Abstract
Abstract. Climate sensitivity in Earth system models (ESMs) is an emergent property that is affected by structural (missing or inaccurate model physics) and parametric (variations in model parameters) uncertainty. This work provides the first quantitative assessment of the role of compensation between uncertainties in aerosol forcing and atmospheric parameters, and their 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 aerosol radiative forcing and distribution, as well as five atmospheric parameters related to clouds, convection, and radiative flux. In this experimental setup where aerosols do not affect the properties of clouds, the atmospheric parameters explain the majority of variance in climate sensitivity, with two parameters being the most important: one controlling low cloud amount, and one controlling the timescale for deep convection. Although the aerosol parameters strongly affect aerosol optical depth, their impacts on climate sensitivity are substantially weaker than the impacts of the atmospheric parameters, but this result may depend on whether aerosol–cloud interactions are simulated. Based on comparisons to inter-model spread of other ESMs, we conclude that structural uncertainties in this configuration of CAM4 likely contribute 3 times more to uncertainty in climate sensitivity than parametric uncertainties. We provide several parameter sets that could provide plausible (measured by a skill score) configurations of CAM4, but with different sulfate aerosol radiative forcing, black carbon radiative forcing, and climate sensitivity.
Highlights
Climate models are exceptional tools for understanding contributions of different forcing agents to climate change
A recent, and highly relevant, contribution is that of Regayre et al (2018), who show that uncertainties in historical aerosol radiative forcing, and topof-atmosphere shortwave radiative flux, in a comprehensive chemistry-climate model are controlled by a combination of aerosol parameters and emissions, as well as uncertain atmospheric parameters
A multivariate skill score is used to determine the plausibility of each combination of parameters, and to constrain plausible parameter ranges, and the spread of an important emergent property of the model: its climate sensitivity (λ)
Summary
Climate models are exceptional tools for understanding contributions of different forcing agents to climate change. A recent, and highly relevant, contribution is that of Regayre et al (2018), who show that uncertainties in historical aerosol radiative forcing, and topof-atmosphere shortwave radiative flux, in a comprehensive chemistry-climate model are controlled by a combination of aerosol parameters and emissions, as well as uncertain atmospheric parameters Their results show that, in recent decades, constraining aerosol and atmospheric parameters allows regional climate impacts of aerosols to be more faithfully reproduced. While the conceptual idea of the Kiehl curve has existed for more than a decade, this work is, to our knowledge, the first explicit attempt to move a single climate model to different parts of the curve, and to quantify the impact on climate sensitivity We stress that this contribution does not tackle the problem of calibration to observations: all simulations are conducted as perturbations to preindustrial (pre-1850) conditions, and are compared to the default version of CAM4 as a reference. The idea from this proof-of-concept study is to configure a series of candidate models with high and low CS, which could be run with interactive ocean components, to produce transient RCP-type simulations (historical and future scenarios) beginning in the preindustrial era
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