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Updated: Jan 12, 2026

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

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

Madhurima Panja¹, Dhiman Das², Tanujit Chakraborty^1,3

¹Department of Science and Engineering, Sorbonne University, Abu Dhabi, United Arab Emirates.

Chaos (Woodbury, N.Y.)

|November 4, 2025

Summary

Accurate Arctic precipitation forecasting is vital for climate risk assessment. This study introduces a machine learning framework combining causal analysis and probabilistic methods for reliable predictions in vulnerable marine regions.

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Last Updated: Jan 12, 2026

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Area of Science:

Climate Science
Machine Learning
Arctic Research

Background:

Arctic maritime environments face significant climate risks due to precipitation variability.
Accurate forecasting is essential for developing early warning systems in these vulnerable regions.

Purpose of the Study:

To develop a probabilistic machine learning framework for modeling and predicting Arctic precipitation dynamics and severity.
To analyze scale-dependent relationships between precipitation and atmospheric drivers.
To quantify causal influences among variables for improved forecasting.

Main Methods:

Wavelet coherence analysis to identify scale-dependent relationships between precipitation and atmospheric drivers (temperature, humidity, cloud cover, pressure).
Synergistic-Unique-Redundant (SUR) decomposition to assess joint causal influences and interaction effects.
Conformal prediction method for generating calibrated, non-parametric prediction intervals to account for uncertainty.

Main Results:

Identified scale-dependent relationships between key atmospheric drivers and precipitation in Arctic marine environments.
Quantified the causal impact of variable interactions on future precipitation dynamics.
Developed a data-driven forecasting model incorporating historical data and causal drivers.

Conclusions:

A comprehensive framework combining causal analysis and probabilistic forecasting enhances the reliability and interpretability of Arctic precipitation predictions.
The proposed method is crucial for climate risk assessment and early warning systems in vulnerable marine regions.