Core-Argo Temperature Data
Pasqualina Vonlanthen, David Stappard & Jens Daniel Müller
19 December, 2023
Last updated: 2023-12-19
Checks: 7 0
Knit directory:
bgc_argo_r_argodata/analysis/
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| Rmd | 64fd104 | ds2n19 | 2023-12-14 | revised coverage analysis and SO focused cluster analysis. |
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| html | cec2a2a | ds2n19 | 2023-11-24 | Build site. |
| Rmd | 59f5cc4 | ds2n19 | 2023-11-23 | Moved spatiotemporal analysis to use aligned profiles. |
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| Rmd | 723c772 | ds2n19 | 2023-10-12 | refresh coverage and analysis after full core load 2013 - 2023 |
| Rmd | 1ae81b3 | ds2n19 | 2023-10-11 | reworked core load process to work initially by year and then finally create consolidated all years files. |
| Rmd | 44f5720 | ds2n19 | 2023-10-09 | manual commit |
| html | 7b3d8c5 | pasqualina-vonlanthendinenna | 2022-08-29 | Build site. |
| Rmd | 8e81570 | pasqualina-vonlanthendinenna | 2022-08-29 | load and add in core-argo data (1 month) |
Task
Explore the core-Argo temperature data (not only BGC)
Dependencies
temp_bgc_observed.rds - core preprocessed folder, created by temp_core_align_climatology. Not this file is written BEFORE the vertical alignment stage.
path_argo_core <- '/nfs/kryo/work/updata/core_argo_r_argodata'
path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_basin_mask <- "/nfs/kryo/work/updata/reccap2/"
path_argo_core_preprocessed <- paste0(path_argo_core, "/preprocessed_core_data")
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_argo_core <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/core_argo_r_argodata'
path_argo_core_preprocessed <- paste0(path_argo_core, "/preprocessed_core_data")Load Core-SST data
Using only core-temperature flag A profiles
# read validated temperature profile and restrict to the top 20 m
sst <-
read_rds(file = paste0(path_argo_core_preprocessed, "/temp_core_observed.rds")) %>%
filter(between(depth, 0, 20))
# load in biome separations
nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))Southern Ocean SST
sst_SO <- sst %>%
filter(lat <= -30)SST offset with depth
Difference between the in-situ measured sst (20 m) and the profile-mean 20m temperature
# calculate the mean sst for each surface profile
mean_profile_sst <- sst_SO %>%
group_by(file_id) %>%
mutate(mean_prof_sst = mean(temp_adjusted, na.rm = TRUE),
.before = depth) %>%
ungroup() %>%
mutate(offset = temp_adjusted-mean_prof_sst,
.after = mean_prof_sst) # subtract the mean profile sst from the measured in situ sst
mean_profile_sst %>%
ggplot()+
geom_point(aes(x = offset, y = depth, col = as.character(year)), size = 0.3, pch = 19) +
scale_y_reverse()+
geom_vline(xintercept = 0, col = 'red', linewidth = 0.6)+
labs(x = 'offset (ºC)',
y = 'depth (m)',
col = 'year',
title = 'in situ sst - mean profile sst')
Bin the sst data into 2m-depth intervals and calculate the offset for each sst observation in each depth interval relative to the profile-mean sst
# bin the sst values into 2m bins and calculate the offset for each 2m bin
mean_profile_sst_binned <- sst_SO %>%
mutate(depth = cut(depth, seq(0, 20, 2), seq(1, 19, 2)),
depth = as.numeric(as.character(depth))) %>%
group_by(file_id) %>%
mutate(mean_prof_sst = mean(temp_adjusted, na.rm = TRUE),
.before = depth) %>%
ungroup() %>%
mutate(offset = temp_adjusted-mean_prof_sst,
.after = mean_prof_sst)
# plot the offset of the depth-binned values
mean_profile_sst_binned %>%
ggplot()+
geom_point(aes(x = offset, y = depth, col = as.character(year)), size = 0.3, pch = 19) +
scale_y_reverse()+
geom_vline(xintercept = 0, col = 'red', linewidth = 0.6)+
labs(x = 'offset (ºC)',
y = 'depth (m)',
col = 'year',
title = 'in situ sst - mean profile sst (2m depth bins)')
Mean binned offset
# bin the ph values into 2m bins and calculate the offset for each 2m bin
profile_sst_binned_ave <- sst_SO %>%
mutate(depth = cut(depth, seq(0, 20, 2), seq(1, 19, 2)),
depth = as.numeric(as.character(depth))) %>%
group_by(file_id) %>%
mutate(mean_prof_sst = mean(temp_adjusted, na.rm = TRUE),
.before = depth) %>%
ungroup() %>%
mutate(offset = temp_adjusted-mean_prof_sst,
.after = mean_prof_sst) %>%
group_by(depth) %>%
summarise(mean_offset = mean(offset))
# plot the offset of the depth-binned values
profile_sst_binned_ave %>%
ggplot()+
geom_point(aes(x = mean_offset, y = depth), size = 1, pch = 19) +
geom_line(aes(x = mean_offset, y = depth))+
scale_y_reverse()+
geom_vline(xintercept = 0, col = 'red', linewidth = 1)+
labs(x = 'mean offset (ºC)',
y = 'depth (m)',
col = 'year',
title = 'in situ sst - mean profile sst (2m depth bins)')
Monthly climatological SST (core-Argo)
Map of monthly climatological Argo temperature (core-Argo floats, flag A profiles only), January 2013-April 2022 (January 1st - February 1st 2013 only for test purposes)
# average pH values in the top 20 m for each month in each 2 x 2º longitude/latitude grid
sst_clim_SO <- sst_SO %>%
group_by(lat, lon, month) %>%
summarise(sst_clim_month = mean(temp_adjusted))
# read in the map from updata
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))
# map a monthly climatology of pH (April 2014 - August 2021)
map +
geom_tile(data = sst_clim_SO,
aes(lon, lat, fill = sst_clim_month)) +
lims(y = c(-85, -25)) +
scale_fill_viridis_c() +
labs(x = 'lon',
y = 'lat',
fill = 'SST',
title = 'Monthly climatological \nArgo SST (Jan 2013 - 2022)') +
theme(legend.position = 'right') +
facet_wrap(~month, ncol = 2)
basemap(limits = -32, data = sst_clim_SO) + # change to polar projection
geom_spatial_tile(data = sst_clim_SO,
aes(x = lon,
y = lat,
fill = sst_clim_month),
linejoin = 'mitre',
col = 'transparent',
detail = 60)+
scale_fill_viridis_c()+
theme(legend.position = 'bottom')+
labs(x = 'lon',
y = 'lat',
fill = 'SST',
title = 'monthly climatological \nArgo SST')+
facet_wrap(~month, ncol = 2)Monthly timeseries
Timeseries of monthly SST values, for each Mayot biome
# plot the region separations on a map
map +
geom_raster(data = nm_biomes,
aes(x = lon,
y = lat,
fill = biome_name)) +
labs(title = 'Southern Ocean Mayot biomes',
fill = 'biome')
| Version | Author | Date |
|---|---|---|
| 7b3d8c5 | pasqualina-vonlanthendinenna | 2022-08-29 |
# plot a timeseries of monthly values over the whole southern ocean south of 30ºS
sst_SO <- inner_join(sst_SO, nm_biomes)
sst_month_SO <- sst_SO %>%
group_by(year, month, biome_name) %>%
summarise(sst_ave = mean(temp_adjusted, na.rm = TRUE))
# timeseries of monthly pH values over 2014-2021 (separate panels for each month)
sst_month_SO %>%
ggplot(aes(x = year,
y = sst_ave,
group = biome_name,
col = biome_name)) +
facet_wrap(~month) +
geom_line() +
geom_point() +
labs(x = 'year',
y = 'SST (ºC)',
title = 'monthly mean Argo SST (Southern Ocean)',
col = 'region')
Monthly average Southern Ocean SST, for each biome
# timeseries of monthly sst values for each year (separate years on the same plot)
sst_month_SO %>%
# filter(year != 2014) %>% # remove the year that is missing data
ggplot(aes(x = month,
y = sst_ave,
group = year,
col = as.character(year)))+
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 2))+
facet_wrap(~biome_name)+
labs(x = 'month',
y = 'SST (ºC)',
title = 'monthly mean Argo SST (Southern Ocean regions)',
col = 'year')
# calculate a yearly average SST (one SST value per year, for the whole biome)
sst_year_SO <- sst_SO %>%
group_by(year, biome_name) %>%
summarise(sst_ave = mean(temp_adjusted, na.rm = TRUE))
# plot a timeseries of the yearly average SST value (one value per year)
sst_year_SO %>%
ggplot(aes(x = year, y = sst_ave, group = biome_name, col = biome_name))+
geom_line()+
geom_point()+
labs(x = 'year',
y = 'SST (ºC)',
title = 'yearly mean Argo SST (south of 30ºS)',
col = 'region')
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] ggOceanMaps_1.3.4 ggspatial_1.1.7 oce_1.7-10 gsw_1.1-1
[5] lubridate_1.9.0 timechange_0.1.1 forcats_0.5.2 stringr_1.5.0
[9] dplyr_1.1.3 purrr_1.0.2 readr_2.1.3 tidyr_1.3.0
[13] tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] fs_1.5.2 sf_1.0-9 httr_1.4.4
[4] rprojroot_2.0.3 tools_4.2.2 backports_1.4.1
[7] bslib_0.4.1 utf8_1.2.2 R6_2.5.1
[10] KernSmooth_2.23-20 rgeos_0.5-9 DBI_1.1.3
[13] colorspace_2.0-3 raster_3.6-11 withr_2.5.0
[16] sp_1.5-1 tidyselect_1.2.0 compiler_4.2.2
[19] git2r_0.30.1 cli_3.6.1 rvest_1.0.3
[22] xml2_1.3.3 labeling_0.4.2 sass_0.4.4
[25] scales_1.2.1 classInt_0.4-8 proxy_0.4-27
[28] digest_0.6.30 rmarkdown_2.18 pkgconfig_2.0.3
[31] htmltools_0.5.3 highr_0.9 dbplyr_2.2.1
[34] fastmap_1.1.0 rlang_1.1.1 readxl_1.4.1
[37] rstudioapi_0.15.0 farver_2.1.1 jquerylib_0.1.4
[40] generics_0.1.3 jsonlite_1.8.3 googlesheets4_1.0.1
[43] magrittr_2.0.3 Rcpp_1.0.10 munsell_0.5.0
[46] fansi_1.0.3 lifecycle_1.0.3 terra_1.7-39
[49] stringi_1.7.8 whisker_0.4 yaml_2.3.6
[52] grid_4.2.2 parallel_4.2.2 promises_1.2.0.1
[55] crayon_1.5.2 lattice_0.20-45 haven_2.5.1
[58] hms_1.1.2 knitr_1.41 pillar_1.9.0
[61] codetools_0.2-18 reprex_2.0.2 glue_1.6.2
[64] evaluate_0.18 modelr_0.1.10 vctrs_0.6.4
[67] tzdb_0.3.0 httpuv_1.6.6 cellranger_1.1.0
[70] gtable_0.3.1 assertthat_0.2.1 cachem_1.0.6
[73] xfun_0.35 broom_1.0.5 e1071_1.7-12
[76] later_1.3.0 viridisLite_0.4.1 class_7.3-20
[79] googledrive_2.0.0 gargle_1.2.1 workflowr_1.7.0
[82] units_0.8-0 ellipsis_0.3.2