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Updated: Jun 10, 2026
06:10
Using Generative Art to Convey Past and Future Climate Transitions
Published on: March 31, 2023
Quantifying uncertainty in climate change science through empirical information theory.
Andrew J Majda1, Boris Gershgorin
1Department of Mathematics and Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA. jonjon@cims.nyu.edu
Summary
This study introduces a new information metric to quantify errors in Atmosphere Ocean Science (AOS) models, improving climate change predictions. It also provides formulas to identify key climate change directions for better forecasting.
Area of Science:
- Climate Science
- Atmosphere Ocean Science (AOS) modeling
- Empirical Information Theory
Background:
- Quantifying uncertainty in current climate and future climate change predictions from Atmosphere Ocean Science (AOS) models is crucial.
- Existing AOS models are imperfect, necessitating robust methods for error assessment and uncertainty quantification.
Purpose of the Study:
- To develop a systematic, mathematically-grounded approach to quantify uncertainties in AOS models.
- To propose an information metric for assessing climate model errors, including mean errors and covariance ratios.
- To derive formulas for identifying sensitive directions of climate change.
Main Methods:
- Utilized empirical information theory to develop a novel information metric.
- Incorporated coarse-grained mean model errors and covariance ratios into the metric in a transformation-invariant manner.
- Employed a statistically exactly solvable test model and a one-dimensional stochastic model for illustration and validation.
Main Results:
- A new information metric effectively quantifies subtle behaviors of AOS model errors.
- Formulas were developed to identify the most sensitive climate change directions using climate statistics and model approximations.
- The approach was illustrated on a solvable model relevant to atmospheric low-frequency variability and tracer gas behavior (e.g., CO2).
Conclusions:
- The proposed information metric offers a robust way to assess and quantify errors in Atmosphere Ocean Science (AOS) models.
- The derived formulas provide a pathway to identify critical climate change drivers, enhancing prediction accuracy.
- The study offers a systematic framework and practical algorithms for improving climate model uncertainty quantification.
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