Last updated: 2022-03-08

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File	Version	Author	Date	Message
Rmd	18dff1b	pasqualina-vonlanthendinenna	2022-03-08	subsetted profiles with flag A only for extremes
html	33238fe	pasqualina-vonlanthendinenna	2022-03-02	Build site.
Rmd	17af9b5	pasqualina-vonlanthendinenna	2022-03-02	added January 2018 profile
html	97a098b	pasqualina-vonlanthendinenna	2022-03-02	Build site.
Rmd	5a073cb	pasqualina-vonlanthendinenna	2022-03-02	removed facet wrap
html	9d97f25	pasqualina-vonlanthendinenna	2022-03-02	Build site.
Rmd	9ccabc6	pasqualina-vonlanthendinenna	2022-03-02	removed facet wrap
html	e4188d2	pasqualina-vonlanthendinenna	2022-03-01	Build site.
Rmd	6ca535c	pasqualina-vonlanthendinenna	2022-03-01	updated profiles
html	da665ab	pasqualina-vonlanthendinenna	2022-03-01	Build site.
Rmd	57ada58	pasqualina-vonlanthendinenna	2022-03-01	updated figure aspects
html	5ef4df2	pasqualina-vonlanthendinenna	2022-03-01	Build site.
Rmd	8ef1277	pasqualina-vonlanthendinenna	2022-03-01	plotted Atlantic mean seasonal profiles
html	8ef1277	pasqualina-vonlanthendinenna	2022-03-01	plotted Atlantic mean seasonal profiles
Rmd	73463cc	pasqualina-vonlanthendinenna	2022-03-01	changed line thickness for H and L raw profiles
html	c4362e5	pasqualina-vonlanthendinenna	2022-02-28	Build site.
Rmd	5b0901d	pasqualina-vonlanthendinenna	2022-02-28	corrected dates and titles
html	ab29b31	pasqualina-vonlanthendinenna	2022-02-28	Build site.
Rmd	6e27fb1	pasqualina-vonlanthendinenna	2022-02-28	update with eval = false for single profile line thickness
html	d299359	pasqualina-vonlanthendinenna	2022-02-28	Build site.
Rmd	aad1df4	pasqualina-vonlanthendinenna	2022-02-28	plotted specific profiles
html	21582c5	pasqualina-vonlanthendinenna	2022-02-25	Build site.
Rmd	fe0b970	pasqualina-vonlanthendinenna	2022-02-25	plotted line profiles and changed HNL colors
html	fd521d1	pasqualina-vonlanthendinenna	2022-02-25	Build site.
Rmd	64c2c71	pasqualina-vonlanthendinenna	2022-02-25	plotted line profiles and changed HNL colors
html	daa0a8f	pasqualina-vonlanthendinenna	2022-02-24	Build site.
Rmd	4557a6e	pasqualina-vonlanthendinenna	2022-02-24	added st dev for pH profiles
html	08c8d4b	pasqualina-vonlanthendinenna	2022-02-23	Build site.
Rmd	7517b78	pasqualina-vonlanthendinenna	2022-02-23	updated regression and merging for extreme_pH
html	71ced5f	pasqualina-vonlanthendinenna	2022-02-23	Build site.
Rmd	48803ef	pasqualina-vonlanthendinenna	2022-02-23	updated regression and merging for extreme_pH
html	905d82f	pasqualina-vonlanthendinenna	2022-02-15	Build site.
html	54ea512	pasqualina-vonlanthendinenna	2022-02-10	Build site.
html	f2fa56a	pasqualina-vonlanthendinenna	2022-02-10	Build site.
Rmd	eda8ca8	pasqualina-vonlanthendinenna	2022-02-10	code review
html	25c9e6b	pasqualina-vonlanthendinenna	2022-02-03	Build site.
Rmd	ecf2f74	pasqualina-vonlanthendinenna	2022-02-03	corrected surface mean pH
html	2d1bdae	pasqualina-vonlanthendinenna	2022-02-03	Build site.
Rmd	dcc269c	pasqualina-vonlanthendinenna	2022-02-03	changed figure aspect
html	71958c4	pasqualina-vonlanthendinenna	2022-02-03	Build site.
Rmd	3f38f15	pasqualina-vonlanthendinenna	2022-02-03	corrected mean argo surface pH
html	d7debab	pasqualina-vonlanthendinenna	2022-02-02	Build site.
Rmd	a73c7cf	pasqualina-vonlanthendinenna	2022-02-02	changed to log scale and mean surface argo ph
html	d14b7f1	pasqualina-vonlanthendinenna	2022-02-02	Build site.
Rmd	bb15149	pasqualina-vonlanthendinenna	2022-02-02	changed map figure aspect
html	31e4d42	pasqualina-vonlanthendinenna	2022-02-02	Build site.
Rmd	09ab7e9	pasqualina-vonlanthendinenna	2022-02-02	changed map figure aspect
html	7376be6	pasqualina-vonlanthendinenna	2022-02-02	Build site.
Rmd	ce1bbab	pasqualina-vonlanthendinenna	2022-02-02	updated bar charts and argo vs oceansoda ph
html	de183c6	pasqualina-vonlanthendinenna	2022-02-01	Build site.
Rmd	db007b5	pasqualina-vonlanthendinenna	2022-02-01	updated figure aspect
html	44a2ec3	pasqualina-vonlanthendinenna	2022-02-01	Build site.
Rmd	f62e851	pasqualina-vonlanthendinenna	2022-02-01	added flat maps, bar charts and OceanSODA vs argo pH
html	44fcfb6	pasqualina-vonlanthendinenna	2022-02-01	Build site.
Rmd	b45a03e	pasqualina-vonlanthendinenna	2022-02-01	added sigma maps and log transform depth
html	28c8d17	jens-daniel-mueller	2022-01-29	Build site.
Rmd	c0c12d0	jens-daniel-mueller	2022-01-28	code cleaning: basinmask and regression
html	cfd734c	jens-daniel-mueller	2022-01-28	Build site.
Rmd	5024768	jens-daniel-mueller	2022-01-28	code review: basinmask and regression
html	5635ef2	pasqualina-vonlanthendinenna	2022-01-27	Build site.
Rmd	23dc282	pasqualina-vonlanthendinenna	2022-01-27	failed attempt at updating basinmask and regression
html	c44ff0f	pasqualina-vonlanthendinenna	2022-01-25	Build site.
Rmd	3851824	pasqualina-vonlanthendinenna	2022-01-25	added basin-mean profiles
html	962cdb9	pasqualina-vonlanthendinenna	2022-01-25	Build site.
Rmd	825a50a	pasqualina-vonlanthendinenna	2022-01-25	added seasonal and biome profiles
html	3ae43e4	pasqualina-vonlanthendinenna	2022-01-24	Build site.
Rmd	3f8e824	pasqualina-vonlanthendinenna	2022-01-24	updated 24/01
html	6b22341	pasqualina-vonlanthendinenna	2022-01-21	Build site.
Rmd	e72d7ca	pasqualina-vonlanthendinenna	2022-01-21	updated linear regression to monthly
html	587755e	pasqualina-vonlanthendinenna	2022-01-21	Build site.
Rmd	7a9209b	pasqualina-vonlanthendinenna	2022-01-21	updated threshold calculation 2
html	c96ad5e	pasqualina-vonlanthendinenna	2022-01-21	Build site.
Rmd	58b3b3b	pasqualina-vonlanthendinenna	2022-01-21	updated threshold calculation
html	ed3fef2	jens-daniel-mueller	2022-01-07	Build site.
Rmd	3d2f8fc	jens-daniel-mueller	2022-01-07	code review
html	486c9c8	jens-daniel-mueller	2022-01-07	Build site.
Rmd	e9ad067	jens-daniel-mueller	2022-01-07	code review
html	343689f	pasqualina-vonlanthendinenna	2022-01-06	Build site.
Rmd	f53cc2d	pasqualina-vonlanthendinenna	2022-01-06	updated profile page
html	b8a6482	pasqualina-vonlanthendinenna	2022-01-03	Build site.
Rmd	054f8a6	pasqualina-vonlanthendinenna	2022-01-03	added Argo profiles

Task

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

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

HNL_colors_map <- c('H' = 'red3',
                    'N' = 'gray90',
                    'L' = 'blue3')

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/"

# RECCAP2-ocean region mask

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

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

# WOA 18 basin mask

basinmask <-
  read_csv(
    paste(path_emlr_utilities,
          "basin_mask_WOA18.csv",
          sep = ""),
    col_types = cols("MLR_basins" = col_character())
  )

basinmask <- basinmask %>%
  filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>% 
  select(-c(MLR_basins, basin))

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

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

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

full_argo <- read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_flag_A.rds")) %>% 
  select(-c(temp_adjusted:temp_adjusted_error,
            profile_temp_qc))

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

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

Regions

Biomes

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

Remove coastal data

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

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

Version	Author	Date
d14b7f1	pasqualina-vonlanthendinenna	2022-02-02
7376be6	pasqualina-vonlanthendinenna	2022-02-02
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01

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

Grid reduction

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

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

Version	Author	Date
d14b7f1	pasqualina-vonlanthendinenna	2022-02-02
7376be6	pasqualina-vonlanthendinenna	2022-02-02
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01

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

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

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

Version	Author	Date
d14b7f1	pasqualina-vonlanthendinenna	2022-02-02
7376be6	pasqualina-vonlanthendinenna	2022-02-02
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01

Basins

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

Grid reduction

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

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

Version	Author	Date
d14b7f1	pasqualina-vonlanthendinenna	2022-02-02
7376be6	pasqualina-vonlanthendinenna	2022-02-02
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01

basinmask_2x2 <- basinmask %>%
  mutate(
    lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)), 
    lat = as.numeric(as.character(lat)),
    lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)), 
    lon = as.numeric(as.character(lon))
   ) # regrid into 2x2º grid

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

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

rm(basinmask)

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

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

Version	Author	Date
d14b7f1	pasqualina-vonlanthendinenna	2022-02-02
7376be6	pasqualina-vonlanthendinenna	2022-02-02
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01

OceanSODA

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

Apply region masks

# keep only Southern Ocean data
OceanSODA_2x2_SO <- inner_join(OceanSODA_2x2, region_masks_all_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))

OceanSODA pH anomalies

Grid level

Climatological thresholds

Fit lm models

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

OceanSODA_regression <- OceanSODA_2x2_SO %>% 
  # filter(basin_AIP == "Indian",
  #        biome == "2",
  #        lon < 40) %>%
  nest(data = -c(lon, lat, month)) %>%
  mutate(fit = map(.x = data,
                   .f = ~ lm(ph_total ~ 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 = 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:year, .resid)

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

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

Slope maps

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

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

Version	Author	Date
31e4d42	pasqualina-vonlanthendinenna	2022-02-02
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01

Residual st. dev. maps

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

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

Version	Author	Date
31e4d42	pasqualina-vonlanthendinenna	2022-02-02
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01

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

OceanSODA_2x2_SO_extreme_grid <-
  full_join(
    OceanSODA_regression_augmented,
    OceanSODA_regression_glanced %>%
      select(lat:month, sigma)
  )

# identify observations in anomaly classes

OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
  mutate(
    ph_extreme = case_when(
      .resid < -sigma*2 ~ 'L',
      .resid > sigma*2 ~ 'H',
      TRUE ~ 'N'
    )
  ) 

OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
  mutate(ph_extreme = fct_relevel(ph_extreme, "H", "N", "L"))


# combine with regression coefficients

OceanSODA_2x2_SO_extreme_grid <-
  full_join(OceanSODA_2x2_SO_extreme_grid,
            OceanSODA_regression_tidied)

OceanSODA_2x2_SO_extreme_grid %>%
  group_split(lon, lat, month) %>%
  head(6) %>%
  map(~ ggplot(data = .x) +
        geom_point(aes(x = year,
                       y = ph_total,
                       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
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
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
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
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
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
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

Anomaly maps

Anomalies on a 2x2 grid

Location of OceanSODA pH extremes

OceanSODA_2x2_SO_extreme_grid %>% 
  group_split(year) %>% 
  # head(2) %>%
  map(
    ~ basemap(limits = -32, data = .x)+
      geom_spatial_tile(data = .x,
                        aes(x = lon,
                            y = lat,
                            fill = ph_extreme),
                        linejoin = 'mitre',
                        col = 'transparent',
                        detail = 60
                        ) +
      scale_fill_manual(values = HNL_colors_map) +
      facet_wrap(~month, ncol = 2)+
      labs(title = paste("Year:", unique(.x$year)),
           fill = 'pH')
  )

OceanSODA_2x2_SO_extreme_grid %>% 
  group_split(year) %>% 
  map(
    ~map +
      geom_tile(data = .x,
                aes(x = lon,
                    y = lat, 
                    fill = ph_extreme))+
      scale_fill_manual(values = HNL_colors_map)+
      facet_wrap(~month, ncol = 2)+
      lims(y = c(-85, -30))+
      labs(title = paste('Year:', unique(.x$year)),
           fill = 'pH')
  )

[[1]]

Version	Author	Date
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01


[[2]]

Version	Author	Date
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01


[[3]]

Version	Author	Date
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01


[[4]]

Version	Author	Date
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01


[[5]]

Version	Author	Date
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01


[[6]]

Version	Author	Date
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01


[[7]]

Version	Author	Date
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01


[[8]]

Version	Author	Date
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
de183c6	pasqualina-vonlanthendinenna	2022-02-01
44a2ec3	pasqualina-vonlanthendinenna	2022-02-01

Anomalies on a 1x1 grid

#use the original lat/lon grid to plot the extreme on a 1x1

OceanSODA_2x2_SO_extreme_grid %>% 
  group_split(year) %>% 
  # head(3) %>% 
  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(~month, ncol = 2)+
      lims(y = c(-85, -30))+
      labs(title = paste('Year:', unique(.x$year)),
           fill = 'pH')
  )

[[1]]

Version	Author	Date
da665ab	pasqualina-vonlanthendinenna	2022-03-01


[[2]]

Version	Author	Date
da665ab	pasqualina-vonlanthendinenna	2022-03-01


[[3]]

Version	Author	Date
da665ab	pasqualina-vonlanthendinenna	2022-03-01


[[4]]

Version	Author	Date
e4188d2	pasqualina-vonlanthendinenna	2022-03-01


[[5]]

Version	Author	Date
e4188d2	pasqualina-vonlanthendinenna	2022-03-01


[[6]]

Version	Author	Date
e4188d2	pasqualina-vonlanthendinenna	2022-03-01


[[7]]

Version	Author	Date
e4188d2	pasqualina-vonlanthendinenna	2022-03-01


[[8]]

Version	Author	Date
e4188d2	pasqualina-vonlanthendinenna	2022-03-01

Anomaly time series

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

OceanSODA_2x2_SO_extreme_grid %>% 
  group_by(year, biome, 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)+
  theme(legend.position = 'bottom')

Version	Author	Date
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_2x2_SO_extreme_grid %>%
  ggplot(aes(ph_total, col = ph_extreme)) +
  geom_density() +
  scale_color_manual(values = HNL_colors) +
  facet_grid(basin_AIP ~ biome) +
  coord_cartesian(xlim = c(8, 8.2)) +
  labs(x = 'value',
       y = 'density',
       col = 'pH anomaly') +
  theme(legend.position = 'bottom')

Version	Author	Date
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

Apply region masks

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

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

Join OceanSODA

# rename OceanSODA columns
OceanSODA_2x2_SO_extreme_grid <- OceanSODA_2x2_SO_extreme_grid %>%
  select(-c(lon, lat)) %>% 
  rename(OceanSODA_ph = ph_total,
         lon = lon_raw,
         lat = lat_raw)

# combine the argo profile data to the surface extreme data
profile_extreme <- inner_join(
  full_argo %>% 
    select(year, month, date, lon, lat, depth,
           ph_in_situ_total_adjusted,
           platform_number,
           cycle_number),
  OceanSODA_2x2_SO_extreme_grid %>% 
    select(year, month, lon, lat,
           OceanSODA_ph, ph_extreme,
           biome, basin_AIP)) %>% 
  unite('platform_cycle', platform_number:cycle_number, sep = '_', remove = FALSE)

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, basin_AIP, year) %>% 
   head(15) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(
        x = ph_in_situ_total_adjusted,
        y = depth,
        group = platform_cycle,
        col = ph_extreme
      )
    ) +
      geom_path(data = .x %>% filter(ph_extreme == 'N'),
                aes(x = ph_in_situ_total_adjusted,
                    y = depth,
                    group = platform_cycle,
                    col = ph_extreme),
                size = 0.3) +
      geom_path(data = .x %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
                aes(x = ph_in_situ_total_adjusted,
                    y = depth,
                    group = platform_cycle,
                    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)
        ),
        col = 'OceanSODA pH \nanomaly'
      )
  )

[[1]]

Version	Author	Date
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
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
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
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
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
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


[[7]]

Version	Author	Date
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


[[8]]

Version	Author	Date
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


[[9]]

Version	Author	Date
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


[[10]]

Version	Author	Date
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


[[11]]

Version	Author	Date
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
21582c5	pasqualina-vonlanthendinenna	2022-02-25
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
71ced5f	pasqualina-vonlanthendinenna	2022-02-23
28c8d17	jens-daniel-mueller	2022-01-29


[[12]]

Version	Author	Date
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
21582c5	pasqualina-vonlanthendinenna	2022-02-25
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
71ced5f	pasqualina-vonlanthendinenna	2022-02-23
28c8d17	jens-daniel-mueller	2022-01-29


[[13]]

Version	Author	Date
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
21582c5	pasqualina-vonlanthendinenna	2022-02-25
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
71ced5f	pasqualina-vonlanthendinenna	2022-02-23
28c8d17	jens-daniel-mueller	2022-01-29


[[14]]

Version	Author	Date
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
21582c5	pasqualina-vonlanthendinenna	2022-02-25
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
71ced5f	pasqualina-vonlanthendinenna	2022-02-23
28c8d17	jens-daniel-mueller	2022-01-29


[[15]]

Version	Author	Date
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
21582c5	pasqualina-vonlanthendinenna	2022-02-25
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
71ced5f	pasqualina-vonlanthendinenna	2022-02-23
28c8d17	jens-daniel-mueller	2022-01-29

Atlantic basin, biome 1, August 2017

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

OceanSODA_2x2_SO_extreme_grid_2017 <-  
  OceanSODA_2x2_SO_extreme_grid %>% 
  filter(date == '2017-08-15')

map+
  geom_tile(data = OceanSODA_2x2_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 == '1')

profile_extreme_Atl_2017 %>% 
  ggplot(aes(y = depth, 
             x = ph_in_situ_total_adjusted,
             group = platform_cycle,
             col = ph_extreme))+
  geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'N'),
            aes(x = ph_in_situ_total_adjusted,
                y = depth, 
                group = platform_cycle,
                col = ph_extreme),
            size = 0.3)+
  geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'H'| ph_extreme == 'L'),
            aes(x = ph_in_situ_total_adjusted,
                y = depth,
                group = platform_cycle,
                col = ph_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Atlantic basin, biome 1, August 2017',
       col = 'OceanSODA\npH anomaly',
       x = 'Argo pH')

Atlantic basin, biome 1, December 2017

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

OceanSODA_2x2_SO_extreme_grid_2017 <-   
  OceanSODA_2x2_SO_extreme_grid %>% 
  filter(date == '2017-12-15')

map+
  geom_tile(data = OceanSODA_2x2_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 == '1')

profile_extreme_Atl_2017 %>% 
  ggplot(aes(y = depth, 
             x = ph_in_situ_total_adjusted,
             group = platform_cycle,
             col = ph_extreme))+
  geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'N'),
            aes(x = ph_in_situ_total_adjusted,
                y = depth,
                group = platform_cycle,
                col = ph_extreme),
            size = 0.3)+
  geom_path(data = profile_extreme_Atl_2017 %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
            aes(x = ph_in_situ_total_adjusted,
                y = depth,
                group = platform_cycle,
                col = ph_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Atlantic basin, biome 1, December 2017',
       col = 'OceanSODA\npH anomaly',
       x = 'Argo pH')

Atlantic basin, biome 1, January 2018

OceanSODA_2x2_SO_extreme_grid_2018 <-   
  OceanSODA_2x2_SO_extreme_grid %>% 
  filter(date == '2018-01-15')

map+
  geom_tile(data = OceanSODA_2x2_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 == '1')

profile_extreme_Atl_2018 %>% 
  ggplot(aes(y = depth, 
             x = ph_in_situ_total_adjusted,
             group = platform_cycle,
             col = ph_extreme))+
  geom_path(data = profile_extreme_Atl_2018 %>% filter(ph_extreme == 'N'),
            aes(x = ph_in_situ_total_adjusted,
                y = depth,
                group = platform_cycle,
                col = ph_extreme),
            size = 0.3)+
  geom_path(data = profile_extreme_Atl_2018 %>% filter(ph_extreme == 'H' | ph_extreme == 'L'),
            aes(x = ph_in_situ_total_adjusted,
                y = depth,
                group = platform_cycle,
                col = ph_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Atlantic basin, biome 1, January 2018',
       col = 'OceanSODA\npH anomaly',
       x = 'Argo pH')

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(
    depth = Hmisc::cut2(
      depth,
      cuts = c(10, 20, 30, 50, 70, 100, 300, 500, 800, 1000, 1500, 2000, 2500),
      m = 5,
      levels.mean = TRUE
    ),
    depth = as.numeric(as.character(depth))
  ) %>%
  mutate(
    season = case_when(
      between(month, 3, 5) ~ 'autumn',
      between(month, 6, 8) ~ 'winter',
      between(month, 9, 11) ~ 'spring',
      month == 12 | 1 | 2 ~ 'summer'
    ),
    .after = date
  )

Overall mean

profile_extreme_mean <- profile_extreme %>%
  group_by(ph_extreme, depth) %>%
  summarise(ph_mean = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
            ph_std = sd(ph_in_situ_total_adjusted, 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 = 'sqrt(depth)',
       x = 'Argo mean pH') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Version	Author	Date
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

profile_count_mean <- profile_extreme %>% 
  distinct(ph_extreme, platform_number, cycle_number) %>% 
  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
71ced5f	pasqualina-vonlanthendinenna	2022-02-23
d7debab	pasqualina-vonlanthendinenna	2022-02-02
7376be6	pasqualina-vonlanthendinenna	2022-02-02

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, platform_number, cycle_number) %>% 
  summarise(argo_surf_ph = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
            OceanSODA_surf_ph = mean(OceanSODA_ph, na.rm = TRUE))


  # mutate(argo_surf_ph = mean(ph_in_situ_total_adjusted, na.rm = TRUE)) %>% 
  # select(ph_extreme, 
  #        platform_number,
  #        cycle_number,
  #        OceanSODA_ph,
  #        argo_surf_ph) %>% 
  # distinct()

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
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
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
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

Season x biome

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

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 = 'sqrt(depth)',
       x = 'Biome mean Argo pH') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
  facet_grid(season ~ biome)

Version	Author	Date
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 season x biome

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

Version	Author	Date
71ced5f	pasqualina-vonlanthendinenna	2022-02-23
d7debab	pasqualina-vonlanthendinenna	2022-02-02
7376be6	pasqualina-vonlanthendinenna	2022-02-02

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

surface_ph_biome <- profile_extreme %>% 
  filter(depth < 20) %>% 
  group_by(season, biome, ph_extreme, platform_number, cycle_number) %>% 
  summarise(argo_surf_ph = mean(ph_in_situ_total_adjusted, 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~biome) +
    labs(title = paste( 'pH extreme:', unique(.x$ph_extreme)),
         x = 'OceanSODA pH',
         y = 'Argo pH')
  )

[[1]]

Version	Author	Date
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
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
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

Season x basin

profile_extreme_basin <- profile_extreme %>% 
  group_by(season, basin_AIP, ph_extreme, depth) %>% 
  summarise(ph_basin = mean(ph_in_situ_total_adjusted, na.rm = TRUE),
            ph_basin_std = sd(ph_in_situ_total_adjusted, 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 = 'sqrt(depth)',
       x = 'Basin mean Argo pH')+
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
  facet_grid(season~basin_AIP)

Version	Author	Date
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

Number of profiles season x basin

profile_count_basin <- profile_extreme %>% 
  distinct(season, basin_AIP, ph_extreme, platform_number, cycle_number) %>% 
  group_by(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~basin_AIP)+
  scale_y_continuous(trans = 'log10')+
  labs(y = 'log(number of profiles)',
       title = 'Number of profiles season x basin')

Version	Author	Date
71ced5f	pasqualina-vonlanthendinenna	2022-02-23
d7debab	pasqualina-vonlanthendinenna	2022-02-02
7376be6	pasqualina-vonlanthendinenna	2022-02-02

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, basin_AIP, ph_extreme, platform_number, cycle_number) %>% 
  summarise(surf_argo_ph = mean(ph_in_situ_total_adjusted, 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~basin_AIP) +
    labs(title = paste('pH extreme:', unique(.x$ph_extreme)),
         x = 'OceanSODA pH',
         y = 'Argo pH')
  )

[[1]]

Version	Author	Date
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
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
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

Season x biome x basin

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

profile_extreme_season %>%
  arrange(depth) %>%
  group_split(season) %>%
  # 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)
  )

[[1]]

Version	Author	Date
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
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
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
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, biome, basin_AIP,
           ph_extreme, platform_number, cycle_number) %>% 
  group_by(season, biome, basin_AIP, ph_extreme) %>% 
  count(ph_extreme)


profile_count_season %>% 
  group_by(season) %>% 
  group_split(season) %>% 
  map(
    ~ggplot()+
      geom_col(data =.x, 
               aes(x = ph_extreme,
                   y = n,
                   fill = ph_extreme),
               width = 0.5)+
      facet_grid(basin_AIP ~ biome)+
      scale_y_continuous(trans = 'log10')+
      labs(y = 'log(number of profiles)',
           title = paste('season:', unique(.x$season)))
  )

[[1]]

Version	Author	Date
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
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
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
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

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, 
           basin_AIP, 
           biome, 
           ph_extreme, 
           platform_number, 
           cycle_number) %>% 
  summarise(surf_argo_ph = mean(ph_in_situ_total_adjusted, na.rm=TRUE),
            surf_OceanSODA_ph = mean(OceanSODA_ph, na.rm = TRUE)) 

surface_ph_season %>% 
  group_by(season, ph_extreme) %>% 
  group_split(season, 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) +
    labs(title = paste('season:', unique(.x$season), 
                        '| pH extreme:', unique(.x$ph_extreme)),
         x = 'OceanSODA pH',
         y = 'Argo pH')
  )

[[1]]

Version	Author	Date
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
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
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
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
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
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
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
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
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
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
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

Atlantic basin, biome 2, winter

profile_extreme_season %>%
  filter(basin_AIP == 'Atlantic',
         season == 'winter',
         biome == '1') %>% 
  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, biome 1, 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
e4188d2	pasqualina-vonlanthendinenna	2022-03-01

Atlantic basin, biome 1, summer

profile_extreme_season %>%
  filter(basin_AIP == 'Atlantic',
         season == 'summer',
         biome == '1') %>% 
  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, biome 1, 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
e4188d2	pasqualina-vonlanthendinenna	2022-03-01

sessionInfo()

R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3

Matrix products: default
BLAS:   /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so

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

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

other attached packages:
 [1] ggforce_0.3.3     metR_0.11.0       scico_1.3.0       ggOceanMaps_1.2.6
 [5] ggspatial_1.1.5   broom_0.7.11      lubridate_1.8.0   forcats_0.5.1    
 [9] stringr_1.4.0     dplyr_1.0.7       purrr_0.3.4       readr_2.1.1      
[13] tidyr_1.1.4       tibble_3.1.6      ggplot2_3.3.5     tidyverse_1.3.1  
[17] workflowr_1.7.0  

loaded via a namespace (and not attached):
  [1] colorspace_2.0-2    ellipsis_0.3.2      class_7.3-20       
  [4] rgdal_1.5-28        rprojroot_2.0.2     htmlTable_2.4.0    
  [7] base64enc_0.1-3     fs_1.5.2            rstudioapi_0.13    
 [10] proxy_0.4-26        farver_2.1.0        bit64_4.0.5        
 [13] fansi_1.0.2         xml2_1.3.3          codetools_0.2-18   
 [16] splines_4.1.2       knitr_1.37          polyclip_1.10-0    
 [19] Formula_1.2-4       jsonlite_1.7.3      cluster_2.1.2      
 [22] dbplyr_2.1.1        png_0.1-7           rgeos_0.5-9        
 [25] compiler_4.1.2      httr_1.4.2          backports_1.4.1    
 [28] assertthat_0.2.1    Matrix_1.4-0        fastmap_1.1.0      
 [31] cli_3.1.1           later_1.3.0         tweenr_1.0.2       
 [34] htmltools_0.5.2     tools_4.1.2         gtable_0.3.0       
 [37] glue_1.6.0          Rcpp_1.0.8          cellranger_1.1.0   
 [40] jquerylib_0.1.4     raster_3.5-11       vctrs_0.3.8        
 [43] xfun_0.29           ps_1.6.0            rvest_1.0.2        
 [46] lifecycle_1.0.1     terra_1.5-12        getPass_0.2-2      
 [49] MASS_7.3-55         scales_1.1.1        vroom_1.5.7        
 [52] hms_1.1.1           promises_1.2.0.1    parallel_4.1.2     
 [55] RColorBrewer_1.1-2  yaml_2.2.1          gridExtra_2.3      
 [58] sass_0.4.0          rpart_4.1-15        latticeExtra_0.6-29
 [61] stringi_1.7.6       highr_0.9           e1071_1.7-9        
 [64] checkmate_2.0.0     rlang_0.4.12        pkgconfig_2.0.3    
 [67] evaluate_0.14       lattice_0.20-45     sf_1.0-5           
 [70] htmlwidgets_1.5.4   labeling_0.4.2      bit_4.0.4          
 [73] processx_3.5.2      tidyselect_1.1.1    magrittr_2.0.1     
 [76] R6_2.5.1            generics_0.1.1      Hmisc_4.6-0        
 [79] DBI_1.1.2           foreign_0.8-82      pillar_1.6.4       
 [82] haven_2.4.3         whisker_0.4         withr_2.4.3        
 [85] units_0.7-2         nnet_7.3-17         survival_3.2-13    
 [88] sp_1.4-6            modelr_0.1.8        crayon_1.4.2       
 [91] KernSmooth_2.23-20  utf8_1.2.2          tzdb_0.2.0         
 [94] rmarkdown_2.11      jpeg_0.1-9          grid_4.1.2         
 [97] readxl_1.3.1        data.table_1.14.2   callr_3.7.0        
[100] git2r_0.29.0        reprex_2.0.1        digest_0.6.29      
[103] classInt_0.4-3      httpuv_1.6.5        munsell_0.5.0      
[106] viridisLite_0.4.0   bslib_0.3.1

Extreme pH Profiles

Pasqualina Vonlanthen & Jens Daniel Müller

08 March, 2022

Task

Load data

Regions

Biomes

Remove coastal data

Grid reduction

Basins

Grid reduction

OceanSODA

Grid reduction

Apply region masks

OceanSODA pH anomalies

Grid level

Climatological thresholds

Fit lm models

Slope maps

Residual st. dev. maps

Anomaly identification

Anomaly maps

Anomalies on a 2x2 grid

Anomalies on a 1x1 grid

Anomaly time series

Anomaly histogram

Argo

Grid reduction

Apply region masks

Join OceanSODA

Plot profiles

Raw

Atlantic basin, biome 1, August 2017

Atlantic basin, biome 1, December 2017

Atlantic basin, biome 1, January 2018

Averaged profiles

Overall mean

Season x biome

Season x basin

Season x biome x basin

Atlantic basin, biome 2, winter

Atlantic basin, biome 1, summer