In the context of the IMPACT2C project, one of the objectives is to estimate the pan-European impacts of a global 2-degree increase in temperature on human health, including change in air pollution. Climate change will affect atmospheric dispersion, biogenic and fire emissions, chemistry, and the frequency of extreme weather situations such as heat waves. These changes will have an impact on air quality with subsequent health consequences that must be evaluated. In order to evaluate how climate change will potentially affect the efficiency of emission abatement policies and how this will potentially affect health, several simulations have been conducted using different chemistry-transport models (CTMs): CHIMERE (IPSL), EMEP MSC-W (MET.NO), MATCH (SMHI), and MOCAGE (Météo-France). The use of four CTMs provide an estimate of the uncertainty in projections with the spread between models and driving meteorological data. To compare with future climate, the first step is to perform air quality simulations for the current climate: HINDCAST (CTMs forced by reanalysis boundary forcing ERA Interim) and HISTORICAL (global climate model boundary forcings). The comparisons between HINDCAST and HISTORICAL simulations allow to evaluate how global climate models modify climate hindcasts by boundary conditions inputs. In this study, we focus on particulate matter (PM10 and PM2.5) and its chemical composition. We first analyze whether the chemical composition of PM is affected by the use of climate models. We then investigate the contributions of the changes in meteorological parameters (frequency of precipitation, 2-m temperature, etc.) as well as emissions and depositions processes on surface PM. These results are the basis for analyzing future 2° warming climates. Under the RCP4.5 scenario, simulations have been performed in order to calculate the effect of climate change on emission reduction scenarios, the climate penalty, as well as the effect of emission mitigation. This analysis also provide uncertainties associated to future AQ projections.

Predicting how European air quality could evolve over the next decades in the context of changing climate requires the use of climate models to produce results that can be averaged in a climatologically and statistically sound manner. This is a very different approach from the one that is generally used for air quality hindcasts for the present period; analysed meteorological fields are used to represent specifically each date and hour. Differences arise both from the fact that a climate model run results in a pure model output, with no influence from observations (which are useful to correct for a range of errors), and that in a "climate" set-up, simulations on a given day, month or even season cannot be related to any specific period of time (but can just be interpreted in a climatological sense). Hence, although an air quality model can be thoroughly validated in a "realistic" set-up using analysed meteorological fields, the question remains of how far its outputs can be interpreted in a "climate" set-up. For this purpose, we focus on Europe and on the current decade using three 5-yr simulations performed with the multiscale chemistry-transport model MOCAGE and use meteorological forcings either from operational meteorological analyses or from climate simulations. We investigate how statistical skill indicators compare in the different simulations, discriminating also the effects of meteorology on atmospheric fields (winds, temperature, humidity, pressure, etc.) and on the dependent emissions and deposition processes (volatile organic compound emissions, deposition velocities, etc.). Our results show in particular how differing boundary layer heights and deposition velocities affect horizontal and vertical distributions of species. When the model is driven by operational analyses, the simulation accurately reproduces the observed values of O3, NOx, SO2 and, with some bias that can be explained by the set-up, PM10. We study how the simulations driven by climate forcings differ, both due to the realism of the forcings (lack of data assimilated and lower resolution) and due to the lack of representation of the actual chronology of events. We conclude that the indicators such as mean bias, mean normalized bias, RMSE and deviation standards can be used to interpret the results with some confidence as well as the health-related indicators such as the number of days of exceedance of regulatory thresholds. These metrics are thus considered to be suitable for the interpretation of simulations of the future evolution of European air quality.