Sea salt aerosol in global transport models

Transcription

Sea salt aerosol in global transport models - results, validations
and model improvements.
Marcin ukasz Witek
University of Warsaw
Faculty of Physics
Submitted to the Faculty of the University of Warsaw in fullment of the requirement
for the degree of Doctor of Physics
- Warsaw 2008 -
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Acknowledgments
Completing this dissertation would not have been possible without the support of many people.
First of all, I would like to thank my supervisors Dr. Piotr Flatau for his helpful and invaluable
assistance, encouragement and guidance, and Prof. Szymon Malinowski for his constructive suggestions and helpfulness throughout the doctoral studies at the Institute of Geophysics.
I am deeply grateful to Dr. Krzysztof Markowicz for useful discussions, inspiration and support. I also would like to thank everyone at the Institute of Geophysics, University of Warsaw
for providing the friendly and open working environment.
I would like to thank Dr. Doug Wesphal for providing NAAPS model and for helpful discussions and assistance in scientic and after-work matters during my visits at NRL. I am very
grateful to Dr. Frank Giraldo for all his help and inspiring lectures on numerical methods. I also
wish to thank all the people who helped me during my visits at NRL.
I would like to express my special gratitude to Dr. Joao Teixeira for lively discussions and
the inspiring and fruitful collaboration. Working with him at NRL, CA and at NURC, Italy was
the privilege.
I am also very grateful to Prof. Sonia Kreidenweis for cooperation and support during my
visits at CSU, Fort Collins and to Kelley Wells-Johnson for working with me and for companionship and sympathy which made my stays in Fort Collins enjoyable.
Above everyone, I would like to thank Joanna, for sharing the experience with me.
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4
Aerozol soli morskiej w globalnych modelach
transportu porównanie wyników i rozwój
modelu
Streszczenie
Aerozol soli morskiej jest jednym z najistotniejszych skªadników aerozolu atmosferycznego. Emitowany w wyniku zaªamywania si¦ fal wyst¦puje w atmosferze nad morzem i w stree przybrze»nej. Istotnie ingeruje w szereg zycznych i chemicznych procesów zachodz¡cych w atmosferze,
wpªywa na widzialno±¢, procesy radiacyjne, mikrozyk¦ chmur, transfer ciepªa i wilgoci w warstwie granicznej (Lewis and Schwartz, 2004). Dokªadny opis aerozolu soli morskiej w modelach
transportu jest istotny do prawidªowego oszacowaniu bilansu energetycznego Ziemi.
Celem pracy jest poszerzenie stanu wiedzy na temat aerozolu soli morskiej i jego cyrkulacji w
atmosferze. Tematyka pracy obejmuje walidacj¦ symulacji soli morskiej przeprowadzonych przy
u»yciu numerycznego modelu transportu aerozolu, badanie klimatologii soli morskiej, popraw¦
parametryzacji emisji soli morskiej do atmosfery oraz okre±lenie roli tego aerozolu w bilansie
radiacyjnym Ziemi.
Do realizacji powy»szych celów zastosowano takie metody badawcze jak
symulacje numeryczne, analiz¦ danych pomiarowych, rozwa»ania teoretyczne oraz testy czuªo±ci
modelu na zmian¦ parametryzacji. Sposób realizacji celów pracy podzielono na cztery etapy:
1. Przeprowadzenie wieloletnich symulacji numerycznych aerozolu soli morskiej, analiza uzyskanych rezultatów oraz porównanie wyników z danymi pochodz¡cymi z pomiarów na
otwartym oceanie (Witek, Flatau, Quinn & Westphal, 2007).
2. Opracowanie nowej parametryzacji emisji soli morskiej do atmosfery uwzgl¦dniaj¡cej parametry falowania oceanów, któr¡ mo»na zastosowa¢ globalnie przy obecno±ci dªugich fal
oceanicznych (swell) (Witek, Flatau, Teixeira & Westphal, 2007).
3. Wprowadzenie i zastosowanie nowego odwzorowania aerozolu soli morskiej w globalnym
modelu transportu. Odwzorowanie to uwzgl¦dnia podziaª na klasy wielko±ci oraz dokªadn¡
parametryzacj¦ procesów depozycji.
4. Okre±lenie bezpo±redniego wpªywu aerozolu soli morskiej na wymuszanie radiacyjne na
szczycie atmosfery.
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Do symulacji numerycznych aerozolu soli morskiej wykorzystano zmodykowana wersj¦ globalnego modelu transportu Navy Aerosol Analysis and Prediction System (NAAPS). Poprawno±¢
dziaªania modelu i wyników globalnych symulacji zostaªa sprawdzona przez porównanie z danymi z pi¦ciu kampanii pomiarowych, przeprowadzonych pomi¦dzy latami 1999 a 2004 i obejmuj¡cych szeroki zakres warunków oceanicznych.
Pomiary koncentracji masy soli morskiej w
atmosferze wykonane zostaªy na pokªadzie statku badawczego przez laboratorium Pacic Marine Environmental Laboratory (PMEL). S¡ one o tyle wyj¡tkowe, gdy» wolne s¡ od wpªywów
strefy przybrze»nej charakterystycznych dla pomiarów l¡dowych. Wyniki analiz potwierdzaj¡ dobr¡ zgodno±¢ symulacji numerycznych z warto±ciami zmierzonymi. Wspóªczynniki korelacji dla
poszczególnych eksperymentów wahaj¡ si¦ pomi¦dzy 0.55 a 0.84. Dla wszystkich 359 punktów
porównawczych korelacja wyniosªa 0.76, natomiast po wyeliminowaniu 106 przypadków, w trakcie których obserwowano opad deszczu, wspóªczynnik korelacji wzrósª do 0.87. Innym ¹ródªem
niezgodno±ci na jaki wskazuje analiza, poza wymywaniem przez opad, jest poprawno±¢ funkcji
emisji dla du»ych pr¦dko±ci wiatru. W warunkach silnych wiatrów modelowane koncentracje s¡
cz¦sto zawy»ane w porównaniu z obserwowanymi. Innym czynnikiem zwi¡zanym z emisj¡ jest
brak progowej warto±ci pr¦dko±ci wiatru, dla której rozpoczyna si¦ produkcja aerozolu. Wskazuj¡
na to niezgodno±ci i zawy»one warto±ci z modelu numerycznego przy niskich pr¦dko±ciach wiatru.
Rozbie»no±ci w wynikach s¡ tak»e skutkiem zastosowania uproszczonej, niezale»nej od wielko±ci
cz¡stek, parametryzacji osiadania aerozolu. Dla odmiany analiza wpªywu temperatury wody na
mierzone i modelowane koncentracje aerozolu przemawia za brakiem sprz¦»enia temepratury z
funkcj¡ emisji.
Kolejnym elementem pracy byªo opracowanie nowej parametryzacji emisji soli morskiej do
atmosfery. Zaproponowana funkcja emisji zostaªa zastosowana w modelu NAAPS wraz z danymi
pochodz¡cymi z globalnego modelu falowania oceanów Wave Watch III. Podstaw¡ zaproponowanej parametryzacji s¡ pr¦dko±¢ wiatru 10 metrów nad powierzchni¡ oceanu podniesiona do
2
kwadratu (U10 ) oraz pr¦dko±¢ orbitalna fal, zdeniowana jako
wysoko±ci¡ fali znacznej i
Tp
Vorb = πHs /Tp ,
gdzie
Hs
jest
okresem fali w piku widma falowego. Taka forma parametryzacji
zachowuje wªasno±ci tradycyjnych sformuªowa« poprzez istnienie silnej korelacji mi¦dzy
Vorb
a
pr¦dko±ci¡ wiatru, wprowadzaj¡c jednocze±nie dodatkow¡ zale»no±¢ od stanu falowania oceanu.
Wyniki symulacji numerycznych zostaªy tak»e porównane z danymi obserwacyjnymi pochodz¡cymi z pi¦ciu kampanii laboratorium PMEL. Zaobserwowano istnienie silnej zale»no±ci przebiegu
Vorb
i mierzonych koncentracji soli morskiej. Symulacje numeryczne z zastosowaniem nowej pa-
rametryzacji wykazuj¡ lepsz¡ zgodno±¢ z warto±ciami mierzonymi ni» symulacje z zastosowaniem
funkcji emisji zale»nej jedynie od pr¦dko±ci wiatru. Nowa parametryzacja, uwzgl¦dniaj¡ca synergi¦ parametrów meteorologicznych i falowania oceanów, daje mo»liwo±¢ lepszego odzwierciedlenia
faktycznych procesów zaªamywania si¦ fal i produkcji aerozolu. W rezultacie mo»e to prowadzi¢
do zmniejszenia niepewno±ci w modelowaniu soli morskiej.
Kolejnym zagadnieniem badanym w pracy jest wpªyw podziaªu na klasy wielko±ci modelowanego aerozolu na wyniki symulacji numerycznych. Problem ten byª zazwyczaj pomijany w
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studiach dotycz¡cych aerozolu soli morskiej. Celem przeprowadzonych bada« byªa analiza zachowania caªkowitej masy soli morskiej oraz ±redniej warto±ci grubo±ci optycznej (AOT) w modelu
w funkcji ilo±ci klas wielko±ci oraz zastosowanego algorytmu wyznaczania przedziaªów. Wyniki
symulacji modelem NAAPS wskazuj¡ na okoªo 20% decyt masy oraz 35% niedoszacowanie AOT
w przypadku zastosowania dwu klas wielko±ci, w porównaniu do symulacji z rozdzielczo±ci¡ 15
przedziaªów. Tak du»e ró»nice mi¦dzy wynikami maj¡ istotne znaczenie w szacowaniu bilansu
masy aerozolu oraz wpªywu soli morskiej na bilans radiacyjny Ziemi. Ró»nice mi¦dzy symulacjami mo»na wyja±ni¢ nadmiern¡ depozycj¡ aerozolu w sytuacji niewielkiej liczby klas wielko±ci
zastosowanych w modelu. W pracy u»yto izo-gradientowy algorytm podziaªu na przedziaªy wielko±ci, zale»ny od rozkªadu pr¦dko±ci suchego osiadania w funkcji wielko±ci cz¡stek. Rezultaty
symulacji pokazuj¡, »e nowy algorytm przewy»sza dokªadno±ci¡ schemat izo-logarytmiczny, który
jest powszechnie stosowany w modelach transportu.
Wieloklasowa reprezentacja aerozolu soli morskiej w modelu transportu NAAPS umo»liwiªa
wyznaczenie globalnego wymuszania aerozolowego na szczycie atmosfery. Przeprowadzona symu-
−2 ) wpªywu aerozolu soli morskiej
lacja dla roku 2004 wskazuje na istnienie ujemnego (−1.2 Wm
na bilans radiacyjny Ziemi. Odpowiada to ±redniej warto±ci grubo±ci optycznej wynosz¡cej 0.043
oraz parametrowi asymetrii 0.767. Studia nad wymuszaniem aerozolowym potwierdziªy równie»
zasugerowany uprzednio wpªyw liczby przedziaªów wielko±ci aerozolu na wymuszanie radiacyjne.
Ró»nice si¦gaj¡ce 33% dowodz¡, i» kwestie podziaªu na klasy s¡ istotnym ¹ródªem niepewno±ci
w modelowaniu klimatu i aerozolu atmosferycznego.
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Abstract
Sea salt is one of the most abundant aerosol types in the atmosphere and exerts strong impact
on the Earth's radiation budget. As a consequence, adequate simulation of the sea salt aerosol
(SSA) in global transport models is fundamental for developing our understanding of climate and
climate behavior. In this thesis numerical modeling, theoretical considerations and experimental
data are combined to improve our understanding of sea salt aerosol in the atmosphere. An assessment of the performance of the single-mode global sea salt modeling versus direct shipboard
measurements is performed. Results show that the simulations reproduce surface concentration
in most of the investigated areas. Wind-speed uncertainties, precipitation events which are not
resolved and source function limitations are limiting model accuracy. A new sea salt parameterization is developed to improve on modeling of its source function. This new parameterization is
implements in Navy Aerosol Analysis and Prediction System (NAAPS) global transport model
along with data from global Wave Watch III (WWIII) model. It is shown that accounting for
ocean state characteristics like the signicant wave height and peak wave period improves model
predictions and leads to more realistic sea-spray production representation. A new approach to
the sea salt physical processes modeling is developed within NAAPS. It is based on detailed representation of the dry deposition velocity and multi-bin approach to the aerosol size spectrum.
Bin division is based on the iso-gradient method, which is proved to be more ecient over
typically used iso-log segregation procedure. Results show strong dependence of the simulated
aerosol mass on the number of size bins in the model. The total sea salt mass simulated with two
size intervals is underestimated by about 20% as compared to the simulation with fteen size
bins. The dierences are even larger for the modeled aerosol optical depth, which varies by up to
40% between simulations. This is indicative of a possible source of uncertainty in climate models.
Applications of parameterizations to the sea salt radiative forcing are also discussed. The clear
sky shortwave SSA radiative forcing at -1.2
Wm−2
level is predicted, which is within the range
of previous estimates. Furthermore, analyses of regional and global sea salt concentrations and
radiative forcing distributions are performed. Finally, directions for further research are outlined
along with possible strategies for model developments.
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10
Contents
1 Introduction
13
1.1
Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
1.2
Research goals and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
1.3
Problem overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
1.4
Dissertation plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
2 Sea salt aerosol emission processes and parameterizations
2.1
Main SSA production mechanisms
. . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1
Wind speed and wind friction velocity
2.1.2
Sea state characteristics
2.1.3
Microscale factors
19
19
. . . . . . . . . . . . . . . . . . . .
20
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
2.2
Relative humidity inuence on sea salt aerosol particles . . . . . . . . . . . . . . .
23
2.3
Methods of determining sea salt aerosol production uxes
. . . . . . . . . . . . .
26
2.4
Whitecap method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
3 Navy Aerosol Analysis and Prediction System model
31
3.1
NAAPS overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
32
3.2
Governing equations and parameterizations
. . . . . . . . . . . . . . . . . . . . .
32
3.3
Sea salt emission
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
4 Global sea salt modeling: results and validation against multi-campaign shipboard measurements
37
4.1
Experimental methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
4.2
Results comparison with the observational data . . . . . . . . . . . . . . . . . .
39
4.2.1
Aerosols99-INDOEX experiment
. . . . . . . . . . . . . . . . . . . . . . .
39
4.2.2
ACE-Asia experiment
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
4.2.3
NEAQS-2002 experiment
. . . . . . . . . . . . . . . . . . . . . . . . . . .
44
4.2.4
NEAQS ITCT 2004 experiment . . . . . . . . . . . . . . . . . . . . . . .
44
4.3
Comparison with experimental data discussion
. . . . . . . . . . . . . . . . . .
47
4.4
Summary
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
5 Global sea salt aerosol emission: results from multi-year model simulations
5.1
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
55
55
CONTENTS
5.2
Summary
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6 Coupling an ocean wave model with a global aerosol transport model
6.1
Wave model description
6.2
Approach
59
61
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
61
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
64
6.3
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
6.4
Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
7 Numerical assessment of an optimal aerosol size bin division scheme for the
sea salt aerosol in global transport models
71
7.1
Dry deposition velocity model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
71
7.2
Size bin division method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
76
7.3
Sea salt optical properties model
. . . . . . . . . . . . . . . . . . . . . . . . . . .
79
7.4
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83
7.4.1
Total sea salt mass analysis
. . . . . . . . . . . . . . . . . . . . . . . . . .
83
7.4.2
Average sea salt optical depth analysis . . . . . . . . . . . . . . . . . . . .
87
7.5
Summary
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8 Sea salt aerosol radiative forcing
90
91
8.1
Simple model of the direct aerosol forcing
. . . . . . . . . . . . . . . . . . . . . .
91
8.2
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
93
8.2.1
Global distributions in 2004 . . . . . . . . . . . . . . . . . . . . . . . . . .
93
8.2.2
Multi-bin simulations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
95
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
97
8.3
Summary
9 Conclusions and outlook
99
A Table A
103
Bibliography
105
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Chapter 1
Introduction
1.1
Problem statement
Sea salt aerosol (SSA) particles are emitted from the ocean's surface almost continuously. As
oceans cover over two-thirds of Earth's surface, the area where small drops of seawater, also
called sea-spray, are released into the atmosphere exceeds 335 million square kilometers. For that
reason SSA is one of the most abundant components of the marine atmosphere. It exerts a strong
inuence on a broad range of geophysical and chemical processes occurring over oceans. It aects
atmospheric chemistry, cloud physics, atmospheric radiation, remote sensing, and air quality.
Recently, SSA has been a subject of increased interest due to its direct inuence on climate
through interactions with solar radiation as well as its indirect inuence on cloud microphysics.
Despite scientic eorts, there are still many unresolved issues concerning SSA which need
to be understood and described. For instance, SSA production mechanisms, as noted by Lewis
and Schwartz (2004) in their geophysical monograph, are still . . . far less well understood than
is indicated by much of the modeling community and indeed than appears to be appreciated by
some investigations, who may be familiar with only a portion of the relevant literature. Emission parameterizations available in literature dier by several orders of magnitude in predicting
number and mass production rate. There is no agreement about the size distribution of emitted
particles. Current estimates of the annual mass emission vary from
0.3 × 1012
to
30 × 1012
kg
(Lewis and Schwartz, 2004). This indicates that SSA is a major contributor to the total particulate matter present in the atmosphere but also that its total mass is not estimated precisely.
The observed uncertainties point out the need to nd alternative approaches in parameterizing
SSA emission.
Simulating the life cycle of SSA has also been a challenge that revealed large uncertainties
between modeling communities. Various model results have not been reconciled or favored over
each other, often due to diculties in validation of underlying assumptions.
Most numerical
eorts were focused on large scale SSA characteristics, either spatial or temporal. Validations
were typically based on monthly averaged observations at selected locations and often modelers
did not comprehensively consider the size limits of measured particles and techniques used for
obtaining mass concentrations. Use of high temporal and spatial resolution SSA measurements
to validate global transport models was not common.
13
CHAPTER 1. INTRODUCTION
One compelling reason for current interest in SSA is its inuence on climate. Little is known
about radiative impact of SSA and its inuence on Earth's radiation budget. Understanding
these inuences [direct and indirect aerosol eect] is essential to understanding and quantifying
such inuences of anthropogenic aerosols (Lewis and Schwartz, 2004).
They also note that
understanding SSA and representing it in models is central to decreasing this key uncertainty
in climate change . Therefore, further studies need to be undertaken to rene current estimates
of the SSA radiative impact.
1.2
Research goals and methods
The broad goal of the research undertaken here is to improve our understanding of the sea salt
aerosol life cycle in the atmosphere. This knowledge is important to reduce key uncertainties
related to SSA such as an adequate emission parameterization or extent of the impact of SSA
on climate.
To this end we performed numerical simulations, analysis of observational data, theoretical
derivations, and numerical sensitivity studies. Our results cover several aspects of the aerosol
research which can be categorized as follows:

Employment of high temporal and spatial resolution ship measurements to validate the sea
salt emission source function and analyze performance of a global aerosol transport model.

Development of a new approach to the sea salt emission parameterization which incorporates wind-wave characteristics into the emission function and that can be employed
globally and under swell-inuenced conditions.

Development and numerical investigation of a new size bin division scheme that can eciently and adequately represent the behavior of SSA in global simulations.

Investigation of the SSA shortwave radiative forcing.
These topics are investigated through the use of parameters characterizing SSA, such as mass
concentration, extinction coecient and asymmetry parameter. Derived optical properties provide additional information about the SSA size distribution in the atmosphere.
1.3
Problem overview
During recent years there were many attempts to improve prediction of the atmospheric sea salt
aerosol. Some of the eorts have focused on global sea salt modeling and air quality studies. The
inuence of the source formulation on modeling of the global sea salt distribution was investigated by Guelle et al. (2001). Grini et al. (2002), Gong et al. (2002) and Takemura et al. (2000)
employed sophisticated multi-bin sectional aerosol transport models to investigate the annual cycle, global budged and radiative impact on climate of the SSA. However, most of these numerical
14
eorts focused on large scale characteristics averaged either spatially or temporally. Typically,
modelers validated monthly observations at selected locations often without considering properly
the size limits of measured particles and the techniques used for the measurement of the mass
concentration (Guelle et al., 2001; Grini et al., 2002; Gong et al., 2002). Often the accuracy of
simulations was limited by a coarse horizontal resolution, typical for general circulation models.
Some other eorts centered on rening the sea salt emission function (Andreas, 1998; Guelle
et al., 2001; Lewis and Schwartz, 2004). Initial studies of emission parameterizations by Monahan
et al. (1986) and Smith et al. (1993) were further improved and evaluated by Andreas (1998),
Hoppel et al. (2002), Gong (2003), and other investigators (c.f.
Lewis and Schwartz, 2004).
Other measurement methods and parameterization approaches were developed by Reid et al.
(2001), who used aircraft measurements and the concentration buildup method, and Petelski
et al. (2005), who performed sea salt concentration gradient measurements in the Baltic Sea and
developed a sea salt emission parameterization based on the surface wind speed and signicant
wave height. Accompanying studies of whitecap coverage (Zhao and Toba, 2001; Stramska and
Petelski, 2003) resulted in improvements of emission parameterizations based on the whitecap
method.
The observed uncertainties, however, put into question the legitimacy of parameterizations
based solely on atmospheric parameters, such as wind speed or wind friction velocity.
It was
suggested (e.g., Xu et al., 2000; Zhao and Toba, 2001; Stramska and Petelski, 2003) that ocean
wave characteristics, like signicant wave height
Hs ,
peak wave period
Tp
or wave steepness,
could serve as good candidates for emission parameterization. Unfortunately, simultaneous measurements of the SSA properties and
Hs
or
Tp
are not common. On the other hand, it is possible
to develop more sophisticated approaches based on numerical global ocean wave models. Such
models provide wave quantities, such as
Hs
or
Tp
and allow investigation of their impact on the
sea salt emission.
Recently some wave-state dependent sea salt emission parameterizations were proposed. Zhao
and Toba (2001) relate the whitecap ratio
Rb = u2? /νωp , where u?
ν
W
is the friction velocity,
to the nondimensional breaking wave parameter
ωp
is the peak angular frequency of the waves and
is the kinematic viscosity of air. As an alternative, Zhao and Toba (2001) proposed another
nondimensional scaling parameter
Hs
Rh = u? Hs /ν ,
which incorporates the signicant wave height
into the whitecap ratio parameterization. However, both functions are valid for the case of
wind-waves in local equilibrium with the wind.
In the open ocean there are often conditions
where swell is predominant. Independent studies of sea surface roughness (Taylor and Yelland,
2001; Drennan et al., 2005) show that the presence of swell signicantly decreases the eective
wave steepness and hence the mean roughness compared to that for a pure wind-driven sea.
Consequently, this aects the momentum transfer between the air and the sea as well as the
wave breaking probability.
Another example is the study of Petelski et al. (2005), where the sea spray emission parameterization is proposed as
1/2 2 2/3
FE = A Hs U10
+ B.
15
In their approach
FE µgm−2 s−1
is the
emission ux,
Hs
[m] is the signicant wave height,
above the sea surface and the coecients
FE , Hs
and
U10 . A =
1.2 × 10−6 ;
B=
A and B
U10
[m/s] is the wind speed at 10 meters
are in units dened by the above listed units of
−1.6 × 10−7 . This expression was formulated specically
for the Baltic Sea and is not supposed to be applied over open oceans globally.
The importance of SSA on Earth's radiation budget has recently been recognized (Winter
and Chylek, 1997; Haywood et al., 1999; Jacobson, 2001; Dobbie et al., 2003; Satheesh and Lubin,
2003; Ma et al., 2008). SSA particles scatter solar radiation and aect the microphysics of marine
clouds by serving as condensation nuclei for cloud drops (Feingold et al., 1999; O'Dowd et al.,
1999a,b; Mason, 2001; Rosenfeld et al., 2002; Lohmann and Feichter, 2005). Estimates of the
global mean direct SSA radiative forcing range between
−0.6
and
−3.5 Wm−2
(Haywood et al.,
1999; Grini et al., 2002; Ma et al., 2008) and are based on model simulations. The evaluation of
the SSA indirect eect is even more complicated due to the complex microphysical interactions
between aerosols and clouds. Some recent model studies by Ma et al. (2008) indicate the rst
indirect SSA radiative forcing to be
−1.34 Wm−2 and the total indirect forcing to be −2.9 Wm−2 ,
if climatic feedback is taken into account.
1.4
Dissertation plan
This dissertation is organized as follows.
Chapter 2 provides a broad overview of the topic. It discusses the SSA production mechanisms
and the impact of relative humidity on the internal properties of sea salt particles. It also presents
sea salt emission parameterizations, paying special attention to the whitecap method employed
in this study.
Chapter 3 introduces and thoroughly describes the Navy Aerosol Analysis and Prediction
System (NAAPS) model. This part also discusses the development of the model.
Chapters 4 and 5 introduce new results from global sea salt simulations. Chapter 4 attempts
to compare and validate NAAPS based results against several shipboard measurements.
The
results help to assess model performance and examine factors aecting the accuracy of the
simulation. Chapter 5 analyzes long range trends of the total sea salt mass and global sea salt
emission. It also presents a mean distribution of the column integrated sea salt mass.
Chapter 6 develops a new sea salt emission source function. This parameterization incorporates sea-state dependent parameters in order to improve the performance of sea salt simulations
in global transport models.
Chapter 7 identies and investigates the inuence of the size bin selection on sea salt modeling
accuracy. This section also aims to construct and describe a size bin division algorithm, along
with the optical properties module for SSA. These algorithms and codes are tested in global
multiple bin simulations in which total sea salt mass and average optical depth are derived.
Furthermore, chapter 7 describes a dry deposition scheme that was incorporated into the NAAPS
model.
16
Chapter 8 analyzes top of the atmosphere shortwave SSA climate forcing. A description of
a model used to derive the radiative forcing is followed by investigation of the global forcing
distribution and analysis of the impact of various size bin choices on modeled climate forcing.
Chapter 9 is a summary of the thesis and presents some conclusions together with a brief
discussion on the way forward.
17
18
Chapter 2
Sea salt aerosol emission processes and
parameterizations
In this chapter we examine physical processes related to the emission of SSA particles. We also
investigate factors that inuence both magnitude and size spectrum of emitted aerosol. Those
factors can be divided into three main categories: a) meteorological factors, e.g.
wind speed
or wind friction velocity; b) sea state related factors, e.g. signicant wave height or wave eld
spectrum; and c) microscale factors, e.g. the presence of surface active materials, water salinity,
or water temperature. Also in this chapter we present the most important methods of deriving
the SSA emission uxes. In particular, we descibe the whitecap method as it will be employed
in our research.
2.1
Main sea salt aerosol production mechanisms and factors
aecting emission
Two main mechanisms are believed to be responsible for sea salt particles production in the
atmosphere: (a) bursting of air bubbles at the water-air interface and (b) mechanical disruption
of wave crests by wind. Bubble bursting, an indirect mechanism, results primarily from wave
breaking, which entrains air into water. Such breaking creates two types of drops
−
lm and
jet ones. This mechanism is schematically illustrated in Fig. 2.1. Film drops are created from
bursting cap of the bubble (c and d in Fig. 2.1), whereas jet drops are created by the collapse
of the water gap remaining after the initial burst (e and f in Fig. 2.1). Film drops are generally
smaller in size but are generated in greater numbers in comparison to jet drops. These two types
of drops are thought to comprise the majority of SSA particles of small and medium sizes, that
is of radius smaller than 0.1 micron and up to several microns.
Second SSA production mechanism is related to the mechanical action of wind on wave crests.
If the wind stress is suciently strong, the wind can tear sea water directly from the wave tops,
generating a ux of sea salt particles denoted by Monahan et al. (1986) as spume drops. This
direct formation process has been little studied (Koga, 1981; Zhao et al., 2006; Bortkovskii, 1987,
pp. 1-5) and the exact spectra of particles produced as well as conditions at which the process
initiates remain unsolved.
19
CHAPTER 2. SSA EMISSION - PROCESSES AND PARAMETERIZATIONS
Figure 2.1: Sea salt aerosol production from bubble bursting. a-c) The entrained air bubble rises
to the surface and forms the bubble lm; d) the bubble lm bursts and lm drops are produced;
e-f ) the collapsing water gap ejects jet drops (Lewis and Schwartz, 2004).
2.1.1
Wind speed and wind friction velocity
Wind is the key factor governing the production and life cycle of SSA. It causes sea waves to
develop, rise and eventually break, forming whitecaps and bubble-produced lm and jet drops.
Mechanical action of wind on wave crests is responsible for the spume drop formation. Wind
aects the entrainment of newly generated particles into the marine boundary layer and controls
their advection and vertical transport. Wind velocity is an easily measured and modeled quantity,
which makes it a commonly available meteorological parameter.
Measurements of the wind
velocity at specic height is performed during virtually all experimental studies pertinent to the
SSA production. The height at which wind velocities are reported is typically 10 meters above
the sea level (asl), but some other measurement levels (for instance 19.5 m asl) are also reported
in the literature.
The wind friction velocity if often used instad of the wind speed to dene the SSA production.
Dened as
ρa
u? = (τ /ρa )1/2 ,
where
τ
is the Reynolds stress (horizontal force per unit area) and
is the air density is one of the imprtant variables describing atmosphere-ocean interactions
(Stull, 1988).
Its advantage over the wind speed is that friction velocity carries information
about atmospheric stability
−
a factor controlling turbulence and entrainment in the boundary
layer. For given stability conditions (stable, neutral or unstable), the friction velocity is nearly
proportional to the horizontal wind speed, with the proportionality constant being a square
root of the dimensionless wind stress coecient
proposed by Taylor (1916), where
Cd
Cd : u? =
√
Cd U10 .
This relation was initially
is a bulk momentum transfer coecient, also called the
drag coecient (Stull, 1988). The experimentally derived value of
under neutral stability conditions. A variety of formulations for
u?
Cd
is between 0.001-0.0015
and
Cd
have been proposed
(see e.g., Wu, 1980, 1982; Jones and Toba, 2001; Drennan et al., 2005; Stull, 1988).
them include other parameters, like the aerodynamic roughness length
z0 ,
Some of
the wave height or
wave age (Panofsky and Dutton, 1984; Toba et al., 1990; Yelland and Taylor, 1996; Taylor and
20
Yelland, 2001; Hsu and Blanchard, 2003; Kara et al., 2007). However, because of the dicult
measurement techniques and associated scatter of the data choosing one function over another
is not possible.
Another argument for choosing friction velocity in sea salt emission parameterizations originates from ocean dynamics models. In ocean wave models, wave energy dissipation is usually
parameterized in terms of the cube of air friction velocity
u3?
(Hasselmann, 1974; Phillips, 1985;
Hanson and Phillips, 1999). The main wave dissipation mechanism is whitecapping, which in
turn leads to the generation of SSA particles.
u3? .
Though friction velocity
u?
SSA emission should be then proportional to
is believed to be more closely coupled to the dissipation (and
generation) of sea waves or production of SSA particles than wind speed, most available parameterizations are a function of the horizontal wind speed rather than
situ measurements of
u?
u? .
This is because in
are very dicult and require sophisticated experimental techniques.
Furthermore, there is a close connection between the two velocities which favors the use of the
more easily available one, i.e. the wind speed.
2.1.2
Sea state characteristics
Sea state, quantitatively characterized by the wave spectrum (Phillips, 1966), plays a signicant
role in description of the wave breaking. The wave spectrum describes the distribution of wave
energy within a range of wave frequencies and directions.
The quantity commonly used to
describe the strength of a wave eld is the signicant wave height
where
E [m2 ]
is the average wave energy spectrum (E
wave energy spectrum and
f
and
directly when a wave spectrum
θ
N
=
´ 2π ´ ∞
0
0
Hs ,
dened as
N (f, θ) df dθ,
are frequency and direction, respectively).
√
Hs = 4 E ,
where
Hs
N
is the
can be derived
is available. However, it can be also derived experimentally
as the average height of the largest one-third of the measured wave heights.
This empirical
denition has a clear physical meaning which has given this quantity a broad recognition in the
ocean science community (Sorensen, 1993).
Other wave spectrum properties of particular importance in characterizing the air-sea interactions are the wave period
Ts ,
the wave phase velocity
cs ,
and the wave frequency
fs .
These
variables are often used to construct more specic quantities, for example those representing
the spectrum parts characterized by highest wave energy. These include the peak wave period
Tp ,
the peak wave angular frequency
ωp ,
and the peak wave direction
θp .
Also, some quanti-
ties can be separately specied for a low frequency part of the spectra (swell, long waves) and
wind-generated short period waves (high frequencies).
Other parameters that have been identied to inuence wave spectra, and thus whitecapping,
are the wind fetch and the wave age
β.
The wind fetch, the horizontal distance over water along
which a given wind has blown, can determine both size and spectrum of generated waves (Ebuchi
et al., 1992). The longer the fetch and the faster the wind speed, the larger and stronger the waves
will become. The wave age
β
is a measure of the time the wind has been acting on a given wave
group. It is usually expressed as a dimensionless number, the phase speed
21
cp
of the peak of the
wave spectrum divided by the wind speed or by the friction velocity (β
= cp /U10 or cp /u? ).
wave age is a useful quantity that describes the sea state development, with small
undeveloped or rising seas and larger
β
β
The
representing
representing fully developed or falling seas. The sea state
development is believed to aect many aspects of the air-sea interactions, including whitecapping
and SSA emission.
The sea state characteristics presented above have not commonly been used as parameters of
the SSA emission parameterization. This is partially because the sea state measurements were
not typically performed together with the SSA emission or the whitecap ratio measurements.
There were only a few attempts to investigate possible relationships between them (Toba and
Chaen, 1973; Toba and Koga, 1986; Iida et al., 1992; Zhao and Toba, 2001; Petelski et al., 2005).
Recent studies of Zhao and Toba (2001) exploit the peak angular frequency
(without swell), the wave age
β,
and the signicant wave height
Hs
ωp
of wind-waves
in the parameterization of
the whitecap ratio. Petelski et al. (2005) formulate a new SSA emission function for the Baltic
Sea basin, based on empirically derived
Hs
values and measured wind velocity. They argue that
their parameterization is equivalent to considering the parameter proportional to the average
wave energy dissipation rate raised to the 2/3 power.
2.1.3
Microscale factors
It has been suggested that some other factors play a role in the SSA generation. They can be
loosely related either to thermodynamical properties of sea water or to the interfacial phenomena
that aect properties of the water-air interface. These can be described as microscale factors, in
contrast to such macroscopic properties as the wind speed or the signicant wave height. The
microscale factors of interest for the SSA parameterizations include:

sea water temperature;
sea water salinity;
surface-active materials.
The microscale physical properties of water aect the number and size distribution of generated
SSA particles through modifying the kinematic viscosity of sea water, the air bubble rise velocity, the rate of gas exchange between a bubble and surrounding it uid as well as the bubble
bursting behavior. Quantitative assessments of such dependencies are rare and mostly limited to
laboratory investigations. Of particular importance is the study of Martensson et al. (2003), who
investigated the impact of water temperature and salinity on the number and size distribution
of emitted SSA particles. Some other studies that analyze the inuence of physical properties of
water on sea state and wave breaking probability include Bortkovskii (1987) or Bortkovskii and
Novak (1993).
22
2.2
Relative humidity inuence on sea salt aerosol particles
SSA particles are hygroscopic, i.e. they readily exchange water vapor with the surrounding air,
and therefore they change their chemical and physical properties under dierent atmospheric
conditions. Such equilibrium physical, chemical and optical parameters as radius, density, water
content, solute concentration or refractive index are controlled almost entirely by the ambient relative humidity (RH) (Tang et al., 1997; Lewis and Schwartz, 2004). The temperature dependence
of the equilibrium radius is very small and can be considered negligible (Arons and Kientzler,
1954). In this section, the inuence of RH on particle radius, density and refractive index will
be examined, as only these parameters are required for the model simulations performed in this
study.
Let us consider the formation of a sea water drop with a given mass of solute that corresponds

to the salinity of 35
. As the mole fraction of water in such a solution drop is very close to

0.98 (DOE, 1994), the vapor pressure of water in equilibrium with a liquid drop of salinity 35
is approximately 98% of the saturation vapor pressure of pure water at the same temperature.
The Kelvin eect (Thomson, 1871), which accounts for the impact of the drop curvature on the
equilibrium vapor pressure, is not taken into account due to its negligible eect in the range
of particle sizes considered in this study (r
> 0.05).
Therefore, the equilibrium RH of a newly
formed SSA particle is equal to 98%. This value can be generalized, with an accuracy of about

0.1%, to the majority of the world's oceans, which have salinities within the range of 33 to 37
.
The ratio of the radius of a forming drop (r98 ) to its equivalent dry radius (rdry ) is equal to:
r98
=
rdry
Here
rdry
salt (∼
The
is based on the equation
2.2
ρss
ρ
m98
mdry
1
3
≈ 4.0 .
3 ; ρ
mdry = 4/3πρss rdry
ss
and
g/cm3 ) and that of sea water with the salinity of 35
m98 /mdry
ratio, from the denition of salinity, is equal to
ρ

are the densities of pure sea
(∼
1.0 g/cm3 ),
respectively.
1000/35.
Based on similar considerations and data provided by Tang et al. (1997), the ratio of the
radius of a drop at 80% RH (r80 ) to
rdry
can be approximated as
3
drop at 80% RH has the density close to 1.2 g/cm and
r80 /rdry ≈ 2.0,
m80 /mdry = 4.2.
given that a
Therefore, the following
convenient relation for a drop growth can be formulated:
rdry : r80 : r98 = 1 : 2 : 4 ,
(2.1)
which is a close approximation to the observed sea salt hygroscopic behavior.
A growth function is typically used to scale the size of a sea water drop from one ambient RH
to another. It requires a reference RH, which has been chosen by many investigators to be 80%
(Lewis and Schwartz, 2004). In this work we follow that choice, except for Section 7.1, where
the reference RH is assumed to be 0%. Nonetheless, the resulting growth function, according to
23
2
Sea salt
NaCl
growth function
r/r
80
1.5
1
< <
> >
0.5
0
<
>
<
20
40
>
60
80
100
RH [%]
Figure 2.2: Dependence of equilibrium radii of SSA and NaCl particles on relative humidity,
after Lewis and Schwartz (2004) and Tang et al. (1997). The black line represents the growth
function used in this study, described by Eq. 2.2.
Eq. 2.1, diers by a factor of 2.
The growth function is dened as
number of expressions for
ξ
ξ = r/r80 ,
assuming the reference RH is 80%.
are proposed in the literature.
A large
Here we limit our analysis to the
expression that was proposed by Lewis and Schwartz (2004) and that agrees to within
∼1% with
the measurements of Tang et al. (1997). Their growth function has the following form:

0.54/ (1 − rh) 13
ξ (rh) =
0.67/ (1 − rh) 14
where
rh = RH/100,
rh > 0.93 ,
(2.2)
rh ≤ 0.93 ,
and it will be used in our studies. Eq. 2.2 is plotted in Fig. 2.2 together
with the experimental results obtained by Tang et al. (1997) showing the growth of sea salt and
pure NaCl particles.
Fig.
2.2 illustrates a hysteresis eect related to the cycle of hydration-dehydration of a
crystal particle. A solid particle remains in its initial state upon increasing RH, until it reaches
a deliquesce point, which in the case of sea salt is at about 74% RH. At this point the particle
becomes a homogeneous solution of water and sea salt and grows continuously and smoothly as
RH rises further.
The sea salt deliquescence transition is not as sharp as that of pure NaCl,
and occurs more gradually, starting at about 70% RH. The drying scenario is dierent. As RH
decreases, a droplet gradually loses its mass by water evaporation but remains hydrated in the
metastable phase, until the eorescence point is reached at about 44% RH. Upon crystallization,
however, a sea salt particle does not shed all its water content as it is observed in the case of
24
a NaCl particle. Some residual water remains in the sea salt particle at all relative humidities
(Tang et al., 1997), which is illustrated in Fig. 2.2 with the growth function for sea salt at 0%
RH being slightly higher than 0.5.
The growth function parameterization, expressed by Eq. 2.2 and plotted in Fig. 2.2, does
not account for the hysteresis eect.
The function follows the upper branch of the hysteresis
and extends smoothly to low RH values without the eorescence size drop.
We assume that
sea salt particles in the model form solution drops at all atmospheric RH conditions. This is a
useful modeling assumption and it is expected to have negligible impact on the model results.
Indeed, in most cases RH over oceans, where most SSA resides, does not drop below 45%, where
eorescence occurs. At the same time, these particles are expected to be on the upper branch of
the hysteresis, since they do not have a chance to dry below 40% RH. Furthermore, the dierence
between approximate and measured behavior in the 0-40 % range of RH is small and does not
exceed 25% of the particle radius. On the basis of these arguments and existing discrepancies,
we conclude that the assumed parameterization approximates the growth behavior of sea salt
particles suciently well. At the same time it eliminates the need for particle history tracking in
order to determine whether the particles were on the lower or higher branch of the hysteresis.
As the SSA droplet exchanges water vapor with the surrounding air, it encounters changes
in density
ρ (rh)
and index of refraction
m (rh).
These modications are presented in Fig. 2.3
and described by the following equations:
ρ (rh) = −0.11 rh2 − 0.45 rh + 1.61 ,
(2.3)
m (rh) = −0.1944 rh + 1.5305 .
(2.4)
[g/cm3]
[nondimensional]
1.6
1.5
1.4
Density
Index of refraction
1.3
1.2
1.1
1
10
20
30
40
50
60
70
Relative humidity [%]
80
90
100
Figure 2.3: RH dependencies of the sea salt density and real part of the index of refraction (at
visible wavelengths), adopted after Lewis and Schwartz (2004).
25
Eqs 2.3 and 2.4 are adopted from a review by Lewis and Schwartz (2004,
Fig. 4 on page 28)
and extrapolated to a low RH regime. The equation for the sea salt density at low RH values is
far from being correct. For instance, the density at 0% RH derived from Eq. 2.3 is 1.61
whereas it is well-known that this value should be close to 2.2
g/cm3 .
g/cm3 ,
Furthermore, the density of
3
dry sea salt is taken as 2.2 g/cm in many equations used in this study. However, this discrepancy
and the formulation of Eq. 2.3 are intentional and carefully evaluated in order to assure a proper
and more realistic behavior of the product
ρ (rh) ξ 2 (rh)
in the full RH range.
ρ (rh) ξ 2 (rh)
is a
part of the Stokes velocity equation and is responsible for the gravitational sedimentation and
dry deposition of sea salt particles in the model.
2.3
Methods of determining sea salt aerosol production uxes
Despite many scientic eorts, quantication of the direct and indirect sea salt production mechanisms remains highly uncertain. Determining the SSA particle production ux by direct measurements in natural conditions is very dicult and therefore has rarely been undertaken by
investigators. Research mostly focused on developing indirect methods that are based on laboratory measurements and/or eld measurements. The most common and extensively used methods
include:
1. Steady state dry deposition method (Fairall et al., 1983, 1994; Iida et al., 1992; Smith
et al., 1993; Smith and Harrison, 1998).
This technique requires measurements of the
mean size-dependent SSA number concentrations
zref
n̄ (r80 , zref )
at a given reference height
and a model-based determination of the dry deposition velocity
vd (r80 , zref )
of those
SSA particles. The SSA emission function is described as a product:
f80 = n̄ (r80 , zref ) vd (r80 , zref ) .
In this method, it is essential that a local balance between production and deposition at
the reference height is established, which is a condition better satised by larger particles
(r80
& 5µm).
The semi-empirical model of the dry deposition velocity introduces additional
uncertainties in the formulation. An example of such a model can be found in Section 7.1.
2. Whitecap method.
In this procedure, both laboratory and eld measurements are em-
ployed. The laboratory stage comprises derivation of the size dependent drop production
from an articially generated whitecap area. The eld stage involves measurements of the
fraction of the ocean surface covered with whitecaps. A limitation of this method is that
drops produced by direct tearing of wave crests, i.e. the spume drops, are not included
in resulting parameterizations. Also, the method assumes that the whitecap area generated mechanically in a laboratory and the one observed naturally on the ocean surface are
similar and produce equal number of drops at a comparable size spectrum. This method
is further described in Section 2.4.
26
Other SSA ux derivation methods which have recently been discussed are the concentration
buildup method (Reid et al., 2001) and the vertical gradient method (Petelski, 2003). The concentration buildup method calculates the size-dependent SSA production ux from eld measurements of the column integrated SSA number concentration. Additionally, column integration
has to be performed along the trajectory that follows the wind direction during oshore wind
conditions.
It is assumed that during an oshore wind scenario the vertical integral of SSA
particles will increase according to the local production rate. The vertical integral is expected
to be zero over land and to reach some saturation value at a distance away from the coast. The
saturation value strongly depends on particle size and is governed by the local balance between
production and removal mechanisms. Some concerns associated with this method include the
extent to which the production rate in near-shore regions is representative of that in the open
ocean. The inuence of continental (or land) aerosol may also be an issue as it might be dicult to distinguish between locally produced and advected aerosol components within measured
concentrations.
The vertical gradient method, developed by Petelski (2003), is an interesting modern approach
towards determining the SSA production ux. It is based on measurements of the SSA number
concentration at several altitudes above the water surface and implementation of the Monin
Obukhov (1953) similarity theory. The method requires the condition of horizontal homogeneity,
which means that horizontal gradients of the SSA concentrations are negligible. This condition is
much better fullled by larger (> 1
mm) aerosol particles for which it is expected that long range
transport through advection does not play a considerable role. The vertical gradient method was
successfully applied over the Baltic Sea and the North Polar Waters of the Atlantic (Petelski,
2003; Petelski and Piskozub, 2006) and led to derivation of the emission source function based
on the sea-state dependent parameters (Petelski et al., 2005).
2.4
Whitecap method
In this section we will describe the whitecap method in more detail as our parameterization of
the SSA emission function is based on it. Thus, it is important to better understand its basis
and assess its range of validity. More information can also be found in Lewis and Schwartz (2004,
Sections 3.3, 4.5 and 5.3).
The whitecap method is used to calculate the SSA production ux from:
(a) laboratory
measurements of the size-dependent drop production over articially generated whitecaps and
(b) eld measurements of the fraction of the ocean surface covered with whitecaps. These are
two separate and independent stages that are often performed by dierent investigators. The
whitecap method was rst suggested by Blanchard (1963, p.
128).
The production ux is
expressed as a product:
f (r80 ) = fwc (r80 ) W,
where
W
[dimensionless] is the whitecap ratio,
fwc (r80 )
27
(2.5)
is the size-dependent SSA production
ux over the whitecap area only, i.e. the dierential whitecap aerosol productivity, and
r80
is the
particle radius at 80% relative humidity. The production ux represents the number of emitted
particles in a unit interval of
r80
per unit time per unit area
1/ m2 sµm .
The choice of 80% as
a reference RH is somewhat arbitrary and is related to a typical RH found over oceans. Emission
parameterizations at this RH value are typically reported by investigators, often after conversion
from the ambient RH at which air samples were investigated.
Derivation of the dierential whitecap aerosol productivity
fwc (r80 )
is based on two labora-
tory methods:
1. Continuous method that involves a steady state whitecap formed either by a continuous
waterfall (Cipriano and Blanchard, 1981; Cipriano et al., 1983) or by forcing air through a
glass lter located underwater (Martensson et al., 2003).
2. Discrete whitecap method that employs a whitecap formed by the collision of two parcels
of water in a wave tank (Monahan et al., 1982; Cipriano et al., 1987).
In the continuous whitecap method
fwc (r80 )
is calculated as
fwc (r80 ) =
where
p (r80 )
p (r80 )
,
Awc
is the number of particles in a unit logarithmic interval of
time from the laboratory whitecap with the area of
Awc .
p (r80 )
Both
r80
produced per unit
and
Awc
are derived
experimentally, the former being measured with a variety of instrumentation, including optical
particle counters, electrical aerosol analyzers, or condensation nuclei (CN) counter.
The discrete laboratory whitecap method relies on measurement of the increase in
the number concentration of SSA particles per unit logarithmic interval of
results from a single breaking wave with the initial white area
volume
V.
The resulting dierential whitecap aerosol productivity
fwc (r80 ) =
where
sion of
τwc
τwc
Awc,0
r80 .
δn (r80 ),
This increase
enclosed in a tank of air
fwc (r80 )
is given by
δn (r80 ) V
,
τwc Awc,0
is an experimentally determined characteristic whitecap decay time.
The inclu-
rests on the assumption that the area of any whitecap, independent of its size
and means of generation, decreases exponentially in time according to the equation
Awc,0 exp (−t/τwc ).
Awc (t) =
This trend was determined in several investigations, typically from pho-
tographs or video recordings of individual whitecaps. However, in many cases the exponential
decay was not supported by the data, making the assumption uncertain.
Oceanic whitecaps and their frequency of occurrence characterized by the whitecap ratio
W
are another component of the SSA production ux (see Eq.
manifestation of the wave breaking process.
2.5).
A whitecap is a visible
The bright white area of a whitecap arises from
multiple light scattering by air bubbles present at or near the water surface.
28
Therefore, the
whiter the observed area is, the more bursting bubbles there are at the surface and the larger
the number of SSA particles emitted to the atmosphere.
There were many experimental eorts to describe the whitecap ratio accurately. The knowledge of the whitecap coverage, in addition to the SSA production, is important for many other
applications, like satellite remote sensing of the oceans and the atmosphere, air-sea exchange of
heat and moisture, and transport and transformation of organic matter, to name just a few. A
broad overview of pertinent research can be found in Lewis and Schwartz (2004, section 4.5).
The whitecap coverage is typically classied by the surface wind speed (U10 ) for the reasons that
were addressed in Section 2.1.2. The data are typically tted to a power law expression:
p
W ∝ aU10
,
where
a is a constant.
The value of
p ranges from less than 2 to more than 5, when power law ts
to individual data sets are performed (Hanson and Phillips, 1999; Monahan and Mac Niocaill,
1986). Some commonly used values include
p = 3.0, p = 3.41,
and
p = 3.75.
In this study, the
expression for the whitecap ratio introduced by Monahan and Muircheartaigh (1980) is used,
with coecient
p = 3.41,
i.e.
3.41 .
W = 3.84 × 10−6 U10
This function was applied in the model
simulations discussed in Chapter 4.
Existing uncertainty in determination of the value of
to three orders of magnitude, in the observed values of
p
W
manifests itself by a large scatter, up
within each wind speed category (see
Fig. 36 in Lewis and Schwartz, 2004, p. 261). Some of this scatter can be assigned to measurement
techniques used in the observations (e.g. Koepke, 1984; Bortkovskii, 1987; Stramska and Petelski,
2003). Typically, whitecap ratios were derived by means of sea surface photographs taken from
ships, aircrafts or towers, or from video recordings taken from ships. It was found that whitecap
ratios determined by photographs are typically greater, by one to two orders of magnitude, than
those determined by video recordings. Various picture analysis algorithms also result in dierent
whitecap ratio values, even if applied to the same set of photographs. Besides these technical
diculties, there are suggestions that large scatter in
W,
when parameterized by
U10
alone, can
be decreased by taking into account factors other than just the wind speed. Such potentially
important factors include sea temperature, atmospheric stability, sea state, fetch, salinity, and
surface active materials. Some of these factors were already discussed in Section 2.1.2. A new
formulation of
W
and the resulting SSA emission function based on the sea-state dependent
parameters and surface wind speed is introduced in Chapter 6.
29
30
Chapter 3
Navy Aerosol Analysis and Prediction System
model
In this chapter we describe the Navy Aerosol Analysis and Prediction System (NAAPS) model,
a tool used to perform parts of the research projects described in this dissertation. NAAPS is
a global three-dimensional aerosol and air pollution transport model that was developed in the
Naval Research Laboratory in Monterey, California. The original version of NAAPS was developed and improved by us in order to tackle problems pertinent to SSA and also to advance our
skill of global modeling and to make more reliable predictions concerning SSA in the atmosphere.
These modications include:
1. Adding sea salt aerosol to the model.
The original version of NAAPS included four species: gaseous
SO2 ,
particulate sulfates
(SO4 ), mineral dust, and smoke/soot aerosols. We implemented a new aerosol class in the
model and tested its performance in global simulations.
SSA was added as a single bin
with the emission function parameterized as a function of the surface wind velocity (see
details in Sec. 3.3) and deposition processes parameterized in a similar manner to that of
the sulphate aerosol. Global simulations for the period 1998-2006 were performed with the
modied model. The results are described as a whole in Chapter 5 and analyzed in details
in Chapter 4.
Chapter 4 includes a model results analysis and a validation against ne
resolution sea salt mass observations over oceans.
In addition, our sea salt aerosol code was implemented in the operational NAAPS simulations carried out by NRL, which are available at
http://www.nrlmry.navy.mil/aerosol.
2. Coupling transport model with results from the global ocean wave model.
The second development direction was to merge results from the global ocean wave model
Wave Watch III with NAAPS in order to improve the SSA emission function. All other
physical process relevant to the sea salt transport and removal remained unchanged, but the
emission parameterization was modied to incorporate a range of the sea state parameters.
Results of investigations with this code are described in Chapter 6.
3. Multi-bin sea salt simulations.
This step required a large set of model developments that have changed NAAPS features
31
CHAPTER 3. NAAPS MODEL DESCRIPTION
and performance in a considerable extent. Several modications that were implemented
include changes in the dry deposition parameterization (see Sec.
7.1), vertical mixing
routine, and adding gravitational sedimentation. Furthermore, a new method for deriving
size bin intervals was developed (Sec. 7.2). In addition, several post-processing programs
and models were developed to analyze results from the multi-bin simulations (e.g.
see
Sec. 7.3). NAAPS modications and results obtained with this version of the model are
described in Chapters 7 and 8.
3.1
NAAPS overview
NAAPS is a global semi-lagrangian aerosols and air pollution transport model, developed in the
Naval Research Laboratory in Monterey, California. (http://www.nrlmry.navy.mil/aerosol).
The current version of the model contains ve species: gaseous
SO2 ,
particulate sulfates (SO4 ),
mineral dust, smoke/soot, and sea salt aerosol. All these species are treated as passive tracers,
not reacting with each other, with the exception of gaseous
SO2 ,
which can be transformed to
particulate sulfates. Physical processes that apply to each species include: (a) emission from the
surface, (b) mixing and diusion in the boundary layer, (c) dispersion and advection by the wind,
and (d) removal from the atmosphere by wet and dry deposition. The NAAPS model is driven
by meteorological elds obtained from Navy Operational Global Atmospheric Prediction System
(NOGAPS) (Hogan and Rosmond, 1991).
◦
is 0.5
×
Although current operational NOGAPS resolution
0.5◦ , the simulations performed in this dissertation were based on the coarser
1◦ × 1◦
resolution. NOGAPS analyses are available every 6 hours (at 00, 06, 12 and 18 UTC).
Current strengths of the model include the near-real time operation with the use of operational
dynamics, 5 days global forecast, and detailed emission inventories for smoke and dust aerosols.
More information about the emission inventories can be found at
aerosol_web/Docs/globaer_model.html.
http://www.nrlmry.navy.mil/
Recent publications on dust and smoke modeling and
validation include the work by Wells et al. (2007), Reid et al. (2004), and McKendry et al. (2007).
3.2
Governing equations and parameterizations
NAAPS is a global three-dimensional aerosol and air pollution model, based primarily on the
Danish Eulerian Hemispheric Model (DEHM) (Christensen, 1997). Many modications, however,
were applied to the original DEHM model, before it became the operational, real-time forecast
system for the Naval Research Laboratory. The following description of the model is partially
based on Christensen (1997) paper, with special emphasis on the processes relevant to the sea
salt prediction.
32
The set of equations solved in the model has the following form:
∂qi
∂t
∂qi
∂qi
∂qi
+
= − u
+v
+ς
∂x
∂y
∂σ


2 K ∂qi
∂
Γ
z ∂σ
Kx ∂qi + Ky ∂qi +
 + Pi − Qi
2
2
∂x
∂y
∂σ
,
(3.1)
i = 1, nq
qi
where
and
ρ
is the mass mixing ratio for the specie
is the density of air,
vertical coordinate (σ
v
x
and
= p/ps ,
are horizontal velocities and
y
where
ci
p
is the vertical velocity in
σ
Qi
surface pressure),
Kx
and
Ky
Kx = Ky = 6 × 104 m2 s−1 ),
hydrostatic equilibrium and introducing the ideal gas law,
and
is the terrain-following
coordinates.
the vertical diusion coecient, which will be described further on.
Pi
σ
ps
is the present pressure and
horizontal diusion coecients (assumed to be constant:
the acceleration due to gravity,
is the mass concentration
are the horizontal coordinates and
where
ς
i: qi = ci /ρ,
Γ = dσ/dz ,
are the production and loss terms for modeled species and
and
are the
and
Kz
is
and assuming
Γ = −gp/ps = −gσ/RT ,
R is the gas constant (for ambient air), and T
u
where
g
is
is the temperature.
nq is the number of species
in the model.
The boundary condition at the ground is represented by the mass uxes due to the dry
Kσ ∂qi /∂σ = −Γvd qi + EG/σ , where vd (m/s) is the dry
2
g/m s is the surface emission. Free boundary conditions are applied
deposition and the surface emission:
deposition velocity, and
Es
in the top layer.
The vertical diusion coecient parameterization
ilarity theory for the surface layer. The
Kz
Kz
is based on the MoninObukhov sim-
prole is extended to the whole boundary layer by
using a simple extrapolation (Hertel et al., 1995):
Kz = max
where
κu? z
φ Lz
z
1−
, 0.1
zmix
!
2 −1
m s
,
φ (z/L) is the similarity function for heat, κ is the von Karman constant, u?
velocity,
z
is the height above the surface,
zmix
is the friction
is the height of the mixing layer, and
L
is the
MoninObukhov length.
The mixing layer height
zmix
is calculated by a simple parameterization based on the energy
balance equation for the internal boundary layer (e.g., Gryning and Batchvarova, 1990):
where
N
ature),
w
u2?
∂zmix
∂zmix
∂zmix
N zmix + 1.9
+u
+v
+w
zmix
∂t
∂x
∂y
w3
1.25u2? u2? w?2
= ? +
−
−
,
zmix
zmix
τ
2τ
2
is the BruntVaisala frequency,
is the vertical velocity at
N 2 = γg/T (γ
is the lapse rate and
T
1/3
zmix , w? = (g/T max (Hsen /ρcp , 0) zmix )
33
is the temper-
is the turbulent
velocity scale, and
τ
is the dissipation timescale (=
1000
s). The eect of entrainment is ignored
in the above balance equation.
The dry deposition of sea salt particles is treated in a very simplied manner. Dry uxes
are equal to the mass concentration in the lower level times a dry deposition velocity, which
is dened dierently for the open water surface and for all other types of surfaces.
The dry
deposition velocity over open water is given by the formula in Slinn and Slinn (1980), assuming
a dry mass mean radius near 1
µm:
u2
vd ∼
= ? = Cd U10 ,
U10
where
U10
Cd ≈ 1.3 × 10−3
is the wind velocity at 10 m above the sea surface and
is the drag
coecient.
Over non-open water surfaces the dry deposition velocity is based on the formulation by
Walcek et al. (1986):
vd =
  u? 1 +
a
−300
L
2 3
 u?
a
where
a = 500
(except for a deciduous forest, where
for
L<0,
for
L>0,
a = 100),
and
L
is the MoninObukhov
length. The choice of the above deposition velocities is arbitrary, and these parameterizations
will be improved as size dierentiation is introduced to the model.
The wet deposition of sea salt particles is assumed to be similar to the sulfate aerosol and
is based on a simple scavenging ratio formulation (see, e.g.,
coecient
W
s−1
at a given
σ
W (σ) =
Pa (σ)
and
mass at a given

 Λbc Pa (σ)
below cloud scavenging
 Λc P (σ)
in cloud scavenging
H
P (σ) kg m−2 s−1
ρw
ρw
,
,
are the total downward ux densities of the precipitation
σ -level below or in a precipitating cloud, respectively. H
for scavenging (set to 1000 m),
The scavenging
level is given as:
H
where
Iversen, 1989).
Λbc = 1 ×
is the in-cloud scavenging ratio, and
ρw
is the eective thickness
105 is the below-cloud scavenging ratio,
Λc = 7 × 105
is the density of water. The condensation scheme is the
same as in the NOGAPS atmospheric model and is further described in Hogan and Rosmond
(1991).
Eq. (3.1) is solved on the spherical Gaussian grid with horizontal resolution
vertical irregular
σ -coordinate
1◦ × 1◦
and 24
levels. The average depth of the rst layer is around 34 meters,
and consecutive layers gradually increase in depth towards the top layer, which ends at around
18 km. Time integration is performed by splitting Eq. (3.1) into three sub-modules: (a) 3D
advection, (b) vertical diusion coupled with emission and dry deposition, and (c) horizontal
diusion.
34
The three-dimensional advection is solved by a semi-lagrangian algorithm (Staniforth and
Cote, 1991; Ritchie, 1987). A one-dimensional algorithm that is implicit in time and has a nite
element space discretization is used for the horizontal and vertical diusion equations.
The
integration time step in operational NAAPS performance is half an hour for vertical diusion
and 1 hour for all other processes, whereas in the simulations performed in this study it was 15
minutes for vertical diusion and 30 minutes for other processes.
3.3
Sea salt emission
The most commonly used formulations of the size dependent source functions are those of Monahan et al. (1986) and Smith et al. (1993). Monahan based his equation on measurements of the
size-resolved sea salt number concentration over laboratory-generated whitecaps. His expression
is valid for particles with radii from 0.8 to 8 micrometers at a 80% RH. Smith et al. (1993)
based their formulation on the steady state dry deposition method, which is valid for particles
with radii greater than about 5 microns at 80% relative humidity (RH) (Lewis and Schwartz,
2004; Hoppel et al., 2002). Such particles are mostly larger jet drops and spume drops and their
residence time in the atmosphere is much shorter than for the particles with smaller radii, due
to greater dry deposition and gravitational fallout.
In NAAPS, the sea salt dry mass ux from the surface is given by the equation
where
a
and
b
U10
are constants and
b ,
F = a U10
is the wind speed at 10 meters above the sea surface.
This formulation of the source function is based on the whitecap method. We used Monahan's
formulation of the source function:
1.19 exp(−B 2 )
dF
3.41 −3
1.05
= 1.373 U10
r80 1 + 0.057 r80
10
,
dr80
where
r80
is the particle radius in
µm
at 80% RH and
B = [0.38 − log (r80 )] /0.65. dF/dr80
describes the areal ux of the number of sea salt particles with the radius
the ocean surface.
It is given in units of
m−2 s−1 µm−1 .
(3.2)
r ± dr
produced over
In the original work, Monahan et al.
(1986) dened this function for particles with radii ranging from 0.8 to 8 microns at 80% RH.
Gong (2003) showed that this formulation can be extended down to 0.2
much error in the number of emitted particles.
results obtained by Martensson et al. (2003).
µm
without introducing
This is also supported by recent laboratory
Therefore, we extended the applicability of the
source function given by Eq. 3.2 to radii ranging from 0.2 to 8 microns at 80% RH to capture
most of the emitted mass.
Eq.
3.2 can be used to derive dry sea salt mass emitted to the
atmosphere. The radius of dry sea salt particles is a factor of two lower than the radius at 80%
RH
rdry = 0.5 r80
(see Eq. 2.1). Therefore,
dF/drdry = 2 dF/dr80 .
35
The dry sea salt mass ux
emitted to the atmosphere is given by the integral:
Fdry
4
= πρss
3
ˆ4
dF 3
4
r drdry = πρss
drdry dry
3
0.2
where
ρss = 2.2 × 103
kg/m3
ˆ4
2
dF 3
r drdry ,
dr80 dry
0.2
is the dry sea salt density and the radius
rdry
is in microns.
The limits of integration are for the dry particles. Evaluating the integral, we obtain the source
function:
−13
F = 1.37 × 10
3.41
U10
kg
m2 s
.
(3.3)
In this parameterization we assume that the mass emitted from the sea surface is conned to
one size bin with the size distribution given by Eq. 3.2. The rate of wet and dry deposition, as
well as diusion strength, is assumed constant for all particles regardless of their radii. Such an
approach is similar to considering monodisperse size distribution with the particles whose radius
is close to 1 micrometer. Under these assumptions, the mass mixing ratio, together with the size
distribution described by Eq. 3.2, are the only parameters required to dene the sea salt state
in the atmosphere.
36
Chapter 4
Global sea salt modeling: results and
validation against multi-campaign shipboard
measurements1
In this chapter, we analyze and compare the results from the NAAPS model simulations to the
open ocean measurements of sea salt concentrations. Five dierent campaigns are used to validate
the sea salt parameterization in a numerical model: Aerosols99, INDOEX, ACE-Asia, NEAQS
2002, and NEAQS-ITCT 2004 experiments. The data set is unique as it comes from shipboard
measurements, which alleviate typical problems associated with onshore wave breaking on land
stations (surf zone). This is, to our knowledge, rst-of-a-kind attempt to merge ne resolution
sea salt observations with global simulations.
First, we discuss experimental methods and measurements techniques carried out by the
Pacic Marine Experimental Laboratory. Next section presents timelines of the measured and
modeled sea salt concentrations and other relevant meteorological parameters. After a discussion
of specic experiments, we follow with an analysis of the dataset as a whole. The nal part of
this chapter identies model-measurement discrepancies and suggests directions for further model
improvements.
4.1
Experimental methods
Concentrations of ambient air chemical components in the sub- and supermicron size ranges were
determined by the National Oceanic and Atmospheric Administration (NOAA) Pacic Marine
Environmental Laboratory (PMEL). Measurements were carried out on board a research vessel
Ronald B. Brown (Fig. 4.1). Aerosol particles were sampled 18 m above the sea surface through
a heated mast that extended 5 m above the aerosol measurement container (see Fig. 4.1). The
mast was capped with a horizontal inlet nozzle that was rotated into the relative wind to maintain
nominally isokinetic ow and minimize the loss of supermicron particles. Air entered the inlet
1
Most of this chapter is based on a published paper:
Marcin L. Witek, Piotr J. Flatau, Patricia K. Quinn, and Douglas L. Westphal, Global sea salt modeling:
Results and validation against multi-campaign shipboard measurements,
doi:10.1029/2006JD007779, 2007.
37
J. Geophys.
Res.,
112 (D08215),
CHAPTER 4. GLOBAL SSA MODELING: RESULTS AND VALIDATION
Figure 4.1: NOAA R/V Ronald B. Brown and the air samples collection facility.
through a 5 cm diameter hole, passed through a 7 degree expansion cone, and then into the 20
cm inner diameter sampling mast. The ow through the mast was 1
m3 min−1 .
Wind tunnel
tests have shown that the transmission eciency for particles with aerodynamic diameters less
than 6.5
µm (the larger size tested in wind tunnels) is greater than 95% (Bates et al., 2002).
particles in the 6.5 to 10
µm
For
size range, for which collection eciency is expected to decrease,
there may have been a loss in sea salt mass of up to 10%.
The lower part of the mast was heated to establish a stable reference relative humidity (RH)
equal to
55 ± 5%.
A stable reference RH allowed for constant instrumental size segregation in
spite of varying ambient relative humidity. Two-stage multijet cascade impactors (Berner et al.,
1979) were used at the lower end of the mast to collect atmospheric particles.
cuto diameters were 1.1
µm
and 10
µm
Aerodynamic
for sub- and supermicron size ranges respectively, with
the segregation at 55% RH. Sampling periods ranged from 4 to 6 hours. The ion chromatography
method (Quinn et al., 1998) was used to specify chemical composition of collected aerosol samples.
The analyzed components include sea salt, sulfate, nitrate, total organic carbon, elemental carbon
and mineral dust. Methodology of the chemical analysis is described in Quinn et al. (2001) and
Quinn et al. (2002).
Non-sea salt sulfate concentrations were calculated from
Na+
concentrations and the ratio of
sulfate to sodium in seawater. Sea salt aerosol concentrations were calculated as:
sea salt µgm−3 = Cl− µgm−3 + Na+ µgm−3 × 1.47 ,
where 1.47 is the seawater ratio of
−
Na+ + K+ + Mg+2 + Ca+2 + SO−
4 + HCO3
1978). This approach prevents the inclusion of non-sea salt
38
/
Na+ (Holland,
K+ , Mg+2 , Ca+2 , SO−
4,
and
HCO−
3
in the sea salt mass and allows for the loss of
assumes that all measured
Na+
and
Cl−
Cl−
mass through
Cl−
depletion processes. It also
are derived from seawater.
The equation that was used to derive sea salt mass concentrations explicitly includes the
concentration instead of assuming a
Cl−
Cl−
concentration based on the Na/Cl ratio in seawater. If
the Na/Cl ratio and measured Na are used to determine the sea salt concentration, the concentration may be overestimated as the amount of Cl that is lost by replacement with SO4 will not
be taken into account. Since the measured Cl concentration was used, the amount of Cl that is
lost by replacement with other acids was taken into account.
The upper limit for the supermicron size range, which is dened by the particle aerodynamic
radius of 5
µm
at 55% RH, corresponds almost exactly to the size limit for modeled sea salt
particles, which is 4
µm
(dry radius). Therefore, the total measured sea salt mass and the mass
modeled with NAAPS reect the same size range, allowing for direct comparisons.
Additional measurements made abroad included wind speed, wind direction, rainfall rate,
water temperature and water salinity. The true wind speed and direction were calculated from
measurements obtained with the Ships IMET wind sensor, mounted 14 meters above the sea
surface.
The true North and East components of the wind vector were calculated and then
averaged in 15 minute intervals. The true wind vector was calculated from these components
and is given as wind speed and wind direction in compass degrees. The measured wind speed,
additionally averaged over 6 hours encompassing the time of model output, was compared to the
wind speed from the model at 10 meters above the sea surface. The rainfall rate was measured
with a Scientic Technology Inc.
ORG-100 Optical Precipitation Intensity Sensor.
minute averaged data is reported in mm/hr.
The 15
The rain rate was compared to the rate of the
sea salt wet deposition inferred from the model, given in milligrams per square meter per 6
hours. The sea surface temperature and salinity were measured with the ship's online Sea-Bird
thermosalinograph. The inlet for the sample water into the thermosalinograph was near the bow
at an approximat depth of 4 m.
4.2
Results comparison with the observational data
In this study we analyzed data from ve eld campaigns conducted by the PMEL Atmospheric
Chemistry Group. Maps of the regions together with corresponding ship tracks are presented in
Figs. 4.2, 4.5, 4.7, and 4.9.
4.2.1
Aerosols99-INDOEX experiment
This project was conducted between January and March, 1999. NOAA R/V Ronald H. Brown,
equipped with the PMEL facility, sailed from Norfolk, Virginia to Male, Maldives via Cape Town
in South Africa and Mauritius in the South Indian Ocean.
The rst part of the experiment,
while the ship headed from the eastern coast of the US towards the Indian Ocean was named
39
Aerosols99, while its main part, after departing Mauritius and exploring the Indian Ocean, was
named INDOEX.
Fig. 4.3 (upper panel) presents comparisons between the measurements and NAAPS modeled sea salt mass concentrations during the Aerosols99 and the INDOEX experiments.
The
middle panel presents surface wind velocities measured from the ship during the cruise and those
predicted by the model. The lower panel shows rain rate (left axis) as observed from the ship
and the rate of sea salt wet deposition in the model (right axis).
There is good agreement between the two data sets presented in the upper panel of Fig. 4.3,
as indicated by the high correlation coecient equal to 0.75 for all data points. Average values
from the model and the measurements agree within 48% and 73% in the case of Aerosols99
and INDOEX respectively, indicating that NAAPS reproduces well the sea salt concentrations
measured at the surface. There are, however, instances where the model overestimates measured
concentrations.
In particular, model concentrations are too large for the period between days 18 25. During
this period concentrations over 20
µg/m3
were measured but no strong winds were observed.
Prior to this high loading episode, there was no observed rainfall that could have accounted for
the sea salt removal and decreased surface concentrations. There is also no evidence of increased
surface wind velocity in the radius of 150 km around the ship.
Bates et al. (2001) suggested
the inuence of the marine boundary layer (MBL) height on observed sea salt concentration
increase on day 25.
Radiosonde measurements (see Bates et al., 2001) of relative humidity
indicated that the MBL decreased to 1.3 km on day 25, whereas before that date it ranged from
2.0 to 2.5 km (Fig. 4.4). Reduced vertical mixing due to lower MBL height, under relatively
similar emission conditions, would result in increased aerosol concentrations. Such dependence
is expected for aerosols well mixed within the boundary layer, which can be coarsely assumed
for the measured and modeled particles considered in this paper. An independent study of Park
et al. (1990) showed such dependence, but the researchers also noted that the relation between
aerosol concentrations and mixing height depends on wind speed or atmospheric stability and
this can aect both the dilution of particles already present in MBL and the production and
subsequent entrainment of the aerosol particles upward. On the other hand, model predictions
of the MBL height did not show a substantial reduction in comparison to that observed from
radiosondes. This could explain why the modeled concentrations were relatively constant.
4.2.2
ACE-Asia experiment
The Aerosol Characterization Experiment (ACE-Asia) was conducted in March and April of
2001 in the Western Pacic region. The NOAA R/V Ronald H. Brown sailed from Hawaii to the
Japan Sea, performing a number of radiation and chemical measurements on board. Fig. 4.6,
upper panel, presents comparisons of measurements from the ship to the NAAPS sea salt mass
concentrations during the cruise. The middle panel shows time trends of measured and modeled
surface wind velocities, and the bottom panel presents rain rate measurements (left axis) and
40
Figure 4.2: Map and corresponding ship track during the Aerosols99-INDOEX experiment. The
blue line denotes regions where PMEL chemical measurements were taken.
Aerosols99−INDOEX
40
Aerosols99−INDOEX measurements
NAAPS mass concentration
[µg/m3]
30
Aerosols99 = 9.28 , NAAPS = 14.1 , R = 0.60
INDOEX = 4.86 , NAAPS = 6.19 , R = 0.83
all data = 6.74 , NAAPS = 9.56 , R = 0.75
20
0
15
NAAPS wind speed − local
Wind speed − ship measurements
5
10
[mm/h]
measured rain rate
Modeled wet deposition
5
0
0
2
1
20
30
40
50
60
Julian day of 1999
70
80
[mgm−2/6h]
10
[ms−1]
10
0
90
Figure 4.3: Top panel: comparison of the model concentrations with the measurements during
the the Aerosols99-INDOEX experiment; middle panel: measured and the NAAPS-modeled wind
velocity during the cruise; lower panel, the rain rate measured from the ship (left axis) and the
rate of sea salt wet deposition in the model (right axis).
41
Figure 4.4:
Relative humidity proles and horizontal wind vectors obtained from radiosonde
− 8
http://saga.pmel.noaa.gov/data/rsond.php?cruise=AEROINDO99.
measurements during the Aerosols99 experiment, Leg 1, 16 Jan
Feb, 1999.
Fig.
source:
modeled rate of sea salt wet deposition (right axis).
For the entire experiment, the average NAAPS sea salt mass concentration is almost two times
higher than the measured one. In particular, NAAPS overestimates the measured concentrations
between days 79 and 95 when the ship sailed over open ocean. On the contrary, near the shore and
in the Sea of Japan the model's predictions show much better agreement with the measurements.
Surface wind speed and deposition processes have to be considered to investigate the open
ocean discrepancy. Between days 79 and 95, the ship encountered the passage of several frontal
systems with intense storms and very strong surface winds.
velocity, averaged over 6 hours, reached 18
ms−1 .
On day 81, the measured wind
The measured average for this open ocean
−1 , almost twice as high as the wind speeds during the INDOEX and NEAQS
period is 8.9 ms
experiments (see Table 4.2). A reasonable explanation for too high model concentrations might
be related to weaknesses of the source function formulation, especially for strong surface winds.
The emission function, proportional to wind speed to the power of 3.41, is accurate for calm
and moderate conditions, but might fail in windy conditions, overpredicting emissions (Andreas,
1998).
Wet deposition processes may also contribute to the observed discrepancies. Ship measurements of rain rate indicate the presence of persistent rainfalls, but mostly of small magnitude,
during the open ocean period, with several more intense downpours associated with the frontal
passages. Not all of these precipitation events were captured by NOGAPS, therefore aecting
the magnitude of wet deposition.
42
Figure 4.5: Map and corresponding ship track during the ACE-Asia experiment. The blue dots
correspond to the mid-points of the observations.
ACE−ASIA 2001
70
ACE−Asia measurements
60
[µg/m3]
50
ACE−Asia average = 5.6
NAAPS average = 11.1
40
30
R = 0.71
20
10
15
5
[mm/h]
10
measured rain rate
modeled wet deposition
5
0
0
4
2
80
85
90
95
Julian day of 2001
100
105
Figure 4.6: Same as Fig. 4.3 but for the ACE-Asia experiment.
43
0
110
[mgm−2/6h]
10
[ms−1]
0
4.2.3
NEAQS-2002 experiment
The New England Air Quality Study (NEAQS) 2002 was a multi-institutional research project
focusing on the understanding of the atmospheric processes that control production and distribution of air pollutants in the New England region. It took place in July and August of 2002.
The NOAA ship R/V Ronald H. Brown was deployed into the Gulf of Maine and surrounding
waters with a complete set of chemical and meteorological sensors to track the transport and
transformation of air pollution. Fig. 4.8 presents comparisons between the experimental values
and the NAAPS modeled sea salt mass concentrations together with corresponding wind velocities obtained from the measurements and the model (middle panel) as well as the measured rain
rate and modeled sea salt wet deposition rate (lower panel).
For the entire experiment, the correlation coecient for measured and modeled sea salt mass
concentrations is equal to 0.55, the lowest of all experiments analyzed here (see Table 4.1). The
same tendency for NAAPS to overestimate the concentrations is observed. A large disagreement
between the model and the observations is noted during two events around day 204 and at
the end of the experiment. Both periods are characterized by surface winds stronger than the
average during the experiment.
Thus, we should observe an increase in the sea salt concen-
tration, assuming that particles were not removed by wet deposition.
Indeed, this increase is
seen in the model predictions, whereas the measurements indicate either a decrease or a small
amplitude increase of the sea salt concentration. Additionally, these cases were not aected by
wet deposition. Rain was observed on day 205, just when the winds were becoming calmer. For
the entire experiment, the average measured concentration is as low as 1.3
µg/m3
(see also Ta-
ble 4.1). These low concentrations suggest that whitecapping, being the main source of the sea
spray, did not occur often in the region. Many observations of the whitecap ratio, collected by
Lewis and Schwartz (2004), indicate that there is a threshold value of wind speed for the onset of
wave breaking. Suggestions for such a threshold value are about
∼35 m/s (Lewis and Schwartz,
2004) or larger (Stramska and Petelski, 2003), depending on other environmental parameters
aecting wave breaking. For the Gulf of Maine, due to the proximity of land and limited wind
fetch, the wind speed threshold may be on its higher end. In this case, sea salt emissions would
be lower than predicted assuming no threshold. Analyzing Fig. 4.8 from this perspective, we
conclude that the periods of winds stronger than the threshold for whitecapping might not have
been long enough to sustain higher background sea salt concentrations and to raise sea salt mass
concentration to local production-deposition equilibrium.
4.2.4
NEAQS ITCT 2004 experiment
The New England Air Quality Study Intercontinental Transport and Chemical Transformation
project was conducted between 5 July and 12 August of 2004.
The study was the continua-
tion of the NEAQS-2002 project with the focus on air quality along the Eastern Seaboard and
transport of North American emissions into the North Atlantic. The Gulf of Maine was again
44
Figure 4.7: Map and corresponding ship track during the NEAQS-2002 experiment. The blue
dots correspond to the mid-points of the observations.
NEAQS−2002
12
NEAQS−2002 measurements
10
NEAQS−2002 average = 1.3
NAAPS average = 2.8
[µg/m3]
8
6
R = 0.55
4
2
10
5
[mm/h]
10
measured rain rate
modeled wet deposition
5
0
[ms−1]
15
0
1
0.5
195
200
205
210
Julian day of 2002
215
220
Figure 4.8: Same as Fig. 4.3 but for the NEAQS-2002 experiment.
45
0
[mgm−2/6h]
0
Figure 4.9: Map and corresponding ship track during the NEAQS - ITCT 2004 experiment. The
blue dots correspond to the mid-points of the observations.
NEAQS−2004
12
10
NEAQS−2004 measurements
NEAQS−2004 average = 1.5
NAAPS average = 2.4
R = 0.84
[µg/m3]
8
6
4
2
10
[mm/h]
5
10
5
0
0
3
modeled wet deposition 2
1
0
200
205
210
215
220
225
Julian day of 2004
measured rain rate
190
195
Figure 4.10: Same as Fig. 4.3 but for the NEAQS-2004 experiment.
46
−1
15
[ms ]
[mgm−2/6h]
0
the operational eld for the NOAA research vessel Ronald H. Brown, deployed with the PMEL
instrumentation performing aerosol chemical measurements near the surface. Fig. 4.10 presents
the comparisons between the measured and modeled sea salt mass concentrations (top panel).
Accompanying surface wind velocities are plotted in the middle panel and the bottom panel
presents the measured rain rate and the modeled sea salt wet deposition rate during the cruise.
A high correlation coecient, equal to 0.84, is observed between the modeled and measured
values for the total of 86 comparison points. The average modeled sea salt mass concentration
is higher than the average of the observations, but the amplitude of peak concentrations during
specic events is often well preserved by NAAPS. Additionally, some disagreements between
the two data sets may be explained by occurring rainfall, which was not resolved in the model.
The rainfall aected the rst data point, higher modeled loading around day 192, and the events
between days 200 and 203. These cases are good examples of how rain events may have suppressed
the measured sea salt loading and lead to discrepancy between the model and observations. The
opposite trend is observed around days 194 and 217, where observations show temporary sharp
increases in the sea salt mass, exceeding the modeled values.
Despite no wind speed increase
during these days and small rainfall observed prior to the second episode, observations indicate
larger concentrations. We cannot oer a good explanation for these cases.
4.3
Fig.
Comparison with experimental data discussion
4.11 shows a scatter plot for the measured and modeled sea salt mass concentrations,
based on all the experiments discussed above.
excluding one outlier, it is 0.79.
The overall correlation coecient is 0.76 and,
All statistics, including the average measured and modeled
values from investigated PMEL experiments, together with corresponding correlation coecients
are presented in Table 4.1. The values in parentheses in Table 4.1 exclude one outlier point. The
average measured sea salt mass concentration is 4.6
equals 7.3
µg/m3 .
µg/m3
For concentrations higher than about 5
while that derived from NAAPS
µg/m3
deviations are substantial (see
Fig. 4.11), with NAAPS generally overestimating measured mass.
One possible reason for the disagreement is the representation of wet removal processes, due
to problems with moist thermodynamics in the global weather forecast model. To investigate the
role of precipitation, we excluded measurements aected by rainfalls from some comparisons.
In particular, Fig. 4.12 presents the correlation coecient (solid line, left axis) together with
the slope (dashed line, right axis) between measured and modeled sea salt concentrations as a
function of rain rate cumulative count (one outlier was omitted in the analysis).
Out of 358 data points, 252 are free of precipitation.
The correlation coecient for this
dataset increased from 0.79 to 0.87 for cases with no precipitation.
the impact of storm activity on model accuracy.
This result demonstrates
The relationship between the modeled and
measured sea salt concentrations is closer to 1:1 for dry cases (left side of the dashed line in
Fig. 4.12). For all data points, this relationship is approximately 1:1.3, mostly because of the
47
70
NAAPS results [µgm−3]
60
50
40
30
Aerosols99−INDOEX
20
ACE−Asia
NEAQS−2002
10
NEAQS−2004
0
0
10
20
30
40
50
−3
PMEL measurements [µgm ]
60
70
Figure 4.11: Measured versus modeled sea salt concentrations based on four PMEL experiments
(359 data points). The solid circles represent Aerosols99-INDOEX, open circles represent ACEAsia, open squares represent NEAQS-2002, and open diamonds represent NEAQS-2004 data.
Experiment
Measurements
average
µg/m3
Aerosols99
9.3
NAAPS average
Correlation
µg/m3
coecient: R
14.1
0.60
INDOEX
4.9
6.2
0.83
ACE-Asia
5.6
11.1 (10.1)
0.71 (0.75)
NEAQS-2002
1.3
2.8
0.55
NEAQS-2004
1.5
2.4
0.84
All data
4.6
7.3 (7.1)
0.76 (0.79)
Table 4.1: Surface sea salt concentration statistics from analyzed PMEL experiments measurements, model values and correlation coecient. The values in the parentheses exclude one
outlier point.
48
0.88
0.86
1.3
R
1.25
1.2
0.82
1.15
0.8
1.1
A
R
0.84
0.78
−3
10
−2
10
−1
10
RRcum [mm/h]
0
10
1.05
1
10
R (left axis)
A (right axis), according to the equation CONCmodel = A×CONCPMEL +
B , as a function of rain rate cumulative count RRcum . The lowest value of rain rate represent the
Figure 4.12: Modeled versus measured sea salt concentration correlation coecient
and linear t slope rate
situation when all observations with precipitation were excluded in the computation, whereas
the highest rain rate stands for the situation where all points were included in the computations.
One outlier point was omitted in analysis.
model overestimations during the ACE-Asia and the Aerosols99 experiments.
As suggested previously (ACE-Asia results), the discrepancies between the model and measurements might vary depending on surface wind conditions. In Fig. 4.13, the dierence between
the measured and modeled sea salt mass is plotted as a function of the local wind speed for points
without precipitation. Indeed, for stronger winds larger dierences are observed, although there
is a large scatter in the data.
Negative values, indicating NAAPS overpredictions, dominate
when the wind is stronger.
One possible mechanism for such a trend was already mentioned when the results from
the ACE-Asia experiment were discussed.
It is the strong nonlinearity of the source function
versus surface wind speed (power law with exponent of 3.41), which might lead to emission
overestimation for higher wind speed regimes (Andreas, 1998).
However, this trend can also
be associated with systematic errors related to the method by which dry deposition is handled
in the model. Due to the lack of deposition velocity dierentiation for dierent particle sizes,
a systematic excess of mass can be modeled.
For stronger wind speeds, with larger sea salt
loading, this systematic error may become substantial and dominate among factors determining
discrepancies. Systematic study of this eect is needed to assess its magnitude and to improve
the model.
Table 4.2 summarizes statistics of the observed and modeled wind speeds during all analyzed
experiments. The values in the parentheses reect similar statistics but with the precipitation
49
5
Diff (measured − NAAPS) [µg/m3]
0
−5
−10
−15
y = − 0.53*x + 1.3
−20
0
5
10
15
U10 [m/s]
Figure 4.13: Dierence Di between measured and modeled sea salt mass concentration as a
function of surface wind velocity
U10 .
Only points without precipitation inuence are considered.
The solid line represents linear t to the data.
cases excluded from the analysis. The total average is slightly higher for the measurements than
for the model, mostly due to underestimated NOGAPS wind velocities during the two NEAQS
experiments. Model agreement with observations is good: correlation varies from 0.69 to 0.83
depending on the project, with an average value of 0.81.
Two interesting facts can be inferred when statistics from Table 4.2 are analyzed together
with average sea salt values from the measurements.
First, we can see that the ACE-Asia average wind speed is higher than during the Aerosols99.
However, the average sea salt mass concentration is lower (5.6 versus 9.28
µg/m3 ), which contra-
dicts our expectations. Most likely, this is because during the Aerosols99 the ship cruised almost
all the time in the open ocean, whereas during the ACE-Asia it spent some time on the calmer
Experiment
Table 4.2:
Measurements
average
NAAPS average
Correlation
[m/s]
coecient: R
[m/s]
Aerosols99
6.6 (6.6)
7.1 (6.7)
0.69 (0.6)
INDOEX
5.0 (4.8)
5.0 (4.8)
0.79 (0.84)
ACE-Asia
6.9 (5.4)
6.9 (5.3)
0.88 (0.86)
NEAQS-2002
5.1 (5.0)
4.4 (4.3)
0.69 (0.62)
NEAQS-2004
4.8 (4.5)
3.6 (3.4)
0.83 (0.82)
All data
5.6 (5.1)
5.3 (4.8)
0.81 (0.8)
Surface wind speed statistics from analyzed PMEL experiments measurements,
model values and correlation coecient. The values in parentheses are for data points without
precipitations.
50
Japan Sea and closer to the shore, where wave characteristics are aected by the proximity of
land (Stramska and Petelski, 2003). Other factors like the dierence in the rain rate could also
be important, but on average, more rain was observed during the Aerosols99 than during the
ACE-Asia. It is also important that very strong wind speed episodes during the ACE-Asia experiment, sometimes exceeding 15 m/s, were not reected in high sea salt loadings. The measured
sea salt mass concentration never exceeded 20
µg/m3 .
An analogous situation is observed in the
case of the two NEAQS projects. The average sea salt mass concentration for NEAQS-2002 and
NEAQS-2004 are very similar (1.3 and 1.5
µg/m3
respectively, see also Table 4.1), despite the
fact that on average wind speed was stronger during the NEAQS-2002 (see Table 4.2).
Secondly, it is interesting to compare the NEAQS projects with the INDOEX experiment.
During the INDOEX, the average wind velocity was 5.0 m/s, i.e. similar to those observed during
the NEAQS experiments.
However, the sea salt measurements during the INDOEX indicate
much higher concentrations, with the average (4.9
NEAQS averages.
µg/m3 )
being a factor of 3-4 larger than the
The wet deposition cannot account for the low NEAQS values.
Similarly,
it is not likely that the dry deposition was so dierent in both regions to produce signicant
variation is surface concentrations.
It is evident from this example that besides wind speeds
there are other factors inuencing the sea salt surface concentrations. The proximity of a large
continent and the importance of advection from the source free direction can be important issues,
as indicated by Lewis and Schwartz (2004). Reid et al. (2001) concluded that during oshore
winds a steady state vertical distribution of sea salt particles was achieved at a distance of
∼35
50 km from shore, depending on the wind speed. Therefore, we investigated measurements from
both NEAQS projects to determine if they exhibit any relation to the average wind direction.
We divided the measurements into two advection categories: (a) o-shore, advection from the
land, dened by angles between 250
by angles between 70
200.
20, and (b) on-shore, advection from the ocean, dened
Table 4.3 presents statistics from the performed analysis.
Only
measurements without precipitation are considered. For both NEAQS experiments, the average
sea salt mass concentration measured during the on-shore advection is higher than during the
o-shore winds, 1.55
vs.
0.60
µg/m3
µg/m3
vs. 0.85
µg/m3
in the case of the NEAQS 2002, and 1.89
in the case of the NEAQS 2004.
µg/m3
These results seem to indicate that the sea
salt surface concentrations measured during the experiments were sometimes inuenced by the
vicinity of land. However, stronger winds from the on-shore direction, as indicated in Table 4.3,
can account for the dierences in some part.
This is especially apparent in the case of the
NEAQS 2004, where the dierence between average wind speeds from both directions is 1.2 m/s.
Therefore, an additional study needs to be performed to explain quantitatively the inuence of
land on the sea salt surface concentrations, even as far as tens or hundreds kilometers from the
shore.
The other aspects that can be of importance when comparing the INDOEX and NEAQS
averages are potential weaknesses of the assumed source function, as well as the possible variability in the whitecapping and emissions in both regions.
51
We already suggested the possible
NEAQS 2002
NEAQS 2004
Average
Average
Average
Wind
Number
Average
Wind
Number
Conc.
Speed
of Obser-
Conc.
Speed
of Obser-
µg/m3
[m/s]
vations
O-shore
1.55
4.8
15
On-shore
0.85
4.7
Total
1.21
5.0
µg/m3
[m/s]
vations
1.89
5.1
20
10
0.60
3.9
17
40
1.31
4.5
55
Table 4.3: Statistics from the NEAQS 2002 and NEAQS 2004 experiments computed for oshore and on-shore average wind directions and for the total experiment period. The on-shore
direction is dened by angles between 70 and 200 degrees, whereas the o-shore direction by
angles between 250 and 20 degrees, clockwise.
Only measurements without precipitation are
considered.
importance of threshold wind speed for the onset of wave breaking. The problem with this parameter is that it is not precisely determined by wind speed alone. Stramska and Petelski (2003)
noticed that the overall degree of whitecapping is determined by a combination of various conditions characterizing both wind and wave eld. Therefore, the onset of wave breaking cannot be
reduced to single parameter dependency. Stramska and Petelski (2003) also showed that wave
breaking depends on the duration of wind action, which is related to the sea state development.
They observed more whitecaps under developed seas than under rising seas or under decreasing
wind conditions. They also suggested that local conditions and regional sea state characteristics
inuence wave-breaking probability and, in result, sea salt emission.
Zhao and Toba (2001),
on the other hand, showed that the whitecap ratio is better described by the non-dimensional
breaking-wave parameter, dened as square friction velocity divided by peak wave angular frequency and kinematic viscosity of air. These and other studies (Hanson and Phillips, 1999; Xu
et al., 2000; Petelski et al., 2005) may suggest the need to incorporate parameters other than
wind speed to improve wave breaking and sea salt emission parameterizations. Our ndings also
support this necessity.
Other parameters identied to have potential inuence on the process of the SSA production
are sea surface temperature
Tw
and water salinity
S
(O'Muircheartaigh and Monahan, 1986;
Lewis and Schwartz, 2004; Stramska and Petelski, 2003). A recent study of Martensson et al.
(2003) shows the dependence of the SSA emission, originating from bursting air bubbles at the
water's surface, on water salinity and temperature. Consequently, the sea salt mass ux should
be larger for higher salinities and warmer sea waters. These laboratory results have not yet been
tested in the open ocean observations, despite several attempts to investigate the relationship
(Bortkovskii, 1987; Stramska and Petelski, 2003; Andreas, 1998).
We analyzed the data to
examine the temperature dependence on the surface SSA mass loadings. The eect of salinity is

not considered here due to a small span of salinities, which ranged between 30

and 37
.
In Fig. 4.14 the dierence between the measured and modeled sea salt mass concentration
is plotted as a function of the sea surface temperature. Only points without precipitations are
52
Diff(measured − NAAPS) [µg/m3]
5
0
−5
−10
−15
−20
5
Aerosols99−INDOEX
ACE−Asia
NEAQS−2002
NEAQS−2004
10
15
20
Tw [0C]
25
30
35
Figure 4.14: Dierence Di between the measured and modeled sea salt mass concentration as a
function of the water temperature
Tw .
Only points without precipitation inuence are considered.
considered. If there was a relation between the measured sea salt mass concentration and water
temperature, some trend should be observed on Fig. 4.14 as the model results do not account
for such dependency. We fail to observe any trend in our dataset. This indicates that the SSA
emission dependence on the sea water temperature cannot be concluded from our analysis. If
such dependence exists, more sophisticated measurements and model simulations are needed to
depict it.
4.4
Summary
In this study, we used open ocean measurements of the sea salt mass concentrations from ve
dierent campaigns, spread in time and space. This data set is from shipboard observations, free
from typical problems associated with the impact of surf zone on land stations measurements.
To investigate the validity of the sea salt parameterizations we employed a global forecasting
model and a transport model with detailed representation of dry and wet deposition, advection
and diusion as well as other physical processes. We show that inclusion of these processes leads
to good agreement with the shipboard measurements. The model's ability to predict surface sea
salt mass concentrations, measured by the correlation coecient, varied from 0.55 in the case of
the NEAQS 2002 to 0.84 during the NEAQS 2004 experiment. Combining all experiments, 359
data points were available for validation. For the combined dataset we obtained a correlation
coecient of 0.76. When 106 cases inuenced by precipitation were excluded from the analysis,
53
we obtained a correlation coecient of 0.87. Apart from the wet deposition uncertainties, the
model-measurement discrepancies were found to be inuenced by uncertainties in emissions at
high wind speeds.
Other suggested factors aecting the comparisons were the lack of a wind
speed threshold for the emission onset, and the lack of size dierentiation for deposition velocity.
Some of these aspects, like the wet deposition, were proved to reduce substantially the model's
accuracy.
On the other hand, the water temperature changes showed no discernible eect on
the measured sea salt concentrations and performed comparisons. The impact of other factors
requires further studies to determine their signicance and magnitude.
54
Chapter 5
Global sea salt aerosol emission: results from
multi-year model simulations
In this chapter we present results from nine years of global aerosol transport model simulations.
In particular, we are interested in time series of the total mass and total emission of SSA as well
as SSA global distributions.
5.1
Results
We performed NAAPS simulations for a period of nine years in order to analyze the SSA global
distribution. Analyzed meteorological data from the NOGAPS model was provided as input for
the simulations. Meteorological elds were updated every 6 hours, at 00, 06, 12, and 18 UTC.
The NAAPS equations (Eq. 3.1) were integrated using the fractional steps approach with a time
step set to 1800 s for all processes except for the vertical diusion, which was set to 900 s. A
shorter time interval for the vertical mixing was chosen to decrease aerosol concentration vertical
gradients imposed by emissions and to reduce numerical errors.
The SSA production in each of the nine years is presented in Table 5.1. The annual global
emission oscillates around
3×1012 kg and the interannual variability is lower than 20%.
of other investigators range from 0.3 to
30 ×
Estimates
1012 kg/year (Lewis and Schwartz, 2004). Such a
large discrepancy results from dierent, sometimes indirect methods of deriving the annual SSA
mass production.
Fig. 5.1 illustrates daily (red line) and monthly (black dashed line) averaged values of the total
sea salt mass in the atmosphere simulated by the model. Blue line on the same Figure shows daily
emission values (additionally averaged over one week to reduce day-to-day oscillations). Several
Year
1998
1999
2000
2001
2002
2003
2004
2005
2006
3.5
3.1
2.9
2.7
2.8
2.8
2.8
3.0
2.9
Annual
sea salt
production
12 kg]
[×10
Table 5.1: Annual global production of the sea salt mass for nine years encompasing the NAAPS
simulations.
55
CHAPTER 5. GLOBAL SSA EMISSION: MULTI-YEAR SIMULATIONS
9
11
x 10
Total sea salt mass − daily average [kg]
Global daily sea salt emission [kg/day]
10
Monthly average sea salt mass [kg]
9
8
7
6
5
4
Jan98
Jan99
Jan00
Jan01
Jan02
Jan03
Jan04
Jan05
Jan06
Jan07
Figure 5.1: Timelines of the total SSA mass (daily average - red line, monthly average - black
dashed line) and daily SSA production (weekly average - blue line), as simulated by NAAPS.
features can be observed in Fig. 5.1. First, one can notice that both the total mass and emission
are larger in the rst two years of the simulation, namely from 1998 to the beginning of 2000. The
sharp decrease in total mass, lasting from February to May, 2000, can be perceived as closing the
period of increased concentrations. To some extent, this behavior can be explained by changes
that were introduced to the NOGAPS model during that time. A new cumulus parameterization
scheme was transitioned into the model in May 2000 (Hogan et al., 2002), which inuenced
meteorological elds that drive the transport model. Although that modication is expected to
inuence behavior of the transport model and in particular, the total SSA mass and emission
values, it can not be considered as the only factor explaining the trend in 2000. For example, in
the beginning of 2005, a short-time amplitude decrease of similar amplitude to the one in 2000 is
observed. No NOGAPS changes took place at that time. Other sharp uctuations in total mass
are commonly observed throughout the simulation period. Furthermore, the change in cumulus
parameterization scheme took place in May 2000, therefore it could not account for most part of
the decreasing trend that is observed from the beginning of 2000.
The total mass peak in the beginning of 2000, followed by the analyzed decrease, ts well to
another pattern that can be seen in Fig. 5.1. This feature is a seasonal oscillation with maximum
values during northern hemisphere winters, at beginning of each year. Winter maxima, observed
56
11
3
x 10
[kg]
2.5
2
1.5
total emission − monthly values
NH emission
1
SH emission
0.5
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 5.2: Average monthly SSA emission modeled with NAAPS. Years 2001 - 2006 are analyzed.
Total emission - red line; southern hemisphere emission - blue line; northern hemisphere emission
- green line.
almost every year, are usually preceded by secondary mid-year peak with lower amplitude. For
instance, the peak in 2005 was followed by the maximum in the beginning of 2006. A possible
explanation for the oscillation could be assigned to a similar pattern in emission. The blue line in
Fig. 5.1, however, does not show such a seasonal variability. Moreover, as illustrated in Fig. 5.2
(red line), the model predicts only a small, less than 10% month-to-month variation in emission.
Enhanced production is modeled during the summer and early fall due to higher emission in the
southern hemisphere. This trend in the global emission is in contradiction to the modeled winter
maxima in the sea salt mass.
Therefore, only removal processes are left to account for these
maxima, presumably a seasonal variability in the wet deposition processes.
Fig. 5.3 presents an average distribution of the column integrated sea salt mass over nine
years. Maxima are observed between
400
and
500 S
latitude in the southern hemisphere and also
in the regions of active cyclogenesis in the mid-latitudes of the northern hemisphere. Relatively
low sea salt loadings are modeled close to the equator and in the tropics, due to low surface
wind speeds in those regions. Latitudinal distribution of the mean SSA surface concentration
over oceans, shown in Fig. 5.4, conrms small sea salt production in the tropics. Around the
equator surface concentrations of about 8
peak to almost 50 and over 30
µg/m3
µg/m3
are predicted, whereas mid-latitude maxima
in the south and north, respectively.
Column loadings
2
have a similar distribution, with the minimum of about 8 mg/m and the southern and northern
maxima at 40 and 27
mg/m2 ,
respectively.
Fig.
5.4 supports the formulation of a simple
approximate relation between the sea salt surface concentration and the column loading over the
57
Figure 5.3: Average distribution of the column integrated sea salt aerosol mass in the atmosphere
over nine years.
0.05
50
average SSA surface concentration
average SSA column loading
0.04
40
30
0.02
20
0.01
10
g/m2
µg/m3
0.03
0
−50
0
LAT [degree]
50
0
Figure 5.4: Latitudinal distribution of the average SSA surface concentration (right axis) and
the column integrated SSA loading (left axis). Only values over oceans are considered.
58
oceans. These two variables dier by about a factor of 1000, which provides a useful formula for
a rst guess assessment of the column loading based on the concentrations measured over the
ocean.
5.2
Summary
In this chapter, we briey discussed basic trends in the global sea salt production. The dataset is
unique in that it covers nine years of the sea salt simulations at one degree horizontal resolution.
To our knowledge, it is one of the longest series examined so far. The simulation was carried
out by the author on a single PC machine under the Linux operating system. The emitted SSA
mass exhibits less than 20% year-to-year variability, with the mean value equal to
3 × 1012
kg.
On average, the emission is strongest in July and August, due to enhanced production in the
southern hemisphere oceans.
The total SSA mass features seasonal oscillations in the model,
with maxima during the northern hemisphere winters, which is presumably caused by a seasonal
variability in the wet deposition processes. The lowest sea salt column loadings are modeled over
the tropical oceans, and the maxima are predicted over the mid-latitudes oceans of both southern
and northern hemispheres.
The modeled column loadings over oceans are almost 1000 times
higher than their corresponding surface concentrations, which provides a useful approximate
relationship between the two parameters.
59
60
Chapter 6
Coupling an ocean wave model with a global
aerosol transport model1
In this chapter, we propose a new approach to sea salt parameterization. Our aim is to develop
a sea salt emission function that incorporates wind-wave characteristics and can be employed
Such parameterization is based on theoretical
considerations, numerical simulations and global data analysis.
The new source function is
applied into the NAAPS model together with predictions from the global wave model Wave
Watch III. It is shown that the proposed emission parameterization has the potential to improve
simulations of sea salt emission in aerosol transport models.
The chapter begins with a description of the global wave model and an analysis of parameters
and data employed in this study.
Then, the new source function is introduced.
Next section
describes results from the global simulations and compares them to the PMEL Atmospheric
Chemistry group measurements dataset. This chapter ends with a thorough discussion of the
obtained results.
6.1
Wave model description
The following description of the WAVEWATCH III model is based on the user manual and system
documentation by Tolman (2002) and the information provided on the NOAA WAVEWATCH
III home page (http://polar.ncep.noaa.gov/waves/wavewatch/wavewatch.html).
WAVEWATCH III (Tolman, 2002) is a third generation wave model developed at the Marine Modeling and Analysis Branch (MMAB) of the Environmental Modeling Center (EMC)
of the National Center for Environmental Prediction (NCEP). It is a further development of
the model WAVEWATCH I, as developed at Delft University of Technology (Tolman, 1991)
and WAVEWATCH II, developed at NASA, Goddard Space Flight Center (e.g. Tolman, 1992).
WAVEWATCH III diers from its predecessors in all important aspects, such as the governing
equations, program structure, numerical methods and physical parameterizations.
1
Large part of this chapter is based on a published paper:
Marcin L. Witek, Piotr J. Flatau, Joao Teixeira, and Douglas L. Westphal, Coupling an ocean wave model with a
global aerosol transport model: A sea salt aerosol parameterization perspective,
doi:10.1029/2007GL030106, 2007.
61
Geophys. Res. Lett.,
34 (L14806),
CHAPTER 6. NAAPS WAVEWATCH III COUPLING
WAVEWATCH III solves the spectral action density balance equation for wave spectra described by wavenumber and direction. The implicit assumption of this equation is that properties
of medium (e.g. water depth and current) as well as the wave eld itself vary on time and space
scales that are much larger than the variation scales of a single wave. A further constraint is
that the parameterizations of physical processes included in the model do not address conditions
where the waves are strongly depth-limited. These two basic assumptions imply that the model
can generally by applied on spatial scales larger than 1 to 10 km, and outside the surf zone.
The main model features include:

the governing equations include refraction and straining of the wave eld due to temporal
and spatial variations of the mean water depth and of the mean current (tides, surges etc.),
when applicable;

wave propagation is considered to be linear. Relevant nonlinear eects such as resonant
interactions are, therefore, included in the source terms (physics);

parameterizations of physical processes (source terms) include wave growth and decay due
to the actions of wind (windwave interaction term), nonlinear resonant interactions (wavewave interaction term), and dissipation (whitecapping).
In shallow waters additional
processes of wave-bottom interactions (bottom friction) are included;

the model includes sub-grid representation of unresolved islands;
the model includes dynamically updated ice coverage;
the model uses a regularly spaced longitude-latitude grid (longitude and latitude increment
do not need to be equal) and, optionally, a Cartesian grid;

wave energy spectra are discretized using a constant directional increment (covering all
directions) and a spatially varying wavenumber grid.
The latter grid corresponds to an
invariant logarithmic intrinsic frequency grid;

the source terms are integrated in time using a dynamically adjusted time stepping algorithm, which concentrates computational eorts in conditions with rapid spectral changes.
The wave model provides output of 18 gridded elds of derived mean wave parameters (such
as the signicant wave height, direction, frequencies) and model input parameters (such as the
mean water depth, wind speed). Examples of such elds are presented in Fig. 6.1 and 6.2. These
are initial (00 hour) forecast elds of the signicant wave height (Fig. 6.1) and the peak wave
period (Fig. 6.2) from 12 UTC, August the 7th, 2004. Additionally, on Fig. 6.2 white arrows
display the wave directions.
Fig.
6.1 shows that increased values of the signicant wave height are a good proxy for
storm activity in those locations. Surface wind speed strongly inuences wave heights, but the
62
Figure 6.1: Signicant wave height from the NOAA/NCEP WWIII global model analysis for
August 7, 12 UTC, 2004.
Figure 6.2: Peak wave period and direction from the NOAA/NCEP WWIII global model analysis
for August 7, 12 UTC, 2004.
63
eect is mostly local and without wind action wave amplitudes quickly become smaller. Dierent
behavior is observed in case of the peak wave period (Fig. 6.2). The highest values are away from
the storm centers, as they do not correlate with the regions of increased signicant wave height.
Another characteristic feature visible in Fig. 6.2 is swell fronts marked by high contrast lines with
the reddish colour. The swell front comprises waves of the longest period (the highest energy),
which propagate with the highest velocity, according to the water wave dispersion relation.
6.2
Approach
Our approach is based on the whitecap method (Lewis and Schwartz, 2004, pp.
105).
The
size-dependent SSA production ux over the whitecap area, i.e. the dierential whitecap aerosol
productivity, is parameterized following the function presented by Monahan et al. (1986, eq. 4).
Integrated over dry particle sizes, it gives the sea salt mass emission per unit area per unit of
time. The multiplication by the fraction of sea surface covered by whitecaps (W ) completes the
emission parameterization. The whitecap ratio
W
2
W = aU10
where
U10
is dened as
Hs
,
Tp
[m/s] is the wind speed at 10 meters above the sea surface,
wave height,
Tp
[s] is the peak wave period, and
a s3 /m3
is proportional to the dominant wave orbital velocity
Hs
[m] is the signicant
is a constant. The parameter
Vorb = πHs /Tp ,
Hs /Tp
which is the velocity of
water at the air-sea interface in its circular movement in a wave.
Fig. 6.3 presents a conceptual model of the expected impact of the wave orbital velocity on
the wave breaking probability and the SSA production. Large and steep waves are more subject
to breaking than the small and at ones. It is anticipated that on average faster water movement
on the surface, reected by high values of
Vorb ,
would result in a higher probability of breaking,
taking into account the highly variable structure of the wave eld and complex wave interactions
which occur in real conditions.
Following the whitecap method approach (Lewis and Schwartz, 2004, pp. 105), the sea salt
mass emitted from the ocean's surface is
2
F = 1.055 × 10−2 U10
where
F
is expressed in
µg m−2 s−1
Hs
,
Tp
and is dened for particles with dry radius
0.1 ≤ rdry ≤ 4
microns.
The new source function is implemented into the NAAPS model, which was modied to
incorporate output data from the global wave model Wave Watch III (WW3) (Tolman, 2002).
The parameters that were used in NAAPS were the signicant wave height
wave period
Tp .
64
Hs
and the peak
Figure 6.3: Conceptual model of the anticipated impact of the wave orbital velocity on the wave
breaking probability.
Results of the simulations are validated against multi-campaign shipboard measurements of
the sea salt surface mass concentrations carried out by the NOAA Pacic Marine Environmental
Laboratory. The experiments considered here are Aerosols99-INDOEX, ACE-Asia, NEAQS-2002
and NEAQS ITCT 2004. Chemical measurements of sub- and supermicron mass concentrations were carried out on board of the NOAA research vessel Ronald B. Brown. The temporal
resolution was about 6 hours. The observations covered parts of the Pacic, Atlantic and Indian
Oceans.
6.3
Results
Fig. 6.4 presents a comparison of emission values obtained with the new and the wind-speed-only
dependent (Monahan et al., 1986; Witek et al., 2007) source functions. A sample output data
from the global
1×1.25 degree WW3 model (date:
7-Aug-2004, 12UTC; source: NOAA archives,
ftp://polar.ncep.noaa.gov/pub/history/waves) was used as a source of U10 , Hs and Tp .
The
new parameterization produces a large scatter in emissions for a given wind velocity. Emission
varies over one order of magnitude at 5 m/s and the spread gradually decreases as the wind
speed increases. This variation could partially explain the large scatter in emissions predicted
by the existing parameterizations (Andreas, 1998; Lewis and Schwartz, 2004). Lower emission
in the high wind speed regime concurs with previous suggestions (Andreas, 1998; Witek et al.,
2007) that Monahan's parameterization might overestimate emission under strong wind speed
conditions.
65
Figure 6.4: Sea salt mass emission as a function of the surface wind velocity. The black points
represent the reference Monahan's parameterization, whereas the gray points represent the proposed parameterization.
A detailed analysis of the measured and modeled sea salt surface mass concentrations from
the Aerosols99 and INDOEX experiments was performed.
Fig. 6.5 presents the PMEL group
measurements and the model results from the southern Atlantic Ocean (Aerosols99Leg 1).
Fig.
6.6 presents similar results but for data from the Indian Ocean (INDOEX). Fig.
6.6
(top) shows the observed and modeled sea salt surface mass concentrations during the cruise.
Two independent transport model simulations were performed: one with the reference Monahan
emission source function (blue dotted line) and the other with the proposed parameterization
(green dashed line). In Fig. 6.6 (bottom), the left axis shows the measured surface wind speed
(red solid line), averaged over the aerosol lter collection period (typically 6 hours) and the model
wind velocity (blue dotted line). The right axis in Fig. 6.6 (bottom) (green dashed line) shows
the wave orbital velocity
Vorb
during the cruise.
A rst-glance evaluation of the results reveals a good correlation between the wave orbital
velocity and the measured sea salt mass concentrations. Episodes of increased
higher aerosol loadings on days 29, 34 and 36. Lower values of
minimum sea salt mass concentrations.
Vorb
Vorb
correspond to
correspond to the observed
Such behavior is often observed despite local wind
conditions. For instance, around day 31 and 57 the winds were strong enough (over 6 m/s) to
expect increased aerosol concentrations. Such increase is not observed, though, suggesting that
the low
Vorb
might have suppressed aerosol production.
Fig.
6.6 illustrating another episode
between days 76-82 shows a possible inuence of the orbital velocity on the sea salt emission.
During that period the surface wind velocity gradually decrease from 11 to about 6 m/s. The
66
20
PMEL measurements
NAAPS ref.
15
NAAPS new
10
5
26
10
30
31
32
33
34
35
Vorb
36
37
38
1.2
1
modeled U10
8
U10
27
28 U 29
measured
10
0.8
6
0.6
4
0.4
2
0.2
0
25
26
27
28
29
30
31
32
33
34
35
36
37
38
Vorb
0
0
39
Day of 1999
Figure 6.5: (top) Sea salt surface concentrations during the Aerosols99 (Leg 1, southern hemisphere only) experiment. The solid line represents the PMEL group observations; the NAAPS
results from two simulations, one with reference emission parameterization and the other with the
proposed emission scheme, are presented by the dotted and dashed lines, respectively. (bottom)
Left axis, ship measurements (solid line) and the NOGAPS (dotted line) surface wind velocities;
right axis, wave orbital velocity (dashed line)
(Vorb = πHs /Tp ).
20
PMEL measurements
15
NAAPS ref.
NAAPS new
10
5
10
U10
8
54 56 58measured
60 62 U64 66 68 70 72 74 76 78 80 82 84 86 88
Vorb
10
modeled U10
1.2
1
0.8
6
0.6
4
0.4
2
0.2
0
0
52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90
Day of 1999
Figure 6.6: Same as Fig. 6.5 but for the INDOEX experiment.
67
Vorb
0
Aerosols99
INDOEX
ACE-Asia
NEAQS-2002
NEAQS-2004
48
83
37
39
52
Old
0.72
0.84
0.57
0.45
0.94
New
0.79
0.77
0.62
0.47
0.93
Number of
observations
Table 6.1: Correlation coecient between the PMEL group sea salt aerosol observations and the
NAAPS results from two simulation of each of the experiments. Òld' stands for the NAAPS
simulation with the reference emission function. `New' stands for the NAAPS simulation with
the proposed emission parameterization. The numbers of comparison points free of precipitation
are also given.
surface sea salt mass concentrations exhibit the opposite trend, rising from about 7 to 13
This increase corresponds well with the changes in
Vorb .
µgm−3 .
The transport model simulations show
that the proposed emission parameterization is able to capture the trend of the surface sea
salt mass concentration change during this episode, surpassing the accuracy of wind-speed-only
parameterization.
In Table 6.1 correlation coecients between the PMEL group sea salt measurements and the
NAAPS model results are presented. Cases where no precipitation was observed were selected.
For most analyzed campaigns, with the exception of the INDOEX, an improved or similar correlation is observed. The lower INDOEX correlation, however, contradicts the previous analysis
which indicated a better agreement with observations (see Fig. 6.6). This may indicate a limited
ability of the correlation coecient to fully characterize the model's performance. Therefore, a
detailed investigation of the concentration trends is needed to evaluate the model's performance
properly.
6.4
Discussion and conclusions
The squared surface wind velocity
U10
and the wave orbital velocity
Vorb = πHs /Tp
are shown
to be key parameters for the proposed parameterization of the sea salt emission. The parameter
Vorb ,
although new in such application, can be inferred from some previous papers. Studies of
Zhao and Toba (2001) and Petelski et al. (2005) suggest that emission is proportional to the
signicant wave height
Hs
(F ∝ Hs ).
Their arguments are additionally supported by the fact that
is proportional to the square root of the total wave energy
E.
Higher
E
value implies larger
dissipation, reected by more frequent wave breaking and, thus, enhanced aerosol production.
However, the inverse proportionality to the peak wave period
mented in the literature.
of a wave, therefore
F ∝ Tp−1
is not well docu-
The wave period is proportional to the square root of the length
Hs /Tp
is related to the wave steepness
S = Hs /L.
L
A more quantitative
argument can be derived from statistical analysis of the output data from the wave model. A
68
3
0.6
2
0.4
1
Hys
0.2
0
−1
0
x=−1
y=1
R=0.78
−0.2
−0.4
−2
−0.6
−3
−3
−2
−1
0
Txp
1
2
Figure 6.7: Correlation coecient between the surface wind velocity
and
Tpx Hsy Tp
x
, where
x
and
y
x
and
U10
and the product of
Hsy
range between [-3, 3].
correlation coecient between the surface wind velocity
Tp values (Hsy Tpx , where
3
y
U10
and a combination of the
Hs
and
ranged between [-3, 3]) was computed. The results are shown
in Fig. 6.7. One of the highest correlations
indicates a closer relationship between
U10
(R = 0.78)
and
Hs /Tp
was found with
x = −1
rather than between
U10
and
and
y = 1.
Hs
This
alone.
The presented parameterization for the sea salt emission function, due to the high correlation
between
Vorb
and
U10 ,
preserves the characteristics of the previously published wind-speed-only
parameterizations. At the same time, it introduces additional variability, as shown in Fig. 6.4,
dependent on wave characteristics and wave-state development. The computed orbital velocity
correlates well with the observed trends of the sea salt surface mass concentrations, often giving
better agreement than that based on the surface wind velocity only. Such behavior indicates a
potential for the use of
Vorb
in improving the emission parameterizations. Another novel aspect
of this work is that the new source function was applied into the NAAPS model together with
the predictions from the global wave model WW3. Parameters that were used in NAAPS were
the signicant wave height
Hs
and the peak wave period
Tp .
The results of the simulations were
validated against multi-campaign shipboard measurements of the sea salt aerosol, including the
Aerosols99-INDOEX, ACE-Asia, NEAQS-2002 and NEAQS ITCT 2004. The validations indicate good correlation between the simulated and measured sea salt surface mass concentrations.
Further research should be undertaken to validate these ndings and to assess the usefulness
of the sea salt emission parameterization as well as the advantages of the coupling between
transport models and global wave models.
69
70
Chapter 7
Numerical assessment of an optimal aerosol
size bin division scheme for the sea salt aerosol
in global transport models
In this chapter we investigate the inuence of size bin selection on the accuracy of the sea salt
modeling. This extended sensitivity study addresses an important but often neglected issue of
the impact of the size resolution on model results. Here, various multi-bin model simulations are
investigated in terms of the total sea salt mass and the average optical thickness. In addition, we
construct a size bin division algorithm that accounts for the size-dependent eciency of aerosol
deposition from the atmosphere.
The new scheme is compared to the typically used iso-log
division procedure.
We will begin with a detailed description of the dry deposition scheme, which has been applied to NAAPS in order to perform multi-bin model simulations. Next section introduces an
iso-gradient size division procedure. Section 7.3 describes a method of deriving optical properties based on simulated mass. Section 7.4 contains analyses of NAAPS multi-bin simulations
performed with two alternative division techniques.
Behavior of the total SSA mass and the
average optical depth are investigated. Also, the inuence of the number of size bins employed
in a simulation is examined.
7.1
Dry deposition velocity model
The parameterization of particles dry deposition is based on the concept of dry deposition velocity
Vd
(Slinn and Slinn, 1980; Slinn, 1982; Zhang et al., 2001; Lewis and Schwartz, 2004).
This
approach has already been described in several publications, however, due to some modications
made specically for NAAPS and for the sake of clarity, the concept is described herein.
The theory originally developed by Slinn and Slinn (1980) divides the lowest part of the
atmosphere into two virtual layers: the surface layer (up to about 10 meters above the surface)
and the viscous sublayer (rst few centimeters of the atmosphere). Particles mix between the
upper and lower layer, but actual deposition takes place only in the lower layer. In each layer
dierent processes inuence aerosol transport and depletion. In the viscous sublayer, where the
71
CHAPTER 7. OPTIMAL SSA SIZE BIN DIVISION SCHEME
actual ground deposition takes place, the key processes involve the Brownian diusion, impaction,
and gravitational settling. In the surface layer, the dominant processes are the turbulent mixing
and gravitational settling, which force particles to move towards the lower viscous sublayer. All
these mechanisms are parameterized by corresponding transfer velocities which strongly depend
on particle size and meteorological conditions.
The overall size-dependent dry deposition velocity
Vd
[m/s] in the model is described by the
following equation:
Vd =
where
VV VS
,
VV Vturb
(7.1)
VV = VgV +Vdiff +Vimp and VS = VgS +Vturb are the cumulative deposition velocities [m/s]
Vdiff
in the viscous sublayer and the surface layer, respectively.
Vimp
is the impaction velocity,
Vturb
is the Brownian diusion velocity,
is the turbulent diusion velocity,
VgV
and
VgS
are the
gravitational sedimentation velocities in the viscous sublayer and the surface layer, respectively.
These two sedimentation velocities are distinguished because of dierent relative humidities in
the two layers.
A constant 98% RH is assumed in the viscous sublayer over oceans, whereas
the RH in the viscous sublayer over land and in the surface layer is computed on the basis of
temperature and pressure.
The gravitational settling velocity in each layer is represented by the Stokes velocity:
Vg =
2ra2 ρgCc
,
9µair
where ra [m] is the particle radius at the ambient RH (particles are assumed to be spherical), ρa
g/m3 is the particle density, g m2 /s is the acceleration due to gravity, Cc is the Cunningham
correction factor and
µair
[Pa s] is the dynamic viscosity of air. The Cunningham factor is not
negligible only for small particles (ra smaller than 1
µm) and is expressed as (Seinfeld and Pandis,
1998):
Cc = 1 +
6.6 × 10−8 1.257 + 0.45 exp −1.667 × 107 ra .
ra
The air dynamic viscosity, according to the Sutherland equation (Sutherland, 1893), is a
function of air temperature and is given by (White, 1991):
µair (T ) =
where
b
is a constant,
110.4 [K] and T
b = 1.458 × 10−6 kgm−1 s−1 K−1/2 , S
[K] is air temperature. Both
use of the growth function:
rh
bT 3/2
,
T +S
ra
and
ρa
is the Sutherland constant,
depend on RH;
ra (rh) = rdry f (rh). f (rh),
ra
can be derived with the
dened as the ratio of particle radius at
relative humidity to its dry equivalent, is described as (Lewis and Schwartz, 2004):

2 × 0.67/ (1 − rh) 41
f (rh) =
2 × 0.54/ (1 − rh) 13
72
S =
rh ≤ 0.93 ,
0.93 < rh ≤ 0.98 .
The change in particle density with
polynomial
rh
is approximated with the following second order
ρa (rh) = −0.11 rh2 − 0.45 rh + 1.61
to ensure proper behavior of the product
(see Fig. 2.3). Such approximation was chosen
ρa (rh) f (rh)2
(a part of the Stokes velocity equation)
for the full RH range.
The Brownian diusion velocity
Vdiff
is typically parameterized in terms of the Schmidt
number:
Vdiff = u? Sc−p ,
where
u?
p
[m/s] is the wind friction velocity, and
type (the value
1/2
surface categories).
(7.2)
is a coecient which accounts for the surface
is used for open water and snow/ice areas, whereas
Sc
2/3
is used for all other
(dimensionless) represents the ratio of the kinematic viscosity of air to
the Brownian diusivity of an aerosol particle. In practice,
Sc
can be approximated as (after
Lewis and Schwartz, 2004):
Sc ≈ 3 × 1012
(ra /µm)
.
Cc
The impaction accounts for the inability of a particle to respond rapidly to a non-uniform
ow near the surface. It plays considerable role only for larger particles
impaction velocity
Vimp
(ra > 1 µm).
The inertial
is parameterized as:
Vimp = u? 10− /St ,
3
where
St
is the dimensionless Stokes number. For numerical purposes, the Stokes number can
be approximated by (Lewis and Schwartz, 2004):
12
St ≈ 0.004 × 10
ra
µm
2 U10
ms−1
The turbulent transfer or turbulent diusion velocity
2
Vturb
.
accounts for mixing processes
in the surface layer which transport particles towards the lower viscous sublayer. An accurate
parameterization of this transfer velocity is important since, in general, it is a very ecient
mechanism for particle removal. The turbulent transfer mostly depends on atmospheric stability
and surface roughness. For neutral atmospheric conditions (assumed here), the expression for
Vturb
is:
Vturb =
where
k = 0.4 is the von Karman constant, u? [m/s] is the wind friction velocity, z is the reference
height for wind velocity (10m) and
The surface roughness
z0
z0
[m] is the roughness length of the surface.
in each of the model grids is computed according to the formula:
z0 =
where
ku?
,
ln (z/z0 )
vegi
X
vegi z0i ,
is a fraction of vegetation type in the grid and
73
z0i
is the prescribed roughness length
Winter
Spring
Summer
Fall
1. Coniferous forest
0.90
0.80
0.80
0.90
2. Deciduous forest
0.55
0.75
1.05
0.95
3. Cultivated land
0.02
0.05
0.10
0.02
4. Grassland
0.02
0.05
0.10
0.05
5. Urban
1.00
1.00
1.00
1.00
6. Swamp
0.02
0.03
0.15
0.10
f (u? , U10 )
7. Open water
8. Snow/ice
0.01
Table 7.1: Surface roughness length
z0i
0.01
0.01
0.01
[m] for 8 land categories during 4 dierent seasons.
corresponding to each vegetation type. Eight surface types (Table 7.1) are dened in the model.
Roughness lengths for these vegetation types varyies with season and are taken after Voldner
et al. (1986) with some modications after Zhang et al. (2001).
All values in Table 7.1 are
prescribed except for the open water category, where the following equation is applied:
U10 k
f (u? , U10 ) = 10 exp −
u?
.
This parameterization is derived from the logarithmic wind prole under neutral atmospheric
stability.
Fig. 7.1 pictures all components of the dry deposition velocity (averaged over model domain
180x360) computed with the use of sample meteorological elds from the NOGAPS global circulation model (date 1 July 2006, 00 UTC). Many deposition velocities exhibit large spreads
within each size category (only average values are presented), up to several orders of magnitude,
due to dierent meteorological conditions. The average trends agree well with those presented
by Lewis and Schwartz (2004) and Foret et al. (2006). Average
Vd
exhibits a strong dependence
on the particle size and may change by three orders of magnitude from 0.1
is a minimum in
Vd
µ
m to 30
µ
m. There
at about 0.3 microns. As small increase towards smaller sizes is caused by
the Brownian diusion mechanism, the velocity increase towards larger sizes is caused mainly by
the gravitational sedimentation and impaction. In general, Fig. 7.1 shows that the described parameterization can reasonably resolve the dry deposition velocity under variable meteorological
conditions taken from a global atmospheric circulation model.
One of the characteristics of the described parameterization is the dierence in dry deposition
velocity over the land and over the ocean, which is clearly visible in Fig. 7.2. This dierence is
caused by the assumption that in the viscous sublayer the ambient RH over the ocean is 98%,
whereas over the land is the same as in the surface layer, which is usually lower than 98%. This
assumption causes particles to have larger radius over the ocean than over the land. This, in
turn, enhances the impaction and gravitational settling and results in a higher deposition velocity
over the ocean. However, this mechanism does not explain a larger dierence in the deposition
velocity in the lower submicron regime (particles smaller than 0.5 micron in radius).
In this
size regime, the impaction and gravitational settling are much less eective than the Brownian
74
Figure 7.1: Average dry deposition velocity and dierent transfer velocities calculated from the
described model as a function of the ambient aerosol radius.
Averaging was done over values
based on the NOGAPS meteorological elds (180x360 data points).
For a description of the
symbols see the text.
Figure 7.2: Average dry deposition velocities in the model as a function of the ambient aerosol
radius. The land and ocean values are indicated by the open squares and the open diamonds,
respectively. The solid circles indicate averaged land-ocean values.
75
diusion, which plays a key role in particles deposition. The observed trend can be explained by
the fact that dierent parameterizations are used for the open water area and all other surface
categories. Coecient
p
in Eq. 7.2 is set to
1980; Zhang et al., 2001) and to
7.2
2/3
1/2
for the open water area (after Slinn and Slinn,
for other land types (Slinn, 1982; Foret et al., 2006).
Size bin division method
A typical approach in aerosol transport models is to divide size spectra in terms of the iso-log
size intervals, i.e. following a constant geometric progression of the particle radius. Foret et al.
(2006) showed limitations of such an approach and proposed an alternative iso-gradient size
segregation procedure. This method relies on the size-dependency of the dry deposition velocity.
Foret et al. (2006) applied this method to mineral dust aerosol and showed its usefulness in a
simple box model numerical study.
The method described in this dissertation follows the iso-gradient concept introduced by
Foret et al. (2006), but uses a dierent procedure of determining the size ranges of dierent bins.
Conceptually, this proposed method minimizes dierences in the dry deposition velocity at the
edges of each size interval.
In other words, constrained by a limited number of size bins, the
method forces the dry deposition velocity to be as much constant as possible within the limits
of each bin. This minimizes the deposition variability of particles in a given bin.
Determining the limits of size bins in the iso-gradient division method is performed using
the procedure described below. This technique can be applied to any given size range and any
shape of the deposition velocity function. However, in this manuscript the procedure is described
for a size range spanning from
rmin = 0.2 µm
up to
rmax = 8
microns. This choice corresponds
to the size range of Monahan's emission function at 80% RH.
First, a deposition velocity
an average
Vd
Vd
has to be established within specic size limits. Here we use
derived numerically from the model described in 7.1. This function is presented
in Fig. 7.3 with the red dashed line (right axis). The solid black line (left axis) in Fig. 7.3 shows
the normalized deposition velocity change as a function of radius,
k∇Vd k.
Note the dierence in
vertical scales on the right and left axes. Normalization is dened as:
r80max
ˆ
∇Vd (r80 ) dlog (r80 ) ,
Ψ=
r80min
and then:
∇Vd
k∇Vd k =
Ψ
r80max
ˆ
⇒
k∇Vd k dlog (r80 ) = 1 .
r80min
It is important to notice that integration over
log (r80 )
rather than
r80
is performed. This
allows for a better representation of the sub-micron size range, where deposition velocity changes
considerably. The area below
k∇Vd k
shown in Fig. 7.3 integrates to unity and can be divided
76
Figure 7.3: Right axis, average dry deposition velocity (dashed line) derived numerically from
the model described in 7.1. Left axis, normalized gradient of the dry deposition velocity (solid
line). Dotted vertical lines divide the area below
k∇Vd k
into four equal segments.
into a desired number of equal area segments using a simple procedure.
Mathematically the
limits of equal area parts are derived progressively from the equation:
r(i+1)
ˆ
k∇Vd k d log (r) =
1
,
n
r(i)
where
n is the number of size bins, i = 1, 2, . . . , n + 1, and r (1) = rmin , r (n + 1) = rmax . Fig. 7.3
is an example of the division into four equal segments (cf. vertical dotted lines). These division
lines dene the limits of the size bins.
The sea salt mass emission within a size bin is derived according to the equation:
4
Fdry (i) = πρss
3
rî+1
2
ri
where
dF 3
rdry drdry g/m2 s ,
dr80
ρss = 2.2 × 106 g/m3 is the dry sea salt density and dF/dr80
(7.3)
is Monahan's equation (see
Eq. 3.2). The equivalent radius of particles assigned to a bin (reqv [µm]) is obtained by using a
weight function in the form
3 dF/dr .
vd (r) rdry
80
It is expressed by:
 r
  ri+1

ˆ
 î+1

dF 3
dF 3
reqv (i) = exp 
log (r) vd (r)
rdry dr / 
vd (r)
rdry dr ,


dr80
dr80
ri
ri
77
(7.4)
where
vd (r)
is given by Eq. 7.1. Such an arbitrarily adopted weight function accounts for the
deposition velocity variability and the emitted mass size distribution.
Fig. 7.4 presents the average size dependent dry deposition velocity (top panel) and size bins
limits (lower panel) for a various number of divisions. Size intervals both for the iso-gradient
(thick colored lines) and the iso-log (black dotted lines) division methods are presented for
comparison. Detailed information about the size intervals, corresponding equivalent radii, and
emission values are collected in Appendix A.
For two size bins, the division is at about 1 micron regardless of the method used, which can
be interpreted as the ne and coarse aerosol mode selection. As more sections are introduced,
the iso-log procedure generates equal-length bin intervals in the logarithmic progression.
In
the case of the iso-gradient division procedure, it can be observed that as the number of size
intervals increases the regions of highly variable
Vd
are more accurately covered with new bins.
This is reected by the tightening of the bins in the critical area at around 1 micron size range
(see Fig.
7.4).
On the other hand, all particles smaller than 0.5
interval even when as many as 22 size bins are used.
all these particles, according to the shape of the
Vd
µm
are enclosed in one size
This can be explained by the fact that
function, deposit at a similar rate.
The
enumerated features of this bin division technique highlight its advantage over the previous isolog method. The generated iso-gradient size intervals are characterized by the minimal variability
−2
Vd [m/s]
10
−3
10
−4
10
0
10
22
12
6
2
0.2
0.4
0.6
0.8
1
2
3
4
5
6
7 8
r80 [µm]
Figure 7.4: Top panel, average dry deposition velocity (see Section 7.1). Lower panel, size limits
for a set of 2, 6, 12, and 22 size bins derived with the iso-gradient size division procedure (thick
colored lines). The black dashed lines above the colored lines denote a similar set of boundaries
but for the iso-log size intervals.
78
in the deposition rate of all particles enclosed in a bin. This, in turn, is expected to improve the
accuracy of model simulations. We will verify this statement in next sections.
7.3
Sea salt optical properties model
In this section, we discuss the SSA optical properties in each size interval. The optical properties
required in the two-stream approximation of the radiative transfer calculations for solar radiation
are the extinction coecient
g.
σext ,
the single scattering albedo
ω,
and the asymmetry parameter
Since sea salt particles are nearly non absorbing in the visible spectrum, the single scattering
albedo is assumed to be 1.
The extinction cross-section
σe m2
of a sea salt particle with the radius
r
is given by:
σe = πr2 Qext ,
where
Qext (2πr/λ, m)
parameter
x = 2πr/λ
(7.5)
is the nondimensional extinction eciency being a function of the size
and real part of the refractive index (m̃
= m + ik ),
where
λ
[m] is the
wavelength of scattered light. Sea salt particles are non absorbing, therefore the imaginary part
k
of the refractive index is omitted in the formulation of
Qext .
For spherical particles,
Qext
can
be derived from the Lorentz-Mie theory (Bohren and Human, 1983; Hulst, 1957).
The asymmetry parameter for a single particle is dened as:
ˆ
2π
ˆ
π
ge =
0
where
P (Θ)
0
P (Θ)
cosΘsinΘdΘdφ ,
4π
is the nondimensional scattering phase function. The phase function describes the
angular distribution of scattered energy and is dened in such a way as to fulll the relation:
ˆ
2π
0
where
Θ
ˆ
0
π
P (Θ)
sinΘdΘdφ = 1 ,
4π
is the scattering angle. It can be noticed that the asymmetry parameter is dened as
the rst moment of the scattering phase function or the average cosine of the scattering angle.
Typically, many aerosol particles in a given unit of volume need to be considered. For that
reason, the more useful extinction coecient is dened as:
2
σext = πr Qext N (r)
where
1
m
,
N (r) is the number of SSA particles of radius r per unit volume.
(7.6)
In real situations, aerosol
particles have dierent sizes. For a collection of particles in a size range between
79
rmin
and
rmax ,
Eq. 7.6 takes form of the following integral:
rˆmax
πr2 Qext n (r) dr ,
σext =
(7.7)
rmin
where
n (r) = dN (r) /dr
1/ m3 µm
is a SSA number concentration function, that is the
number of SSA particles per unit volume in a unit interval of
r.
The corresponding asymmetry parameter for a collection of particles is obtained from
´ rmax
πr2 Qext n (r) ge dr
r
g = ´min
.
rmax
2
rmin πr Qext n (r) dr
(7.8)
The extinction weighting rule for the asymmetry parameter is general and will have equivalent
forms for other formulations of the extinction coecient presented in this section.
In the case of highly hygroscopic SSA particles, the change in size of a solution droplet with
ambient RH needs to be taken into account in the derivation of optical properties. Therefore, a
transformation relation into a dierent RH that conserves the total number of particles needs to
be formulated. This transformation takes the form:
n (ra ) dra = ñ (r80 ) dr80 .
Here we also used referential RH of 80%.
reference RH. The index
a
ñ (r80 )
is the number concentration function at that
denotes ambient RH. Eq. 7.9 can be rewritten as:
n (ra ) =
where the ratio
(7.9)
ξa = dra /dr80
ñ (r80 )
,
ξa
(7.10)
is equivalent to the previously dened growth function (see Sec-
tion 2.2).
The extinction coecient dened by Eq. 7.7 can then be rewritten as:
rmax,a
ˆ
πra2 Qext (RH) n (ra ) dr
σext (RH) =
rmin,a
rmax,80
ˆ ξa
2
=
π [r80 ξa ] Qext
2πr80 ξa
, m (RH)
λ
ñ (r80 )
dr .
ξa
(7.11)
rmin,80 ξa
Such a formulation allows for the derivation of
concentration
ñ (r80 )
at 80% RH is known.
σext
at any RH, given that the SSA number
It should be noticed that the transport model
simulates the SSA dry mass instead of the number concentration
ñ (r80 ).
Additionally, the use
of a limited number of size bins precludes modeling a continuous
ñ (r80 )
function. In order to
reconstruct the
ñ (r80 )
function, the emission source function
80
dF/dr
is used. That information
can be employed assuming that the shape of the size-dependent source function describes entirely
the distribution of aerosol particles within a bin. This assumption allows for a direct and accurate
derivation of the aerosol optical properties based on the model output.
To facilitate this approach, we will introduce the concept of a virtual particle which will help
reconstruct
ñ (r80 ).
Such a virtual particle is constructed as a single unit which is described by
µ
nvp (r) [1/
some predened size distribution probability function
and
r2
m] dened for radii between
r1
and which fullls the relation:
ˆr2
nvp (r) dr = 1 .
(7.12)
r1
The area and mass of a virtual particle are computed as integrals of second and third moments
of its size distribution:
ˆr2
2
Avp = π
nvp (r) r dr ,
mvp
4
= πρ
3
r1
ˆr2
nvp (r) r3 dr .
(7.13)
r1
Additionally, the extinction cross-section
σe,vp
ˆr2
πr2 Qext
σe,vp =
is derived from equation:
2πr
, m nvp (r) dr .
λ
(7.14)
r1
The corresponding asymmetry parameter of such a particle can be obtained using a weighting
procedure similar to the one presented in Eq. 7.8.
The predened size distribution probability function which describes a virtual particle can
be any function that satises the condition 7.12. Here it is assumed to have the same shape as
Monahan's emission source function. To fulll the condition 7.12, the emission function
dF/dr
(see Eq. 3.2) has to be normalized, which is given by:
nvp (r) =
dF
dr
ˆr2
dF
/
dr .
dr
r1
Let us notice that function
nvp (r)
nvp (r)
is dened at 80% RH. For any other humidity values, the
is scaled according to Eq. 7.10 with the use of the growth function
virtual particle encompassing the range of sizes
r80 ∈ (0.2, 8) [µm],
In the case of a
that is the sizes for which
Monahan's function is dened, the extinction cross-section at 80% RH
σe,vp
ξa .
(ξa = 1.0)
is:
ˆ8.0
1
2πr
−12
2
= πr Qext
, m (80%) nvp (r) dr = 8.2186 × 10
.
λ
m
0.2
The mass of the pure sea salt of such a virtual particle, without the addition of water, is derived
81
n-bin bin rdry range [µm]
2
0.1 − 0.675
0.675 − 4.0
0.1 − 0.415
0.415 − 0.675
0.675 − 1.548
1.548 − 4.0
0.1 − 0.354
0.354 − 0.484
0.484 − 0.675
0.675 − 1.099
1.099 − 2.192
2.192 − 4.0
1
2
1
4
2
3
4
1
2
6
3
4
5
6
Table 7.2: Extinction cross-section
size intervals.
σext
mvp [g]
σe,vp
2.5913 · 10−13
3.3279 · 10−12
7.6124 · 10−14
1.8301 · 10−13
1.1828 · 10−12
2.1451 · 10−12
5.1401 · 10−14
6.0392 · 10−14
1.4734 · 10−13
5.3018 · 10−13
1.4569 · 10−12
1.3408 · 10−12
and dry mass
mvp
1
(RH = 80%)
m
2.1975 · 10−12
6.0212 · 10−12
1.1068 · 10−12
1.0907 · 10−12
3.3821 · 10−12
2.6391 · 10−12
9.1621 · 10−13
4.9832 · 10−13
7.8295 · 10−13
1.9073 · 10−12
2.7775 · 10−12
1.3364 · 10−12
of virtual particles dened for dierent
The shape of the size distribution function is based on Monahan et al. (1986)
emission parameterization (see Eq. 3.2). Division of the original range (0.1
− 4.0
dry radius)
into 2, 4 and 6 size bins is presented.
from combined Eqs. 7.13 and 7.10 (the growth parameter
mvp
4
= πρ
3
ξa = 0.5
for dry particles):
ˆ4.0
nvp (r)
dr = 3.587 × 10−12 [g] ,
r3
0.5
0.1
where the salt density
ρ
is set to
2.2 × 106 g/m3 .
Table 7.2 presents pairs of extinction cross-
section and dry mass for several dierent size bins. The limits of the size bins were derived using
the iso-gradient division procedure described in Section 7.2.
Having the mass of a virtual particle, the NAAPS generated sea salt mass concentration
within a bin can be transformed into the total number of such particles:
ρsea salt
.
mvp
Nvp =
(7.15)
In the next step, the actual extinction coecient is computed by using a modied version of
Eq. 7.6:
σext = Nvp σs,vp .
(7.16)
Eqs. 7.15 and 7.16 complete the derivation of the SSA optical properties. The aerosol optical
depth is then computed according to the equation:
τ=
X
σext (zi ) δzi ,
i=1,nlev
where
δzi
is the height of the i'th model layer and
in the i'th model layer.
82
σext (zi )
is the predicted extinction coecient
9
6
x 10
5.5
[kg]
5
4.5
2 bins
4 bins
6 bins
8 bins
10 bins
15 bins
4
3.5
3
01
07
13
19
25
31
July 2004
06
12
18
24
30
August 2004
Figure 7.5: Total sea salt dry mass as simulated by the model with various numbers of size bins.
All simulations are initiated with the zero aerosol mass (cold-start) and are for the time period
of July 1 - August 13, 2004. The simulation with 15 size bins extends until August 31, 2004.
7.4
Results
In this section, the NAAPS simulations carried out with the use of multiple SSA size bins are
investigated and analyzed. The performance of the two dierent bin division techniques - the
commonly used iso-log method and the iso-gradient method - is tested. The analyzed quantities
include the total SSA mass and the average sea salt optical thickness.
7.4.1
Total sea salt mass analysis
Fig. 7.5 presents the sea salt dry mass as simulated by the model with various numbers of size
bins. The iso-gradient size division method was applied in these simulations. Mass accumulation
can be observed at the beginning of each model run, which is related to the initialization process
(cold-start). The vertical axis in Fig. 7.5 is scaled to show only values above
3 × 109
kg. The
simulations were performed for the period between July 1 August 13, 2004. The simulation
with 15 size bins, which is extended until August 31, demonstrates that a local maximum in
the total mass is modeled on August 12, 2004. This solves a potential controversy regarding the
sharp mass increase around August the 10th, which could be falsely interpreted as an additional
mass accumulation in the model. Further analysis will be limited to the date August 13, 2004.
Fig. 7.5 indicates that there is the total mass increase in the model when more size intervals
are introduced. However, dierences between the simulations become smaller with an increase in
the number of size bins and the simulated mass seems to converge to some value. For example,
83
9
2.5
x 10
2
sea salt mass [kg]
0.1−0.35 µm
0.35−0.5 µm
0.5 − 0.7 µm
0.7 − 1.1 µm
1.1 − 2.2 µm
2.2 − 4.0 µm
bin 1:
bin 2:
bin 3:
bin 4:
bin 5:
bin 6:
1.5
1
0.5
0
01
07
13
July 2004
19
25
31
06
12
August 2004
Figure 7.6: Sea salt mass in each of the bins in the 6-bin model simulation. Size intervals were
derived using the iso-gradient division procedure. Colored arrows on the right hand side denote
relative mass changes at the end of the simulation as compared to the relative mass proportions
derived from the emission function.
the simulations with 10 and 15 size bins generate very similar sea salt mass and the dierences
between them can be considered negligible. In this dissertation the simulation with 15 size bins
is selected as reference for various analyses.
Fig. 7.6 presents the sea salt mass contributions from each of the size intervals in the 6-bin
simulation. The highest contribution, constituting about 40% of the total mass, is modeled in
the 5th bin. Somewhat surprisingly the mass in bin 6 is more than twice lower even though there
are similar emissions (5.55
× 1012
and
5.11 × 1012 kg/m2 s ,
see Apendix A) in both bins. This
is caused by stronger deposition processes aecting larger particles in the sixth bin.
To assess the relative mass change in each size bin during the simulation versus the mass
ratios at emission, vertical arrows are drawn on the right hand side of Fig. 7.6. The model output
at the last day of the simulation is analyzed. The mass values are extended with dotted lines.
In Fig. 7.6 beginnings of arrows are placed at the levels determined by the total modeled mass
multiplied by a fraction of emission within a bin derived from the emission function. Therefore,
they are at the positions which would be observed if all particles were deposited at a constant
rate. These positions are proportional to the emission ratios for each of the bins. The arrowheads
point to the actual modeled mass values at the end of the simulation. An arrow's direction has
a specic interpretation.
A facing-up arrow indicates a relative mass accumulation in a bin,
whereas a downward pointing arrow is interpreted as a relative loss of mass in a bin, caused by
deposition excess over emission.
As can be seen in Fig. 7.6, in the rst four size bins relative mass accumulation is observed.
84
In the 6th bin, on the other hand, a large relative mass decrease is simulated (cyan arrow). The
aerosol mass buildup is expected in the case of small particles for which the deposition processes
are weak. This buildup can result in a situation where a size bin with less ecient mass emission
gains more mass than a bin where emission is stronger, but deposition plays a more dominant
role. Bins 4 (green line) and 6 (cyan line) are examples of such situations, where at some point
the mass in bin 4 surpasses that in bin 6. Another example are bins 1 (red line) and 2 (blue
line). Even though less mass is emitted in bin 1, the simulated values quickly overtake the mass
modeled in bin 2. Again, less ecient deposition processes allow for more mass buildup in bin 1.
Aerosol accumulation in a bin results in a smooth mass change within that bin, which is only
slightly aected by temporal variations in global emissions. For example (Fig. 7.5), the mass
changes in bins 1, 2, and 3 show almost negligible temporal variability despite substantial spikes
in the total mass. Temporal variations are due to larger particles with a shorter lifetime, which
prevents them to be accumulated.
Fig. 7.7 presents relative dierences between the total mass and the mass obtained with 15
size bins. The iso-gradient and iso-log cases are represented by solid and dashed lines, respectively. The reference 15-bin simulation has iso-gradient divisions.
In all cases a mass decit is observed; there is up to 20% less mass when 2 size bins are used
instead of 15. The higher number of size bins, the better accuracy of the simulations - the mass
decit is smaller than 1% in the case of 10 size intervals. Fig. 7.7 shows that simulations with
the iso-gradient division method are more accurate than simulations with the iso-log divisions.
For instance, 8 iso-gradient size bins are required to reproduce the accuracy obtained with 10
iso-log intervals.
The reasons behind the mass increase with an increasing number of size bins (Fig.
7.5)
require further explanation. What processes are responsible for the situation in which a ner
resolution generates more, not less, sea salt mass? To address these questions we constructed a
simple conceptual model of mass deposition caused by the dry processes.
In this 0-dimensional model, we assume that the mass reduction in a bin is only a function
of the deposition velocity multiplied by the emission value:
Mdep,i = Fi v̄d,i .
Here
Fi
is given by Eq. 7.3 and represents the sea salt mass emitted within a size bin;
characteristic velocity at which the mass
The characteristic deposition velocity
assigned to a bin,
r̄eqv ,
v̄d,i
Fi
is deposited, and index
i
v̄d,i
is the
denotes a given size bin.
is strictly related to the equivalent radius of particles
as derived from Eq. 7.4. Moreover, since most of the deposition occurs
over oceans, the eective radius at 80% RH is provided as an argument to the deposition velocity
v̄d,i .
According to the sea salt growth function,
The characteristic deposition velocity
r̄eqv,80 = 2 r̄eqv .
v̄d,i (r̄eqv,80 )
is then obtained with the use of Eq. 7.1.
In order to derive the total mass deposition, the following summation over all `n' size bins has
85
0
−2
−4
−6
%
−8
−10
2 bins
4 bins
6 bins
8 bins
10 bins
−12
−14
−16
−18
−20
01
07
13
19
25
31
July 2004
06
12
August 2004
Figure 7.7: Mass decit (in percent) obtained from simulations with various numbers of size
bins. The reference simulation has 15 size intervals. Simulations with the iso-gradient divisions
are plotted as solid lines; the iso-log divisions are plotted as dashed lines.
to be evaluated:
Mdep =
X
Fi v̄d,i (r̄eqv,80 ) .
(7.17)
i=1,n
For a given emission and dry deposition functions,
Mdep
depends only on the value of
is the number of size intervals at which the initial size range is divided.
Mdep
n,
that
can also be derived
in a similar way to that in Eq. 7.17, using continuous forms of emission and deposition functions:
Mdep,a
4
= πρss
3
ˆr2
1
dF 3
r v̄d (r) dr ,
ξdry dr80
r1
where
ρss = 2.2×106 g/m3 is the dry sea salt density, dF/dr80
ξdry = 0.5
is the growth function for dry particles and
v̄d (r)
is Monahan's emission function,
is the average deposition velocity
derived from Eq. 7.1.
In Fig. 7.8, the black line represents the relative dierence, with the minus sign, between
Mdep (n) and the analytically derived Mdep,a .
The red and blue lines on the same gure represent
relative mass decits on August 13, 2004, obtained with various numbers of size bins.
The
simulation with 15 size intervals was used as reference. Simulations with the iso-gradient division
are represented as the blue line and the iso-log divisions as the red line.
The results of the
conceptual model (black line) indicate that the more size bins are used, the less mass is lost in
the model. In other words, a coarser resolution in representing the deposition processes leads
86
0
−2
−4
−6
%
−8
iso−log bins
−10
iso−gradient bins
−12
deposition excess,
with the minus sign
−14
−16
−18
0
2
4
6
8
10
12
14
16
Number of size bins
Figure 7.8: Relative mass decit (in percents) on the last day of the simulations as a function
of the number of applied size intervals. The red and blue lines represent simulated mass decit
on August 13, 2004, obtained with the iso-log and iso-gradient size intervals, respectively. The
black line is the conceptually derived mass deposition excess, with the minus sign, computed
according to the Eq. 7.17.
to deposition which is too high, or excess of deposition in the model. In the case of a one size
interval, almost 10% more mass is deposited, as compared to the
Mdep,a
value. The results of the
conceptual mass deposition model provide a compelling explanation for the total mass increase
with the rising number of size bins observed in Figs. 7.5 and 7.7.
The mass decit simulated with the global model, as can be observed in Fig. 7.8, is almost
three times higher than the deposition excess obtained with the conceptual deposition model.
This could be assigned to the whole complexity of the global model, where mass deposition is
inuenced by many mechanisms not included in a simple, 0-dimensional scheme. However, as
the number of size bins increases, global simulations converge to the conceptually derived errors.
The simulations with 10 size bins already provide sucient accuracy, with the error being smaller
than 0.5% when the iso-gradient bin division procedure is applied.
7.4.2
Average sea salt optical depth analysis
The NAAPS simulations performed with various numbers of size intervals and the two alternative
size division methods were further analyzed with the optical code described in Section 7.3. We
investigated the global average sea salt optical thickness (AOT) at 550 nm wavelength.
In Fig.
7.9, computed AOT is presented with each line representing a simulation with a
dierent number of size bins. As it can be seen, the average sea salt AOT reaches about 0.05 for
most detailed simulations. Also, a converging trend, similar to the one presented in Fig. 7.5, is
87
0.05
0.04
AOT
0.03
2 − bins
4 − bins
6 − bins
8 − bins
10 − bins
15 − bins
0.02
0.01
0
01
07
July 2004
13
19
25
31
06
August 2004
12
Figure 7.9: Trends of computed average AOT at 550 nm wavelength as derived from the NAAPS
simulations with various numbers of size bins. Size intervals are derived using the iso-gradient
division procedure.
40
AOT contribution [%]
35
bin 1:
bin 2:
bin 3:
bin 4:
bin 5:
bin 6:
30
25
20
0.1−0.35 µm
0.35−0.5 µm
0.5 − 0.7 µm
0.7 − 1.1 µm
1.1 − 2.2 µm
2.2 − 4.0 µm
15
10
5
0
01
07
13
19
25
July 2004
31
06
12
August 2004
Figure 7.10: Trends of AOT contribution, in percent, from dierent bins in the 6-bin model
simulation. Size intervals are derived using the iso-gradient division procedure.
88
observed. An important dierence between the trends in total mass (Fig. 7.5) and average AOT
(Fig. 7.9) is that the AOT exhibits less temporal variability. For instance, the over 10% mass
increase between August 6-13 corresponds to the less than 5% AOT increase during the same
period of time. This is related to the relative impact of fractional AOT from individual bins.
Fig. 7.10 illustrates the relative AOT contribution from each bin in case of the 6-bin model
simulation. About one-third of the total AOT is generated by the smallest particles (red line).
The particles with dry radius smaller than 1.1 micron constitute about
3/4 of the modeled AOT.
This large contribution of submicron particles explains relatively smooth temporal trends of the
total AOT: the sea salt mass modeled in these bins, due to mass accumulation, exhibits weak
temporal variability (see Fig. 7.6).
The impact of bin 6 on the total AOT is of the order of 5% despite a relatively wide size
range of particles included in that bin. On the other hand, bin 5 contributes about 20% of the
total AOT even though the mass emission in both bins is of similar magnitude (see Appendix A).
The AOT dierence in bins 5 and 6 can be understood as follows. The mass simulated in bin
5, due to less ecient deposition, is more than twice higher than in bin 6 (see Fig. 7.6). This
mass dierence results in a similar change in the number of particles, given a comparable weight
of virtual particles in both bins (1.46
× 10−12
and
1.34 × 10−12
g).
Also, the virtual particle
extinction cross-section in bin 5 is more than twice higher than in bin 6 (see Table 7.2). These
two factors result in a 4-fold dierence in the total AOT contribution of bins 5 and 6.
Fig. 7.11 illustrates relative dierences between the AOT obtained with various size intervals
versus the simulation with 15 size bins. Simulations with the iso-gradient and iso-log divisions
are presented as solid and dashed lines, respectively.
Fig. 7.11 shows that the simulations with 2 size intervals underestimate the average AOT
by more than 35% with respect to the reference run. When the AOT averaging is performed
only over oceans (results not shown), this dierence reaches 50%. A comparison with Fig. 7.7
indicates that the AOT dierences are larger than the corresponding total mass dierences. Such
AOT underestimations can substantially inuence radiative forcing estimates and assessment of
the planetary albedo.
Fig. 7.11 illustrates that the proposed iso-gradient division scheme oers some advantage for
6 and more bins. A lower division resolution results in slightly smaller errors for the iso-log
simulations. This is because smaller particles, which have dominant impact on AOT, are better
represented by the iso-log intervals in the case of a limited number of size bins (see Appendix A).
For ner divisions, the iso-gradient size division leads to more accurate results, analogically
to the total mass case. Also, it should be noted that the simulated AOT exhibits more regular
convergence with respect to the number of size bins in the case of the iso-gradient division method.
The convergence of the iso-log simulations is less stable, which is expressed by a similar 5%
dierence in the 6 and 8 division simulations. Performed simulations and analyzes show that the
proposed iso-gradient size division procedure has advantages over the iso-log scheme.
89
0
−5
−10
−15
%
2 bins
−20
4 bins
6 bins
−25
8 bins
−30
10 bins
−35
−40
01
07
13
19
25
31
July 2004
06
12
August 2004
Figure 7.11: Average AOT relative dierence, in percent, obtained from simulations with various
numbers of size bins. The reference simulation has 15 size intervals. The solid lines represent
model runs where the iso-gradient size division procedure was applied, whereas the dashed lines
represent the corresponding iso-log simulations.
7.5
Summary
In this chapter, we investigated the impact of the SSA size resolution on the accuracy of the
global model simulations. This problem is often inadequately addressed in the existing literature.
The NAAPS multi-bin simulations reveal total mass decit close to 20% and AOT underprediction of about 35% when one examines the least accurate two bin simulation versus the fteen
size interval simulation which we assume to be our reference.
size intervals are needed to assure 5% accuracy.
It is suggested that at least 8
A conceptual model of the mass deposition
is constructed in order to identify possible sources of uncertainties.
The observed dierences
are related to the mass deposition excess caused by low size resolution.
We also show that a
choice of the bin division method inuences the model performance. Sensitivity studies with the
proposed iso-gradient division procedure show improved accuracy of the simulated sea salt mass
and average optical thickness in comparison to the more commonly used iso-log size intervals.
It is found that up to twice fewer iso-gradient size bins are required to reproduce the results
of the iso-log simulations. In addition, the iso-gradient simulations exhibit better and more
regular asymptotic convergence to the reference results. The impact of the size resolution on the
simulated radiative forcing will be evaluated further in chapter 8.
90
Chapter 8
Sea salt aerosol radiative forcing
In this chapter, we describe a simple model of the SSA direct clear sky radiative forcing. Based
on that model and on data from the global sea salt simulations, we derive and analyze average
radiative forcing values.
Two types of transport data are employed in this section.
The rst
one is a long-term, one-year simulation for 2004 with the SSA parameterization based on the 10
iso-gradient size intervals. This dataset is analyzed in terms of the annually averaged parameters
and global distributions of the sea salt optical properties and radiative forcing values. The other
data type is based on a collection of short-term simulations with various numbers of size intervals.
These results are employed to analyze the impact of the size resolution on predicted radiative
forcing.
8.1
Simple model of the direct aerosol forcing
The direct radiative forcing is dened as the dierence between the net radiative uxes in the
absence and presence of aerosols in the atmosphere:
A = F↓ − F↑
aerosols
− F↓ − F↑
clear
.
It is therefore the perturbation of the Earth-Atmosphere system radiative budget caused by
the aerosols. Here
F↓
and
F↑
are the downward and upward propagating uxes (irradiances),
obtained by integrating the radiation intensity over the hemispheric solid angles:
ˆ
F
↓(↑)
=
I (µ, ϕ) µdµdϕ ,
0
where
0(1)
ˆ
2π
−1(0)
µ is the cosine of the solar zenith angle, ϕ is the solar azimuth angle, and I (µ, ϕ) represents
the solar radiation intensity integrated over the solar spectrum.
For the top of the atmosphere (TOA), the radiative forcing takes a simplied form due to
equal downward radiative uxes in both the aerosol-burdened and the aerosol-free scenarios:
AT OA = FT↑ OA
clear
91
− FT↑ OA
aerosol
.
CHAPTER 8. SSA RADIATIVE FORCING
Now let us consider a simple one-layer aerosol model (Seinfeld and Pandis, 1998) below which
there is a surface characterized by reectivity
Rs
(Fig. 8.1). Additionally, let us assume that the
solar radiation interacts only with the aerosol (molecular scattering is neglected) and the sun is
directly overhead. For the aerosol layer, we can dene transmission
t and reection r
coecients,
which are expressed as:
where
τ , ω,
and
β
t = e−τ + 1 − e−τ ω (1 − β) ,
(8.1)
r = 1 − e−τ ωβ ,
(8.2)
are the optical properties which characterize the aerosol layer:
the opti-
cal depth, the single scattering albedo and the backscatter coecient, respectively.
In order
to substitute the backscatter coecient with the asymmetry parameter, the approximation of
Wiscombe and Grams (1976) is used:
β = 0.5 (1 − g).
Figure 8.1: Solar radiative budget in the aerosol layer
Using the notation of aerosol transmission t, aerosol reectance
r, and surface reectivity Rs ,
and allowing for multiple scattering between the surface and aerosol layer, the aerosol forcing at
the TOA can be expressed as (Markowicz, 2003):
AT OA =
FT↓ OA
Rs − r −
Rs t2
1 − rRs
.
(8.3)
Eq. 8.3 together with Eqs. 8.1 and 8.2 complete the simple model by which the SSA forcing at the
TOA can be derived. The optical properties are derived based on the optical parameterization
software described in Section 7.3 and the NAAPS simulated aerosol concentrations. The unknown
parameters are the incident solar ux at the top of the atmosphere
averaged surface albedo
Rs .
92
FT↓ OA
and the spectrally
At this stage further assumptions regarding these two parameters are made. The main focus
is on deriving an annually and globally averaged SSA radiative forcing.
The monthly mean
values of the surface albedo and the mean solar elevation are obtained for each latitude and
longitude position. Surface reectivity is interpolated to 1 degree horizontal resolution from the
2.5 degree surface albedo database obtained from the International Satellite Cloud Climatology
Project web site
http://isccp.giss.nasa.gov
(Rossow and Shier, 1999). The average solar
↓
↓
elevation is used to derive the FT OA parameter, according to the equation FT OA = F0 sin (ψ),
−2 and ψ is the average solar elevation angle given in
where F0 is the solar constant 1378 Wm
radians.
8.2
Results
8.2.1
Global distributions in 2004
In order to derive the annual average global SSA radiative forcing, the NAAPS simulation with
10 iso-gradient size intervals for the year 2004 was performed. A spin-up time was set to one
month.
In Fig. 8.2 global distribution of the SSA optical thickness at 550 nm wavelength is presented.
The results are averaged over one year (2004). They follow closely the distribution of the average
column integrated SSA mass, presented in Fig. 5.3. The smallest sea salt AOT is modeled over
tropical oceans, with values of about 0.02.
Such small AOT is not exceptional, and is often
measured and reported in the literature (Wilson and Forgan, 2002; Halthore and Carey, 2006;
Sakerin et al., 2007; Smirnov et al., 2006). The highest sea salt AOT values are located in the
40-50 degree latitude belt of the southern hemisphere and in the regions of active cyclogenesis in
mid-latitudes of the northern hemisphere, over the Atlantic and Pacic Oceans. The top values
reach 0.15 over southern oceans but do not exceed 0.1 in the northern hemisphere.
How does the average distribution of the sea salt AOT aect the TOA radiative forcing?
Results from the simple radiative forcing model are presented in Fig.
8.3.
Radiative forcing
exhibits similar horizontal distribution, which is more uniform than optical depth though. This
is caused by increased forcing in the sub-tropical regions. Such areas as the Arabian Sea and
Mid-Indian Basin in the Indian Ocean, the Philippine Sea, Central Pacic Basin, Peru and Chile
Basins in the Pacic Ocean, the Caribbean Sea, Cape Verde Islands, Angola and Cape Basins
in the Atlantic Ocean are characterized by intensied radiative forcing, despite their relatively
low sea salt optical depths. This is caused by higher values of the downward solar radiative ux
FT↓ OA
in these regions, related to the higher solar elevation.
The low TOA forcing values in most parts of the Tropics are a visible manifestation of the
tropical convergence zone and the associated low surface wind velocities. However, the resulting
average forcing is not the lowest around the Equator. As presented in Fig. 8.4, the longitudinal
average at 0 degree latitude is about
and below about 60 degree South.
−1 Wm−2 ,
and exceeds all values above 30 degree North
Despite the lowest average sea salt AOT at the Equator
93
Figure 8.2: Global distribution of the SSA optical thickness at 550 nm, averaged for 2004. Land
and glacier mask is applied.
Figure 8.3: Distribution of the sea salt aerosol top of the atmosphere clear sky shortwave radiative
forcing. Land and glacier mask is applied.
94
−3 −2.5 −2 −1.5 −1 −0.5
0
80 N
TOA forcing W/m2
60
40
20
sea salt AOT
0
−20
−40
−60
−80 S
0.02
0.05
0.08
0.11
0.14
Figure 8.4: Latitudinal distribution of the average sea salt TOA clear sky shortwave radiative
forcing (left panel) and the average sea salt optical thickness at 550 nm (right panel), obtained
from the 2004 simulation with 10 iso-gradient size intervals.
(0.02), the high solar elevations and
FT↓ OA
are able to raise the forcing values to a modest level.
An opposite inuence is observed at high latitudes, especially in the southern hemisphere. Low
solar elevation values are one of the factors responsible for the observed shift in the location
of the AOT and TOA forcing maxima.
The sea salt optical depth picks at about 60 degree
South whereas the forcing maximum is shifted 10 degrees North, at 50 degree South. Another
factor is the change in the surface albedo caused by the presence of sea ice around the Antarctic
Continent. Enhanced surface albedo results in smaller, i.e. less negative TOA forcing values, as
the aerosol layer reectivity is dimmed by brighter background of the surface.
Table 8.1 summarizes average statistics from the 2004 simulation, presenting the annual global
mean sea salt optical depth, the asymmetry parameter and the clear sky direct radiative forcing.
The table also lists a comparison of the direct clear sky forcings in various studies. In general, the
global forcing value obtained in this study is lower than previous predictions. However, given a
large spread in the various forecasts present in the literature, our result appears to be well within
the range of results of other studies.
8.2.2
Multi-bin simulations
We obtained similar AOT data series with various numbers of size intervals (2, 4, 6, 8 and 15)
but for shorter simulation times. The performed simulations cover the period between July 1 August 13, 2004. Additionally, to eliminate errors related to the cold-start and model spin-up
time, only the period of the last 2 weeks of the simulations, namely August 1 - 13 , 2004 is
analyzed.
We assume here that the global TOA forcing values obtained in such a simplied
manner are representative to assess relative dierences between simulations.
95
Description
Value
Mean sea salt AOT
0.043
0.767
−1.22
−1.52
−2.2
−0.6 to −2.2 (low wind)
−1.5 to −4.0 (high wind)
−1.51 (low wind)
−5.3 (high wind)
Asymmetry parameter
Radiative forcing
W
m2
Other clear sky radiative
forcing estimations
W
m2
Reference
This study
Ma et al. (2008)
Grini et al. (2002)
Winter and Chylek (1997)
Haywood et al. (1999)
Table 8.1: Global statistics from the 2004 sea salt simulation with 10 iso-gradient size intervals
and a comparison of the global annual mean sea salt clear sky direct radiative forcing in various
studies.
Table 8.2 presents the relative sea salt TOA forcing dierences as derived from the simulations
with various numbers of size intervals. The results dier from the most accurate 15 bin simulation
by about 2% in the case of the simulation with 10 size intervals and up to 33% in the case of the
least accurate 2 bin simulation. These values are in close agreement with the results obtained
for the average sea salt optical thickness (see Fig.
7.11).
The AOT underestimation has a
similar relative magnitude to that of the radiative forcing. Such behavior becomes clearer when
the average asymmetry parameters in each of the simulations are considered.
averaged
g
values are almost identical in all simulations.
These globally
Therefore, only the average AOT
values can account for the dierences in the average forcing, which explains their similar relative
variations.
Number of iso-gradient size intervals
TOA forcing decit
2
−33 %
−12 %
−5 %
−3 %
−2 %
4
6
8
10
Table 8.2: Relative dierences in the sea salt TOA forcing between simulations with various
numbers of size intervals. The simulation with 15 size bins is used as reference.
It is somewhat unexpected to nd the same values of the asymmetry parameters in the multiple bin simulations. The computed equal average asymmetry parameters in all simulations are
somehow against common sense expectations. We expected dierences on that grounds since a
better size resolution should result in a change in the size distribution of modeled particles, and
thus in a modied
g
value. This mechanism is indeed observed between the performed simula-
tions, especially when individual grid points are examined. However, these changes are usually
small and have a limited impact on the magnitude of the asymmetry parameter. Additionally,
horizontal and vertical averaging procedures applied to the data eectively reduce any variations in the asymmetry parameter distributions. We anticipate that forcing computations with
96
a more sophisticated radiative transfer code would show stronger dependence on the modeled
asymmetry parameter values.
8.3
Summary
In this chapter, the SSA clear sky shortwave radiative forcing is derived and analyzed. A simple
scheme of the radiative forcing is constructed, based on a one-layer aerosol model, monthly
averaged surface albedo and solar elevation values. The average results from the 2004 NAAPS
simulation with the 10 iso-gradient size intervals indicate that the climate forcing by SSA is
negative and equals to -1.2
Wm−2 .
This value corresponds to the average AOT at 550 nm equal
to 0.043 and the asymmetry parameter 0.767. The set of simulations with various numbers of size
bins reveals a large, up to 33%, forcing underestimation, caused by an inadequate representation
of the aerosol size distribution. This result points to the need for a more careful consideration
of the issue of size resolution in global modeling, which is shown to be an important source of
uncertainty in climate predictions.
97
98
Chapter 9
Conclusions and outlook
This study addresses some of the key problems related to the physics and prediction of SSA
in the atmosphere.
This component of the atmospheric aerosol has been long recognized to
play an important role in many aspects of the geophysical, chemical and radiative processes
occurring in the atmosphere. It is one of the most abundant aerosol types present in the marine
boundary layer. Understanding SSA and representing it in models is central to reducing the key
uncertainties in many aspects of the atmospheric sciences and in understanding climate behavior.
The objective of the research performed in this thesis was to improve our understanding of
the SSA life cycle in the atmosphere. These goals were approached by numerical simulations,
analysis of experimental data, theoretical derivations, and sensitivity studies. The results cover
several aspects of the aerosol research, which can be categorized as follows:
1. Use of high temporal and spatial resolution ship measurements to validate the sea salt
emission source function and performance of a global aerosol transport model.
2. Development of a new approach to the sea salt emission parameterization which incorporates wind-wave characteristics into the emission function and that can be employed
3. Development and numerical investigation of a new size bin segregation scheme that can
eciently and adequately represent the behavior of SSA in global simulations.
4. Investigation of the sea salt aerosol shortwave radiative forcing.
In the rst part of the dissertation, open-ocean measurements of the sea salt concentrations from
ve dierent campaigns were used to validate the sea salt parameterization in numerical models.
The data set is unique in that it is from open-ocean shipboard measurements, which alleviates
typical problems associated with the onshore wave breaking on land stations (surf zone). The
validity of the sea salt parameterizations was tested by employing a global forecasting model
and the NAAPS transport model with a detailed representation of the dry and wet deposition,
advection and diusion, as well as other physical processes. It is shown that the inclusion of these
processes leads to good agreement with the shipboard measurements. The correlation coecient
of the measured and modeled sea salt mass concentrations for all data points was 0.76 and varied
99
CHAPTER 9. CONCLUSIONS AND OUTLOOK
from 0.55 to 0.84 for dierent experiments.
µg/m3
from the measurements and 7.3
The average sea salt mass concentration was 4.6
µg/m3
from the model, for all considered experiments.
It was found that the model-measurements discrepancies were aected by the wet deposition
uncertainties, but also suggested was the inuence of the emission uncertainties in the strong
wind-speed regime, the lack of a wind-speed threshold for emission onset, and the lack of size
dierentiation in the applied deposition velocity. No apparent relationship between the water
temperature and the measured sea salt concentration was found in the analyzed data set.
Also, we performed an analysis of the global sea salt production and its distribution. Model
results cover nine years of the sea salt simulations at one degree horizontal resolution.
The
emitted SSA mass exhibits less than 20% year-to-year variability with the mean value equal to
3 × 1012
kg.
On average, the emission is strongest in July and August, due to the enhanced
production in the southern hemisphere oceans.
The total SSA mass exhibits seasonal oscilla-
tions with maxima during the northern hemisphere winters, presumably caused by the seasonal
variability in the wet deposition processes. The lowest sea salt column loadings are modeled over
the tropical oceans and maxima are predicted over the mid-latitude oceans of both southern and
northern hemispheres. The modeled column loadings over oceans are almost 1000 times higher
than the corresponding surface concentrations, which is a useful approximate relation between
the two parameters.
Another contribution of this work was development of a new approach to the sea salt parameterization which incorporates wind-wave characteristics into the SSA emission function and can
be employed globally and under swell-inuenced conditions. This parameterization of the source
function was applied to the NAAPS model together with the wave eld predictions based on the
global wave model Wave Watch III. The squared surface wind velocity
velocity
Vorb = πHs /Tp
U10
and the wave orbital
are shown to be the key parameters in the proposed parameterization.
The results of the model simulations were validated against the multi-campaign shipboard measurements of the sea salt aerosol.
These validations indicate a good correlation between
Vorb
and the measured surface concentrations. The model simulations with the new parameterization
exhibit an improved agreement with the observations when compared to a wind-speed-only approach. The proposed emission parameterization has the potential to improve the simulations of
sea salt emission in aerosol transport models.
The extensive NAAPS development allowed for investigation of the impact of the SSA size
bin assumptions on the accuracy of global model predictions. The NAAPS multi-bin simulations
reveal that about 20% total mass decit and about 35% AOT underestimation in results are
obtained when a two bin simulations versus the reference fteen bin simulations are compared.
It has also been established that at least 8 size intervals are needed to assure 5% accuracy.
The observed dierences are found to be inuenced by the mass deposition excess caused by
low size resolution. It is also shown that a choice of the bin segregation method inuences the
model performance. Sensitivity studies with the iso-gradient division procedure demonstrate an
improved accuracy in the simulated sea salt mass and the average optical thickness versus the
100
CHAPTER 9. CONCLUSIONS AND OUTLOOK
typically used iso-log size intervals. It is found that the iso-gradient size bins are more ecient in
representing transport processes. In addition, the iso-gradient simulations exhibit a better and
more regular asymptotic convergence to the reference values.
We also studied the SSA clear sky shortwave radiative forcing. This provided an opportunity
to examine the role of the developed parameterizations on the climate related topics. A simple
conceptual scheme of the radiative forcing was constructed based on a one-layer aerosol model,
monthly averaged surface albedo and solar elevation values. The average results from the 2004
NAAPS simulation with 10 iso-gradient size intervals indicate that the climate forcing by SSA
is negative and equals -1.2
Wm−2 .
This value corresponds to the average AOT of 0.043 at 550
nm and the asymmetry parameter 0.767. The set of simulations with varying numbers of size
bins reveals large, of up to 33%, forcing underestimation caused by an inadequate representation
of the aerosol size distribution. This result points to the need for a more careful consideration
of the issue of size resolution in global modeling, which is shown to be an important source of
uncertainty in climate predictions.
Further research along the lines taken in this dissertation can include investigation of the
indirect impact of SSA on climate through its interaction with clouds.
Two important issues
are of interest for the scientic community: the impact of large sea salt particles on drizzle and
warm rain formation and the assessment of the role of sub-micron SSA particles acting as cloud
condensation nuclei. Both problems could be investigated with the modied version of NAAPS
using the extended sea salt parameterizations and size bin sectioning. Another unresolved problem is the longwave greenhouse eect of the SSA. This topic has recently been addressed by some
scientists, but it still requires further consideration. Some other interesting issues include investigation of the sea salt budget variability caused by seasonal sea-ice melting in the Arctic Ocean.
Recent climate predictions indicate that the Arctic can be ice-free in as soon as the summer of
2015. These changes would certainly inuence the sea salt production and aerosol burden in this
region, which - in turn - would modify the local and global radiation balance. In addition to the
SSA-climate interaction issues, some NAAPS parameterizations should be further developed in
order to improve representation of the physical processes in the model. For instance, the wet
deposition processes should be improved by using more realistic size dependent formulas. Furthermore, many other prospects for the model and sea salt parameterization development were
already suggested in this dissertation, which could be continued in future scientic explorations.
101
102
Appendix A
Table A
103
APPENDIX A. TABLE A
n-bin
2
4
6
8
10
bin
rdry range [µm]
reqv [µm]
iso-log
1
0.515
2.196
0.186
0.521
1.163
2.677
0.143
0.292
0.530
0.935
1.655
3.011
0.129
0.212
0.348
0.541
0.839
1.302
2.036
3.219
0.122
0.178
0.267
0.386
0.552
0.788
1.126
1.613
2.321
3.355
9
8
7
6
5
4
3
2
1
8
7
6
5
4
3
2
1
6
5
4
3
2
1
4
3
2
1
2
0.1 − 0.632
0.632 − 4.0
0.1 − 0.251
0.251 − 0.632
0.632 − 1.591
1.591 − 4.0
0.1 − 0.185
0.185 − 0.342
0.342 − 0.632
0.632 − 1.169
1.169 − 2.163
2.163 − 4.0
0.1 − 0.159
0.159 − 0.251
0.251 − 0.399
0.399 − 0.632
0.632 − 1.003
1.003 − 1.591
1.591 − 2.522
2.522 − 4.0
0.1 − 0.144
0.144 − 0.209
0.209 − 0.302
0.302 − 0.437
0.437 − 0.632
0.632 − 0.915
0.915 − 1.323
1.323 − 1.912
1.912 − 2.766
2.766 − 4.0
10
Fi g/m2 s
8.3958 × 10−12
1.2833 × 10−10
8.5617 × 10−13
7.5397 × 10−12
4.8862 × 10−11
7.9473 × 10−11
3.9711 × 10−13
1.3978 × 10−12
6.601 × 10−12
2.5572 × 10−11
5.0456 × 10−11
5.2307 × 10−11
2.5487 × 10−13
6.013 × 10−13
1.7668 × 10−12
5.7728 × 10−12
1.6483 × 10−11
3.2379 × 10−11
4.0923 × 10−11
3.855 × 10−11
1.8722 × 10−13
3.5714 × 10−13
7.8925 × 10−13
1.9682 × 10−12
5.0936 × 10−12
1.1937 × 10−11
2.2224 × 10−11
3.08 × 10−11
3.2904 × 10−11
3.047 × 10−11
rdry
0.547
2.207
0.349
0.573
1.153
2.641
0.284
0.431
0.593
0.909
1.634
3.028
0.255
0.378
0.476
0.607
0.824
1.257
2.041
3.255
0.236
0.347
0.421
0.506
0.617
0.783
1.075
1.579
2.355
3.398
iso-gradient
range [µm] reqv [µm]
Fi g/m2 s
9.8777 × 10−12
1.2685 × 10−10
2.9017 × 10−12
6.976 × 10−12
4.5085 × 10−11
8.1768 × 10−11
1.9593 × 10−12
2.3021 × 10−12
5.6163 × 10−12
2.021 × 10−11
5.5536 × 10−11
5.1108 × 10−11
1.5799 × 10−12
1.3219 × 10−12
2.286 × 10−12
4.69 × 10−12
1.2195 × 10−11
3.2889 × 10−11
4.5414 × 10−11
3.6354 × 10−11
1.3711 × 10−12
9.2312 × 10−13
1.3609 × 10−12
2.1862 × 10−12
4.0364 × 10−12
8.6189 × 10−12
2.0295 × 10−11
3.4472 × 10−11
3.5371 × 10−11
2.8097 × 10−11
0.1 − 0.675
0.675 − 4.0
0.1 − 0.415
0.415 − 0.675
0.675 − 1.548
1.548 − 4.0
0.1 − 0.354
0.354 − 0.484
0.484 − 0.675
0.675 − 1.099
1.099 − 2.192
2.192 − 4.0
0.1 − 0.325
0.325 − 0.415
0.415 − 0.523
0.523 − 0.675
0.675 − 0.949
0.949 − 1.548
1.548 − 2.586
2.586 − 4.0
0.1 − 0.306
0.306 − 0.378
0.378 − 0.456
0.456 − 0.548
0.548 − 0.675
0.675 − 0.877
0.877 − 1.255
1.255 − 1.911
1.911 − 2.843
2.843 − 4.0
Table A.2: Size bin intervals and corresponding equivalent radii for the iso-log and iso-gradient size division procedures.
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