Load Biome Information
Pasqualina Vonlanthen & Jens Daniel Müller
31 March, 2024
Last updated: 2024-03-31
Checks: 7 0
Knit directory:
bgc_argo_r_argodata/analysis/
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| Rmd | 3e1ac14 | pasqualina-vonlanthendinenna | 2022-04-26 | separated loading data pages, added mayot biomes, switched to pH and temp flag A |
Task
Load in biome information and regional separations.
Dependencies
RECCAP2_region_masks_all_v20210412.nc
map_landmask_WOA18.rds
Outputs (in BGC preprocessed folder)
region_masks_all_seamask_1x1.rds
region_masks_all_seamask_2x2.rds
region_masks_all_1x1.rds
region_masks_all_2x2.rds
ph_surface_1x1.rds
ph_surface_2x2.rds
nm_biomes.rds
RECCAP-2 Biome Separations
# load in the RECCAP biome separations
region_masks_all <-
stars::read_ncdf(paste(
path_basin_mask, "RECCAP2_region_masks_all_v20221025.nc", sep = "")) %>%
as_tibble() %>%
mutate(seamask = as.factor(seamask))Harmonise RECCAP biomes
# harmonise the latitude longitude bands of the biomes to the pH data (2x2 grid)
region_masks_all_seamask_2x2 <- region_masks_all %>%
select(lat, lon, seamask) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(
lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
lon = as.numeric(as.character(lon))
)
region_masks_all_seamask_1x1 <- region_masks_all %>%
select(lat, lon, seamask) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(
lat = cut(lat, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 1), seq(20.5, 379.5, 1)),
lon = as.numeric(as.character(lon))
)
region_masks_all <- region_masks_all %>%
select(-seamask) %>%
pivot_longer(open_ocean:southern,
names_to = 'region',
values_to = 'value') %>%
mutate(value = as.factor(value))
# harmonise the lat/lon of the regional separations to our pH data
region_masks_all_1x1 <- region_masks_all %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(
lat = cut(lat, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 1), seq(20.5, 379.5, 1)),
lon = as.numeric(as.character(lon))
)
region_masks_all_2x2 <- region_masks_all %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(
lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
lon = as.numeric(as.character(lon))
)
# add the region names to the surface pH dataframes
ph_surface_1x1 <- read_rds(file = paste0(path_argo_preprocessed, "/ph_surface_1x1.rds"))
ph_surface_2x2 <- read_rds(file = paste0(path_argo_preprocessed, "/ph_surface_2x2.rds"))
ph_surface_2x2 <- inner_join(ph_surface_2x2, region_masks_all_2x2)
ph_surface_1x1 <- inner_join(ph_surface_1x1, region_masks_all_1x1)Maps of Southern Ocean RECCAP biomes
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))
# restrict base map to Southern Ocean
map <- map +
lims(y = c(-85, -30))
region_masks_all_1x1 <- region_masks_all_1x1 %>%
filter(region == 'southern',
value != 0) %>%
mutate(coast = as.character(coast))Coastal regions
map +
geom_tile(data = region_masks_all_1x1,
aes(x = lon,
y = lat,
fill = coast))+
scale_fill_brewer(palette = 'Dark2')
Biomes
map+
geom_tile(data = region_masks_all_1x1,
aes(x = lon,
y = lat,
fill = value))+
scale_fill_brewer(palette = 'Dark2')+
labs(title = 'RECCAP biomes')
basemap(limits = -30)+
geom_spatial_tile(data = region_masks_all_1x1,
aes(x = lon,
y = lat,
fill = value),
col = NA)+
scale_fill_brewer(palette = 'Dark2')+
labs(title = 'RECCAP biomes')
Write RECCAP biomes to file
region_masks_all_seamask_1x1 %>%
write_rds(file = paste0(path_argo_preprocessed, "/region_masks_all_seamask_1x1.rds"))
region_masks_all_seamask_2x2 %>%
write_rds(file = paste0(path_argo_preprocessed, "/region_masks_all_seamask_2x2.rds"))
region_masks_all_1x1 %>%
write_rds(file = paste0(path_argo_preprocessed, "/region_masks_all_1x1.rds"))
region_masks_all_2x2 %>%
write_rds(file = paste0(path_argo_preprocessed, "/region_masks_all_2x2.rds"))
# joined RECCAP-biomes to surface pH data
ph_surface_1x1 %>%
write_rds(file = paste0(path_argo_preprocessed, "/ph_surface_1x1.rds"))
ph_surface_2x2 %>%
write_rds(file = paste0(path_argo_preprocessed, "/ph_surface_2x2.rds"))Mayot biomes
nm_biomes <- tidync::hyper_tibble(paste0(path_argo, "/SouthernOcean_mask_NM.nc"))
# 1 degree lon/lat grid
# table(nm_regions$LATITUDE) # 1 degree intervals
# table((nm_regions$LONGITUDE)) # 1 degree longitude intervalsHarmonise Mayot biomes
nm_biomes <- nm_biomes %>%
rename(lon = LONGITUDE,
lat = LATITUDE) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
nm_biomes <- nm_biomes %>%
filter(ICE == 1 | STSS == 1 | SPSS == 1)
nm_biomes <- nm_biomes %>%
pivot_longer(cols = c(STSS, SPSS, ICE),
values_to = 'biome_mask',
names_to = 'biome_name')
nm_biomes <- nm_biomes %>%
filter(biome_mask==1,
lat <= -30)Maps of Southern Ocean Mayot biomes
map+
geom_tile(data = nm_biomes,
aes(x = lon,
y = lat,
fill = biome_name))+
scale_fill_brewer(palette = 'Dark2')+
labs(title = 'Mayot biomes')
basemap(limits = -30)+
geom_spatial_tile(data = nm_biomes,
aes(x = lon,
y = lat,
fill = biome_name),
col = NA)+
scale_fill_brewer(palette = 'Dark2')+
labs(title = 'Mayot biomes')
Write Mayot biomes to file
# write data to file
# nm_biomes %>%
# write_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5
Matrix products: default
BLAS: /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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] lubridate_1.9.0 timechange_0.1.1 ggOceanMaps_1.3.4 ggspatial_1.1.7
[5] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
[9] readr_2.1.3 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[13] tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 sf_1.0-9 RColorBrewer_1.1-3
[4] httr_1.4.4 rprojroot_2.0.3 tools_4.2.2
[7] backports_1.4.1 bslib_0.4.1 rgdal_1.6-2
[10] utf8_1.2.2 R6_2.5.1 KernSmooth_2.23-20
[13] rgeos_0.5-9 DBI_1.1.3 colorspace_2.0-3
[16] raster_3.6-11 withr_2.5.0 sp_1.5-1
[19] tidyselect_1.2.0 processx_3.8.0 compiler_4.2.2
[22] git2r_0.30.1 cli_3.6.1 rvest_1.0.3
[25] RNetCDF_2.6-1 xml2_1.3.3 labeling_0.4.2
[28] sass_0.4.4 scales_1.2.1 classInt_0.4-8
[31] ggOceanMapsData_1.0.1 callr_3.7.3 proxy_0.4-27
[34] digest_0.6.30 rmarkdown_2.18 pkgconfig_2.0.3
[37] htmltools_0.5.3 highr_0.9 dbplyr_2.2.1
[40] fastmap_1.1.0 tidync_0.3.0 rlang_1.1.1
[43] readxl_1.4.1 rstudioapi_0.15.0 farver_2.1.1
[46] jquerylib_0.1.4 generics_0.1.3 jsonlite_1.8.3
[49] googlesheets4_1.0.1 magrittr_2.0.3 ncmeta_0.3.5
[52] Rcpp_1.0.10 munsell_0.5.0 fansi_1.0.3
[55] abind_1.4-5 lifecycle_1.0.3 terra_1.7-65
[58] stringi_1.7.8 whisker_0.4 yaml_2.3.6
[61] grid_4.2.2 parallel_4.2.2 promises_1.2.0.1
[64] crayon_1.5.2 lattice_0.20-45 stars_0.6-0
[67] haven_2.5.1 hms_1.1.2 knitr_1.41
[70] ps_1.7.2 pillar_1.9.0 codetools_0.2-18
[73] reprex_2.0.2 glue_1.6.2 evaluate_0.18
[76] getPass_0.2-2 modelr_0.1.10 vctrs_0.6.4
[79] tzdb_0.3.0 httpuv_1.6.6 cellranger_1.1.0
[82] gtable_0.3.1 assertthat_0.2.1 cachem_1.0.6
[85] xfun_0.35 lwgeom_0.2-10 broom_1.0.5
[88] e1071_1.7-12 later_1.3.0 ncdf4_1.19
[91] class_7.3-20 googledrive_2.0.0 gargle_1.2.1
[94] units_0.8-0 ellipsis_0.3.2