ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-12-1031-2012 The scale problem in quantifying aerosol indirect effects McComiskey A. 1 2 Feingold G. 2 Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, USA NOAA Earth System Research Laboratory, Boulder, USA 23 01 2012 12 2 1031 1049 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/12/1031/2012/acp-12-1031-2012.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/12/1031/2012/acp-12-1031-2012.pdf

A wide range of estimates exists for the radiative forcing of the aerosol effect on cloud albedo. We argue that a component of this uncertainty derives from the use of a wide range of observational scales and platforms. Aerosol influences cloud properties at the microphysical scale, or the "process scale", but observations are most often made of bulk properties over a wide range of resolutions, or "analysis scales". We show that differences between process and analysis scales incur biases in quantification of the albedo effect through the impact that data aggregation and computational approach have on statistical properties of the aerosol or cloud variable, and their covariance. Measures made within this range of scales are erroneously treated as equivalent, leading to a large uncertainty in associated radiative forcing estimates. Issues associated with the coarsening of observational resolution particular to quantifying the albedo effect are discussed. Specifically, the omission of the constraint on cloud liquid water path and the separation in space of cloud and aerosol properties from passive, space-based remote sensors dampen the measured strength of the albedo effect. We argue that, because of this lack of constraints, many of these values are in fact more representative of the full range of aerosol-cloud interactions and their associated feedbacks. Based on our understanding of these biases we propose a new observationally-based and process-model-constrained, method for estimating aerosol-cloud interactions that can be used for radiative forcing estimates as well as a better characterization of the uncertainties associated with those estimates.

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