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European Geosciences Union, Atmospheric Chemistry and Physics Discussions, 20(15), p. 28361-28393

DOI: 10.5194/acpd-15-28361-2015

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Using proxies to explore ensemble uncertainty in climate impact studies: the example of air pollution

Journal article published in 2015 by V. E. P. Lemaire, A. Colette ORCID, L. Menut ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projection. However, the computing cost of such method requires optimizing ensemble exploration techniques. By using a training dataset of deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for 8 regions in Europe and developed simple statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows concluding on the robustness of the climate impact on air quality. The climate benefit for PM 2.5 was confirmed −0.96 (±0.18), −1.00 (±0.37), −1.16 ± (0.23) μg m −3 , for resp. Eastern Europe, Mid Europe and Northern Italy and for the Eastern Europe, France, Iberian Peninsula, Mid Europe and Northern Italy regions a climate penalty on ozone was identified 10.11 (±3.22), 8.23 (±2.06), 9.23 (±1.13), 6.41 (±2.14), 7.43 (±2.02) μg m −3 . This technique also allows selecting a subset of relevant regional climate model members that should be used in priority for future deterministic projections.