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Greater climate sensitivity implied by anvil cloud thinning
High clouds produced by tropical convection are expected to shrink in area as the climate warms, and the radiative feedback associated with this change has long been the subject of controversy. In a recent assessment of climate sensitivity, the World Climate Research Programme estimated that this fe...
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Published in: | Nature geoscience 2024-05, Vol.17 (5), p.398-403 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | High clouds produced by tropical convection are expected to shrink in area as the climate warms, and the radiative feedback associated with this change has long been the subject of controversy. In a recent assessment of climate sensitivity, the World Climate Research Programme estimated that this feedback is substantially negative, albeit with substantial uncertainty. Here we examine the cloud response using an approach that treats high clouds as part of an optical continuum rather than entities with fixed opacity. We show that a substantial negative feedback is not supported by an ensemble of high-resolution atmospheric models. Rather, the models suggest that changes in cloud area and opacity together act as a weakly positive feedback. The positive opacity component arises from the disproportionate reduction in the area of thick, climate-cooling clouds relative to thin, climate-warming clouds. This suggests that thick cloud area is tightly coupled to the rate of convective overturning—which is expected to slow with warming—whereas thin cloud area is influenced by other, less certain processes. The positive feedback differs markedly from previous estimates and leads to a +0.3 °C shift in the median estimate of equilibrium climate sensitivity relative to a previous community assessment.
Changes in anvil clouds with warming do not produce a negative feedback on climate sensitivity as previously thought, according to an ensemble of cloud-resolving models. |
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ISSN: | 1752-0894 1752-0908 |
DOI: | 10.1038/s41561-024-01420-6 |