Uncertainty-Aware Anomaly Detection in Spatiotemporal Climate Data

Abstract

Accurate detection and quantification of uncertainty in anomalous climate events are needed to improve our understanding of climate extremes, particularly snow and ice melt processes in the polar regions. Despite the advances in anomaly detection methods, existing frameworks often neglect uncertainties inherent in spatiotemporal processes, leading to unreliable anomaly detection. We propose an uncertainty-aware anomaly detection framework that integrates measurement uncertainty and modeling bias. Our approach leverages the Three-Cornered-Hat (3CH) error variance estimator to quantify input uncertainties and incorporate them into the anomaly detection process through an uncertainty-weighted loss function and Monte Carlo Dropout (MCD) for total predictive uncertainty estimation. Using our approach, we detect and evaluate the uncertainty associated with anomalies from three surface melt products (ERA5, MAR, GEMB) of the Greenland Ice Sheet surface. Experiments on synthetic datasets and modeling output demonstrate that our uncertainty-aware method significantly enhances anomaly detection reliability, reducing false positives and improving detection confidence. Our results across the three models provide robust insights into the simulation of ice sheet surface melt dynamics, highlighting that GEMB, which includes the most complex physical representation of snow evolution, exhibits the strongest reliability in detecting melt regions with low uncertainty. Our findings provide insights into the simulation of ice sheet surface melt dynamics and underscore the importance of uncertainty quantification in Earth system modeling.