# General overview: European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI) - integrating aerosol research from nano to global scales

M. Kulmala, A. Asmi, H. K. Lappalainen, Urs Baltensperger, J. L. Brenguier, M. C. Facchini, H. C. Hansson, O. Hov, C. D. O'Dowd, U. Poschl, et al.

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## ► To cite this version:

M. Kulmala, A. Asmi, H. K. Lappalainen, Urs Baltensperger, J. L. Brenguier, et al. General overview: European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI) - integrating aerosol research from nano to global scales. Atmospheric Chemistry and Physics, 2011, 11, pp.13061-13143. 10.5194/acp-11-13061-2011. hal-00699685

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# General overview: European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI) – integrating aerosol research from nano to global scales

M. Kulmala<fnref n="1" />, A. Asmi<fnref n="1" />, H. K. Lappalainen<fnref n="1" />,<fnref n="2" />, U. Baltensperger<fnref n="3" />, J.-L. Brenguier<fnref n="4" />, M. C. Facchini<fnref n="5" />, H.-C. Hansson<fnref n="6" />,
Ø. Hov<fnref n="7" />, C. D. O'Dowd<fnref n="8" />, U. Pöschl<fnref n="9" />, A. Wiedensohler<fnref n="10" />, R. Boers<fnref n="11" />, O. Boucher<fnref n="12" />,*, G. de Leeuw<fnref n="1" />,<fnref n="2" />,<fnref n="13" />,
H. A. C. Denier van der Gon<fnref n="13" />, J. Feichter<fnref n="14" />, R. Krejci<fnref n="16" />, P. Laj<fnref n="15" />, H. Lihavainen<fnref n="2" />, U. Lohmann<fnref n="16" />, G. McFiggans<fnref n="17" />,
T. Mentel<fnref n="18" />, C. Pilinis<fnref n="19" />, I. Riipinen<fnref n="1" />,³⁶, M. Schulz<fnref n="7" />,<fnref n="20" />, A. Stohl<fnref n="21" />, E. Swietlicki<fnref n="22" />, E. Vignati<fnref n="23" />, C. Alves<fnref n="25" />,
M. Amann<fnref n="24" />, M. Ammann⁵⁹, S. Arabas²⁶, P. Artaxo²⁷, H. Baars<fnref n="10" />, D. C. S. Beddows²⁸, R. Bergström²⁹, J. P. Beukes³⁰,
M. Bilde³¹, J. F. Burkhart<fnref n="21" />, F. Canonaco<fnref n="3" />, S. L. Clegg³², H. Coe<fnref n="17" />, S. Crumeyrolle³³, B. D'Anna³⁴, S. Decesari<fnref n="5" />,
S. Gilardoni<fnref n="23" />, M. Fischer³⁵, A. M. Fjaeraa<fnref n="21" />, C. Fountoukis³⁶, C. George³⁴, L. Gomes<fnref n="4" />, P. Halloran<fnref n="12" />,
T. Hamburger³⁷, R. M. Harrison²⁸, H. Herrmann<fnref n="10" />, T. Hoffmann³⁸, C. Hoose³⁹, M. Hu⁴⁰, A. Hyvärinen<fnref n="2" />,
U. Hõrrak⁴¹, Y. Iinuma<fnref n="10" />, T. Iversen<fnref n="7" />, M. Josipovic³⁰, M. Kanakidou⁴², A. Kiendler-Scharr<fnref n="18" />, A. Kirkevåg<fnref n="7" />, G. Kiss⁴³,
Z. Klimont<fnref n="24" />, P. Kolmonen<fnref n="2" />, M. Komppula⁴⁴, J.-E. Kristjánsson⁴⁵, L. Laakso<fnref n="1" />,<fnref n="2" />,³⁰, A. Laaksonen<fnref n="2" />,⁴⁶, L. Labonnote⁴⁷,
V. A. Lanz<fnref n="3" />, K. E. J. Lehtinen⁴⁴,⁴⁶, L. V. Rizzo²⁷, R. Makkonen<fnref n="1" />, H. E. Manninen<fnref n="1" />, G. McMeeking<fnref n="17" />, J. Merikanto<fnref n="1" />,
A. Minikin³⁷, S. Mirme³⁵, W. T. Morgan<fnref n="17" />, E. Nemitz⁴⁸, D. O'Donnell<fnref n="14" />, T. S. Panwar⁴⁹, H. Pawlowska²⁶, A. Petzold³⁷,
J. J. Pienaar³⁰, C. Pio<fnref n="25" />, C. Plass-Duelmer⁵⁰, A. S. H. Prévôt<fnref n="3" />, S. Pryor⁵¹, C. L. Reddington⁵², G. Roberts<fnref n="11" />,
D. Rosenfeld⁵³, J. Schwarz⁵⁴, Ø. Seland<fnref n="7" />, K. Sellegri⁵⁵, X. J. Shen⁵⁶, M. Shiraiwa<fnref n="9" />, H. Siebert<fnref n="10" />, B. Sierau<fnref n="16" />,
D. Simpson<fnref n="7" />,⁶⁰, J. Y. Sun⁵⁶, D. Topping<fnref n="17" />, P. Tunved<fnref n="6" />, P. Vaattovaara⁴⁶, V. Vakkari<fnref n="1" />, J. P. Veefkind<fnref n="11" />, A. Visschedijk<fnref n="13" />,
H. Vuollekoski<fnref n="1" />, R. Vuolo<fnref n="20" />, B. Wehner<fnref n="10" />, J. Wildt<fnref n="18" />, S. Woodward<fnref n="12" />, D. R. Worsnop<fnref n="1" />,<fnref n="2" />,⁵⁷, G.-J. van Zadelhoff<fnref n="11" />,
A. A. Zardini³¹,⁵⁸, K. Zhang<fnref n="14" />, P. G. van Zyl³⁰, V.-M. Kerminen<fnref n="2" />, K. S Carslaw⁵², and S. N. Pandis³⁶

<footnote n="1">¹Department of Physics, University of Helsinki, 00014 Helsinki, Finland</footnote>

<footnote n="2">²Finnish Meteorological Institute, Research and Development, 00101 Helsinki, Finland</footnote>

<footnote n="3">³Paul Scherrer Institut, Laboratory of Atmospheric Chemistry, Villigen PSI, Switzerland</footnote>

<footnote n="4">⁴French Meteorological Service, 31057 Toulouse, France</footnote>

<footnote n="5">⁵Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Bologna, Italy</footnote>

<footnote n="6">⁶Department of Applied Environmental Science (ITM), Stockholm University, 10691 Stockholm, Sweden</footnote>

<footnote n="7">⁷Norwegian Meteorological Institute, 0313 Oslo, Norway</footnote>

<footnote n="8">⁸School of Physics & Centre for Climate and Air Pollution Studies, Environmental Change Institute, National University of Ireland, Galway, Ireland</footnote>

<footnote n="9">⁹Max Planck Institute for Chemistry, Mainz, Germany</footnote>

<footnote n="10">¹⁰Leibniz Institute for Tropospheric Research, 04318 Leipzig, Germany</footnote>

<footnote n="11">¹¹Earth Observation and Climate Department/Climate and Seismology Sector, KNMI, The Netherlands</footnote>

<footnote n="12">¹²Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK</footnote>

<footnote n="13">¹³TNO Built Environment and Geosciences, Utrecht, The Netherlands</footnote>

<footnote n="14">¹⁴Max Planck Institute for Meteorology, Hamburg, Germany</footnote>

<footnote n="15">¹⁵Laboratoire de Glaciologie et Géophysique de l'Environnement (LGGE), CNRS/University of Grenoble, Grenoble, France</footnote>

<footnote n="16">¹⁶Institute of Atmospheric and Climate Science, ETH Zurich, Switzerland</footnote>

<footnote n="17">¹⁷School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, UK</footnote>

<footnote n="18">¹⁸Institut fuer Energie- und Klimaforschung – Troposphaere, Forschungszentrum GmbH Jülich, 52425 Jülich, Germany</footnote>

<footnote n="19">¹⁹Department of Environment, University of the Aegean, Mytilene, Greece</footnote>

<footnote n="20">²⁰Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France</footnote>

<footnote n="21">²¹NILU, Norwegian Institute for Air Research, Kjeller, Norway</footnote>

<footnote n="22">²²Division of Nuclear Physics, Lund University, P.O. Box 118, 22100 Lund, Sweden</footnote>

<footnote n="23">²³European Commission, Institute for Environment and Sustainability, Climate Change and Air Quality Unit, Ispra, Italy</footnote>

<footnote n="24">²⁴IIASA, International Institute for Applied Systems Analysis, Laxenburg, Austria</footnote>

<footnote n="25">²⁵CESAM & Department of Environment, University of Aveiro, Portugal</footnote>
<sup>26</sup>Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland

<sup>27</sup>Institute of Physics University of São Paulo Rua do Matão, Travessa R, 187 CEP05508-090, Sao Paulo, Brazil

<sup>28</sup>University of Birmingham, Division of Environmental Health and Risk Management, Birmingham, UK

<sup>29</sup>Department of Chemistry, University of Gothenburg, 412 96 Gothenburg, Sweden

<sup>30</sup>School of Physical and Chemical Sciences, North-West University, Potchefstroom, South Africa

<sup>31</sup>Department of Chemistry, University of Copenhagen, Copenhagen, Denmark

<sup>32</sup>School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK

<sup>33</sup>Laboratoire de Météorologie Physique, Université Blaise Pascal, Clermont-Ferrand, France

<sup>34</sup>CNRS, UMR5256, IRCELYON, Institut de recherches sur la catalyse et l'environnement de Lyon, Villeurbanne, Université de Lyon, Lyon, France

<sup>35</sup>Airel Ltd, Tähe 4, Tartu, Estonia

<sup>36</sup>Institute of Chemical Engineering and High Temperature Chemical Processes (ICE-HT), Foundation for Research and Technology Hellas (FORTH), Patras, Greece

<sup>37</sup>Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany

<sup>38</sup>Institut für Anorganische und Analytische Chemie, Johannes Gutenberg-University, Mainz, Germany

<sup>39</sup>Karlsruhe Institute of Technology, Karlsruhe, Germany

<sup>40</sup>State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China

<sup>41</sup>Institute of Physics, University of Tartu, 18 Ülikooli St., Tartu, 50090, Estonia

<sup>42</sup>Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, 71003, P.O. Box 2208, Heraklion, Greece

<sup>43</sup>Air Chemistry Group of Hungarian Academy of Sciences, University of Pannonia, P.O. Box 158, 8201 Veszprém, Hungary

<sup>44</sup>Finnish Meteorological Institute, Kuopio Unit, 70211 Kuopio, Finland

<sup>45</sup>Department of Geosciences, University of Oslo, Oslo, Norway

<sup>46</sup>University of Eastern Finland, Dept. Applied Physics, POB 1627, 70211 Kuopio, Finland

<sup>47</sup>Laboratoire d'Optique Atmosphérique – Université des Sciences et Technologies de Lille/CNRS, Villeneuve d'Ascq Cedex, France

<sup>48</sup>Centre for Ecology & Hydrology Bush Estate, Penicuik, Midlothian, EH26 0QB, UK

<sup>49</sup>TERI Darbari Seth Block IHC complex Lodhi Road, New Delhi, 110003, India

<sup>50</sup>Hohenpeissenberg Meteorological Observatory, Deutscher Wetterdienst, Germany

<sup>51</sup>Risø National Laboratory Fredriksborgvej 399, P.O. Box 49, 4000 Roskilde, Denmark

<sup>52</sup>University of Leeds, School of Earth and Environment, Leeds, LS2 9JT, UK

<sup>53</sup>Institute of Earth Sciences, The Hebrew University of Jerusalem, Israel

<sup>54</sup>Institute of Chemical Process Fundamentals AS CR, Rozvojova 135, Prague, Czech Republic

<sup>55</sup>Laboratoire de Météorologie Physique, UMR6016, CNRS/University of Clermont-Ferrand, Clermont-Ferrand, France

<sup>56</sup>Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing 100081, China

<sup>57</sup>Aerodyne Research, Inc. 45 Manning Rd, Billerica, MA 0182, USA

<sup>58</sup>Institute for Energy and Transport, Sustainable Transport Unit, JRC-European Commission, 21027 Ispra (Va), Italy

<sup>59</sup>Paul Scherrer Institut, Laboratory of Radiochemistry and Environmental Chemistry, Switzerland

<sup>60</sup>Dept. Earth & Space Sciences, Chalmers University of Technology, Gothenburg, Sweden

*now at: Laboratoire de Météorologie Dynamique, IPSL/CNRS, Tour 45-55, Université P. et M. Curie, 4 place Jussieu, 75252 Paris Cedex 05, France

Received: 19 April 2011 – Published in Atmos. Chem. Phys. Discuss.: 24 June 2011

Revised: 24 November 2011 – Accepted: 5 December 2011 – Published: 21 December 2011

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**Abstract.** In this paper we describe and summarize the main achievements of the European Aerosol Cloud Climate and Air Quality Interactions project (EUCAARI). EUCAARI started on 1 January 2007 and ended on 31 December 2010 leaving a rich legacy including: (a) a comprehensive database with a year of observations of the physical, chemical and optical properties of aerosol particles over Europe, (b) comprehensive aerosol measurements in four developing countries, (c) a database of airborne measurements of aerosols and clouds over Europe during May 2008, (d) comprehensive modeling tools to study aerosol processes from nano to global scale and their effects on climate and air quality. In addition a new Pan-European aerosol emissions inventory was developed and evaluated, a new cluster spectrometer was built and tested in the field and several new aerosol parameterizations and computations modules for chemical transport and global climate models were developed and evaluated. These achievements and related studies have substantially improved our understanding and reduced the uncertainties of aerosol radiative forcing and air quality-climate interactions. The EUCAARI results can be utilized in European and global environmental policy to assess the aerosol impacts and the corresponding abatement strategies.

# 1 Introduction

## 1.1 Background

The study of atmospheric physics and chemistry as a scientific discipline goes back to the 18th century when the principal issue was identifying the major chemical components of the atmosphere. In the late 19th and 20th century attention turned to the so-called trace gases and aerosol particles. Recently, the importance of atmospheric aerosols to global radiation, cloud formation, and human health effects has motivated several investigations. Trace gases and atmospheric aerosols are tightly connected with each other via physical, chemical, meteorological and biological processes occurring in the atmosphere and at the atmosphere-biosphere interface (see e.g. Seinfeld and Pandis, 1998; Fowler et al., 2009). Human actions, such as emission policy, forest management and land use changes, as well as various natural feedback mechanisms involving the biosphere and atmosphere, have substantial impacts on the complicated couplings between atmospheric aerosols, trace gases, air quality and climate (Brasseur and Roeckner, 2005; Monks et al., 2009; Arneth et al., 2009; Raes and Seinfeld, 2009; Carslaw et al., 2010).

Atmospheric aerosol particles affect the quality of our life in many different ways. First of all, they influence the Earth's radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei (CCN) (e.g. Charlson et al., 1992). The interaction between atmospheric aerosols and climate system is the dominant uncertainty in predicting the radiative forcing and future climate (IPCC, 2007; Andreae and Rosenfeld, 2008; Myhre,

2009; Quaas et al., 2009; Isaksen et al., 2009). The effects of aerosols on temperature and precipitation patterns can be seen mainly over regional and continental scales. Secondly, aerosol particles deteriorate both human health and visibility in urban areas (Pope and Dockery, 2006; Hand and Malm, 2007; Anderson, 2009). The interactions between air quality and climate are largely unknown, although some links have been identified (e.g. Swart, 2004; Arneth et al., 2009) or even quantified (Dentener et al., 2005). Thirdly, aerosol particles modify the intensity and distribution of radiation that reaches the earth surface, having direct influences on the terrestrial carbon sink (Gu et al., 2002). Better understanding and quantifying of the above aerosol effects in the atmosphere requires detailed information on how different sources (including those related to the biosphere) and atmospheric transformation processes modify the properties of atmospheric particles and the concentrations of trace gases. It also requires the development of advanced instrumentation and methodologies for measuring and validating atmospheric composition changes and understanding key atmospheric processes (Laj et al., 2009).

The European Aerosol Cloud Climate and Air Quality Interactions project EUCAARI is an EU Research Framework 6 integrated project focusing on understanding the interactions of climate and air pollution (Kulmala et al., 2009). EUCAARI has integrated in a multidisciplinary way atmospheric processes from the nano to global scale. The project brought together several leading European research groups, state-of-the-art infrastructure and some key scientists from non-EU countries to investigate the role of aerosol on climate and air quality. Altogether 48 partners from 24 countries participated in the project. EUCAARI has established a pan-European measurement network for Lagrangian studies and four stations in developing countries. Here we present the main research achievements, improved scientific methods, and the answers to the main scientific questions and objectives of EUCAARI. We focus on the quantification of different processes related to aerosol radiative forcing of our climate. We also quantify the side effects of possible air quality directives on aerosol concentrations and present the roadmap for future analysis. All of this is possible since we have improved the general understanding of aerosol life cycle, which enabled us to improve the description of radiative forcing and different feedbacks. It also allowed us to assess aerosol effects on climate and air quality and analyze a range of abatement strategies.

We present first an overview of the main tools (Sect. 2) and results of the project (Sect. 3). In Sect. 4 we focus on our objectives and specific questions (given in Sect. 1.2). In Sect. 4 we also describe the major improvements of the description of the aerosol life cycle, which resulted in major improvements of the climate and air quality models. In Sect. 4 we also present our legacy including data banks, implementations of process-based parameterisations in chemical transport and global climate models improving their performance.
## 1.2 Mission and objectives

Originally The EUCAARI mission and objectives were determined in 2005–2006 (Kulmala et al., 2009):

1. Reduction of the current uncertainty of the impact of aerosol particles on climate by 50% and quantification of the relationship between anthropogenic aerosol particles and regional air quality. To achieve this objective EUCAARI concentrated on the areas of greatest uncertainty to:

1. Identify and quantify the processes and sources governing global and regional aerosol concentrations.

2. Quantify the physico-chemical properties of atmospheric aerosols.

3. Quantify the feedback processes that link climate change and atmospheric aerosol concentrations with emphasis on the production and loading of natural aerosols and their precursors.

2. Quantification of the side effects of European air quality directives on global and regional climate, and providing tools for future quantifications for different stakeholders.

EUCAARI also aimed to make technological developments by improving aerosol and ion measurement techniques. This was achieved by using prototypes of new equipment in field experiments, thus providing the relevant technological trials for product development by small and medium companies (SME). EUCAARI also produced advanced aerosol and cloud models that can be used by the global and regional climate and air quality modelling communities.

EUCAARI Objective 1 was achieved by improving our understanding of atmospheric aerosol physics and chemistry. This improvement enhanced our ability to forecast short-term regional air quality as well as to estimate the long-term aerosol effects on current and future global climate. Objective 2 facilitated the linking of the air quality and global climate change problems and provided the necessary tools for their quantification for use by different stakeholders.

The impact of EUCAARI can be measured by its ability to achieve its objectives and its contribution to (a) research, (b) technological improvements; (c) mitigation strategies and (d) solution of air pollution problems.

During project planning the consortium identified 12 key scientific topics from nanometre scale processes to the overall aerosol-cloud effect on climate. These were:

1. In-situ formation (nucleation) of aerosols.

2. Number and mass emissions of primary aerosol from natural and anthropogenic sources at urban, regional, and global scales.

3. Formation of secondary organic aerosol and the partitioning of semi-volatile compounds between the gas and aerosol phases.

4. Ageing of aerosols and evolution of their properties during their atmospheric lifetime.

5. Attribution of the different aerosol mass components in Europe to specific sources.

6. Current and future contributions of natural versus anthropogenic, and primary versus secondary sources to particle number concentrations.

7. Long-range transport of aerosol particles and their precursors from and to Europe as well as their transport within Europe.

8. Seasonal behaviour of aerosol particles in economically developing countries.

9. Air quality and local climate interactions inside and outside Europe.

10. The impact of aerosols and trace gases on cloud droplet activation, cloud lifetime, and extent (the aerosol indirect effects).

11. Interactions between the aerosol cycle, the water cycle, and the biosphere.

12. Climatic feedbacks related to anthropogenic/biosphere-aerosol-cloud-climate interactions.

Highlights of EUCAARI contributions to our knowledge in these key areas are summarized in Sect. 4 and Appendix E.

## 2 Scientific approach

In order to achieve our objectives and to address the research questions we performed multidisciplinary research including both experimental and theoretical methods. Laboratory and field experiments, including development of novel instrumental techniques, provided new information about aerosol and cloud properties and processes, while developments in basic theory, simulations, and models gave us a way to integrate and compare the results in a broader context. EUCAARI utilized the European long-term aerosol observation network established by EUSAAR (European Super sites for Atmospheric Aerosol Research), and also established long-term ground-based aerosol measurements in economically growing countries in Asia, Africa, and South-America to plug significant gaps in our global aerosol observation capability. In parallel, EUCAARI performed intensive airborne measurements over Europe during May 2008. Additional details of the technical approach are given in Appendix A and references therein.
Figure 1 shows the research chain (the “EUCAARI arrow”) utilized as the backbone of EUCAARI research. It begins at the molecular scale extending to the regional and global scale. The scientific approach starts from basic theories of nucleation and chemical processes followed by models of detailed aerosol dynamic/atmospheric chemistry and vegetation-atmosphere exchange, laboratory experiments with continuous field measurements at several research stations and global-scale modelling. Understanding the highly non-linear processes related to the earth system at different spatial and temporal scales gave insights that allowed us to achieve our objectives. The main corresponding disciplines are aerosol and environmental physics and technology, atmospheric chemistry and physics, analytical chemistry, micrometeorology, climate modelling and forest ecology. This multidisciplinarity provides an opportunity to add value and gain synergy.

We have focused on those topics in the research chain where the uncertainties are largest. At small scales, we used molecular simulations (Monte Carlo and molecular dynamics) to understand nucleation and aerosol thermodynamic processes. These microscopic processes of nucleation together with condensation/evaporation and coagulation are required to understand aerosol dynamics, particle concentrations and composition. Significant advances in laboratory data and modelling techniques were needed for a number of important aerosol systems. Fundamental aerosol processes are needed to be understood in order to quantify the aerosol radiative properties and the influence of aerosols on cloud microphysics and dynamics at the scale of individual clouds. At larger scales, advances in our understanding of boundary layer meteorology were needed to understand atmospheric aerosol transport, trace gas (e.g. CO₂, methane, N₂O, O₃, SO₂, NOₓ, VOCs) and water vapour exchange and deposition processes. Boundary layer studies form a link to regional-scale and global-scale processes. To simulate global climate and air quality, the most recent progress in this chain of processes was compiled, integrated and implemented into climate change and air quality numerical models.

The understanding of different processes and their inclusion in climate models is crucial. For example, if only the aerosol mass loading in the atmosphere is simulated based on emissions of precursor gases (like SO₂) and primary emissions, the number concentration of aerosol particles and further cloud condensation nuclei (CCN) concentration might be seriously under- or sometimes overestimated unless the size and number of particles is considered (see e.g. Spracklen et al., 2006, 2008b).

EUCAARI has built on available data from previous field campaigns and long-term measurements in order to establish global datasets. The data integration within EUCAARI involved a combination of data analysis (accuracy, consistency and representativeness), modelling and field experiments. The experimental and model data was collected in a web-based platform located at partner NILU in Norway

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The “EUCAARI arrow” or research chain connecting molecular scale processes with the global scale through integrated measurements, modeling and theory (Kulmala et al., 2009).</figcaption></figure>

(ebas Nilu.no). The EUCAARI observation system combines long-term and spatially extensive surface-based measurements both in Europe and developing countries (China, India, Brazil, South-Africa), including the European network of supersites for aerosol research (EUSAAR), with satellite retrievals of key parameters. EUCAARI used west-east and north-south station-to-station networks together with Lagrangian and Eulerian airborne measurements and field measurements to quantify the effects on regional aerosol properties of emissions, aerosol formation, transformation, transport and deposition. These measurements included parameters relevant for climate change (the radiative fields in clear and cloudy skies, and their susceptibility to aerosol fields) and air quality (particulate matter (PM) mass, size-resolved aerosol chemical composition, ozone, and NOₓ). During May 2008 an intensive campaign of airborne measurements over Europe was performed. During 2008–2009 a sub-network of supersites provided higher resolution data and detailed chemical composition and extended the observations into the free troposphere.

Due the wide variety of experiments and studies done in EUCAARI, a comprehensive description of technical solutions and methodologies can not be explained fully in single document. More details are given in the EUCAARI Special Issue in the journal *Atmospheric Chemistry and Physics* (http://www.atmos-chem-phys.net/special issue11.html).

Here, we present some of the key methods used in the main studies of EUCAARI. The laboratory studies focused on atmospheric nucleation, ice nucleation and secondary organic aerosol formation/ageing studies. In order to cover the time scales involved in these processes (from seconds to days), flow tubes as well as atmospheric simulation chambers of different sizes were applied. Table A1 in Appendix A provides information about the main laboratory infrastructures used in EUCAARI. Field studies in EUCAARI used multiple stations in Europe and developing countries. Table A2 in Appendix A summarizes the main field infrastructures used in the studies. Airborne measurements in 2008 included two main airborne experiments named Intensive Cloud Aerosol
Measurement Campaign (IMPACT) and Long-range Experiment (LONGREX). The airborne platforms are shown in Table A3 in Appendix A.

The computational methods span from simulations of the behaviour of single modules to compute rates of specific processes to Earth system models. Table A4 in Appendix A show some of the main methods used in the small scale studies, and Table A5 in Appendix A methods used in the large scale studies. The used data bases and emission inventories are described in Table A6 in Appendix A.

EUCAARI studies deployed an unprecedented array of instruments for in situ measurements. Table A7 in Appendix A shows some of the main instruments used in experiments concentrating on the current state-of-art instrumentation.

# 3 Main results

## 3.1 Emissions

### 3.1.1 Size-resolved anthropogenic carbonaceous aerosols and particle number emission database for Europe

To assess the impact of air pollution on climate, among the major knowledge gaps are size-resolved emissions of carbonaceous aerosols and particle numbers (PN). Both have been addressed in EUCAARI (Denier van der Gon et al., 2009, 2011a, b). European emissions of anthropogenic particulate elemental carbon (EC) and organic carbon (OC) were estimated and separated in <1 µm, 1–2.5 µm and 2.5–10 µm size classes. More detailed mass particle size distributions for EC < 1 µm and OC < 2.5 µm in the range 20 nm–2.5 µm by source sector was derived from the literature. Particle mass size distributions show a maximum in the range of 80–200 nm, indicating that the carbonaceous aerosol emissions are highly relevant for long-range atmospheric transport. The emission of OC < 2.5 µm in Europe is dominated by the residential combustion of wood and coal. The largest sources of EC < 1 µm are transport (diesel use) and residential combustion. Total carbonaceous aerosol in the PM₁₀ range for Europe in 2005 amounts to ~2000 kt C yr⁻¹, of which ~10% is due to international shipping. For details see Table 1.

A first size-resolved anthropogenic particle number (PN) emission inventory for the reference year 2005 was compiled (Denier van der Gon et al., 2011a). The emission data base includes all particles in the size range of 10–300 nm and distributes the particle number emissions in 15 different size bins. The preferred approach to calculate PN emissions uses direct emission factors (EFs). For the key sources, such EFs were compiled from the literature with specific emphasis on road transport and residential combustion. Especially wood combustion is an uncertain source in Europe. The wood-use data have been updated and new PN emission factors have been compiled. A remarkable observation from these data is

that PM emission is highly dependent on the type of wood stove with modern stoves emitting much less PM but that PN emissions are quite comparable. The emissions are grided on a 1/8° × longitude 1/16° latitude resolution (or approximately 7 × 7 km) using especially prepared distribution maps. Particular attention has been given to the spatial distribution of transport emission and emission due to residential combustion. An example is presented in Fig. 2.

In polluted areas, road traffic is usually the main source of particles evaluated by number. In comparison to emission factors for mass, the particle number emission factors are very poorly known. There are two sources of available information:

- Emission factors determined from laboratory chassis dynamometer studies, in which particle number emissions from engines with a range of capacities, fuels and technologies have been measured. There has been only one systematic study (the PARTICULATES project), from which data can be taken.

- Inversion of particle number concentrations measured in the field to infer the source strength of particles from a known fleet of vehicles.

Whilst the latter approach has the attraction of deriving data from actual on-road vehicles, it has the weakness that the highest level of disaggregation according to vehicle type which can be achieved is between heavy duty and light duty vehicles, and the method requires accurate knowledge of the source strength of another traffic generated pollutant such as NOₓ. The latter demands high quality emission factor information combined with detailed knowledge of the traffic fleet composition. Whilst the former approach of deriving data from chassis dynamometer studies is superficially attractive, the measurements are sensitive to exhaust gas dilution conditions and may not replicate well between different test facilities.

A number of published studies have used the field measurement approach to estimate average emission factors for light duty and heavy duty vehicles. In some cases, the vehicle fleet composition was published and has been used to infer emission factors. In other cases, it was not, and has been reconstructed from published data on vehicle registrations and usage. By combining such data on the vehicle fleet with the laboratory determined emission factors relating to different fuels and engine technologies, fleet average emission factors can be calculated which can then be compared with those determined in the field (Beddows and Harrison, 2008).

### 3.1.2 Particle number emissions from biomass burning

Aerosol emissions from vegetation fires have a large impact on air quality and climate. Dynamic particle number (EFPN, unit: number of particles per kg of burnt fuel) and mass emission factors (EFPM, unit: mass of particles per
<table><caption>**Table 1.** Size-differentiated EC and OC emissions (tonnes) for UNECE Europe in 2005.</caption><thead><tr><th>SNAP</th><th>Source sector</th><th>EC &lt;1µm</th><th>EC 1–2.5 µm</th><th>EC 2.5–10 µm</th><th>OC &lt;2.5µm</th><th>OC 2.5–10 µm</th></tr></thead><tbody><tr><td>1</td><td>Combustion in energy industries</td><td>2390</td><td>17 600</td><td>29 000</td><td>11 200</td><td>2700</td></tr><tr><td>2</td><td>Non-industrial combustion plants</td><td>143 000</td><td>43 000</td><td>50 200</td><td>395 000</td><td>4340</td></tr><tr><td>3</td><td>Combustion in manufacturing industry</td><td>1850</td><td>3960</td><td>5460</td><td>9050</td><td>543</td></tr><tr><td>4</td><td>Production processes</td><td>32 300</td><td>3990</td><td>19 000</td><td>81 000</td><td>29 000</td></tr><tr><td>5</td><td>Extraction and distribution fossil fuels</td><td>1330</td><td>2720</td><td>55 200</td><td>1430</td><td>6310</td></tr><tr><td>6</td><td>Solvent use</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>7</td><td>Road transport (total)</td><td>184 000</td><td>16 800</td><td>7800</td><td>104 000</td><td>25 500</td></tr><tr><td>8</td><td>Other mobile sources and machinery</td><td>90 200</td><td>4880</td><td>5250</td><td>71 200</td><td>0</td></tr><tr><td>9</td><td>Waste treatment and disposal</td><td>35 200</td><td>1880</td><td>1870</td><td>63 000</td><td>0</td></tr><tr><td>10</td><td>Agriculture</td><td>35 000</td><td>1010</td><td>2250</td><td>112 000</td><td>145 000</td></tr><tr><td colspan="2">Total excl. international shipping*</td><td>525 000</td><td>95 900</td><td>176 000</td><td>848 000</td><td>214 000</td></tr><tr><td colspan="2">International shipping</td><td>79 400</td><td>44 700</td><td>6890</td><td>83 700</td><td>0</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Emission of particle numbers from 100–300 nm in Europe for the year 2005 (Denier van der Gon et al., 2009).</figcaption></figure>

kg of burnt fuel) as well as characteristic carbon monoxide (CO)-referenced emission ratios (PN/CO, PM/CO) were determined from experimental data (Janhall et al., 2009). For the particle number emission factor of vegetation fires, we found no dependence on fuel type and obtained the following parametrization as a function of modified combustion efficiency (MCE):

$$EFPN = 34 \times 10^{15} \times (1 - \text{MCE}) \text{ kg}^{-1} \pm 10^{15} \text{ kg}^{-1}$$

with regard to dry fuel mass. For the fine particle mass emission factors (EFPM) we obtained (86–85 × MCE) g kg⁻¹ ± 3 g kg⁻¹ as an average for all investigated fires; (93–90 × MCE) g kg⁻¹ ± 4 g kg⁻¹ for forest; (67–65 × MCE) g kg⁻¹ ± 2 g kg⁻¹ for savanna; (63–62 × MCE) g kg⁻¹ ± 1 g kg⁻¹ for grass (Janhall et al., 2009). For the PN/CO emission ratio we obtained an average of (34 ± 16) cm⁻³ ppb⁻¹ exhibiting no systematic dependence on fuel type or combustion efficiency. The average PM/CO emission ratios were (0.09 ± 0.04) g g⁻¹ for all investigated fires; (0.13 ± 0.05) g g⁻¹ for forest; (0.08 ± 0.03) g g⁻¹ for savanna; and (0.07 ± 0.03) g g⁻¹ for grass. These results

were based on a rather limited amount of experimental data and should be complemented by additional measurements. Nevertheless, the presented parameterizations represent the current state of knowledge and appear sufficiently robust for exploring the influence of vegetation fires on aerosol particle number and mass concentrations in regional and global model studies.

### 3.1.3 Particle emissions from oceans

Marine aerosols comprise the largest natural source of particulate matter globally. A critical review of the production of aerosol from the ocean shows the current status of knowledge, including extension of the source function to particles as small as 10 nm, new insights in the production mechanisms and the contribution of organic matter to the sea spray aerosol composition (de Leeuw et al., 2011a). Although the enrichment of primary marine aerosol with organics compared to the average sea-water composition has been known for decades, before O’Dowd et al. (2008) this organic fraction had not been included in a source function. This EUCAARI contribution provides global emission inventories for number, mass and organic enrichment fraction of primary marine aerosol and presents a scheme to calculate these parameters online in chemical transport models. The combined organic-inorganic sea-spray source function combines 10 m wind speed, chlorophyll-a concentrations and sea-spray source function to produce a size-resolved emission of number, mass and water insoluble organic matter enrichment as a function of the wind speed and chlorophyll-a concentration. A key finding of this research is that the organic enrichment is observed in submicron aerosol sizes.

## 3.2 Aerosol microscale processes

In this section we summarise new aerosol process understanding obtained during EUCAARI combining theory,

<footnote>* Deviations in the summed totals are due to rounding of the source sector contributions.</footnote>
process models and laboratory experiments with field obser-
vations.

**3.2.1 Nucleation and growth**

The most important technical achievement of the EUCAARI nucleation studies was the development of new instruments for measuring sub-3 nm particle populations, along with the extensive application of these instruments in both the laboratory and the field measurements. One of these instruments is the Neutral cluster and Air Ion Spectrometer (NAIS; Kulmala et al., 2007a; Manninen et al., 2009a, b), and a more sophisticated version of it suitable for airborne operations at different altitudes (Mirme et al., 2010).

All the scientific results obtained during EUCAARI indi-
cate that sulphuric acid plays a central role in atmospheric
nucleation (Kerminen et al., 2010). However, also vapours
other than sulphuric acid are needed to explain the nucleation
and the subsequent growth processes of particles, at least in
continental boundary layers. Organic vapours are seen to par-
ticipate at least in the growth of freshly formed particles.

Both field and laboratory measurements demonstrate that the nucleation rate scales to the first or second power of the nucleating vapour concentration(s). This agrees with the few earlier field observations, but is in stark contrast with the predictions of classical thermodynamic nucleation theories. However, the recent laboratory experiments show somewhat higher power dependence on sulphuric acid. The average formation rates of 2-nm particles were found to vary by almost two orders of magnitude between the different EUCAARI field sites, whereas the formation rates of charged 2-nm particles varied very little between the sites. Overall, our observations are indicative of frequent, yet moderate, ion-induced nucleation usually outweighed by much stronger neutral nucleation events in the continental lower troposphere.

The most concrete outcomes of the nucleation studies are
the new semi-empirical nucleation rate parametrizations for
neutral and ion-induced nucleation based on field observa-
tions, along with updated aerosol formation parametrizations
(see Sect. 3.4.3.8).

Laboratory experiments

Homogeneous nucleation experiments with various mixtures of sulphuric acid, water, ammonia, amines and organic vapours were made in three laboratories by using two different flow tubes (Ift in Leipzig, Finnish Meteorological Institute) and an environmental smog chamber (Paul Scherrer Institute). The conducted H₂SO₄-H₂O nucleation experiments resolved the apparent discrepancies between most earlier experiments is largely a measurement artifact arising from the high sensitivity of the measured “nucleation rate” to the temporal and spatial profile of the gaseous H₂SO₄ concentration inside the measurement device and the detection efficiency of the instrument used to measure nucleated particles (Sipilä

et al., 2010). The new H₂SO₄-H₂O nucleation experiments are in line with EUCAARI field observations both predicting a slope between about 1 and 2 in a plot of the nucleation rate versus gaseous H₂SO₄ concentration. Experiments with different inlet NH₃ concentrations showed that the presence of NH₃ increased slightly the mean diameter of nucleated particles, as well as their total number concentration. The enhancing effect of NH₃ addition on the nucleation was found to be more pronounced under drier conditions. The presence of tert-butylamine enhanced nucleation rates more than NH₃ under similar conditions (Berndt et al., 2010). A series of photo-oxidation experiments in the presence of 1,3,5-trimethylbenzene (TMB), NOₓ and SO₂ at various mixing ratios showed that the measurement data can only be explained with a dependence of the nucleation rate on both sulphuric acid and a nucleating organic compound (Metzger et al., 2010).

The UEF Kuopio plant chamber was used in experimental
studies of nucleation and growth resulting from oxidation of
VOC's emitted by Scots pine and Norway spruce seedlings.
It was found that ozonolysis products of the VOCs are more
efficient than OH products in causing new particle growth
(Hao et al., 2009). On the other hand, the new particle for-
mation rates were several hundred times higher in the OH ex-
periments compared with the ozonolysis experiments. This is
in agreement with findings in the Jülich Plant chamber where
OH radicals are needed to initiate particle formation at ppb
VOC and atmospheric ozone levels (Mentel et al., 2009). The
studies in both plant chambers suggest that at organics par-
ticipated in the nucleation. Jülich observed linear relations
between VOC concentration and nucleation rate. Kuopio
showed that even if trace amounts of SO₂ had been present
- the modelled peak OH concentration was 1.07 × 10⁶, and
it is not likely that sulphuric acid would have been formed
enough to produce the observed particle formation rate of
360 cm⁻³ s⁻¹ without any contribution from the organics.
In a later set of experiments (Hao et al., 2011) the ozonol-
ysis products were somewhat surprisingly found to be less
volatile than the OH products.

Laboratory experiments on the effect of electric charge
(both negative and positive) on the heterogeneous nuclea-
tion probability were performed at University of Vienna
(Winkler et al., 2008). The experiments showed that when
the saturation ratio of the vapour responsible for heteroge-
neous nucleation (here n-propanol) is gradually increased,
the negatively-charged particles or clusters will activate first,
then the positively-charged ones, and finally also the neutral
ones. This kind of behaviour was evident in the sub-4 nm
size range, and the effect was more pronounced for smaller
particle sizes.

Atmospheric nucleation mechanism

In practically all lower-tropospheric environments, naturally
charged particles, called also air ions, were found to have
an almost persistent and narrow mode of cluster ions, close to the mobility diameter of 1 nm (e.g. Hórrak et al., 2008; Hirsikko et al., 2011 and the references therein). The aircraft measurements made during the LONGREX experiment as well as the ground based-measurements at high elevation sites revealed that this cluster ion mode can be seen in the free troposphere (Mirme et al., 2010; Boulon et al., 2010). The first quantitative estimates on the concentrations of neutral sub-3 nm particles were obtained for both the continental boundary layer (Kulmala et al., 2007a; Lehtipalo et al., 2009) and the free troposphere (Mirme et al., 2010). The concentrations of neutral sub-3 nm particles seem to exceed those of similar-size charged particles in the lower troposphere (Manninen et al., 2010). During the LONGREX aircraft measurements, concentrations of neutral particles in the diameter range 2–10 nm were, on average, roughly two orders of magnitude larger than those of charged particles throughout the tropospheric column (Mirme et al., 2010). First observations of large scale particle production in the open ocean were detected (O'Dowd et al., 2010).

The EUCAARI field measurements indicate that sulphuric acid plays a central role in atmospheric nucleation. On the other hand, both field measurements and laboratory experiments showed that vapours other than sulphuric acid are needed to explain the aerosol formation process. Such vapours are very likely of organic origin, at least in the continental boundary layer. The in situ UFO-TDMA field measurements showed that oxidized organics dominate the fresh particle growth processes in a European polluted environment (San Pietro Capofiume, Italy) too, in addition to previously observed organics dominance in the boreal forest region. The field data indicate that the atmospheric nucleation rate scales to the first or second power of the gaseous $H_2SO_4$ concentration or, more generally, of the nucleating vapour concentrations.

The field measurements brought plenty of new insight into the role of ions in atmospheric nucleation (Laakso et al., 2007; Kerminen et al., 2007; Gagné et al., 2008, 2010; Manninen et al., 2010; Mirme et al., 2010; Boulon et al., 2010). The contribution of charged particles to the total formation rate of 2-nm particles was usually found to be well below 10%, but it showed substantial temporal variability both during a nucleation event and between the different event days. In general, our observations are indicative of frequent, yet moderate, ion-induced nucleation usually outweighed by much stronger neutral nucleation in the continental lower troposphere. No evidence on the enhanced role of ion-induced nucleation in the upper free troposphere, as suggested by some theoretical studies, was obtained from our aircraft measurements, although a higher contribution of ion-induced nucleation is found at high altitude sites compared to low altitude sites (Manninen et al., 2010; Boulon et al., 2010).

## Quantum chemical calculations

By using quantum chemical methods, atmospherically relevant molecular clusters were studied, with the final aim of elucidating the molecular mechanism behind observed atmospheric nucleation. Quantum chemical calculations provide evaporation rates, or equivalently formation free energies, of different clusters that can be involved in nucleation. Evaporation rates are needed to assess the stability of various clusters and to identify the pathways through which clusters nucleate. The evaporation rates of a wide variety of clusters were calculated, ranging from clusters containing only sulphuric acid to clusters containing complex molecules like amines or large organic acids. Our main findings can be summarized as follows: (i) ammonia can enhance neutral sulphuric acidwater nucleation to some extent, but has a smaller role in corresponding ion-induced nucleation (Ortega et al., 2008), (ii) dimethylamine enhances neutral and ion-induced sulphuric acid-water nucleation in the atmosphere more effectively than ammonia (Kurtén et al., 2008; Loukonen et al., 2010), (iii) some of the organic acids resulting from monoterpane oxidation can form very stable clusters with sulphuric acid, being good candidates to explain the pool of neutral clusters found in field measurements, and (iv) organosulphates can be involved in ion-induced nucleation.

## Future needs

Although our understanding on atmospheric nucleation has enhanced substantially during EUCAARI, several issues requiring further research can be identified. Firstly, it is unclear whether atmospheric nucleation is dominated by a single nucleation pathway, or whether multiple different mechanisms are competing with each other. Secondly, we have not yet resolved the relative importance of kinetic and thermodynamic factors controlling the atmospheric nucleation rate. Thirdly, the identity and role of organic vapours in the nucleation process are still unknown. Finally, although our results suggest ion-induced nucleation to be of minor significance in continental boundary layers, this may not be the case in the free troposphere or above the oceans.

In order to address the remaining issues and scientific questions, we need to find out how the nucleation rate is connected with the chemical composition, physical properties, evaporation rates and dynamics of the smallest atmospheric clusters. Getting such information requires further advances in both experimental tools and theoretical approaches. These include construction and application of highly sensitive and selective instruments capable of operating at the sub-2 nm size range, making laboratory experiments at highly-controlled conditions (e.g. Kirkby et al., 2011), developing a new generation of kinetic molecular-scale models, and using various theoretical approaches relying on both quantum chemistry and classical thermodynamics.
### 3.2.2 BSOA formation and OA partitioning

Results from EUCAARI show that the yields of biogenic secondary organic aerosol (BSOA) from emissions of Boreal and Mediterranean trees were between 5 and 10%, independent of the monoterpane mixture (Mentel et al., 2009; Lang-Yona et al., 2010; Buchholz et al., 2011), whereas Hao et al. (2011) found a somewhat broader range for the yields (1.9–17.7%). The formed particle mass and number concentration increased linearly with increasing monoterpane concentrations in accordance with the analysis of Boreal field data by Tunved et al. (2008). Based on this result, climate warming of a few degrees leading to increasing monoterpane emissions will enhance future BSOA formation. Via direct and indirect aerosol effects this will contribute to the negative feedback as postulated by Kulmala et al. (2004a). Monoterpane emissions of Mediterranean tree species are stronger dependent on temperature (Lang-Yona et al., 2010; Staudt and Bertin, 1998), leading to stronger BSOA formation in the Mediterranean compared to Boreal regions for the same degree of warming. However, the coupling of increasing monoterpane emissions and enhanced BSOA formation is diminished, if with the warming relatively more isoprene is emitted. The presence of isoprene suppressed the nucleation as well as the formation of mass of BSOA (Kiendler-Scharr et al., 2009b). The effect of stress-induced emissions caused by droughts, heat waves, or nutrition deficits in a changing climate still needs to be investigated. Moreover, we observed indications that stress-induced emissions have the potential to enhance SOA formation but also to suppress particle formation (Mentel et al., 2011).

EUCAARI included a complete set of chamber experiments of aerosol aging, where the main results showed an increase of the O/C ratio of aged aerosol and good agreement between different methodologies of organic aerosol analysis. A set of models and chemical mechanisms have been developed that enable a consistent description of the chemical transformation and aging of organic aerosol components under a wide range of different conditions. Studies of surface chemistry, physics and laboratory studies of aerosol aging, including a wide variety of modeling, measurements and experiments are detailed in Appendix B.

### 3.2.3 Ice nucleation experiments

In terms of the ability of aerosols to act as ice nuclei (IN), significant progress was made. Ice nucleation in supercooled water clouds with temperatures between 0 and -35 °C can be initiated in four different ways: deposition nucleation, immersion freezing, condensation freezing and contact freezing.

Deposition nucleation refers to the direct deposition of vapour onto an ice nucleus. It requires that the saturation ratio with respect to ice exceeds 1. Deposition nucleation is important for cirrus clouds, when vapour is deposited for

instance onto mineral dust particles that act as IN. Deposition nucleation does not seem to be important for mixed-phase clouds, because LIDAR observations revealed that liquid clouds are required before ice crystals form via heterogeneous freezing mechanisms (Ansmann et al., 2008). Immersion freezing refers to freezing that is initiated from within the droplet. It requires that the IN is fully immersed in the droplet when the droplet reaches a temperature at which it can freeze. Obviously, the liquid phase requires saturation with respect to water. Sometimes condensation freezing is distinguished from immersion freezing. It is thought that condensation freezing refers to a different pathway such that the IN enters ambient conditions supersaturated with respect to water only at low temperatures at which heterogeneous freezing of the forming droplet is likely. In that way, the ice crystal can form in the liquid phase, but at the interface between the forming droplet and the vapour phase. This has been shown theoretically to be energetically more favourable than forming an ice crystal on a fully immersed IN (Djikaev, 2008). Condensation freezing can be observed in laboratory studies on deposition nucleation when the relative humidity exceeds water saturation (Welti et al., 2009). However, condensation freezing is very difficult to be unambiguously distinguished from other ice nucleation mechanisms in an experiment. Therefore, it is still subject of ongoing research to what extent condensation freezing is fundamentally different from immersion freezing. Contact freezing refers to the collision of an IN with a supercooled cloud droplet. It requires saturation with respect to water.

We have built devices to perform experiments on all four modes of ice nucleation. The continuous flow diffusion chamber ZINC (Zurich Ice Nucleation Chamber) can be used to study ice nucleation in the deposition and condensation mode in the laboratory (Stetzer et al., 2008; Welti et al., 2009) and the portable version PINC (Portable Ice Nucleation Chamber, PINC) can be used to study ice nucleation in the deposition and condensation mode in the field (Chou et al., 2011). The design of ZINC and PINC was based on Rogers et al. (1988) except that the cylindrical walls were replaced by plane-parallel walls in order to optically distinguish between liquid droplets and ice crystals (Nicolet et al., 2010).

In addition, we built a chamber for immersion freezing (Lüönd et al., 2010). Here we could not follow any design as most immersion freezing experiments were done with droplets immersed in emulsions containing oil (e.g. Zuberi et al., 2002; Marcolli et al., 2007) or based on wind tunnel studies of levitated droplets of the size of drizzle drops (e.g. Diehl et al., 2002). Both are not representative of typical atmospheric conditions. Our immersion freezing device (IMCA = Immersion Mode Cooling Chamber) is comparable to the one simultaneously built in Leipzig (Niedermeier et al., 2010). IMCA is also a continuous flow diffusion chamber (CFDC) designed such that first the aerosol particles are activated as cloud droplets. This ensures that there is only
one single aerosol particle inside each droplet and therefore, IMCA mimics conditions similar to those found in the atmosphere. The droplets are then cooled down and ice formation can occur inside ZINC. This approach will enable the comparison of deposition/condensation freezing with immersion freezing.

Cloud droplets formed on soluble aerosol particles can only freeze homogeneously. Accordingly, the data for immersion freezing of ammonium sulphate particles show an onset of freezing close to 235 K and a steep increase of the frozen fraction with decreasing temperature. The measurements with immersed kaolinite particles clearly show that the droplets freeze at higher temperatures, indicating that freezing occurred heterogeneously, i.e. kaolinite particles act as IN. We could also observe droplets to freeze at slightly higher temperatures (around 2 K for a frozen fraction of 50 %) if the particle diameter is increased from 200 nm to 800 nm. The slope of the frozen fraction with decreasing temperature is less steep for heterogeneous freezing than for homogeneous freezing. This suggests that in terms of heterogeneous ice nucleation, IN surfaces can not be described appropriately by assuming all particles to have equal and uniform surface properties (Lüönd et al., 2010; Hartmann et al., 2011). These findings can be important for the initiation of precipitation in numerical models, as a small fraction of aerosol particles acting as IN is sufficient to initiate precipitation. Therefore, the measurements suggest that kaolinite particles might initiate precipitation at temperatures significantly higher than if the first ice crystals nucleate homogeneously.

### 3.2.4 CCN formation and cloud droplet activation

Laboratory experiments in a controlled laboratory environment were carried out on single component, binary and ternary particles to investigate the effect of organic molecules with different properties on cloud droplet activation. Frosch et al. (2010) investigated the ability of oxo-dicarboxylic acids to act as cloud condensation nuclei and Kristensson et al. (2010) addressed the cloud droplet activation of aminoacids with limited solubility. Frosch et al. (2011) studied the combined effect of inorganic salts and organic acids. Prisle et al. (2008, 2010) investigated the effect of surface active organic molecules on cloud droplet activation and found that it is important to account correctly for partitioning of the surfactant molecules between the bulk and surface of the growing droplet to match measured critical supersaturations.

Laboratory experiments of CCN activity of biogenic secondary organic aerosols generated in smog chambers were performed by Asa-Awuku et al. (2009) and Engelhart et al. (2008, 2010). The SOA becomes more CCN active in all cases due to continued reactions with the OH radical (Engelhart et al., 2008; Asa-Awuku et al., 2009). The water uptake of organics could be modeled using kappa-Köhler theory following Petters and Kreidenweis (2007) applying a kappa value of ~0.1, which is consistent with other recent

laboratory and field studies of (secondary) organic aerosol hygroscopicity and CCN activity (Gunthe et al., 2009; Shinozuka et al., 2009; King et al., 2010; Dusek et al., 2010; Roberts et al., 2010; Pöschl et al., 2010, 2011, and references therein).

A synthesis paper is in progress focusing on parametrizations describing the activation of ambient and SOA particles that can be used in global models (Sierau et al., 2011). This paper also combines CCN measurements in the laboratory with those in the field during EUCAARI. Herein, extensive and intensive CCN parameters compiled from the exceptionally broad data set from CCN measurements that were carried out at locations all over the world, including long-term as well as intensive field studies, will be statistically analysed and reported as monthly, daily and/or hourly mean values to account for seasonal, weekly, and diurnal pattern. The overall kappa-variability will be inferred and discussed in context with the effective average kappa of 0.3 ± 0.1 and 0.7 ± 0.2 as estimated by Andreae and Rosenfeld (2008) for the continental and marine background aerosol, respectively. The former value has recently been superseded by 0.3 ± 0.2 (Pringle et al., 2010) which seems still fairly well constrained with regard to cloud droplet formation (Reutter et al., 2009; Arabas and Pawlowska, 2010, 2011). Kappa deduced from the CCN data (i.e. measured in the supersaturated regime) will be further compared with kappa deduced from Hygroscopicity Tandem Differential Mobility Analyser (HTDMA) data measured under subsaturated conditions (Swietlicki et al., 2011). Moreover, measured CCN activity for secondary organic aerosol (SOA) from real tree emissions of boreal and Mediterranean trees as measured at the Jülich Plant Atmosphere Chamber will be related to the CCN activity parameters obtained from the field stations in Hyytiälä, FI, and Finokalia, GR, respectively.

## 3.3 Aerosol characterization

### 3.3.1 Long-term field observations in Europe

Development of an observing capacity suited to follow and understand atmospheric composition changes and to account for regional specificities is a primary objective of EUCAARI. Sustained long-term observations of short-lived species of atmospheric importance outside of the few policy-regulated variables have, in fact, been crucially missing in Europe. Monitoring of species essential to climate and air quality studies was left to quasi-independent initiatives of scientists contributing to collection and analysis of atmospheric data, resulting in difficulties to assess data quality, access and intercompare data sets. Limited availability of long time-series of atmospheric parameters, and in particular aerosol related species, over Europe was a major obstacle for the validation of satellite observations and chemical transport model evaluation. Recent initiatives to integrate information on aerosol chemical and physical properties (Putaud et al., 2004; Van
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Stations and station codes of EUSAAR stations used in EU-CAARI (adapted from Asmi et al., 2011).</figcaption></figure>

Dingenen et al., 2004) represent a first attempt to provide a synthetic view of aerosols over Europe. However, these studies were mostly based upon data provided on a voluntary basis and, for the major part, derived from campaign-based initiatives rather than long-term observations. A limitation of past work was also the absence of coordinated control on data quality, not available at that time in Europe. Recent intercomparison exercises performed in Europe indeed demonstrated the need for improving standardization of operating procedures for many aerosol measurements (Kahnert et al., 2004; Cavalli et al., 2010).

EUCAARI, in a joint effort with the EU-funded Integrated Infrastructure Initiative EUSAAR (European Super sites for Atmospheric Aerosol Research), provided the framework for the first pan-European coordinated initiative on aerosol observations. By the end of the EUCAARI project, the network provided the most comprehensive record of aerosol observation ever produced in Europe. In addition to basic aerosol variables as recommended by Global Atmospheric Watch (GAW) (namely aerosol absorption and scattering coefficients, aerosol number and size, aerosol chemistry), observations were expanded to provide the change in particle size with relative-humidity using novel Hygroscopicity Tandem Differential Mobility Analyzer techniques (Duplissy et al., 2008; Fors et al., 2009; Massling et al., 2010), atmospheric ion size distribution using novel EUCAARI-developed ion spectrometers (Manninen et al., 2010), and size-segregated chemistry using aerosol mass spectrometers (Prévôt et al., 2011). Not all measurements, in particular the more advanced ones, were performed for the whole EUCAARI period but rather during intensive observing periods that took place in the Spring and Autumn of 2008 and Winter of 2009. For the first time, all groups worked together to ensure similar procedures and methodologies. A number of metrology papers from EUSAAR activities have been recently submitted/published providing the data quality framework for EUCAARI (Cavalli et al., 2010; Collaud-Coen et al., 2010; Mueller et al., 2009; Wiedensohler et al., 2011).

EUCAARI and EUSAAR measurements provided a pan-European view of aerosol properties (Fig. 3, Asmi et al., 2011). A first observation relates to the large variability of aerosol properties encountered over Europe. This is largely due to the geographical location of observing stations representing different climates and environment, although significant variability is also observed for single stations. An integration of measurements over such an extended network leads to simplifications in particular related to station representativeness. Overall, the most suited categorization of sites related to particle variability appears to be a mix between the categorization by Henne et al. (2010) and the more classical geographical classification (Asmi et al., 2011). The concept of catchment area (the area in which the surface fluxes are creating detectable and significant signals at the site) seems to apply well to a number of sites located in the plains of Central and Western Europe (KPO, OBK, CBW, JRC, MPZ for example). At these sites, even if some may be classified as rural according to air base classification, aerosol physical parameters and in particular the smallest particle range (below 50 nm) are clearly influenced by a regional catchment that varies from 50 to a few hundred km. Stations under this situation have in common the following features:

- To a first approximation, the dynamics of aerosol number concentration are driven by large catchment area for particles with particle diameter $d_p > 100$ nm as opposed to a typically smaller area for the smallest particles. The particles regional background is therefore ranging from 2000 to $3000 \text{ cm}^{-3}$. According to Van Dingenen et al. (2004) this concentration range is associated to a particle mass ranging from 10 to $20 \text{ µg m}^{-3}$. Considering that most particles are in the sub-2.5 µm range, the quality objective for PM$_{2.5}$ of $20 \text{ µg m}^{-3}$ by 2015 is a very optimistic target.

- A fairly constant particle number concentration throughout the year. The change in boundary layer height appears to be compensated by a more intense secondary particle formation during summer months.

- Elevated number concentrations of particles generally over $3000 \text{ cm}^{-3}$ and up to $7000 \text{ cm}^{-3}$ (JRC in the Po Valley-Italy) on an annual basis for particles with sizes approximately larger than 30 nm.

- The elevated background of particles somehow hides the diurnal cycle of particles which stays fairly constant.

- The ratio between particles with $30 \text{ nm} < d_p < 100 \text{ nm}$ and $d_p > 100 \text{ nm}$ rarely exceeds 2, but is always higher than unity (Asmi et al., 2011). The dynamics of the smallest particles seems to be more easily explained in models by including a substantial particle formation rate in the boundary layer (Spracklen et al., 2010; Merikanto et al., 2009). However, the link between sub-50 nm to the larger super-100 nm particles, which are generally
involved in cloud formation is not direct and involves
processes that are outside the 48-h catchment area. This
is confirmed by model work of Spracklen et al. (2008b)
and Merikanto et al. (2009) showing that CCN concen-
trations are fairly insensitive to large changes in the BL
nucleation intensity. Clearly, data are still missing in
the sub-15 nm range. Results from the EUSAAR in-
tercomparison experiments showed that DMPS/SMPS
instruments are at present not suited to provide a coher-
ent framework below 15 nm (Wiedensohler et al., 2011).
However, NAIS/AIS/BSMA measurements have shown
to be able to give reasonable and significant results on
new particle formation (Manninen et al., 2010).

- Measurements of hygroscopic properties confirm the difference in origin of the sub-30 nm particles with respect to particles with $d_p$ > 100 nm. However, this is based on a limited number of stations. More hygroscopic particles, characterized by high hygroscopic growth factor (HGF = 1.5) which are internally mixed between evolved SOA and inorganic material constitute most of the super-100 nm fraction. On the contrary, below 30 nm particles are generally less hygroscopic (HGF 1.3) and result from freshly formed particles. The seasonal variability of the HGF also agrees with the weaker SOA contribution to the super-100 nm particles (lower HGF) (Swietlicki et al., 2011).

- There is no simple feature explaining optical properties at stations of Central and Western Europe. Different mass absorption coefficients and chemical composition lead to more variability in comparing absorption and scattering coefficients than for the number concentration. Differences in energy production and in the automobile fleet may explain the variability.

- A classification of aerosol properties under the conditions encountered at stations like CBW, KPO, MPZ, OBK or JRC (see Fig. 3) can be performed without considering the air mass origin but rather considering first a very large regional catchment area driving optical properties and the larger particle properties (CCN concentrations in particular), and a smaller catchment area driving the more variable sub-50 nm particles, of which a still unknown fraction directly arises from direct particle emissions.

- For these stations, the strong difference between boundary layer characteristics and the air aloft leads to a strong decoupling between aerosol parameters (such as Angström coefficient or single scattering albedo) retrieved in-situ and using sun photometers (Kinne et al., 2011). In addition, the in-situ measurements of the optical properties are usually performed under dry conditions, for comparison with remote sensing techniques they need to be corrected for their dependences on the relative humidity (Zieger et al., 2011).

Stations located at elevated sites (JFJ, MTC, BEO, PDD) also present some common features. These stations have been obviously placed at those sites to provide a more regional view of aerosol properties. There, the local catchment area, as defined by Henne et al. (2010), has in fact, much less influence on the variability of the aerosol parameters. The driving factors explaining observed changes are related to both long-range transport (air mass origin) and station altitude. The station altitude and its surrounding topography control thermally-driven upslope/downslope flows which are a common feature of all mountain sites. Characteristics of the mountain sites are:

- Strong seasonal variability, in particular for sites located above 2000 m. This is clearly due to the stronger influence of thermal winds during mid-spring to mid-autumn with resulting advection of boundary the layer air. This is not exclusively linked to slope winds but may also result from an increased boundary layer thickness for medium altitude sites such as PDD in France or Hohenpeissenberg or Schauinsland in Germany. This explains a large fraction of the higher aerosol concentrations in summer months with respect to winter periods.

- At sites strongly affected by thermal winds, a strong bias is introduced if the local dynamics is not accounted for. The thickness of the thermally-driven air mass is rather limited (a few hundred meters) and the station for a fair amount of time may not represent the regional background, but rather air from lower levels. Venting boundary layer air by mountain topography is not well represented in regional models and is an efficient way to transport air pollutants into the free troposphere.

- The interface between polluted air from the BL and the FT air was found to be the location for nucleation events. This is observed at mountain stations (Venzac et al., 2009; Boulon et al., 2011) but also during airborne measurements above central Europe (Crumeyrolle et al., 2010). This is clearly an additional source of small particles to the FT.

- The particle number concentration is a function of station height decreasing down to a couple of hundred particles per cm³ for the highest EU station of JFJ. This corresponds to mass concentrations of approximately 5 to 10 µg m⁻³ during summer and 2.5 to 5 µg m⁻³ in winter for stations right above 1000 m, and concentrations below 5 µg m⁻³ in summer and below 2 µg m⁻³ in winter above 2000 m.

- The relative contributions by organic material, inorganic species and elemental carbon do not seem to be height dependent to a first approximation. The fraction of organic material remains at all sites close to 50%.
- Within EUCAARI, new information was made available from HGF measurements at high altitude sites. Our results show that the long-range component of aerosol sampled at high altitudes (>2000 m) is internally mixed with HGF close to 1.4 at 90 % for 75 nm particles (Kammermann et al., 2010). At lower altitudes, aerosol properties are driven by injection from the BL at regional scale and features that are described for the site in Central/Western European plains are still valid. The aerosol is composed of several modes with the more hygroscopic mode having a HFG of around $1.5 \pm 0.1$ for 110 nm particles at RH=90 %, and the less hygroscopic mode around $1.2 \pm 0.1$ for the same conditions. The number fraction of particles in each hygroscopic mode is variable but the more hygroscopic mode seems to dominate in winter, likely for the same reasons as for BL sites.

- The optical properties of particles at high altitude sites follow the features of particles number concentrations with marked diurnal and seasonal variations. The absorption and scattering coefficients are larger in summer leading to larger extinction coefficient without significant changes in the single scattering albedo.

- Contrary to BL sites, in situ measurements performed at high-elevation stations seem to provide a fairly good representation of the atmospheric column, at least on a monthly average basis (provided the in situ measurements are corrected for humidity effects). This is confirmed by the general agreement between optical properties derived from sun-photometers and in-situ measurements.

A third category of stations are those in the Nordic/Baltic countries stations (ASP, BIR, PAL, PLA, SMR and VHL). They can be differentiated from the Central/Western European BL stations mainly by the generally lower concentrations of aerosol particles and gases as well as the presence of a higher proportion of particles with $d_p < 100$ nm. The lower concentrations might be explained by the specific locations of the Nordic stations, often chosen far away from urbanized areas in particular for the Finnish stations. The occurrence of frequent new particle formation events explain the higher fraction of particles with diameter less than 30 nm with respect to more continental BL sites. The seasonal variability is not well marked at these Nordic stations. A larger variability at BIR results from changing source regions during summer rather than from a change in BL height. The single Baltic station within EUSAAR shows distinct differences with respect to other Nordic stations, with considerably higher concentrations. The Baltic area is possibly the interface between the polluted central European BL and the cleaner Nordic area. Similarly, the Zeppelin station in the Svalbard demonstrates very specific variability due to the Arctic haze phenomena in Spring and Summer. Hygroscopic growth measurements

in the Nordic station of VHL are also somewhat intermediate between the internally mixed free tropospheric aerosol and the multi-mode or less hygroscopic modes encountered in the Central European plains.

The European network also includes single stations that cannot be classified into a specific category. The marine stations FKL and MHD do not show the expected similarities based on their geographical location within the marine BL. This is due to the fact that their average aerosol levels and composition reflect also local and regional influence. It also shows that aerosol processes taking place at regional scale modify the atmospheric composition leading to difficulties in defining typical marine aerosol parameters in Europe.

The hypothesis that aerosol properties (size, hygroscopicity) may be estimated based on their evolution during transport is very difficult to test using long-term measurements (Crumeyrolle et al., 2011). This evolution can only be followed during airborne campaigns, which are limited in time and space. The regional context can be addressed by indicators similar to those listed by Henne et al. (2010).

The bias between model and measurements is much higher for in-situ observations (both positive and negative) than for the integrated column (AERONET) measurements. This is due to the fact that AERONET observations are less sensitive to local variability but also that they are not well suited for regional air quality forecasting. Direct comparison of just absolute values is at present of limited use for the testing of the transport and transformation components of CTMs as most of the model variability derives from emission inventories. Andrews et al. (2011) derived some interesting secondary parameters such as Ångstrom coefficient versus single scattering albedo that may be more discriminating for testing model/observation discrepancies. Asmi et al. (2011) have also performed a synthesis and data reduction on aerosol number concentrations required for direct model/measurements comparisons.

### 3.3.2 Long-term field observations outside Europe

Detecting atmospheric trends of key atmospheric compounds requires long (>10 yr) high quality records. Such datasets are rare in Europe, and nonexistent in many parts of World. This crucial lack of data is a limiting factor for many applications, including forecasting atmospheric composition changes. Tools developed at the EU level to improve provision and access to high quality atmospheric data have been applied within the international collaborating framework of EUCAARI.

In cooperation with partners from universities and research institutes in China, India, Brazil, and South Africa, long-term aerosol measurements were performed to obtain additional insights into the physical, optical and chemical particle properties in these important areas. We carefully selected the observation sites in these four countries to be representative for the regional atmospheric aerosol. This activity completes
efforts of EU scientists to develop and sustain monitoring activities of short-lived species in developing and emerging countries (see for example Zhang et al., 2011; Henne et al., 2008; Bonasoni et al., 2008, 2010). In this section, we highlight findings from the EUCAARI measurements in these four countries.

In China, we performed measurements at a regional site in the North China Plain 150 km northeast of Beijing. In India, we chose a site 30 km from Delhi in a highly polluted non-urban area. In South-Africa, we performed measurements in a clean savannah area as well in the polluted area east of Johannesburg. Finally, we characterized the aerosol over the Amazonian Basin near Manaus in Brazil.

## South Africa

EUCAARI obtained the longest data series of aerosol optical properties and number size distributions in continental Africa. We found that over the background savannah, nucleation and particle growth takes place in more than 80 % of the days (Laakso et al., 2008; Vakkari et al., 2011). The observed particle formation and growth rates (Vakkari et al., 2011) were among the highest observed (Kulmala et al., 2004b). Comparisons with regional vegetation maps and emission inventories clearly show that particle growth is related to biogenic organic vapors whereas formation is dominated by sulfur compounds.

At the station Elandsfontein east of Johannesburg, the light absorption measurements revealed an annual cycle of black carbon (Laakso et al., 2010). In the industrial area around Elandsfontein, black carbon results from industrial activities as well as from domestic burning and natural fires. The peak concentration during the local winter is due to wild fires combined with increased domestic small scale burning.

On a regional scale, meteorology of the area is characteristically strongly layered (Garstang et al., 1996). These layers trap emissions at different levels. The aerosol emission from large natural fires may be injected to higher altitudes. These layers are clearly visible in vertically resolved aerosol profiles, but significantly complicate the interpretation of satellite observations.

The observations in South Africa have significantly increased knowledge on levels, sources and dynamics of atmospheric aerosol particles. Domestic burning and natural wildfires pose a significant threat to human health, whereas acidic particulate matter, combined with gaseous compounds from industrial activities may seriously affect local agriculture. In the context of climate change, high black carbon concentrations may result in significant local heating of the lower atmosphere.

## China

The Chinese GAW-site Shangdianzi, at which the EUCAARI measurements took place, is influenced by two different

types of air masses. From the South, highly polluted air is transported from the North China Plain to the site, while from the North, cleaner continental air is observed. During periods of northerly winds, particle formation occurred on 205 out of 565 days in 2008 and 2009 (Shen et al., 2011).

Particle formation occurred in the morning with a maximum average number concentration of $18\,000\,\text{cm}^{-3}$ around noon. At midnight, the number concentration decreased due to coagulation to approximately $3000\,\text{cm}^{-3}$. The mean particle growth rate was $3.8\,\text{nm}\,\text{h}^{-1}$ and the mode diameter reached 80–100 nm at midnight. The mean mass growth rate was $2.6\,\mu\text{g}\,(\text{m}^{-3}\,\text{h})$ and the mean mass concentration reached $45\,\mu\text{g}\,\text{m}^{-3}$ at midnight when assuming a particle density of $1.5\,\text{g}\,\text{cm}^{-3}$. The mean number concentration of particles $d_p > 100\,\text{nm}$ was between 5000 and $6500\,\text{cm}^{-3}$ throughout the day. The mean PM₁ mass concentration varied from 70 to $90\,\mu\text{g}\,\text{m}^{-3}$ during the day.

Lidar measurements revealed that the top of the aerosol layer was around 900 m above ground in all seasons, only slightly higher during the summer months. Frequently, elevated aerosol layers were observed, especially during winter and spring. A mean aerosol optical depth (AOD) of 0.95 was observed for air masses arriving from the North China Plain. In contrast, the mean AOD was about 0.42 for northerly air masses.

## India

Aerosol measurements in Gual Pahari, India, were performed from December 2007 to January 2010. The seasonal variation of the aerosol characteristics was very distinct in Gual Pahari (Hyvärinen et al., 2011a). The highest concentrations were observed during the winter and the lowest ones during the rainy season. The average monsoon-time mass concentrations of PM₁₀, PM₂.₅, and black carbon (BC) were 55–70 % lower than the pre-monsoon average concentrations, having a linear relationship with the total local rainfall during the monsoon season. The average PM₁₀ mass concentration (at STP conditions) was $216\,\mu\text{g}\,\text{m}^{-3}$ and the average PM₂.₅ concentration was $126\,\mu\text{g}\,\text{m}^{-3}$. A high percentage (4–9 %) of the PM₁₀ mass consisted of BC, indicating strong anthropogenic influence. The percentage of BC was higher during the winter and according to the diurnal pattern of the BC fraction, the peak occurred during active traffic hours. The rainy season decreased the average fraction of particle mass in the PM₂.₅ size range.

The diurnal variation of the aerosol properties was much dependent on the prevailing season, even though outside the rainy season the general characteristics were rather similar. Figure 4 illustrates the PM behaviour during different seasons. Maximum concentrations occurred in the morning around 07:00–08:00, because of the low boundary layer height and the morning traffic in the area. The warm day time temperatures initiated convective mixing, which is visible as a minimum in the diurnal PM mass data. The afternoon
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Diurnal variation of the PM₁₀ mass during different seasons in the Indian EUCAARI site.</figcaption></figure>

concentration was more than 50% lower compared to the morning values. Another maximum in mass concentration was observed in the evening due to traffic and reduction of the boundary layer height. During the strong daytime mixing, the fraction of particles smaller than 1 µm decreased.

New particle formation events were observed frequently at Gual Pahari. The decreased condensation sink due to convective mixing and dilution was the key factor enabling the new particle formation. Apparently, the vapor source rate in Gual Pahari was very high, because nucleation events were observed in over 60% of the measurement days. The particles grew rapidly reaching the Aitken and accumulation mode size thus contributing considerably to the aerosol mass concentration. In November, fewer particle formation events were observed, as the low night- and day-time temperatures resulted in weaker natural convection and a higher condensation sink.

Vertical profiles of aerosol properties were measured for more than one year (Komppula, 2010; Hyvärinen et al., 2011b). The vertical profiles of backscatter, extinction, and lidar ratio and their variability during each season were analyzed. The measurements revealed that the aerosol layer was on average highest in spring (5.5 km). In summer, the vertically averaged (between 1–3 km) backscatter and extinction coefficients had the highest average values. Aerosol concentrations were slightly higher in summer compared with other seasons. The autumn showed the highest lidar ratio and a high extinction-related Ångström exponent, indicating the presence of smaller absorbing particles. The winter had the lowest backscatter and extinction coefficients, but the extinction-related Ångström exponent was the highest.

## Brazil

Aerosol measurements were performed at a pristine Amazonian forest site near Manaus. We highlight here the seasonal variation of the optical and physical aerosol properties. The scattering coefficients ranged between 1 and 600 Mm⁻¹ at 450 nm, while absorption ranged between 1 and 25 Mm⁻¹ at 637 nm. A strong seasonal behavior was observed, with higher aerosol mass concentrations during the dry season (July–November) compared to the wet season (December–June). During the wet season, the single scattering albedo calculated from our measurements ranged from 0.90 to 0.99, whereas during the dry season, it ranged from 0.75 to 0.95. Although the site is remote, it receives the influence of regional biomass burning emissions during the dry season. Also, measurements of aerosol elemental composition indicate events of long range transport of African dust to the Amazonian forest site. These trans-continental sources of particles affect the optical properties of the natural aerosol population, with implications to the regional climate and to the forest nutrient cycle.

Measurements of submicrometer number size distributions indicated only a few events of new particle formation and subsequent growth along three years of measurements. From wet to dry season, integrated number concentrations increased approximately by a factor of 3. The shape of the particle number size distribution also changed. During the wet season, the Aitken mode (~30–100 nm) was prominent, suggesting the presence of secondary aerosol, most likely originated from the condensation of biogenic volatile organic compounds to the particle phase. In contrast, during the dry season the accumulation mode (100–500 nm) dominates the number size distribution, indicating the presence of primary biomass combustion and/or aged aerosol.

In Fig. 5 monthly averages of particle number size distributions are shown. The black curve represents the mean of all seasons, while the upper and lower curves are monthly averages of the dry and wet season, respectively. During the dry season, the Amazonian biomass burning aerosol dominates the number size distribution, while the concentrations are low during the wet season.

The number size distribution, light scattering and absorption coefficient data are the first long-term aerosol in-situ measurements ever performed in Amazonia, elucidating the differences between the biogenic aerosol population and the anthropogenic and long-range transport influences.

Lidar measurements were performed from January to November 2008 to obtain vertical aerosol profiles (Althausen et al., 2009) in Amazonia, determining the backscatter coefficient (wavelength: 355 nm, 532 nm, 1064 nm), the extinction coefficient (355 nm and 532 nm) and the depolarization ratio (355 nm). Furthermore, microphysical properties such as the effective radius and the volume concentration as well as the single scattering albedo were retrieved using the inversion algorithm by Ansmann and Müller (2005). The aerosol
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Seasonal behaviour of the particle number size distribution in the Brazilian EUCAARI site. The black curve represents the mean of all seasons. The upper and lower curves are the averages of the dry and wet season, respectively.</figcaption></figure>

optical depth (AOD) was derived by integrating the vertical extinction coefficient profiles.

A wide variety of aerosol conditions with a complex vertical aerosol layer structure were observed. During the wet season, clean conditions occurred occasionally with an AOD (532 nm) less than 0.03. This low AOD value is in the order of the lowest values measured for remote marine conditions (Andreae, 2009) and one of the lowest values ever measured on a continent. Beside such clean conditions, frequent intrusions of Saharan mineral dust and African biomass burning aerosol were observed (Ansmann et al., 2009). The mineral dust fraction in these African aerosol plumes was usually below 50%. The biomass burning aerosol from Africa seems to be as important as the Saharan dust in terms of trans-continental transport. During the dry season, Amazonian biomass burning dominated the optical aerosol properties with AODs up to 0.55 (532 nm). One major finding from the dry season observations is that virtually no cleaning due to short showers was observed.

The lidar measurements performed during EUCAARI were the first long-term observations of the vertical aerosol structure in Amazonia ever. It was also the first time that a multi-wavelength-Raman lidar was operated in the Amazon Basin.

### 3.3.3 Source apportionment of organic aerosol

Organic aerosol components (OA) account for a large mass fraction of the European aerosol, and accurate quantification, source apportionment and model descriptions are necessary in order to determine their effect on the radiative balance and air quality. The importance of biogenic sources and their response to climate change and air quality policy measures is

not yet adequately quantified, but is likely to be significant. The main accomplishments of EUCAARI in this area have been:

- A large mass fraction of the European aerosol is organic, and a large fraction of that carbonaceous aerosol is modern carbon (i.e. deriving from non-fossil fuel sources).

- A new comprehensive European AMS data set, which was analyzed by positive matrix factorization (PMF) and further supported by HNMR data, provided detailed information on the different sources of OC at urban and rural sites, including biomass burning aerosol, fossil-fuel POA, and oxidized organic aerosol (OOA). The latter fraction, both freshly-produced and aged typically comprised the largest fraction of OA. The origin of these oxidized components remains uncertain but can be considered as an upper limit for the total SOA contribution. The reconstructed carbon budget for selected stations indicates that most of such OOA must be apportioned to modern carbon sources.

- Major sources to modern carbon in Europe are wood combustion and secondary biogenic OA. The latter includes not only the products of terpenes oxidation, but also amines, which were found to characterize a variety of environments (Po Valley, Crete, Boreal forest). Since their source strengths are expected to vary in response to climate change, these sources of modern carbon may constitute important feedback mechanisms in the climate system.

- Both global and regional OA model parameterizations were developed. Secondary OA formation via multiphase reactions was shown to be an important contributor to global background OA.

- The comprehensive OA data set acquired within EUCAARI offers a unique opportunity to evaluate OA models on a wide range of spatial and temporal scales, and will be valuable beyond the EUCAARI timeframe.

The wide variety of co-supporting methodologies and their results are detailed in Appendix C. Figure 6 shows the source apportionment for TC for Hyytiälä, SPC and Melpitz, based on the different methods used ($^{14}$C, NMR, AMS). AMS HOA was approximated as fossil-fuel POA. Although there is an uncertainty due to the unknown modern fractions of EC for these stations, the carbon budget indicates that the sum of the OOA classes having no clear source characterization must actually be apportioned to modern carbon, but for the single classes we cannot exclude important contributions from fossil fuel carbon.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Source apportionment for TC for Hyytiälä, SPC and Melpitz, based on the different methods used (¹⁴C, H-NMR, AMS). “Biomass burning POA” are AMS “BBOA” and NMR factors correlated to biomass burning tracers (levoglucosan). “SOA biogenic & amines” include AMS and NMR factors for amines, and the NMR factor for terpene SOA (Finessi et al., 2011b). “SOA anthropo-/bio-genic” include semivolatile organic compounds determined by AMS (SV-OOA) of unspecified anthropogenic or biogenic origin; or NMR factors for oxygenated WSOC compounds of unspecified origin. “Aged OOA anthropo-/bio-genic” stand for low-volatility organic compounds determined by AMS (LV-OOA) of unspecified anthropogenic or biogenic origin, or NMR factors for oxygenated WSOC showing spectral profiles characteristic of humic-like substances (HULIS). “Fossil-fuel combustion POA” are approximated to AMS hydrocarbon-like compounds (HOA). All concentrations are calculated on a carbon basis. A circular arc of 360° refers to the average total carbon (TC = OC + EC) concentration at each site.</figcaption></figure>

### 3.3.4 Field observations of organic aerosol ageing

During EUCAARI, the atmospheric transformation of OA was studied during 30 ground-based field experiments using AMS and other OA characterization techniques, providing a unique European data set of OA “types”, defined by spectral fingerprints (and chemical composition) reflecting both sources and chemical ageing. Main results of the analysis of these measurements are:

- a new European phenomenology of submicron aerosol chemical composition based on time-resolved mass spectrometric (AMS) measurements;

- a first European phenomenology of “organic aerosol types”, defined by spectral fingerprints from both AMS and HNMR spectroscopies (Decesari et al., 2011a);

- LV-OOA (low volatility oxygenated organic aerosol), which are end-products of OA ageing, were associated by H-NMR analysis to HULIS-containing aerosols, and are the most common constituents of the European regional continental polluted background under clear sky and stable meteorological conditions (from May 2008 IOP).

An overview of how the various H-NMR and AMS source types correspond to each other is shown in Fig. 7. The AMS data are plotted in the space spanned by the organic mass fractions of *m/z* 44 versus *m/z* 43 (Ng et al., 2010). Extensively aged OA is found in the upper apex of the blue triangle (high organic mass fraction of *m/z* 44), while freshly emitted OA is typically found at the base of it. The larger diversity of compositions observed for fresh OOA reflects the multiple fingerprints of anthropogenic (e.g. biomass burning) and

biogenic (e.g. terpene SOA) sources, while the aged OOA exhibits a consistent composition dominated by carboxylic acids. H-NMR analysis highlights a sharp contrast between the composition of OOA in the lower left corner of the triangle (samples from the Po Valley) and that of samples from regional background stations (Mace Head, Montseny) which fall in the upper corner of the diagram. The former are dominated by aliphatic chains, poorly functionalized alkyl groups, amines, aromatic and alcohols (originating from wood burning emissions), while the functional group composition of the background stations is characterized by a variable amount of aromatic groups and by very functionalized aliphatic groups lacking of methylenic chains, hence pointing to HULIS. In the centre of the triangle, compositions of “atypical HULIS” are also found, which are interpreted as OOA of intermediate ageing state and are found at some polluted stations (Barcelona, Cabauw). On the other hand, H-NMR analysis identifies HULIS also in samples not characterized by extensive ageing (Melpitz), therefore missing a full overlap with the AMS categorization for OOA. Reasons for this discrepancy can be inherent to the methodologies: H-NMR characterization reflects more the structure of the backbone of the organic molecules and their functionalization degree rather than their actual oxidation state, therefore looking at the ageing processes with a different perspective compared to AMS. An alternative explanation is that in environments, such as Melpitz, the less oxidized OOA are accounted for by water-insoluble compounds, which were not analyzed by H-NMR spectroscopy in this study.
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Average H-NMR compositions from various EUCAARI campaigns compared to the corresponding AMS fingerprints for oxidized organic aerosols. NMR compositions are expressed as relative proportions of main WSOC chemical classes derived by factor analysis: Factor 1 (“Aromatic + polyols”) is associated with fresh biomass burning sources, Factor 2 (“Poly-substituted aliphatics”) are associated with atmospheric humic-like substances (HULIS), while Factor 3 includes minor components, such as amines. The AMS composition for OOA is shown as a function of the abundance of carbonyl (mass fragment *m/z* 43) and carboxyl (mass fragment *m/z* 44) functional groups (Ng et al., 2010).</figcaption></figure>

### 3.3.5 Results of the intensive observation period

#### Particulate pollution over Europe under anticyclonic conditions

During the EUCAARI-LONGREX campaign in May 2008 the DLR Falcon and FAAM BAe-146 research aircraft were deployed to measure microphysical, chemical and optical properties of atmospheric aerosol over Europe throughout the tropospheric column. The first half of May 2008 was characterized by the occurrence of a well developed, blocking anticyclonic system, which enabled the development of a very stable boundary layer over central Europe (Hamburger et al., 2011). Reduced horizontal wind velocities averaging below $7 \text{ m s}^{-1}$ at low levels and the stable vertical layering of the lower troposphere resulted in high total particle number concentration over the continent. The airborne measurements of aerosol number concentrations discussed by Hamburger et al. (2011) show a “C-shaped” vertical structure for particles with $d_p > 10 \text{ nm}$ (Fig. 8) with considerable day-to-day (flight-to-flight) variability throughout the tropospheric column. Boundary layer aerosol number concentrations ranged from 5000 to 20 000 particles cm$^{-3}$ in polluted regions to around 1000–2000 particles cm$^{-3}$ in rather remote areas. A significant number of freshly formed particles have been detected

during many flights (Crumeyrolle et al., 2010). Accumulation mode particles ($d_p > 150 \text{ nm}$) accounted typically for approximately for 10–20% of the aerosol population. A rather strong gradient between high number concentrations inside the boundary layer and the much cleaner free troposphere was characteristic for the high pressure conditions, whereas the contrast was clearly weakened after passage of frontal systems later on.

Almost undisturbed transport of continental anthropogenic pollutants to remote regions can occur. This process was observed for instance around 14 May 2008, contributing to about 90% of fairly high aerosol optical depths (AOD) over the Atlantic south west of Ireland (Fig. 9). Closure of optical aerosol properties determined from the High Spectral Resolution Lidar (HSRL) and the in situ aerosol optical aerosol spectrometers carried on the Falcon was successfully obtained for this case (Hamburger et al., 2011). Furthermore, the AOD as retrieved from satellite observations (here PARASOL; averaged AOD $0.31 \pm 0.03$ for the box marked in Fig. 9b) could be validated against the HSRL measurement (averaged AOD $0.36 \pm 0.05$ along the flight track in Fig. 9b).
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Vertical distribution of aerosol particles >10 nm (left panel) and >250 nm (right panel) over Europe during May 2010 from 43 individual vertical profiling missions with the DLR Falcon aircraft, measured by condensation and optical particle counters (see Hamburger et al. (2011) for details). In-cloud data are excluded. Number concentration refers to standard conditions (STP) 273 K and 1013 hPa.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Long-range transported central European pollution observed over the Atlantic off the west coast of Ireland on 14 May 2008. (a) Averaged vertical profile of the aerosol scattering coefficient as measured by High Spectral Resolution Lidar (HSRL; green line) and derived from the in situ particle size distribution measurements (square symbols) on board the DLR Falcon. (b) Map of aerosol optical thickness (AOD) at 532 nm wavelength retrieved from satellite (PARASOL) and HSRL measurements colour coded, where available, on the Falcon flight track.</figcaption></figure>

## Sub-micron aerosol chemical composition

The spatial distribution of sub-micron aerosol chemical composition has been characterized based upon airborne measurements in the planetary boundary layer across Europe (Morgan et al., 2010b). Downwind of major source regions total submicron mass loadings from the AMS exceeded $15 \mu g m^{-3}$ with organic aerosol (OA) and ammonium nitrate being the dominant chemical components, contributing 20–50% each to the non-refractory mass. OA dominates over

sulphate over most of Europe, with OA concentrations typically 1.3–2.5 times greater than that of sulphate. A positive matrix factorisation analysis of the OA component was conducted, revealing the dominance of oxidised organic aerosol (OOA) over hydrocarbon-like organic aerosol (HOA), which is consistent with previous literature (Jimenez et al., 2009; Zhang et al., 2007) as well as with the ground-based data. An empirical estimate based upon previous research indicated that HOA contributes less than 15% to the OA burden. Two separate OOA components were identified; one
representing an aged-more oxidised organic aerosol and an-
other representing fresher-less oxidised organic aerosol. OA
data can be viewed as a continuum with a progression from a
less oxidised, more-volatile component to a highly oxidised,
less-volatile component. This progression was observed to
occur as a function of the distance from source, with more
oxidised components dominating further downwind. Ammo-
nium nitrate dominates in North-Western Europe where the
emissions of NOₓ and ammonia reach their maximum.

Airborne measurements revealed complex partitioning of the semi-volatile aerosol components in the planetary boundary layer. Measurements revealed an increase in secondary aerosol mass with an increasing altitude in the boundary layer, causing an increase of the aerosol direct radiative forcing (Morgan et al., 2010a). Specifically, in-situ measurements in the vicinity of a ground-based measurement site at Cabauw, the Netherlands, showed that ammonium nitrate was the dominant chemical component aloft, while at the ground OA dominated. Furthermore, the fractional contribution to the sub-micron aerosol mass of ammonium nitrate increased with height in the boundary layer. This was primarily attributed to partitioning of semi-volatile gas phase precursors to the particle phase at reduced temperature and enhanced RH, a phenomenon which has been observed previously in California (Neuman et al., 2003). By comparing the optical properties measured on the aircraft with coincident measurements from the ground, a strong enhancement in the aerosol optical depth (AOD) and direct forcing was shown to occur when taking into account the additional mass associated water uptake and hence scattering caused by the partitioning phenomenon. Consequently, the radiative impact of anthropogenic aerosols is likely to be severely underestimated in Europe, where ammonium nitrate and OA are major components of the sub-micron aerosol burden. Such increases in AOD and radiative forcing have major implications for regional weather and climate, particularly as semi-volatile compounds are often not included in global and regional aerosol models.

Black carbon

The EUCAARI airborne IOP delivered also first measure-
ments of refractory black carbon (rBC) in the lower tropo-
sphere on a European scale (McMeeking et al., 2010). Aver-
aged rBC mass concentrations in the boundary layer (<3 km)
ranged from roughly 300 ng m⁻³ in near-urban regions to
50 ng m⁻³ in background environments. The rBC mass con-
centrations increased from the East to the West during a pe-
riod dominated by easterly flow, although fraction of sub-
micron mass was between 0.5–3% and displayed a weak
geographic dependence. Mass concentrations in the bound-
ary layer were more than a factor of 10 higher than in the
lower free troposphere, decreasing on average from about
100 ng m⁻³ to 5 ng m⁻³.

Airborne CCN and cloud property measurements

During EUCAARI, simultaneous observations of cloud con-
densation nuclei and lidar extinction profiles, as well as
simultaneous ground-based and airborne CCN concentra-
tions measurements provided the opportunity to quantify
the vertical distribution of CCN. Results from the ground-
based/airborne intercomparison based on data from the in-
tensive measurement period at Cabauw, Netherlands, in May
2008, indicate that CCN measurements on the ground of-
ten over-estimate the concentrations at levels where clouds
form. During the clean background conditions when the
air masses originated from the North Sea and cloud bases
were relatively low, the boundary layer was well mixed and
CCN concentrations at the ground resembled those at cloud
base. The difference between ground-based and airborne
measurements is especially important at higher concentra-
tions associated with local pollution, when boundary layer
mixing timescales are greater than the timescales for trans-
port. Ground-based and airborne lidar observations detect
multiple aerosol layers, provide insight to boundary layer
mixing and are useful tools to investigate the relationships
between ground-based and airborne measurements.

Lagrangian parcel model simulations

Lagrangian parcel model simulations were carried out to assess the closure among the aerosol and cloud-droplet measurements carried out on board the SAFIRE ATR-42 during the IMPACT campaign. The physicochemical aerosol characteristics measured below cloud base are used for initializing an air parcel model. The model covers detailed treatment of the evolution of aerosol size spectrum and predicts the shape of the spectrum of activated cloud droplets. The evolution of the spectrum is driven by changes in humidity that are in turn caused by adiabatic displacement of the air parcel. Results of multiple simulations performed using different vertical velocities were matched with the measured vertical wind speed spectrum to obtain statistics of droplet-spectrum parameters. The predicted statistics were compared with the in-situ measurements made just above the cloud base using the FSSP-100 cloud-droplet size spectrometer on 13 and 15 May (Arabas and Pawlowska, 2010). The result of that study is hoped to help a creation of a novel parametrization of the activation process in cloud models with detailed description of microphysics. A novel approach for solving the evolution of particle spectrum in an air-parcel model was developed for the purpose of this study (Arabas and Pawlowska, 2011, model code released with the paper).

Analysis of vertical velocity statistics on different levels in
the atmosphere and statistics of cloud micro-physical param-
eters (cloud droplet number concentration, liquid water con-
tent, cloud droplet radius) were performed (Fig. 10). That in-
formation has been used in the evaluation of the droplet acti-
vation model developed at the University of Warsaw (Arabas
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Statistics of cloud droplet spectrum plotted as a function of altitude for two research flights (RF51 – upper row, and RF52 – lower row) on 15 May above the North Sea in a Sc layer. A cumulative distribution of cloud droplet number concentration, liquid water content and effective radius on each level is represented by a set of 7 percentiles (1/8, 2/8, 3/8, 4/8, 5/8, 6/8 and 7/8) using vertical bars. The analysis carried out separately for regions with a positive (w>0) and negative (w<0) vertical velocity shows median value marked by a green (w<0) and red (w>0) crosses.</figcaption></figure>

and Pawlowska, 2011). Statistics of vertical velocity have been used also in the CAM-Oslo GCM model (Hoose et al., 2010a).

Profiles derived from the aircraft data taken during the flight on 15 May over the North Sea (RF51) were used to initialize Large Eddy Simulations (LES). Simulation was run using the Eulerian version of the EULAG model (www.mmm.ucar.edu/eulag) that solves anelastic equations with a 2-moment microphysics scheme that predicts liquid water mixing ratio and cloud droplet concentration. To study the process of turbulent mixing in clouds, the microphysics scheme was improved. In the original version of the scheme, the mixing scenario is determined by a single parameter, which is assumed to be constant in space and time during the simulation. To include in the model the variability of the mixing scenario in clouds, we took advantage of the direct numerical simulations (DNS) results (Andrejczuk et al.,

2009). Results from these simulations suggest that a simple relationship exists between the ratio of the time scales of droplet evaporation and turbulent homogenization and the slope of the mixing line on the diagram representing the relative change of the droplet concentration versus the change of the droplet radius cubed. To calculate the ratio of the time scales two new variables were added to model: the scale (or width) of cloudy filaments and the fraction of cloud air in the grid box.

The IMPACT stratocumulus case was used to compare the models with new 2-moment microphysics scheme and with the traditional 2-moment scheme (assuming homogenous or extremely inhomogenous mixing for entire simulation). Results from all simulations are in relatively good agreement with experimental data. The cloud water profiles show a bilinear structure, with different slopes in the layer between 400 and 600 m, and the layer above 700 m. This might
suggest a layer of cumuli beneath the stratocumulus growing into the stratocumulus deck. The differences between models are insignificant. This is what one might expect because homogeneity of mixing should not affect bulk cloud properties such as cloud water and cloud fraction profiles. It is not true for the mean droplet number concentration where differences between models should be important.

## LES simulations

High resolution Large Eddy Simulation (LES) studies are important in our effort to understand cloud processes. Data derived from the North Sea case studies provide excellent material for the initialization of LES models that were used to simulate the PBL-cloud formation and evolution. Development of these models now include (a) CCN parametrization, (b) the option to study in-depth the various mixing scenario's of the clouds with ambient [overlying air], (c) inclusion of the observed aerosol chemical and physical properties. A parametrization testbed has been used to compare the output from LES models to that from larger scale Single Column Models that are reduced versions of full scale 3-D regional/global climate models.

## Radiative closure

Our results from IMPACT sub-campaign show that a detailed model of the effects of aerosols on atmospheric radiation is able to capture the observed radiative signatures at the surface with a high degree of accuracy. This suggests that an accurate modelling of the direct aerosol radiative effect in global climate models is within reach provided that the global and regional distribution of aerosols is known.

The focus in this work was on the radiation budget of stratosphere clouds. To study this complex system, an atmospheric model capable of computing three dimensional cloud fields is needed. This requires high spatial and temporal resolution at large domain sizes. The gpuASAM model developed at the IfT in Leipzig uses graphical processing units (GPU) to provide the necessary computing power. It is a three dimensional atmospheric model with a two moment microphysics based on Seifert and Beheng (2006). With that it is possible to study the effects of different CCN concentrations on cloud structures. The model was evaluated using simple test cases (Bryan and Fritsch, 2002) but also some more complex GCSS test cases (BOMEX, DYCOMS). It is capable of producing three-dimensional cloud fields even with features like open and closed cell structures, at computation times of several hours using one GPU. More GPU's can be combined to enhance domain size or increase model resolution. With this new model large eddy simulations of cloud fields became possible on ordinary desktop computers or even on notebooks. In future, using actual high-end GPU servers, it will be possible to do these calculations in a forecast mode for example to accompany field measure-

ments. The produced three dimensional cloud fields are good input fields for usage in more sophisticated radiation transfer models, but none of these are currently implemented in the model framework.

## 3.4 Regional and global aerosol and clouds

### 3.4.1 Satellite data

EUCAARI used satellite data to understand regional and global variations of aerosol and cloud properties, and aerosol-cloud interactions. The resulting aerosol and cloud information on regional and global scales was used in process, transport and effects studies in support of the assessment of air quality and climate. The observations were made using experimental state-of-the-art space-borne instruments (MODIS, AATSR, PARASOL, OMI, Cloudsat, CALIPSO and MSG SEVIRI) (see de Leeuw et al., 2011b). This required the development of new methods/algorithms and the improvement of existing ones to improve the quality of retrieved aerosol and cloud parameters and the retrieval of new parameters by optimum utilization of the technical characteristics of the available instruments. Tools have been developed to visualize and analyse combined data sets. The analysis focused on the EUCAARI campaigns LONGREX and IMPACT, on the distributions and effects over Europe and around the EUCAARI sites in China, India and South Africa, and on regional, global and seasonal variations of aerosol and cloud properties and radiative effects. Retrieval results were analyzed and inter-compared to improve their quality and the understanding of the retrieval products. Aerosol optical depth (AOD) and Fine Mode Fraction (FMF) were compared with independent ground-based and airborne measurements as well as with model results. These comparisons served to evaluate both the retrieval and the model outcomes. Cloud properties retrieved from satellite observations show the effects of aerosols on cloud microphysical and optical properties and the evolution of clouds as well as information on cloud phase, which leads to better understanding of cloud properties and effects.

Satellite-based instruments provide information on the spatial distribution of atmospheric constituents on regional to global scales (Burrows et al., 2011). Of particular interest for EUCAARI was the retrieval of aerosol and cloud properties using radiometers or lidar systems. Satellite-retrieved aerosol and cloud properties provide information on atmospheric processes and especially on aerosol-cloud interactions and radiative effects of clouds through their macroscopic and microphysical properties. The current status of the retrieval of cloud properties has been described by Kokhanovsky et al. (2011). The current status of the retrieval of aerosol properties over land has been described by Kokhanovsky and de Leeuw (2009) and de Leeuw et al. (2011b). The validation of aerosol retrieval is described in Piters et al. (2011). Together these publications provide a
good overview of the instruments and algorithms used in EU-CAARI to provide aerosol information from space. Aerosol and cloud properties have been retrieved using instruments flying on sun-synchronous satellites as well as on geostationary satellites. The former provide information on a global scale within one to a few days, the latter provide information on part of the globe but with temporal resolution of multiples of 15 min.

## Satellite detection of aerosols

Aerosol retrieval products over Europe are available from AQUA/MODIS, PARASOL, OMI, and the CALIOP lidar, all flying in the A-Train constellation, as well as AATSR (on ENVISAT) and SEVIRI (on MSG, geostationary). For all instruments algorithms have been further developed and improved.

The AATSR dual view algorithm was further developed and improved (Kolmonen et al., 2011). Results for 2008 are shown in Fig. 11. Global aerosol retrieval results on the aerosol optical depth (AOD) over land were compared with ground-based AERONET (Holben et al., 1998) AOD data. This comparison showed where the algorithm provides good and less good results, with the latter needing further development (Kolmonen et al., 2011). For the determination of the aerosol altitude using data from a passive sensor, a new approach has been developed that uses the oxygen A-band of POLDER (Dubuisson et al., 2009). The method provides insight into the vertical distribution of aerosol on a global scale. A new approach for the retrieval of aerosol optical thickness above clouds from merged POLDER and MODIS observations has been developed by Waquet et al. (2009). The method provides a new perspective for studying aerosol properties and radiative forcing in the presence of underlying clouds. The OMI multi-wavelength aerosol algorithm (OMAERO product) has been developed (Torres et al., 2007) to retrieve the aerosol optical thickness and a best fitting aerosol type. The single scattering albedo, the layer height and the size distribution associated with the best fitting aerosol type are provided. OMAERO aerosol products have been improved by the use of the surface albedo climatology from OMI over land (Kleipool et al., 2008) in the retrieval algorithm to account for surface effects on the radiation measured at the top of the atmosphere. Further OMAERO improvement is expected from combining the MODIS-AQUA cloud screening with the OMI data, in order to improve the cloud screening for OMAERO. MODIS-AQUA and OMI are both part of the A-Train satellite constellation and the two sensors observe the same area within 10 min.

Apart from the aerosol optical thickness and the aerosol type retrieval, the OMAERO product also produces the Aerosol Absorbing Index (AAI). The AAI is not a geophysical parameter, but an indicator of the presence of elevated layers of absorbing aerosol, such as desert dust or biomass

<figure><img src="image_14.png" /><figcaption>Fig. 11. AOD at 555 nm over Europe, as an aggregate of the individual data for the whole year 2008. White areas (e.g. over the Alps) indicate the absence of data.</figcaption></figure>

burning plumes. The multi-year OMI AAI data series show the inter-annual variability of this parameter. A unique new result is the retrieval of the AOD above clouds from merged POLDER and MODIS observations (Waquet et al., 2009). It provides a new perspective for studying aerosols properties and radiative forcing in the presence of underlying clouds.

## Satellite detection of clouds

A study on the information content analysis of Multi-viewing polarization measurements for liquid cloud microphysical retrievals by Labonnote et al. (2011) showed that the physics governing the radiative transfer in clouds is relatively well known, whereas the impact of model uncertainties and instrument noise in the information content of an observing system is not as clear. The study further showed that there is potentially more information than only the two first modes of the size distribution (effective radius and effective variance) in such measurements. It might be possible to retrieve the entire shape of the size distribution depending on the angular resolution and the signal to noise ratio of the instrument.

MODIS and POLDER cloud fraction, cloud optical thickness and cloud phase as well as seasonal variations and spatial distribution of high, middle and low, ice/liquid cloud fractions have been compared by Zhang et al. (2010) and Zeng et al. (2010). Seasonal variations and spatial distribution of high, middle and low, ice/liquid cloud fractions have been compared and analyzed. The impact of microphysical model uncertainties on cloud optical thickness retrievals and subsequent errors on the estimate of ice cloud radiative forcing has been quantified.
An algorithm has been developed to retrieve an improved cloud top phase product using POLDER/PARASOL and MODIS/AQUA (Riedi et al., 2007). The resulting product provides a semi-continuous confidence index ranging from confident liquid to confident ice instead of the usual discrete classification of liquid, ice, mixed or simply unknown phase clouds.

A sophisticated method using a variational technique has been developed to retrieve cloud size distribution parameters (effective radius and effective variance) from multi-viewing/spectral polarized measurements (Labonnote et al., 2009). Due to the data used (polarization) this microphysical information primarily comes from cloud top and is mainly sensitive to narrow size distribution (e.g. small effective variance).

The OMI effective cloud fraction represents the cloud influence on the reflectance, not a geometric coverage. The OMI effective cloud fraction has been validated against the MODIS/Aqua cloud optical thickness (Sneep et al., 2006). The MODIS instrument uses thermal infrared radiation to determine the cloud (top) pressure. OMI uses reflected sunlight close to the O₂-O₂ absorption band at 477 nm and yields a pressure near the mid-level of the cloud. The clouds observed near 40 degrees latitude are probably a multi-layer cloud deck (cirrus over water clouds) where MODIS observes the top layer, and OMI the bottom layer. Model comparisons have shown that the cloud pressure retrieved by OMI is near the mid-level of the cloud. This is very different from the cloud pressure derived in the thermal infra-red, where a cloud top is found. Comparisons with CloudSat have confirmed this behaviour (Sneep et al., 2008).

## Performance criteria and trend analysis of satellite aerosol observations

A new scoring system has been introduced to quantify the performance of MISR and MODIS satellite sensor retrieval products for aerosol optical depth (AOD). Based on comparisons to highly accurate ground-based sun-photometer data of AERONET here stratified into 25 regions and 12 months, scores for bias and variability are assigned. These regional and temporal sub-scores are then combined into single annual global overall scores. MODIS (0.61) and MISR (0.58) global annual scores are at the top of available multi-annual AOD data-sets. Both data-sets (based on multi-annual statistics) score even better than the usually well behaved multi-model median (0.58). MODIS scores better over oceans and MISR scores better over land. Another aspect of this new scoring is the diagnostics, which allows tracing poor retrieval performance back to failure at temporal and spatial sub-scales. Such analysis for instance suggests that MODIS suffers from retrieval issues over continents in mid-latitudes during winter (possibly due to sub-pixel snow) and that MISR suffers from retrieval issue at high latitudes (certainly related to MISR's relatively poor temporal sampling).

Differences in scores at these sub-scales allow the identification of regional and seasonal retrieval strengths and help in making more objective choices when picking one retrieval over another.

## 3.4.2 Natural vs. anthropogenic contribution to aerosol number concentrations

### Global particle number concentrations with GLOMAP

Model runs on the global impact of secondary particle formation on CN (condensation nuclei, i.e. measured particle numbers) and CCN (cloud condensation nuclei) numbers were conducted with the global CTM GLOMAP (Spracklen et al., 2006, 2008b, 2010; Merikanto et al., 2009, 2010). The computational schemes based on linear or square dependence on sulphuric acid for boundary layer (BL) nucleation (developed within EUCAARI, Sect. 3.2.1 and Kulmala et al., 2006; Sihto et al., 2006; Spracklen et al., 2006; Kerminen et al., 2010) were used and the binary homogeneous nucleation in the upper troposphere was also accounted for (Merikanto et al., 2009). The model runs with a global focus were conducted using pre-existing inventories for particulate emissions, while in the model runs concentrating on the European domain the particle number emission inventories developed in EUCAARI were used.

The results suggested that the primary emissions can reproduce the spatial variation of the particle number concentrations on a global scale (Spracklen et al., 2010). A clear influence of secondary particle formation on the total CN numbers was seen particularly on the seasonal behavior of particle number concentrations (Spracklen et al., 2010). The results reported by Merikanto et al. (2010) suggest that on average about 75% of predicted global surface level number concentrations of $d_p > 3$ nm particles had originated from nucleation. Over the continents BL nucleation is the primary source of these nucleated particles, whereas near the equator a large contribution from upper tropospheric nucleation is predicted (Fig. 12) (Merikanto et al., 2010).

Merikanto et al. (2010) concluded that 45% of global low-level cloud CCN at 0.2% supersaturation are secondary aerosol derived from nucleation (ranging between 30–50% taking into account uncertainties in primary emissions and nucleation rates), with the remainder from primary emissions. Boundary layer nucleation and emissions of biogenic organics are coupled, when looking at their effect on CCN numbers. On average the forest emissions increase boreal CCN by a factor of 2 (Spracklen et al., 2008a). However, in a run without nucleation the forest emissions increase CCN only by a factor of 1.5 The contribution of biogenic SOA formation to total aerosol mass, on the other hand, was estimated using a physico-chemical box model applied in a Lagrangian manner over Scandinavia (Tunved et al., 2008). A recently-developed parametrization for the aerosol mass
<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Ground level total particle (above 3 nm in diameter) average concentrations of (a) all particles (sum of panels b, c and d); (b) primary particles; (c) nucleated particles entrained from upper troposphere; (d) particles nucleated in the boundary layer, as predicted with GLOMAP (published in Merikanto et al., 2009).</figcaption></figure>

yield from biogenic terpenes was used (see also Tunved et al., 2006). It was demonstrated that the forest itself could produce up to 200 CCN per cm³ on average over Scandinavia. Model runs comparing global CCN, cloud droplet numbers (CDN) and cloud reflectivity in 1850 and 2000 were conducted with GLOMAP. The results indicate that the global impact of nucleation on the 1850-to-2000 change in cloud reflectivity is small (few percent) but regionally it may be as high as 50% and can be either positive or negative (see Fig. 13 and Merikanto et al., 2010). These results suggest that boundary layer nucleation is important in the first indirect forcing calculations on a regional scale.

GLOMAP predictions of particle number over Europe were compared to aircraft and ground-based measurements recorded during the May 2008 EUCAARI intensive campaign and Long Range Experiment (LONGREX) by Reddington et al. (2011). It was found that the spatial distributions of campaign-mean number concentrations >50 nm ($N_{50}$) and >100 nm ($N_{100}$) dry diameter were well captured by the model ($R^2>0.8$) and the normalised mean bias was also small (−18% for $N_{50}$ and 1% for $N_{100}$) if a small emission size was assumed for primary carbonaceous particles, as used by AEROCOM (Dentener et al., 2006). Number con-

centrations of particles <50 nm dry diameter ($N<50$) were substantially underpredicted at most ground sites unless an empirical mechanism was included to simulate BL nucleation. Comparisons with aircraft observations were consistent with these findings. The results of a t-test showed that by including BL nucleation, a statistically significant difference between modelled and observed $N<50$ was removed at roughly half the ground sites. Including BL nucleation increased simulated $N_{50}$ and $N_{100}$ over Europe by ~10–40% and ~5–20% respectively, depending on the mechanism and on the emission size of primary particles. However, within the uncertainty of the observations and accounting for the uncertainty in the size of emitted primary particles, BL nucleation made a statistically significant contribution to CCN-sized particles ($d_p > 50$ nm) at less than a quarter of the ground sites. Despite testing several empirical parametrisations for BL nucleation (including new parametrisations developed within EUCAARI, Paasonen et al., 2010), the agreement between hourly time series of modelled and observed nucleation events in this period was fairly poor. From this 1-month intensive European dataset it was not possible to determine a reliable estimate of the fraction of CCN-sized particles from primary and secondary sources, although the
<figure><img src="image_16.png" /><figcaption>**Fig. 13.** The 1850–2000 change in cloud albedo with and without boundary layer particle formation (BLPF) at 300–1000 m above ground level, as predicted with GLOMAP. Results are shown for an initial albedo $R_c = 0.35$ assuming cloud updraft velocity $w = 0.4 \text{ m s}^{-1}$ (published in Merikanto et al., 2010).</figcaption></figure>

size of primary emitted particles was shown to be a major
source of uncertainty.

## Global particle number concentrations with ECHAM5-HAM

The global climate model ECHAM5-HAM (Stier et al., 2005) was modified to improve the representation of new particle formation in the boundary layer. The effect of nucleation on cloud droplet number was studied with the modified version of the model by Makkonen et al. (2009, see Fig. 14). In these runs the simple particle formation scheme introduced in EUCAARI (see also Sihto et al., 2006) was implemented together with a model of condensation of biogenic organics, which are able to contribute particle growth. Comparisons to observations indicated that simple nucleation scheme used in the study is a promising way to improve the ECHAM5-HAM model towards the average values observed over different locations.

## European number concentrations with PMCAMx-UF and GLOMAP using emission inventories developed in EUCAARI

The regional 3-D-model PMCAMx-UF was developed (Jung et al., 2008), and its first tests were conducted for the Eastern United States for which input data such as emission inventories were readily available (Jung et al., 2010). The model simulates the aerosol number (from 1 nm to 10 µm) and mass distributions for a variety of chemical components, with a spatial resolution of 36 km × 36 km and temporal resolution of one hour. To test the impact of boundary layer nucleation on aerosol number, the model allows the user to select among several different nucleation parametrisations, including the ones developed in EUCAARI (Sihto et al., 2006; Paasonen et al., 2010). Furthermore, a version of PMCAMx-UF simulating the European domain was developed, and the newly-developed anthropogenic particle inventory (see Sect. 3.1) was implemented (Fountoukis et al., 2011b). Figure 15 shows the average predicted total number concentration of particles larger than 3 nm ($N_3$), 50 nm, ($N_{50}$), and 100 nm ($N_{100}$) for the ground level for May 2008. The major nucleation areas are in the S-SE of Europe. The maps
<figure><img src="image_17.png" /><figcaption>**Fig. 14.** Annual zonal average concentrations (cm⁻³ STP (1013.25 hPa, 273.15 K)) of (a) nucleation mode aerosol number; (b) sum of Aitken mode number concentration: centre ones experiment A1 (using activation nucleation in addition to the standard binary nucleation) as predicted by the GCM ECHAM5-HAM; and right panels show the ratio of the yearly averages for experiments B (only binary nucleation) and A1. Notice the different color scales between figures (published in Makkonen et al., 2009).</figcaption></figure>

of average *N*<sub>50</sub> and *N*<sub>100</sub> concentration fields have in general similar features, while the spatial distribution of *N*<sub>3</sub> is quite different. The average ground concentrations over the whole modelling domain are predicted to be 6667, 1465, and 390 cm<sup>-3</sup> for *N*<sub>3</sub>, *N*<sub>50</sub>, and *N*<sub>100</sub>, respectively. Additional simulations were performed for the same period with (1) nucleation turned off to study the secondary contribution to particle number concentrations; (2) anthropogenic SO<sub>2</sub> and primary aerosol emissions reduced with 50%, respectively, to study the anthropogenic impact on aerosol particle number concentrations. Corresponding simulations were conducted with GLOMAP using the same nucleation parameterisations, EUCAARI-developed emission inventories, and meteorological fields.

The evaluation of the anthropogenic particle number emission inventories developed in EUCAARI (Sect. 3.1.1) is an important contribution to the assessment of the anthropogenic impact on atmospheric aerosol numbers in the European boundary layer. These emission inventories were imple-

mented in PMCAMx-UF and GLOMAP, and comparisons between the predicted particle number concentrations against observations at the EUCAARI field sites during the Intensive Observation periods in May 2008 and March 2009 were conducted to evaluate the performance of the inventories. The results indicate that the models performed relatively well in both capturing the levels of particle number concentrations and size distributions, as well as their temporal variation (see Fig. 16, for the comparison for *N*<sub>50</sub> in May 2008).

The average impact of secondary particle production by nucleation on average number concentrations of particles larger than 3 nm (*N*<sub>3</sub>) and 50 nm (*N*<sub>50</sub>) in Europe, and their sensitivity to 50% reductions in anthropogenic SO<sub>2</sub>, primary particle and biogenic VOC emissions are depicted in Table 2. The estimates are based on the PMCAMx-UF and/or GLOMAP simulations. The results suggest, in line with the previous global model studies (Merikanto et al., 2010), that a considerable fraction of >3 nm and >50 nm aerosol and CCN number concentrations are of secondary origin. The number concentrations also seem to be relatively insensitive to potential emission reductions. The impacts of the emission reductions on the size distribution are notable: while reducing SO<sub>2</sub> emissions will reduce number concentrations in all size classes, primary aerosol emission reductions will have the largest impact on >50 nm particles. According to predictions with GLOMAP, the similar behavior is predicted for the impact of VOC emissions. It should be noted, however, that large uncertainties exist in the role of organic compounds – and therefore in the role of VOCs – in defining the lifetimes and size distributions of atmospheric aerosol populations.

### Observed changes in particle formation due SO₂ emission reductions

In order to study the effects of past SO₂ emission reductions on small particle concentrations via possible reduction in new particle formation, we examined two long term datasets of aerosol size distributions recorded in Melpitz, Germany, in 1996–1997 and in 2003–2006 (Hamed et al., 2010). Between the two periods, SO₂ concentrations decreased on average by 65%. This decrease was accompanied by a 45% decrease in the frequency of new particle formation events, and a 68% decrease in the average new particle formation rate. Examination of the various factors affecting sulfuric acid concentrations (i.e. SO₂ concentration, intensity of solar radiation, and the condensation sink) allowed us to conclude that the SO₂ reductions were indeed the reason in the decreased new particle formation. However, the growth rate of the freshly formed particles increased by 22% between 1996–1997 and 2003–2006, resulting in increased likelihood of the new particles to grow to sizes above 100 nm. Therefore, and rather counterintuitively, the production of 100 nm (and larger) particles origination from new particle formation events was increased in the 2003–2006 period compared to 1996–1997, although new particle formation
<figure><img src="image_18.png" /><figcaption>**Fig. 15.** The average number concentrations (cm⁻³) at the ground level above (A) 3 nm, (B) 50 nm, and (C) 100 nm as predicted by the regional CTM PMCAMx-UF for 1–29 May 2008 simulation (Fountoukis et al., 2011b).</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>**Fig. 16.** Comparison of measured and modeled (PMCAMx-UF, left; GLOMAP, right) number concentrations of particles larger than 50 nm during May 2008 in a selection of EUCAARI field stations (see Fig. 3 for explanations of the letter codes for the stations). The model runs have been conducted using the nucleation parametrisations and primary aerosol emission inventories developed in EUCAARI.</figcaption></figure>

itself decreased. The reason for the increased growth rate may have to do with increased emissions of biogenic VOC's as the new particle formation day temperatures were on average clearly higher in 2003–2006 than in 1996–1997.

**From primary vs. secondary to natural vs. anthropogenic contributions to particle number concentrations**

Summarizing, the importance of anthropogenic emissions as compared with natural aerosol background to the aerosol loading in atmosphere was one of the core questions of EUCAARI. To address this question for atmospheric aerosol number concentrations, it is crucial to unravel the fraction of particles with primary as compared with secondary origin – the former having primarily anthropogenic sources over the European continent, and the latter being typically a complicated mixture of both natural and anthropogenic components. We studied the sources of atmospheric aerosol particle

numbers with global and regional models, equipped with the state-of-the-science parameterisations for secondary aerosol formation and primary aerosol number emission inventories – both developed within EUCAARI. The main results of these studies are:

1. Nucleation is a major source of aerosol particle number concentrations, and usually several tens of percents of sub-micron aerosol particles have originated from condensation of atmospheric vapours – thus being of secondary origin. As a rough approximation one could estimate that about half of aerosol particles in terms of their total number concentration in the European boundary layer have originated from nucleation.

2. Secondary aerosol formation is a combination of both natural and anthropogenic influence: while anthropogenic sulfate emissions are a major factor governing formation of new particles, natural emissions of
<table><caption>**Table 2.** Impact of nucleation and emission reductions on particle number concentrations in Europe. Where available, the values represent the average of PMCAMx-UF and GLOMAP. Otherwise the numbers are based on one of the models only.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Relative increase due to nucleation (%)</th><th colspan="3">Relative PN change for 50% reduction in emissions</th></tr><tr><th>SO<sub>2</sub> (%)</th><th>Primary PN (%)</th><th>VOC (%)</th></tr></thead><tbody><tr><td>N<sub>3</sub></td><td>263</td><td>-15</td><td>-8</td><td>3</td></tr><tr><td>N<sub>50</sub></td><td>63</td><td>-12</td><td>-15</td><td>-8</td></tr></tbody></table>

biogenic organic vapours play an important role in defining the aerosol size distributions and the climatic impact of aerosols. Our results suggest that the anthropogenic contribution (both primary and secondary) is dominating in the most parts of Europe, the biogenic component being of less importance. However, halving SO₂ and anthropogenic primary particle emissions (see Table 2) would result in reductions of the order of 20% on the total particle number concentrations – which might suggest that the natural aerosol production might compensate somewhat for the reductions in the anthropogenic aerosol production.

3. Air quality-driven reductions of global anthropogenic SO₂ emissions are likely to decrease the cooling effect of aerosols during the next hundred years – due to their impact on secondary aerosol formation. This effect is likely to overwhelm the potential changes in natural emissions of aerosol precursors.

It is important to mention that impact of different air quality-driven reduction scenarios are still associated with a lot of uncertainty

### 3.4.3 Parameterisations of processes

Due to the required long computing time complex processes usually need simplification before they can be implemented in global and regional models. These simplifications should still represent the full processes from the physical and/or chemical point of view. Therefore they need extensive testing before their results can be trusted. The EUCAARI project produced quality controlled parameterizations and investigated the accuracy of essential assumptions used in the models. Key results of this work are:

1. The role of a minimum in cloud droplet number concentration assumed in models was quantified, thus reducing the sensitivity of the estimated aerosol first indirect effect to this arbitrary choice.

2. The parameterization of the cloud updraft velocity was improved, allowing a better determination of the activation of aerosol particles to cloud droplets.

3. Ice nucleation parameterizations were tested and improved with emphasis on the role of bioaerosols in atmospheric ice nucleation in mixed-phase clouds.

4. The treatment of boundary layer was improved. The new description allows better results for relation between Liquid Water Path and Aerosol Optical Depth.

5. The description of stratocumuli thickness was improved.

6. An improved scheme for CCN activation was developed filtering numerical artifacts.

7. A parameterization of surface partitioning was developed that can be included in large-scale models.

8. New nucleation parameterizations were developed, evaluated and tested. The existing parameterization for aerosol formation was updated.

9. The relative accuracies and differences between modal- and bin-descriptions of the aerosol size distribution were evaluated.

#### Cloud droplet number concentration

Some global aerosol-climate models impose a lower bound to cloud droplet number concentration (CDNC) or aerosol concentrations. Typical values of this lower bound range between 5 and $40 \text{ cm}^{-3}$. In the pre-industrial era very low aerosol concentrations were not as uncommon as they are today. A constraint on the CDNC influences simulated clouds strongly and in a non-physical way. The common practice of prescribing a lower bound on the droplet number concentration is avoided in CAM-Oslo. Hoose et al. (2009) showed that arbitrary lower bounds on the droplet concentration lead to suppression of the simulated first indirect effect (from -1.9 W m² up to -0.6 W m²), especially over oceans. Constraining aerosol concentration instead of droplet concentration has a weaker effect on the change in short-wave cloud forcing and can be considered physically more correct, because global aerosol-cloud models lack some aerosol species like primary biological particles or non-desert dust.
## Cloud updraft velocity

The parametrization of the in-cloud updraft velocity, which determines the activation of aerosol particles to cloud droplets, has been the subject of model studies and comparison to observations from the EUCAARI-IMPACT campaign (Hoose et al., 2010a). The onset of the Wegener-Bergeron-Findeisen process in mixed-phase clouds is also related to the distribution of in-cloud updraft velocity. Therefore the updraft velocity parametrization has also an influence on the simulated 1st indirect effect in mixed-phase clouds (Lohmann and Hoose, 2009). Updraft velocity is a critical parameter in cloud formation, because it determines how many CCN are activated. GCMs cannot resolve the updrafts, so they need to be parametrized. In the CAM-Oslo model, a parametrization from Abdul-Razzak and Ghan (2000) is used, calculating the probability density function (PDF) of the vertical velocity in each grid box, and relating the width ($\sigma_w$) of the PDF to the eddy exchange coefficient that is given by the turbulence scheme. Comparing the obtained values of $\sigma_w$ from the model with various observations, e.g. EUCAARI-IMPACT data (Cabauw), a rather poor agreement has been found, and in particular an underestimation on cloudy days.

Based on these results, a new parametrization has been derived, in which an additional term, proportional to LWC (liquid water content) is added to the formulation of $\sigma_w$. The physical idea behind this is that clouds not only depend on updrafts for their formation, they also produce turbulence via two mechanisms: cloud top cooling and latent heat release. In fact it has been known for many years that in marine stratocumulus, it is the cloud top cooling that is the main source of turbulence in the boundary layer, and it is precisely that turbulence which transports moisture from the surface to the cloud base, thereby maintaining the cloud.

With the new formulation, the agreement between the model-predicted $\sigma_w$ and the observed $\sigma_w$ is greatly improved. When the new formulation is tested in the CAM-Oslo model, it yields an overall 36% increase in cloud droplet number, significantly reducing the model negative bias.

## Ice nucleation on biological and mineral dust particles

Global model simulation of bacterial, fungal spore and pollen with the CAM-model showed that simple bioaerosol emission parametrisations can reproduce average measured concentrations. The modelled average bioaerosol contribution to heterogeneous ice nucleation in mixed-phase clouds is very small. If they are present in high enough concentrations (significantly higher than the climatological concentrations simulated in this study), they might trigger glaciation of clouds at warmer temperatures and lower altitudes than in their absence (Hoose et al., 2010b).

Most assessments of the aerosol first indirect effect so far only deal with CCN influencing cloud droplet formation.

While some climate models calculate ice nucleation from natural and anthropogenic aerosols, so far they have only used quite simple, empirical parametrizations. In order to reduce the uncertainty associated with the aerosol indirect effect, a new parametrization of heterogeneous ice nucleation has been developed, in which the ice nucleation rates and their temperature dependence are derived from classical nucleation theory and laboratory data (Hoose et al., 2010c). The parametrization treats three types of IN: mineral dust, soot and primary biological aerosol particles (PBAP: bacteria, pollen and fungal spores). This is the most detailed parametrization of heterogeneous ice nucleation developed so far in any global climate model.

Multi-year simulations were carried out with the CAM-Oslo model, using the new parametrization. Comparing simulated IN concentrations to observations using the airborne CFDC instrument shows generally good agreement over the whole temperature range from 0° to -40°. It was found that mineral dust accounts globally average of 77% of the ice crystal nucleation in mixed-phase clouds, followed by with soot at 23%, while the PBAP contribution is much less than 1%. Even when rather extreme assumptions are made on the nucleation ability of PBAP, their contribution remains small. Hence, our results do not support earlier suggestions in the literature of a large contribution from biological particles to ice nucleation (Christner et al., 2008; Prenni et al., 2009). However, we do not rule out a significant role of such particles in certain areas and at certain times.

With the new ice nucleation scheme in place, the simulated aerosol indirect forcing is reduced by about 25% compared to simulations that do not treat ice nuclei. The reduction is caused by a reduced lifetime effect, as anthropogenic soot stimulates the freezing of supercooled water. Precipitation release is much more efficient when ice crystals are present, and therefore the freezing stimulates precipitation, which is the opposite of the Albrecht effect in warm clouds.

## Improvements of boundary layer parametrization

The ECHAM5 model uses a turbulent kinetic energy (TKE)-scheme, which simulates the cloud top fluxes in function of the local turbulence. It reproduces relatively well the clear convective and stratocumulus topped boundary layers in a quite high resolution model (e.g. Duynkerke and Driedonks, 1987). Nevertheless, a new version of the boundary layer including a parametrisation of cloud-top entrainment in the stratocumulus regions has been included in ECHAM5 (ENTR). The standard version (STD) includes turbulent diffusion on non-conserved variables. The highest values of TKE in ENTR are more in the range of measurements (between 0.1 and 0.5 m s⁻¹) than the huge values in STD. Although using the new scheme the relation between Liquid Water Path and Aerosol Optical Depth becomes closer to the satellite measurements it is still not enough as the observations show a negative slope of all the variables for
AOD > 0.2. Nevertheless, the explicit entrainment reduces the sensitivity of LWP to AOD on a global scale as well.

### Improved description of stratocumuli thickness

It has been observed that stratocumuli are too shallow to be well represented in the standard vertical grid of models like ECHAM5. To better simulate these low clouds without increasing the vertical resolution tremendously, two levels are added dynamically wherever a stratocumulus could form. More precisely, the thickness of the stratocumulus is found following the approach presented in Grenier and Bretherton (2001), and a new grid containing 2 more levels based on it is defined. The algorithm which computes the cloud top and the vertical thickness of the stratocumulus-topped boundary layer in ECHAM5 and defines the new grid has been implemented. The method allows the existence of stratocumuli in the right place in the GCM with a reasonable pressure for the inversion (cloud top).

### Supersaturation simulation to reduce spurious CCN activation

CCN activation is among the shortest time scale processes in cloud physics and effective prediction of the number of activated CCN requires time steps that are not feasible in models. Supersaturation prognostic schemes show spurious peaks leading to unrealistic activation. A new scheme has been developed, tested in a parcel model and implemented in 3-D Large Eddy Simulation framework (CNRM), based on advection of supersaturation, even though supersaturation is not a conservative variable. Combined with the supersaturation prognostic derived from heat and moisture, the scheme allows filtering numerical artifacts and it provides an accurate prediction of CCN activation. It has been extensively tested in a parcel model against explicit calculation of supersaturation at a 0.01 s time resolution. Implemented in a 3-D LES framework to simulate cumulus and stratocumulus clouds, it appears very efficient at suppressing spurious CCN activation at cloud boundaries.

### Surface partitioning of surface active compounds

Simplified descriptions for surface partitioning of surface active compounds in liquid droplets were developed (Prisle et al., 2010; Raatikainen and Laaksonen, 2011). These parametrizations are computationally affordable and can be applied in regional-scale and global-scale models.

### Nucleation and aerosol formation rate parametrizations

Several parametrizations related to modelling atmospheric aerosol formation were derived. We concentrated on developing semi-empirical ones, in which the nucleation rate is assumed to follow a simple power-law dependence on the gaseous sulphuric acid (and organic vapour) concentration

(Riipinen et al., 2007a; Paasonen et al., 2010). Data from 12 European field sites with different types of air ion and cluster spectrometer measurements were used to derive a semi-empirical parameterization for ion-induced nucleation (Nieminen et al., 2011).

Direct application of nucleation rate parameterisations in large-scale models is not possible, or at least not desirable because of the numerical cost. For this reasons, an updated parametrisation that relates the apparent formation rate of particles at any diameter to the nucleation rate was derived (Lehtinen et al., 2007). This parametrization was developed further to take into account the nuclei self-coagulation (Anttila et al., 2010). All these parametrizations are readily applicable in large-scale atmospheric models.

### Modal vs. sectional model intercomparison

A modal aerosol model was evaluated in the GLOMAP model against a more comprehensive section or “bin” model. GLOMAP-mode simulates the aerosol size distribution using several log-normal modes while GLOMAP-bin uses 20 size sections. GLOMAP was run at 2.8° resolution for 1 yr using both mode and bin schemes. Particle size distributions have been compared for 12 distinct regions around the world. The modal and bin schemes agree very well at most sizes, although there tends to be some overprediction of particle concentrations in the nucleation mode of the modal model. Aerosol optical depth has also been evaluated against MODIS and AERONET observations. Both models are in close agreement but differ by approximately the same amount from the retrieved size distributions. It was therefore concluded that there are general adjustments to make to the aerosol model, rather than a specific issue with the simpler modal model. Global cloud condensation nuclei have been evaluated against a collection of CCN measurements compiled from several field campaigns. There is reasonable agreement in the model versus observations (with a lot of scatter). But again the difference between the bin and modal schemes is smaller than the difference between model and the observations. Modal aerosol schemes are a computationally efficient but nonetheless accurate way of simulating the aerosol size distribution, optical properties and CCN concentrations on a global scale. Thus EUCAARI model comparisons show that the microphysical treatment in state of the art climate models is now sufficiently well developed to be able to capture many of the details of the aerosol properties on a global scale.

### 3.4.4 Air quality

The role of aerosols in European air quality was one of the major foci of EUCAARI. We have improved a regional Chemical Transport Model (PMCAMx) and used it together with the emission inventories developed in the project to
<figure><img src="image_20.png" /><figcaption>**Fig. 17.** Comparison of predicted versus observed PM₁ sulfate and organic aerosol concentration (µg m⁻³) from 4 measurement stations during the EUCAARI May 2008 campaign. Each point corresponds to a 24-h average value. Also shown the 1:1, 2:1 and 1:2 lines. Observed data represent AMS measurements.</figcaption></figure>

evaluate our understanding of the sources and atmospheric processing of fine PM in Europe. Major findings include:

The carbonaceous aerosol emissions of EUCAARI together with the new organic aerosol module used in PMCAMx based on the volatility basis set approach resulted in very good agreement with measured organic aerosol concentrations on the European continent (Fig. 17). There is evidence that the residential wood-burning emissions in at least some regions (e.g. Sweden, East Germany, Switzerland) are significantly underestimated. Also the wintertime emissions of ammonia are probably overestimated by a factor of 3 or so.

The reduction in ammonia emissions is one of the most effective ways to reduce aerosol mass concentrations in Europe. Reduction in NOₓ is also effective, but might lead to higher ozone levels. Reduction in SO₂ emissions will reduce particulate air pollution especially in the Eastern Mediterranean area. Reduction of organic aerosol concentrations is a lot more challenging and will require reductions of gas and aerosol emissions from transportation and biomass burning.

Besides PMCAMx also EMEP MSC-W chemical transport model was used (see Appendix D).

## Evaluation of current understanding of regional fine PM in Europe

During EUCAARI, the organic and inorganic aerosol modules of PMCAMx were improved (Fountoukis et al., 2011a) and the resulting model (called PMCAMx-2008) was applied for the first time in Europe. The domain consisted of the whole European continent, and extended from the Atlantic Ocean to the Middle East and from the North Pole to North

Africa. The results of the model were compared against measurements during the May 2008 EUCAARI intensive campaign both at ground level and aloft. The comparison of the model predictions with the ground measurements in four measurement stations is encouraging. The model reproduces more than 86% of the daily averaged data and more than 77% of the hourly data within a factor of 2, for both PM₁ OA and sulphate respectively (Fig. 17).

The model tends to predict relatively flat diurnal profiles for PM₁ OA in many areas, both rural and often urban, in agreement with the available measurements. The model performance against the high time resolution airborne measurements at multiple altitudes and locations is as good as its performance against the ground level hourly measurements (Fig. 18). There is no evidence of missing sources of OA aloft over Europe. The major sources of OA during the summer are predicted to be photo-oxidation of biogenic VOCs and anthropogenic Intermediate Volatility Compounds (IVOCs) and evaporated primary organic aerosols.

The highest predicted concentrations for fine sulphate are seen over the Mediterranean region while organic matter is predicted to be the dominant PM₁ species in central and northern Europe (Fig. 19). The model predicts low levels of fresh POA and a ubiquity of oxygenated species in organic aerosol, which is predicted to be predominantly composed of SOA of biogenic origin.

PMCAMx was also evaluated against the EUCAARI measurements at the ground level during the winter intensive period of February/March 2009. Measurements from the field stations in Barcelona, Cabauw, Finokalia, Helsinki, Hyytiälä, Mace Head, Melpitz, Payerne, Puy de Dome and Vavihill. While the performance of the model for sulfate and organic aerosol in most areas was quite good, ammonium nitrate levels were overpredicted. Sensitivity analysis indicated that this was probably due to an overestimation of the wintertime ammonia emissions. A uniform reduction of these emissions
<figure><img src="image_21.png" /><figcaption>**Fig. 18.** Comparison of predicted (PMCAMx) vs. observed (AMS) vertical profiles of aerosol chemical composition averaged over the entire EUCAARI-LONGREX campaign for PM₁ (a) organics, (b) sulfate, (c) ammonium and (d) nitrate. The horizontal bars indicate one standard deviation for each 500 m altitude bin mean value.</figcaption></figure>

resulted in significant improvement of the performance of the model. A second problem was the serious undprediction of organic aerosol levels in Melpitz, Payerne and Vavihill suggesting an underestimation of the residential wood burning emissions in these areas.

## Sensitivity of fine PM to emissions

We performed a series of emissions sensitivity runs to quantify the responses of the concentrations of fine PM to the changes of emissions of sulphur dioxide, oxides of nitrogen, anthropogenic VOCs, and ammonia. In these tests we examined the effects of uniform reductions of the emissions of NH₃, NOₓ, POA, anthropogenic VOCs and SO₂ by 50%. The differences in composition and concentration of the fine PM in Europe result in different spatial reductions of the fine PM levels. Reductions in ammonia emissions are one of the most promising strategies for the areas with the highest PM levels during the late spring period investigated. For example a 50% reduction in ammonia emissions is predicted to result in a 16% reduction of the PM₂.₅ levels in downtown London during that month (Table 2). Reductions in NOₓ emissions would also result in significant reductions in PM₂.₅ levels; however they will be accompanied by increases in ozone levels in these areas according to the model predictions. Reductions in SO₂ emissions will be helpful across Europe and will represent a significant reduction of the fine PM especially in the Eastern Mediterranean.

### 3.4.5 Aerosols and climate

The present-day direct and first indirect radiative forcings by atmospheric aerosols have been updated using a combination of model, satellite and other ancillary data. In this chapter, unless specifically mentioned otherwise, we mean by aerosol “indirect effect” the aerosol first indirect effect (or cloud-albedo effect).

EUCAARI model results and sensitivity studies, done within EUCAARI with an offline radiative transfer model, suggest that a central estimate for the total direct aerosol forcing is not more negative than −0.45 W m⁻². This is substantially less than the upper limit estimate of −0.9 W m⁻² by the IPCC AR4. The best estimate for the 1st indirect forcing is −0.7 ± 0.5 W m⁻², which can be compared to the first indirect forcing estimate of −0.3 to −1.8 W m⁻² by the IPCC AR4.

Different aerosol and aerosol precursor emission scenarios reflecting possible future control strategies for air pollution have been simulated with a global climate atmospheric chemistry model (ECHAM5-HAM). When stringent air pollution control measures are implemented worldwide, the present-day negative total aerosol top-of-the-atmosphere aerosol radiative forcing will be reduced by 50% by 2030. The net effect of increasing GHG concentrations and lower aerosol concentrations is a global annual mean equilibrium temperature increase is predicted to be 2.2 K, as compared with the temperature increase of 1.2 K due to increasing GHG concentrations alone. As aerosols strongly impact surface forcings, the consequences for precipitation increases associated with global warming are even stronger. These
<figure><img src="image_22.png" /><figcaption>**Fig. 19.** Ground-level concentration predictions averaged over the entire simulation period (1–29 May 2008) for PM<sub>2.5</sub> total organic aerosol and sulphate (in µg m<sup>-3</sup>). Different scales are used.</figcaption></figure>

results highlight the potential impact of future air pollution mitigation strategies on climate.

The performance of the improved EUCAARI global atmospheric chemistry models has been evaluated against pre-existing and also the new EUCAARI observational datasets. The simulation of absorbing aerosols above cloud has been identified as a major cause of the discrepancies among models. Simulated total optical depth shows considerable low bias especially in South-East Asia and the biomass burning dominated regions in South America and South Africa. The model bias in South-East Asia has decreased with the new IPCC emissions used in the EUCAARI models, but considerable discrepancy still exists for South Africa and South America. A zonally-averaged comparison to AERONET derived column absorption exhibits considerable bias in several models. Mass absorption coefficients, dust contributions to measured total absorption, sampling bias of AERONET and emission uncertainties were identified as major reasons for the documented bias.

## Estimated aerosol direct effect on climate

The fourth assessment report of the Intergovernmental Panel on Climate Change struggled in assessing the aerosol direct effect on climate. The vast majority of the results suggests now that aerosol on a global annual average exerts a cooling on the Earth-Atmosphere-System (see discussion in Haywood and Schulz, 2007). However, there was a discrepancy between observational-based estimates at or near -1.0 W m<sup>2</sup> and model-based estimates near -0.4 W m<sup>2</sup>. Since then several independent studies of EUCAARI partners have explored details responsible for this discrepancy. These studies demonstrate that more negative estimates of observation-based approaches are most likely incorrect. These observation-based approaches involve many assumptions

and approximations, most of which introduce a negative bias:

- aerosol optical depth (AOD) of most satellites retrievals may be biased high;

- the anthropogenic fraction is uncertain since it is only part of the fine mode attributable aerosol optical depth;

- the switch from clear- to all-sky is not a linear function of the cloud-fraction (as absorbing aerosols above clouds can exert a warming impact on climate) (Schulz et al., 2006);

- areas over desert (regions of potential warming) are not well characterized by satellite based aerosol observations;

- albedo uncertainties are still important, e.g. the MODIS surface albedo product does not consider snow-cover and the range used in models varies largely (Stier et al., 2007).

Bellouin et al. (2008) demonstrated that just by using an improved year 2002 AOD data-set of MODIS (with less snow contamination at high latitudes) and improved assumptions for the fine-mode fraction, the best estimate is reduced from near approximately -1.0 W m<sup>-2</sup> to near -0.65 W m<sup>-2</sup>. This estimate still may be biased high, due to choices for surface albedo, AOD, clear-sky to all-sky ratio and assumptions on missing AOD information over desert regions. In fact, sensitivity studies performed by the EUCAARI partners demonstrate that direct cooling in access of -0.45 W m<sup>-2</sup> is highly unlikely. The use of quasi-global sun-/sky photometer data of AERONET, which define not only AOD but also simultaneous information on aerosol absorption and size (without making a-priori assumptions), suggests a -0.2 W m<sup>-2</sup> aerosol
direct cooling (Kinne, 2011). This value is identical to the average from AEROCOM models (Schulz et al., 2006), in which however contributions from nitrate and anthropogenic dust are missing. Altogether, this may suggest that there is less model-data discrepancy.

## Estimated aerosol indirect effect on climate

Although many different types of interactions between aerosols and clouds have been suggested, IPCC AR4 only considered the cloud albedo effect of warm clouds, and for that effect alone a wide uncertainty range of -0.3 to -1.8 W m⁻² was given, cancelling anywhere from 12% to 69% of the radiative forcing from well-mixed greenhouse gases. EUCAARI has contributed a better understanding and a narrowing of this uncertainty range. For instance, we investigated the implications for the aerosol first indirect effect of using lower bounds for cloud droplet number concentration of aerosol number concentration (CDNC), which are being used in many climate models. We found (Hoose et al., 2009) that the use of such lower bounds on CDNC leads to a suppression of the simulated first indirect effect from -1.9 W m⁻² to -0.6 W m², depending on what threshold is chosen. The suppression is strongest over oceans where CDNC is low. This means that a rather arbitrary and sometimes poorly justified choice of a threshold value has a huge impact on the simulated aerosol first indirect effect.

In Iversen et al. (2010) this was further elaborated by estimating the impact on the equilibrium climate response of direct and the warm-cloud first indirect aerosol effects by adding a “background” level of cloud droplets. The added background cloud droplet number concentration (CDNC) were 3 cm⁻³ over oceans and the Antarctica and 17 cm⁻³ over continents. The effect on the estimated global equilibrium 2-m temperature response to anthropogenic aerosol forcing was a reduction from -2.09 to -1.50 °C, while the global equilibrium precipitation response was reduced from -5.7% to -4.5%.

We have also investigated the influence on the simulated indirect effect of a prognostic treatment of ice crystal concentrations (Storelvmo et al., 2008) and ice nucleation by natural and anthropogenic particles (soot). Hoose et al. (2010c) found a reduction in the simulated aerosol indirect effect by about 10 to 26%. The reduction is mainly caused by a reduced lifetime effect due to anthropogenic soot (glaciation effect). This is because soot particles stimulate the freezing of supercooled water, thereby stimulating the release of precipitation from the clouds. The net effect is a reduction in cloud lifetime.

Several EUCAARI models have contributed to the study by Quaas et al. (2009) where a model based range for the first indirect effect was established, considering constraints from satellite observations of cloud droplet number concentration and liquid water path. An estimate obtained by scaling simulated clear and cloudy-sky forcings with estimates

of anthropogenic and satellite-retrieved N<sub>d</sub>-AOD regression slopes, respectively, yields a global cloudy-sky (aerosol indirect effect) estimate of -0.7 ± 0.5 W m⁻².

## Sensitivity of climate models to aerosol nucleation

Studies (e.g. Spracklen et al., 2006; Merikanto et al., 2009; Kazil et al., 2010) indicate that nucleation of new particles from the gas phase is an important source of cloud condensation nuclei. A realistic simulation of new particle formation in the planetary boundary layer has been problematic in the past. Recently, it has been found that observed nucleation can be explained by sulphuric acid or by both sulphuric acid plus organic species (Sipilä et al., 2010; Metzger et al., 2010; Paasonen et al., 2010). Earlier, models were based upon a range of other assumptions, now known to be incorrect.

ECHAM5-HAM was used with varying emission environments of years 1750, 2000 and 2100 to assess the effect of nucleation on indirect aerosol forcing (Makkonen et al., 2011). Forcing was calculated as radiative flux perturbation. According to the results, the effect of nucleation is clearly visible in present-day climate: including nucleation in the model increases the present-day indirect forcing from -0.27 W m⁻² to -1.16 W m². In simulations without nucleation, aerosol number concentrations are a result of primary emissions only. A strong decrease is seen in the indirect aerosol forcing when moving to year 2100, relating to a decrease in both anthropogenic SO₂ emissions and primary emissions. Two additional experiments were performed to see the effect of potentially rising future emissions of biogenic VOCs and oceanic DMS. Even with the high-level of elevation in emissions (+50% BVOCs or +10% DMS), the natural emissions cannot counteract the cooling from the strong decrease in anthropogenic emissions.

## Air quality and climate

Specific economic sectors and/or source regions emit a wide variety of climate relevant gases and particles, influencing climate and air quality. This includes emissions of greenhouse gases, chemical species that affect the oxidation capacity of the atmosphere and the concentrations of ozone and methane, and aerosol particles or aerosol precursors. Most of the studies so far assessed the climate impact of specific chemical components (e.g. carbon dioxide, sulphate particles etc.). However, the different climate effects add non-linearly and thus interactions between warming and the water and aerosol cycles have to be taken into account. For the purpose of climate protection and improvement of air quality, we applied a more integrative approach assessing the total climate effect of gaseous and particulate emissions from a specific economic sector. Additionally, we have to take into account that the implementation of air quality measures affects the climate system as well.
<table><caption>**Table 3.** Predicted PM<sub>2.5</sub> Concentrations Reduction in Selected Cities for May 2008.</caption><thead><tr><th rowspan="2">City</th><th colspan="5">Reduction in PM<sub>2.5</sub> (µg m<sup>-3</sup>) for a 50% Emissions Reduction</th></tr><tr><th>SO<sub>2</sub></th><th>NO<sub>x</sub></th><th>VOCs</th><th>POA</th><th>NH<sub>3</sub></th></tr></thead><tbody><tr><td>London</td><td>1.1 (4.2 %)</td><td>2.9 (11 %)</td><td>0.8 (3.1 %)</td><td>0.7 (2.6 %)</td><td>4.0 (16 %)</td></tr><tr><td>Paris</td><td>1.3 (6.1 %)</td><td>1.4 (6.7 %)</td><td>0.2 (1 %)</td><td>1.9 (8.9 %)</td><td>0.8 (4.2 %)</td></tr><tr><td>Athens</td><td>1.0 (10 %)</td><td>0.4 (3.5 %)</td><td>0.1 (0.8 %)</td><td>0.2 (1.7 %)</td><td>0.6 (6.1 %)</td></tr><tr><td>Marseille</td><td>0.7 (3.7 %)</td><td>1.1 (6 %)</td><td>0.3 (1.3 %)</td><td>0.5 (2.5 %)</td><td>2.3 (12 %)</td></tr><tr><td>Ruhr</td><td>1.1 (10 %)</td><td>1.7 (15 %)</td><td>0.1 (1 %)</td><td>0.2 (1.8 %)</td><td>0.7 (6.4 %)</td></tr></tbody></table>

Different aerosol and aerosol precursor emission scenarios reflecting possible future control strategies for air pollution have been applied in the ECHAM5-HAM model coupled to a mixed-layer ocean model to simulate the resulting effects on the Earth's radiation budget and climate. Two opposing future mitigation strategies for the year 2030 have been used: one in which emission reduction legislation decided in countries throughout the world are effectively implemented (current legislation; CLE 2030) and one in which all technical options for emission reductions are being implemented independent of their cost (maximum feasible reduction, MFR). The importance of the combined industrial and power generation sector on the one hand have been assessed and domestic and transport related emission on the other hand. In addition, regional experiments have been performed to evaluate the influence aerosol emissions from Europe and Asia have on other world regions. A number of sensitivity studies address the non-linear chemical and microphysical couplings in the context of these scenarios (Kloster et al., 2008, 2009).

CAM-Oslo coupled to a slab ocean model was used to estimate interactions between GHG-driven and anthropogenic aerosol-driven global changes under various assumptions (Iversen et al., 2010), Anthropogenic aerosols largely counteract the effects of increased CO<sub>2</sub>, even though there are significant exceptions for precipitation in the subtropics and in the southern extra-tropics. Increased CO<sub>2</sub> shortens the atmospheric residence time of aerosols and aerosol precursors due to increased precipitation amounts in major anthropogenic air pollution emission regions. This reduction is estimated to be larger for present-day (~2000) aerosol emissions than it would have been if aerosols had been maintained at pre-industrial levels (~1850). The climate effects of aerosols are thus reduced by the CO<sub>2</sub>-increase. Since the two effects largely counteracts each other, this feedback may potentially cause a non-linear reinforcement of the CO<sub>2</sub> driven global changes when reduction protocols for aerosols and precursors are implemented.

When more stringent air pollution abatements are implemented worldwide, utilizing the presently available most advanced control technologies, the present-day negative total aerosol top-of-the-atmosphere radiative forcing will be strongly reduced (by 50%) by 2030 (Iversen et al., 2010). As a consequence, climate change thereafter will be controlled

to a larger extent by changes in greenhouse gas emissions. The temperature response of increasing GHG concentrations and reduced aerosol emissions leads to a global annual mean equilibrium temperature response of 2.18 K. When aerosols will be only abated in the Industry and Power Plant sector, whereas the Domestic and Transport sectors stay with currently enforced regulations, the temperature response is 1.89 K. In contrast, a maximum feasible abatement only applied in the Domestic and Transport sector, leads to a smaller temperature response of 1.39 K. Increasing GHG concentrations alone lead to a temperature response of 1.20 K. Our study thus highlights the huge potential impact of future air pollution mitigation strategies on climate and supports the need for urgent GHG emission reductions. As aerosols strongly impact surface forcings and have thus a high hydrological sensitivity, the consequences for precipitation increases associated with global warming are even stronger. GHG and aerosol forcings are not independent as they both affect and are influenced by changes in the hydrological cycle.

## Model intercomparisons

The aerosol direct radiative effect has been shown to be associated with considerable model diversity (Schulz et al., 2006) and thus uncertainty in the 4thAR IPCC total radiative forcing uncertainty for present day. Uncertainty in the computation of the direct effect is due to several factors involved, among which dominate the vertical distribution of the aerosol (Schwarz et al., 2010), the relative position and interaction of clouds and aerosols (Quaas et al., 2009), the amount of aerosol absorption (Koch et al., 2009), the anthropogenic aerosol load, humidity growth and possibly also details of the radiative transfer calculation itself. It is thus of interest to compare new EUCAARI model simulations to earlier pre-EUCAARI results. Such comparison is possible for the models (ECHAM, CAM-Oslo and INCA) which have been further developed and changed considerably from the earlier AEROCOM phase I. The GLOMAP and HadGem models did not participate in earlier AEROCOM phase I experiments. All GCM models have developed new coupled aerosol climate model versions with changed resolutions and atmospheric host models. This has involved changes to the
model environment beyond the aerosol model structure. Dif-
ferences in performances are then difficult to track down
to individual aerosol process formulation, because several
changes were in effect at the same time of the model struc-
ture. Model improvement with respect to the selected obser-
vational datasets of relevance is fair for several parameters
and models.

The new observational datasets that became available in
the EUCAARI framework and from other sources make sev-
eral characteristics, successes and biases of the EUCAARI
global aerosol model exercise apparent, with direct conse-
quences for an improved direct aerosol forcing estimate. The
error in the vertical distribution of the aerosol (as evaluated
against CALIOP vertical extinction profiles) above the in-
dustrial regions of the Northern Hemisphere is probably not
so much an error in the form of the profile, but rather an er-
ror in absolute aerosol loads and column optical depth. The
average European vertical profile for 2008 (annual and May)
and the simulated contributions from aerosol species as well
as humidity growth serves as an example here. The role of
absorbing aerosols above cloud is identified as one factor of
important differences between models and sensitivity simu-
lations are performed in the INCA model to identify the con-
tribution from just that fraction of the aerosol to total aerosol
forcing.

Total optical depth is a crucial parameter for the direct ra-
diative effect of the aerosol. Simulated total optical depth
shows considerable low bias especially in South-East Asia
and the biomass burning dominated regions in South Amer-
ica and South Africa. The model bias in South-East Asia
has decreased with the new IPCC emissions used in the
EUCAARI models, but considerable discrepancy still exists
for South Africa and South America. Surface observations
from EUSAAR/EUCAARI on dry aerosol extinction and ab-
sorption coefficients confirm such findings on independent
grounds.

Aerosol absorption due to black carbon and brown car-
bon is a major uncertainty for the direct aerosol forcing. A
zonally averaged comparison to AERONET derived column
absorption exhibits considerable bias in several models. The
underestimate goes only partially along with simultaneous
underestimates in total aerosol optical depth, suggesting that
especially the black carbon fraction is underestimated in sev-
eral model simulations. Mass absorption coefficients, dust
contributions to measured total absorption, sampling bias of
AERONET and emission uncertainties are identified as ma-
jor reasons for the documented bias in modelling.

**3.5 Feedback processes and interactions**

A number of anthropogenic perturbations are being applied
to the climate system since pre-industrial times through
changes in atmospheric composition, land use, and other
changes. These perturbations are known as radiative forc-
ings. The climate system responds to these perturbations

through a series of changes called feedbacks in order to re-
turn to some equilibrium. A lot of research has focused on
understanding and quantifying the feedbacks of the physical
climate system (Arneth et al., 2010); however the develop-
ment of Earth System models has revealed the importance
of biogeochemical feedbacks. The EUCAARI project has
focused on climate feedbacks involving natural and anthro-
pogenic aerosols.

An important task of EUCAARI was to quantify the un-
certainties in the various interactions between aerosols and
the Earth system as one important aspect of the overall bio-
geochemical feedbacks. EUCAARI reviewed and assessed
the role of aerosols in climate and Earth system feedbacks
(Carslaw et al., 2010) and quantified the magnitude of feed-
back loops involving natural and anthropogenic aerosols.
Available observations and model studies suggest that the
regional radiative perturbations are potentially several Watts
per square metre due to changes in natural aerosol emissions
in a future climate. The review produced new estimates of
the direct radiative effect due to aerosol feedbacks related to
dust, dimethyl-sulphide (DMS) from marine biota, wildfires
and terrestrial biogenic secondary organic aerosol. Taking
into account only the direct radiative effect of changes in the
atmospheric burden of natural aerosols, and neglecting po-
tentially large effects on other parts of the Earth system, a
global mean radiative perturbation approaching 1 W m⁻² is
possible by the end of the century. The level of scientific un-
derstanding of the climate drivers, interactions and impacts
was assessed as very low.

EUCAARI has pushed our understanding a bit further con-
cerning biogenic SOA, marine DMS and dust, and has also
studied the effect of climate change on sea spray particle
emissions and their radiative effect. This was done through
the use of three Earth System models including aerosols and
their couplings to other components of the Earth System.

**3.5.1 Climate Change impact on global aerosol cycling**

Climate change experiments have been performed to anal-
yse the change in atmospheric cycling of both natural and
anthropogenic aerosols through changes in temperature, hu-
midity, precipitation, convection and oxidant concentrations
(HadGEM2-ES, Rae et al., 2007). We found that, when ox-
idants alone are changed, the global total sulphate burden
decreases by approximately 3%, due mainly to a reduction
in the OH burden. When climate alone is changed, our re-
sults show that the global total sulphate burden increases
by approximately 9%; we conclude that this is probably at-
tributable to reduced precipitation in regions of high sulphate
abundance. When both oxidants and climate are changed
simultaneously, we find that the effects of the two changes
combine approximately linearly.

The HadGEM2-ES model includes interactive sources and
sinks of aerosols, coupled to the tropospheric chemistry
scheme through interactive oxidants, and coupled to the
marine carbon cycle through emissions of DMS and depo-
sition of dust. Nitrate aerosols have also been added to the
default HadGEM2-ES climate model as it is an important
aerosol species in some regions. Interactive biogenic emis-
sions and a simple secondary organic aerosol scheme have
been added in a research version of HadGEM2-ES.

The MPI Hamburg coupled climate model version used in EUCAARI is based on the atmospheric model ECHAM6 middle atmosphere version with the extension to gas-phase chemistry (MOZART3) and aerosol physics (HAM2) merged with the most recent carbon cycle model system (MPI-OM, HAMMOC, JS-BACH). The aerosol module HAM (Stier et al., 2005) has been extended by a scheme for the treatment of SOA (O'Donnell, 2009). The time integration of the nucleation and condensation schemes has been improved and different nucleation schemes have been implemented and tested (Kazil et al., 2008). HAM2 includes additionally a new impaction and below cloud scavenging scheme. The new two-moment cloud physics (Lohmann et al., 2007) enables a more physically based treatment of aerosol-cloud interactions. The new model version includes also source models for marine sources of DMS and biogenic sources of VOC.

The IPSL model includes the LMDZ atmospheric model (Hourdin et al., 2006) and several ESM components. Two model versions were used to study the aerosol-climate feedbacks: the standard IPSL coupled model, which uses pre-calculated aerosol mass fields and the interactive IPSL-AER aerosol-climate model, which is using offline pre-calculated oxidant fields. The latter includes interactive sources and sinks of dust and sea salt aerosols, coupled to biogeochemistry through emissions of DMS. Dust deposition of iron is read in into the NEMO-PISCES module as forcing. Biogenic VOC emissions are also pre-calculated for present day conditions from the land-biosphere model ORCHIDEE. Several control keys have been implemented into the model to investigate the trajectory of the climate in the model with/without the radiative aerosol effects, with/without nudging the synoptic meteorology, with/without prescribed aerosol optical properties. The comparisons between the different model versions have shown that the radiative forcing due to aerosols differs within 30% and makes the models comparable, despite differences in complexity.

The global CAM-Oslo model version used in EUCAARI includes an aerosol life-cycling scheme for sea-salt, dust, sulphate, black and organic carbon. The sulphur chemistry relies on prescribed oxidants. Through tabulations that takes into account sulphate-nucleation, condensation, coagulation and processes in liquid cloud droplets, the aerosol optical and water-activation properties are implicitly described with 44 size-bins (Seland et al., 2008), and aerosol interactions with clear-air radiation and warm cloud microphysics are parametrized. The model was coupled to a slab ocean for estimations of equilibrium climate sensitivity (Kirkevåg et al., 2008) and was used for EUCAARI for estimating inter-

actions between GHG-driven changes and changes caused by
anthropogenic aerosols (Iversen et al., 2010). The sensitiv-
ity of the equilibrium climate sensitivity and with respect to
selected uncertain aerosol assumptions was also estimated.

3.5.2 Climate feedbacks involving natural secondary organic aerosols

The global aerosol-climate model ECHAM5-HAM has been extended with a suite of parametrizations that describe the complete life cycle of secondary organic aerosols, starting from emission of the precursor gases, proceeding through the chemical formation of condensible species, the partitioning of those species between the aerosol and gas phases, the microphysics of the aerosol phase and finally to the sink processes that remove both gases and aerosols from the atmosphere (O'Donnell et al., 2011). The model treats primary and secondary, organic and inorganic aerosols, prognostically resolves their composition, size distribution and mixing state, and computes their impact on both the short-wave and longwave radiation budget. The model is coupled to the ECHAM5-HAM double-moment cloud scheme that calculates cloud droplet number concentration and mass concentration as prognostic variables as functions of (inter alia) aerosol properties. The model thus contains the elements necessary for estimation of the effects of SOA on radiation and on liquid water clouds. To the second end, a global model of vegetation emissions of precursor gases has been implemented and linked to the aforementioned SOA extension to the ECHAM5-HAM model. As an alternative, the option to use externally-generated sources of biogenic emission data has been added, making it possible to use the output of, for example, a dynamic vegetation model as input to the SOA module. With these tools, biosphere-atmosphere interactions via biogenic emissions have been investigated in a number of simulations, both in the present atmospheric state, and in hypothetical atmospheric states, that are conceived to examine interactions between anthropogenic activities, biogenic emissions and climate.

Different biogenic emission models were employed, one
an empirically-based model lacking any CO2 response, the
other a dynamic vegetation model with leaf process-based
calculation of emissions includes CO2 concentration in its
calculation of biogenic emissions. Both models were used
to drive the SOA model in two different climate states, one
present day, and one warmer climate state, each climate state
being constrained on the large scale by fixed sea surface tem-
peratures. The warmer climate state used SRES scenario A2
CO2 levels, and sea surface temperatures previously calcu-
lated for that scenario by the coupled ECHAM5/MPI-OM
atmosphere-ocean model in the context of IPCC AR4 sce-
nario runs. When driven with the empirically based emis-
sion model, the SOA model, in common with published stud-
ies, predicts a large increase in biogenic SOA in the warmer
climate state (O'Donnell, 2009), however this requires fur-
ther studies.

**3.5.3 Climate feedbacks involving DMS and sulphate production**

HadGEM2-ES was used to study a feedback that links
climate-driven changes in terrestrial dust production, iron
stimulated (or limited) marine-biological growth changes,
oceanic DMS production, and climate change. As observed
in the contemporary ocean, HadGEM2-ES simulates iron
limitation of phytoplankton growth in the Pacific Ocean.
Coupling between the deposition of dust from the atmo-
sphere and oceanic iron concentrations within HadGEM2-
ES, allows modification of iron limitation in response to ter-
restrial land-use change or drying events, increasing wind
intensity, or changes in wind direction. Atmospheric CO2
concentrations were increased by 1% each year, following
the IPCC CMIP5 experimental protocol. This drives in in-
crease in dust production, and subsequently iron deposition
in the North Pacific. The increase in dust deposition pro-
gressively alleviates surface ocean iron limitation through-
out the experiment. Increased photic zone iron concentration
stimulates two changes; firstly, overall phytoplankton pro-
duction is enhanced, partially offsetting the background de-
cline in primary production occurring due to increased sur-
face water stratification limiting the upward movement of
macro-nutrients (in this case nitrogen), secondly, the higher
affinity for iron demonstrated by the diatom phytoplankton
group relative to the non-diatom group initiates a popula-
tion shift towards diatoms. The ecosystem shift will re-
sult in a relative increase in carbon drawdown (due to the
faster sinking rate of the larger diatom functional type), but
also since the model's DMS scheme allows DMS produc-
tion by only the non-diatom phytoplankton type (Halloran et
al., 2010), a relative increase in DMS emissions. Although
from a global temperature standpoint this feedback is likely
to be of minor importance, since the land-based warming
which initiates the observed chain of events is spatially sepa-
rated from the DMS-driven surface ocean (relative) cooling,
it can be hypothesised that this feedback will act to intensify
monsoon processes in landmasses neighbouring iron-limited
ocean basins.

We have also compared some of the results with earlier re-
sults from the ECHAM5 model. DMS emissions increase
in UM, but decrease in ECHAM, although, the effect on
sulphate aerosol is negligible compared with anthropogenic
changes. Such opposite responses were also reported in the
Carslaw et al. (2010) review, so it is important to understand
why two models can differ so fundamentally. Differences
between models are large, often exceeding the differences
between historical and future simulation for one model. Sur-
face temperatures are similar in both models, but the pattern
of warming in ECHAM is more inhomogeneous than in UM,
with more warming in the Northern Hemisphere and less in

the Southern. 10 m windspeeds, which drive DMS sea-to-
air flux, are broadly similar in both models, but are higher
in ECHAM in the Southern Ocean. Changes – both positive
and negative – are larger in ECHAM than the UM. Mixed
layer depth (MLD) is much greater in ECHAM than the UM
in the Southern Ocean and North Atlantic. Whilst the UM
MLD is almost unchanged over time, the ECHAM MLD
is significantly reduced, particularly in those areas where it
was highest, though it remains greater them UM MLD in
those regions. Ocean DMS concentrations are higher in the
UM than ECHAM: 25% in 1860–1889 and 50% in 2060–
2089 in the global mean. The change is caused mainly by
a drop in ECHAM concentrations particularly at high lati-
tudes and near the equator, while UM global mean values
increase very slightly, with some increases at high latitudes,
but with some decreases in equatorial regions. In both cases
patterns of emission change follow those of DMS ocean con-
centration change. SO₂ and sulphate loads are different in
the two models, but in each case are dominated by anthro-
pogenic emissions. The pattern of sulphate loading shows
negligible contributions from DMS. In conclusion, there are
different mechanisms in action in the two models and the
changes in DMS are too small to induce a significant feed-
back in present-day conditions.

**3.5.4 Climate feedbacks involving natural dust and sea-salt production**

Jones et al. (2007) investigated the change in the burden of the natural primary aerosols (sea-salt and mineral dust) in climate change experiments. These changes are purely in response to the anthropogenic aerosol/CO₂ changes, as no alterations to the emission schemes for natural aerosols were made in these experiments (vegetation cover was also kept constant). The changes at low latitudes are dominated by mineral dust, while those at high latitudes are due to sea-salt. The changes in dust burden show complex patterns of increases and decreases, with some areas of increase immediately adjacent to areas of decrease. Such complex distributions of changes are to be expected given the highly variable nature of dust emission (Woodward, 2001): whereas one might explain the changes in the dust plume over the Arabian Sea in terms of the change in wind speed over the horn of Africa, the same explanation does not hold for the plume extending over the Atlantic. These results indicate that changes in dust production in this major source region are in general more strongly affected by changes in soil moisture content than in wind speed in this model. Further investigations using HadGEM2-ES showed that the CO₂ fertilisation contributes to change soil moisture in semi-arid regions, but not in arid and hyper-arid regions, through an increase in the shrub cover. This results in a decrease in dust emissions over these regions.

Turning to the changes in sea-salt, these appear more
straightforwardly related to changes in sea-ice fraction and