Temporal autocorrelations of aerosol are often reported, but poorly understood. We use simple box models, Perturbed Parameters Ensembles of global aerosol models, AEROCOM (AEROsol Comparison of Observations and Models) simulations as well as satellite and AERONET (AErosol RObotic NETwork) observations to study temporal autocorrelations in aerosol optical depth (AOD). In particular, we present the first global climatology of observed temporal autocorrelations.</p> <p>We develop a conceptual model for autocorrelations and relate them to important timescales, in particular lifetimes. We identify aerosol processes that affect autocorrelations and find autocorrelations provide information independent from yearly AOD, in particular on deposition processes. It is possible to estimate temporal autocorrelations in AOD from satellite observations by sensors like MODIS (MODerate resolution Imaging Spectroradiometer) or POLDER (POLarization and Directionality of the Earth’s Reflectances).</p> <p>In our unique global climatology of observed temporal autocorrelations, regional variation is significant. Over remote oceans, the autocorrelation after 6 days tends to be low (∼ 0.2 or lower) but it is quite high in tropical outflow regions (∼ 0.5). Over land, it varies considerably, from 0.2 to 0.7. These spatial variations are much larger than observed year-to-year variation.</p> <p>AEROCOM models often significantly overestimate autocorrelations. This suggests that loss processes are underestimated and/or contributions from seasonal sources are overestimated, which should have a marked impact on aerosol forcing estimates. Autocorrelations offer a new way to understand aerosol processes and evaluate models. Autocorrelations can be derived from existing observational datasets, for example surface black carbon mass concentrations or cloud condensation nuclei.