The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei
By Lindsay A. Lee, K. Pringle, C. Reddington, G. Mann, P. Stier, Dominick V. Spracklen, J. Pierce, K. Carslaw. 2013
247 citations recorded by the source corpus.
Abstract. Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN). Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/e7f76dafbb5dcf64e6d46450a199416eb20323dc
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In Atmospheric Science
In Global and Planetary Change
Citations in the corpus
- Machine learning and artificial intelligence to aid climate change research and preparedness (2019, unknown): “Lee et al (2013) use a Gaussian process to emulate the impact of parameter uncertainty on climate model output, to determine sources of uncertainty in cloud-aerosol processes.”
- Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity (2014, background): “Lee et al. (2011) considered the effect on simulated CCN concentrations of co-varying eight parameters in a global aerosol microphysics model, showing that in the European free troposphere, simulated CCN concentrations are highly sensitive to parameters associated with the treatment of nucleation sc”