Senne Van Loon, Maria Rugenstein, Elizabeth A Barnes
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引用次数: 0
Abstract
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 [Formula: see text] 0.21 Wm[Formula: see text] 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.
期刊介绍:
The Proceedings of the National Academy of Sciences (PNAS), a peer-reviewed journal of the National Academy of Sciences (NAS), serves as an authoritative source for high-impact, original research across the biological, physical, and social sciences. With a global scope, the journal welcomes submissions from researchers worldwide, making it an inclusive platform for advancing scientific knowledge.