Marine Heat Waves categorisation
Marguerite Larriere & Jens Daniel Müller
17 April, 2024
Last updated: 2024-04-17
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bgc_argo_r_argodata/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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_fileid.rds - not used yet
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
MHWs 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]Argo floats
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.#reorganize dataset
argo_data_2023 <- sst_with_biomes_2023[, c("file_id",
"depth", "lat", "lon", "date",
"profile_range", "biome_value", "temp")]
#Time in the same format in the 2 dataframes
argo_data_2023 <- argo_data_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(file_id)) %>% #select only locations where there is an argo float
rename(biome_value=biome_value.x) %>%
select(file_id,
depth, lat, lon, time,
avg_intensity, most_freq_category,
biome_value, temp) #Cleaning dataset
#Adding temperature anomaly
temp_anomaly_600m_2023 <- temp_anomaly_va_biomes_2023 %>%
filter(profile_range==1) %>% #only profile until 600m
mutate(time = as.Date(date))
temp_anomaly_filtered_2023<- temp_anomaly_600m_2023[, c("file_id",
"depth", "lat", "lon", "time",
"biome_value", "anomaly")]
#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("file_id","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,temp_anomaly_filtered_2023, category='I Moderate')
argo_anomaly_strong<- mhws_surface_categorisation(argo_mhws_categ,temp_anomaly_filtered_2023, category='II Strong')
argo_anomaly_severe<- mhws_surface_categorisation(argo_mhws_categ,temp_anomaly_filtered_2023, category='III Severe')
argo_anomaly_extreme<- mhws_surface_categorisation(argo_mhws_categ,temp_anomaly_filtered_2023, category='IV Extreme')
distribution_anomaly <- function(argo_anomaly){
#from oct to dec (most extreme floats) and only the first 100m
argo_anomaly<- argo_anomaly %>%
filter(depth<=100, format(time, "%m")==c("10","11","12"))
# Calculate mean and 99th percentile of anomaly values
mean_anomaly <- mean(argo_anomaly$anomaly)
n_floats<-length(unique(argo_anomaly$file_id))
# Create histogram of anomaly values
p <- ggplot(argo_anomaly, aes(x = anomaly)) +
geom_histogram(binwidth = 0.2, fill = "skyblue", color = "black") +
geom_vline(aes(xintercept = mean_anomaly, linetype = "Mean"), color = "red") +
labs(title = "Distribution of Anomaly",
subtitle=paste0("Surface MHWs = ", unique(argo_anomaly$most_freq_category),
"\nDepth: 0-100m, Oct-Nov-Dec 2023 \nNumber of floats: ", n_floats),
x = "Anomaly",
y = "Frequency",
linetype = "Statistic") +
scale_linetype_manual(values = c("Mean" = "dashed"),
name = "Statistic",
labels = c(paste0("Mean = ", round(mean_anomaly, 2)))) +
scale_color_manual(values = "red", name = "Statistic") +
theme_minimal() +
theme(legend.position = "top",
legend.box.background = element_rect(color = "black", size = 0.5))
p
}
distribution_moderate<-distribution_anomaly(argo_anomaly_moderate)
distribution_strong<-distribution_anomaly(argo_anomaly_strong)
distribution_severe<-distribution_anomaly(argo_anomaly_severe)
distribution_extreme<-distribution_anomaly(argo_anomaly_extreme)
combined_stat<- grid.arrange(distribution_moderate, distribution_strong, distribution_severe,distribution_extreme, ncol = 2, nrow=2)
print(combined_stat)TableGrob (2 x 2) "arrange": 4 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
3 3 (2-2,1-1) arrange gtable[layout]
4 4 (2-2,2-2) arrange gtable[layout]# Defining the anomaly class as a function of anomaly value and depth
threshold <- argo_anomaly_moderate %>%
filter(anomaly >= 1) %>% # Look only at positive anomaly values
group_by(depth) %>%
summarise(depth_threshold = max(depth))
argo_anomaly_dataset <- argo_anomaly_moderate %>%
mutate(anomaly_class = ifelse(anomaly >= 1 & depth <= threshold$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)TableGrob (1 x 3) "arrange": 3 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 (1-1,3-3) arrange gtable[layout]Argo floats clustering
To define the optimal number of cluster we use the elbow method. Then we apply a K-mean clustering on the final_mhw_argo_cat dataset.
Elbow Method
Kmean clustering per biomes in 2023
# code adapted from cluster_analysis_extreme.Rmd
plot_anomaly_clusters <- function(data_input, inum_clusters = elbow_optimal,
opt_max_iterations = 8, opt_n_start = 15) {
#-------Preprocessing
# wide table with each depth becoming a column
anomaly_va_wide <- data_input %>%
select(file_id, depth, anomaly) %>%
pivot_wider(names_from = depth, values_from = anomaly)
# Drop any rows with missing values N/A caused by gaps in climatology data
anomaly_va_wide <- anomaly_va_wide %>%
drop_na()
# Table for cluster analysis
points <- anomaly_va_wide %>%
column_to_rownames(var = "file_id")
#-------Clustering
kclusts <- tibble(k = inum_clusters) %>%
mutate(
kclust = map(k, ~ kmeans(points, .x, iter.max = opt_max_iterations, nstart = opt_n_start)),
tidied = map(kclust, tidy),
glanced = map(kclust, glance),
augmented = map(kclust, augment, points)
)
profile_id <- kclusts %>%
unnest(cols = c(augmented)) %>%
select(file_id = .rownames,
cluster = .cluster) %>%
mutate(file_id = as.numeric(file_id),
cluster = as.character(cluster))
data_output <- full_join(data_input, profile_id)
data_output <- data_output %>%
filter(!is.na(cluster))
# Summarize data for plotting
anomaly_cluster_mean <- data_output %>%
group_by(cluster, depth) %>%
summarise(
count_cluster = n(),
anomaly_mean = mean(anomaly, na.rm = TRUE),
anomaly_sd = sd(anomaly, na.rm = TRUE)
) %>%
ungroup()
#-----Plotting
#defining colors
num_colors <- min(5, inum_clusters) # 5 colors maximum (max number of cluster=5)
cluster_palette <- paletteer::paletteer_d("colorBlindness::Blue2DarkRed12Steps")
cluster_colors<-cluster_palette[1:num_colors]
ggplot(anomaly_cluster_mean, aes(x = anomaly_mean, y = depth, color = factor(cluster))) +
geom_path() +
geom_ribbon(aes(xmax = anomaly_mean + anomaly_sd,
xmin = anomaly_mean - anomaly_sd,
y = depth, fill = factor(cluster)), alpha = 0.3, color=NA) +
geom_vline(xintercept = 0) +
scale_y_reverse() +
labs(title = paste0('Anomaly Profiles by Cluster -- 2023'),
subtitle = paste0('Months: ', paste(substr(month.abb[unique(month(data_input$time))], 1, 3)
[order(unique(month(data_input$time)))], collapse = ", "),
"\nBiomes: ", paste(unique(data_input$biome_value), collapse = ", ")),
x = "Mean Anomaly", y = "Depth (m)") +
scale_fill_manual(name = "Cluster", values = cluster_colors) +
scale_color_manual(name = "Cluster", values = cluster_colors) +
theme_minimal() +
theme(legend.position = "bottom") +
facet_wrap(~cluster, nrow = 1) #1 subplot per cluster
}
#Clustered anomaly profiles
cluster_SPSS<-plot_anomaly_clusters(final_mhw_argo_cat %>%
group_by(file_id, lat, lon) %>%
filter(biome_value==3))
cluster_STSS<-plot_anomaly_clusters(final_mhw_argo_cat %>%
group_by(file_id, lat, lon) %>%
filter(biome_value==2))
cluster_STPS<-plot_anomaly_clusters(final_mhw_argo_cat %>%
group_by(file_id, lat, lon) %>%
filter(biome_value==1))
cluster_subplots_biome<- grid.arrange(cluster_SPSS, cluster_STSS, cluster_STPS, ncol=3, nrow=1)
print(cluster_subplots_biome)TableGrob (1 x 3) "arrange": 3 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 (1-1,3-3) arrange gtable[layout]Kmean clustering per seasons in 2023
cluster_winter<-plot_anomaly_clusters(final_mhw_argo_cat %>%
group_by(file_id, lat, lon, biome_value) %>%
filter(format(time, "%m") %in% c("01","02")))
cluster_spring<-plot_anomaly_clusters(final_mhw_argo_cat %>%
group_by(file_id, lat, lon) %>%
filter(format(time, "%m") %in% c("03","04", "05")))
cluster_summer<-plot_anomaly_clusters(final_mhw_argo_cat %>%
group_by(file_id, lat, lon) %>%
filter(format(time, "%m") %in% c("06","07", "08")))
cluster_autumn<-plot_anomaly_clusters(final_mhw_argo_cat %>%
group_by(file_id, lat, lon) %>%
filter(format(time, "%m") %in% c("09","10", "11", "12")))
cluster_subplots<- grid.arrange(cluster_winter, cluster_spring, cluster_summer, cluster_autumn, ncol=2, nrow=2)
print(cluster_subplots)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]#Track 1 argo profile in lat, lon ,depth
test <- final_mhw_argo_cat %>%
filter(file_id==10) %>%
mutate(point_order = row_number())
# Convert most_freq_category to factor
test$most_freq_category <- factor(test$most_freq_category)
test <- test %>%
arrange(desc(depth))
test$depth_reversed <- test$depth * -1
# Create a color palette based on the levels of most_freq_category
color_palette <- c("I Moderate" = "darkgoldenrod1",
"II Strong" = "darkorange",
"III Severe" = "darkred",
"IV Extreme" = "#21152B")
# Create the 3D scatter plot
scatterplot3d(x = test$lon,
y = test$lat,
z = test$depth_reversed,
color = color_palette[as.numeric(test$most_freq_category)],
pch = 16,
type = "p",
main = "3D Scatter Plot of 1 Argo Float",
xlab = "Longitude",
ylab = "Latitude",
zlab = "Depth"
)
# Add legend
legend("topright",
legend = levels(test$most_freq_category),
pch = 16,
col = color_palette,
title = "Most Frequency Category")#reorganize dataset
argo_data_2023 <- sst_with_biomes_2023[, c("file_id",
"depth", "lat", "lon", "date",
"profile_range", "biome_value", "temp")]
selected_latitude <- 60.5
# Filter data frames
mhw_subset <- mhw_1x1_natlantic_2023_biomes %>%
filter(lat == selected_latitude, month=="02")# most_freq_category ==c ("IV Extreme", "III Severe"))
# argo_subset <- argo_data_2023 %>%
# filter(lat == selected_latitude)
# argo_subset$depth_reversed <- argo_subset$depth * -1
anomaly_subset <- temp_anomaly_va_biomes_2023 %>%
filter(lat == selected_latitude)
# Define color palette for MHWs category
color_palette <- c("I Moderate" = "darkgoldenrod1",
"II Strong" = "darkorange",
"III Severe" = "darkred",
"IV Extreme" = "#21152B")
plot_mhw <- ggplot() +
geom_point(data = anomaly_subset, aes(x = lon, y = depth, fill = anomaly), shape=21, size = 2, color="transparent") +
geom_point(data = mhw_subset, aes(x = lon, y = 0, color = most_freq_category), size = 1) +
scale_fill_continuous(low = "darkblue", high = "lightgreen", name = "Anomaly") +
scale_color_manual(name = "MHWs category", values = color_palette) +
labs(x = "Longitude", y = "Depth", color = "MHWs category") +
ggtitle(paste0("Argo temperature anomaly for latitude=", selected_latitude, "°, in december 2023")) +
theme_minimal() +
scale_y_reverse() +
guides(fill = guide_legend(title = "Anomaly \n climatology-obs"))
plot_mhw
test_anomaly<-temp_anomaly_va_biomes_2023 %>% filter(file_id==6879)
test_mhw<-mhw_subset%>% filter(lon==-59.5)
plot_anomaly_profiles <- function(data, max_depth) {
anomaly_overall_mean <- data %>%
filter(depth <= max_depth) %>%
group_by(depth) %>%
summarise(temp_count = n(),
temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
temp_anomaly_sd = sd(anomaly, na.rm = TRUE))
ggplot(anomaly_overall_mean) +
geom_path(aes(x = temp_anomaly_mean, y = depth)) +
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(-4.5, 6)) +
scale_x_continuous(breaks = c(-4, -2, 0, 2, 4, 6)) +
labs(title = paste0('Mean anomaly profile (lat:60.5°, lon: -59.5) in dec 2023'),
x = "temperature anomaly (°C)", y = 'depth (m)')
}
plot_anomaly_profiles(test_anomaly, "600")
#Temperature VS Depth
# look at the anomaly under catgoery
temp_anomaly_filtered_2023 <- temp_anomaly_va_biomes_2023 %>%
mutate(time = as.Date(date)) %>%
filter(depth==5, lat<35, lat>23, lon<(-20), lon>-40)
mean(temp_anomaly_filtered_2023$anomaly)
base_map <- base_map + lims(x= c(-50, -10), y = c(10, 40))
#Select data in season_of_interest
sept<-mhw_1x1_natlantic_2023_biomes %>%
filter(month %in% c("09"), lat<35, lat>23, lon<(-20), lon>-40)
oct<-mhw_1x1_natlantic_2023_biomes %>%
filter(month %in% c("10"), lat<35, lat>23, lon<(-20), lon>-40)
nov<-mhw_1x1_natlantic_2023_biomes %>%
filter(month %in% c("11"),lat<35, lat>23, lon<(-20), lon>-40)
dec<-mhw_1x1_natlantic_2023_biomes %>%
filter(month %in% c("12"),lat<35, lat>23, lon<(-20), lon>-40)
#Plot MHWs
sept_plot_1x1<-mhw_season_plot(sept, "sept")
oct_plot_1x1<-mhw_season_plot(oct, "oct")
nov_plot_1x1<-mhw_season_plot(nov, "nov")
dec_plot_1x1<-mhw_season_plot(dec, "dec")
combined_plot <- grid.arrange(sept_plot_1x1, oct_plot_1x1, nov_plot_1x1, dec_plot_1x1, ncol = 2, nrow=2)
print(combined_plot)#Argo float per season/group by month
argo_winter<-filter(argo_mhws_categ, month(argo_mhws_categ$time) %in% c("1","2"))
argo_spring<-filter(argo_mhws_categ, month(argo_mhws_categ$time) %in% c("3","4","5"))
argo_summer<-filter(argo_mhws_categ, month(argo_mhws_categ$time) %in% c("6","7","8"))
argo_autumn<-filter(argo_mhws_categ, month(argo_mhws_categ$time) %in% c("9","10","11", "12"))
argo_categorisation<-function(argo_dataset, chosen_months){
categorisation_map <- base_map +
geom_point(data = argo_dataset, aes(x = lon, y = lat, color = factor(most_freq_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("MHWs Categorization of Argo profiles in 2023"),
subtitle = paste0("months: ", chosen_months))
return(categorisation_map)
}
#Plot Argo distrib
spring_argo<-argo_categorisation(argo_spring, "03, 04, 05")
summer_argo<-argo_categorisation(argo_summer, "06, 07, 08")
autumn_argo<-argo_categorisation(argo_autumn, "09, 10, 11, 12")
winter_argo<-argo_categorisation(argo_winter, "01, 02")
combined_plot <- grid.arrange(winter_argo, spring_argo, summer_argo, autumn_argo, ncol = 2, nrow = 2)
print(combined_plot)Area of interest
#Defining are of interest spatially and temporally
month_interest=c("9", "10", "11", "12")
area_extent <- c(-45, 0, 20, 40)
argo_mhws_categ_focus<-argo_mhws_categ %>%
filter(month(time) %in% month_interest, lat>20, lat<40, lon>-45, lon<0, most_freq_category %in% c("III Severe", "IV Extreme"))
extreme_float<-argo_mhws_categ_focus %>%
filter(most_freq_category=="IV Extreme")
print(unique(extreme_float$file_id))
base_map <- base_map + lims(x= c(-50, 5), y = c(10, 50))
argo_focus_map<-argo_categorisation(argo_mhws_categ_focus, "09, 10, 11, 12")3D maps
# devtools::install_github("tylermorganwall/rayshader")
# install.packages("rnaturalearth")
# library(scatterplot3d)
surface_argo5m<-temp_anomaly_va_biomes_2023 %>%
filter(depth==5)
library(marmap)
library(rayshader)
library(ggplot2)
#Bathymetric data
north_atlantic_bathymetry <- getNOAA.bathy(lon1 = 30, lon2 = -100,
lat1 = 0, lat2 = 80, resolution = 10)
#Creating a custom palette of blues
blues <- c("lightsteelblue4", "lightsteelblue3","lightsteelblue2", "lightsteelblue1")
# Plotting the bathymetry with different colors for land and sea
plot(north_atlantic_bathymetry, image = TRUE, land = TRUE, lwd = 0.1,
bpal = list(c(0, max(north_atlantic_bathymetry), "grey"),
c(min(north_atlantic_bathymetry),0,blues)))
# Making the coastline more visible
plot(north_atlantic_bathymetry, deep = 0, shallow = 0, step = 0,
lwd = 0.4, add = TRUE)
scaleBathy(north_atlantic_bathymetry, deg = 2, x = "bottomleft", inset = 5) #adding scale
# points(surface_argo5m$lon, surface_argo5m$lat, pch = 19, cex = 0.3, asp = 1)
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 prismatic_1.1.2
[52] yaml_2.3.6 sass_0.4.4 stringi_1.7.8
[55] highr_0.9 e1071_1.7-12 rlang_1.1.1
[58] pkgconfig_2.0.3 evaluate_0.18 lattice_0.20-45
[61] sf_1.0-9 labeling_0.4.2 processx_3.8.0
[64] tidyselect_1.2.0 magrittr_2.0.3 R6_2.5.1
[67] generics_0.1.3 DBI_1.2.2 pillar_1.9.0
[70] haven_2.5.1 whisker_0.4 withr_2.5.0
[73] units_0.8-0 stars_0.6-0 abind_1.4-5
[76] sp_1.5-1 modelr_0.1.10 crayon_1.5.2
[79] KernSmooth_2.23-20 utf8_1.2.2 tzdb_0.3.0
[82] rmarkdown_2.18 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