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File	Version	Author	Date	Message
Rmd	5a5f21a	vgfroh	2025-04-07	Final code push
Rmd	9445dea	vgfroh	2025-02-19	Updates to files/coding fixes
html	91e2272	vgfroh	2025-02-19	Build site.
Rmd	a36bcfe	vgfroh	2025-02-19	Mixing depth and air-sea co2 flux anlaysis

Introduction

Map air-sea CO2 flux of each surface cell (all 3 phases, monthly maps)
Map dFlux of each cell (OAE - control), monthly maps, also on zoomed in region
Map dFlux difference from mean of each phase on zoomed region
Calculate moles of CO2 uptake per month dist and time series
Mapping ddpCO2 during months of interest

#loading packages
library(tidyverse)
library(tidync)
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 original grid intermediate computation outputs
path_outputs_og <- "/net/sea/work/vifroh/oae_ccs_roms_data/original_grid/"

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

# loading in previous saved surface variable data
lanina_surf_data <- read_feather(
  paste0(path_outputs, "lanina_surf_dataRG2.feather"))
neutral_surf_data <- read_feather(
  paste0(path_outputs, "neutral_surf_dataRG2.feather"))
elnino_surf_data <- read_feather(
  paste0(path_outputs, "elnino_surf_dataRG2.feather"))

# loading in previous saved data with area
lanina_dTA_mol <- read_feather(
  paste0(path_outputs,"lanina_dTA_mol_mdataRG2.feather"))

# loading in original grid integrated CDR data
lanina_cdreff_sum <- read_feather(
  paste0(path_outputs_og, "lanina_CDReff_integrated.feather"))
neutral_cdreff_sum <- read_feather(
  paste0(path_outputs_og, "neutral_CDReff_integrated.feather"))
elnino_cdreff_sum <- read_feather(
  paste0(path_outputs_og, "elnino_CDReff_integrated.feather"))

# loading in pCO2 gradient data
ddpco2_data <- read_feather(
  paste0(path_outputs, "ddpco2_dataRG2.feather"))

phase_data <- list(lanina_surf_data, neutral_surf_data, elnino_surf_data)

# Convert lat/lon to numeric and subset first two years
phase_data <- phase_data %>%
  lapply(function(table) {
    setDT(table)
    setorder(table, time)
    table[, lat := as.numeric(lat)]
    table[, lon := as.numeric(lon)]
    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_surf_data <- phase_data[[1]]
neutral_surf_data <- phase_data[[2]]
elnino_surf_data <- phase_data[[3]]

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

# adding phase and combining all 3 into one table for plotting
phase_list <- Map(function(table, phase) {
    table[, phase := phase]
  }, phase_data, phases)

surface_data <- rbindlist(phase_list)

# subset down to just area data; figure out how to subset just the unique?
setDT(lanina_dTA_mol)
lanina_dTA_mol <- unique(lanina_dTA_mol[, .(lat, lon, area)])

# create combined table of original grid data with "phase" column
full_cdreff_og <- rbindlist(
  list(lanina = lanina_cdreff_sum, neutral = neutral_cdreff_sum,
       elnino = elnino_cdreff_sum),
  idcol = "phase")

# add column for months since oae addition begins for x-axis
full_cdreff_og <- full_cdreff_og[, month := seq_len(.N), by = phase]

rm(elnino_cdreff_sum, lanina_cdreff_sum, neutral_cdreff_sum)
gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   1477172   78.9    2710424   144.8    2186244   116.8
Vcells 211059815 1610.3 7913311213 60373.8 9637976277 73532.0

Air-Sea Flux Regional Distribution

Regional distribution of flux in OAE runs

# can rerun code for months of choice
create_fgOAE_distmap <- function(phase_name, title_text) {
  phase_dt <- surface_data[surface_data$phase == phase_name & surface_data$month == 14]
  plot <- ggplot(phase_dt, aes(x = lon, y = lat, fill = FG_CO2_OAE)) +
    geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group), 
                 fill = "lightgray", color = "white") +
    geom_raster() +
    scale_fill_distiller(limit = c(-0.0055, 0.0055), palette = "PRGn") +  # set the color range
    theme_light() +
    coord_fixed(xlim = c(-170, -85),
              ylim = c(10, 60)) +
    scale_x_continuous(breaks = seq(-170, -85, by = 10)) +
    scale_y_continuous(breaks = seq(10, 60, by = 10)) +
    labs(x = "Longitude",
         y = "Latitude",
         fill = "Air-Sea CO2 Flux (mmol/m^2/s)",
         title = paste0(title_text, " Phase, Air-Sea CO2 Flux (Month 14)")) +
    theme(panel.border = element_blank())
  
  # print(plot)

  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_fgOAEmap_month14.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

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

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rm(create_fgOAE_distmap, phase_data, phase_list)
gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   1689382   90.3    2710424   144.8    2710424   144.8
Vcells 217201256 1657.2 6330648971 48299.1 9637976277 73532.0

Regional distribution of dflux in OAE runs (OAE flux - control flux)

# can rerun code for months of choice,
create_dfg_distmap <- function(phase_name, title_text) {
  phase_dt <- surface_data[surface_data$phase == phase_name & surface_data$month == 22]
  plot <- ggplot(phase_dt, aes(x = lon, y = lat, fill = dFG)) +
    geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group), 
                 fill = "lightgray", color = "white") +
    geom_raster() +
    # scale_fill_viridis_c(limit = c(0, 0.00022)) +
    scale_fill_gradient2(low = "green", mid = "white", high = "purple", midpoint = 0,
                         limit = c(0, 0.00022)) +
    theme_light() +
    coord_fixed(xlim = c(-170, -85),
              ylim = c(10, 60)) +
    scale_x_continuous(breaks = seq(-170, -90, by = 10)) +
    scale_y_continuous(breaks = seq(10, 60, by = 10)) +
    labs(x = "Longitude",
         y = "Latitude",
         fill = "dCO2 Flux 
         (mmol/m^2/s)",
         title = paste0(title_text, " Phase, Change in Air-Sea CO2 Flux (Month 22)")) +
    theme(panel.border = element_blank())
  
  # print(plot)

  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_dfgmap_month22.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

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

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rm(create_dfg_distmap)
gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   1695647   90.6    2710424   144.8    2710424   144.8
Vcells 217218702 1657.3 5064519177 38639.3 9637976277 73532.0

Zoomed in maps of dflux in OAE runs (OAE flux - control flux) near add site

# can rerun code for months of choice
create_dfg_distmap <- function(phase_name, title_text) {
  phase_dt <- surface_data[surface_data$phase == phase_name & surface_data$month == 15]
  plot <- ggplot(phase_dt, aes(x = lon, y = lat, fill = dFG)) +
    geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group), 
                 fill = "lightgray", color = "white") +
    geom_raster() +
    # scale_fill_viridis_c(limit = c(0, 0.00022)) +
    scale_fill_gradient2(low = "green", mid = "white", high = "purple", midpoint = 0,
                         limit = c(0, 0.0000575)) +
    theme_light() +
    coord_fixed(xlim = c(-135, -100),
              ylim = c(10, 45)) +
    scale_x_continuous(breaks = seq(-135, -100, by = 10)) +
    scale_y_continuous(breaks = seq(10, 45, by = 10)) +
    labs(x = "Longitude",
         y = "Latitude",
         fill = "dCO2 Flux 
         (mmol/m^2/s)",
         title = paste0(title_text, " Phase, Change in Air-Sea CO2 Flux (Month 15)")) +
    theme(panel.border = element_blank())
  
  # print(plot)

  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_dfgmapzoom_month15.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

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

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rm(create_fgd_distmap)

Warning in rm(create_fgd_distmap): object 'create_fgd_distmap' not found

gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   1694753   90.6    2710424   144.8    2710424   144.8
Vcells 217216163 1657.3 4051615342 30911.4 9637976277 73532.0

Zoomed in difference from mean dflux maps

# calculating mean max delta flux across all 3 phases for each grid cell
surface_data[, ":=" (
  dFG_mean = mean(dFG, na.rm = TRUE)
), by = .(lat, lon, month)]

# can rerun code for months of choice, subset coordinates in place
create_dfg_meanmap <- function(phase_name, title_text) {
  phase_dt <- surface_data[surface_data$phase == phase_name & surface_data$month == 1]
  plot <- ggplot(phase_dt, aes(x = lon, y = lat, fill = dFG - dFG_mean)) +
    geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group), 
                 fill = "lightgray", color = "white") +
    geom_raster() +
    scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
                        limits = c(-0.00005, 0.00005)) + 
                        # breaks = c(-0.000005, -0.0000025, 0, 0.0000025, 0.000005)) +
    theme_light() +
    coord_fixed(xlim = c(-135, -100),
              ylim = c(10, 45)) +
    scale_x_continuous(breaks = seq(-135, -100, by = 10)) +
    scale_y_continuous(breaks = seq(10, 45, by = 10)) +
    labs(x = "Longitude",
         y = "Latitude",
         fill = "Difference from Mean dCO2 Flux 
         (mmol/m^2/s)",
         title = paste0(title_text, " Phase, Difference from Mean delta Air-Sea CO2 Flux (Month 1)")) +
    theme(panel.border = element_blank())
  
  # print(plot)

  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_dfgmapdfm_month1.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

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

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rm(create_dfg_meanmap)
gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   1696314   90.6    2710424   144.8    2710424   144.8
Vcells 229624971 1751.9 3241292274 24729.1 9637976277 73532.0

Quantifying Uptake

# merging area data to dFG and calculating moles per month from the dFG rate
uptake_data <- surface_data[, .SD, .SDcols = c("lat", "lon", "time", "dFG", 
                                               "month", "phase")] %>% 
  merge(lanina_dTA_mol, by = c("lon", "lat"), all.x = TRUE) 

uptake_data[, secs := as.numeric(days_in_month(ymd(paste0(time, "-01"))))
            * 60 * 60 * 24] # pulls days in the month and converts to seconds
uptake_data[, CO2_uptake := dFG * area * secs / 1000] # mmol/m2/s -> moles

# can rerun code for months of choice, using zoomed region
create_uptake_distmap <- function(phase_name, title_text) {
  phase_dt <- uptake_data[uptake_data$phase == phase_name & uptake_data$month == 24]
  plot <- ggplot(phase_dt, aes(x = lon, y = lat, fill = CO2_uptake)) +
    geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group), 
                 fill = "lightgray", color = "white") +
    geom_raster() +
    # scale_fill_viridis_c(limit = c(0, 91000000)) +
    scale_fill_gradient2(low = "green", mid = "white", high = "purple", midpoint = 0,
                         limit = c(0, 1500000)) +
    theme_light() +
    coord_fixed(xlim = c(-135, -100),
              ylim = c(10, 45)) +
    scale_x_continuous(breaks = seq(-135, -100, by = 10)) +
    scale_y_continuous(breaks = seq(10, 45, by = 10)) +
    labs(x = "Longitude",
         y = "Latitude",
         fill = "CO2 Uptake (moles)",
         title = paste0(title_text, " Phase, CO2 Uptake (Month 24)")) +
    theme(panel.border = element_blank())
  
  # print(plot)

  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_co2uptakemap_month24.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

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

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# integrate by time and compare it to the native grid dDIC to check scope
# # need to add these up sequentially
uptake_data_int <- uptake_data[, .(CO2_sum_ind = sum(CO2_uptake, na.rm = TRUE)),
                                   by = .(phase, month)] %>% 
  .[, CO2_sum_cum := cumsum(CO2_sum_ind), by = phase]

uptake_data_int <- merge(uptake_data_int, full_cdreff_og[, .(phase, month, dDIC_sum)], 
                         by = c("phase", "month"), all.x = TRUE) %>% 
  .[, fraction := CO2_sum_cum / dDIC_sum]

# saving uptake individual and sum value tables
# write_feather(uptake_data, paste0(path_outputs,
#                                       "fguptake_data.feather"))
# write_feather(uptake_data_int, paste0(path_outputs,
#                                       "fguptake_data_intsum.feather"))

# plot time series of accumulating uptaken CO2
ggplot(uptake_data_int, aes(x = month, y = CO2_sum_cum/1e10, color = phase)) +
  scale_x_continuous(breaks = seq(0, 24, by = 3), limits = c(0,24),
                     expand = c(0,0)) +
  scale_y_continuous(limits = c(0, 18), expand = c(0,0)) +
  geom_vline(xintercept = seq(0, 24, by = 12), color = "grey75", linewidth = 0.4) +
  geom_hline(yintercept = 16.7, linetype = "dashed", color = "black", linewidth = 0.6) +
  geom_line(linewidth = 1) +
  labs(title = "Cumulative Uptaken CO2 from Surface Flux Values", 
       x = "Months Since OAE Began", 
       y = "Moles of CO2 (10^10)") +
  theme_bw()

Version	Author	Date
91e2272	vgfroh	2025-02-19

# ggsave(paste0(path_plots, "total_fg_co2uptake.png"), plot = last_plot(),
#               width = 10, height = 6, dpi = 300)

rm(create_uptake_distmap)
gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   1745581   93.3    2710424   144.8    2710424   144.8
Vcells 331519428 2529.3 2593033820 19783.3 9637976277 73532.0

Induced pCO2 Gradient Distribution

Calculating 3-month mean

# assigning group
ddpco2_data[, period := (month - 1) %/% 3 + 1]

Warning in `[.data.table`(ddpco2_data, , `:=`(period, (month - 1)%/%3 + :
Invalid .internal.selfref detected and fixed by taking a (shallow) copy of the
data.table so that := can add this new column by reference. At an earlier
point, this data.table has been copied by R (or was created manually using
structure() or similar). Avoid names<- and attr<- which in R currently (and
oddly) may copy the whole data.table. Use set* syntax instead to avoid copying:
?set, ?setnames and ?setattr. If this message doesn't help, please report your
use case to the data.table issue tracker so the root cause can be fixed or this
message improved.

# calculating 3-month mean
ddpco2_data[, ":=" (
  ddpco2_3mean = mean(ddpCO2, na.rm = TRUE)),
  by = .(lat, lon, phase, period)]

Regional distribution of ddpCO2 (3-month means)

# can rerun code for months of choice, using CCS region grid
create_ddpco2_distmap <- function(phase_name, title_text) {
  phase_dt <- ddpco2_data[ddpco2_data$phase == phase_name & ddpco2_data$month == 3]
  plot <- ggplot(phase_dt, aes(x = lon, y = lat, fill = ddpco2_3mean)) +
    geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group), 
                 fill = "lightgray", color = "white") +
    geom_raster() +
    scale_fill_gradient2(low = "green", mid = "white", high = "purple", midpoint = 0,
                         limit = c(-1, 130)) +
    theme_light() +
    coord_fixed(xlim = c(-135, -110),
              ylim = c(22.5, 47.5)) +
    scale_x_continuous(breaks = seq(-140, -110, by = 5)) +
    scale_y_continuous(breaks = seq(20, 50, by = 5)) +
    labs(x = "Longitude",
         y = "Latitude",
         fill = "dpCO2 Gradient (ppm)",
         title = paste0(title_text, " Phase, Induced pCO2 Gradient 3-Month Mean (Month 3)")) +
    theme(panel.border = element_blank())
  
  # print(plot)

  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_ddpco2map_3mo_month3.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

lapply(seq_along(phases), function(i) {
  create_ddpco2_distmap(phases[i], phase_titles[i])
})

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rm(create_ddpco2_distmap)
gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   1737361   92.8    2710424   144.8    2710424   144.8
Vcells 449934135 3432.8 2074427056 15826.7 9637976277 73532.0

Zoomed in difference from mean ddpCO2 maps

# calculating mean ddpco2 across all 3 phases for each grid cell
setDT(ddpco2_data)
ddpco2_data[, ":=" (
  ddpco2_mean = mean(ddpCO2, na.rm = TRUE)
), by = .(lat, lon, month)]

# can rerun code for months of choice, subset coordinates in place
create_ddpco2_meanmap <- function(phase_name, title_text) {
  phase_dt <- ddpco2_data[ddpco2_data$phase == phase_name & ddpco2_data$month == 15]
  plot <- ggplot(phase_dt, aes(x = lon, y = lat, fill = ddpCO2 - ddpco2_mean)) +
    geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group), 
                 fill = "lightgray", color = "white") +
    geom_raster() +
    scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
                        limits = c(-40, 40)) + 
                        # breaks = c(-0.000005, -0.0000025, 0, 0.0000025, 0.000005)) +
    theme_light() +
    coord_fixed(xlim = c(-135, -110),
              ylim = c(22.5, 47.5)) +
    scale_x_continuous(breaks = seq(-135, -100, by = 10)) +
    scale_y_continuous(breaks = seq(10, 45, by = 10)) +
    labs(x = "Longitude",
         y = "Latitude",
         fill = "Difference from Mean\ndpCO2 Gradient (ppm)",
         title = paste0(title_text, " Phase, Difference from Mean OAE Induced pCO2 Gradient (Month 15)")) +
    theme(panel.border = element_blank())
  
  # print(plot)

  # # save plot
  # ggsave(paste0(path_plots, phase_name, "_ddpco2mapdfm_month15.png"), plot = plot,
  #        width = 8, height = 6, dpi = 300)
}

lapply(seq_along(phases), function(i) {
  create_ddpco2_meanmap(phases[i], phase_titles[i])
})

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rm(list = ls())
gc()

          used (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells 1735632 92.7    2710424   144.8    2710424   144.8
Vcells 9146278 69.8 1659541645 12661.3 9637976277 73532.0

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 tidync_0.4.0     
 [5] lubridate_1.9.3   forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4      
 [9] purrr_1.0.2       readr_2.1.5       tidyr_1.3.1       tibble_3.2.1     
[13] ggplot2_3.5.1     tidyverse_2.0.0   workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          bslib_0.8.0        processx_3.8.4    
 [5] callr_3.7.6        tzdb_0.4.0         vctrs_0.6.5        tools_4.4.2       
 [9] ps_1.8.1           generics_0.1.3     fansi_1.0.6        pkgconfig_2.0.3   
[13] RColorBrewer_1.1-3 assertthat_0.2.1   lifecycle_1.0.4    farver_2.1.2      
[17] compiler_4.4.2     git2r_0.35.0       munsell_0.5.1      getPass_0.2-4     
[21] ncdf4_1.23         httpuv_1.6.15      htmltools_0.5.8.1  maps_3.4.2.1      
[25] sass_0.4.9         yaml_2.3.10        later_1.4.1        pillar_1.9.0      
[29] jquerylib_0.1.4    whisker_0.4.1      cachem_1.1.0       RNetCDF_2.9-2     
[33] ncmeta_0.4.0       tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[37] labeling_0.4.3     rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.2        
[41] colorspace_2.1-1   cli_3.6.3          magrittr_2.0.3     utf8_1.2.4        
[45] withr_3.0.2        promises_1.3.2     bit64_4.5.2        timechange_0.3.0  
[49] rmarkdown_2.29     httr_1.4.7         bit_4.5.0          hms_1.1.3         
[53] evaluate_1.0.1     knitr_1.49         rlang_1.1.4        Rcpp_1.0.13-1     
[57] glue_1.8.0         rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1          
[61] fs_1.6.5

Drivers - Air-Sea CO2 Flux

Victoria Froh

07 April, 2025