The importance of temporal collocation for the evaluation of aerosol models with observations
Atmospheric Chemistry and Physics
European Geosciences Union (EGU) and Copernicus Publications
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It is often implicitly assumed that over suitably long periods the mean of observations and models should be comparable, even if they have different temporal sampling. We assess the errors incurred due to ignoring temporal sampling and show that they are of similar magnitude as (but smaller than) actual model errors (20–60 %). Using temporal sampling from remote-sensing data sets, the satellite imager MODIS (MODerate resolution Imaging Spectroradiometer) and the ground-based sun photometer network AERONET (AErosol Robotic NETwork), and three different global aerosol models, we compare annual and monthly averages of full model data to sampled model data. Our results show that sampling errors as large as 100 % in AOT (aerosol optical thickness), 0.4 in AE (Ångström Exponent) and 0.05 in SSA (single scattering albedo) are possible. Even in daily averages, sampling errors can be significant. Moreover these sampling errors are often correlated over long distances giving rise to artificial contrasts between pristine and polluted events and regions. Additionally, we provide evidence that suggests that models will underestimate these errors. To prevent sampling errors, model data should be temporally collocated to the observations before any analysis is made. We also discuss how this work has consequences for in situ measurements (e.g. aircraft campaigns or surface measurements) in model evaluation. Although this study is framed in the context of model evaluation, it has a clear and direct relevance to climatologies derived from observational data sets.
This work was supported by the Natural Environmental Research Council grant nr NE/J024252/1 (Global Aerosol Synthesis And Science Project). Computational resources for the ECHAM-HAM runs were made available by Deutsches Klimarechenzentrum (DKRZ) through support from the Bundesministerium für Bildung und Forschung (BMBF). The ECHAM-HAMMOZ model is developed by a consortium composed of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute and the Leibniz Institute for Tropospheric Research, and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich. P. Stier would like to acknowledge funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) ERC project ACCLAIM (grant agreement no. FP7-280025). HadGEMUKCA was run on the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk). The development of the UKCA model (www.ukca.ac.uk) was supported by the UK’s Natural Environment Research Council (NERC) through the NERC Centres for Atmospheric Science (NCAS) initiative. MIROC-SPRINTARS was run on the SX-9 supercomputer at NIES (CGER) in Japan. The figures in this paper were prepared using David W. Fanning’s coyote library for IDL. The authors thank an anonymous reviewer and in particular Andrew Sayer for useful comments that helped improve the manuscript.
This is the final version of the article. Available from European Geosciences Union (EGU) and Copernicus Publications via the DOI in this record.
Vol. 16, pp. 1065 - 1079