Recent cloud trends and extremes reaffirm established bounds on cloud feedback and aerosol-cloud interactions
Year: 2026
Type: article
Abstract: Earth's energy imbalance exhibits a robust positive trend over the past two decades, reaching a record value in 2023. Both the trend and year-to-year anomalies are primarily attributable to decreased reflection of sunlight by low-level clouds, which is pronounced over the Northern Hemisphere oceans. However, the causes of these low-cloud anomalies and their implications for future Earth system evolution are unknown, leading to speculation that the cloud feedback may be larger than expected. Here we quantify the meteorological factors that have driven interannual cloud-radiative anomalies, several of which aligned to cause the extreme global value observed in 2023. These meteorological variations are superposed on a background of decreasing sulfate aerosol concentrations, leading to a robust trend of reduced cloud reflection over the past 22 years. Constraints on cloud feedback and aerosol forcing implied by these observations support previous analyses and reaffirm an equilibrium climate sensitivity near 3°C with a likely (66% confidence) range of 2.7 to 4.1°C. Hence recent observations do not imply that a stronger-than-expected cloud feedback is emerging in nature or that future warming has been underestimated.
Institutions: Lawrence Livermore National Laboratory, National Oceanic and Atmospheric Administration, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, NOAA Physical Sciences Laboratory, Pacific Northwest National Laboratory, Laoshan Laboratory, Stockholm University, Bolin Centre for Climate Research, Imperial College London
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $3,490
Funders: U.S. Department of Energy, U.S. Department of Commerce, Battelle, Government of the United Kingdom, UK Research and Innovation, National Energy Research Scientific Computing Center, European Commission, National Oceanic and Atmospheric Administration, Office of Science, HORIZON EUROPE Framework Programme, Natural Environment Research Council, Biological and Environmental Research, Lawrence Livermore National Laboratory, Pacific Northwest National Laboratory
Awards: Machine learning approaches to constrain and understand the role of clouds in climate change (ML4CLOUDS), G1202769797, G125460123, G1272229657, G1356433355, G138090931, G14554683, G1899721271, G2145270357, G2236757247, G2506945562, G2510423872, G2812390658, G2820049545, G2988778103, G3063447758, CIRCULATES: A comprehensive investigation of clouds, circulation and constraints on climate sensitivity, G3669113783, G3861279505, G391136020, G4932404565, G5321382714, G5373841109, G5726405315, G574795485, G5869080520, G6886818189, G7198630863, G7665161096, G7749092492, G8135867528, G8163035529, G821644656, G8506127299, G8520247653, G8612102656, G895790057, G903483436, G991281326
OpenAlex ID: W7143797235