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

Journal Article · Mon Nov 21 04:00:00 EST 2022 · Proceedings of the National Academy of Sciences of the United States of America

DOI:https://doi.org/10.1073/pnas.2209431119· OSTI ID:1898925

Po-Chedley, Stephen ^[1]; Fasullo, John T. ^[2]; Siler, Nicholas ^[3]; Labe, Zachary M. ^[4]; Barnes, Elizabeth A. ^[4]; Bonfils, Céline J. W. ^[1]; Santer, Benjamin D. ^[5]

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1. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550
2. Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, CO 80305
3. College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331
4. Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523
5. Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA 90095, Physical Oceanography Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543

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⋅decade ⁻¹ between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade ⁻¹ . 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⋅decade ⁻¹ . 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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1898925
• Alternate ID(s): OSTI ID: 1899434; OSTI ID: 1903578
• Journal Information: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 47 Vol. 119; ISSN 0027-8424
• Publisher: Proceedings of the National Academy of Sciences
• Country of Publication: United States
• Language: English