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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 CO2in 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 CO2doubling when estimated using the historical GSAT trend. Does the climate respond in the same way to carbon dioxide as it does to methane or aerosol changes? The simple way of thinking about forcing and response in the Earth system assumes that it does, such that, a Watt per square meter forcing from CO2has the same response as an equivalent forcing from aerosols. Recent work has suggested that this might not be true and that differences in how effective different forcings are at increasing surface temperature (their efficacy) may account for a low estimate of climate sensitivity when examining historical change. We show this all depends on how you estimate your Watts per meter squared forcing in the first place. Using the effective radiative forcing concept to estimate forcing strength makes temperature changes far more predictable, and a lot of these issues with efficacy variation are not as pronounced as they were with earlier definitions. Multimodel mean efficacies computed using effective radiative forcing are close to one for major anthropogenic drivers, but there is notable intermodel spreadSurface temperature‐driven radiative feedbacks are generally not constant through time across forcing experimentsPDRMIP results suggest that the efficacy impact on equilibrium climate sensitivity derived from the historical period is limited to ±0.4 °C or better

Future projections of east Amazonian precipitation indicate drying, but they are uncertain and poorly understood. In this study we analyze the Amazonian precipitation response to individual atmospheric forcings using a number of global climate models. Black carbon is found to drive reduced precipitation over the Amazon due to temperature‐driven circulation changes, but the magnitude is uncertain. CO2drives reductions in precipitation concentrated in the east, mainly due to a robustly negative, but highly variable in magnitude, fast response. We find that the physiological effect of CO2on plant stomata is the dominant driver of the fast response due to reduced latent heating and also contributes to the large model spread. Using a simple model, we show that CO2physiological effects dominate future multimodel mean precipitation projections over the Amazon. However, in individual models temperature‐driven changes can be large, but due to little agreement, they largely cancel out in the model mean. Climate models show that rainfall in the eastern Amazon may decrease during the 21st century; however, the changes are uncertain and there are many factors which could affect rainfall in the region. In this study we use a range of global climate model experiments to investigate how Amazonian rainfall responds to different drivers, such as carbon dioxide in the atmosphere. We find that increasing carbon dioxide reduces east Amazonian rainfall, and this is due to the response of plant stomata to carbon dioxide. Plant stomata do not open as wide when carbon dioxide is increased, which is known as the physiological effect. The physiological effect reduces evaporation from plants which means that there is less moisture available to fuel rainfall. We construct a simple model to estimate future rainfall changes over the Amazon to help fully understand the importance of physiological effects. The simple model shows that the physiological effect of carbon dioxide is the main driver of future drying over the eastern Amazon. This implies that future changes in rainfall are independent of how much the climate warms. Our findings show the importance of improving understanding of how plants affect atmospheric processes. Increased carbon dioxide consistently drives reduced eastern and central Amazonian precipitation in global climate modelsProjected Amazonian precipitation changes are dominated by the carbon dioxide physiological effectHighlights importance of reducing uncertainties associated with vegetation schemes

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. Important physical and quantitative differences in precipitation adjustment and feedback results arise due to computation methodFixed SST method provides a consistent and clear mechanistic decompositionFixed SST method is less dependent on methodological choices and exhibits less variability

An amendment to this paper has been published and can be accessed via a link at the top of the paper.