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

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JournalAtmospheric Chemistry and Physics
DatePublished - 11 Jul 2016
Issue number13
Volume16
Number of pages14
Pages (from-to)8295-8308
Original languageEnglish

Abstract

Models of atmospheric composition play an es- sential role in our scientific understanding of atmospheric processes and in providing policy strategies to deal with so- cietally relevant problems such as climate change, air qual- ity, and ecosystem degradation. The fidelity of these models needs to be assessed against observations to ensure that er- rors in model formulations are found and that model lim- itations are understood. A range of approaches are neces- sary for these comparisons. Here, we apply a spectral anal- ysis methodology for this comparison. We use the Lomb– Scargle periodogram, a method similar to a Fourier trans- form, 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 spec- trum with regimes indicative of meteorological processes (weather, macroweather) and specific peaks observed at the daily and annual timescales together with corresponding har- monic 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 mea- surements. We focus on the amplitude and phase of diurnal and seasonal cycles and present observational/model com- parisons and discuss model performance. We find large bi- ases notably for the seasonal cycle in the mid-latitude North- ern Hemisphere where the amplitudes are generally overesti- mated 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 suit- able for Multimodel Intercomparison Projects (MIPs).

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© Author(s) 2016.

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