Marine Heat Waves categorisation
Marguerite Larriere & Jens Daniel Müller
23 April, 2024
Last updated: 2024-04-23
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Knit directory:
bgc_argo_r_argodata/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ae8003f | mlarriere | 2024-04-23 | Building test |
| html | 0ada9ee | mlarriere | 2024-04-22 | Build site. |
| Rmd | fc5ac8c | mlarriere | 2024-04-22 | Adding SST anomaly subsection |
| html | 83b6d9a | mlarriere | 2024-04-22 | Build site. |
| Rmd | 40fff85 | mlarriere | 2024-04-22 | Adding SST anomaly subsection |
| Rmd | 4690882 | mlarriere | 2024-04-17 | Elbow method graph and 2 clusters |
| html | 924babc | mlarriere | 2024-04-17 | Build site. |
| Rmd | 550ab72 | mlarriere | 2024-04-17 | Cluster analysis test |
| html | 4a8157c | mlarriere | 2024-04-16 | Build site. |
| Rmd | 5acdc98 | mlarriere | 2024-04-16 | Some explanations on categorisation and beginning of cluster analysis |
| html | ad0d737 | mlarriere | 2024-04-16 | Build site. |
| Rmd | f22c4b5 | mlarriere | 2024-04-16 | Some explanations on categorisation and beginning of cluster analysis |
| Rmd | 2c60853 | mlarriere | 2024-04-15 | Some explanations on categorisation and beginning of cluster analysis |
| html | 9910de2 | mlarriere | 2024-04-14 | Build site. |
| html | 50596ee | mlarriere | 2024-04-14 | Build site. |
| Rmd | ac8de8b | mlarriere | 2024-04-14 | Figures MHWs |
| html | 718d84b | mlarriere | 2024-04-14 | Build site. |
| Rmd | a2e118c | mlarriere | 2024-04-14 | Figures MHWs |
| html | c789e3f | mlarriere | 2024-04-13 | Build site. |
| Rmd | acdc838 | mlarriere | 2024-04-13 | Figures MHWs |
| html | bbbc1bb | mlarriere | 2024-04-13 | Build site. |
| Rmd | b080fc9 | mlarriere | 2024-04-13 | Figures MHWs |
| html | 83d1fa3 | mlarriere | 2024-04-13 | Build site. |
| Rmd | c8b3f67 | mlarriere | 2024-04-13 | Figures MHWs |
| html | 1ab66f1 | mlarriere | 2024-04-13 | Build site. |
| Rmd | 25928b0 | mlarriere | 2024-04-13 | Figures MHWs |
| html | 5475def | mlarriere | 2024-04-13 | Build site. |
| Rmd | e2b710d | mlarriere | 2024-04-13 | build North Atlanatic section |
| html | c076fba | mlarriere | 2024-04-12 | Build site. |
| Rmd | a52cee0 | mlarriere | 2024-04-08 | adding subsection MHWs categorisation |
| html | efc6aab | mlarriere | 2024-04-08 | Build site. |
| Rmd | a9ea2a8 | mlarriere | 2024-04-08 | test MHWs cat |
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_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_mhw<- '/net/kryo/work/datasets/gridded/ocean/2d/obs/mhw'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 entire North Atlantic basin
plot_float_location <- function(data, month_chosen, base_map) {
month_biome_dataset <- data %>%
filter(month == sprintf("%02d", match(month_chosen, month.abb)))
#Changing subtitle and color based on biome_value
subtitles <- c("SPSS", "STSS", "STPS")
colors <- c("darkblue", "darkorange", "darkred")
base_map +
geom_point(data = month_biome_dataset, aes(x = lon, y = lat, color = factor(biome_value)), size = 2) +
scale_color_manual(values = colors, name = "Biome", labels = subtitles) +
labs(title = paste("Float Locations in North Atlantic Ocean in 2023"),
subtitle = paste0("Month: ", month_chosen))
}
#Anomaly T°C plot for each month
# plot_list <- list()
# for (month in month.abb) {
# plot <- plot_float_location(core_argo_with_platform_2023, month, base_map)
# plot_list[[month]]<- plot
# }
# plot_list
#Histogram -- number of floats per month and biome
unique_platforms_per_month <- core_anomaly_with_platform_2023 %>%
group_by(month, biome_value) %>%
summarize(unique_platforms = n_distinct(platform_number))
hist <- ggplot(unique_platforms_per_month, aes(x = factor(month), y = unique_platforms, fill=factor(biome_value))) +
geom_bar(stat = "identity")+#, position = "dodge") +
labs(title= paste0("Amount of platform per month and per biome, in 2023"),
x = "Months", y = "Number of argo floats", fill = "Biomes") +
scale_fill_manual(values = c("1" = "#1034A6", "2" = "#f59c04", "3" = "darkred"),
labels = c("1" = "SPSS", "2" = "STSS", "3" = "STPS")) +
theme_minimal()
print(hist)
Area of interest
Maps area of interest
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.
# 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)
#------Defining area of interest spatially and temporally depending on SST anomaly
#Hotspot in JJA + SON in agreement with climate reanaliser
north_west_base_map<- base_map + lims(x=c(-70,-30), y=c(30,65))
north_west_sst_anomaly<-sst_anomaly_northAtlantic %>%
filter(lat>30, lat<65, lon>-70, -30>lon)
map_anomaly_north_west <-
north_west_base_map +
geom_tile(data=north_west_sst_anomaly, aes(x = lon, y = lat, fill = SST_anomaly)) +
scale_fill_gradientn(colors = continuous_palette, limits = scale_limits, breaks = scale_breaks) +
labs(title = "Temperature Anomaly (°C) in North Atlantic - 2023",
subtitle = "Extent: North west",
x = "Longitude", y = "Latitude") +
theme(legend.position = 'right', legend.key.height = unit(2, "cm")) +
facet_wrap(~ month, ncol=2)
print(map_anomaly_north_west)
#Annual hotspot (+MAM + JJA + a bit SON) in agreement with climate reanaliser
east_base_map<- base_map + lims(x=c(-40,0), y=c(0,65))
east_sst_anomaly<-sst_anomaly_northAtlantic %>%
filter(lat>0, lat<65, lon>-40, 0>lon)
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 = "Temperature Anomaly (°C) in North Atlantic - 2023",
subtitle = "Extent: East",
x = "Longitude", y = "Latitude") +
theme(legend.position = 'right', legend.key.height = unit(2, "cm")) +
facet_wrap(~ month, ncol=2)
print(map_anomaly_east)
Argo floats available
#Join the SST anomaly with the anomaly profiles
complete_anomaly_profile<- full_join(sst_anomaly_northAtlantic, core_anomaly_with_platform_2023, by=c("lat", "lon", "month"))
complete_anomaly_profile<-complete_anomaly_profile %>%
filter(!is.na(platform_number), !is.na(depth)) #remove profile where there is only SST anomaly and not deeper
# mutate(depth = ifelse(is.na(depth), 0, depth)) #depth=NA correspond to SST anomaly, i.e. depth==0 (surface)
#Floats present during heatwaves
#North West
heatwaves_data_north_west<-complete_anomaly_profile %>%
group_by(month) %>%
filter(lat>30, lat<65, lon>-70, -30>lon)
num_floats_available_NW <- length(unique(heatwaves_data_north_west$platform_number))
#East
heatwaves_data_east<-complete_anomaly_profile %>%
group_by(month, platform_number) %>%
filter(lat>0, lat<65, lon>-40, 0>lon)
platform_colors <- rainbow(length(unique(heatwaves_data_east$platform_number)))
map_anomaly_east <-
east_base_map +
geom_tile(data = east_sst_anomaly, aes(x = lon, y = lat, fill = SST_anomaly)) +
geom_path(data = heatwaves_data_east %>% arrange(platform_number, cycle_number),
aes(x = lon, y = lat, group = platform_number, color = factor(platform_number))) +
scale_fill_gradientn(colors = continuous_palette, limits = scale_limits, breaks = scale_breaks) +
scale_color_manual(values = platform_colors) +
labs(title = "Temperature Anomaly (°C) in North Atlantic - 2023",
subtitle = "Extent: East",
x = "Longitude", y = "Latitude") +
facet_wrap(~ month, ncol = 2) +
guides(color = guide_legend(title = "Argo Platform"),
fill = guide_colorbar(title = "SST Anomaly")) +
theme(legend.position = "bottom",
legend.box = "vertical", legend.key.width = unit(2,"cm"))
print(map_anomaly_east)
#Annual mean anomaly at surface with the different floats passing by this region (legend= each month = 1 color)
# plot_list <- list()
# for (month in month.abb) {
# plot <- plot_float_location(complete_anomaly_profile, month, east_base_map)
# plot_list[[month]]<- plot
# }
#
# plot_listMHWs category
Upscaling Surface Marine Heat Waves
#Aggregate the dataset into 1°x1° grid
argo_grid<-sst_natlantic_2023 %>%
ungroup() %>%
select(lat, lon)
mhw_1x1_natlantic_2023 <- mhw_raw_2023_north_atlantic %>% #----------->long process (few min)
mutate(lat_upscale = floor(lat) + 0.5, #rounding to the lower nearest value and add an offset of 0.5 to match with Argo data grid
lon_upscale = floor(lon) + 0.5,
row_id = row_number() #row identifier - to facilitate comparison with original dataset
) %>%
group_by(lat = lat_upscale, #group the data by lon, lat and time
lon = lon_upscale,
time) %>%
summarise(avg_intensity = mean(intensity), # average intensity
most_freq_category = names(sort(table(category), decreasing = TRUE))[1], #Most frequent category
row_id = first(row_id)
) %>%
ungroup() %>%
arrange(row_id)
test<- mhw_raw_2023_north_atlantic %>% filter(time=="2023-01-01", lat<1, lat>0, lon<1, lon>0)
print(mean(test$intensity))
test1<- mhw_1x1_natlantic_2023 %>% filter(time=="2023-01-01", lat<1, lat>0, lon<1, lon>0)
print(test1)
#Adding biomes value to the surface MHWs
mhw_1x1_natlantic_2023_biomes<-left_join(mhw_1x1_natlantic_2023, biomes_subset, by=c("lat", "lon")) %>%
filter(!is.na(biome_value))
mhw_1x1_natlantic_2023_biomes$month <- format( as.Date(mhw_1x1_natlantic_2023_biomes$time), "%m")#adding month attribute
#Write upscaled MHWs dataset (1°x1°)
mhw_1x1_natlantic_2023_biomes %>% write_rds(file = paste0(path_argo_core_preprocessed,"/", "2023_surface_mhws_1x1.rds"))Surface intensity maps
#Read upscale data
mhw_1x1_natlantic_2023_biomes <- read_rds(file = paste0(path_argo_core_preprocessed,"/", "2023_surface_mhws_1x1.rds"))
# original_plot <- base_map +
# geom_point(data=mhw_raw_2023_north_atlantic, aes(x = lon, y = lat, color = intensity), size=0.1) +
# scale_color_gradient(low = "blue", high = "red") +
# labs(title = "MHW intensity - Original Data") +
# theme_minimal()
# print(original_plot)
mhw_biomes_plot <- base_map +
geom_point(data= mhw_1x1_natlantic_2023_biomes, aes(x = lon, y = lat, color = avg_intensity)) +
scale_color_gradient(low = "blue", high = "red") +
labs(title = "MHW intensity - Upscaled Data") +
theme_minimal()
print(mhw_biomes_plot)
Surface category maps per season
mhw_season_plot_comparison<-function(original_dataset, upscale_dataset, season_of_interest, name_season){
#Select data in season_of_interest
season_original<-original_dataset %>%
filter(month %in% season_of_interest)
season_1x1<-upscale_dataset %>%
filter(month %in% season_of_interest)
#Plot
plot_original<- base_map +
geom_point(data = season_original, aes(x = lon, y = lat, color = factor(category))) +
scale_color_manual(values = c("I Moderate" = "darkgoldenrod1",
"II Strong" = "darkorange",
"III Severe" = "darkred",
"IV Extreme" = "#21152B"), name = "Category")+
guides(color = guide_legend(override.aes = list(shape = 15, size = 10)))+
labs(title = paste0("Surface MHWs in ", name_season, " 2023 - resolution 0.25°x0.25°"))
plot_upscale<- base_map +
geom_point(data = season_1x1, aes(x = lon, y = lat, color = factor(most_freq_category)), size = 0.3) +
scale_color_manual(values = c("I Moderate" = "darkgoldenrod1",
"II Strong" = "darkorange",
"III Severe" = "darkred",
"IV Extreme" = "#21152B"), name = "Category")+
guides(color = guide_legend(override.aes = list(shape = 15, size = 10)))+
labs(title = paste0("Surface MHWs in ", name_season, " 2023 - resolution 1°x1°"))
combined_plot <- grid.arrange(plot_original, plot_upscale, ncol = 2)
print(combined_plot)
}
mhw_season_plot_comparison(mhw_raw_2023_north_atlantic, mhw_1x1_natlantic_2023_biomes, c("03", "04", "05"), "spring")
mhw_season_plot_comparison(mhw_raw_2023_north_atlantic, mhw_1x1_natlantic_2023_biomes, c("06", "07", "08"), "summer")
mhw_season_plot_comparison(mhw_raw_2023_north_atlantic, mhw_1x1_natlantic_2023_biomes, c("09", "10", "11", "12"), "autumn")
mhw_season_plot_comparison(mhw_raw_2023_north_atlantic, mhw_1x1_natlantic_2023_biomes, c("01", "02"), "winter")mhw_season_plot<-function(upscale_dataset, months){
months<-unique(format(as.Date(upscale_dataset$time), "%m"))
print(months)
#Plot
plot_upscale<- base_map +
geom_point(data = upscale_dataset, aes(x = lon, y = lat, color = factor(most_freq_category)), size = 0.3) +
scale_color_manual(values = c("I Moderate" = "darkgoldenrod1",
"II Strong" = "darkorange",
"III Severe" = "darkred",
"IV Extreme" = "#21152B"), name = "Category")+
guides(color = guide_legend(override.aes = list(shape = 15, size = 10)))+
labs(title = paste0("Surface MHWs in 2023"),
subtitle = paste0("Months: ", paste(months, collapse = ", ") ,"\nresolution 1°x1°"))
return(plot_upscale)
}
#Select data in season_of_interest
spring_1x1<-mhw_1x1_natlantic_2023_biomes %>%
filter(month %in% c("03", "04", "05"))
summer_1x1<-mhw_1x1_natlantic_2023_biomes %>%
filter(month %in% c("06", "07", "08"))
autumn_1x1<-mhw_1x1_natlantic_2023_biomes %>%
filter(month %in% c("09", "10", "11", "12"))
winter_1x1<-mhw_1x1_natlantic_2023_biomes %>%
filter(month %in% c("01", "02"))
#Plot MHWs
spring_plot_1x1<-mhw_season_plot(spring_1x1, "spring")[1] "03" "04" "05"summer_plot_1x1<-mhw_season_plot(summer_1x1, "summer")[1] "06" "07" "08"autumn_plot_1x1<-mhw_season_plot(autumn_1x1, "autumn")[1] "09" "10" "11" "12"winter_plot_1x1<-mhw_season_plot(winter_1x1, "winter")[1] "01" "02"combined_plot <- grid.arrange(winter_plot_1x1, spring_plot_1x1, summer_plot_1x1, autumn_plot_1x1, ncol = 2, nrow=2)
print(combined_plot)TableGrob (2 x 2) "arrange": 4 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
3 3 (2-2,1-1) arrange gtable[layout]
4 4 (2-2,2-2) arrange gtable[layout]Strong MHWs regions
#Time in the same format in the 2 dataframes
argo_data_2023 <- sst_with_platform_2023 %>%
mutate(time = as.Date(date))
mhw_1x1_natlantic_2023_biomes <- mhw_1x1_natlantic_2023_biomes %>%
mutate(time = as.Date(time))
#Associating mhws category to the argo profiles
argo_mhws_categ<-left_join(mhw_1x1_natlantic_2023_biomes, argo_data_2023, by=c("lat", "lon", "time")) %>%
filter(!is.na(platform_number)) %>% #select only locations where there is an argo float
rename(biome_value=biome_value.x) %>%
select(platform_number,cycle_number,
depth, lat, lon, time,
avg_intensity, most_freq_category,
biome_value, temp) #Cleaning dataset
#Adding temperature anomaly
argo_anomaly_2023 <- argo_anomaly_2023 %>%
mutate(time = as.Date(date))
#Datasets for each surface MHWs category
mhws_surface_categorisation<- function(argo_categ_dataset, anomaly_dataset, category){
argo_surf_cat<-filter(argo_categ_dataset, most_freq_category==category)
argo_anomaly_cat<- left_join(anomaly_dataset, argo_surf_cat,
by=c("platform_number", "cycle_number","lat", "lon", "depth", "time", "biome_value")) %>%
filter(!is.na(anomaly), !is.na(most_freq_category))
return(argo_anomaly_cat)
}
argo_anomaly_moderate<- mhws_surface_categorisation(argo_mhws_categ, argo_anomaly_2023, category='I Moderate')
argo_anomaly_strong<- mhws_surface_categorisation(argo_mhws_categ, argo_anomaly_2023, category='II Strong')
argo_anomaly_severe<- mhws_surface_categorisation(argo_mhws_categ, argo_anomaly_2023, category='III Severe')
argo_anomaly_extreme<- mhws_surface_categorisation(argo_mhws_categ, argo_anomaly_2023, category='IV Extreme')
final_mhw_argo_cat<-bind_rows(argo_anomaly_moderate,
argo_anomaly_strong,
argo_anomaly_severe,
argo_anomaly_extreme)Mean anomaly temperature profile
Per biomes and months
We investigate the mean temperature anomaly profile for each biome and over months in 2023.
plot_anomaly_profiles_biomes <- function(data, biome_chosen) {
# Filter data for the chosen biome
biome_data <- filter(data, biome_value == biome_chosen)
# Anomaly statistics
anomaly_overall_mean <- biome_data %>%
group_by(depth, month, biome_value) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
# Subtitle and color based on biome chosen
subtitle <- switch(biome_chosen,
"1" = "SPSS",
"2" = "STSS",
"3" = "STPS")
color_SPSS <- "darkblue"
color_STSS <- "darkorange"
color_STPS <- "darkred"
colors <- c("1" = color_SPSS, "2" = color_STSS, "3" = color_STPS)
# Anomaly plot
ggplot(anomaly_overall_mean) +
geom_path(aes(x = temp_anomaly_mean, y = depth, color = factor(month))) +
geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
xmin = temp_anomaly_mean - temp_anomaly_sd,
y = depth, fill = factor(biome_value)), alpha = 0.4) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
coord_cartesian(xlim = c(-4.5, 6)) +
scale_x_continuous(breaks = c(-4, -2, 0, 2, 4, 6)) +
labs(title = paste0('Mean Temperature anomaly profiles - up to 600m depth'),
x = "Temperature anomaly (°C)", y = 'depth (m)', color = "Biome") +
scale_color_manual(values = colors[biome_chosen],
labels = subtitle) +
scale_fill_manual(values = colors[biome_chosen],
name = "Biome", labels = subtitle) +
facet_wrap(~ month, ncol = 3, labeller = labeller(month.abb)) +
theme(axis.text.x = element_text(angle = 0, hjust = 1)) +
theme(legend.position = "bottom")
}
plot_anomaly_profiles_biomes(final_mhw_argo_cat, 1)
plot_anomaly_profiles_biomes(final_mhw_argo_cat, 2)
plot_anomaly_profiles_biomes(final_mhw_argo_cat, 3)Floats classification
We classify marine heat waves according to their intensity and propagation over the water column:
When the temperature anomaly is equal to or greater than 1°C, we consider the anomaly to be big, otherwise it is considered as small.
Then we look at the vertical propagation of the MHWS: - Shallow MHWs: When a big anomaly is detected between 0 and 100 meters depth.
- Medium MHWs: When a big anomaly is detected between 0 and 200 meters depth.
- Deep MHWs: When a big anomaly is detected between 0 and 600 meters depth.# Defining the anomaly class as a function of anomaly value and depth
threshold <- argo_anomaly_moderate %>%
filter(anomaly >= 1) %>% # Look only at positive anomaly values
group_by(depth) %>%
summarise(depth_threshold = max(depth))
argo_anomaly_dataset <- argo_anomaly_moderate %>%
mutate(anomaly_class = ifelse(anomaly >= 1 & depth <= threshold$depth_threshold, "Big", "Small")) %>%
mutate(anomaly_class = factor(anomaly_class, levels = c("Big", "Small")))
# Group the data by float identifiers
max_depth_by_float <- argo_anomaly_moderate %>%
filter(anomaly >= 1) %>%
group_by(file_id, lat, lon) %>%
summarise(max_depth = max(depth))
# Define mhw_class based on the maximum depth
max_depth_by_float <- max_depth_by_float %>%
mutate(mhw_class = case_when(
max_depth <= 100 ~ "Shallow",
max_depth <= 200 ~ "Medium",
TRUE ~ "Deep"
))
#Combining datasets
final_mhw_argo_cat<-left_join(max_depth_by_float, argo_anomaly_dataset, by=c("file_id", "lat","lon") )
plot_anomaly_categorisation <- function(anomaly_data) {
anomaly_classes <- anomaly_data %>%
distinct(depth, anomaly_class)
ggplot(anomaly_data) +
geom_rect(data = anomaly_data %>% distinct(depth, anomaly_class),
aes(xmin = -Inf, xmax = Inf, ymin = lag(depth), ymax = depth, fill = anomaly_class),
inherit.aes = FALSE) +
geom_path(aes(x = anomaly , y = depth)) +
geom_vline(xintercept = 0) +
scale_fill_manual(values = c("Big" = "lightblue", "Small" = "lightyellow"),
name = "Anomaly Class", labels = c("Big Anomaly", "Small Anomaly")
) +
scale_y_reverse() +
coord_cartesian(xlim = c(-4.5, 6)) +
scale_x_continuous(breaks = c(-4, -2, 0, 2, 4, 6)) +
labs(title = paste0('Anomaly profile in ', month.name[unique(month(anomaly_data$time))], ' 2023'),
subtitle = paste0('Location: (', unique(anomaly_data$lat), ',', unique(anomaly_data$lon),')\n',
'Type of surface MHW: ', unique(anomaly_data$most_freq_category),"\n",
'Type of argo MHW: ', unique(anomaly_data$mhw_class)),
x = "Temperature anomaly (°C)", y = 'depth (m)')
}
shallow<-plot_anomaly_categorisation(final_mhw_argo_cat %>% filter(file_id==20))
medium<-plot_anomaly_categorisation(final_mhw_argo_cat %>% filter(file_id==10))
deep<-plot_anomaly_categorisation(final_mhw_argo_cat %>% filter(file_id==6396))
combined_plot <- grid.arrange(shallow, medium, deep, ncol = 3)
print(combined_plot)
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