Last updated: 2024-05-14

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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, biome_value) %>%
  filter(!is.na(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)

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#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)
# A tibble: 752 × 2
   platform_number cycle_count
   <chr>                 <int>
 1 1901730                  37
 2 1901731                  37
 3 1902073                  35
 4 1902180                  22
 5 1902181                  27
 6 1902208                  37
 7 1902299                  37
 8 1902300                  36
 9 1902301                  37
10 1902302                  37
# ℹ 742 more rows
#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, -biome_value  )

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(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

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Anomaly profiles

Individual float

platform_6903067<-heatwaves_data_east %>% 
  filter(platform_number==6903067)
custom_palette <- c("#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b",
                    "#e377c2", "#7f7f7f", "#bcbd22", "#17becf", "#aec7e8", "#ffbb78")
ggplot(platform_6903067, aes(x = anomaly, y = depth, color = factor(month))) +
  geom_path() +
  geom_vline(xintercept = 0) +
  scale_y_reverse() +
  coord_cartesian(xlim = c(-4, 4), ylim = c(200, 0)) +
  labs(title = 'Anomaly profiles by month',
       subtitle= '1 subplot per cycle',
       x = 'Temperature (°C)', y = 'Depth (m)', color = 'Month') +
  scale_color_manual(values = custom_palette) + 
  facet_wrap(~cycle_number, scales = "free_x", ncol = 5)

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platform_6903067_base_map<- base_map + lims(x=c(-20,-10), y=c(10,20))

unique_coords_with_cycles <- platform_6903067 %>%
  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 <- platform_6903067_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"))

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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) %>%
  summarise(
    temp_count = n(),
    temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
    temp_anomaly_sd = sd(anomaly, na.rm = TRUE)
  )


ggplot(anomaly_lat_lon, aes(x = temp_anomaly_mean, y = depth)) +
  geom_path(aes(color = factor(month))) +
  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(limits = c(200, 0)) +
  coord_cartesian(xlim = c(-4, 4)) +
  facet_grid(lat ~ lon, scales = "free", space = "free") +
  labs(title = "Mean Anomaly Profile for Latitude and Longitude",
       x = "Temperature (°C)", y = "Depth (m)") +
  theme_minimal() +
  scale_color_discrete(name = "Month") +
  guides(color = guide_legend(override.aes = list(alpha = 1)))

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# filter specific lat for better visualisation
specific_anomaly_lat <- anomaly_lat_lon %>%
  filter(lat == 7.5)

ggplot(specific_anomaly_lat, aes(x = temp_anomaly_mean, y = depth)) +
  geom_path(aes(color = factor(month))) +
  geom_vline(xintercept = 0) +
  scale_y_reverse(limits = c(200, 0)) +
  coord_cartesian(xlim = c(-4, 4)) +
  facet_wrap(~ lon, scales = "free", ncol = 2) +
  labs(title = paste0("Mean Anomaly Profile for Latitude ", unique(specific_anomaly_lat$lat), " and Different Longitudes"),
       x = "Temperature (°C)", y = "Depth (m)") +
  scale_color_discrete(name = "Month") +
  guides(color = guide_legend(override.aes = list(alpha = 1)))

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Coloring by SST anomaly

# Define category breaks and labels for SST anomaly
anomaly_categories <- list(
  list(label = "(-0.5, 0)", filter = quote(SST_anomaly < 0 & SST_anomaly > -0.5)),
  list(label = "(0, 0.5)", filter = quote(SST_anomaly > 0 & SST_anomaly < 0.5)),
  list(label = "(0.5, 1)", filter = quote(SST_anomaly > 0.5 & SST_anomaly < 1)),
  list(label = "(1, 1.5)", filter = quote(SST_anomaly > 1 & SST_anomaly < 1.5)),
  list(label = "(1.5, 2)", filter = quote(SST_anomaly > 1.5 & SST_anomaly < 2)),
  list(label = "> 2", filter = quote(SST_anomaly > 2))
)

generate_plot <- function(category) {
  # filter data
  anomaly_data <- heatwaves_data_east %>% filter(eval(category$filter))
  
  # calculate mean anomaly data
  anomaly_summary <- anomaly_data %>%
    group_by(platform_number, depth, month) %>%
    summarise(
      temp_count = n(),
      temp_anomaly_mean = mean(anomaly, na.rm = TRUE)
    )
  
  num_floats <- n_distinct(anomaly_summary$platform_number)
  num_cycles <- n_distinct(anomaly_summary$month)
  
  #plot for the current category
  ggplot(anomaly_summary, aes(x = temp_anomaly_mean, y = depth)) +
    geom_path(aes(color = factor(month))) +
    geom_vline(xintercept = 0) +
    scale_y_reverse(limits = c(200, 0)) +
    coord_cartesian(xlim = c(-4, 4)) +
    labs(title = "Mean Anomaly Profile",
         subtitle = paste("Floats having SST anomaly falls within the range", category$label,                                                    "\nNumber of floats:", num_floats),
         x = "Temperature (°C)", y = "Depth (m)") +
    scale_color_discrete(name = "Month") +
    guides(color = guide_legend(override.aes = list(alpha = 1))) +
    theme_bw() +
    facet_wrap(~ month, scales = "free", ncol = 2)
}

plots <- lapply(anomaly_categories, generate_plot)
plots
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# anomaly_SST_cat2 <- heatwaves_data_east %>% filter(SST_anomaly<0, SST_anomaly>-0.5) # SST anomaly falls within the range (-0.5, 0)
# anomaly_SST_cat3 <- heatwaves_data_east %>% filter(SST_anomaly>0, SST_anomaly<0.5) # SST anomaly falls within the range (0, 0.5)
# anomaly_SST_cat4 <- heatwaves_data_east %>% filter(SST_anomaly>0.5, SST_anomaly<1)# SST anomaly falls within the range (0.5, 1)
# anomaly_SST_cat5 <- heatwaves_data_east %>% filter(SST_anomaly>1, SST_anomaly<1.5)# SST anomaly falls within the range (1, 1.5)
# anomaly_SST_cat6 <- heatwaves_data_east %>% filter(SST_anomaly>1.5, SST_anomaly<2)# SST anomaly falls within the range (1.5, 2)
# anomaly_SST_cat7 <- heatwaves_data_east %>% filter(SST_anomaly>2) #SST anomaly is greater than 2

Monthly anomaly profiles

#Comparison 1st and 2nd half of the month

heatwaves_data_east <- heatwaves_data_east %>%
  mutate(day = as.numeric(as.character(day)))
# create datasets for the 1st and second half of each month
first_half <- heatwaves_data_east %>% filter(day < 15)
second_half <- heatwaves_data_east %>% filter(day >= 15)

create_anomaly_profile <- function(data, subtitle) {
  data %>%
    group_by(depth, month) %>%
    summarise(temp_count = n(),
              temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
              temp_anomaly_sd = sd(anomaly, na.rm = TRUE)) %>%
    ggplot(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 = 'Monthly anomaly profile',
         subtitle = subtitle,
         x = 'Temperature (°C)', y = 'Depth (m)') +
    theme_minimal() +
    guides(color = FALSE) +
    facet_wrap(~month)
}

plot_first_half <- create_anomaly_profile(first_half, 
                                          subtitle = "Extent: East\nfrom surface to 200m\nfirst half of the month (day<15)")
plot_second_half <- create_anomaly_profile(second_half, 
                                           subtitle = "Extent: East\nfrom surface to 200m\nsecond half of the month (day>=15)")

plot_first_half

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plot_second_half

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af6594f mlarriere 2024-05-13 
# 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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TableGrob (1 x 2) "arrange": 2 grobs
  z     cells    name           grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]

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TableGrob (1 x 2) "arrange": 2 grobs
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1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]

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TableGrob (1 x 2) "arrange": 2 grobs
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1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]

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TableGrob (1 x 2) "arrange": 2 grobs
  z     cells    name           grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]

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TableGrob (1 x 2) "arrange": 2 grobs
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2 2 (1-1,2-2) arrange gtable[layout]

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TableGrob (1 x 2) "arrange": 2 grobs
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1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]

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TableGrob (1 x 2) "arrange": 2 grobs
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1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]

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TableGrob (1 x 2) "arrange": 2 grobs
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TableGrob (1 x 2) "arrange": 2 grobs
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TableGrob (1 x 2) "arrange": 2 grobs
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TableGrob (1 x 2) "arrange": 2 grobs
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# 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)') +
  theme_minimal()+
  guides(color = FALSE)+
  facet_wrap(~month)

print(anomaly_plot_monthly)

Version Author Date
af6594f mlarriere 2024-05-13 

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