Extreme Core-Temperature Profiles
Pasqualina Vonlanthen, David Stappard & Jens Daniel Müller
26 April, 2024
Last updated: 2024-04-26
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bgc_argo_r_argodata/analysis/
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| 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 |
| Rmd | ce19a66 | ds2n19 | 2023-10-04 | Revised version of OceanSODA product -v2023 |
| 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
Compare Argo depth profiles of normal core-temperature and of extreme core-temperature, as identified in the surface OceanSODA data product, in extreme_temp.Rmd
Dependencies
temp_core_va.rds - core preprocessed folder, created by temp_core_align_climatology.
temp_anomaly_va.rds - core preprocessed folder, created by temp_core_align_climatology.
OceanSODA_SST_anomaly_field_01.rds (or _02.rds) - bgc preprocessed folder, extreme_temp.
theme_set(theme_bw())
HNL_colors <- c("H" = "#b2182b",
"N" = "#636363",
"L" = "#2166ac")
HNL_colors_map <- c('H' = 'red3',
'N' = 'transparent',
'L' = 'blue3')
# opt_min_profile_range
# profiles with profile_range >= opt_min_profile_range will be selected 1 = profiles of at least 600m, 2 = profiles of at least 1200m, 3 = profiles of at least 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 <- 2Load data
path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_argo_core <- '/nfs/kryo/work/updata/core_argo_r_argodata_2024-03-13'
path_argo_core_preprocessed <- paste0(path_argo_core, "/preprocessed_core_data")
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_updata <- '/nfs/kryo/work/updata'
path_argo_clim_temp <- paste0(path_updata, "/argo_climatology/temperature")
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")nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))
# 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))
# load validated and vertically aligned temp profiles,
full_argo <-
read_rds(file = paste0(path_argo_core_preprocessed, "/temp_core_va.rds")) %>%
filter(profile_range >= opt_min_profile_range) %>%
mutate(date = ymd(format(date, "%Y-%m-15")))
# # load in core-temperature data with profile QC flags of A and B
# full_argo <- read_rds(file = paste0(path_argo_core_preprocessed, "/core_temp_flag_A.rds"))
#
# full_argo <- full_argo %>%
# mutate(year = year(date),
# month = month(date)) %>%
# mutate(date = ymd(format(date, '%Y-%m-15')))
# OceanSODA extremes detected
if (opt_extreme_determination == 1){
OceanSODA_temp_SO_extreme_grid <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_01.rds"))
} else if (opt_extreme_determination == 2){
OceanSODA_temp_SO_extreme_grid <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field_02.rds"))
}
# base map for plotting
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))Core-Argo Grid Reduction
# Note: While reducing lon x lat grid,
# we keep the original number of observations
full_argo_2x2 <- full_argo %>%
mutate(
lat_raw = lat,
lon_raw = lon,
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))) # re-grid to 2x2Join OceanSODA anomaly field
# revert OceanSODA to regular 1x1 grid
OceanSODA_temp_SO_extreme_grid <- OceanSODA_temp_SO_extreme_grid %>%
select(-c(lon, lat)) %>%
rename(OceanSODA_temp = temperature,
lon = lon_raw,
lat = lat_raw) %>%
filter(year >=2013)
# 925 056 obs
# combine the argo profile data to the surface extreme data
profile_temp_extreme <- inner_join(
full_argo %>%
select(c(year, month, date, lon, lat, depth,
temp,
file_id)), # 567 327 obs
OceanSODA_temp_SO_extreme_grid %>%
select(c(year, month, date, lon, lat,
OceanSODA_temp, temp_extreme,
clim_temp, clim_diff,
basin_AIP, biome_name)))
# profile_temp_extreme <- profile_temp_extreme %>%
# unite('platform_cycle', platform_number:cycle_number, sep = '_', remove = FALSE)Location of Core-Temperature Profiles
OceanSODA_temp_SO_extreme_grid %>%
group_split(month) %>%
# head(1) %>%
map(
~ map +
geom_tile(
data = .x,
aes(x = lon,
y = lat,
fill = temp_extreme),
alpha = 0.5
) +
scale_fill_manual(values = HNL_colors_map) +
new_scale_fill() +
geom_tile(
data = profile_temp_extreme %>%
distinct(lon, lat, file_id, year, month),
aes(
x = lon,
y = lat,
fill = 'argo\nprofiles',
height = 1,
width = 1
),
alpha = 0.5
) +
scale_fill_manual(values = "springgreen4",
name = "") +
facet_wrap(~ year, ncol = 1) +
lims(y = c(-85, -30)) +
labs(title = paste('month:', unique(.x$month))
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
Plot profiles
Argo profiles plotted according to the surface OceanSODA temperature
L profiles correspond to a low surface temperature event, as recorded in OceanSODA
H profiles correspond to an event of high surface temperature, as recorded in OceanSODA
N profiles correspond to normal surface OceanSODA temperature
Raw
By Mayot biomes
profile_temp_extreme %>%
group_split(biome_name, basin_AIP, year) %>%
head(6) %>%
map(
~ ggplot() +
geom_path(data = .x %>% filter(temp_extreme == 'N'),
aes(x = temp,
y = depth,
group = file_id,
col = temp_extreme),
linewidth = 0.3) +
geom_path(data = .x %>% filter(temp_extreme == 'H' | temp_extreme == 'L'),
aes(x = temp,
y = depth,
group = file_id,
col = temp_extreme),
linewidth = 0.5)+
scale_y_reverse() +
scale_color_manual(values = HNL_colors) +
facet_wrap(~ month, ncol = 6) +
labs(
x = 'Argo temperature (ºC)',
y = 'depth (m)',
title = paste(
unique(.x$basin_AIP),
"|",
unique(.x$year),
"| biome:",
unique(.x$biome_name)
),
col = 'OceanSODA temp \nanomaly'
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
Atl, STSS biome, Oct17
# Temperature extreme:
# Atlantic biome 1, 2018, months 2 and 3
OceanSODA_temp_SO_extreme_grid_2017 <- OceanSODA_temp_SO_extreme_grid %>%
filter(date == '2017-10-15')
map+
geom_tile(data = OceanSODA_temp_SO_extreme_grid_2017,
aes(x = lon,
y = lat,
fill = temp_extreme))+
scale_fill_manual(values = HNL_colors_map)+
labs(title = 'October 2017',
fill = 'OceanSODA SST \nextreme')
profile_temp_Atl_2017 <- profile_temp_extreme %>%
filter(date == '2017-10-15',
basin_AIP == 'Atlantic',
biome_name == 'STSS')
profile_temp_Atl_2017 %>%
ggplot(aes(x = temp,
y = depth,
group = file_id,
col = temp_extreme))+
geom_path(data = profile_temp_Atl_2017 %>% filter(temp_extreme == 'N'),
aes(x = temp,
y = depth,
group = file_id,
col = temp_extreme),
linewidth = 0.3)+
geom_path(data = profile_temp_Atl_2017 %>% filter(temp_extreme == 'H'| temp_extreme == 'L'),
aes(x = temp,
y = depth,
group = file_id,
col = temp_extreme),
linewidth = 0.5)+
scale_y_reverse()+
scale_color_manual(values = HNL_colors)+
labs(title = 'Atlantic, STSS biome, October 2017',
col = 'OceanSODA SST\nextreme',
x = 'Argo temperature (ºC)')
rm(profile_temp_Atl_2017, OceanSODA_temp_SO_extreme_grid_2017)Averaged profiles
# cut depth levels at 10, 20, .... etc m
# add seasons
# Dec, Jan, Feb <- summer
# Mar, Apr, May <- autumn
# Jun, Jul, Aug <- winter
# Sep, Oct, Nov <- spring
profile_temp_extreme <- profile_temp_extreme %>%
# mutate(
# depth = Hmisc::cut2(
# depth,
# cuts = c(10, 20, 30, 50, 70, 100, 300, 500, 800, 1000, 1500, 2000, 2500),
# levels.mean = TRUE,
# digits = 3
# ),
# depth = as.numeric(as.character(depth))
# ) %>%
mutate(
season = case_when(
between(month, 3, 5) ~ 'autumn',
between(month, 6, 8) ~ 'winter',
between(month, 9, 11) ~ 'spring',
month == 12 | 1 | 2 ~ 'summer'
),
season_order = case_when(
between(month, 3, 5) ~ 2,
between(month, 6, 8) ~ 3,
between(month, 9, 11) ~ 4,
month == 12 | 1 | 2 ~ 1
),
.after = date
)Overall mean
profile_temp_extreme_mean <- profile_temp_extreme %>%
group_by(temp_extreme, depth) %>%
summarise(temp_mean = mean(temp, na.rm = TRUE),
temp_std = sd(temp, na.rm = TRUE)) %>%
ungroup()
profile_temp_extreme_mean %>%
arrange(depth) %>%
ggplot(aes(y = depth)) +
geom_ribbon(aes(xmin = temp_mean - temp_std,
xmax = temp_mean + temp_std,
fill = temp_extreme),
alpha = 0.2)+
geom_path(aes(x = temp_mean,
col = temp_extreme))+
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(title = "Overall mean",
col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'mean Argo temperature (ºC)') +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))
rm(profile_temp_extreme_mean)Number of profiles
profile_temp_count_mean <- profile_temp_extreme %>%
distinct(temp_extreme, file_id) %>%
count(temp_extreme)
profile_temp_count_mean %>%
ggplot(aes(x = temp_extreme, y = n, fill = temp_extreme))+
geom_col(width = 0.5)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = 'Number of profiles')
# rm(profile_temp_count_mean)Surface Core-Argo temperature vs surface OceanSODA temperature (20 m)
# calculate surface-mean argo SST, for each profile
surface_temp_mean <- profile_temp_extreme %>%
filter(depth <= 20) %>%
group_by(temp_extreme, file_id) %>%
summarise(argo_surf_temp = mean(temp, na.rm = TRUE),
OceanSODA_surf_temp = mean(OceanSODA_temp, na.rm = TRUE))
surface_temp_mean %>%
group_by(temp_extreme) %>%
group_split() %>%
# head(1) %>%
map(
~ggplot(data = .x, aes(x = OceanSODA_surf_temp,
y = argo_surf_temp))+
geom_bin2d(data = .x, aes(x = OceanSODA_surf_temp,
y = argo_surf_temp), linewidth = 0.3, bins = 60) +
scale_fill_viridis_c()+
geom_abline(slope = 1, intercept = 0)+
coord_fixed(ratio = 1,
xlim = c(-3, 28),
ylim = c(-3, 28))+
labs(title = paste('temp extreme:', unique(.x$temp_extreme)),
x = 'OceanSODA temp',
y = 'Argo temp')
)[[1]]
[[2]]
[[3]]
rm(surface_temp_mean)Season x Mayot biome
profile_temp_extreme_biome <- profile_temp_extreme %>%
group_by(season_order, biome_name, temp_extreme, depth) %>%
summarise(temp_biome = mean(temp, na.rm = TRUE),
temp_std_biome = sd(temp, na.rm = TRUE)) %>%
ungroup()
facet_label <- as_labeller(c("1"="summer",
"2"="autumn",
"3"="winter",
"4"="spring",
"ICE" = "ICE",
"SPSS" = "SPSS",
"STSS" = "STSS",
"Atlantic" = "Atlantic",
"Indian" = "Indian",
"Pacific" = "Pacific"
))
profile_temp_extreme_biome %>%
ggplot(aes(
x = temp_biome,
y = depth,
group = temp_extreme,
col = temp_extreme
)) +
geom_ribbon(aes(xmax = temp_biome + temp_std_biome,
xmin = temp_biome - temp_std_biome,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors)+
labs(col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'biome mean Argo temperature (ºC)') +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
lims(x = c(-3, 18))+
facet_grid(season_order ~ biome_name, labeller = facet_label)
rm(profile_temp_extreme_biome)Number of profiles per season per Mayot biome
profile_temp_count_biome <- profile_temp_extreme %>%
distinct(season_order, biome_name, temp_extreme, file_id) %>%
group_by(season_order, biome_name, temp_extreme) %>%
count(temp_extreme)
profile_temp_count_biome %>%
ggplot(aes(x = temp_extreme, y = n, fill = temp_extreme))+
geom_col(width = 0.5)+
facet_grid(season_order ~ biome_name, labeller = facet_label)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = 'Number of profiles season x Mayot biome')
# rm(profile_temp_count_biome)Surface Core-Argo temp vs surface OceanSODA temp season x Mayot biome (20 m)
surface_temp_biome <- profile_temp_extreme %>%
filter(depth <= 20) %>%
group_by(season_order, biome_name, temp_extreme, file_id) %>%
summarise(argo_surf_temp = mean(temp, na.rm=TRUE),
OceanSODA_surf_temp = mean(OceanSODA_temp, na.rm = TRUE))
surface_temp_biome %>%
group_by(temp_extreme) %>%
group_split(temp_extreme) %>%
map(
~ggplot(data = .x, aes(x = OceanSODA_surf_temp,
y = argo_surf_temp))+
geom_bin2d(data = .x, aes(x = OceanSODA_surf_temp,
y = argo_surf_temp)) +
scale_fill_viridis_c()+
geom_abline(slope = 1, intercept = 0)+
coord_fixed(ratio = 1,
xlim = c(-3, 25),
ylim = c(-3, 25))+
facet_grid(season_order~biome_name, labeller = facet_label) +
labs(title = paste( 'Temp extreme:', unique(.x$temp_extreme)),
x = 'OceanSODA temp',
y = 'Argo temp')
)[[1]]
[[2]]
[[3]]
rm(surface_temp_biome)Season x basin
profile_temp_extreme_basin <- profile_temp_extreme %>%
group_by(season_order, basin_AIP, temp_extreme, depth) %>%
summarise(temp_basin = mean(temp, na.rm = TRUE),
temp_basin_std = sd(temp, na.rm = TRUE)) %>%
ungroup()
profile_temp_extreme_basin %>%
ggplot(aes(x = temp_basin,
y = depth,
group = temp_extreme,
col = temp_extreme))+
geom_ribbon(aes(xmin = temp_basin - temp_basin_std,
xmax = temp_basin + temp_basin_std,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_path()+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
labs(col = 'OceanSODA\ntemp anomaly\n(mean ± st dev)',
fill = 'OceanSODA\ntemp anomaly\n(mean ± st dev)',
y = 'depth (m)',
x = 'basin-mean Argo temperature (ªC)')+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
facet_grid(season_order~basin_AIP, labeller = facet_label)
rm(profile_temp_extreme_basin)Number of profiles season x basin
profile_temp_count_basin <- profile_temp_extreme %>%
distinct(season_order, basin_AIP, temp_extreme, file_id) %>%
group_by(season_order, basin_AIP, temp_extreme) %>%
count(temp_extreme)
profile_temp_count_basin %>%
ggplot(aes(x = temp_extreme, y = n, fill = temp_extreme))+
geom_col(width = 0.5)+
facet_grid(season_order~basin_AIP, labeller = facet_label)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = 'Number of profiles season x basin')
# rm(profile_temp_count_basin)Surface Argo temperature vs surface OceanSODA temperature (20 m) season x basin
# calculate surface-mean argo pH to compare against OceanSODA surface pH (one value)
surface_temp_basin <- profile_temp_extreme %>%
filter(depth <= 20) %>%
group_by(season_order, basin_AIP, temp_extreme, file_id) %>%
summarise(surf_argo_temp = mean(temp, na.rm=TRUE),
surf_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE))
surface_temp_basin %>%
group_by(temp_extreme) %>%
group_split(temp_extreme) %>%
map(
~ggplot(data = .x, aes(x = surf_OceanSODA_temp,
y = surf_argo_temp))+
geom_bin2d(data = .x, aes(x = surf_OceanSODA_temp,
y = surf_argo_temp)) +
scale_fill_viridis_c()+
geom_abline(slope = 1, intercept = 0)+
coord_fixed(ratio = 1,
xlim = c(-3, 25),
ylim = c(-3, 25))+
facet_grid(season_order~basin_AIP, labeller = facet_label) +
labs(title = paste('Temp extreme:', unique(.x$temp_extreme)),
x = 'OceanSODA temp',
y = 'Argo temp')
)[[1]]
[[2]]
[[3]]
rm(surface_temp_basin)Season x Mayot biome x Basin
profile_temp_extreme_season <- profile_temp_extreme %>%
group_by(season_order, season, biome_name, basin_AIP, temp_extreme, depth) %>%
summarise(temp_mean = mean(temp, na.rm = TRUE),
temp_std = sd(temp, na.rm = TRUE)) %>%
ungroup()
profile_temp_extreme_season %>%
arrange(depth) %>%
group_split(season_order) %>%
# head(1) %>%
map(
~ ggplot(
data = .x,
aes(x = temp_mean,
y = depth,
group = temp_extreme,
col = temp_extreme)) +
geom_ribbon(aes(xmax = temp_mean + temp_std,
xmin = temp_mean - temp_std,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors) +
labs(title = paste("season:", unique(.x$season)),
col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'mean Argo temperature (ºC)') +
scale_y_continuous(
trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))
) +
facet_grid(basin_AIP ~ biome_name)
)[[1]]
[[2]]
[[3]]
[[4]]
Number of profiles season x Mayot biome x basin
profile_temp_count_season <- profile_temp_extreme %>%
distinct(season_order, season, biome_name, basin_AIP,
temp_extreme, file_id) %>%
group_by(season_order, season, biome_name, basin_AIP, temp_extreme) %>%
count(temp_extreme)
profile_temp_count_season %>%
group_by(season_order) %>%
group_split(season_order) %>%
map(
~ggplot()+
geom_col(data =.x,
aes(x = temp_extreme,
y = n,
fill = temp_extreme),
width = 0.5)+
facet_grid(basin_AIP ~ biome_name)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = paste('season:', unique(.x$season)))
)[[1]]
[[2]]
[[3]]
[[4]]
# rm(profile_temp_count_season)Surface Core-Argo temperature vs surface OceanSODA temperature (20m) in each season, Mayot biome, basin
# calculate surface-mean argo pH, for each season x biome x basin x ph extreme
surface_temp_season <- profile_temp_extreme %>%
filter(depth <= 20) %>%
group_by(season_order,
season,
basin_AIP,
biome_name,
temp_extreme,
file_id) %>%
summarise(surf_argo_temp = mean(temp, na.rm=TRUE),
surf_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE))
surface_temp_season %>%
group_by(season_order, temp_extreme) %>%
group_split(season_order, temp_extreme) %>%
map(
~ggplot(data = .x, aes(x = surf_OceanSODA_temp,
y = surf_argo_temp))+
geom_bin2d(data = .x, aes(x = surf_OceanSODA_temp,
y = surf_argo_temp)) +
scale_fill_viridis_c()+
geom_abline(slope = 1, intercept = 0)+
coord_fixed(ratio = 1,
xlim = c(-3, 25),
ylim = c(-3, 25))+
facet_grid(basin_AIP ~ biome_name) +
labs(title = paste('season:', unique(.x$season),
'| temp extreme:', unique(.x$temp_extreme)),
x = 'OceanSODA temp',
y = 'Argo temp')
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
rm(surface_temp_season)Atlantic, SPSS biome, winter
profile_temp_extreme_season %>%
filter(basin_AIP == 'Atlantic',
biome_name == 'SPSS',
season == 'winter') %>%
arrange(depth) %>%
ggplot(aes(x = temp_mean,
y = depth,
group = temp_extreme,
col = temp_extreme)) +
geom_ribbon(aes(xmax = temp_mean + temp_std,
xmin = temp_mean - temp_std,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors) +
labs(title = 'Atlantic basin, SPSS biome, winter',
col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'mean Argo temperature (ºC)') +
scale_y_continuous(
trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) 
Atlantic, STSS biome, spring
profile_temp_extreme_season %>%
filter(basin_AIP == 'Atlantic',
biome_name == 'STSS',
season == 'spring') %>%
arrange(depth) %>%
ggplot(aes(x = temp_mean,
y = depth,
group = temp_extreme,
col = temp_extreme)) +
geom_ribbon(aes(xmax = temp_mean + temp_std,
xmin = temp_mean - temp_std,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_path() +
scale_color_manual(values = HNL_colors) +
scale_fill_manual(values = HNL_colors) +
labs(title = 'Atlantic basin, STSS biome, spring',
col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'mean Argo temperature (ºC)') +
scale_y_continuous(
trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) 
rm(profile_temp_extreme_season)Remove climatology
Plot the H/L/N profiles as anomalies relative to the CSIO-MNR Argo temperature climatology
Argo profiles
# profile_temp_extreme_binned <- profile_temp_extreme %>%
# group_by(lon, lat, year, month, file_id,
# biome_name, basin_AIP, temp_extreme,
# depth) %>%
# summarize(temp_adjusted_binned = mean(temp_adjusted, na.rm = TRUE)) %>%
# ungroup()ARGO climatology
# boa_temp_clim <- read_rds(file = paste0(path_argo_preprocessed, '/boa_temp_clim.rds'))
#
# # compatibility with profile_temp_extreme_jan
# boa_temp_clim_SO <- boa_temp_clim %>%
# filter(lat <= -30) %>%
# mutate(depth_boa = depth)
#
# # grid average climatological temp into the argo depth bins
# boa_temp_clim_SO <- boa_temp_clim_SO %>%
# mutate(
# depth = cut(
# depth_boa,
# breaks = c(0, 10, 20, 30, 50, 70, 100, 300, 500, 800, 1000, 1500, 2000),
# include.lowest = TRUE,
# labels = as.factor(unique(profile_temp_extreme$depth))[1:12]
# ),
# depth = as.numeric(as.character(depth))
# )
#
#
# # calculate mean climatological pH per depth bin
# boa_temp_clim_SO_binned <- boa_temp_clim_SO %>%
# group_by(lon, lat, depth, month) %>%
# summarise(clim_temp_binned = mean(clim_temp, na.rm = TRUE)) %>%
# ungroup()
#
#
# # join climatology and ARGO profiles
#
# remove_clim <- inner_join(profile_temp_extreme_binned,
# boa_temp_clim_SO_binned)
remove_clim <-
read_rds(file = paste0(path_argo_core_preprocessed, "/temp_anomaly_va.rds")) %>%
filter(profile_range >= opt_min_profile_range) %>%
mutate(date = ymd(format(date, "%Y-%m-15")))
remove_clim <- inner_join(
remove_clim %>%
select(
file_id,
year,
month,
date,
lon,
lat,
depth,
temp,
clim_temp,
anomaly
),
OceanSODA_temp_SO_extreme_grid %>%
select(
year,
month,
date,
lon,
lat,
OceanSODA_temp,
temp_extreme,
biome_name,
basin_AIP
)
)
remove_clim <- remove_clim %>%
mutate(
season = case_when(
between(month, 3, 5) ~ 'autumn',
between(month, 6, 8) ~ 'winter',
between(month, 9, 11) ~ 'spring',
month == 12 | 1 | 2 ~ 'summer'
),
season_order = case_when(
between(month, 3, 5) ~ 2,
between(month, 6, 8) ~ 3,
between(month, 9, 11) ~ 4,
month == 12 | 1 | 2 ~ 1
),
.after = date
) Profiles
Points are the climatological temperature, lines are the depth-binned Argo profiles colored by H/N/L classification
Absolute
remove_clim %>%
group_split(biome_name, basin_AIP, year) %>%
head(6) %>%
map(
~ ggplot() +
geom_path(
data = .x %>%
filter(temp_extreme == 'N'),
aes(
x = temp,
y = depth,
group = file_id,
col = temp_extreme
),
size = 0.3
) +
geom_path(
data = .x %>%
filter(temp_extreme == 'H' | temp_extreme == 'L'),
aes(
x = temp,
y = depth,
group = file_id,
col = temp_extreme
),
size = 0.5
) +
geom_point(
data = .x,
aes(x = clim_temp,
y = depth,
col = temp_extreme),
size = 0.5
) +
scale_y_reverse() +
scale_color_manual(values = HNL_colors) +
labs(
x = 'Argo temperature (ºC)',
y = 'depth (m)',
title = paste(
"Biome:",
unique(.x$biome_name),
"| basin:",
unique(.x$basin_AIP),
" | ",
unique(.x$year)
),
col = 'OceanSODA temp \nanomaly'
)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
# calculate the difference between the binned climatological argo and in-situ argo for each depth level and grid cell
# remove_clim <- remove_clim %>%
# mutate(argo_temp_anomaly = temp_adjusted_binned - clim_temp_binned,
# season = case_when(between(month, 3, 5) ~ 'autumn',
# between(month, 6, 8) ~ 'winter',
# between(month, 9, 11) ~ 'spring',
# month == 12 | 1 | 2 ~ 'summer'),
# season_order = case_when(between(month, 3, 5) ~ 2,
# between(month, 6, 8) ~ 3,
# between(month, 9, 11) ~ 4,
# month == 12 | 1 | 2 ~ 1))Anomaly
remove_clim %>%
group_split(month) %>%
#head(6) %>%
map(
~ggplot()+
geom_path(data = .x %>% filter(temp_extreme == 'N'),
aes(x = anomaly,
y = depth,
group = file_id,
col = temp_extreme),
size = 0.2)+
geom_path(data = .x %>% filter(temp_extreme == 'H'| temp_extreme == 'L'),
aes(x = anomaly,
y = depth,
group = file_id,
col = temp_extreme),
size = 0.3)+
geom_vline(xintercept = 0)+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
facet_grid(basin_AIP~biome_name)+
labs(title = paste0('month: ', unique(.x$month)))
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
Overall mean anomaly
remove_clim_overall_mean <- remove_clim %>%
group_by(temp_extreme, depth) %>%
summarise(temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
remove_clim_overall_mean %>%
ggplot()+
geom_path(aes(x = temp_anomaly_mean,
y = depth,
group = temp_extreme,
col = temp_extreme))+
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
y = depth,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_vline(xintercept = 0)+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
# geom_text_repel(data = profile_temp_count_mean,
# aes(x = 1,
# y = 1500,
# label = paste0(n),
# col = temp_extreme),
# size = 7,
# segment.color = 'transparent')+
geom_text(data = profile_temp_count_mean[2,],
aes(x = -4.0,
y = 1200,
label = paste0(n),
col = temp_extreme),
size = 6)+
geom_text(data = profile_temp_count_mean[1,],
aes(x = -4.0,
y = 1400,
label = paste0(n),
col = temp_extreme),
size = 6)+
geom_text(data = profile_temp_count_mean[3,],
aes(x = -4.0,
y = 1600,
label = paste0(n),
col = temp_extreme),
size = 6)+
coord_cartesian(xlim = c(-4.5, 4.5))+
scale_x_continuous(breaks = c(-4, -2, 0, 2, 4))+
labs(title = 'Overall mean anomaly profiles')
rm(remove_clim_overall_mean, profile_temp_count_mean)Biome x season mean anomaly
remove_clim_biome_mean <- remove_clim %>%
group_by(temp_extreme, depth, season_order, season, biome_name) %>%
summarise(temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
remove_clim_biome_mean %>%
ggplot(aes(x = temp_anomaly_mean,
y = depth,
group = temp_extreme,
col = temp_extreme))+
geom_path()+
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_vline(xintercept = 0)+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
scale_fill_manual(values = HNL_colors)+
scale_color_manual(values = HNL_colors)+
labs(title = 'Biome-mean anomaly profiles')+
# geom_text_repel(data = profile_temp_count_biome,
# aes(x = 3,
# y = 1500,
# label = paste0(n),
# col = temp_extreme),
# size = 4,
# segment.color = 'transparent')+
geom_text(data = profile_temp_count_biome %>% filter (temp_extreme == 'N'),
aes(x = -3.5,
y = 800,
label = paste0(n),
col = temp_extreme),
size = 4)+
geom_text(data = profile_temp_count_biome %>% filter (temp_extreme == 'H'),
aes(x = -3.5,
y = 1200,
label = paste0(n),
col = temp_extreme),
size = 4)+
geom_text(data = profile_temp_count_biome %>% filter (temp_extreme == 'L'),
aes(x = -3.5,
y = 1600,
label = paste0(n),
col = temp_extreme),
size = 4)+
coord_cartesian(xlim = c(-4.5, 4.5))+
scale_x_continuous(breaks = c(-4, -2, 0, 2, 4))+
facet_grid(season_order~biome_name, labeller = facet_label)
| Version | Author | Date |
|---|---|---|
| c0a5b90 | mlarriere | 2024-04-01 |
| f9de50e | ds2n19 | 2024-01-01 |
| cf5dd20 | ds2n19 | 2023-12-04 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 80c16c2 | ds2n19 | 2023-11-15 |
| 7004f76 | ds2n19 | 2023-10-17 |
| f9c091b | ds2n19 | 2023-10-17 |
| d0535b5 | ds2n19 | 2023-10-17 |
| c16000b | ds2n19 | 2023-10-12 |
| 7b3d8c5 | pasqualina-vonlanthendinenna | 2022-08-29 |
rm(remove_clim_biome_mean, profile_temp_count_biome)Basin x season mean anomaly
remove_clim_basin_mean <- remove_clim %>%
group_by(basin_AIP, temp_extreme, depth, season_order, season) %>%
summarise(temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
remove_clim_basin_mean %>%
ggplot(aes(x = temp_anomaly_mean,
y = depth,
group = temp_extreme,
col = temp_extreme))+
geom_path()+
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_vline(xintercept = 0)+
facet_grid(season_order~basin_AIP, labeller = facet_label)+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
# geom_text_repel(data = profile_temp_count_basin,
# aes(x = 2,
# y = 1500,
# label = paste0(n),
# col = temp_extreme),
# size = 4,
# segment.color = 'transparent')+
geom_text(data = profile_temp_count_basin %>% filter (temp_extreme == 'N'),
aes(x = -3.5,
y = 800,
label = paste0(n),
col = temp_extreme),
size = 4)+
geom_text(data = profile_temp_count_basin %>% filter (temp_extreme == 'H'),
aes(x = -3.5,
y = 1200,
label = paste0(n),
col = temp_extreme),
size = 4)+
geom_text(data = profile_temp_count_basin %>% filter (temp_extreme == 'L'),
aes(x = -3.5,
y = 1600,
label = paste0(n),
col = temp_extreme),
size = 4)+
coord_cartesian(xlim = c(-4.5, 4.5))+
scale_x_continuous(breaks = c(-4, -2, 0, 2, 4))+
labs(title = 'Basin-mean anomaly profiles')
| Version | Author | Date |
|---|---|---|
| c0a5b90 | mlarriere | 2024-04-01 |
| f9de50e | ds2n19 | 2024-01-01 |
| cf5dd20 | ds2n19 | 2023-12-04 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 80c16c2 | ds2n19 | 2023-11-15 |
| 7004f76 | ds2n19 | 2023-10-17 |
| f9c091b | ds2n19 | 2023-10-17 |
| d0535b5 | ds2n19 | 2023-10-17 |
| c16000b | ds2n19 | 2023-10-12 |
| 7b3d8c5 | pasqualina-vonlanthendinenna | 2022-08-29 |
rm(remove_clim_basin_mean, profile_temp_count_basin)Basin x biome x season mean anomaly
remove_clim_basin_biome_mean <- remove_clim %>%
group_by(basin_AIP, biome_name, temp_extreme, season_order, season, depth) %>%
summarise(temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
remove_clim_basin_biome_mean %>%
group_by(season_order) %>%
group_split(season_order) %>%
map(
~ggplot(data = .x,
aes(x = temp_anomaly_mean,
y = depth,
group = temp_extreme,
col = temp_extreme))+
geom_path()+
geom_ribbon(data = .x,
aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
group = temp_extreme,
fill = temp_extreme),
col = NA,
alpha = 0.2)+
geom_vline(xintercept = 0)+
facet_grid(basin_AIP~biome_name)+
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
scale_color_manual(values = HNL_colors)+
scale_fill_manual(values = HNL_colors)+
# geom_text_repel(data = profile_temp_count_season,
# aes(x = 1,
# y = 1400,
# label = paste0(n),
# col = temp_extreme,
# group = temp_extreme),
# size = 4,
# segment.color = 'transparent')+
geom_text(data = profile_temp_count_season %>% filter (temp_extreme == 'N' & season == unique(.x$season)),
aes(x = -3.5,
y = 800,
label = paste0(n),
col = temp_extreme),
size = 4)+
geom_text(data = profile_temp_count_season %>% filter (temp_extreme == 'H' & season == unique(.x$season)),
aes(x = -3.5,
y = 1200,
label = paste0(n),
col = temp_extreme),
size = 4)+
geom_text(data = profile_temp_count_season %>% filter (temp_extreme == 'L' & season == unique(.x$season)),
aes(x = -3.5,
y = 1600,
label = paste0(n),
col = temp_extreme),
size = 4)+
coord_cartesian(xlim = c(-4.5, 4.5))+
scale_x_continuous(breaks = c(-4, -2, 0, 2, 4))+
labs(title = paste0('biome-basin mean anomaly profiles ', unique(.x$season)))
)[[1]]
[[2]]
[[3]]
[[4]]
rm(remove_clim_basin_biome_mean, profile_temp_count_season)
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 ggrepel_0.9.2 oce_1.7-10 gsw_1.1-1
[5] ggforce_0.4.1 metR_0.13.0 scico_1.3.1 ggOceanMaps_1.3.4
[9] ggspatial_1.1.7 broom_1.0.5 lubridate_1.9.0 timechange_0.1.1
[13] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
[17] readr_2.1.3 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[21] 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] httr_1.4.4 rprojroot_2.0.3 tools_4.2.2
[7] backports_1.4.1 bslib_0.4.1 utf8_1.2.2
[10] R6_2.5.1 KernSmooth_2.23-20 rgeos_0.5-9
[13] DBI_1.2.2 colorspace_2.0-3 raster_3.6-11
[16] withr_2.5.0 sp_1.5-1 tidyselect_1.2.0
[19] bit_4.0.5 compiler_4.2.2 git2r_0.30.1
[22] cli_3.6.1 rvest_1.0.3 xml2_1.3.3
[25] labeling_0.4.2 sass_0.4.4 checkmate_2.1.0
[28] scales_1.2.1 classInt_0.4-8 proxy_0.4-27
[31] digest_0.6.30 rmarkdown_2.18 pkgconfig_2.0.3
[34] htmltools_0.5.8.1 highr_0.9 dbplyr_2.2.1
[37] fastmap_1.1.0 rlang_1.1.1 readxl_1.4.1
[40] rstudioapi_0.15.0 farver_2.1.1 jquerylib_0.1.4
[43] generics_0.1.3 jsonlite_1.8.3 vroom_1.6.0
[46] googlesheets4_1.0.1 magrittr_2.0.3 Rcpp_1.0.10
[49] munsell_0.5.0 fansi_1.0.3 lifecycle_1.0.3
[52] terra_1.7-65 stringi_1.7.8 whisker_0.4
[55] yaml_2.3.6 MASS_7.3-58.1 grid_4.2.2
[58] parallel_4.2.2 promises_1.2.0.1 crayon_1.5.2
[61] lattice_0.20-45 haven_2.5.1 hms_1.1.2
[64] knitr_1.41 pillar_1.9.0 codetools_0.2-18
[67] reprex_2.0.2 glue_1.6.2 evaluate_0.18
[70] data.table_1.14.6 modelr_0.1.10 tweenr_2.0.2
[73] vctrs_0.6.4 tzdb_0.3.0 httpuv_1.6.6
[76] cellranger_1.1.0 polyclip_1.10-4 gtable_0.3.1
[79] assertthat_0.2.1 cachem_1.0.6 xfun_0.35
[82] e1071_1.7-12 later_1.3.0 viridisLite_0.4.1
[85] class_7.3-20 googledrive_2.0.0 gargle_1.2.1
[88] memoise_2.0.1 workflowr_1.7.0 units_0.8-0
[91] ellipsis_0.3.2