Comment on egusphere-2026-2656
Abstract
Abstract. 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. 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). 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. 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.
Authors 11
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Nick Schutgens corresponding
Affiliation as printed
Vrije Universiteit, 1081 HV Amsterdam, the Netherlands
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National Oceanic and Atmospheric Administration · Cooperative Institute for Research in Environmental Sciences · University of Colorado Boulder · University of Colorado System · NOAA Global Monitoring Laboratory
Affiliation as printed
CIRES, University of Colorado, Boulder CO 80309, USA
NOAA/Global Monitoring Laboratory, Boulder CO 80305, USA
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Finnish Meteorological Institute
Affiliation as printed
FMI, P.O. Box 1627, FI-70211 Kuopio, Finland
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Space Research Organisation Netherlands
Affiliation as printed
SRON Space Research Organisation Netherlands, 2333 CA Leiden, the Netherlands
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Space Research Organisation Netherlands
Affiliation as printed
SRON Space Research Organisation Netherlands, 2333 CA Leiden, the Netherlands
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Space Research Organisation Netherlands
Affiliation as printed
SRON Space Research Organisation Netherlands, 2333 CA Leiden, the Netherlands
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Finnish Meteorological Institute
Affiliation as printed
FMI, P.O. Box 1627, FI-70211 Kuopio, Finland
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Finnish Meteorological Institute
Affiliation as printed
FMI, P.O. Box 1627, FI-70211 Kuopio, Finland
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Finnish Meteorological Institute
Affiliation as printed
FMI, P.O. Box 1627, FI-70211 Kuopio, Finland
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Met Office · University of Leeds
Affiliation as printed
Centre for Environmental Modelling and Computation, University of Leeds, Leeds, UK
Met Office Hadley Centre, Exeter, UK
School of Earth and Environment, University of Leeds, University of Leeds, Leeds, UK
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University of Maryland, Baltimore · Goddard Space Flight Center
Affiliation as printed
Goddard Earth Sciences Technology And Research (GESTAR) II, University of Maryland, Baltimore, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
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