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Introduction

Plotting dTA, dDIC, and CDR efficiency across depth and time of the OAE addition site. Also plotting their differences from a mean of all 3 phases to compare.

#loading packages
library(tidyverse)
library(data.table)
library(arrow)
library(scales)

# Path to intermediate computation outputs
path_outputs <- "/net/sea/work/vifroh/oae_ccs_roms_data/regrid_2/"

# Path to save practice plots when working on them
path_plots <- "/net/sea/work/vifroh/test_plots/"

# loading in previous saved depth integrated data for hovmo plots
lanina_depthint <- read_feather(
  paste0(path_outputs, "lanina_depthintRG2.feather"))
neutral_depthint <- read_feather(
  paste0(path_outputs, "neutral_depthintRG2.feather"))
elnino_depthint <- read_feather(
  paste0(path_outputs, "elnino_depthintRG2.feather"))

# plotting
phase_titles <- c("La Niña", "Neutral", "El Niño")
phases <- c("lanina", "neutral", "elnino")

Regular Hovmoeller Plots

Change in total (added) alkalinity

# formatting tables, subsetting
phase_data <- list(lanina_depthint, neutral_depthint, elnino_depthint)

phase_data <- phase_data %>%
  lapply(function(table) {
    setDT(table)
    table[, depth := as.numeric(depth)]
    table[, dTA_sum := as.numeric(as.character(dTA_sum))]
    table[, CDR_eff := as.numeric(as.character(CDR_eff))]
    table[, month := .GRP, by = time] # gives index in order to each unique time
    table <- table[month <= 24] # subsets to first two years
    return(table)
  })

# return tables to each item 
lanina_depthint_sub <- phase_data[[1]]
neutral_depthint_sub <- phase_data[[2]]
elnino_depthint_sub <- phase_data[[3]]

# # removing negative data points and transform
# lanina_depthint_sub <- lanina_depthint_sub[dTA_sum > 0]
# lanina_depthint_sub[, dTA_log := log10(dTA_sum)]

# setting boundaries of bins
depth_ranges <- unique(lanina_depthint_sub[, .(depth)])
setorder(depth_ranges, depth)

depth_ranges[, ":=" (
    depth_upper = (shift(depth, type = "lag", fill = 0) + depth) / 2, # set shallower boundary
    depth_lower = (shift(depth, type = "lead", fill = max(depth)) + depth) / 2 
  )] # deeper bound

# merge depth ranges back to tables and divide into surface and subsurface
phase_data <- list(lanina_depthint_sub, neutral_depthint_sub, elnino_depthint_sub)

phase_data <- phase_data %>%
  lapply(function(table) {
    table <- merge(table, depth_ranges, by = "depth", all.x = TRUE)
    table[, category := fifelse(depth < 100, "Surface", "Subsurface")]
    table$category <- factor(table$category, levels = c("Surface", "Subsurface"))
    return(table)
  })

# return tables to each item
lanina_depthint_sub <- phase_data[[1]]
neutral_depthint_sub <- phase_data[[2]]
elnino_depthint_sub <- phase_data[[3]]

# plotting
create_hovmo_dTA <- function(data, title_text, phase) {
  plot <- ggplot(data, aes(x = month, y = depth, fill = dTA_sum)) +
    geom_rect(aes(ymin = depth_upper, ymax = depth_lower, 
                  xmin = month - 1, xmax = month)) +
    scale_x_continuous(breaks = seq(0, 24, by = 3), limits = c(0,24),
                       expand = c(0,0)) + 
    scale_y_reverse(expand = c(0, 0)) +
    scale_fill_viridis_c(guide = guide_colorbar(
      barwidth = unit(0.7, "cm"), barheight = unit(6, "cm")),
      limits = c(-926000, 3443000000)) +
      # limits = c(0, 3443000000)) +
    facet_wrap(~category, scales = "free_y", ncol = 1) + # splits into two panels
    labs(x = "Months Since OAE Start",
         y = "Depth (m)",
         fill = "Added Alkalinity\n(mol/m)",
         title = paste0(title_text, " Phase Added Alkalinity")) + 
    theme_bw() + 
    theme(strip.text = element_blank(),
          axis.ticks.length = unit(0.15, "inches"),
          panel.border = element_blank(),  # Remove box around facets
          panel.grid = element_blank(),
          panel.spacing = unit(0.5, "lines"),
          strip.background = element_blank())
  
  print(plot)
  
  # # save plot
  # ggsave(paste0(path_plots, phase, "_hovmo_dTA.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

lapply(seq_along(phase_data), function(i) {
  create_hovmo_dTA(phase_data[[i]], phase_titles[i], phases[i])
})

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rm(create_hovmo_dTA, lanina_depthint, elnino_depthint, neutral_depthint)
gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1726524 92.3    3411250 182.2  3101578 165.7
Vcells 3198303 24.5    8388608  64.0  6011053  45.9

Change in DIC

# building off of data ran in chunk above

# plotting dDIC
create_hovmo_dDIC <- function(data, title_text, phase) {
  plot <- ggplot(data, aes(x = month, y = depth, fill = dDIC_sum)) +
    geom_rect(aes(ymin = depth_upper, ymax = depth_lower, 
                  xmin = month - 1, xmax = month)) +
    scale_y_reverse(expand = c(0, 0)) +
    scale_x_continuous(breaks = seq(0, 24, by = 3), limits = c(0,24),
                       expand = c(0,0)) +
    scale_fill_viridis_c(guide = guide_colorbar(
      barwidth = unit(0.7, "cm"), barheight = unit(6, "cm")),
      limits = c(-480, 2008000000)) +
      # limits = c(0, 2008000000)) +
    facet_wrap(~category, scales = "free_y", ncol = 1) + # splits into two panels
    labs(x = "Months Since OAE Start",
         y = "Depth (m)",
         fill = "Change in DIC\n(mol/m)",
         title = paste0(title_text, " Phase Change in DIC")) + 
    theme_bw() + 
    theme(strip.text = element_blank(),
          axis.ticks.length = unit(0.15, "inches"),
          panel.border = element_blank(),  # Remove box around facets
          panel.grid = element_blank(),
          panel.spacing = unit(0.5, "lines"),
          strip.background = element_blank())
  
  print(plot)
  
  # # save plot
  # ggsave(paste0(path_plots, phase, "_hovmo_dDIC.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

lapply(seq_along(phase_data), function(i) {
  create_hovmo_dDIC(phase_data[[i]], phase_titles[i], phases[i])
})

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rm(create_hovmo_dDIC)
gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1732728 92.6    3411250 182.2  3411250 182.2
Vcells 3212742 24.6    8388608  64.0  6011053  45.9

Integrated CDR Efficiency (full total dDIC per layer/full total dTA per layer)

# building off of data ran in chunk above

# plotting CDReff
create_hovmo_CDReff <- function(data, title_text, phase) {
  plot <- ggplot(data, aes(x = month, y = depth, fill = CDR_eff)) +
    geom_rect(aes(ymin = depth_upper, ymax = depth_lower, 
                  xmin = month - 1, xmax = month)) +
    scale_y_reverse(expand = c(0, 0)) +
    scale_x_continuous(breaks = seq(0, 24, by = 3), limits = c(0,24),
                       expand = c(0,0)) +
    scale_fill_viridis_c(guide = guide_colorbar(
      barwidth = unit(0.7, "cm"), barheight = unit(6, "cm")),
      limits = c(0, 1)) + # cutting off weird negative or >1 CDR effs
    facet_wrap(~category, scales = "free_y", ncol = 1) + # splits into two panels
    labs(x = "Months Since OAE Start",
         y = "Depth (m)",
         fill = "CDR Efficiency\n(Fraction)",
         title = paste0(title_text, " Phase CDR Efficiency")) + 
    theme_bw() + 
    theme(strip.text = element_blank(),
          axis.ticks.length = unit(0.15, "inches"),
          panel.border = element_blank(),  # Remove box around facets
          panel.grid = element_blank(),
          panel.spacing = unit(0.5, "lines"),
          strip.background = element_blank())
  
  print(plot)
  
  # # save plot
  # ggsave(paste0(path_plots, phase, "_hovmo_CDReff.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

lapply(seq_along(phase_data), function(i) {
  create_hovmo_CDReff(phase_data[[i]], phase_titles[i], phases[i])
})

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rm(create_hovmo_CDReff)
gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1733124 92.6    3411250 182.2  3411250 182.2
Vcells 3216505 24.6    8388608  64.0  6011053  45.9

Hovmoeller plots of the differences from the means of all 3 phases

Difference in added alkalinity from the phases’ mean

# building off of data ran in chunk above

# combining all three tables in to one
phase_list <- Map(function(table, phase) {
    table[, phase := phase]
    table[, .(month, depth, depth_upper, depth_lower, category, dTA_sum, dDIC_sum, 
              CDR_eff, phase)]
  }, phase_data, phases)

phase_fulldata <- rbindlist(phase_list)

# calculating mean dTA, dDIC, and CDR-eff across all 3 phases
phase_fulldata[, ":=" (
  dTA_mean = mean(dTA_sum, na.rm = TRUE),
  dDIC_mean = mean(dDIC_sum, na.rm = TRUE),
  CDR_effmean = mean(CDR_eff[CDR_eff >= 0 & CDR_eff <= 1] , na.rm = TRUE)
), by = .(month, depth)]

# plotting delta dTA from mean
create_hovmo_ddTA <- function(phase_name, title_text) {
  phase_dt <- phase_fulldata[phase_fulldata$phase == phase_name,]
  plot <- ggplot(phase_dt, aes(x = month, y = depth, fill = dTA_sum - dTA_mean)) +
    geom_rect(aes(xmin = month - 1, xmax = month,
                  ymin = depth_upper, ymax = depth_lower)) +
    scale_y_reverse(expand = c(0, 0)) +
    scale_x_continuous(breaks = seq(0, 24, by = 3), limits = c(0,24),
                       expand = c(0,0)) +
    scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
                         guide = guide_colorbar(
      barwidth = unit(0.7, "cm"), barheight = unit(6, "cm")),
      limits = c(-410000000, 430000000)) +
    facet_wrap(~category, scales = "free_y", ncol = 1) + # splits into two panels
    labs(x = "Months Since OAE Start",
         y = "Depth (m)",
         fill = "Added Alkalinity\nDifference (mol/m)",
         title = paste0(title_text, " Phase, Added Alkalinity Difference from Mean")) +
    theme_bw() +
    theme(strip.text = element_blank(),
          axis.ticks.length = unit(0.15, "inches"),
          panel.border = element_blank(),  # Remove box around facets
          panel.grid = element_blank(),
          panel.spacing = unit(0.5, "lines"),
          strip.background = element_blank())
  print(plot)
  
  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_hovmo_ddTA.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

lapply(seq_along(phases), function(i) {
  create_hovmo_ddTA(phases[i], phase_titles[i])
})

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[[3]]

rm(phase_list, create_hovmo_ddTA)
gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1735736 92.7    3411250 182.2  3411250 182.2
Vcells 3268101 25.0    8388608  64.0  6011053  45.9

Difference in dDIC from the phases’ mean

# building off of data ran in chunk above

# plotting delta dDIC from mean
create_hovmo_ddDIC <- function(phase_name, title_text) {
  phase_dt <- phase_fulldata[phase_fulldata$phase == phase_name,]
  plot <- ggplot(phase_dt, aes(x = month, y = depth, fill = dDIC_sum - dDIC_mean)) +
    geom_rect(aes(xmin = month - 1, xmax = month,
                  ymin = depth_upper, ymax = depth_lower)) +
    scale_x_continuous(breaks = seq(0, 24, by = 3), limits = c(0,24),
                       expand = c(0,0)) +
    scale_y_reverse(expand = c(0, 0)) +
    scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
                         guide = guide_colorbar(
      barwidth = unit(0.7, "cm"), barheight = unit(6, "cm")),
      limits = c(-200000000, 200000000)) +
    facet_wrap(~category, scales = "free_y", ncol = 1) + # splits into two panels
    labs(x = "Months Since OAE Start",
         y = "Depth (m)",
         fill = "dDIC Difference\n(mol/m)",
         title = paste0(title_text, " Phase, dDIC Difference from Mean")) +
    theme_bw() +
    theme(strip.text = element_blank(),
          axis.ticks.length = unit(0.15, "inches"),
          panel.border = element_blank(),  # Remove box around facets
          panel.grid = element_blank(),
          panel.spacing = unit(0.5, "lines"),
          strip.background = element_blank())
  print(plot)
  
  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_hovmo_ddDIC.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

lapply(seq_along(phases), function(i) {
  create_hovmo_ddDIC(phases[i], phase_titles[i])
})

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rm(create_hovmo_ddDIC)
gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1736023 92.8    3411250 182.2  3411250 182.2
Vcells 3265579 25.0    8388608  64.0  6011053  45.9

Difference in CDR efficiency from the phases’ mean; wonky efficiencies outside the range from 0 to 1 were filtered out (these occurred primarily below 150m)

# building off of data ran in chunk above; CDR efficiencies outside 0-1 were
# excluded from the mean calculation since they are casued by errors

# plotting delta dDIC from mean
create_hovmo_dCDReff <- function(phase_name, title_text) {
  phase_dt <- phase_fulldata[phase_fulldata$phase == phase_name,]
  # filter out negative dTA/dDIC/wrong CDReff as these will make weird/incorrect efficiency
  phase_dt <- phase_dt[dTA_sum < 0, dTA_sum := NA] 
  phase_dt <- phase_dt[dDIC_sum < 0, dDIC_sum := NA]
  phase_dt <- phase_dt[CDR_eff < 0 | CDR_eff > 1, CDR_eff := NA]
  
  plot <- ggplot(phase_dt, aes(x = month, y = depth, fill = CDR_eff - CDR_effmean)) +
    geom_rect(aes(xmin = month - 1, xmax = month,
                  ymin = depth_upper, ymax = depth_lower)) +
    scale_x_continuous(breaks = seq(0, 24, by = 3), limits = c(0,24),
                       expand = c(0,0)) +
    scale_y_reverse(expand = c(0, 0)) +
    scale_fill_gradient2(na.value = "lightgray", low = "blue", mid = "white", 
                         high = "red", midpoint = 0,
                         guide = guide_colorbar(
      barwidth = unit(0.7, "cm"), barheight = unit(6, "cm")),
      limits = c(-0.152, 0.152)) +
    facet_wrap(~category, scales = "free_y", ncol = 1) + # splits into two panels
    labs(x = "Months Since Addition Start",
         y = "Depth (m)",
         fill = "CDR Efficiency\nDifference",
         title = paste0(title_text, " Phase, CDR Efficiency Difference from Mean")) +
    theme_bw() +
    theme(strip.text = element_blank(),
          axis.ticks.length = unit(0.15, "inches"),
          panel.border = element_blank(),  # Remove box around facets
          panel.grid = element_blank(),
          panel.spacing = unit(0.5, "lines"),
          strip.background = element_blank())
  print(plot)
  
  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_hovmo_dCDReff.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

lapply(seq_along(phases), function(i) {
  create_hovmo_dCDReff(phases[i], phase_titles[i])
})

Version Author Date
dd18a78 vgfroh 2025-02-03

Version Author Date
dd18a78 vgfroh 2025-02-03

Version Author Date
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rm(list = ls())
gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1737840 92.9    3411250 182.2  3411250 182.2
Vcells 3230628 24.7    8388608  64.0  6011053  45.9

sessionInfo()
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: openSUSE Leap 15.6

Matrix products: default
BLAS/LAPACK: /usr/local/OpenBLAS-0.3.28/lib/libopenblas_haswellp-r0.3.28.so;  LAPACK version 3.12.0

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       

time zone: Europe/Zurich
tzcode source: system (glibc)

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

other attached packages:
 [1] scales_1.3.0      arrow_18.1.0.1    data.table_1.16.2 lubridate_1.9.3  
 [5] forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4       purrr_1.0.2      
 [9] readr_2.1.5       tidyr_1.3.1       tibble_3.2.1      ggplot2_3.5.1    
[13] tidyverse_2.0.0   workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] sass_0.4.9        utf8_1.2.4        generics_0.1.3    stringi_1.8.4    
 [5] hms_1.1.3         digest_0.6.37     magrittr_2.0.3    timechange_0.3.0 
 [9] evaluate_1.0.1    grid_4.4.2        fastmap_1.2.0     rprojroot_2.0.4  
[13] jsonlite_1.8.9    processx_3.8.4    whisker_0.4.1     ps_1.8.1         
[17] promises_1.3.2    httr_1.4.7        fansi_1.0.6       viridisLite_0.4.2
[21] jquerylib_0.1.4   cli_3.6.3         rlang_1.1.4       bit64_4.5.2      
[25] munsell_0.5.1     withr_3.0.2       cachem_1.1.0      yaml_2.3.10      
[29] tools_4.4.2       tzdb_0.4.0        colorspace_2.1-1  httpuv_1.6.15    
[33] assertthat_0.2.1  vctrs_0.6.5       R6_2.5.1          lifecycle_1.0.4  
[37] git2r_0.35.0      bit_4.5.0         fs_1.6.5          pkgconfig_2.0.3  
[41] callr_3.7.6       pillar_1.9.0      bslib_0.8.0       later_1.4.1      
[45] gtable_0.3.6      glue_1.8.0        Rcpp_1.0.13-1     xfun_0.49        
[49] tidyselect_1.2.1  rstudioapi_0.17.1 knitr_1.49        farver_2.1.2     
[53] htmltools_0.5.8.1 labeling_0.4.3    rmarkdown_2.29    compiler_4.4.2   
[57] getPass_0.2-4