Extreme Temperature Profiles

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Last updated: 2022-02-15

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Knit directory: bgc_argo_r_argodata/

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Rmd	01ae9da	pasqualina-vonlanthendinenna	2022-02-15	added OceanSODA-Argo SST comparison
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Task

Compare depth profiles of normal temperature and of extreme temperature, as identified in the surface OceanSODA data product

theme_set(theme_bw())
HNL_colors <- c("H" = "#b2182b",
                "N" = "#636363",
                "L" = "#2166ac")

Load data

path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_updata <- '/nfs/kryo/work/updata'

# RECCAP2-ocean region mask

region_masks_all_2x2 <- read_rds(file = paste0(path_argo_preprocessed,
                                               "/region_masks_all_2x2.rds"))

region_masks_all_2x2 <- region_masks_all_2x2 %>%
  rename(biome = value) %>% 
  mutate(coast = as.character(coast))

# 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))

# OceanSODA temperature

OceanSODA_temp <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_temp.rds"))

OceanSODA_temp <- OceanSODA_temp %>%
  mutate(year = year(date),
         month = month(date))

# full argo data
full_argo <- read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_pH_qc_1.rds"))

# change the date format for compatibility with OceanSODA pH data
full_argo <- full_argo %>%
  mutate(year = year(date),
         month = month(date)) %>%
  mutate(date = ymd(format(date, "%Y-%m-15")))

map <-
  read_rds(paste(path_emlr_utilities,
                 "map_landmask_WOA18.rds",
                 sep = ""))

Regions

Biomes

region_masks_all_2x2 <- region_masks_all_2x2 %>%
  filter(region == 'southern',
         biome != 0) %>% 
  select(-region)

Remove coastal data

basemap(limits = -32) +
  geom_spatial_tile(
    data = region_masks_all_2x2,
    aes(x = lon,
        y = lat,
        fill = coast),
    col = 'transparent'
  ) +
  scale_fill_brewer(palette = "Dark2")

map + 
  geom_tile(data = region_masks_all_2x2, 
            aes(x = lon, 
                y = lat, 
                fill = coast))+
  lims(y = c(-85, -30))+
  scale_fill_brewer(palette = 'Dark2')

Past versions of coast_flat_map-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

# remove coastal data 

region_masks_all_2x2 <- region_masks_all_2x2 %>% 
  filter(coast == "0")

Grid reduction

basemap(limits = -32) +
  geom_spatial_tile(
    data = region_masks_all_2x2,
    aes(x = lon,
        y = lat,
        fill = biome),
    col = 'transparent'
  ) +
  scale_fill_brewer(palette = "Dark2")

map +
  geom_tile(data = region_masks_all_2x2, 
            aes(x = lon, 
                y = lat, 
                fill = biome))+
  lims(y = c(-85, -30))+
  scale_fill_brewer(palette = 'Dark2')

Past versions of biomes_flat_map_pre_grid_reduction-1.png

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region_masks_all_2x2 <- region_masks_all_2x2 %>%
  count(lon, lat, biome) %>%
  group_by(lon, lat) %>%
  slice_max(n, with_ties = FALSE) %>%
  ungroup()

basemap(limits = -32) +
  geom_spatial_tile(
    data = region_masks_all_2x2,
    aes(x = lon,
        y = lat,
        fill = biome),
    col = 'transparent'
  ) +
  scale_fill_brewer(palette = "Dark2")

map+
  geom_tile(data = region_masks_all_2x2,
            aes(x = lon,
                y = lat, 
                fill = biome))+
  lims(y = c(-85, -30))+
  scale_fill_brewer(palette = 'Dark2')

Past versions of biomes_flat_map_post_grid_reduction-1.png

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Basins

basinmask <- basinmask %>%
  filter(lat < -30)

Grid reduction

basemap(limits = -32) +
  geom_spatial_tile(
    data = basinmask,
    aes(x = lon,
        y = lat,
        fill = basin_AIP),
    col = 'transparent'
  ) +
  scale_fill_brewer(palette = "Dark2")

map +
  geom_tile(data = basinmask, 
            aes(x = lon, 
                y = lat, 
                fill = basin_AIP))+
  lims(y = c(-85, -30))+
  scale_fill_brewer(palette = 'Dark2')

Past versions of basin_flat_map_pre_grid_reduction-1.png

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basinmask_2x2 <- basinmask %>%
  mutate(
    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))
   ) # regrid into 2x2º grid

# assign basins from each pixel to to each 2 Lon x Lat pixel, based on the majority of basins in each 2x2 grid  

basinmask_2x2 <- basinmask_2x2 %>%
  count(lon, lat, basin_AIP) %>%
  group_by(lon, lat) %>%
  slice_max(n, with_ties = FALSE) %>%
  ungroup() %>% 
  select(-n)

rm(basinmask)

basemap(limits = -32) +
  geom_spatial_tile(
    data = basinmask_2x2 %>% filter(lat < -30),
    aes(x = lon,
        y = lat,
        fill = basin_AIP),
    col = 'transparent'
  ) +
  scale_fill_brewer(palette = "Dark2")

map+
  geom_tile(data = basinmask_2x2 %>% filter(lat < -30),
            aes(x = lon,
                y = lat, 
                fill = basin_AIP))+
  lims(y = c(-85, -30))+
  scale_fill_brewer(palette = 'Dark2')

Past versions of basin_flat_map_post_grid_reduction-1.png

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f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

OceanSODA

Grid reduction

# Note: While reducing lon x lat grid,
# we keep the original number of observations

OceanSODA_temp_2x2 <- OceanSODA_temp %>% 
  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))) # regrid into 2x2º grid 

Apply region masks

# keep only Southern Ocean data
OceanSODA_temp_2x2_SO <- inner_join(OceanSODA_temp_2x2, region_masks_all_2x2)

# add in basin separations
OceanSODA_temp_2x2_SO <- inner_join(OceanSODA_temp_2x2_SO, basinmask_2x2)
# expected number of rows from -30 to -70º latitude, 360º longitude, for 12 months, 8 years:
# 40 lat x 360 lon x 12 months x 8 years = 1 382 400 rows 
# actual number of rows: 1 134 177 (in line with expectations)

OceanSODA_temp_2x2_SO <- OceanSODA_temp_2x2_SO %>% 
  filter(!is.na(temperature))
# resulting number of rows: 925 056 
# (removes 209 121 observations)

OceanSODA temperature anomalies

Grid level

Fit lm models

# fit a linear regression of OceanSODA pH against time (temporal trend)
# in each lat/lon/month grid

OceanSODA_temp_regression <- OceanSODA_temp_2x2_SO %>% 
  # filter(basin_AIP == "Indian",
  #        biome == "2",
  #        lon < 40) %>%
  nest(data = -c(lon, lat, month)) %>%
  mutate(fit = map(.x = data,
                   .f = ~ lm(temperature ~ year, data = .x)),
         tidied = map(.x = fit, .f = tidy),
         glanced = map(.x = fit, .f = glance),
         augmented = map(.x = fit, .f = augment))


OceanSODA_temp_regression_tidied <- OceanSODA_temp_regression %>%
  select(-c(data, fit, augmented, glanced)) %>%
  unnest(tidied)

OceanSODA_temp_regression_tidied <- OceanSODA_temp_regression_tidied %>% 
  select(lon:estimate) %>% 
  pivot_wider(names_from = term,
              values_from = estimate) %>% 
  rename(intercept = `(Intercept)`,
         slope = year)

OceanSODA_temp_regression_augmented <- OceanSODA_temp_regression %>%
  select(-c(fit, tidied, glanced, data)) %>%
  unnest(augmented) %>% 
  select(lon:year, .resid)

OceanSODA_temp_regression_augmented <- bind_cols(
  OceanSODA_temp_regression_augmented,
  OceanSODA_temp_2x2_SO %>% 
    select(
    lon_raw, lat_raw, basin_AIP, biome)
)

OceanSODA_temp_regression_glanced <- OceanSODA_temp_regression %>%
  select(-c(data, fit, tidied, augmented)) %>%
  unnest(glanced)

Slope maps

basemap(limits = -32) +
  geom_spatial_tile(data = OceanSODA_temp_regression_tidied,
                    aes(x = lon,
                        y = lat,
                        fill = slope),
                    col = 'transparent') +
  scale_fill_scico(palette = "vik", midpoint = 0) +
  facet_wrap( ~ month, ncol = 2)

map+
  geom_tile(data = OceanSODA_temp_regression_tidied,
            aes(x = lon,
                y = lat, 
                fill = slope))+
  scale_fill_scico(palette = 'vik', midpoint = 0)+
  lims(y = c(-85, -30))+
  facet_wrap(~month, ncol = 2)

Past versions of map_temp_regression_slopes_flat-1.png

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Residual st. dev. maps

basemap(limits = -32)+
  geom_spatial_tile(data = OceanSODA_temp_regression_glanced,
                    aes(x = lon, 
                        y = lat, 
                        fill = sigma),
                    col = 'transparent')+
  scale_fill_viridis_c()+
  facet_wrap(~month, ncol = 2)+
  labs(fill = '1 residual \nst. dev.')

map+
  geom_tile(data = OceanSODA_temp_regression_glanced,
            aes(x = lon,
                y = lat, 
                fill = sigma))+
  scale_fill_viridis_c()+
  lims(y = c(-85, -30))+
  facet_wrap(~month, ncol = 2)+
  labs(fill = '1 residual \nst. dev.')

Past versions of temp_flat_map_sigma-1.png

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f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Anomaly identification

Calculate OceanSODA surface temperature anomalies; L for abnormally low, H for abnormally high, and N for normal

# when the in-situ OceanSODA temperature is lower than the 5th percentile (predicted - 2*residual.st.dev), assign 'L' for low extreme
# when the in-situ OceanSODA temperature exceeds the 95th percentile (predicted + 2*residual.st.dev), assign 'H' for high extreme
# when the in-situ OceanSODA temperature is within 95% of the range, then assign 'N' for normal pH

# combine observations and regression statistics

OceanSODA_temp_2x2_SO_extreme_grid <-
  full_join(
    OceanSODA_temp_regression_augmented,
    OceanSODA_temp_regression_glanced %>%
      select(lon:month, sigma)
  )

# identify observations in anomaly classes

OceanSODA_temp_2x2_SO_extreme_grid <- OceanSODA_temp_2x2_SO_extreme_grid %>%
  mutate(
    temp_extreme = case_when(
      .resid < -sigma*2 ~ 'L',
      .resid > sigma*2 ~ 'H',
      TRUE ~ 'N'
    )
  ) 

OceanSODA_temp_2x2_SO_extreme_grid <- OceanSODA_temp_2x2_SO_extreme_grid %>%
  mutate(temp_extreme = fct_relevel(temp_extreme, "H", "N", "L"))


# combine with regression coefficients

OceanSODA_temp_2x2_SO_extreme_grid <-
  full_join(OceanSODA_temp_2x2_SO_extreme_grid,
            OceanSODA_temp_regression_tidied)

OceanSODA_temp_2x2_SO_extreme_grid %>%
  group_split(lon, lat, month) %>%
  head(6) %>%
  map(~ ggplot(data = .x) +
        geom_point(aes(x = year,
                       y = temperature,
                       col = temp_extreme)) +
        geom_abline(data = .x, aes(slope = slope,
                    intercept = intercept)) +
        geom_abline(data = .x, aes(slope = slope,
                    intercept = intercept + 2*sigma),
                    linetype = 2) +
        geom_abline(data = .x, aes(slope = slope,
                    intercept = intercept - 2*sigma),
                    linetype = 2) +
        labs(title = paste(fititle = paste(
          "lon:", unique(.x$lon),
          "| lat:", unique(.x$lat),
          "| month:", unique(.x$month)
          ))) +
        scale_color_manual(values = HNL_colors))

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Anomaly maps

Location of OceanSODA temperature extremes

OceanSODA_temp_2x2_SO_extreme_grid %>% 
  group_split(year) %>% 
  # head(2) %>%
  map(
    ~ basemap(limits = -32, data = .x)+
      geom_spatial_tile(data = .x,
                        aes(x = lon,
                            y = lat,
                            fill = temp_extreme),
                        linejoin = 'mitre',
                        col = 'transparent',
                        detail = 60
                        ) +
      scale_fill_manual(values = HNL_colors) +
      facet_wrap(~month, ncol = 2)+
      labs(title = paste("Year:", unique(.x$year)),
           fill = 'temperature')
  )

OceanSODA_temp_2x2_SO_extreme_grid %>% 
  group_split(year) %>% 
  map(
    ~map +
      geom_tile(data = .x,
                aes(x = lon,
                    y = lat, 
                    fill = temp_extreme))+
      scale_fill_manual(values = HNL_colors)+
      facet_wrap(~month, ncol = 2)+
      lims(y = c(-85, -30))+
      labs(title = paste('Year:', unique(.x$year)),
           fill = 'pH')
  )

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Past versions of flat_map_OceanSODA_temp_extremes_grid-1.png

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Anomaly timeseries

# calculate a regional mean temperature for each biome, basin, and ph extreme (H/L/N) and plot a timeseries 

OceanSODA_temp_2x2_SO_extreme_grid %>% 
  group_by(year, biome, basin_AIP, temp_extreme) %>% 
  summarise(temp_regional = mean(temperature, na.rm = TRUE)) %>% 
  ungroup() %>% 
  ggplot(aes(x = year, y = temp_regional, col = temp_extreme))+
  geom_point(size = 0.3)+
  geom_line()+
  scale_color_manual(values = HNL_colors) +
  facet_grid(basin_AIP~biome)+
  theme(legend.position = 'bottom')

Past versions of extreme_temp_timeseries_grid-1.png

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f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Anomaly histogram

OceanSODA_temp_2x2_SO_extreme_grid %>%
  ggplot(aes(temperature, col = temp_extreme)) +
  geom_density() +
  scale_color_manual(values = HNL_colors) +
  facet_grid(basin_AIP ~ biome) +
  coord_cartesian(xlim = c(-2, 28)) +
  labs(x = 'value',
       y = 'density',
       col = 'temp anomaly') +
  theme(legend.position = 'bottom')

Past versions of extreme_temp_histogram_grid-1.png

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f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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

Apply region masks

# keep only Southern Ocean argo data
full_argo_2x2_SO <- inner_join(full_argo_2x2, region_masks_all_2x2)

# add in basin separations
full_argo_2x2_SO <- inner_join(full_argo_2x2_SO, basinmask_2x2)

Join OceanSODA

# rename OceanSODA columns
OceanSODA_temp_2x2_SO_extreme_grid <- OceanSODA_temp_2x2_SO_extreme_grid %>%
  select(-c(lon, lat)) %>% 
  rename(OceanSODA_temp = temperature,
         lon = lon_raw,
         lat = lat_raw)

# combine the argo profile data to the surface extreme data
profile_temp_extreme <- inner_join(
  full_argo %>% 
    select(year, month, date, lon, lat, depth,
           temp_adjusted,
           platform_number,
           cycle_number),
  OceanSODA_temp_2x2_SO_extreme_grid %>% 
    select(year, month, lon, lat,
           OceanSODA_temp, temp_extreme,
           biome, basin_AIP))

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

profile_temp_extreme %>%
  group_split(biome, basin_AIP, year) %>% 
  #head(1) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(
        x = temp_adjusted,
        y = depth,
        group = temp_extreme,
        col = temp_extreme
      )
    ) +
      geom_point(pch = 19, size = 0.3) +
      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)
        ),
        col = 'OceanSODA temp \nanomaly'
      )
  )

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f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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),
      m = 5,
      levels.mean = TRUE
    ),
    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)) %>%
  ungroup()

profile_temp_extreme_mean %>%
  arrange(depth) %>%
  ggplot(aes(
    x = temp_mean,
    y = depth,
    group = temp_extreme,
    col = temp_extreme
  )) +
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  labs(title = "Overall mean",
       col = 'OceanSODA\ntemp\nanomaly',
       y = 'log(depth)') +
  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
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Surface Argo temperature vs surface OceanSODA temperature (20 m)

# calculate surface-mean argo pH, 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(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)) +
  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
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of argo_vs_OceanSODA_temp_mean-2.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of argo_vs_OceanSODA_temp_mean-3.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Season x biome

profile_temp_extreme_biome <- profile_temp_extreme %>% 
  group_by(season, biome, temp_extreme, depth) %>% 
  summarise(temp_biome = mean(temp_adjusted, na.rm = TRUE)) %>% 
  ungroup()
  

profile_temp_extreme_biome %>%
  ggplot(aes(
    x = temp_biome,
    y = depth,
    group = temp_extreme,
    col = temp_extreme
  )) +
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  labs(col = 'OceanSODA\ntemp\nanomaly',
       y = 'log(depth)') +
  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)

Past versions of mean_temp_profiles_season_biome-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Number of profiles season x biome

profile_temp_count_biome <- profile_temp_extreme %>% 
  distinct(season, biome, temp_extreme, platform_number, cycle_number) %>% 
  group_by(season, biome, 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)+
  scale_y_continuous(trans = 'log10')+
  labs(y = 'log(number of profiles)',
       title = 'Number of profiles season x biome')

Past versions of count_temp_profiles_season_biome-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Surface Argo temp vs surface OceanSODA temp season x biome (20 m)

surface_temp_biome <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season, biome, 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)) +
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 21),
              ylim = c(-3, 21))+
  facet_grid(season~biome) +
    labs(title = paste( 'Temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_biome-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of argo_vs_OceanSODA_temp_biome-2.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of argo_vs_OceanSODA_temp_biome-3.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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)) %>% 
  ungroup()

profile_temp_extreme_basin %>% 
  ggplot(aes(x = temp_basin, 
             y = depth, 
             group = temp_extreme, 
             col = temp_extreme))+
  geom_path()+
  scale_color_manual(values = HNL_colors)+
  labs(col = 'OceanSODA\ntemp\nanomaly',
       y = 'log(depth)')+
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
  facet_grid(season~basin_AIP)

Past versions of mean_basin_temp_profiles-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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')

Past versions of count_basin_temp_profiles-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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)) +
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 21),
              ylim = c(-3, 21))+
  facet_grid(season~basin_AIP) +
    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
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of argo_vs_OceanSODA_temp_basin-2.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of argo_vs_OceanSODA_temp_basin-3.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Season x biome x basin

profile_temp_extreme_season <- profile_temp_extreme %>%
  group_by(season, biome, basin_AIP, temp_extreme, depth) %>%
  summarise(temp_mean = mean(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_path() +
      scale_color_manual(values = HNL_colors) +
      labs(title = paste("season:", unique(.x$season)),
           col = 'OceanSODA\ntemp\nanomaly',
           y = 'log(depth)') +
      scale_y_continuous(
        trans = trans_reverser("sqrt"),
        breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))
      ) +
      facet_grid(basin_AIP ~ biome)
  )

[[1]]

Past versions of mean_temp_profiles_season_biome_basin-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of mean_temp_profiles_season_biome_basin-2.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of mean_temp_profiles_season_biome_basin-3.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[4]]

Past versions of mean_temp_profiles_season_biome_basin-4.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Number of profiles season x biome x basin

profile_temp_count_season <- profile_temp_extreme %>% 
  distinct(season, biome, basin_AIP,
           temp_extreme, platform_number, cycle_number) %>% 
  group_by(season, biome, 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)+
      scale_y_continuous(trans = 'log10')+
      labs(y = 'log(number of profiles)',
           title = paste('season:', unique(.x$season)))
  )

[[1]]

Past versions of number_season_biome_basin_temp_profiles-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of number_season_biome_basin_temp_profiles-2.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of number_season_biome_basin_temp_profiles-3.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[4]]

Past versions of number_season_biome_basin_temp_profiles-4.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Surface Argo temperature vs surface OceanSODA temperature (20 m) season x biome x 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, 
           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)) +
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 21),
              ylim = c(-3, 21))+
  facet_grid(basin_AIP ~ biome) +
    labs(title = paste('season:', unique(.x$season), 
                        '| temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of surface_OceanSODA_vs_argo_temp_season-1.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of surface_OceanSODA_vs_argo_temp_season-2.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of surface_OceanSODA_vs_argo_temp_season-3.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[4]]

Past versions of surface_OceanSODA_vs_argo_temp_season-4.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[5]]

Past versions of surface_OceanSODA_vs_argo_temp_season-5.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[6]]

Past versions of surface_OceanSODA_vs_argo_temp_season-6.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[7]]

Past versions of surface_OceanSODA_vs_argo_temp_season-7.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[8]]

Past versions of surface_OceanSODA_vs_argo_temp_season-8.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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Past versions of surface_OceanSODA_vs_argo_temp_season-9.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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Past versions of surface_OceanSODA_vs_argo_temp_season-10.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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Past versions of surface_OceanSODA_vs_argo_temp_season-11.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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Past versions of surface_OceanSODA_vs_argo_temp_season-12.png

Version	Author	Date
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Session information

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] ggforce_0.3.3     metR_0.11.0       scico_1.3.0       ggOceanMaps_1.2.6
 [5] ggspatial_1.1.5   broom_0.7.11      lubridate_1.8.0   forcats_0.5.1    
 [9] stringr_1.4.0     dplyr_1.0.7       purrr_0.3.4       readr_2.1.1      
[13] tidyr_1.1.4       tibble_3.1.6      ggplot2_3.3.5     tidyverse_1.3.1  
[17] 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] assertthat_0.2.1    Matrix_1.4-0        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_0.4.12        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