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Related Experiment Video

Updated: Jun 27, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Bayesian weighting of climate models based on climate sensitivity.

Elias C Massoud¹, Hugo K Lee², Adam Terando^3,4

¹Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN USA.

Communications Earth & Environment

|April 26, 2024

Summary

Bayesian Model Averaging refines climate projections by weighting climate models, avoiding bias from

Keywords:

Environmental impact Projection and prediction

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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
Earth system science
Climate modeling

Background:

Climate model ensembles can produce biased projections due to 'hot models' with high climate sensitivities.
Existing methods like model emulators or culling have limitations in addressing this bias.

Purpose of the Study:

To implement Bayesian Model Averaging (BMA) to address the 'hot model' problem without excluding models.
To produce unbiased posterior probability distributions of model weights using multiple lines of evidence.

Main Methods:

Utilized Bayesian Model Averaging (BMA) as a statistical framework.
Constructed model weights based on multiple lines of evidence for Earth's climate sensitivity.

Main Results:

BMA yields updated multi-model ensemble projections of end-of-century global mean surface temperature increases: 2°C (SSP1-2.6) and 5°C (SSP5-8.5).
These BMA-derived estimates are lower than those from a simple multi-model mean of the CMIP6 ensemble.
The approach retains some weight on low-probability models, acknowledging potential extreme climate sensitivity values.

Conclusions:

Bayesian Model Averaging offers a robust framework for unbiased climate change projection.
This method integrates scientific evidence effectively, providing more reliable future climate scenarios.