Articles | Volume 16, issue 2
https://doi.org/10.5194/acp-16-1065-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-1065-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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. Schutgens
CORRESPONDING AUTHOR
Department of Physics, University of Oxford, Parks road, Oxford OX1 3PU, England
D. G. Partridge
Department of Environmental Science and Analytical Chemistry, Stockholm University, Stockholm, Sweden
Bert Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Department of Physics, University of Oxford, Parks road, Oxford OX1 3PU, England
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- Will a perfect model agree with perfect observations? The impact of spatial sampling N. Schutgens et al. 10.5194/acp-16-6335-2016
- Short Black Carbon lifetime inferred from a global set of aircraft observations M. Lund et al. 10.1038/s41612-018-0040-x
- Spatiotemporal Heterogeneity of Aerosol and Cloud Properties Over the Southeast Atlantic: An Observational Analysis I. Chang et al. 10.1029/2020GL091469
- The resolution dependence of cloud effects and ship‐induced aerosol‐cloud interactions in marine stratocumulus A. Possner et al. 10.1002/2015JD024685
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- Site representativity of AERONET and GAW remotely sensed aerosol optical thickness and absorbing aerosol optical thickness observations N. Schutgens 10.5194/acp-20-7473-2020
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Latest update: 24 Apr 2024
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.
When comparing models against observations, researchers often use long-term averages without due...
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