Climate Models Underestimate Global Decreases in High‐Cloud Amount With Warming DOI Creative Commons
Sarah Wilson Kemsley, Peer Nowack, Paulo Ceppi

et al.

Geophysical Research Letters, Journal Year: 2025, Volume and Issue: 52(7)

Published: April 9, 2025

Abstract Cloud feedback has prevailed as a leading source of uncertainty in climate model projections under increasing atmospheric carbon dioxide. Cloud‐controlling factor (CCF) analysis is an approach used to observationally constrain cloud feedback, and subsequently the sensitivity. Although high clouds contribute significantly toward uncertainty, they have received comparatively little attention CCF other observational analyses. Here we use for first time ‐cloud radiative focusing on amount component owing its dominant contribution high‐cloud feedback. Globally, observations indicate larger decreases cloudiness than state‐of‐the‐art models suggest. In fact, half 16 considered here predict feedbacks inconsistent with observations, likely due misrepresenting stability iris mechanism. Despite suggested strong warming, point near‐neutral net almost canceling longwave shortwave contributions.

Language: Английский

Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels DOI Creative Commons
Mark D. Zelinka, Li‐Wei Chao, Timothy A. Myers

et al.

Atmospheric chemistry and physics, Journal Year: 2025, Volume and Issue: 25(3), P. 1477 - 1495

Published: Feb. 3, 2025

Abstract. The cloud radiative kernel method is a popular approach to quantify feedbacks and rapid adjustments increased CO2 concentrations partition contributions from changes in amount, altitude, optical depth. However, because this relies on property histograms derived passive satellite sensors or produced by simulators models, obscuration of lower-level clouds upper-level can cause apparent low-cloud adjustments, even the absence properties. Here, we provide methodology for properly diagnosing impact changing these effects across climate models. Averaged globally global accounting leads weaker positive stronger while simultaneously removing mostly artificial anti-correlation between them. Given that using kernels has evolved over several papers, have only occasionally been considered recent paper serves establish recommended best practices corresponding code base community use.

Language: Английский

Citations

1
Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective DOI Creative Commons
Peer Nowack, Duncan Watson‐Parris

Atmospheric chemistry and physics, Journal Year: 2025, Volume and Issue: 25(4), P. 2365 - 2384

Published: Feb. 21, 2025

Abstract. Global climate change projections are subject to substantial modelling uncertainties. A variety of emergent constraints, as well several other statistical model evaluation approaches, have been suggested address these However, they remain heavily debated in the science community. Still, central idea relate future already observable quantities has no real substitute. Here, we highlight validation perspective predictive skill machine learning community a promising alternative viewpoint. Specifically, argue for quantitative approaches which each constraining relationship can be evaluated comprehensively based on out-of-sample test data – top qualitative physical plausibility arguments that commonplace justification new constraints. Building this perspective, review ideas types controlling-factor analyses (CFAs). The principal behind CFAs is use find climate-invariant relationships historical hold approximately under strong scenarios. On basis existing archives, validated perfect-climate-model frameworks. From such three reasons: (a) objectively both past and data, (b) provide more direct and, by design, physically plausible links between observations potential climates, (c) take high-dimensional complex into account functions learned constrain response. We demonstrate advantages two recently published CFA examples form constraints feedback mechanisms (clouds, stratospheric water vapour) discuss further challenges opportunities using example rapid adjustment mechanism (aerosol–cloud interactions). avenues work, including strategies non-linearity, tackle blind spots ensembles, integrate helpful priors Bayesian methods, leverage physics-informed learning, enhance robustness through causal discovery inference.

Language: Английский

Citations

1
Climate Models Underestimate Global Decreases in High‐Cloud Amount With Warming DOI Creative Commons
Sarah Wilson Kemsley, Peer Nowack, Paulo Ceppi

et al.

Geophysical Research Letters, Journal Year: 2025, Volume and Issue: 52(7)

Published: April 9, 2025

Abstract Cloud feedback has prevailed as a leading source of uncertainty in climate model projections under increasing atmospheric carbon dioxide. Cloud‐controlling factor (CCF) analysis is an approach used to observationally constrain cloud feedback, and subsequently the sensitivity. Although high clouds contribute significantly toward uncertainty, they have received comparatively little attention CCF other observational analyses. Here we use for first time ‐cloud radiative focusing on amount component owing its dominant contribution high‐cloud feedback. Globally, observations indicate larger decreases cloudiness than state‐of‐the‐art models suggest. In fact, half 16 considered here predict feedbacks inconsistent with observations, likely due misrepresenting stability iris mechanism. Despite suggested strong warming, point near‐neutral net almost canceling longwave shortwave contributions.

Language: Английский

Citations

0