Extreme Temperature Profiles

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-02-24

Checks: 7 0

Knit directory: bgc_argo_r_argodata/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20211008)

The command set.seed(20211008) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 58d2846

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 58d2846. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    output/

Untracked files:
    Untracked:  code/OceanSODA_argo_extremes.R
    Untracked:  code/creating_dataframe.R
    Untracked:  code/creating_map.R
    Untracked:  code/merging_oceanSODA_Argo.R
    Untracked:  code/pH_data_timeseries.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/extreme_temp.Rmd) and HTML (docs/extreme_temp.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	58d2846	pasqualina-vonlanthendinenna	2022-02-24	added st dev for temp profiles
html	c68163a	pasqualina-vonlanthendinenna	2022-02-22	Build site.
Rmd	818ac54	pasqualina-vonlanthendinenna	2022-02-22	updated regression and merging for extreme_temp
html	f98c744	pasqualina-vonlanthendinenna	2022-02-18	Build site.
Rmd	8b99ab3	pasqualina-vonlanthendinenna	2022-02-18	updates
html	19aa73d	pasqualina-vonlanthendinenna	2022-02-16	Build site.
Rmd	d955d28	pasqualina-vonlanthendinenna	2022-02-16	updated extreme temperature
html	905d82f	pasqualina-vonlanthendinenna	2022-02-15	Build site.
Rmd	01ae9da	pasqualina-vonlanthendinenna	2022-02-15	added OceanSODA-Argo SST comparison
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

---

Task

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

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

Load data

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

# RECCAP2-ocean region mask

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

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

# WOA 18 basin mask

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

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

# OceanSODA temperature

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

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

# full argo data
full_argo <- read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_temp_qc_1.rds"))

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

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

Regions

Biomes

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

Remove coastal data

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

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

Past versions of coast_flat_map-1.png

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

# remove coastal data 

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

Grid reduction

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

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

Past versions of biomes_flat_map_pre_grid_reduction-1.png

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

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

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

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

Past versions of biomes_flat_map_post_grid_reduction-1.png

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

Basins

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

Grid reduction

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

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

Past versions of basin_flat_map_pre_grid_reduction-1.png

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

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

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

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

rm(basinmask)

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

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

Past versions of basin_flat_map_post_grid_reduction-1.png

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

OceanSODA

Grid reduction

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

OceanSODA_temp_2x2 <- OceanSODA_temp %>% 
  mutate(
    lat_raw = lat,
    lon_raw = lon,
    lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)), 
    lat = as.numeric(as.character(lat)),
    lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)), 
    lon = as.numeric(as.character(lon))) # regrid into 2x2º grid 

Apply region masks

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

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

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

OceanSODA temperature anomalies

Grid level

Fit lm models

# fit a linear regression of OceanSODA pH against time (temporal trend)
# in each lat/lon/month grid
OceanSODA_temp_2x2_SO <- OceanSODA_temp_2x2_SO %>% 
  mutate(row_number = row_number())


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


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

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

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

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

# OceanSODA_temp_regression_augmented <- bind_cols(
#   OceanSODA_temp_regression_augmented,
#   OceanSODA_temp_2x2_SO %>% 
#     select(
#     date, lon_raw, lat_raw, lon, lat, basin_AIP, biome)
# )

OceanSODA_temp_regression_augmented <- bind_cols(
  OceanSODA_temp_regression_augmented,
  OceanSODA_temp_regression_data %>% 
    select(lon_raw, lat_raw, date, row_number, biome, basin_AIP))

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

Slope maps

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

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

Past versions of map_temp_regression_slopes_flat-1.png

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

Residual st. dev. maps

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

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

Past versions of temp_flat_map_sigma-1.png

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

Anomaly identification

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

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

# combine observations and regression statistics

OceanSODA_temp_2x2_SO_extreme_grid <-
  full_join(
    OceanSODA_temp_regression_augmented,
    OceanSODA_temp_regression_glanced %>%
      select(lon:month, sigma)
  )
# results in 925 056 rows 

# identify observations in anomaly classes

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

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


# combine with regression coefficients

OceanSODA_temp_2x2_SO_extreme_grid <-
  full_join(OceanSODA_temp_2x2_SO_extreme_grid,
            OceanSODA_temp_regression_tidied) 
# 925 056 rows, in line with expectations for 40 lat x 360 lon x 12 months x 8 years (1 382 400 obs minus NA values)

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

[[1]]

Past versions of temp_anomaly_test_plot_grid-1.png

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

[[2]]

Past versions of temp_anomaly_test_plot_grid-2.png

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

[[3]]

Past versions of temp_anomaly_test_plot_grid-3.png

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

[[4]]

Past versions of temp_anomaly_test_plot_grid-4.png

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

[[5]]

Past versions of temp_anomaly_test_plot_grid-5.png

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

[[6]]

Past versions of temp_anomaly_test_plot_grid-6.png

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

Anomaly maps

Location of OceanSODA temperature extremes

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

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

[[1]]

Past versions of flat_map_OceanSODA_temp_extremes_grid-1.png

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

[[2]]

Past versions of flat_map_OceanSODA_temp_extremes_grid-2.png

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

[[3]]

Past versions of flat_map_OceanSODA_temp_extremes_grid-3.png

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

[[4]]

Past versions of flat_map_OceanSODA_temp_extremes_grid-4.png

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

[[5]]

Past versions of flat_map_OceanSODA_temp_extremes_grid-5.png

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

[[6]]

Past versions of flat_map_OceanSODA_temp_extremes_grid-6.png

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

[[7]]

Past versions of flat_map_OceanSODA_temp_extremes_grid-7.png

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

[[8]]

Past versions of flat_map_OceanSODA_temp_extremes_grid-8.png

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

Anomaly timeseries

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

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

Past versions of extreme_temp_timeseries_grid-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Anomaly histogram

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

Past versions of extreme_temp_histogram_grid-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Argo

Grid reduction

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

full_argo_2x2 <- full_argo %>%
  mutate(
    lat_raw = lat,
    lon_raw = lon,
    lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
    lat = as.numeric(as.character(lat)),
    lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
    lon = as.numeric(as.character(lon)))  # re-grid to 2x2

Apply region masks

# keep only Southern Ocean argo data
full_argo_2x2_SO <- inner_join(full_argo_2x2, region_masks_all_2x2)
# expected number of rows: 
# number of points in a profile, for sampling every 2 m from 0 to 1000 m and every 50 m from 1000 to 2500 m: 530 obs (rows)
# for 12 months x 9 years x 360 lon x 30 lat: 
# 618 000 000 rows 
# actual number of rows: 15 770 055 
# (not all Argo profiles go down to 2500 m and spatial coverage isn't as large as OceanSODA)

# With 50% of Argo profiles to 2500 m and half of longitude coverage: 
# sampling every 2 m from 0 to 1000 m (500 obs) + 50% every 50 m from 1000 to 2500 m (15 obs): 515 obs 
# for 12 months x 9 years x 180 lon x 30 lat:
# 299 181 600 rows 
# actual number of obs: 15 770 055 

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

Join OceanSODA

# rename OceanSODA columns
OceanSODA_temp_2x2_SO_extreme_grid <- OceanSODA_temp_2x2_SO_extreme_grid %>%
  select(-c(lon, lat)) %>%
  rename(OceanSODA_temp = temperature,
         lon = lon_raw,
         lat = lat_raw)
# 925 056 obs 

# OceanSODA_temp_2x2_SO_extreme_grid <- OceanSODA_temp_2x2_SO_extreme_grid %>% 
#   rename(OceanSODA_temp = temperature)

# full_argo_2x2_SO <- full_argo_2x2_SO %>% 
#   rename(argo_temp = temp_adjusted)

# check lat/lon distributions for argo and OceanSODA 
# OceanSODA_temp_2x2_SO_extreme_grid %>% 
#   ggplot(aes(x = lon))+
#   geom_density()
# 
# OceanSODA_temp_2x2_SO_extreme_grid %>% 
#   ggplot(aes(x = lat))+
#   geom_density()
# 
# OceanSODA_temp_2x2_SO_extreme_grid %>% 
#   ggplot(aes(x = OceanSODA_temp))+
#   geom_density()

# OceanSODA_temp_2x2_SO_extreme_grid %>% 
#   ggplot(aes(x = lon, y = lat))+
#   geom_bin2d(aes(x = lon, y = lat), size = 0.3, bins = 60)

# 
# full_argo_2x2_SO %>% 
#   ggplot(aes(x = lon))+
#   geom_density()
# 
# full_argo_2x2_SO %>% 
#   ggplot(aes(y = lat))+
#   geom_density(aes(y = lat))
#   
# full_argo_2x2_SO %>% 
#   ggplot(aes(x = argo_temp))+
#   geom_density()

# full_argo_2x2_SO %>% 
#   ggplot(aes(x = lon, y = lat))+
#   geom_bin2d(aes(x = lon, y = lat))

# combine the argo profile data to the surface extreme data
profile_temp_extreme <- inner_join(
  full_argo %>% 
    select(c(year, month, date, lon, lat, depth,
           temp_adjusted,
           platform_number,
           cycle_number)),                 # 15 770 055 obs 
  OceanSODA_temp_2x2_SO_extreme_grid %>% 
    select(c(year, month, date, lon, lat,
           OceanSODA_temp, temp_extreme,
           biome, basin_AIP)))            # 925 056 obs 
  
# results in 14 816 885 rows 

# profile_temp_extreme %>% 
#   ggplot(aes(x = lon))+
#   geom_density()
# 
# profile_temp_extreme %>% 
#   ggplot(aes(y = lat))+
#   geom_density()

Plot profiles

Argo profiles plotted according to the surface OceanSODA temperature

L profiles correspond to a low surface temperature event, as recorded in OceanSODA

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

N profiles correspond to normal surface OceanSODA temperature

Raw

profile_temp_extreme %>%
  group_split(biome, basin_AIP, year) %>% 
  #head(1) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(
        x = temp_adjusted,
        y = depth,
        group = temp_extreme,
        col = temp_extreme
      )
    ) +
      geom_point(pch = 19, size = 0.3) +
      scale_y_reverse() +
      scale_color_manual(values = HNL_colors) +
      facet_wrap(~ month, ncol = 6) +
      labs(
        x = 'Argo temperature (ºC)',
        y = 'depth (m)',
        title = paste(
          unique(.x$basin_AIP),
          "|",
          unique(.x$year),
          "| biome:",
          unique(.x$biome)
        ),
        col = 'OceanSODA temp \nanomaly'
      )
  )

[[1]]

Past versions of temp_profiles_raw-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of temp_profiles_raw-2.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of temp_profiles_raw-3.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[4]]

Past versions of temp_profiles_raw-4.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[5]]

Past versions of temp_profiles_raw-5.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[6]]

Past versions of temp_profiles_raw-6.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[7]]

Past versions of temp_profiles_raw-7.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[8]]

Past versions of temp_profiles_raw-8.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[9]]

Past versions of temp_profiles_raw-9.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[10]]

Past versions of temp_profiles_raw-10.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[11]]

Past versions of temp_profiles_raw-11.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[12]]

Past versions of temp_profiles_raw-12.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[13]]

Past versions of temp_profiles_raw-13.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[14]]

Past versions of temp_profiles_raw-14.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[15]]

Past versions of temp_profiles_raw-15.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[16]]

Past versions of temp_profiles_raw-16.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[17]]

Past versions of temp_profiles_raw-17.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[18]]

Past versions of temp_profiles_raw-18.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[19]]

Past versions of temp_profiles_raw-19.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[20]]

Past versions of temp_profiles_raw-20.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[21]]

Past versions of temp_profiles_raw-21.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[22]]

Past versions of temp_profiles_raw-22.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[23]]

Past versions of temp_profiles_raw-23.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[24]]

Past versions of temp_profiles_raw-24.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[25]]

Past versions of temp_profiles_raw-25.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[26]]

Past versions of temp_profiles_raw-26.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[27]]

Past versions of temp_profiles_raw-27.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[28]]

Past versions of temp_profiles_raw-28.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[29]]

Past versions of temp_profiles_raw-29.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[30]]

Past versions of temp_profiles_raw-30.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[31]]

Past versions of temp_profiles_raw-31.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[32]]

Past versions of temp_profiles_raw-32.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[33]]

Past versions of temp_profiles_raw-33.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[34]]

Past versions of temp_profiles_raw-34.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[35]]

Past versions of temp_profiles_raw-35.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[36]]

Past versions of temp_profiles_raw-36.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[37]]

Past versions of temp_profiles_raw-37.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[38]]

Past versions of temp_profiles_raw-38.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[39]]

Past versions of temp_profiles_raw-39.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[40]]

Past versions of temp_profiles_raw-40.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[41]]

Past versions of temp_profiles_raw-41.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[42]]

Past versions of temp_profiles_raw-42.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[43]]

Past versions of temp_profiles_raw-43.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[44]]

Past versions of temp_profiles_raw-44.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[45]]

Past versions of temp_profiles_raw-45.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[46]]

Past versions of temp_profiles_raw-46.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[47]]

Past versions of temp_profiles_raw-47.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[48]]

Past versions of temp_profiles_raw-48.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[49]]

Past versions of temp_profiles_raw-49.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[50]]

Past versions of temp_profiles_raw-50.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[51]]

Past versions of temp_profiles_raw-51.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[52]]

Past versions of temp_profiles_raw-52.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[53]]

Past versions of temp_profiles_raw-53.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[54]]

Past versions of temp_profiles_raw-54.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[55]]

Past versions of temp_profiles_raw-55.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[56]]

Past versions of temp_profiles_raw-56.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[57]]

Past versions of temp_profiles_raw-57.png

Version	Author	Date
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[58]]

Past versions of temp_profiles_raw-58.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[59]]

Past versions of temp_profiles_raw-59.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[60]]

Past versions of temp_profiles_raw-60.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[61]]

Past versions of temp_profiles_raw-61.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[62]]

Past versions of temp_profiles_raw-62.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[63]]

Past versions of temp_profiles_raw-63.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[64]]

Past versions of temp_profiles_raw-64.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[65]]

Past versions of temp_profiles_raw-65.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[66]]

Past versions of temp_profiles_raw-66.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[67]]

Past versions of temp_profiles_raw-67.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[68]]

Past versions of temp_profiles_raw-68.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[69]]

Past versions of temp_profiles_raw-69.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

[[70]]

Past versions of temp_profiles_raw-70.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16

Averaged profiles

# cut depth levels at 10, 20, .... etc m
# add seasons 
# Dec, Jan, Feb <- summer 
# Mar, Apr, May <- autumn 
# Jun, Jul, Aug <- winter 
# Sep, Oct, Nov <- spring 

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

Overall mean

profile_temp_extreme_mean <- profile_temp_extreme %>%
  group_by(temp_extreme, depth) %>%
  summarise(temp_mean = mean(temp_adjusted, na.rm = TRUE),
            temp_std = sd(temp_adjusted, na.rm = TRUE)) %>%
  ungroup()

profile_temp_extreme_mean %>%
  arrange(depth) %>%
  ggplot(aes(x = temp_mean, 
             y = depth, 
             group = temp_extreme, 
             col = temp_extreme)) +
  geom_ribbon(aes(xmin = temp_mean - temp_std,
                  xmax = temp_mean + temp_std,
                  col = NA,
                  fill = temp_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\ntemp anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       y = 'sqrt(depth)',
       x = 'mean Argo temperature (ºC)') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Past versions of mean_temp_profiles-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Number of profiles

profile_temp_count_mean <- profile_temp_extreme %>% 
  distinct(temp_extreme, platform_number, cycle_number) %>% 
  count(temp_extreme)

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

Past versions of mean_temp_profile_count-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Surface Argo temperature vs surface OceanSODA temperature (20 m)

# calculate surface-mean argo pH, for each profile 
surface_temp_mean <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(temp_extreme, platform_number, cycle_number) %>% 
  summarise(argo_surf_temp = mean(temp_adjusted, na.rm = TRUE),
            OceanSODA_surf_temp = mean(OceanSODA_temp, na.rm = TRUE))

# test by averaging surface temp over lat/lon instead of profile 
# surface_temp_mean <- profile_temp_extreme %>% 
#   filter(depth <= 20) %>% 
#   group_by(temp_extreme, lon, lat) %>% 
#   summarise(argo_surf_temp = mean(temp_adjusted, na.rm = TRUE),
#             OceanSODA_surf_temp = mean(OceanSODA_temp, na.rm = TRUE))

# test 2, without averaging surface temperature 
# surface_temp_mean <- profile_temp_extreme %>% 
#   filter(depth <= 20)

# test plot without averaging surface temperature 
# surface_temp_mean %>% 
#   ggplot(aes(x = OceanSODA_temp, y = temp_adjusted)) +
#   geom_bin2d(aes(x = OceanSODA_temp, y = temp_adjusted)) +
#   geom_abline(slope = 1, intercept = 0) +
#   coord_fixed(ratio = 1,
#               xlim = c(-3, 28),
#               ylim = c(-3, 28)) +
#   facet_wrap(~temp_extreme) +
#   labs(x = 'OceanSODA temp',
#        y = 'Argo temp')


surface_temp_mean %>% 
  group_by(temp_extreme) %>% 
  group_split(temp_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = OceanSODA_surf_temp, 
             y = argo_surf_temp))+
  geom_bin2d(data = .x, aes(x = OceanSODA_surf_temp, 
                 y = argo_surf_temp), size = 0.3, bins = 60) +
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1,
              xlim = c(-3, 28),
              ylim = c(-3, 28))+
    labs(title = paste('temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_mean-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of argo_vs_OceanSODA_temp_mean-2.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of argo_vs_OceanSODA_temp_mean-3.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Season x biome

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

profile_temp_extreme_biome %>%
  ggplot(aes(
    x = temp_biome,
    y = depth,
    group = temp_extreme,
    col = temp_extreme
  )) +
  geom_ribbon(aes(xmax = temp_biome + temp_std_biome,
                  xmin = temp_biome - temp_std_biome,
                  group = temp_extreme,
                  fill = temp_extreme),
              col = NA, 
              alpha = 0.2)+
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       y = 'sqrt(depth)',
       x = 'biome mean Argo temperature (ºC)') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
  lims(x = c(-3, 18))+
  facet_grid(season ~ biome)

Past versions of mean_temp_profiles_season_biome-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Number of profiles season x biome

profile_temp_count_biome <- profile_temp_extreme %>% 
  distinct(season, biome, temp_extreme, platform_number, cycle_number) %>% 
  group_by(season, biome, temp_extreme) %>% 
  count(temp_extreme)

profile_temp_count_biome %>% 
  ggplot(aes(x = temp_extreme, y = n, fill = temp_extreme))+
  geom_col(width = 0.5)+
  facet_grid(season ~ biome)+
  scale_y_continuous(trans = 'log10')+
  labs(y = 'log(number of profiles)',
       title = 'Number of profiles season x biome')

Past versions of count_temp_profiles_season_biome-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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

surface_temp_biome <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season, biome, temp_extreme, platform_number, cycle_number) %>% 
  summarise(argo_surf_temp = mean(temp_adjusted, na.rm=TRUE),
            OceanSODA_surf_temp = mean(OceanSODA_temp, na.rm = TRUE))

surface_temp_biome %>% 
  group_by(temp_extreme) %>% 
  group_split(temp_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = OceanSODA_surf_temp, 
             y = argo_surf_temp))+
  geom_bin2d(data = .x, aes(x = OceanSODA_surf_temp, 
                 y = argo_surf_temp)) +
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 25),
              ylim = c(-3, 25))+
  facet_grid(season~biome) +
    labs(title = paste( 'Temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_biome-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of argo_vs_OceanSODA_temp_biome-2.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of argo_vs_OceanSODA_temp_biome-3.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Season x basin

profile_temp_extreme_basin <- profile_temp_extreme %>% 
  group_by(season, basin_AIP, temp_extreme, depth) %>% 
  summarise(temp_basin = mean(temp_adjusted, na.rm = TRUE),
            temp_basin_std = sd(temp_adjusted, na.rm = TRUE)) %>% 
  ungroup()

profile_temp_extreme_basin %>% 
  ggplot(aes(x = temp_basin, 
             y = depth, 
             group = temp_extreme, 
             col = temp_extreme))+
  geom_ribbon(aes(xmin = temp_basin - temp_basin_std,
                  xmax = temp_basin + temp_basin_std,
                  group = temp_extreme, 
                  fill = temp_extreme),
              col = NA, 
              alpha = 0.2)+
  geom_path()+
  scale_color_manual(values = HNL_colors)+
  scale_fill_manual(values = HNL_colors)+
  labs(col = 'OceanSODA\ntemp anomaly\n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly\n(mean ± st dev)',
       y = 'sqrt(depth)',
       x = 'basin-mean Argo temperature (ªC)')+
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
  facet_grid(season~basin_AIP)

Past versions of mean_basin_temp_profiles-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Number of profiles season x basin

profile_temp_count_basin <- profile_temp_extreme %>% 
  distinct(season, basin_AIP, temp_extreme, platform_number, cycle_number) %>% 
  group_by(season, basin_AIP, temp_extreme) %>% 
  count(temp_extreme)

profile_temp_count_basin %>% 
  ggplot(aes(x = temp_extreme, y = n, fill = temp_extreme))+
  geom_col(width = 0.5)+
  facet_grid(season~basin_AIP)+
  scale_y_continuous(trans = 'log10')+
  labs(y = 'log(number of profiles)',
       title = 'Number of profiles season x basin')

Past versions of count_basin_temp_profiles-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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

# calculate surface-mean argo pH to compare against OceanSODA surface pH (one value)
surface_temp_basin <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season, basin_AIP, temp_extreme, platform_number, cycle_number) %>% 
  summarise(surf_argo_temp = mean(temp_adjusted, na.rm=TRUE),
            surf_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE)) 

surface_temp_basin %>% 
  group_by(temp_extreme) %>% 
  group_split(temp_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = surf_OceanSODA_temp, 
             y = surf_argo_temp))+
  geom_bin2d(data = .x, aes(x = surf_OceanSODA_temp, 
                 y = surf_argo_temp)) +
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 25),
              ylim = c(-3, 25))+
  facet_grid(season~basin_AIP) +
    labs(title = paste('Temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_basin-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of argo_vs_OceanSODA_temp_basin-2.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of argo_vs_OceanSODA_temp_basin-3.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Season x biome x basin

profile_temp_extreme_season <- profile_temp_extreme %>%
  group_by(season, biome, basin_AIP, temp_extreme, depth) %>%
  summarise(temp_mean = mean(temp_adjusted, na.rm = TRUE),
            temp_std = sd(temp_adjusted, na.rm = TRUE)) %>%
  ungroup()

profile_temp_extreme_season %>%
  arrange(depth) %>%
  group_split(season) %>%
  # head(1) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(x = temp_mean,
          y = depth,
          group = temp_extreme,
          col = temp_extreme)) +
      geom_ribbon(aes(xmax = temp_mean + temp_std,
                      xmin = temp_mean - temp_std,
                      group = temp_extreme,
                      fill = temp_extreme),
                  col = NA,
                  alpha = 0.2)+
      geom_path() +
      scale_color_manual(values = HNL_colors) +
      scale_fill_manual(values = HNL_colors) +
      labs(title = paste("season:", unique(.x$season)),
           col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
           fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
           y = 'sqrt(depth)',
           x = 'mean Argo temperature (ºC)') +
      scale_y_continuous(
        trans = trans_reverser("sqrt"),
        breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))
      ) +
      facet_grid(basin_AIP ~ biome)
  )

[[1]]

Past versions of mean_temp_profiles_season_biome_basin-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of mean_temp_profiles_season_biome_basin-2.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of mean_temp_profiles_season_biome_basin-3.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[4]]

Past versions of mean_temp_profiles_season_biome_basin-4.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Number of profiles season x biome x basin

profile_temp_count_season <- profile_temp_extreme %>% 
  distinct(season, biome, basin_AIP,
           temp_extreme, platform_number, cycle_number) %>% 
  group_by(season, biome, basin_AIP, temp_extreme) %>% 
  count(temp_extreme)


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

[[1]]

Past versions of number_season_biome_basin_temp_profiles-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of number_season_biome_basin_temp_profiles-2.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of number_season_biome_basin_temp_profiles-3.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[4]]

Past versions of number_season_biome_basin_temp_profiles-4.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

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

# calculate surface-mean argo pH, for each season x biome x basin x ph extreme 
surface_temp_season <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season, 
           basin_AIP, 
           biome, 
           temp_extreme,  
           platform_number, 
           cycle_number) %>%  
  summarise(surf_argo_temp = mean(temp_adjusted, na.rm=TRUE),
            surf_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE)) 

surface_temp_season %>% 
  group_by(season, temp_extreme) %>% 
  group_split(season, temp_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = surf_OceanSODA_temp, 
             y = surf_argo_temp))+
  geom_bin2d(data = .x, aes(x = surf_OceanSODA_temp, 
                 y = surf_argo_temp)) +
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 25),
              ylim = c(-3, 25))+
  facet_grid(basin_AIP ~ biome) +
    labs(title = paste('season:', unique(.x$season), 
                        '| temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of surface_OceanSODA_vs_argo_temp_season-1.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of surface_OceanSODA_vs_argo_temp_season-2.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of surface_OceanSODA_vs_argo_temp_season-3.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[4]]

Past versions of surface_OceanSODA_vs_argo_temp_season-4.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[5]]

Past versions of surface_OceanSODA_vs_argo_temp_season-5.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[6]]

Past versions of surface_OceanSODA_vs_argo_temp_season-6.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[7]]

Past versions of surface_OceanSODA_vs_argo_temp_season-7.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[8]]

Past versions of surface_OceanSODA_vs_argo_temp_season-8.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[9]]

Past versions of surface_OceanSODA_vs_argo_temp_season-9.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[10]]

Past versions of surface_OceanSODA_vs_argo_temp_season-10.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[11]]

Past versions of surface_OceanSODA_vs_argo_temp_season-11.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[12]]

Past versions of surface_OceanSODA_vs_argo_temp_season-12.png

Version	Author	Date
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Session information

sessionInfo()

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

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

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

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

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

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