Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming

Tools

Po-Chedley, Stephen, Fasullo, John T., Siler, Nicholas, Labe, Zachary M., Barnes, Elizabeth A., Bonfils, Céline J.W. and Santer, Benjamin D. (2022) Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming. Proceedings of the National Academy of Sciences of the United States of America, 119 (47). ISSN 0027-8424

[thumbnail of rba08-po-chedley-et-al-2022-int] Microsoft Word (rba08-po-chedley-et-al-2022-int) - Published Version
Available under License Creative Commons Attribution.
Download (1MB)

Abstract

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K.decade21 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K.decade21. Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 K.decade21. The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models.

Item Type: Article
Additional Information: Publisher Copyright: Copyright © 2022 the Author(s).
Uncontrolled Keywords: climate change,general circulation models,natural climate variability,satellite data,general ,/dk/atira/pure/subjectarea/asjc/1000
Faculty \ School: Faculty of Science > School of Environmental Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 18 Mar 2026 14:30
Last Modified: 19 Mar 2026 09:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/102433
DOI: 10.1073/pnas.2209431119

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item