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A Parameterization of in-cloud Sulphate Production

by

Qingyuan Song

Department of Atmospheric and Oceanic Sciences McGiil University, Montreal

A thesis subrnitted to the Faculty of Graduare Studies and Research in partial fulfilment of the requirements for the degree of

Doctor of Philosophy

8 Qingyuan Song 1997

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ABSTRACT

A parameterization that describes in-cloud oxidation of S(IV) by hydrogen peroxide and

ozone, has been developed for use in large scale models. This pararneterization, which is based on

the reaction rate equations and basic cloud characteristics, is an explicit function of the concentration

of ambient chernical species and some gross cloud parameters. Cornparisons of the panmeterization

scheme with a well-established three-dimensional cloud chemistry model. and also with the cloud

chemistry module of a regional model have been used to formulate and test this parameterization

scheme. Results show that the parameterization agrees with the 3-D chernistry model very well and

that the parameterization holds considenble potentiai for application in large-scale models.

Preliminary application in a regional climate model confirms that the parametentation is able

to improve the agreement of the mass budget and specirum distribution of sulphate aerosol with

observations.

Une paramétrisation qui décrit l'oxydation intra-nuage de S(1V) par l'ozone et le peroxyde

d'hydrogène a été développée pour l'emploi dans des modèles à grande échelle. Cette

paramétrisation, qui est basée sur les équations de taux de réaction et les caractéristiques

fondamentales des nuages, est une fonction explicite de la concentration d'espèces chimiques

ambiantes et de certains paramètres grossies des nuages. Des comparaisons de cette paramétrisation

avec un modèle tri-dimensionnel bien établi de chimie des nuages et aussi avec le module de chimie

des nuages d'un modele régional ont été utilisées pour formuler et tester la parmétrisation. Les

résultats montrent que la parmétrisation s'accorde très bien avec le rnodde chimique 3-D et que

cette paramétrisation détient un potentiel considérable pour I'application dans des modèles à grande

échelle.

L'application préliminaire dans un modèle climatique régional confirme que la paramétrisation

est capable d'améliorer l'accord du budget de masse et du spectre de distribution d'aérosol de sulfate

avec les observations.

Acknowledgements

1 would like to express my deep appreciation to Prof. Henry Leighton. my Ph.D thesis supervisor.

for his guidance, encouragement and understanding. Dunng rny five years at McGill, he has

influenced me far beyond atmospheric science, but also culturally. socially and even recreationally.

Many thanks to Dr. Fanyou Kong for his invaluable assistance in the mnning of his cloud dynamic

model and countless discussions. Frankly speaking, his arrivai ai McGill hastened the progress of

my Ph.D study. Prof. Jean-Pierre Blanchet at UQAM also deserves thanks. Patient assistance and

suppon from him and his research group in mnning a regiond ciirnate model helped me to finish the

1 s t part of my research.

Special thanks should go to my own funily. Studying abroad with a fvnily is indeed a challenge. The

support frorn my wife. Mei, made it possible for me to go through ail difficulties. The responsibility

and cornmitment to my children, Jiaqi and Jialin, make me firm and persistent. As the rnonths and

years go by. the restless young man matures; high adventure gives way to family and professionai

responsibility.

Many thuiks to Louis-Philippe for conecting, or more precisely, rewriting my French translation of

the thesis abstract. 1 appreciate the assistance from Sunling, Lubos and Alex in preparing some

gnphs in this thesis. 1 would like to acknowledge the financial suppon for rny study in the form of

a McGilYCIDA Fellowship and NSERC funding.

Last but not least, 1 would like to thank al1 my classrnates in this department for their help in

overcoming the cultural shock and language bmier. Unreserved assistance from Hai Lin, Chunqiang

Li, Zonghui Huo and Bao Ning made my initiai life at McGill easier. Interesting and constructive

talks with Hdldor, Patrick, Werner ... colored my days from time to tirne. The culture of mutual

encouragement between students made it an e ~ c h i n g , unforgettable experience. The unique

scientific atmosphere created by the faculty and students of the department enabled me to acquire

a solid background and a sound interest in atmosphenc science.

TABLE OF CONTENTS

Abstract

Resume

Acknowledgement

Table of contents

List of figures

List of tables

Statement of originality

1. Introduction

1 . 1 General Background

1.2 Sulphite aerosols and their clirnate implications

1.3 Cloud-aerosol interaction

1.4 Purpose of the study

1.5 Brief description of the developmen t and application

of the parameterization

2. Model description

2.1 Cloud chemistry model

2.1.1 Brief description of the cloud chemistry model

2.1.2 Review of previous work

2.1.3 Model modifications and results

2.2 Cloud dynamics model

2.3 A cloud case study

2.3.1 Instruction

2.3.2 Simulation

2.3.3 Conclusion

3. Experiments and methodology

3.1 Expenments

3.2 Methodology

3.2.1 Effective cloud lifetime correction factor

3.2.2 Recalculation of pH

4. Results and discussions

4.1 Comparison between the resul ts frorn parameterization

and the 3-D cloud chemistry model

4.2 Discussion

4.3 Comparison between the results frorn parameterization,

3-D cloud chemistry model and ADOM cloud module

5 . Prel irninary application in NARCM

5.1 The structure of NARCM

5.1.1 General dynamic features of CGCM

5.1.2 LocaI Climate Mode1 (LCM)

5.2 Cloud representation in present NARCM

5.3 Aerosol scheme in NARCM

5.4 Climate implication of arctic anthropogenic aerosols

5.5 Results from current NARCM

5.6 Results from the preliminary application

6. Summary and conclusion

Appendix

References

LIST OF FIGURES

Figure

Fig. 2.1

Fig. 2.2

Fig. 2.3

Fig. 2.4

Fig. 2.5

Fig. 2.6

Fig. 2.7

Fig. 2.8

Fig. 3.1

HNO, concentration in air and cloud at (a) O s, (b) 300 s,

and (c)720 s; in min at (d) 720 S.

HNO, concentration in ice phase hydrometeor

(a) in ice or snow at 1000s ; (b) in graupel or hail at 2000 S.

M icrophysical processes

Mixing ratios of cloud. rain, ice and graupel in the idealized case

Y-Z cross-section of total water content at

(a) x=50 km. t=56 min. (b) xdOkrn, t=80 min.

(c) x=55 km, t=120 min.(d), x=60 km, t-168 min..

Time series of maximum vertical velocity

Time series of average ratio of (a) cloud water. (b) min.

(c) ice and snow. and (d) graupel and hail.

Radar reflectivity (dBZ) in the X-Z plane at y=56 km, t=80 min.

One set of observed chernical profile and its idealized mode1 input

Page

Fig. 3.2

Fig. 3.3

Fig. 3.4

Fig. 4.1

Fig. 4.2.

Fig. 4.3

Fig. 4.4

Fig. 4.5

Variation in time of sulphate production frorn the 3-D chemistry

mode1 and production from the original parameterization scheme.

(a) by the oxidation of HzOZ, (b) by the oxidation of O, 6 1

As Fig. 3.2 but with the lifetirne correction factor included in the

pararnetenzation. 64

As Fig. 3.2 but with the lifetime correction factor and pH

recalculation included in the pararneterization.

Cornparison of sulphate production for the 24 cases with

normal chemical concentrations in the smafl doud frorn

the 3-D chemistry model and from the pararneterization.

a) oxidation by H102; b) oxidation by O,

As Fig. 4.1 but for the 12 cases with extreme chemical concentrations. 69

Cornparison of sulphate production for the first 12 normal

concentration cases from the 3-D chemistry model and

from the parameterization, with 1-9 for deep cloud and

10-1 2 for moderate cloud, a) oxidation by Hz02;

b) oxidation by O,.

Cornparison of sulphate production for the 12 extreme

concentration cases in deep cloud from the 3-D chemistry

model and frorn the panmeterizaiion. a) oxidation by H202;

b) oxidation by O,.

Scatter plot of total sulphate production by H,O, and O, from

3-D model and pararneterization. The drshed lines represent

error of &O%, a) - d) Data from Figs. 4.1-4.4respectively.

Fig. 4.6 Comparison between sulphate production from the 3-D

cloud chemistry model, the panmeterization, and the

ADOM cloud module. The nurnber 1-12 identify 12

cases with different chernical profiles from Glazer et al ( 1993).

a) for Cloud A, b) for Cloud B. 79

Fig. 5.1 Scales of the three climate models used for NARCM

Fig. 5.2 Arctic haze distribution at Ny-Alesund of 12" E, 79" N,

Heintzenberg (1980)

Fig. 5.3 The simulation dornain of NARCM

Fig. 5.4 Sulphate aerosol size distributions at the 60-th day of the simulation 96

Fig. 5.5 The sulphate aerosol size distribution at 70" N, 80" W. from the

simulation results of Fig. 5.4

Fig. 5.6 The modified sulphate mrosol size distribution by

irnposing 2.5 pg/m3 sulphate from in-cloud production.

viii

LIST OF TABLES

Table 1 .1

Table 2.1

Table 2.2

Table 3.1

Table 3.2

Table 3.3

Table 3.4

Table 4.1

Table 4.2

Table 5.1

1 Estimates of global sulfur emission

(in Tg S yr-') from Moller, (1994)

Input data of 24 June 1992 Colorado thunderstorm case

(Brandes. 1996)

Corn parison between mode1 resul ts and observations

The input of the parameterkation

Gross cioud parameters

Ambient chemical concentrations in normal concentration

cûtegory at the surface (ppb. SO,' in 10" mollm')

Ambient chemical concentrations in extreme concentration

category at the surface (ppb, SO,' in 10-8 moVm3)

Gross cloud parameters of cloud A and B

12 ambient chemical concentrations at the surface

(ppb. SO,' in 1W8 mol/m3)

Vertical levels of LCM

STATEMENT OF ORIGINALITY

Anthropogenic aerosols, mainly sulphate particles, rnay have an important impact on

global climate and hence changing aerosol concentrations rnay influence the global clirnate

change. However, the cloud processing of S(IV). which has been speculated to contribute 70-

90% of the total sulfur oxidation in atmosphere and also to rnodify the size distribution of

sulphate aerosol particles through aqueous chemical reactions, has been identified as one of

the most serious uncertainties in global suiphur modelling. The originality of this study is that

the pararneterization that has been developed has explicit dependence on the concentration of

ambient chemical species and gross cloud parameters that are generally available in large

scale models, and hence diminishes the uncenainties in numerical modeling. The

implementation of the parameterization in a climate model c m be considered as the first

attempt to calculate in-cloud sulphate production in a chemically explicit wny in a climate

rnodel. The preliminary application produces encouraging results and more irnportantly.

provides constructive suggestions for future research.

Chapter 1

1.1 General background

Warning of global warming caused by greenhouse gases such as carbon dioxide,

CFCs and methane was given more than a decade ago. Images of a world of relentlessly

rising temperatures where farmlands are scorched into desen. the polar ice caps dissolve

and ocean levels rise, swamping low-lying islands and coastal areas have been played up

in the mass media. while more careful investigations of global climate change are being

perforrned in scientific circles. One indeed sees an increase in average temperatures around

the globe by between 0.5 and 1 .O°C during the lasr century ([PCC, 1990). However, this

could possi bly be the result of a natural climate fluctuation. Actually. evidence even shows

that parts of the globe. including western Europe, eastern North Arnerica and eastern Asia

have become cooler rather than warmer in the pest 60 years (Jones, 1988; Engardt and

Rodhe 1993; Hunter et al, 1993). It is recognized that under the scenano of global

warming, regional climates need more specific and detailed studies.

Attention that has been given to the climate changes as a consequence of increased

concentration of gases that absorb infrared radiation is well justified. However,

anthropogenic aerosol particles are also able to change the radiation budget directly by

increasing the planetary albedo (or decreasing it over highly reflecting surface). They rnay

also influence the radiation budget indirectly by changing cloud optical properties and

cloud lifetime by modifying cloud droplet size distributions. These effects on the earth

radiation budget may be comparable in magnitude but opposite in sign to ihose caused by

greenhouse forcing (Charlson et al, 1992), thus rnay off-set increases in greenhouse

warming. Uniike greenhouse gases, which are long-lived and hence well-mixed in the

atmosphere, aerosols have short lifetimes and have their highest concentrations in regions

influenced by industrial emissions. Therefore, the aerosol distribution is highly variable in

space and time. This nonuniform distribution of aerosols, in conjunction with greenhouse

forcing rnay lead to a differential spatial forcing with net heating in some areas and net

cooling in others (Penner et al, 1994). Modelling results (IPCC. 1994) show that i n western

and central Europe, eastern North America and eastern Asia. where sulphate aerosols are

abundant due to strong industnal activity, the direct radiative effect of the aerosols rnay

have caused the observed local cooling.

Aerosols cm change the features of cloud. Clouds are also important sources of

aerosol. Through in-cloud chemistry reactions, clouds can influence the global budget of

aerosols, and modify aerosol size distribution spectra and cloud condensation nuclei (CCN)

concentrations (Hegg, 1990). The interaction between cloud and aerosols may have strong

implications for clirnate change (e.g Lelieveld and Henzenberg, 1992). So both the impact

of aerosol on clouds and impact of cloud on aerosol must be included in climate rnodels.

AI1 these cornplex features make studies of atmospheric aerosols difficult, their climate

impact has. therefore, not been thoroughly evaluated. Deficiencies in numerical models

such as the inaccurate estimate of cloud and aerosol radiation effects, and the inadequate

description of the interaction between cloud and aerosol may lead to poor prediction of

regional climate changes.

1.2 Sulphate aerosols and their climate implications

Since the beginning of industrialization. pollutants from the combustion of fossil

fuels have increased dramatically. It is likely that the present global anthropogenic sulfur

emission exceeds the natural emission of the sulfur by a factor of 2 or more (Table 1.

Moller, 1994).

Table 1 . Estimates of global sulfur emission ( in Tg S yr")

Source A B C D E F G

Volcanic 3-20 9.2 9.3 7.4-9.3 9 8.5 7

Terrestrial 0.1 -5 1.2 0.3 3.8-4.3 I 1 7

Oceanic 12-20 19.5 15.4 19-58 12 16 36 (non-sea salt)

Biomass burning 1-4 3.0 2.2 2.8 2 2.5 --- Anthropogenic 70-85 92.4 76.8 --- 78 70 103

Total natural 16-69 33 27 33-75 24 28 50

A-G identify results from diffcrcnt authors (Mollcr, 1994)

Initial attention on sulphur dioxide as a major pollutant was centred on i t being a

precursor for acid deposition. The recent realization that anthropogenic sulphate aerosols

may play a significant role in climate change has made it mandatory to improve the

undentanding of the physical and chernical processes that influence the global distribution

of atmosphenc sulphate. Charlson et al (1992) speculated that anthropogenic aerosol

climate forcing may be comparable in magnitude to greenhouse gas forcing and

counteracting its warming effect.

Most sulphur is discharged into the atmosphere in the form of SOz or S(1V). There

are two pathways for the oxidation of S(1V) to sulphate in the atmosphere: photo-chernical

reactions in clear air and heterogeneous oxidation reactions in cloud and rain. The newly-

generated sulphate may form new aerosol particles by condensation, i.e. homogenous

nucleation of aerosol particles as a consequence of clear air chemistry oxidation, or deposit

on pre-existing aerosols when cloud or haze droplets evaporate. The pas phase oxidaiion

is dominated by the renction of S 0 2 with hydroxyl (OH) radicals, for example:

SO1 + OH (g) --> HOSOt photo-chemical oxidûtion ( 1.1)

HOSO, + H,O----> H$O, nucleation

Since OH radicals are a result of atrnosphenc photochernical reactions, $as phase

oxidation of SO, will only be significant during the daytime. The proportion of S(IV) that

4

is oxidized to S(VI) by photochemical reactions is not large because the photochemical

reaction rates are relatively slow. Most of the sulphur dioxide is either oxidized in cloud

droplets or dry-deposited on the ground. The main in-cloud oxidation reactions are

S(IV),,, + 02(,) + Fe(II1) ---> S(VI),,,+ Fe (III)

Hegg (1985) concluded that for the whole troposphere, the in-cloud conversion of SO, to

sulphate was about 10 to 15 times greater than homogenous gas-phase oxidation. In the

rnodelling results of Langer and Rodhe (1991), the aqueous phase oxidation within clouds

contributes more than 90% to the total S 0 2 oxidation. According to Moller (1994). on

global average. the importance of different sulphate formation pathways is de pendent on

the time of day and season. and the frequency and duration of clouds. He also concluded

that the aqueous phase oxidation is always dominant. In the sulphur cycle. about 50% of

$O2 is dry-deposited at the surface, 15% wet-eavenged by cloud and min. more than 25%

oxidized into sulphate within cloud, and only 7% oxidized in clear air (Moller, 1994).

Nevertheless. gas phase oxidation processes produce significant concentrations of fine

atmospheric sulphate particles, which can be identified in the aerosol number distribution

spectrum.

5

Unlike gaseous pollutants that only need to have their molar concentrations

specified in order to describe their signatures in the atmosphere, aerosol panicles, with

radii ranging from 0 . 0 0 1 p to 1Opm. have another important physical property, their

number or volume size distribution. The aerosol size distributions are critical in describing

processes such as aerosol coagulation, transport. scavenging. CCN nucleation and solar

radiation scattering. Whitby (1975) classified the aerosols into three groups, nucleation

mode (0.00 1-0.1 pm), accumulation mode (O. 1 - I pm) and coarse particle mode (s 1 pm).

based on the aerosol production mechanism. Sulphate aerosols from different pathways also

have different identities in their size distribution spectrum. The nucleation mode. which

is usually the most prominent mode in the aerosol number distribution spectrum, is mainly

produced by gas-to-particle conversion, as described in Equ. 1.1 and 1.2. Langner et al

(1991) indicated that the rate of formation of new sulphate panicles may have doubled

si nce pre-industrial ti mes by the increasi ng anthropogenic sulphur discharge. Heterogeneous

oxidation of SO, leads to rapid sulphate production. With the evaporation of haze or cloud

droplets. the pre-existing nuclei that were activated in the droplet formation and which may

have been sulphate particles, combine with additional sulphate from in-cloud oxidation and

are released back into atmosphere. 'me accumulation mode is mainly formed by

coagulation of fine particles and heterogeneous oxidation. Heterogeneous oxidation may

add substantial amounts of mass to the accumulation mode. This mode is normally the

most pronounced in the aerosol mass or volume size distribution spectrum.

The estimation of the relative importance of clear air and heterogenous sulphate

production corne from global models. But since aqueous sulfur chemistry depends on

ambient chernical concentrations and cloud properties. which are poorly known and

inaccurately represented in di fferent models, inconsistencies in the magnitude of the

conversion terms from different researchers are considerable. For exampie. Hegg (1985)

and Pham et al (1995) estimated that more than 90% of atmospheric sulphate is from in-

cloud oxidation, while the result from Feichter et al (1996) is 66%. Nevertheless,

consensus has been reached that the photo-chernical process dominates the sulphate aerosol

numbcr concentration, but heterogenous processes are important in influencing the sulphate

mass concentration.

Aerosols may be a significant source of clirnate forcing through their direct effect

on the solar radiation balance (Bal1 and Robinson, 1982; Charlson et al, 1991). The first

estimation of the direct radiative sulfate forcing was made by Charlson et al (199 1 ) who

estimated a magnitude of -0.6 W ma' on global average. Other estimations of the direct

effect range from -0.3 W m" (Kiehl and Briegleb. 1993) to -0.9 W m-' (Taylor and

Penner, 1994). The Intergovemmental Panel on Climate Change indicated chat the global

mean direct forcing due to anthropogenic sulphate and biomass combustion aerosol may

lie in the range -1.0 to -2.0 W me' (IPCC. 1994), which could counteract the greenhouse

effect of similar magnitude of around 2.430.4 W m" (IPCC, 1994).

Aerosols may also affect climate indirectly through their modification of the optical

properties and lifetime of clouds by acting as CCN. Twomey et al. (1984) showed that

7

under the assumption that liquid water content remains the same, an increased nurnber of

CCN yields more cloud droplets with smaller radii. This increases the scattering of solar

radiation within the ctoud and thus the cloud albedo. Observations on the increased cloud

reflectivi ty of ship tracks due to increased cloud droplet concentrations cnused by su lphate

aerosol particle concentration from the discharge of ships strongly supports the aerosol

indirect effect (Coakiey et al, 1987).

Precipitation basically acts as a link between fractional cloudiness and aerosols

(Albrecht, 1989). The rnechanism responsi ble for precipiiation in warm clouds is

coalescence arnong cloud droplets. Small droplets have small collision cross sections and

slow settling speeds and hence have little chance of colliding with one another. Therefore.

the formation of precipitation i s much more efficient for clouds with fewer but larger

droplets. Any increase in CCN may also reduce the precipitation efficiency and increase

the life-time of clouds and hence their radiative impact.

Indirect forcing is still too uncenain to quantify in a rigorous way because the

interactions between aerosols, CCN and cloud optical propenies are poorly understood. It

surely causes negative forcing, and may have significant magnitude (Tworney, 1977),

possibiy being more important ihan direct forcing (Grassel, 1988). Charlson et a1 (1987)

made attempts to calculate roughly the magnitude of the indirect forcing and obtained a

value of -1.7 Wm". Similar estimates have been obtained by Slingo (1990). Recently,

Jones et al (1994) and Boucher and Anderson (1995) used climate modeIs to study the

8

indirect forcing. Their estimation of global average indirect forcing is in the range from -

0.5 to -1.5 W m.*.

I .3 Cloud-aerosol interaction

It is impossible to discuss aerosois and clouds separately. The activation of aerosols

is a necessary condition for cloud formation. Sulphate mrosols comprise an important pan

of cloud condensation nuclei due to their abundance in the atmosphere, their size and their

hygroscopic nature. In a precipitaring cloud, sulphate will be removed by rain drops. snow,

graupel and hail, possibly leading to acidic precipitation. Also, with the evaporation of

cloud, dissolved sulphate from nucleûtion of pre-existing sulphare particies and ûqueous

oxidation of S(IV) will return to the atmosphere as an aerosol. Hence clouds act not only

as a sink but also as a source of atmospheric sulphate.

In-cloud oxidation of S(1V) can strongly influence the global sulphate budget, and

can also modify the sulphate aerosol distribution specirum. Modification of the size

distribution has significant implications for both the direct and indirect effects of sulphate

clirnate forcing. This section will be focused on the discussions of these effects.

As we have seen in the previous section, about 50% of S 0 2 is removed by dry

deposition at the eanh's surface. Of the remaining 50% percent, only a small fraction is

oxidized to sulfate in the gaseous phase by photochernical reactions, and a large fraction

9

is oxidized to sulfate via heterogenous processes, because of the fact that most clouds

evaponte rather than precipitate (Pruppacher and Klett, 1978). and in-cloud oxidation of

SO, is rather fast. In-cloud sulfur chemistry involves microphysical and chemical aspects

which are determined by the pH, the concentrations of oxidants and reactants and also

cloud parameters such as cloud water content etc. H,O, - - and 0, are believed tu usually be

the most important oxidants in aqueous oxidation of S(1V) to sulphate (Equ. 1.3 and 1.4).

catalytic oxidation by iron being relative1 y unimportant except in the unusual circumstances

of low HzO, and 0, concentrations and high iron concentrations (Seinfeld. 1980). H,O, - - and

0, are the only oxidants considered in the present study.

The size distribution of atrnospheric aerosol particles c m be modified by clouds

through heterogeneous chemical reactions. With evaporation of the cloud. the extra

suiphate frorn in-cloud oxidation of S(IV) combined with pre-existing dissolved sulphate

will be released to the atmosphere. The modification of the size distribution of sulphate

aerosols may significantly increase the aerosol light-scattering efficiency. In the work of

Lelieveld and Heintzenberg (1992). the modifications of an aerosol size distribution

spectrum due to gas phase oxidation and queous phase oxidation are investigated and are

compared to each other. They found that the cloud processed aerosol is more efficient in

scattering solar radiation than clear-air processed aerosol and background aerosol.

The modification of the size of aerosols by cloud processes may significantly

enhance the number of CCN avaiilable for subsequent stratifonn cloud formation (Charlson

1 0

et al., 1992). The essentiai facts of heterogenous nucleation are well summarized by the

Kohler curves, which describe the equilibrium supersaturation over droplets of various

sizes and containing various masses of dissolved salt, Le. the combined effects of curvature

effect and solution effect. Due to these effects, the increase in the size of CCNs leads to

a decrease in the supersaturation required to initiate a cloud droplet formation. Many

observations (e.g. Radke and Hobbs, 199 1 ) demonstrate that CCN concentrations active at

a given supersaturation are often higher in air that has been processed by clouds than in

the ambient air. Hegg (1990) concluded that. in the remote marine atmosphere. aerosols

have to be processed by cumuliform clouds before they will be large enough to serve as

CCN at the low supersaturation typical of marine stratus clouds with a concentration

usually observed. He used a model to predict the CCN spectrurn left behind by an

evaporating cumulus cloud to compare to the CCN spectrum that entered the cloud from

homogenous gas phase processes (without or before cloud processi ng). In a stratiform

cloud. if the CCNs entering the cloud base are the unprocessed aerosols and the maximum

supersaturarion in the cloud is 0.58, then only 50 droplets cm" will be activnted. However.

about 130 droplets cm" at the same maximum supersaturation will be activated after these

CCNs have been processed by a convective cloud.

1.4 Purpose of the study

From the discussions in the eûrlier sections, the importance of making quantitative

studies on in-cloud sulphate production on a global or a regional scale become clear. Many

11

studies have accordingly examined the global sulphur budget and the impact of sulphate

aerosols on the solar radiation budget. Langner and Rodhe (1991 ) made the first atternpt

to investigate the tropospheric sulphur cycle by using a global three-dimensional mode].

They achieved remarkable results which are broadly consistent with observations of

concentrations in air and precipitation in polluted regions of Europe and Nonh America.

They aiso discussed the uncertainties in global sulphur budget modelling, arnong which,

the rate of oxidation of SO, in clouds was identified as one of the most serious.

Cloud chemistry rnodelling involves rnodelling of cloud dynamics. cloud

microphysics and chemistry. The sinks and sources of chemical species change temporrlly

and spatially with the deveiopment of cloud and the subsequent in-cloud chemical

processes. It is computationally impractical to include either an explicit cloud dynamics

and microphysics scheme with an explicit aqueous phase oxidation scheme in general

circulation models or regional models to obtain even a total column amount of in-cloud

sulphate production. The uncenainty of in-cloud sulphate production presents serious

limitations to the modeling of climate. A parameterization of the in-cloud oxidation of SO,

by H202 and 0, is essential.

Much effort has gone into parameteriring aqueous-phase oxidation of SO,. For

instance, in the work of Langner and Rodhe (1991) mentioned earlier, the estimation of

S(IV) oxidation is based on the magnitude of some charactenstic time scales: the average

time taken by an air parcel between successive cloud encounters; the average time the air

1 2

parcel stays inside a cloud once a cloud is reached; and the average chemical Iifetime due

to transformations inside the cloud. However, in such a pararneterization, the ambient

chernical concentrations are not accounted for explicitly but instead are included irnplicitly

in the chemical transformation time scale. Also, due to the crude cloud scheme in their

global model, cloud physical processes could not be treated in an adequate way, and the

associated interaction between suiphate and clouds faiied CO be accounted for. Langner and

Rodhe point out that an aqueous-phase reaction scherne preferably having explicit

dependence on the ambient concentration of oxidants should be utilized in futtire work.

In some climate models, such as the founh generation Max-Planck-Institute rnodel.

ECHAM-4, (Lohmann et al, 1996) and the third generation of Canadian General

Circulation Model, CGCM III. (McFariane, 1997). explicit cloud schemes are available to

describe stratiform clouds. Richter et al (1996) used the ECHAM-4, coupled with a

chemistry model to study the direct and indirect forcing caused by anthropogenic sulphate.

In ECHAM-4. water vapor and cloud water are prognostic variables, so that aqueous phase

concentrations can be calculated from rmbient concentrations of pollutants wi th

temperature dependent Henry's law constants. Cloud chemistry therefore can be described

explicitly. They investigated the oxidation of S(IV) and found the aqueous phase oxidation

accounted for 66% of the total oxidation and clear air photochemistry the remaining 34%.

H,O, was the dominant aqueous phase oxidant accounting for 90% of the heterogenous

oxidation. In ECHAM-4, the explicit cloud scheme is only appiied to stratiform clouds that

have large cloud cover and long lifespan. Moreover, in the calculation of aqueous sulphate

13

production, the fact that the pH value is assumed always to between 3-5 limits the choice

of initial chernical concentrations. For example, HNO, has been neglected in their study.

Also under such an assumption, the concentration of NH,, presumably, has to be rather

small in order to avoid a large pH value. Berge (1990) took advantage of an explicit cloud

parameterization (Sundqvist. 1993) to simulate sulfur dispersion in a chemically explicit

way. For most climate models, such an explicit treatrnent of clouds is not available.

Furthermore, Berge's simulation is limited to acidic arnbient conditions since NH, is not

included in his model.

1.5 Brief description of the developrnent and application of the parameterization scheme

In this study, we have developed a parameteriwtion that describes oxidation of

S(IV) by H,02 and O, in convective clouds. Based on the equilibrium and reaction rate

equations descnbing dissolution, dissociation and oxidation processes (e.g. Leighton et al.

1 990). the pararneterization scheme is an explicit function of the concentrations of ambient

chemical species. namely sulphur dioxide, sulphate aerosol. hydrogen peroxide, ozone,

ammonia and nitric acid. The parameterization is also a function of some gross cloud

parameters such as average cloud water content, cloud base height. cloud thickness. cloud

lifetime md cloud total water content. Thus. given ambient chemical profiles and these

cloud parameters, the parameterization may be applied in large-scale models to provide a

better description of in-cloud sulphate production. Details about the formulation of the

parameterization scheme are given in section 3.

The parameterization has been formulated and tested by cornparisons with a

well-established 3-D cloud chemistry model (Tremblay and Leighton, 1986) for a series

of cases with different chemical and dynamical conditions. As an indication of a potential

application of the parameterization, we compare the sulphate production by the

parameterization with results from the cloud module of the Acid Deposition and Oxidant

Model (ADOM) and 3-D cloud model simulations (Glazer et al., 1994) for identical initiai

conditions. Results (shown in detail in Chapter 4) show a satisfactory agreement between

out- parameteriwtion and the 3-D cloud chemistry model. Furtherrnore. a cornparison

between the results of the parametentation and the results from the ADOM cloud module

with results from the 3-D model demonstrates the superiority of the parameterization to the

ADOM cloud module. These results suggest that the parameterization holds considerable

promise for use in regional and global chemical models.

In Canada. in order to improve the understanding of the roles of aerosol particles

in radiative forcing and climate change, especially the effects of aerosols on northern

regional climates, the Nonhern Aerosol Regional Climate Model (NARCM) has been

proposed by scientists from the Atmospheric Environment Service (AES). University of

Quebec at Montreal (UQAM) and several other universities.

The major troposphenc aerosols such as sulphate, black carbon, non-black

carbonaceous aerosols, mil dust, and sea salt will be included in NARCM. The unique

feature of NARCM will be its ability to handle size-segregated aerosols. Actually, since

15

1994, sire-distributed sea salt aerosols have been added to the 2nd generation of the

Canadian general circulation model (GCM II) to produce a first version of NARCM. The

number and mass distribution and the wind dependence of total sea-salt aerosol mass

concentration predicted by the model agrees well with observations (Gong et al, 1997). In

a refined GCM II, the spectrurn and concentration of sulphate aerosols derived solely from

gaseous phase oxidation and transport. coagulation, condensation. wet and dry-deposition

is also included. Since in-cloud sulphate production has not been accounted for. clouds

only act as sinks of suiphate aerosols by washout of the aerosol. The model is unable to

account for observed aerosol concentrations (Heintzen berg. 1 980). This suggests an

application of the parameterization in NARCM to study the sulphate budget of the arctic

region. In this study, the mass production of sulphate from in-cloud oxidation by hydrogen

peroxide and ozone is investigated as a first application of the parameterization developed.

Based on this preliminary application. the investigation of the modification of the size

distribution spectrurn of atmospheric aerosols due to heterogenous oxidation. and even the

indirect effect of cloud processed sulphate aerosols on the formation of stratiform clouds

can be performed in future work.

Chapter 2 Mode1 description

The parameterization has been formulated and tested by cornparisons with a

well-established 3-D cloud chemistry model. In this study, the dynarnic and microphysical

fields of the chemistry model are provided by a rnixed-phase three-dimensional cloud

dynamics model. The structure, modification and performance of the modeis are introduced

in the following sections.

2.1 Cloud chemistry mode1

7.1.1 Brief description of the model

The McGill cloud chemistry model (Tremblay and Leighton, 1986) was originally

a warm cloud chemistry model that was supported by a warm-cloud dynamics mode1 (Yau

et al, 1980). A set of conservation equations is formulated and solved to iovestigate the

aqueous chemistry and interactions of the cloud wiih gaseous and particuiar pollutants.

Qna, Qnr and Q,, represent the molar densities of pollutant n in air, cloud and rain water,

respectively. In Equ. 2.1 and 2.2, we assume that doud droplets follow air motion. while

rain drops expenence an additional vertic21 falling velocity V, which depends on

precipitation water content as shown in Equ. 3 (Manton and Cotton, 1977),

where p is the density of moist air, and q, is the mixing ratio of min. V is the three

dimensional wind velocity, and U, an eddy diffusivity coefficient. Here the diffusion of

min drops i s not considered. Sn,, Sn, and Sn, are the sink and source terms of each

chemical species associated with microphysical and chemical processes in air, cloud and

min. The chemistry mode1 has been rnodified into a rnixed-phase chemistry mode1 which

includes two new ice categories, ice or snow. and graupel or hail. Consequently, two more

continuity equations are needed to described the ice-phase processes. Details of the

modifications will be introduced in 2.1.3.

The chemical species included in the cloud chemistry model include sulphur

dioxide, nitric acid, ammonia, ozone and carbon dioxide. and an aerosol consisting of a

mixture of (NH,),SO, and H2S0.+. When soluble gases such as SO,, Hz02, CO2 enter the

cloud, an equilibrium state between the gas dissolved in the cloud droplets and ambient

gas will be reached according to Henry's Law, while extremely soluble gases such as NH,

and HNO, are assumed to be totally dissolved into cloud water. AI1 of the suiphate

aerosols are assumed to enter the cloud water by nucleation at cloud base. After the

dissolution and nucleation processes, al1 the aqueous chemical contents are carried along

as the water substance is transformed from one category of hydrometeor to another by

different in-cloud microphysical processes. Pollutants are washed out by precipitation by

both in-cloud and below-cloud scavenging . Chernical reactions take place only in the

aqueous phase. Gas-phase or ice-phase chemistry is not considered in this study. The main

emphasis of the mode! is on sulphur chemistry. and so in-cloud oxidation of S(IV) by

hydrogen peroxide and ozone, nucleation of sulphate aerosols and below-cloud scavenging

are ail included in this model. Detailed descriptions of the chemistry model have been

given by Tremblay and Leighton (1986) and Leighton et al (1990). Only the essential of

the chemistry processes which are utilized in both the 3-D mode1 and the parametenzation

scheme are described in detail below.

Gaseous sulphur in this rnodel is in the forrn of SO,. This is sufficient to fulfil Our

purpose to investigate the effect of anthropogenic sulphur that is mainly emitted as SO,.

After SO, dissolves in cloud droplets with a concentration given by Henry's Law, aqueous

19

rn sulphur dissociates into bisulphite (HSOJ and sulphite (SOJ. The equilibrium between

gaseous suiphur dioxide and aqueous sulfur is represented as:

From equations 2.5, 2.6 and 2.7, the pH value of cloud droplets. or [H']. plays û

determining role in the initial aqueous S(IV) concentration. i.e., SO, ,,,,, HSO,' and SO,'.

The initial pH value is determined by the concentrations of ambient chemical species like

HNO,, NH, and sulphate aerosols.

The dominant aqueous phase chernical reactions are the oxidation of S(IV) by

hydrogen peroxide and ozone. Oxidation by O, catalyzed by metal ions such as iron or

rnanganese may be significant given sufficient catalyst concentration (Barth et al, 1992),

which, however, are not well monitored and documented yet. Hence the catalyzed

oxidations of S(1V) are not inciuded in this model.

The oxidation by hydrogen peroxide described by Equ. 2.8 is usually considered

as the most important process in aqueous phase sulphur chemistry. According to Martin

( l983), the oxidation rate is

and is only slightly dependent on pH. However, ozone oxidation is very sensitive to pH

value (Maahs, 1983),

R,, = (4.4 * 10" exp(-4 13 1 /T)+2.6 * 1 O' exp(-966/T)/[H+] } [o~][s(Iv)] Mls (7.1 1 )

The ozone oxidaiion rate increases rapidly with decreasing [H'], or increasing pH.

However, at an initially high pH. much SO, is quickly oxidized, and simultaneously the

concentration of EH'] increases. The ozone oxidation rate then decreases. Therefore, ozone

oxidation of S(1V) is a self-limiting process. This unique feaiure of aqueous sulphur

chemistry has to be interpreted in the parameterization scheme (details in Chapter 3).

2.1.2 Review of previous work

The warm-cloud chemistry mode1 was originally developed for the investigation the

interaction between trace pollutants, atrnospheric aerosol and clouds. more specifically, acid

deposition in cumulus clouds. Cloud chemistry such as the in-cloud oxidation of SO, by

H,O1 and O, is included. Tremblay (1987) examined the vertical redistribution of several

species by clouds in several case studies of cumulus clouds near North Bay, Ontario. in

that study, the observed aerosol distribution in and around towenng cumulus clouds

showed evidence of nucleation scavenging and cloud vertical transport. The rotai aerosol

number concentrations exhibited sharp minima in the lower portion of clouds and sharp

maxima near cloud tops. With adjustment of several unmeasured mode1 parameters,

reasonable agreement could be obtained between the simulated and observed cloud

chemistry and aerosol distribution in clouds. The mode1 was also used to examine the

effects of in-cloud H202 production on SO, oxidation (Macdonald and Leighton, 1990).

Aqueous phase H20, production was incorporated into this chemistry mode1 and

simulations compared with those in which aqueous phase HL02 came oniy from the

dissolution of gaseous H202 in the cloud interstitial air. Their results showed that in special

situations such as low initial sulphate and high initial SOI, the additional oxidrtion caused

by in-cloud peroxide production results in a 5- 10% increase in the arnount of sulphate

deposited on the surface. However, in normal situations, this source of additional oxidation

could be considered negligible. A rnodified 2-D cold version (with one ice category) was

used to study rainband chemistry (Leighton et al, 1990). Numencal results were compared

with observations from a field study of a rainband in southem Ontario. In this particular

precipitation system, oxidation of S(1V) by H,O, and 0, within the cloud was found to be

a relatively unimporîant pathway, and the two most important sources of sulphate in the

22

rain are nucleation and washout of sulphate aerosol. Later, this model was utilized to

evaluate the Acid Deposition and Oxidant Model (ADOM) cloud module (Glazer and

Leighton, 1994), some results from which will be used in this study (Chapter 4). The

results of the ADOM cloud module is sensitive to cloud top height. If the clouds simulated

in ADOM and in the 3-d cloud chernistry model are similar, the ADOM tends to

overpredict the sulphate production by the oxidation of H20z and O, in comparison with

the results from the 3-d model. The cloud chemistry model itself has nlso been evaluated

(Leighton et al., 1996) by comparisons with data availnble the Eulerian Model Evaluation

Field Study (EMEFS) summer field project (Liu et al., 1993). In these simulations (i.e.

Tremblay and Leighton. 1986; Trernblay, 1987; Leighton et al. 1996). results such as acid

deposition. vertical transport of pollutants from the cloud chemistry model agree with

observational data reasonabl y well.

2.1.3 Model modification and results

Ice may form when cloud top temperatures fa11 below O°C. Freezing is an important

factor in cloud microphysical processes. Once an ice crystal forms, it is in a favourable

environment to grow rapidly by diffusion because the saturation vapor pressure over ice

is lower than that over water. This is the basis of the Bergeron processes. When the ice

phase is present, other microphysical processes are involved, such as freezing/melting,

riming, deposition/sublimation, etc. Chernical species may be transfened from one

hydrometeor category to another by some of these processes. Clouds. especially in the mid-

23

latitude and arctic regions, often extend to altitudes where the temperatures are lower than

0°C and are thus mainly mixed-phase or ice clouds. In NARCM, the mid-latitude and arctic

regions are the main regions of interest. therefore. it is mandatory to include processes to

describe the cloud physics and chemistry more precisely.

In the present work, the 3-D chemistry model has been modified to include two

categories of ice. ice crystals. and graupel or hail. In numerical descriptions, ice crystals

are often considered to move with air, while snow flakes have a terminal velocity. There

is, however, no distinction between ice crystals and snow in this model. Ice crystals are

hornogeneous hexagonal plates with a monodispersed spectrum. The diameter and terminal

velocity of a crystal is determined by its mass. Therefore, the category of ice crystal can

be considered to include both crystals and snow. depending on the mass or diameter of

each crystal. The two additional continuity equations describing the ice-phase physical and

chernical processes are

Here, i represents ice crystal or snow and g graupel or hail. V,, is the fall-speed of graupel

or hail relative to the moving air, given by Cotton et a1 (1982).

This is the mass-weighted mean terminal velocity where p is the density of moist air, and

q, is the mixing ratio of graupel or hail.

Chernical reactions in ice are not considered. Therefore. Sn, and Sn,, being different

from Sn,. Sn, and Sn, of Equ. 2.1, 2.2 and 2.3, do not involve chernical conversions but

only the transformations between ice categories and the aqueous phase or gaseous phase

resulting from processes, such as freezing, accretion. melting, scavenging and evaporation

etc. The new processes associated with the two ice categories are explained in detail in the

following section.

A. Aerosol scavenging by ice phase precipitation

Precipitation contributes significantly to the removal of atmospheric pollution. As

stated earlier, in convective clouds, ice phase hydrometeors appear at upper levels where

temperatures are lower than zero. With the formation of ice, aerosols may be scavenged

by impaction scavenging, which is a nsult of aerosol particles becoming attached to the

snow crystals by Brownian motion, inertial, hydrodynamic, and electric forces. If graupel

25

or hail with mixing ratio Q, fa11 through air containing Q,,, units of rnass of sulphate

aerosol per unit volume, the graupel or hail accumulates sulphate at a rate given by

(dfdt)Q m 4 . g = A s m.,, Q s*, a

where, the washout coefficient

D is the melted diameter of snow. n(D)dD is number per unit volume of air of snow with

dimensions between D and D+dD. According to Scott (1978). the washout coefficient is

given by

A sor. ar = C E Qg7'-

C , a constant dependent on the fa11 speed and diameter of precipitation, is 3.7 x 10" for

snow. Es is an average collection efficiency. According to Mitra et al (1990), Es is sensitive

to temperature,

In the model, when temperature is between - 10°C and O°C. E is taken as 1 x IO"; and

5x IO4, when temperature less than - 10°C.

B. Gas scavenging by ice precipitation

HNO, may contribute significantly to the acidity of precipitation. The scavenging

of HNO, by snow has been studied by Chang (1984).

where the collection coefficient

R is the snow precipitation rate. Here. the snow population is assumed to be distributed

in size according to the Gunn-Marshall (1958) distribution. It may also be described as

where Q, is the mixing ratio of snow.

In the studies of Mitra et al (1990) and Chang (1984). the scavenging efficiencies

are snow scavenging efficiencies. In our work, we use the same efficiencies for graupei or

hail scavenging. The absorption or attachment of other gaseous pollutants on ice is not

included in this study.

C. Cloud droplet to ice crystal transport of water-dissolved species

This process includes ice crystal growth by riming of cloud drops and hornogeneous

freezing of supercooled cloud droplets when the temperature is below -40" C. The impact

of phase change on chernicals such as sulphur dioxide and hydrogen peroxide that are

present in the aqueous phase is cornplex. When droplets freeze, the solutes are rejected in

a higher or lesser proportion by the growing ice lattice. depending on the nature of the

solute and the growth conditions. If the solute is non-volatile. it will be totally retained in

the ice after freezing. According to Iribarne et al (1990). HNO, NH, and H201 remain

entirely in the frozen droplets. However, volatile species cnn be released into the air during

freezing. According to the laboratory results of Iribarne et al (1983). only 2 5 8 of SO, is

retained in ice dunng freering.

Snider et al (1992) conducted H20, retention rate measurements in stratiform

orographie cloud in order to possibly avoid unrealistic conditions associated with laboratory

studies by Iribame and Pyshnov (1990). They found the H,O, retention rate to be 30%.

Therefore in the freezing process, there are only 25% of SO, and 30% of Hz02 remaining

28

in the ice phase, while 100% of HNO,, NH,, and SO,' remains.

D. Cloud to graupel transport of soluble species

Graupel can grow by accreting cloud droplets. This is also a freezing process,

where, similarly. the retention rates are 100% for highly soluble species such as HNO,,

NH, and SO,', and 0.25, 0.3 for &O, and Sot, respectively

E. Rain to graupel transport of soluble species

Rain drops can freeze to form graupel. Supercooled rain drops can also accrete onto

hail. Al1 the chernical transfers are treated in the same way as in C and D.

F. Ice crystals to graupel transport of soluble species

Graupel can be directly initiated from ice crystals by auto-conversion of rimed ice

crystals. and can grow by collecting ice crystals. In this process. gases are not panially

released in this processes since there is no phase-change.

G. Ice crystal to cloud transport of pollutants

Once the temperature is above O°C, ice crystals are assumed to melt completely. Al1

29

chemical species stay in the newly-generated cloud droplets.

H. Graupel particle to min transport of pollutants

Due to their larger size relative to ice crystals, graupel or hail should not be simply

assumed to melt completely when T> O°C. Their melting rate is a function of ambient

temperature. supersaturation and graupel size (Kong et al, 1990). Chernicals from the

melted portion of graupel remain in rain drops.

I . Graupel to air transport of pollutants

Sublimation of graupel particles takes place whenever ambient air is unsaturated.

Melting graupel can also be easily evaporated in an unsaturated environment. In this

process. proportional amount of al1 pollutants except sulphate are released into the air with

evaporation of the graupel. Sulphate remains in the graupel particle and will not be

released into the air until the graupel particle is completely evaporated.

The above processes are the transport terms of different species with phase changes

between cloud. min, air. ice and graupel. The coefficients of conversion between different

hydrometeors. are identical to Kong et al (1990).

2.1.4 Modification of description of ozone

The treatment of ozone i n the present version of the model has also been irnproved.

In the original version, the ozone concentration was considered to be hornogeneous and

constant during the whole simulation tirne. This simplification can only be accepted or

justified in a shallow cumulus cloud system that has a short lifetime. In reality, ozone

concentration tends to be high near and above tropopause. In order to avoid the

overproduction of sulphate by oxidation of ozone under the circumstance of utilizing a

constant ozone concentration, the spatial and temporal changes of ozone concentration have

to be described in this model. Accordingly, the O, concentration is included as one of the

prognostic parameters, n in Equ. 2.1, 2.2 and 2.3 of the cloud chemistry rnodel, which

changes with cloud dynamics. microphysics and chernical reactions. Considenng the fact

that the solubility of ozone is small, we do not include ozone in the ice phase processes,

thus in Equ 2.1 2 and 2.13. n does not identify ozone concentration.

The vertical inhornogeneity of atmospheric ozone concentration, cumulus cloud

systems and the associated updraft and downward transport may also have implication to

the ozone budget. In a study of the role of deep cfoud convection in the tropospheric ozone

budget, Lelieveld and Crutzen (1994) found that convective clouds can cause a 20%

overall reduction in total troposphenc O,. The improvement of the treatment in the present

model also allows us to extend our rerarch into the ozone budget studies.

2.1.5 Demonstration of the transfer of chemicals with phase changes

In this section, the time evolution of the concentrations of HNO, and O, in different

hydrometeors are depicted to demonstrate the transfer of chemicals with phase changes.

A simple idealized cloud case is simulated with the same initial conditions as Kong et al

(1990). The cloud dynamic model (Kong et al. 1992. details in 2.2) requires as input the

initial temperature and hurnidity profiles, the vertical profile of the horizontal wind and the

surface pressure. These fields are assumed to be horizontally uniform over the domain of

integration at the beginning. A convective system is initialized by a temperature impulse

of 10 km x 10 km x 2 km dong the x. y and z directions, within which the temperature

increases from the ambient temperature To to T, + 2°C at the center of the impulse by a

Gaussian function. The domain size is set as 30km x 30km in the horizontal with a

resolution of lkm, and 30km in the vertical with a resolution of 500m. The cloud lasts

about one and a half hours. The cloud top reaches to around lOkm at 30 min..

The transfer processes for HNO, is shown in Fig. 2.1. All xz sections presented

here are located at y = 20 km, which is roughly the center of the domain in the y direction.

The dotted line identifies the cloud boundary taken as the 0.01 gm" contour. The tirne

evolution of vertical sections of HNO, concentration at Os, 720s, 1000s in air and cloud

are depicted in Fig. 2.15 b and c, where we can x e the initial HNO, concentration has a

constant value of 0.4 ppb below 2.5 km and decreases gradually to zero at 5 km. HNO,

vapor is totally dissolved if there exists cloud water in a grid box. The interstitial

32

f 1 I I I ' 1 ' 1 1 1 6 1 1 I 1

CONSTANT FIELD - VAL= 1s 0 9

I

Fig 2.1 (a)

Fig. 2.1 (b)

10.

7 . 5

5 .

* , I i 1 1 I ' 1 1 I I 1 4 I 1

m d

- - m

a

- . O 1 .O& Q1

œ - 2 . 5

o.

, 0.4 m

- a

, 1 1 i 1 t 1 t I I 1 1 I t I 1 I

0 . S . 10. 15. 20. 25. 30. 35. 40.

Fig. 2.1 (c)

HNO, concenmtion (d) in min at 720s

Fig. 2.1 (d)

(ppb) in air and claid ai (a) O S. (b) MO S. ad (c)720 s;

concentration of HNO, is zero. Therefore, the concentration outside the cloud boundary is

the gas phase HNO,, while the concentration within cloud boundary represents cloud-

dissolved HNO,. As shown in Fig. 2.1 b, HNO, is advected and diffused upward mainly

within the cloud volume with vertical convection during the eariy stage. However, after

720 s, with the formation of rain. the transfer of HNO, into precipitation and the

scavenging by precipitaiion become significant. This is illustrated by the descent of the 0.2

ppb isopleth in Fig. 2.1~. In Fig 2.ld. the HNO, concentration in rain at 720 s. we can

clearly see that the loss of HNO, from cloud (Fig. 2.1~) has been transferred to rain. After

1350s. ice Stans forming in the upper levels of the cloud. Fig. 2.2a shows the HNO,

concentration in ice or snow. Graupel or hail formed at around 25 min.. and precipitates

and transports HNO, downwards (Fig. 2.2b). The dotted lines in Fig. 2.1 b,c identify cloud

boundary (0.1 g k g ) . The ones in Fig. 2.ld and Fig. 2.2a.b identify the 0.1 g k g mixing

ratio of rain, ice or snow. and graupel or hail. respectively.

2.2. Cloud dynamics model

The cloud dynamics and microphysics fields of the 3-d warm cloud chemistry

model were provided by the cloud dynamics mode1 of Yau (1980) that describes only

warm cloud-rain microphysics processes. Only one ice category was included in a 2-d

version for studies of ckmistry of rainband (Leighton et al, 1990). In the present work

these fields are provided by the 3-D, bulk water, mixed-phase, cloud dynamics model of

Kong et al (1990).

Fig. 2.2 (a)

2.2.1 Model structure

The dynamic framework of Kong's rnodel is based on that of Klemp and

Wilhelmson (1978). There are 10 prognostic variables in the model, the wind velocity

components, potential temperature, pressure perturbation, specific humidity, mixing ratios

of cloud water, rain, and two categories of ice, ice crystals or snow, and graupel or hail.

Parameterization of warm min microphysical processes is based on Kessler ( 1969). i .e.

condensation of cloud water, auto-conversion of cloud water to rain water, collection of

cloud water by min drops and evaporation of rain water in unsaturated regions. The auto-

conversion threshold is set at lg/m3. Parameterization for icç-phase microphysical

processes, which include ice nucleation, sublimation. riming, dry- and wet-growth of

hailstone and melting, are rnainly based on Orville and Kopp ( 1977) and Cotton et al

(1982). Mixed phase cloud is always assumed between -40" and d°C. The main

microphysical processes are shown in Fig 2.3. Open boundary conditions are appiied at the

lateral boundaries, which makes it necessary to change the original periodic boundary

condition of the cloud chemistry mode1 to a rrdiative boundary condition.

In previous case studies, this model has captured many features of deep convective

storms. In the simulation of an intense thunderstom on July 19, 1977 in the South Park

Area Cumulus Experiment. the evolution and the lifetime of the storm are well represented

(Kong et al 1992). A h , the vertical echo structure of the rnodeled hailstorm was quite

similar to the structure observed by radar (Knupp and Cotton. 1982).

37

2.3 4 case study

We also used this mode1 to simulate the June 24, 1992 Colorado heavy rain and

hail storm for the 4th International Cloud Modelling Workshop (Song. et al. 1996). n i e

following are the results from the workshop.

3.3.1. Introduction

This storm. which has been well documented by Brandes et al. ( 1995) and Bringi

et al. (1995). developed over the Rocky mountains near Fon Collins. Colorado in the early

afternoon of June 24, 1992, and moved eastward onto the plains alter 2000UTC. Early

conditions were not favourable for convection because of lack of sufficient moisture in the

boundary layer. Mesoscale forcing in the forrn of a gust front that converged with

increasingly moist surface air subsequently modified the thermodynarnic environment. A

composite environmental sounding provided from sounding at two stations is the basis of

this simulation and other simulations of the storm presented at the workshop. This

composite sounding that was adjusted with surface conditions to produce a lifting

condensation level that roughly matched observed clouds, has considerably more moisture

than in the 2030UTC release. The sounding shows sirong wind shear and strong rotation

of the wind direction with height, making three dimensional simulation of the storm

essential in order to capture its dynamic structure. It has been stated that the changes with

height of horizontal wind profile, dong with the quantity of available thermodynamic

39

instability, have significant impacts upon the evolution of convection (Weisrnan and

Klernp. 1984).

2.3.2. Simulation

Results of the northeasiern Colorado hail storm of 24 June 1992 are compared with

observations and rnay also be compared with other simulations presenred by participants

at the 4th International Cloud Modelling Workshop. The inter-comparison study was

composed of two pans: an idealized case simulation for the purpose of cornparing the

microphysical representations in the different models; and a 3-D simulation with a

composite initial profile for cornparisons with observations.

2.3.2A. Idealized test

Different cloud models have different descriptions of microphysical processes, such

as the formuIations for saturated water vapor pressure over wrtter and ice; constant Iatent

heats or temperature-dependent latent heat; different levels of sophistication with regard

to precipitation representation; different treatment of the ice phase. In order to compare

cloud parameters from microphysics schemes used in different cloud models, a highly

idealized test case was designed (Grabowski. 1996). The framework of the idealized case

is an adiabatic parcel with a constant updraft velocity. Precipitation out of the parcel is not

allowed, and microphysical processes respond only to temperature and pressure changes

40

associated with updraft.

where p is pressure. T is temperature. F, are mixing ratios of different hydrometeor such

as water vapor, cloud water. min, cloud ice. graupel. g is the acceleration of gravity; Le.

L,, and L, are latent heats of condensation, sublimation and fusion, respectively.; Se, S,,

and S , are rates of evaporation. sublimation and freeUng inside the parcel; G , , is the

transfer matrix which describes transfer of water substances between different

categorieslphases of hydrometeor i and j. Initial conditions are determined by the density

p,, initial pressure p,. temperature T,. relative humidity RH, and the rate of ascent W.

given as

RH, = 0.7

p, = 850 h W

To = IOOC

p, = 0.8 kg m"

W = 8 m s-'

Parcel vertical displacement = 6 km

41

Fie. 2 4 Mixing ratios of cloud. min. icc and graupl in the ideaiized case

The results of the idealized case simulation. such as water vapour mixing ratio.

cloud water mixing ratio, rain water mixing ratio. cloud base height and the temperature

at the final level are presented in Fig. 2.4. The initial water vapour mixing ratio is 6.3 g

kg-' and the rnixing ratio at the final level of 8 km is 0.54 g kg*'. The parce1 temperature

at 8 km is -34.2 O C . Condensation commences at 640 m and auto-conversion at 1280 m.

Because a11 forms of condensed water remain in the parce1 in this idealized case, with the

increase of height and corresponding decrease in temperature. most of the water is

converted to graupel and hail by riming. The total water rnixing ratio remains constant at

6.3 g kg" dernonstrating the good mass conservation of the microphysics module of the

cloud model.

2.3.2B. Three-dimensional case simulation

a. Initinlization

The actual s tom was initiated by a gust-front that moved off the mountains. IdeaIly

the Storm should be simulated by a mesoscale model that includes topography and that is

initialized by well-documented initial and boundary conditions. However, for the purpose

of the workshop, the s t o n is simulated by a cloud-scale model in which the initial

conditions, that are based on a composite sounding, are horitontally homogeneous over the

simulation domain (Table 2.1).

Table 2.1 Input data of 24 June 1992 Colorado thunderstorm case

Pressure in hPa:

Temperature in deg C:

Water vapor mixing ratio in g k g :

Wind direction (deg):

Wind speed (rnfs):

The model domain is set at 101 x 101 x 38 grid points dong the x. y, and z

directions, respectively. The horizontal grid increment is 2 km and the vertical grid

increment is 0.5 km. The coordinates are oriented so that x, y correspond to east and nonh.

respectively. The integration timestep is 15 s with time splitting to 3 s in order to eliminate

the computation instability caused by the presence of sound wave. The initial field is

assumed to be horizontally homogeneous in temperarure, mixing ratio, and wind speed at

values given by the composite sounding at 2030 UT. In order to initiate convection i t was

necessary to introduce a Gaussian potential temperature perturbation with a maximum

value of 2S°C at the lowest level in the centre of domain. First atternpts dernonstrated that

the low-level moisture and convergence were too small to initiate convection. When the

dimensions of the warm bubble increased to 20 km x 20 km x 4 km dong the x. y and z

directions. a convective system was generated. It was found that convection could not be

triggered by a smaller or weaker bubble. Other participants of this workshop also

acknowledged the same problern with the provided temperature and humidity soundings.

and some even had to rnodify the initial conditions such as the value of humidity at low

levels in order to trigger a convective system.

b. Comparisons of the 3-D simulations with observations

The storm resulting from the above initialization was simulated for a total period

of three hours. Vertical cross-sections in the y-z plane through the centre of the storm at

different times are shown in Figs 2Sa-d. The model storm starts at the centre of the

45

l b .

a . te. 48. 66. m. tee. 126. 148. 160. 100. 200.

Fig. 2.5 (c)

Fig. 2.5 (d)

Fig. 1.5 Y-Z cross-section of total water mixing ratio (glkg) at (a) x-50 km, t-56 min. (b) x=SOlun, t J O min. (c) x=SS km, t=120 min.(d). x=6û km. t=168 min-

domain, and then moves slowly southeastward, in agreement with observations. The size

of the srom continues to increase throughout this period and at 168 min there is evidence

of the dcvelopment of a secondary convection ce11 (Fig. 25d). The heaviest precipitation

reached the surface at 40-60 min. and an anvil started forming at about 70 min.

4 a . I 1 I 1 L I I I * 1 r I b 1 I 1 r

D d

30. 3

h a

2 0 . - C

10. - œ

e 2a. 4a. 60. B I . Wa. ta. 148. t6a. lm.

nae: . (m.) Fig. 2.6 Time xrits of maxium vcnical velociry

Fig. 2.6 shows the time evolution of the maximum vertical velocity in the

simulation domain. The maximum updraft increases sharply during the first 50 min after

initialization reaching a maximum value of 35 m s". Following the initial vcry strong

updraft there is a secondary peak in the maximum updraft of 18 m s". In fact, for about

75% of the total simulation tirne. the maximum updraft is between 18 m s" t 40%.

Aircrzft penetrations through the storm betwan 2130 and 2300 UT mcasund updrafts of

about 18 m S.' (Brandes et al., 1995; Bringi et al., 1995). At about 170 min the maximum

updraft s t v t s to increase for a third time. In this case the increasc is associated with the

48

development of the new convective cell. Plots of the distribution of graupeühail (not

shown) indicates that hail reached the surface starting at 40 mins. The maximum

accumulation of precipitation at the surface after three hours was 103 mm. which c m be

compared with the total accumulation of precipitation at Fort Collins of more than 76 mm

(Brandes, I 996).

The tirne series of the average cloud. min, ice crystal and hail mixing ratios are

shown in Figs. 2.7a-d. The quantities are averaged over the volume of space for which

their mixing ratio is non-zero.

It is interesting to compare Fig. 2.6 and Figs. 2.7b and d. Strong updrafts at about

50 min and 145 min produce sharp increases in the amount of condensation and thus in

the production of precipitation by autoconversion and accretion and in the production of

graupel by rirning. The increased drag resuiting from the increase in precipitation

subsequently leads to significant reductions in the maximum vertical velocity.

Bringi et al (1996) report radar reflectivity analyses of the storm during the time

period 2135 to 2205 UT. They found that reflectivities generally exceeded 55 dBZ with

peak values of -65 dB2 and the 10 dB contour at about 15 km. Fig. 2.8 shows a vertical

section of the radar reflectivity from the model at 80 min in the x-z plane through the

centre of the storrn at y=56 km. The altitude of 10 dB2 contour and the maximum

reflectivity from the model agree well with observations.

49

Fig. 2.7 (a)

Fig. 2.7 (b)

50

Fig. 2.7 (c)

Fig. 2.7 (d)

Ttg. 2.7 'func Mcs of average mixing ratio of (a) cloud watcr, (b) rain. (c) icc and snow, and (d) mupl and hail.

51

x (KM) Fig. 2.8 Radar rcflccrivity (dB21 in Ihe X-Z at y=56 km. r d 0 min.

2.3.3 Conclusion

Table 2.2 Cornparison bctwccn model rcsults and observations

Max. Storm Top Prccipitation Max. Vertical Velocity rcficctivity ( I O dBZ)

Simulation 64 dBZ 15 km 101 mm (96 min.)

35 d s nt the fint peak t 8mls at the second peak

The hailstorrn that crossed NE. Colorado on Junc 24. 1992 has been simulatcd by

û three-dimensional mixed phase dynamical cloud model. Mmy of the storm fcatures are

reproduced by the model. These include the maximum radar reflectivity, height of the 10

d B 2 contour. precipitation amount. maximum vertical velocity and storm track. which

ûgree relûtively well with observations (Table 2.2). The results from the idealired case

D agrce well with ihc results of other participants of the workshop.

Chapter 3 Experiments and Methodology

The cloud chemistry and dynamics models that we use in this study have been

tested and have been found to perform well. Results from the models are therefore taken

as standards to evaluate the performance of the parameterization scheme. As introduced

in Chapter 1. the parameterization scheme is an explicit function of the concentrations of

ambient chemical species. narnely sulphur dioxide. sulphate aerosol, hydrogen peroxide,

ozone, arnmonia and nitric acid. It is also a function of some gross cloud parameters such

as average cloud water content, cloud base height, cloud thickness, cloud lifetime and

cioud total water content. Therefore. given ambient chemical profiles and these cloud

parameters shown in Table 3.1. the pararneterization may be applied in large-scale models

to provide a better description of in-cloud sulphate production.

Table 3.1 The input of the parameterization

Gross cloud parameters Ambient chernical concentraions

Average cloud water content

Cloud base height

Cloud thickness

Average cloud temperature

Cloud lifetime

Average toatal cloud mass

Sulphur dioxide

Hydrogen peroxide

Sulphate aerosol

Nitric acid

Ammonia

Ozone and Carbon dioxide

In this chapter, the senes of numerical experiments with different chernical and

dynamical conditions that have been used to test the parameterization will be introduced,

and the methodology of the parameterization will be described in detail.

3.1 Experiments

Different sets of temperature. humidity and wind profiles taken from Kong et al.

(1992) and Bringi et al (1995) are used to simulate three cloud cases: a shallow warm

cloud. a moderate mixed-phase cloud and a deep convective cloud. The gross cloud

pararneters are given in Table 3.2.

Table 3.2. Gross cloud parameters

parameters small cloud rnoderate cloud deep cloud

average cloud water content (g/m3) 0.29 0.26 0.19

cloud life tirne (mins) 23. 42. 75.

average temperature (K) 273. 265. 260.

total water content ( 1 06xkg) 0.89 78. 177.

cloud base height (rn) 2,500 4,3 O0 2,400

cloud depth (m) 4,000 1 3,000 13,800

A total of 36 different chernical environments identified by the concentrations of

SO,, HNO,, NH,, H,Oz, SO,' and O,, grouped into two categories, are used to test the

parameterization. The larger group (Table 3.3) contains more normal concentrations

whereas in order to test the parameterization for unusual concentrations the second group

(Table 3.4) contains concentrations that can be considered as extreme. The initial

concentrations of H,O, - - and 0, are taken as being uniform in the whole dornain. Other

chernical concentrations are assumed to be uniform below cloud base and to decrease

linearly with height to zero a: IO km. The aerosol composition is specified in terms 6: the

relative acidity chat is the rnolar ratio of sulphuric acid to total sulphate. Alihough these

vertical distributions are to sorne extent arbitrary, they are in a reasonable agreement with

observations (Leaitch et al., 1991; Liu et al. 1993). Fig 3.h and b show a set of observed

chernical concentrations and the corresponding idealized model input. S ince the goal of

these experiments is to compare the results from the parameterization with the results from

the 3-D chemistry model, details in the shape of the profiles are not critical. In order to

make cornparisons between the 3-D chemistry model and the parameterization, for each

simulation with the cloud model, the parameterization was applied with chernical

concentrations extracted from the profiles used in the cloud model simulation and gross

cloud parameters extracted from the output of the cloud dynamics model that drives the

particular cloud c hemistry simulation.

Table 3.3 Ambient chemical concentrations in normal concentration category at the surface (ppb, SO,' in 10" moI/m3)

Case I 2 3 4 5 6 7 8 9 10 11 12

NH, 1.0 1.0 1 .O 2.0 1 .O 1.0 1.0 1.0 1.0 1.0 1.0 1 .O

Relative acidity O O O 1 O O O 1 1 1 O O

Case 13 14 15 16 17 18 19 20 21 22 23 24

NH, 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 05 1 .O

Relative acidity O O O O O O O O 05 O 05 O

Table 3.4 Ambient chernical concentrations in extreme concentration category at the surface (ppb, SO,' in 10" mo~m')

Case 1 2 3 4 5 6 7 8 9 10 1 1 12

HNO, 0.0 0.0 1 .O 1 .O 1.0 0.0 0.0 1 .O 1 .O 1 .O 1.0 1.0

NH, 1.0 1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 10. 5.0 5.0

H,O, 4.0 4.0 4.0 5.5 4.0 53 5.5 4.0 55 6.0 6.0 6.0

SO,' 3.1 3.1 3 3.1 3.1 3.1 3.1 3.1 3.1 6. 6. 6.

Relative acidity O O O O O O O 1 1 0.5 05 1

3.2 Methodology

The parameterization describes the oxidation of S(IV) by hydrogen peroxide and

ozone in convective clouds. The chemical species SO,, H1O2, HN03, COi, O,, NH1 and

SO,' are included in the scheme. In order to be consistent with the coarse spatial and

temporal resolution in GCMs and regional climate rnodels, the chemical concentrations and

cloud properties in the parameteridon are simplified and represented by averaged

quantities. From many sensitivity studies, it was found that the chemical concentrations at

cloud base and in the vicinity of the cloud above cloud base are important factors in the

aqueous phase production of sulphate. Sulphate production was much less sensitive to the

shape of the concentration profiles below cloud base. Consequently. the average ambient

concentrations of the chemical species between cloud base and cloud top in the vicinity

of the cloud from the profiles used in the 3-D mode1 run are used as initial concentrations

in the parameterization. Similarly, the average temperature within the cloud is used to

define the temperature at which dissolution and aqueous phase reactions take place. The

temporal and spatial average of the cloud liquid water content is used to define the

aqueous concentrations of the chemical species. These are cenainly rather gross

simplifications. Since it is not possible to develop and test a parameterization direcily on

the basis of observations, the usefulness of these simplifications is evaluated by

cornparisons with the results from the more realistic three-dimensional simulations.

Based on the equilibriurn and reaction rate equations that describe the dissolution.

dissociation and oxidation processes (e.g. Leighton et al., 1990). and the cloud lifetime. the

parameterization scheme is applied to obtain the sulphate production from a unit volume

of cloud. Finally, the total production is obtained from the time average of the total amount

of cloud liquid water. In the parameterization, the gross doud properties such as average

cloud water content, cloud base height, doud thickness. average temperature, cloud life

time and cloud total water content, and the ambient chemical concentrations are considered

to be static throughout the lifetime of the cloud. In principle these parameters may be

obtained from GCMs or regional rnodels allowing the parameterization to be used i n such

large-scale models.

There are, however, problems that anse from the assumptions. The oxidation rate

is constant throughout the cloud lifetime and is determined by the initial chemical

concentrations and static cloud properties. For instance, a high hydrogen peroxide oxidation

rate caused by high concentrations of reactants or a high ozone oxidation rate caused by

m initially high pH value. which is kept constant during whole cloud lifetirne, rnay cause

senous overestimation of in-cloud sulphate production. As an example, for the shallow

warrn cloud case with chemical environment 7 (Table 3.3), the parameterization

overestimates the in-cloud production of sulphate by the oxidation of ozone and hydrogen

peroxide compared to results from the 3-D cloud chemistry mode1 (Figs. 3.2a.b). There are

two aspects to the overprediction of oxidation. One is that the initial oxidation rate is

maintained constant over the entire cloud lifetime, even when the cloud is dissipating. The

60

Fig. 3.2 (a)

- 3-0 L o o -

Fig. 3.2 @)

Fig. 3.2 Variation in tirne of sulphate production h m the 3-D chemimy mode1 and production h m the o n g i ~ l pmrncteriwtion schemc. (a) by the oxidation of HA, (b) by the oxidation of 0:.

other is due to high oxidation rates by ozone at high pH which normally reduce quickly

as the pH drops. The following sections describe the approaches that are introduced to

compensate for these effects.

3.2.1 Effective cloud lifetime correction factor

An implicit assumption in the parameterization is that there is sufficient transport

of chernical species into the cloud to maintain the chemical concentrations at a constant

value throughout the cloud lifetime. Clearly this is not reasonable. At least during the

decaying phase of the cloud when there are no updraughts, the gas phase concentrations

will start to decrease. To capture this effect a constant oxidation rate is applied but for a

time period that corresponds only to the active period of the cloud, that is the period

dunng which the cloud is characterized by updraughts at cloud base. rather than the full

lifetirne of the cloud. An alternative approach that has been used for example by Feichter

et al., (1996) to avoid overpredicting sulphate production is to limit the oxidation of S(1V)

in a single tirnestep to the available amount of aqueous S(1V) or H202. However. this

approach may lead to an underestimation of the amount of sulphate produced because

transport of S(IV) and oxidants into the cloud during the active phase of the cloud may be

substantial dunng the long timestep over which the parameterization is applied.

The 3-D cloud chemistry mode1 results do in fact show that in-cloud sulphate

production becomes relatively unimportant in the later stages of the cloud lifetime

(Fig.3.2a, b). Accordingly, a cloud lifetime correction factor is introduced, which accounts

for the Iow concentrations of reactants dunng the decaying phase of the cloud. Fig. 3.3a

shows that the sulphate production by hydrogen peroxide from the parameterization would

agree with that from the 3-D simulation if this lifetime correction factor were taken as 0.45

for this particular case. Funhermore, the agreement between parameterization and 3-D

simulation for sulphate production by ozone (Fig.3.3 b) is also improved. Similar results

were obtained from cornparisons between parameterization and simulation for the other

results shown here. On the basis of these results a single cloud lifetime correction factor

of 0.5 was applied to al1 results. This is the single tuned parameter in the parameterization

and it is fixed at the same value for al1 of the results shown here.

In-cloud oxidation by ozone is iikely to dominate for pH larger than 5.5 whereas

hydrogen peroxide is likely to be most important at lower pH. The acidity increase at high

initial pH due to oxidation by ozone tends to limit the amount of sulphate produced by

oxidation by ozone. Thus. if in the pararneterization the pH is kept fixed at a value

determined by the dissolution of the various species in the cloud water, the

parameterization may significantly overpredict the oxidation rate by ozone and hence

sulphate production. One approach to avoiding this problem is to fix the pH value of the

cloud water at a constant value which is independent of the concentration of the ambient

chernical species and which is small enough to avoid the rapid oxidation by ozone (e.g.

63

Fig. 3.3 (a)

Fig. 3.3 (b)

Fig. 3.3 As Fig. 3.2 but with the lifctime correction factor in the paramctcrivtio

Barth et al., 1996). However, under certain conditions. oxidation by ozone rnay be

significant and the above approach may resui t in sy stematic underestimation of sulphate

production. In the present parameterization, an alternative to these two extreme approaches

is employed. Instead of keeping the pH fixed at its initial value for the whole cloud

lifetime, we first assume total oxidation of the aqueous S(1V) that is initially in the cloud.

The resulting increase in acidity is used to detenine the subsequent oxidation rate. If the

initial pH of the cloud water is low. the solubility of SO, is small and hence assuming that

the S(IV) initially in the cloud water is quickly oxidized will have a negligible influence

on the subsequent pH of the cloud water. Thus, this procedure alleviates the problem of

the very high oxidation rate by ozone at high pH but has little effect on oxidation rates at

low initial pH. A mathematical description of the parameterization can be found the

Appendix.

It is important to state that the correction factor to the cloud lifetime and the

redefinition of pH are not tuned arbitrarily in different cases in order to obtain acceptable

results. They are physically based on cloud dynamics and cloud chemistry considerations

and the only adjustable parameter (the factor that modifies the cloud lifetime) is set to a

fixed and reasonable value. Fig.3.4a. b show that after these adjustments. for the panicular

case being illustrated, the final amounts of in-cloud sulphate production by the oxidation

of ozone and hydrogen peroxide from the parameterization agree with the result from the

3-D chemistry mode1 well. Identical correction procedures have been applied to al1 of the

results in the following sections.

m - S-D 1

Fig .3.4 (a)

Fig. 3.4 @)

Fig 3.4 As Fip. 3.2 but with the Iifetirne comction facror and pH cecaiculation includcd in the p~~nceri ta8on.

Chapter 4 Results and discussions

Three different types of clouds with a series of 36 chemical conditions are used as

input to the pararneterization. The results from the pararneterization are compared with the

results from the McGill 3-d cloud chemistry rnodel to formulate and test the

parameterization scheme. and the results are also cornpared with the results from the cloud

module of a long-range transport model to demonstrate a potential application of the

parameterization scheme in large-scale models.

4.1 . Cornparison between the results from parameterization and 3-D cloud chemistry

model I

The comparisons of the sulphate production by HZ02 and 0, are investigated

separately. Results for the shallow warm cloud case are shown in Figs. 4.la,b for the

normal air concentrations. For some cases H,Oz is the dominant oxidant, in others it is O,,

and in others the two oxidants are comparable. In al1 cases, regardless of the dominant

oxidant, the agreement between the parameterization and the 3-D model is very good, the

largest differences being 20%. Results for the shallow cloud and the chemical

concentrations classified as being extreme are shown in Fig. 4.2a, b. In these cases the

oxidation is dominated by H,O, for which the agreement is again good except for cases

8 and 12 which are characterized by very high S 0 2 concentrations. The agreement for

Fig. 4.1 (a)

Fig. 4.1 @)

Fig. 4.1 CompYison of sulphatc production for the 24 cases wiih n d chernid concentrations in ihe small cloud from the 3-O chernistry mode1 and from the p~~ncterizaiion, 3 oxidation by HP,; b) oxidation by O,.

Fig. 4.2 (a)

Fig. 4.2 (b)

Fig. 4.1 As Fig. 4.1 but for the 12 cxvcmc chernical concentrations.

69

ozone is not quite as good where for some cases (1, 2 and 10) the pararneterization gives

significantly smaller oxidations than the 3-D rnodel. These cases are characterized by high

initial values of pH for which the procedure described in section 3.2 suppresses the

oxidation by 0, too much compared to the 3-D model.

Due to computational limitations, we have not simulated ail of the 36 chemistry

cases for the moderate and deep clouds. Instead only the first 8 cases in the normal air

category havc been simulated with the deep cloud. and cases 9-12 with the mourrate

cloud. Results are shown in Fig. 4.3 a, b, where cases 1-12 refer to the concentrations in

Table 3.23. Again, the agreement between the two sets of results are quite acceptable being

within 30%. Figs. 4.4a. b show the results from the extreme concentration category and

the deep cold cloud. Similar to Fig. 4.2a. the parameterization results for cases 8 and 12

in Fig. 4 . 4 ~ have noticeable (>100$) differences from the cloud model results, but

otherwise the two sets of data agree well. Suiphate production by ozone is substantially

underestimated in cases 3, 4, 8 and 9 of Fig. 4.4b.

4.2. Discussion

For ozone oxidation, in the shallow and moderate clouds (Figs. 4.lb, 4.2b and

cases 10. 1 1, 12 in Fig. 4.3b). for the most part the parameterization performs very well.

For cases in which the cloud is initially acidic, either because of the acidity of the ambient

aerosol (8, 9 and 10 in Fig. 4.1 b; 8, 9 in Fig. 4.2b) or the excess of the ambient HNO,

70

Fig. 4.3 (a)

Fig. 4.3 (b)

Fig. 4.3 Cornpuison of sulpharc production for the first 12 normai concenîra~on cases fmm the 3-D cheMmy m d c l and from the pmmcteriwtion. with 1-9 for dcep cloud and IO- 12 for rn~deracc cloud. a) oxidation by H,oz: b) oxidotion by O,.

Fig. 4.4 (a)

Fig. 4.4 (b)

Fig. 4.4 Cornpuison of sulphare production for the 12 exmmc concenaption casa in decp cioud fmm the 3-D chcniisîry mode! and from the pmelm'zation. a) oxi&Oon by HA; b) oxidation by O,.

72

concentration over that of NH, (16, 17, and 23 in Fig. 4.1 b), both the 3-D rnodel and the

parameterization produced very srnail sulphate amoun ts. However, in the deep convective

cloud with strongly acidic ambient conditions (3. 4. 8, and 9 in Fig, 4.4b). the

parameterhtion scheme tends to underpredict the ozone oxidation. This is because the

parameterization implicitly assumes that the pH of cloud water is uniforrnly low over the

whole cloud. However, the pH in the middle and upper levels of the cloud will tend to be

higher because of the greater liquid water content and hence dilution of the hydrogen ions

resulting from the dissolution of HNO, and the scavenging of acidic aerosols at the cloud

base. Therefore, the ozone oxidation rate in the middle and upper levels of the cloud can

be higher than in the lower portions of the cloud. In deep convective clouds the vertical

variation of pH is much larger than in shallower clouds (Wang and Chang, 1992), and so

in the shallower clouds underprediction of oxidation by ozone is much less important. This

sensitivity of oxidation by ozone to the vertical variation of pH is not able to be captured

by a prrarneterization that assumes uniform and static cloud propenies. In order to evaluate

the parameterization for ozone oxidation and to test the effectiveness of recalculating the

pH. we intentionally simulate many cases for which the ambient conditions result in

initially low cloud water acidity. The parameterization works well in these cases and the

recalculation of pH described in section 3.2.2 does prevent the parameterization from

overpredicting sulphate production in cornparison to the cloud model results (Fig. 4.1. 2,

3, and 4).

Typically hydrogen peroxide will be the most important oxidant of S(1V). The

73

initial conditions used to generate the 60 sets of results reported here are consistent with

this since in the majority of results oxidation by hydrogen peroxide dominates. However,

at high initial pH, ozone may be the dominant oxidant of S(1V) (e.g. 2, 5 and 14 in Fig.

4.1 b; 2 and 4 in Fig. 4.3b; 10 in Figs. 4.2 and 4.4b). Figs. 4.13, 4.2a. 4.3a and 4.4a show

that the parameterization is able to descnbe the oxidation by HzOz very well. The poorest

agreement is for cases 8 and 12 in Figs. 4.2a and 4.4a where sulphate production is

overestirnated by the parameterization. These cases are characterized by exceptionally high

concentrations of HzO2 and SO?, which imply a high initial aqueous concentration of H.0, - -

and S(IV), and consequently a high oxidation rate in the parameterization.

Fig. 4.5 surnmarizes the cornparisons of the total of the sulphate production by

hydrogen peroxide and ozone in each case from the parameterization and cloud model.

Except for cases with the deep cloud and with environments that we have characterized

as being extreme, for which the parameterization persistentiy overestirnates sulphate

production, the parameterkation agrees well with the results from the cloud model, most

of the parameterization results lying within d208 of the cloud model results.

4.3. Cornparison between the results from parameterization, 3-D cloud chemistry model and

ADOM cloud module

As an indication of a potential application of the parameterization. we compare the

sulphate production by the parameterization with results from the cloud module of the Acid

SO i- Production from 3-D Model (lo2 moles)

Fig. 4.5 (a)

-

SU Production from 3-D Modd (1d moles)

Fig. 4 J (b)

Fig. 45 (d)

Deposition and Oxidant Model (ADOM) and 3-D cloud mode1 simulations for identical

initial conditions (Glazer et al., 1994). The ADOM is a Eulerian long-range transport of

atmospheric pollutant models, developed by Environmental Research and Technology and

Meteorological and Environmental Planning Company of Canada (Venkatram et al, 1988).

For the purpose of acid rain study, one of the most important components of ADOM is its

cloud module that describes cloud formation. pollutant scavenging, aqueous-phase

chemistry and wet deposition (Karamchandani and Venkatram, 1992). In-cloud processes

such as scavenging, aqueous-phase reactions can make major contributions to acid

precipitation. However, the cloud module is likely to be one of the most highly

parameterized and least well established pans of ADOM. For example. the cloud base and

cloud top heights are important inputs to the module, to which the chemistry results are

sensitive.

Table 4.1 Gross cloud parameters

parameters Cloud A Cloud B

average cloud waier content (g/rn3) 0.3 O .3

cloud Iife tirne (mins) 30. 30.

average temperature (K) 273. 273.

total water content (1 06xkg) 1.45 1.83

cloud base height (m) 1350 1350

Glazer and Leighton (1994) evaluated the cloud module of ADOM by comparing

results from simulations with the module with the results from simulations with equivalent

conditions with the 3-d McGill cloud chemistry rnodei. Two cloud cases (Cloud A and B)

generated by the dynamical cloud model of Yau (1980) wirh 12 different initial chemical

conditions were simulated in their study. Table 4.1 and 4.2 give the gross cloud pararneters

of the two clouds and the surface concentrations of the 12 chemical conditions,

respectively .

Table 4.2 12 ambient chernical concentrations at the surface (ppb. SO,= in 1 O-' rnoi/rn3)

SO? 1.0 1.0 5.3 1.0 5.3 5.3 1.0 20. 5.3 20. 20. 20.

HNO, 1.0 1 .O 1 .O I .O !.O 1.0 1 .O I .O 1 .O 1 .O 1 .O I .O

NH, 1.0 I .O 1.0 2.0 1.0 1.0 4.0 1 .O 1.0 4.0 I .O 1 .O

SO,' 20. 5.2 5.2 5.2 5.2 20. 5.2 20. 20. 20. 20.

Relative acidi ty0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.65

Similar to the expenments in Chapter 3, the concentrations of 0, and H,O, are kept

uniform through the vertical domain of the model, and the concentrations of other

chernicals are assumed to be uniforrn below cloud base and to decrease exponentially with

Fig. 4.6 (a)

Fig. 4.6 (b)

Fig. 4.6 Cornpaison ktween sulphrte production fmm the 3-D cloud chemistry model. the p~;imetaiution. and the ADûM cloud module. 'Ihe number 1-12 identim 12 cases with dinercnt chemicai profila h m Cilater et al (1993). a) for Cloud A, b) for Cloud B.

height. The scale height is taken as one kilometre. Their results show that the aqueous

oxidation of S(IV) from ADOM is in almost al1 cases significantly greater than that from

the cloud chemistry model (Glazer and Leighton. 1994).

It may make the pararneterization scheme of in-cloud sulphate production more

convincing by companng it with results that are generated by orher researchers. The clouds

A and B with 12 different chemical conditions used by Gluer and Leighton (1994) are

chosen to test the parameteriwtion. The cloud gross parameters from Table 4.1. and the

chemical conditions from Table 4.2 are utilized as input of the pararneterization. As shown

in Figs. 4.6, the cloud module of ADOM tends to overestirnate the amount of sulphate

production compared to the results of the 3-D cloud chemistry model. However. the

agreement between the pararneterization and the cloud chemistry rnodel is very good

indeed,

Chapter 5 Preliminary application in NA RCM

In the previous two chapters, the methodology and performance of the

pararneterization has been introduced. This parameterization scheme is an expiicit function

of cioud gross parameters and chemical concentrations. It is based on equilibrium equations

describing the dissolution of gases into cloud water (Henry's Law), dissociation equations,

and renction equations describing the aqueous oxidation of S(IV) by H,O, and 0,. Two

factors brised on dynamical and chemical considerations were also introduced in order to

compensate for the simplifying assumption that the cloud properties are constant over the

cloud lifetime. A series of experiments with different cloud and chemical conditions

demonstrated that the pararneterization agrees with 3-D cloud chemistry mode1 well. I n this

chapter, the preliminary application of the pararnetenwtion in NARCM (introduced in

chapter one) will be described.

5.1 The structure of NARCM

NARCM is an application of the Canadian Regional CIimate Mode1 (RCM, Laprise

et al, 1997) to simulate the rnass budget and the size distribution of atmosphenc aerosols

resulting from emission processes, clear air transformation and removal processes. and

aerosol-cloud processes. The RCM is utilized as a framework to provide NARCM

rneteorological fields such as wind velocity , temperature, pressure, relative humidi ty , land

surface information etc. Aerosol algorithms developed for NARCM can. in principle, also

be used in the Canadian General Circulation Model. At the present tirne, GCMs are the

most sophistical tools available to address the issues of global climate change. However

due to the limitations of spatial resolution as well as insufficient scientific basis, the

parameterizations included in GCMs are often too crude. Pararneterizations for aerosols

need to be developed in higher resolution models and tested against observations in order

to gain more confidence in their validity before they are incorporated in lower resolution

GCMs. Therefore, a graduated scaling approach for NARCM has ken proposed and

implemented. in which three different scale climate models have been used for various

purposes: first the Local Climate Model (LCM. Blanchet et al. 1997). a one-dimensional

version of the Canadian GCM II is used to develop. diagnose and validate various

parameterizations. then the Regional Climate Model (RCM, Laprise et al. 1997) is used

to perform 3-D simulations, and ultirnately the Canadian General Circulation Model (GCM

II, McFarlane et al. 1992) will be used to investigate the aerosol global climate forcing.

The three models are actually a family of models based on the GCM II. They share the

same dynnmic structures: the semi-Lagrangian advection and a semi-implicit scheme, but

have different xales as shown in Fig 5.1.

The GCM II serves as the foundation of al1 these climate models by providing

dynamics and physics packages and boundary conditions to the RCM and LCM. The RCM

possesses its own dynamics, and calculates al1 the physics in a regional domain, while the

LCM does the physics at only a single grid point with the dynamics fields from GCM II.

82

Fig. 5.1 Scdcs of the thne climate rnodcls used for NARCM

As a preliminary application, we coupled our parametenzation to the LCM to investigate

the impact of clouds on the sulphate aerosol budget and its size distribution in the Arctic

region. Therefore, in the following sections, the GCM and LCM will be introduced in

some detail.

S. 1.1 General dynamic features of GCM II

All general circulation models bear a resernblance. They are based on the primitive

equations of motion, and include explicit representations of the main physical processes

that determine the atmospheric circulation on seasonal and longer tirne-scales. They dso

have sufficient resolution to represent atmospheric structures at synoptic and planetary

scales. Atmospheric climate models have been recognized as powerful tools for quantitative

studies of climate due to their ability to represent more or less realistically the radiation

budget of the atmosphere and surface, the global circulation, and the associated

hydroiogical cycle.

The Canadian GCM II (McFarlance, et al, 1992) is a spectral model that makes use

of a truncated expansion in spherical harmonics to represent model variables in the

horizontal. A feature of the numerics of this model is its semi-Lagrangian and semi-

implicit scheme (Robert, 1982; Robert et al, 1985). It has been proved that the semi-

Lagrangian and semi-implicit techniques make possible the integration of Eulerian

equations at large scale with little computationai expense compared to more traditional

84

schemes. Horizontal motion is descnbed by equations for the vonicity and velocity

divergence. Other basic prognostic equations include the thermodynamic equation written

in terms of a function of geopotential height. Specific humidity is a propnostic variable.

5.1.2 Local Climate Model (LCM)

For the convenience of developing and testing new physicûl or chernical processes

in NARCM, the Local Clirnate Model (LCM). a column (one-dimensional) version of

GCM 11. has been developed. It is a full-physics and serni-prognostic atmospheric climate

model. The model requires initial conditions of the column region and lateral transport of

prognostic variables from the upstream region, such as horizontal wind field U and V.

temperature T. moisture Q, and surface pressure P,. The dynamic variables U, V. T and

Q are updated regularly from the results of a precalculated GCM 11 reference run of a

global climate simulation. However, al1 physical processes such as radiation, hydrology.

convection, precipitation, snow, frost, sea-ice, topography, heat fluxes. surface energy

balance are recalculated by the LCM with subroutines identical to their counterparts in

RCM or GCM II. The general methodology of the LCM may be described as follows:

where F is a prognostic variable. i+. U, V, T or Q. The variable F at time step n+l is

85

obtained frorn the value at the previous timestep F(n) by adding the dynamic tendency

D(n) and the physics tendency P(n). The dynamic tendency D(n) has been saved in the

GCM archive frorn the reference global simulation. The tendencies due to al1 the physical

processes mentioned above are generated by the LCM itself.

The basic reason to utilize the LCM is to avoid the complications and expense of

running the three-dimensional general circulation mode1 for testing the parameterization.

It is more straightforward to develop new physics and chemistry parameterizations and

perforrn various sensitive studies with the LCM. When confidence in a new physics or

chemistry parameterization is gained from its applications in the LCM, it is relatively easy

to couple the new processes to the three-dimensional GCM II. Here. the parameterization

of in-cloud sulphate production that we have developed is coupled to the LCM as a

preliminary application to investigate the effect of cloud on the sulphate budget and size

distribution of sulphate aerosols.

5.2 Cloud representation in present NARCM

On average, 50% of the Earth is always covered with clouds. Clouds scatter solar

radiation to cool the atmosphere by 48 WmJ, and also absorb infrared radiation to warm

the atmosphere by 30 Wm'* on global average (Collins et al. 1994). Additionally, it has

been found that the cloud processed aerosol particles may be more efficient in their direct

and indirect forcing (Charlson et al, 1991). Clouds exert a major impact on the climate

86

system. However. clouds. in spite of their importance, have not been well represented in

climate models. The different treatment of cloud processes in GCMs is one of the main

reasons for the discrepancies in the climate predictions of various models (Cess et al.

1 990).

In GCM II, cloud cover is diagnosed from large-scale variables such as relative

humidity. which is a prognostic parameter. Cloud water content is a diagnostic estimate

from relative hurnidity and temperature. The fractional cloud cover < C > is

where c h >, the mean relative hurnidity in the grid square, is evaluated from prognostic

variables in the model. < h> is the threshold value that is a prescribed function of

atrnospheric pressure

where = p/p,, p, being the pressure at the surface.

5.3 Aerosol scheme in NARCM

As introduced in Chapter one, in the first version of NARCM, routines to mode1

a size-segregated sea-salt aerosol were included in the LCM (Gong et al, 1996). In their

studies. atmospheric sea-sali aerosol concentrations taken frorn long-term observation of

Na+ at seven stations are cornpared with modelling results. Good agreement is achieved in

the northern hemisphere. In another study, the physical and chemical evolution of sulphate

aerosols has also been included in the LCM and the RCM. However, the spectrum and

concentration of sulphate aerosols are derived solely from emission, transport, coagulation,

condensation, wet and dry-deposition and gaseous phase oxidation. Aqueous chemistry,

which may have a significant impact on the global sulphate budget, and hence on the direct

and indirect effects of aerosol forcing of clirnate has not been considered yet. This

framework provide an ideal opportunity to apply the parameterization that we have

developed to investigate the potential implications of aqueous oxidation of S(1V).

In the LCM. the evolution of sea-salt or sulphate aerosols are governed by a senes

of physical equations describing transport, coagulation, dry and wet deposition, and clear

air chemical transformations. Usually there are 8 or 16 size bins that range between 0.001

pm and 10 pm to identify the radius of aerosol particles. Nevertheless, the number of size

bins and the radius range can be easily modified according to need. A generalized

prognostic mass balance equation for size i of type j aerosol particle is

where X , is the type j aerosol's mixing ratio in the i th size range. V is the horizontal wind

velocity vector that is obtained from the GCM II archive of the reference run, p is air

density, and S, is the source and sink terms that includes processes such as: natural and

anthropogenic surface sources; clear-air processes including particle nucleation, panicle

coagulation, and chernical transformation; in-cloud processes such as activation of aerosols,

scavenging of aerosols by cloud, dry deposition and precipitation scavenging. As

mentioned previously, aqueous phase chemistry has not yet been included. Term 1,

represents the rate of intersectional transfer which moves the aerosol particles from one

size bin to another by processes such as coagulation or breaking, condensation or

evaporation.

5.4 Ciirnate implication of arctic anthropogenic aerosols

The Arctic region is one of the main foci of NARCM. The Arctic region was

always considered as a remote area with little aerosol burden due to its distance from the

major aerosol sources and the short residence times of the particles in the atmosphere.

Fenn (1960) made the first Arctic aerosol measurements, and indicated a nearly particle-

free Arctic region. However, with the improvement of instruments and persistent

observations, better insight into the properties of Arctic aerosol has been obtained (Flyger

et al, 1973; Heintzenberg, 1980; Bame, 1986; Li and Barrie, 1993; and Heintzenberg and

89

Leck, 1994). The concentration of Arctic aerosol has been observed as significant in

winter, and with strong seasonal variation (Flyger et al, 1973). By analyzing the number

size distributions of Arctic aerosol and the global background aerosol, Heintzenberg ( 1 980)

revealed that in the range 0.05 pm to 0.2 Fm radius (Aiken particles) there is an excess

concentration of about one order of magnitude in Arctic haze that causes the rernarkably

bluish visual appearance in Arctic air. while there is a very sharp decrease in concentration

with particle size from about 0.3 pm up to 10 pm (coarse mode). This result strongly

suggests that the Arctic aerosol is well aged aerosol transported from its surrounding

source regions, because during the long-distance transport, rerosol particles with larger

radii are more easily removed by dry/wet deposition. However, the removal rates of Aitken

nuclei whose radius is less than 0.1 pm is small when the concentration is about a few

hundred per cm' (Junge, 1963). During winter, Eurasian industrial regions are the principal

sources of Arctic pollution (Barrie, 1986). because of the high latitudes of Eurasia sources

and the existence of a persistent anticycloiie over northern Asia during Winter (the Siberian

High). Sulfur isotope and other studies indicate that 70% of the lower troposphenc aerosol

in the Arctic is anthropogenic in origin (Li and Barrie. 1993; Heinzenberg and Leck,

1 994).

In the polar region, aerosols a h have strong interactions with clouds. According

to observations (Gultepe and Isaac, 1996; Isaac and Stuart, 1996), in summer when the

northern polar ngion is covered by arctic stratus, aerosol mass concentrations are 10 to 20

times lower than in the polluted winter. The high concentration and long residence tirne

90

of winter arctic aerosols attributed to a minimum in removal processes due to a low

preci pitation rate.

Sulphate aerosols are almost non-absorbing. In arctic regions, due to the high

surface albedo of the snow covered surface. the solar radiation reflected by sulphates is not

very imponant. Increasing concentration of sulphate aione will not be important to the

arctic radiation budget. However. aerosols may impact on the radiation budget as a result

of the rernoval of water vapor from the arctic atmosphere due to longwave cooling caused

by aerosols. Blanchet and Girard (1995) speculated that the depletion of water vapor rnay

contribute to greater infrared cooling and result in a positive feedback loop for cooling in

the Arctic, which rnight be responsible for the decrease in arctic surface temperatures over

the last four decades. even though the predictions of global climate models show an

increase of temperature in the Arctic under the influence of greenhouse gases (Kahl et ai.

199 1 ; Bradley et al, 1993)

5.5 Results from current NARCM

In the last section, we discussed the importance of arctic aerosols on climate

forcing. Most of the arctic aerosols are transported from Eurasian industrial regions. Fig.

5.2 shows the winter arctic haze sîze distribution from a set of 19-day continuous

observations in Aprii a i Ny-Alesund of 12" E, 79" N by Heintzenberg (1980). Since the

arctic aerosols are aged aerosols, the aerosol mass is five to six times greater in the

91

-8.00 -7.00 -6.00 radius (logr, m)

Fig. 5.2 Aictic hare distribution et Ny-Aiesund of 12 E, 79 N. (Heintzcnbcrg. 1980)

accumulation mode than in the coarse mode, which is different from common continentai

aerosol size distributions. The peak of the volume size distribution dvld(1ogr). lies ai

around 0.1 pm with a magnitude of 5.5 ~ r n ~ cm-'. nie aerosol mass concentration at Ny-

Alesund is between 2 and 7 pg m.). This spectrum, which will be used later in our study.

can be considered as a typical of the in Arctic region since many observation made after

Heintzenberg agree with it well. Shaw (1984) investigated the particle size spectra of

Arctic-derived air masses during late winter and spring 1983 from Ester Dome Observatory

in central Alaska and also found a large accumulation mode centred around 0.2 prn

diameter. The Canadian Arctic aerosols show the same features according to the discussion

of Barrie and Hoff (1985) from long-term observations made at three stations of the

Canadian Arctic Aerosol Sampling Network (CAASN) between 1979 and 1984.

Arctic aerosols originate mainly from surrounding source regions. Thus, in order

to adequatel y rnodel polar aerosols it is necessary to si mulate the movement and evolution

of anthropogenic aerosols and their precursors from the mid-latitudes to the nonh.

In the RCM, aerosols from 30 O N to the nonh pole are studied (Fig 5.3). A global

emissions inventory on a 1 O x 1 gnd from the Global Ernission Inventory Activity

(GEIA) of IGAC for SO, for 1985 has been used. In the present version of NARCM.

sulphate aerosol is distnbuted in 8 size bins ranging from 0.01 pm to 1 Pm. The main

sulphur emission is in the f o m of SO,. Primary sulphate emission accounts only for about

10% of the total sulphur emission. In this simulation, all the prirnary sulphate is distributed

93

Fig. 5.3 The simulation domain of NARCM

in the first size bin 0.01 pm - 0.019 Pm. The sulphate generated from photo-chemical

reactions is also distributed in the first size bin. The photochernical conversion rate to

S(VI) from S(1V) is based on Barrie et al (1 984):

R = Max ( 0.0779 F, 0-lW7, 0.05 1 } 55 1 hr.

where F, is the solar flux received at the surface [ ~ l m ' ] . A simulation was run for four

winter months from December to Apnl. After about 60 days, the sulphate aerosol size

distribution reached an alrnost steady state where the budget and size spectrum of sulphate

do not change much with location and time in the region nonh of 60" N. (Fig. 5.4).

Now we select one of the arctic sulphate volume size distributions that is an

average over 5" x 5" around the grid point of latitude 70" nonh. longitude 80" West in the

Northwest Territories of Canada (Fig. 5.5). Aerosols are distributed in eight size bins that

are averaged divided between from 0.0 1 pn and 0.1 Pm, i.e. 0.0 1-0.0 19 Fm. 0.0 19-0.037

Fm, 0.037-0.073 pm, 0.073-0.14 Fm. 0.14-0.27 Pm. 0.27-0.53 Fm. 0.53-0.1 Fm. In this

version of the RCM/NARChf. the behaviour of sulphate aerosols are described by

processes such as transport. coagulation. dry and wet deposition, and clear air chemical

transformations. Fig. 5.5 shows that after three months, the arctic sulphate aerosol

distribution becomes stable with a peak value at 0.1 Pm. This feature is in a good

agreement with the Heintzenberg's observation. However, the mass of the aerosols from

the simulation is less than the observation by a factor of 10. Assuming a sulphate particle

95

Fig. 5.4 Sulphate aerosol size distributions at the 60& day of the simulation

Fig. 5.5 The sulphate aerosol size distribution at 70 O N, 80 O W from the simulation results of Fig. 5.4.

density of 1.835 kg m", the total aerosol mass at this site is about 0.27 pg m", while the

observed values are 2-7 pg rn" (Heintzenberg, 1980).

This version of the RCM/NARCM was unable to account for the observed aerosol

concentrations. A too large removal rate has been postulated as a reason for the

discrepancy. However, since Gong et al (1997). using the same wet renioval processes,

found good agreement with observations for sea-sali aerosol. i t is unlikely that a too high

wet scavenging rare in the model is the reason for the discrepancy. The other possi bility

is that the production of sulphate in the model is too small. Clouds may play an important

role in the arctic aerosol budget and its size distribution. As introduced in Chapter One,

more than 70-908 of atrnospheric sulphate is produced in cloud. and in-cloud processes

can have a signifiant impact on the sulphate aerosol size distribution. However. this

important process has not yet been accounted for in NARCM. Clouds here only acc as

sinks of sulphate aerosols by washout of the aerosol.

5.6 Results from the preliminary application

To get a better representation of the evolution of the arctic sulphate aerosol. the

mass production of sulphate from in-cloud oxidation by hydrogen peroxide and ozone is

studied as a fint application of the parameterizaiion that we have developed. The

investigation of the modification of the size distribution spectrum of atmospheric aerosols

due to heterogenous oxidation is then performed. One grid point of NARCM is chosen as

98

the domain of the LCM in which the parameterization is incorporated. The horizontal

domain size is 256 km x 256 km. There are ten vertical layers frorn the surface to 30 km

(Table 5.1). The integration timestep is 20 minute. Chernical concentrations of SO?. HNO,,

NH,, H202, SO; and O) and their tendencies are not yet available in LCM. Therefore. only

SO,. SO,=, H202 and O,, which are considered to be the most important chemical species

in the aqueous oxidation of S(IV), are included in this simulation. The concentrations of

these aerosols are assumed to be horizontally homogeneous during the whole integration

time. Cloud parameters such as liquid water rnixing ratio and cloud cover are from the

archive of GCM run. There is no depletion of the arnbient chemical concentrations as a

result of in-cloud oxidation.

In this run, the surface concentrations of SO,, SO,', H20, and O, are 0.4 ppb. 0.1

pg m.'. 3.8 ppb and 50 ppb respectively. The sulphate is assumed to be entirely in the form

of sulphuric acid. This set of concentrations is in the range of observations in northern

Canada (Barrie, personal communication). Except for the concentrations of H20, and 0,

which are uniform over the whole vertical domain, the concentrations of other chernicals

are assumed to be constant below 5 km and to decrease linearly from 5 km to zero at 10

km. The clouds in this case mainly occur in layers between 0.5 km and 10 km. The cloud

depths Vary. ranging from 1 km to 7 km, while the cloud cover within a cloudy layer is

almost always 1. The clouds last from one to two days.

Table 5.1. Vertical Ievels of LCM

Since the ambient chemical concentrations are presently assumed to be constant

during the whole process, the oxidation rate, only dependent on cloud characteristics. varies

in a narrow range. Aqueous sulphate production within cloud at each timestep is 2.0 - 4.0

pg m-'. Due to the long integrating timestep in the LCM, al1 the S(1V) within the cloud

might be oxidated into S(1V) with constant oxidation rate. The sulphate production is

actually larger than the amount of S(1V) within the cloud. Mass conservation can be easily

kept by forcing the maximum production term equal to the source term. A more realistic

result would require calculations with a small timestep, sornething that is not feasible with

the present computational resources.

In order to get a general idea of the impact of in-cloud oxidation, the modification

of sulphate size distribution in the cloudy layer has been investigated. An initial sulphate

size distribution is taken from Fig. 5.5, based on which the panicle number in each size

bin can be calculated. If we take the maximum in-cloud supersaturation as big as 0.1%.

according to Kohler curve (Rogers and Yau. 1989)- panicles with radius of 0.02 pm and

larger can be activated. We also assume that 2.5 pg m-' new sulphate is homogenously

distributed on each activated nucleus within cloud domain. Then the total aerosol mass

becomes about 2.8 pg mes, a value within the range of 2-7 pg m.' observed by

Heintzenberg (1980). Fig. 5.6 shows that, after the cloud processing, al1 the panicles in

size bins of 0.019 pm - 0.037 Fm and 0.037 pm - 0.073 prn have evolved into size bins

of 0.073 pm - 0.14 pm and 0.14 pm - 0.27 Pm. The maximum in the volume-size

distribution is still around 0.1 prn. however, with a magnitude of 3.5 pm3 cm'' that is in

a much better agreement with the observation (Fig. 5.2).

In the comparison, we only use the aerosols from the cloudy layers to compare with

the observation. The observed aerosols are implicitly assumed to have been processed by

clouds. This assumption begs many questions: what amount of aerosols has been activated

in cloud? what is the portion of cloud that evaporates and release new aerosols? etc. These

problems exist in the LCM inherently, and can be solved with the development of a more

sophisticated version of NARCM.

-8.00 -7.00 -6.00 radius (logr, m)

Fig. 5.6 nie modified sulphate aerosol size distribution by imposing 2.5 ug /rn**3 sulphatc h m incloud production

It is necessary to state that the purpose of this preliminary study is to evaluate the

importance of in-cloud sulphate production and to evaluate to what extent the

parameterkation can reproduce this process. These preliminary applications should not be

considered as a final scheme of the sulphate aerosol in NARCM due to the 1-d restriction

and the arbitrarily assumed constant chernical concentrations.

Chapter 6 Summary and Conclusions

To give a better description of in-cloud sulphate production in large-scale rnodels,

a simple parameterization has been developed. The parameterization in based on the

standard reaction rate equations applied to average air concentrations of the relevant

chemical species in the vicinity of the cloud and gross cloud propenies. The chemical

species SO,, H20z, HNO,, CO2, O,. NH, and SO,= are included in the scheme. In order

to be consistent with the coarse spatial and temporal resolution in GCMs and regional

climate rnodels, the chemical concentrations and cloud propenies in the parameterization

are sirnplified and represented by averaged quantities. Consequently, the average ambieni

concentrations of the chemical species between cloud base and cloud top in the vicinity

of the cloud from the profiles used in the 3-D mode1 run are used as initial concentrations

in the parameterization. Similarly. the average temperature wi thin the cloud is used to

define the temperature at which dissolution and aqueous phase reactions take place. The

temporal and spatial average of the cloud liquid water content is used to define the

aqueous concentrations of the chemical species. These are certainly rather gross

simplifications but since it is not possible to develop and test a parameteriration directly

on the basis of observations, the usefulness of these simplifications is evaluated by

cornparisons with the results from the more realistic three-dimensional simulations.

Based on the equilibrium and reaction rate equations that describe the dissolution.

dissociation and oxidation pmcesses (e.g. Leighton et al., 1990). and the cloud Iifetime, the

parameterization scheme is applied to obtain the sulphate production from unit volume of

cloud. Finally, the total production is obtained from the tirne average of the total arnount

of cloud liquid water. In the parameterization, the gross cloud properties such as average

cloud water content, cloud base height. cloud thickness, average temperature, cloud life

time and cloud total water content, and the arnbient chemical concentrations are considered

to be static throughout the lifetime of the cloud. In pnnciple these parameters may be

obtained from GCMs or regional models allowing the parameterization to be used in such

large-scûle models.

The assumptions of constant cloud and chemistry properties iead to problems such

as constant oxidation rate and constant pH value. The oxidation rate is constant throughout

the cloud lifetime and is determined by the initial chemical concentrations and static cloud

properties. For instance, a high hydrogen peroxide oxidation rate caused by high

concentrations of reactants or a high ozone oxidation rate caused by an initially high pH

value. which are kept constant during whole cloud lifetime, may cause serious

overestimation of in-cloud sulphate production. The initial oxidation rate should not be

maintained constant over the entire cloud lifetime. especially when the cloud is dissipating.

The high oxidation rates by ozone at high pH which nonnally reduce quickly as the pH

drops should also be recalculated. Therefore, two adjusting factors. which account for the

effective cloud lifetime and the changing cloud pH value, respectively, have been

105

developed to compensate for these effects.

The parameterization has been tested by comparing it against the results of many

detailed cloud mode1 simulations. Different sets of temperature, humidity and wind profiles

taken from Kong et al. (1992) and Bringi et al (1995) are used to sirnulate three cloud

cases: a shallow warm cloud. a moderate rnixed-phase cloud and a deep convective cloud.

A total of 36 different chemical environments identified by the concentrations of SO,,

HNO,, NH,, H20,. SO,' and O,, grouped into two categories, are used to test the

parameterization. The same sets of chernical conditions and the gross cloud parameters that

are extracted frorn the 3-D clouds are used as the initial conditions of the parameterizaion

scheme to get the corresponding sets of results.

Cornparisons of the two sets of results have shown a satisfactory agreement

between the parameterization scheme and the 3-D rnodel. with differences within +20%.

The oxidation of S(IV) by hydrogen peroxide is very well reproduced by the

parameterizaiion in ail but a few cases with extreme ambient chemical concentrations. The

oxidation by ozone is also reasonably represented and the recalculation of pH seems to be

able to capture the self-limiting oxidation feature of ozone. In the simulations involving

a deep convective cloud, oxidation by ozone tends to be undenstimated by the

parametenzation in conditions where the initial ambient chemical concentrations tend to

make the cloud water strongly acidic. Nevertheless, sulphate production by oxidation by

ozone i s generall y less important than by hydrogen peroxide, especiall y in acidic cases,

106

which reduces the net effect of the disagreement.

The results from the parameterization with the 3-D chemistry mode1 are also

compared with the results from the cloud module of the regional model ADOM, which has

been evaluated by Glazer and Leighton (1994). Their results show that the aqueous

oxidation of S(1V) from ADOM is in almost al1 cases significantly greater than that from

the cloud chemistry model (Glazer and Leighton. 1994). However. the agreement between

the parameterization and the cloud chernistry model is very good indeed. The cornparison

suggests that the parameterization is superior to the ADOM cloud chemistry module and

hence that it holds considerable promise for use in regional and large-scale cloud chemistry

models.

As a first application, we have made an effort to implement the parameterization

in a large scale climate model to investigate the effect of rqueous oxidation of SO? on the

sulphate budget and its size distribution spectrum. The NARCM has been developed by

coupling a size-segregated anthropogenic sulphate aerosol routine with the RCM (Barrie

et al. 1996). The physical and chernical evolution of sulphate aerosols has also been

included. The aerosol particles are distributed into 8 intervals with diameters ranging from

0.01 to 2.00 microns. They are formed by gas-to-particle conversion and grow by

coagulation to form an accumulation mode near 0.1 micron. The aerosol is transponed by

the atmosphere and undergoes gravitational settling, in-cloud and below-cloud improved

scavenging in accordance with their diameters. The results showed some nsemblance with

107

observations. However, the spectrurn and concentration of sulphate aerosols are derived

solely from emission, transport, coagulation, condensation, wet and dry-deposition and

gaseous phase oxidation. Aqueous chemistry, which may have a significant impact on the

sulphate global budget, and on the direct and indirect effects of sulphaie to climate forcing

has not been considered yet. The description of sulphate aerosols has to be funher

irnproved by including the aqueoos chemistry. nierefore, we use the NARCMlLCM as a

framework to apply the parameterization to investigate the implication of aqueous oxidation

of S(IV).

A typical arctic sulphate aerosol spectrum from Heintrenberg ( 1 980) has been

utilized in our application. The peak of the volume size distribution dv/d(logr), lies around

0.1 pm with a magnitude of 5.5 pm' cm". The total aerosol mass concentration is between

2 and 7 pg m". However, the mode1 is unable to account for observed aerosol

concentrations. The simulation results are smaller than the observed concentrations by a

factor of 10 (Barrie et al, 1996). Clouds rnay be an important source of sulphate aerosol.

It has been postulated that more than 70 -90% of atmospheric sulphate is produced in

clouds, and in-cloud processes have significant impact on the sulphate aerosol size

distribution. Nevertheless, this important process has not yet been accounted for in

NARCM. Clouds here only act as sinks of sulphate aerosols by washout of the aerosol. In

the first application of the piirameterkation, the modification of aerosol spectrum in the

cloudy layer has been investigated. After imposing the aqueous production of sulphate in

the pre-existing aerosols, the aerosol mass in the cloudy region becomes around 2.8 pg m",

.. 108

in agreement with the observations of Heintzenberg (1980). The size spectrum of sulphate O

aerosol is also in a better agreement with the observation.

In this application, many gross assumptions have been made, because there are

many major limits in the descriptions of clouds and pollutants in the present NARCM. As

introduced in 5.2, the cloud scheme in NARCM is rather crude in terms of the dynamic

and rnicrophysics of clouds. The only prognostic parameter concerning clouds is relative

humidity. The cloud cover and cloud mixing ratio are both diagnostic estimates from

relative humidity and temperature. Therefore, from the cloud scherne, it is impossible to

explicitly acquire cloud lifetimes and the amount of cloud evaporated. Moreover, the

concentrations of various pollutants such as S02. SO,'. H,O, and 0, are not yet prognostic

variables in NARCM. Therefore, we first assume that the ambient chemicd concentrations

are constant. This is obviously unrealistic in a long simulation. Plus. in a !ong simulation.

the crude cloud scheme poses an even more serious problem to the application of the

parameterization that requires the cloud lifetime to estimate the total amount of sulfate

production. and requires the portion of cloud evaporated in order to estimate the

modification of spectrum of cloud-processed aerosols. In order not to jeopardize the effort

to investigate the cloud effects on the aerosol budget and spectrum, a compromise is made

to investigate the production from a single timestep. The results are encouraging, however.

there remains much to improve.

In future work, there are many modifications and revisions of the parameterization

and its application to be considered. The parameterization is developed based a on

convective cloud model. The dynamic structures of stratiform clouds are different from

convective clouds. In GCMs, most clouds should be considered as stratiforrn clouds.

Therefore, it is necessary to further the study to investigate the stratiform cloud chemistry.

Since the LCM is just one slice of the CGCM, many physical processes that can

be described in GCM cannot be included in the LCM. There are challenges frorn both

chernical part and cloud dynamics pan to make the parameterization work in the NARCM.

The assumption o f constant ambient concentrations implies that there is no sink of SO:,

H202 and 0,. Accordingly. a long period of integration becomes virtually meaningless.

Therefore we only investigated the in-cloud sulphate production from one timestep. As an

indication of a better representation of the atmospheric aerosol spectrum by including

aqueous chemistry, the parameterization shows promising results in the preliminary

application. Nevertheless. with the improvement of NARCM. the cloud chemistry schemes

will be refined. A more sophisticated chernical diffusion scherne is being included in

NARCMKGCM II (Gong. 1997, personal communication), in which the concentrations

of pollutants are prognostic variables. Also a more advanced cloud dynamic scheme

including many explicit cloud parameters (Lohmann. 1996) will be adopted in CGCM

(McFarlane, 1997) so thit i much better framework will be available in future applications

of this pararneterization.

Appendix

Mathematical description of the parameterization

The parameterization is an explicit function of ambient chemical concentrations and some gross

cloud parameterization thal are listed in Table 3.1 (reproduced as Table A 1 ;.

Table A l The input of the parameterization

Gross cloud parameters Ambient chemical concentrations

Ci@) (mole/m3) I

Average cloud water content Q,

Cloud base height HI,

Cloud thickness H c

Average cloud temperature T

Cloud lifetime t c

Average total cloud mass C,

Sulphur dioxide (SOJ

Hydrogen peroxide ( H m

Sulphate aerosol (so4=) Nitnc acid (HNod

Ammonia (NHd

Ozone (03)

Carbon dioxide (Co,)

The parameterization describes the production of aqueous sulphate from oxidation of S(1V) by H20z

and O,.

P represents the sulphate production during cloud lifetime t, averaged over the cloud volume. The

two terms of ds/dt identify the oxidation rate of S(N) by H202 and O, respectively.

The oxidation rate of S(N) by hydrogen peroxide is a function of the aqueous concentration of

S(IV), H,O,, and temperature. It is slightly dependent on pH value. i.e.. H'.

The oxidation nie of S(1V) by ozone increases rapidly with pH. It is aiso a function of the aqueous

concentration of S(N). O,, and temperature.

in the parameterization. the sub-grid convective cloud is assumed to be a static system with constant

gross parameten. The intloud gaseous chernical concentrations are assumed to be uniform over the

whole cloud domain. This assumption cm be panly justified on the bais of strong mixing feature

within convective cloud. For species i, the average ambient concentration Ci is

O Here, as in Table Al, Ci(z) is the original arnbient concentration of species i (SO,. KNO,, NH,. H@,.

S04= and O,). The average cloud water pH and aqueous concentrations of al1 chemicais Ci (aq) are

determined from the gaseous phase-aqueous phase equilibrium according to Henry's Law and from

ion balance. The magnitude of pH and C,(aq) are also strongly dependent on the average cloud water

content Q,.

The correction factor CO the cloud Iifetime and the redefinition of pH can be ideniified in Equ. 1- 3.

After these adjustments. the equations appear as

Oxidation by hydrogen peroxide.

Oxidation by ozone will be heavily modified by the recalculation of pH

H" is the recdculated acidity. or pH descnbed in Chapter three. Basically, the existing S(W) in

cloud water is assumed to be totally oxidized and a new H' concentration is defined by

Finally the total sulphate production in the whole cloud volume, P,,, (mole), can be get by

P,,, = P CJpw

here p, is the density of water, and C, the total arnount of cloud water.

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