I. Kioutsioukis

Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3

The impact of air pollution on human health and the associated external costs in Europe and the United States (US) for the year 2010 are modeled by a multi-model ensemble of regional models in the frame of the third phase of the Air Quality Modelling Evaluation International Initiative (AQMEII3). The modeled surface concentrations of O3, CO, SO2 and PM2.5 are used as input to the Economic Valuation of Air Pollution (EVA) system to calculate the resulting health impacts and the associated external costs from each individual model. Along with a base case simulation, additional runs were performed introducing 20 % anthropogenic emission reductions both globally and regionally in Europe, North America and east Asia, as defined by the second phase of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP2). Health impacts estimated by using concentration inputs from different chemistry–transport models (CTMs) to the EVA system can vary up to a factor of 3 in Europe (12 models) and the United States (3 models). In Europe, the multi-model mean total number of premature deaths (acute and chronic) is calculated to be 414 000, while in the US, it is estimated to be 160 000, in agreement with previous global and regional studies. The economic valuation of these health impacts is calculated to be EUR 300 billion and 145 billion in Europe and the US, respectively. A subset of models that produce the smallest error compared to the surface observations at each time step against an all-model mean ensemble results in increase of health impacts by up to 30 % in Europe, while in the US, the optimal ensemble mean led to a decrease in the calculated health impacts by ~ 11 %. A total of 54 000 and 27 500 premature deaths can be avoided by a 20 % reduction of global anthropogenic emissions in Europe and the US, respectively. A 20 % reduction of North American anthropogenic emissions avoids a total of ~ 1000 premature deaths in Europe and 25 000 total premature deaths in the US. A 20 % decrease of anthropogenic emissions within the European source region avoids a total of 47 000 premature deaths in Europe. Reducing the east Asian anthropogenic emissions by 20 % avoids ~ 2000 total premature deaths in the US. These results show that the domestic anthropogenic emissions make the largest impacts on premature deaths on a continental scale, while foreign sources make a minor contribution to adverse impacts of air pollution.

AQMEII 1, 2 and 3: Direct and Indirect Benefits of Community Model Evaluation Exercises

Now that the third model evaluation exercise has been launched, a critical review of the activities performed under the Air Quality Model Evaluation International Initiative (AQMEII) is presented. Attention will be focused on the scientific results obtained by individual modeling groups and by the overall community activity. In particular, we critically review the contributions of AQMEII to operational, diagnostic, dynamic, and probabilistic model evaluation. In addition, the role of community collaborations around coordinated modeling activities will be analyzed. Aspects considered in this analysis are the coverage of multiple topics and research interests, the distribution of the workload among several players, the exploitation of web technology for data exchange, the rationalization of the organization of information, and the exploitation of existing data from emission inventory to niche ad hoc and operational monitoring data. Finally, we discuss the channeling of efforts towards the collaboration with other international activities such as the LTRAP Task Force on Hemispheric Transport of Air Pollutant (TF-HTAP) thus multiplying the benefits for the community.

De praeceptis ferendis: Air Quality Multi-model Ensembles

Ensembles of air quality models have been shown to outperform single models in many cases. Starting from the theoretical evidence behind this empirical ascertainment, we present the conditions granting an ensemble superior to any single model. As those conditions are not systematically met, we also investigate two additional ensemble estimators for which a sound mathematical framework exists. In view of producing a single improved forecast out of the ensemble, the three candidate ensemble estimators, namely the unconditional ensemble mean, the weighted ensemble mean and the mean of the sub-ensemble with the right trade-off between accuracy and diversity, are evaluated against data generated in the context of AQMEII (Air Quality Model Evaluation International Initiative). The pitfalls of training such ensembles are investigated. Overall, following a proper training procedure, the sophisticated ensemble averaging techniques were shown to have higher skill compared to solely ensemble averaging forecasts.