CCS ENSO evaluation metrics
Victoria Froh & Jens Daniel Müller
22 November, 2024
Last updated: 2024-11-22
Checks: 7 0
Knit directory: oae_ccs_roms/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e8bd044 | vgfroh | 2024-11-22 | enso_indices update and plot edits |
| Rmd | 1cd6532 | vgfroh | 2024-11-21 | Manually committing updated enso file and file location changes |
| html | c12634a | vgfroh | 2024-11-21 | Build site. |
| Rmd | 163f18c | vgfroh | 2024-11-21 | rebuild after code update and new enso file |
| Rmd | 043c113 | jens-daniel-mueller | 2024-11-18 | rebuild after code review |
| html | 02a42aa | jens-daniel-mueller | 2024-11-18 | Build site. |
| html | 3facddd | jens-daniel-mueller | 2024-11-18 | Build site. |
| html | 2509718 | jens-daniel-mueller | 2024-11-18 | Build site. |
| html | 9508443 | vgfroh | 2024-11-15 | Build site. |
| html | 2c9eb7e | vgfroh | 2024-11-15 | Build site. |
| Rmd | a2b33f0 | vgfroh | 2024-11-15 | Adding full enso indices file, removing just temperature file |
| html | fa48b39 | vgfroh | 2024-11-15 | Build site. |
| Rmd | 0437728 | vgfroh | 2024-11-15 | Adding full enso indices file |
Read this
this is the script to open the data
# 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 = 11),
axis.text.y = element_text(size = 11),
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 = 12),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(0.2, 0.945),
legend.title = element_blank(),
legend.text = element_text(size = 10),
legend.direction = "horizontal",
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)# 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) +
theme(legend.position = c(0.7, 0.08))
| Version | Author | Date |
|---|---|---|
| c12634a | vgfroh | 2024-11-21 |
# 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
| Version | Author | Date |
|---|---|---|
| c12634a | vgfroh | 2024-11-21 |
# 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 +
theme(legend.position = c(0.43, 0.965))
| Version | Author | Date |
|---|---|---|
| c12634a | vgfroh | 2024-11-21 |
# 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())
| Version | Author | Date |
|---|---|---|
| c12634a | vgfroh | 2024-11-21 |
# 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) +
theme(legend.position = c(0.43, 0.965))
| Version | Author | Date |
|---|---|---|
| c12634a | vgfroh | 2024-11-21 |
# 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 +
theme(legend.position = c(0.6, 0.965))
| Version | Author | Date |
|---|---|---|
| c12634a | vgfroh | 2024-11-21 |
# 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))
| Version | Author | Date |
|---|---|---|
| c12634a | vgfroh | 2024-11-21 |
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5
Matrix products: default
BLAS: /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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] zoo_1.8-11 units_0.8-0 lubridate_1.9.0 timechange_0.1.1
[5] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
[9] readr_2.1.3 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[13] tidyverse_1.3.2 stars_0.6-0 sf_1.0-9 abind_1.4-5
[17] ncdf4_1.19 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 httr_1.4.4 rprojroot_2.0.3
[4] tools_4.2.2 backports_1.4.1 bslib_0.4.1
[7] utf8_1.2.2 R6_2.5.1 KernSmooth_2.23-20
[10] DBI_1.1.3 colorspace_2.0-3 withr_2.5.0
[13] tidyselect_1.2.0 processx_3.8.0 compiler_4.2.2
[16] git2r_0.30.1 cli_3.6.1 rvest_1.0.3
[19] RNetCDF_2.6-1 xml2_1.3.3 labeling_0.4.2
[22] sass_0.4.4 scales_1.2.1 classInt_0.4-8
[25] callr_3.7.3 proxy_0.4-27 digest_0.6.30
[28] rmarkdown_2.18 pkgconfig_2.0.3 htmltools_0.5.3
[31] highr_0.9 dbplyr_2.2.1 fastmap_1.1.0
[34] rlang_1.1.1 readxl_1.4.1 rstudioapi_0.15.0
[37] jquerylib_0.1.4 generics_0.1.3 farver_2.1.1
[40] jsonlite_1.8.3 googlesheets4_1.0.1 magrittr_2.0.3
[43] ncmeta_0.3.5 Rcpp_1.0.10 munsell_0.5.0
[46] fansi_1.0.3 lifecycle_1.0.3 stringi_1.7.8
[49] whisker_0.4 yaml_2.3.6 grid_4.2.2
[52] parallel_4.2.2 promises_1.2.0.1 crayon_1.5.2
[55] lattice_0.20-45 haven_2.5.1 hms_1.1.2
[58] knitr_1.41 ps_1.7.2 pillar_1.9.0
[61] reprex_2.0.2 glue_1.6.2 evaluate_0.18
[64] getPass_0.2-2 modelr_0.1.10 vctrs_0.6.4
[67] tzdb_0.3.0 httpuv_1.6.6 cellranger_1.1.0
[70] gtable_0.3.1 assertthat_0.2.1 cachem_1.0.6
[73] xfun_0.35 lwgeom_0.2-10 broom_1.0.5
[76] e1071_1.7-12 later_1.3.0 class_7.3-20
[79] googledrive_2.0.0 gargle_1.2.1 ellipsis_0.3.2