Quantifying biases in aerosol-cloud-radiation interactions associated with spatiotemporal averaging

Aerosol-cloud interactions represent one of the largest sources of error in projections of future climate states. Quantification is uncertain in part because of the inability of climate models to resolve clouds, aerosol, and their interaction at important small scales, and in part because of uncertainties in satellite-based retrievals of key cloud properties. Model output and satellite data are typically aggregated to monthly mean averages, over areas on the order of 100 km, and inferences regarding radiative forcing or effect are calculated based thereon. Here we consider another source of uncertainty, namely the spatiotemporal averaging of aerosol and cloud fields and how it creates biases in the strength of albedo response to aerosol perturbations. We quantify this bias for a range of cloud and aerosol conditions through the use of simple ad hoc modeling exercises, as well as satellite-based observations.