Extreme pH Profiles

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Last updated: 2022-01-03

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Knit directory: bgc_argo_r_argodata/

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File	Version	Author	Date	Message
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())

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/"

region_masks_all_1x1 <- read_rds(file = paste0(path_argo_preprocessed,
                                               "/region_masks_all_1x1.rds"))

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

# keep only Southern Ocean data (southern ocean RECCAP biomes)
OceanSODA_SO <-
  inner_join(region_masks_all_1x1, OceanSODA) %>%
  filter(region == 'southern',
         value != 0) %>%
  mutate(month = month(date)) %>%
  mutate(date = format_ISO8601(date, precision = 'ym'))

# add in basin separations
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(lon, lat, basin_AIP)

OceanSODA_SO <- inner_join(OceanSODA_SO, basinmask)

OceanSODA pH

Climatological monthly OceanSODA pH and the 5th and 95th percentiles, calculated for 2013-2021, with the full spatial OceanSODA data

# calculate climatological average OceanSODA pH, and the 95th percentile of the monthly OceanSODA pH
OceanSODA_SO_clim <- OceanSODA_SO %>%
  group_by(lon, lat, month) %>%
  summarise(
    clim_OceanSODA_ph = mean(ph_total, na.rm = TRUE),
    threshold_high = quantile(ph_total, 0.95, na.rm = TRUE),
    threshold_low = quantile(ph_total, 0.05, na.rm = TRUE)
  ) %>%
  ungroup()

OceanSODA_SO_extreme <- inner_join(OceanSODA_SO, OceanSODA_SO_clim,
                                     by = c('lon', 'lat', 'month'))

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

# when the in-situ OceanSODA pH is lower than the 5th percentile, assign 'L' for low extreme
# when the in-situ OceanSODA pH exceeds the 95th percentile, assign 'H' for high extreme
# when the in-situ OceanSODA pH is within 95% of the range, then assign 'N' for normal pH
OceanSODA_SO_extreme <- OceanSODA_SO_extreme %>%
  mutate(extreme = case_when(ph_total < threshold_low ~ 'L',
                             ph_total > threshold_high ~ 'H',
                             TRUE ~ 'N')) %>%
  drop_na()

Argo data

Load in full Argo data

# load in the full argo data
full_argo <- read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge.rds"))

# change the date format for compatibility with OceanSODA pH data
full_argo <- full_argo %>%
  mutate(year = year(date),
         month = month(date)) %>%
  mutate(date = format_ISO8601(date, precision = 'ym'))

# add in RECCAP biomes, and keep only Southern Ocean argo data
full_argo_SO <- inner_join(full_argo, region_masks_all_1x1) %>%
  filter(region == 'southern',
         value != 0)

# add in basin separations to full profile argo data
full_argo_SO <- inner_join(full_argo_SO, basinmask)

# select only relevant columns
full_argo_SO <- full_argo_SO %>%
  select(depth:platform_number,
         cycle_number,
         float_serial_no,
         date:lon,
         profile_pres_qc:basin_AIP) %>%
  filter(ph_in_situ_total_adjusted_qc == '1')

# rename OceanSODA columns
OceanSODA_SO_extreme <- OceanSODA_SO_extreme %>%
  rename(OceanSODA_ph_uncert = ph_total_uncert,
         OceanSODA_ph = ph_total)

# combine the argo profile data to the surface extreme data
profile_extreme <- inner_join(full_argo_SO, OceanSODA_SO_extreme)

Argo 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

Atlantic basin

2019

pH

profile_extreme %>%
  filter(basin_AIP == 'Atlantic',
         year == '2019') %>%
  group_split(value) %>%
  map(
    ~ ggplot(data = .x,
         aes(x = ph_in_situ_total_adjusted,
             y = depth,
             group = extreme,
             col = extreme))+
      geom_point(pch = 19, size = 0.5)+
      scale_y_reverse()+
      theme_bw()+
      facet_wrap(~month, ncol = 6)+
      labs(x = 'Argo pH (total scale)',
       y = 'depth (m)',
       title = paste('Atlantic basin, 2019, biome', unique(.x$value)),
       col = 'OceanSODA pH \n(high/low/normal)')
  )

[[1]]

[[2]]

[[3]]

Temperature

# plot temperature profiles for the Atlantic
profile_extreme %>%
  filter(basin_AIP == 'Atlantic',
         year == '2019') %>%
  group_split(value) %>%
  map(
    ~ ggplot(data = .x,
             aes(x = temp_adjusted,
                 y = depth,
                 group = extreme,
                 col = extreme))+
      geom_point(pch = 19, size = 0.5)+
      scale_y_reverse()+
      theme_bw()+
      facet_wrap(~month, ncol = 6)+
      labs(x = 'Argo temperature (ºC)',
           y = 'depth (m)',
           title = paste('Atlantic basin, 2019, biome', unique(.x$value)),
           col = 'OceanSODA pH \n(high/low/normal)')
  )

[[1]]

[[2]]

[[3]]

Pacific basin

2019

pH

profile_extreme %>%
  filter(basin_AIP == 'Pacific',
         year == '2019') %>%
  group_split(value) %>%
  map(
    ~ ggplot(data = .x,
             aes(x = ph_in_situ_total_adjusted,
                 y = depth,
                 group = extreme,
                 col = extreme))+
      geom_point(pch = 19, size = 0.5)+
      scale_y_reverse()+
      theme_bw()+
      facet_wrap(~month, ncol = 6)+
      labs(x = 'Argo pH (total scale)',
           y = 'depth (m)',
           title = paste('Pacific basin, 2019, biome', unique(.x$value)),
           col = 'OceanSODA pH \n(high/low/normal)')
  )

[[1]]

[[2]]

[[3]]

Temperature

profile_extreme %>%
  filter(basin_AIP == 'Pacific',
         year == '2019') %>%
  group_split(value) %>%
  map(
    ~ ggplot(data = .x,
             aes(x = temp_adjusted,
                 y = depth,
                 group = extreme,
                 col = extreme))+
      geom_point(pch = 19, size = 0.5)+
      scale_y_reverse()+
      theme_bw()+
      facet_wrap(~month, ncol = 6)+
      labs(x = 'Argo temperature (ºC)',
           y = 'depth (m)',
           title = paste('Pacific basin, 2019, biome', unique(.x$value)),
           col = 'OceanSODA pH \n(high/low/normal)')
  )

[[1]]

[[2]]

[[3]]

Indian basin

2019

pH

profile_extreme %>%
  filter(basin_AIP == 'Indian',
         year == '2019') %>%
  group_split(value) %>%
  map(
    ~ ggplot(data = .x,
             aes(x = ph_in_situ_total_adjusted,
                 y = depth,
                 group = extreme,
                 col = extreme))+
      geom_point(pch = 19, size = 0.5)+
      scale_y_reverse()+
      theme_bw()+
      facet_wrap(~month, ncol = 6)+
      labs(x = 'Argo pH (total scale)',
           y = 'depth (m)',
           title = paste('Indian basin, 2019, biome', unique(.x$value)),
           col = 'OceanSODA pH \n(high/low/normal)')
  )

[[1]]

[[2]]

[[3]]

Temperature

profile_extreme %>%
  filter(basin_AIP == 'Indian',
         year == '2019') %>%
  group_split(value) %>%
  map(
    ~ ggplot(data = .x,
             aes(x = temp_adjusted,
                 y = depth,
                 group = extreme,
                 col = extreme))+
      geom_point(pch = 19, size = 0.5)+
      scale_y_reverse()+
      theme_bw()+
      facet_wrap(~month, ncol = 6)+
      labs(x = 'Argo temperature (ºC))',
           y = 'depth (m)',
           title = paste('Indian basin, 2019, biome', unique(profile_extreme$value)),
           col = 'OceanSODA pH \n(high/low/normal)')
  )

[[1]]

[[2]]

[[3]]

Session information

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] lubridate_1.7.9 forcats_0.5.0   stringr_1.4.0   dplyr_1.0.7    
 [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.4     tibble_3.1.6   
 [9] ggplot2_3.3.5   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7        assertthat_0.2.1  rprojroot_2.0.2   digest_0.6.29    
 [5] utf8_1.2.2        R6_2.5.1          cellranger_1.1.0  backports_1.1.10 
 [9] reprex_0.3.0      evaluate_0.14     httr_1.4.2        highr_0.8        
[13] pillar_1.6.4      rlang_0.4.12      readxl_1.3.1      rstudioapi_0.13  
[17] whisker_0.4       jquerylib_0.1.4   blob_1.2.1        rmarkdown_2.10   
[21] labeling_0.4.2    munsell_0.5.0     broom_0.7.9       compiler_4.0.3   
[25] httpuv_1.6.2      modelr_0.1.8      xfun_0.25         pkgconfig_2.0.3  
[29] htmltools_0.5.1.1 tidyselect_1.1.1  fansi_0.5.0       crayon_1.4.2     
[33] dbplyr_1.4.4      withr_2.4.3       later_1.3.0       grid_4.0.3       
[37] jsonlite_1.7.2    gtable_0.3.0      lifecycle_1.0.1   DBI_1.1.2        
[41] git2r_0.27.1      magrittr_2.0.1    scales_1.1.1      cli_3.1.0        
[45] stringi_1.7.6     farver_2.1.0      fs_1.5.2          promises_1.2.0.1 
[49] xml2_1.3.2        bslib_0.2.5.1     ellipsis_0.3.2    generics_0.1.1   
[53] vctrs_0.3.8       tools_4.0.3       glue_1.6.0        hms_1.1.1        
[57] yaml_2.2.1        colorspace_2.0-2  rvest_0.3.6       knitr_1.33       
[61] haven_2.3.1       sass_0.4.0