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Updated: Sep 19, 2025
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Published on: June 24, 2019
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Observation-based estimate of Earth's effective radiative forcing
Senne Van Loon1, Maria Rugenstein1, Elizabeth A Barnes1
1Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80521.
Summary
Human emissions significantly impact Earth's climate. This study estimates effective radiative forcing using machine learning and observations, revealing a substantial increase since 2021, consistent with recent warming trends.
Area of Science:
- Climate Science
- Earth System Science
- Atmospheric Physics
Background:
- Human emissions are a primary driver of climate change.
- Effective radiative forcing (ERF) quantifies anthropogenic and natural impacts on Earth's energy balance.
- Direct observation of ERF is challenging, necessitating reliance on climate models.
Purpose of the Study:
- To develop an observational estimate of effective radiative forcing.
- To utilize machine learning to bridge the gap between climate models and observational data.
- To provide an independent assessment of ERF trends.
Main Methods:
- Employed machine learning to model the relationship between surface temperature and radiation from internal climate variability.
- Integrated multimodel ensemble data with observed surface temperature and net radiative imbalance.
- Calculated the effective radiative forcing trend from 2001-2024.
Main Results:
- Estimated an effective radiative forcing trend of 0.71 ± 0.21 Wm⁻² per decade for 2001-2024.
- Demonstrated an independent assessment of observed ERF since 1985.
- Showed a substantial increase in ERF since 2021, not offset by radiative response until 2024.
Conclusions:
- The study provides a novel, observationally constrained estimate of effective radiative forcing.
- Findings align with physical understanding of radiative feedbacks and recent exceptional warmth.
- Advances the ability to close the Earth's energy budget on annual timescales.
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