Quantifying Albedo Susceptibility Biases in Shallow Clouds

Abstract. The evaluation of radiative forcing associated with aerosol-cloud interactions remains a significant source of uncertainty in future climate projections. The problem is confounded by the fact that aerosol particles influence clouds locally, and that averaging to larger spatial and/or temporal scales carries biases that depend on the heterogeneity and spatial correlation of the interacting fields and the non-linearity of the responses. Mimicking commonly applied satellite data analyses for calculation of albedo susceptibility So, we quantify So aggregation biases using an ensemble of 127 large eddy simulations of marine stratocumulus. We explore the cloud field properties that control this spatial aggregation bias, and quantify the bias for a large range of shallow stratocumulus cloud conditions manifesting a variety of morphologies and range of cloud fractions. We show that So spatial aggregation biases can be on the order of 100s of percent, depending on methodology. Key uncertainties emanate from the typically applied adiabatic drop concentration Nd retrieval, the correlation between aerosol and cloud fields, and the extent to which averaging reduces the variance in cloud albedo Ac and Nd. Biases are more often positive than negative. So biases are highly correlated to biases in the adjustment. Temporal aggregation biases are shown to offset spatial averaging biases. Both spatial and temporal biases have significant implications for observationally based assessments of aerosol indirect effects and our inferences of underlying aerosol-cloud-radiation effects.