Performance of European chemistry transport models as function of horizontal resolution
Journal article, 2015
Air pollution causes adverse effects on human health as well as ecosystems and crop yield and also has an impact on climate change trough short-lived climate forcers. To design mitigation strategies for air pollution, 3D Chemistry Transport Models (CTMs) have been developed to support the decision process. Increases in model resolution may provide more accurate and detailed information, but will cubically increase computational costs and pose additional challenges concerning high resolution input data. The motivation for the present study was therefore to explore the impact of using finer horizontal grid resolution for policy support applications of the European Monitoring and Evaluation Programme (EMEP) model within the Long Range Transboundary Air Pollution (LRTAP) convention. The goal was to determine the “optimum resolution” at which additional computational efforts do not provide increased model performance using presently available input data. Five regional CTMs performed four runs for 2009 over Europe at different horizontal resolutions.
The models’ responses to an increase in resolution are broadly consistent for all models. The largest response was found for NO2 followed by PM10 and O3. Model resolution does not impact model performance for rural background conditions. However, increasing model resolution improves the model performance at stations in and near large conglomerations. The statistical evaluation showed that the increased resolution better reproduces the spatial gradients in pollution regimes, but does not help to improve significantly the model performance for reproducing observed temporal variability. This study clearly shows that increasing model resolution is advantageous, and that leaving a resolution of 50 km in favour of a resolution between 10 and 20 km is practical and worthwhile. As about 70% of the model response to grid resolution is determined by the difference in the spatial emission distribution, improved emission allocation procedures at high spatial and temporal resolution are a crucial factor for further model resolution improvements.
The models’ responses to an increase in resolution are broadly consistent for all models. The largest response was found for NO2 followed by PM10 and O3. Model resolution does not impact model performance for rural background conditions. However, increasing model resolution improves the model performance at stations in and near large conglomerations. The statistical evaluation showed that the increased resolution better reproduces the spatial gradients in pollution regimes, but does not help to improve significantly the model performance for reproducing observed temporal variability. This study clearly shows that increasing model resolution is advantageous, and that leaving a resolution of 50 km in favour of a resolution between 10 and 20 km is practical and worthwhile. As about 70% of the model response to grid resolution is determined by the difference in the spatial emission distribution, improved emission allocation procedures at high spatial and temporal resolution are a crucial factor for further model resolution improvements.
Ozone
Model evaluation
Particulate matter
Air quality
Nitrogen oxides
Author
M. Schaap
Netherlands Organisation for Applied Scientific Research (TNO)
C. Cuvelier
European Commission (EC)
C. Hendriks
Netherlands Organisation for Applied Scientific Research (TNO)
B. Bessagnet
Institut National de l'Environnement Industriel et des Risques (INERIS)
J. M. Baldasano
Barcelona Supercomputing Center (BSC)
A. Colette
Institut National de l'Environnement Industriel et des Risques (INERIS)
P. Thunis
European Commission (EC)
D. Karam
European Commission (EC)
H. Fagerli
Norwegian Meteorological Institute
A. Graff
German Environment Agency (UBA)
R. Kranenburg
Netherlands Organisation for Applied Scientific Research (TNO)
A. Nyiri
Norwegian Meteorological Institute
M. T. Pay
Barcelona Supercomputing Center (BSC)
L. Rouil
Institut National de l'Environnement Industriel et des Risques (INERIS)
M. Schulz
Norwegian Meteorological Institute
David Simpson
Geoscience and Remote Sensing
R. Stern
Freie Universität Berlin
E. Terrenoire
Institut National de l'Environnement Industriel et des Risques (INERIS)
P. Wind
Norwegian Meteorological Institute
University of Tromsø – The Arctic University of Norway
Atmospheric Environment
1352-2310 (ISSN) 1873-2844 (eISSN)
Vol. 112 90-105ModElling the Regional and Global Earth system (MERGE)
Lund University (9945095), 2010-01-01 -- .
Subject Categories
Energy Systems
DOI
10.1016/j.atmosenv.2015.04.003