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

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).