Extreme Core-Temperature Profiles
Pasqualina Vonlanthen & Jens Daniel Müller
29 August, 2022
Last updated: 2022-08-29
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Knit directory: bgc_argo_r_argodata/
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| 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
theme_set(theme_bw())
HNL_colors <- c("H" = "#b2182b",
"N" = "#636363",
"L" = "#2166ac")
HNL_colors_map <- c('H' = 'red3',
'N' = 'transparent',
'L' = 'blue3')Load 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'
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")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 in core-temperature data with profile QC flags of A and B
full_argo <- read_rds(file = paste0(path_argo_core_preprocessed, "/core_merge_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
OceanSODA_temp_SO_extreme_grid <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field.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_adjusted,
platform_number,
cycle_number)), # 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, platform_cycle, 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_adjusted,
y = depth,
group = platform_cycle,
col = temp_extreme),
size = 0.3) +
geom_path(data = .x %>% filter(temp_extreme == 'H' | temp_extreme == 'L'),
aes(x = temp_adjusted,
y = depth,
group = platform_cycle,
col = temp_extreme),
size = 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_adjusted,
y = depth,
group = platform_cycle,
col = temp_extreme))+
geom_path(data = profile_temp_Atl_2017 %>% filter(temp_extreme == 'N'),
aes(x = temp_adjusted,
y = depth,
group = platform_cycle,
col = temp_extreme),
size = 0.3)+
geom_path(data = profile_temp_Atl_2017 %>% filter(temp_extreme == 'H'| temp_extreme == 'L'),
aes(x = temp_adjusted,
y = depth,
group = platform_cycle,
col = temp_extreme),
size = 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'
),
.after = date
)Overall mean
profile_temp_extreme_mean <- profile_temp_extreme %>%
group_by(temp_extreme, depth) %>%
summarise(temp_mean = mean(temp_adjusted, na.rm = TRUE),
temp_std = sd(temp_adjusted, 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, platform_number, cycle_number) %>%
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, platform_number, cycle_number) %>%
summarise(argo_surf_temp = mean(temp_adjusted, 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), size = 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, biome_name, temp_extreme, depth) %>%
summarise(temp_biome = mean(temp_adjusted, na.rm = TRUE),
temp_std_biome = sd(temp_adjusted, na.rm = TRUE)) %>%
ungroup()
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 ~ biome_name)
rm(profile_temp_extreme_biome)Number of profiles per season per Mayot biome
profile_temp_count_biome <- profile_temp_extreme %>%
distinct(season, biome_name, temp_extreme, platform_number, cycle_number) %>%
group_by(season, 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 ~ biome_name)+
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, biome_name, temp_extreme, platform_number, cycle_number) %>%
summarise(argo_surf_temp = mean(temp_adjusted, 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~biome_name) +
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, basin_AIP, temp_extreme, depth) %>%
summarise(temp_basin = mean(temp_adjusted, na.rm = TRUE),
temp_basin_std = sd(temp_adjusted, 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~basin_AIP)
rm(profile_temp_extreme_basin)Number of profiles season x basin
profile_temp_count_basin <- profile_temp_extreme %>%
distinct(season, basin_AIP, temp_extreme, platform_number, cycle_number) %>%
group_by(season, 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~basin_AIP)+
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, basin_AIP, temp_extreme, platform_number, cycle_number) %>%
summarise(surf_argo_temp = mean(temp_adjusted, 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~basin_AIP) +
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, biome_name, basin_AIP, temp_extreme, depth) %>%
summarise(temp_mean = mean(temp_adjusted, na.rm = TRUE),
temp_std = sd(temp_adjusted, na.rm = TRUE)) %>%
ungroup()
profile_temp_extreme_season %>%
arrange(depth) %>%
group_split(season) %>%
# 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$season)),
col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
y = 'depth (m)',
x = 'mean Argo temperature (ºC)') +
scale_y_continuous(
trans = trans_reverser("sqrt"),
breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))
) +
facet_grid(basin_AIP ~ biome_name)
)[[1]]
Number of profiles season x Mayot biome x basin
profile_temp_count_season <- profile_temp_extreme %>%
distinct(season, biome_name, basin_AIP,
temp_extreme, platform_number, cycle_number) %>%
group_by(season, biome_name, basin_AIP, temp_extreme) %>%
count(temp_extreme)
profile_temp_count_season %>%
group_by(season) %>%
group_split(season) %>%
map(
~ggplot()+
geom_col(data =.x,
aes(x = temp_extreme,
y = n,
fill = temp_extreme),
width = 0.5)+
facet_grid(basin_AIP ~ biome_name)+
scale_y_continuous(trans = 'log10')+
labs(y = 'log(number of profiles)',
title = paste('season:', unique(.x$season)))
)[[1]]
# 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,
basin_AIP,
biome_name,
temp_extreme,
platform_number,
cycle_number) %>%
summarise(surf_argo_temp = mean(temp_adjusted, na.rm=TRUE),
surf_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE))
surface_temp_season %>%
group_by(season, temp_extreme) %>%
group_split(season, 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]]
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, platform_cycle,
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$depth))[1:12]
),
depth = as.numeric(as.character(depth))
)
# calculate mean climatological pH per depth bin
boa_temp_clim_SO_binned <- boa_temp_clim_SO %>%
group_by(lon, lat, depth, month) %>%
summarise(clim_temp_binned = mean(clim_temp, na.rm = TRUE)) %>%
ungroup()
# join climatology and ARGO profiles
remove_clim <- inner_join(profile_temp_extreme_binned,
boa_temp_clim_SO_binned)Profiles
Points are the climatological temperature, lines are the depth-binned Argo profiles colored by H/N/L classification
Absolute
remove_clim %>%
group_split(biome_name, basin_AIP, year) %>%
head(6) %>%
map(
~ ggplot() +
geom_path(
data = .x %>%
filter(temp_extreme == 'N'),
aes(
x = temp_adjusted_binned,
y = depth,
group = platform_cycle,
col = temp_extreme
),
size = 0.3
) +
geom_path(
data = .x %>%
filter(temp_extreme == 'H' | temp_extreme == 'L'),
aes(
x = temp_adjusted_binned,
y = depth,
group = platform_cycle,
col = temp_extreme
),
size = 0.5
) +
geom_point(
data = .x,
aes(x = clim_temp_binned,
y = depth,
col = temp_extreme),
size = 0.5
) +
scale_y_reverse() +
scale_color_manual(values = HNL_colors) +
labs(
x = 'Argo temperature (ºC)',
y = 'depth (m)',
title = paste(
"Biome:",
unique(.x$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'))Anomaly
remove_clim %>%
group_split(month) %>%
#head(6) %>%
map(
~ggplot()+
geom_path(data = .x %>% filter(temp_extreme == 'N'),
aes(x = argo_temp_anomaly,
y = depth,
group = platform_cycle,
col = temp_extreme),
size = 0.2)+
geom_path(data = .x %>% filter(temp_extreme == 'H'| temp_extreme == 'L'),
aes(x = argo_temp_anomaly,
y = depth,
group = platform_cycle,
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]]
Overall mean anomaly
remove_clim_overall_mean <- remove_clim %>%
group_by(temp_extreme, depth) %>%
summarise(temp_anomaly_mean = mean(argo_temp_anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(argo_temp_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')+
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, biome_name) %>%
summarise(temp_anomaly_mean = mean(argo_temp_anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(argo_temp_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')+
facet_grid(season~biome_name)
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) %>%
summarise(temp_anomaly_mean = mean(argo_temp_anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(argo_temp_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~basin_AIP)+
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')+
labs(title = 'Basin-mean anomaly profiles')
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, depth) %>%
summarise(temp_anomaly_mean = mean(argo_temp_anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(argo_temp_anomaly, na.rm = TRUE))
remove_clim_basin_biome_mean %>%
group_by(season) %>%
group_split(season) %>%
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')+
labs(title = paste0('biome-basin mean anomaly profiles ', unique(.x$season)))
)[[1]]
rm(remove_clim_basin_biome_mean, profile_temp_count_season)
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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_0.4.5 ggrepel_0.9.1 oce_1.5-0 gsw_1.0-6
[5] ggforce_0.3.3 metR_0.11.0 scico_1.3.0 ggOceanMaps_1.2.6
[9] ggspatial_1.1.5 broom_0.7.11 lubridate_1.8.0 forcats_0.5.1
[13] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[17] tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[21] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ellipsis_0.3.2 class_7.3-20
[4] rgdal_1.5-28 rprojroot_2.0.2 htmlTable_2.4.0
[7] base64enc_0.1-3 fs_1.5.2 rstudioapi_0.13
[10] proxy_0.4-26 farver_2.1.0 bit64_4.0.5
[13] fansi_1.0.2 xml2_1.3.3 codetools_0.2-18
[16] splines_4.1.2 knitr_1.37 polyclip_1.10-0
[19] Formula_1.2-4 jsonlite_1.7.3 cluster_2.1.2
[22] dbplyr_2.1.1 png_0.1-7 rgeos_0.5-9
[25] compiler_4.1.2 httr_1.4.2 backports_1.4.1
[28] Matrix_1.4-0 assertthat_0.2.1 fastmap_1.1.0
[31] cli_3.1.1 later_1.3.0 tweenr_1.0.2
[34] htmltools_0.5.2 tools_4.1.2 gtable_0.3.0
[37] glue_1.6.0 Rcpp_1.0.8 cellranger_1.1.0
[40] jquerylib_0.1.4 raster_3.5-11 vctrs_0.3.8
[43] xfun_0.29 ps_1.6.0 rvest_1.0.2
[46] lifecycle_1.0.1 terra_1.5-12 getPass_0.2-2
[49] MASS_7.3-55 scales_1.1.1 vroom_1.5.7
[52] hms_1.1.1 promises_1.2.0.1 parallel_4.1.2
[55] RColorBrewer_1.1-2 yaml_2.2.1 gridExtra_2.3
[58] sass_0.4.0 rpart_4.1-15 latticeExtra_0.6-29
[61] stringi_1.7.6 highr_0.9 e1071_1.7-9
[64] checkmate_2.0.0 rlang_1.0.2 pkgconfig_2.0.3
[67] evaluate_0.14 lattice_0.20-45 sf_1.0-5
[70] htmlwidgets_1.5.4 labeling_0.4.2 bit_4.0.4
[73] processx_3.5.2 tidyselect_1.1.1 magrittr_2.0.1
[76] R6_2.5.1 generics_0.1.1 Hmisc_4.6-0
[79] DBI_1.1.2 foreign_0.8-82 pillar_1.6.4
[82] haven_2.4.3 whisker_0.4 withr_2.4.3
[85] units_0.7-2 nnet_7.3-17 survival_3.2-13
[88] sp_1.4-6 modelr_0.1.8 crayon_1.4.2
[91] KernSmooth_2.23-20 utf8_1.2.2 tzdb_0.2.0
[94] rmarkdown_2.11 jpeg_0.1-9 grid_4.1.2
[97] readxl_1.3.1 data.table_1.14.2 callr_3.7.0
[100] git2r_0.29.0 reprex_2.0.1 digest_0.6.29
[103] classInt_0.4-3 httpuv_1.6.5 munsell_0.5.0
[106] viridisLite_0.4.0 bslib_0.3.1