compare_gcms: Compare General Circulation Models (GCMs)

Description

This function compares future climate projections from multiple General Circulation Models (GCMs) based on their similarity in terms of variables. The function uses three clustering algorithms — k-means, hierarchical clustering, and closestdist — to group GCMs, and generates visualizations for the resulting clusters.

Usage

compare_gcms(
  s,
  var_names = c("bio_1", "bio_12"),
  study_area = NULL,
  scale = TRUE,
  k = 3,
  clustering_method = "closestdist"
)

Value

A list with two items: suggested_gcms (the names of the GCMs suggested for further analysis) and statistics_gcms (a grid of plots visualizing the clustering results).

Arguments

s: A list of stacks of General Circulation Models (GCMs).
var_names: Character. A vector with the names of the variables to compare, or 'all' to include all available variables.
study_area: An Extent object, or any object from which an Extent object can be extracted. Defines the study area for cropping and masking the rasters.
scale: Logical. Whether to apply centering and scaling to the data. Default is TRUE.
k: Numeric. The number of clusters to use for k-means clustering.
clustering_method: Character. The clustering method to use. One of: "kmeans", "hclust", or "closestdist". Default is "closestdist".

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

Run this code

var_names <- c("bio_1", "bio_12")
s <- import_gcms(system.file("extdata", package = "chooseGCM"), var_names = var_names)
study_area <- terra::ext(c(-80, -30, -50, 10)) |> terra::vect(crs="epsg:4326")
compare_gcms(s, var_names, study_area, k = 3, clustering_method = "closestdist")