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FINNISH METEOROLOGICAL INSTITUTE
CONTRIBUTIONS
No. 114
AEROSOLS AND CLIMATE: FROM REGIONAL TO
GLOBAL MODELLING
Joni-Pekka Pietikäinen
Finnish Meteorological Institute
Helsinki, Finland
Academic dissertation
To be presented with the permission of the Faculty of Science and Forestry of the
University of Eastern Finland, Kuopio, for public examination in the Auditorium
Brainstorm in the Dynamicum Building at the Finnish Meteorological Institute,
Helsinki, on February 23, 2015, at 12 o’clock noon.
Finnish Meteorological Institute
Helsinki, 2015
Author’s Address:
Finnish Meteorological Institute
Climate Research Unit
P.O. BOX 503
FI-00101 Helsinki, Finland
e-mail [email protected]
Supervisors:
Professor Ari Laaksonen, Ph.D.
Department of Applied Physics
University of Eastern Finland, Kuopio, Finland
and
Climate Research Unit
Finnish Meteorological Institute, Helsinki, Finland
Docent Harri Kokkola, Ph.D.
Atmospheric Research Centre of Eastern Finland
Finnish Meteorological Institute, Kuopio, Finland
Reviewers:
Professor Hans-F. Graf, Ph.D.
Department of Geography
University of Cambridge, Cambridge, United Kingdom
Professor Markku Rummukainen, Ph.D.
Department of Physical Geography and Ecosystem
Lund University, Lund, Sweden
Opponent:
Elisabetta Vignati, Ph.D.
Air and Climate Unit
Institute for Environment and Sustainability
Joint Research Centre, Ispra, Italy
Custos:
Professor Ari Laaksonen, Ph.D.
Department of Applied Physics
University of Eastern Finland, Kuopio
and
Climate Research Unit
Finnish Meteorological Institute, Helsinki, Finland
ISBN 978-951-697-859-1 (paperback)
ISSN 0782-6117
Unigrafia Oy
Helsinki 2015
ISBN 978-951-697-860-7 (pdf)
UEF Electronic Publications
http://epublications.uef.fi
Kuopio 2015
Published by
Finnish Meteorological Institute
P.O. Box 503
FI-00101 Helsinki, Finland
Series title, number and report code of publication
Finnish Meteorological Institute
Contributions 114, FMI-CONT-114
Date
February 2015
Author
Joni-Pekka Pietikäinen
Title
Aerosols and climate: from regional to global modelling
Abstract
Our surrounding climate is a puzzle of different physical, chemical and dynamical
puzzle, such as aerosol particles, can impact many processes and have complicated
the ability to change the radiation budget directly or indirectly makes the aerosol
piece of the climate puzzle. However, many aerosol-related processes, and even
climate, still leave quite a lot of gaps in our scientific understanding.
processes. Some parts of the
feedback loops. For example,
particles one very interesting
their total impact upon the
In recent years, both aerosol measurements and the instruments used have evolved quite significantly. The same
has also happened in the modelling of particles. Global and regional climate models are one sub-group of the
total spread of the models that can be used to study aerosols and aerosol-related processes; and, in the last 10
years, many of these models have been developed towards a better representation of aerosol-climate effects.
In this work, two different climate models were used. First, the regional aerosol-climate model REMO-HAM was
developed and used to study black carbon properties over Finland. In addition, the model was further modified
for European boundary layer new particle formation (NPF) studies. Second, the global aerosol-climate model
ECHAM-HAMMOZ was used and modified to investigate the Asian monsoon-aerosol interactions and the impact
of aerosol reductions on future aerosol forcing.
It was shown that REMO-HAM can reproduce the observed aerosol concentrations and the main meteorological
variables. This model version was then used for studying black carbon over Finland and shown to be able to
predict the measured values. Some underestimation was seen in surface air concentrations; this could be related
to, for example, errors in the residential wood-burning sector of the used emissions. For the European boundary
layer NPF studies, the model was further developed and the new version showed much better agreement of NPF
related statistics with measurements than the original version. With ECHAM-HAMMOZ, aerosol absorption was
shown to have a role in the Asian monsoon, although the solar dimming (scattering effect) can decrease or even
cancel out the influence of absorption. The future emission reductions study showed that targeted reductions to
short-lived climate forcers, especially for black carbon, can be a more climate-friendly pathway compared to the
maximum potential of overall reductions.
Publishing unit
Climate Research
Classification (UDC)
551.510.412
551.58
ISSN and series title
0782-6117 Finnish Meteorological Institute Contributions
ISBN
978-951-697-859-1 (paperback)
978-951-697-860-7 (pdf)
Keywords
aerosols, regional modelling, global modelling,
nucleation
Language
English
Pages
180
Julkaisija
Ilmatieteen laitos
Erik Palménin aukio 1
PL 503, 00101 Helsinki
Julkaisun sarja, numero ja raporttikoodi
Finnish Meteorological Institute
Contibutions 114, FMI-CONT-114
Julkaisuaika
Helmikuu 2015
Tekijä
Joni-Pekka Pietikäinen
Nimeke
Aerosolit ja ilmasto: alueellisesta mallinnuksesta globaaliin mallinnukseen
Tiivistelmä
Ympäröivä ilmakehämme on fysikaalisten, kemiallisten ja dynaamisten prosessien palapeli. Jotkut palat, kuten
aerosolipartikkelit, ovat yksi iso tärkeä osajoukko, jolla on kyky vaikuttaa useampaan muuhun osajoukkoon
ja jotka sisältävät monimutkaisia takaisinkytkentämekanismeja. Aerosolit esimerkiksi muokkaavat ilmakehän säteilybalanssia suoraan ja epäsuorasti, mikä tekeekin niiden tutkimisesta yhden mielenkiintoisimmista ilmastopalapelin paloista. Monet aerosoleihin liittyvät prosessit ja jopa niiden kokonaisvaikutus ilmastoon sisältävät
edelleen paljon aukkoja tieteellisessä ymmärryksessämme.
Viime vuosina sekä aerosolien mittaukset että mittauslaitteet ovat kehittyneet huomattavasti. Tämä sama trendi
on ollut myös nähtävissä aerosolien mallinnuksessa. Globaalit ja alueelliset ilmastomallit ovat yksi osa aerosolien
mallinnukseen liittyvästä mallijoukosta, jota voidaan käyttää aerosolien ja niihin liittyvien prosessien tutkimiseen.
Viimeisen 10 vuoden aikana nämä mallit ovatkin kehittyneet kohti parempia aerosoli-ilmastovaikutusmenetelmiä.
Tässä työssä on käytetty kahta erilaista ilmastomallia. Työssä on ensinnäkin kehitetty alueellinen aerosoli-ilmasto
malli nimeltä REMO-HAM ja sitä on käytetty sekä Suomen alueen mustan hiilen ominaisuuksien tutkimiseen että
Euroopan rajakerroksen uusien hiukkasten muodostumisen tarkasteluun. Toinen käytetty malli REMO-HAM
-mallin lisäksi on globaalia aerosoli-ilmastomalli ECHAM-HAMMOZ, jota on kehitetty Aasian monsuunin ja
aerosolien välisen vuorovaikutuksen tutkimiseen, sekä tulevaisuuden aerosolivähennysten ilmastopakotteen vaikutuksen tutkimiseen.
REMO-HAM kykenee tuottamaan mitatut aerosolipitoisuudet ja meteorologiset muuttujat luotettavasti. Sitä
käytettiinkin Suomen alueen mustan hiilen tutkimiseen ja pystyttiin näyttämään, että malli ennustaa mitatut arvot hyvin. Pintailmapitoisuuksissa tosin oli aliarviointia, joka johtunee käytettyjen päästölähdekarttojen epätarkkuudesta esimerkiksi puun pienpolton suhteen. Euroopan rajakerroksen pienhiukkasmuodostumisen
mallintamista varten REMO-HAM -mallia muokattiin aerosolikemian osalta ja uusi versio antoikin paljon parempia tuloksia mitatun uusien hiukkasten muodostumisen statistiikan suhteen verrattuna vanhempaan versioon.
ECHAM-HAMMOZ -mallilla osoitettiin, että aerosolien absorptiolla on rooli Aasian monsuunin muodostumisessa,
vaikkakin aerosolien aiheuttama ilmakehän himmeneminen pystyy osaksi tai kokonaan kumoamaan tämän vaikutuksen. Tulevaisuuden päästörajoituksia koskevassa tutkimuksessa pystyttiin osoittamaan, että lyhytikäiset ilmastoon vaikuttavat yhdisteet, varsinkin musta hiili, ovat potentiaalinen ilmastoystävällinen vähennysten kohde
maksimaaliseen kokonaisvähennykseen tähtäävään skenaarioon verrattuna.
Julkaisijayksikkö
Ilmastontutkimus
Luokitus (UDC)
551.510.412
551.58
ISSN and avainnimike
0782-6117 Finnish Meteorological Institute Contributions
ISBN
978-951-697-859-1 (nidottu)
978-951-697-860-7 (pdf)
Asiasanat
aerosolit, alueellinen ilmastomallinnus,
globaali ilmastomallinnus, nukleaatio
Kieli
Englanti
Sivumäärä
180
Acknowledgements
The work presented in this thesis has been carried out at the Regional Modelling
group (Max Planck Institute for Meteorology MPI-M, Hamburg, Germany) during
2008–2011 and afterwards at the Atmospheric and Ocean Modelling Group (former Climate Modelling Group, Finnish Meteorological Institute (FMI), Helsinki).
I thank both organizations for providing excellent working facilities and resources.
I want to especially thank the leader of the Atmosphere in the Earth System department at MPI-M, director Prof. Bjorn Stevens, and the FMI’s research director,
Prof. Yrjö Viisanen, for giving me the opportunity to work in the corresponding
institutes. Since during my stay in Germany, the regional modelling group started
to move from MPI-M to the Climate Service Center (CSC), I also want to thank
the former director of CSC, Prof. Guy Brasseur.
I am especially grateful to my principal supervisor, scientific superman Prof.
Ari Laaksonen, who gave me the opportunity to work in his environmental/aerosol
physics group, especially in the field of modelling. Thank you, Ari, for all the support
and guidance during my work. I also want to thank my other supervisor, Dr. Harri
Kokkola, for all the help, especially during our common time in Hamburg. I am
also thankful to Dr. Sami Romakkaniemi for all the help and many scientific/nonscientific discussions we had during this work.
I really want to thank the regional modelling group leader, Prof. Daniela Jacob,
who warmly welcomed me into the group and taught me the secrets of regional
modelling (I want to acknowledge her contribution as an unofficial supervisor). Also,
I want to thank the rest of the regional modelling group for all the support and
friendship (the list of names would be too long to show here). In addition, all the
colleagues and personnel at the Max Planck Institute for Meteorology (and Climate
Service Center) deserve a big “Danke Schön!” for their help and support.
I wish to thank the official reviewers, Prof. Hans-F. Graf and Prof. Markku
Rummukainen, for carefully reading the manuscript and giving valuable suggestions
for the thesis. I also want to thank all the co-authors of the papers included.
In 2011, when I moved back to Finland, I was given the opportunity to work at
FMI, Helsinki. I am thankful to the group leader (at that time), Dr. Leif Backman,
for all the help during the moving process and after. I want to also thank Dr. Declan
O’Donnell for all the help and support during the time in Hamburg and Helsinki.
The help from Dr. Anca Hienola, Dr. Svante Henriksson, Dr. Antti-Ilari Partanen,
7
Dr. Tiina Markkanen, and Dr. Santtu Mikkonen has been also invaluable. My
current group leader, Dr. Hannele Korhonen, also deserves many thanks for the
help in the final stages of my work. Naturally, the help and support from both the
former Climate Modelling Group and current Atmospheric and Ocean Modelling
Group is gratefully acknowledged. The help and support from all other personnel
of FMI is also acknowledged.
I want to thank the German Climate Computation Centre (Deutsches Klimarechenzentrum; DKRZ) for the support and computational resources during my
time in Germany. Likewise, the help and support from the IT department at FMI
is gratefully acknowledged. In addition, the support from different foundations is
appreciated.
I want to thank all my friends and relatives. My fellow students, Dr. Timo, Dr.
Pasi, Dr. Henri, Dr. Jukka and Dr. Aki, are gratefully acknowledged by my liver.
My three godsons (and their families) deserve many, many thanks as each of
them, in their own unique ways, have the ability to “clear my work cache”. Paljon
kiitoksia Eppu, Joakim ja Akseli!
During the past five years, my summer holidays at the beautiful city of Odesa
have also been a “steam valve” for me. Thus, I want to thank my parents-in-law
Ld and Valer$
i, as well as my brother-in-law Serg$
i, for their support. Also,
many thanks to all of my Ukrainian friends for making my holidays so relaxing.
Millions of thanks to my parents Marja and Jorma for their love and support
during my life. Likewise, my sister Nina and her family deserves special thanks for
their love and support. Lämpimät kiitokset!
Inna, my beautiful and lovely wife, deserves the deepest and warmest thanks I
can possibly give. She really supported me during this work and pushed me forward
when I lost the focus. Due dku, mo malen~ka ukraı̈nka. tebe koha!
Helsinki, January 2015
Joni-Pekka Pietikäinen
Contents
List of publications
9
1 Introduction
10
2 Aerosols
13
2.1 Absorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Black carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Nucleation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Modelling tools
3.1 ECHAM global circulation model . . . . . . . . . . .
3.2 REMO regional climate model . . . . . . . . . . . . .
3.3 HAM aerosol module . . . . . . . . . . . . . . . . . .
3.4 ECHAM-HAMMOZ global aerosol-climate model . .
3.5 REMO-HAM regional aerosol-climate model . . . . .
3.5.1 HAM implementation . . . . . . . . . . . . . .
3.5.2 Lateral boundary conditions . . . . . . . . . .
3.5.3 Other input files . . . . . . . . . . . . . . . .
3.5.4 MPI parallelization and halo zone . . . . . . .
3.6 Sulphate chemistry . . . . . . . . . . . . . . . . . . .
3.6.1 Modifications to the sulphate chemistry . . . .
3.7 Nucleation methods . . . . . . . . . . . . . . . . . . .
3.7.1 Modifications to the kinetic nucleation scheme
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4 Modelling aerosols and climate
31
4.1 Black carbon over Finland . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 European boundary layer NPF . . . . . . . . . . . . . . . . . . . . . . 34
4.3 Targeted black carbon reductions . . . . . . . . . . . . . . . . . . . . 40
5 Review of the papers and the author’s contribution
43
6 Conclusions
45
References
47
8
List of publications
This thesis consist of an introductory review, followed by 5 research articles. In the
introductory part, these paper are cited according to their roman numerals.
I Pietikäinen, J.-P., O’Donnell, D., Teichmann, C., Karstens, U., Pfeifer, S., Kazil,
J., Podzun, R., Fiedler, S., Kokkola, H., Birmili, W., O’Dowd, C., Baltensperger,
U., Weingartner, E., Gehrig, R., Spindler, G., Kulmala, M., Feichter, J., Jacob,
D. and Laaksonen, A.: The regional aerosol-climate model REMO-HAM. Geosci.
Model Dev., 5, 1323–1339, doi:10.5194/gmd-5-1323-2012, 2012.
II Hienola, A. I., Pietikäinen, J.-P., Jacob, D., Podzun, R., Petäjä, T., Hyvärinen, A.-P., Sogacheva, L., Kerminen, V.-M., Kulmala, M., and Laaksonen, A.:
Black carbon concentration and deposition estimations in Finland by the regional aerosol-climate model REMO-HAM. Atmos. Chem. Phys., 13, 4033–4055,
doi:10.5194/acp-13-4033-2013, 2013.
III Henriksson, S. V., Pietikäinen, J.-P., Hyvärinen, A.-P., Räisänen, P., Kupiainen,
K., Tonttila, J., Hooda, R., Lihavainen, H., Backman, L., Klimont, Z., and Laaksonen, A.: Spatial distributions and seasonal cycles of aerosol climate effects in
India seen in global climate-aerosol model. Atmos. Chem. Phys., 14, 10177–
10192, doi:10.5194/acp-14-10177-2014, 2014.
IV Pietikäinen, J.-P., Mikkonen, S., Hamed, A., Hienola, A. I., Birmili, W., Kulmala,
M., and Laaksonen, A.: Analysis of nucleation events in the European boundary
layer using the regional aerosol-climate model REMO-HAM with a solar radiationdriven OH-proxy. Atmos. Chem. Phys., 14, 11711–11729, doi:10.5194/acp-1411711-2014, 2014.
V Pietikäinen, J.-P., Kupiainen, K., Klimont, Z., Makkonen, R., Korhonen, H.,
Karinkanta, R., Hyvärinen, A.-P., Karvosenoja, N., Laaksonen, A., Lihavainen,
H., and Kerminen, V.-M.: Impacts of emission reductions on aerosol radiative
effects. Atmos. Chem. Phys. Discuss., 14, 31899-31942, doi:10.5194/acpd-1431899-2014, 2014.
9
Chapter I
Introduction
Earth’s atmosphere is a playground for various different physical, chemical and
dynamical processes. To understand all of these, the scientific community has developed different kinds of measurements techniques and atmospheric models. These
models can range from the nano-scale (process models) to larger scales (large eddy
simulations, dispersion models), reaching regional scales (regional climate models,
numerical weather prediction models) and end up at the global scale (global climate
models, chemical transport models).
Aerosol particles have been and are an important part of the climate (Forster
et al., 2007; Stocker et al., 2013). So, what are these aerosol particles? The definition
says, in its simplest form, “a collection of of solid or liquid particles suspended in
a gas” (Hinds, 1999). Aerosols can be directly emitted to the atmosphere from
anthropogenic and natural sources (Seinfeld and Pandis, 1998), or they can form
in the atmosphere through gas-to-particle conversion, initiated by nucleation, and
followed by growth to bigger sizes (condensation and coagulation). The process of
new particle formation has been observed around the world (Kulmala et al., 2004).
There are many different kinds of species of aerosols; for example, black and organic carbon, sea salt, sulphate, nitrate and dust. Thus, aerosol modelling, whether
being more process oriented (e.g. Kokkola et al., 2003; Romakkaniemi et al., 2004;
Raatikainen and Laaksonen, 2005) or more climate oriented (e.g. Stier et al., 2005;
Spracklen et al., 2006; Makkonen et al., 2009; O’Donnell et al., 2011; Partanen et al.,
2013; Scott et al., 2014), is needed to understand the complex nature of aerosols and
aerosol-climate interactions. Besides the climatic influence, aerosols also affect our
everyday life through health effects (Pöschl, 2005), air quality (Kulmala et al., 2011)
and visibility (Horvath, 1995).
In the atmosphere, aerosol particles have many different impacts. They have the
ability to scatter and absorb solar radiation. Scattering mainly cools the atmosphere
and is most effective for particles with a diameter less than 1 µm (Seinfeld and
Pandis, 1998). Particle species like black carbon and dust can absorb radiation, thus
warming the atmosphere. The scattering and absorption effects are more significant
for the incoming solar radiation; that is, short-wave radiation, but aerosols also have
10
1. Introduction
11
the ability to influence long-wave radiation through absorption and emission. Large
particles in particular; for example, dust, influence the long-wave radiation budget,
whereas the smaller anthropogenic aerosols do not have such a big impact in this
respect (Ramanathan et al., 2001; Ramanathan and Feng, 2009).
Solar radiation reaching the Earth’s surface had a decreasing trend between
∼1960–1990; a phenomenon also known as solar/global dimming (Wild et al., 2005).
This was caused by the increased scattering and absorption by aerosols and the
aerosol-related changes in cloud properties resulting from regionally increased atmospheric concentrations. Since the late 1980s, sulphur dioxide and black carbon
emissions are known to have decreased in some regions (Streets et al., 2006; Hamed
et al., 2010), which has lead to solar/global brightening; that is, more solar radiation in Europe and North America reaches the surface (Wild, 2009). However,
in some parts of the Earth (e.g. China, India, Zimbabwe, Chile, Venezuela and a
few European locations), the dimming has continued (Wild, 2009). In addition, the
absorption of solar radiation by aerosols heats the atmosphere around the absorbing
aerosol layer, but cools the layers below (surface). This leads to local and regional
variations in the circulation patterns and can further influence the regional scale
meteorological phenomena, such as the Asian monsoon (Lau et al., 2006).
Another important aerosol-related atmospheric impact is their influence on
clouds. Aerosol particles can act as cloud condensation nuclei (CCN) for cloud
droplets. The increase in the number concentration of aerosol particles usually leads
to the increase of CCN number concentration, which eventually increases the cloud
droplet number concentrations (CDNC), thus affecting cloud properties (Twomey,
1974). As the amount of liquid water does not not depend on the amount of cloud
droplets in a non-precipitating cloud, the increase in the number of CCN also increases the condensation sink of water molecules and leads to smaller sizes of the
cloud droplets. Reduced droplet size, together with increased droplet concentration,
will increase the cloud albedo and more incoming solar radiation will be reflected
back to space, thus leading to a cooling effect. In addition, the higher CDNC (assuming constant liquid water content) may lead to longer cloud lifetime and surface
cooling as smaller droplet size tends to delay precipitation formation (Albrecht,
1989).
Global and regional climate models are needed when aerosol-climate interactions
are studied. Aerosols and their effects, for example, on warm clouds have been
considered in climate models for years (Jones et al., 1994). These first climate
models used sulfate aerosols as a surrogate for all anthropogenic aerosols. Since
then, both global and regional climate models have been developed significantly and
the major global aerosol components and their interactions (e.g. with other aerosol
species, clouds and radiation) have been included in them (e.g. Stier et al., 2005;
Grell et al., 2005; Spracklen et al., 2005; Sotiropoulou et al., 2006; Lohmann et al.,
2007; Langmann et al., 2008; Spracklen et al., 2008; Vogel et al., 2009; Kazil et al.,
2010; Rummukainen, 2010; Hommel et al., 2011; Zubler et al., 2011a). However,
the models still have deficiencies and missing processes. For example, an online
1. Introduction
12
aerosol approach might be missing or is incomplete, or the aerosol modules have
too simplified approaches for certain processes, such as nucleation. Nucleation is a
process that occurs in very fine scales (Kulmala et al., 2004), but influences largerscale phenomena (Laaksonen et al., 2005). It has been studies on global scales quite
widely (e.g. Spracklen et al. (2008); Makkonen et al. (2009); Kazil et al. (2010)),
but not that much with regional-scale aerosol-climate models. Different model scales
also bring new questions, such as the accuracy of emissions and the need for more
detailed process descriptions.
From all the interesting and important issues related to aerosols and their impact
on the climate system, the focus of this work has been mainly on regional modelling,
black carbon and nucleation. In addition, this work presents scientific issues that
have been discussed on a global scale, such as future aerosol forcing. The need for an
online coupled regional aerosol model was the starting point for the development of
REMO-HAM (Paper I). REMO-HAM was further used to study the black carbon
concentrations over Finland (Paper II). Black carbon is one of the most interesting
aerosol species due to its unique characteristics, for example the ability to change
snow albedo and absorb solar radiation (Bond et al., 2013). In Paper III, absorption
and the subsequent heating of air masses was studied in terms of the Asian monsoon.
The work was motivated by the need for more detailed modelling of the influence of
the so-called elevated heat pump hypothesis (Lau et al., 2006). For this study, the
global aerosol-model ECHAM-HAMMOZ was used and the emissions database was
updated to get the best possible representation of black carbon concentration over
the area of interest. The need for high-resolution nucleation studies motivated us to
test the ability of REMO-HAM to represent the European boundary layer nucleation
statistics. For this study, partly based on deficiencies already discussed in Paper I,
REMO-HAM was modified in terms of aerosol chemistry. Finally, the changes in
the emissions already partly used in Paper III lead to the last phase of this thesis
(Paper V). In this study, the future forcing changes were studies with ECHAMHAMMOZ with state-of-the-art emission scenarios. This work was motivated by
the need of further information regarding how future aerosol concentrations can be
controlled and what will be the radiative influence of such measures.
Chapter II
Aerosols
Aerosol particles can be divided into two main classes: natural and anthropogenic
aerosols. Natural aerosols come from sources like volcanoes, forest fires, oceans and
deserts, and they make a major contribution to the global aerosol load. Anthropogenic aerosols, on the other hand, come from sectors that produce emissions, like
industrial, traffic, residential wood burning and flaring. If the mass load of natural
and anthropogenic aerosols is compared, the anthropogenic is smaller, being ∼ 10%
of the natural. However, the anthropogenic aerosols are much smaller and higher
in total number, which leads to similar optical depths between these two classes
(Ramanathan et al., 2001; Carslaw et al., 2013).
The Intergovernmental Panel on Climate Change (IPCC) has estimated that the
effective1 anthropogenic forcing of aerosol-radiation interactions is -0.35 W/m2 with
-0.85 to +0.15 W/m2 uncertainty levels (Stocker et al., 2013). If the aerosol-cloud
interactions are taken into account, the estimate changes to -0.9 W/m2 with a 5
to 95% uncertainty range of -1.9 to -0.1 W/m2 . Based on the IPCC report, the
anthropogenic aerosols have a big impact upon the global mean forcing, despite the
large uncertainty range (Stocker et al., 2013). As was mentioned in the introduction
(Section 1), many global and regional models with an aerosol module have been
developed. With these models, the uncertainty related to aerosol-climate effects was
decreased, but still much work remains to be done, especially concerning aerosolcloud interactions, nucleation, secondary organic aerosols, black carbon and related
phenomena, and aerosol interactions with radiative transfer.
2.1
Absorption
Although the direct effect of aerosols is globally negative (cooling), it still has a
positive warming component through absorption. This is mainly caused by black
carbon (BC; details in the next section), but dust and brown carbon also contribute
to it (e.g. Stier et al., 2007). The absorption of radiation, which can be both short1
Includes all rapid adjustments, but is typically estimated from simulation with fixed sea surface
temperatures
13
2. Aerosols
14
and long-wave, heats the surrounding atmosphere. This can suppress convection
and lead to the evaporation of cloud droplets, and is known as the semi-direct effect
of aerosol particles (Lohmann and Feichter, 2005).
Absorption and scattering blocks solar radiation from reaching the surface. Between ∼1960–1990 the amount of solar radiation reaching the Earth’s surface decreased due to increased aerosol concentrations, which is called solar dimming (Wild
et al., 2005). Since the late 1980s, regional emission reductions have caused corresponding reductions in aerosol concentrations, leading to solar brightening, i.e.
areas, where more solar radiation is reaching the surface than before (Wild, 2009).
Although solar dimming influences the global radiation budget, the phenomenon
itself is more regional than global. In some regions, the impact is much higher than
in others; today, dimming still influences the region’s meteorology. For example, in
the Asian monsoon region, the dimming effect is large due to heavy aerosol loads
(Lau and Kim, 2006). It has been proposed that dimming can influence the Asian
monsoon by reducing the rainfall. Another aerosol-related impact related to the Indian monsoon is the elevated heat pump (EHP) hypothesis (Lau et al., 2006), where
aerosol heats the air in the upper troposphere over the monsoon region, which leads
to the rising of the heated air; eventually, warm and moist air is drawn over the
Indian subcontinent from the Indian Ocean (Lau et al., 2006). The EHP hypothesis
has not been accepted by all (e.g. Kuhlmann and Quaas, 2010); and, in Paper III,
the counter-acting EHP and solar dimming effects over India have been studied in
more detail.
2.2
Black carbon
One of the main aerosol species studied in this work is black carbon (BC). In Paper II, the BC properties in Finland were studied; and, in Paper V, BC emission
reductions and their impact on the future aerosol forcing was investigated. BC is
formed in flames during the combustion of carbon-based fuels. It has a unique combination of climate-relevant properties, such as the ability to absorb visible light at
all visible wavelengths and the potential to modify snow albedo. There are no other
substances like that in the atmosphere in significant quantities. Bond et al. (2013)
estimated that the total anthropogenic direct radiative forcing of black carbon in
the atmosphere is +0.71 W/m2 (with 90% uncertainty bounds of +0.08 to +1.27
W/m2 ). Including the pre-industrial natural background BC concentrations, the
estimate from Bond et al. (2013) changes to +0.88 W/m2 (with 90% uncertainty
levels of +0.17 to +1.48 W/m2 ). The latest IPCC report estimates that the total BC radiative forcing from fossil fuel and bio fuel to be +0.4 (+0.05 to +0.8)
W/m2 (Stocker et al., 2013). BC’s direct effect is included in the model used in Papers III and V, but not in Papers I, II and IV. Besides the absorption, BC can
also change cloud properties by altering the CCN budget and eventually changing
the albedo. Moreover, it can affect the ice clouds (cirrus) by enhancing the clouds’
long-wave emissivity (Garrett and Zhao, 2006). Bond et al. (2013) estimate that the
2. Aerosols
15
industrial-era climate forcing from black carbon cloud effects is +0.23 W/m2 (with
substantial uncertainty from -0.47 to +1.0 W/m2 ). These aerosol-cloud processes
are included in all the models used within this work.
One important climate effect of BC is its ability to change snow albedo. BC ends
up on the surface of Earth by dry and wet deposition. The deposited BC will then
decrease the albedo of snow, which leads to enhanced absorption and eventually
accelerated snow melting. As the background surface albedo is usually always lower
than the snow albedo, this will increase the warming of the surface (Quinn et al.,
2011). Again, based on estimates from Bond et al. (2013), the best industrial era
climate forcing estimate for black carbon deposition on snow and sea ice is +0.13
W/m2 (uncertainty from +0.04 to +0.33 W/m2 ). This estimate is the effective
forcing, whereas the estimate from IPCC, +0.04 W/m2 (with +0.02 to +0.09 W/m2
uncertainty levels), does not include the rapid change feedbacks, such as albedo
changes due to melting of snow caused by BC deposition on snow (Stocker et al.,
2013). It is noteworthy that the IPCC reports the surface temperature change per
unit forcing to be two to four times more responsive to the BC albedo forcing change
than to changes in CO2 forcing (Boucher et al., 2013).
BC is emitted to the atmosphere from various sources and is usually co-emitted
with other species, like organic carbon (OC). BC is formed from bio fuel burning,
like wood, crops and cooking, and from fossil fuel burning, mainly from diesel and
coal (Ramanathan and Carmichael, 2008). The lifetime of BC in the atmosphere is
much shorter (∼1 week) than, for example, that of carbon dioxide (CO2 ), which can
remain in the atmosphere from a few years to a couple hundred years (Stocker et al.,
2013). Although the lifetime of BC is short, it still can undergo long-range transport;
for example, to the Arctic, when the so-called polar dome is formed (Quinn et al.,
2011). The polar dome forms when a cold and stable lower troposphere becomes
isolated as the moist and warm air masses from outside cannot directly penetrate
it; the air masses outside a cold region start to ascend above the cold air, which
can be seen as a dome (Law and Stohl, 2007). The polar dome can extend to about
40◦ N (it is not symmetric), which makes northern Eurasia (under the dome) a major
source of the arctic BC (Stohl, 2006). On the other hand, for the Antarctic (another
remote location), the continental long-range BC transport is minimal (Stohl and
Sodemann, 2010) and the local sources, such as shipping emissions, might play the
key role for BC concentrations (Graf et al., 2010).
The short lifetime and the potential to change the radiation budget makes BC a
very interesting species for targeted emission reductions (Bond and Sun, 2005; Bond
et al., 2013). This has been studied in Paper V, where different future scenarios for
anthropogenic BC, OA and sulphur dioxide (SO2 ) were used in ECHAM-HAMMOZ.
Two of the scenarios were based on current legislation, one showed the technically
maximum reduction potential and the last one was targeted to BC reductions.
2. Aerosols
2.3
16
Nucleation
Atmospheric new particle formation (NPF) occurs when a cluster of critical size
is formed from molecules (nucleates) and condensable vapors cause the cluster to
grow (Kulmala and Kerminen, 2008; Andreae, 2013). The clusters in atmospheric
conditions can be solid or liquid. NPF is a process that has been detected almost
everywhere in the world (Kulmala et al., 2004). The clusters are formed either homogeneously (clustering of vapor molecules without a nucleus) or heterogeneously
(clustering around a nucleus or on a surface). After the sub-nanometer clusters have
formed, they start to grow; although, during the growth, many of them coagulate
with bigger particles (Kerminen et al., 2004). The coagulation sink naturally depends on the size and number of background particles, as these properties change
the available surface area and mobility of particles.
Different compounds have been proposed to influence nucleation. Sulphuric acid
(H2 SO4 ) has been suggested to be the main driving compound (Weber et al., 1996;
Sipilä et al., 2010). Regionally, for example in coastal areas, iodine can cause the
nucleation (O’Dowd et al., 2002). Some compounds, like ammonia, can increase
H2 SO4 nucleation rates and reduce the critical cluster sizes (Ziereis and Arnold,
1986; Korhonen et al., 1999; Kirkby et al., 2011). Also, amines have been shown to
influence the nucleation (Kurtén et al., 2008; Almeida et al., 2013). In addition to
these compounds, ion-induced nucleation can play a part in the atmospheric new
particle formation (Kazil et al., 2006; Yu et al., 2010; Yu, 2010). There is, however,
some evidence that the ion-induced nucleation has a fairly small contribution to the
particle formation (Eisele et al., 2006; Kulmala et al., 2007, 2010; Hirsikko et al.,
2011), but this is still a matter of debate.
NPF is an important process in the atmosphere and its climate relevance has
been demonstrated in many studies (Spracklen et al., 2006, and references therein).
Besides influencing the aerosol number concentrations and chemical composition,
it can affect the global and regional cloud condensation nuclei concentrations (Lihavainen et al., 2003; Laaksonen et al., 2005; Kerminen et al., 2005; Kulmala et al.,
2004; Merikanto et al., 2009; Kazil et al., 2010; Makkonen et al., 2012). In this work,
the European boundary layer NPF has been studied in Paper IV.
Chapter III
Modelling tools
The only method for studying the global atmospheric effects of aerosol particles
and their interactions is to use models. Observations, whether in situ or satellite
based, can be used to study regional and global aerosol properties, but a fully
coupled physical-chemical-dynamical system (aerosol-climate interactions) can only
be studied by models. However, observations are, in a way, the foundation for
modelling, as the model evaluation is one major part of the modelling process.
Furthermore, the set of basic aerosol properties, such as their size distributions and
chemical compositions, are at least partially based on observations.
In this work, the modelling approach has been used to study the aerosol-climate
interactions on both regional and global scales. In this chapter, the models used in
this study are presented. In Papers III and V the global aerosol-climate model
ECHAM-HAMMOZ was used; and, in Papers I, II and IV the regional aerosolclimate model REMO-HAM was used. In addition, in all REMO-HAM simulations,
ECHAM-HAMMOZ has been used to provide lateral aerosol boundary data.
3.1
ECHAM global circulation model
The European Centre Hamburg Model (ECHAM) is a global atmospheric model
that can be used to study the evolution of atmospheric states. The model describes
all the relevant atmospheric processes (radiation, clouds, precipitation, convection
etc.) and calculates their interactions and state time dependently. ECHAM was
developed at the Max Planck Institute for Meteorology, Hamburg, and is based on
the spectral weather prediction model of the European Centre for Medium Range
Weather Forecasts (ECMWF; Simmons et al., 1989). Currently, the newest version
of the model is the sixth-generation ECHAM6 (Giorgetta et al., 2013). However,
in this work, the fifth-generation version of ECHAM5 (Roeckner et al., 2003) (with
aerosols, details in Section 3.4) was used.
ECHAM calculates the time-dependent derivatives for the following prognostic
variables: vorticity, divergence, temperature, logarithm of surface pressure, cloud
liquid water and ice (Roeckner et al., 2003). Other calculations in the model are
17
3. Modelling tools
18
based on these prognostic variables and can either have feedbacks or simply be for
diagnostics. For example, the cloud calculations are performed in each time step
and they feed information back to the model through radiation and the water cycle.
The model uses, for the vertical calculations, a terrain following sigma-pressure coordinate system that reaches 10 hPa at the top level. In this work, 31 vertical levels
have been used in Papers I, II, IV and IV and 19 levels in Paper III. For horizontal calculations, in Papers I, II, IV and IV, T63-resolution1 was used, which
corresponds to a 1.9◦ × 1.9◦ resolution (∼200 km × ∼200 km), and in Paper III,
T42-resolution was used, which corresponds to a 2.8◦ × 2.8◦ resolution (∼300 km
× ∼300 km). Time integration in the model is done using a semi-implicit leapfrog
time integration scheme. The time step used in Papers I, II, IV and IV was 12
min; in Paper III, it was 30 min. ECHAM5 uses the flux form semi-Lagrangian
transport scheme by Lin and Rood (1996) on a Gaussian grid.
3.2
REMO regional climate model
The regional climate model REMO can be used to study the climate on a finer/higher
resolution than global models are usually able to use. This has been achieved by
restricting the REMO calculation area to be within a predetermined, although almost freely selectable domain. REMO is then forced from the domain boundaries
(lateral boundaries; Jacob and Podzun, 1997), which compensates for the missing global information. Inside the domain, the model calculates the dynamics and
physics without any direct outside forcing (REMO can also be used in weather forecast mode, which means that the whole domain is frequently initialized back to the
outside state (Karstens et al., 1996)).
REMO is a hydrostatic, three-dimensional atmosphere model, which was developed at the Max Planck Institute for Meteorology in Hamburg and is currently
maintained at the Climate Service Center in Hamburg. The core of the model is
based on the Europa Model, the former numerical weather prediction model of the
German Weather Service (Jacob and Podzun, 1997; Jacob, 2001). The spatial resolution of REMO goes from from 10×10 km2 upwards and the model domain can
be freely chosen. The restrictions for the domain comes from the arrangement of
boundaries, which should not be located over orographically challenging areas, such
as mountains.
Prognostic variables in REMO include the horizontal wind components, surface
pressure, temperature, specific humidity, cloud liquid water and ice. The physical
packages are originally from the global circulation model ECHAM4 (Roeckner et al.,
1996), although many updates have been done (Hagemann, 2002; Semmler et al.,
2004; Pfeifer, 2006; Kotlarski, 2007; Rechid, 2009; Teichmann, 2010; Preuschmann,
2012; Wilhelm et al., 2014). A leap-frog scheme with semi-implicit correction is
used for the temporal discretization and longer time steps are made possible with
a time filtering by Asselin (1972). The vertical levels in REMO are represented in
1
Spectral transform method with triangular truncation at wave number 63
3. Modelling tools
19
a hybrid sigma-pressure coordinate system, which follows the surface orography in
the lower levels and becomes independent of it at higher atmospheric model levels.
Horizontally, REMO uses a spherical Arakawa-C grid (Arakawa and Lamb, 1977),
where all prognostic variables, except wind, are calculated at the center of a grid
box. The wind components are calculated at the edges of the grid boxes. If the
model domain is far from the equator, a rotation of the grid is performed. The
rotation makes the equator run across the center of the domain, thus leading to a
more equal size of grid boxes over the domain.
The original stratiform cloud scheme of REMO (Roeckner et al., 1996) was updated by Pfeifer (2006) to include cloud ice as a prognostic variable. Other prognostic variables in the cloud scheme are cloud water and water vapor. The model uses
the empirical cloud cover scheme by Sundqvist et al. (1989). A height-dependent
parameterization for the cloud droplet number concentration is used separately for
continental and maritime climates (Roeckner et al., 1996).
The convective cloud parameterization is based on the mass-flux scheme from
Tiedtke (1989) with modifications by Nordeng (1994). A new convection type for
the cloud scheme (so called cold convection) was introduced to the model by Pfeifer
(2006). This type of convection occurs in cold air outbreaks over the sea in the
extra-tropical atmosphere.
Aerosols are represented in REMO only as a form of climatology (Tanre et al.,
1984). The spatial distributions of the optical thickness of land, sea, urban, and
desert aerosols, along with well-mixed tropospheric and stratospheric background
aerosols, are represented in the climatology. It is based on a global T10 spectral
distribution (≈1300 km), is fixed in time and the aerosols have no direct influence on
the clouds; that is, the indirect aerosol effects are not represented. It is known that
the climatology is highly absorbing, mainly over Africa and in Southern and Eastern
Europe (Zubler et al., 2011b). This comes from the unrealistic dust component,
which dominates the aerosol optical depth over these areas. Naturally, the coarse
resolution of the climatology is also problematic in regional scales.
Because the REMO domain does not cover the whole globe, information about
large-scale circulation outside the domain is needed. This forcing can be derived from
GCM simulations, observations, or global-scale analysis and re-analysis products
(Kotlarski, 2007). The outside forcing is calculated usually for a certain zone around
the regional model domain. In REMO, the lateral forcing uses the relaxation scheme
developed by Davies (1976). In this scheme, the prognostic variables of REMO are
adjusted towards large-scale forcing at the 8 outermost grid boxes. The outside
forcing decreases exponentially in this zone towards the inner model domain. The
boundary data used in this work is from the ECMWF and is the analysis data in
Paper I and Paper II, whereas in Paper IV the ERA-Interim re-analysis data is
used.
Besides the lateral boundary forcing, REMO also needs information about the
surface-related parameters. Over the oceans, the sea surface temperature (SST) and
sea ice distribution are prescribed from external data, unless the REMO simulation
3. Modelling tools
20
is coupled with a regional ocean model (e.g. Elizalde (2011)). At land points,
a modified land surface scheme is used and details can be found, for example, in
Kotlarski (2007) and Rechid (2009).
Nesting methods
The usage of the lateral boundary forcing data has some limitations. If the spatial
resolution of the driving data is too coarse, artificial wave reflections and breaking
might occur (Sieck, 2013). To avoid this, it is possible to use a nesting method,
where a high-resolution domain is nested to a lower-resolution domain. This means
that the results from the low resolution domain are used as boundary data for the
high-resolution domain (Rummukainen, 2010). This approach also allows the study
of the target area with two resolutions, if needed.
Figure 3.1: Example of a nesting.
Figure 3.1 represents the basic principle of the nesting. The European domain
is driven by the global model (or by (re)analysis data). The output from this
simulation is then further used as a lateral forcing data for the smaller domain.
3. Modelling tools
21
This method, using an intermediate step, is called nesting; or, in this case, doublenesting. The different plots in Fig. 3.1 represent ECHAM T63, REMO 0.44◦ and
REMO 0.088◦ orographies. It is noteworthy to point out how the coarse resolution
smooths the surface orography. This is also an important factor when the resolution
of a simulation is planned.
The common configuration for regional models is to use 1-way nesting, where the
results are not fed back to the driving model. It is also possible to do 2-way nesting,
where the regional model feeds back information to the driving model. In this way,
for example, a global model can use a regional model to resolve specific areas with a
finer temporal and spatial resolution. An example from such a nesting with REMO
can be found in Lorenz and Jacob (2005). Basically, a regional model can also be
included in a global model with 1-way/2-way nesting option, if local “zooming” is
supported.
3.3
HAM aerosol module
The Hamburg Aerosol Model (HAM) is an aerosol module that uses the aerosol
microphysical model M7 (Vignati et al., 2004; Stier et al., 2005). The module
solves the main aerosol processes, such as water uptake, chemistry (gas- and liquid
phase), emissions, nucleation, sedimentation, deposition (wet and dry) and cloud
processes (scavenging). The original implementation (HAM) is presented in Stier
et al. (2005) and the recent updates (HAM2) are described in Zhang et al. (2012). In
Papers I, II and III, the original HAM version was used and, in Papers IV and V,
the updated HAM2 version was used. The regional model REMO-HAM includes
many of the updates from HAM2, excluding the Secondary Organic Aerosol (SOA)
module by O’Donnell et al. (2011) and updated tracer structure, which defines the
tracer characteristics in the code level in a different way.
The aerosol microphysical processes in HAM are described through the aerosol
microphysics module M7 (Vignati et al., 2004), where the size distribution is represented by a superposition of seven log-normal modes: soluble and insoluble Aitken,
accumulation, and coarse modes, and one soluble nucleation mode. The mass and
number concentration are prognosed in each of the modes for the following species:
sulfate (SO4 ), black carbon (BC), organic carbon (OC), sea salt (SS) and mineral
dust (DU) (Stier et al., 2005). In reality, the model does not simulate organic carbon;
instead, it simulates organic matter (OM) using a transformation factor (OC×1.4
= OM; Dentener et al., 2006).
The species are divided into the modes as follows: the (soluble) nucleation mode
includes only sulphate, the soluble Aitken modes include BC, OM and SO4 , the
soluble accumulation and coarse mode includes BC, OM, SO4 , SS and DU, the
insoluble Aitken mode includes BC and OM; and, finally, the insoluble accumulation
and coarse modes include DU only. Insoluble-mode particles become soluble either
by coagulating with soluble-mode particles or by condensation of sulphate on their
surface. In the latter case, when a mono-layer of sulphate is reached, particles are
3. Modelling tools
22
moved to the corresponding soluble mode (Vignati et al., 2004; Stier et al., 2005).
Emissions are based on the AeroCom emission inventory, excluding Dimethyl
Sulfide (DMS) and sea salt emissions (Dentener et al., 2006). DMS emissions from
the marine biosphere are calculated based on DMS sea water concentrations using
an air-sea exchange rate, which is based on the model 10 m wind speed. Terrestrial
biogenic emissions of DMS are prescribed. The sea salt emissions are based on
the approach by Stier et al. (2005), where the emissions of sea salt particles are
based on a look-up table for wind speeds between 1 and 40 m/s. All the emitted
species, except the sulfur compounds, are treated as primary emissions (emitted
directly). The emissions are injected to the lowest model layer, except for explosive
and continuous volcanoes, biomass burning emissions, and emissions coming from
industry, shipping and power plants. Noteworthy is that, in Papers III and V,
the emissions were partly changed and partly updated for the ECHAM-HAMMOZ
model.
3.4
ECHAM-HAMMOZ global aerosol-climate model
The ECHAM model, with the coupled aerosol module HAM, is called ECHAMHAMMOZ2 (earlier ECHAM-HAM) (Stier et al., 2005; Zhang et al., 2012).
ECHAM-HAMMOZ is a model that describes all the major aerosol components
(see Section 3.3) and included both the aerosol-cloud and aerosol-radiation interactions. It is a model that can be used to study the aerosol-climate interactions
globally with different grid sizes.
The convective cloud parameterization is based on the same mass-flux scheme
from Tiedtke (1989) with modifications by Nordeng (1994) as in REMO(-HAM).
The double moment microphysical scheme was reported by Lohmann et al. (2007)
with improvement by Lohmann et al. (2007). The convective, large-scale and turbulent transport of aerosols and their precursors are based on the same approach
as other passive tracers (e.g. water vapor and hydrometeors) in the main model.
The activation of aerosols can be chosen from Lin and Leaitch (1997) or AbdulRazzak and Ghan (2000) parameterizations. The radiation module is coupled with
the HAM aerosol module and is described in (Stier et al., 2005; Zhang et al., 2012,
and references therein) publications (for HAM1 and HAM2).
In this work, the versions used are: in Papers I and II, ECHAM5.5-HAM (the
most recent version at that time); in Paper III, ECHAM5.3-HAM (this version was
used as it was validated in terms of aerosols over Asia in an earlier study by Henriksson et al. (2011). In addition, in some simulations, the model was coupled with the
mixed-layer ocean model); and, in Papers IV and V, ECHAM5.5-HAM2 (the most
recent version at that time). These model versions use the emissions from AeroCom
2
Acknowledgment: The ECHAM-HAMMOZ model is developed by a consortium composed
of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of
Oxford, and the Finnish Meteorological Institute and managed by the Center for Climate Systems
Modeling (C2SM) at ETH Zurich
3. Modelling tools
23
(Dentener et al., 2006), except in Papers III and V, where the emission module
was updated with IIASA’s (International Institute for Applied Systems Analysis3 )
GAINS (Greenhouse gas – Air pollution Interactions and Synergies) model emissions, global (Wang et al., 2008) and arctic (Corbett et al., 2010) shipping emissions
and GFED (Global Fire Emissions Database) version 3 emission (Giglio et al., 2010;
van der Werf et al., 2010). Additionally, in Paper V, new emissions were included
for aviation BC emissions from the QUANTIFY (Quantifying the Climate Impact
of Global and European Transport Systems) project (Lee et al., 2005; Owen et al.,
2010). Emission updates were done for Paper III, because the best possible representation for BC emissions was needed and the most recent AeroCom II phase
emissions were not available at that time. For Paper V, updates were done to get
the best possible representation of carbon-based emissions with consistent emission
scenarios for the future. Updating the emissions meant writing the pre-processors for
the different emissions sources and modifying the model code. The pre-processor’s
main idea is to collect all necessary data, do necessary unit conversions and remap
the modified data for the model grid. Otherwise, in this study, ECHAM-HAMMOZ
was not modified.
3.5
REMO-HAM regional aerosol-climate model
Modelling the aerosols in a global scale is, computationally, quite a demanding task.
To do aerosol-climate simulations on a higher resolution that allows small-scale
studies of aerosol-related processes, a decision was made to implement the HAM
aerosol module to REMO. In addition, to use the information about the aerosol in
the model, a double-moment stratiform cloud scheme was implemented. This work
is represented in Paper I and the REMO version with the HAM aerosol module is
called REMO-HAM.
3.5.1
HAM implementation
The starting point for the REMO-HAM development was the following: previously,
a chemistry module, RADMII, had been implemented to the vector version (calculation done in vector processor architecture) of REMO by Teichmann (2010), which
was partly adopted from the older implementation by Langmann (2000). Parallel to
RADMII implementation, a bigger structural change was conducted to the model:
an MPI version (massive parallel version that uses the Message Passing Interface,
MPI) was developed for scalar processors. As the implementation of the HAM
module was done to the new MPI version, some of the old (vector version) tracer
treatment had to be updated to support the MPI version. Some of these updates
are described in more details in the following sections.
REMO-HAM has two major updates if compared to REMO: an online aerosol
module and an updated stratiform cloud scheme. The HAM module (details in
3
http://www.iiasa.ac.at
3. Modelling tools
24
3.3) was implemented following the ECHAM-HAMMOZ structure, although some
changes had to be done as REMO calculates the physics before dynamics, which is
done the other way round in ECHAM. Due to this, the aerosol physics were divided
into two parts, where first, the cloud calculations are done (convective transport
+ wet deposition); and, in the second part, the M7 calculations are done. The
second part is calculated after the dynamics of the main model, which also means
that the aerosol dynamics (vertical and horizontal advection + dry deposition) are
calculated before the M7 module. Moreover, as the advection needs information
about the updated lateral boundaries, the module that updates them is called before
the aerosol dynamics.
Using two-phase physics for aerosols keeps the diagnostics consistent with other
output variables, but introduces one error source. The gas phase sulphuric acid
time integration scheme by Kokkola et al. (2009) is now partly calculated before the
dynamics and partly after. This will introduce some numerical error, but will stay
within reasonable limits due to the short time steps used with REMO-HAM. In addition, the time integration differs from the approach used in ECHAM-HAMMOZ:
the leap-frog methods for aerosols was replaced with the more straightforward Eulerstyle approach (this is also done in the RADMII implementation). Euler-style calculation is not as precise as leap-frog (with filter), but makes the integration very
fast. In the future, a leap-frog scheme-based integration can be easily implemented,
if needed.
After the implementation of the HAM module, the cloud schemes of REMO
were updated, because the original approaches did not include any aerosols effects.
The convective part was modified so that the tracer transport and wet deposition were included, but no microphysical interactions with the aerosol module was
implemented. There was some work towards convective microphysics in ECHAMHAMMOZ at that time, and the same preliminary structure was implemented in
REMO-HAM. As this project did not continue and the model version was never
officially released (due to some problems), the convective microphysics package is
not active in the model. The stratiform cloud scheme was also updated and is now
based on the Lohmann et al. (2007) double-moment cloud microphysics scheme (with
improvements by Lohmann and Hoose (2009), same as in ECHAM-HAMMOZ). In
this scheme, the aerosol information is used to calculate the CDNC and ice crystal number concentration (ICNC). Two parameterizations were included for the
cloud droplet activation: by Abdul-Razzak and Ghan (2000) and Lin and Leaitch
(1997). The connection of clouds to the radiation module was done as in ECHAMHAMMOZ: the information about CDNC, ICNC, cloud liquid water and ice content
is passed to the radiation module, and based on these, the effective radius of droplets
and crystals is calculated (which determines cloud properties, like albedo).
The vertical diffusion fluxes caused by the sub-grid scale turbulence are calculated for the lowest layer as reported by Louis (1979). A second-order closure scheme
by Mellor and Yamada (1974) is used for other model levels. Tracer transport in
REMO-HAM is based on the mass conserving, positive definite and computationally
3. Modelling tools
25
efficient scheme by Smolarkiewicz (1983, 1984). The implementation was already
done for the vector version of REMO and, for REMO-HAM, the parallelization was
needed. As the model has a halo-zone (see Section 3.5.4), the implementation of
the advection scheme was possible without any major modifications to the existing
code.
Aerosol representation through coarse climatology is a problem for climate models, as was shown in a regional study by Zubler et al. (2011b). To overcome this
problem, a better climatology can be implemented (e.g. Kinne et al. (2013)), or
an online aerosol model coupled with radiation can be used. As an aerosol module
was implemented during this work, the possibility of coupling the module (HAM)
with REMO’s radiation code was investigated. The current REMO radiation code
is based on ECHAM4 radiation and does not directly support online radiation calculations. The outcome of the coupling investigation was that, for example, the
ECHAM5/6 radiation module should be implemented rather than modifying the
existing code. As this would have been a very time consuming task, it was excluded
from the work done in Paper I. This means that the direct effect is not based on the
HAM aerosol module information and the model is not suitable for similar studies as
was done in Paper III. Also, as only the indirect aerosol effect is included through
online coupling of HAM with clouds, the model cannot be fully used to study aerosol
forcing, like in Paper V.
Because Paper I is a peer-reviewed scientific article focusing on the validation
of REMO-HAM, it did not include all the details about the implementation of the
HAM aerosol module to REMO. Above, some details are shown and, in the following
sections, more insights into the structure of REMO-HAM will be discussed, although
the aim is not to provide a technical manual.
3.5.2
Lateral boundary conditions
The average residence time of aerosol particles in the atmosphere depends on their
size and where they are vertically located. Lifetime can be a few days in the lower
troposphere, a few weeks in the upper troposphere and one to two years at higher
levels above the troposphere (Pruppacher and Klett, 1997). In order to get the
information of (longer lifetime) aerosol species from outside the calculation domain,
the aerosol module needs lateral boundary forcing. In Paper I, Paper II and
Paper IV, this has been done by running ECHAM-HAM and pre/post-processing4
the aerosol data for REMO. The aerosol pre-processor for REMO-HAM basically
reads in the global data, remaps it horizontally and vertically for the desired grid,
and writes out the REMO-HAM-readable (IEG format) input files.
The lateral boundary update frequency of any tracer in REMO-HAM can be
freely chosen between 0–99 hours. Between the update time steps, the forcing for
the boundary is calculated by interpolating the boundary data linearly (in time).
4
Depends on the model’s point of view
3. Modelling tools
26
The “sponge” zone5 for the tracers in REMO-HAM includes two of the outermost
grid boxes throughout the domain. The lateral boundary treatment at the “sponge”
zone is based on Pleim et al. (1991), where the flux divergence in the grid boxes
next to the boundary are set to zero in order to minimize the discontinuities in the
advective flux. At the upper boundary, the influence of downward transport can
be taken into account by setting the number of levels (top-down) that are updated
from the boundary data.
3.5.3
Other input files
Besides the lateral aerosol boundary forcing, the model also requires input data
for other parameters. The most important data is for the emission module, which
uses emissions from AeroCom (Dentener et al., 2006). The AeroCom emission preprocessor creates the emission files from the original AeroCom data and gives the
remapped emission files in flux form, thus making them easily usable for the model.
In short, the pre-processor gathers the data from different AeroCom source files, does
the necessary collecting to different emission sectors (e.g. fossil and bio fuels), does
the necessary units conversions (mostly to kg(substance)/m2 /s; this way unnecessary
calculations during the simulation are avoided); and, finally, does a flux-conserving
remapping to the REMO grid.
In addition, other input data is needed by the HAM aerosol module. Below,
only the data that needed modifications (pre-processing) is discussed. All the other
input files needed by the HAM module remain untouched and are directly read in
by the model.
The aerosol chemistry part requires information about the oxidant species, as is
described in Section 3.6. The concentration fields of these species are created from
the global 3D maps and are then remapped in the pre-processor both horizontally
and vertically for the REMO domain.
The dry deposition module requires information about the land properties. For
SO2 (gas phase), the model requires information on soil resistance, which is a function of soil pH. The pH values are divided into 5 classes as reported by Batjes (1995).
Last, as REMO does not include information about the canopy height, this is also
read in from offline fields. Both the soil pH and canopy height are pre-processed
from ECHAM input files to REMO input files by horizontal remapping.
3.5.4
MPI parallelization and halo zone
The MPI version of the model uses MPI (Message Passing Interface) parallelization.
Figure 3.2 shows the main principle of the parallelization. The domain (top left corner) is divided between the processors into sub-domain (path 1) and each processor
creates a so called halo zone for the sub-domain (step 2, grey areas). In actuality, the
halo zone is a copy of the neighboring grid boxes (which would overlap with them if
the original domain were gathered). The halo zone must be created; otherwise, the
5
The area, where the lateral boundary forcing directly affects the modelled values
3. Modelling tools
27
horizontal advection would not be able to work (because it needs information from
the neighboring grid boxes).
Figure 3.2: Example of REMO parallelization principle and halo regions (grey color).
The model basically does two major steps in each cycle: calculates physics (including soil, vertical diffusion, clouds, radiation, etc.) and calculates the dynamical
changes (advection, winds, etc.). The physics calculation does not take into account
any horizontal processes. Thus, calculations in each sub-domain can be divided
into smaller sections and looped over the whole domain. The vertical dimension
remains the same all the time and the sub-domain shown in Fig. 3.2 is calculated
in smaller vectors (this would basically give support for OpenMP and lead to hybrid MPI/OpenMP parallelization). The length of the calculation vector can be
freely chosen, which makes the overall calculation of physics faster (the reason is
that, with smaller vector length, the processor needs only to use the fastest cache
level(s)). As the halo zone is updated by the neighboring processors after physics
calculations are done for the prognostic variables, calculation of halo zone for these
is basically unnecessary. This does not make a big difference for the normal REMO
(small number of affected variables), but when the aerosol module is coupled (or
a similar module with many tracers), the tracer-related calculations in the physics
core significantly increases the burden of unnecessary calculations.
This problem was discussed with colleagues, and one solution was implemented.
After considering the issue, the vector length used for the physics looping was set to
3. Modelling tools
28
be the same as the edge length of the sub-domain without the halo zone. This works,
but might lead to situations where the edge length is so long that the processors
start to use higher-level caches, which eventually slows down the calculations. One
way to solve this problem, without losing the ability to set calculation vector size,
would be to use pointers, but this was never introduced as it would require quite
significant structural changes to the main REMO code.
3.6
Sulphate chemistry
The sulphate chemistry module of HAM is based on the work done by Feichter et al.
(1996). In this simple aerosol chemistry scheme, both the gas and the aqueous phase
reactions leading to sulphate production are taken into account. In the aqueous
phase, sulphur dioxide (SO2 ) is oxidised by hydrogen peroxide (H2 O2 ) and ozone
(O3 ) and in the gas phase, SO2 and DMS are oxidized by hydroxyl radical (OH)
during the daytime while DMS reacts with the nitrate radical (NO3 ) during the night
time. In the latter, the assumption is that NO3 is in steady state with its production
and loss terms, which both include reactions with nitrogen dioxide (NO2 ). The gas
phase sulphuric acid concentration is calculated with an improved time integration
scheme by Kokkola et al. (2009).
The oxidation fields for OH, H2 O2 , O3 and NO2 originate from the calculations
of the Model for OZone and Related chemical Tracers (MOZART) chemical transport model (Horowitz et al., 2003). ECHAM-HAMMOZ reads in the pre-processed
MOZART fields, which are on a monthly time resolution (constant values over a
month). Although this method is a good approach for a simple chemistry module,
it has deficiencies for species like OH, which has a strong diurnal cycle (peak during
day time; Seinfeld and Pandis, 1998). This problem has been solved in HAM by introducing an artificial diurnal cycle for OH. In this approach, the OH concentrations
follow a cosine function between sunrise and sunset so that the values are always
positive (cosine between −π/2 and π/2) and that the peak value is during midday.
The peak amplitude has been scaled with day length so that the original monthly
mean values are preserved. With this method, the OH concentrations have a diurnal
cycle and the sulphate chemistry routine calculates the oxidation more realistically.
3.6.1
Modifications to the sulphate chemistry
In ECHAM-HAMMOZ and REMO-HAM, the formation of sulphuric acid occurs
via the reaction between OH and SO2 . The latter is directly emitted from various
anthropogenic and natural sources, and is also produced in a reaction between DMS
and OH. This means that OH concentrations play an important role in the sulphate
formation. Still, as was described earlier, the OH concentrations are monthly mean
fields that are read in by the model. OH has a clear diurnal cycle, which means that
usage of the monthly mean values will create some error. On the other hand, the
introduced artificial diurnal cycle does cancel out some of this, but this approach
may overestimate the values for short days, and more importantly, it does not take
3. Modelling tools
29
into account the attenuation of solar radiation (main driver for OH formation) by
clouds.
Previously, in the work by Mikkonen et al. (2011), a proximity measure (proxy)
approach was developed for the sulphuric acid concentrations. In this approach,
using experiment data from multiple measurement sites, a H2 SO4 proxy was derived.
The proxy was based on global solar radiation, SO2 concentration, condensation sink
and relative humidity, because this form could describe the overall data set very well.
Following this approach, an OH-proxy was created in Paper IV and implemented
in REMO-HAM. The proxy was derived from OH data measured in Hyytiälä and
is based only on the downward flux of solar radiation (SWF↓). Data from other
sites was also analysed; but, based on the test done for the European domain,
only Hyytiälä data was finally accepted. The test included comparison of H2 SO4
concentrations from the OH-proxy version of REMO-HAM with different forms of
proxy against measurements.
Details about the proxy can be found in Paper IV. Here, only the final form of
the OH-proxy, OHproxy , is shown:
(
OHproxy =
3081.0 · SWF↓0.8397 day time
,
6.033 × 104
night time
(3.1)
where the units are [molec/cm3 ] for the OH-proxy and [W/m2 ] for SWF↓. This
method does not increase the computational burden and links the OH concentration
to the incoming solar radiation, thus including the influence of clouds as well as
location, day of year, and time of day.
3.7
Nucleation methods
The HAM module includes many alternative formulations for aerosol nucleation (see
Section 2.3). The default approaches are the binary water–sulphuric acid nucleation
by Vehkamäki et al. (2002) and the neutral and ion-induced nucleation of sulphuric
acid and water by Kazil et al. (2010). In addition, two nucleation parameterizations
for the forested boundary layer are included: the cluster activation mechanism (Kulmala et al., 2006; Sihto et al., 2006; Riipinen et al., 2007) and the kinetic mechanism
(Laakso et al., 2004; Sihto et al., 2006; Kuang et al., 2008). In Paper III, the nucleation by Vehkamäki et al. (2002) was used, in Papers I, II and V the approach
by Kazil et al. (2010) was used and in Paper IV the modified kinetic nucleation
was used.
In Paper IV (also Section 3.6.1), a measurement-based OH-proxy for OH concentrations was implemented, which improved the model’s chemistry. Earlier, as
discussed in Paper I, it has been noticed, that the simulated SO2 concentrations
are too high in ECHAM-HAMMOZ and REMO-HAM. With the OH-proxy, the simulated SO2 and H2 SO4 concentration are at more realistic levels; which, on the other
hand, means that the H2 SO4 concentration-based nucleation schemes have potential
to give more realistic results.
3. Modelling tools
3.7.1
30
Modifications to the kinetic nucleation scheme
Within the work done in Paper IV, the kinetic nucleation scheme by Laakso et al.
(2004) was modified. In the original scheme, nucleation rates were calculated for
the aerosol particles, which included two H2 SO4 molecules. However, the kinetic nucleation mechanism is based on aerosol size distribution measurements for particles
larger than 3 nm, and deriving the ∼1 nm nucleation rate introduces some uncertainty related to particle growth between 1 and 3 nm. Therefore, in the modified
scheme, the calculations are done for 3 nm (in diameter) sized particles (a similar
approach has been used in Makkonen et al. (2009)). The assumption within this
method is that the newly formed particles consist only of H2 SO4 (and a corresponding amount of H2 SO4 is removed from the gas phase as the particles are formed).
The modified 3 nm nucleation rate J3nm is calculated with the following equation
J3nm = K × [H2 SO4 ]2 ,
(3.2)
where K = 1.417 × 10−15 [cm3 s−1 ] is the kinetic coefficient and [H2 SO4 ] is the
sulphuric acid concentration in molec/cm3 . The K is based on comparison of
measured H2 SO4 concentrations and J3nm values from three measurement stations:
Hyytiälä (Finland), Melpitz (Germany) and San Pietro Capofiume (Italy).
Chapter IV
Modelling aerosols and climate
This chapter includes some of the main findings of this work. More detailed and
comprehensive analysis can be found from the papers attached. First, the modelled REMO-HAM BC concentrations over Finland will be discussed and the influence of Finnish BC emissions to the surface air concentrations will be investigated.
Next, examples from the study of the European boundary layer NPF will be given.
These results show how much the new approach of calculating the OH concentrations improved the modelled nucleation. In addition, some of the limitations of the
aerosol model will be discussed. Moreover, an analysis of the spatial distribution
of Europe-wide nucleation events will be shown and the link between SO2 emissions and nucleation rates will be discussed. Finally, from the global point of view
(ECHAM-HAMMOZ), the influence of future emission reductions will be discussed
in terms of aerosol direct radiative effects and cloud radiative effects. Additionally,
some results for the BC snow effect are described.
4.1
Black carbon over Finland
In Paper II, the modelled BC surface air concentrations were compared against observations. The analysis showed that the model underestimated the observed values,
especially for winter time. In addition, as it is known that REMO has a wet bias
over Finland, the possibility of too efficient wet removal was also investigated. However, the results showed that the excess in precipitation can only partially explain
the underestimation. The results indicated that the underestimation most probably originates from deficiencies in the emission database used; specifically, from
underestimated residential wood burning emissions.
The simulations done in Paper II were for the year 2005. As some of the used
measurement stations did not have observation data for 2005, the first available
measurement year was used in the analysis. For this thesis, two extra simulations
were done. First, instead of 2005, the year 2008 was simulated, and in the second
simulation, updated emissions from GAINS model were used (Paper V emission
structure including sectors and height dependency used with ECHAM-HAMMOZ
31
4. Modelling aerosols and climate
PUIJO
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REMO-HAM GAINS
REMO-HAM AeroCom
OBSERVATIONS
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32
Figure 4.1: Monthly averaged black carbon concentrations for 2008. Yellow triangles represent REMO-HAM results from Paper II (AeroCom, 2005), blue diamonds
represent REMO-HAM results for 2008 with AeroCom emissions, cyan circles represent REMO-HAM results for 2008 with GAINS emissions and red stars represent the
observed 2008 values.
was implemented into REMO-HAM). In addition, the results shown here are from
the latest version of REMO-HAM model; the model version used in Paper II was
the first official release of REMO-HAM and the model has been updated since (both
the aerosol module and main model). The domain used here is slightly bigger than
in Paper II simulations, but the spatial resolution is the same. Vertically, the
simulations includes only 27 levels, whereas 31 levels were used in Paper II. The
meteorological boundary data was changed from analysis data to ERA-Interim reanalysis data (Dee et al., 2011). Moreover, the model output frequency was changed
from 3 h to 1 h. Furthermore, updated measurement data was used because it is
now more precisely calibrated.
In Figure 4.1, the observed and modelled values from 5 locations in Finland
(Pallas, Puijo, Hyytiälä, Utö and Virolahti) are shown. If results from Paper II
are compared against the first simulation done now (REMO-HAM AeroCom), two
bigger differences show up. First of all, the late-winter values are in better accord
with Paper II’s results. The latest model version cannot capture the late-winter
peaks and tends to even have a decreasing trend during that time. This is not
surprising as the emissions do not have a yearly cycle for residential combustion
sector (which is the biggest sector in Finland); however, what is interesting are the
4. Modelling aerosols and climate
33
winter peaks seen with Paper II results. As the emissions are exactly the same,
this is coming from the different lateral boundary forcing and differences in the
model versions. The second big difference between the old and new run is that the
underestimation during summer seen in Paper II’s results is not anymore present
(the newer model version actually slightly overestimates the June and July values
at some of the stations).
If the latest model version is used with updated emissions (REMO-HAM
GAINS), the results looks more realistic. New emissions have better spatial and
time resolution, but the biggest advantage, at least for these simulations, is the
yearly cycle for domestic combustion sector. This does improve the underestimation during the late-winter, but does not remove it completely. The method used
(details in Paper V) for the yearly cycle could have some problems for Northern
Europe (as it was derived from Asian data), but tests done at the Finnish Environmental Institute (SYKE) have shown that the method actually represents the
yearly cycle very realistically when compared against their own detailed emission
data (personal communications, 2013). This means that the emission data could
still slightly underestimate the late-winter values or simply that the model has some
deficiencies that smooth out the peak values. During the rest of the year, the model
with updated emissions can reproduce the observed values fairly realistically and
show similar features in the yearly cycle.
As Finland is located at high latitudes (roughly above 60◦ N), the winter temperatures are normally quite cold and the whole country has a snow cover. Cold
weather leads to quite high winter BC emissions, which mainly come from the residential burning sector (Kupiainen et al., 2006). High BC emissions during snow
season means that the Finnish emissions impact the regional snow-albedo forcing
(which is also modified by the long-range transport of BC from neighboring regions). In addition, BC emissions from Finland can influence the Arctic region’s BC
concentrations. Although the emissions are not as high as from some other Arctic
countries, they still can have impacts as the Arctic region is quite sensitive to even
small increases in BC concentrations (Quinn et al., 2011). To investigate how much
BC is transported to and from Finland, a simulation without Finnish BC emissions
was conducted in Paper II. It should be mentioned that, in Paper II, the focus
of analysis was more upon BC concentrations and deposition, not so much on the
snow-albedo effect or long-range transport to the Arctic. However, here the latter
is also briefly discussed.
The relative fraction of 2005 BC surface air concentrations when the Finnish
BC emissions are switched off is shown in Figure 4.2. On annual scale near the
high emissions sources (Southern Finland and Oulu region), the surface air fraction
of long-range transported BC is only 10–30%, whereas in Eastern and Northern
Finland, the long-range transported BC fraction can be up to 50–60%. In addition,
the transport from Finland can be seen in the northeast part of the plot, where the
non-Finnish emissions can explain 80–90% of the surface values. Since the winter
time is more relevant due to colder air and the forming polar dome (Stohl, 2006),
4. Modelling aerosols and climate
34
Figure 4.2: The relative fraction of 2005 BC surface air concentrations when the
Finnish BC emissions are removed for yearly and winter values. The fraction is based
on the simulations in Papers I and II.
winter values are separately shown in Figure 4.2. The long-range transport from
Finland towards the Arctic is now much more visible than on the yearly scale.
Emissions outside Finland are still a bigger source at the northeast part of the plot,
but the values decrease to 60–80%. This means that, during winter time, the longrange transport from Finland can have significant impact on the Arctic near-surface
values. However, a more detailed study should be done to quantify the effect of
long-range transported BC from Finland to the Arctic. Such a study should, as
an example, use better emission data, as was indicated in the study by Stohl et al.
(2013), as well as a longer simulation period.
4.2
European boundary layer NPF
The background aerosol properties change through NPF, which leads to changes in
the regional and global CCN concentrations (e.g. Lihavainen et al., 2003; Merikanto
et al., 2009). Laaksonen et al. (2005) have shown that in Po Valley, Italy, NPF can
contribute up to a third of the regional CCN budget. This indicates the importance
of NPF inclusion in climate models.
The European boundary layer NPF was studied in Paper IV. The models used
were REMO-HAM with modifications presented in Chapter 3.6.1 (henceforth in this
section called REMO-OHP), and an unmodified REMO-HAM version. The model
results were compared against measurements from 13 ground-based sites. Actual
4. Modelling aerosols and climate
35
measurement data was obtained from three stations; whereas, for the other ten
stations, processed datasets published in the literature were used. Here, results
showing the differences in nucleation rates between REMO-OHP, REMO-HAM and
observations are presented. In addition, the capability (or lack thereof) of REMOOHP to represent NPF events in terms of the growth of the particles is demonstrated.
Also, the spatial distribution of the modelled nucleation rates is shown with the link
to SO2 emissions.
HYYTIÄLÄ
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Years 2003-2004
Figure 4.3: Measured and modelled daily mean (filtered) J3 nm rates for event days at
Hyytiälä, Melpitz and San Pietro Capofiume. REMO-OHP is the OH-proxy version
of REMO-HAM and REMO-HAM is the unmodified version.
In Paper IV, the daily mean formation rates of 3 nm particles J3 nm were shown
for Hyytiälä (Finland), Melpitz (Germany) and San Pietro Capofiume (Italy). The
4. Modelling aerosols and climate
36
means were calculated only for event days (measured data was classified differently
than the modelled data; details in Paper IV), but no additional filtering was done.
Here, the lower detection limit of the instruments used in Hyytiälä and San Pietro
Capofiume (0.01 cm−3 s−1 ) is used to further filter the event days: all values below
this limit are removed from the daily mean calculations. With this approach, the
daily means become daily event means, which are more informative in regards to
how well the model can capture the nucleation rates during a nucleation event.
The results from REMO-OHP and the unmodified REMO-HAM are compared
against observations in Figure 4.3 (here the filtering procedure explained above is
used for all data). Overall, it is clear that REMO-OHP can reproduce the measured
values much better than REMO-HAM. The latter tends to overestimate the nucleation rates at all locations, although in Hyytiälä the bias is not very strong. On
the other hand, REMO-OHP is capable to simulate realistic values at all locations.
Some underestimation can be seen with REMO-OHP in San Pietro Capofiume, but
it is much smaller than the overestimation with REMO-HAM. Figure 4.3 shows
clearly how much the model results improved with the new OH-proxy.
Hyytiälä measured
D [nm]
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Figure 4.4: Example distribution plots from Hyytiälä. Upper panel shows the evolution of the distribution for one day from measurements; the lower panel, the same
from the model results (based on Paper IV).
After being formed by nucleation, the new particles start to grow. The particles
might eventually reach bigger sizes; for example, it can take 1–2 days for the particles
to reach ∼100 nm sizes (Pierce et al., 2012). An example of a NPF event can
4. Modelling aerosols and climate
37
be seen in Figure 4.4, where the upper panel shows an observed event and the
lower panel shows a modelled (REMO-OHP) event. The measurements show how
the nucleated particles start to grow and eventually reach ∼100 nm sizes. From
the model results, the start of the event can be clearly seen (nucleation), but the
following growth is missing. There are many reasons for this, but it is mainly caused
by two factors: first, REMO-HAM does not include condensation of low-volatility
organic species, which would cause the particles to grow to bigger sizes (only H2 SO4
is condensing in the current setup). Second, the used modal structure does not
allow continuous growth of the particles, as was shown in Korhola et al. (2014).
The reason why the nucleation continues much longer in the model comes directly
from the missing growth. Larger particles would cause a bigger condensation sink of
H2 SO4 ; but, because the model cannot produce enough of these, the excess H2 SO4
will continue to activate nucleation. This leads to overestimations in the length of
nucleation, although the used OH-proxy approach in Paper IV did improve the
results significantly.
Nucleation is generally simulated quite realistically (Paper IV), but the problem
is the growth of freshly nucleated particles. Currently in REMO-HAM, the coagulation and H2 SO4 condensation will eventually grow the particles to reach Aitken
and accumulation modes, as can be seen in Figure 4.4. However, as the growth
of nucleated particles has deficiencies in the model, this will impact the simulated
climate. The reason for this is that NPF events will change the CCN production
and the cloud droplet number concentrations (Korhola et al., 2014), because the
CCN particles are in the sizes of Aitken and accumulation modes (Makkonen et al.,
2009), which are influenced by NPF. The description of particle growth could be
improved with an online SOA scheme (e.g. O’Donnell et al., 2011); which would,
on the other hand, increase the computational burden. Another approach would
be to use a more simple approach for organics (e.g. Makkonen et al., 2009), but
these methods would still suffer from the limitations of the modal structure. Moving to sectional aerosol models (e.g. Kokkola et al., 2008) would solve this problem
(assuming that the sectional model also includes organic condensation, which happens to be the case in Kokkola et al. (2008)), but would also lead to increased
computational burden. However, as the (super) computers continue to get faster
and model development continues, this problem might decrease or even disappear
in the future because more detailed aerosol modules, such as sectional models, can
be used more frequently. To date, simulations with sectional models (fully coupled
with physics) have been already performed, for example with ECHAM-HAMMOZ
(Bergman et al., 2012).
The spatial distribution of nucleation events can be studied with climate models.
In Figure 4.5, an example of the modelled1 evolution of one European-wide event
day is represented. Overall, nucleation rates show clearly the diurnal cycle; spatially,
the events can be very local or reach hundreds of kilometers in size, which is in good
1
REMO-OHP, simulated date 21.04.2009, based on Paper IV
4. Modelling aerosols and climate
38
Figure 4.5: An example timeseries of the evolution of an European nucleation event
(time is in GMT). The result are based on the simulations in Paper V.
agreement with previous studies, for example, with Crippa and Pryor (2013).
Most of the intensive nucleation events occur near high SO2 emissions sources.
The emission burden of the used AeroCom emissions (for April 2000) is presented
in Figure 4.6. Strong nucleation rates can be clearly seen over the Baltic Sea in
Figure 4.5, where nucleation is influenced by the high SO2 emission burden coming
from the shipping sector (Figure 4.6). In addition, over continental areas, such as
Bulgaria, the nucleation is strong and there, based on Figure 4.6, the emissions are
very high. Interestingly, by comparing the areas over Northern Germany in Figures
4. Modelling aerosols and climate
39
4.5 and 4.6, it can be seen that nucleation rates there are strong and cannot be
linked directly to the local emission sources. Instead, nucleation is affected by the
atmospheric transport. The wind direction in the simulation (on that day) was
from east to west, and part of the SO2 that oxidizes to H2 SO4 and activates the
nucleation was coming from big industrial sources in the Czech Republic, maybe
even from Poland.
Figure 4.6: SO2 emission burden from AeroCom 2000 emissions (only for April) for
the European REMO-HAM domain.
The example data shown in Figure 4.5 is from a quite cloud-free day, which
explains why almost the whole domain is experiencing nucleation events. In some
areas, such as Sweden and Norway, clouds are blocking the radiation, which eventually leads to lower nucleation rates. In addition, the local emissions over these areas
are fairly low, although high enough to cause nucleation, if the regional atmospheric
conditions were favorable.
Figure 4.6 also shows the problems discussed in Paper I related to emission
accuracy. For example, the shipping lanes are on a coarse resolution, which shows
up in the modelled results. However, there are many other emission databases, and
it should be mentioned that the AeroCom emissions have been also updated. Nowadays, they include datasets from the Atmospheric Chemistry and Climate Model
Intercomparison Project (ACCMIP; Lamarque et al., 2013). These updates improve the emissions by introducing more emissions years, better spatial and time
resolution, include more species and can have more emission sectors (for details,
4. Modelling aerosols and climate
40
see http://aerocom.met.no/emissions.html). In Paper V, the global anthropogenic emissions were updated and these emissions can be also used with REMOHAM. Although with REMO-HAM, even more regional (country-wise) and higher
resolution (spatial and time-wise) emission datasets could (or should) be used to
gain all the benefits of the higher resolution of the model.
4.3
Targeted black carbon reductions
As the lifetime of aerosol particles is relatively short, the atmospheric concentrations
respond rapidly to changes in primary or aerosol precursor gas emissions. Recently,
in some regions, such as Europe and North America, the atmosphere has experienced brightening while in some regions, for example India, dimming has increased
(Cermak et al., 2010; Haywood et al., 2011). This means that the cooling effect of
aerosols has also changed (globally and regionally). As the trend to decrease aerosol
and aerosol precursor emissions still continues, one climatological issue raising from
this is the aerosol cooling effect: how will it change? Since the different aerosol
species have different climate impacts, the emission reductions should be studied
also from species, or even sector point of view. In Paper V, the emission scenarios
based on current legislation for BC, OC and SO2 were included. Additionally, one
of the scenarios was targeted to reductions in short-lived climate forcers (in this case
BC and OC) and one showed the maximum reduction potential of BC, OC and SO2 .
In this thesis, the results from the targeted and maximum potential scenarios
are shown (henceforth called BCadd and MTFR, respectively). The MTFR scenario
includes a portfolio of most important measures that could produce the largest reductions in their global radiative forcing. Measures with small increases in radiative
forcing (or net impact) have been excluded (UNEP, 2011; Shindell et al., 2012).
The maximum reduction potential of anthropogenic aerosols (BC and OC) and SO2
with currently available technologies is also demonstrated. This scenario includes
primarily end-of-pipe measures and excludes any further efficiency or fuel switching
potential (Cofala et al., 2007; Klimont et al., 2009). These two chosen scenarios
are represented so that the targeted reductions could be compared to the maximum
potential of reductions.
The influence of the aerosol direct radiative effect for short-wave radiation
(DRESW, cloud-free) and the cloud radiative effect for short-wave radiation
(CRESW) at the top of the atmosphere (TOA) can be seen in Figure 4.7. The
top panels show the absolute values from the reference simulation and the middle and lower panels show the difference between the scenarios and the reference
simulation.
Clearly, the MTFR simulation reduces the cooling effect of aerosols over the
globe2 . Over India, the BCadd simulation predicts increased DRESW cooling, which
2
Because the absolute values are mainly negative, the difference plots show basically the difference in cooling, thus a positive difference means that the cooling has decreased in the scenario
simulation (and vice versa)
4. Modelling aerosols and climate
Figure 4.7: The yearly mean direct radiative effect for
(DRESW, cloud-free) and yearly mean cloud radiative effect
tion (CRESW) at the top of the atmosphere (TOA) from the
difference between scenarios and the reference run. The result
ulations in Paper V.
41
short-wave radiation
for short-wave radiareference run and the
are based on the sim-
comes partly from the reduced absorption component (BC), but mainly from the
increased scattering component (sulphate). The cooling increases over India as the
SO2 emission pathway follows one of the current legislation scenarios (and it is esti-
4. Modelling aerosols and climate
42
mated that SO2 emission will increase over India, mainly coming from the industrial
and energy sectors). If the emissions are reduced as much as it is technically feasible,
the cooling over the globe would decrease significantly. Globally, the BCadd simulation predicts 0.06 W/m2 reduced cooling for DRESW and 0.38 W/m2 for CRESW,
whereas the MTFR simulation predicts 0.4 W/m2 and 0.82 W/m2 cooling, respectively. This makes the targeted BC emission reductions clearly more beneficial for
climate than the technically maximum reductions, which could probably be very
harmful for climate practically in all continental regions.
The influence of BC upon snow albedo was not included in the simulations done
in Paper V. Overall, the BC snow albedo effect is estimated to cause global warming that can reach ∼0.3 W/m2 (Bond et al., 2013). One thing not shown in Paper V
is how much the BC snow deposition decreases in different scenarios. For example,
in the BC emission reduction scenario, the simulation predicts that the BC snow
deposition decreases -60% globally and -37% over the Arctic area3 if compared to
the reference year (2005) simulation. For the technically maximum reduction scenario, the simulation predicts -56% and -32% decrements, respectively. It should
be mentioned that these values are not directly comparable, as the snow cover was
changing according to the simulated climate (which explains why in the BC targeted
simulation the change is slightly higher). Nevertheless, these values indicate that
the BC snow forcing (warming) can be decreased by the targeted reductions as much
as with the technical maximum reduction measures. The results above (and in Paper V) show that the technical maximum reduction measures can be quite harmful
from the climate point of view. All of this implies that, with the BC targeted reductions, the BC snow albedo effect can be reduced very efficiently without climatically
harmful side effects. However, as the values here are based on an offline approach,
they can only be used as rough estimates. Nevertheless, the potential for BC snow
effect reductions can be seen and this is important especially over the Arctic area,
where the air is very clean and small changes in BC concentrations/deposition can
lead to significant changes in the overall forcing (Quinn et al., 2011).
3
Definition used for the Arctic area: the area above the arctic circle; 66.55◦ N
Chapter V
Review of the papers and the author’s contribution
Paper I describes the regional aerosol-climate model REMO-HAM and the evaluation of the new model version against measurements and the global aerosol-climate
model ECHAM-HAMMOZ. REMO-HAM is able to reproduce measured aerosol
concentrations and aerosol distributions, and was able to improve 2-m temperatures
in Eastern Europe. I was the main developer of REMO-HAM and did all the simulations. I also wrote the paper and did all the data analysis and calculations.
Paper II studies how well REMO-HAM can reproduce the measured black carbon
(BC) concentrations over Finland using several statistical tools. The paper shows
that the model underestimates the BC concentrations due to deficiencies in the emissions, mainly coming from domestic wood burning. The role of sinks, such as wet
deposition, was also discussed because the model is known to precipitate too much
over Finland. I did the simulations with REMO-HAM and helped write the model
description part. Also, I did the map figures representing the REMO-HAM results
and helped with the data analysis.
Paper III investigates aerosol forcing in India with temperature and precipitation
responses. It shows that aerosol absorption can play a role in the monsoon precipitation, but the solar dimming of aerosol can have an extinctive effect. I helped to
create the emission files and helped with the simulations and analysis. Also, I wrote
the emission description and helped with the model description part.
Paper IV describes the implementation of a measurement-based OH-proxy and
shows that the new scheme improves simulated nucleation statistics of REMOHAM for European boundary layer significantly. Together with Dr. Mikkonen, we
designed the new approach of OH calculations, which I implemented into REMOHAM and did all the simulations. I wrote most parts of the paper and did almost
all the data analysis.
Paper V studies the impacts of global aerosol emissions reductions on aerosol forc43
5. Review of the papers and the author’s contribution
44
ing. Four different emissions scenarios were used to investigate the forcing changes
in 2020 and 2030. The paper shows that targeted emission reductions for shortlived climate forcers can be very beneficial to the climate, even if compared against
the technically maximum reductions scenario. I pre-processed the emission files for
ECHAM-HAMMOZ and did the coding for the new and updated emission modules.
I did all the simulations and data analysis. In addition, I wrote most parts of the
paper and prepared all the figures.
Chapter VI
Conclusions
The main focus of this thesis has been in increasing the understanding of aerosolclimate modelling and aerosol-climate effects by developing and using both global
and regional aerosol-climate models. The main tool has been the regional aerosolclimate model REMO-HAM, which was introduced in Paper I and used to study
black carbon over Finland in Paper II. In Paper IV, REMO-HAM was further
modified for studying the European boundary layer nucleation. Additionally, the
global aerosol-climate model, ECHAM-HAMMOZ, was used to study the Asian
monsoon and future climate forcing of aerosols (Papers III and V). This was
achieved by modifying the existing emission data used by the model.
Aerosol nucleation is a climatically important process that impacts the aerosol
and CCN concentrations on regional and global scales (Laaksonen et al., 2005;
Spracklen et al., 2006). Paper I shows that both of the models used in this study
have a tendency to overestimate sulphur dioxide concentrations, which leads to overestimations in sulphuric acid concentrations and to too high nucleation rates. The
reasons behind the overestimation were not fully resolved, but modifications to the
chemistry improved the model performance. As shown in Paper IV, with the
modifications, the European boundary layer nucleation can be modelled fairly accurately, although many error sources, starting from the underlying understanding
of the nucleation process, still exists.
From all of the aerosol species, black carbon (BC) was studied most closely in
this work. Paper II describes how well REMO-HAM can reproduce the measured
BC concentration over Finland. As is the case with ECHAM-HAMMOZ, REMOHAM also underestimates the measured concentrations. Besides the error coming
from, for example, too-efficient wet deposition, the role of an inaccurate emission
database was discussed. It is known that domestic (residential) wood burning is a
major source of BC in Finland and any inaccuracies in this sector will have a big
impact upon the modelled results.
Using more recent and updated emission databases in the climate models is
important. In Papers III and V, the anthropogenic emissions were updated and
both Asian monsoon and future aerosol forcing were studied. Paper III shows that
45
6. Conclusions
46
aerosol absorption can play a role in monsoon rainfall, but also that decreased surface
radiation and evaporation caused by solar dimming can decrease or even cancel out
this effect. In Paper V, the near future (years 2020 and 2030) was studied with
different emission scenarios. One of the main findings was that emission reductions
of the short-lived climate forcers, such as BC, can be climatically beneficial. Even
if the technologically maximum emissions reductions are used, the targeted actions
provide a much better choice from the climate point of view.
The development of REMO-HAM was successfully carried out in this work. Some
model components still need updating in order to make use of all the benefits of an
online aerosol module. For example, an interactive radiation code is needed to connect the aerosol information to the radiative budget. This work will be probably
done in the future and then, for example, the optical properties of aerosols can be
studied extensively. The aerosol model itself has been updated since its implementation (Zhang et al., 2012) and some of these updates, such as an organic aerosol
module (O’Donnell et al., 2011), should also be implemented in the future. The
condensation of organics, as an example, would improve the initial growth of the
nucleated particles (Paper IV). The model development done for REMO-HAM in
Paper IV (in terms of the OH-proxy) will be also used in ECHAM-HAMMOZ in
the work by Bergman et al. (2015). Also, all the new and updated emissions used
in Paper V are already included in REMO-HAM, although the evaluation is not
yet finished.
Bibliography
Abdul-Razzak, H. and Ghan, S. J. A parameterization of aerosol activation: 2. multiple aerosol types. Journal of Geophysical Research: Atmospheres, 105(D5):6837–
6844, doi:10.1029/1999JD901161, 2000.
Albrecht, B. A. Aerosols, cloud microphysics, and fractional cloudiness. Science,
245(4923):1227–1230, doi:10.1126/science.245.4923.1227, 1989.
Almeida, J., Schobesberger, S., Kürten, A., Ortega, I. K., Kupiainen-Määttä, O.,
Praplan, A. P., Adamov, A., Amorim, A., Bianchi, F., Breitenlechner, M., David,
A., Dommen, J., Donahue, N. M., Downard, A., Dunne, E., Duplissy, J., Ehrhart,
S., Flagan, R. C., Franchin, A., Guida, R., Hakala, J., Hansel, A., Heinritzi, M.,
Henschel, H., Jokinen, T., Junninen, H., Kajos, M., Kangasluoma, J., Keskinen, H., Kupc, A., Kurtén, T., Kvashin, A. N., Laaksonen, A., Lehtipalo, K.,
Leiminger, M., Leppä, J., Loukonen, V., Makhmutov, V., Mathot, S., McGrath,
M. J., Nieminen, T., Olenius, T., Onnela, A., Petäjä, T., Riccobono, F., Riipinen,
I., Rissanen, M., Rondo, L., Ruuskanen, T., Santos, F. D., Sarnela, N., Schallhart,
S., Schnitzhofer, R., Seinfeld, J. H., Simon, M., Sipilä, M., Stozhkov, Y., Stratmann, F., Tomé, A., Tröstl, J., Tsagkogeorgas, G., Vaattovaara, P., Viisanen, Y.,
Virtanen, A., Vrtala, A., Wagner, P. E., Weingartner, E., Wex, H., Williamson,
C., Wimmer, D., Ye, P., Yli-Juuti, T., Carslaw, K. S., Kulmala, M., Curtius,
J., Baltensperger, U., Worsnop, D. R., Vehkamäki, H., and Kirkby, J. Molecular understanding of sulphuric acid-amine particle nucleation in the atmosphere.
Nature, 502(7471):359–363. Letter, 2013.
Andreae, M. O. The aerosol nucleation puzzle.
doi:10.1126/science.1233798, 2013.
Science, 339(6122):911–912,
Arakawa, A. and Lamb, V. Computational design and the basic dynamical processes of the UCLA general circulation model. Methods in Computational Physics,
17:173–265, 1977.
Asselin, R. Frequency filter for time integrations. Monthly Weather Review, 100:487–
490, 1972.
Batjes, N. H. A global data set of soil ph properties. Technical Report 27, International Soil reference and Information Centre, Wageningen, Netherlands, 1995.
47
References
48
Bergman, T., Kerminen, V.-M., Korhonen, H., Lehtinen, K. J., Makkonen, R.,
Arola, A., Mielonen, T., Romakkaniemi, S., Kulmala, M., and Kokkola, H.
Evaluation of the sectional aerosol microphysics module SALSA implementation in ECHAM5-HAM aerosol-climate model. Geoscientific Model Development,
5(3):845–868, doi:10.5194/gmd-5-845-2012, 2012.
Bergman, T., Laaksonen, A., Mielonen, T., Komppula, M., Korhonen, H., Lehtinen,
K., Dunne, E., Malila, J., and Kokkola, H. Amine enhanced nucleation in the
boundary layer. in Preparation, 2015.
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo,
B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y.,
Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C.,
Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson,
M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D.,
Storelvmo, T., Warren, S. G., and Zender, C. S. Bounding the role of black
carbon in the climate system: A scientific assessment. Journal of Geophysical
Research: Atmospheres, 118(11):5380–5552, doi:10.1002/jgrd.50171, 2013.
Bond, T. C. and Sun, H. Can reducing black carbon emissions counteract
global warming?
Environmental Science & Technology, 39(16):5921–5926,
doi:10.1021/es0480421, 2005.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P.,
Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S.,
Sherwood, S., B., S., and Zhang, X. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report
of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex
and P.M. Midgley (eds.)], chapter Clouds and Aerosols, pages 571–658. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
doi:10.1017/CBO9781107415324.016, 2013.
Carslaw, K. S., Lee, L. A., Reddington, C. L., Pringle, K. J., Rap, A., Forster, P. M.,
Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre, L. A., and Pierce,
J. R. Large contribution of natural aerosols to uncertainty in indirect forcing.
Nature, 503(7474):67–71. Article, 2013.
Cermak, J., Wild, M., Knutti, R., Mishchenko, M. I., and Heidinger, A. K. Consistency of global satellite-derived aerosol and cloud data sets with recent brightening
observations. Geophysical Research Letters, 37(21), doi:10.1029/2010GL044632,
2010.
Cofala, J., Amann, M., Klimont, Z., Kupiainen, K., and Höglund-Isaksson,
L.
Scenarios of global anthropogenic emissions of air pollutants and
References
49
methane until 2030.
Atmospheric Environment, 41(38):8486 – 8499,
doi:http://dx.doi.org/10.1016/j.atmosenv.2007.07.010, 2007.
Corbett, J. J., Lack, D. A., Winebrake, J. J., Harder, S., Silberman, J. A., and
Gold, M. Arctic shipping emissions inventories and future scenarios. Atmospheric
Chemistry and Physics, 10(19):9689–9704, doi:10.5194/acp-10-9689-2010, 2010.
Crippa, P. and Pryor, S. Spatial and temporal scales of new particle formation
events in eastern North America. Atmospheric Environment, 75(0):257 – 264,
doi:http://dx.doi.org/10.1016/j.atmosenv.2013.04.051, 2013.
Davies, H. C. A laterul boundary formulation for multi-level prediction models. Quarterly Journal of the Royal Meteorological Society, 102(432):405–418,
doi:10.1002/qj.49710243210, 1976.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S.,
Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.
C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes,
M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen,
L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut,
J.-N., and Vitart, F. The era-interim reanalysis: configuration and performance
of the data assimilation system. Quarterly Journal of the Royal Meteorological
Society, 137(656):553–597, doi:10.1002/qj.828, 2011.
Dentener, F., Kinne, S., Bond, T., Boucher, O., Cofala, J., Generoso, S., Ginoux,
P., Gong, S., Hoelzemann, J. J., Ito, A., Marelli, L., Penner, J. E., Putaud, J.-P.,
Textor, C., Schulz, M., van der Werf, G. R., and Wilson, J. Emissions of primary
aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for
aerocom. Atmospheric Chemistry and Physics, 6(12):4321–4344, doi:10.5194/acp6-4321-2006, 2006.
Eisele, F. L., Lovejoy, E. R., Kosciuch, E., Moore, K. F., Mauldin, R. L., Smith,
J. N., McMurry, P. H., and Iida, K. Negative atmospheric ions and their potential
role in ion-induced nucleation. Journal of Geophysical Research: Atmospheres,
111(D4):n/a–n/a, doi:10.1029/2005JD006568, 2006.
Elizalde, A. The Water Cycle in the Mediterranean Region and the Impacts of
Climate Change. PhD thesis, Max Planck Institute for Meteorology, Hamburg,
Germany, Reports on Earth System Science, No. 103, 2011.
Feichter, J., Kjellström, E., Rodhe, H., Dentener, F., Lelieveldi, J., and Roelofs,
G.-J. Simulation of the tropospheric sulfur cycle in a global climate model. Atmospheric Environment, 30(10-11):1693 – 1707, doi:http://dx.doi.org/10.1016/13522310(95)00394-0. Joint 8th CAGCP and 2nd IGAC Conference on Global Atmospheric Chemistry, 1996.
References
50
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D., Haywood, J., Lean, J., Lowe, D., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz,
M., and Dorland, R. V. Changes in Atmospheric Constituents and in Radiative
Forcing. In: Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental Panel
on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis,
K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 2007.
Garrett, T. J. and Zhao, C.
Increased arctic cloud longwave emissivity
associated with pollution from mid-latitudes.
Nature, 440(7085):787–789,
doi:10.1038/nature04636, 2006.
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J.,
Morton, D. C., and DeFries, R. S. Assessing variability and long-term trends in
burned area by merging multiple satellite fire products. Biogeosciences, 7(3):1171–
1186, doi:10.5194/bg-7-1171-2010, 2010.
Giorgetta, M. A., Roeckner, E., Mauritsen, T., Bader, J., Crueger, T., Esch, M.,
Rast, S., Kornblueh, L., Schmidt, H., Kinne, K., Hohenegger, C., Möbis, B.,
Krismer, T., Wieners, K.-H., and Stevens, B. The atmospheric general circulation
model ECHAM6 - model description. Max Planck Institute for Meteorology,
Hamburg, Germany, Reports on Earth System Science, Report No. 135, 2013.
Graf, H.-F., Shirsat, S. V., Oppenheimer, C., Jarvis, M. J., Podzun, R., and Jacob,
D. Continental scale antarctic deposition of sulphur and black carbon from anthropogenic and volcanic sources. Atmospheric Chemistry and Physics, 10(5):2457–
2465, doi:10.5194/acp-10-2457-2010, 2010.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G.,
Skamarock, W. C., and Eder, B.
Fully coupled ”online” chemistry
within the {WRF} model. Atmospheric Environment, 39(37):6957 – 6975,
doi:http://dx.doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Hagemann, S. An improved land surface parameter data set for global and regional
climate models. Max Planck Institute for Meteorology report series, Hamburg,
Germany, Report No. 336, 2002.
Hamed, A., Birmili, W., Joutsensaari, J., Mikkonen, S., Asmi, A., Wehner, B.,
Spindler, G., Jaatinen, A., Wiedensohler, A., Korhonen, H., Lehtinen, K. E. J.,
and Laaksonen, A. Changes in the production rate of secondary aerosol particles
in central europe in view of decreasing so2 emissions between 1996 and 2006.
Atmospheric Chemistry and Physics, 10(3):1071–1091, doi:10.5194/acp-10-10712010, 2010.
References
51
Haywood, J. M., Bellouin, N., Jones, A., Boucher, O., Wild, M., and Shine,
K. P. The roles of aerosol, water vapor and cloud in future global dimming/brightening. Journal of Geophysical Research: Atmospheres, 116(D20),
doi:10.1029/2011JD016000, 2011.
Henriksson, S. V., Laaksonen, A., Kerminen, V.-M., Räisänen, P., Järvinen, H.,
Sundström, A.-M., and de Leeuw, G. Spatial distributions and seasonal cycles
of aerosols in india and china seen in global climate-aerosol model. Atmospheric
Chemistry and Physics, 11(15):7975–7990, doi:10.5194/acp-11-7975-2011, 2011.
Hinds, W. Aerosol technology: properties, behavior, and measurement of airborne
particles. Wiley-Interscience. Wiley, 1999.
Hirsikko, A., Nieminen, T., Gagné, S., Lehtipalo, K., Manninen, H. E., Ehn, M.,
Hõrrak, U., Kerminen, V.-M., Laakso, L., McMurry, P. H., Mirme, A., Mirme,
S., Petäjä, T., Tammet, H., Vakkari, V., Vana, M., and Kulmala, M. Atmospheric ions and nucleation: a review of observations. Atmospheric Chemistry
and Physics, 11(2):767–798, doi:10.5194/acp-11-767-2011, 2011.
Hommel, R., Timmreck, C., and Graf, H. F. The global middle-atmosphere aerosol
model maecham5-sam2: comparison with satellite and in-situ observations. Geoscientific Model Development, 4(3):809–834, doi:10.5194/gmd-4-809-2011, 2011.
Horowitz, L. W., Walters, S., Mauzerall, D. L., Emmons, L. K., Rasch, P. J.,
Granier, C., Tie, X., Lamarque, J.-F., Schultz, M. G., Tyndall, G. S., Orlando,
J. J., and Brasseur, G. P. A global simulation of tropospheric ozone and related
tracers: Description and evaluation of mozart, version 2. Journal of Geophysical
Research: Atmospheres, 108(D24), doi:10.1029/2002JD002853, 2003.
Horvath, H. Estimation of the average visibility in central europe. Atmospheric Environment, 29(2):241 – 246, doi:http://dx.doi.org/10.1016/1352-2310(94)00236-E,
1995.
Jacob, D. A note to the simulation of the annual and inter-annual variability of the
water budget over the baltic sea drainage basin. Meteorology and Atmospheric
Physics, 77(1-4):61–73, doi:10.1007/s007030170017, 2001.
Jacob, D. and Podzun, R.
Sensitivity studies with the regional climate
model REMO.
Meteorology and Atmospheric Physics, 63(1-2):119–129,
doi:10.1007/BF01025368, 1997.
Jones, A., Roberts, D. L., and Slingo, A. A climate model study of indirect radiative forcing by anthropogenic sulphate aerosols. Nature, 370(6489):450–453,
doi:10.1038/370450a0, 1994.
References
52
Karstens, U., Nolte-Holube, R., and Rockel, B. Calculation of the water budget
over the baltic sea catchment area using the regional forecast model remo for june
1993. Tellus A, 48(5), 1996.
Kazil, J., Lovejoy, E. R., Barth, M. C., and O’Brien, K. Aerosol nucleation over
oceans and the role of galactic cosmic rays. Atmospheric Chemistry and Physics,
6(12):4905–4924, doi:10.5194/acp-6-4905-2006, 2006.
Kazil, J., Stier, P., Zhang, K., Quaas, J., Kinne, S., O’Donnell, D., Rast, S., Esch,
M., Ferrachat, S., Lohmann, U., and Feichter, J. Aerosol nucleation and its
role for clouds and earth’s radiative forcing in the aerosol-climate model echam5ham. Atmospheric Chemistry and Physics, 10(22):10733–10752, doi:10.5194/acp10-10733-2010, 2010.
Kerminen, V.-M., Lehtinen, K. E. J., Anttila, T., and Kulmala, M. Dynamics of
atmospheric nucleation mode particles: a timescale analysis. Tellus B, 56(2):135–
146, doi:10.1111/j.1600-0889.2004.00095.x, 2004.
Kerminen, V.-M., Lihavainen, H., Komppula, M., Viisanen, Y., and Kulmala, M. Direct observational evidence linking atmospheric aerosol formation and cloud droplet activation.
Geophysical Research Letters, 32(14),
doi:10.1029/2005GL023130, 2005.
Kinne, S., O’Donnel, D., Stier, P., Kloster, S., Zhang, K., Schmidt, H., Rast, S.,
Giorgetta, M., Eck, T. F., and Stevens, B. Mac-v1: A new global aerosol climatology for climate studies. Journal of Advances in Modeling Earth Systems,
5(4):704–740, doi:10.1002/jame.20035, 2013.
Kirkby, J., Curtius, J., Almeida, J., Dunne, E., Duplissy, J., Ehrhart, S., Franchin,
A., Gagné, S., Ickes, L., Kürten, A., Kupc, A., Metzger, A., Riccobono, F., Rondo,
L., Schobesberger, S., Tsagkogeorgas, G., Wimmer, D., Amorim, A., Bianchi,
F., Breitenlechner, M., David, A., Dommen, J., Downard, A., Ehn, M., Flagan,
R. C., Haider, S., Hansel, A., Hauser, D., Jud, W., Junninen, H., Kreissl, F.,
Kvashin, A., Laaksonen, A., Lehtipalo, K., Lima, J., Lovejoy, E. R., Makhmutov,
V., Mathot, S., Mikkilä, J., Minginette, P., Mogo, S., Nieminen, T., Onnela, A.,
Pereira, P., Petäjä, T., Schnitzhofer, R., Seinfeld, J. H., Sipilä, M., Stozhkov, Y.,
Stratmann, F., Tomé, A., Vanhanen, J., Viisanen, Y., Vrtala, A., Wagner, P. E.,
Walther, H., Weingartner, E., Wex, H., Winkler, P. M., Carslaw, K. S., Worsnop,
D. R., Baltensperger, U., and Kulmala, M. Role of sulphuric acid, ammonia and
galactic cosmic rays in atmospheric aerosol nucleation. Nature, 476(7361):429–
433, doi:10.1038/nature10343, 2011.
Klimont, Z., Cofala, J., Xing, J., Wei, W., Zhang, C., Wang, S., Kejun, J., Bhandari,
P., Mathur, R., Purohit, P., Rafaj, P., Chambers, A., Amann, M., and Hao, J.
Projections of so2 , nox and carbonaceous aerosols emissions in asia. Tellus B,
61(4):602–617, 2009.
References
53
Kokkola, H., Hommel, R., Kazil, J., Niemeier, U., Partanen, A.-I., Feichter, J., and
Timmreck, C. Aerosol microphysics modules in the framework of the ECHAM5
climate model – intercomparison under stratospheric conditions. Geoscientific
Model Development, 2(2):97–112, doi:10.5194/gmd-2-97-2009, 2009.
Kokkola, H., Korhonen, H., Lehtinen, K. E. J., Makkonen, R., Asmi, A., Järvenoja,
S., Anttila, T., Partanen, A.-I., Kulmala, M., Järvinen, H., Laaksonen, A., and
Kerminen, V.-M. SALSA – a Sectional Aerosol module for Large Scale Applications. Atmospheric Chemistry and Physics, 8(9):2469–2483, doi:10.5194/acp-82469-2008, 2008.
Kokkola, H., Romakkaniemi, S., and Laaksonen, A. On the formation of radiation fogs under heavily polluted conditions. Atmospheric Chemistry and Physics,
3(3):581–589, doi:10.5194/acp-3-581-2003, 2003.
Korhola, T., Kokkola, H., Korhonen, H., Partanen, A.-I., Laaksonen, A., Lehtinen,
K. E. J., and Romakkaniemi, S. Reallocation in modal aerosol models: impacts on
predicting aerosol radiative effects. Geoscientific Model Development, 7(1):161–
174, doi:10.5194/gmd-7-161-2014, 2014.
Korhonen, P., Kulmala, M., Laaksonen, A., Viisanen, Y., McGraw, R., and
Seinfeld, J. H. Ternary nucleation of h2so4, nh3, and h2o in the atmosphere. Journal of Geophysical Research: Atmospheres, 104(D21):26349–26353,
doi:10.1029/1999JD900784, 1999.
Kotlarski, S. A Subgrid Glacier Parameterisation for Use in Regional Climate Modelling. PhD thesis, Max Planck Institute for Meteorology, Hamburg, Germany,
Reports on Earth System Science, No. 42, 2007.
Kuang, C., McMurry, P. H., McCormick, A. V., and Eisele, F. L. Dependence of nucleation rates on sulfuric acid vapor concentration in diverse atmospheric locations. Journal of Geophysical Research: Atmospheres, 113(D10),
doi:10.1029/2007JD009253, 2008.
Kuhlmann, J. and Quaas, J. How can aerosols affect the asian summer monsoon?
assessment during three consecutive pre-monsoon seasons from CALIPSO satellite
data. Atmospheric Chemistry and Physics, 10(10):4673–4688, doi:10.5194/acp-104673-2010, 2010.
Kulmala, M., Asmi, A., Lappalainen, H. K., Baltensperger, U., Brenguier, J.-L., Facchini, M. C., Hansson, H.-C., Hov, Ø., O’Dowd, C. D., Pöschl, U., Wiedensohler,
A., Boers, R., Boucher, O., de Leeuw, G., Denier van der Gon, H. A. C., Feichter,
J., Krejci, R., Laj, P., Lihavainen, H., Lohmann, U., McFiggans, G., Mentel, T.,
Pilinis, C., Riipinen, I., Schulz, M., Stohl, A., Swietlicki, E., Vignati, E., Alves,
C., Amann, M., Ammann, M., Arabas, S., Artaxo, P., Baars, H., Beddows, D.
C. S., Bergström, R., Beukes, J. P., Bilde, M., Burkhart, J. F., Canonaco, F.,
References
54
Clegg, S. L., Coe, H., Crumeyrolle, S., D’Anna, B., Decesari, S., Gilardoni, S.,
Fischer, M., Fjaeraa, A. M., Fountoukis, C., George, C., Gomes, L., Halloran, P.,
Hamburger, T., Harrison, R. M., Herrmann, H., Hoffmann, T., Hoose, C., Hu,
M., Hyvärinen, A., Hõrrak, U., Iinuma, Y., Iversen, T., Josipovic, M., Kanakidou, M., Kiendler-Scharr, A., Kirkevåg, A., Kiss, G., Klimont, Z., Kolmonen, P.,
Komppula, M., Kristjánsson, J.-E., Laakso, L., Laaksonen, A., Labonnote, L.,
Lanz, V. A., Lehtinen, K. E. J., Rizzo, L. V., Makkonen, R., Manninen, H. E.,
McMeeking, G., Merikanto, J., Minikin, A., Mirme, S., Morgan, W. T., Nemitz,
E., O’Donnell, D., Panwar, T. S., Pawlowska, H., Petzold, A., Pienaar, J. J., Pio,
C., Plass-Duelmer, C., Prévôt, A. S. H., Pryor, S., Reddington, C. L., Roberts,
G., Rosenfeld, D., Schwarz, J., Seland, Ø., Sellegri, K., Shen, X. J., Shiraiwa, M.,
Siebert, H., Sierau, B., Simpson, D., Sun, J. Y., Topping, D., Tunved, P., Vaattovaara, P., Vakkari, V., Veefkind, J. P., Visschedijk, A., Vuollekoski, H., Vuolo,
R., Wehner, B., Wildt, J., Woodward, S., Worsnop, D. R., van Zadelhoff, G.-J.,
Zardini, A. A., Zhang, K., van Zyl, P. G., Kerminen, V.-M., S Carslaw, K., and
Pandis, S. N. 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, 11(24):13061–13143,
doi:10.5194/acp-11-13061-2011, 2011.
Kulmala, M. and Kerminen, V.-M.
On the formation and growth
of atmospheric nanoparticles.
Atmospheric Research, 90(2–4):132–150,
doi:http://dx.doi.org/10.1016/j.atmosres.2008.01.005. 17th International Conference on Nucleation and Atmospheric Aerosols {ICNAA07} O’Dowd {SI}, 2008.
Kulmala, M., Lehtinen, K. E. J., and Laaksonen, A. Cluster activation theory as an
explanation of the linear dependence between formation rate of 3nm particles and
sulphuric acid concentration. Atmospheric Chemistry and Physics, 6(3):787–793,
doi:10.5194/acp-6-787-2006, 2006.
Kulmala, M., Riipinen, I., Nieminen, T., Hulkkonen, M., Sogacheva, L., Manninen,
H. E., Paasonen, P., Petäjä, T., Dal Maso, M., Aalto, P. P., Viljanen, A., Usoskin,
I., Vainio, R., Mirme, S., Mirme, A., Minikin, A., Petzold, A., Hõrrak, U., PlaßDülmer, C., Birmili, W., and Kerminen, V.-M. Atmospheric data over a solar
cycle: no connection between galactic cosmic rays and new particle formation.
Atmospheric Chemistry and Physics, 10(4):1885–1898, doi:10.5194/acp-10-18852010, 2010.
Kulmala, M., Riipinen, I., Sipilä, M., Manninen, H. E., Petäjä, T., Junninen, H.,
Maso, M. D., Mordas, G., Mirme, A., Vana, M., Hirsikko, A., Laakso, L., Harrison, R. M., Hanson, I., Leung, C., Lehtinen, K. E. J., and Kerminen, V.-M.
Toward direct measurement of atmospheric nucleation. Science, 318(5847):89–92,
doi:10.1126/science.1144124, 2007.
References
55
Kulmala, M., Vehkamäki, H., Petäjä, T., Maso, M. D., Lauri, A., Kerminen, V.-M.,
Birmili, W., and McMurry, P. Formation and growth rates of ultrafine atmospheric
particles: a review of observations. Journal of Aerosol Science, 35(2):143 – 176,
doi:http://dx.doi.org/10.1016/j.jaerosci.2003.10.003, 2004.
Kupiainen, K., Karvosenoja, N., Porvari, P., Johansson, M., Tainio, M., and
Tuomisto, J. Emissions of primary carbonaceous particles, their uncertainties
and spatial allocation in finland. Proceedings of IUAPPA regional conference /
17th EFCA speciality conference. Lille, France, September 6 - 8, 8pp, 2006.
Kurtén, T., Loukonen, V., Vehkamäki, H., and Kulmala, M. Amines are likely to
enhance neutral and ion-induced sulfuric acid-water nucleation in the atmosphere
more effectively than ammonia. Atmospheric Chemistry and Physics, 8(14):4095–
4103, doi:10.5194/acp-8-4095-2008, 2008.
Laakso, L., Anttila, T., Lehtinen, K. E. J., Aalto, P. P., Kulmala, M., Hõrrak, U.,
Paatero, J., Hanke, M., and Arnold, F. Kinetic nucleation and ions in boreal forest
particle formation events. Atmospheric Chemistry and Physics, 4(9/10):2353–
2366, doi:10.5194/acp-4-2353-2004, 2004.
Laaksonen, A., Hamed, A., Joutsensaari, J., Hiltunen, L., Cavalli, F., Junkermann,
W., Asmi, A., Fuzzi, S., and Facchini, M. C. Cloud condensation nucleus production from nucleation events at a highly polluted region. Geophysical Research
Letters, 32(6), doi:10.1029/2004GL022092, 2005.
Lamarque, J.-F., Shindell, D. T., Josse, B., Young, P. J., Cionni, I., Eyring, V.,
Bergmann, D., Cameron-Smith, P., Collins, W. J., Doherty, R., Dalsoren, S.,
Faluvegi, G., Folberth, G., Ghan, S. J., Horowitz, L. W., Lee, Y. H., MacKenzie,
I. A., Nagashima, T., Naik, V., Plummer, D., Righi, M., Rumbold, S. T., Schulz,
M., Skeie, R. B., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S., Voulgarakis,
A., and Zeng, G. The atmospheric chemistry and climate model intercomparison
project (accmip): overview and description of models, simulations and climate
diagnostics. Geoscientific Model Development, 6(1):179–206, doi:10.5194/gmd-6179-2013, 2013.
Langmann, B. Numerical modelling of regional scale transport and photochemistry directly together with meteorological processes. Atmospheric Environment,
34(21):3585 – 3598, doi:http://dx.doi.org/10.1016/S1352-2310(00)00114-X, 2000.
Langmann, B., Varghese, S., Marmer, E., Vignati, E., Wilson, J., Stier, P., and
O’Dowd, C. Aerosol distribution over europe: a model evaluation study with
detailed aerosol microphysics. Atmospheric Chemistry and Physics, 8(6):1591–
1607, doi:10.5194/acp-8-1591-2008, 2008.
References
56
Lau, K., Kim, M., and Kim, K. Asian summer monsoon anomalies induced by
aerosol direct forcing: the role of the tibetan plateau. Climate Dynamics, 26(78):855–864, doi:10.1007/s00382-006-0114-z, 2006.
Lau, K.-M. and Kim, K.-M. Observational relationships between aerosol and
asian monsoon rainfall, and circulation. Geophysical Research Letters, 33(21),
doi:10.1029/2006GL027546, 2006.
Law, K. S. and Stohl, A. Arctic air pollution: Origins and impacts. Science,
315(5818):1537–1540, doi:10.1126/science.1137695, 2007.
Lee, D. S., Owen, B., Graham, A., Fichter, C., Lim, L. L., and Dimitriu, D. Allocation of international aviation emissions from scheduled air traffic - present day
and historical (report 2 of 3). Manchester Metropolitan University, Centre for Air
Transport and the Environment, Manchester, UK, 2005.
Lihavainen, H., Kerminen, V.-M., Komppula, M., Hatakka, J., Aaltonen, V.,
Kulmala, M., and Viisanen, Y. Production of ”potential” cloud condensation nuclei associated with atmospheric new-particle formation in northern
finland. Journal of Geophysical Research: Atmospheres, 108(D24):n/a–n/a,
doi:10.1029/2003JD003887, 2003.
Lin, H. and Leaitch, W. R. Development of an in-cloud aerosol activation parameterization for climate modelling. In Measurement of Cloud Properties for Forecast
of Weather, Air Quality and Climate, pages 328–335. World Meteorological Organization, Geneva, 1997.
Lin, S.-J. and Rood, R. B. Multidimensional flux-form semi-lagrangian transport schemes. Monthly Weather Review, 124(9):2046–2070, doi:10.1175/15200493(1996)124¡2046:MFFSLT¿2.0.CO;2, 1996.
Lohmann, U. and Feichter, J. Global indirect aerosol effects: a review. Atmospheric
Chemistry and Physics, 5(3):715–737, doi:10.5194/acp-5-715-2005, 2005.
Lohmann, U. and Hoose, C. Sensitivity studies of different aerosol indirect effects
in mixed-phase clouds. Atmospheric Chemistry and Physics, 9(22):8917–8934,
doi:10.5194/acp-9-8917-2009, 2009.
Lohmann, U., Stier, P., Hoose, C., Ferrachat, S., Kloster, S., Roeckner, E., and
Zhang, J. Cloud microphysics and aerosol indirect effects in the global climate model echam5-ham. Atmospheric Chemistry and Physics, 7(13):3425–3446,
doi:10.5194/acp-7-3425-2007, 2007.
Lorenz, P. and Jacob, D. Influence of regional scale information on the global
circulation: A two-way nesting climate simulation. Geophysical Research Letters,
32(18), doi:10.1029/2005GL023351, 2005.
References
57
Louis, J.-F. A parametric model of vertical eddy fluxes in the atmosphere. BoundaryLayer Meteorology, 17(2):187–202, doi:10.1007/BF00117978, 1979.
Makkonen, R., Asmi, A., Kerminen, V.-M., Boy, M., Arneth, A., Hari, P., and
Kulmala, M. Air pollution control and decreasing new particle formation lead to
strong climate warming. Atmospheric Chemistry and Physics, 12(3):1515–1524,
doi:10.5194/acp-12-1515-2012, 2012.
Makkonen, R., Asmi, A., Korhonen, H., Kokkola, H., Järvenoja, S., Räisänen, P.,
Lehtinen, K. E. J., Laaksonen, A., Kerminen, V.-M., Järvinen, H., Lohmann,
U., Bennartz, R., Feichter, J., and Kulmala, M. Sensitivity of aerosol concentrations and cloud properties to nucleation and secondary organic distribution in
ECHAM5-HAM global circulation model. Atmospheric Chemistry and Physics,
9(5):1747–1766, doi:10.5194/acp-9-1747-2009, 2009.
Mellor, G. L. and Yamada, T. A hierarchy of turbulence closure models for planetary boundary layers. Journal of the Atmospheric Sciences, 31(7):1791–1806,
doi:10.1175/1520-0469(1974)031¡1791:AHOTCM¿2.0.CO;2, 1974.
Merikanto, J., Spracklen, D. V., Mann, G. W., Pickering, S. J., and Carslaw,
K. S. Impact of nucleation on global CCN. Atmospheric Chemistry and Physics,
9(21):8601–8616, doi:10.5194/acp-9-8601-2009, 2009.
Mikkonen, S., Romakkaniemi, S., Smith, J. N., Korhonen, H., Petäjä, T., PlassDuelmer, C., Boy, M., McMurry, P. H., Lehtinen, K. E. J., Joutsensaari, J.,
Hamed, A., Mauldin III, R. L., Birmili, W., Spindler, G., Arnold, F., Kulmala,
M., and Laaksonen, A. A statistical proxy for sulphuric acid concentration. Atmospheric Chemistry and Physics, 11(21):11319–11334, doi:10.5194/acp-11-113192011, 2011.
Nordeng, T. Extended versions of the convective parametrization scheme at
ECMWF and their impact on the mean and transient activity of the model in tropics. ECMWF Tech. Memo., 206, Available from ECMWF, Shineld Park, Reading,
RG2 9AX, United Kingdom, 1994.
O’Donnell, D., Tsigaridis, K., and Feichter, J. Estimating the direct and indirect effects of secondary organic aerosols using ECHAM5-HAM. Atmospheric Chemistry
and Physics, 11(16):8635–8659, doi:10.5194/acp-11-8635-2011, 2011.
O’Dowd, C. D., Hämeri, K., Mäkelä, J., Väkeva, M., Aalto, P., de Leeuw, G., Kunz,
G. J., Becker, E., Hansson, H.-C., Allen, A. G., Harrison, R. M., Berresheim, H.,
Kleefeld, C., Geever, M., Jennings, S. G., and Kulmala, M. Coastal new particle formation: Environmental conditions and aerosol physicochemical characteristics during nucleation bursts. Journal of Geophysical Research: Atmospheres,
107(D19):PAR 12–1–PAR 12–17, doi:10.1029/2000JD000206, 2002.
References
58
Owen, B., Lee, D. S., and Lim, L. L. Flying into the future: aviation emissions
scenarios to 2050. American Chemical Society, 44(7), doi:10.1021/es902530z, 2010.
Partanen, A. I., Laakso, A., Schmidt, A., Kokkola, H., Kuokkanen, T., Pietikäinen,
J.-P., Kerminen, V.-M., Lehtinen, K. E. J., Laakso, L., and Korhonen, H. Climate
and air quality trade-offs in altering ship fuel sulfur content. Atmospheric Chemistry and Physics, 13(23):12059–12071, doi:10.5194/acp-13-12059-2013, 2013.
Pfeifer, S. Modeling cold cloud processes with the regional climate model REMO. PhD
thesis, Reports on Earth System Science, Max Planc Institute for Meteorology,
Hamburg, 2006.
Pierce, J. R., Leaitch, W. R., Liggio, J., Westervelt, D. M., Wainwright, C. D.,
Abbatt, J. P. D., Ahlm, L., Al-Basheer, W., Cziczo, D. J., Hayden, K. L., Lee, A.
K. Y., Li, S.-M., Russell, L. M., Sjostedt, S. J., Strawbridge, K. B., Travis, M.,
Vlasenko, A., Wentzell, J. J. B., Wiebe, H. A., Wong, J. P. S., and Macdonald,
A. M. Nucleation and condensational growth to ccn sizes during a sustained
pristine biogenic soa event in a forested mountain valley. Atmospheric Chemistry
and Physics, 12(7):3147–3163, doi:10.5194/acp-12-3147-2012, 2012.
Pleim, J. E., Chang, J. S., and Zhang, K. A nested grid mesoscale atmospheric
chemistry model. Journal of Geophysical Research: Atmospheres, 96(D2):3065–
3084, doi:10.1029/90JD02026, 1991.
Pöschl, U. Atmospheric aerosols: Composition, transformation, climate and
health effects. Angewandte Chemie International Edition, 44(46):7520–7540,
doi:10.1002/anie.200501122, 2005.
Preuschmann, S. Regional surface albedo characteristics - analysis of albedo data and
application to land-cover changes for a regional climate model. PhD thesis, Max
Planck Institute for Meteorology, Hamburg, Germany, Reports on Earth System
Science, No. 117, 2012.
Pruppacher, H. and Klett, J. Microphysics of Clouds and Precipitation. Atmospheric
and Oceanographic Sciences Library. Springer, 1997.
Quinn, P. K., Stohl, A., Arneth, A., Berntsen, T., Burkhart, J. F., Christensen, J.,
Flanner, M., Kupiainen, K., Lihavainen, H., Shepherd, M., Shevchenko, V., Skov,
H., and Vestreng, V. The impact of black carbon on arctic climate. Technical
Report 4, Arctic Monitoring and Assessment Programme (AMAP), Oslo, 2011.
Raatikainen, T. and Laaksonen, A. Application of several activity coefficient models
to water-organic-electrolyte aerosols of atmospheric interest. Atmospheric Chemistry and Physics, 5(9):2475–2495, doi:10.5194/acp-5-2475-2005, 2005.
Ramanathan, V. and Carmichael, G. Global and regional climate changes due to
black carbon. Nature Geosci, 1(4):221–227, doi:10.1038/ngeo156, 2008.
References
59
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.
Aerosols, climate, and the hydrological cycle. Science, 294(5549):2119–2124,
doi:10.1126/science.1064034, 2001.
Ramanathan, V. and Feng, Y. Air pollution, greenhouse gases and climate
change: Global and regional perspectives. Atmospheric Environment, 43(1):37
– 50, doi:http://dx.doi.org/10.1016/j.atmosenv.2008.09.063. Atmospheric Environment - Fifty Years of Endeavour, 2009.
Rechid, D.
Land surface scheme of REMO. Internal report, Max
Planck Institute for Meteorology, Hamburg, 3 pp.
Downloadable
at
http://www.remo-rcm.de/fileadmin/user_upload/remo/UBA/pdf/
LandSurfaceScheme_REMO_short-summary.pdf, last access:
19.05.2014,
2009.
Riipinen, I., Sihto, S.-L., Kulmala, M., Arnold, F., Dal Maso, M., Birmili, W.,
Saarnio, K., Teinilä, K., Kerminen, V.-M., Laaksonen, A., and Lehtinen, K. E. J.
Connections between atmospheric sulphuric acid and new particle formation during QUEST III-IV campaigns in Heidelberg and Hyytiälä. Atmospheric Chemistry
and Physics, 7(8):1899–1914, doi:10.5194/acp-7-1899-2007, 2007.
Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M., Dumenil, L.,
Esch, M., Schlese, U., and Schulzweida, U. The atmospheric general circulation
model ECHAM4: Model description and simulation of present-day climate. Max
Planck Institute for Meteorology report series, Hamburg, Germany, Report No.
218, 1996.
Roeckner, E., Baeuml, G., Bonventura, L., Brokopf, R., Esch, M., Giorgetta, M.,
Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E., Rhodin, A., Schlese, U.,
Schulzweida, U., and Tompkins, A. The atmospheric general circulation model
ECHAM5. PART I: Model description. Max Planck Institute for Meteorology
report series, Hamburg, Germany, Report No. 349, 2003.
Romakkaniemi, S., Kokkola, H., Petzold, A., and Laaksonen, A. Growth of upper
tropospheric aerosols due to uptake of hno3 . Atmospheric Chemistry and Physics,
4(2):549–556, doi:10.5194/acp-4-549-2004, 2004.
Rummukainen, M. State-of-the-art with regional climate models. Wiley Interdisciplinary Reviews: Climate Change, 1(1):82–96, doi:10.1002/wcc.8, 2010.
Scott, C. E., Rap, A., Spracklen, D. V., Forster, P. M., Carslaw, K. S., Mann,
G. W., Pringle, K. J., Kivekäs, N., Kulmala, M., Lihavainen, H., and Tunved,
P. The direct and indirect radiative effects of biogenic secondary organic aerosol.
Atmospheric Chemistry and Physics, 14(1):447–470, doi:10.5194/acp-14-447-2014,
2014.
References
60
Seinfeld, J. and Pandis, S. Atmospheric Chemistry And Physics: From Air Pollution
to Climate Change. A Wiley-Interscience publication, 1998.
Semmler, T., Jacob, D., Schlünzen, K. H., and Podzun, R. Influence of sea ice
treatment in a regional climate model on boundary layer values in the fram strait
region. Monthly Weather Review, 132:985–999, 2004.
Shindell, D., Kuylenstierna, J. C. I., Vignati, E., van Dingenen, R., Amann, M.,
Klimont, Z., Anenberg, S. C., Muller, N., Janssens-Maenhout, G., Raes, F.,
Schwartz, J., Faluvegi, G., Pozzoli, L., Kupiainen, K., Höglund-Isaksson, L.,
Emberson, L., Streets, D., Ramanathan, V., Hicks, K., Oanh, N. T. K., Milly,
G., Williams, M., Demkine, V., and Fowler, D. Simultaneously mitigating nearterm climate change and improving human health and food security. Science,
335(6065):183–189, doi:10.1126/science.1210026, 2012.
Sieck, K. Internal Variability in the Regional Climate Model REMO. PhD thesis,
Max Planck Institute for Meteorology, Hamburg, Germany, Reports on Earth
System Science, No. 142, 2013.
Sihto, S.-L., Kulmala, M., Kerminen, V.-M., Dal Maso, M., Petäjä, T., Riipinen, I.,
Korhonen, H., Arnold, F., Janson, R., Boy, M., Laaksonen, A., and Lehtinen, K.
E. J. Atmospheric sulphuric acid and aerosol formation: implications from atmospheric measurements for nucleation and early growth mechanisms. Atmospheric
Chemistry and Physics, 6(12):4079–4091, doi:10.5194/acp-6-4079-2006, 2006.
Simmons, A., Burridge, D., Jarraud, M., Girard, C., and Wergen, W. The ecmwf
medium-range prediction models development of the numerical formulations and
the impact of increased resolution. Meteorology and Atmospheric Physics, 40(13):28–60, doi:10.1007/BF01027467, 1989.
Sipilä, M., Berndt, T., Petäjä, T., Brus, D., Vanhanen, J., Stratmann, F., Patokoski,
J., Mauldin, R. L., Hyvärinen, A.-P., Lihavainen, H., and Kulmala, M. The
role of sulfuric acid in atmospheric nucleation. Science, 327(5970):1243–1246,
doi:10.1126/science.1180315, 2010.
Smolarkiewicz, P. K. A simple positive definite advection scheme with small
implicit diffusion. Monthly Weather Review, 111:479–486, doi:10.1175/15200493(1983)111¡0479:ASPDAS¿2.0.CO, 1983.
Smolarkiewicz, P. K. A fully multidimensional positive definite advection transport algorithm with small implicit diffusion. Journal of Computational Physics,
54(2):325 – 362, doi:http://dx.doi.org/10.1016/0021-9991(84)90121-9, 1984.
Sotiropoulou, R. E. P., Tagaris, E., Pilinis, C., Anttila, T., and Kulmala, M. Modeling new particle formation during air pollution episodes: Impacts on aerosol
and cloud condensation nuclei. Aerosol Science and Technology, 40(7):557–572,
doi:10.1080/02786820600714346, 2006.
References
61
Spracklen, D. V., Carslaw, K. S., Kulmala, M., Kerminen, V.-M., Mann, G. W.,
and Sihto, S.-L. The contribution of boundary layer nucleation events to total
particle concentrations on regional and global scales. Atmospheric Chemistry and
Physics, 6(12):5631–5648, doi:10.5194/acp-6-5631-2006, 2006.
Spracklen, D. V., Carslaw, K. S., Kulmala, M., Kerminen, V.-M., Sihto, S.-L.,
Riipinen, I., Merikanto, J., Mann, G. W., Chipperfield, M. P., Wiedensohler,
A., Birmili, W., and Lihavainen, H. Contribution of particle formation to global
cloud condensation nuclei concentrations. Geophysical Research Letters, 35(6),
doi:10.1029/2007GL033038, 2008.
Spracklen, D. V., Pringle, K. J., Carslaw, K. S., Chipperfield, M. P., and Mann,
G. W. A global off-line model of size-resolved aerosol microphysics: I. model
development and prediction of aerosol properties. Atmospheric Chemistry and
Physics, 5(8):2227–2252, doi:10.5194/acp-5-2227-2005, 2005.
Stier, P., Feichter, J., Kinne, S., Kloster, S., Vignati, E., Wilson, J., Ganzeveld, L.,
Tegen, I., Werner, M., Balkanski, Y., Schulz, M., Boucher, O., Minikin, A., and
Petzold, A. The aerosol-climate model ECHAM5-HAM. Atmospheric Chemistry
and Physics, 5(4):1125–1156, doi:10.5194/acp-5-1125-2005, 2005.
Stier, P., Seinfeld, J. H., Kinne, S., and Boucher, O. Aerosol absorption and radiative
forcing. Atmospheric Chemistry and Physics, 7(19):5237–5261, doi:10.5194/acp7-5237-2007, 2007.
Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels,
A., Xia, Y., Bex, V., and Midgley, P. The Physical Science Basis. Working Group
I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change. In IPCC, 2013: Climate Change 2013. Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 2013.
Stohl, A.
Characteristics of atmospheric transport into the arctic troposphere. Journal of Geophysical Research: Atmospheres, 111(D11):n/a–n/a,
doi:10.1029/2005JD006888, 2006.
Stohl, A., Klimont, Z., Eckhardt, S., Kupiainen, K., Shevchenko, V. P., Kopeikin,
V. M., and Novigatsky, A. N. Black carbon in the arctic: the underestimated role
of gas flaring and residential combustion emissions. Atmospheric Chemistry and
Physics, 13(17):8833–8855, doi:10.5194/acp-13-8833-2013, 2013.
Stohl, A. and Sodemann, H. Characteristics of atmospheric transport into
the antarctic troposphere. Journal of Geophysical Research: Atmospheres,
115(D2):n/a–n/a, doi:10.1029/2009JD012536, 2010.
Streets, D. G., Wu, Y., and Chin, M. Two-decadal aerosol trends as a likely explanation of the global dimming/brightening transition. Geophysical Research Letters,
33(15), doi:10.1029/2006GL026471, 2006.
References
62
Sundqvist, H., Berge, E., and Kristiansson, J. E. Condensation and Cloud Parameterization Studies with a Mesoscale Numerical Weather Prediction Model.
Monthly Weather Review, 117:1641–1657, 1989.
Tanre, D., Geleyn, J.-F., and Slingo, J. First results of the introduction of an
advanced aerosol-radiation interaction in the ECMWF low resolution global model,
pages 133–177. H. Gerber and A. Deepak (Eds.), A. Deepak, Hampton, Virginia,
1984.
Teichmann, C. Climate and Air Pollution Modelling in South America with Focus
on Megacities. PhD thesis, Max Planck Institute for Meteorology, Hamburg,
Germany, Reports on Earth System Science, No. 76, 2010.
Tiedtke, M. A comprehensive mass flux scheme for cumulus parametrisation in
large-scale models. Monthly Weather Review, 117:1779–1800, 1989.
Twomey, S. Pollution and the planetary albedo. Atmospheric Environment (1967),
8(12):1251 – 1256, doi:http://dx.doi.org/10.1016/0004-6981(74)90004-3, 1974.
UNEP. Near-term climate protection and clean air benefits: Actions for controlling
short-lived climate forcers. United Nations Environment Programme (UNEP),
Nairobi, Kenya, 78pp, 2011.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla,
P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T. Global fire
emissions and the contribution of deforestation, savanna, forest, agricultural, and
peat fires (1997-2009). Atmospheric Chemistry and Physics, 10(23):11707–11735,
doi:10.5194/acp-10-11707-2010, 2010.
Vehkamäki, H., Kulmala, M., Napari, I., Lehtinen, K. E. J., Timmreck, C.,
Noppel, M., and Laaksonen, A. An improved parameterization for sulfuric acid–water nucleation rates for tropospheric and stratospheric conditions.
Journal of Geophysical Research: Atmospheres, 107(D22):AAC 3–1–AAC 3–10,
doi:10.1029/2002JD002184, 2002.
Vignati, E., Wilson, J., and Stier, P. M7: An efficient size-resolved aerosol microphysics module for large-scale aerosol transport models. Journal of Geophysical
Research: Atmospheres, 109(D22), doi:10.1029/2003JD004485, 2004.
Vogel, B., Vogel, H., Bäumer, D., Bangert, M., Lundgren, K., Rinke, R., and
Stanelle, T. The comprehensive model system cosmo-art – radiative impact of
aerosol on the state of the atmosphere on the regional scale. Atmospheric Chemistry and Physics, 9(22):8661–8680, doi:10.5194/acp-9-8661-2009, 2009.
Wang, C., Corbett, J. J., and Firestone, J. Improving spatial representation of global
ship emissions inventories. Environmental Science & Technology, 42(1):193–199,
doi:10.1021/es0700799. PMID: 18350896, 2008.
References
63
Weber, R. J., Marti, J. J., McMurry, P. H., Eisele, F. L., Tanner, D. J., and Jefferson, A. Measured atmospheric new particle formation rates: implications for
nucleation mechanisms. Chemical Engineering Communications, 151(1):53–64,
doi:10.1080/00986449608936541, 1996.
Wild, M. Global dimming and brightening: A review. Journal of Geophysical
Research: Atmospheres, 114(D10), doi:10.1029/2008JD011470, 2009.
Wild, M., Gilgen, H., Roesch, A., Ohmura, A., Long, C. N., Dutton, E. G., Forgan, B., Kallis, A., Russak, V., and Tsvetkov, A. From dimming to brightening:
Decadal changes in solar radiation at earth’s surface. Science, 308(5723):847–850,
doi:10.1126/science.1103215, 2005.
Wilhelm, C., Rechid, D., and Jacob, D. Interactive coupling of regional atmosphere
with biosphere in the new generation regional climate system model REMOiMOVE. Geoscientific Model Development, 7(3):1093–1114, doi:10.5194/gmd-71093-2014, 2014.
Yu, F. Ion-mediated nucleation in the atmosphere: Key controlling parameters,
implications, and look-up table. Journal of Geophysical Research: Atmospheres,
115(D3), doi:10.1029/2009JD012630, 2010.
Yu, F., Luo, G., Bates, T. S., Anderson, B., Clarke, A., Kapustin, V., Yantosca,
R. M., Wang, Y., and Wu, S. Spatial distributions of particle number concentrations in the global troposphere: Simulations, observations, and implications for nucleation mechanisms. Journal of Geophysical Research: Atmospheres,
115(D17):n/a–n/a, doi:10.1029/2009JD013473, 2010.
Zhang, K., O’Donnell, D., Kazil, J., Stier, P., Kinne, S., Lohmann, U., Ferrachat, S.,
Croft, B., Quaas, J., Wan, H., Rast, S., and Feichter, J. The global aerosol-climate
model ECHAM-HAM, version 2: sensitivity to improvements in process representations. Atmospheric Chemistry and Physics, 12(19):8911–8949, doi:10.5194/acp12-8911-2012, 2012.
Ziereis, H. and Arnold, F. Gaseous ammonia and ammonium ions in the free troposphere. Nature, 321(6069):503–505, doi:10.1038/321503a0, 1986.
Zubler, E. M., Folini, D., Lohmann, U., Lüthi, D., Muhlbauer, A., PousseNottelmann, S., Schär, C., and Wild, M. Implementation and evaluation of aerosol
and cloud microphysics in a regional climate model. Journal of Geophysical Research: Atmospheres, 116(D2), doi:10.1029/2010JD014572, 2011a.
Zubler, E. M., Lohmann, U., Lüthi, D., and Schär, C. Intercomparison of aerosol
climatologies for use in a regional climate model over europe. Geophysical Research
Letters, 38(15), doi:10.1029/2011GL048081, 2011b.