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An observationally constrained time series of historical aerosol effective radiative forcing (ERF) from 1750 to 2019 is developed in this study. We find that the time history of aerosol ERFs diagnosed in CMIP6 models exhibits considerable variation and explore how the time history of aerosol forcing influences the probability distributions of present‐day aerosol forcing and emergent metrics such as climate sensitivity. Using a simple energy balance model, trained on CMIP6 climate models and constrained by observed near‐surface warming and ocean heat uptake, we derive estimates for the historical aerosol forcing. We find 2005–2014 mean aerosol ERF to be −1.1 (−1.8 to −0.5) W m−2 relative to 1750. Assuming recently published historical emissions from fossil fuel and industrial sectors and biomass burning emissions from SSP2‐4.5, aerosol ERF in 2019 is −0.9 (−1.5 to −0.4) W m−2. There is a modest recovery in aerosol forcing (+0.025 W m−2 decade−1) between 1980 and 2014. This analysis also gives a 5%–95% range of equilibrium climate sensitivity of 1.8°C –5.1°C (best estimate 3.1°C) with a transient climate response of 1.2°C –2.6°C (best estimate 1.8°C). Plain Language Summary: There are two main human drivers of climate change: (a) Greenhouse gas emissions, which warm the planet; and (b) air pollution (aerosols) that offset some of this warming. Unfortunately, disentangling the effects of historical aerosol cooling is difficult based on the available observations. Therefore, we often use climate models to estimate how much aerosols have cooled the Earth since the start of the Industrial Revolution. Over the mid‐to‐late 20th Century, some climate models simulate less warming compared to 1850 than has been observed. This may be because aerosol cooling in some climate models is too strong. Our approach combines the relationships between aerosol emissions and their cooling effects on temperature from 11 climate models with simpler representations of the underlying physics. This simpler mathematical framework allows us to more fully account for uncertainty in both aerosol cooling and its effects on surface temperature and ocean heat uptake by running a much larger set of simulations. Our results suggest that the effect of aerosol cooling has only unwound slowly since 1980, and that it is difficult to determine how sensitive the climate is from this method. Key Points: We determine the most plausible time history of aerosol forcing that matches surface temperature and Earth energy uptake constraintsConstrained aerosol forcing shows a modest recovery between 1980 and 2014, slower than the rate simulated by many CMIP6 modelsThe best estimate aerosol forcing using this method is −1.10 W m−2 for 2005–2014 relative to 1750 [ABSTRACT FROM AUTHOR]

Black carbon (BC) aerosols influence precipitation through a range of processes. The climate response to the presence of BC is however highly dependent on its vertical distribution. Here, we analyze the changes in the energy budget and precipitation impacts of adding a layer of BC at a range of altitudes in two independent global climate models. The models are run with atmosphere‐only and slab ocean model setup to analyze both fast and slow responses, respectively. Globally, precipitation changes are tightly coupled to the energy budget. We decompose the precipitation change into contributions from absorption of solar radiation, atmospheric longwave radiative cooling, and sensible heat flux at the surface. We find that for atmosphere‐only simulations, BC rapidly suppresses precipitation, independent of altitude, mainly because of strong atmospheric absorption. This reduction is offset by increased atmospheric radiative longwave cooling and reduced sensible heat flux at the surface, but not of sufficient magnitude to prevent reduced precipitation. On longer timescales, when the surface temperature is allowed to respond, we find that the precipitation increase associated with surface warming can compensate for the initial reduction, particularly for BC in the lower atmosphere. Even though the underlying processes are strikingly similar in the two models, the resulting change in precipitation and temperature by BC differ quite substantially. Plain Language Summary: Soot particles change precipitation by absorbing solar radiation and heating the surrounding air. The atmosphere rapidly adjusts to this added warming by changing relative humidity, clouds, and precipitation. We use two climate models to investigate these rapid adjustments in the atmosphere caused by soot particles. We insert soot particles in different vertical layers in the models and find that soot particles quickly warm the atmosphere and reduce precipitation. Soot particles at higher altitudes stabilize the atmosphere and increase cloud cover located below. Given all the processes soot particles influence in the atmosphere, the similarities in underlying processes by the two climate models are striking. The resulting change in precipitation and temperature differ quite substantially. Key Points: The strong atmospheric shortwave absorption by black carbon suppresses precipitationRapid adjustments decrease the direct radiative effect of black carbon in two independent modelsEven though the underlying processes are strikingly similar in the models, the resulting change in precipitation by black carbon differs [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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 CO2 has 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

We compare six methods of estimating effective radiative forcing (ERF) using a set of atmosphere‐ocean general circulation models. This is the first multiforcing agent, multimodel evaluation of ERF values calculated using different methods. We demonstrate that previously reported apparent consistency between the ERF values derived from fixed sea surface temperature simulations and linear regression holds for most climate forcings, excluding black carbon (BC). When land adjustment is accounted for, however, the fixed sea surface temperature ERF values are generally 10–30% larger than ERFs derived using linear regression across all forcing agents, with a much larger (~70–100%) discrepancy for BC. Except for BC, this difference can be largely reduced by either using radiative kernel techniques or by exponential regression. Responses of clouds and their effects on shortwave radiation show the strongest variability in all experiments, limiting the application of regression‐based ERF in small forcing simulations. Plain Language Summary: Climate drivers such as greenhouse gases and aerosols influence the Earth's climate by perturbing the Earth's energy budget at the top of the atmosphere, which is referred to as effective radiative forcing (ERF) when the atmospheric response is included in the calculation. ERF plays a crucial role in understanding the climate response to these drivers and predicting long‐term climate change. Previously, ERFs have been estimated for greenhouse gases using two techniques that generally lead to similar values. Here we show that such consistency holds for most climate drivers. ERF values estimated from different methods may differ by 10–50%, and this difference may reach 70–100% for black carbon. Regression techniques do not work well in some models when imposed forcings are relatively small. Key Points: ERF estimated using fixed SST simulations and linear regression are fairly consistent for most climate forcingsMultimodel mean ERF values vary by 10–50% with different methods, and this difference may reach 70–100% for black carbonInternal variability limits the application of linear regression technique in small‐forcing experiments [ABSTRACT FROM AUTHOR]