Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers

TL;DR

This paper introduces a probabilistic machine learning approach that integrates causal climate drivers and uncertainty quantification to improve precipitation forecasting in Arctic marine regions.

Contribution

It combines wavelet coherence, causal decomposition, and conformal prediction to create a novel, interpretable, and reliable precipitation forecasting framework for Arctic environments.

Findings

Causal climate drivers significantly influence Arctic precipitation dynamics.

The framework provides calibrated prediction intervals for precipitation forecasts.

Incorporating causal analysis improves forecast reliability and interpretability.

Abstract

Understanding and forecasting precipitation events in the Arctic maritime environments, such as Bear Island and Ny-{\AA}lesund, is crucial for assessing climate risk and developing early warning systems in vulnerable marine regions. This study proposes a probabilistic machine learning framework for modeling and predicting the dynamics and severity of precipitation. We begin by analyzing the scale-dependent relationships between precipitation and key atmospheric drivers (e.g., temperature, relative humidity, cloud cover, and air pressure) using wavelet coherence, which captures localized dependencies across time and frequency domains. To assess joint causal influences, we employ Synergistic-Unique-Redundant Decomposition, which quantifies the impact of interaction effects among each variable on future precipitation dynamics. These insights inform the development of data-driven forecasting models that incorporate both historical precipitation and causal climate drivers. To account for uncertainty, we employ the conformal prediction method, which enables the generation of calibrated non-parametric prediction intervals. Our results underscore the importance of utilizing a comprehensive framework that combines causal analysis with probabilistic forecasting to enhance the reliability and interpretability of precipitation predictions in Arctic marine environments.