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Rmd 72a65a7 pasqualina-vonlanthendinenna 2022-04-05 added new biomes to extreme pH
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Rmd b9a42f9 pasqualina-vonlanthendinenna 2022-03-29 added january plots and changed pH anomaly detection to mean
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Rmd e4d1d1e pasqualina-vonlanthendinenna 2022-03-15 updated to new only flag A data
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Rmd b1bb0ec pasqualina-vonlanthendinenna 2022-03-08 subsetted profiles with flag A only for extremes
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Rmd 8ef1277 pasqualina-vonlanthendinenna 2022-03-01 plotted Atlantic mean seasonal profiles
html 8ef1277 pasqualina-vonlanthendinenna 2022-03-01 plotted Atlantic mean seasonal profiles
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Rmd 5b0901d pasqualina-vonlanthendinenna 2022-02-28 corrected dates and titles
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Rmd 6e27fb1 pasqualina-vonlanthendinenna 2022-02-28 update with eval = false for single profile line thickness
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Rmd aad1df4 pasqualina-vonlanthendinenna 2022-02-28 plotted specific profiles
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Rmd fe0b970 pasqualina-vonlanthendinenna 2022-02-25 plotted line profiles and changed HNL colors
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Rmd 64c2c71 pasqualina-vonlanthendinenna 2022-02-25 plotted line profiles and changed HNL colors
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Rmd 4557a6e pasqualina-vonlanthendinenna 2022-02-24 added st dev for pH profiles
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Rmd 48803ef pasqualina-vonlanthendinenna 2022-02-23 updated regression and merging for extreme_pH
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Rmd ecf2f74 pasqualina-vonlanthendinenna 2022-02-03 corrected surface mean pH
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Rmd 3f38f15 pasqualina-vonlanthendinenna 2022-02-03 corrected mean argo surface pH
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Rmd a73c7cf pasqualina-vonlanthendinenna 2022-02-02 changed to log scale and mean surface argo ph
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Rmd bb15149 pasqualina-vonlanthendinenna 2022-02-02 changed map figure aspect
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Rmd ce1bbab pasqualina-vonlanthendinenna 2022-02-02 updated bar charts and argo vs oceansoda ph
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Rmd db007b5 pasqualina-vonlanthendinenna 2022-02-01 updated figure aspect
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Rmd f62e851 pasqualina-vonlanthendinenna 2022-02-01 added flat maps, bar charts and OceanSODA vs argo pH
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Rmd b45a03e pasqualina-vonlanthendinenna 2022-02-01 added sigma maps and log transform depth
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Rmd c0c12d0 jens-daniel-mueller 2022-01-28 code cleaning: basinmask and regression
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Rmd 5024768 jens-daniel-mueller 2022-01-28 code review: basinmask and regression
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Rmd 23dc282 pasqualina-vonlanthendinenna 2022-01-27 failed attempt at updating basinmask and regression
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Rmd 3851824 pasqualina-vonlanthendinenna 2022-01-25 added basin-mean profiles
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Rmd 3f8e824 pasqualina-vonlanthendinenna 2022-01-24 updated 24/01
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Task

Compare depth profiles of normal pH and of extreme pH, as identified in the surface OceanSODA pH data product. BGC pH profiles have already been validate for coverage and aligned to climatology depths.

Dependencies

OceanSODA.rds - bgc preprocessed folder, created by OceanSODA_argo_pH.

pH_bgc_va.rds - bgc preprocessed folder, created by ph_align_climatology.

pH_anomaly_va.rds - bgc preprocessed folder, created by ph_align_climatology.

Outputs

OceanSODA_pH_anomaly_field_01.rds (or _02.rds)

OceanSODA_global_pH_anomaly_field_01.rds (or _02.rds)

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

HNL_colors_map <- c('H' = 'red3',
                    'N' = 'transparent',
                    'L' = 'blue3')

# opt_min_profile_range
# profiles with profile_range >= opt_min_profile_range will be selected 1 = profiles of at least 614m, 2 = profiles of at least 1225m, 3 = profiles of at least 1600m
opt_min_profile_range = 3

# opt_extreme_determination
# 1 - based on the trend of de-seasonal data - we believe this results in more summer extremes where variation tend to be greater.
# 2 - based on the trend of de-seasonal data by month. grouping is by lat, lon and month.
opt_extreme_determination <- 2

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_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
# /nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo/preprocessed_bgc_data
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
# load in new Mayot biomes 

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))

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

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

# load validated and vertically aligned pH profiles, 
full_argo <-
  read_rds(file = paste0(path_argo_preprocessed, "/pH_bgc_va.rds")) %>%
  filter(profile_range >= opt_min_profile_range) %>%
  mutate(date = ymd(format(date, "%Y-%m-15")))

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

Regions

Biome grid reduction

map +
  geom_tile(data = nm_biomes, 
            aes(x = lon, 
                y = lat, 
                fill = biome_name))+
  lims(y = c(-85, -30))+
  scale_fill_brewer(palette = 'Dark2')+
  labs(title = 'Mayot biomes pre-grid reduction')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
d14b7f1 pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
# Commented
# nm_biomes_2x2 <- nm_biomes %>% 
#   mutate(lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
#          lon = as.numeric(as.character(lon)),
#          lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
#          lat = as.numeric(as.character(lat)))
# 
# nm_biomes_2x2 <- nm_biomes_2x2 %>% 
#   count(lon, lat, biome_name) %>% 
#   group_by(lon, lat) %>% 
#   slice_max(n, with_ties = FALSE) %>% 
#   ungroup()

# New
nm_biomes <- nm_biomes %>% 
  count(lon, lat, biome_name) %>% 
  group_by(lon, lat) %>% 
  slice_max(n, with_ties = FALSE) %>% 
  ungroup()

# Commented
#rm(nm_biomes)
# map+
#   geom_tile(data = nm_biomes_2x2,
#             aes(x = lon,
#                 y = lat,
#                 fill = biome_name))+
#   lims(y = c(-85, -30))+
#   scale_fill_brewer(palette = 'Dark2')+
#   labs(title = 'Mayot biomes post-grid reduction')

Basin grid reduction

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

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f110b74 ds2n19 2023-12-13
8805f99 pasqualina-vonlanthendinenna 2022-04-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
d14b7f1 pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
# Commented
# 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))
#    )
# 
# # 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)

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

# commented
#rm(basinmask)
# 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')

OceanSODA

Monthly clim surface pH

OceanSODA <- OceanSODA %>% 
  group_by(lon, lat, month) %>% 
  mutate(clim_ph = mean(ph_total, na.rm = TRUE),
         clim_diff = ph_total - clim_ph,
         .after = ph_total) %>% 
  ungroup()

Grid reduction

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

# OceanSODA_2x2 <- OceanSODA %>%
#   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

OceanSODA <- OceanSODA %>%
  mutate(
    lat_raw = lat,
    lon_raw = lon)

# rm(OceanSODA)

Apply region masks

# # keep only Southern Ocean data
# OceanSODA_2x2_SO <- inner_join(OceanSODA_2x2, nm_biomes_2x2)
# 
# # add in basin separations
# OceanSODA_2x2_SO <- inner_join(OceanSODA_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: 919 768
# 
# OceanSODA_2x2_SO <- OceanSODA_2x2_SO %>% 
#   filter(!is.na(ph_total))

## keep only Southern Ocean data
# OceanSODA_SO <- inner_join(OceanSODA, nm_biomes %>% 
#                                       select(-n)) 

# add in basin separations
OceanSODA <- inner_join(OceanSODA, basinmask)

OceanSODA <- OceanSODA %>% 
   filter(!is.na(ph_total))

Maps

OceanSODA %>% 
  filter(year == 2020) %>% 
  ggplot(aes(lon_raw, lat_raw, fill = clim_ph)) +
  geom_tile() +
  scale_fill_viridis_c() +
  facet_wrap(~ month, ncol = 2) +
  coord_quickmap(expand = 0)

Version Author Date
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44f5720 ds2n19 2023-10-09
710edd4 jens-daniel-mueller 2022-05-11
# map of climatological OceanSODA pH 
OceanSODA %>% 
  group_split(month) %>% 
  #head(1) %>% 
  map(
    ~map+
      geom_tile(data = .x,
                aes(x = lon_raw,
                    y = lat_raw,
                    fill = clim_ph))+
      scale_fill_viridis_c()+
      labs(title = paste('climatological OceanSODA pH (1995-2020) month:', unique(.x$month)))
  )
[[1]]

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[[12]]

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9875dd0 pasqualina-vonlanthendinenna 2022-04-05
# map of monthly anomaly relative to climatology

OceanSODA %>%
  group_split(month) %>%
  #head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(
                  x = lon_raw,
                  y = lat_raw,
                  fill = clim_diff
                )) +
      scale_fill_divergent(mid = 'grey80') +
      facet_wrap( ~ year, ncol = 2) +
      labs(
        title = paste(
          'in-situ OceanSODA pH - clim OceanSODA pH, month:',
          unique(.x$month)
        )
      ) +
      theme(legend.position = 'right')
  )
[[1]]

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[[2]]

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9875dd0 pasqualina-vonlanthendinenna 2022-04-05

OceanSODA pH anomalies

Grid level

Climatological thresholds

Fit lm models

OceanSODA <- OceanSODA %>%
  mutate(decimal_year = decimal_date(date), .after = year)

# fit a linear regression of OceanSODA pH against time (temporal trend)
# in each lat/lon/month grid
# in each lat/lon/month grid, month is used depending on opt_extreme_determination
if (opt_extreme_determination == 1){
  OceanSODA_regression <- OceanSODA %>%
    nest(data = -c(lon, lat)) %>%
    mutate(
      fit = map(
        .x = data,
        .f = ~ lm(clim_diff ~ decimal_year, data = .x)
      ),
      tidied = map(.x = fit, .f = tidy),
      glanced = map(.x = fit, .f = glance),
      augmented = map(.x = fit, .f = augment)
    )
} else if (opt_extreme_determination == 2){
  OceanSODA_regression <- OceanSODA %>%
    nest(data = -c(lon, lat, month)) %>%
    mutate(
      fit = map(
        .x = data,
        .f = ~ lm(clim_diff ~ decimal_year, data = .x)
      ),
      tidied = map(.x = fit, .f = tidy),
      glanced = map(.x = fit, .f = glance),
      augmented = map(.x = fit, .f = augment)
    )
}

OceanSODA_regression_tidied <- OceanSODA_regression %>%
  select(-c(data, fit, augmented, glanced)) %>%
  unnest(tidied)

OceanSODA_regression_tidied <- OceanSODA_regression_tidied %>% 
  select(lat:estimate) %>% 
  pivot_wider(names_from = term,
              values_from = estimate) %>% 
  rename(intercept = `(Intercept)`,
         slope = decimal_year)

OceanSODA_regression_data <- OceanSODA_regression %>% 
  select(-c(fit, tidied, glanced, augmented)) %>% 
  unnest(data)

OceanSODA_regression_augmented <- OceanSODA_regression %>%
  select(-c(fit, tidied, glanced, data)) %>%
  unnest(augmented) %>% 
  select(lat:decimal_year, .resid)

OceanSODA_regression_augmented <- bind_cols(
  OceanSODA_regression_augmented,
  OceanSODA_regression_data %>% select(
    date, basin_AIP,   
    clim_ph, ph_total,
    lon_raw, lat_raw))

OceanSODA_regression_glanced <- OceanSODA_regression %>%
  select(-c(data, fit, tidied, augmented)) %>%
  unnest(glanced)

Fit mean

# identify the mean value
# in each lat/lon/month grid

# OceanSODA_regression_tidied <- OceanSODA_SO %>% 
#   # filter(basin_AIP == "Indian",
#   #        biome_name == "SPSS",
#   #        lon < 40) %>%
#   group_by(lon, lat, month) %>%
#   summarise(slope = 0,
#             intercept = mean(clim_diff, na.rm = TRUE)) %>% 
#   ungroup()
# 
# OceanSODA_regression_glanced <- OceanSODA_SO %>% 
#   # filter(basin_AIP == "Indian",
#   #        biome_name == "SPSS",
#   #        lon < 40) %>%
#   group_by(lon, lat, month) %>%
#   summarise(sigma = sd(clim_diff, na.rm = TRUE)) %>% 
#   ungroup()
# 
# OceanSODA_regression_augmented <- OceanSODA_SO %>% 
#   # filter(basin_AIP == "Indian",
#   #        biome_name == "SPSS",
#   #        lon < 40) %>%
#   mutate(.resid = clim_diff)

Slope maps

if (opt_extreme_determination == 1){
  map +
    geom_tile(data = OceanSODA_regression_tidied,
              aes(x = lon,
                  y = lat,
                  fill = slope)) +
    scale_fill_scico(palette = 'vik', midpoint = 0)
} else if (opt_extreme_determination == 2){
  map +
    geom_tile(data = OceanSODA_regression_tidied,
              aes(x = lon,
                  y = lat,
                  fill = slope)) +
    scale_fill_scico(palette = 'vik', midpoint = 0) +
    facet_wrap( ~ month, ncol = 2)
}

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Sigma maps

if (opt_extreme_determination == 1){
  map +
    geom_tile(data = OceanSODA_regression_glanced,
              aes(x = lon,
                  y = lat,
                  fill = sigma)) +
    scale_fill_viridis_c() +
    labs(fill = '1 residual \nst. dev.')
} else if (opt_extreme_determination == 2){
  map +
    geom_tile(data = OceanSODA_regression_glanced,
              aes(x = lon,
                  y = lat,
                  fill = sigma)) +
    scale_fill_viridis_c() +
    labs(fill = '1 residual \nst. dev.') +
    facet_wrap(~month, ncol = 2)
}

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Anomaly identification

Calculate OceanSODA pH anomalies: L for abnormally low, H for abnormally high, N for normal pH

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

# combine observations and regression statistics

if (opt_extreme_determination == 1){
  OceanSODA_extreme_grid <-
    full_join(
      OceanSODA_regression_augmented,
      OceanSODA_regression_glanced %>%
        select(lat:lon, sigma)
    )
} else if (opt_extreme_determination == 2){
  OceanSODA_extreme_grid <-
    full_join(
      OceanSODA_regression_augmented,
      OceanSODA_regression_glanced %>%
        select(lat:month, sigma)
    )
}

# identify observations in anomaly classes

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

OceanSODA_extreme_grid <- OceanSODA_extreme_grid %>%
  mutate(ph_extreme = fct_relevel(ph_extreme, "H", "N", "L"))


# combine with regression coefficients

OceanSODA_extreme_grid <-
  full_join(OceanSODA_extreme_grid,
            OceanSODA_regression_tidied)

OceanSODA_extreme_grid <- OceanSODA_extreme_grid %>%
  mutate(year = year(date),
         month = month(date),
         .after = decimal_year)

Write anomalies to file

# Restrict to SO by inner join to nm_biomes
OceanSODA_SO_extreme_grid <- inner_join(OceanSODA_extreme_grid, nm_biomes %>% 
                                        select(-n)) 



# if (opt_extreme_determination == 1){
#   OceanSODA_SO_extreme_grid %>%
#     write_rds(file = paste0(path = path_argo_preprocessed, "/OceanSODA_pH_anomaly_field_01.rds"))
#   OceanSODA_extreme_grid %>%
#     write_rds(file = paste0(path = path_argo_preprocessed, "/OceanSODA_global_pH_anomaly_field_01.rds"))
# } else if (opt_extreme_determination == 2){
#   OceanSODA_SO_extreme_grid %>%
#     write_rds(file = paste0(path = path_argo_preprocessed, "/OceanSODA_pH_anomaly_field_02.rds"))
#   OceanSODA_extreme_grid %>%
#     write_rds(file = paste0(path = path_argo_preprocessed, "/OceanSODA_global_pH_anomaly_field_02.rds"))
# }
if (opt_extreme_determination == 1){
  OceanSODA_SO_extreme_grid %>%
    group_split(lon, lat) %>%
    head(8) %>%
    map(
      ~ ggplot(data = .x) +
        geom_point(aes(
          x = year,
          y = clim_diff,
          col = ph_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))
        )) +
        scale_color_manual(values = HNL_colors)
    )
} else if (opt_extreme_determination == 2){
  OceanSODA_SO_extreme_grid %>%
    group_split(lon, lat, month) %>%
    head(8) %>%
    map(
      ~ ggplot(data = .x) +
        geom_point(aes(
          x = year,
          y = clim_diff,
          col = ph_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)
    )
}
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21
c96ad5e pasqualina-vonlanthendinenna 2022-01-21
486c9c8 jens-daniel-mueller 2022-01-07

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21
c96ad5e pasqualina-vonlanthendinenna 2022-01-21
486c9c8 jens-daniel-mueller 2022-01-07

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21
c96ad5e pasqualina-vonlanthendinenna 2022-01-21
486c9c8 jens-daniel-mueller 2022-01-07

[[4]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21
c96ad5e pasqualina-vonlanthendinenna 2022-01-21
486c9c8 jens-daniel-mueller 2022-01-07

[[5]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21
c96ad5e pasqualina-vonlanthendinenna 2022-01-21
486c9c8 jens-daniel-mueller 2022-01-07

[[6]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21
c96ad5e pasqualina-vonlanthendinenna 2022-01-21
486c9c8 jens-daniel-mueller 2022-01-07

[[7]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21

[[8]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21

Anomaly maps

#use the original lat/lon grid to plot the extreme on a 1x1
#(anomalies detected with 1995-2020 data but mapped only from 2013 to 2020)

OceanSODA_SO_extreme_grid %>%
  filter(year >= 2013) %>% 
  group_split(month) %>%
  #head(1) %>%
  map(
    ~map +
      geom_tile(data = .x,
                aes(x = lon_raw,
                    y = lat_raw,
                    fill = ph_extreme))+
      scale_fill_manual(values = HNL_colors_map)+
      facet_wrap(~year, ncol = 2)+
      lims(y = c(-85, -30))+
      labs(title = paste('month:', unique(.x$month)),
           fill = 'pH')
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
8805f99 pasqualina-vonlanthendinenna 2022-04-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
da665ab pasqualina-vonlanthendinenna 2022-03-01

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
8805f99 pasqualina-vonlanthendinenna 2022-04-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
da665ab pasqualina-vonlanthendinenna 2022-03-01

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
8805f99 pasqualina-vonlanthendinenna 2022-04-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
da665ab pasqualina-vonlanthendinenna 2022-03-01

[[4]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
e4188d2 pasqualina-vonlanthendinenna 2022-03-01

[[5]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
8805f99 pasqualina-vonlanthendinenna 2022-04-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
e4188d2 pasqualina-vonlanthendinenna 2022-03-01

[[6]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
8805f99 pasqualina-vonlanthendinenna 2022-04-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
e4188d2 pasqualina-vonlanthendinenna 2022-03-01

[[7]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
e4188d2 pasqualina-vonlanthendinenna 2022-03-01

[[8]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
e4188d2 pasqualina-vonlanthendinenna 2022-03-01

[[9]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31

[[10]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31

[[11]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31

[[12]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
4173c20 jens-daniel-mueller 2022-05-12
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31

Anomaly time series

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

OceanSODA_SO_extreme_grid %>% 
  group_by(year, biome_name, basin_AIP, ph_extreme) %>% 
  summarise(ph_regional = mean(ph_total, na.rm = TRUE)) %>% 
  ungroup() %>% 
  ggplot(aes(x = year, y = ph_regional, col = ph_extreme))+
  geom_point(size = 0.3)+
  geom_line()+
  scale_color_manual(values = HNL_colors) +
  facet_grid(basin_AIP~biome_name)+
  theme(legend.position = 'bottom')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
8805f99 pasqualina-vonlanthendinenna 2022-04-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
71ced5f pasqualina-vonlanthendinenna 2022-02-23
28c8d17 jens-daniel-mueller 2022-01-29
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21
587755e pasqualina-vonlanthendinenna 2022-01-21
c96ad5e pasqualina-vonlanthendinenna 2022-01-21
486c9c8 jens-daniel-mueller 2022-01-07

Anomaly histogram

OceanSODA_SO_extreme_grid %>%
  ggplot(aes(ph_total, col = ph_extreme)) +
  geom_density() +
  scale_color_manual(values = HNL_colors) +
  facet_grid(basin_AIP ~ biome_name) +
  coord_cartesian(xlim = c(8, 8.2)) +
  labs(x = 'value',
       y = 'density',
       col = 'pH anomaly') +
  theme(legend.position = 'bottom')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
8805f99 pasqualina-vonlanthendinenna 2022-04-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
71ced5f pasqualina-vonlanthendinenna 2022-02-23
28c8d17 jens-daniel-mueller 2022-01-29
cfd734c jens-daniel-mueller 2022-01-28
962cdb9 pasqualina-vonlanthendinenna 2022-01-25
6b22341 pasqualina-vonlanthendinenna 2022-01-21
587755e pasqualina-vonlanthendinenna 2022-01-21
c96ad5e pasqualina-vonlanthendinenna 2022-01-21
486c9c8 jens-daniel-mueller 2022-01-07

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

full_argo <- full_argo %>%
  mutate(
    lat_raw = lat,
    lon_raw = lon)

Apply region masks

# keep only Southern Ocean argo data
full_argo_SO <- inner_join(full_argo, nm_biomes)

# add in basin separations
full_argo_SO <- inner_join(full_argo_SO, basinmask)

Join OceanSODA

# rename OceanSODA columns
OceanSODA_SO_extreme_grid <- OceanSODA_SO_extreme_grid %>%
  select(-c(lon, lat)) %>%
  rename(OceanSODA_ph = ph_total,
         lon = lon_raw,
         lat = lat_raw) %>% 
  filter(year >= 2013)

# combine the argo profile data to the surface extreme data
profile_extreme <- inner_join(
  full_argo %>%
    select(
      file_id, 
      year, 
      month, 
      date, 
      lon, 
      lat, 
      depth, 
      pH
      ),
  OceanSODA_SO_extreme_grid %>%
    select(
      year,
      month,
      date,
      lon,
      lat,
      OceanSODA_ph,
      ph_extreme,
      clim_ph,
      clim_diff,
      biome_name,
      basin_AIP
    )
)

# profile_extreme <- profile_extreme %>% 
#   unite('platform_cycle', platform_number:cycle_number, sep = '_', remove = FALSE)

Location of Argo profiles

OceanSODA_SO_extreme_grid %>%
  group_split(month) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(
        data = .x,
        aes(x = lon,
            y = lat,
            fill = ph_extreme),
        alpha = 0.5
      ) +
      scale_fill_manual(values = HNL_colors_map) +
      new_scale_fill() +
      geom_tile(
        data = profile_extreme %>%
          distinct(lon, lat, file_id, year, month),
        aes(
          x = lon,
          y = lat,
          fill = 'argo\nprofiles',
          height = 1,
          width = 1
        ),
        alpha = 0.5
      ) +
      scale_fill_manual(values = "springgreen4",
                        name = "") +
      facet_wrap( ~ year, ncol = 1) +
      lims(y = c(-85, -30)) +
      labs(title = paste('month:', unique(.x$month)))
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
ca30beb pasqualina-vonlanthendinenna 2022-05-05
4cf88e4 pasqualina-vonlanthendinenna 2022-05-05
f46b9da pasqualina-vonlanthendinenna 2022-05-05
4e3571d pasqualina-vonlanthendinenna 2022-05-03
2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[2]]

Version Author Date
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23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
4e3571d pasqualina-vonlanthendinenna 2022-05-03
2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[3]]

Version Author Date
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23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
4e3571d pasqualina-vonlanthendinenna 2022-05-03
2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[4]]

Version Author Date
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cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
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2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[5]]

Version Author Date
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23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
4e3571d pasqualina-vonlanthendinenna 2022-05-03
2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[6]]

Version Author Date
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23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
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1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
4e3571d pasqualina-vonlanthendinenna 2022-05-03
2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[7]]

Version Author Date
1a72545 mlarriere 2024-04-12
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23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
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2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[8]]

Version Author Date
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23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
4e3571d pasqualina-vonlanthendinenna 2022-05-03
2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[9]]

Version Author Date
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1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
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[[10]]

Version Author Date
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1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
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[[11]]

Version Author Date
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23a24d1 ds2n19 2023-12-05
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ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
4e3571d pasqualina-vonlanthendinenna 2022-05-03
2d44f8a pasqualina-vonlanthendinenna 2022-04-29

[[12]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
71e58d6 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
710edd4 jens-daniel-mueller 2022-05-11
4e3571d pasqualina-vonlanthendinenna 2022-05-03
2d44f8a pasqualina-vonlanthendinenna 2022-04-29

Plot profiles

Argo profiles plotted according to the surface OceanSODA pH

L profiles correspond to a surface acidification event (low pH), as recorded in OceanSODA

H profiles correspond to an event of high surface pH, as recorded in OceanSODA

N profiles correspond to normal surface OceanSODA pH

Raw

profile_extreme %>%
  group_split(biome_name, basin_AIP, year) %>% 
  head(6) %>% 
  map(
    ~ ggplot(
      data = .x,
      aes(
        x = pH,
        y = depth,
        group = file_id,
        col = ph_extreme
      )
    ) +
      geom_path(data = .x %>% filter(ph_extreme == 'N'),
                aes(x = pH,
                    y = depth,
                    group = file_id,
                    col = ph_extreme),
                size = 0.3) +
      geom_path(data = .x %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
                aes(x = pH,
                    y = depth,
                    group = file_id,
                    col = ph_extreme),
                size = 0.5)+
      scale_y_reverse() +
      scale_color_manual(values = HNL_colors) +
      facet_wrap(~ month, ncol = 6) +
      labs(
        x = 'Argo pH (total scale)',
        y = 'depth (m)',
        title = paste(
          unique(.x$basin_AIP),
          "|",
          unique(.x$year),
          "| biome:",
          unique(.x$biome_name)
        ),
        col = 'OceanSODA pH \nanomaly'
      )
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
ff31fef ds2n19 2023-11-20
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
8ef1277 pasqualina-vonlanthendinenna 2022-03-01
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
28c8d17 jens-daniel-mueller 2022-01-29

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
8ef1277 pasqualina-vonlanthendinenna 2022-03-01
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
28c8d17 jens-daniel-mueller 2022-01-29

[[3]]

Version Author Date
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23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
8ef1277 pasqualina-vonlanthendinenna 2022-03-01
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
28c8d17 jens-daniel-mueller 2022-01-29

[[4]]

Version Author Date
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879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
8ef1277 pasqualina-vonlanthendinenna 2022-03-01
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
28c8d17 jens-daniel-mueller 2022-01-29

[[5]]

Version Author Date
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23a24d1 ds2n19 2023-12-05
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
8ef1277 pasqualina-vonlanthendinenna 2022-03-01
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
28c8d17 jens-daniel-mueller 2022-01-29

[[6]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
8ef1277 pasqualina-vonlanthendinenna 2022-03-01
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
28c8d17 jens-daniel-mueller 2022-01-29

Atl, STSS biome, Aug 2017

# plot profiles for the Atlantic basin, biome 1, month 08, 2017 

OceanSODA_SO_extreme_grid_2017 <-  
  OceanSODA_SO_extreme_grid %>% 
  filter(date == '2017-08-15')

map+
  geom_tile(data = OceanSODA_SO_extreme_grid_2017,
            aes(x = lon,
                y = lat,
                fill = ph_extreme))+
  scale_fill_manual(values = HNL_colors_map)+
  lims(y = c(-85, -30))+
  labs(title = 'August 2017',
       fill = 'OceanSODA pH \nextreme')

profile_extreme_Atl_2017 <- profile_extreme %>% 
  filter(date == '2017-08-15',
         basin_AIP == 'Atlantic',
         biome_name == 'STSS')

profile_extreme_Atl_2017 %>% 
  ggplot(aes(y = depth, 
             x = pH,
             group = file_id,
             col = ph_extreme))+
  geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'N'),
            aes(x = pH,
                y = depth, 
                group = file_id,
                col = ph_extreme),
            size = 0.3)+
  geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'H'| ph_extreme == 'L'),
            aes(x = pH,
                y = depth,
                group = file_id,
                col = ph_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Atlantic basin, STSS biome, August 2017',
       col = 'OceanSODA\npH anomaly',
       x = 'Argo pH')

rm(OceanSODA_SO_extreme_grid_2017, profile_extreme_Atl_2017)

Atl, STSS biome, Dec 2017

# Plot profiles for the Pacific basin, biome 3, months 12, 2017

OceanSODA_SO_extreme_grid_2017 <-   
  OceanSODA_SO_extreme_grid %>% 
  filter(date == '2017-12-15')

map+
  geom_tile(data = OceanSODA_SO_extreme_grid_2017,
            aes(x = lon,
                y = lat,
                fill = ph_extreme))+
  scale_fill_manual(values = HNL_colors_map)+
  lims(y = c(-85, -30))+
  labs(title = 'December 2017',
       fill = 'OceanSODA pH \nextreme')

profile_extreme_Atl_2017 <- profile_extreme %>% 
  filter(date == '2017-12-15',
         basin_AIP == 'Atlantic',
         biome_name == 'STSS')

profile_extreme_Atl_2017 %>% 
  ggplot(aes(y = depth, 
             x = pH,
             group = file_id,
             col = ph_extreme))+
  geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'N'),
            aes(x = pH,
                y = depth,
                group = file_id,
                col = ph_extreme),
            size = 0.3)+
  geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
            aes(x = pH,
                y = depth,
                group = file_id,
                col = ph_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Atlantic basin, STSS biome, December 2017',
       col = 'OceanSODA\npH anomaly',
       x = 'Argo pH')

rm(OceanSODA_SO_extreme_grid_2017, profile_extreme_Atl_2017)

Atl, STSS biome, Jan 2018

OceanSODA_SO_extreme_grid_2018 <-   
  OceanSODA_SO_extreme_grid %>% 
  filter(date == '2018-01-15')

map+
  geom_tile(data = OceanSODA_SO_extreme_grid_2018,
            aes(x = lon,
                y = lat,
                fill = ph_extreme))+
  scale_fill_manual(values = HNL_colors_map)+
  lims(y = c(-85, -30))+
  labs(title = 'January 2018',
       fill = 'OceanSODA pH \nextreme')

profile_extreme_Atl_2018 <- profile_extreme %>% 
  filter(date == '2018-01-15',
         basin_AIP == 'Atlantic',
         biome_name == 'STSS')

profile_extreme_Atl_2018 %>% 
  ggplot(aes(y = depth, 
             x = pH,
             group = file_id,
             col = ph_extreme))+
  geom_path(data = profile_extreme_Atl_2018 %>% filter(ph_extreme == 'N'),
            aes(x = pH,
                y = depth,
                group = file_id,
                col = ph_extreme),
            size = 0.3)+
  geom_path(data = profile_extreme_Atl_2018 %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
            aes(x = pH,
                y = depth,
                group = file_id,
                col = ph_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Atlantic basin, STSS biome, January 2018',
       col = 'OceanSODA\npH anomaly',
       x = 'Argo pH')

rm(OceanSODA_SO_extreme_grid_2018, profile_extreme_Atl_2018)

Averaged profiles

# calculate mean profiles in each basin and biome, for each month between 2014 and 2021 
# 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_extreme <- profile_extreme %>%
  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_extreme_mean <- profile_extreme %>%
  group_by(ph_extreme, depth) %>%
  summarise(ph_mean = mean(pH, na.rm = TRUE),
            ph_std = sd(pH, na.rm = TRUE)) %>%
  ungroup()

profile_extreme_mean %>%
  arrange(depth) %>%
  ggplot(aes(
    x = ph_mean,
    y = depth,
    group = ph_extreme,
    col = ph_extreme
  )) +
  geom_ribbon(aes(xmin = ph_mean - ph_std,
                  xmax = ph_mean + ph_std,
                  group = ph_extreme,
                  fill = ph_extreme),
              col = NA,
              alpha = 0.2)+
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(title = "Overall mean",
       col = 'OceanSODA\npH anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\npH anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'Argo mean pH') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
daa0a8f pasqualina-vonlanthendinenna 2022-02-24
71ced5f pasqualina-vonlanthendinenna 2022-02-23
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
44fcfb6 pasqualina-vonlanthendinenna 2022-02-01
28c8d17 jens-daniel-mueller 2022-01-29
rm(profile_extreme_mean)

Number of profiles

profile_count_mean <- profile_extreme %>% 
  distinct(ph_extreme, file_id) %>% 
  count(ph_extreme)

profile_count_mean %>% 
  ggplot(aes(x = ph_extreme, y = n, fill = ph_extreme))+
  geom_col(width = 0.5)+
  scale_y_continuous(trans = 'log10')+
  labs(y = 'log(number of profiles)',
       title = 'Number of profiles')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
71ced5f pasqualina-vonlanthendinenna 2022-02-23
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
# rm(profile_count_mean)

Surface Argo pH vs surface OceanSODA pH (20 m)

# calculate surface-mean argo pH, for each profile 
surface_ph_mean <- profile_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(ph_extreme, file_id) %>% 
  summarise(argo_surf_ph = mean(pH, na.rm = TRUE),
            OceanSODA_surf_ph = mean(OceanSODA_ph, na.rm = TRUE)) %>%
  ungroup()



surface_ph_mean %>% 
  group_by(ph_extreme) %>% 
  group_split(ph_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = OceanSODA_surf_ph, 
             y = argo_surf_ph))+
  geom_bin2d(data = .x, aes(x = OceanSODA_surf_ph, 
                 y = argo_surf_ph)) +
  scale_fill_viridis_c()+
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(7.9, 8.21),
              ylim = c(7.9, 8.21))+
  # facet_grid(basin_AIP ~ biome) +
    labs(title = paste('pH extreme:', unique(.x$ph_extreme)),
         x = 'OceanSODA pH',
         y = 'Argo pH')
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
rm(surface_ph_mean)

January Overall Mean Profiles

profile_extreme_mean_jan <- profile_extreme %>%
  filter(month == 1) %>% 
  group_by(ph_extreme, depth) %>%
  summarise(ph_mean = mean(pH, na.rm = TRUE),
            ph_std = sd(pH, na.rm = TRUE)) %>%
  ungroup()

profile_extreme_mean_jan %>%
  arrange(depth) %>%
  ggplot(aes(
    x = ph_mean,
    y = depth,
    group = ph_extreme,
    col = ph_extreme
  )) +
  geom_ribbon(aes(xmin = ph_mean - ph_std,
                  xmax = ph_mean + ph_std,
                  group = ph_extreme,
                  fill = ph_extreme),
              col = NA,
              alpha = 0.2)+
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(title = "Overall mean January profiles",
       col = 'OceanSODA\npH anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\npH anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'Argo mean pH') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
rm(profile_extreme_mean_jan)

Season x Mayot biome

profile_extreme_biome <- profile_extreme %>% 
  group_by(season_order, season, biome_name, ph_extreme, depth) %>% 
  summarise(ph_biome = mean(pH, na.rm = TRUE),
            ph_biome_std = sd(pH, na.rm = TRUE)) %>% 
  ungroup()

facet_label <- as_labeller(c("1"="summer", 
                             "2"="autumn", 
                             "3"="winter", 
                             "4"="spring", 
                             "ICE" = "ICE", 
                             "SPSS" = "SPSS",
                             "STSS" = "STSS",
                             "Atlantic" = "Atlantic",
                             "Indian" = "Indian",
                             "Pacific" = "Pacific"
                             ))

profile_extreme_biome %>%
  ggplot(aes(
    x = ph_biome,
    y = depth,
    group = ph_extreme,
    col = ph_extreme
  )) +
  geom_ribbon(aes(xmin = ph_biome - ph_biome_std,
                  xmax = ph_biome + ph_biome_std,
                  group = ph_extreme,
                  fill = ph_extreme),
              col = NA, 
              alpha = 0.2)+
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(col = 'OceanSODA\npH anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\npH anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'Biome mean Argo pH',
       title = 'Biome-mean Argo profiles') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
  facet_grid(season_order ~ biome_name, labeller = facet_label)

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
daa0a8f pasqualina-vonlanthendinenna 2022-02-24
71ced5f pasqualina-vonlanthendinenna 2022-02-23
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
44fcfb6 pasqualina-vonlanthendinenna 2022-02-01
28c8d17 jens-daniel-mueller 2022-01-29
rm(profile_extreme_biome)

Number of profiles season x biome

profile_count_biome <- profile_extreme %>% 
  distinct(season_order, season, biome_name, ph_extreme, file_id) %>% 
  group_by(season_order, season, biome_name, ph_extreme) %>% 
  count(ph_extreme)

profile_count_biome %>% 
  ggplot(aes(x = ph_extreme, y = n, fill = ph_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 biome')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
71ced5f pasqualina-vonlanthendinenna 2022-02-23
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
# rm(profile_count_biome)

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

surface_ph_biome <- profile_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season_order, season, biome_name, ph_extreme, file_id) %>% 
  summarise(argo_surf_ph = mean(pH, na.rm=TRUE),
            OceanSODA_surf_ph = mean(OceanSODA_ph, na.rm = TRUE))

surface_ph_biome %>% 
  group_by(ph_extreme) %>% 
  group_split(ph_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = OceanSODA_surf_ph, 
             y = argo_surf_ph))+
  geom_bin2d(data = .x, aes(x = OceanSODA_surf_ph, 
                 y = argo_surf_ph)) +
  scale_fill_viridis_c()+
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(7.94, 8.21),
              ylim = c(7.94, 8.21))+
  facet_grid(season_order~biome_name, labeller = facet_label) +
    labs(title = paste( 'pH extreme:', unique(.x$ph_extreme)),
         x = 'OceanSODA pH',
         y = 'Argo pH')
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
rm(surface_ph_biome)

Season x basin

profile_extreme_basin <- profile_extreme %>% 
  group_by(season_order, season, basin_AIP, ph_extreme, depth) %>% 
  summarise(ph_basin = mean(pH, na.rm = TRUE),
            ph_basin_std = sd(pH, na.rm = TRUE)) %>% 
  ungroup()

profile_extreme_basin %>% 
  ggplot(aes(x = ph_basin, 
             y = depth, 
             group = ph_extreme, 
             col = ph_extreme))+
  geom_ribbon(aes(xmax = ph_basin + ph_basin_std,
                  xmin = ph_basin - ph_basin_std,
                  group = ph_extreme,
                  fill = ph_extreme),
              col = NA,
              alpha = 0.3)+
  geom_path()+
  scale_color_manual(values = HNL_colors)+
  scale_fill_manual(values = HNL_colors)+
  labs(col = 'OceanSODA\npH anomaly\n(mean ± st dev)',
       fill = 'OceanSODA\npH anomaly\n(mean ± st dev)',
       y = 'depth (m)',
       x = 'Basin mean Argo pH',
       title = 'Basin-mean Argo profiles')+
  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)

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
daa0a8f pasqualina-vonlanthendinenna 2022-02-24
71ced5f pasqualina-vonlanthendinenna 2022-02-23
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
44fcfb6 pasqualina-vonlanthendinenna 2022-02-01
28c8d17 jens-daniel-mueller 2022-01-29
cfd734c jens-daniel-mueller 2022-01-28
c44ff0f pasqualina-vonlanthendinenna 2022-01-25
rm(profile_extreme_basin)

Number of profiles season x basin

profile_count_basin <- profile_extreme %>% 
  distinct(season_order, season, basin_AIP, ph_extreme, file_id) %>% 
  group_by(season_order, season, basin_AIP, ph_extreme) %>% 
  count(ph_extreme)

profile_count_basin %>% 
  ggplot(aes(x = ph_extreme, y = n, fill = ph_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')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
71ced5f pasqualina-vonlanthendinenna 2022-02-23
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
# rm(profile_count_basin)

Surface Argo vs surface OceanSODA pH (20 m) season x basin

# calculate surface-mean argo pH to compare against OceanSODA surface pH (one value)
surface_ph_basin <- profile_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season_order, season, basin_AIP, ph_extreme, file_id) %>% 
  summarise(surf_argo_ph = mean(pH, na.rm=TRUE),
            surf_OceanSODA_ph = mean(OceanSODA_ph, na.rm = TRUE)) 

surface_ph_basin %>% 
  group_by(ph_extreme) %>% 
  group_split(ph_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = surf_OceanSODA_ph, 
             y = surf_argo_ph))+
  geom_bin2d(data = .x, aes(x = surf_OceanSODA_ph, 
                 y = surf_argo_ph)) +
  scale_fill_viridis_c()+
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(7.94, 8.21),
              ylim = c(7.94, 8.21))+
  facet_grid(season_order~basin_AIP, labeller = facet_label) +
    labs(title = paste('pH extreme:', unique(.x$ph_extreme)),
         x = 'OceanSODA pH',
         y = 'Argo pH')
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
2d1bdae pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
2d1bdae pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
2d1bdae pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
rm(surface_ph_basin)

Biome-basin January mean

profile_extreme_biome_basin_jan <- profile_extreme %>%
  filter(month == 1) %>% 
  group_by(biome_name, basin_AIP, ph_extreme, depth) %>%
  summarise(ph_mean = mean(pH, na.rm = TRUE),
            ph_std = sd(pH, na.rm = TRUE)) %>%
  ungroup()

profile_extreme_biome_basin_jan %>%
  arrange(depth) %>%
  ggplot(aes(x = ph_mean,
             y = depth)) +
  geom_ribbon(aes(xmin = ph_mean - ph_std,
                  xmax = ph_mean + ph_std,
                  fill = ph_extreme), 
              alpha = 0.2)+
  geom_path(aes(x = ph_mean,
                col = ph_extreme))+
  facet_grid(basin_AIP~biome_name)+
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(title = "Basin-biome-mean January profiles",
       col = 'OceanSODA\nph anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\nph anomaly \n(mean ± st dev)',
       y = 'sqrt(depth)',
       x = 'mean Argo pH)') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
a2271df pasqualina-vonlanthendinenna 2022-03-30
rm(profile_extreme_biome_basin_jan)

Season x biome x basin

profile_extreme_season <- profile_extreme %>%
  group_by(season_order, season, biome_name, basin_AIP, ph_extreme, depth) %>%
  summarise(ph_mean = mean(pH, na.rm = TRUE),
            ph_std = sd(pH, na.rm = TRUE)) %>%
  ungroup()

profile_extreme_season %>%
  arrange(depth) %>%
  group_split(season_order) %>%
  # head(1) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(
        x = ph_mean,
        y = depth,
        group = ph_extreme,
        col = ph_extreme
      )
    ) +
      geom_ribbon(aes(xmax = ph_mean + ph_std,
                      xmin = ph_mean - ph_std,
                      group = ph_extreme,
                      fill = ph_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\npH anomaly\n(mean ± st dev)',
           fill = 'OceanSODA\npH anomaly\n(mean ± st dev)',
           y = 'sqrt(depth)',
           x = 'Argo pH') +
      scale_y_continuous(
        trans = trans_reverser("sqrt"),
        breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))
      ) +
      facet_grid(basin_AIP ~ biome_name)
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
daa0a8f pasqualina-vonlanthendinenna 2022-02-24
71ced5f pasqualina-vonlanthendinenna 2022-02-23
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
44fcfb6 pasqualina-vonlanthendinenna 2022-02-01
28c8d17 jens-daniel-mueller 2022-01-29

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
daa0a8f pasqualina-vonlanthendinenna 2022-02-24
71ced5f pasqualina-vonlanthendinenna 2022-02-23
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
44fcfb6 pasqualina-vonlanthendinenna 2022-02-01
28c8d17 jens-daniel-mueller 2022-01-29

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
daa0a8f pasqualina-vonlanthendinenna 2022-02-24
71ced5f pasqualina-vonlanthendinenna 2022-02-23
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
44fcfb6 pasqualina-vonlanthendinenna 2022-02-01
28c8d17 jens-daniel-mueller 2022-01-29

[[4]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
daa0a8f pasqualina-vonlanthendinenna 2022-02-24
71ced5f pasqualina-vonlanthendinenna 2022-02-23
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
44fcfb6 pasqualina-vonlanthendinenna 2022-02-01
28c8d17 jens-daniel-mueller 2022-01-29

Number of profiles per season x biome x basin x pH extreme

profile_count_season <- profile_extreme %>% 
  distinct(season_order, season, biome_name, basin_AIP,
           ph_extreme, file_id) %>% 
  group_by(season_order, season, biome_name, basin_AIP, ph_extreme) %>% 
  count(ph_extreme)


profile_count_season %>% 
  group_by(season_order) %>% 
  group_split(season_order) %>% 
  map(
    ~ggplot()+
      geom_col(data =.x, 
               aes(x = ph_extreme,
                   y = n,
                   fill = ph_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]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
71ced5f pasqualina-vonlanthendinenna 2022-02-23
2d1bdae pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
71ced5f pasqualina-vonlanthendinenna 2022-02-23
2d1bdae pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
71ced5f pasqualina-vonlanthendinenna 2022-02-23
2d1bdae pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01

[[4]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
71ced5f pasqualina-vonlanthendinenna 2022-02-23
2d1bdae pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01
# rm(profile_count_season)

Surface OceanSODA pH vs surface Argo pH (20 m)

# calculate surface-mean argo pH, for each season x biome x basin x ph extreme 
surface_ph_season <- profile_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season_order,
           season, 
           basin_AIP, 
           biome_name, 
           ph_extreme, 
           file_id) %>% 
  summarise(surf_argo_ph = mean(pH, na.rm=TRUE),
            surf_OceanSODA_ph = mean(OceanSODA_ph, na.rm = TRUE)) 

surface_ph_season %>% 
  group_by(season_order, ph_extreme) %>% 
  group_split(season_order, ph_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = surf_OceanSODA_ph, 
             y = surf_argo_ph))+
  geom_bin2d(data = .x, aes(x = surf_OceanSODA_ph, 
                 y = surf_argo_ph)) +
  scale_fill_viridis_c()+
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(7.94, 8.21),
              ylim = c(7.94, 8.21))+
  facet_grid(basin_AIP ~ biome_name) +
    labs(title = paste('season:', unique(.x$season), 
                        '| pH extreme:', unique(.x$ph_extreme)),
         x = 'OceanSODA pH',
         y = 'Argo pH')
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
de183c6 pasqualina-vonlanthendinenna 2022-02-01
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
de183c6 pasqualina-vonlanthendinenna 2022-02-01
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
de183c6 pasqualina-vonlanthendinenna 2022-02-01
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01

[[4]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
de183c6 pasqualina-vonlanthendinenna 2022-02-01
44a2ec3 pasqualina-vonlanthendinenna 2022-02-01

[[5]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[6]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[7]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[8]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[9]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[10]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[11]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02

[[12]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
fd521d1 pasqualina-vonlanthendinenna 2022-02-25
71ced5f pasqualina-vonlanthendinenna 2022-02-23
25c9e6b pasqualina-vonlanthendinenna 2022-02-03
71958c4 pasqualina-vonlanthendinenna 2022-02-03
d7debab pasqualina-vonlanthendinenna 2022-02-02
7376be6 pasqualina-vonlanthendinenna 2022-02-02
rm(surface_ph_season)
Atl, SPSS biome, winter
profile_extreme_season %>%
  filter(basin_AIP == 'Atlantic',
         season == 'winter',
         biome_name == 'SPSS') %>% 
  arrange(depth) %>%
  ggplot(aes(x = ph_mean,
             y = depth,
             group = ph_extreme,
             col = ph_extreme)) +
  geom_ribbon(aes(xmax = ph_mean + ph_std,
                  xmin = ph_mean - ph_std,
                  group = ph_extreme,
                  fill = ph_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\npH anomaly\n(mean ± st dev)',
       fill = 'OceanSODA\npH anomaly\n(mean ± st dev)',
       y = 'sqrt(depth)',
       x = 'Argo pH') +
  scale_y_continuous(
        trans = trans_reverser("sqrt"),
        breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
a2271df pasqualina-vonlanthendinenna 2022-03-30
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
e4188d2 pasqualina-vonlanthendinenna 2022-03-01
Atl, STSS biome, summer
profile_extreme_season %>%
  filter(basin_AIP == 'Atlantic',
         season == 'summer',
         biome_name == 'STSS') %>% 
  arrange(depth) %>%
  ggplot(aes(x = ph_mean,
             y = depth,
             group = ph_extreme,
             col = ph_extreme)) +
  geom_ribbon(aes(xmax = ph_mean + ph_std,
                  xmin = ph_mean - ph_std,
                  group = ph_extreme,
                  fill = ph_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, summer',
       col = 'OceanSODA\npH anomaly\n(mean ± st dev)',
       fill = 'OceanSODA\npH anomaly\n(mean ± st dev)',
       y = 'sqrt(depth)',
       x = 'Argo pH') +
  scale_y_continuous(
        trans = trans_reverser("sqrt"),
        breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
b917bd0 jens-daniel-mueller 2022-05-11
9875dd0 pasqualina-vonlanthendinenna 2022-04-05
eb8e3be pasqualina-vonlanthendinenna 2022-03-31
a2271df pasqualina-vonlanthendinenna 2022-03-30
968ad85 pasqualina-vonlanthendinenna 2022-03-29
dfd75e9 pasqualina-vonlanthendinenna 2022-03-29
e12a216 pasqualina-vonlanthendinenna 2022-03-15
1ffe07f pasqualina-vonlanthendinenna 2022-03-11
7540ae4 pasqualina-vonlanthendinenna 2022-03-08
e4188d2 pasqualina-vonlanthendinenna 2022-03-01

Anomaly profiles

Read anomalies

# profile_extreme_binned <- profile_extreme %>%
#   group_by(lon, lat, year, month, file_id,
#            biome_name, basin_AIP, ph_extreme,
#            depth, season, season_order) %>%
#   summarise(ph_adjusted_binned = mean(pH, na.rm = TRUE)) %>%
#   ungroup()
# broullon_clim <- read_rds(file = paste0(path_argo_preprocessed, 
#                                         '/broullon_TA_DIC_clim_SO_pH.rds'))
# 
# # compatibility with profile_extreme
# broullon_clim <- broullon_clim %>% 
#   mutate(depth_broullon = depth)
# 
# # grid average climatological temp into the argo depth bins 
# broullon_clim <- broullon_clim %>%
#   mutate(
#     depth = cut(
#       depth_broullon,
#       breaks = c(0, 10, 20, 30, 50, 70, 100, 300, 500, 800, 1000, 1500, 2000),
#       include.lowest = TRUE,
#       labels = as.factor(unique(profile_extreme$depth))[1:12]
#     ),
#     depth = as.numeric(as.character(depth))
#   )
# 
# 
# # calculate mean climatological pH per depth bin
# broullon_clim_binned <- broullon_clim %>% 
#   group_by(lon, lat, depth, month, basin_AIP, biome_name) %>% 
#   summarise(clim_ph_binned = mean(pH, na.rm = TRUE)) %>%
#   ungroup()
# 
# 
# # join climatology and ARGO profiles
# remove_clim <- inner_join(profile_extreme_binned, 
#                               broullon_clim_binned)


remove_clim <-
  read_rds(file = paste0(path_argo_preprocessed, "/pH_anomaly_va.rds")) %>%
  filter(profile_range >= opt_min_profile_range) %>%
  mutate(date = ymd(format(date, "%Y-%m-15")))

remove_clim <- inner_join(
  remove_clim %>%
    select(
      file_id, 
      year, 
      month, 
      date, 
      lon, 
      lat, 
      depth, 
      pH,
      clim_pH,
      anomaly_pH
      ),
  OceanSODA_SO_extreme_grid %>%
    select(
      year,
      month,
      date,
      lon,
      lat,
      OceanSODA_ph,
      ph_extreme,
      biome_name,
      basin_AIP
    )
)

remove_clim <- remove_clim %>%
  mutate(
    season = case_when(
      between(month, 3, 5) ~ 'autumn',
      between(month, 6, 8) ~ 'winter',
      between(month, 9, 11) ~ 'spring',
      month == 12 | 1 | 2 ~ 'summer'
    ),
    season_order = case_when(
      between(month, 3, 5) ~ 2,
      between(month, 6, 8) ~ 3,
      between(month, 9, 11) ~ 4,
      month == 12 | 1 | 2 ~ 1
    ),
    .after = date
  ) 

Profiles - Absolute

remove_clim %>%
  group_split(biome_name, basin_AIP, year) %>%
  head(6) %>%
  map(
    ~ ggplot() +
      geom_path(
        data = .x %>%
          filter(ph_extreme == 'N'),
        aes(
          x = pH,
          y = depth,
          group = file_id,
          col = ph_extreme
        ),
        size = 0.3
      ) +
      geom_path(
        data = .x %>%
          filter(ph_extreme == 'H' | ph_extreme == 'L'),
        aes(
          x = pH,
          y = depth,
          group = file_id,
          col = ph_extreme
        ),
        size = 0.5
      ) +
      geom_point(
        data = .x,
        aes(x = clim_pH,
            y = depth,
            col = ph_extreme),
        size = 0.5
      ) +
      scale_y_reverse() +
      scale_color_manual(values = HNL_colors) +
      labs(
        x = 'Argo pH',
        y = 'depth (m)',
        title = paste(
          "Biome:",
          unique(.x$biome_name),
          "| basin:",
          unique(.x$basin_AIP),
          " | ",
          unique(.x$year)
        ),
        col = 'OceanSODA pH \nanomaly'
      )
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[4]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[5]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[6]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

Profiles - Anomaly

remove_clim %>% 
  group_split(month) %>% 
  #head(1) %>% 
  map(
    ~ggplot()+
      geom_path(data = .x %>% filter(ph_extreme == 'N'),
                aes(x = anomaly_pH,
                    y = depth,
                    group = file_id,
                    col = ph_extreme),
                size = 0.2)+
      geom_path(data = .x %>% filter(ph_extreme == 'H'| ph_extreme == 'L'),
                 aes(x = anomaly_pH,
                     y = depth,
                     group = file_id,
                     col = ph_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 = paste('month:', unique(.x$month)))
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[4]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[5]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[6]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[7]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[8]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[9]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[10]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
91f08a6 mlarriere 2024-04-07
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[11]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

[[12]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
a107add pasqualina-vonlanthendinenna 2022-05-03

Overall mean anomaly profiles

remove_clim_overall_mean <- remove_clim %>% 
  group_by(ph_extreme, depth) %>% 
  summarise(ph_anomaly_mean = mean(anomaly_pH, na.rm = TRUE),
            ph_anomaly_sd = sd(anomaly_pH, na.rm = TRUE)) %>%
  ungroup()

remove_clim_overall_mean %>% 
  ggplot()+
  geom_path(aes(x = ph_anomaly_mean,
                y = depth,
                group = ph_extreme,
                col = ph_extreme))+
  geom_ribbon(aes(xmax = ph_anomaly_mean + ph_anomaly_sd,
                  xmin = ph_anomaly_mean - ph_anomaly_sd,
                  y = depth,
                  group = ph_extreme,
                  fill = ph_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_count_mean,
  #                 aes(x = -0.075,
  #                     y = 1500,
  #                     label = paste0(n),
  #                     col = ph_extreme),
  #                 size = 7,
  #                 segment.color = 'transparent')+
  geom_text(data = profile_count_mean[2,],
          aes(x = -0.07,
              y = 1200,
              label = paste0(n),
              col = ph_extreme),
          size = 6)+
  geom_text(data = profile_count_mean[1,],
          aes(x = -0.07,
              y = 1400,
              label = paste0(n),
              col = ph_extreme),
          size = 6)+
  geom_text(data = profile_count_mean[3,],
          aes(x = -0.07,
              y = 1600,
              label = paste0(n),
              col = ph_extreme),
          size = 6)+
  coord_cartesian(xlim = c(-0.08, 0.08))+
  scale_x_continuous(breaks = c(-0.08, -0.04, 0, 0.04, 0.08))+
  labs(title = 'Overall Mean Anomaly Profiles')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
708f923 pasqualina-vonlanthendinenna 2022-05-04
4e3571d pasqualina-vonlanthendinenna 2022-05-03
a107add pasqualina-vonlanthendinenna 2022-05-03
rm(remove_clim_overall_mean, profile_count_mean)

Biome-mean anomaly profiles

remove_clim_biome <- remove_clim %>% 
  group_by(depth, ph_extreme, biome_name, season_order, season) %>% 
  summarise(ph_anomaly_mean = mean(anomaly_pH, na.rm = TRUE),
            ph_anomaly_sd = sd(anomaly_pH, na.rm = TRUE))

remove_clim_biome %>% 
  ggplot()+
  geom_path(aes(x = ph_anomaly_mean,
                y = depth,
                group = ph_extreme,
                col = ph_extreme))+
  geom_ribbon(aes(xmax = ph_anomaly_mean + ph_anomaly_sd,
                  xmin = ph_anomaly_mean - ph_anomaly_sd,
                  y = depth, 
                  group = ph_extreme,
                  fill = ph_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)))+
  facet_grid(season_order~biome_name, labeller = facet_label)+
  scale_color_manual(values = HNL_colors)+
  scale_fill_manual(values = HNL_colors)+
  # geom_text_repel(data = profile_count_biome,
  #                 aes(x = -0.085,
  #                     y = 1500,
  #                     label = paste0(n),
  #                     col = ph_extreme),
  #                 size = 4,
  #                 segment.color = 'transparent')+
  geom_text(data = profile_count_biome %>% filter (ph_extreme == 'N'),
          aes(x = -0.07,
              y = 800,
              label = paste0(n),
              col = ph_extreme),
          size = 4)+
  geom_text(data = profile_count_biome %>% filter (ph_extreme == 'H'),
          aes(x = -0.07,
              y = 1200,
              label = paste0(n),
              col = ph_extreme),
          size = 4)+
  geom_text(data = profile_count_biome %>% filter (ph_extreme == 'L'),
          aes(x = -0.07,
              y = 1600,
              label = paste0(n),
              col = ph_extreme),
          size = 4)+
  coord_cartesian(xlim = c(-0.08, 0.08))+
  scale_x_continuous(breaks = c(-0.08, -0.04, 0, 0.04, 0.08))+
  labs(title = 'Biome-mean anomaly profiles')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
708f923 pasqualina-vonlanthendinenna 2022-05-04
b923426 pasqualina-vonlanthendinenna 2022-05-03
4e3571d pasqualina-vonlanthendinenna 2022-05-03
a107add pasqualina-vonlanthendinenna 2022-05-03
rm(remove_clim_biome, profile_count_biome)

Basin-mean anomaly profiles

remove_clim_basin <-  remove_clim %>% 
  group_by(depth, ph_extreme, basin_AIP, season_order, season) %>% 
  summarise(ph_anomaly_mean = mean(anomaly_pH, na.rm = TRUE),
            ph_anomaly_sd = sd(anomaly_pH, na.rm = TRUE))

remove_clim_basin %>% 
  ggplot()+
  geom_path(aes(x = ph_anomaly_mean,
                y = depth,
                group = ph_extreme,
                col = ph_extreme))+
  geom_ribbon(aes(xmax = ph_anomaly_mean + ph_anomaly_sd,
                  xmin = ph_anomaly_mean - ph_anomaly_sd,
                  y = depth,
                  group = ph_extreme,
                  fill = ph_extreme),
              col = NA,
              alpha = 0.2)+
  scale_y_continuous(trans = trans_reverser('sqrt'),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
  geom_vline(xintercept = 0)+
  facet_grid(season_order~basin_AIP, labeller = facet_label)+
  scale_color_manual(values = HNL_colors)+
  scale_fill_manual(values = HNL_colors)+
  # geom_text_repel(data = profile_count_basin,
  #                 aes(x = -0.1,
  #                     y = 1500,
  #                     label = paste0(n),
  #                     col = ph_extreme),
  #                 size = 4,
  #                 segment.color = 'transparent')+
  geom_text(data = profile_count_basin %>% filter (ph_extreme == 'N'),
          aes(x = -0.07,
              y = 800,
              label = paste0(n),
              col = ph_extreme),
          size = 4)+
  geom_text(data = profile_count_basin %>% filter (ph_extreme == 'H'),
          aes(x = -0.07,
              y = 1200,
              label = paste0(n),
              col = ph_extreme),
          size = 4)+
  geom_text(data = profile_count_basin %>% filter (ph_extreme == 'L'),
          aes(x = -0.07,
              y = 1600,
              label = paste0(n),
              col = ph_extreme),
          size = 4)+
  coord_cartesian(xlim = c(-0.08, 0.08))+
  scale_x_continuous(breaks = c(-0.08, -0.04, 0, 0.04, 0.08))+
  labs(title = 'Basin-mean anomaly profiles')

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
708f923 pasqualina-vonlanthendinenna 2022-05-04
b923426 pasqualina-vonlanthendinenna 2022-05-03
4e3571d pasqualina-vonlanthendinenna 2022-05-03
a107add pasqualina-vonlanthendinenna 2022-05-03
rm(remove_clim_basin, profile_count_basin)

Basin-biome-mean anomaly profiles

remove_clim_basin_biome <- remove_clim %>% 
  group_by(depth, ph_extreme, season_order, season, basin_AIP, biome_name) %>% 
  summarise(ph_anomaly_mean = mean(anomaly_pH, na.rm=TRUE),
            ph_anomaly_sd = sd(anomaly_pH, na.rm = TRUE))

remove_clim_basin_biome %>% 
  group_by(season_order) %>% 
  group_split(season_order) %>% 
  map(
    ~ggplot()+
      geom_path(data = .x,
            aes(x = ph_anomaly_mean,
                y = depth,
                group = ph_extreme,
                col = ph_extreme))+
      geom_ribbon(data = .x,
              aes(xmax = ph_anomaly_mean+ph_anomaly_sd,
                  xmin = ph_anomaly_mean-ph_anomaly_sd,
                  y = depth,
                  group = ph_extreme,
                  fill = ph_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)))+
      facet_grid(basin_AIP~biome_name)+
      scale_color_manual(values = HNL_colors)+
      scale_fill_manual(values = HNL_colors)+
      geom_text(data = profile_count_season %>% filter (ph_extreme == 'N' & season == unique(.x$season)),
              aes(x = -0.07,
                  y = 800,
                  label = paste0(n),
                  col = ph_extreme),
              size = 4)+
      geom_text(data = profile_count_season %>% filter (ph_extreme == 'H' & season == unique(.x$season)),
              aes(x = -0.07,
                  y = 1200,
                  label = paste0(n),
                  col = ph_extreme),
              size = 4)+
      geom_text(data = profile_count_season %>% filter (ph_extreme == 'L' & season == unique(.x$season)),
              aes(x = -0.07,
                  y = 1600,
                  label = paste0(n),
                  col = ph_extreme),
              size = 4)+
      coord_cartesian(xlim = c(-0.08, 0.08))+
      scale_x_continuous(breaks = c(-0.08, -0.04, 0, 0.04, 0.08))+
      labs(title = paste0('basin-biome-mean profiles ', unique(.x$season)))
  )
[[1]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
2efb8f2 ds2n19 2023-10-17
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
b923426 pasqualina-vonlanthendinenna 2022-05-03
4e3571d pasqualina-vonlanthendinenna 2022-05-03
a107add pasqualina-vonlanthendinenna 2022-05-03

[[2]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
2efb8f2 ds2n19 2023-10-17
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
b923426 pasqualina-vonlanthendinenna 2022-05-03
4e3571d pasqualina-vonlanthendinenna 2022-05-03
a107add pasqualina-vonlanthendinenna 2022-05-03

[[3]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
2efb8f2 ds2n19 2023-10-17
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
b923426 pasqualina-vonlanthendinenna 2022-05-03
4e3571d pasqualina-vonlanthendinenna 2022-05-03
a107add pasqualina-vonlanthendinenna 2022-05-03

[[4]]

Version Author Date
1a72545 mlarriere 2024-04-12
c076fba mlarriere 2024-04-12
f9de50e ds2n19 2024-01-01
23a24d1 ds2n19 2023-12-05
cec2a2a ds2n19 2023-11-24
ff31fef ds2n19 2023-11-20
80c16c2 ds2n19 2023-11-15
1cd9ec1 ds2n19 2023-10-19
879821d ds2n19 2023-10-18
2efb8f2 ds2n19 2023-10-17
a97d9c1 ds2n19 2023-10-17
1ae81b3 ds2n19 2023-10-11
44f5720 ds2n19 2023-10-09
ae0f995 jens-daniel-mueller 2022-05-12
4173c20 jens-daniel-mueller 2022-05-12
b917bd0 jens-daniel-mueller 2022-05-11
b923426 pasqualina-vonlanthendinenna 2022-05-03
4e3571d pasqualina-vonlanthendinenna 2022-05-03
a107add pasqualina-vonlanthendinenna 2022-05-03
rm(remove_clim_basin_biome)

sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggnewscale_0.4.8  ggrepel_0.9.2     ggforce_0.4.1     metR_0.13.0      
 [5] scico_1.3.1       ggOceanMaps_1.3.4 ggspatial_1.1.7   broom_1.0.5      
 [9] lubridate_1.9.0   timechange_0.1.1  forcats_0.5.2     stringr_1.5.0    
[13] dplyr_1.1.3       purrr_1.0.2       readr_2.1.3       tidyr_1.3.0      
[17] tibble_3.2.1      ggplot2_3.4.4     tidyverse_1.3.2   workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] googledrive_2.0.0   colorspace_2.0-3    ellipsis_0.3.2     
 [4] class_7.3-20        rprojroot_2.0.3     fs_1.5.2           
 [7] rstudioapi_0.15.0   proxy_0.4-27        farver_2.1.1       
[10] bit64_4.0.5         fansi_1.0.3         xml2_1.3.3         
[13] codetools_0.2-18    cachem_1.0.6        knitr_1.41         
[16] polyclip_1.10-4     jsonlite_1.8.3      dbplyr_2.2.1       
[19] rgeos_0.5-9         compiler_4.2.2      httr_1.4.4         
[22] backports_1.4.1     assertthat_0.2.1    fastmap_1.1.0      
[25] gargle_1.2.1        cli_3.6.1           later_1.3.0        
[28] tweenr_2.0.2        htmltools_0.5.8.1   tools_4.2.2        
[31] gtable_0.3.1        glue_1.6.2          Rcpp_1.0.10        
[34] cellranger_1.1.0    jquerylib_0.1.4     raster_3.6-11      
[37] vctrs_0.6.4         xfun_0.35           ps_1.7.2           
[40] rvest_1.0.3         lifecycle_1.0.3     googlesheets4_1.0.1
[43] terra_1.7-65        getPass_0.2-2       MASS_7.3-58.1      
[46] scales_1.2.1        vroom_1.6.0         hms_1.1.2          
[49] promises_1.2.0.1    parallel_4.2.2      RColorBrewer_1.1-3 
[52] yaml_2.3.6          memoise_2.0.1       sass_0.4.4         
[55] stringi_1.7.8       highr_0.9           e1071_1.7-12       
[58] checkmate_2.1.0     rlang_1.1.1         pkgconfig_2.0.3    
[61] evaluate_0.18       lattice_0.20-45     sf_1.0-9           
[64] labeling_0.4.2      bit_4.0.5           processx_3.8.0     
[67] tidyselect_1.2.0    magrittr_2.0.3      R6_2.5.1           
[70] generics_0.1.3      DBI_1.2.2           pillar_1.9.0       
[73] haven_2.5.1         whisker_0.4         withr_2.5.0        
[76] units_0.8-0         sp_1.5-1            modelr_0.1.10      
[79] crayon_1.5.2        KernSmooth_2.23-20  utf8_1.2.2         
[82] tzdb_0.3.0          rmarkdown_2.18      grid_4.2.2         
[85] readxl_1.4.1        data.table_1.14.6   callr_3.7.3        
[88] git2r_0.30.1        reprex_2.0.2        digest_0.6.30      
[91] classInt_0.4-8      httpuv_1.6.6        munsell_0.5.0      
[94] viridisLite_0.4.1   bslib_0.4.1