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Anthropogenic aerosols are an important driver of historical climate change but the climate response is not fully understood and the climate model simulations suffer large uncertainties. On the basis of a multimodel ensemble of historical aerosol forcing simulation for a period of global aerosol increase during 1965 to 1989, here, we show that the precipitation response shares a common southward displacement of tropical rain bands but the magnitude differs markedly among models, accounting for 76% of the intermodel uncertainty in zonal-mean precipitation change. Our analysis of atmospheric energetics further reveals key mechanisms for magnitude uncertainty: aerosol radiative forcing drives, cloud radiative feedback amplifies, and ocean circulation damps the intermodel uncertainty in cross-equatorial atmospheric energy transport change and the meridional shift of tropical rain bands. This has important implications for understanding and reducing intermodel uncertainty in anthropogenic climate change.
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http://dx.doi.org/10.1126/sciadv.adv5013 | DOI Listing |
Sci Adv
September 2025
Laoshan Laboratory, Qingdao, China.
Anthropogenic aerosols are an important driver of historical climate change but the climate response is not fully understood and the climate model simulations suffer large uncertainties. On the basis of a multimodel ensemble of historical aerosol forcing simulation for a period of global aerosol increase during 1965 to 1989, here, we show that the precipitation response shares a common southward displacement of tropical rain bands but the magnitude differs markedly among models, accounting for 76% of the intermodel uncertainty in zonal-mean precipitation change. Our analysis of atmospheric energetics further reveals key mechanisms for magnitude uncertainty: aerosol radiative forcing drives, cloud radiative feedback amplifies, and ocean circulation damps the intermodel uncertainty in cross-equatorial atmospheric energy transport change and the meridional shift of tropical rain bands.
View Article and Find Full Text PDFNat Commun
August 2025
Key Laboratory of Ocean Observation and Forecasting & Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.
The El Niño-Southern Oscillation (ENSO) profoundly impacts global climate, but its sea surface temperature (SST) variability projected by climate models remains uncertain, with a substantial inter-model spread in 21st-century projections. Model-observation discrepancies in ENSO physics contribute to this uncertainty, necessitating observational constraints to refine projections. However, methods to achieve this constraint remain unclear.
View Article and Find Full Text PDFEnergy Clim Chang
November 2024
U.S. Environmental Protection Agency, 1200 Pennsylvania Avenue, SW, Washington, DC 20585, United States of America.
Carbon dioxide and non-greenhouse gas air pollutants are emitted from many of the same sources. Decarbonization actions thus typically yield air pollutant emission reductions, resulting in significant air quality benefits. Although several studies have highlighted this connection, including in the context of net zero carbon emission targets, substantial uncertainty remains regarding how alternative technological pathways to this goal will affect the spatial distribution and magnitude of air pollutants.
View Article and Find Full Text PDFNat Geosci
April 2025
Finnish Meteorological Institute, Kuopio, Finland.
The susceptibility of cloud droplet number to cloud condensation nuclei number is one of the major factors controlling the highly uncertain change in the amount of solar radiation reflected by clouds when aerosol emissions are perturbed (the radiative forcing due to aerosol-cloud interactions). We investigate this susceptibility in low-level stratiform clouds using long-term (3-10-yr) in situ observations of aerosols and clouds at three high-latitude locations. The in situ observations show higher susceptibility for low-level stratiform clouds than values reported for satellite data.
View Article and Find Full Text PDFSci Adv
March 2025
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences & Key Laboratory of Polar Atmosphere-Ocean-Ice System for Weather and Climate, Ministry of Education, Fudan University, 200438 Shanghai, China.
Credible projections of Arctic warming and wetting (AWW) are essential for informed decision-making in a changing climate. However, current AWW projections from state-of-the-art climate models carry uncertainties. Using observational datasets and CMIP6 model simulations, we demonstrate that the observed historical global warming trend and the climatological mean pattern of Arctic sea ice can serve as effective constraints on AWW projections.
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