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Download Arctic/Antarctic Sea Ice Extent Data

envir_get_sea_ice_extent.Rd

Fetches daily sea ice extent data from the National Snow and Ice Data Center (NSIDC) REST API https://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/. Allows specifying years, pole (North/Arctic or South/Antarctic), and optionally applying a smoothing window. Also retrieves the 1981-2010 quantile data (median, IQR, range) for comparison, applying the same smoothing if specified.

Usage

envir_get_sea_ice_extent(years = NULL, pole = "north", window = 5)

Arguments

years

A numeric vector of years for which to retrieve daily data.

pole

Character string: "north" (default) for Arctic or "south" for Antarctic.

window

Numeric: The size of the moving average smoothing window (days). Defaults to 5. Set window = 1 to retrieve raw, unsmoothed data. Must be a positive integer.

Value

A tidy data frame with the following columns:

  • date: The date (as Date objects). For yearly data, this is the actual date. For quantile data, this is the day-of-year mapped onto the base year 2000 (a leap year) for consistent plotting.

  • ice_extent_mi_sqkm: Sea ice extent in millions of square kilometers.

  • variable: Character string indicating the data type:

    • The year (e.g., "2023") for specific year data.

    • The quantile label (e.g., "q50_1981_to_2010", "q25_1981_to_2010") for aggregate data.

  • pole: Character string, either "north" or "south".

  • smoothing_window: Numeric, the window size used (1 indicates raw data).

Examples

if (FALSE) { # \dontrun{
# Get Arctic data for 2022 and 2023 with default 5-day smoothing
ice_data_arctic_smooth = fetch_sea_ice_extent(years = c(2022, 2023))
print(head(ice_data_arctic_smooth))

# Get Arctic data for 2022 and 2023 with NO smoothing (raw data)
ice_data_arctic_raw = fetch_sea_ice_extent(years = c(2022, 2023), window = 1)
print(head(ice_data_arctic_raw))

# Example of plotting (requires ggplot2, dplyr, lubridate)
if (requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE) &&
    requireNamespace("lubridate", quietly = TRUE)) {

  library(ggplot2)
  library(dplyr)
  library(lubridate)

  # Plot 2023 Arctic raw data against historical raw range/median
  fetch_sea_ice_extent(years = 2023, pole = "north", window = 1) %>%
    filter(variable %in% c("2023", "q0_1981_to_2010", "q100_1981_to_2010", "q50_1981_to_2010")) %>%
    # Add a temporary DOY column for plotting alignment
    mutate(doy = yday(date)) %>%
    ggplot(aes(x = doy, y = ice_extent_mi_sqkm, color = variable)) +
    geom_line(linewidth = 1) +
    labs(title = "Arctic Sea Ice Extent (2023 Raw vs 1981-2010 Raw)",
         x = "Day of Year",
         y = "Ice Extent (Million sq km)",
         color = "Data Series") +
    theme_minimal()
}
} # }