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

摘要

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