Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction

TL;DR

This paper introduces a factor-model approach to correct biases in climate model outputs by capturing unobserved confounders, significantly improving precipitation prediction accuracy.

Contribution

It presents a novel causality-inspired method that learns latent confounders from data to enhance climate model bias correction, addressing limitations of traditional statistical techniques.

Findings

Improved precipitation forecast accuracy

Effective identification of unobserved confounders

Robust bias correction across climate variables

Abstract

Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, the 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.