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New paper finds climate model assumptions on cloud-aerosol interactions may be off by 100%: Published in Atmospheric Chemistry and Physics


New paper finds climate model assumptions on cloud-aerosol interactions may be off by 100%


A paper published today in Atmospheric Chemistry and Physics demonstrates the huge uncertainties of computer modeling of aerosol–cloud interaction effects, which are one of the “major sources of uncertainty in climate models.” According to the authors, the standard deviation around the mean cloud condensation nuclei varies globally between a minimum of about ± 30% over some marine regions to ± 40–100% over most land areas and high latitudes. This is only one of the factors affecting clouds in climate models, and clouds are but one of the many major uncertainties in climate models. 

Dr. Judith Curry points out why climate models might be wrong in her “uncertainty monster” paper, and has pointed out for years the need for realistic assessments of the uncertainty of climate projections. This paper takes one step in that much need direction and shows only a tiny fraction [but still huge] of the “uncertainty monster.” Meanwhile, the IPCC remains blissfully ignorant of the “uncertainty monster” and increases its confidence level from 90 to 95% without any basis in statistical analysis or science. 

Atmos. Chem. Phys., 13, 8879-8914, 2013www.atmos-chem-phys.net/13/8879/2013/doi:10.5194/acp-13-8879-2013

The magnitude and causes of uncertainty in global model simulations of cloud condensation nucleiL. A. Lee1, K. J. Pringle1, C. L. Reddington1, G. W. Mann1, P. Stier2, D. V. Spracklen1, J. R. Pierce3, and K. S. Carslaw11Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK2Department of Physics, University of Oxford, Oxford, UK3Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USAAbstract. 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.

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