Marine Heat Waves anomaly profiles
Marguerite Larriere & Jens Daniel Müller
24 May, 2024
Last updated: 2024-05-24
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Knit directory:
bgc_argo_r_argodata/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a4c7c05 | mlarriere | 2024-05-24 | Absolute maximum anomalies categorisation |
| Rmd | cebda03 | mlarriere | 2024-05-22 | Hovmoeller plot |
| html | 3ac87c9 | mlarriere | 2024-05-20 | Build site. |
| Rmd | 5304024 | mlarriere | 2024-05-20 | choosing extent |
| html | b0fb164 | mlarriere | 2024-05-16 | Build site. |
| 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'#Area of interest: North Atlantic north west - lat:(60,30), lon:(-70,-30), North Atlantic east - lat:(0,40), lon:(-30,0)
chosen_extent <- list(
lat_min = 0, #30
lat_max = 40, #60
lon_min = -30, #-70
lon_max = 0 #-30
)
name_extent<- "East" #NorthwestLoad 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) %>%
filter(lat>0, lat<70, lon>-80, lon<0) %>%
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)
complete_anomaly_profile<- core_anomaly_with_platform_2023 %>%
inner_join(sst_anomaly_northAtlantic, by = c("lat", "lon", "month"))
#---Chosen subset
core_anomaly_2023_natlantic_subset<- complete_anomaly_profile %>%
filter(lat > chosen_extent$lat_min, lat < chosen_extent$lat_max,
lon > chosen_extent$lon_min, lon < chosen_extent$lon_max) %>%
select(-interpolated_temp)
cycles_per_platform <-core_anomaly_2023_natlantic_subset %>%
group_by(platform_number) %>%
summarize(unique_cycles = toString(unique(cycle_number)))
months_per_float <- core_anomaly_2023_natlantic_subset %>%
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 = core_anomaly_2023_natlantic_subset, FUN = function(x) length(unique(x)))
cycle_count_per_platform_month <- core_anomaly_2023_natlantic_subset %>%
group_by(month, platform_number) %>%
summarise(cycle_count = n_distinct(cycle_number))
custom_labeller <- function(variable, value) {
month_count <- platform_counts[platform_counts$month == value, "platform_number"]
return(paste("Month:", value, "\nNumber of floats:", month_count))
}
#PLot
sst_anomaly_northAtlantic_subset<-sst_anomaly_northAtlantic %>%
filter(lat > chosen_extent$lat_min, lat < chosen_extent$lat_max,
lon > chosen_extent$lon_min, lon < chosen_extent$lon_max)
ggplot() +
geom_tile(data=sst_anomaly_northAtlantic_subset, aes(x = lon, y = lat, fill = SST_anomaly)) + #tile with SST
geom_point(data = core_anomaly_2023_natlantic_subset, aes(x = lon, y = lat), color = "black") + # Point for float position
geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), fill = "grey") + #base map
lims(x = c(chosen_extent$lon_min, chosen_extent$lon_max), y=c(chosen_extent$lat_min, chosen_extent$lat_max)) +
coord_quickmap(expand = 0) +
scale_fill_viridis_c(option = "magma") +
labs(title = "Platform Locations",
subtitle = "Resolution: 1°x1°, SST from SOM",
color = "Months with float") +
theme(plot.title = element_text(size = 16),
plot.subtitle = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.key.width = unit(0.5, "cm"),
legend.key.height = unit(2, "cm"))+
facet_wrap(~month, ncol = 3, labeller = custom_labeller)
Anomaly profiles
Individual float
unique_platform<-core_anomaly_2023_natlantic_subset %>%
filter(platform_number==1902323)
# ggplot(unique_platform, aes(x = anomaly, y = depth, color = factor(cycle_number))) +
# geom_path() +
# geom_vline(xintercept = 0) +
# scale_y_reverse() +
# coord_cartesian(xlim = c(-6, 6), ylim = c(200, 0)) +
# labs(title = 'Anomaly profiles by month',
# subtitle= paste0('Platform ', unique(unique_platform$platform_number)),
# x = 'Temperature (°C)', y = 'Depth (m)', color = 'Cycle Number') +
# scale_color_viridis(discrete = TRUE) +
# facet_wrap(~month, scales = "free", ncol = 3)
ggplot() +
geom_path(data=unique_platform, aes(x = anomaly, y = depth, color = factor(month), group = cycle_number)) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = c(-6, 6), ylim = c(200, 0)) +
scale_color_manual(values = colorRampPalette(c("#2796A5", "#F3712B", "#880D1E"))(12)) +
labs(title = 'SST anomalies propagation \nof a single float',
subtitle = 'Location: Canaries Islands (Eddies corridor)',
x = 'Temperature (°C)', y = 'Depth (m)', color = 'Months') +
theme(plot.title = element_text(size = 18),
plot.subtitle = element_text(size = 14),
legend.text = element_text(size = 12),
legend.title = element_text(size = 14, face = "bold"),
legend.background = element_rect(fill = "transparent", color='transparent'),
legend.position=c(.91,.28),
)
# 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 <- core_anomaly_2023_natlantic_subset %>%
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
longitude<- 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
latitude<- 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()
combined_plot <- latitude + longitude + plot_layout(ncol = 2)
combined_plot
Coloring by SST anomaly
# calculate mean anomaly data
anomaly_summary <- core_anomaly_2023_natlantic_subset %>%
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 as a function of SST anomaly",
x = "Temperature (°C)", y = "Depth (m)", color = "SST Anomaly") +
scale_color_viridis_c()
Day within the month
# calculate mean anomaly data
anomaly_summary_day <- core_anomaly_2023_natlantic_subset %>%
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<- core_anomaly_2023_natlantic_subset %>%
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 <- core_anomaly_2023_natlantic_subset %>%
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(core_anomaly_2023_natlantic_subset$month)) {
# Filter data
map_data <- filter(sst_anomaly_northAtlantic_subset, month == m)
anomaly_data <- filter(anomaly_mean, month == m)
# Plot for temperature anomaly map
map_plot <- ggplot() +
geom_tile(data=map_data, aes(x = lon, y = lat, fill = SST_anomaly)) +
geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), fill = "grey") + #base map
lims(x = c(chosen_extent$lon_min, chosen_extent$lon_max), y=c(chosen_extent$lat_min, chosen_extent$lat_max)) +
coord_quickmap(expand = 0) +
scale_fill_viridis_c(option = "magma") +
labs(title = paste("SST Anomaly (°C) in North Atlantic - 2023", month.name[as.numeric(m)]),
subtitle = paste0("Extent: ", name_extent),
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)) +
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)
}
TableGrob (1 x 2) "arrange": 2 grobs
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2 2 (1-1,2-2) arrange gtable[layout]# Vertical anomaly profile for the North Atlantic subset 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)) +
labs(title = paste('Monthly anomaly profile'),
subtitle = paste0("Extent: ", name_extent),
x = 'Temperature (°C)', y = 'Depth (m)') +
guides(color = FALSE)+
facet_wrap(~month)
print(anomaly_plot_monthly)
# Calculating monthly mean anomaly over the east area by averaging the anomaly of each float present
anomaly_2months <- core_anomaly_2023_natlantic_subset %>%
mutate(period=(as.numeric(month)+1)%/%2)
anomaly_2months<-anomaly_2months %>%
group_by(depth, period) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
anomaly_2023<-core_anomaly_2023_natlantic_subset %>%
group_by(depth) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
# Vertical anomaly profile for the North atlatinc east region - monthly
ggplot() +
geom_path(data=anomaly_2months, aes(x = temp_anomaly_mean, y = depth, color = factor(period))) +
geom_ribbon(data=anomaly_2023, aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
y = depth, fill = "spread"), alpha = 0.2) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = c(-4, 4), ylim = c(200, 0)) +
labs(title = paste('Propagation of SST anomalies in the water column'),
subtitle = paste0("Extent: ", name_extent, " North Atlantic bassin in 2023"),
x = 'Temperature (°C)', y = 'Depth (m)') +
scale_color_manual(values = colorRampPalette(c("blue", "orange", "darkred"))(6),
breaks = unique(anomaly_2months$period),
labels =c("Jan-Feb", "March-April", "May-June", "July-Aug", "Sept-Oct", "Nov-Dec")) +
scale_fill_manual(values = "grey", # Manual fill color setting
labels = "yearly") + # Label for the legend
theme(plot.title = element_text(size = 18),
plot.subtitle = element_text(size = 12),
legend.text = element_text(size = 14)) +
guides(color = guide_legend(title="Period"),
fill = guide_legend(title = "Spread")) # Legend for the ribbon
| Version | Author | Date |
|---|---|---|
| 3ac87c9 | mlarriere | 2024-05-20 |
Hovmoeller plot
#---WEEKLY
#adding the week of the year
core_anomaly_2023_natlantic_subset <- core_anomaly_2023_natlantic_subset %>%
mutate(week = week(date))
#---WEEKLY
anomaly_weekly<-core_anomaly_2023_natlantic_subset %>%
group_by(depth, week) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE))
#---2WEEKS
# Calculating monthly mean anomaly over the east area by averaging the anomaly of each float present
anomaly_2weekly <- core_anomaly_2023_natlantic_subset %>%
mutate(week2=(week+1)%/%2) %>%
group_by(depth, week2) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE))
#---MONTHLY
anomaly_monthly <- core_anomaly_2023_natlantic_subset %>%
group_by(depth, month) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE))
#---Hovmoeller plot
breaks_seq=seq(-0.5,2, by=0.5)
labels_name=as.character(seq(-0.5,2, by=0.5))
ggplot(data=anomaly_2weekly, aes(x = week2, y = depth, z = temp_anomaly_mean)) +
geom_contour_filled(aes(fill = after_stat(level_mid))) + #color = 'gray20', linewidth = 0.5,
scale_fill_gradient2(name='T°C anomaly', low = "darkblue", high = "darkred",
breaks=seq(-0.5,2, by=0.5), labels=as.character(seq(-0.5,2, by=0.5))) +
coord_cartesian(ylim = c(200, 0), expand = 0) +
labs(title = "Temporal progression of SST anomalies penetration in 2023",
subtitle = paste0("Extent: ", name_extent, ", temporal resolution: 2 weeks"),
x = "Week of Year", y = "Depth (m)") +
theme_minimal() +
theme(plot.title = element_text(size = 16),
plot.subtitle = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.key.width = unit(0.5, "cm"),
legend.key.height = unit(2, "cm")
)
Absolute maximum anomalies categorisation
#Absolute max anomalies for each lat-lon
abs_max_SSTanomalies<-core_anomaly_2023_natlantic_subset %>%
filter(!is.na(anomaly), depth<=200) %>%
group_by(lat, lon) %>%
summarize(max_SST_anomaly = max(abs(anomaly), na.rm = TRUE))
ggplot()+
geom_tile(data=abs_max_SSTanomalies, aes(lon, lat, fill = max_SST_anomaly)) +
geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), fill = "grey") +
lims(x = c(chosen_extent$lon_min, chosen_extent$lon_max), y=c(chosen_extent$lat_min, chosen_extent$lat_max)) +
scale_fill_viridis_c(option = "magma") +
labs(title = "Absolute Maximum SST anomaly, annual average 2023",
fill = "Absolute max SST anomalies [°C]")+
coord_quickmap(expand = 0)+
theme(plot.title = element_text(size = 16),
plot.subtitle = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 11) )
#Mean depth at which max anomaly is found
mean_depth_max_anomaly<- inner_join(abs_max_SSTanomalies,core_anomaly_2023_natlantic_subset, by=c('lat', 'lon'))
mean_depth_max_anomaly <- mean_depth_max_anomaly %>%
filter(abs(anomaly) == max_SST_anomaly)
library(scico)
ggplot()+
geom_tile(data=mean_depth_max_anomaly, aes(lon, lat, fill = depth)) +
geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), fill = "grey") +
lims(x = c(chosen_extent$lon_min, chosen_extent$lon_max), y=c(chosen_extent$lat_min, chosen_extent$lat_max)) +
scale_fill_scico(palette = "lajolla", direction = -1) +
# scale_fill_gradient2(name = 'Depth[m]', low = "darkblue", high = "darkred", midpoint = 100,
# guide = "colorbar",
# breaks=seq(0,1500, by=100), labels=as.character(seq(0,1500, by=100))) +
labs(title = "Depth of max anomaly [m]",
subtitle = 'midpoint: 200m',
fill = "Absolute max SST anomalies [°C]")+
coord_quickmap(expand = 0)+
theme(plot.title = element_text(size = 16),
plot.subtitle = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 11),
legend.key.width = unit(0.5, "cm"),
legend.key.height = unit(2, "cm")
)
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] scico_1.3.1 patchwork_1.1.2 broom_1.0.5
[4] paletteer_1.6.0 cluster_2.1.6 gridExtra_2.3
[7] scatterplot3d_0.3-44 viridis_0.6.2 viridisLite_0.4.1
[10] ggOceanMaps_1.3.4 ggspatial_1.1.7 oce_1.7-10
[13] gsw_1.1-1 lubridate_1.9.0 timechange_0.1.1
[16] forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3
[19] purrr_1.0.2 readr_2.1.3 tidyr_1.3.0
[22] tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2
[25] 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] isoband_0.2.6 grid_4.2.2 readxl_1.4.1
[85] callr_3.7.3 git2r_0.30.1 reprex_2.0.2
[88] digest_0.6.30 classInt_0.4-8 httpuv_1.6.6
[91] munsell_0.5.0 bslib_0.4.1