[1] This paper presents the aerosol modeling now part of the ECMWF Integrated Forecasting System (IFS). It includes new prognostic variables for the mass of sea salt, dust, organic matter and black carbon, and sulphate aerosols, interactive with both the dynamics and the physics of the model. It details the various parameterizations used in the IFS to account for the presence of tropospheric aerosols. Details are given of the various formulations and data sets for the sources of the different aerosols and of the parameterizations describing their sinks. Comparisons of monthly mean and daily aerosol quantities like optical depths against satellite and surface observations are presented. The capability of the forecast model to simulate aerosol events is illustrated through comparisons of dust plume events. The ECMWF IFS provides a good description of the horizontal distribution and temporal variability of the main aerosol types. The forecastonly model described here generally gives the total aerosol optical depth within 0.12 of the relevant observations and can therefore provide the background trajectory information for the aerosol assimilation system described in part 2 of this paper.

Abstract.A new methodology for the estimation of smokeinjection height from wild-land fires is proposed and evaluated. It is demonstrated that the approaches developed for estimating the plume rise from stacks, such as the formulas of G. Briggs, can be formally written in terms characterising the wild-land fires: fire energy, size and temperature. However, these semi-empirical methods still perform quite poorly because the physical processes controlling the uplift of the wildfire plumes differ from those controlling the plume rise from stacks. The proposed new methodology considers wildfire plumes in a way similar to Convective Available Potential Energy (CAPE) computations. The new formulations are applied to a dataset collected within the MISR Plume Height Project for about 2000 fire plumes in North America and Siberia. The estimates of the new method are compared with remote-sensing observations of the plume top by the MISR instrument, with two versions of the Briggs' plume-rise formulas, with the 1-D plume-rise model BUOYANT, and with the prescribed plume-top position (the approach widely used in dispersion modelling). The new method has performed significantly better than all these approaches. For two-thirds of the cases, its predictions deviated from the MISR observations by less than 500 m, which is the uncertainty of the observations themselves. It is shown that the fraction of "good" predictions is much higher (>80 %) for the plumes reaching the free troposphere.

This paper considers the feasibility of numerical simulation of large-scale atmospheric transport of allergenic pollen. It is shown that at least small grains, such as birch pollen, can stay in the air for a few days, which leads to a characteristic scale for their transport of approximately 10(3) km. The analytical consideration confirmed the applicability of existing dispersion models to the pollen transport task and provided some reference parameterizations of the key processes, including dry and wet deposition. The results were applied to the Finnish Emergency Dispersion Modelling System (SILAM), which was then used to analyze pollen transport to Finland during spring time in 2002-2004. Solutions of the inverse problems (source apportionment) showed that the main source areas, from which the birch flowering can affect Finnish territory, are the Baltic States, Russia, Germany, Poland, and Sweden-depending on the particular meteorological situation. Actual forecasting of pollen dispersion required a birch forest map of Europe and a unified European model for birch flowering, both of which were nonexistent before this study. A map was compiled from the national forest inventories of Western Europe and satellite images of broadleaf forests. The flowering model was based on the mean climatological dates for the onset of birch forests rather than conditions of any specific year. Utilization of probability forecasting somewhat alleviated the problem, but the development of a European-wide flowering model remains the main obstacle for real-time forecasting of large-scale pollen distribution.

We evaluate public health and climate impacts of low-sulphur fuels in global shipping. Using high-resolution emissions inventories, integrated atmospheric models, and health risk functions, we assess ship-related PM2.5 pollution impacts in 2020 with and without the use of low-sulphur fuels. Cleaner marine fuels will reduce ship-related premature mortality and morbidity by 34 and 54%, respectively, representing a ~ 2.6% global reduction in PM2.5 cardiovascular and lung cancer deaths and a ~3.6% global reduction in childhood asthma. Despite these reductions, low-sulphur marine fuels will still account for ~250k deaths and ~6.4 M childhood asthma cases annually, and more stringent standards beyond 2020 may provide additional health benefits. Lower sulphur fuels also reduce radiative cooling from ship aerosols by ~80%, equating to a ~3% increase in current estimates of total anthropogenic forcing. Therefore, stronger international shipping policies may need to achieve climate and health targets by jointly reducing greenhouse gases and air pollution.

Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multimodel ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O 3 , NO 2 , SO 2 , CO, PM 10 , PM 2.5 , NO, NH 3 , total NMVOCs (non-methane volatile organic compounds) and PAN+PAN Published by Copernicus Publications on behalf of the European Geosciences Union. V. Marécal et al.:A regional air quality forecasting system over Europe precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations.The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO 2 and PM 10 . The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 µg m −3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30-50 µg m −3 . Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM 10 for winter 2013-1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 µg m −3 . The ensemble median fractional gross error is larger for PM 10 (∼ 0.52) than for ozone and the correlation is lower (∼ 0.35 for PM 10 and ∼ 0.54 for ...