Abstract
Abstract. 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.
Highlights
Boundary layer clouds have been identified as a major source of uncertainty in climate sensitivity and climate change (Bony and Dufresne, 2006; Medeiros et al, 2008)
Measurements of aerosol and cloud properties are taken from the Department of Energy (DOE) deployment of the Atmospheric Radiation Measurement (ARM) Mobile Facility to Pt
We have shown that for processes such as the albedo effect that operate on the microphysical scale, the use of aggregated data results in errors of statistics and sampling, leading to biases in associated radiative forcing estimates
Summary
Boundary layer clouds have been identified as a major source of uncertainty in climate sensitivity and climate change (Bony and Dufresne, 2006; Medeiros et al, 2008). Aerosol has potentially substantial impacts on cloud radiative forcing (“aerosol indirect effects”), cloud-climate feedbacks, and water resources through changing patterns of precipitation; quantifying the associated mechanisms and impacts through observation, and representing those processes in models, has proven to be extremely challenging. The sensitivity of cloud microphysical (and albedo) response to an increase in aerosol is still a matter of much debate, and at the heart of this study. The sign of this forcing is agreed to be negative but a large uncertainty in the estimated magnitude has persisted through time (Lohmann et al, 2010). A few studies have produced purely observational estimates of the first indirect effect radiative forcing (e.g., Quaas et al, 2008; Lebsock et al, 2008) and inverse calculations based on observations have been performed
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