CCS ENSO evaluation metrics
Victoria Froh & Jens Daniel Müller
07 April, 2025
Last updated: 2025-04-07
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Knit directory: oae_ccs_roms/
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Introduction
CCS Enso Index proxy calculations for choosing ideal location
# loading packages
library(ncdf4)
library(stars)
library(tidyverse)
library(lubridate)
library(units)
library(zoo)
# Path to the files:
path_ROMSv2RG_results <-
"/net/sea/work/loher/ROMS/Alk_enh_formatted_2024_08/regrid_2024_10/1979-2009_2/"
#2x2 degree location boxes for each location + TrP 3.4 region:
#Columbia = 45N-47N, 124W-126W
#San Francisco = 36.5N-38.5N, 122.5W-124.5W
#Huntington = 32N-34N, 117W-119W
#Tropical Pacific = 5S-5N, 120W-170W
# saving theme formatting for all plots in this code
custom_theme <- theme_bw() +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 13),
axis.ticks = element_line(linewidth = 0.3, color = "black"),
axis.ticks.length = unit(-0.3, "cm"),
axis.title.x = element_blank() ,
axis.title.y = element_text(size = 14),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(size = 12),
legend.direction = "vertical",
legend.background = element_rect(color = "grey", linewidth = 0.25),
panel.border = element_rect(color = "black", linewidth = 0.5, fill = NA))
colors <- c("tropPacific" = "black", "ColumbiaRiver" = "#F8766D",
"SanFrancisco" = "#00BFC4", "Huntington" = "#7CAE00")
lines <- c("tropPacific" = "dashed", "ColumbiaRiver" = "solid",
"SanFrancisco" = "solid", "Huntington" = "solid")# 42 year time series of historical monthly averages for all 4 locations
years_past <- 1979:2021
locations <- c("ColumbiaRiver", "SanFrancisco", "Huntington", "tropPacific")
annual_cycles <- list()
annual_monthly_sst_list <- list()
for (i in locations){
# creating the 2x2 degree grid subsets for 3 CCS locations, tropP already set
subset <- if (i == "ColumbiaRiver") {
cbind(start = c(70, 40, 1, 1), count = c(16, 16, 1, 12))
} else if (i == "SanFrancisco") {
cbind(start = c(33, 39, 1, 1), count = c(16, 16, 1, 12))
} else if (i == "Huntington") {
cbind(start = c(50, 44, 1, 1), count = c(16, 16, 1, 12))
} else if (i == "tropPacific") {
cbind(start = c(1, 1, 1, 1), count = c(400, 80, 1, 12))
}
# reading in data for each location into a stars object
annual_cycles[[i]] <- do.call(c, lapply(years_past, function(year)(
read_ncdf(paste0(path_ROMSv2RG_results,
"pactcs30_", year, "_monthly_", i, ".nc"),
ncsub = subset,
var = "temp",
proxy = FALSE
))))
# converting each stars object into a dataframe and cleaning up
annual_cycles[[i]] <- annual_cycles[[i]] %>%
drop_units() %>%
as_tibble %>%
select(-s_rho)
# creating a new data frame with the monthly average over each region
annual_monthly_sst_list[[i]] <- annual_cycles[[i]] %>%
mutate(yr_mon = paste0(format(time, "%Y-%m"), "-15"), yr_mon = ymd(yr_mon)) %>%
group_by(yr_mon) %>%
summarise(sst_mean = mean(temp, na.rm = TRUE)) %>%
ungroup() %>%
mutate(region = i)
}
# rearranging all location lists into a single data frame
annual_monthly_sst <- reduce(annual_monthly_sst_list, bind_rows)
# clearing unneeded objects
rm(annual_cycles, annual_monthly_sst_list)Proxies
Sea Surface Temperature
# computing the climatological monthly mean for each location
monthly_mean_baseline <- annual_monthly_sst %>%
mutate(month = format(as.Date(yr_mon), "%m")) %>%
group_by(month, region) %>%
summarise(sst_clim = mean(sst_mean)) %>%
ungroup()
# plot
monthly_mean_baseline %>%
mutate(month = as.numeric(month)) %>%
ggplot(aes(month, sst_clim, col = region)) +
geom_path() +
labs(title = "Climatological Sea Surface Temperature Baseline",
x = "Month", y = "Monthly Mean SST (°C)") +
scale_color_manual(values = colors) +
custom_theme +
scale_x_continuous(breaks = 1:12, labels = month.abb)
# calculate the anomaly for each location/month (time series - base month mean)
annual_monthly_sst <- annual_monthly_sst %>%
mutate(month = format(as.Date(yr_mon), "%m")) %>%
group_by(month, region) %>%
mutate(sst_clim = mean(sst_mean)) %>%
ungroup() %>%
mutate(sst_anom = sst_mean - sst_clim)
# plot anomalies
annual_monthly_sst %>%
ggplot(aes(x = yr_mon, y = sst_anom, color = region, linetype = region)) +
geom_path() +
labs(title = "Monthly Sea Surface Temperature Anomalies",
y = "SST anomaly (°C)") +
scale_color_manual(values = colors) +
scale_linetype_manual(values = lines) +
custom_theme
# Computing running annual mean (mean sst/anom for one year starting each month)
running_annual_mean_sst <- annual_monthly_sst %>%
select(yr_mon, region, sst_mean, sst_anom) %>%
pivot_longer(starts_with("sst")) %>%
group_by(region, name) %>%
mutate(value = rollmean(value, k=12, fill=NA, align = "left")) %>%
ungroup()
# plot both RAMs
running_annual_mean_sst %>%
ggplot(aes(x = yr_mon, y = value, color = region, linetype = region)) +
geom_path() +
labs(title = "Sea Surface Temperature Running Annual Means",
y = "Running Annual Mean (°C)") +
scale_color_manual(values = colors) +
scale_linetype_manual(values = lines) +
facet_grid(name ~ ., scales = "free_y") +
scale_y_continuous(expand = expansion(mult = 0.07)) + # add more space
custom_theme 
# Calculating correlation coefficients for the sst anomalies and plot
annual_monthly_sst %>%
select(yr_mon, region, sst_anom) %>%
pivot_wider(names_from = region,
values_from = sst_anom) %>%
pivot_longer(-c(yr_mon, tropPacific)) %>% #this recombines the indices col
drop_na() %>%
group_by(name) %>% # name is now where the regions are stored
summarise(cor = cor(tropPacific, value)) %>% #value is the indice
ungroup() %>%
ggplot(aes(name, cor)) +
geom_col() +
labs(title =
"Correlation Coefficients of SST Anomalies with the Tropical Pacific",
y = "Correlation Coefficient",
x = "Region") +
custom_theme +
theme(panel.grid.major.y = element_line())
50m Temperature, Isopycnal 26 kg/m3 density and salinity
# Loading in time series for temperature at 50m depth
locations_2 <- c("ColumbiaRiver", "SanFrancisco", "Huntington")
annual_cycles <- list()
annual_monthly_t50_list <- list()
for (j in locations_2){
# creating the 2x2 degree grid subsets for 3 CCS locations, tropP already set
subset <- if (j == "ColumbiaRiver") {
cbind(start = c(70, 40, 1), count = c(16, 16, 12))
} else if (j == "SanFrancisco") {
cbind(start = c(33, 39, 1), count = c(16, 16, 12))
} else if (j == "Huntington") {
cbind(start = c(50, 44, 1), count = c(16, 16, 12))
}
# reading in data for each location into a stars object
annual_cycles[[j]] <- do.call(c, lapply(years_past, function(year)(
read_ncdf(paste0(path_ROMSv2RG_results,
"pactcs30_", year, "_monthly_", j, "_50m.nc"),
ncsub = subset,
var = "temp",
proxy = FALSE
))))
# converting each stars object into a dataframe and cleaning up
annual_cycles[[j]] <- annual_cycles[[j]] %>%
drop_units() %>%
as_tibble()
# creating a new data frame with the monthly average over each region
annual_monthly_t50_list[[j]] <- annual_cycles[[j]] %>%
mutate(yr_mon = paste0(format(time, "%Y-%m"), "-15"), yr_mon = ymd(yr_mon)) %>%
group_by(yr_mon) %>%
summarise(t50_mean = mean(temp, na.rm = TRUE)) %>%
ungroup() %>%
mutate(region = j)
}
# rearranging all stored location lists into a single data frame
annual_monthly_t50 <- reduce(annual_monthly_t50_list, bind_rows)
# clearing unneeded objects
rm(annual_cycles, annual_monthly_t50_list)# 42 year time series of historical monthly averages for all 4 locations
annual_cycles <- list()
annual_monthly_iso26_list <- list()
for (j in locations_2){
# creating the 2x2 degree grid subsets for 3 CCS locations, tropP already set
subset <- if (j == "ColumbiaRiver") {
cbind(start = c(70, 40, 1), count = c(16, 16, 12))
} else if (j == "SanFrancisco") {
cbind(start = c(33, 39, 1), count = c(16, 16, 12))
} else if (j == "Huntington") {
cbind(start = c(50, 44, 1), count = c(16, 16, 12))
}
# reading in data for each location into a stars object
annual_cycles[[j]] <- do.call(c, lapply(years_past, function(year)(
read_ncdf(paste0(path_ROMSv2RG_results,
"isopycnal_monthly_", year, "_", j, ".nc"),
ncsub = subset,
var = c("depth_26", "salt_26"),
proxy = FALSE
))))
# converting each stars object into a data frame and cleaning up
annual_cycles[[j]] <- annual_cycles[[j]] %>%
drop_units() %>%
as_tibble()
# Weird values showing up in SF, need to filter out
if (j == "SanFrancisco") {
annual_cycles[[j]] <- annual_cycles[[j]] %>%
filter(depth_26 >= 1, salt_26 >= 1)
}
# creating a new data frame with the monthly average over each region
annual_monthly_iso26_list[[j]] <- annual_cycles[[j]] %>%
mutate(yr_mon = paste0(format(time, "%Y-%m"), "-15"), yr_mon = ymd(yr_mon)) %>%
group_by(yr_mon) %>%
summarise(depth_26_mean = mean(depth_26, na.rm = TRUE),
salt_26_mean = mean(salt_26, na.rm = TRUE)) %>%
ungroup() %>%
mutate(region = j)
}
# rearranging all stored location lists into a single data frame
annual_monthly_iso26 <- reduce(annual_monthly_iso26_list, bind_rows)
# clearing unneeded objects
rm(annual_cycles, annual_monthly_iso26_list)# Joining together the 50m T and iso26 dataframes and reorganizing
enso_metric_monthly <- full_join(annual_monthly_t50, annual_monthly_iso26)
enso_metric_monthly <- enso_metric_monthly %>%
pivot_longer(
cols = c(t50_mean, depth_26_mean, salt_26_mean),
names_to = "metric",
values_to = "metric_mean",
names_transform = list(metric = ~ case_when(
. == "t50_mean" ~ "t50",
. == "depth_26_mean" ~ "depth26",
. == "salt_26_mean" ~ "salt26"
))
)
# computing the climatological monthly mean for each metric at each location
monthly_mean_baseline <- enso_metric_monthly %>%
mutate(month = format(as.Date(yr_mon), "%m")) %>%
group_by(month, region, metric) %>%
summarise(metric_clim = mean(metric_mean)) %>%
ungroup()
# plot (still want to update/change this later)
monthly_mean_baseline %>%
mutate(month = as.numeric(month)) %>%
ggplot(aes(x = month, y = metric_clim, color = region)) +
geom_path() +
labs(title = "Climatological Baseline",
x = "Month", y = "Climatological Monthly Mean") +
scale_color_manual(values = colors) +
facet_wrap(
~ metric, scales = "free_y", ncol = 1,
labeller = labeller(
metric = c(
"t50" = "50m Temperature (°C)",
"depth26" = "Isopycnal 26 kg/m³ Depth (m)",
"salt26" = "Isopycnal 26 kg/m³ Salinity (psu)"))) +
custom_theme +
scale_x_continuous(breaks = 1:12, labels = month.abb)
# Calculating anomaly index values
enso_metric_monthly <- enso_metric_monthly %>%
mutate(month = format(as.Date(yr_mon), "%m")) %>%
group_by(month, region, metric) %>%
mutate(metric_clim = mean(metric_mean)) %>%
ungroup() %>%
mutate(metric_anom = metric_mean - metric_clim)
# plot (want to do more with this and add in tropP sst later)
enso_metric_monthly %>%
ggplot(aes(x = yr_mon, y = metric_anom, color = region)) +
geom_path() +
labs(title = "Mean Monthly Anomalies",
y = "Anomaly") +
scale_color_manual(values = colors) +
facet_wrap(
~ metric, scales = "free_y", ncol = 1,
labeller = labeller(
metric = c(
"t50" = "50m Temperature (°C)",
"depth26" = "Isopycnal 26 kg/m³ Depth (m)",
"salt26" = "Isopycnal 26 kg/m³ Salinity (psu)"))) +
custom_theme 
# Computing running annual mean (sst and anomaly for one year starting that month)
running_annual_mean_metrics <- enso_metric_monthly %>%
select(yr_mon, region, metric, metric_mean, metric_anom) %>%
pivot_longer(starts_with("metric_")) %>%
group_by(region, metric, name) %>%
mutate(value = rollmean(value, k=12, fill=NA, align = "left")) %>%
ungroup()
# plot (this is not correct currently and i want to add in tropP sst later)
running_annual_mean_metrics %>%
ggplot(aes(x = yr_mon, y = value, color = region)) +
geom_path() +
labs(title = "Metric Running Annual Means",
y = "Running Annual Mean") +
scale_color_manual(values = colors) +
scale_y_continuous(expand = expansion(mult = 0.07)) +
facet_wrap(~ metric + name, scales = "free_y", ncol = 2,
labeller = labeller(
metric = c(
"t50" = "50m Temperature (°C)",
"depth26" = "Isopycnal 26 kg/m³ Depth (m)",
"salt26" = "Isopycnal 26 kg/m³ Salinity (psu)"),
name = label_value)) +
custom_theme #+ 
# theme(legend.position = "top", legend.justification = "right",
# plot.title = element_text(hjust = 0.5, margin = margin(b = -20)),
# strip.text = element_text(size = 10))#Calculating correlation coeffs for the anomalies vs tropical Pacific sst
#isolate the tropical Pacific sst anomaly data for a comparison metric
tropP_sstanom <- annual_monthly_sst %>%
select(yr_mon, region, sst_anom) %>%
pivot_wider(names_from = region,
values_from = sst_anom) %>%
select(yr_mon, tropPacific)
enso_metric_monthly %>%
select(yr_mon, region, metric, metric_anom) %>%
pivot_wider(names_from = metric,
values_from = metric_anom) %>%
full_join(tropP_sstanom, by = "yr_mon") %>%
pivot_longer(-c(yr_mon, region, tropPacific)) %>%
drop_na() %>%
group_by(name, region) %>%
summarise(cor = cor(tropPacific, value)) %>%
ungroup() %>%
ggplot(aes(x = region, y = cor)) +
geom_col() +
facet_wrap(~ name,
labeller = labeller(
name = c(
"t50" = "50m Temperature",
"depth26" = "Isopycnal 26 kg/m³ Depth",
"salt26" = "Isopycnal 26 kg/m³ Salinity"))) +
labs(title =
"Correlation Coefficients of Metric Anomalies and Tropical Pacific SST Anomaly",
y = "Correlation Coefficient",
x = "Region") +
custom_theme +
theme(panel.grid.major.y = element_line(),
axis.text.x = element_text(angle = 90))
rm(list = ls())
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 1745251 93.3 2872160 153.4 2872160 153.4
Vcells 3100497 23.7 160931147 1227.9 314304603 2398.0
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] zoo_1.8-12 units_0.8-5 lubridate_1.9.3 forcats_1.0.0
[5] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[9] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[13] stars_0.6-7 sf_1.0-19 abind_1.4-8 ncdf4_1.23
[17] 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 tzdb_0.4.0 callr_3.7.6 vctrs_0.6.5
[9] tools_4.4.2 ps_1.8.1 generics_0.1.3 parallel_4.4.2
[13] proxy_0.4-27 fansi_1.0.6 pkgconfig_2.0.3 KernSmooth_2.23-24
[17] lifecycle_1.0.4 farver_2.1.2 compiler_4.4.2 git2r_0.35.0
[21] munsell_0.5.1 getPass_0.2-4 httpuv_1.6.15 htmltools_0.5.8.1
[25] class_7.3-22 sass_0.4.9 yaml_2.3.10 CFtime_1.4.1
[29] later_1.4.1 pillar_1.9.0 jquerylib_0.1.4 whisker_0.4.1
[33] classInt_0.4-10 cachem_1.1.0 RNetCDF_2.9-2 ncmeta_0.4.0
[37] tidyselect_1.2.1 digest_0.6.37 stringi_1.8.4 labeling_0.4.3
[41] rprojroot_2.0.4 fastmap_1.2.0 grid_4.4.2 colorspace_2.1-1
[45] cli_3.6.3 magrittr_2.0.3 utf8_1.2.4 e1071_1.7-16
[49] withr_3.0.2 scales_1.3.0 promises_1.3.2 timechange_0.3.0
[53] rmarkdown_2.29 httr_1.4.7 hms_1.1.3 evaluate_1.0.1
[57] knitr_1.49 rlang_1.1.4 Rcpp_1.0.13-1 glue_1.8.0
[61] DBI_1.2.3 rstudioapi_0.17.1 jsonlite_1.8.9 R6_2.5.1
[65] fs_1.6.5