Extreme Weather Attribution Studies

climate
weather
Trends in climate change predictions
Author

Cole Baril

Published

August 12, 2025

Load Packages

require(pacman)
p_load(tidytuesdayR, magick, trashpanda, tidyverse, janitor, tidytext, slider, here, TTR)

options(scipen = 999)

Load Data

tuesdata <- tidytuesdayR::tt_load('2025-08-12')
attribution_studies <- tuesdata$attribution_studies
attribution_studies_raw <- tuesdata$attribution_studies_raw

Data Exploration

attribution_studies |> 
  distinct(event_type)
# A tibble: 12 × 1
   event_type      
   <chr>           
 1 Heat            
 2 Rain & flooding 
 3 Storm           
 4 Drought         
 5 Cold, snow & ice
 6 Oceans          
 7 Impact          
 8 Compound        
 9 Wildfire        
10 Atmosphere      
11 Sunshine        
12 River flow      
attribution_studies |> 
  distinct(classification)
# A tibble: 4 × 1
  classification                               
  <chr>                                        
1 More severe or more likely to occur          
2 No discernible human influence               
3 Insufficient data/inconclusive               
4 Decrease, less severe or less likely to occur

Plot

plot <- attribution_studies |> 
  filter(event_type %in% c("Cold, snow & ice", "Drought", "Heat", "Rain & flooding", "Wildfire")) |> 
  reframe(n = n(),
          .by = c("publication_year", "event_type", "classification")) |> 
  ggplot(aes(x = publication_year, y = n, colour = classification)) +
  geom_point(aes(size = n)) + 
  geom_smooth(aes(groups = classification), method = "lm", se = FALSE) +
  facet_wrap(~event_type) +
  labs(x = "Publication Year",
       y = "Number of Studies",
       title = "Charting Attribution: Studies Linking Climate Change to Extreme Weather (2000–2024)",
       subtitle = "Studies across all event types have grown, with most finding that climate change increases the severity or likelihood of extremes.",
       size = "Number of Attribution Studies",
       colour = "Study Finding") +
  scale_colour_brewer(palette = "Set2") +
  theme_bw(base_size = 30) +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_blank(),
        strip.text = element_text(face = "bold", size = 20),
        plot.title = element_text(face = "bold", hjust = 0.5, size = 30),
        plot.subtitle = element_text(hjust = 0.5, size = 30),
        legend.position = "inside",
        legend.position.inside = c(.85, .25),
        legend.title.position = "top",
        legend.title = element_text(hjust = 0),
        legend.key.width = unit(2.5, "lines"),
        legend.key.height = unit(1, "lines"),
        axis.line = element_line(colour = "black"),
        plot.margin = margin(0.1, 2, 0.1, 0.1, "cm")) +
  guides(color = guide_legend(override.aes = list(size = 7))) 

# Save and display images
current_dir <- dirname(knitr::current_input())
plot_name <- "att_studies.png"

ggsave(plot = plot, 
       dpi = "screen",
       width = 25,
       height = 20,
       device = ragg::agg_png,
       filename = file.path(current_dir, plot_name))


# Read the big plot
img <- image_read(file.path(current_dir, plot_name)) 


# Force 16:9 aspect ratio with minimal padding
# Target size: 1200x675 px (16:9)
img_card <- image_scale(img, "1200x675")           # scale to fit inside 16:9
img_card <- image_extent(
  img_card,
  geometry = "1200x675",
  gravity = "center"
)

# Save as card preview
image_write(img_card, path = file.path(current_dir, "preview.png"))

knitr::include_graphics(
  file.path(current_dir, plot_name)
)

References

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