EagleEye: Unsupervised Detection and Quantification of Local Density Anomalies in Climate Data

Detecting subtle, spatially localized changes in climate datasets is essential for understanding evolving regional dynamics and extreme events—key topics in Earth and planetary sciences. We present EagleEye, a novel distribution-free approach for identifying local density anomalies between two multivariate datasets. By leveraging a nearest-neighbor strategy and a binomial-based test, EagleEye pinpoints regions of significant deviations without relying on complex model assumptions.

We illustrate its potential using temperature reanalysis products, where EagleEye reveals localized shifts in historical climate patterns—including notable positive anomalies around Greenland—that may indicate emerging changes in temperature fields. Owing to its scalability and interpretability, EagleEye can handle large, high-dimensional datasets and integrate multiple climatic variables within a single region. This framework offers an innovative avenue for probing evolving climate dynamics, highlighting areas undergoing rapid change, and supporting an enhanced understanding of climate variability—making it a valuable tool for geoscience applications and beyond.