Extreme pH Profiles
Pasqualina Vonlanthen, David Stappard & Jens Daniel Müller
04 December, 2023
Last updated: 2023-12-04
Checks: 7 0
Knit directory: bgc_argo_r_argodata/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3cb4b17 | ds2n19 | 2023-12-04 | Cluster under surface extreme. |
| Rmd | fa1083d | ds2n19 | 2023-12-01 | Additional analysis to cluster process. |
| html | cec2a2a | ds2n19 | 2023-11-24 | Build site. |
| Rmd | 3dc557d | ds2n19 | 2023-11-24 | Switched to new profile details. |
| Rmd | 59f5cc4 | ds2n19 | 2023-11-23 | Moved spatiotemporal analysis to use aligned profiles. |
| html | 80c16c2 | ds2n19 | 2023-11-15 | Build site. |
| html | 4b55c43 | ds2n19 | 2023-10-12 | Build site. |
| Rmd | ce19a66 | ds2n19 | 2023-10-04 | Revised version of OceanSODA product -v2023 |
| html | 7b3d8c5 | pasqualina-vonlanthendinenna | 2022-08-29 | Build site. |
| html | bdd516d | pasqualina-vonlanthendinenna | 2022-05-23 | Build site. |
| html | 4173c20 | jens-daniel-mueller | 2022-05-12 | Build site. |
| html | dfe89d7 | jens-daniel-mueller | 2022-05-12 | Build site. |
| html | 710edd4 | jens-daniel-mueller | 2022-05-11 | Build site. |
| Rmd | 2f20a76 | jens-daniel-mueller | 2022-05-11 | rebuild all after subsetting AB profiles and code cleaning |
Task
Explore double extremes in the temp and pH anomaly fields
Load Data
library(tidyverse)
library(lubridate)
library(ggnewscale)theme_set(theme_bw())
HNL_colors_map <- c('H' = 'red3',
'N' = 'gray90',
'L' = 'blue3')
# opt_min_profile_range
# profiles with profile_range >= opt_min_profile_range will be selected
# 1 = profiles of at least pH = 614m temp = 600m, 2 = profiles of at least pH = 1225m temp = 1200m, 3 = profiles of at least pH = 1600m temp = 1500m
opt_min_profile_range = 3
# opt_extreme_determination
# 1 - based on the trend of de-seasonal data - we believe this results in more summer extremes where variation tend to be greater.
# 2 - based on the trend of de-seasonal data by month. grouping is by lat, lon and month.
opt_extreme_determination <- 2path_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_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")map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))
if (opt_extreme_determination == 1){
pH_extreme <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_pH_anomaly_field_01.rds"))
temp_extreme <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_01.rds"))
} else if (opt_extreme_determination == 2){
pH_extreme <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_pH_anomaly_field_02.rds"))
temp_extreme <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_02.rds"))
}
# load validated and vertically aligned pH profiles,
argo_pH <- read_rds(file = paste0(path_argo_preprocessed, "/pH_bgc_va.rds")) %>%
filter(profile_range >= opt_min_profile_range) %>%
select(file_id,
date,
year,
month,
lat,
lon,
depth,
profile_range_pH = profile_range,
pH,
h_plus)
argo_temp <- read_rds(file = paste0(path_argo_preprocessed, "/temp_bgc_va.rds")) %>%
filter(profile_range >= opt_min_profile_range) %>%
select(file_id,
date,
year,
month,
lat,
lon,
depth,
profile_range_temp = profile_range,
temp)
full_argo <- full_join(argo_pH, argo_temp)
# list where both pH and temp exist and apply to full_argo
argo_file_id <- inner_join(argo_pH %>% distinct(file_id), argo_temp %>% distinct(file_id))
full_argo <- left_join(argo_file_id, full_argo)
# change the date format for compatibility with OceanSODA pH data
full_argo <- full_argo %>%
mutate(date = ymd(format(date, "%Y-%m-15")))
rm(argo_pH, argo_temp, argo_file_id)Anomaly maps
pH
pH_extreme %>%
filter(year >= 2013) %>%
group_split(month) %>%
#head(1) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon_raw,
y = lat_raw,
fill = ph_extreme))+
scale_fill_manual(values = HNL_colors_map)+
facet_wrap(~year, ncol = 2)+
lims(y = c(-85, -32))+
labs(title = paste('month:', unique(.x$month)),
fill = 'pH')
)[[1]]
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SST
temp_extreme %>%
filter(year >= 2013) %>%
group_split(month) %>%
#head(1) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon_raw,
y = lat_raw,
fill = temp_extreme))+
scale_fill_manual(values = HNL_colors_map)+
facet_wrap(~year, ncol = 2)+
lims(y = c(-85, -32))+
labs(title = paste('month:', unique(.x$month)),
fill = 'pH')
)[[1]]
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Join data
anomaly_field <- full_join(pH_extreme %>%
select(lon_raw, lat_raw, month, year, date, basin_AIP, biome_name, ph_extreme),
temp_extreme %>%
select(lon_raw, lat_raw, month, year, date, basin_AIP, biome_name, temp_extreme))# chisq.test(anomaly_field$ph_extreme, anomaly_field$temp_extreme, correct = FALSE)
anomaly_field <- anomaly_field %>%
mutate(
double_extreme = case_when(
temp_extreme == 'H' & ph_extreme == 'H' ~ 'warm_HpH',
temp_extreme == 'L' &
ph_extreme == 'H' ~ 'cold_HpH',
temp_extreme == 'H' &
ph_extreme == 'L' ~ 'warm_LpH',
temp_extreme == 'L' &
ph_extreme == 'L' ~ 'cold_LpH',
temp_extreme == 'H' &
ph_extreme == 'N' ~ 'warm',
temp_extreme == 'L' &
ph_extreme == 'N' ~ 'cold',
temp_extreme == 'N' &
ph_extreme == 'H' ~ 'HpH',
temp_extreme == 'N' &
ph_extreme == 'L' ~ 'LpH',
TRUE ~ 'N'
)
) %>%
mutate(
double_extreme = fct_relevel(
double_extreme,
'warm_HpH',
'cold_HpH',
'warm_LpH',
'cold_LpH',
'warm',
'cold',
'HpH',
'LpH',
'N'
)
)Compound extreme maps
HNL_colors_map_temp <- c('H' = "#CD534CFF",
'N' = 'transparent',
'L' = "#0073C2FF")
HNL_colors_map_ph <- c('H' = "#009E73",
'N' = 'transparent',
'L' = "#EFC000FF")
anomaly_field %>%
filter(year >= 2013,
double_extreme != "N") %>%
group_split(month) %>%
# tail(1) %>%
map(
~ map +
geom_tile(data = .x,
aes(
x = lon_raw,
y = lat_raw,
fill = ph_extreme
), alpha = 0.4) +
scale_fill_manual(values = HNL_colors_map_ph) +
new_scale_fill() +
geom_tile(data = .x,
aes(
x = lon_raw,
y = lat_raw,
fill = temp_extreme
), alpha = 0.4) +
scale_fill_manual(values = HNL_colors_map_temp) +
facet_wrap(~ year, ncol = 2) +
lims(y = c(-85, -32)) +
labs(title = paste0('month:', unique(.x$month)))
)[[1]]
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rm(HNL_colors_map_temp, HNL_colors_map_ph)anomaly_field %>%
filter(year >= 2013) %>%
group_split(month) %>%
map(
~map+
geom_tile(data = .x,
aes(x = lon_raw,
y = lat_raw,
fill = double_extreme))+
facet_wrap(~year, ncol = 2)+
scale_fill_manual(values = c('warm_HpH' = 'brown',
'warm_LpH' = 'yellow',
'cold_HpH' = 'beige',
'cold_LpH' = 'cyan',
'cold' = 'blue',
'warm' = 'red',
'LpH' = 'orange',
'HpH' = 'green',
'N' = NA),
na.value = NA)+
lims(y = c(-85, -32))+
labs(title = paste0('month:', unique(.x$month)),
fill = 'double extreme')
)[[1]]
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Join Argo profile data
anomaly_field <- anomaly_field %>%
rename(lat = lat_raw,
lon = lon_raw) %>%
filter(year >= 2013)
profile_double_extreme <- inner_join(full_argo, anomaly_field)
# profile_double_extreme <- profile_double_extreme %>%
# unite('platform_cycle', platform_number:cycle_number, sep = '_', remove = FALSE)anomaly_field %>%
filter(year >= 2013) %>%
group_split(month) %>%
map(
~map+
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = double_extreme))+
facet_wrap(~year, ncol = 2)+
scale_fill_manual(values = c('warm_HpH' = 'brown',
'warm_LpH' = 'yellow',
'cold_HpH' = 'beige',
'cold_LpH' = 'cyan',
'cold' = 'blue',
'warm' = 'red',
'LpH' = 'orange',
'HpH' = 'green',
'N' = NA),
na.value = NA)+
geom_point(data = profile_double_extreme,
aes(x = lon,
y = lat),
size = 0.2)+
lims(y = c(-85, -32))+
labs(title = paste0('month:', unique(.x$month)),
fill = 'double extreme')
)[[1]]
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Plot Profiles
Raw
pH
profile_double_extreme %>%
group_split(biome_name, basin_AIP, year) %>%
map(
~ggplot(data = .x,
aes(x = pH,
y = depth,
group = file_id,
col = double_extreme))+
geom_path(data = .x %>% filter(double_extreme == 'N'),
aes(x = pH,
y = depth,
group = file_id,
col = double_extreme),
size = 0.3)+
geom_path(data = .x %>% filter(double_extreme == 'E'),
aes(x = pH,
y = depth,
group = file_id,
col = double_extreme),
size = 0.5)+
scale_y_reverse()+
scale_color_manual(values = c('N' = 'gray', 'E' = 'red'))+
facet_wrap(~month, ncol = 6)+
labs(title = paste0('biome: ', unique(.x$biome_name), '| ', unique(.x$basin_AIP), '| ', unique(.x$year)),
col = 'double extreme',
x = 'Argo pH',
y = 'depth')
)[[1]]
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Temperature
profile_double_extreme %>%
group_split(biome_name, basin_AIP, year) %>%
map(
~ggplot(data = .x,
aes(x = temp,
y = depth,
group = file_id,
col = double_extreme))+
geom_path(data = .x %>% filter(double_extreme == 'N'),
aes(x = temp,
y = depth,
group = file_id,
col = double_extreme),
size = 0.3)+
geom_path(data = .x %>% filter(double_extreme == 'E'),
aes(x = temp,
y = depth,
group = file_id,
col = double_extreme),
size = 0.5)+
scale_y_reverse()+
scale_color_manual(values = c('N' = 'gray', 'E' = 'red'))+
facet_wrap(~month, ncol = 6)+
labs(title = paste0('biome: ', unique(.x$biome_name), '| ', unique(.x$basin_AIP), '| ', unique(.x$year)),
col = 'double extreme',
x = 'Argo temperature',
y = 'depth')
)[[1]]
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Pacific basin, STSS biome, December 2017
Pacific_STSS_2017 <- profile_double_extreme %>%
filter(date == '2017-12-15',
basin_AIP == 'Pacific',
biome_name == 'STSS')
# pH:
Pacific_STSS_2017 %>%
ggplot()+
geom_path(data = Pacific_STSS_2017 %>% filter(double_extreme == 'N'),
aes(x = pH,
y = depth,
group = file_id,
col = double_extreme),
size = 0.3)+
geom_path(data = Pacific_STSS_2017 %>% filter(double_extreme == 'E'),
aes(x = pH,
y = depth,
group = file_id,
col = double_extreme),
size = 0.5)+
scale_y_reverse()+
scale_color_manual(values = c('E' = 'red', 'N' = 'grey'))+
labs(title = 'Pacific, STSS biome, December 2017',
col = 'double extreme',
x = 'Argo pH',
y = 'depth')
# Temperature
Pacific_STSS_2017 %>%
ggplot()+
geom_path(data = Pacific_STSS_2017 %>% filter(double_extreme == 'N'),
aes(x = temp,
y = depth,
group = file_id,
col = double_extreme),
size = 0.3)+
geom_path(data = Pacific_STSS_2017 %>% filter(double_extreme == 'E'),
aes(x = temp,
y = depth,
group = file_id,
col = double_extreme),
size = 0.5)+
scale_y_reverse()+
scale_color_manual(values = c('E' = 'red', 'N' = 'grey'))+
labs(title = 'Pacific, STSS biome, December 2017',
col = 'double extreme',
x = 'Argo pH',
y = 'depth')
rm(Pacific_STSS_2017)Pacific basin, STSS biome, December 2019
No profiles for December 2019 in SPSS or STSS Pacific
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] ggnewscale_0.4.8 lubridate_1.9.0 timechange_0.1.1 forcats_0.5.2
[5] stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2 readr_2.1.3
[9] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 assertthat_0.2.1 rprojroot_2.0.3
[4] digest_0.6.30 utf8_1.2.2 R6_2.5.1
[7] cellranger_1.1.0 backports_1.4.1 reprex_2.0.2
[10] evaluate_0.18 highr_0.9 httr_1.4.4
[13] pillar_1.9.0 rlang_1.1.1 googlesheets4_1.0.1
[16] readxl_1.4.1 rstudioapi_0.15.0 whisker_0.4
[19] jquerylib_0.1.4 rmarkdown_2.18 labeling_0.4.2
[22] googledrive_2.0.0 munsell_0.5.0 broom_1.0.5
[25] compiler_4.2.2 httpuv_1.6.6 modelr_0.1.10
[28] xfun_0.35 pkgconfig_2.0.3 htmltools_0.5.3
[31] tidyselect_1.2.0 workflowr_1.7.0 fansi_1.0.3
[34] crayon_1.5.2 withr_2.5.0 tzdb_0.3.0
[37] dbplyr_2.2.1 later_1.3.0 grid_4.2.2
[40] jsonlite_1.8.3 gtable_0.3.1 lifecycle_1.0.3
[43] DBI_1.1.3 git2r_0.30.1 magrittr_2.0.3
[46] scales_1.2.1 cli_3.6.1 stringi_1.7.8
[49] cachem_1.0.6 farver_2.1.1 fs_1.5.2
[52] promises_1.2.0.1 xml2_1.3.3 bslib_0.4.1
[55] ellipsis_0.3.2 generics_0.1.3 vctrs_0.6.4
[58] tools_4.2.2 glue_1.6.2 hms_1.1.2
[61] fastmap_1.1.0 yaml_2.3.6 colorspace_2.0-3
[64] gargle_1.2.1 rvest_1.0.3 knitr_1.41
[67] haven_2.5.1 sass_0.4.4