Marine Heat Waves anomaly profiles
Marguerite Larriere & Jens Daniel Müller
16 May, 2024
Last updated: 2024-05-16
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Knit directory:
bgc_argo_r_argodata/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | fbf0c64 | mlarriere | 2024-05-16 | latitude, longitude and day of the month graphs |
| html | 22e0206 | mlarriere | 2024-05-16 | Build site. |
| Rmd | d0f58b6 | mlarriere | 2024-05-16 | latitude, longitude and day of the month graphs |
| html | 96d4b76 | mlarriere | 2024-05-14 | Build site. |
| Rmd | 91e6028 | mlarriere | 2024-05-13 | adding subsection CESM comparison |
| html | af6594f | mlarriere | 2024-05-13 | Build site. |
| Rmd | 30f9250 | mlarriere | 2024-05-13 | Adding CESM subsection |
Task
Focusing on 2023 Only core Argo - focus on temperature anomalies
Dependencies
temp_core_va.rds - temperature of core argo floats after vertical alignment.
core_metadata.rds - File with metadata concerning the floats such as platform number, cycle number, date, lat, lon and quality control results.
temp_anomaly_va.rds - file containing the temperature anomalies (temp core - climatology).
2023_mhw_raw.csv - CSV file containing the categorization of surface marine heatwaves, in 2023 and in a 0.25°x0.25° grid.
Outputs
2023_surface_mhws_1x1.rds - file containing the categorization of surface marine heatwaves in a 1°x1° grid, in 2023.
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_basin_mask <- "/nfs/kryo/work/datasets/gridded/ocean/interior/reccap2/supplementary/"
path_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
path_argo_core <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/core_argo_r_argodata_2024-03-13'
path_argo_core_preprocessed <- paste0(path_argo_core, "/preprocessed_core_data")
path_mhw<- '/net/kryo/work/datasets/gridded/ocean/2d/obs/mhw'opt_xbreaks <- c(-4, -2, 0, 2, 4)
# Define colors palette to match ~ color of ClimateReanalyser (for comparison)
colors <- c("lavender", "#9867C5", "darkblue", "lightblue", "white", "orange", "darkred", "red", "#FFCBCB")
palette <- colorRampPalette(colors)
n <- 20 #number of colors
continuous_palette <- palette(n) #continuous color palette
scale_limits <- c(-10, 10)
scale_breaks <- seq(scale_limits[1], scale_limits[2], length.out = n + 1)Load data
Load biomes
Load Argo floats data
Load SST anomaly
#Read SST anomaly, computed using climatology:2009-2019 (from anomaly_SST_2023.Rmd)
sst_anomaly_northAtlantic<- read_rds(paste(path_argo_core_preprocessed, "/SST_anomaly2023_NorthAtlantic_clim2004-2019.rds", sep = ""))Argo floats
Distribution over North Atlantic basin
#Histogram -- number of floats per month and biome
unique_platforms_per_month <- core_anomaly_with_platform_2023 %>%
group_by(month) %>%
summarize(unique_platforms = n_distinct(platform_number))
hist <- ggplot(unique_platforms_per_month, aes(x = factor(month), y = unique_platforms)) +
geom_bar(stat = "identity", fill = "darkred") +
labs(title = "Amount of platform per month, in North Atlantic, 2023",
x = "Months", y = "Number of argo floats", fill = "Biomes") +
theme_minimal() +
guides(fill = FALSE)
# scale_fill_manual(values = c("1" = "#1034A6", "2" = "#f59c04", "3" = "darkred"),
# labels = c("1" = "SPSS", "2" = "STSS", "3" = "STPS")) +
# theme_minimal()
print(hist)
#Number of cycle per platform
# cycle_counts <- core_anomaly_with_platform_2023 %>%
# group_by(platform_number) %>%
# summarise(cycle_count = n_distinct(cycle_number))
# print(cycle_counts)#Join the SST anomaly with the anomaly profiles
core_anomaly_with_platform_2023<-core_anomaly_with_platform_2023 %>%
filter(!is.na(depth)) %>%
select(-profile_range, -day_of_year, -clim_temp)
complete_anomaly_profile<- core_anomaly_with_platform_2023 %>%
inner_join(sst_anomaly_northAtlantic, by = c("lat", "lon", "month"))
#Floats present during heatwaves
# East
heatwaves_data_east<-complete_anomaly_profile %>%
group_by(month) %>%
filter(lat>0, lat<40, lon>-30, 0>lon)
cycles_per_platform <-heatwaves_data_east %>%
group_by(platform_number) %>%
summarize(unique_cycles = toString(unique(cycle_number)))
months_per_float <- heatwaves_data_east %>%
group_by(platform_number) %>%
summarize(unique_months = toString(unique(month)))Distribution over East-North Atlantic
Using the SST map computed in “anomaly_SST_2023.Rmd” and ClimateReanalyser, we identify 2 areas of particular interest for SST anomalies in 2023 in the North Atlantic Ocean:
Northeast, near the Canada/USA coast. SST anomaly particularly strong in summer and autumn (JJA and SON).
East coast of the North Atlantic Ocean. Here, SST anomalies are high on an annual basis, with a sharp increase from June onwards.
#PLatfrom in the east north atlantic over 2023
platform_counts <- aggregate(platform_number ~ month, data = heatwaves_data_east, FUN = function(x) length(unique(x)))
cycle_count_per_platform_month <- heatwaves_data_east %>%
group_by(month, platform_number) %>%
summarise(cycle_count = n_distinct(cycle_number))
#Annual hotspot (+MAM + JJA + a bit SON) in agreement with climate reanaliser
east_base_map<- base_map + lims(x=c(-30,0), y=c(0,40))
east_sst_anomaly<-sst_anomaly_northAtlantic %>%
filter(lat>0, lat<40, lon>-30, 0>lon)
# Tiles for SST anomaly
map_anomaly_east <- east_base_map+
geom_tile(data=east_sst_anomaly, aes(x = lon, y = lat, fill = SST_anomaly)) +
scale_fill_gradientn(colors = continuous_palette, limits = scale_limits, breaks = scale_breaks) +
labs(title= "Area of interest: east", fill = "SST Anomaly") +
theme_minimal()
custom_labeller <- function(variable, value) {
month_count <- platform_counts[platform_counts$month == value, "platform_number"]
return(paste("Month:", value, "\nnumber of floats:", month_count))
}
platform_positions <- map_anomaly_east +
geom_point(data = heatwaves_data_east, aes(x = lon, y = lat), color = "dodgerblue3") +
labs(title = "Platform Locations") +
facet_wrap(~month, ncol = 3, labeller = custom_labeller)
platform_positions
Anomaly profiles
Individual float
unique_platform<-heatwaves_data_east %>%
filter(platform_number==6903067 )
ggplot(unique_platform, aes(x = anomaly, y = depth, color = factor(cycle_number))) +
geom_path() +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = c(-6, 6), ylim = c(200, 0)) +
labs(title = 'Anomaly profiles by month',
subtitle= paste0('Platform ', unique(unique_platform$platform_number)),
x = 'Temperature (°C)', y = 'Depth (m)', color = 'Cycle Number') +
scale_color_viridis(discrete = TRUE) +
facet_wrap(~month, scales = "free", ncol = 3)
unique_platform_base_map<- base_map + lims(x=c(-30,-10), y=c(0,20))
unique_coords_with_cycles <- unique_platform %>%
distinct(lat, lon, .keep_all = TRUE) %>%
group_by(lat, lon) %>%
summarise(cycle_numbers = toString(unique(cycle_number)))
#base map and SST anomaly
map_anomaly_east <- unique_platform_base_map +
geom_tile(data = east_sst_anomaly, aes(x = lon, y = lat, fill = SST_anomaly)) +
scale_fill_gradientn(colors = continuous_palette, limits = scale_limits, breaks = scale_breaks) +
labs(fill = "SST Anomaly") +
theme_minimal()
#trajetory
map_anomaly_east +
geom_point(data = unique_coords_with_cycles, aes(x = lon, y = lat, color = cycle_numbers), size = 2) +
scale_color_discrete(name = "Cycle Number", guide = guide_legend(title.position = "top")) +
labs(title = 'Float position') +
theme(legend.position = "bottom")+
guides(fill = guide_colorbar(barwidth = 20, barheight = 1, title.position = "top"))
Coloring by lat/lon
#Calculating monthly mean anomaly + std for each lat/lon pair of the area
anomaly_lat_lon <- heatwaves_data_east %>%
group_by(lat, lon, depth, month, platform_number, cycle_number) %>%
summarise(
temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE)
)
# Longitude - gradient north-south
ggplot(anomaly_lat_lon, aes(x = temp_anomaly_mean, y = depth, group = interaction(platform_number, cycle_number), color = as.numeric(lon))) +
geom_path() +
geom_vline(xintercept = 0) +
scale_y_reverse(limits = c(200, 0)) +
coord_cartesian(xlim = c(-6, 6)) +
facet_wrap(~ month, ncol = 3) +
labs(title = "Anomaly Profiles for all longitudes",
subtitle = "All floats and cycles included",
x = "Temperature (°C)", y = "Depth (m)", color = "Longitude") +
scale_color_viridis_c()
# Latitude - gradient west-east
ggplot(anomaly_lat_lon, aes(x = temp_anomaly_mean, y = depth, group = interaction(platform_number, cycle_number), color = as.numeric(lat))) +
geom_path() +
geom_vline(xintercept = 0) +
scale_y_reverse(limits = c(200, 0)) +
coord_cartesian(xlim = c(-6, 6)) +
facet_wrap(~ month, ncol = 3) +
labs(title = "Anomaly Profile for all latitudes",
subtitle = "All floats and cycles included",
x = "Temperature (°C)", y = "Depth (m)", color = "Latitude") +
scale_color_viridis_c()
| Version | Author | Date |
|---|---|---|
| 22e0206 | mlarriere | 2024-05-16 |
Coloring by SST anomaly
# calculate mean anomaly data
anomaly_summary <- heatwaves_data_east %>%
group_by(platform_number, depth, month, cycle_number, SST_anomaly) %>%
summarise(
temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE)
)
#plot for the SST anomalies
ggplot(anomaly_summary, aes(x = temp_anomaly_mean, y = depth, group = interaction(platform_number, cycle_number), color = as.numeric(SST_anomaly))) +
geom_path() +
geom_vline(xintercept = 0) +
scale_y_reverse(limits = c(200, 0)) +
coord_cartesian(xlim = c(-6, 6)) +
facet_wrap(~ month, ncol = 3) +
labs(title = "Mean Anomaly Profile",
x = "Temperature (°C)", y = "Depth (m)", color = "SST Anomaly") +
scale_color_viridis_c()
Day within the month
# calculate mean anomaly data
anomaly_summary_day <- heatwaves_data_east %>%
group_by(platform_number, depth, month, cycle_number, day) %>%
summarise(
temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE)
)
#plot for time during month
ggplot(anomaly_summary_day, aes(x = temp_anomaly_mean, y = depth, group = interaction(platform_number, cycle_number), color = as.numeric(day))) +
geom_path() +
geom_vline(xintercept = 0) +
scale_y_reverse(limits = c(200, 0)) +
coord_cartesian(xlim = c(-6, 6)) +
facet_wrap(~ month, ncol = 3) +
labs(title = "Mean Anomaly Profile",
x = "Temperature (°C)", y = "Depth (m)", color = "day within the month") +
scale_color_viridis_c()
Monthly anomaly profiles
# datsets with the cycle number per float across months
cycles_per_platform_month<- heatwaves_data_east %>%
group_by(platform_number, month) %>%
summarize(unique_cycles = toString(unique(cycle_number)))
# Calculating monthly mean anomaly over the east area by averaging the anomaly of each float present
anomaly_mean <- heatwaves_data_east %>%
group_by(depth, month) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
#For each mont put the SST anomaly next to the anomaly path
for (m in unique(east_sst_anomaly$month)) {
# Filter data
map_data <- filter(east_sst_anomaly, month == m)
anomaly_data <- filter(anomaly_mean, month == m)
# Plot for temperature anomaly map
map_plot <- east_base_map +
geom_tile(data=map_data, aes(x = lon, y = lat, fill = SST_anomaly)) +
scale_fill_gradientn(colors = continuous_palette, limits = scale_limits, breaks = scale_breaks) +
labs(title = paste("SST Anomaly (°C) in North Atlantic - 2023", month.name[as.numeric(m)]),
subtitle = "Extent: East",
x = "Longitude", y = "Latitude") +
theme(legend.position = 'right', legend.key.height = unit(2, "cm"))
# Plot for anomaly profiles
anomaly_plot <- ggplot(anomaly_data, aes(x = temp_anomaly_mean, y = depth, color = factor(month))) +
geom_path() +
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
y = depth), alpha = 0.2) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = c(-4, 4), ylim = c(200, 0)) +
scale_x_continuous(breaks = opt_xbreaks) +
labs(title = paste('Associated anomaly profile in', month.name[as.numeric(m)]),
subtitle = paste0("from surface to 200m", "\nNumber of platform:", nrow(filter(cycles_per_platform_month, month==m))),
x = 'Temperature (°C)', y = 'Depth (m)') +
theme_minimal()+
guides(color = FALSE)
#plots side by side
combined_plot <- grid.arrange(map_plot, anomaly_plot, ncol = 2)
print(combined_plot)
}
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2 2 (1-1,2-2) arrange gtable[layout]# Vertical anomaly profile for the North atlatinc east region - monthly
anomaly_plot_monthly <- ggplot(anomaly_mean, aes(x = temp_anomaly_mean, y = depth, color = factor(month))) +
geom_path() +
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
y = depth), alpha = 0.2) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = c(-4, 4), ylim = c(200, 0)) +
scale_x_continuous(breaks = opt_xbreaks) +
labs(title = paste('Monthly anomaly profile'),
subtitle = paste0("Extent: East", "\nfrom surface to 200m"),
x = 'Temperature (°C)', y = 'Depth (m)') +
guides(color = FALSE)+
facet_wrap(~month)
print(anomaly_plot_monthly)
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] broom_1.0.5 paletteer_1.6.0 cluster_2.1.6
[4] gridExtra_2.3 scatterplot3d_0.3-44 viridis_0.6.2
[7] viridisLite_0.4.1 ggOceanMaps_1.3.4 ggspatial_1.1.7
[10] oce_1.7-10 gsw_1.1-1 lubridate_1.9.0
[13] timechange_0.1.1 forcats_0.5.2 stringr_1.5.0
[16] dplyr_1.1.3 purrr_1.0.2 readr_2.1.3
[19] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[22] tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 colorspace_2.0-3 ellipsis_0.3.2
[4] class_7.3-20 rprojroot_2.0.3 fs_1.5.2
[7] rstudioapi_0.15.0 proxy_0.4-27 farver_2.1.1
[10] fansi_1.0.3 xml2_1.3.3 codetools_0.2-18
[13] cachem_1.0.6 knitr_1.41 jsonlite_1.8.3
[16] dbplyr_2.2.1 rgeos_0.5-9 compiler_4.2.2
[19] httr_1.4.4 backports_1.4.1 assertthat_0.2.1
[22] fastmap_1.1.0 gargle_1.2.1 cli_3.6.1
[25] later_1.3.0 htmltools_0.5.8.1 tools_4.2.2
[28] gtable_0.3.1 glue_1.6.2 maps_3.4.1
[31] Rcpp_1.0.10 cellranger_1.1.0 jquerylib_0.1.4
[34] RNetCDF_2.6-1 raster_3.6-11 vctrs_0.6.4
[37] lwgeom_0.2-10 xfun_0.35 ps_1.7.2
[40] rvest_1.0.3 lifecycle_1.0.3 ncmeta_0.3.5
[43] googlesheets4_1.0.1 terra_1.7-65 getPass_0.2-2
[46] scales_1.2.1 hms_1.1.2 promises_1.2.0.1
[49] parallel_4.2.2 rematch2_2.1.2 yaml_2.3.6
[52] sass_0.4.4 stringi_1.7.8 highr_0.9
[55] e1071_1.7-12 rlang_1.1.1 pkgconfig_2.0.3
[58] evaluate_0.18 lattice_0.20-45 sf_1.0-9
[61] labeling_0.4.2 processx_3.8.0 tidyselect_1.2.0
[64] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[67] DBI_1.2.2 pillar_1.9.0 haven_2.5.1
[70] whisker_0.4 withr_2.5.0 units_0.8-0
[73] stars_0.6-0 abind_1.4-5 sp_1.5-1
[76] modelr_0.1.10 crayon_1.5.2 KernSmooth_2.23-20
[79] utf8_1.2.2 tzdb_0.3.0 rmarkdown_2.18
[82] grid_4.2.2 readxl_1.4.1 callr_3.7.3
[85] git2r_0.30.1 reprex_2.0.2 digest_0.6.30
[88] classInt_0.4-8 httpuv_1.6.6 munsell_0.5.0
[91] bslib_0.4.1