Extreme pH Profiles

workflowr

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Last updated: 2022-05-12

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.

---

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---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/extreme_compound.Rmd) and HTML (docs/extreme_compound.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
html	710edd4	jens-daniel-mueller	2022-05-11	Build site.
Rmd	2f20a76	jens-daniel-mueller	2022-05-11	rebuild all after subsetting AB profiles and code cleaning

---

Task

Explore double extremes in the temp and pH anomaly fields

Load Data

library(tidyverse)
library(lubridate)
library(ggnewscale)

theme_set(theme_bw())

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

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/"

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

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

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


# argo pH data (flag A only)
full_argo <- read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_flag_AB.rds"))


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

Anomaly maps

pH

pH_extreme %>%
  filter(year >= 2013) %>% 
  group_split(month) %>%
  #head(1) %>%
  map(
    ~map +
      geom_tile(data = .x,
                aes(x = lon_raw,
                    y = lat_raw,
                    fill = ph_extreme))+
      scale_fill_manual(values = HNL_colors_map)+
      facet_wrap(~year, ncol = 2)+
      lims(y = c(-85, -32))+
      labs(title = paste('month:', unique(.x$month)),
           fill = 'pH')
  )

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SST

temp_extreme %>%
  filter(year >= 2013) %>% 
  group_split(month) %>%
  #head(1) %>%
  map(
    ~map +
      geom_tile(data = .x,
                aes(x = lon_raw,
                    y = lat_raw,
                    fill = temp_extreme))+
      scale_fill_manual(values = HNL_colors_map)+
      facet_wrap(~year, ncol = 2)+
      lims(y = c(-85, -32))+
      labs(title = paste('month:', unique(.x$month)),
           fill = 'pH')
  )

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Join data

anomaly_field <- full_join(pH_extreme %>% 
                             select(lon_raw, lat_raw, month, year, date, basin_AIP, biome_name, ph_extreme),
                           temp_extreme %>% 
                             select(lon_raw, lat_raw, month, year, date, basin_AIP, biome_name, temp_extreme))

# chisq.test(anomaly_field$ph_extreme, anomaly_field$temp_extreme, correct = FALSE)

anomaly_field <- anomaly_field %>%
  mutate(
    double_extreme = case_when(
      temp_extreme == 'H' & ph_extreme == 'H' ~ 'warm_HpH',
      temp_extreme == 'L' &
        ph_extreme == 'H' ~ 'cold_HpH',
      temp_extreme == 'H' &
        ph_extreme == 'L' ~ 'warm_LpH',
      temp_extreme == 'L' &
        ph_extreme == 'L' ~ 'cold_LpH',
      temp_extreme == 'H' &
        ph_extreme == 'N' ~ 'warm',
      temp_extreme == 'L' &
        ph_extreme == 'N' ~ 'cold',
      temp_extreme == 'N' &
        ph_extreme == 'H' ~ 'HpH',
      temp_extreme == 'N' &
        ph_extreme == 'L' ~ 'LpH',
      TRUE ~ 'N'
    )
  ) %>%
  mutate(
    double_extreme = fct_relevel(
      double_extreme,
      'warm_HpH',
      'cold_HpH',
      'warm_LpH',
      'cold_LpH',
      'warm',
      'cold',
      'HpH',
      'LpH',
      'N'
    )
  )

Compound extreme maps

HNL_colors_map_temp <- c('H' = "#CD534CFF",
                    'N' = 'transparent',
                    'L' = "#0073C2FF")

HNL_colors_map_ph <- c('H' = "#009E73",
                    'N' = 'transparent',
                    'L' = "#EFC000FF")


anomaly_field %>%
  filter(year >= 2013,
         double_extreme != "N") %>%
  group_split(month) %>%
  # tail(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(
                  x = lon_raw,
                  y = lat_raw,
                  fill = ph_extreme
                ), alpha = 0.4) +
      scale_fill_manual(values = HNL_colors_map_ph) +
      new_scale_fill() +
      geom_tile(data = .x,
                aes(
                  x = lon_raw,
                  y = lat_raw,
                  fill = temp_extreme
                ), alpha = 0.4) +
      scale_fill_manual(values = HNL_colors_map_temp) +
      facet_wrap(~ year, ncol = 2) +
      lims(y = c(-85, -32)) +
      labs(title = paste0('month:', unique(.x$month)))
  )

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rm(HNL_colors_map_temp, HNL_colors_map_ph)

anomaly_field %>% 
  filter(year >= 2013) %>% 
  group_split(month) %>% 
  map(
    ~map+
      geom_tile(data = .x,
                aes(x = lon_raw,
                    y = lat_raw,
                    fill = double_extreme))+
      facet_wrap(~year, ncol = 2)+
      scale_fill_manual(values = c('warm_HpH' = 'brown',
                                   'warm_LpH' = 'yellow',
                                   'cold_HpH' = 'beige',
                                   'cold_LpH' = 'cyan',
                                   'cold' = 'blue',
                                   'warm' = 'red',
                                   'LpH' = 'orange',
                                   'HpH' = 'green',
                                   'N' = NA),
                        na.value = NA)+
      lims(y = c(-85, -32))+
      labs(title = paste0('month:', unique(.x$month)),
           fill = 'double extreme')
  )

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Join Argo profile data

anomaly_field <- anomaly_field %>% 
  rename(lat = lat_raw,
         lon = lon_raw) %>% 
  filter(year >= 2013)
  
profile_double_extreme <- inner_join(full_argo %>% 
                                       select(-c(temp_adjusted_error, 
                                                 temp_adjusted_qc,
                                                 ph_in_situ_total_adjusted_error,
                                                 ph_in_situ_total_adjusted_qc,
                                                 profile_temp_qc,
                                                 profile_ph_in_situ_total_qc)),
                                     anomaly_field)

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

anomaly_field %>% 
  filter(year >= 2013) %>% 
  group_split(month) %>% 
  map(
    ~map+
      geom_tile(data = .x,
                aes(x = lon,
                    y = lat,
                    fill = double_extreme))+
      facet_wrap(~year, ncol = 2)+
      scale_fill_manual(values = c('warm_HpH' = 'brown',
                                   'warm_LpH' = 'yellow',
                                   'cold_HpH' = 'beige',
                                   'cold_LpH' = 'cyan',
                                   'cold' = 'blue',
                                   'warm' = 'red',
                                   'LpH' = 'orange',
                                   'HpH' = 'green',
                                   'N' = NA),
                        na.value = NA)+
      geom_point(data = profile_double_extreme, 
                 aes(x = lon,
                     y = lat),
                 size = 0.2)+
      lims(y = c(-85, -32))+
      labs(title = paste0('month:', unique(.x$month)),
           fill = 'double extreme')
  )

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Plot Profiles

Raw

pH

profile_double_extreme %>% 
  group_split(biome_name, basin_AIP, year) %>% 
  map(
    ~ggplot(data = .x,
            aes(x = ph_in_situ_total_adjusted,
                y = depth,
                group = platform_cycle,
                col = double_extreme))+
      geom_path(data = .x %>% filter(double_extreme == 'N'),
                aes(x = ph_in_situ_total_adjusted,
                    y = depth, 
                    group = platform_cycle,
                    col = double_extreme),
                size = 0.3)+
      geom_path(data = .x %>% filter(double_extreme == 'E'),
                aes(x = ph_in_situ_total_adjusted,
                    y = depth, 
                    group = platform_cycle,
                    col = double_extreme),
                size = 0.5)+
      scale_y_reverse()+
      scale_color_manual(values = c('N' = 'gray', 'E' = 'red'))+
      facet_wrap(~month, ncol = 6)+
      labs(title = paste0('biome: ', unique(.x$biome_name), '| ', unique(.x$basin_AIP), '| ', unique(.x$year)),
           col = 'double extreme',
           x = 'Argo pH',
           y = 'depth')
  )

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Temperature

profile_double_extreme %>% 
  group_split(biome_name, basin_AIP, year) %>% 
  map(
    ~ggplot(data = .x,
            aes(x = temp_adjusted,
                y = depth,
                group = platform_cycle,
                col = double_extreme))+
      geom_path(data = .x %>% filter(double_extreme == 'N'),
                aes(x = temp_adjusted,
                    y = depth, 
                    group = platform_cycle,
                    col = double_extreme),
                size = 0.3)+
      geom_path(data = .x %>% filter(double_extreme == 'E'),
                aes(x = temp_adjusted,
                    y = depth, 
                    group = platform_cycle,
                    col = double_extreme),
                size = 0.5)+
      scale_y_reverse()+
      scale_color_manual(values = c('N' = 'gray', 'E' = 'red'))+
      facet_wrap(~month, ncol = 6)+
      labs(title = paste0('biome: ', unique(.x$biome_name), '| ', unique(.x$basin_AIP), '| ', unique(.x$year)),
           col = 'double extreme',
           x = 'Argo temperature',
           y = 'depth')
  )

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Pacific basin, STSS biome, December 2017

Pacific_STSS_2017 <- profile_double_extreme %>% 
  filter(date == '2017-12-15',
         basin_AIP == 'Pacific',
         biome_name == 'STSS')

# pH: 
Pacific_STSS_2017 %>% 
  ggplot()+
  geom_path(data = Pacific_STSS_2017 %>% filter(double_extreme == 'N'),
            aes(x = ph_in_situ_total_adjusted,
                y = depth,
                group = platform_cycle, 
                col = double_extreme),
            size = 0.3)+
  geom_path(data = Pacific_STSS_2017 %>% filter(double_extreme == 'E'),
            aes(x = ph_in_situ_total_adjusted,
                y = depth,
                group = platform_cycle, 
                col = double_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = c('E' = 'red', 'N' = 'grey'))+
  labs(title = 'Pacific, STSS biome, December 2017',
       col = 'double extreme',
       x = 'Argo pH',
       y = 'depth')

Past versions of pacific_STSS_dec_2017-1.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11

# Temperature 

Pacific_STSS_2017 %>% 
  ggplot()+
  geom_path(data = Pacific_STSS_2017 %>% filter(double_extreme == 'N'),
            aes(x = temp_adjusted,
                y = depth,
                group = platform_cycle, 
                col = double_extreme),
            size = 0.3)+
  geom_path(data = Pacific_STSS_2017 %>% filter(double_extreme == 'E'),
            aes(x = temp_adjusted,
                y = depth,
                group = platform_cycle, 
                col = double_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = c('E' = 'red', 'N' = 'grey'))+
  labs(title = 'Pacific, STSS biome, December 2017',
       col = 'double extreme',
       x = 'Argo pH',
       y = 'depth')

Past versions of pacific_STSS_dec_2017-2.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11

rm(Pacific_STSS_2017)

Pacific basin, STSS biome, December 2019

No profiles for December 2019 in SPSS or STSS Pacific

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] ggnewscale_0.4.5 lubridate_1.8.0  forcats_0.5.1    stringr_1.4.0   
 [5] dplyr_1.0.7      purrr_0.3.4      readr_2.1.1      tidyr_1.1.4     
 [9] tibble_3.1.6     ggplot2_3.3.5    tidyverse_1.3.1  workflowr_1.7.0 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8       getPass_0.2-2    ps_1.6.0         assertthat_0.2.1
 [5] rprojroot_2.0.2  digest_0.6.29    utf8_1.2.2       R6_2.5.1        
 [9] cellranger_1.1.0 backports_1.4.1  reprex_2.0.1     evaluate_0.14   
[13] highr_0.9        httr_1.4.2       pillar_1.6.4     rlang_1.0.2     
[17] readxl_1.3.1     rstudioapi_0.13  whisker_0.4      callr_3.7.0     
[21] jquerylib_0.1.4  rmarkdown_2.11   labeling_0.4.2   munsell_0.5.0   
[25] broom_0.7.11     compiler_4.1.2   httpuv_1.6.5     modelr_0.1.8    
[29] xfun_0.29        pkgconfig_2.0.3  htmltools_0.5.2  tidyselect_1.1.1
[33] fansi_1.0.2      withr_2.4.3      crayon_1.4.2     tzdb_0.2.0      
[37] dbplyr_2.1.1     later_1.3.0      grid_4.1.2       jsonlite_1.7.3  
[41] gtable_0.3.0     lifecycle_1.0.1  DBI_1.1.2        git2r_0.29.0    
[45] magrittr_2.0.1   scales_1.1.1     cli_3.1.1        stringi_1.7.6   
[49] farver_2.1.0     fs_1.5.2         promises_1.2.0.1 xml2_1.3.3      
[53] bslib_0.3.1      ellipsis_0.3.2   generics_0.1.1   vctrs_0.3.8     
[57] tools_4.1.2      glue_1.6.0       hms_1.1.1        processx_3.5.2  
[61] fastmap_1.1.0    yaml_2.2.1       colorspace_2.0-2 rvest_1.0.2     
[65] knitr_1.37       haven_2.4.3      sass_0.4.0