The tropical belt has widened during the last several decades, and both internal variability and anthropogenic forcings have contributed. Although greenhouse gases and stratospheric ozone depletion have been implicated as primary anthropogenic drivers of tropical expansion, the possible role of other drivers remains uncertain. Here, we analyze the tropical belt width response to idealized perturbations in multiple models. Our results show that absorbing black carbon (BC) aerosol drives tropical expansion, and scattering sulfate aerosol drives contraction. BC, especially from Asia, is more efficient per unit radiative forcing than greenhouse gases in driving tropical expansion, particularly in the Northern Hemisphere. Tropical belt expansion (contraction) is associated with an increase (decrease) in extratropical static stability induced by absorbing (scattering) aerosol. Although a formal attribution is difficult, scaling the normalized expansion rates to the historical time period suggests that BC is the largest driver of the Northern Hemisphere tropical widening but with relatively large uncertainty. Plain Language Summary The tropical belt has widened over the past several decades, and this is associated with poleward movement of the descending branches of the Hadley Cell and the subtropical dry zones. Internal climate variability and anthropogenic forcers-including greenhouse gases and stratospheric ozone depletion-are important contributors. Leveraging idealized single-forcing experiments, we show that anthropogenic aerosols, including black carbon and sulfate, drive significant tropical expansion and contraction, respectively. Aerosols, particularly those emitted from Asia, are more efficient than greenhouse gases in perturbing tropical belt width. Although relatively large uncertainty exists, linearized scaling suggests that black carbon is the dominant driver of the Northern Hemisphere tropical widening over the historical time period.

Quantifying the efficacy of different climate forcings is important for understanding the real-world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near-surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy-induced bias of +/- 0.4 K in equilibrium climate sensitivity to CO2 doubling when estimated using the historical GSAT trend.

The precipitation adjustment and feedback framework is a useful tool for understanding global and regional precipitation changes. However, there is no definitive method for making the decomposition. In this study we highlight important differences which arise in results due to methodological choices. The responses to five different forcing agents (CO2, CH4, SO4, black carbon, and solar insolation) are analyzed using global climate model simulations. Three decomposition methods are compared: using fixed sea surface temperature experiments (fSST), regressing transient climate change after an abrupt forcing (regression), and separating based on timescale using the first year of coupled simulations (YR1). The YR1 method is found to incorporate significant SST-driven feedbacks into the adjustment and is therefore not suitable for making the decomposition. Globally, the regression and fSST methods produce generally consistent results; however, the regression values are dependent on the number of years analyzed and have considerably larger uncertainties. Regionally, there are substantial differences between methods. The pattern of change calculated using regression reverses sign in many regions as the number of years analyzed increases. This makes it difficult to establish what effects are included in the decomposition. The fSST method provides a more clear-cut separation in terms of what physical drivers are included in each component. The fSST results are less affected by methodological choices and exhibit much less variability. We find that the precipitation adjustment is weakly affected by the choice of SST climatology.

Precipitation is expected to respond differently to various drivers of anthropogenic climate change. We present the first results from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where nine global climate models have perturbed CO2, CH4, black carbon, sulfate, and solar insolation. We divide the resulting changes to global mean and regional precipitation into fast responses that scale with changes in atmospheric absorption and slow responses scaling with surface temperature change. While the overall features are broadly similar between models, we find significant regional intermodel variability, especially over land. Black carbon stands out as a component that may cause significant model diversity in predicted precipitation change. Processes linked to atmospheric absorption are less consistently modeled than those linked to top-of-atmosphere radiative forcing. We identify a number of land regions where the model ensemble consistently predicts that fast precipitation responses to climate perturbations dominate over the slow, temperature-driven responses.