SIM2 climate data

Temperature change in France – CC-BY by University of Reading

As France enters its second heatwave of 2026, can we produce more detailed plots than the excellent visualizations provided by ShowYourStripes?

MétéoFrance offers its monthly SIM2 dataset albeit over a shorter time span (currently 1970–2025). The dataset includes temperature, precipitation and other variables on an 8 km resolution grid.

We will select a French city, retrieve its geographic coordinates, build the grid for a specific month over the 1970–2025 period, extract the data from the grid at that location and plot the temperature anomaly.

We will use {terra} to create the grid from the tabular files containing cell centers and weather variables, and terra::extract() to get all temperatures.

library(tidyverse)
library(fs)
library(janitor)
library(osmdata)
library(sf)
library(terra)
library(glue)
library(memoise)

invisible(
  Sys.setlocale(category = "LC_ALL", 
                locale = "en_GB.UTF8"))


#' Geolocate using OSM (Nominatim API)
#'
#' using first result
#' Memoized function
#'
#' @param location (char): place name (geocodable via OSM)
#'
#' @returns (SpatVector): first result
geolocate <- memoise(function(location) {
  loc <- getbb(location, format_out = "sf_polygon")  |> 
    st_point_on_surface() |>
    slice_head(n = 1)
  
  message(glue("{loc$display_name} — WGS84 : {loc$geometry}"))
  
  return(loc|>
           st_transform("IGNF:NTFLAMB2E") |>
           as("SpatVector"))
})

#' Generate a monthly temperature chart since 1970
#'
#' @param sim2 (data.frame): Météo-France SIM2 data over the period
#' @param month (char): month number "01"..."12"
#' @param location (char): place name (geocodable via OSM); memoized
#' @param output_dir (char): directory path where a PNG file will be written, if not NULL
#'
#' @returns (ggplot and optionally a file on disk)
generate_chart <- function(
    sim2,
    month,
    location,
    output_dir = NULL) {
  stopifnot(month %in% sprintf("%02d", 1:12))
  month_name <- format(ymd(glue("0000-{month}-01")), "%B")
  
  sim2_raster <- sim2 |>
    filter(str_detect(date, glue("{month}$"))) |>
    mutate(
      x = lambx * 100,
      y = lamby * 100,
      layer = date,
      temp = t,
      .keep = "none") |>
    rast(
      type = "xylz",
      crs = "IGNF:NTFLAMB2E")
  
  loc <- geolocate(location)
  
  temperatures <- sim2_raster |>
    terra::extract(loc) |>
    select(-ID) |>
    pivot_longer(
      cols = everything(),
      names_to = "month",
      values_to = "temperature") |>
    mutate(
      year = as.integer(str_sub(month, 1, 4)),
      anomaly = temperature - mean(temperature[year >= 1991 & year <= 2020],
                                   na.rm = TRUE))
  
  p <- temperatures |>
    ggplot(aes(year, anomaly)) +
    geom_col(aes(fill = anomaly)) +
    geom_smooth(method = "loess",
                formula = y ~ x) +
    scale_fill_gradient2(
      high = scales::muted("red"),
      mid = "white",
      low = scales::muted("blue")) +
    scale_x_continuous(breaks = scales::breaks_pretty()) +
    scale_y_continuous(breaks = scales::breaks_pretty()) +
    labs(
      title = glue("Average monthly anomaly temperature — {month_name}"),
      subtitle = location,
      x = "year",
      y = "departure from average* (°C)",
      fill = "°C",
      caption = glue(
        "https://r.iresmi.net/ — {Sys.Date()}
         data: Météo-France SIM2 — *baseline: 1991–2020 normal for {month_name}")) +
    theme(
      text = element_text(family = "Ubuntu"),
      plot.caption = element_text(size = 7))
  
  if (!is.null(output_dir)) {
    dir_create(output_dir)
    
    ggsave(
      glue("{output_dir}/tm_{month}_{make_clean_names(location)}.png"),
      plot = p,
      width = 20,
      height = 20 / 1.618,
      units = "cm",
      dpi = 150)
  }
  
  return(p)
}

The data is a bunch of compressed CSV.

# https://meteo.data.gouv.fr/datasets/65e040c50a5c6872ebebc711
# Climate change data - monthly SIM
# all files MENS_SIM2_*-*.csv.gz
sim2 <- dir_ls("data") |>
  read_delim(
    delim = ";",
    locale = locale(decimal_mark = "."),
    name_repair = make_clean_names)

Now we just call our function.

generate_chart(sim2,
               month = "06",
               location = "Paris, France")

Figure 1: June temperature in Paris 1970–2025

If we want each month:

# for each month
sprintf("%02d", 1:12) |>
  map(\(x) generate_chart(sim2,
                           month = x,
                           location = "Grenoble, France",
                           output_dir = "results"), 
  .progress = TRUE)

Note that scales are not constant across plots; if we want to compare month (or places) we should fix the y-axis and the color scale. It’s left as an exercise to the reader if you want to make a nice poster…