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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 13
Atmos. Chem. Phys., 16, 8295–8308, 2016
https://doi.org/10.5194/acp-16-8295-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 16, 8295–8308, 2016
https://doi.org/10.5194/acp-16-8295-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 11 Jul 2016

Research article | 11 Jul 2016

Spectral analysis of atmospheric composition: application to surface ozone model–measurement comparisons

Dene R. Bowdalo, Mathew J. Evans, and Eric D. Sofen Dene R. Bowdalo et al.
  • Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, Heslington, York, YO10 5DD, UK

Abstract. Models of atmospheric composition play an essential role in our scientific understanding of atmospheric processes and in providing policy strategies to deal with societally relevant problems such as climate change, air quality, and ecosystem degradation. The fidelity of these models needs to be assessed against observations to ensure that errors in model formulations are found and that model limitations are understood. A range of approaches are necessary for these comparisons. Here, we apply a spectral analysis methodology for this comparison. We use the Lomb–Scargle periodogram, a method similar to a Fourier transform, but better suited to deal with the gapped data sets typical of observational data. We apply this methodology to long-term hourly ozone observations and the equivalent model (GEOS-Chem) output. We show that the spectrally transformed observational data show a distinct power spectrum with regimes indicative of meteorological processes (weather, macroweather) and specific peaks observed at the daily and annual timescales together with corresponding harmonic peaks at one-half, one-third, etc., of these frequencies. Model output shows corresponding features. A comparison between the amplitude and phase of these peaks introduces a new comparison methodology between model and measurements. We focus on the amplitude and phase of diurnal and seasonal cycles and present observational/model comparisons and discuss model performance. We find large biases notably for the seasonal cycle in the mid-latitude Northern Hemisphere where the amplitudes are generally overestimated by up to 16 ppbv, and phases are too late on the order of 1–5 months. This spectral methodology can be applied to a range of model–measurement applications and is highly suitable for Multimodel Intercomparison Projects (MIPs).

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We introduce a new methodology for the assessment of atmospheric models with observations. We apply a spectral analysis methodology to hourly ozone observations and the equivalent model output. The spectrally transformed observational data show significant peaks on daily and annual timescales. Comparison between the amplitude and phase of these peaks introduces a new comparison methodology between model and measurements. We find the model shows significant biases on an annual timescale.
We introduce a new methodology for the assessment of atmospheric models with observations. We...
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