Regrid Checks and Column Integrated Plots
Victoria Froh
07 April, 2025
Last updated: 2025-04-07
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Knit directory: oae_ccs_roms/
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| Rmd | a36bcfe | vgfroh | 2025-02-19 | Mixing depth and air-sea co2 flux anlaysis |
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| Rmd | 639b38d | vgfroh | 2025-02-03 | Column integrated plots and hovmoeller plots completed |
Introduction
- Checking regrid subregion
- Verifying scope of dTA in regrid
- Column-integrated dTA maps
#loading packages
library(tidyverse)
library(data.table)
library(arrow)
library(scales)
library(maps)
library(geosphere)
# 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 dTA conc data to make a surface plot/columnint
lanina_dTA_conc <- read_feather(
paste0(path_outputs,"lanina_dTA_concdataRG2.feather"))
neutral_dTA_conc <- read_feather(
paste0(path_outputs,"neutral_dTA_concdataRG2.feather"))
elnino_dTA_conc <- read_feather(
paste0(path_outputs,"elnino_dTA_concdataRG2.feather"))
# loading in dTA full integration data for competency check
lanina_dTA_int<- read_feather(
paste0(path_outputs,"lanina_CDReff_intRG2.feather"))
# loading in dTA sum original grid data for competency check
lanina_intdata_ogs <- read_feather(
"/net/sea/work/vifroh/oae_ccs_roms_data/regrid/lanina_dTAint_comparegrids.feather")Looking at the boundaries of the regridded subregion
# filtering dTA conc data to make a surface plot
surface_data <- lanina_dTA_conc[depth == 0 & time == "1998-09"]
# Convert lat and lon to numeric
surface_data$lat <- as.numeric(surface_data$lat)
surface_data$lon <- as.numeric(surface_data$lon)
# # Convert longitude to -180 to 180 range
# surface_data$lon <- surface_data$lon - 360
# Define the bounding box for the plot
lat_range <- range(surface_data$lat, na.rm = TRUE)
lon_range <- range(surface_data$lon, na.rm = TRUE)
# plotting surface map
ggplot() +
geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group),
fill = "lightgray", color = "white") +
geom_raster(data = surface_data, aes(x = lon, y = lat, fill = dTA)) +
scale_fill_viridis_c() + # Change the color scale to suit your data
theme_minimal() +
coord_fixed(xlim = c(lon_range[1] - 2, lon_range[2] + 2),
ylim = c(lat_range[1] - 2, lat_range[2] + 2)) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 8),
axis.text.y = element_text(size = 8),
axis.ticks = element_line(size = 0.5),
panel.grid = element_blank()
) +
labs(title = "Subset Domain (La Niña, September 1998)",
x = "Longitude",
y = "Latitude",
fill = "dTA (mmol/m^3)")
| Version | Author | Date |
|---|---|---|
| dd18a78 | vgfroh | 2025-02-03 |
# # save plot
# ggsave(paste0(path_plots, "regrid_domainRG2.png"), plot = last_plot(),
# width = 8, height = 6, dpi = 300)
rm(surface_data)
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 1813457 96.9 3101934 165.7 2975083 158.9
Vcells 3148786932 24023.4 4534077182 34592.3 3227357167 24622.8Checking the alkalinity containment within the regridded subregion over the time series
# using full integrated data from cdr_eff_molar file
setDT(lanina_dTA_int)
# combine with original dTA sum data to compare; subtracting regrid from original
lanina_dTAint_compare <- merge(lanina_intdata_ogs, lanina_dTA_int[, .(time, dTA_sum_rg2 = dTA_sum)],
by = "time", all.x = FALSE) %>%
.[, dTA_dif_rg2 := dTA_sum_og - dTA_sum_rg2] %>%
.[, frac_miss_rg2 := dTA_dif_rg2 / dTA_sum_og]
# # save data file
# write_feather(lanina_dTAint_compare, paste0(path_outputs,
# "lanina_dTAint_comparegridsRG2.feather"))
rm(lanina_dTAint_compare, lanina_intdata_ogs)
gc()Column integrated plots of added alkalinity
To check lateral movement during the time series
# Defining Subregions (currently abandoned for now)
# loc_box <- c(30, 35, -115, -122)
# nep_box <- c(10, 60, -155, -95)
# #work in progress
# coastline <- map("world", plot = FALSE, fill = FALSE,
# xlim = c(-130, -113), ylim = c(20, 50))
# coastline <- data.table(lon = coastline$x, lat = coastline$y)[!is.na(lon) & !is.na(lat)]
# coastline <- coastline[lon >= -130 & lon <= -113 & lat >= 20 & lat <= 50]
#
# # filtering
#
#
# rm()
# gc()Column Integrated Plots
# rerunning these on own for different phases/months; can load saved column int
# files from folder
# using conc data then multiplied by depth bin size so have mmol/m^2
setDT(lanina_dTA_conc)
lanina_dTA_conc$depth <- as.numeric(lanina_dTA_conc$depth)
# filter out only top 100m for CDReff integrated plot
lanina_dTA_conc <- lanina_dTA_conc[depth <= 100]
lanina_dTA_conc <-
lanina_dTA_conc[, thickness :=
ifelse(depth == 0, 2.5,
ifelse(depth < 80, 5,
ifelse(depth == 80, 7.5,
ifelse(depth < 100, 10, #edit here for top 100
5
# ifelse(depth == 100, 15,
# ifelse(depth < 300, 20,
# 10
))))
] # two )) removed for top100
lanina_dTA_conc <- lanina_dTA_conc[, dTA_m2 := dTA * thickness] %>% # units now moles/m2
.[, dDIC_m2 := dDIC * thickness] %>%
.[, CDReff_m2 := fifelse(dDIC_m2/dTA_m2 == Inf, NaN, dDIC_m2/dTA_m2)]
# calculating CDR Efficiency per cell, replacing dDIC calcs producing Inf -> Na
# grouping by lat/lon and integrating vertically, averaging the CDR Efficiency
lanina_dTA_columnint<- lanina_dTA_conc[, .(dTA_column = sum(dTA_m2, na.rm = TRUE),
dDIC_column = sum(dDIC_m2, na.rm = TRUE),
CDReff_avg = mean(CDReff_m2, na.rm = TRUE)),
# averaging CDReff of individual grid cells
by = c("lat", "lon", "time")] %>%
.[, CDReff_col := fifelse(dDIC_column/dTA_column == Inf, NaN, dDIC_column/dTA_column)]
# total CDReff for column; this is fine for top 100m if thinking this is mixing
# Convert lat, lon to numeric
lanina_dTA_columnint$lat <- as.numeric(lanina_dTA_columnint$lat)
lanina_dTA_columnint$lon <- as.numeric(lanina_dTA_columnint$lon)
# filter by time to create a timestop plot
surface_data <- lanina_dTA_columnint[time == "2000-05"]
# # Convert longitude to -180 to 180 range
# surface_data$lon <- surface_data$lon - 360
# plotting column integrated map
ggplot() +
geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group),
fill = "lightgray", color = "white") +
geom_raster(data = surface_data, aes(x = lon, y = lat, fill = CDReff_col)) +
scale_fill_viridis_c(limit = c(0, 1)) + # 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(title = "Vertically Integrated CDR Efficiency, top 100m (La Niña, May 2000)", # La Niña
x = "Longitude",
y = "Latitude",
fill = "CDR Efficiency") + # dTA (mmol/m^2)
theme(panel.border = element_blank())
# # save plot
# ggsave(paste0(path_plots, "lanina_columnint_May2000_CDReff100.png"), plot = last_plot(),
# width = 8, height = 6, dpi = 300)
# save column integrated data
# write_feather(lanina_dTA_columnint, paste0(path_outputs,
# "lanina_columnintRG2_top100.feather"))
# write_feather(neutral_dTA_columnint, paste0(path_outputs,
# "neutral_columnintRG2_top100.feather"))
# write_feather(elnino_dTA_columnint, paste0(path_outputs,
# "elnino_columnintRG2_top100.feather"))
rm(lanina_dTA_columnint, surface_data, lanina_dTA_conc)
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 1833058 97.9 3101934 165.7 3101934 165.7
Vcells 6031566 46.1 6710651979 51198.3 8374565549 63892.9Difference From Mean Plots
# load in full table with column ints and mean
colint_data <- read_feather(paste0(path_outputs, "colint_RG2.feather"))
# calc dfm
# dTA
col_dt <- colint_data[colint_data$phase == "neutral" & colint_data$month == 10]
ggplot() +
geom_polygon(data = map_data("world"), aes(x = long, y = lat, group = group),
fill = "lightgray", color = "white") +
geom_raster(data = col_dt, aes(x = lon, y = lat, fill = dTA_column - dTA_mean)) +
geom_rect(aes(xmin = -118.0625, xmax = -117.9375,
ymin = 33.5625, ymax = 33.6875),
fill = NA, color = "black", size = 0.5) + # Outline only (no fill)
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
guide = guide_colorbar(
barwidth = unit(0.7, "cm"), barheight = unit(6, "cm"), title.hjust = 0.5)#,
#limits = c(-200000000, 200000000)
)+
# scale_fill_viridis_c(limit = c(-0.5, 103), guide = guide_colourbar(
# barwidth = 15, barheight = 0.5, title.position = "bottom", title.hjust = 0.5
# )) + # set the color range
theme_light() +
coord_fixed(xlim = c(-125, -115), #nep grid
ylim = c(25, 35)) +
# coord_fixed(xlim = c(-127, -115), # loc grid
# ylim = c(27.5, 37.5)) +
scale_x_continuous(breaks = seq(-155, -85, by = 5)) +
scale_y_continuous(breaks = seq(10, 60, by = 5)) +
labs(#title = "Added Alkalinity Mean Column Concentration (Month 13)",
x = "Longitude",
y = "Latitude",
fill = "\u0394TA' [mmol/m\u00B2]") + # dTA (mmol/m^2)
theme(panel.border = element_blank(),
axis.text = element_text(size = 11, color = "black"),
axis.title = element_text(size = 12),
axis.ticks = element_line(color = "black", size = 0.5),
axis.ticks.length = unit(0.2, "cm"),
legend.text = element_text(size = 9),
legend.title = element_text(size = 10)#,
#legend.position = "bottom",
#legend.direction = "horizontal"
)Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
# # save plot
# ggsave(paste0(path_plots, "dTA_col_mean_10mo_neutdfm.png"), plot = last_plot(),
# width = 6, height = 6, dpi = 300)
rm(list = ls())
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 1835504 98.1 3101934 165.7 3101934 165.7
Vcells 6623831 50.6 5368521584 40958.6 8374565549 63892.9
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] geosphere_1.5-20 maps_3.4.2.1 scales_1.3.0 arrow_18.1.0.1
[5] data.table_1.16.2 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[9] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[13] tibble_3.2.1 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] lattice_0.22-6 callr_3.7.6 tzdb_0.4.0 vctrs_0.6.5
[9] tools_4.4.2 ps_1.8.1 generics_0.1.3 fansi_1.0.6
[13] pkgconfig_2.0.3 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] httpuv_1.6.15 htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.10
[25] crayon_1.5.3 later_1.4.1 pillar_1.9.0 jquerylib_0.1.4
[29] whisker_0.4.1 cachem_1.1.0 tidyselect_1.2.1 digest_0.6.37
[33] stringi_1.8.4 labeling_0.4.3 rprojroot_2.0.4 fastmap_1.2.0
[37] grid_4.4.2 colorspace_2.1-1 cli_3.6.3 magrittr_2.0.3
[41] utf8_1.2.4 withr_3.0.2 promises_1.3.2 sp_2.1-4
[45] bit64_4.5.2 timechange_0.3.0 rmarkdown_2.29 httr_1.4.7
[49] bit_4.5.0 hms_1.1.3 evaluate_1.0.1 knitr_1.49
[53] viridisLite_0.4.2 rlang_1.1.4 Rcpp_1.0.13-1 glue_1.8.0
[57] rstudioapi_0.17.1 jsonlite_1.8.9 R6_2.5.1 fs_1.6.5