Observation-based estimate of Earth's effective radiative forcing

• 成果类型: Article
• 署名作者: Van Loon, Senne; Rugenstein, Maria; Barnes, Elizabeth A.
• 署名单位: Colorado State University System; Colorado State University Fort Collins
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11014
• DOI: 10.1073/pnas.2425445122
• 发表日期: 2025-06-10
• 关键词: internal variability; climate sensitivity; SYSTEM; feedback; dependence; patterns; driven; FUTURE; cmip5; state

摘要

Human emissions continue to influence Earth's climate. Effective radiative forcing quantifies the effect of such anthropogenic emissions together with natural factors on Earth's energy balance. Evaluating the exact rate of effective radiative forcing is challenging, because it can not be directly observed. Therefore, estimating the effective forcing usually relies on climate models. Here, we present an estimate of effective radiative forcing that makes optimal use of observations. We use machine learning to learn the relationship between surface temperature and radiation caused by internal variability in a multimodel ensemble. Combining this with observations of surface temperature and the Earth's net radiative imbalance, we predict an effective forcing trend of 0.71 +/- 0.21 Wm-2 per decade for 2001-2024. This is an independent assessment of the observed effective radiative forcing since 1985, that can be updated simultaneously with available observations and aligns with our physical understanding of radiative feedbacks. We make advances to close the Earth's energy budget on annual timescales, by separating the influence of forcing versus the radiative response to surface temperature variations. Effective radiative forcing has substantially increased since 2021 and has not been countered by a strongly negative radiative response until 2024, consistent with exceptional warmth in 2023 and 2024.