Comparisons between climate models and satellite observations consistently find that simulated warming of the tropical troposphere outpaces observations after 1979. There are a number of factors that may contribute to this discrepancy. Using machine learning and large ensembles of climate model simulations, we find that internal variations in Earth’s climate have, by chance, reduced real-world tropospheric warming over the satellite era. A spurious discontinuity in prescribed biomass-burning aerosol emissions has also artificially enhanced simulated warming. These two effects largely explain the difference between simulated and observed tropical tropospheric warming. This offsetting effect of internal climate variability on greenhouse warming cannot, however, be relied on to reduce future warming and may instead lead to periods of accelerated change.
Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming in the most recent generation of climate models and are important considerations when evaluating climate models. Moreover, discrepancies between models and observations of recent warming should not be interpreted as an indication of excessive climate sensitivity unless these effects are first accounted for.
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 mid-troposphere (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.