Many of the next generation of global climate models will include aerosol schemes which explicitly simulate the microphysical processes that determine the particle size distribution. These models enable aerosol optical properties and cloud condensation nuclei (CCN) concentrations to be determined by fundamental aerosol processes, which should lead to a more physically based simulation of aerosol direct and indirect radiative forcings. This study examines the global variation in particle size distribution simulated by 12 global aerosol microphysics models to quantify model diversity and to identify any common biases against observations. Evaluation against size distribution measurements from a new European network of aerosol supersites shows that the mean model agrees quite well with the observations at many sites on the annual mean, but there are some seasonal biases common to many sites. In particular, at many of these European sites, the accumulation mode number concentration is biased low during winter and Aitken mode concentrations tend to be overestimated in winter and underestimated in summer. At high northern latitudes, the models strongly underpredict Aitken and accumulation particle concentrations compared to the measurements, consistent with previous studies that have highlighted the poor performance of global aerosol models in the Arctic. In the marine boundary layer, the models capture the observed meridional variation in the size distribution, which is dominated by the Aitken mode at high latitudes, with an increasing concentration of accumulation particles with decreasing latitude. Considering vertical profiles, the models reproduce the observed peak in total particle concentrations in the upper troposphere due to new particle formation, although modelled peak concentrations tend to be biased high over Europe. Overall, the multimodel-mean data set simulates the global variation of the particle size distribution with a good degree of skill, suggesting that most of the individual global aerosol microphysics models are performing well, although the large model diversity indicates that some models are in poor agreement with the observations. Further work is required to better constrain size-resolved primary and secondary particle number sources, and an improved understanding of nucleation and growth (e. g. the role of nitrate and secondary organics) will improve the fidelity of simulated particle size distributions.

Belgium is one of the areas within Europe experiencing the highest levels of air pollution. A high-resolution (3 km) modelling experiment is employed to provide guidance to policymakers about expected air quality changes in the near future (2026–2035). The regional air quality model AURORA (Air quality modelling in Urban Regions using an Optimal Resolution Approach), driven by output from a regional climate model, is used to simulate several 10-year time slices to investigate the impact of climatic changes and different emission scenarios on near-surface O3 concentrations, one of the key indices for air quality. Evaluation of the model against measurements from 34 observation stations shows that the AURORA model is capable of reproducing 10-year mean concentrations, daily cycles and spatial patterns. The results for the Representative Concentration Pathways (RCP)4.5 emission scenario indicate that the mean surface O3 concentrations are expected to increase significantly in the near future due to less O3 titration by reduced NOx emissions. Applying an alternative emission scenario for Europe is found to have only a minor impact on the overall concentrations, which are dominated by the background changes. Climate change alone has a much smaller effect on the near-surface O3 concentrations over Belgium than the projected emission changes. The very high horizontal resolution that is used in this study results in much improved spatial correlations and simulated peak concentrations compared to a standard 25 km simulation. An analysis of the number of peak episodes during summer revealed that the emission reductions in RCP4.5 result in a 25% decrease of these peak episodes.

Five air quality models were applied over Portugal for July 2006 with an ensemble purpose. These models were used, with their own meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and its ensemble for ozone (O3) and particulate matter was performed using monitoring data from 22 background stations over Portugal. After removing the bias from each model, different ensemble techniques were applied and compared. Besides the median, several weighted ensemble approaches were tested and intercompared: static (SLR) and dynamic (DLR) multiple linear regressions (using less-square optimization method) and the Bayesian Model Averaging (BMA) methodology. The goal of the comparison is to estimate to what extent the ensemble analysis is an improvement with respect to the single model results. The obtained results revealed that no one of the 4 tested ensembles clearly outperforms the others on the basis of statistical parameters and probabilistic analysis (reliability and resolution properties). Nevertheless, statistical results have shown that the application of the weights slightly improves ensemble performance when compared to those obtained from the median ensemble. The same statistical analysis together with the probabilistic measures demonstrates that the SLR and BMA methods are the best performers amongst the assessed methodologies.

A novel implementation of neural networks for a measure-correlate-predict (MCP) technique is being introduced. This approach represents a state-of-the-art machine learning procedure making use of sophisticated methods to limit generalisation errors. Moreover, the implementation is fully tailored to the requirements of MCP methods in the context of wind resource assessment. We show the application of this method to a set of different locations and compare the results to a simple linear fit to the wind speed frequency distribution as well as to a standard linear regression MCP. The neural network based MCP outperforms both other methods with respect to correlation, root-mean-square error and the error in the wind speed frequency distribution. Hence, this approach can be regarded as a novel, high-quality tool reducing uncertainties in the long-term reference problem using siteassessment products and tools. It therefore tackles one of the most important steps developing a wind energy project.

The uncertain, future development of emissions of short-lived trace gases and aerosols forms a key factor for future air quality and climate forcing. The Representative Concentration Pathways (RCPs) only explore part of this range as they all assume that worldwide ambitious air pollution control policies will be implemented. In this study, we explore how different assumptions on future air pollution policy and climate policy lead to different concentrations of air pollutants for a set of RCP-like scenarios developed using the IMAGE model. These scenarios combine low and high air pollution variants of the scenarios with radiative forcing targets in 2100 of 2.6 W m(-2) and 6.0 W m(-2). Simulations using the global atmospheric chemistry and transport model TM5 for the present-day climate show that both climate mitigation and air pollution control policies have large-scale effects on pollutant concentrations, often of similar magnitude. If no further air pollution policies would be implemented, pollution levels could be considerably higher than in the RCPs, especially in Asia. Air pollution control measures could significantly reduce the warming by tropospheric ozone and black carbon and the cooling by sulphate by 2020, and in the longer term contribute to enhanced warming by methane. These effects tend to cancel each other on a global scale. According to our estimates the effect of the worldwide implementation of air pollution control measures on the total global mean direct radiative forcing in 2050 is +0.09 W m(-2) in the 6.0 W m(-2) scenario and -0.16 W m(-2) in the 2.6 W m(-2) scenario. (C) 2013 Elsevier Ltd. All rights reserved.

The measure-correlate-predict technique is state-of-the-art for assessing the quality of a wind power resource based on long term numerical weather prediction systems. On-site wind speed measurements are correlated to meteorological reanalysis data, which represent the best historical estimate available for the atmospheric state. The different variants of MCP more or less correct the statistical main attributes by making the meteorological reanalyses bias and scaling free using the on-site measurements. However, by neglecting the higher order correlations none of the variants utilize the full potential of the measurements. We show that deep neural networks make use of these higher order correlations. Our implementation is tailored to the requirements of MCP in the context of wind resource assessment. We show the application of this method to a set of different locations and compare the results to a simple linear fit to the wind speed frequency distribution as well as to a standard linear regression MCP, that represents the state-of-the-art in industrial aerodynamics. The neural network based MCP outperforms both other methods with respect to correlation, root-mean-square error and the distance in the wind speed frequency distribution. Site assessment can be considered one of the most important steps developing a wind energy project. To this end, the approach described can be regarded as a novel, high-quality tool for reducing uncertainties in the long-term reference problem of on-site measurements.

Ozone (O3) from 17 atmospheric chemistry models taking part in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) has been used to calculate tropospheric ozone radiative forcings (RFs). All models applied a common set of anthropogenic emissions, which are better constrained for the present-day than the past. Future anthropogenic emissions follow the four Representative Concentration Pathway (RCP) scenarios, which define a relatively narrow range of possible air pollution emissions. We calculate a value for the pre-industrial (1750) to present-day (2010) tropospheric ozone RF of 410 mW m−2. The model range of pre-industrial to present-day changes in O3 produces a spread (±1 standard deviation) in RFs of ±17%. Three different radiation schemes were used – we find differences in RFs between schemes (for the same ozone fields) of ±10%. Applying two different tropopause definitions gives differences in RFs of ±3%. Given additional (unquantified) uncertainties associated with emissions, climate-chemistry interactions and land-use change, we estimate an overall uncertainty of ±30% for the tropospheric ozone RF. Experiments carried out by a subset of six models attribute tropospheric ozone RF to increased emissions of methane (44±12%), nitrogen oxides (31 ± 9%), carbon monoxide (15 ± 3%) and non-methane volatile organic compounds (9 ± 2%); earlier studies attributed more of the tropospheric ozone RF to methane and less to nitrogen oxides. Normalising RFs to changes in tropospheric column ozone, we find a global mean normalised RF of 42 mW m−2 DU−1, a value similar to previous work. Using normalised RFs and future tropospheric column ozone projections we calculate future tropospheric ozone RFs (mW m−2; relative to 1750) for the four future scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) of 350, 420, 370 and 460 (in 2030), and 200, 300, 280 and 600 (in 2100). Models show some coherent responses of ozone to climate change: decreases in the tropical lower troposphere, associated with increases in water vapour; and increases in the sub-tropical to mid-latitude upper troposphere, associated with increases in lightning and stratosphere-to-troposphere transport. Climate change has relatively small impacts on global mean tropospheric ozone RF.

We compare ground-based measurements of aerosol optical depth and Ångström parameter at six Arctic stations in the period 2001–2006 with the results from two global aerosol dynamics and transport models, ECHAM-HAM and TM5. Satellite measurements from MODIS and the MACC reanalysis product are used to examine the spatial distribution and the seasonality of these parameters and to compare them with model results. We find that both models provide a good reproduction of the Ångström parameter but significantly underestimate the observed AOD values. We also explore the effects of changes in emissions, model resolution and the parametrization of wet scavenging.

Global air quality datasets with a coarse resolution have to be downscaled to become useful for regional interpretation, for instance by applying dynamical downscaling. Here, the downscaling ability of the regional air quality model AURORA (Air quality modelling in Urban Regions using an Optimal Resolution Approach) for surface ozone concentrations is evaluated over a model domain covering a large part of Belgium. The impact of two different one-way nesting resolution jumps is studied. Additionally, the effect of horizontal grid spacing on the simulation results is investigated. Model evaluation against measurements from a number of urban/suburban and rural background stations and a gridded interpolation map shows that the model is capable of reproducing the observed temporal and spatial patterns. The results indicate that the two applied nesting resolution jumps are comparably successful in simulating the surface ozone concentrations, despite the large resolution jump (25-3 km) in one of the approaches. The impact of horizontal resolution on the modelled time series at different types of locations is relatively small, with the average difference around 1% of the mean concentrations. However, the benefits of the higher resolution express themselves in the spatial correlations and temporal variances of the simulation results. (C) 2012 Elsevier Ltd. All rights reserved.

Background: In panel datasets information on environmental exposures is scarce. Thus, our goal was to probe the use of area-wide geographically referenced data for air pollution from an external data source in the analysis of physical health. Methods: The study population comprised SOEP respondents in 2004 merged with exposures for NO2, PM10 and O3 based on a multi-year reanalysis of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM). Apart from bivariate analyses with subjective air pollution we estimated cross-sectional multilevel regression models for physical health as assessed by the SF-12. Results: The variation of average exposure to NO2, PM10 and O3 was small with the interquartile range being less than 10µg/m3 for all pollutants. There was no correlation between subjective air pollution and average exposure to PM10 and O3, while there was a very small positive correlation between the first and NO2. Inclusion of objective air pollution in regression models did not improve the model fit. Conclusions: It is feasible to merge environmental exposures to a nationally representative panel study like the SOEP. However, in our study the spatial resolution of the specific air pollutants has been too little, yet.

A very high ozone episode with observed hourly values above 350 mu g m(-3) occurred in July 2005 at the Lamas d'Olo air quality monitoring station, located in a mountainous area in the north of Portugal. Aiming to identify the origin and formation of this ozone-rich episode, a statistical analysis and a modelling approach were applied. A cross-spectrum analysis in the frequency domain and a synoptic analysis of the meteorological and air quality time series were performed. In order to go further in this analysis, a numerical modelling approach was applied. The results indicate that the transport of ozone and its precursors is the main responsible for the high ozone concentrations. Together with the local mountain breeze and subsidence conditions, the sea-breeze circulation transporting pollutants from the coastal urban and industrialized areas that reach the site during late afternoon turn out to be the driving forces for the ozone peaks. (C) 2011 Elsevier Ltd. All rights reserved.

The EURAD-IM chemistry transport model and its 4d-var inverse model extension is applied to one summer and one winter episode, in order to identify the benefit of tropospheric NO2 column retrievals for estimating near-surface nitrogen dioxide concentrations. Initial values and emission rates are jointly optimised by assimilating tropospheric NO2 data from the OMI, while European ground based observations are used for impact evaluation. Results show a moderate improvement of surface level nitrogen dioxide estimates during the summer episode, successfully sustained after the assimilation period through emission adjustments. In the winter case, the OMI data is of limited value due to lower boundary layer heights and thus smaller impact on emission rates.

Atmospheric models are used both to assist in the retrieval of information about tropospheric composition and to interpret the results. The models themselves encompass much of our knowledge and understanding about trace components in the atmosphere and so they themselves must be tested against data. Care must be taken not to mix the dual role so that one can be sure of the conclusions drawn, and also be alert to any new phenomena that may be revealed by the data. These issues are dealt with and illustrated here. Chapter 9 goes on to discuss inverse modelling which is used, for example, to deduce emissions from regional and global data sets. Another important topic is data assimilation in which data and models are combined to derive improved concentration fields and, for example, to make forecasts. A summary provides some perspectives for future work. There is also a useful appendix on the principles of inverse modelling.

The chemistry transport model system EURAD-IM and its variational data assimilation module are applied to assess air quality conditions during the measurement campaign VERTIKO. The inverse modelling system has been designed to run in a nested setup, assimilating in-situ observations and measurements by remote sensing. The benefits of joint initial value and emission rate optimisation are emphasised, significantly enhancing analysis skill for air quality related constituents including ammonia and benzene.

During July 2005, a particularly high ozone episode (values above 350 μg m−3) occurred at Lamas d’Olo, a rural station in northern Portugal. The main objective of the work is to identify the origin of this ozone-rich episode. Data analysis together with air quality numerical simulations were applied. The results indicate that long-range transport of ozone and its precursors instead of local chemical production is responsible for the high ozone concentrations. The sea-breeze circulation turns out to be the driving force for the transport of pollutants from the coastal urban and industrialized areas.