Capturing Aleatoric Uncertainty in Climate Models

2026-04-16

AI Summary

This study addresses the quantification of irreducible aleatoric uncertainty in the climate system arising from intrinsic chaotic dynamics to inform risk-based climate decision-making. The uncertainty is directly characterized by the spread among members of a single-model large ensemble and modeled using generalized additive models (GAMs) to capture its spatiotemporal structure, with validation against ERA5-Land reanalysis data. The work establishes, for the first time, a theoretical link between internal climate variability and aleatoric uncertainty, proposing a model-agnostic statistical framework that is transferable across multiple variables and scales. The approach successfully reproduces key features of observed variability and reveals a significant reduction in summer variability over the Iberian Peninsula’s arid regions under a +3°C warming scenario, indicating an elevated risk of persistent drought conditions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Internal climate variability arises from the climate system's inherently chaotic dynamics. Quantifying it is essential for climate science, as it enables risk-based decision-making and differentiates between externally forced change and internal fluctuations. In statistical terms, natural variability corresponds to aleatoric uncertainty, i.e., irreducible stochastic variability. Despite this close conceptual alignment, the link between internal climate variability and aleatoric uncertainty has not yet been formalized. We establish a theoretical link by showing that member-to-member differences in single-model large ensembles provide a direct representation of aleatoric uncertainty. To quantify the spatio-temporal structure of aleatoric uncertainty, we employ generalized additive models. The proposed framework is validated through comparison with ERA5-Land reanalysis data, demonstrating that ensemble-derived estimates reproduce key spatial and temporal patterns of real-world variability. Applied to the water balance over the Iberian Peninsula, our approach reveals coherent variability structures and pronounced regional heterogeneity. We find a decline in variability in drought-prone regions and seasons, a pattern that strengthens under +3 °C global warming, implying an increased risk of persistent summer drought conditions. Beyond this application, the framework is climate-model agnostic and transferable to other variables and spatial scales, providing a statistical basis for quantifying internal climate variability as aleatoric uncertainty.

Problem

• aleatoric uncertainty
• internal climate variability
• climate models
• large ensembles
• spatio-temporal structure

Innovation

• aleatoric uncertainty
• internal climate variability
• large ensemble
• generalized additive models
• climate model agnostic