Argo Temperature Variability
Pasqualina Vonlanthen, David Stappard & Jens Daniel Müller
19 April, 2024
Last updated: 2024-04-19
Checks: 7 0
Knit directory:
bgc_argo_r_argodata/analysis/
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Task
Explore the spatial variability of Argo temperature profiles
Dependencies
temp_bgc_observed.rds - bgc preprocessed folder, created by temp_align_climatology. Not this file is written BEFORE the vertical alignment stage.
theme_set(theme_bw())Load Data
path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_updata <- '/nfs/kryo/work/updata'
path_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
# /nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo/preprocessed_bgc_data
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")region_masks_all_1x1 <- read_rds(file = paste0(path_argo_preprocessed,
"/region_masks_all_1x1.rds"))
region_masks_all_1x1 <- region_masks_all_1x1 %>%
rename(biome = value) %>%
mutate(coast = as.character(coast))
# WOA 18 basin mask
basinmask <-
read_csv(
paste(path_emlr_utilities,
"basin_mask_WOA18.csv",
sep = ""),
col_types = cols("MLR_basins" = col_character())
)
basinmask <- basinmask %>%
filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>%
select(-c(MLR_basins, basin))
# # full argo data (temperature)
# full_argo <-
# read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_flag_AB.rds")) %>%
# select(
# -c(
# ph_in_situ_total_adjusted:ph_in_situ_total_adjusted_error,
# profile_ph_in_situ_total_qc
# )
# )
#
# # change the date format for compatibility with OceanSODA data
# full_argo <- full_argo %>%
# mutate(year = year(date),
# month = month(date)) %>%
# mutate(date = ymd(format(date, "%Y-%m-15")))
# load validated and vertically aligned temp profiles,
full_argo <-
read_rds(file = paste0(path_argo_preprocessed, "/temp_bgc_observed.rds")) %>%
mutate(date = ymd(format(date, "%Y-%m-15")))
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))Regions
Biomes
# keep only southern ocean biomes
region_masks_all_1x1 <- region_masks_all_1x1 %>%
filter(region == 'southern',
biome != 0) %>%
select(-region)
# remove coastal data
region_masks_all_1x1 <- region_masks_all_1x1 %>%
filter(coast == "0")map +
geom_tile(data = region_masks_all_1x1,
aes(x = lon,
y = lat,
fill = biome))+
lims(y = c(-85, -30))+
scale_fill_brewer(palette = 'Dark2')
Basins
basinmask <- basinmask %>%
filter(lat < -30)map +
geom_tile(data = basinmask,
aes(x = lon,
y = lat,
fill = basin_AIP))+
lims(y = c(-85, -30))+
scale_fill_brewer(palette = 'Dark2')
Southern Ocean Argo temperature
full_argo_SO <- inner_join(full_argo, region_masks_all_1x1)
full_argo_SO <- inner_join(full_argo_SO, basinmask)
# full_argo_SO <- full_argo_SO %>%
# unite('platform_cycle', platform_number:cycle_number, sep = '_', remove = FALSE)Profiles by longitude
# plot the argo temperature profiles according to their longitude, in each biome, basin, and year
full_argo_SO %>%
group_split(biome, basin_AIP, year) %>%
head(12) %>%
map(
~ ggplot(data = .x,
aes(x = temp_adjusted,
y = depth,
group = file_id,
col = lon))+
geom_path(data = .x,
aes(x = temp_adjusted,
y = depth,
group = file_id,
col = lon),
linewidth = 0.3)+
scale_y_reverse()+
scale_color_viridis_c()+
facet_wrap(~month, ncol = 6)+
labs(title = paste0('biome: ', unique(.x$biome), '| basin: ', unique(.x$basin_AIP), ' |', unique(.x$year)),
x = 'Argo temperature (ºC)',
y = 'depth (m)',
col = 'longitude')
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 710edd4 | jens-daniel-mueller | 2022-05-11 |
| b917bd0 | jens-daniel-mueller | 2022-05-11 |
| e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
| c8451b9 | pasqualina-vonlanthendinenna | 2022-03-14 |
[[12]]
Profiles by latitude
# color the argo profiles according to their latitude, for each biome, basin, and year
full_argo_SO %>%
group_split(biome, basin_AIP, year) %>%
head(12) %>%
map(
~ ggplot(data = .x,
aes(x = temp_adjusted,
y = depth,
group = file_id,
col = lat))+
geom_path(data = .x,
aes(x = temp_adjusted,
y = depth,
group = file_id,
col = lat),
linewidth = 0.3)+
scale_y_reverse()+
scale_color_viridis_c()+
facet_wrap(~month, ncol = 6)+
labs(title = paste0('biome: ', unique(.x$biome), '| basin: ', unique(.x$basin_AIP), ' |', unique(.x$year)),
x = 'Argo temperature (ºC)',
y = 'depth (m)',
col = 'latitude')
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 710edd4 | jens-daniel-mueller | 2022-05-11 |
| b917bd0 | jens-daniel-mueller | 2022-05-11 |
| e12a216 | pasqualina-vonlanthendinenna | 2022-03-15 |
| c8451b9 | pasqualina-vonlanthendinenna | 2022-03-14 |
[[12]]
Profiles by year
# plot all years in each month
full_argo_SO %>%
group_split(biome, basin_AIP) %>%
head(3) %>%
map(
~ ggplot(data = .x,
aes(x = temp_adjusted,
y = depth,
group = file_id,
col = as.character(year)))+
geom_path(data = .x,
aes(x = temp_adjusted,
y = depth,
group = file_id,
col = as.character(year)),
linewidth = 0.3)+
scale_y_reverse()+
facet_wrap(~month, ncol = 6)+
labs(title = paste0('biome: ', unique(.x$biome), '| basin: ', unique(.x$basin_AIP)),
x = 'Argo temperature (ºC)',
y = 'depth (m)',
col = 'year')
)
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] ggforce_0.4.1 metR_0.13.0 scico_1.3.1 ggOceanMaps_1.3.4
[5] ggspatial_1.1.7 broom_1.0.5 lubridate_1.9.0 timechange_0.1.1
[9] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
[13] readr_2.1.3 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[17] tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] fs_1.5.2 sf_1.0-9 bit64_4.0.5
[4] RColorBrewer_1.1-3 httr_1.4.4 rprojroot_2.0.3
[7] tools_4.2.2 backports_1.4.1 bslib_0.4.1
[10] utf8_1.2.2 R6_2.5.1 KernSmooth_2.23-20
[13] rgeos_0.5-9 DBI_1.2.2 colorspace_2.0-3
[16] raster_3.6-11 withr_2.5.0 sp_1.5-1
[19] tidyselect_1.2.0 bit_4.0.5 compiler_4.2.2
[22] git2r_0.30.1 cli_3.6.1 rvest_1.0.3
[25] xml2_1.3.3 labeling_0.4.2 sass_0.4.4
[28] checkmate_2.1.0 scales_1.2.1 classInt_0.4-8
[31] proxy_0.4-27 digest_0.6.30 rmarkdown_2.18
[34] pkgconfig_2.0.3 htmltools_0.5.8.1 highr_0.9
[37] dbplyr_2.2.1 fastmap_1.1.0 rlang_1.1.1
[40] readxl_1.4.1 rstudioapi_0.15.0 farver_2.1.1
[43] jquerylib_0.1.4 generics_0.1.3 jsonlite_1.8.3
[46] vroom_1.6.0 googlesheets4_1.0.1 magrittr_2.0.3
[49] Rcpp_1.0.10 munsell_0.5.0 fansi_1.0.3
[52] lifecycle_1.0.3 terra_1.7-65 stringi_1.7.8
[55] whisker_0.4 yaml_2.3.6 MASS_7.3-58.1
[58] grid_4.2.2 parallel_4.2.2 promises_1.2.0.1
[61] crayon_1.5.2 lattice_0.20-45 haven_2.5.1
[64] hms_1.1.2 knitr_1.41 pillar_1.9.0
[67] codetools_0.2-18 reprex_2.0.2 glue_1.6.2
[70] evaluate_0.18 data.table_1.14.6 modelr_0.1.10
[73] tweenr_2.0.2 vctrs_0.6.4 tzdb_0.3.0
[76] httpuv_1.6.6 cellranger_1.1.0 polyclip_1.10-4
[79] gtable_0.3.1 assertthat_0.2.1 cachem_1.0.6
[82] xfun_0.35 e1071_1.7-12 later_1.3.0
[85] viridisLite_0.4.1 class_7.3-20 googledrive_2.0.0
[88] gargle_1.2.1 memoise_2.0.1 workflowr_1.7.0
[91] units_0.8-0 ellipsis_0.3.2