Po-Chedley Stephen

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Po-Chedley
First Name
Stephen
ORCID
0000-0002-0390-238X

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  • Article
    Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming
    (National Academy of Sciences, 2022-11-22) Po-Chedley, Stephen ; Fasullo, John T. ; Siler, Nicholas ; Labe, Zachary M. ; Barnes, Elizabeth A. ; Bonfils, Céline J. W. ; Santer, Benjamin D.
    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 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade. 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 . 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.
  • Article
    Exceptional stratospheric contribution to human fingerprints on atmospheric temperature
    (National Academy of Sciences, 2023-05-16) Santer, Benjamin D. ; Po-Chedley, Stephen ; Zhao, Lilong ; Zou, Cheng-Zhi ; Fu, Qiang ; Solomon, Susan ; Thompson, David W. J. ; Mears, Carl ; Taylor, Karl E.
    In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO2 should warm Earth’s troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth’s surface (S25 − 50). To date, however, S25 − 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a “fingerprint” study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S25 − 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S25 − 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S25 − 50 signal and noise patterns are accompanied by large cooling of S25 − 50 (1 to 2C over 1986 to 2022) and low S25 − 50 noise levels. Our results explain why extending “vertical fingerprinting” to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth’s atmosphere.