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

TY - JOUR

T1 - Spectral analysis of atmospheric composition: application to surface ozone model–measurement comparisons

AU - Bowdalo, D. R.

AU - Evans, M. J.

AU - Sofen, E. D.

N1 - © Author(s) 2016.

PY - 2016/7/11

Y1 - 2016/7/11

N2 - 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).

AB - 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).

U2 - 10.5194/acp-16-8295-2016

DO - 10.5194/acp-16-8295-2016

M3 - Article

SN - 1680-7316

VL - 16

SP - 8295

EP - 8308

JO - Atmospheric Chemistry and Physics

JF - Atmospheric Chemistry and Physics

IS - 13

ER -