Space-time analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 air quality simulations

Abstract

This study presents an evaluation of summertime ozone concentrations over North America (NA) and Europe (EU) using the database generated from Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation metrics and to test the extent to which the various modeling systems can replicate the features seen in the observations. Using a synoptic map typing approach, it is demonstrated that model performance varies with meteorological conditions associated with specific synoptic-scale flow patterns over both eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone was twice as high when cloud fractions were high compared with when cloud fractions were low over eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better reproduce the observed variance in ambient ozone levels than the global model used to specify chemical boundary conditions, although the variance simulated by almost all regional models is still less that the observed variance on all spatiotemporal scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intraday time scale over both NA and EU. Furthermore, a methodology is introduced to distinguish between locally influenced and regionally representative sites for the purpose of model evaluation. Results reveal that all models have worse model performance at locally influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance statistics and to test the modeling systems’ ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture.

Implications: 

The analyses presented in this paper demonstrate how observed temporal and spatial information can be used to stratify operational model performance and to test the modeling systems’ ability to replicate observed temporal and spatial features. Decisions for the improvement of regional air quality models should be based on the information derived from only regionally representative sites.

Acknowledgments

The authors gratefully acknowledge the contribution of various groups to the first Air Quality Model Evaluation International Initiative (AQMEII) activity. The modeling simulations analyzed in this study were performed by IPSL, CEA/CNRS/UVSQ, France; Aarhus University, Denmark; University of Aveiro, Portugal; Helmholtz-Zentrum Geesthacht, Germany; U.S. Environmental Protection Agency, USA; Environ International Corporation, USA; Environment Canada, Canada; CEREA, France; Leibniz Institute for Tropospheric Research, Germany; Finnish Meteorological Institute, Finland; Meteorological and Hydrological Service, Croatia; TNO, The Netherlands; University of Herfordshire, United Kingdom; IMK-IFU, Germany; and National Oceanic and Atmospheric Administration, USA. The following agencies have prepared the data sets used in this study: U.S. Environmental Protection Agency (North American emissions processing and gridded meteorology); U.S. Environmental Protection Agency, Environment Canada, Mexican Secretariat of the Environment and Natural Resources (Secretaría de Medio Ambiente y Recursos Naturales, SEMARNAT), and National Institute of Ecology (Instituto Nacional de Ecología, INE) (North American national emissions inventories); TNO (European emissions processing); Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CEA/CNRS/UVSQ (gridded meteorology for Europe); and ECMWF/GEMS project and Météo-France/CNRM-GAME (chemical boundary conditions). Ambient North American ozone concentration measurements were extracted from Environment Canada’s National Atmospheric Chemistry Database (NAtChem) database and provided by several U.S. and Canadian agencies (AQS, CASTNet, and NAPS networks); for European air quality data the following data centers were used: EMEP European Environment Agency/European Topic Center on Air and Climate Change/AirBase provided European ozone data. The Finish Meteorological Institute provided biomass burning emission data for Europe. Joint Research Center Ispra/Institute for Environment and Sustainability provided its ENSEMBLE system for model output harmonization and analyses and evaluation. The authors also thank OAA/OAR/ESRL PSD, Boulder, Colorado, USA, for providing the NCEP Reanalysis 2 data via their Web site at http://www.esrl.noaa.gov/psd/. Finally, the authors thank ECMWF for providing the ERA interim reanalysis fields via their Web site at http://data-portal.ecmwf.int/data/d/interim_full_daily.

The views expressed here are those of the authors and do not necessarily reflect the views and policies of the U.S. Environmental Protection Agency (EPA) or any other organization participating in the AQMEII project. This paper has been subjected to EPA review and approved for publication.

Additional information

Notes on contributors

C. Hogrefe

C. Hogrefe, S. Roselle, and R. Mathur are with the Atmospheric Modeling and Analysis Division in the National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC.

S.T. Rao

S.T. Rao is an adjunct professor at the Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC. He previously was the director of the Atmospheric Modeling and Analysis Division in the National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC.

S. Galmarini

S. Galmarini is with the European Commission Joint Research Centre in Ispra, Italy.