A novel and efficient method for computing the shortwave direct radiative effect of above-cloud aerosol

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Monday, 3 February 2014
Hall C3 (The Georgia World Congress Center )
Zhibo Zhang, University of Maryland, Baltimore, MD; and K. Meyer, S. Platnick, and L. Oreopoulos

The shortwave direct radiative effect (DRE) of aerosols at TOA is strongly dependent on the underlying surface. Over dark surfaces (e.g. ocean surface), the scattering effect of aerosols is generally dominant, leading to negative DRE (i.e., cooling) at TOA (Yu et al. 2006). In contrast, when light-absorbing aerosols reside above clouds, aerosol absorption is significantly amplified by cloud reflection, which offsets or even exceeds the scattering effect of aerosol leading to less negative or even positive (i.e., warming) TOA DRE (Keil and Haywood 2003; Abel et al. 2005). In order to understand the full complexity of aerosol radiative effects on climate, it is important to quantify the DRE under both clear-sky and cloudy-sky conditions. While the DRE of aerosols in clear-sky regions is well studied and relatively well constrained based on satellite remote sensing data (Yu et al. 2006), the cloudy-sky DRE is still poorly understood. Globally averaged cloudy-sky radiative forcing associated with anthropogenic aerosols simulated in nine AeroCom models show a large spread ranging from -0.16 (cooling) to +0.34 W m-2 (warming), which is much larger than inter-model diversity in aerosol load, aerosol optical depth and clear-sky radiative forcing (Schulz et al. 2006). There is a clear need for an observational constraint on the DRE of above-cloud aerosols (ACA). Recently, we have developed a flexible and efficient method to compute the ACA DRE using CALIOP aerosol and MODIS cloud observations. Our method has several unique features: 1) it takes sub-grid scale cloud and aerosol variation into account in DRE computation 2) it treats the overlapping of aerosol and cloud rigorously; 3) it has high computational efficiency. In this talk, we will introduce the key assumption made in our method and main features that make our method substantially different from those in previous studies.