Future precipitation increase constrained by climatological pattern of cloud effect.

The fractional increase in global mean precipitation ([Formula: see text]) is a first-order measure of the hydrological cycle intensification under anthropogenic warming. However, [Formula: see text] varies by a factor of more than three among model projections, hindering credible assessments of the associated climate impacts. The uncertainty in [Formula: see text] stems from uncertainty in both hydrological sensitivity (global mean precipitation increase per unit warming) and climate sensitivity (global mean temperature increase per forcing). Here, by investigating hydrological and climate sensitivities in a unified surface-energy-balance perspective, we find that both sensitivities are significantly correlated with surface shortwave cloud feedback, which is further linked to the climatological pattern of cloud shortwave effect. The observed pattern of cloud effect thus constrains both sensitivities and consequently constrains [Formula: see text]. The 5%-95% uncertainty range of [Formula: see text] from 1979-2005 to 2080-2100 under the high-emission (moderate-emission) scenario is constrained from 6.34[Formula: see text]3.53% (4.19[Formula: see text]2.28%) in the raw ensemble-model projection to 7.03[Formula: see text]2.59% (4.63[Formula: see text]1.71%). The constraint thus suggests a higher most-likely [Formula: see text] and reduces the uncertainty by ~25%, providing valuable information for impact assessments.

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

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.