Extreme Core-Temperature Profiles

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Last updated: 2023-10-17

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Knit directory: bgc_argo_r_argodata/

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File	Version	Author	Date	Message
Rmd	c218546	ds2n19	2023-10-17	revised order of seasons
html	4b55c43	ds2n19	2023-10-12	Build site.
html	c16000b	ds2n19	2023-10-12	Build site.
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
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

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")

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 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 

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 2x2

Join 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]]

Past versions of map_location_argo_profiles-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of map_location_argo_profiles-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of map_location_argo_profiles-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[4]]

Past versions of map_location_argo_profiles-4.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[5]]

Past versions of map_location_argo_profiles-5.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[6]]

Past versions of map_location_argo_profiles-6.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[7]]

Past versions of map_location_argo_profiles-7.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[8]]

Past versions of map_location_argo_profiles-8.png

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c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[9]]

Past versions of map_location_argo_profiles-9.png

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c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[10]]

Past versions of map_location_argo_profiles-10.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[11]]

Past versions of map_location_argo_profiles-11.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[12]]

Past versions of map_location_argo_profiles-12.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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),
                linewidth = 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),
                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]]

Past versions of temp_profiles_raw_Mayot_biomes-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of temp_profiles_raw_Mayot_biomes-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of temp_profiles_raw_Mayot_biomes-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[4]]

Past versions of temp_profiles_raw_Mayot_biomes-4.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[5]]

Past versions of temp_profiles_raw_Mayot_biomes-5.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[6]]

Past versions of temp_profiles_raw_Mayot_biomes-6.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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')

Past versions of select_specific_temp_profiles_2017-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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),
            linewidth = 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),
            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)')

Past versions of select_specific_temp_profiles_2017-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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_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)))

Past versions of mean_temp_profiles-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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')

Past versions of mean_temp_profile_count-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

# 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]]

Past versions of argo_vs_OceanSODA_temp_mean-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of argo_vs_OceanSODA_temp_mean-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of argo_vs_OceanSODA_temp_mean-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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_adjusted, na.rm = TRUE),
            temp_std_biome = sd(temp_adjusted, na.rm = TRUE)) %>% 
  ungroup()
  
facet_label <- as_labeller(c("1"="summer", "2"="autumn", "3"="winter", "4"="spring"))

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)

Past versions of mean_temp_profiles_season_new_biomes-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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, platform_number, cycle_number) %>%
  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')

Past versions of count_temp_profiles_season_new_biome-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

# 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, 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_order~biome_name, labeller = facet_label) +
    labs(title = paste( 'Temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_new_biome-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of argo_vs_OceanSODA_temp_new_biome-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of argo_vs_OceanSODA_temp_new_biome-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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_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_order~basin_AIP, labeller = facet_label)

Past versions of mean_basin_temp_profiles-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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, platform_number, cycle_number) %>% 
  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')

Past versions of count_basin_temp_profiles-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

# 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, 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_order~basin_AIP, labeller = facet_label) +
    labs(title = paste('Temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_basin-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of argo_vs_OceanSODA_temp_basin-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of argo_vs_OceanSODA_temp_basin-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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_adjusted, na.rm = TRUE),
            temp_std = sd(temp_adjusted, 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]]

Past versions of temp_profiles_new_biomes_basin-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of temp_profiles_new_biomes_basin-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[3]]

Past versions of temp_profiles_new_biomes_basin-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[4]]

Past versions of temp_profiles_new_biomes_basin-4.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

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, platform_number, cycle_number) %>% 
  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]]

Past versions of count_number_of_profiles_season_new_biomes-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of count_number_of_profiles_season_new_biomes-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[3]]

Past versions of count_number_of_profiles_season_new_biomes-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[4]]

Past versions of count_number_of_profiles_season_new_biomes-4.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

# 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,  
           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_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]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[4]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-4.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[5]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-5.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[6]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-6.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[7]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-7.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[8]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-8.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[9]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-9.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[10]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-10.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[11]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-11.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[12]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-12.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

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))) 

Past versions of temp_profile_season_biome_Atlantic-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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))) 

Past versions of Atlantic_biome_1_spring_mean_profiles-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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]]

Past versions of plot_raw_profiles_with_clim-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of plot_raw_profiles_with_clim-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of plot_raw_profiles_with_clim-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[4]]

Past versions of plot_raw_profiles_with_clim-4.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[5]]

Past versions of plot_raw_profiles_with_clim-5.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[6]]

Past versions of plot_raw_profiles_with_clim-6.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

# 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 = 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]]

Past versions of plot_anomaly_profiles-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of plot_anomaly_profiles-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of plot_anomaly_profiles-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[4]]

Past versions of plot_anomaly_profiles-4.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[5]]

Past versions of plot_anomaly_profiles-5.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[6]]

Past versions of plot_anomaly_profiles-6.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[7]]

Past versions of plot_anomaly_profiles-7.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[8]]

Past versions of plot_anomaly_profiles-8.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[9]]

Past versions of plot_anomaly_profiles-9.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[10]]

Past versions of plot_anomaly_profiles-10.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[11]]

Past versions of plot_anomaly_profiles-11.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[12]]

Past versions of plot_anomaly_profiles-12.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

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')

Past versions of plot_overall_mean_clim_removed_profiles-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

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(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_order~biome_name, labeller = facet_label)

Past versions of plot_biome_mean_clim_removed_profiles-1.png

Version	Author	Date
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(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, 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')+
  labs(title = 'Basin-mean anomaly profiles')

Past versions of plot_basin_mean_anomaly_profiles-1.png

Version	Author	Date
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(argo_temp_anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(argo_temp_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')+
      labs(title = paste0('biome-basin mean anomaly profiles ', unique(.x$season)))
    )

[[1]]

Past versions of plot_basin_biome_mean_anomaly_profiles-1.png

Version	Author	Date
c16000b	ds2n19	2023-10-12
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of plot_basin_biome_mean_anomaly_profiles-2.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[3]]

Past versions of plot_basin_biome_mean_anomaly_profiles-3.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

[[4]]

Past versions of plot_basin_biome_mean_anomaly_profiles-4.png

Version	Author	Date
c16000b	ds2n19	2023-10-12

rm(remove_clim_basin_biome_mean, profile_temp_count_season)

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.4

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_1.0.1       lubridate_1.9.0   timechange_0.1.1 
[13] forcats_0.5.2     stringr_1.4.1     dplyr_1.0.10      purrr_0.3.5      
[17] readr_2.1.3       tidyr_1.2.1       tibble_3.1.8      ggplot2_3.4.0    
[21] tidyverse_1.3.2  

loaded via a namespace (and not attached):
  [1] googledrive_2.0.0   colorspace_2.0-3    deldir_1.0-6       
  [4] ellipsis_0.3.2      class_7.3-20        rprojroot_2.0.3    
  [7] htmlTable_2.4.1     base64enc_0.1-3     fs_1.5.2           
 [10] rstudioapi_0.14     proxy_0.4-27        farver_2.1.1       
 [13] bit64_4.0.5         fansi_1.0.3         xml2_1.3.3         
 [16] splines_4.2.2       codetools_0.2-18    cachem_1.0.6       
 [19] knitr_1.41          polyclip_1.10-4     Formula_1.2-4      
 [22] jsonlite_1.8.3      workflowr_1.7.0     cluster_2.1.4      
 [25] dbplyr_2.2.1        png_0.1-8           rgeos_0.5-9        
 [28] compiler_4.2.2      httr_1.4.4          backports_1.4.1    
 [31] Matrix_1.5-3        assertthat_0.2.1    fastmap_1.1.0      
 [34] gargle_1.2.1        cli_3.4.1           later_1.3.0        
 [37] tweenr_2.0.2        htmltools_0.5.3     tools_4.2.2        
 [40] gtable_0.3.1        glue_1.6.2          Rcpp_1.0.10        
 [43] cellranger_1.1.0    jquerylib_0.1.4     raster_3.6-11      
 [46] vctrs_0.5.1         xfun_0.35           rvest_1.0.3        
 [49] lifecycle_1.0.3     googlesheets4_1.0.1 terra_1.7-39       
 [52] MASS_7.3-58.1       scales_1.2.1        vroom_1.6.0        
 [55] hms_1.1.2           promises_1.2.0.1    parallel_4.2.2     
 [58] RColorBrewer_1.1-3  yaml_2.3.6          gridExtra_2.3      
 [61] memoise_2.0.1       sass_0.4.4          rpart_4.1.19       
 [64] latticeExtra_0.6-30 stringi_1.7.8       highr_0.9          
 [67] e1071_1.7-12        checkmate_2.1.0     rlang_1.1.1        
 [70] pkgconfig_2.0.3     evaluate_0.18       lattice_0.20-45    
 [73] sf_1.0-9            htmlwidgets_1.5.4   labeling_0.4.2     
 [76] bit_4.0.5           tidyselect_1.2.0    magrittr_2.0.3     
 [79] R6_2.5.1            generics_0.1.3      Hmisc_4.7-2        
 [82] DBI_1.1.3           foreign_0.8-83      pillar_1.8.1       
 [85] haven_2.5.1         whisker_0.4         withr_2.5.0        
 [88] units_0.8-0         nnet_7.3-18         survival_3.4-0     
 [91] sp_1.5-1            modelr_0.1.10       crayon_1.5.2       
 [94] interp_1.1-3        KernSmooth_2.23-20  utf8_1.2.2         
 [97] tzdb_0.3.0          rmarkdown_2.18      jpeg_0.1-10        
[100] grid_4.2.2          readxl_1.4.1        data.table_1.14.6  
[103] git2r_0.30.1        reprex_2.0.2        digest_0.6.30      
[106] classInt_0.4-8      httpuv_1.6.6        munsell_0.5.0      
[109] viridisLite_0.4.1   bslib_0.4.1