Articles | Volume 16, issue 2
Atmos. Chem. Phys., 16, 1065–1079, 2016
Atmos. Chem. Phys., 16, 1065–1079, 2016

Research article 29 Jan 2016

Research article | 29 Jan 2016

The importance of temporal collocation for the evaluation of aerosol models with observations

N. A. J. Schutgens1, D. G. Partridge2,3, and P. Stier1 N. A. J. Schutgens et al.
  • 1Department of Physics, University of Oxford, Parks road, Oxford OX1 3PU, England
  • 2Department of Environmental Science and Analytical Chemistry, Stockholm University, Stockholm, Sweden
  • 3Bert Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

Abstract. 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.

Short summary
When comparing models against observations, researchers often use long-term averages without due regard for the temporal sampling of the underlying data sets. We study the errors introduced by this practice and show they are often larger than observational errors and comparable to model errors. We further analyse what causes these errors and suggest best practices for eliminating them.
Final-revised paper