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Volume 16, issue 4
Atmos. Chem. Phys., 16, 2559–2574, 2016
https://doi.org/10.5194/acp-16-2559-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 16, 2559–2574, 2016
https://doi.org/10.5194/acp-16-2559-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 02 Mar 2016

Research article | 02 Mar 2016

Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

Vincent E. P. Lemaire1, Augustin Colette1, and Laurent Menut2 Vincent E. P. Lemaire et al.
  • 1Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France
  • 2Laboratoire de Météorologie Dynamique, UMR CNRS8539, Ecole Polytechnique, Ecole Normale Supérieure, Université P.M. Curie, Ecole Nationale des Ponts et Chaussées, Palaiseau, France

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 projections. However, the computing cost of such methods requires optimizing ensemble exploration techniques.

By using a training data set from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for eight regions in Europe and developed 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 selecting the members of the EuroCordex ensemble of regional climate projections that should be used in priority for future air quality projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM following the EuroCordex terminology).

After having tested the validity of the statistical model in predictive mode, we can provide ranges of uncertainty attributed to the spread of the regional climate projection ensemble by the end of the century (2071–2100) for the RCP8.5.

In the three regions where the statistical model of the impact of climate change on PM2.5 offers satisfactory performances, we find a climate benefit (a decrease of PM2.5 concentrations under future climate) of −1.08 (±0.21), −1.03 (±0.32), −0.83 (±0.14) µg m−3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the Iberian Peninsula and the Mediterranean, the statistical model is not considered skillful enough to draw any conclusion for PM2.5.

In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern Italy, the statistical model of the impact of climate change on ozone was considered satisfactory and it confirms the climate penalty bearing upon ozone of 10.51 (±3.06), 11.70 (±3.63), 11.53 (±1.55), 9.86 (±4.41), 4.82 (±1.79) µg m−3, respectively. In the British-Irish Isles, Scandinavia and the Mediterranean, the skill of the statistical model was not considered robust enough to draw any conclusion for ozone pollution.

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Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change. Its impact is typically assessed using deterministic chemistry-transport models forced by an ensemble of climate projection. Because of the high computational cost of such initiative, elaborated techniques are required to optimize the exploration of ensemble of future projections. We develop such a technique, which also allows quantifying uncertainties in climate and air quality projections.
Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to...
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