Country-level temperature trends

library(varunayan)
library(dplyr)
library(ggplot2)
library(tidyr)
library(patchwork)

get_era5_country_temperature() does three steps. First, the function finds the boundary of a country. Then it downloads monthly ERA5 data. Then it returns national averages with cosine-latitude area weights.

One call does all of these steps. You do not have to find boundaries, write GeoJSON, call era5ify_geojson(), and aggregate grid cells manually.

In this example, we get the monthly mean, maximum, and minimum temperatures for 2024. We get these data for four European countries and four South Asian countries.

Download data

We give the short values "mean", "max", and "min" to the variables argument.

countries <- c(
  "France", "Germany", "Spain", "Norway",
  "India", "Pakistan", "Bangladesh", "Nepal"
)

all_data <- lapply(countries, function(cntry) {
  df <- get_era5_country_temperature(
    country    = cntry,
    start_date = "2024-01-01",
    end_date   = "2024-12-31",
    variables  = c("mean", "max", "min")
  )
  df$country <- cntry
  df
})

temps <- bind_rows(all_data)

head(temps)
#   year month temperature_max temperature_mean temperature_min country
# 1 2024     1        6.852207         6.953495        6.225498  France
# 2 2024     2       10.040868        10.223489        9.456424  France
# 3 2024     3       11.683986        12.049970       11.041785  France
# 4 2024     4       13.844017        14.307139       13.269198  France
# 5 2024     5       16.621092        17.035453       16.117049  France
# 6 2024     6       20.049674        20.751232       19.515942  France

We add the region of each country to the data. The plots use the region to make facets.

region_map <- c(
  France = "Europe", Germany = "Europe", Spain = "Europe", Norway = "Europe",
  India = "South Asia", Pakistan = "South Asia",
  Bangladesh = "South Asia", Nepal = "South Asia"
)

temps$region <- region_map[temps$country]
temps$month_label <- factor(month.abb[temps$month], levels = month.abb)

# Cool colors for Europe, warm colors for South Asia
country_colors <- c(
  France = "#2166AC", Germany = "#4393C3",
  Spain = "#92C5DE", Norway = "#053061",
  India = "#B2182B", Pakistan = "#D6604D",
  Bangladesh = "#F4A582", Nepal = "#67001F"
)

Seasonal heatmap

ggplot(temps, aes(x = month_label, y = country, fill = temperature_mean)) +
  geom_tile(color = "white", linewidth = 0.4) +
  facet_grid(region ~ ., scales = "free_y", space = "free_y") +
  scale_fill_distiller(
    palette = "RdYlBu", direction = -1,
    name = "Mean temp (\u00B0C)"
  ) +
  labs(
    x = NULL, y = NULL,
    title = "Mean monthly temperature by country (2024)"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    panel.grid = element_blank(),
    strip.text.y = element_text(angle = 0, face = "bold")
  )

The temperatures of South Asian countries stay more than 20 °C for all of the year. The temperatures of European countries change by more than 20 °C between winter and summer. In Norway, the temperature is less than 0 °C from November to March.

Monthly trend lines with min/max ribbon

ggplot(temps, aes(x = month, color = country, fill = country)) +
  geom_ribbon(aes(ymin = temperature_min, ymax = temperature_max),
    alpha = 0.15, color = NA
  ) +
  geom_line(aes(y = temperature_mean), linewidth = 0.9) +
  geom_point(aes(y = temperature_mean), size = 1.5) +
  facet_wrap(~region, ncol = 1, scales = "free_y") +
  scale_color_manual(values = country_colors) +
  scale_fill_manual(values = country_colors) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  labs(
    x = NULL, y = "Temperature (\u00B0C)",
    title = "Monthly temperature with min-max range (2024)",
    subtitle = "Ribbon: national-average daily min to daily max",
    color = NULL, fill = NULL
  ) +
  theme_minimal(base_size = 12) +
  theme(legend.position = "bottom")

Diurnal range

The diurnal temperature range (DTR) is the difference between the daily maximum and the daily minimum. The DTR is larger in continental and arid climates than in humid tropical climates.

temps$dtr <- temps$temperature_max - temps$temperature_min

ggplot(temps, aes(x = month, y = dtr, color = country)) +
  geom_line(linewidth = 0.9) +
  geom_point(size = 1.5) +
  facet_wrap(~region, ncol = 1) +
  scale_color_manual(values = country_colors) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  labs(
    x = NULL, y = "Diurnal temperature range (\u00B0C)",
    title = "Monthly diurnal temperature range (2024)",
    color = NULL
  ) +
  theme_minimal(base_size = 12) +
  theme(legend.position = "bottom")

Regional comparison

Last, we show all eight countries on one axis. The line type shows the region of each country.

ggplot(temps, aes(
  x = month, y = temperature_mean,
  color = country, linetype = region
)) +
  geom_line(linewidth = 0.8) +
  geom_point(size = 1.5) +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  scale_color_manual(values = country_colors) +
  scale_linetype_manual(values = c("Europe" = "solid", "South Asia" = "dashed")) +
  labs(
    x = NULL, y = "Mean temperature (\u00B0C)",
    title = "Europe vs South Asia: monthly temperature (2024)",
    color = NULL, linetype = NULL
  ) +
  theme_minimal(base_size = 12) +
  theme(legend.position = "bottom")