A Deconfounding Approach to Climate Model Bias Correction

2024-08-22

arXiv.org

AI Summary

Global climate models (GCMs) suffer from systematic biases induced by unobserved confounding factors, limiting conventional statistical bias correction methods that neglect such confounders. This paper introduces, for the first time, temporal deconfounding—a causal inference technique—into GCM bias correction. We propose a multi-cause latent factor model that jointly integrates GCM outputs and observational data to explicitly identify and adjust for unobserved confounding effects. The framework synergistically combines latent factor modeling, causal deconfounding principles, and advanced time-series architectures (e.g., Transformer and Neural ODEs). Evaluated on precipitation forecasting, our method achieves significant improvements in predictive accuracy while enhancing physical consistency and observational fidelity. By rigorously accounting for confounding, it overcomes a fundamental theoretical limitation of traditional approaches that assume no unmeasured confounding.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation of complex climate phenomena. Traditional bias correction methods, which rely on historical observation data and statistical techniques, often neglect unobserved confounders, leading to biased results. This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders. Inspired by recent advances in causality based time series deconfounding, our method first constructs a factor model to learn latent confounders from historical data and then applies them to enhance the bias correction process using advanced time series forecasting models. The experimental results demonstrate significant improvements in the accuracy of precipitation outputs. By addressing unobserved confounders, our approach offers a robust and theoretically grounded solution for climate model bias correction.

Problem

• Addresses systematic biases in Global Climate Model outputs
• Identifies multi-cause latent confounders using factor models
• Improves bias correction accuracy for precipitation predictions

Innovation

• Factor model captures multi-cause latent confounders
• Causality based time series deconfounding enhances correction
• Advanced forecasting models improve precipitation accuracy