(-region)
# 4 basins incl arctic
basinmask_04 <- basinmask_01 %>%
mutate(basin = basin_AIP) %>%
mutate(MLR_basins = "4")
# 1 basins
basinmask_01 <- basinmask_01 %>%
filter(basin_AIP != "Arctic") %>%
mutate(basin = "global",
MLR_basins = "1")
# 2 basins
basinmask_2 <- basinmask_01 %>%
mutate(basin = if_else(basin_AIP == "Atlantic",
"Atlantic",
"Indo-Pacific"),
MLR_basins = "2")
# 5 basins
basinmask_5 <- basinmask_01 %>%
mutate(
basin = case_when(
basin_AIP == "Atlantic" & lat > params_global$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$lat_equator ~ "S_Pacific",
basin_AIP == "Indian" ~ "Indian"
)
) %>%
mutate(MLR_basins = "5")
# SO_2 basin separate
basinmask_SO_2 <- basinmask_01 %>%
mutate(
basin = if_else(basin_AIP == "Atlantic",
"Atlantic",
"Indo-Pacific"),
basin = if_else(
lat < params_global$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 & lat >= params_global$lat_min_SO ~ "Atlantic",
basin_AIP == "Atlantic" & lat < params_global$lat_min_SO ~ "S_Atlantic",
basin_AIP == "Pacific" & lat > 35 ~ "N_Pacific",
basin_AIP == "Pacific" & lat < 35 & lat >= params_global$lat_min_SO ~ "Pacific",
basin_AIP == "Pacific" & lat < params_global$lat_min_SO ~ "S_Pacific",
basin_AIP == "Indian" & lat >= params_global$lat_min_SO ~ "Indian",
basin_AIP == "Indian" & lat < params_global$lat_min_SO ~ "S_Indian"
)) %>%
mutate(MLR_basins = "SO_5")
# SO basin separate, with others being AIP
basinmask_SO_AIP <- basinmask_01 %>%
mutate(
basin = if_else(
lat < params_global$lat_min_SO, "SO", basin_AIP)
) %>%
mutate(MLR_basins = "SO_AIP")
# 3 basins
basinmask_AIP <- basinmask_01 %>%
mutate(
basin = basin_AIP) %>%
mutate(MLR_basins = "AIP")
# join basin masks into one file
basinmask_all <- bind_rows(basinmask_04, basinmask_01, basinmask_2, basinmask_5,
basinmask_SO_2, basinmask_SO_5,
basinmask_SO_AIP, basinmask_AIP)for (i_MLR_basins in unique(basinmask_all$MLR_basins)) {
i_MLR_basins <- unique(basinmask_all$MLR_basins)[6]
print(
map +
geom_raster(
data = basinmask_all %>% filter(MLR_basins == i_MLR_basins),
aes(lon, lat, fill = basin)
) +
scale_fill_brewer(palette = "Dark2") +
labs(title = paste("MLR basin label:", i_MLR_basins))
)
}
| Version | Author | Date |
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| dde77eb | jens-daniel-mueller | 2022-04-01 |
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2.3 Global section
To plot sections from the North Atlantic south to the Southern Ocean, around Antarctica and back North across the Pacific Ocean, corresponding coordinates were subsetted from the basin mask and distances between coordinate grid points calculated.
section <- basinmask_01 %>%
select(lon, lat)
# subset individual section parts
Atl_NS <- section %>%
filter(
lon == params_global$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 |
|---|---|---|
| dde77eb | jens-daniel-mueller | 2022-04-01 |
2.4 Write files
# land sea mask
landseamask %>%
write_csv(paste(path_files,
"land_sea_mask_WOA18.csv",
sep = ""))
# basin mask
basinmask_all %>%
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 = ""))
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] stars_0.5-5 sf_1.0-5 abind_1.4-5 patchwork_1.1.1
[5] geosphere_1.5-14 oce_1.5-0 gsw_1.0-6 reticulate_1.23
[9] tidync_0.2.4 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[13] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6
[17] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 lubridate_1.8.0 RColorBrewer_1.1-2
[5] httr_1.4.2 rprojroot_2.0.2 tools_4.1.2 backports_1.4.1
[9] bslib_0.3.1 utf8_1.2.2 R6_2.5.1 KernSmooth_2.23-20
[13] DBI_1.1.2 colorspace_2.0-2 withr_2.4.3 sp_1.4-6
[17] tidyselect_1.1.1 processx_3.5.2 bit_4.0.4 compiler_4.1.2
[21] git2r_0.29.0 cli_3.1.1 rvest_1.0.2 RNetCDF_2.5-2
[25] xml2_1.3.3 labeling_0.4.2 sass_0.4.0 scales_1.1.1
[29] classInt_0.4-3 proxy_0.4-26 callr_3.7.0 digest_0.6.29
[33] rmarkdown_2.11 pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9
[37] dbplyr_2.1.1 fastmap_1.1.0 rlang_0.4.12 readxl_1.3.1
[41] rstudioapi_0.13 farver_2.1.0 jquerylib_0.1.4 generics_0.1.1
[45] jsonlite_1.7.3 vroom_1.5.7 magrittr_2.0.1 ncmeta_0.3.0
[49] Matrix_1.4-0 Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2
[53] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4 yaml_2.2.1
[57] grid_4.1.2 parallel_4.1.2 promises_1.2.0.1 crayon_1.4.2
[61] lattice_0.20-45 haven_2.4.3 hms_1.1.1 knitr_1.37
[65] ps_1.6.0 pillar_1.6.4 reprex_2.0.1 glue_1.6.0
[69] evaluate_0.14 getPass_0.2-2 modelr_0.1.8 png_0.1-7
[73] vctrs_0.3.8 tzdb_0.2.0 httpuv_1.6.5 cellranger_1.1.0
[77] gtable_0.3.0 assertthat_0.2.1 xfun_0.29 lwgeom_0.2-8
[81] broom_0.7.11 e1071_1.7-9 later_1.3.0 viridisLite_0.4.0
[85] class_7.3-20 ncdf4_1.19 units_0.7-2 ellipsis_0.3.2