Abstract. Regional-scale air quality models are being used for studying the
sources, composition, transport, transformation, and deposition of fine
particulate matter (PM2.5). The availability of decadal air quality
simulations provides a unique opportunity to explore sophisticated model
evaluation techniques rather than relying solely on traditional operational
evaluations. In this study, we propose a new approach for process-based
model evaluation of speciated PM2.5 using improved complete ensemble
empirical mode decomposition with adaptive noise (improved CEEMDAN) to
assess how well version 5.0.2 of the coupled Weather Research and
Forecasting model–Community Multiscale Air Quality model (WRF-CMAQ)
simulates the time-dependent long-term trend and cyclical variations in
daily average PM2.5 and its species, including sulfate (SO4),
nitrate (NO3), ammonium (NH4), chloride (Cl), organic carbon (OC),
and elemental carbon (EC). The utility of the proposed approach for model
evaluation is demonstrated using PM2.5 data at three monitoring
locations. At these locations, the model is generally more capable of
simulating the rate of change in the long-term trend component than its
absolute magnitude. Amplitudes of the sub-seasonal and annual cycles of
total PM2.5, SO4, and OC are well reproduced. However, the
time-dependent phase difference in the annual cycles for total PM2.5,
OC, and EC reveals a phase shift of up to half a year, indicating the need for
proper temporal allocation of emissions and for updating the treatment of
organic aerosols compared to the model version used for this set of
simulations. Evaluation of sub-seasonal and interannual variations
indicates that CMAQ is more capable of replicating the sub-seasonal cycles
than interannual variations in magnitude and phase.
Evaluating Trends and Seasonality in Modeled PM<sub>2.5</sub> Concentrations Using Empirical Mode Decomposition
