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Related Experiment Video

Updated: Jun 26, 2025

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

Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields.

Fabrizio Falasca¹, Pavel Perezhogin¹, Laure Zanna¹

¹Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.

Physical Review. E

|May 17, 2024

Summary

This summary is machine-generated.

This study introduces a data-driven framework to understand climate variability by identifying causal links between key patterns. It helps visualize climate responses and rank pattern influence for better system dynamics analysis.

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Area of Science:

Climate Science
Data Science
Complex Systems

Background:

Spatiotemporal climate data is high-dimensional and complex.
Understanding causal relationships in climate variability is crucial for prediction and mitigation.

Purpose of the Study:

To develop a data-driven framework for describing spatiotemporal climate variability.
To infer causal linkages between climate patterns using a novel methodology.
To provide uncertainty estimation for identified causal relationships.

Main Methods:

Dimensionality reduction of climate fields into regionally constrained patterns.
Causal inference using the fluctuation-response formalism and an analytical null model.
Application to sea surface temperature data from a climate model.

Main Results:

Successful identification of known causal relations in tropical basins.
Visualization of system-wide responses to climate variability from selected regions.
Ranking of climate patterns by causal strength, quantifying their influence.

Conclusions:

The proposed framework offers a rigorous and interpretable method for exploring causal relations in spatiotemporal fields.
It enhances understanding of climate dynamics and variability.
The methodology aids in quantifying the influence of different climate patterns on the system.