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. Climate sensitivity in Earth system models (ESMs) is an emergentproperty that is affected by structural (missing or inaccurate model physics)and parametric (variations in model parameters) uncertainty. This workprovides the first quantitative assessment of the role of compensationbetween uncertainties in aerosol forcing and atmospheric parameters, andtheir impact on the climate sensitivity of the Community Atmosphere Model,Version 4 (CAM4). Running the model with prescribed ocean and ice conditions,we perturb four parameters related to sulfate and black carbon aerosolradiative forcing and distribution, as well as five atmospheric parametersrelated to clouds, convection, and radiative flux. In this experimental setupwhere aerosols do not affect the properties of clouds, the atmosphericparameters explain the majority of variance in climate sensitivity, with twoparameters being the most important: one controlling low cloud amount, andone controlling the timescale for deep convection. Although the aerosolparameters strongly affect aerosol optical depth, their impacts on climatesensitivity are substantially weaker than the impacts of the atmosphericparameters, but this result may depend on whether aerosol–cloud interactionsare simulated. Based on comparisons to inter-model spread of other ESMs, weconclude that structural uncertainties in this configuration of CAM4 likelycontribute 3 times more to uncertainty in climate sensitivity thanparametric uncertainties. We provide several parameter sets that couldprovide plausible (measured by a skill score) configurations of CAM4, butwith different sulfate aerosol radiative forcing, black carbon radiativeforcing, and climate sensitivity.

Quantifying uncertainty from aerosol and atmospheric parameters and their impact on climate sensitivity