A Modeling Study of Seasonal and Inter-annual Variations ... · Arctic BC show a strong dependence...

A Modeling Study of Seasonal and Inter-annual Variations of the Arctic Black Carbon and Sulphate Aerosols

by

Li Huang

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Chemical Engineering and Applied Chemistry University of Toronto

© Copyright by Li Huang, 2010

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A Modeling Study of Seasonal and Inter-annual Variations of the

Arctic Black Carbon and Sulphate Aerosols

Li Huang

Doctor of Philosophy

Department of Chemical Engineering and Applied Chemistry University of Toronto

2010

Abstract The modeling results of current global aerosol models agree, generally within a factor of two,

with the measured surface concentrations of black carbon (BC) and sulphate (SF) aerosols in

rural areas across the northern continents. However, few models are able to capture the observed

seasonal cycle of the Arctic aerosols. In general, the observed seasonality of the Arctic aerosols

is determined by complex processes, including transport, emissions and removal processes. In

this work, the representations of aerosol deposition processes (i.e., dry deposition, in-cloud and

below-cloud scavenging) within the framework of the Canadian Global Air Quality Model –

GEM-AQ are first enhanced. Through the enhancements in GEM-AQ, the seasonality of the

Arctic BC and SF is reproduced, and the improvement in model performance extends to the rest

of the globe as well. Then, the importance of these deposition processes in governing the Arctic

BC and SF seasonality is investigated. It is found that the observed seasonality of the Arctic BC

and SF is mainly caused by the seasonal changes in aerosol wet scavenging, as well as the

seasonal injection of aerosols from surrounding source regions.

Being able to reproduce the seasonality of the Arctic BC, the enhanced GEM-AQ allows more

accurate assessment of the contributions of anthropogenic sources to the BC abundance in the

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Arctic air and deposition to the Arctic surface. Simulating results on regional contributions to the

Arctic BC show a strong dependence on altitude. The results reinforce the previous finding of

Eurasia being the dominant contributor to the surface BC in the Arctic, and suggest a significant

contribution from Asian Russia. In addition to the seasonality of the Arctic aerosols, the inter-

annual variation in the Arctic BC surface concentration is also investigated. To complement the

3-D GEM-AQ model, the atmospheric backward trajectory analysis, together with estimated BC

emissions, is implemented as a computational effective approach to reconstruct BC surface

concentrations observed at the Canadian high Arctic station, Alert. Strong correlations are found

between the reconstructed and the measured BC in the cold season at Alert between 1990 and

2005, which implies that atmospheric transport and emissions are the major contributors to the

observed inter-annual variations and trends in BC. The regional contributions estimated annually

from 1990 through 2005 suggest that Eurasia is the major contributor in winter and spring to the

near-surface BC level at Alert with a 16-year average contribution of over 85% (specifically 94%

in winter and 70% in spring). A decreasing trend in the Eurasian contribution to the Arctic is

found in this study, which is mainly due to regional emission reduction. However, the inter-

annual variation in the North American contribution shows no clear trend.

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Acknowledgments I would like to express my deepest gratitude to my thesis supervisors, Dr. Sunling Gong and

Professor Charles Q. Jia, for their guidance, encouragements and financial support. I am very

grateful to both of them for their patient supervision, leading to a successful completion of this

thesis.

I would also like to express my sincere thanks to the rest of my committee members,

Professors Don W. Kirk and Ramin R. Farnood, for their valuable suggestions and feedbacks to

my research.

Special thanks to the following research scientists from Environment Canada who provided

technical assistance on my work, and contributed to the preparation of journal papers for

publication: Ping Huang, Tianliang Zhao, Sangeeta Sharma, and David Lavoué.

Very special thanks to my current and former members of the Green Technology Group for

their help in diverse ways: Eric Morris, Tarek Ayash, Hui Cai, Chijuan Hu, Yesul Kim, Laura

Fuentes de Maria, Zhiwei Zhang, Shamia Hoque, Azadeh Nama, Xiaodong Jiang, and Mingjiang

Yuan. My warm thanks to my friends and the staff of the Department of Chemical Engineering

and Applied Chemistry at the University of Toronto for their warm support and continual

assistance.

I dedicate this work to my wife, Juan Liu, and our parents, Xinzhen Shi, Guiying Liu and

Shenglin Huang, for their love and dedication.

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Table of Contents

ABSTRACT.................................................................................................................................. II

ACKNOWLEDGMENTS .......................................................................................................... IV

TABLE OF CONTENTS ............................................................................................................ V

LIST OF TABLES ...................................................................................................................VIII

LIST OF FIGURES ..................................................................................................................... X

LIST OF APPENDICES .........................................................................................................XIII

1 INTRODUCTION................................................................................................................. 1

2 ATMOSPHERIC AEROSOLS: AN OVERVIEW............................................................ 4

2.1 DEFINITION AND CLASSIFICATION SCHEMES .................................................................... 4

2.2 CHARACTERIZATION........................................................................................................ 4

2.2.1 Ambient particle size and size distribution................................................................. 4

2.2.2 Major chemical components ....................................................................................... 8

2.3 DYNAMIC PROCESSES OF ATMOSPHERIC AEROSOLS ....................................................... 10

2.3.1 Formation of atmospheric aerosols........................................................................... 10

2.3.2 Growth of atmospheric aerosols ............................................................................... 14

2.3.3 Removal of aerosol particles from the atmosphere................................................... 17

2.4 IMPACTS OF ATMOSPHERIC AEROSOLS: .......................................................................... 23

2.4.1 Impacts on human health .......................................................................................... 23

2.4.2 Impacts on chemical composition of the atmosphere ............................................... 23

2.4.3 Impacts on radiative balance of the atmosphere ....................................................... 24

3 MODELING THE FATE AND TRANSPORT OF ATMOSPHERIC AEROSOLS... 27

3.1 METEOROLOGICAL HOST MODEL: GEM......................................................................... 29

3.2 AEROSOL MODULE: CAM.............................................................................................. 30

3.2.1 Emissions .................................................................................................................. 31

3.2.2 Transformation.......................................................................................................... 32

3.2.3 Removal from the atmosphere .................................................................................. 36

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3.3 PERFORMANCE OF EXISTING GLOBAL AEROSOL MODELS................................................ 37

3.3.1 Modeling aerosol surface concentration and burden on a global scale .................... 38

3.3.2 Modeling aerosol surface concentration and burden in the Arctic troposphere ....... 42

3.4 ATMOSPHERIC TRANSPORT OF AEROSOLS INTO THE ARCTIC .......................................... 43

3.5 RESEARCH RESULTS....................................................................................................... 45

4 IMPORTANCE OF DEPOSITION PROCESSES IN SIMULATING THE

SEASONALITY OF THE ARCTIC BC AND SF AEROSOL............................................... 47

ABSTRACT ................................................................................................................................. 47

4.1 INTRODUCTION .............................................................................................................. 47

4.2 GEM-AQ MODEL AND MODIFICATIONS TO AEROSOL DEPOSITION SCHEMES.................. 50

4.2.1 Host meteorological model: Global Environmental Multiscale model .................... 50

4.2.2 Air quality modules and modifications to aerosol deposition schemes.................... 50

4.2.3 Simulation setup........................................................................................................ 57

4.3 SIMULATION RESULTS AND DISCUSSION......................................................................... 59

4.3.1 Surface concentration of BC and SF over the Arctic................................................ 59

4.3.2 Surface concentration of BC and SF in North America and Europe ........................ 69

4.3.3 Atmospheric transport of air masses indicated by CO.............................................. 73

4.3.4 Effect of dry deposition on the seasonality of Arctic BC ......................................... 78

4.3.5 Seasonal changes in in-cloud and below-cloud scavenging ..................................... 82

4.3.6 Atmospheric transport and deposition of BC to the Arctic....................................... 86

4.3.7 BC global budget compared with AeroCom and other models ................................ 91

4.4 CONCLUSIONS................................................................................................................ 94

REFERENCE................................................................................................................................ 95

5 REGIONAL CONTRIBUTIONS OF ANTHROPOGENIC EMISSIONS TO THE

ARCTIC BC: AIR CONCENTRATION AND DEPOSITION............................................ 102

ABSTRACT ............................................................................................................................... 102

5.1 INTRODUCTION ............................................................................................................ 102

5.2 MODEL DESCRIPTION ................................................................................................... 105

5.2.1 Host meteorological model: Global Environmental Multiscale model .................. 105

5.2.2 Aerosol module: Canadian Aerosol Module........................................................... 106

5.2.3 Model setup and simulations .................................................................................. 106

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5.3 MODEL VALIDATIONS AGAINST AVAILABLE OBSERVATIONS........................................ 108

5.4 SOURCE CONTRIBUTIONS TO THE ARCTIC BC .............................................................. 114

5.5 DEPOSITION OF BC TO THE ARCTIC ............................................................................. 119

5.6 CONCLUSIONS.............................................................................................................. 121

REFERENCE.............................................................................................................................. 123

6 BACK TRAJECTORY ANALYSIS OF INTER-ANNUAL VARIATIONS OF THE

ARCTIC BLACK CARBON AEROSOL (1990-2005).......................................................... 127

ABSTRACT ............................................................................................................................... 127

6.1 INTRODUCTION ............................................................................................................ 127

6.2 DATA AND METHODS ................................................................................................... 129

6.2.1 Equivalent black carbon data .................................................................................. 129

6.2.2 Trajectory data and transport frequency ................................................................. 129

6.2.3 Surface flux of BC .................................................................................................. 130

6.2.4 Simple linear regression model............................................................................... 131

6.3 RESULTS AND DISCUSSION ........................................................................................... 133

6.3.1 Transport pathways affecting Alert ........................................................................ 133

6.3.2 Inter-annual variations of BC at Alert explained by the model .............................. 140

6.3.3 Source contributions to BC at Alert........................................................................ 142

6.4 CONCLUSIONS.............................................................................................................. 142

ACKNOWLEDGEMENTS............................................................................................................. 143

REFERENCES ............................................................................................................................ 144

7 CONCLUSIONS ............................................................................................................... 147

8 FUTURE WORK.............................................................................................................. 149

REFERENCES.......................................................................................................................... 150

APPENDICES........................................................................................................................... 167

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List of Tables TABLE 1. MAJOR SOURCES OF ATMOSPHERIC AEROSOLS. NOTE: VOC = VOLATILE ORGANIC COMPOUND. ................10 TABLE 2. TIME FOR A PARTICLE TO FALL 1 KM IN THE ATMOSPHERE BY SEDIMENTATION UNDER NEAR-SURFACE

CONDITIONS [JACOBSON, 1999]...........................................................................................................................18 TABLE 3. GAS PHASE CHEMICAL REACTIONS OF SULPHUR SPECIES IN CAM (ADAPTED FROM [GONG ET AL., 2003A]) .34

TABLE 4. RATIO OF MODEL AVERAGE TO OBSERVED BC SURFACE CONCENTRATION AND RETRIEVED BC BURDEN

WITHIN SELECTED REGIONS OVER THE GLOBE [KOCH ET AL., 2009]. A RATIO GREATER THAN 1 SUGGESTS AN

OVERESTIMATION BY MODELS AND VICE VERSA. HERE, DIVERSITY IS SHOWN IN THE BRACKETS, WHICH IS

EXPRESSED AS THE STANDARD DEVIATION AMONG 17 MODEL RESULTS NORMALIZED BY THE AVERAGE IN

PERCENTAGE.......................................................................................................................................................40

TABLE 5. DIVERSITIES OF ESTIMATED AEROSOL LIFE-CYCLE PARAMETERS BASED ON AEROCOM MODEL SIMULATIONS

[TEXTOR ET AL., 2006]. IN THEIR STUDY, DIVERSITY IS EXPRESSED AS THE STANDARD DEVIATION AMONG 16

MODEL RESULTS NORMALIZED BY THE AVERAGE IN PERCENTAGE. .....................................................................41

TABLE 6. LIST OF SYMBOLS USED IN COLLISION EFFICIENCY CALCULATION. ...............................................................56

TABLE 7. SUMMARY OF BC EMISSION DATA USED IN CAM. ........................................................................................57

TABLE 8. CORRELATION COEFFICIENTS (R) AND MODEL TO OBSERVATION RATIOS (R) BETWEEN MODEL SIMULATIONS

AND SURFACE OBSERVATIONS OF BC AT THE SELECTED SITES IN THE ARCTIC FOR 2001 AND 2002. ..................63

TABLE 9. CORRELATION COEFFICIENTS (R) AND MODEL TO OBSERVATION RATIOS (R) BETWEEN MODEL SIMULATIONS

AND SURFACE OBSERVATIONS OF SF AT THE SELECTED SITES IN THE ARCTIC FOR 2001 AND 2002. ...................68

TABLE 10. SUMMARY OF MODEL AGAINST OBSERVATION COMPARISON: PEARSON’S CORRELATION COEFFICIENTS (R),

RATIOS (R) BETWEEN MODELED AND OBSERVED AEROSOL CONCENTRATIONS, AND PERCENTAGE OF AGREEMENT

WITHIN A FACTOR OF 2........................................................................................................................................72

TABLE 11. SAME AS TABLE 10, BUT FOR SF.................................................................................................................73

TABLE 12. CORRELATION COEFFICIENTS (R) AND MODEL-TO-OBSERVATION RATIOS (R) BETWEEN MODEL

SIMULATIONS AND SURFACE OBSERVATIONS OF CO AT THE SELECTED SITES IN THE ARCTIC FOR 2001 AND 2002.

...........................................................................................................................................................................77

TABLE 13. CORRELATION COEFFICIENTS (R) AND MODEL-TO-OBSERVATION RATIOS (R) BETWEEN MODEL

SIMULATIONS AND SURFACE OBSERVATIONS OF BC AT THE SELECTED SITES IN THE ARCTIC FOR 2001, OBTAINED

BY VARYING THE GLOBAL AND ANNUAL AVERAGE DRY DEPOSITION VELOCITY AND WITH THE ENHANCED WET

DEPOSITION SCHEMES. ........................................................................................................................................79

TABLE 14. ANNUAL BUDGET OF BC DEPOSITION TO THE ARCTIC ESTIMATED BY THE ORIGINAL AND THE ENHANCED

GEM-AQ MODEL RUNS. .....................................................................................................................................90

TABLE 15. SUMMARY OF BC EMISSION DATA USED IN THIS STUDY BY TYPE AND REGION..........................................108

TABLE 16. RATIO OF MODEL SIMULATED TO OBSERVED BC SURFACE CONCENTRATIONS AMONG THE POTENTIAL

SOURCE REGIONS AFFECTING THE ARCTIC: THE ENHANCED GEM-AQ (THIS STUDY) VS. THE AEROCOM

AVERAGE ([KOCH ET AL., 2009]). ......................................................................................................................112

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TABLE 17. COMPARISON OF ANNUAL AVERAGE ANTHROPOGENIC SOURCE CONTRIBUTIONS TO THE ARCTIC BC (70-90

ºN) ESTIMATED BY SHINDELL ET AL. [2008] AND GEM-AQ IN THIS STUDY......................................................118

TABLE 18. RELATIVE CONTRIBUTIONS OF REGIONAL EMISSIONS TO BC DEPOSITION TO THE ARCTIC (EXCLUDING

GREENLAND). ...................................................................................................................................................120

TABLE 19. BC DEPOSITED TO THE ENTIRE ARCTIC (INCLUDING GREENLAND) PER UNIT BC EMITTED. ......................121

TABLE 20. INTER-ANNUAL VARIATION OF TRANSPORT FREQUENCY (TRAJECTORY NUMBER OF EACH SECTOR DIVIDED

BY THE TOTAL NUMBER OF TRAJECTORIES, IN PERCENTAGE) AFFECTING ALERT IN JANUARY, 1990-2005. ......136

TABLE 21. SAME AS TABLE 20, BUT FOR APRIL, 1990-2005.......................................................................................137

TABLE 22. VALUES OF BI FACTORS FOR JANUARY AND APRIL, 1990-2005. ................................................................138

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List of Figures FIGURE 1. IDEALIZED SCHEMATIC OF PROCESSES RESPONSIBLE FOR FORMATION, GROWTH AND REMOVAL OF

ATMOSPHERIC AEROSOLS (ADAPTED FROM [SEINFELD AND PANDIS, 2006]). .........................................................6

FIGURE 2. TYPICAL POLAR AEROSOL NUMBER (TOP), SURFACE (MIDDLE) AND VOLUME (BOTTOM) DISTRIBUTIONS

(ADAPTED FROM [SEINFELD AND PANDIS, 2006]). .................................................................................................7

FIGURE 3. CHEMICAL COMPONENTS (INCLUDING SULPHATE, NITRATE, AMMONIUM, CHLORIDE, SODIUM, AND

HYDROGEN ION) OF ATMOSPHERIC PARTICLES MEASURED AT CLAREMONT, CA (ADAPTED FROM [SEINFELD AND

PANDIS, 2006]). ....................................................................................................................................................9 FIGURE 4. TIME SERIES PLOT OF SULPHURIC ACID VAPOUR AND 3-6 NM PARTICLES (N3) DURING NUCLEATION EVENTS

AT HYYTIALA (BOREAL FOREST) (ADAPTED FROM [CURTIUS, 2006])..................................................................13

FIGURE 5. SCHEMATIC REPRESENTATION OF THE NUCLEATION AND SUBSEQUENT GROWTH PROCESS FOR BINARY

HOMOGENEOUS NUCLEATION OF H2SO4 AND H2O (ADAPTED FROM [CURTIUS, 2006]) .......................................13

FIGURE 6. SCHEMATIC DIAGRAM OF AN AEROSOL SIZE DISTRIBUTION, N(R), AS A FUNCTION OF RADIUS R, IN WHICH

MASS TRANSFERS ARE TAKING PLACE BOTH FROM MOLECULAR CONDENSATION AND ALSO BY COAGULATION

WITH SMALL PARTICLES IN THE NUCLEATION MODE (ADAPTED FROM [COLBECK, 2008]). ..................................15

FIGURE 7. MODEL SIMULATED AEROSOL NUMBER AND VOLUME CONCENTRATIONS (AT 902 HPA), SUMMED OVER 16

SIZE DISTRIBUTIONS, BEFORE (SOLID LINES) AND AFTER (SHORT-DASHED LINES) WASHOUT BELOW CLOUD BASE.

THE SIMULATION PERIOD WAS ONE HOUR (ADAPTED FROM [JACOBSON, 1999])..................................................20

FIGURE 8. CONTRIBUTIONS OF BROWNIAN DIFFUSION, INTERCEPTION AND IMPACTION TO THE COLLISION EFFICIENCY

(E) BETWEEN AN AEROSOL PARTICLE AND A RAINDROP AS A FUNCTION OF PARTICLE SIZE (DP). THE

REPRESENTATIVE DIAMETER FOR RAINDROPS IS DP = 0.1 MM. TOTAL: BLACK SOLID LINE; BROWNIAN: RED SOLID

LINE WITH DOTS; INTERCEPTION: BLUE DASHED LINE; AND IMPACTION: RED DOTTED LINE (ADAPTED FROM

[ANDRONACHE, 2003]). .......................................................................................................................................21

FIGURE 9. RESIDENCE TIME OF AEROSOL PARTICLES IN THE ATMOSPHERE AS A FUNCTION OF THEIR RADIUS (ADAPTED

FROM [GÖTZ ET AL., 1991]). ................................................................................................................................23 FIGURE 10. FRAMEWORK OF GEM-AQ MODELING SYSTEM.........................................................................................28

FIGURE 11. MODELED GLOBAL ANNUAL AVERAGE BC SURFACE CONCENTRATION (IN NG M-3) [REDDY AND BOUCHER,

2004]. .................................................................................................................................................................39

FIGURE 12. OBSERVED AND MODELED SEASONAL CYCLES OF BC SURFACE CONCENTRATIONS AT TWO ARCTIC SITES:

BARROW (LEFT PANEL) AND ALERT (RIGHT PANEL) [SHINDELL ET AL., 2008]. BC OBSERVATION DATA ARE FROM

THE IMPROVE SITE AT BARROW DURING 1996-1998 (RED), AND FROM [SHARMA ET AL., 2006A] FOR BOTH

BARROW AND ALERT USING EQUIVALENT BC OVER 1989-2003 (PURPLE). MULTI-MODEL SIMULATION RESULTS

ARE IN GREY FROM [SHINDELL ET AL., 2008]. ......................................................................................................42

FIGURE 13. SEASONAL CHANGES IN AIR CIRCULATION AROUND THE ARCTIC: WINTER VS. SUMMER [AMAP, 2006]. ...44

FIGURE 14. GLOBAL AND ANNUAL-AVERAGE DRY DEPOSITION VELOCITIES (CM/S) PREDICTED BY GEM-AQ AS A

FUNCTION OF DRY PARTICLE SIZE COMPARED WITH TRIVITAYANURAK ET AL. [2008]. .......................................52

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FIGURE 15. COMPARISON OF GEM-AQ SIMULATED MONTHLY AVERAGE BC SURFACE CONCENTRATIONS AGAINST

OBSERVATIONS AT (A) ALERT, (B) BARROW, AND (C) ZEPPELIN [ELEFTHERIADIS ET AL., 2009] FOR THE YEAR OF

2001. ..................................................................................................................................................................61

FIGURE 16. SAME AS FIGURE 15, BUT FOR THE YEAR OF 2002......................................................................................62

FIGURE 17. COMPARISON OF THE SIMULATED MONTHLY AVERAGE SF SURFACE CONCENTRATIONS AGAINST

OBSERVATIONS AT (A) ALERT, (B) BARROW, AND (C) SPITSBERGEN FOR THE YEAR OF 2001. R VALUES INDICATE

THE PEARSON’S CORRELATION COEFFICIENTS BETWEEN OBSERVED AND SIMULATED MONTHLY SF. .................66


FIGURE 19. COMPARISON OF MODEL SIMULATED BC SURFACE CONCENTRATIONS BEFORE AND AFTER MODIFICATIONS

AGAINST SURFACE OBSERVATIONS IN NORTH AMERICA FROM THE IMPROVE MONITORING NETWORK (A) AND

EUROPE FROM THE EMEP MONITORING NETWORK (B). TWO DASHED LINES REPRESENT MODEL-TO-

OBSERVATION RATIOS OF 2:1 AND 1:2. ...............................................................................................................70

FIGURE 20. SAME AS FIGURE 19, BUT FOR SF. .............................................................................................................71

FIGURE 21. COMPARISON OF GEM-AQ SIMULATED MONTHLY AVERAGE SURFACE CONCENTRATIONS AGAINST

OBSERVATIONS OF CO AT (A) ALERT, (B) BARROW, AND (C) ZEPPELIN FOR THE YEAR OF 2001.........................75


FIGURE 23. THE EFFECT OF DRY DEPOSITION ON THE SURFACE BC CONCENTRATION AT THREE ARCTIC SITES: (A)

ALERT, (B) BARROW, AND (C) ZEPPELIN.............................................................................................................80

FIGURE 24. THE SEASONAL CHANGE IN THE RATE OF THE ORIGINAL DRY PARTICLE REMOVAL (1/S) AVERAGED OVER

THE ARCTIC OCEAN (70-90 ºN) FOR FOUR SELECTED PARTICLE SIZE BINS. THE RATE OF THE MODIFIED DRY

PARTICLE REMOVAL (NOT SHOWN) IS 50% LOWER THAN THAT OF THE ORIGINAL SCHEME. ................................81

FIGURE 25. THE EFFECT OF DRY DEPOSITION ON THE BC MASS IN THE LOWEST 5 KM OF THE ARCTIC TROPOSPHERE. .82

FIGURE 26. COMPARISON OF THE SEASONAL CHANGE IN WET SCAVENGING RATE BETWEEN THE ORIGINAL AND THE

MODIFIED PARAMETERIZATIONS, AVERAGED BELOW 5 KM IN ALTITUDE ABOVE THE ARCTIC OCEAN (70-90 ºN):

(A) ORIGINAL IN-CLOUD SCAVENGING RATE, (B) ORIGINAL BELOW-CLOUD SCAVENGING RATE, (C) MODIFIED IN-

CLOUD SCAVENGING RATE, AND (D) MODIFIED BELOW-CLOUD SCAVENGING RATE............................................85 FIGURE 27. COMPARISON OF GEM-AQ SIMULATED MONTHLY RATE OF PRECIPITATION WITHIN THE ARCTIC (70-90 ºN)

AGAINST OBSERVATIONS FROM CMAP DATASET FOR THE YEAR OF (A) 2001 AND (B) 2002...............................86 FIGURE 28. SEASONAL CHANGES IN (A) THE OVERALL AND (B) THE SECTIONAL SOUTH-TO-NORTH FLUX OF BC MASS

CROSSING 70 ºN LATITUDE, AVERAGED BELOW 5 KM IN ALTITUDE AND WEIGHTED BY LAYER THICKNESS. ........88 FIGURE 29. ZONAL MEAN JANUARY BC CONCENTRATION (IN µG/M3) CALCULATED FOR 4 SECTORS: (A) EUROPE, (B)

FORMER USSR, (C) NORTH ATLANTIC, AND (D) NORTH AMERICA.....................................................................89 FIGURE 30. BC GLOBAL BUDGET FROM GEM-AQ COMPARED WITH AEROCOM MULTI-MODEL AVERAGE. THE ERROR

BARS ON TOP OF THE AEROCOM AVERAGES REPRESENT STANDARD DEVIATIONS...............................................92

FIGURE 31. ZONAL DISTRIBUTION OF THE ATMOSPHERIC LOAD OF BC FROM MODEL SIMULATIONS: GEM-AQ (BLACK

LINE) VS. AEROCOM MODELS [SCHULZ ET AL., 2006] (GRAY LINES)....................................................................93 FIGURE 32. ZONAL DISTRIBUTION OF THE ATMOSPHERIC LOAD OF BC FROM GEM-AQ MODEL SIMULATIONS: WINTER

(BLACK) VS. SUMMER (GRAY) OF THE NORTHERN HEMISPHERE. ........................................................................94

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FIGURE 33. (A) ANNUAL AVERAGE DISTRIBUTION OF THE SURFACE CONCENTRATIONS OF BC IN THE ARCTIC AND ITS

SURROUNDING REGIONS (IN µG/M3). AND COMPARISON OF MODEL SIMULATED BC SURFACE CONCENTRATIONS

AGAINST SURFACE OBSERVATIONS IN NORTH AMERICA (FROM THE IMPROVE MONITORING NETWORK) (B),

EUROPE (FROM THE EMEP MONITORING NETWORK) (C), AND ASIA (FROM THE ACE-ASIA EXPERIMENT) (D).

THE DASHED LINES IN THE PANELS (B)-(D) INDICATE 1:2 AND 2:1 RATIO BETWEEN SIMULATIONS AND

OBSERVATIONS, AND THE R REPRESENTS THE SIMULATION TO OBSERVATION RATIO, DETAILED IN TEXT. ........110

FIGURE 34. MONTHLY AVERAGE VERTICAL PROFILES OF BC AEROSOL CONCENTRATION FROM THE GEM-AQ OVER

ALASKA COMPARED WITH DAILY AIRCRAFT OBSERVATIONS FROM THE ARCTIC RESEARCH OF THE COMPOSITION

OF THE TROPOSPHERE FROM AIRCRAFT AND SATELLITES (ARCTAS) CAMPAIGN. AREA BARS ON THE

SIMULATED PROFILE REPRESENT THE RANGES OF DAILY BC CONCENTRATIONS AT VARIOUS ALTITUDES.........113

FIGURE 35. BC VERTICAL PROFILES AND SOURCE CONTRIBUTIONS TO BC IN THE ARCTIC (70-90ºN) IN WINTER AND

SUMMER............................................................................................................................................................117

FIGURE 36 ANNUAL AVERAGE BC SURFACE FLUX (NG/M2/S) FROM EUROPEAN UNION, THE FORMER USSR, AND

NORTH AMERICA: 1990-2005...........................................................................................................................131

FIGURE 37 TRANSPORT PATHWAYS AFFECTING ALERT, NUNAVUT IN JANUARY (A) AND APRIL (B) FROM 1990

THROUGH 2005 IDENTIFIED BY CLUSTER ANALYSIS ON THE HYSPLIT TRAJECTORIES. THE NUMBER OUTSIDE

THE BRACKETS SERVES ONLY AS AN IDENTIFICATION OF EACH CLUSTER; THE ONE INSIDE THE BRACKETS GIVES

THE FREQUENCY OF OCCURRENCE OF THE UNDERLINING TRANSPORT PATHWAY..............................................134

FIGURE 38. TIME-SERIES OF THE MODEL RECONSTRUCTED AND THE OBSERVED MONTHLY AVERAGE BC IN JANUARY

(A) AND APRIL (B), 1990-2005. THE R2 SHOWN IN BOTH PLOTS ARE THE SQUIRES OF THE PEARSON’S

CORRELATION COEFFICIENTS BETWEEN THE RECONSTRUCTED AND OBSERVED BC CONCENTRATIONS RATHER

THAN THOSE FOR LINEAR REGRESSIONS. ...........................................................................................................139

FIGURE 39. MODEL ESTIMATED SOURCE CONTRIBUTIONS OF BC FROM THE NORTH AMERICAN AND THE EURASIAN

SECTORS BASED ON THE AVERAGE OF JANUARY AND APRIL FROM 1990 THROUGH 2005. THE INTER-ANNUAL

CHANGES IN BC EMISSION INTENSITY ARE SHOW BY TWO DASHED LINES. ........................................................141

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List of Appendices

APPENDIX A: GEM-AQ MODEL INSTALLATION......................................................... 167

APPENDIX B: PROCEDURE OF RUNNING GEM-AQ.................................................... 169

APPENDIX C: OBTAINING AEROSOL DATA FROM GEM-AQ OUTPUT................. 170

APPENDIX D: MODIFIED DORLING’S CLUSTERING ALGORITHM....................... 171

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1 Introduction In the past several decades, atmospheric aerosols have attracted serious interest of scientific

communities in response to issues concerning human health [Davidson et al., 2005; Kappos et al.,

2004; Oberdorster et al., 1994; Pope et al., 1995], global climate change [Charlson et al., 1992;

Lohmann and Feichter, 2005; Penner et al., 1998; Tegen et al., 1996], and atmospheric

chemistry [Andreae and Crutzen, 1997; George et al., 2007; Jacob, 2000]. Aerosols are known

to affect the Earth’s climate through their direct radiative effects [Boucher and Anderson, 1995;

Haywood and Ramaswamy, 1998], and through their impacts on clouds and precipitation

[Ramanathan et al., 2001]. Suspended in the air, aerosol particles can directly absorb and reflect

the incoming solar and outgoing long-wave radiation, resulting in direct changes in air and

surface temperatures. In addition, aerosol particles can actively participate in the formation of

clouds and precipitation, which indirectly disturbs the Earth’s energy balance by modifying the

radiative properties of clouds. According to the most recent report from the Intergovernmental

Panel on Climate Change (IPCC) [IPCC, 2007], the magnitude of the estimated radiative forcing

of atmospheric aerosols is comparable with that of CO2, but with a negative sign (i.e. a cooling

effect) and a very large uncertainty.

Characterized by the extreme cold and dry air and the high surface reflectivity of ice/snow,

the Arctic region plays important roles in the Earth’s climate system, and is particularly sensitive

to changes of the global climate. Studies indicate that the Arctic warmed faster than the global

average over the 20th century, and it may continue to warm at a rate about twice as fast as the rest

of the globe [Holland and Bitz, 2003; Shindell and Faluvegi, 2009]. Meanwhile, the change in

the Arctic climate is expected to affect other parts of the world [Murray and Simmonds, 1995;

Serreze et al., 2007]. The melting of sea ice and glacier due to a warming Arctic climate could

contribute significantly to the raise of the global sea level. The change in the Arctic surface

albedo, partly due to the deposition of black carbon and dust aerosol onto the surface of snow/ice,

could result in additional warming of the global climate and accelerated melting of sea ice. As

such, the Arctic interacts with the global climate.

As a principle component of the atmosphere, aerosol is found to be highly variable in its

physical characteristics and chemical composition. The level of our current understanding on

atmospheric aerosols remains low as concluded by IPCC 2007 report [IPCC, 2007]. This is

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partly due to the instrumental limitations associated with the current aerosol measurement

techniques [McMurry, 2000]. The low level of understanding associated with atmospheric

aerosols also arise from modeling limitations, such as uncertainties in particle emissions (initial

particle size, shape, mixing state), particle uptake in clouds and precipitation, and model’s

meteorology (transport, clouds, precipitation). Especially in such a remote environment as the

Arctic, experimental investigations on aerosols have only been carried out at very limited spatial

and temporal resolutions. Current aerosol models, on the other hand, are not able to reasonably

reproduce the abundance and the seasonal patterns of aerosols in the Arctic. Due to inherent

complexities of aerosol dynamics, coupled with modeling limitations, the estimation of aerosol

spatial and temporal distributions in the Arctic atmosphere is still marked by significant

uncertainties [Shindell et al., 2008; Textor et al., 2006]. As part of the effort to fill these

knowledge gaps and reduce the uncertainties associated with aerosols, the Global Environmental

Multiscale Model with Air Quality processes (GEM-AQ) was developed in Canada [Kaminski et

al., 2007; Kaminski et al., 2008; O'Neill et al., 2006]. As a global-scale 3-dimentional modeling

framework, GEM-AQ was designed to simulate the fate and transport of air pollutants including

aerosols. However, its ability to simulate the behavior of aerosols over the globe, especially in

the Arctic, has not yet been evaluated.

The overall goal of this work is to better understand the fate and transport of two important

aerosols-black carbon and sulphate, over the Arctic, with a focus on their temporal variations of

different time scales. To achieve this goal, following tasks are carried out:

(a) Evaluating the performance of GEM-AQ against observations and identifying its

shortcomings;

(b) Improving GEM-AQ with a focus on the representations of aerosol deposition in the

Canadian Aerosol Module (CAM);

(c) Using the improved GEM-AQ as an enhanced tool to investigate the behavior of BC and SF

aerosols in the Arctic troposphere.

Following this introduction, the definition, characterization, dynamic processes, and

potential impacts of atmospheric aerosols are summarized in Chapter two. In Chapter three the

fundamentals of aerosol models are illustrated using GEM-AQ as an example. This chapter is

- 3 -

concluded with a summary of knowledge gaps and limitations of global-scale aerosol models.

The rest of this document is a collection of three papers submitted to Journal of Geophysical

Research-Atmospheres and Atmospheric Chemistry and Physics, in which the research

methodology and findings are presented. In the first paper, the importance of aerosol dry and wet

depositions in determining the seasonal cycle of BC and SF aerosol in the Arctic is demonstrated,

with modifications made on CAM. In the second paper, the improved GEM-AQ is first evaluated

using available observations and then used to assess source contributions to the Arctic BC from

regional anthropogenic emissions. In the third paper, the governing factors of the inter-annual

variation in the surface BC observed in the Arctic (i.e. emission and atmospheric transport) are

investigated using a linear regression model. Finally, this document ends with an overall

conclusion section, which is followed by recommendations for future investigations.

- 4 -

2 Atmospheric aerosols: an overview

2.1 Definition and classification schemes Atmospheric aerosol refers to the minute solid or liquid particles suspended in the

atmosphere. Aerosols are ubiquitous in the environment. Examples of solid particles include

carbon black in smoke and fumes as well as soil dust, liquid particles forms cloud or fog. Haze

and smog, however, are a combination of smoke and fog and usually consist of both solid and

liquid particles.

There are a number of classification schemes for atmospheric aerosols. Based on chemical

compositions, typical aerosols in the atmosphere are grouped into sulphate (SF), sea salt (SS),

soil dust (SD), black carbon (BC), and organic carbon (OC). The combination of BC and OC is

often referred to as carbonaceous aerosols. Particles can also be geographically classified into

maritime, continental, and background aerosols, or by size into Aitken (or nucleation) mode

(0.001 – 0.1 µm), accumulation mode (0.1 – 1 µm), and coarse particle mode (> 1 µm).

Depending on origins, aerosols are commonly grouped into two groups: natural aerosols (e.g., SS

and SD) and anthropogenic aerosols (e.g., non-sea salt SF, BC and OC from fossil fuel

combustion). Moreover, atmospheric aerosols are commonly divided into primary and secondary

aerosols according to production mechanisms. Primary aerosols are produced by direct ejection

into the atmosphere as particles from sources (e.g. SS and SD), while secondary aerosols are

produced from gaseous species by chemical reactions and physical processes (e.g. SF).

2.2 Characterization

2.2.1 Ambient particle size and size distribution

Particle size and size distribution are among the most important parameters that affect

physical properties, environmental impacts, and fate of atmospheric aerosols. For instance, both

the dry and wet deposition rates of particles, and therefore, their residence time in the

atmosphere, strongly depend on particle size. Light scattering and absorption properties, which

are important in both visibility and radiative forcing, are also determined by particle size. Under

ambient conditions, atmospheric aerosols contain a wide range of sizes, usually over four orders

of magnitude from a few nanometers to several hundred micrometers in diameter. The lower

- 5 -

limit in the above size range approximates roughly a collection of a few molecules while the

upper limit prevents particles from settling for a sufficient length of time.

Although many particle shapes are possible, it is convenient to treat aerosol particles as

spheres for calculations, and this also helps visualize the processes taking place. Very often, a

particle diameter is defined in terms of particle settling velocity. All particles having similar

settling velocities are considered to be the same size, regardless of their actual size or shape.

Aerodynamic diameter, as an example, is defined as the diameter of a unit density sphere

(density = 1 g/cm3) having the same aerodynamic properties as the particle of interest. For a

spherical particle of nonunit density the aerodynamic diameter is different from its physical

diameter and depends on its density. Aerosol instruments like the cascade impactor and

aerodynamic particle sizer measure the aerodynamic diameter of atmospheric particles, which is

in general different from the physical diameter of the particles even if they are spherical.

In the ambient air, particle size is determined by the production mechanism and subsequent

physical processes and chemical reactions. Figure 1 shows an idealized schematic of the

processes that are responsible for the growth of aerosol particles. In the Aitken nuclei range, as

shown in the left-hand side of Figure 1, homogeneous nucleation and heterogeneous

condensation contribute to the production of new particles and the growth of particles,

respectively, from condensable gases. Homogeneous nucleation takes place when there is no or

insufficient existing surface of particles for the uptake of condensable gases, while

heterogeneous condensation occurs on the surface of existing particles in the air. Combustion

and other high-temperature processes are largely responsible for direct emissions of fine-mode

particles, while mechanical processes such as grinding, entrainment of soil dust and droplet

formation by waves generate coarse-mode particles. Particle removal mechanisms for each mode

are also shown in Figure 1. For particles in the Aitken size range, they are subject to rapid

coagulation and/or condensation of vapors due to significant Brownian motion, and thus enter

the accumulation mode. Accumulation-mode particles, whose typical atmospheric lifetime is

about few weeks, are mainly removed by precipitation scavenging. Particles in this size range are

therefore involved in atmospheric long-range transport. The coarse mode particles (greater than

few micrometers in diameter) are effectively removed by dry deposition due to their substantial

weight.

- 6 -

Figure 1. Idealized schematic of processes responsible for formation, growth and removal of

atmospheric aerosols (adapted from [Seinfeld and Pandis, 2006]).

There are several commonly used forms of size distribution of atmospheric aerosols, such as

particle number, surface, and volume (or mass) distributions. In practice, a given size distribution

- 7 -

can look very different if described in different forms. As shown in the top and bottom panels of

Figure 2, the particle number distribution curve peaks only at 0.15 µm in diameter for a typical

polar aerosol while its volume distribution curve peaks approximately at both 0.4 and 3 µm. The

reason is that particles at the small end of the size distribution can be very abundant in number,

but contain only a small amount of the total volume/mass due to their small size.

Figure 2. Typical polar aerosol number (top), surface (middle) and volume (bottom) distributions

(adapted from [Seinfeld and Pandis, 2006]).

- 8 -

2.2.2 Major chemical components

Together with particle size, chemical composition of atmospheric aerosols determines their

physical properties, environmental effects, and fate. The chemical composition of atmospheric

aerosols is seldom simple, with their composition being determined by production mechanisms,

as well as subsequent physical processes and chemical reactions. Therefore, the composition of

atmospheric aerosol particles is highly variable with both time and space. In general, however,

the predominant chemical components of aerosol particles, given by Seinfeld and Pandis [2006],

are sulphate, nitrate, ammonium, sodium, chloride, trace metals, organic compounds, black

carbon and water. The predominance of these chemical components and their size distribution

are closely linked to the emitting source and the dynamic evolution of atmospheric aerosols.

Following [Colbeck, 2008], the common forms and the major emitting sources of atmospheric

particles can be summarized as follows.

Sulphate – arises primarily as a secondary component from atmospheric oxidation of SO2;

Nitrate – typically presents as NH4NO3, resulting from the neutralization of HNO3 vapour by

NH3, or as sodium nitrate (NaNO3), due to displacement of hydrogen chloride from NaCl by

HNO3 vapour;

Ammonium – usually present in the form of (NH4)2SO4 or NH4NO3;

Sodium and chloride – from sea salt;

Black carbon – formed due to incomplete combustion of fossil fuels and biofuels;

Organic carbon – carbon in the form of organic compounds, could be either primary,

resulting from the same source emitting black carbon, or secondary, resulting from the

oxidation of volatile organic compounds;

Aluminum, silicon, iron and calcium – from mineral substances such as soil and sand, which

mainly present in the coarse fraction;

Water – may be present within, for example (NH4)2SO4, NH4NO3, and NaCl; may also be

taken by water-soluble components from the air at high relative humidity.

Figure 3 is an example of the measured aerosol components, showing concentrations of

some of these constituents as equivalent number concentration as a function of particle size. It is

noted that the sodium ion is concentrated only in the coarse mode (about several micrometers in

diameter), which is the typical signature of primary aerosols produced mechanically (such as sea

salt or dust). A major peak of the chloride ion can be found in the same particle range, however,

- 9 -

with two minor peaks in the accumulation mode. It suggests that the major source for the

measured chloride ion is from primary production but with relatively small contributions from

atmospheric processes such as condensation. Sulphate and ammonium, on the other hand, have

their major peaks in the accumulation mode indicating a strong secondary source from gas-to-

particle conversion. Sulphate, for example, arises primarily as a secondary component from

atmospheric oxidation of SO2.

Figure 3. Chemical components (including sulphate, nitrate, ammonium, chloride, sodium, and

hydrogen ion) of atmospheric particles measured at Claremont, CA (Adapted from [Seinfeld and

Pandis, 2006]).

- 10 -

2.3 Dynamic processes of atmospheric aerosols The dynamic processes of atmospheric aerosols alter the particle size distribution and

ultimately determine the fate of the aerosols. These processes include formation, growth, and

removal of particles, which are reviewed in the present chapter.

2.3.1 Formation of atmospheric aerosols

Table 1. Major sources of atmospheric aerosols. Note: VOC = Volatile Organic Compound.

Natural Anthropogenic

Soil dust Industrial and agricultural dust

Sea salt Black carbon from fossil and bio-fuel

Volcanic dust Organic aerosols from biofuel burning

Organic aerosols

Primary

Black carbon from forest fire

Sulphate from DMS Sulphate from industrial SO2

Sulphate from volcanic SO2 Organic aerosols from VOCs

Organic aerosols from VOCs Nitrate from NOx

Secondary

Nitrate from NOx

A combination of chemical and physical processes results in the formation of atmospheric

aerosols. Table 1 summarizes the major sources of atmospheric aerosols. Anthropogenic sources

are those associated with human activity: industrial wastes from chimneys, pollutants exhausts

from cars, and soil erosion in agriculture and open mining. As an example of primary aerosols,

black carbon particles are directly emitted from the high-temperature combustion of fossil fuels

- 11 -

and biofuels (anthropogenic source) and forest fire (natural source) into the air. The available

data suggest that anthropogenic emissions dominate in the global BC budget, with BC

contributing regions being the northern hemisphere mid-latitudes (due to fossil fuel burning) and

the tropics (due to biofuel and biomass burning).

Photochemical and chemical reactions and nucleation process in the atmosphere are

responsible for the in situ production of secondary aerosols, which contribute substantially to the

fine aerosol mass and the total particle number. Precursor gases of secondary aerosols include

volatile organic compounds (VOCs), sulphur dioxide (SO2), dimethyl sulphide (DMS), ammonia

(NH3), nitrogen oxides (NOx), and some other gases and radicals, as well as water vapour.

According to Kondratrev [2006], there are three basic, generally accepted mechanisms of the

formation of secondary aerosols from these gases in the atmosphere.

(a) Photochemical oxidation, heterogeneous reactions. The process takes place in the middle to

the upper troposphere. Secondary sulphate and nitrate particles are often photochemically

produced from SO2 and NOx, respectively. The estimate of the SO2 consumption rate due to this

process is about 0.03% of SO2 during one hour in pure air.

(b) Catalytic oxidation in the presence of heavy metals. The rate of reaction depends strongly on

the presence of suitable catalysts (ions of heavy metals) and can be sufficiently high in heavily

polluted air. This kind of process takes place both in dry air and in cloud droplets, which may be

affected by pH as well.

(c) The reaction of ammonium (NH4+) with sulphur dioxide (SO2) in the presence of liquid water

(e.g., cloud droplets). The mechanism of the formation of ammonium sulphate ((NH4)2SO4) is

efficient only in the presence of water and the pH value is maintained sufficiently high, for

instance, due to the supply of NH3. Model calculations show that the rate of oxidation in cloud

droplets is 12% per hour, and the particles of ammonium sulphate can remain suspended in the

air after the evaporation of cloud droplets.

Nucleation is often observed as a burst of new ultrafine particles being formed from the gas-

phase products of photochemical reactions with low vapour pressures. Atmospheric aerosol

nucleation processes are critical to understand the dynamics of aerosols as the freshly produced

particles directly influence the size distribution of the atmospheric aerosol. Nucleation has been

- 12 -

observed at many different places in the atmosphere, such as the boundary layer, the free

troposphere, remote regions, coastal areas, boreal forests, and urban areas. Observed nucleation

rates in the boundary layer are typically in the range of 0.01 – 10 cm-3 s-1, in urban areas up to

100 cm-3 s-1 and in coastal areas as high as 104 – 105 cm-3 s-1 [Curtius, 2006]. Binary nucleation

of sulphuric acid and water, ternary nucleation of sulphuric acid, water, and ammonia and ion-

induced nucleation (e.g. [Enghoff and Svensmark, 2008]) are thought to be the most important

aerosol nucleation processes in the atmosphere. Figure 4 shows the observed, strong correlation

between gaseous H2SO4 concentration and ultrafine particle numbers in a boreal forest area. It

was found that concentration of ultrafine particles closely followed the rise of H2SO4 after

sunrise but with approximately a 1.5-hour lag. Pure sulphuric acid (H2SO4) has a low vapour

pressure, which is further reduced in the presence of water due to the large mixing enghalpy that

is freed when the two substances are mixed. When gaseous H2SO4 is produced from SO2, it is

therefore easily super-saturated and starts to condense. Under the condition of no pre-existing

(aerosol) surfaces, gaseous H2SO4 molecules may cluster with other H2SO4 and H2O molecules.

If these clusters continue to grow and overcome the nucleation barrier, then new,

thermodynamically stable aerosol particles are formed form the gas phase. This process, as

shown in Figure 5, is called binary homogeneous nucleation because two substances H2SO4 and

H2O nucleate with no foreign surface involved. Organic vapours could, in principle, participate

in nucleation, but nucleation mechanisms that involve organics have not yet been identified

[Kulmala et al., 2004].

- 13 -

Figure 4. Time series plot of sulphuric acid vapour and 3-6 nm particles (N3) during nucleation

events at Hyytiala (boreal forest) (adapted from [Curtius, 2006]).

Figure 5. Schematic representation of the nucleation and subsequent growth process for binary

homogeneous nucleation of H2SO4 and H2O (adapted from [Curtius, 2006])

- 14 -

2.3.2 Growth of atmospheric aerosols

Once in the air, aerosol particles have a lifetime of a few hours to several weeks. During the

period of their lifetime, the chemical composition and size distribution of particles are highly

variable in both time and space due to a number of processes. For example, changes occur in

aerosol population when a vapour compound condenses on the particles and when the particles

collide and adhere, which are termed condensation and coagulation, respectively. Typical

particle growth rates are in the range of 1 – 20 nm/h in the mid-latitudes depending on the

temperature and the availability of condensable vapours, while over polar areas the growth rate

can be as low as 0.1 nm/h [Kulmala et al., 2004]. Aerosol particles may also interact with water

vapour, one of the most abundant gases in the atmosphere, in some cases, resulting in the

formation of clouds and precipitation.

2.3.2.1 Condensation

Apart from the condensation of water vapour, most mass transfer to aerosols in the

atmosphere results from condensation. Atmospheric chemical reactions usually produce

condensable compounds by oxidation of gaseous emissions from natural and anthropogenic

sources. Aerosol particles grow by the addition of such vapours onto particle surfaces, which

modifies both the particle size distribution and the total mass of an aerosol. In forest areas, for

example, the oxidation of biogenic VOCs was suggested to be the main contributor to the growth

of organic particles due to condensation [Kavouras et al., 1998]. From the kinetics percept of

aerosol condensation process, there are three regimes of particle growth commonly considered:

free-molecular growth, continuous growth, and transient growth regime. The growth regime is

characterized by the Knudsen number, the ratio between the free path of an air molecule and the

particle size in radius. If the Knudsen number is large, particles grow under the free-molecular

condition; that is, it is assumed that carrier gas molecules do not interfere with the approach of a

condensable molecule to a particle. The effectiveness of condensation is proportional to the

surface area of the particle. If particles grow under the continuum regime, when a particle is

large relative to the mean free path of an air molecule, the effectiveness of condensation is then

proportional to the diffusion coefficient of the condensable molecule and the radius of the

- 15 -

particle. Condensation under transient conditions, however, is more complex, which can only be

solved using semi-empirical equations [Lushnikov et al., 2008].

In addition to contributing to particle growth, condensation may compete with nucleation

and, therefore, limit the formation of new particles. The pre-existing (aerosol) surfaces provide

an effective sink for the precursor gases, lowering their super-saturation, and prohibit nucleation.

This is fundamentally due to the same driving forcing of both nucleation and condensation,

concentration of condensable compounds. If the removal rate from condensation is not large

enough, the supersaturation will increase and a nucleation event is preferred to occur.

Figure 6. Schematic diagram of an aerosol size distribution, n(R), as a function of radius R, in

which mass transfers are taking place both from molecular condensation and also by coagulation

with small particles in the nucleation mode (adapted from [Colbeck, 2008]).

- 16 -

2.3.2.2 Coagulation

Next to condensation, coagulation evolves particle size distribution effectively. It is easily

observed that at high particle concentrations, individual particles collide to form larger chains or

flocs made up of many particles. In several cases, it has been found that the observed growth rate

of sulphate aerosol is larger by a factor of 2 – 3 than the maximum predicted due to condensation

alone [Stolzenburg et al., 2005]. Coagulation of particles in nucleation mode can account for

some of the above discrepancies because molecules nucleate to tiny particles in the nucleation

mode, and then rapidly coagulate to a growing aerosol. This mechanism competes with the

particle growth due to direct condensation on particles, as shown in Figure 6.

Particles may come into contact in the air due to their Brownian (thermal) motion or as a

result of their motion driven by gravitational, hydrodynamic, electrical, or other forces. The rate

of particle collision during coagulation is commonly considered as the product of a coalescence

efficiency and a collision kernel. The efficiency of coalescence depends on particle shape,

composition, ambient relative humidity, and other factors. When two colliding particles are large,

for example, the kinetic energy of collision is high, increasing the chance of a bounceoff

following collision, and the coalescence efficiency is less than 1. Considering the collision with

at least one small particle involved, however, the chance of a bounceoff is low, and the

coalescence efficiency is approximately unity [Pruppacher and Klett, 1997].

The collision kernel is the sum of the kernels due to individual physical process: Brownian

motion, gravitational settling, turbulence, and others. Brownian and gravitational coagulations

take place everywhere in the atmosphere. Similarly to the above discussion on condensation, the

mechanism of Brownian coagulation is considered in three particle flow regimes: continuum,

free-molecular, and transition regime. Gravitational collection accounts for the coagulation due

to different settling speeds of two particles of different size. It becomes important when at least

one particle is large in size, such as cloud drops or in the presence of sea spray or soil dust

particles. Two additional kernels include those for turbulent inertial motion and turbulent shear,

which were first obtained by Saffman and Tumer [1956]. A large particle is more likely to

collide with a small one because of inertial motion than because of turbulent shear; two small

particles are likely to collide because of turbulent shear.

- 17 -

2.3.2.3 Hygroscopic growth

The size distribution of ambient aerosol varies as a function of the relative humidity (RH)

because of the presence of water-soluble materials in the particulate matter. It is simply because

the water activity of the aqueous solution strives to equilibrate to RH in the surrounding air in

accordance with the Köhler equation (e.g. [Jacobson, 1999]). A recent investigation on the time

required reaching equilibrium between particle phase and the surrounding water vapour [Sjogren

et al., 2007] showed that, depending on particle chemical composition, residence times of > 40 s

were required to reach equilibrium at 85% RH. The equilibrium radius of particle is governed

also by the chemical composition and the curvature of the droplet. Hygroscopic properties of

ambient aerosol vary strongly depending on the origin of the air masses and the location. In

continental, polluted air masses the aerosol is often separated to less- and more-hygroscopic

particles; clean marine aerosol is usually more hygroscopic; in urban air, hydrophobic particles

originated from combustion are mixed with transported background aerosol [Swietlicki et al.,

2008].

2.3.3 Removal of aerosol particles from the atmosphere

2.3.3.1 Sedimentation and dry deposition

Aerosol particles fall in the atmosphere due to the force of gravity with their fall speeds (or

terminal speeds) determined by a balance between gravity and the opposing drag force. Thus, the

removal of particles from the atmosphere at their terminal speed is called sedimentation. Gaseous

species and small particles have negligible fall speeds, but sedimentation of large particles,

particularly soil dust, sea salt, cloud drops and raindrops, is important. The terminal velocity of a

falling particle strongly depends on particle size and flow regimes. At low Reynolds number

regime (< 0.01), corresponding to particles < 20 µm in diameter under near-surface conditions,

particle terminal velocity is simply governed by Stokes’ law with the Cunningham correction

factor. For moderate to large Reynolds number regimes (0.01 < Re < 300 and Re > 300,

respectively), the terminal speeds was empirically parameterized as functions of the physical

properties of the drops and their surroundings by Beard [1976], which accounted for the effects

of turbulence on large raindrops (> 1 mm). The time required for particles to fall 1 km in the

atmosphere by sedimentation under near-surface conditions is shown in Table 2. Most aerosol

particles of atmospheric importance are 0.1 – 1 µm in diameter. Table 2 indicates that these

- 18 -

particles require 36 years – 328 days, respectively, to drop 1 km in the atmosphere. The terminal

speeds of particles are useful to estimate not only the removal of particles by sedimentation but

also coagulation and dry deposition.

Table 2. Time for a particle to fall 1 km in the atmosphere by sedimentation under near-surface

conditions [Jacobson, 1999].

Diameter (µm) Time to fall 1 km Diameter (µm) Time to fall 1 km

0.0005 9630 yr 4 23 d

0.02 230 yr 5 14.5 d

0.1 36 yr 10 3.6 d

0.5 3.2 yr 20 23 hr

1 328 d 100 1.1 hr

2 89 d 1000 4 min

3 41 d 5000 1.8 min

Dry deposition removes particles, as well as gases, at air-surface interfaces when they

contact a surface and stick to or react with the surface. Sedimentation (or gravitational settling) is

one mechanism by which particles in the air contact the Earth’s surface, as well as Brownian

diffusion, turbulent diffusion, and advection. Although it is a complicated process, dry deposition

can be idealized as taking place in three separate steps: aerodynamic transport, boundary layer

transport and surface interactions. Aerodynamic transport refers to the process in which particles

are carried from the free atmosphere to the viscous sublayer due to turbulent diffusion and

sedimentation. The viscous sublayer is a thin (several mm or smaller) layer between a surface

and the free atmosphere, which contains of mainly laminar flow with intermittent bursts of

turbulence. Transport across this layer, in the second step, is governed by Brownian diffusion for

- 19 -

small particles, and by interception, inertial forces, and sedimentation for large (> 1 µm) particles.

Once at the surface, particles may adhere (resulting in removal) or bounce off (resulting in

resuspension) in this final step, depending on the properties of particles and the surface.

Particle dry deposition largely depends on atmospheric, surface, and particle characteristics.

Examples of atmospheric parameters include wind speed, humidity, stability and temperature.

Surface properties which influence deposition include chemical and biological reactivity, surface

roughness, terrain characteristics and wetness. The most important particle properties affecting

deposition are particle size, shape, density, reactivity, hygroscopicity and solubility. Although

both modeling and experimental investigations have been carried out to study particle dry

deposition, our understanding is still limited. On the one hand, none of the available models has

been fully verified over the wide range of conditions likely to be encountered. On the other hand,

the results from laboratory and field experiments are specific to the conditions of measurements,

and thus are difficult to be generalized and applied on a global scale.

2.3.3.2 Wet deposition/scavenging

Compared to dry deposition on a global scale, wet deposition is more efficient aerosol

removal process, partly due to the fact that the falling speed of hydrometeors greatly exceeds the

dry deposition velocity of particles. Although the efficiency of the process can only be estimated

crudely, wet deposition is the major removal process of sulphate aerosol from the atmosphere. It

was estimated using global three-dimensional chemistry-transport models that wet deposition

accounts for approximately 70 – 80% of the total deposition of sulphate aerosol [Langner and

Rodhe, 1991; Pham et al., 1995].

There are two commonly observed mechanisms for aerosol removal by precipitation:

nucleation scavenging in clouds (rainout or in-cloud scavenging) and aerosol-hydrometeor

coagulation scavenging (washout or below-cloud scavenging). Rainout occurs when a particle

activates to form a liquid drop due to nucleation (discussed previously in aerosol formation), and

the drop subsequently grows to become rain or graupel, which eventually falls to the surface,

removing the aerosol particle. Washout occurs when precipitation particles coagulate with

aerosol particles on their way of falling, bringing the aerosol particles to the surface. Thus,

aerosol wet deposition is an episodic event, while sedimentation and dry deposition take place

over long periods. Within a cloud, rainout removes > 50%, whereas washout removes < 0.1% of

- 20 -

aerosol mass (e.g. [Jacobson, 2003; Kreidenweis et al., 1997]). This is due to the fact that rainout

effectively scavenges all large and most midsize particles, which account for a large fraction of

the aerosol mass, before washout has a chance to remove particles within a cloud. However,

below a cloud, rainout does not remove particles since no activation of new drops occurs. Figure

7 shows the modeled effect of washout on the size distribution of aerosol particles. It can be

found from the model simulation that washout removed particles across the entire size

distribution. Therefore, washout generally removes more particles from the atmosphere than

does rainout [Jacobson, 2003].

Figure 7. Model simulated aerosol number and volume concentrations (at 902 hPa), summed

over 16 size distributions, before (solid lines) and after (short-dashed lines) washout below cloud

base. The simulation period was one hour (adapted from [Jacobson, 1999]).

- 21 -

Figure 8. Contributions of Brownian diffusion, interception and impaction to the collision

efficiency (E) between an aerosol particle and a raindrop as a function of particle size (dp). The

representative diameter for raindrops is Dp = 0.1 mm. Total: black solid line; Brownian: red solid

line with dots; Interception: blue dashed line; and Impaction: red dotted line (Adapted from

[Andronache, 2003]).

For washout of aerosol particles to occur, Brownian diffusion, interception and impaction

are among the major mechanisms resulting in the collision between particles and hydrometeors.

The relatively importance of these mechanisms is very sensitive to the size of particle. In the

atmosphere, small particles in the nucleation mode coagulate rapidly with liquid or solid

precipitation, while coarse particles coagulate due to inertial impaction or interception with

precipitation. [Greenfield, 1957] first found that the overall scavenging coefficient had a broad

- 22 -

and distinctive minimum for particles of about 0.1 – 1 µm in diameter. This minimum is referred

to generally as the “Greenfield gap” and is due to the considerably effective removal of both

small and large particles by Brownian diffusion and inertial impaction, respectively. Figure 8

shows the estimated contributions of wet scavenging mechanisms in the atmosphere (i.e.

Brownian diffusion, interception, and impaction) to the collision efficiency between an aerosol

particle and a raindrop of 0.1 mm in diameter [Andronache, 2003].

2.3.3.3 Residence time

Aerosol particles in the atmosphere are continuously produced and removed. Residence

time of particles is an important indicator of the cycling of particles in the atmosphere. Residence

time refers to the typical period of time they spend in the atmosphere under the interaction of

source and sink mechanisms. As discussed in the previous section, precipitation considerably

influences the residence time of atmospheric aerosols, especially water-soluble species such as

sulphate, nitrate, and hydrophilic carbonaceous aerosols. In addition, wet deposition removes

particles across the entire size distribution but with a broad minimum scavenging efficiency for

particles of 0.1 – 1 µm in diameter. Under relatively low relative humidity (RH) conditions,

small particles in the nucleation mode coagulate rapidly with each other and transfer mass to

large particles, while coarse particles fall out of the atmosphere due to sedimentation effectively.

Figure 9 shows the residence time of troposphere aerosol particles as a function of their size. It

can be seen that maximum values occur in the “Greenfield gap” size range, which is just below

10 days for sub-micrometer particles in the lowest 1.5 km of the troposphere. The residence time

of these particles increases with increasing altitude because wet deposition is less frequent at

higher altitudes [Götz et al., 1991; Grieken and Harrison, 1998]. Studies also have been

conducted to estimate the residence time for individual chemical component of the atmospheric

aerosols. [Textor et al., 2006] found sea salt has the shortest residence time in the atmosphere of

about half a day, followed by sulphate and soil dust with about four days, and organic and black

carbons with about six and seven days, respectively.

- 23 -

Figure 9. Residence time of aerosol particles in the atmosphere as a function of their radius

(adapted from [Götz et al., 1991]).

2.4 Impacts of atmospheric aerosols:

2.4.1 Impacts on human health

Once airborne, aerosols in the atmosphere have significant impacts on human health and

environment. They are a respiratory health hazard at the high concentrations found in urban

environments. After many years of study, a statistically significant correlation between levels of

fine particles and health effects has been established. The health effects of aerosols are thought to

be strongly associated with particle size, chemical composition, and concentration, but it remains

a challenge to isolate the health effects of each individual factor [Davidson et al., 2005].

2.4.2 Impacts on chemical composition of the atmosphere

From the atmospheric chemistry perspective, particles provide sites for surface chemistry

and condensed-phase chemistry to take place in the atmosphere. The effect of aerosols on the

chemical budget of the atmosphere has been investigated from several aspects. First, most

- 24 -

atmospheric chemical reactions depend directly or indirectly on incoming solar radiation,

especially the formation of atmospheric oxidants, such as O3, HOx (OH + HO2) and NOx (NO +

NO2). By changing the amount of available solar radiation, aerosols can modify the rate of

photo-chemistry. BC was identified as the most effective factor (followed by SD) in reducing

photodissociation rates of O3 and NO2 with an estimated contribution of 10% [Yang and Levy,

2004].

Second, heterogeneous reactions on the surface of solid particles and multiphase reactions

within the aqueous phase have a considerable potential to affect the budget of gas-phase oxidants

in the troposphere [Jacob, 2000; Ravishankara, 1997]. As many as 60 heterogeneous reactions

were summarized by [Demore et al., 1985], however, only seven of them were considered in the

Model for Ozone and Related Chemical Tracers, version 2 (or MOZART-2) [Tie et al., 2005].

Tie et al. [2005] showed a remarkable reduction in HOx due to the uptake by sulphate aerosol,

and a subsequent reduction in O3 in boundary layer over United States, Europe and eastern Asia.

The oxidization of sulphur dioxide on particle surface, such as sea salt and soil dust, has been

proved to be substantial in the troposphere [Clegg and Toumi, 1998; Usher et al., 2002].

Mozurkewich [1995] described that releasing active halogens from enhanced heterogeneous

chemistry on sea-salt surface could possibly be responsible for the severe surface ozone

depletion in the Arctic during polar sun rise.

The third role played by aerosols that is of concern to atmospheric chemistry is referred to

as sinks for soluble gas-phase species. Soluble species in gas phase have a tendency to dissolve

in liquid droplets or coat solid particles. Thus, transport of suspended droplets or particles

provides a means for these chemicals to travel great distances.

2.4.3 Impacts on radiative balance of the atmosphere

2.4.3.1 Direct radiative forcing

Aerosol particles affect the Earth's climate in both direct (by scattering and absorbing

radiation) and indirect (by modifying amounts and microphysical and radiative properties of

clouds) manners. Scattering of the incoming radiation by an individual particle is largely

determined by its particle size and chemical composition. Thus, it is an important characteristic

of SF and OC aerosols, as they are small enough to scatter radiation back to space and therefore

exert a cooling effect on earth surface. The direct scattering of solar radiation by aerosols was

- 25 -

estimated to have a negative forcing at the top of the atmosphere (TOA) of -1.3 W/m2

[Ramanathan and Carmichael, 2008], which is comparable to the positive forcing of 1.6 W/m2

due to CO2. On the other hand, BC strongly (and soil dust to some extent) absorb both direct and

reflected solar radiation, leading to a significant warming of the atmosphere. Therefore, the

resulting direct forcing of BC is positive, and estimates of its magnitude range from +0.34 W/m2

in the latest IPCC report to +0.9 W/m2 by [Chung et al., 2005; Ramanathan and Carmichael,

2008]. The BC forcing of +0.9 W/m2 at TOA, obtained from the observationally constrained

study of [Chung et al., 2005], is as much as 55% of the CO2 forcing. However, the estimates by

many general circulation models (GCMs) are mostly in the lower range of 0.2 – 0.4 W/m2. This

is due to the fact that many GCMs ignore the internally mixed BC and/or BC from biomass

burning. The BC forcing estimated by models account for the internally mixed and biomass

burning BC are generally in the range of 0.6 – 1.2 W/m2 (e.g. [Chung and Seinfeld, 2002;

Jacobson, 2001; Sato et al., 2003]). On a global level, the TOA BC forcing of +0.9 W m-2

implies that BC by itself has a surface warming effect of about 0.5 – 1 ºC, while all aerosol

species combined have an overall cooling effect of about -0.75 – -2.5 ºC [Andreae et al., 2005].

2.4.3.2 Indirect radiative forcing

The global atmospheric energy budget is substantially affected by clouds, covering about

60% of the globe surface. Aerosol particles, especially those composed of water soluble

components with diameter of sub-micrometer range, can actively involve in cloud processes, and

therefore disturb the atmospheric energy budget (so-called indirect effects). In the aerosol-cloud

interactions, aerosol particles commonly act as cloud condensation nuclei (CCN) and ice-

formation nuclei (IN). First of all, increased aerosol concentration, and therefore CCN

concentration, is expected to result in more, but smaller cloud droplets (so-called cloud albedo

effect). As a result, it increases reflection of solar radiation directly by increasing total area,

which was estimated by recent studies as -0.5 – -1.9 W/m2 [Lohmann and Feichter, 2005;

Ramanathan and Carmichael, 2008]. In addition, the more but smaller cloud droplets reduce the

precipitation efficiency and therefore enhance the cloud lifetime (so-called cloud lifetime effect),

which is roughly as large as the cloud albedo effect. Other studies have indicated that absorbing

aerosols (such as BC), however, can strongly reduce low-level cloud cover by heating the air and

evaporating cloud droplets depending on the location of absorbing aerosols with respect to

clouds [Ackerman et al., 2000; Hansen et al., 1997; Johnson et al., 2004]. The aerosol-cloud

- 26 -

interaction is complicated also due to BC inside cloud drops and ice crystals, which can decrease

the albedo of clouds, leading to enhanced absorption by cloud droplets and ice crystals [Chylek et

al., 1984; Jacobson, 2006; Mikhailov et al., 2006].

In the unique environment of the Arctic, the relatively low aerosol number concentrations

results in a large percentage of particles activating during cloud formation (e.g. [Komppula et al.,

2005]). Hence, changes in aerosol properties are likely to have a significant impact on

microphysical and optical cloud properties. Garrett et al. [2004] showed that low-level Arctic

clouds are highly sensitive to Arctic aerosols. And Arctic haze can increase cloud longwave

emissivity resulting in an estimated surface warming of between 3.3 and 5.2 W/m2 or 1 and 1.6

ºC [Garrett and Zhao, 2006]. After deposit on the surface of snow or sea ice, very small

quantities of absorbing aerosols (such as BC) alter the surface albedo amplifying its positive

radiative forcing on the Arctic climate. The average radiative forcing from BC by altering

surface albedo was estimated as +0.1 W/m2 [Solomon et al., 2007] on a global scale and +0.3

W/m2 [Hansen and Nazarenko, 2004] for the Northern Hemisphere.

- 27 -

3 Modeling the fate and transport of atmospheric aerosols

Aerosol models, in addition to direct measurements, have been commonly implemented to

study airborne particles, and play an important role in understanding the climatic impacts of

atmospheric aerosols on various temporal and spatial scales. Although the information obtained

directly from various observations has a relatively high accuracy in time and space compared

with the latest model simulations, some deficiencies still remain in direct observations, even for

the latest, most comprehensive approaches based on ground-based monitoring and remote

sensing. The ground-based measurements, with relatively accurate information at fixed locations,

are, somehow, both horizontally and vertically limited in spatial coverage. Satellite remote

sensing was developed to improve geographical coverage, but less information can be obtained

for high latitudes and night-time measurement. The accuracy of retrieved aerosol optical

properties from remote sensing is dependent on surface conditions (more accurate over oceans

than above land surface) and cloud contamination (accurate in cloud-free conditions). Therefore,

the retrieved aerosol microphysical and chemical properties are, in some cases, subject to

considerable uncertainties. Various models have been developed as an attempt to capture the

mechanisms of aerosol production, transport, transformation and removal and to provide a

comprehensive description of aerosol properties. Using aerosol models, progress in

understanding the impact of human activities on the global environment can be made by

distinguishing anthropogenic aerosol effects from natural ones, which can hardly be achieved by

measurements alone. As a result of the limited understanding on some complex aerosols

processes, however, simplifications and parameterizations based on assumptions and/or

empirical relationships are unavoidable in studies using aerosol models.

Depending on the scientific questions to be answered, a variety of models have been

constructed and evaluated on various scales in time and space. Regional air quality models in

capable of very high spatial and temporal resolutions including major aerosol processes were

developed to simulate and predict the local aerosol budget. However, these models can only be

restrictively used in limited geographical locations. Owing to the inherent difference in spatial

and temporal resolutions of the regional air quality model and the driving model (usually on a

global scale) there exists an intrinsic inconsistency at the edge of boundary between them.

- 28 -

Furthermore, models on a local scale can not sufficiently take long-range transport into account,

which have been suggested with more and more scientific evidence to play a critical role in

remote areas, such as the North Pole. So attention will be paid, mainly, to aerosol models on a

global scale in the present research.

Air Quality Modules

Canadian Aerosol Module

Gas Phase Chemistry Module

GEM

Temp. Density

Pressure Humidity

Wind

Clouds

Precip.

(Dynamics) (Physics)

(Physics & Chemistry)

Emission

In-cloud

S chem.

Below-cloud

Dry depo.

NucleationCondensationCoagulation

Figure 10. Framework of GEM-AQ modeling system.

Model simulations of a wide spectrum of atmospheric aerosol types are provided by

chemical transport models (CTMs) that are off-line modules driven by meteorological data or by

general circulation models (GCMs) or numerical weather prediction models (NWPs) with online

aerosol processes integrated within the whole framework. The online approach is generally

preferred in that the integrated aerosol module can have a full access to the meteorological fields

- 29 -

from GCMs or NWPs in their inherent temporal and spatial scales. Furthermore, feedbacks from

the aerosol module to the atmospheric model are possible for the online approach. Issues of

intensive interest currently include aerosol temporal and spatial distributions, location and

relative importance of aerosol sources, pathways of aerosol long-range transport, aerosol

radiative forcing (both direct and indirect forcing), and so on.

Recently, GEM-AQ model has been developing based on the Canadian operational weather

prediction model GEM [Cote et al., 1998a; Cote et al., 1998b; Yeh et al., 2002]. Aerosol and

chemistry modules are integrated into the meteorological model GEM online (Figure 10). Thus

GEM-AQ provides a unique opportunity to investigate atmospheric aerosols.

3.1 Meteorological host model: GEM GEM is currently running by the Canadian Meteorological Centre of Environment Canada

as operational weather prediction model (NWP) and data assimilation system. GEM is a

comprehensive numerical weather prediction system, with capability of both globally uniform

and variable horizontal resolutions as the core of this flexible modeling system. High resolution

up to meso-γ scale (0.0033º or about 360 m), for example, can be achieved over a domain of

1.36×1.36º or about 150×150 km [Cote et al., 1998a; Cote et al., 1998b]. The atmospheric

dynamics in this model is based on a set of non-hydrostatic Euler equations [Yeh et al., 2002]. Its

physics package consists of a comprehensive set of unified RPN (Recherche en Prévision

Numérique) physical parameterization schemes [Cote et al., 1998b; Kaminski et al., 2008;

Mailhot et al., 1998]. Parameterizations are available for the following physical phenomena:

(a) turbulent fluxes of momentum, heat and tracers (moisture, gases, and aerosols);

(b) surface-layer effects;

(c) gravity wave drag;

(d) prognostic clouds;

(e) solar and infrared radiation;

(f) deep and shallow convection;

(g) condensation of water vapour; and

(h) precipitation including evaporative effects.

The current operational parameterizations, used by [Kaminski et al., 2008], are adopted by

the current study with one exception in the scheme for land surface processes. The Interactions

- 30 -

Soil-Biosphere-Atmosphere (ISBA) scheme is used in this study instead of the simplified force-

restore method in Kaminski et al. [2008]. Compared to the force-restore method, the ISBA

scheme provides a more detailed calculation of surface heat and moisture fluxes from different

surface types by introducing new prognostic variables. Unlike many gaseous species, the

concentration of aerosol particles is usually high close to the surface of source regions, except

those from forest fire emissions. Therefore, better representations of surface heat and moisture

fluxes are crucial to the model simulations of BC and SF aerosol in this paper.

3.2 Aerosol module: CAM Given global 3D meteorology by host model, aerosol module is developed to account for

microphysical and thermodynamic processes of atmospheric aerosols. Aerosol models on a

global scale often consider atmospheric aerosol as a combination of the following chemical

components: carbonaceous, sulphate, sea salt and soil dust. The geographical distribution of

these particles on a global scale, as well as its dynamic evolution over time, is typically obtained

based on the following physical processes: emission, dry and wet deposition, coagulation,

condensation, nucleation and aerosol chemistry. A set of numerical representations of these

aerosol processes consist a so-called aerosol module, such as Canadian Aerosol Module (or

CAM) [Gong et al., 2002; Gong et al., 2003a]. CAM was developed as a size-resolved, multi-

component, and interactive aerosol module. The aerodynamic size of particles in equilibrium

with relative humidity is used in CAM. As an aerosol particle grows with raising relative

humidity, the particle density changes accordingly. A sectional representation of 12 particle size

bins over a radius spectrum from 0.005 to 20.48 µm was previously found to be adequate to

predict aerosol number or mass size distributions [Gong et al., 2002; Gong et al., 2003a], which

is also used in the current study. In CAM, the evolving of aerosol mixing ratio over time can be

written as

CLOUDBELOW

ip

CLOUDIN

ip

DRY

ip

SKYCLEAR

ip

SOURCES

ip

TRANSPORT

ipip

ttttttt−−−

∂

∂+

∂

∂+

∂

∂+

∂

∂+

∂

∂+

∂

∂=

∂

∂ χχχχχχχ

(3.1)

where ipχ stands for the aerosol mixing ratio of p component with particle size in the i-th size

bin. Both 3D advection and vertical diffusion are performed by the meteorological host model,

- 31 -

GEM, to provide the fist term on the right-hand side of the above equation. Calculation of the

last five terms is summarized in the following sections.

3.2.1 Emissions

While dealing with the injection of particles from various sources into the atmosphere in

models (e.g. the second term on the right-hand side of Eq. 3.1), the assumption that all emissions

are treated as primary emissions except those of sulphur compounds has been widely accepted.

In the latest aerosol models, the injection of soil dust (SD) and sea-salt (SS) aerosol is calculated

online, while the gridded surface fluxes of sulphur compounds and other aerosol species are

mainly based on global inventories. Depending on both wind speed and surface properties, SD

and SS particles are naturally produced by the action of wind at the land and ocean surface,

respectively. As these two aerosol species are not the scope of the current study, one refers to

[Gong et al., 2002; Gong et al., 2003b; Guelle et al., 2001; Luo et al., 2003; Tegen et al., 2002]

for their detailed production mechanisms represented by current aerosol models. The BC and SF

aerosol emission schemes used by CAM, as well as by other aerosol modules, are summarized as

follows.

For carbonaceous particles, a number of inventories on both global and regional scales have

been constructed to account for their emissions from handful types of sources. For instance,

Cooke and Wilson [1994] developed a global BC emission inventory by considering the

combustion of biomass and fossil fuel, including the burning of savanna and forest fires, diesel,

coal, fuel wood, and charcoal sources. The first carbonaceous inventory including both BC and

OC emission sources was constructed by [Liousse et al., 1994], where OC was emitted from

biomass burning sources with BC at the same time, but had a larger portion compared to BC.

Focusing on the northern latitudes, a spatial and monthly inventory was constructed for BC and

OC emitted by boreal and temperate wildfires [Lavoue et al., 2000]. Another significant

contribution was made by [Bond et al., 2004; Bond et al., 2007] by presenting a global inventory

of BC and OC from combined combustion including fossil fuels, biofuels, open biomass burning,

and burning of urban waste. The total uncertainties in this technology-based inventory are about

a factor of 2, and the low-technology combustions contribute greatly to the total emissions as

well as the uncertainties. In GEM-AQ, both BC and OC emissions are provided by three

complementary data sets: forest and savannah fires at the tropical latitudes and domestic and

- 32 -

agricultural fires worldwide [Liousse et al., 1994]; global fossil-fuel burning [Cooke et al., 1999];

and boreal and temperate fires [Lavoue et al., 2000]. Emission fields vary in horizontal

resolutions, so they were regridded to the model grid and mass flux conservation was ensured.

The annual global anthropogenic BC emissions of 6.0 teragram (Tg) are derived from the data

used in GEM-AQ, while [Bond et al., 2007] totalized 4.6 Tg for the year of 2000, i.e. 23% less,

which is well within the estimated uncertainties.

As required by performing sulphur chemistry in aerosol modules, emissions of sulphur

species include oceanic emissions of DMS, anthropogenic emissions of SO2 and primary

sulphate (SO42-), and, in some studies, the volcanic and biomass burning sources. The oceanic

emission of DMS is usually determined based upon estimated DMS seawater concentrations and

10-m wind speed. Sulphur dioxide emissions from anthropogenic sources include fossil fuel and

biofuel emissions and agricultural burning, which is currently considered the major contributor

to sulphate aerosol in the air. For primary emissions of sulphate particles prescribed size

distributions are generally employed in emission schemes. Similar to many other studies (e.g.

[Barrie et al., 2001; Barth et al., 2000]), sources of sulphur species used in this study include

surface emission rate of both natural and anthropogenic sulphur dioxide and sulphate (Global

Emissions Inventory Activity, or GEIA 1985 inventory [Benkovitz et al., 1996]), H2S from land

[Benkovitz and Schwartz, 1997], DMS from oceans [Kettle et al., 1999]. Sulphur dioxide and

sulphate emissions from GEIA inventory have two injection levels (0-100 m and >100 m). In the

current study, lower level injection is applied to the lowest model level, and the higher level

injection contributes to the lowest model level whose upper boundary is higher than 100 m. H2S

and DMS are both injected into the lowest model level.

3.2.2 Transformation

3.2.2.1 Clear-sky sulphur chemistry, nucleation and condensation

The transformation of airborne particles involves sulphur chemistry, aerosol coagulation,

nucleation and condensation. The changes in aerosol populations are accounted for in CAM by

the third term on the right-hand side of Eq. 3.1 (SKYCLEAR

ip

t−

∂

∂χ), for example. To account for

sulphur chemistry under clear-sky conditions (the in-cloud sulphur chemistry will be discussed

separately in this document), gas phase chemical reactions resulting in the production of SO2

- 33 -

from DMS and H2S, followed by the oxidation of SO2 to produce H2SO4(g), are included in

CAM, as shown in Table 3. DMS is the most abundant reduced sulphur compound naturally

emitted by vegetation on land and phytoplankton from oceans into the atmosphere. The major

recognized sink for DMS in the atmosphere is reaction with the OH radical (e.g. [Barnes et al.,

1988]). Although the oxidation mechanism for DMS is extremely complex and has been the

focus of numerous studies, two major DMS oxidation pathways (i.e. the H-abstraction leading to

SO2 and the OH-addition leading to MSA and SO2) are generally adopted in models, critically

dependent on ambient air temperature [Hynes and Wine, 1996]. The major oxidant next to the

OH radical producing SO2 in the atmosphere is NO3, which can be an important nighttime loss

process in continental air masses. Contrary to the oxidation of DMS by OH following two

possible pathways, the oxidation of DMS by NO3 only proceeds through the H-abstraction

mechanism leading to SO2. Only recently, studies (such as [Boucher et al., 2002a]) investigated

the possible roles of O3 and BrO as complementary oxidants in SO2 production from DMS in the

air using a global model. According to previous studies (such as [Cox and Sandalls, 1974;

Geoffrey and Ravishankara, 1991; Jaeschke et al., 1978]), the OH radical also accounts for most

of the observed H2S transformation leading to SO2, while the reactions of H2S with O3 and NO3

are slow. The commonly accepted oxidation mechanism of SO2 under clear-sky conditions

includes the reactions shown in Table 3, with the first elementary reaction being the rate limiting

reaction.

Following the formation of water soluble H2SO4 in gas phase, they are subject to two

competitive transformation processes: nucleation and condensation, which are described in CAM

as

42

2

4242

4231 SOH

CSOHSOH

SOH CCPt

χχχ

−−=∂

∂, (3.2)

where 42SOHP is the H2SO4 production rate from clear-sky sulphur chemistry described above,

coefficients C1, C2 and C3 were derived by Gong et al. [2003a]. The second and third terms on

the right-hand side account for binary nucleation and condensation, respectively. Binary

nucleation of sulphuric acid and water is thought to be the most important nucleation process

resulting in new sulphate particle formation in the atmosphere. The binary nucleation rate,

therefore coefficients C1 and C2, can be expressed as a function of vapor sulphuric acid

- 34 -

concentration, temperature and relative humidity [Kulmala et al., 1998]. As clusters of only a

few molecules, sulphate particles formed due to binary nucleation are generally placed in the

smallest size bin, resulting in modifying particle number- and mass-size distributions at the same

time. Competitively, the condensation rate of H2SO4(g) to an existing particle is proportional to

the diffusion coefficient of H2SO4(g) in air, as well as the particle size, following the modified

Fuchs-Sutugin equation [Fuchs and Sutugin, 1971]. Condensation, however, only modifies the

aerosol mass-size distribution, with the number-size distribution unchanged.

Table 3. Gas phase chemical reactions of sulphur species in CAM (adapted from [Gong et al.,

2003a])

Chemical Reactions Rate coefficients (cm3 molecule-1 s-1)*

DMS + OH SO2 (H-abstraction) 9.6×10-12exp(-234/T)

DMS + OH 0.75SO2 +

0.25MSA

(OH-addition)

][)/7460exp(105.51][)/7810exp(107.1

231

242

OTOT⋅×+

⋅×−

−

DMS + NO3 SO2 + HNO3 1.9×10-13exp(500/T)

SO2

Production

H2S + OH SO2 6.3×10-12exp(-80/T)

SO2

Oxidation

SO2 + OH HSO3

HSO3 + O2 SO3 + HO2 (fast)

SO3 + H2O H2SO4 (fast)

120 })](/])[([log1{

0

0 6.0})(/])[(1

])[({−

∞+

∞

⋅+

TkMTk

TkMTkMTk

*T = air temperature (K), [O2] = O2 density (molecule cm-3), [M] = air density (molecule cm-3), 12105.1 −

∞ ×=k , and 3.3310 )/300(100.3 Tk ××= − .

- 35 -

No analytical solution to Eq. (3.2) can be found, so to simplify the solution it is

approximated as follows:

4242

2

4242

423

101 )( SOHSOH

CSOHSOH

SOH CCPt

χχχχ

−−=∂

∂ − , (3.3)

where 042SOHχ is the initial mass mixing ratio of H2SO4. A further division of model time step (i.e.

30 minutes in this study) into 10 sub-time steps is introduced to ensure the approximation with

acceptable uncertainties under typical atmospheric conditions.

3.2.2.2 In-cloud sulphur chemistry

Next to the clear-sky sulphur chemistry, the in-cloud chemical production of sulphate is also

taken into account in CAM by aqueous-phase oxidation of dissolved sulphur compounds by

hydrogen peroxide (H2O2) and ozone (O3). Laboratory experiments [Penkett et al., 1979]

indicated that the reaction of SO2 with O3 leads to the highest conversion rate for a droplet pH

above 5.5. The rate was, however, found to be inversely proportional to the square of the

hydrogen ion (H+) concentration and so decreased rapidly at low pH. The rate of oxidation by

H2O2 increases linearly with increasing H+ concentration, but the corresponding decrease in SO2

solubility leads to an overall conversion remained roughly constant with pH. Therefore, over the

normally observed pH range of cloud droplets (3 – 6) the oxidation of SO2 in aqueous solution is

dominated by the O3 reaction above pH = 5.5 and by the acid-catalyzed H2O2 reaction at lower

pH.

The in-cloud oxidation of SO2 in CAM is parameterized differently for stratiform and

convective clouds following [von Salzen et al., 2000]. The availability of SO2 and oxidants (i.e.

H2O2 and O3) and the impact of ammonia, nitric acid and carbon dioxide on the in-cloud

oxidation rates are explicitly included in the parameterization. To take into account the

dependence of the oxidation rates on the pH, the H+ concentration is calculated from the ion

balance: ][][][][2][2][][][ 33323

244

−−−−−++ +++++=+ − HCONOHSOSOSOOHNHH , which is

numerically solved by an iteration method.

- 36 -

3.2.2.3 Coagulation

For aerosol coagulation, only binary collisions of particles are considered in CAM.

Following Seinfeld and Pandis [2006], the rate of change in number concentration of aerosol

particles in certain size range due to coagulation can be calculated by

∑∑∞

=

−

=−− −=

1,

1

1,2

1j

jjkk

k

jjkjjkj

k NKNNNKdt

dN , (3.4)

where Kk,j is the coagulation coefficient between particles in size bins k and j, and Nk is the

number concentration of aerosol particles in size bin k. As no analytical solution to this equation

is available for multi-component systems, a semi-implicit numerical solution based on Jacobson

[1994] is adopted in CAM.

3.2.3 Removal from the atmosphere

3.2.3.1 In-cloud scavenging

Aerosol-cloud interactions are considered by modeling in-cloud chemical and physical

processes, including aerosol activation, precipitation formation, and multiphase chemistry.

Clouds are formed in the atmosphere where the condensational growth of aerosol particles (i.e.

aerosol activation) becomes considerable. An empirical approach following [Jones et al., 1994]

is used in CAM to establish a diagnostic relationship between cloud droplet number

concentration and aerosol number concentration in a certain size range, as follows

)]105.2exp(1[1075.3 98adrop NN −×−−×= (3.5)

where Na is aerosol number density of particles that are 0.1—3.0 µm in diameter. This cloud

number concentration is fed to the in-cloud sulphur chemistry code to calculate in-cloud

production of sulphate and modifications of hydrogen peroxide.

The formation of precipitation can remove the activated aerosol mass in the cloud droplets

or ice particles. The rainout removal tendency in CAM has the form of

)( iipCLOUDIN

ip rt

χλχ

×−=∂

∂

−

(3.6)

- 37 -

where λ is the local removal frequency (1/s) based on the calculation of Giorgi and Chameides

[1986].

3.2.3.2 Below-cloud scavenging

Precipitation such as rain and snow is responsible for the removal of aerosol particles as it

falls to earth’s surface (often referred to as scavenging). The consequent change in mass mixing

ratio of atmospheric aerosols can be written as

)()( iipicldCLOUDBELOW

ip rrft

χχ

×Ψ×=∂

∂

−

(3.7)

where ri is the averaged radius of particles in size bin i, fcld the fraction of cloud cover, and Ψ the

scavenging rate depending on the size of both aerosol particles and precipitations [Slinn, 1984].

3.2.3.3 Dry deposition

Both aerosol and gas species undergo dry deposition at the surface level in the atmosphere.

For multicomponent aerosol in CAM, a scheme modified by Zhang et al. [2001] based on sea-

salt aerosol deposition [Gong et al., 1997] is used to calculate particle dry deposition velocity

from the ambient size and density, land surface properties (i.e. surface covered by different

vegetation, ocean, snow and ice), and meteorological conditions (i.e. wind speed). For gas

species, only SO2 is considered based on Padro et al. [1991]. As required in the calculation, a

detailed land use database from the Land Processes Distributed Active Archive Center (LP

DAAC) is introduced into CAM with global resolution of 1×1 km.

3.3 Performance of existing global aerosol models A number of global aerosol models have been developed to predict the distributions of

aerosols in the atmosphere and subsequently to estimate their impact on the global climate. Many

of these models simulate aerosol cycles in the atmosphere similarly as CAM does. This section

summarizes the performance of several such models using two approaches: model-observation

comparison and model intercomparison. The goal is to identify limitations in modeling aerosols,

especially for BC and SF, on a global scale.

- 38 -

3.3.1 Modeling aerosol surface concentration and burden on a global scale

To evaluate the performance of aerosol models, model predictions can be compared directly

with available observations, especially aerosol mass concentrations. Long-term BC surface

measurements are available for the Unite States from the Interagency Monitoring of Protected

Visual Environments (or IMPROVE) network, for Europe from the European Monitoring and

Evaluation Programme (or EMEP) network, for Asia from Zhang et al. [2008], and for selected

sites in the northern high latitudes. Although the measured BC surface concentrations vary

significantly from site to site, there are some generally differences among these regions, with the

higher concentrations in Asia (1000-1400 ng/m3) and Europe (500-5000 ng/m3), followed by the

Unite States (100-500 ng/m3), and the northern high latitudes (10-100 ng/m3), and the lowest

ones at remote locations (<10 ng/m3) [Koch et al., 2009].

The early model investigations [Cooke et al., 1999; Koch and Hansen, 2005a; Liousse et

al., 1996; Reddy and Boucher, 2004] indicated that on an annual average the distribution of

anthropogenic and natural sources resulted in a widely spread BC distribution over the

continental areas in eastern Europe, Asia, and Africa, as well as a greater abundance in northern

hemisphere than that in the southern hemisphere (e.g. in Figure 11). Directly affected by

intensive BC emissions, the annually and zonally averaged BC concentration shows maxima at

the surface in the tropical regions and the northern mid-latitudes. A strong tendency of

atmospheric transport toward the poles in the middle troposphere was revealed [Koch and

Hansen, 2005a; Wang et al., 2009], which resulted in higher aerosol concentration in the middle

troposphere than in the lower altitudes over the Arctic. Models also revealed that carbonaceous

particles in the Arctic region had a fossil fuel origin, while those in the Antarctic had a biomass

burning origin, mainly due to the distance to surrounding sources.

- 39 -

Figure 11. Modeled global annual average BC surface concentration (in ng m-3) [Reddy and

Boucher, 2004].

Surface concentrations predicted by these models commonly agree with the observations

within a factor of 2 for rural sites of North America and Europe, whereas for the remote and

urban areas the models tend to underestimate aerosol surface concentration by up to an order of

magnitude. The correlation coefficient (R) between IMPROVE network data (mostly rural sites)

and modeled BC surface concentrations by Reddy and Boucher [2004], for example, was

reported to be 0.67 with over 70% of the data points falling within a factor of 2. However, the

predictions for Asia (often urban areas) made by the same study were rather scattered with both

overestimations and underestimations by a factor of 2 – 5 depending on the site. The

discrepancies between model and observations may partly arise from the influence of urban

sources not included in the model. Most recently, Koch et al. [2009] evaluated the model-

predicted surface BC concentration and burden (calculated as the total mass of aerosol in a

column of atmosphere) against observations. The results are summarized in Table 4 by

geographic regions. Overall, the 17 participating models tend to overestimate BC concentrations

except for Asia. In North America, simulations of most models agree fairly well with observed

- 40 -

surface concentration (within a factor of 2), but with a couple of outliers of significant

overestimations, resulting in the greatest diversity among models (111%). Comparison over the

European area revealed a general overestimation by a factor of 3, while a relatively consistent

underestimation by a factor of 2 was found for Asia among the models. However, the current

global models consistently underestimate the total burden by a factor of 2 regardless the regions.

It should be pointed out that North America, Europe, and Asia are considered the most important

anthropogenic source regions of the Arctic BC. Such model-observation comparison suggests

that BC concentration is often overestimated near the surface of polluted areas, but

underestimated above the surface level. This may result in underestimations in the atmospheric

transport of BC from the mid-latitudes to the Arctic, because the poleward atmospheric transport

was found to be significant in the middle troposphere.

Table 4. Ratio of model average to observed BC surface concentration and retrieved BC burden

within selected regions over the globe [Koch et al., 2009]. A ratio greater than 1 suggests an

overestimation by models and vice versa. Here, diversity is shown in the brackets, which is

expressed as the standard deviation among 17 model results normalized by the average in

percentage.

N America Europe Asia S America Africa Rest

Surface

concentration

1.6 (111%) 2.8 (82%) 0.54 (41%) N/A N/A 1.5 (72%)

Burden 0.42 (56%) 0.58 (64%) 0.64 (49%) 0.42 (30%) 0.64 (73%) 0.40 (39%)

Model intercomparison is another exercise to assess global aerosol models relative to each

other. Although model intercomparison does not provide a definitive test of model performance,

it can reveal diversities and sensitivities of different process treatments among models and

suggest approaches to eliminate discrepancies among models. To achieve this goal the Aerosol

interComparison (AeroCom) project was established in 2003 to provide a platform to conduct

- 41 -

detailed analysis of global aerosol simulations based on harmonized diagnostics. Textor et al.

[2006] provided basic aspects on model resolution and the treatments of aerosol emission,

transformation, and removal, and conducted the first comparison of aerosol life cycle predicted

by 16 global models using their own emissions data. The diversities among the estimated life-

cycle parameters from the 16 global aerosol models are summarized in Table 5. Comparing the

diversity values listed in the table, it is found that models are rather different in aerosol dry and

wet removal than in emissions. To investigate the model sensitivity to aerosol emission,

harmonized emissions were used by different models and the results showed that model diversity

was not greatly reduced by unifying emissions [Textor et al., 2007]. These studies therefore

implied that modeled aerosol life cycles depend to a large extent on model-specific differences of

atmospheric transport, aerosol transformation, removal, and parameterizations of aerosol

microphysics. To better understand aerosol life cycles at process level, detailed sensitivity

analysis using multiple aerosol models needs to be carried out, which is very rare in the literature.

Table 5. Diversities of estimated aerosol life-cycle parameters based on AeroCom model

simulations [Textor et al., 2006]. In their study, diversity is expressed as the standard deviation

among 16 model results normalized by the average in percentage.

Parameter (unit) BC SF

Mean Diversity (%) Mean Diversity (%)

Emission (Tg/yr) 11.9 23 179 22

Burden (Tg) 0.24 42 1.99 25

Residence time (days) 7.12 33 4.12 18

Wet removal rate (1/day) 0.12 31 0.22 22

Dry removal rate (1/day) 0.03 55 0.03 55

- 42 -

Figure 12. Observed and modeled seasonal cycles of BC surface concentrations at two Arctic

sites: Barrow (left panel) and Alert (right panel) [Shindell et al., 2008]. BC observation data are

from the IMPROVE site at Barrow during 1996-1998 (red), and from [Sharma et al., 2006a] for

both Barrow and Alert using equivalent BC over 1989-2003 (purple). Multi-model simulation

results are in grey from [Shindell et al., 2008].

3.3.2 Modeling aerosol surface concentration and burden in the Arctic troposphere

Long-term measurements of BC surface concentration have been carried out at selected

northern high latitude observatories, such as Alert (82.4 ºN, 62.3 ºW), Nunavut, Point Barrow

(71 ºN, 156.6 ºW), Alaska, and Zeppelin (78.9 ºN, 11.9 ºE), Svalbard. Persistent seasonal cycles,

characterized by higher BC concentrations in the cold seasons than those in the summer, were

revealed from all of the above Arctic sites [Eleftheriadis et al., 2009; Sharma et al., 2006b]. As

shown in Figure 12, however, all 11 global models tested failed to capture the seasonal patterns

[Shindell et al., 2008]. Although the significant discrepancies between observations and model

predictions in the northern high latitudes were attributed to a number of possible model

deficiencies (e.g. [Cooke et al., 1999; Iversen and Seland, 2002; Koch and Hansen, 2005a;

Liousse et al., 1996; Reddy and Boucher, 2004; Wang et al., 2009]), no detailed investigations on

these factors were carried out in their studies. Liousse et al. [1996] suggested that the

underestimation of winter and spring BC in the polar region might result from (1)

underestimation of biomass sources, (2) overestimation of precipitation scavenging, or (3) poor

- 43 -

representation of south-to-north advection. The unrealistic representation of transport in stratified

layers in the Arctic winter by host models was cited as another possible reason for the

significantly underestimated BC surface concentration in the winter months at Barrow (by a

factor of 8) [Cooke et al., 1999; Reddy and Boucher, 2004]. In another study carried out by

Iversen and Seland [2002], the underestimation of BC concentrations in the winter at Barrow and

Alert was linked to the overestimated low-level cloudiness, and therefore the overestimated wet

scavenging. Koch and Hansen [2005a] argued that, to some degree, the model’s inability to

capture BC seasonality in the Arctic might be related to lack of seasonality in emissions and

overestimated precipitation in source regions like China. Based on these individual modeling

studies, a wide range of reasons were suggested for the poor performance of global aerosol

models in the Arctic, which reflects a huge knowledge gap in modeling aerosols in the Arctic.

Only recently, studies of model intercomparison on the diversity of model simulated

pollutants in the Arctic has been carried out (e.g. [Schulz et al., 2006; Shindell et al., 2008]).

Comparing the performance of 17 global models in the Arctic, Shindell et al. [2008] confirmed

that the seasonality of Arctic BC and SF were poorly simulated by all these models, whereas

most of these models reasonably well captured the seasonal pattern of CO. Comparing model

diversities of prescribed lifetime tracers with those of BC and SF, they suggested that aerosol

physical and chemical processing (e.g. removal, oxidation and microphysics) is the principle

source of uncertainty in modeling the aerosol distributions in the Arctic. Using harmonized

emissions, [Textor et al., 2007] showed that the diversities in modeled aerosol abundance in

polar regions (>80º in both hemispheres) depend only to a less extent on emissions, but might

largely depend on differences in aerosol transformation and removal. As such, the current study

focuses on the importance of model representation of aerosol removal in simulating the

seasonality of the Arctic aerosol.

3.4 Atmospheric transport of aerosols into the Arctic Source contributions and atmospheric transport pathways affecting the Arctic have been the

focus of numerous studies for decades. Such information is critical to estimate and possibly

regulate, for example, the human impacts on the Arctic environment. In some cases, however,

contradictory results can be found in literature.

- 44 -

As a remote region, the northern high latitudes have very limited local sources of

anthropogenic aerosols, which are restricted to regions near the Arctic Circle, such as

anthropogenic emissions from conurbations like Mumansk, industrial emission in the northern

parts of Russia, and emissions from the oil industry and shipping [Law and Stohl, 2007]. Thus,

the atmospheric transport of aerosol particles into the Arctic plays an important role in

controlling the abundance of anthropogenic aerosols in the Arctic troposphere. The seasonal

changes in air circulation around the Arctic during winter and summer are shown in Figure 13.

Figure 13. Seasonal changes in air circulation around the Arctic: winter vs. summer [AMAP,

2006].

According to sediment core analyses in earlier studies, the predominant aerosol transport

occurred from Russia to west Greenland (67 ºN), and Western Europe was suggested as another

important source [Bindler et al., 2001]. The sulphate concentration in two Canadian ice cores

was analyzed by Koerner et al. [1999], showing no significant changes during 1980s and 1990s.

Considering the reduction of aerosol emissions from North America and Europe while

- 45 -

unchanged emissions from Russia, it suggested that the aerosol abundance in the Canadian

Arctic was dominated by Russian emissions. Recently, Koch and Hansen [2005a] carried out an

investigation using models. Their results imply that south Asian contribution to the BC

abundance near the Arctic surface is comparable to the European contribution, and it becomes

dominant in the Arctic upper troposphere. The Asian BC, mainly from industrial emissions, is

probably lofted to high altitudes (e.g. above the boundary layer), and transported toward the pole

thereafter. However, contradictory results were reported by [Shindell et al., 2008; Stohl, 2006],

emphasizing preferred atmospheric transport pathways on the European side into the Arctic

lower troposphere. Using a Lagrangian particle dispersion model FLEXPART, [Stohl, 2006]

identified three different pathways leading air pollution from the lower latitudes into the Arctic

troposphere: low-level transport followed by ascent in the Arctic, low-level transport alone, and

uplift outside the Arctic followed by descent in the Arctic. It was pointed out in his study that

only the last pathway is available for pollution originating from North America and Asia, but

European pollution can follow all three pathways in winter. Therefore, near the Arctic surface,

BC source contribution from south Asia was estimated to be less than 10% of the European

contribution, assuming the BC atmospheric lifetime of 10 days. Similar results were presented by

an 11-model assessment [Shindell et al., 2008] that the European contribution dominates at the

near-surface level of Arctic troposphere, followed by east Asian and North American

contributions, while south Asia contributes the least. Although south Asian contribution

increases and European contribution decreases with increasing altitude, south Asian only

becomes comparable to Europe according to [Shindell et al., 2008].

3.5 Research results Based on the review presented above, it was found that global aerosol models agree fairly

well (i.e. within a factor of 2) with the surface observations of BC and SF in the rural areas in

North America and Europe. However, serious underestimations over the remote Arctic are

commonly found in the literature. In addition, the pronounced seasonal cycle of Arctic aerosol is

not captured by state-of-the-art global aerosol models. Although models agree fairly well with

each other on the estimated global burden and residence time of BC and SF, their estimates of

aerosol dynamics are relatively spread on a process level. By excluding aerosol emissions and

atmospheric transport, recent model intercomparisons suggest that aerosol physical and chemical

processing (e.g. removal, oxidation and microphysics) is the principle source of uncertainty in

- 46 -

modeling the Arctic aerosol. Therefore, study on the importance of the suggested individual

aerosol processes in reproducing the seasonality of the Arctic aerosol becomes urgent to close

the knowledge gap. As such, the importance of representations of aerosol depositions in

simulating the Arctic BC and SF is the subject of the first research paper. The effects of

enhanced aerosol deposition parameterizations on the modeled seasonality of Arctic BC and SF

are investigated.

The improved global model, GEM-AQ, is then applied to study the contributions of

anthropogenic BC sources to the Arctic BC abundance in the second research paper. Although

several potential source regions affecting Arctic BC are identified, the latest estimates on their

relative importance in the literature are found contradictory. The contributions of Asian BC to

the surface BC abundance in the Arctic, for example, were estimated quite differently among

models. Performing better in reproducing the abundance of BC, as well as its seasonal cycle, in

the Arctic than previous models, the enhanced GEM-AQ provides latest estimates on source

contributions. The source contributions to the abundance of BC in the Arctic are investigated in

this study as a continuous function of altitude. With more confidence than previous modeling

studies, additional support on the dominant contribution of Eurasia to surface BC in the Arctic is

presented in the second paper.

Other than the seasonal variation, the inter-annual changes in Arctic BC are investigated in

the third research paper, as different factors may control aerosol variability in the Arctic on

different time scales. Although the enhanced GEM-AQ is ideal to study the temporal variations

of the Arctic aerosol, the computational overhead limits its application to effectively study

problems of relative short time scale. Therefore, the atmospheric backward trajectory analysis,

together with estimated BC emissions, is implemented as a computational effective approach to

interpret the year-to-year changes in the BC surface concentration observed at the Canadian high

Arctic observatory, Alert. Among other factors, the relative importance of atmospheric transport

and regional BC emissions in governing inter-annual variability of Arctic BC is investigated. The

source contributions from North America and Eurasia are estimated annually from 1990 through

2005. To estimate the uncertainties of this simplified approach, the estimated source

contributions are compared with studies using comprehensive aerosol models for selected years.

- 47 -

4 Importance of deposition processes in simulating the seasonality of the Arctic BC and SF aerosol

Abstract Anthropogenic aerosol components in the Arctic troposphere, such as black carbon (BC)

and sulphate (SF), show a strong seasonal variation characterized by a peak in later winter and

early spring. The seasonality, however, is not properly simulated by the most existing global

aerosol models. Using the Canadian global air quality model with an on-line aerosol algorithm –

GEM-AQ, this work investigates the mechanisms of the seasonal variation of the Arctic BC and

SF. Through enhancements to parameterizations of wet and dry depositions in the Canadian

Aerosol Module (CAM), the GEM-AQ model is able to simulate the observed seasonality of BC

and SF over the Arctic. The observed seasonality of Arctic BC and SF is mainly attributed to the

seasonal changes in aerosol wet scavenging. Seasonal injection of aerosols (e.g., BC from the

European and the former USSR sectors, and to a less extent from the North Atlantic sector) also

contributes to the seasonality of Arctic aerosols in the lower troposphere. Although dry

deposition has little effect on the seasonal pattern of BC in the Arctic lower troposphere, it

significantly changes BC surface concentration in the Arctic. The enhanced model suggests an

annual budget of BC deposition to the Arctic of 0.11 Tg – a 10% increase over the original

estimation. The enhanced GEM-AQ model also suggests that the below-cloud scavenging

dominates the contribution of BC removal over the Arctic with an estimation of 48% for 2001,

while the contributions of in-cloud scavenging and dry deposition contribute about 27% and 25%,

respectively. The estimated global BC burden is 0.28 Tg, which implies a global average BC

lifetime of 9.2 days, while the AeroCom project suggests a range of 4.9 – 11.4 days.

4.1 Introduction The episodic events of surprisingly high particle concentrations in the Arctic lower

troposphere during winter and spring, known as Arctic haze, have been studied for several

decades [Barrie, 1986; Law and Stohl, 2007; Quinn et al., 2007; Shaw, 1995]. Particles collected

during haze events are mainly sulphate and particulate organic matter and, to a lesser extent,

black carbon (BC), nitrate, ammonium, and dust aerosols [Quinn et al., 2002; Ricard et al., 2002;

Sirois and Barrie, 1999; Xie et al., 1999a]. Haze particles in the Arctic lower troposphere may

- 48 -

result in complex climatic consequences due to the varying mixture of radiative scattering and

absorbing aerosol species [Hu et al., 2005; Koch, 2001] (e.g., SF and BC, respectively), which is

known as the direct aerosol effect. A radiative transfer model investigation suggested that the

overall climate forcing is highly sensitivity to the presence of BC [Kirkevag et al., 1997]. In their

study, a minimum of -5 W/m2 near the most polluted areas was found in Europe, a maximum of

+2 W/m2 over North Africa, and an overall warming effect of values up to +0.4 W/m2 was found

in the Arctic. Once activated in the air, aerosol particles can serve as cloud condensation nuclei,

and therefore modify the radiative forcing of Arctic clouds, which is called the indirect aerosol

effect. A recent study suggested that the indirect aerosol effect contributes more to net radiative

flux changes over the Arctic than the direct aerosol effect [Menon et al., 2008]. After deposit on

the surface of snow and sea ice, very small quantities of BC alter the surface albedo amplifying

its positive radiative forcing on the Arctic climate. The average radiative forcing from BC by

altering surface albedo was estimated as +0.1 W/m2 [Solomon et al., 2007], which varies from

+0.05 to +0.15 W/m2 depending on distribution and deposition of BC on snow [Flanner et al.,

2007; Hansen and Nazarenko, 2004; Hansen et al., 2005; Hansen et al., 2007]. Recently, it was

shown that the decreasing SF and the increasing BC concentrations have substantially

contributed to rapid Arctic warming during the past three decades [Shindell and Faluvegi, 2009].

Thus, the significant sensitivity of the Arctic to radiative forcing from BC and SF makes them

attractive aerosol species in numerous observational and modeling studies.

Long-term observations of BC carried out at Alert (82.4 ºN, 62.3 ºW), Nunavut, Point

Barrow (71 ºN, 156.6 ºW), Alaska, and Zeppelin (78.9 ºN, 11.9 ºE), Svalbard suggested that BC

surface air concentration is highly variable in time, but more importantly, a persistent seasonal

cycle was revealed for these sites [Eleftheriadis et al., 2009; Hirdman et al., 2010; Sharma et al.,

2006a]. The pronounced seasonal cycle is characterized by higher BC concentration values in

winter (i.e., the haze season) than in summer. The formation of the distinct seasonal variation in

BC can be attributed to several seasonally dependent mechanisms, such as atmospheric transport

and removal. Like other pollutants, sources of BC are located in more southerly latitudes than

within the Arctic. Local BC sources are currently small and limited to near the Arctic Circle,

such as anthropogenic emissions from conurbations like Mumansk, industrial emission in the

northern parts of Russia, and emissions from the oil industry and shipping [Law and Stohl, 2007].

Ground-based particle size distribution measurements from the Zeppelin station, Svalbard, and

- 49 -

Point Barrow, Alaska confirmed that more aged particles associated with long-range transport

were collected in winter and spring than in summer [Engvall et al., 2008; Quinn et al., 2002;

Strom et al., 2002]. Based on atmospheric back trajectories, frequent long-range transport

pathways affecting the Arctic were investigated [Eneroth et al., 2002; Kahl et al., 1997; Polissar

et al., 1999; Polissar et al., 2001; Stohl, 2006; Worthy et al., 1994; Xie et al., 1999b; Yli-Tuomi

et al., 2003]. These studies suggest that industrial regions in Eurasia and North America are

likely to be the major sources of the Arctic aerosol during winter and spring. Atmospheric long-

range transport alone, however, cannot explain the observed seasonality of Arctic BC

concentration [Sharma et al., 2004]. Thus, the importance of atmospheric removal processes,

such as wet scavenging and dry deposition, should be investigated to better understand the

seasonal cycle.

Although a qualitative understanding of the Arctic BC seasonal cycle has been established

based on various observations and trajectory analysis, a successful implement of the current

understanding in global models remains a challenge. Since the 1990s’, global models considering

BC aerosol have been developed and been evaluated against monthly surface observations for

the Arctic region [Cooke et al., 1999; Iversen and Seland, 2002; Liousse et al., 1994; Reddy and

Boucher, 2004; Wang et al., 2009]. These studies found that the disagreement between models

and measurements appears to be worst in the Arctic region than the rest of the globe, which was

mainly attributed to the atmospheric transport scheme and removal. Even the most recent multi-

model study revealed that none of the 11 3D global aerosol models can reasonably capture the

seasonality of Arctic BC at Alert and Barrow [Shindell et al., 2008]. Arctic BC concentrations in

winter are likely under-predicted by all participant models. Their study also found that the

intermodel variations in Arctic sensitivity are significant for BC, which was mainly attributed to

different representations of aerosol wet scavenging rather than atmospheric transport among the

models.

The focus of this study is on the impacts of deposition processes on the simulated seasonal

variation of Arctic BC through a 3D global chemical transport model. For this purpose, the

Global Environmental Multiscale model with Air Quality processes (or GEM-AQ) [Kaminski et

al., 2008; O'Neill et al., 2006] is utilized in this study to investigate the effects of atmospheric

transport and removal on the simulated seasonality of Arctic BC. Below, we first describe the 3D

host meteorological model and the integrated air quality modules. Differences in the original and

- 50 -

the modified parameterization schemes of aerosol wet scavenging and dry deposition are

presented. Then the model validation against surface observations of the Arctic BC is presented.

The particle dry deposition and the model simulated rate of aerosol removal by wet scavenging

and horizontal transport into the Arctic region are investigated. Finally, the model simulated BC

global budget is compared with the average of multi-model simulations.

4.2 GEM-AQ model and modifications to aerosol deposition schemes

4.2.1 Host meteorological model: Global Environmental Multiscale model

The Global Environmental Multiscale model (GEM) [Cote et al., 1998b; Yeh et al., 2002] was

developed by the Meteorological Service of Canada (MSC) for operational numerical weather

forecasting. It is used in this work as the host meteorological model. However, the GEM model

used in this study differs from the one used by Kaminski et al. [2008] in the scheme for land

surface processes. The Interactions Soil-Biosphere-Atmosphere (ISBA) scheme is used in this

study instead of the simplified force-restore method used by Kaminski et al. [2008]. Compared to

the force-restore method, the ISBA scheme provides a more detailed calculation of surface heat

and moisture fluxes from different surface types by introducing new prognostic variables. Unlike

many gaseous species, the concentration of BC particles is usually high close to the surface of

source regions, except those from forest fire emissions. Therefore, better representations of

surface heat and moisture fluxes are crucial to the model simulations of BC in this paper.

4.2.2 Air quality modules and modifications to aerosol deposition schemes

Currently, there are two air quality modules implemented on-line in the host meteorological

model – GEM. The integrated gas-phase chemistry module is based on a modified version of the

Acid Deposition and Oxidants Model (ADOM, version 2) [Venkatram et al., 1988]. With the

integration of the Canadian Aerosol Module (CAM) [Gong et al., 2003a], size-resolved aerosol

physical and chemical processes in the atmosphere are included in GEM-AQ. The on-line

implementation of air quality processes in the GEM model provides full access to the simulated

dynamics and physics fields for air quality simulations. At the same time, it allows feedbacks

- 51 -

from air quality simulations on GEM dynamics and physics to study interactions between

weather/climate and chemical and aerosol components.

CAM has been previously used to investigate the global distribution and budget of sea salt

[Gong et al., 2002] and persistent organic pollutants [Gong et al., 2007], the impact of sea salt on

non-sea salt sulphate [Gong and Barrie, 2003], the radiative effects of sea salt [Ayash et al.,

2008a], and global aerosol optical parameters [Ayash et al., 2008b]. In the current version, 5

major aerosol types including sulphate, sea salt, soil dust, black carbon and organic carbon are

divided into 12 logarithmically spaced size bins from 0.01 to 20.48 µm in diameter. The number

of size bins is chosen based on numerical investigations by Gong et al. [2003a] as it balances the

desired accuracy and the computational overhead of global 3D models. Aerosol processes

accounted for in the current CAM include emission, hygroscopic growth, coagulation, nucleation,

condensation, dry deposition, wet scavenging, aerosol activation, aerosol-cloud interaction, and

chemical transformation of sulphur species. A process-splitting technique is used in CAM to

efficiently conduct integration with acceptable numerical errors. Parameterization schemes for

each aerosol process were detailed in Gong et al. [2003a], and references therein, but the

modified aerosol dry deposition and wet scavenging (i.e., in-cloud and below-cloud scavenging)

are presented as follows.

4.2.2.1 Dry deposition

Dry deposition of aerosol particles depends strongly on particle size and density, underlying

surface characteristics, and meteorological conditions. Due to its complexity, however, a number

of studies simulate dry deposition using a prescribed deposition velocity (e.g. [Cooke et al., 1999;

Kim et al., 2008; Liousse et al., 1994; Reddy and Boucher, 2004]). The parameterization

developed for CAM [Zhang et al., 2001] implements a size-resolved resistance-in-series

approach. Dry deposition velocity of a particle is calculated at the lowest model level by

considering gravitational settling, aerodynamic resistance, and surface resistance. CAM’s dry

deposition parameterization distinguishes 15 land use categories (LUCs) and 5 seasonal

categories to account for effects of surface properties (See Tables 2-3 in [Zhang et al., 2001] for

details). Properties of particles under ambient air condition are considered, such as the wet

particle size after growth at high humidity. The global and annual-average dry deposition

velocities calculated by GEM-AQ as a function of particle size are plotted in Figure 14. When

- 52 -

they are compared to the implementation of the same scheme by Trivitayanurak et al. [2008], it

is found that the particle dry deposition velocity predicted by GEM meteorology is about 2 times

higher than that predicted using assimilated meteorology. The discrepancy implies that the GEM

meteorological fields (especially 10-meter wind velocity) or their implementation in CAM lead

to overestimation of aerosol dry deposition. Thus, in the enhanced GEM-AQ simulation setup (is

presented next) dry deposition velocity is reduced by 50%. To further justify this modification,

the particle mass distribution weighted annual and global average dry deposition velocity is

calculated and compared with the commonly used aerosol bulk dry deposition velocity of 0.1

cm/s [Chung and Seinfeld, 2002; Kim et al., 2008; Liousse et al., 1996; Reddy and Boucher,

2004; Wang, 2004]. The obtained annual and global average dry deposition velocity weighted by

particle mass-size distribution is 0.23 cm/s for the original parameterization, which is also about

2 times higher than the commonly used aerosol bulk dry deposition velocity. Thus, the 50%

reduction in GEM-AQ simulated particle dry deposition velocity is considered reasonable in this

study, but simulations using different meteorological fields will be conducted to further

investigate the impact on aerosol dry deposition.

0.01 0.1 1 100.01

0.1

1

10

Dep

ositi

on v

eloc

ity (c

m/s

)

Dry particle diameter (μm)

Trivitayanurak et al., 2008 GEM-AQ

Figure 14. Global and annual-average dry deposition velocities (cm/s) predicted by GEM-AQ as

a function of dry particle size compared with Trivitayanurak et al. [2008].

- 53 -

4.2.2.2 In-cloud scavenging

Aerosol particles are efficiently removed from the atmosphere by precipitation. Wet

scavenging processes considered in more advanced aerosol models include both in-cloud

scavenging (or rainout process) and below-cloud scavenging (or washout process), while other

models do not distinguish them (e.g. [Liousse et al., 1994]). The relative importance of both

mechanisms depends largely on aerosol particle size and meteorological conditions, but in-cloud

scavenging is usually more efficient in removing accumulation mode particles when these

particles serve as cloud condensation nuclei under super saturation. In-cloud scavenging involves

activation of accumulation mode particles, attachment of the activated particles to existing cloud

droplets, and removal of aerosol-containing droplets due to precipitation formation [Gong et al.,

1997]. The parameterization of in-cloud scavenging commonly used in aerosol models is based

on either prescribed scavenging efficiencies (e.g. [Iversen and Seland, 2002; Seland et al., 2008])

or the scheme developed by Giorgi and Chameides [1986] (hereafter GC86, e.g. [Chung and

Seinfeld, 2002; Cooke et al., 1999; Koch and Hansen, 2005a; Liu et al., 2005; Wang et al.,

2009]). The later approach is used in the current version of CAM to remove only activated

particles. GC86 was originally developed to be used with older generation of GCMs, which did

not estimate precipitation and sub-grid scale clouds at every vertical model level. Recent

implementations of GC86, on the other hand, utilize precipitation fluxes and sub-grid scale

clouds by the host model (e.g. [Boucher et al., 2002b; Reddy and Boucher, 2004]). Thus, the

original implementation of GC86 in CAM has been modified to account for precipitation and

sub-grid scale clouds predicted by GEM at every vertical model level. In the original GC86

scheme, the first order in-cloud scavenging of aerosols in a model grid box is given by

CdtdC

cloudin ⋅−= −λ , (4.1)

where C is aerosol mass mixing ratio (kgaero/kgair), t is time (s), and λin-cloud is the local in-cloud

scavenging rate (1/s). The in-cloud scavenging rate (λin-cloud) is calculated in terms of local

precipitation formation, i.e.

)1( tcloudin e

tF Δ⋅−

− −Δ

= βλ , (4.2)

- 54 -

where F is the grid area fraction involved in local precipitation formation, and β is the rate of

precipitation formation (1/s). To be used with the older generation GCMs, both F and β have to

be estimated based on water condensation rate and two prescribed constants. When this scheme

is coupled with GEM, model predicted sub-grid cloud cover should be used in Eq. (4.2) as F.

The rate of precipitation formation (β) in a model grid box can be estimated based on three-

dimensional precipitation flux from the host model, following

LzP

air ⋅Δ⋅Δ

=ρ

β , (4.3)

where ΔP is the difference of precipitation fluxes (kg/m2/s) at upper and lower boundaries of a

grid box, ρair is the local air density (kg/m3), Δz is the thickness (m) of the grid box, and L is the

liquid water content (kgwater/kgair) assumed a constant depending on the type of precipitation

(1.5×10-3 for stratiform and 2.0×10-3 for convective precipitation). A different L value for

stratiform precipitation from the original GC86 scheme is chosen because the suggested value of

0.5×10-3 tends to overestimate the aerosol in-cloud scavenging [Brost et al., 1991; Liu et al.,

2001; Wang et al., 2009].

4.2.2.3 Below-cloud scavenging

When precipitation occurs, aerosols in the air could be collected by hydrometeors falling

through the air, which is known as aerosol below-cloud scavenging or washout. Particles in ultra-

fine mode and coarse mode are subject to more efficient below-cloud scavenging rather than

accumulation mode particles [Andronache, 2003]. Falling hydrometeors create a thin layer of air

flow around them. Ultrafine and coarse aerosol particles can easily overcome the resistance due

to Brownian diffusion and inertial impaction, respectively. Particles with sub-micron radii adjust

their movement to the induced flow around falling hydrometeors so that collision may be

avoided. Aerosol below-cloud scavenging is often modeled using prescribed scavenging

efficiencies (e.g. [Iversen and Seland, 2002; Kim et al., 2008; Koch and Hansen, 2005a; Stier et

al., 2005; Wang et al., 2009]) or based on explicit calculation of collision efficiency [Feng, 2007;

Henzing et al., 2006; Loosmore and Cederwall, 2004]. The later approach is used in the current

version of CAM to calculate size-resolved below-cloud scavenging rate assuming a volume-

average droplet size. The calculation of collision efficiency (E) in CAM is based on Slinn’s

- 55 -

semi-empirical model [Slinn, 1983], which includes the collision efficiency due to Brownian

diffusion, interception, and impaction.

In the calculation of E, terminal velocities of both droplets and aerosol particles in the air

are essential. In the original CAM implementation, terminal velocities are evaluated based on

Stokes law (e.g. [Seinfeld and Pandis, 1998]), which applies to small droplets/aerosol particles

(Dp<20 µm or Re<0.1). For droplet/particle with larger Reynolds number, however, Stokes law

is no longer valid. So in this study, terminal velocity is now evaluated in Eq. (4.4) according to

an explicit analytical expression derived by Feng [2007] as

pa

at d

uρμ Re

= , (4.4)

where

⎪⎪⎪

⎩

⎪⎪⎪

⎨

⎧

×>

×<<⋅+−+−

<

= −

)101.1Re(,)44.0Re(

)101.1Re4.2(),2

)]Relog(44[(log

)4.2Re(,24Re

Re

522/12

522/122

1

22

DD

DD

DD

CC

Ca

Caacbb

CC

and

2

32

34

Rea

cpapD

gCdC

μρρ

= (with symbols listed in Table 6). Implementing the drag coefficient

(CD), Feng’s approach [2007] can estimate terminal velocity for raindrop/particle of any

Reynolds number. This change in the current model mainly affects the below-cloud scavenging

of aerosols by strong precipitation events, such as storms and intensive convective precipitation

events.

Given the collision efficiency, the first order below-cloud scavenging rate (λbelow-cloud) can

be evaluated based on

pcloudbelow D

pE360010 3−

− =λ , (4.5)

- 56 -

where p is the rate of precipitation (mm/h) and Dp is the volume-mean rain drop diameter (i.e.,

25.01

3 )1

(107.0 −−×=

mmhpDp for stable rain precipitation, and the constants suggested by Slinn

[1982] are used for snow precipitation).

Table 6. List of symbols used in collision efficiency calculation.

Cc Slip correction factor

CD Drag coefficient

dp Diameter of aerosol particle

D Particle diffusivity

Dp Diameter of rain droplet

E Collision efficiency

g Acceleration of gravity (9.807 m/s2)

Re Reynolds number of rain droplet

ut Terminal velocity of aerosol particle

Ut Terminal velocity of rain droplet

ρa Air density

ρp Aerosol particle density (1×103 kg/m3)

µa Viscosity of air (1.720×10-5 kg/m/s)

- 57 -

4.2.3 Simulation setup

The host model GEM (dynamics version 3.1.2, physics version 4.1) used in the current

study was configured with 28 hybrid vertical levels with the model top at 10 hPa. Upon this

vertical setup, the lowest model level remains the thinnest with a thickness of about 50 m

depending mainly on latitude. The horizontal model grid was configured as global uniform

resolution of 2 degrees longitude by 2 degrees latitude, which leads to a horizontal resolution of

about 200 km by 200 km close to the Equator. The model time step was set to 1800 seconds for

dynamics, physics, and air quality processes, and the model was run in 24-hour forecast

segments initially driven by meteorological fields from the Canadian Meteorological Centre

global assimilation system [Gauthier et al., 1999; Laroche et al., 2007].

Table 7. Summary of BC emission data used in CAM.

Emission source Type Injection height Amount (Tg/yr)

Tropical forest and savannah fires Natural 1 km 4.60

Domestic and agricultural fires Anthropogenic 200 m 1.05

Boreal and temperate fires Natural 0.33

Canada 6 km

U.S. 2 km

Russia 3 km

Europe 2 km

Fossil fuel Anthropogenic 200 m 4.96

Total 10.94

- 58 -

Although CAM accounts for 5 aerosol species, only BC and SF are included in the current

study to reduce the computational overhead and the results of both species are analyzed. Sources

of sulphur species include surface emission rate of both natural and anthropogenic sulphur

dioxide and sulphate (Global Emissions Inventory Activity, or GEIA 1985 inventory), H2S from

land [Benkovitz and Schwartz, 1997], DMS from oceans [Kettle et al., 1999]. Black carbon

emission is provided by three complementary data sets: forest and savannah fires at the tropical

latitudes and domestic and agricultural fires worldwide [Liousse et al., 1994]; global fossil-fuel

burning [Cooke et al., 1999]; and boreal and temperate fires [Lavoue et al., 2000]. Since

emission fields vary in horizontal resolutions, they were regridded to the model grid, while

ensuring mass flux conservation.

The vertical distribution of emission varies for different species. Sulphur dioxide and

sulphate emissions from GEIA inventory have two injection levels (0-100 m and >100 m). In the

current CAM, lower level injection was applied to the lowest model level, and the higher level

injection contributed to the lowest model level whose upper boundary is higher than 100 m. H2S

and DMS were both injected into the lowest model level. Vertical distribution of BC emissions

injects 50% of the total surface flux into the model level that contains the injection height

corresponding to a certain source (listed in Table 7), 25% into one level below, 12.5% into two

levels below, and so on, with the remaining amount injected into the lowest model level. A

lognormal particle mass-size distribution with a mean diameter of 0.2 µm is assumed for BC

based on recent measurements of BC in urban and biomass burning emissions [Schwarz et al.,

2008].

Freshly emitted BC particles are treated as hydrophobic species, which are not subject to

wet scavenging (in-cloud and below-cloud scavenging). For simplification, an aging process is

currently assumed to convert all BC particles into hydrophilic species in a model time step (or

1800 s in this study), which is faster than the assumed e-folding transformation times on the

order of 1 day (e.g. [Flanner et al., 2007; Koch et al., 2009]). The assumption of a fast transition

from hydrophobic to hydrophilic particles would result in an underestimation on the global total

amount of BC in the atmosphere. However, Croft et al. [2005] showed that the seasonality, as

well as the concentration, of Arctic BC near the surface was not significantly affected by various

parameterizations of BC aging. This is probably due to the fact that BC particles in the Arctic are

- 59 -

mainly internally mixed (or hydrophilic). Therefore, the seasonal patterns in the wet deposition

of BC in the Arctic would not be significantly affected by this assumption.

The GEM-AQ with modified wet scavenging and dry deposition schemes was run for 3

model years from January 2000 to December 2002. Aerosol concentrations start to build up from

zero at the beginning of the first year. Initial gas phase chemical conditions were generated in the

same way as detailed in [Kaminski et al., 2008]. The results presented in this study are based on

the second and the third year of the model run. To investigate the effects of the modifications on

aerosol removal parameterization, a series of simulations are conducted, including the original

code (Original run), one with modified dry deposition (Dry_depo run), one with modified in-

cloud and below-cloud scavenging (Wet_depo run), the Dry_depo run with modified in-cloud

scavenging (In-cloud run), and the In-cloud run with modified below-cloud scavenging

(Enhanced run).

4.3 Simulation results and discussion This section analyzes the results of simulated surface concentration, rate of removal,

atmospheric transport, and global budget.

4.3.1 Surface concentration of BC and SF over the Arctic

- 60 -

Feb Apr Jun Aug Oct Dec

0

30

60

90

120

BC

(ng/

m3 )

Observed Original run Dry_depo run In-cloud run Enhanced run

(a) Alert (2001)


0

20

40

60

BC

(ng/

m3 )

(b) Barrow (2001)

- 61 -


0

30

60

90

120

BC

(ng/

m3 )

(c) Zeppelin (2001)

Figure 15. Comparison of GEM-AQ simulated monthly average BC surface concentrations

against observations at (a) Alert, (b) Barrow, and (c) Zeppelin [Eleftheriadis et al., 2009] for the

year of 2001.

Feb Apr Jun Aug Oct Dec0

30

60

90

120

150

BC

(ng/

m3 )

Observed Original run Dry_depo run In-cloud run Enhanced run

(a) Alert (2002)

- 62 -


0

30

60

90

BC

(ng/

m3 )

(b) Barrow (2002)


0

30

60

90

120

BC

(ng/

m3 )

(c) Zeppelin (2002)

Figure 16. Same as Figure 15, but for the year of 2002.

The model simulated monthly average BC surface concentrations for 2001 and 2002 are

compared with surface observations at Alert, Barrow and Zeppelin [Eleftheriadis et al., 2009] in

Figure 15 and Figure 16, respectively. The results from 4 GEM-AQ model runs (i.e., Original,

Dry_depo, In-cloud, and Enhanced) are considered here in the comparison with observations.

Furthermore, Table 8 summarizes the calculated Pearson’s correlation coefficients (R) and model

to observation ratios (r) between GEM-AQ simulated BC concentrations and observations at the

- 63 -

above selected Arctic sites. For all these Arctic sites, the observed seasonal changes are not

captured by the Original (-0.59<R<-0.05) and the Dry_depo (-0.51<R<0.49) runs. Similar R

values from these two model runs suggest that aerosol dry deposition is not the primary

governing mechanism for the observed seasonality of BC in the Arctic. Although the Dry_depo

run predicts higher surface concentrations than the Original run, BC concentrations in the Arctic

predicted by these two model runs are still substantially lower than the available observations at

all sites during the cold season (e.g. September – May). The r values in Table 8 show that on

annual average, surface BC concentration in the Arctic is underestimated roughly by a factor of 2

or more by the Original and the Dry_depo runs.

Table 8. Correlation coefficients (R) and model to observation ratios (r) between model

simulations and surface observations of BC at the selected sites in the Arctic for 2001 and 2002.

Original Dry_depo In-cloud Enhanced

2001 2002 2001 2002 2001 2002 2001 2002

Alert

R -0.05 -0.43 0.49 0.10 0.91 0.80 0.95 0.91

r 0.04 0.04 0.18 0.16 1.59 1.31 1.12 0.90

Barrow

R -0.59 -0.40 -0.51 -0.25 0.58 0.66 0.50 0.68

r 0.19 0.18 0.57 0.51 2.23 1.78 1.50 1.18

Zeppelin

R -0.24 -0.49 -0.13 -0.20 0.77 0.52 0.81 0.83

r 0.17 0.25 0.51 0.67 1.70 2.13 1.11 1.45

- 64 -

However, the pronounced seasonal variation of BC is fairly well reproduced by the In-cloud

and the Enhanced GEM-AQ runs at all these Arctic sites due to the modifications to the in-cloud

scavenging scheme of CAM. At Alert, the Enhanced run tends to slightly underestimate

(overestimate) the BC surface concentration in the cold (warm) season compared to available

observations (with less than 10% of missing data). At Barrow, however, the overestimation by

these two model runs seems more significant than the comparison for the Alert station. It should

be noticed that the observation data used here is associated with a large amount of missing data

(i.e. 58% for 2001 and 64% for 2002), which may introduce significant uncertainties to this

comparison. But the BC concentration at Barrow in the warm months (e.g. June – August) is

likely overestimated by these model runs. Although it is a remote site within the Arctic, Barrow

is relatively close to the northeast Asia continent, which was previously identified as an

important potential source region affecting Barrow at lower level in the troposphere [Polissar et

al., 2001]. So the overestimation at this site in summer is probably due to the stronger boreal and

temperate fire emissions based on the climatology of the 1980s than the actual emissions in 2001.

The comparison at Zeppelin shows a generally good agreement with observations, but with

moderate overestimations from June through to December.

The Pearson’s correlation coefficients (R values shown in Table 8) between the monthly

observed and simulated BC surface concentrations by the In-cloud and the Enhanced model runs

are significantly higher than the Original and the Dry_depo runs. This is due to a significant

increase in the simulated BC surface concentrations during the Arctic winter, as shown in Figure

15 and Figure 16, after modifying the in-cloud scavenging parameterization. Furthermore, the R

values obtained from the Enhanced run are generally better than those from the In-cloud run,

except for the case of the Barrow station in 2001. Comparison of the model-to-observation ratios

(r values) for In-cloud and the Enhanced runs listed in Table 8 shows that on annual average, the

In-cloud run predicts BC surface concentrations as much as 2 times observations (i.e.

1.31<r<2.23), but the Enhanced run agree well within a factor of 2 with observations (i.e.

0.90<r<1.50). The difference in r values suggests that the surface BC concentrations in the Arctic

are fairly sensitive to the below-cloud scavenging parameterization as well. Thus, the ability of

GEM-AQ model to reproduce BC seasonality in the Arctic is mainly attributed to the

modifications in the in-cloud and below-cloud scavenging schemes. The seasonal changes in the

rate of wet scavenging will be further investigated in Section 4.3.5.

- 65 -


400

800

1200

1600S

F (n

g/m

3 ) Observed (2001) Original run (R=-0.61) Enhanced run (R=0.80)

(a) Alert


400

800

1200

SF

(ng/

m3 )

Observed (2001) Original run (R=-0.27) Enhanced run (R=0.50)

(b) Barrow

- 66 -


400

800

1200

1600

SF

(ng/

m3 )


(c) Spitsbergen

Figure 17. Comparison of the simulated monthly average SF surface concentrations against

observations at (a) Alert, (b) Barrow, and (c) Spitsbergen for the year of 2001. R values indicate

the Pearson’s correlation coefficients between observed and simulated monthly SF.


400

800

1200

1600

SF

(ng/

m3 )


(a) Alert

- 67 -


400

800

1200

SF

(ng/

m3 )


(b) Barrow


400

800

1200

SF

(ng/

m3 )


(c) Spitsbergen


- 68 -

Table 9. Correlation coefficients (R) and model to observation ratios (r) between model

simulations and surface observations of SF at the selected sites in the Arctic for 2001 and 2002.

Original Enhanced

2001 2002 2001 2002

Alert

R -0.61 -0.60 0.80 0.53

r 0.16 0.23 0.87 0.88

Barrow

R -0.27 -0.35 0.50 0.54

r 0.29 0.26 0.71 0.79

Spitsbergen

R -0.22 -0.42 0.46 0.72

r 0.58 0.67 1.02 1.33

The model simulated monthly SF surface concentrations before and after modifications are

compared with surface observations at Alert, Barrow and Spitsbergen for 2001 and 2002, as

shown in Figure 17 and Figure 18, respectively. Similar to the results shown previously for the

Arctic BC, the Original GEM-AQ run seriously underestimates the wintertime SF concentrations

at all these Arctic sites. The Pearson’s correlation coefficients between the observed and the

predicted monthly SF concentrations by the Original run range from -0.22 to -0.61. However,

better agreements between observations and the Enhanced run can be found in the comparison

shown in Table 9. The enhanced GEM-AQ model reproduced higher SF concentrations during

the Arctic winter than the summer, and the correlation coefficients are now within the range from

- 69 -

0.46 to 0.80, indicating moderate to strong positive correlations. Although the enhanced GEM-

AQ reasonably reproduces the seasonal patterns of SF surface concentrations in the Arctic,

discrepancies between model simulations and observations exist in the Arctic spring. As shown

in Figure 17 and Figure 18, the enhanced model underestimates SF by a factor of 2 to 3 in spring,

especially in April and May. This could possibly be a result of a low sulphate chemical

production rate predicted by the model in these months, which should substantially increase after

the polar sunrise. Such discrepencies result in smaller R values for SF than BC, which has no

chemical production in the springtime. Detailed analysis on SF production in the northern high

latitudes during spring months needs to be done in the future to verify this hypothesis.

4.3.2 Surface concentration of BC and SF in North America and Europe

In this section, the model simulated aerosol surface concentrations (both before and after

modifications) are compared with long-term observations available in North America (from

IMPROVE monitoring network) and Europe (from EMEP monitoring network), which are

potential source regions affecting the Arctic. The purpose of conducting such comparison is

twofold. First, the comparison of modeled surface concentrations before and after modifications

is conducted to investigate the sensitivity of the model to changes in deposition schemes in these

areas. Second, the model’s performance in simulating aerosol surface concentrations is assessed

by comparing results to observational data.

Figure 19 and Figure 20 show the scatter plots of model simulations from the Original run and

the Enhanced run against annual average surface concentrations of BC and SF, respectively.

Unlike aerosols in the Arctic region, surface concentrations of BC and SF change only slightly in

these areas. Such difference between the Arctic and its potential source regions implies that the

Arctic aerosols are more sensitive to deposition processes than aerosols in areas close to sources.

In other words, the surface concentrations of aerosol close to the sources tend to remain stable

due to continuous supply from the sources, and are less sensitive to parameterizations of aerosol

depositions than the Arctic aerosols.

- 70 -

10-3 10-2 10-1 100 10110-3

10-2

10-1

100

101

Original run Enhanced run

Mod

eled

BC

(200

1) (μ

g/m

3 )

Observed BC (1989-2003) (μg/m3)

(a) IMPROVE

10-2 10-1 100 10110-2

10-1

100

101

Original run Enhanced run

Mod

eled

BC

(200

1) (μ

g/m

3 )


(b) EMEP

Figure 19. Comparison of model simulated BC surface concentrations before and after

modifications against surface observations in North America from the IMPROVE monitoring

network (a) and Europe from the EMEP monitoring network (b). Two dashed lines represent

model-to-observation ratios of 2:1 and 1:2.

- 71 -

10-1 100 101

10-1

100

101 Original run Enhanced run

Mod

eled

SF

(200

1) (μ

g/m

3 )

Observed SF (1989-2003) (μg/m3)

(a) IMPROVE

10-1 100 101

10-1

100

101 Original run Enhanced run

Mod

eled

SF

(200

1) (μ

g/m

3 )

Observed SF (1978-2006) (μg/m3)

(b) EMEP

Figure 20. Same as Figure 19, but for SF.

Pearson’s correlation coefficient and the average simulation-to-observation ratio are

summarized for North America and Europe in Table 10 for BC and Table 11 for SF, as well as

the percentage of agreement within a factor of 2. The correlation coefficients shown in both

tables are moderate, except for SF in North America. These correlation coefficients indicate the

- 72 -

ability of the model to capture the geographic distributions of aerosols near the surface across

these regions. The R values in Table 10 and Table 11 show that the modified model performs

only slightly better, if any, than the model with original deposition parameterizations. As implied

by model sensitivity to modifications on aerosol deposition schemes, the geographic distributions

of BC and SF near the surface largely depend on emissions, which were compiled for GEM-AQ

simulations based on the GEIA original inventory for the year of 1985. However, the

observations used in the current comparison were mostly conducted in the 1990s and the early

2000s, in which period the geographic distributions of sources might be quite different from

those of 1985, especially for Europe [Bond et al., 2007]. As such, both the inherent uncertainties

in aerosol emissions and the mismatch of the period chosen for this comparison may contribute

to the discrepancy between the geographic distributions of surface aerosol concentrations from

model simulations and observations.

Table 10. Summary of model against observation comparison: Pearson’s correlation coefficients

(R), ratios (r) between modeled and observed aerosol concentrations, and percentage of

agreement within a factor of 2.

BC IMPROVE (167 observations) EMEP (12 observations)

Original Enhanced Original Enhanced

R 0.40 0.42 0.39 0.57

r 0.53 0.83 0.85 1.05

Agreement within a factor of 2 38% 78% 67% 83%

Improvement in simulating aerosol surface concentrations after modifications on deposition

schemes is found for the model-to-observation ratio and the percentage of agreement within a

factor of 2, as shown in the last two rows in Table 10 and Table 11. The calculated average ratio

shows that the original model underestimates BC surface concentrations in North America by

about 50%, which is reduced to less than 20% after modifications. Substantial improvement in

- 73 -

the percentage of agreement within a factor of 2 is also observed for BC in North America. The

improvements of simulating BC in Europe and SF in both regions are less significant compared

to the former. In general, better agreement between modeled and observed aerosol surface

concentrations is achieved in North America and Europe, two important source regions affecting

the Arctic aerosol, by improving aerosol deposition parameterizations. Thus, the modified model

is capable of providing more reasonable source strength to the Arctic than the original one.

Table 11. Same as Table 10, but for SF.

SF IMPROVE (170 observations) EMEP (151 observations)

Original Enhanced Original Enhanced

R 0.92 0.92 0.45 0.48

r 0.79 0.96 1.35 1.15

Agreement within a factor of 2 88% 92% 75% 81%

4.3.3 Atmospheric transport of air masses indicated by CO

Before diagnosing aerosol deposition processes, we first try to understand if the lack of BC

seasonality in the original GAM-AQ simulations is due to poorly simulated atmospheric

transport to the Arctic. Therefore, modeled and observed seasonal cycles of surface CO for

selected stations in the Arctic are first compared in this section. Here, CO is considered as an

ideal species to evaluate the atmospheric transport to the Arctic simulated by the GEM-AQ

model since CO is very often co-emitted with BC, yet has a lifetime (about 1 – 2 months in the

troposphere and even longer at the winter poles) much longer than that of BC [Holloway et al.,

2000]. In Figure 21and Figure 22, the GEM-AQ simulated monthly concentrations of CO are

compared with surface observations within the Arctic at Alert, Barrow, and Zeppelin in 2001 and

2002, respectively. The figures show that the distinct seasonal cycle of CO observed in the

Arctic is captured by GEM-AQ for both years. However, the surface concentration of CO in the

- 74 -

Arctic is found to be slightly overestimated in the cold season by the model. Table 12

summarizes the estimated Pearson’s correlation coefficients (R) and model to observation ratio (r)

between GEM-AQ simulated CO concentrations and observations at the above selected Arctic

sites. For all three sites, the R values are in the range of 0.82 – 0.96, and the r values suggest that

the difference between model simulations and observations are generally within 20% based on

the annual average. Thus, the atmospheric transport of air masses from surrounding regions to

the Arctic troposphere is fairly well captured by GEM-AQ.


50

100

150

200

250

CO

(ppb

v)

GEM-AQ Observed

(a) Alert (2001)

- 75 -


50

100

150

200

250

CO

(ppb

v)

(b) Barrow (2001)


50

100

150

200

250

CO

(ppb

v)

(c) Zeppelin (2001)

Figure 21. Comparison of GEM-AQ simulated monthly average surface concentrations against

observations of CO at (a) Alert, (b) Barrow, and (c) Zeppelin for the year of 2001.

- 76 -


50

100

150

200

250

CO

(ppb

v)

GEM-AQ Observed

(a) Alert (2002)


50

100

150

200

250

CO

(ppb

v)

(b) Barrow (2002)

- 77 -


50

100

150

200

250

CO

(ppb

v)

(c) Zeppelin (2002)


Table 12. Correlation coefficients (R) and model-to-observation ratios (r) between model

simulations and surface observations of CO at the selected sites in the Arctic for 2001 and 2002.

Alert Barrow Zeppelin

2001

R 0.86 0.82 0.85

r 1.13 1.14 1.18

2002

R 0.96 0.82 0.93

r 1.04 1.09 1.03

- 78 -

4.3.4 Effect of dry deposition on the seasonality of Arctic BC

The effect of dry deposition on the model simulated seasonality of the Arctic BC is

investigated in this paper on two different vertical scales, i.e. the surface level and the lowest 5

km in the Arctic troposphere. To study the effect of dry deposition at the surface level over the

Arctic, the model simulations from the Wet_depo (only wet deposition schemes are changed) run

are first compared with the Enhanced run in Figure 23. At all the Arctic sites investigated here,

the original dry deposition scheme significantly underestimates the surface BC concentrations

due to relatively high dry deposition velocity predicted by the model. And no seasonality in the

simulated surface BC concentrations is found for the Wet_depo run. Figure 24 shows the

seasonal change in the rate of removal by the original dry deposition scheme in GEM-AQ

averaged over the Arctic Ocean (70-90 ºN) for four selected particle size bins. For all particle

size ranges, the average rate of particle dry removal above the Arctic surface is relative high in

winter months due to high surface wind speed at this time. Thus, the original dry deposition

process significantly removes BC particles at the surface level in the Arctic. Therefore, when dry

deposition was overestimated by the original model, the Arctic BC near the surface was removed

unrealistically fast, resulting in the lack of wintertime maximum.

To further understand the effects of particle dry deposition on simulated seasonality of the

Arcic BC, a sensitivity analysis is conducted by varying the global and annual average particle

dry deposition velocity. Table 13 summarizes the Pearson’s correlation coefficients (R) and

model to observation ratios (r) between simulations and surface observations of BC at the

selected sites in the Arctic for 2001 as a function of particle dry deposition velocity in the range

of 0 – 0.2 cm/s. As expected, lower average particle dry deposition velocity results in higher

surface concentrations simulated by the model. The model run with global and annual average

dry deposition velocity of 0.1 cm/s predicts the best agreement with observations among four

model runs, indicated by both R and r values shown in Table 13.

- 79 -

Table 13. Correlation coefficients (R) and model-to-observation ratios (r) between model

simulations and surface observations of BC at the selected sites in the Arctic for 2001, obtained

by varying the global and annual average dry deposition velocity and with the enhanced wet

deposition schemes.

Dry Deposition Velocity (cm/s) Alert (2001) Zeppelin (2001)

R r R r

0.00 0.86 4.10 0.79 4.83

0.05 0.92 1.92 0.74 2.62

0.10 0.95 1.12 0.81 1.11

0.20* 0.87 0.23 0.79 0.51

* The global and annual average particle dry deposition velocity estimated by the Original run.


0

30

60

90

Sur

face

BC

at A

lert

(ng/

m3 )

Enhanced run Wet_depo run

(a) Alert

- 80 -


0

30

60

90

Sur

face

BC

at B

arro

w (n

g/m

3 )


(b) Barrow


0

30

60

90

Sur

face

BC

at Z

eppe

lin (n

g/m

3 )


(c) Zeppelin

Figure 23. The effect of dry deposition on the surface BC concentration at three Arctic sites: (a)

Alert, (b) Barrow, and (c) Zeppelin.

- 81 -

Feb Apr Jun Aug Oct Dec0.0

5.0x10-6

1.0x10-5

1.5x10-5

2.0x10-5

2.5x10-5

Orig

inal

dry

rem

oval

rate

(1/s

)

D=120 nm D=240 nm D=480 nm D=960 nm

Figure 24. The seasonal change in the rate of the original dry particle removal (1/s) averaged

over the Arctic Ocean (70-90 ºN) for four selected particle size bins. The rate of the modified dry

particle removal (not shown) is 50% lower than that of the original scheme.

Figure 25 shows the effect of dry deposition on the BC mass in the lowest 5 km of the Arctic

troposphere. As shown later in this paper (Figure 29), the major south-to-north transport routines

are found within the lowest 5 km in altitude, and the Arctic haze events were often observed in

this layer. The BC mass from the Enhanced run in Figure 25 shows a clear seasonal pattern with

more BC accumulated in the winter but less accumulation in the summer. The Arctic BC mass

from Wet_depo run shown in the same figure closely follows the seasonal variation of the

Enhanced run (with Pearson’s correlation coefficient R=0.97), but the mass is constantly lower.

It suggests that with the average dry deposition velocity in the range of 0.1 – 0.2 cm/s the dry

deposition process has little effect on the seasonal pattern of BC in the Arctic lower troposphere.

The effects of wet scavenging and atmospheric transport are investigated in the following

sections.

- 82 -


2

4

6

BC

mas

s in

the

low

est 5

km

of

the

Arc

tic tr

opos

pher

e (T

gx10

-3) Enhanced run

Wet_depo run Original run

Figure 25. The effect of dry deposition on the BC mass in the lowest 5 km of the Arctic

troposphere.

4.3.5 Seasonal changes in in-cloud and below-cloud scavenging

As revealed in the previous section, pronounced seasonal changes in surface BC

concentration is captured by the modified wet scavenging code. In order to investigate the

changes due to the modified code, the rate of removal (in 1/s) by in-cloud and below-cloud

scavenging is output along with BC daily concentrations from GEM-AQ simulations. Given the

layered structure of the Arctic haze, the area average scavenging rate below 5 km in altitude

above the Arctic Ocean (70-90 ºN) is shown in Figure 26 for average dry particle sizes from 120

to 960 nm in diameters. This range of particle size is chosen because BC particles in this size

range dominate the abundance in the atmosphere (over 85% of the total BC mass). As particles

of 120 nm in diameters are often partly activated in CAM [Gong et al., 2003a], the scavenging

rate of these particles due to in-cloud scavenging is generally lower than larger particles, which

are fully activated. Therefore, the particles larger than 120 nm in diameters share very similar in-

cloud scavenging rate, as shown in Figure 26 (a) and (c). Results from the original code, as

shown in Figure 26 (a)-(b), show seasonal patterns only in the below-cloud scavenging for large

- 83 -

particles (D > 240 nm). Applying the modified wet scavenging parameterizations, however,

seasonal changes are found for both scavenging mechanisms (Figure 26 (c)-(d)).

The in-cloud scavenging in the original code is a more effective removal process than the

below-cloud scavenging throughout the year for smaller particles (D<960 nm), resulting in

significantly underestimated surface concentrations in the Arctic (as shown in Figure 15 and

Figure 16). Comparing (a) and (c) in Figure 26, the modified in-cloud scavenging scheme, on the

one hand, significantly reduces the contribution of in-cloud scavenging of BC in the Arctic, and

produces reasonable seasonality on the other hand. The seasonality in the in-cloud scavenging is

the result of the modifications to the empirical parameterization scheme, described previously in

Section 4.2.2.2. It is due to the implementation of precipitation flux from the GEM meteorology

at every model time step. As shown in Figure 27, the seasonality of precipitation over the Arctic

is fairly well captured (R=0.6 – 0.7) by the host model as it is compared to the Climate

Precipitation Center (CPC) Merged Analysis of Precipitation (CMAP) dataset [Huffman et al.,

1997].

Compared to the in-cloud scavenging, the below-cloud scavenging tends to remove large

particles with higher rate, which is further enhanced in the summer Arctic by improving the

calculation of raindrop/particle terminal velocity of any Reynolds number. Comparison between

(b) and (d) in Figure 26 shows that the rate of below-cloud scavenging of particles with D=240

nm in July increases by a factor of 4 after modification, and increases by a factor of 2 for

particles with D=480 nm. The original below-cloud scheme tends to underestimate the collision

efficiency between aerosol particles and large raindrops from intensive precipitation events by

treating turbulent flow around raindrops as laminar flow. By implementing the modified scheme,

however, the below-cloud scavenging in the Arctic summer is realistically predicted by the

model, resulting in the enhanced seasonality of below-cloud scavenging in the Arctic. Therefore,

the obtained seasonal changes in the modified wet (i.e. both in-cloud and below-cloud)

scavenging explain the seasonality of the surface BC concentrations in the Arctic captured by the

In-cloud and the Enhanced runs.

- 84 -


2x10-6

4x10-6

6x10-6

8x10-6

1x10-5

Orig

inal

in-c

loud

sca

veng

ing

rate

(1/s

) D=120 nm D=240 nm D=480 nm D=960 nm

(a)


2x10-6

4x10-6

6x10-6

8x10-6

1x10-5

Orig

inal

bel

ow-c

loud

sca

veng

ing

rate

(1/s

)

D=120 nm D=240 nm D=480 nm D=960 nm

(b)

- 85 -


2x10-6

4x10-6

6x10-6

8x10-6

1x10-5

Mod

ified

in-c

loud

sca

veng

ing

rate

(1/s

) D=120 nm D=240 nm D=480 nm D=960 nm

(c)


2x10-6

4x10-6

6x10-6

8x10-6

1x10-5

Mod

ified

bel

ow-c

loud

sca

veng

ing

rate

(1/s

)

D=120 nm D=240 nm D=480 nm D=960 nm

(d)

Figure 26. Comparison of the seasonal change in wet scavenging rate between the original and

the modified parameterizations, averaged below 5 km in altitude above the Arctic Ocean (70-90

ºN): (a) Original in-cloud scavenging rate, (b) Original below-cloud scavenging rate, (c)

Modified in-cloud scavenging rate, and (d) Modified below-cloud scavenging rate.

- 86 -


0.3

0.6

0.9

1.2

Mon

thly

Rat

e of

Pre

cipi

tatio

n (m

m/d

ay)

CMAP GEM-AQ

(a) Arctic 2001

R=0.70


0.3

0.6

0.9

1.2

Mon

thly

Rat

e of

Pre

cipi

tatio

n (m

m/d

ay) (b) Arctic 2002

R=0.60

Figure 27. Comparison of GEM-AQ simulated monthly rate of precipitation within the Arctic

(70-90 ºN) against observations from CMAP dataset for the year of (a) 2001 and (b) 2002.

4.3.6 Atmospheric transport and deposition of BC to the Arctic

The seasonal change in atmospheric transport of BC into the Arctic region is investigated

here by looking at the south-to-north flux of BC crossing the 70 ºN latitude (in µg/m2/s). To

- 87 -

obtain the flux at a given model level, the BC concentrations (in µg/m3) at 70ºN latitude are

multiplied by the local south-to-north wind velocity (in m/s). The seasonal changes in the overall

and the sectional south-to-north flux of BC mass are summarized in Figure 28 (a) and (b),

respectively. The average flux from surface to 5 km in altitude is weighted by the thickness of

each layer involved. As shown in Figure 28 (a), the flux remains positive throughout the year,

indicating the Arctic being a net sink of the BC aerosols. The overall flux of BC mass from

surrounding regions into the Arctic north of 70 ºN in latitude shows a clear seasonal pattern with

strong injections in winter and fall. The minimum south-to-north transport in the lowest 5 km of

the troposphere is found in May. On top of the seasonal wet scavenging of BC within the Arctic,

the seasonality of atmospheric transport in the lower troposphere into the Arctic contributes to

the model simulated seasonal changes in BC surface concentrations.

Sectional contributions to the overall flux are investigated by dividing the parallel of 70 ºN

latitude into the following sections: former USSR (30 ºE-150 ºE), Europe (10 ºW-30 ºE), North

America (60 ºW-140 ºW), North Atlantic (10 ºW-60 ºW), and North Pacific (150 ºE-140 ºW).

Fluxes from North America and North Pacific to the northern high latitudes, as shown in Figure

28 (b), tend to be negative (or out flux of BC mass from the Arctic region) for most months of

the year. Fluxes from the former USSR and Europe remain positive in most of the time, while

North Atlantic side injects BC mass only in winter and fall. High correlation coefficient (R=0.94)

is found between the overall flux and the European flux, which indicates that the major transport

pathway of BC aerosols to the Arctic lies in the European side. Next to Europe, the former USSR

is found to be the second major pathway followed by North Atlantic. Although the northern part

of North America is found to be the major pathway for north-to-south transport among the five,

the North American BC emissions may arrive first at North Atlantic or even Europe, then

transport along with other sources into the Arctic. So the results shown in Figure 28 only indicate

the major pathways for the atmospheric transport of BC aerosols. A qualitative analysis of the

contributions of the sectional BC emissions follows, and a more quantitative analysis will be

discussed in Chapter 5.

- 88 -


0.02

0.04

0.06

0.08

0.10

Pol

ewar

d tra

nspo

rt flu

x (μ

g/m

2 /s)

Enhanced run Original run

(a) Overall flux


0.0

0.1

0.2

0.3

0.4

0.5

Pol

ewar

d tra

nspo

rt flu

x (μ

g/m

2 /s)

USSR Europe North America North Atlantic North Pacific

(b) Sectional flux

Figure 28. Seasonal changes in (a) the overall and (b) the sectional south-to-north flux of BC

mass crossing 70 ºN latitude, averaged below 5 km in altitude and weighted by layer thickness.

- 89 -

0 20 40 60 80

2

4

6

8

10

Alti

tude

(km

)

Latitude (ON)

(a) Europe

0 20 40 60 80

2

4

6

8

10

Alti

tude

(km

)

Latitude (ON)

(b) USSR

0 20 40 60 80

2

4

6

8

10

Alti

tude

(km

)

Latitude (ON)

(c) North Atlantic

0 20 40 60 80

2

4

6

8

10

Alti

tude

(km

)

Latitude (ON)

(d) North America

0.02 0.03 0.06 0.12 0.25 0.50 0.75 2.50

Figure 29. Zonal mean January BC concentration (in µg/m3) calculated for 4 sectors: (a) Europe,

(b) former USSR, (c) North Atlantic, and (d) North America.

As indicated by the previous investigations on the seasonal changes in atmospheric

transport, the injection of the mid-latitude BC emissions to the Arctic peaks in January.

Therefore, the sectional BC mass concentration profile from the Equator to the Arctic in January

- 90 -

is shown in Figure 29 (a)-(d) for Europe, former USSR, North Atlantic, and North America,

respectively. As shown in Figure 29 (a), strong BC sources on the European side is found

between 40 and 60 ºN in latitude in the lowest 1 km troposphere. BC concentration decrease

gradually from the source region to the northern polar area. The transport of BC mass is found

mainly within the lowest 3 km in altitude, which is consistent with the low level transport

pathways into the Arctic [Stohl, 2006]. Strong BC emissions in the former USSR sector locate

mainly between 20 to 40 ºN, but with relative strong emissions south to 60 ºN. The later

contributes to the low level transport to the Arctic indicated by the south-to-north concentration

gradient shown in Figure 29 (b). Potential source regions found in the North American sector are

concentrated in a relative narrow zone around 40 ºN, while no strong emissions are found in the

mid-latitudes in the North Atlantic sector. The emissions from both sectors are lifted to about 5

km and higher in altitude before reaching the Arctic, which also agrees the possible transport

pathways identified by [Stohl, 2006]. Thus, the impact of BC sources in these two sectors tends

to be very limited on the BC abundance in the lower Arctic troposphere in winter.

Table 14. Annual budget of BC deposition to the Arctic estimated by the Original and the

Enhanced GEM-AQ model runs.

Original Run Enhanced Run

Dry Deposition 0.024 (23%) 0.028 (25%)

In-cloud Scavenging 0.064 (61%) 0.031 (27%)

Below-cloud Scavenging 0.017 (16%) 0.054 (48%)

Wet Scavenging 0.080 (77%) 0.085 (75%)

Total 0.104 (100%) 0.114 (100%)

For each row, values are the accumulated amount of BC deposition to the Arctic in Tg/yr. The

percentage of BC deposition due to the indicated deposition process is given in parentheses. Wet

scavenging is the combined in-cloud and below-cloud scavenging. Total is the combined dry

deposition and wet scavenging.

- 91 -

Besides the atmospheric transport, the annul budget of the BC deposition to the Arctic from

the Enhanced model run differs significantly from the Original run. As shown in Table 14, the

total amount of BC deposition due to combined dry and wet deposition estimated by the

Enhanced run is about 10% greater than the estimate by the Original run. The results in Table 14

also show that the overall increase in BC deposition to the Arctic is due to the increase in both

the dry deposition (by 0.004 Tg/yr) and the wet scavenging (by 0.005 Tg/yr). But the ratio

between the in-cloud and the below-cloud scavenging vary significantly from the Original run

(3.8:1) to the Enhanced run (1:1.7). This comparison suggests that the overall below-cloud

scavenging in the Arctic should be about 2 times greater than the in-cloud scavenging to properly

simulate the seasonality of the Arctic BC.

4.3.7 BC global budget compared with AeroCom and other models

As a chemically inactive aerosol species, sources of atmospheric BC are from primary

emissions and sinks are only wet scavenging and dry deposition. Atmospheric removal processes,

therefore, play important roles in regulating the global distribution of BC. To investigate the

effects of aerosol removal parameterization on BC removal budget on a global scale, the annual

average BC removal (in Tg) from multiple model runs is compared with AeroCom multi-model

average (from Experiment A [Textor et al., 2006]) in Figure 30. The BC emission inventories

used in the Experiment A simulations of the AeroCom models include different types of sources

so that the global annual BC emission varies from 7.8 to 19.4 Tg among models [Textor et al.,

2006]. The global annual BC emission used in GEM-AQ simulations is kept at 10.9 Tg, which is

comparable to the AeroCom average (11.7 Tg). Based on the AeroCom simulations, wet

scavenging of BC particles was found to be the more effective removal mechanism compared to

the dry deposition process. On average, the multi-model estimate of BC global removal due to

wet scavenging is about 8.8 Tg/year, while the dry deposition removes only 2.5 Tg/year. And all

AeroCom models predicted over 2 times higher BC removal due to wet scavenging than dry

deposition. However, the BC global removal budget from the Original run does not agree with

AeroCom average within one standard deviation, and the original parameterization tends to

significantly overestimate dry deposition and underestimate wet scavenging. The Dry_depo, the

In-cloud, and the Enhanced runs, however, agree fairly well with the AeroCom averages in

- 92 -

annual removal budget. The ratio of wet to dry BC removal becomes more reasonable after the

dry deposition velocity has been reduced by 50% in CAM. The atmospheric BC burden from the

Original run is about 0.154 Tg, which is almost 1/3 lower than the AeroCom average. After

modifications to aerosol removal schemes, the estimated BC burden is about 0.277 Tg, and the

global average BC lifetime is 9.2 days, which lies within the range from 4.9 to 11.4 days

estimated from the AeroCom project [Koch et al., 2009]. The comparison implies that the global

removal budget is more sensitive to the parameterization of the CAM dry deposition than the wet

scavenging schemes, but the atmospheric BC burden is quite sensitive to aerosol wet scavenging.

0

2

4

6

8

10

12

14

16

18

Burden(0.02 Tg)

Dry Deposition (Tg/Year)

Wet Scavenging (Tg/Year)

BC

glo

bal b

udge

t

GEM-AQ (Original run) GEM-AQ (Dry_depo run) GEM-AQ (In-cloud run) GEM-AQ (Enhanced run) AeroCom

Emission(Tg/Year)

Figure 30. BC global budget from GEM-AQ compared with AeroCom multi-model average. The

error bars on top of the AeroCom averages represent standard deviations.

The GEM-AQ Enhanced run simulated zonal distribution of atmospheric load of BC is

compared with the estimations by AeroCom models [Schulz et al., 2006] in Figure 31. Although

AeroCom model predictions seem spread in values, they share similar trend along latitude. For

example, the lowest burdens are found in the polar regions, while the highest values are

generally found around the equator and the mid-latitudes in the Northern Hemisphere. Very

- 93 -

similar pattern is reproduced by GEM-AQ, but with values in the upper end of AeroCom

predicted range. Koch et al. [2009] compared the AeroCom estimated BC burden with the

retrieved values from Aerosol Robotic Network (or AERONET) measurements, and found that

on average AeroCom models underestimated BC burden by roughly 50% in North America,

Europe, and Asia, as shown previously in Table 4. Thus, the higher GEM-AQ estimations should

lead to better agreements with the observations than the AeroCom models. Figure 32 shows the

seasonal change in GEM-AQ estimated zonal distribution of BC load. In the mid-latitudes or BC

source regions affecting the Arctic, the model estimated BC load in summer is higher than that in

winter. However, BC load in the northern high latitudes (especially the area north of about 75 ºN)

in winter is found higher than that in summer. It strongly suggests that the seasonal changes in

long-range transport and aerosol removal, as discussed previously in this paper, play the most

important role in governing the seasonal changes in the Arctic BC abundance.

-90 -60 -30 0 30 60 90

0.0

0.2

0.4

0.6

0.8

1.0

Zona

l bur

den

of B

C (m

g/m

2 )

Latitude (degree)

GEM-AQ UMI UIO_CTM LOA LSCE MPI_HAM UIO_GCM SPRINTARS ULAQ GISS

Figure 31. Zonal distribution of the atmospheric load of BC from model simulations: GEM-AQ

(black line) vs. AeroCom models [Schulz et al., 2006] (gray lines).

- 94 -

-90 -60 -30 0 30 60 90

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Zona

l bur

den

of B

C (m

g/m

2 )

Latitude (degree)

Winter Summer

Figure 32. Zonal distribution of the atmospheric load of BC from GEM-AQ model simulations:

winter (black) vs. summer (gray) of the Northern Hemisphere.

4.4 Conclusions 1. Results show that the missing of the Arctic BC and SF seasonality from the original

GEM-AQ simulations is due to the parameterization of deposition processes rather than transport

process. Therefore, GEM-AQ is enhanced by improving the representations of aerosol deposition

processes in the CAM module. The enhanced model is able to capture the observed seasonal

changes in the surface concentrations of the Arctic BC and SF.

2. The observed seasonality of the Arctic BC and SF is due to the seasonal changes in

aerosol removal by wet scavenging and horizontal transport to the Arctic region.

3. With the implementation of 3D clouds, precipitation flux and an improved estimate of

raindrop terminal velocity, a pronounced seasonal cycle in the wet scavenging of aerosol

particles is found within the polar region north of 70 ºN in latitude, which is driven by the

seasonal cycle of the Arctic precipitation.

- 95 -

4. Seasonal injection of aerosols (e.g. BC from the European and the former USSR sectors,

and to a less extent from the North Atlantic sector) also contributes to the seasonality of the

Arctic aerosols in the lower troposphere.

5. With an average velocity of 0.1–0.2 cm/s, dry deposition has little effect on the seasonal

pattern of BC in the Arctic lower troposphere. Since dry deposition can significantly change the

aerosol surface concentration, a realistic dry deposition velocity (a global average of 0.1 cm/s

used in this study) is essential to capturing the seasonality of the Arctic BC and SF near the

surface.

6. The enhanced GEM-AQ model suggests an annual budget of BC deposition to the Arctic

of 0.11 Tg – a 10% increase over the original estimation. It also suggests that the below-cloud

scavenging dominates the contribution of BC removal over the Arctic with an estimation of 48%

for 2001, while the contributions of in-cloud scavenging and dry deposition contributes about

27% and 25%, respectively.

7. The enhanced model estimates a global BC burden of 0.28 Tg, and a global average BC

lifetime of 9.2 days, which lies within the range of 4.9 - 11.4 days from the AeroCom project

estimations.

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5 Regional contributions of anthropogenic emissions to the Arctic BC: air concentration and deposition

Abstract Using the enhanced GEM-AQ, this work estimates the regional contributions to the Arctic

black carbon (BC) aerosol from anthropogenic sources. Comparisons with surface measurements

in North America, Europe, and South Asia confirm that the source strength is reasonably

reproduced by the model. Comparisons with the measured vertical BC profile show that the

vertical structure of BC in the Arctic troposphere is also properly captured by GEM-AQ. Based

on 20% changes in anthropogenic BC emissions from each potential source region, model

simulations suggest that Europe contributes the most (up to 57%) to the Arctic lower troposphere.

The contributions from Asian Russia are significant only near the surface (about 30% at 100 m

above the surface), and decrease rapidly to less than 10% at the altitude of about 5 km in the

Arctic troposphere. The contributions from South and East Asia increase with increasing altitude,

and become more significant than others in the upper troposphere and the lower stratosphere,

with their peak contributions of about 35% and 40%, respectively. North American contribution

to the Arctic troposphere (about 10 – 20%) has the least variations in the vertical direction

among the potential source regions affecting the Arctic.

5.1 Introduction The climate impact of black carbon (BC) particles, emitted from incomplete combustion of

fossil fuel and biofuel, is a result of complex atmospheric processes. Suspended in the air, BC

particles absorb both direct and reflected solar radiation (which is known as the direct radiative

effect), leading to a significant warming of the atmosphere. The estimated direct radiative effect

at the top of the atmosphere has a wide range from +0.34 to +0.9 W m-2 [IPCC, 2007;

Ramanathan and Carmichael, 2008]. On the one hand, once activated in the air, aged BC

particles can serve as the cloud condensation nuclei, and therefore increase the lifetime and

coverage of clouds. On the other hand, absorbing aerosol components (such as BC) can strongly

reduce low-level cloud cover by heating the air and evaporating cloud droplets (e.g. [Ackerman

et al., 2000; Johnson et al., 2004]). After deposit on the surface of snow and sea ice, very small

quantities of BC alter the surface albedo resulting in a further radiative effect on climate. The

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average radiative forcing from BC by altering surface albedo was estimated as +0.1 W m-2

[IPCC, 2007], which varies from 0.05 to 0.15 W m-2 depending on the distribution and

deposition of BC on snow [Flanner et al., 2007; Hansen and Nazarenko, 2004; Hansen et al.,

2005; Hansen et al., 2007]. Highly sensitive to anthropogenic and natural disturbance, the

remote Arctic environment experiences the impact of BC from the lower latitudes. High BC

concentrations in the lower Arctic troposphere seasonally build up during winter and spring

[Barrie, 1986; Law and Stohl, 2007; Quinn et al., 2007; Shaw, 1995]. A radiative transfer model

investigation suggested that the overall climate forcing is highly sensitivity to presence of BC,

and an overall warming effect of values up to 0.4 W m-2 was found in the Arctic [Kirkevag et al.,

1997]. A recent study suggested that the aerosol indirect radiative effect contributes more to net

radiative flux changes over the Arctic than the direct aerosol effect [Menon et al., 2008].

Analysis of long-term observations on the Arctic BC revealed a generally decreasing trend

over the Arctic region. Continuous surface observation of BC aerosols has been carried out at

Alert (82.4 ºN, 62.3 ºW, since 1989) in Nunavut, Canada, Point Barrow (71 ºN, 156.6 ºW, since

1989) in Alaska, United States, and Zeppelin (78.9 ºN, 11.9 ºE, since 1998) near Ny-Alesund,

Svalbard. Trend analysis revealed marked decreasing trends in BC concentration at Alert and

Barrow over the 15-year period from 1989 to 2003, with signs of an increase starting in 2000-

2001 [Sharma et al., 2006a]. Specifically for the winter data (January to April), decreasing trends

were obtained for both sites, but for the summer data (June to September) downward trend is

only found at Alert. Sharma et al. [2006a] suggested that the difference in trends might be a

result of changes in atmospheric circulation in the Arctic and variable source contribution

affecting both sites. On the European side, however, the continuous measurements at Zeppelin

station (474 m above sea level) imply only a small decreasing trend from 2001 to 2007 in BC

concentration [Eleftheriadis et al., 2009]. In their study, the source regions in northern and

central Russia was found to be the major contributors affecting Zeppelin station. Although the

potential source regions affecting these Arctic sites, as well as the impacts of atmospheric

circulation, were investigated, source contributions from different regions were not quantified in

these studies.

Until recently, only a few studies have been conducted to quantify the possible impacts of

regional emissions on the observed BC long-term trend in the Arctic. Using the annual BC

emissions from and the derived geopotential height indices for Eurasia (Europe and Former

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Asian Soviet Union) and North America (U.S. and Canada), Gong et al. [2010] explained 36-

70% of the inter-annual variance of the 16-year BC observations at Alert since 1990. Higher

percentages of the observed variations in the spring (i.e. over 50%) were achieved in their study

than that in the winter (lower than 40%). Based on the same BC emission data used by Gong et

al. [2010] and the atmospheric transport frequency affecting Alert (derived from 10-day

backward trajectory analysis), a linear regression model was constructed to explain 66-81% of

the variance in BC concentration at Alert between 1990 and 2005 [Huang et al., 2010]. The

improvement is probably due to the implement of 3D trajectories followed by cluster analysis to

better represent transport pathways affecting Alert rather than the pressure difference on a

specific pressure level used in the former study. In both studies, the dominant contribution of the

Eurasian emissions was found over that of the North American emissions. Agree well with each

other, both studies revealed a decreasing trend in the contributions from Eurasian emissions.

Thus, both studies suggested that the observed decreasing trend in Alert BC concentration was

mainly due to the significant reduction in Eurasian BC emissions.

Although BC emissions from selected regions and atmospheric transport were analyzed by

Gong et al. [2010] and Huang et al. [2010], emissions from East and South Asia and the effects

of many other processes (such as aerosol wet scavenging and dry deposition, particle diffusion,

and so on) were neglected, which may account for the unresolved variance. A comprehensive

multi-model assessment of regional contributions to the Arctic was carried out by Shindell et al.

[2008], which employed the global emission and the complex dynamics of BC aerosols into their

analysis. It was found based on their multi-model estimation that the European emissions

contribute over 70% to the annual average BC abundance near the surface in the Arctic

troposphere, followed by the East Asian (17%) and the North American (10%) emissions. At

upper level of the Arctic atmosphere, the contributions from East and South Asia increase to

about 50 and 20%, respectively. Koch and Hansen [2005a], however, estimated that the

contributions from South Asia, Europe, and Russia to the low-altitude springtime Arctic haze are

quite comparable (from 20-25% each). Although the contributions of regional BC emissions

were estimated using state-of-the-art global aerosol models, the level of uncertainty remains high

in these studies. First of all, the multi-model inter-comparison in [Shindell et al., 2008] showed

significant variations in simulated BC concentration in the Arctic, which were attributed to the

discrepancy in the aerosol deposition schemes among models. The comparison between model

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simulations with Arctic observations in [Koch and Hansen, 2005a; Shindell et al., 2008]

suggested that models generally underpredict BC in the Arctic, especially during the haze season.

In this paper, the relative contributions of anthropogenic BC emissions from various source

regions are examined using the recently improved Global Environmental Multiscale model with

Air Quality processes (or GEM-AQ, [Huang et al., manuscript submitted to JGR]). From the

perspective of the latest 3D global chemical transport model, we can analyze the long-range

atmospheric transport of BC from different source regions. Along the way of atmospheric

transport, complex aerosol dynamics and removal are represented by the model. Below, we first

describe the 3D host meteorological model and the integrated aerosol module. The model

simulations are compared with available observations in the source regions, as well as in the

Arctic. Sensitivity analysis is then conducted by changing the regional BC emission data to

characterize the impacts of anthropogenic BC emissions on the Arctic BC concentrations at

different altitudes.

5.2 Model description

5.2.1 Host meteorological model: Global Environmental Multiscale model

The Global Environmental Multiscale (GEM) [Cote et al., 1998b; Yeh et al., 2002] model

was developed by the Meteorological Service of Canada (MSC) for operational numerical

weather forecasting. It is used in this work as the host meteorological model to drive aerosol

transport and deposition. However, the GEM model setting used in this study differs from the

one used by Kaminski et al. [2008] in the scheme for land surface processes. The Interactions

Soil-Biosphere-Atmosphere (ISBA) scheme (based on [Noilhan and Planton, 1989]) is used in

this study instead of the simplified force-restore method in Kaminski et al. [2008]. Compared to

the force-restore method, the ISBA scheme provides a more detailed calculation of surface heat

and moisture fluxes from different surface types by introducing new prognostic variables. Unlike

many gaseous species, the concentration of BC aerosol particles is usually high close to the

surface of source regions, except those from forest fire emissions. Therefore, better

representations of surface heat and moisture fluxes are crucial to the model simulations of BC in

this paper.

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5.2.2 Aerosol module: Canadian Aerosol Module

Currently, there are two air quality modules implemented on-line in the host meteorological

model-GEM. The integrated gas-phase chemistry module is based on a modified version of the

Acid Deposition and Oxidants Model (ADOM, version 2) [Venkatram et al., 1988]. With the

integration of the Canadian Aerosol Module (CAM) [Gong et al., 2003a], size-resolved aerosol

physical and chemical processes in the atmosphere are included in GEM-AQ. The on-line

implementation of air quality processes in the GEM model provides full access to the simulated

dynamics and physics fields for air quality simulations. At the same time, it allows feedbacks

from air quality simulations on GEM dynamics and physics to study interactions between

weather/climate and chemical and aerosol components.

CAM is a size-resolved multi-component aerosol module driven by climate or numerical

weather forecast models. In the current version, 5 major aerosol types including sulphate, sea salt,

soil dust, black carbon and organic carbon are divided into 12 logarithmically spaced size bins

from 0.01 to 20.48 µm in diameter. The number of size bins is chosen based on previous

numerical investigations by Gong et al. [2003a] as it balances the desired accuracy and the

computational overhead of global 3D models. This size distribution leads to 60 advective aerosol

tracers, together with (CH3)2S, H2S, NH3, SO2, NO3, OH, O3, H2O2, and HNO3, introduced for

on-line sulphur chemistry. Aerosol processes accounted for in the current CAM include emission,

hygroscopic growth, coagulation, nucleation, condensation, dry deposition, wet scavenging,

aerosol activation, aerosol-cloud interaction, and chemical transformation of sulphur species. A

process-splitting technique is used in CAM to efficiently conduct integration with acceptable

numerical errors. Parameterization schemes for aerosol processes were detailed by Gong et al.

[2003a] and its references, and the recent improvements in aerosol dry deposition and wet

scavenging (i.e., in-cloud and below-cloud scavenging) were documented by Huang et al.

[Huang et al., manuscript submitted to JGR]. Huang et al. [Huang et al., manuscript submitted to

JGR] showed that the improved GEM-AQ is able to reasonably reproduce the observed BC

seasonality in the Arctic.

5.2.3 Model setup and simulations

The host model GEM (dynamics version 3.1.2, physics version 4.1) used in the current

study was configured with 28 hybrid vertical levels with the model top at 10 hPa. Upon this

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vertical setup, the lowest model level remains the thinnest with a thickness of about 50-60 m

depending on latitude. The horizontal model grid was configured as global uniform resolution of

2 degrees longitude by 2 degrees latitude, which leads to a horizontal resolution of about 200 km

by 200 km close to the Equator. The model time step was set to 1800 seconds for dynamics,

physics, and air quality processes, and the model was run in 24 h forecast segments initially

driven by meteorological fields from the Canadian Meteorological Centre global assimilation

system [Gauthier et al., 1999; Laroche et al., 2007].

Although CAM accounts for 5 aerosol species, only sulphate and black carbon are included

in the current study to reduce the computational overhead and the results of black carbon are

analyzed. Black carbon emission is provided by three complementary data sets: forest and

savannah fires at the tropical latitudes and domestic and agricultural fires worldwide [Liousse et

al., 1994]; global fossil-fuel burning [Cooke et al., 1999]; and boreal and temperate fires [Lavoue

et al., 2000]. The global BC emission from anthropogenic sources is about 6 Tg/year, which

concentrates mainly in the Northern Hemisphere. Vertical distribution of BC emissions injects

50% of the total surface flux into the model level that contains the injection height corresponding

to a certain source (listed in Table 15), 25% into one level below, 12.5% into two levels below,

and so on, with the remaining amount injected into the lowest model level. A lognormal particle

mass-size distribution with a mean diameter of 0.2 µm is assumed for BC based on recent

measurements of BC in urban and biomass burning emissions [Schwarz et al., 2008].

Model simulations are conducted with the original BC emission data as the control

experiment and with the 20% perturbations in anthropogenic BC emissions from each region as

sensitivity runs. The difference between the control and a sensitivity run is then used to

investigate the response in the Arctic BC to regional emission, from which the relative

contributions from surrounding source regions to the Arctic BC are calculated. In order to keep

the results comparable with those from literature, geographic definitions of source regions

following [Shindell et al., 2008] are used, however, Asian Russia is included in this study as an

additional one (shown in Table 15). All results for the Arctic are based on area-weighted

averages, and surface values are taken from the lowest model level.

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Table 15. Summary of BC emission data used in this study by type and region.

Emission source Type Injection height Amount (Tg/yr)

Fossil fuel Anthropogenic 200 m 4.96

Domestic and agricultural fires Anthropogenic 200 m 1.05

Boreal and temperate fires Natural 2-6 km 0.33

Tropical forest and savannah fires Natural 1 km 4.60

Total 10.94

Region Longitude Latitude Amount (Tg/yr)

South Asia 50 ºE–95 ºE 5 ºN–35 ºN 0.52

East Asia 95 ºE–160 ºE 15 ºN–50 ºN 1.83

Europe 10 ºW–50 ºE 25 ºN–65 ºN 1.81

North America 125 ºW–60 ºW 15 ºN–55 ºN 0.67

Asian Russia 50 ºE–180 ºE 50 ºN–65 ºN 0.19

Regional total 5.03

5.3 Model validations against available observations

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10.005.0002.5001.0000.5000.2000.1500.1000.0500.0200.0100.0050.001

10-2 10-1 100 10110-2

10-1

100

101

Sim

ulat

ed B

C (μ

g/m

3 )


(b) 167 IMPROVE sites (r=0.83)

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10-2 10-1 100 10110-2

10-1

100

101

Sim

ulat

ed B

C (μ

g/m

3 )


(c) 12 EMEP sites (r=1.05)

10-1 100

10-1

100

Sim

ulat

ed B

C (μ

g/m

3 )

Observed BC (Mar-Apr, 2001) (μg/m3)

(d) 5 ACE-Asia sites (r=0.79)

Figure 33. (a) Annual average distribution of the surface concentrations of BC in the Arctic and

its surrounding regions (in µg/m3). And comparison of model simulated BC surface

concentrations against surface observations in North America (from the IMPROVE monitoring

network) (b), Europe (from the EMEP monitoring network) (c), and Asia (from the ACE-Asia

experiment) (d). The dashed lines in the panels (b)-(d) indicate 1:2 and 2:1 ratio between

simulations and observations, and the r represents the simulation to observation ratio, detailed in

text.

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As a chemically inactive aerosol species, BC has only primary emissions from incomplete

combustion as its source to the atmosphere, and it is removed by wet scavenging and dry

deposition. In this study, the annual global BC emission from fossil fuel and biomass

combustions is 10.94 Tg, which is balanced by the wet scavenging (7.86 Tg) and dry deposition

(2.89 Tg). It was confirmed by Huang et al. [Huang et al., manuscript submitted to JGR] that the

relative importance of aerosol wet and dry removal for BC simulated by GEM-AQ is consistent

with the AeroCOM multi-model assessment. The total abundance of BC in the atmosphere is

calculated to be 0.28 Tg, which implies an average lifetime of 9.2 days. This GEM-AQ

estimation is in agreement with the AeroCom result of 7.3±2.3 days [Schulz et al., 2006]. Given

similar emission dataset used in the models, the large variation in BC lifetime comes mainly

from different aerosol removal schemes implemented by different models. The significant effect

of removal schemes on simulated BC was demonstrated by [Huang et al., manuscript submitted

to JGR].

The distribution of annual average surface BC concentration in the Arctic and its

surrounding regions is shown in Figure 33(a). There are three centers of intensive BC sources

around the Arctic region: North America, Europe, and Asia. Relatively low surface

concentrations are found in the northern part of Canada, Alaska, U.S., and over the oceans. To

evaluate the performance of the GEM-AQ model over the source regions to the Arctic, the

simulated BC concentrations in the lowest model level (about 0~60 m above the ground) are

compared first against the near-surface observations in North America, Europe, and Asia. The

BC surface concentrations from the Interagency Monitoring of Protected Visual Environments

(MPROVE), the European Monitoring and Evaluation Programme (EMEP), and the Asian

Pacific Regional Aerosol Characterization Experiment (ACE-Asia) monitoring sites are used to

evaluate the model performance in the mid-latitude potential source regions. The model

simulations against observations are shown in Figure 33(b)-(d). As shown in Figure 33(b), GEM-

AQ simulated BC surface concentrations agree with observations within a factor of 2 at most

(over 80%) IMPROVE sites. The average ratio of the simulated to the observed BC annual

surface concentrations in North America is estimated as 0.83 with the standard deviation of 0.52.

For comparison against the EMEP observations, as shown in Figure 33(c), agreement within a

factor of 2 is found at 3 out of 12 (or 75%) EMEP sites. The average ratio of the simulated to the

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observed BC annual surface concentrations in Europe is estimated as 1.02 with the standard

deviation of 0.76. The observations taken during the ACE-Asia campaign at 5 islands around

Japan from March to April, 2001 [Uno et al., 2003] are compared with model simulations in

Figure 33(d). Based on fairly limited observation data, the simulation to observation ratio is

calculated as 0.79 for these islands with the standard deviation of 0.09. Table 16 compares the

above ratios to AeroCom averages [Koch et al., 2009]. In this study, better agreements with

surface observations can be found for all regions of comparison than the multi-model average.

The comparison against surface observations shows that the GEM-AQ model performs

reasonably well in simulating the BC surface concentration over the source regions. The

calculated simulation to observation ratios confirm that, on a regional average basis, the source

strength near the surface in North America, Europe, and Asia is reasonably represented by the

model.

Table 16. Ratio of model simulated to observed BC surface concentrations among the potential

source regions affecting the Arctic: the enhanced GEM-AQ (this study) vs. the AeroCom

average ([Koch et al., 2009]).

N America Europe Asia

AeroCom average (standard deviation among models) 1.6 (111%) 2.8 (82%) 0.54 (41%)

GEM-AQ 0.83 1.05 0.79

- 113 -

0

5

10

15

10-3 10-2 10-1 100

BC concentration (μg/m3)

Altu

tude

(Km

)

Simulated (April, 2001) Observed (April 16, 2008) Observed (April 12, 2008)

Figure 34. Monthly average vertical profiles of BC aerosol concentration from the GEM-AQ

over Alaska compared with daily aircraft observations from the Arctic Research of the

Composition of the Troposphere from Aircraft and Satellites (ARCTAS) campaign. Area bars on

the simulated profile represent the ranges of daily BC concentrations at various altitudes.

In the Arctic, comparison of model simulated monthly average BC surface concentration

against long-term observations at Alert (1989-2005), Barrow (1988-2007), and Zeppelin (1998-

2008) was previously shown by Huang et al. [Huang et al., manuscript submitted to JGR]. In

general, pronounced seasonal variations of BC in the Arctic surface atmosphere are reasonably

reproduced by the model. Due to stratification of the Arctic troposphere, however, the BC

concentrations during Arctic haze events were found to be highly inhomogeneous in vertical

over the Arctic [Hansen and Rosen, 1984; Hansen and Novakov, 1989]. In order to obtain

meaningful estimates of the source contributions to the Arctic BC abundance at various altitudes,

it is crucial to ensure that the vertical structure of BC is properly captured by GEM-AQ. Figure

34 compares the monthly average vertical profiles of BC aerosol concentration in April, 2001

from the GEM-AQ over Alaska compared with daily aircraft observations from the Arctic

Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS)

- 114 -

campaign. It should be kept in mind that the model simulations and the observed vertical

structure of BC in Figure 34 are not directly comparable. The observations were conducted on

April 12 and 16, 2008, while the model estimation is based on monthly average profile of April,

2001. Furthermore, the aircraft observations used in the comparison are short-term observations

with specific flight tracks around Alaska, while the model simulated vertical profile is the

average across the whole area. However, the typical layered structure of BC in the Arctic is

reasonably captured by the model except that the modeled profile does not reflect the significant

variation along altitude in observations.

5.4 Source contributions to the Arctic BC Figure 35 shows the GEM-AQ estimated source contributions to the Arctic BC abundance

as a function of Altitude for winter (Dec-Feb) and summer (Jun-Aug) based on 20% changes in

regional anthropogenic emissions, along with the vertical profile of BC averaged over the Arctic

region (70-90ºN). Comparing the vertical profiles for winter and summer shown in Figure 35(b)

and (d), the average wintertime concentration is significantly higher than that in the summer for

the lowest 5 km of the Arctic troposphere, where the haze layers were often observed. Below 5

km above the ground in the Arctic in winter, as shown in Figure 35(a), the European sector (with

contributions of 30 – 57%) is found to be the major contributor to the BC abundance. The

highest contribution from Europe peaks at just the same altitude (~2 km above ground level) of

the vertical profile. Within the lowest 5 km of the Arctic troposphere, the Pearson’s correlation

coefficient between the European contribution and the vertical BC concentration profile is

calculated to be 0.68, which indicates the significant impact by European BC emissions. With

the simulated BC lifetime by GEM-AQ , the dominating contribution from Europe revealed in

this study is consistent with the results obtained using a dispersion model with prescribed BC

lifetime [Stohl, 2006]. Both studies show that near the surface, BC source contribution from

South Asia is about 10% of the European value. Following Europe, East Asia and Asian Russia

contribute about 10–30% each within this layer. Furthermore, the contribution from East Asia

increases with the altitude, however, an opposite pattern is found for that of Asian Russia. South

Asia and North America contribute the least (only about 10%) at this level to the Arctic BC.

Again, it is in agreement with the characteristics of atmosphere transport in winter suggested by

previous studies [Bowling and Shaw, 1992; Stohl, 2006] that air masses from the northern high-

latitudes (such as Asian Russia) have a stronger impact on the Arctic surface than those from the

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northern low-latitudes (such as South Asia). It is not clear, however, how South Asian emissions

may contribute as much as 20–25% to the low-altitude Arctic haze suggested by other models

(such as [Koch and Hansen, 2005a]).

In the Arctic upper troposphere (~5–10 km), the contribution from Europe decreases

significantly to only 10% in winter with increasing altitude and that of East Asia peaks at around

8 km above ground level to about 40%. The contribution from South Asia increases stably from

5 to 10 km, while Asian Russia and North America remain relatively stable at 10-15% level. So

in the Arctic upper troposphere, BC emissions from South and East Asia combined account for

up to 65% of BC in the atmosphere. Above 10 km in the lower stratosphere, East Asian

contribution decreases rapidly with increasing altitude until 15 km above ground level. The peak

contribution from South Asia (about 35%) is also found in the lower stratosphere around the 12.5

km level. Above 15 km in the stratosphere, the contributions from all five regions remain stable

with 25–30% for South Asia, 15–20% for Europe, North America, and Asian Russia, and less

than 15% for East Asia.

Regional contributions along altitude in summer, as shown in Figure 35(c) share some

similarities with those in winter. The North American and Asian Russian contributions remain

largely the same for both seasons from the Arctic surface to the stratosphere. European emissions

contribute the most (up to 50%) in the lowest 5 km of the Arctic troposphere, while East and

South Asian emissions dominate between 10 and 15 km. In contrast to its winter pattern,

however, the East Asian contributes 10% more to the near surface BC in summer than in winter.

Comparing the vertical profiles of BC in the Arctic, BC concentrations between 5 and 10 km in

summer are slightly greater than those in winter, which is likely due to the slightly greater

contributions from East Asian in the warm season. In summer, South Asia contributes the

minimum to the Arctic troposphere below 5 km among all of the five regions, followed by North

America.

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0

5

10

15

20

25

0 10 20 30 40 50 60

Alti

tude

(km

)

East Asia South Asia Europe Asian Russia North America

Contribution (%)

(a) Winter (Dec-Feb)

0

5

10

15

20

25

0.00 0.03 0.06 0.09 0.12 0.15


Alti

tude

(km

)

(b) Winter profile (Dec-Feb)

- 117 -

0

5

10

15

20

25

0 10 20 30 40 50 60

Alti

tude

(km

)

East Asia South Asia Europe Asian Russia North America

Contribution (%)

(c) Summer (Dec-Feb)

0

5

10

15

20

25

0.00 0.03 0.06 0.09 0.12 0.15


Alti

tude

(km

)

(d) Summer profile (Jun-Aug)

Figure 35. BC vertical profiles and source contributions to BC in the Arctic (70-90ºN) in winter

and summer.

- 118 -

Table 17. Comparison of annual average anthropogenic source contributions to the Arctic BC

(70-90 ºN) estimated by Shindell et al. [2008] and GEM-AQ in this study.

East Asia South Asia North America Europe Asian Russia

250 hPa

Shindell et al. [2008] 48% 19% 15% 18% N/A

GEM-AQ (without Russia) 20% 29% 26% 25% N/A

GEM-AQ 16% 23% 21% 20% 21%

500 hPa

Shindell et al. [2008] 38% 4% 17% 41% N/A


GEM-AQ 32% 10% 12% 37% 8%

Surface

Shindell et al. [2008] 17% 1% 10% 72% N/A


GEM-AQ 10% 6% 7% 44% 33%

Table 17 compares the estimated annual average anthropogenic source contributions to the

Arctic BC (70-90 ºN) by Shindell et al. [2008] and GEM-AQ in this study. The definitions of

regions are the same in both studies except for Asian Russia, which is only defined and

investigated in the current study. To better compare the estimated source contributions with the

previous study, Asian Russia is first excluded in this analysis as done in [Shindell et al., 2008].

In this case, the estimated anthropogenic contributions from both studies agree very well for the

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near-surface and the mid-troposphere levels. On the surface level, both studies suggest that the

most dominant contribution (over 60%) is from Europe and the least is from South Asia (less

than 10%). As shown in Table 17, the relative contribution of Europe decreases with increasing

altitude from surface to 250 hPa in both studies, while the contributions of other regions increase,

except for East Asian contributions estimated by GEM-AQ. At 250 hPa level, GEM-AQ

estimates relative comparable contributions from different BC source regions, however almost

50% of BC in the Arctic is attributed to the East Asian emissions in [Shindell et al., 2008]. The

contributions of South Asia at 3 levels estimated in this study are higher than the multi-model

estimates. This is likely due to the relative long BC lifetime (9.2 days) predicted using GEM-AQ,

therefore long-range transport from South Asia is enhanced in this study.

Then, the contribution of Asian Russia is considered in this study, and the results are

presented in Table 17 as well. For all levels shown in Table 17, the relative contributions of the

first four regions decrease as expected. Although the Russian emissions account for only 4% of

the total anthropogenic BC emissions from the underlying source regions, results show that the

relative contribution of Asian Russia is significant (up to 33%) at the near-surface level. This

may partly be due to the fact that the Russian emissions are fairly close to the Arctic cycle [Stohl,

2006] and they are along the northward transport pathways near the surface due to the Siberian

High [Macdonald et al., 2005]. Thus, the neglect of Asian Russian emissions can result in

overestimations at the surface for other source regions, especially Europe. Most recently, studies

using multi-year BC surface observations at Alert, Canada correlated with large scale

atmospheric circulations suggest that the contribution of Eurasia (i.e. Europe and Asian Russia

combined) to Alert is about 80–90% and 10–20% is found for North America [Gong et al., 2010;

Huang et al., 2010]. The slightly lower estimation (i.e. 77%) using GEM-AQ is obtained with

more confidence than the above studies in that aerosol removal and additional potential source

regions (i.e. East and South Asia) are accounted for in this study.

5.5 Deposition of BC to the Arctic The deposition of BC to the Arctic is also estimated and compared with estimations in the

literature in a similar manner. Following [Shindell et al., 2008], the relative importance of the

underlying source regions to BC deposition to the Arctic (excluding Greenland) is first

investigated in terms of the regional contributions in percentage, as shown in Table 18. When

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Asian Russia is excluded in the analysis, the estimated fractional contributions agree very well

with the multi-model averages, derived from [Shindell et al., 2008], except for South Asia. With

consideration of the Asian Russian contribution, the fractional contribution of European

emissions decreases significantly from 67% to 47%, and the Asian Russian contribution is

estimated to be 30%. In both studies, strong correlations (R > 0.99) are found between regional

contributions to the Arctic near-surface BC and those to BC deposited to the Arctic. It reflects

that the estimate of BC deposition in the state-of-the-art aerosol models, including GEM-AQ,

depends largely on the near-surface BC concentration. Thus, relatively large contribution of

Russian emissions to BC abundance near the Arctic surface results in a significant contribution

to BC deposition to the Arctic.

Table 18. Relative contributions of regional emissions to BC deposition to the Arctic (excluding

Greenland).


Shindell et al. [2008]* 19% 1% 10% 69% N/A


GEM-AQ 10% 6% 7% 47% 30%

*Values are estimated based on the multi-model means given in the Table 4 in the reference.

To reflect source contribution to BC deposition on a per Tg emission basis, BC deposited to

the entire Arctic (including Greenland) in response to 20% reduced BC emissions are normalized

by the reduced amount of regional BC emissions (Table 19). Although the fractional

contributions to BC deposited to the Arctic agree very well with [Shindell et al., 2008] without

considering Asian Russia (Table 18), the fractions of regionally emitted BC that are deposited in

the entire Arctic (including Greenland) differ in both studies. Higher fractions are estimated in

the current study using GEM-AQ for all these source regions, regardless of the consideration of

Asian Russia. The difference may partly due to the discrepancy in BC emission dataset used in

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various models, but the enhanced south-to-north atmospheric transport due to modified

deposition schemes in GEM-AQ [Huang et al., manuscript submitted to JGR] largely explains

this.

Table 19. BC deposited to the entire Arctic (including Greenland) per unit BC emitted.


Shindell et al. [2008] 0.10% 0.02% 0.19% 0.70% N/A

GEM-AQ (without Russia) 0.26% 0.55% 0.49% 1.21% N/A

GEM-AQ 0.19% 0.40% 0.36% 0.86% 4.91%

5.6 Conclusions In this study, the enhanced 3D chemical transport model, GEM-AQ, is used to estimate the

anthropogenic contributions of East Asia, South Asia, Europe, Asian Russia, and North America

to the BC in the atmosphere over the Arctic, as well as the regional contributions of BC

deposition to the Arctic.

1. The performance of the enhanced model is further validated using available surface

observations near some potential source regions, such as Asia, North America, and Europe. For

most sites investigated in this study, the simulated surface concentrations of BC agree with the

observations within a factor of 2. The enhanced model is able to capture the characteristics of BC

vertical profile over the Arctic.

2. The results of the sensitivity analysis on anthropogenic BC emissions show that in both

winter and summer, Europe contributes more (up to 57% and 48% in winter and summer,

respectively) to the Arctic BC abundance than other regions to the lowest 5 km of the Arctic

troposphere.

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3. The relative contributions to the Arctic BC from surrounding regions vary with altitude.

In the case of Europe, its relative contribution peaks at about 2 km above the ground level,

reaches 57% in winter, decreases with the further increase in altitude and stabilizes at 20% when

reaching 15 km.

4. The contributions from Asian Russia are significant near the surface (e.g., 30% at 100 m

above the surface) in both winter and summer. It however decreases rapidly to less than 10% at 5

km and above.

5. The contributions from South and East Asia increase with altitude, and become more

significant than others in the upper troposphere and the lower stratosphere, where their

contributions reach 35% and 40%, respectively.

6. North American contribution has much less variation with altitude than other regions.

7. Overall, Europe and Asian Russia contribute the most to the Arctic in the near surface

level where most of the long-term observations were made, while South and East Asia contribute

the most at the level of 10–15 km. The results support the thermodynamic argument that the

Arctic surface is mainly affected by extremely cold air masses from the land surface of the

northern high-latitudes, such as the northern Eurasia; however the upper troposphere and the

lower stratosphere are subject to warmer and more intensive anthropogenic BC emissions from

South and East Asia.

8. Given the direct dependence of BC deposition on the BC concentration near the surface,

one would expect a greater influence of Europe and Asian Russia on BC deposited on the Arctic

ice and snow than other regions. Although Russian anthropogenic emissions are small in

magnitude compared to other source regions, they substantially contribute to both BC abundance

in the lower troposphere and deposition to the Arctic.

9. The enhanced GEM-AQ suggests that the estimated fractions of BC emitted from source

regions that deposits in the Arctic may be greater than previous estimations due to a stronger

south-to-north transport of BC resulted from the current model.

- 123 -

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(2000), Reduction of tropical cloudiness by soot, Science, 288(5468), 1042-1047.


Bowling, S. A., and G. E. Shaw (1992), THE THERMODYNAMICS OF POLLUTANT REMOVAL AS AN INDICATOR OF POSSIBLE SOURCE AREAS FOR ARCTIC HAZE, Atmospheric Environment Part a-General Topics, 26(16), 2953-2961.


Cote, J., S. Gravel, A. Methot, A. Patoine, M. Roch, and A. Staniforth (1998), The operational CMC-MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation, Mon. Weather Rev., 126(6), 1373-1395.




Gong, S. L., T. L. Zhao, S. Sharma, D. Toom-Sauntry, D. Lavoue, X. B. Zhang, W. R. Leaitch, and L. Barrie Sourcing trends and variability of sulphate and black carbon in the Canadian High Arctic: 1981 to 2007, Submitted to Journal of Geophysical Research.

Gong, S. L., L. A. Barrie, J. P. Blanchet, K. von Salzen, U. Lohmann, G. Lesins, L. Spacek, L. M. Zhang, E. Girard, H. Lin, R. Leaitch, H. Leighton, P. Chylek, and P. Huang (2003), Canadian Aerosol Module: A size-segregated simulation of atmospheric aerosol processes for climate and air quality models - 1. Module development, Journal of Geophysical Research-Atmospheres, 108(D1).

Hansen, A. D. A., and H. Rosen (1984), Vertical Distributions of Particulate Carbon, Sulfur, and Bromine in the Arctic Haze and Comparison with Ground-level Measurements at Barrow, Alaska, Geophysical Research Letters, 11(5), 381-384.

Hansen, A. D. A., and T. Novakov (1989), AEROSOL BLACK CARBON MEASUREMENTS IN THE ARCTIC HAZE DURING AGASP-II, J. Atmos. Chem., 9(1-3), 347-361.

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Huang, L., S. L. Gong, and C. Q. Jia Modelling the seasonality of the Arctic black carbon aerosols using GEM-AQ with improved deposition schemes, Submitted to JGR.

Huang, L., S. L. Gong, S. Sharma, D. Lavoue, and C. Q. Jia A Trajectory Analysis of Atmospheric Transport of Black Carbon Aerosols to Canadian High Arctic in Winter and Spring (1990-2005), Submitted to Environmental Science and Technology.

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Johnson, B. T., K. P. Shine, and P. M. Forster (2004), The semi-direct aerosol effect: Impact of absorbing aerosols on marine stratocumulus, Q. J. R. Meteorol. Soc., 130(599), 1407-1422.



Koch, D., and J. Hansen (2005), Distant origins of Arctic black carbon: A Goddard Institute for Space Studies ModelE experiment, J. Geophys. Res.-Atmos., 110(D4), 14.

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Macdonald, R. W., T. Harner, and J. Fyfe (2005), Recent climate change in the Arctic and its impact on contaminant pathways and interpretation of temporal trend data, Sci. Total Environ., 342(1-3), 5-86.



Ramanathan, V., and G. Carmichael (2008), Global and regional climate changes due to black carbon, Nat. Geosci., 1(4), 221-227.

Schulz, M., C. Textor, S. Kinne, Y. Balkanski, S. Bauer, T. Berntsen, T. Berglen, O. Boucher, F. Dentener, S. Guibert, I. S. A. Isaksen, T. Iversen, D. Koch, A. Kirkevag, X. Liu, V. Montanaro, G. Myhre, J. E. Penner, G. Pitari, S. Reddy, O. Seland, P. Stier, and T. Takemura (2006), Radiative forcing by aerosols as derived from the AeroCom present-day and pre-industrial simulations, Atmos. Chem. Phys., 6, 5225-5246.




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Uno, I., G. R. Carmichael, D. Streets, S. Satake, T. Takemura, J. H. Woo, M. Uematsu, and S. Ohta (2003), Analysis of surface black carbon distributions during ACE-Asia using a regional-scale aerosol model, J. Geophys. Res.-Atmos., 108(D23), 11.



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6 Back trajectory analysis of Inter-annual variations of the Arctic black carbon aerosol (1990-2005)

Abstract Black carbon (BC) particles accumulated in the Arctic troposphere and deposited over snow

have significant effects on radiative forcing of the Arctic regional climate. Applying cluster

analysis technique on 10-day backward trajectories, transport pathways affecting Alert (82.5ºN,

62.5ºW), Nunavut in Canada are identified in this work, along with the associated transport

frequency. Based on the atmospheric transport frequency and the estimated BC emission

intensity from surrounding regions, a linear regression model is constructed to investigate the

inter-annual variations of BC observed at Alert between 1990 and 2005 in January and April,

representative of winter and spring respectively. Strong correlations are found between BC

concentrations reconstructed with the regression model and observations at Alert for both

seasons (R2 equals 0.77 and 0.81 for winter and spring, respectively). Results imply that

atmospheric transport and BC emission are the major contributors to the inter-annual variations

in BC concentrations observed at Alert in the cold seasons for the 16-year period. Based on the

regression model, the relative contributions of regional BC emissions affecting Alert are

attributed to Eurasia, composed of the European Union and the former USSR, and North

America. Considering both seasons, the model suggests that Eurasia is the major contributor to

the near-surface BC levels at the Canadian high Arctic site with an average contribution of over

85% during the 16-year period. In winter, the atmospheric transport of BC aerosol from Eurasia

is found to be even more predominant with a multi-year average of 94%. The model estimates

smaller contribution from the Eurasian sector in spring (70%) than that in winter. It is also found

that the decreasing trend in Eurasian contributions is mainly caused by the reduction of regional

emissions, while the changes in both emission and atmospheric transport contributed to the inter-

annual variation of North American contributions.

6.1 Introduction Black carbon (BC) particles accumulated in the Arctic troposphere and deposited onto snow

have significant effects on radiative forcing of the Arctic regional and the global climate

[Flanner et al., 2007; Hansen and Nazarenko, 2004; Jacobson, 2001; Kristjansson et al., 2005].

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Absorbing both the direct and the reflected solar radiation, BC in the atmosphere was suggested

to be the second strongest contributor to current global warming, after carbon dioxide [Chung et

al., 2005; Ramanathan and Carmichael, 2008]. Once deposited onto snow and sea ice, BC

changes the surface albedo and contributes to melting of Arctic sea ice [Clarke and Noone, 1985;

Flanner et al., 2007; Jacobson, 2004; Kim et al., 2005; Koch and Hansen, 2005b]. BC particles

(along with sulphate and organic carbon) intensively accumulate in the Arctic troposphere during

the winter and early spring, as a result of the Arctic haze phenomenon [Barrie, 1986; Law and

Stohl, 2007; Quinn et al., 2007; Shaw, 1995]. The anthropogenic emissions from Europe and

former USSR were suggested to be the major sources of the Arctic haze in general [Quinn et al.,

2007; Shindell et al., 2008; Stohl, 2006], but the relative importance of the potential source

regions may have different impacts on two different sites in the Arctic [Sharma et al., 2006b].

Worthy et al. [1994], for instance, showed that rapid air mass transport from western Russia in

winter were responsible for the highest concentrations of BC measured at Alert.

Based on 13-year continuous observations at Alert (82.5ºN, 62.5ºW), Nunavut, Canada

since 1989, a broad peak in BC concentration was previously found from January to April

[Sharma et al., 2006a], corresponding to the haze season. Recently, a marked monotonic

decreasing trend of BC concentration at Alert between 1989 and 2002 was revealed using a

geometric time variation model [Sharma et al., 2004]. The impact of emission variation was

highlighted in their study as the decreasing trend in BC concentrations was associated to the

reduction of BC emissions from the former USSR sector, rather than the North American and the

European sectors. Additionally, the important influence of atmospheric transport variability on

the inter-annual changes of air pollution levels in the Arctic troposphere was also revealed

recently, particularly the effect of North Atlantic Oscillation (NAO) [Eckhardt et al., 2003].

However, studies emphsizing the simulatanous effects of varing atmospheric transport and BC

emissions are still limited. More recently, the inter-annual variation of BC was correlated with

two atmospheric transport indices derived from the 700 hPa geopotential heights and regional

BC emissions, but only 36 and 54% of the variations can be explained for January and April data

[Gong et al., 2010]. In this paper, an attempt is made to investigate the effect of changes in both

emission and atmospheric transport on the inter-annual variation of BC observed at Alert from

1990 through 2005, and to further quantify the contributions of BC emissions from two sectors

based on trajectory analysis technique. To better isolate the effect of atmospheric transport in the

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cold seasons, 10-day backward trajectories in January and April between 1990 and 2005 are used

in this study. Transport in January and April are specifically investigated in this study because

the atmospheric transport to the Arctic remain strong in these months according to the average

length of the 10-day back trajectories arriving at Alert. Applying the cluster analysis technique,

the transport pathways affecting Alert are identified for both seasons. Based on the obtained

transport frequency from cluster analysis and the estimated BC emission intensity from

surrounding regions, a linear regression model is constructed to reconstruct the year-to-year

changes in BC surface concentrations in winter and spring.

6.2 Data and methods

6.2.1 Equivalent black carbon data

Continuous hourly measurements of the BC at Alert have been conducted since 1989. The

attenuation of light transmitted through particles collected on a quartz fiber filter was measured

using a commercial aethalometer, along with the attenuation of a blank filter. Then the hourly

BC concentrations were calculated based on the difference in attenuation, the filter area, the

sample flow rate, and a specific attenuation coefficient (19 m2/g). The later is determined based

upon calibrations during instrument development and theoretical calculations. More detailed

description of the method and the determination of the specific attenuation coefficient were

documented by Sharma et al. [Sharma et al., 2004].

6.2.2 Trajectory data and transport frequency

Ten-day backward trajectories arriving at 500 m above ground level (or agl) at Alert were

initialized 12 times daily (i.e., 00, 02, 04 … and 22 coordinated universal time) for January and

April between 1990 and 2005 using the HYSPLIT model (HYbrid Single-Particle Lagrangian

Integrated Trajectory, version 4) [Lin et al., 2001]. Three-dimensional wind fields from

NCEP/NCAR global reanalysis data [Kalnay et al., 1996] were used to drive HYSPLIT , which

are available every 6 hours on a 2.5 degree latitude-longitude global grid with 18 vertical levels.

The arrival elevation of 500 m agl locates within the wintertime Arctic inversion layer so that it

is representative of the air sampled at Alert. Worthy et al. [1994], for instance, compared

trajectories arriving 1000, 925, 850, and 700 hPa above Alert and suggested that the 925 hPa

level (about 540 m agl) was the most representative arriving height. Ten-day backward

trajectories are used in this study since trajectories of a shorter duration are usually not long

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enough to indicate possible distant source regions affecting the Arctic [Harris and Kahl, 1990].

Although longer trajectories are generally subject to higher uncertainty, progressive advances in

generating meteorological fields, computing trajectories, and especially, the implementation of

cluster analysis technique on a large set of trajectories in this study may, to some extent, reduce

the effects of individual errors [Harris and Kahl, 1994; Kahl, 1990]. The clustering algorithm

described by Dorling et al. [1992] was modified (refer to the Appendix: Modified Dorling’s

algorithm) to effectively group trajectories. Each group of trajectories represents a distinct

transport pathway bringing air masses into Alert. The transport frequency (dimensionless) for

every pathway can be estimated by computing the percentage of trajectories in that group.

6.2.3 Surface flux of BC

Wintertime black carbon emissions in the northern mid-latitudes are predominantly emitted

from incomplete fossil fuel combustion. Analyzing BC trend in the Arctic required building

annual BC emission inventories by country from 1990 through 2005. BC emissions were

calculated globally from consumption and transaction amounts of 23 different fuel types

compiled by the United Nations [United Nations, 2007]. The method to compute emissions was

initially developed by [Cooke et al., 1999] for 1970-89. The period was extended a first time to

1990-1998 in [Sharma et al., 2004] and through 2005 in [Sharma et al., 2009]. BC emissions

located in the former USSR decreased by more than 50% during the first half of the 1990s, and

since then have progressively increased. In addition, South Asian emissions steadily increased

during the 1990s and have accelerated since the early 2000s, reaching +10% per year. Global

emissions were also developed by Bond et al. [2007]. Only emissions every 10 years until 2000

are made available to the public on their web site (http://www.hiwater.org). For the year 2000,

we determined global emissions of 7.2 Tg, while they totalized 4.6 Tg, i.e. 36% less. For 1990,

the difference calculated is similar.

- 131 -

1989 1992 1995 1998 2001 2004 2007

1

2

3

4

5

6

7

European Union former USSR North America

Ann

ual B

C s

urfa

ce fl

ux (n

g/m

2 /s)

Figure 36 Annual average BC surface flux (ng/m2/s) from European Union, the former USSR,

and North America: 1990-2005.

Based on the previous study on the atmospheric transport into the Arctic troposphere [Stohl,

2006], North America (50-180ºW), European Union (15ºW-15ºE) and the former USSR (15-

180ºE) are considered as the major BC source regions affecting Alert through the lower

troposphere transport in this work. The obtained BC surface fluxes from these regions for 1990-

2005 are shown in Figure 36. A pronounced decreasing trend in BC surface flux is found for the

former USSR sector while the emissions from the European Union and the North American

sectors increase stably during the same period.

6.2.4 Simple linear regression model

A mass balance approach is used to establish the linkage among the intensity of

anthropogenic emission, the frequency of atmospheric transport, and the measured BC, which

has the following formula

- 132 -

∑=

⋅=n

ijijijAprJan CfBC

1,,,/ )(][ (6.1)

where ni ,...2,1= is an arbitrary index for atmospheric transport pathways affecting Alert and

2005,...,1991,1990=j represents year from 1990 through 2005. For the year of j , the left-hand

side of the above equation represents the monthly average BC concentration observed in January

or April (in ng/m3), jif , (in percentage) is the transport frequency of the i-th pathway, and jiC ,

(in ng/m3) is defined as the BC concentration that would be measured if only the i-th transport

pathway had affected the receptor.

It is then assumed in this study that the characteristic BC concentration of a transport

pathway is linearly proportional to the surface flux of BC emission at source region identified by

trajectory cluster analysis. The mass balance model takes the following form,

∑=

⋅⋅=n

ijiijijAprJan EbfBC

1,,,/ )(][ (6.2)

where jiE , (in ng/m2/s) represents the surface flux of BC emission from source region i in the

year of j and ib (in s/m) is a cluster specific proportional constant. The final form of the mass

balance model shows a linear dependence of monthly average BC concentration ( ][BC ) on

transport frequency ( f ) and emission intensity ( E ), which are obtained following the

methodologies described in Sect. 2.1-2.3. It is a simple linear regression model with the

independent variable or predictor ∑=

⋅n

ijiji Ef

1,, )( and the slope ib . It is physically meaningful to

have a zero intercept in this model, which requires that other BC emissions than those considered

in this study have little impact on the near-surface BC observed at Alert. Using a particle

dispersion model, the BC source contribution to the entire Arctic troposphere from south Asia

was estimated to be only 10% of the European value [Stohl, 2006]. The number would be even

smaller for the case of high Arctic surface, such as the underlying receptor, Alert. Given BC

concentrations, transport frequencies, and emission intensities, the slope ib . is estimated using

the least squares method. The purpose of introducing the b factors is to relate the available

surface emission inventories to the observed concentrations at the receptor when atmospheric

- 133 -

transport from source regions takes place. The b factors are assumed to be the ratio of the

concentration in the air to the surface emission flux. So it is region specific and it has the unit of

s/m. It may depend on factors, such as the horizontal wind speed, precipitation, and air mass

mixing during the transport.

6.3 Results and discussion

6.3.1 Transport pathways affecting Alert

Ten-day backward trajectories for January and April from 1990 through 2005 are classified into

7 distinct groups by implementing the modified clustering algorithm. The cluster-mean

trajectories, which indicate the average atmospheric flow patterns, are shown in Figure 37. The

identified 7 transport pathways are distinct in wind direction and speed. First of all, there are

several specific characteristics that can be found for the air masses arriving at Alert in January.

In terms of wind direction, it was found that southerly (clusters 1, 2, and 3) and northerly

(clusters 4, 5, 6, and 7) flows dominate the wintertime atmospheric transport. For the period of

interest, northerly winds constitute slightly over 50% of the total flows and the rest is from

southerly transport. Among southerly transport routes, clusters 2 and 3 indicate transport of air

masses from south and southwest to the receptor Alert. Cluster 2 appears to be the most frequent

transport pathways, which alone account for 20% of the air masses affecting Alert. The cluster-

member plot for cluster 2 (not shown in this paper) indicates that most of the trajectories in this

group originally started from Canada or Alaska, moved first towards southeast of Canada, and

turned north between Baffin Island and Greenland due to the effect of geographic barrier. Cluster

3 (about 11%) is composed of trajectories with a strong westerly wind component. The

trajectories in this cluster are mostly found originated between Eastern Siberia and the Beaufort

Sea, although few are found from Bering Sea. Cluster 1 (17%), however, contains trajectories

passing through the European Arctic region. Trajectories in this cluster initiated from the North

Atlantic Ocean and the Europe.

- 134 -

Figure 37 Transport pathways affecting Alert, Nunavut in January (a) and April (b) from 1990

through 2005 identified by cluster analysis on the HYSPLIT trajectories. The number outside the

brackets serves only as an identification of each cluster; the one inside the brackets gives the

frequency of occurrence of the underlining transport pathway.

- 135 -

Among the northerly transport pathways in Figure 37(a), Cluster 5 is characterized as a

relatively slow northerly moving group, which is found about 14% of the time in January.

Trajectories grouped into this cluster initiated from the northern high latitudes of the former

USSR, but they are found cycling around the Alert site. During the 10-day transport to Alert,

trajectories in this group spent considerable amount of time in traveling above the sea ice

covering the Arctic Ocean. In January, several fully developed long-range transport pathways

bringing air masses from Eurasia into Alert are found with considerable frequency of occurrence.

This type of pathways includes clusters 4, 6, and 7 in Figure 37(a). They are highly potential to

carry anthropogenic emissions during the winter and result in elevated BC measurements at Alert

[Worthy et al., 1994], as the lifetime of BC aerosol in winter could be as long as several weeks

[Barrie, 1986]. Cluster 7 (about 10%) represents the transport of mid-latitude continental air

masses from Eastern Europe. A number of trajectories in this cluster extend deeply into the mid-

latitudes as far south as 45ºN. Many of them traveled eastwards for the first one or two days

before entering the Arctic region. In cluster 6, trajectories started within a wide area of Siberia

and extended also deeply into the continent (about 50ºN in latitude). Such long-range transport is

frequently found in winter for close to 18% of the time. Transport from Eastern Siberia with rare

exceptions from Bering Sea and Alaska is presented by cluster 4 (10%) in Figure 37(a).

Compared to the atmospheric transport patterns in January, the cyclonic feature and the long

extend of trajectories are much weaker in April, which is due to shifted and weakened surface

pressure systems: the Siberian High, the Icelandic Low, and the Aleutian Low [Serreze and

Barry, 2005]. Thus, long-range transport from the mid-latitudes is less frequent in spring

compared to that in winter. The monthly average trajectory length in April is about 38% shorter

than that in January between 1990 and 2005. In Figure 37(b), Clusters 1 and 2 represent transport

originated from the central and the northwestern North America, respectively. Cluster 3 is

composed of trajectories from Eastern Siberia, and Clusters 4 and 5 point to the central of the

northern Siberia. Transport of air mass from Europe in April is only found in Cluster 7.

- 136 -

Table 20. Inter-annual variation of transport frequency (trajectory number of each sector divided

by the total number of trajectories, in percentage) affecting Alert in January, 1990-2005.

North America Former USSR European Union

Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 1 Cluster 7

1990 25% 3% 0% 0% 17% 33% 21%

1991 55% 0% 7% 10% 17% 9% 2%

1992 30% 4% 10% 32% 6% 11% 8%

1993 14% 42% 3% 0% 27% 14% 0%

1994 5% 2% 19% 19% 33% 11% 12%

1995 12% 4% 6% 37% 25% 13% 3%

1996 31% 26% 11% 4% 9% 18% 0%

1997 11% 6% 9% 10% 21% 1% 43%

1998 19% 5% 18% 7% 11% 31% 10%

1999 1% 15% 2% 8% 0% 70% 4%

2000 43% 29% 6% 10% 7% 6% 0%

2001 26% 13% 0% 14% 20% 15% 11%

2002 2% 3% 16% 14% 39% 10% 15%

2003 15% 21% 18% 19% 7% 15% 3%

2004 23% 5% 28% 4% 34% 2% 2%

2005 18% 6% 1% 21% 9% 19% 27%

Average 21% 52% 27%

- 137 -

Table 21. Same as Table 20, but for April, 1990-2005.

North America Former USSR European Union

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7

1990 15% 11% 38% 13% 2% 0% 22%

1991 1% 16% 26% 16% 0% 41% 1%

1992 38% 0% 13% 2% 20% 17% 11%

1993 2% 4% 24% 40% 12% 8% 9%

1994 33% 8% 21% 4% 19% 5% 11%

1995 19% 7% 16% 16% 27% 0% 15%

1996 19% 0% 6% 0% 0% 0% 74%

1997 22% 4% 8% 4% 11% 38% 14%

1998 8% 17% 6% 0% 9% 43% 17%

1999 11% 12% 21% 8% 40% 8% 0%

2000 39% 11% 29% 14% 5% 2% 0%

2001 37% 4% 18% 38% 1% 1% 0%

2002 0% 69% 14% 10% 0% 7% 0%

2003 3% 5% 52% 15% 7% 17% 1%

2004 25% 11% 7% 15% 22% 8% 13%

2005 36% 1% 26% 12% 19% 6% 0%

Average 30% 58% 12%

- 138 -

According to the direction of each identified transport pathway, the linkage between the

source of emission and the receptor is established. Table 20 and Table 21 present the year-to-

year changes in atmospheric transport frequency between 1990 and 2005 for winter and spring,

respectively. In the North American sector, the frequency of atmospheric transport increases by

about 10% from winter to spring. In the former USSR sector, transport from the western and the

central USSR increases by 6% in spring compared to the winter pattern. Compared to that in

winter, the frequency of transport from Europe in spring also decreases significantly by 15%.

Table 22. Values of bi factors for January and April, 1990-2005.

b1 b2 b3 b4 b5 b6 b7

January 11.0 68.4 100.3 192.7 167.1 181.4 43.7

p-value 0.048 0.081 0.088 0.082 0.017 0.006 0.006

April 65.8 54.8 137.6 113.8 129.0 62.6 9.2

p-value 0.037 0.045 0.002 0.046 0.045 0.046 0.048

- 139 -

1989 1992 1995 1998 2001 2004 200750

100

150

200

250

300

BC

(ng/

m3 )

Observed Reconstructed

(a) January

Rec

onst

ruct

ed

Observed

R2=0.77

1989 1992 1995 1998 2001 2004 20070

50

100

150

200

250

300

BC

(ng/

m3 )

Observed Reconstructed

(b) AprilR

econ

stru

cted

Observed

R2=0.81

Figure 38. Time-series of the model reconstructed and the observed monthly average BC in

January (a) and April (b), 1990-2005. The R2 shown in both plots are the squires of the Pearson’s

correlation coefficients between the reconstructed and observed BC concentrations rather than

those for linear regressions.

- 140 -

6.3.2 Inter-annual variations of BC at Alert explained by the model

The transport frequency obtained in the previous section is then used here as f values in

Eq. 6.2. Given monthly average BC measurements ( ][BC ), transport frequency ( f ), and surface

BC flux ( E ), the linear regression model (Eq. 6.2) is solved using the Least Squares method, and

the region specific b factors, as well as the individual p-values, are given in Table 22 for both

seasons. The regressions are significant at 95% confidence level for both seasons. The time-

series and the correlation between model reconstructed and the observed monthly average BC

are shown in Figure 38. Strong positive correlations are found between the model reconstructed

and the observed BC at Alert for both seasons. The square of Pearson’s correlation coefficient

(R2) indicates the inter-annual variations in observations explained by the linear regression

model. As shown in Figure 38, the model is able to explain 77% of the variation in the observed

BC for winter, and over 80% is explained for spring, which is considerably better than the

approach in [Gong et al., 2010]. Given the same BC emission dataset used in both studies, the

better correlations obtained in this study is probably due to the implement of 3D trajectories

followed by cluster analysis to better represent transport pathways affecting Alert rather than the

pressure difference on a specific pressure level. It may also be partially due to the introduction of

the pathway specific b factors, which implicitly account for the effect of atmospheric BC

removal. Inter-annual variations can be considerably explained by this model implies that

atmospheric transport plays the dominant role in connecting source regions and the Canadian

high Arctic site during the Arctic haze season. In such extreme cold season, favorable

meteorological conditions, such as stable stratification, surface temperature inversion, and

extreme dryness, suppress mixing, dry deposition, and wet scavenging of BC in the air and,

therefore, enhance the long-range atmospheric transport.

About 20% of the inter-annual variation in observations cannot be explained by this

approach. The uncertainty of this approach is affected by several assumptions made in the

current study. First, the atmospheric removal mechanisms are not explicitly included in the

current approach. By assuming constant b factors with respect to the identified transport

pathways, constant removal efficiencies during transport are implicitly assumed between 1990

and 2005. This assumption may not perfectly hold for years with extreme precipitation events. In

January, 1997, for instance, the area averaged precipitation accumulation at the European sector

is found the lowest among the period of interest, and it is estimated 33% lower than the multi-

- 141 -

year average based on the Climate Prediction Center (CPC) Merged Analysis of Precipitation

(CMAP) dataset [Huffman et al., 1997]. Thus, the significant underestimation (about 25 ng/m3

lower than the observation) in January, 1997 may be partly due to the extreme dry conditions,

which substantially suppressed the wet scavenging of aerosols. In January, 1995, however, the

highest precipitation accumulation at the European sector (33% higher than the multi-year

average) was found, which may partly explain the overestimation by the model. Another major

source of uncertainty is the assumption that BC particles are uniformly distributed at the regions

of emission. The BC emission intensities used in this study does not consider the geographic

distribution of BC within the potential source regions. Uncertainties of this approach may also

come from trajectory calculation, emission data, and the implicitly treatment of particle dry

deposition and air mass mixing during the transport. To reduce all these sources of uncertainties,

the study implementing state-of-the-art aerosol model, GEM-AQ, is presented in the previous

chapters.

1989 1992 1995 1998 2001 2004 2007

0

50

100

150

200 North American contribution Eurasian contribution

BC

em

issi

on in

tens

ty (n

g/m

2 )

BC

con

tribu

tion

(ng/

m3 )

1.0

1.5

2.0

2.5

3.0

North American emission Eurasian emission

Figure 39. Model estimated source contributions of BC from the North American and the

Eurasian sectors based on the average of January and April from 1990 through 2005. The inter-

annual changes in BC emission intensity are show by two dashed lines.

- 142 -

6.3.3 Source contributions to BC at Alert

According to the model, the contributions of BC transport from the North American and the

Eurasia sectors are estimated based on the average of January and April from 1990 through 2005,

as shown in Figure 39. The annual BC emission intensities of North America and Eurasia are

also shown for comparison. Comparing the importance of these two regions in affecting Alert,

contributions from Eurasia dominate throughout the 16-year period. The model suggests that the

Eurasia emitted BC contributes about 90 ng/m3 (or 85%) to the measured BC particles at Alert,

while the North America contributes less than 15 ng/m3 (or 15%) on 16-year average. It agrees

well with the most recent multi-model estimation (North America: 10% and Eurasia: 90%)

[Shindell et al., 2008], considering the effects of South and East Asian emissions are not

considered in this study. In January, the effect of the Eurasian emission becomes even more

predominant (94%) than that in April (70%), which is due to the enhanced long-range transport

in January.

The model also suggests that the contribution of Eurasia declined significantly in the first 8-

10 years since 1990. However, a slightly increasing trend can be noticed since the late 1990s to

2005 on the Eurasian contribution curve in Figure 39. The relative importance of atmospheric

transport and BC emission in governing the inter-annual variations of regional contributions to

the near-surface BC level at Alert is also investigated. The Pearson’s correlation coefficient

between the Eurasian contribution and BC emission intensity from that region is found to be 0.93,

which indicates that the inter-annual change in Eurasian contributions is mainly attributed to

regional BC emission reduction during the 16-year period rather than the changes in atmospheric

transport. On the other hand, the correlation for the North American side is very poor (R=0.23).

So on the North American side, source contribution to BC levels at Alert for the same period did

not simply depend on regional BC emission, but also on other factors, especially atmospheric

transport patterns.

6.4 Conclusions Based on the atmospheric transport frequency and the estimated BC emission intensity from

surrounding regions, a linear regression model is constructed to investigate the inter-annual

variations of BC observed at Alert in January and April, representative of winter and spring

respectively, from 1990 through 2005. The following conclusions are drawn from this study.

- 143 -

1. The 10-day backward trajectory-based linear regression model reconstructs the BC

concentrations measured at Alert for both winter and spring. The model is able to explain 77% of

the inter-annual variation of BC concentration in winter, and over 80% in spring. The results

imply that atmospheric transport and BC emission are the major contributors to the inter-annual

variations in BC concentrations observed at Alert in the cold seasons for the 16-year period.

2. The model enables the quantification of relative contribution of source regions. For both

seasons, Eurasia, consisting of the European Union and the former USSR, is identified as the

major contributor to the near-surface BC observed at the Canadian high Arctic site, with a

relative contribution over 85% averaged over the 16-year period.

3. There is some difference in the relative contribution between the two seasons. In winter,

the atmospheric transport of BC aerosol from Eurasia is more predominant with a multi-year

average of 94%, while its average contribution in spring is 70%.

4. There are inter-annual variations in relative contributions from source regions. The

decreasing trend in Eurasian contributions is due to the reduction of regional emissions. However,

the inter-annual variation in North American contributions shows no clear trend, and it is

attributed to the changes in both the emission intensity and the atmospheric transport pattern.

Acknowledgements The author gratefully acknowledges the NOAA Air Resources Laboratory (ARL) for the

provision of the HYSPLIT transport and dispersion model and READY website

(http://www.arl.noaa.gov/ready.html) used in this publication.

- 144 -

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- 147 -

7 Conclusions 1. The missing of seasonality of simulated Arctic BC and SF by the original GEM-AQ is

due to the parameterization of deposition processes rather than transport process. Therefore,

GEM-AQ is enhanced by improving the representations of aerosol deposition processes in the

aerosol module – CAM. The enhanced model is able to capture the observed seasonal changes in

the surface concentrations of the Arctic BC and SF.

2. The observed seasonality of the Arctic BC and SF is due to the seasonal changes in

aerosol removal by wet scavenging and in horizontal transport to the Arctic region.

3. With an average velocity of 0.1 – 0.2 cm/s, dry deposition has little effect on the seasonal

pattern of Arctic aerosols in the lower troposphere. Since dry deposition can significantly change

the aerosol surface concentration, a realistic dry deposition velocity (a global average of 0.1 cm/s

used in this study) is essential to capturing the seasonality of the Arctic BC and SF near the

surface.

4. The enhanced GEM-AQ model suggests an annual budget of BC deposition to the Arctic

of 0.11 Tg – a 10% increase over the original estimation. It also suggests that the below-cloud

scavenging dominates BC removal over the Arctic with an estimation of 48% for 2001, while the

contributions of in-cloud scavenging and dry deposition are about 27% and 25%, respectively.

5. The results of the sensitivity analysis on anthropogenic BC emissions show that the

relative contributions to the Arctic BC from surrounding regions depend strongly on altitude.

Europe contributes more (up to 57% and 48% in winter and summer, respectively) than other

regions to the lowest 5 km of the Arctic troposphere. The contributions of Asian Russia are

significant near the surface (e.g. 30% at 100 m above the surface). The contributions from South

and East Asia increase with altitude, and become more significant than others in the upper

troposphere and the lower stratosphere, where their contributions reach 35% and 40%,

respectively. North American contributions remain stable (< 15% and < 20% in summer and

winter, respectively) in the Arctic troposphere.

6. Given the direct dependence of BC deposition on the BC concentration near the surface,

one would expect a greater influence of Europe and Asian Russia on BC deposited on the Arctic

- 148 -

ice and snow than other regions. Although relatively small in their magnitude compared to other

source regions, Russian anthropogenic emissions substantially contribute to both BC abundance

in the lower troposphere and deposition to the Arctic.

7. The 10-day backward trajectory-based linear regression model reconstructs the BC

concentrations measured at Alert for both winter and spring. The model is able to explain 77% of

the inter-annual variation of BC concentration in winter, and over 80% in spring. The results

imply that the changes in atmospheric transport and BC emission are the major reasons behind

the decreasing trend in BC concentrations observed at Alert in the cold seasons of the 16-year

period of 1990-2005.

8. The model enables the quantification of relative contribution of source regions. For both

winter and spring, Eurasia, consisting of the European Union and the former USSR, is identified

as the major contributor to the near-surface BC observed at the Canadian high Arctic site, with a

relative contribution over 85% averaged over the 16-year period.

9. There are inter-annual variations in relative contributions from source regions. The

decreasing trend in Eurasian contributions is mainly due to the reduction of regional emissions.

However, the inter-annual variation in North American contribution, which shows no clear trend,

is attributed to the changes in both the emission intensity and the atmospheric transport pattern.

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8 Future work GEM-AQ has been enhanced in the current study to better simulate the seasonality of BC

and SF aerosol in the Arctic troposphere. Unlike BC, SF particles can be produced in the air

through a series of chemical and physical processes. Especially after the polar sunrise, the

relatively high SF surface concentration in the Arctic is not even captured by the improved

model, which suggests the need of studying oxidation processes (under clear-sky and in-cloud

conditions) related to sulphate production in the northern high latitudes. Model sensitivity to the

parameterizations of sulfur oxidation processes needs to be better understood, as well as the

seasonal change in sulfur oxidation efficiency in the northern high latitudes. In addition to BC

and SF, other chemical components (such as sea salt, soil dust, and organic carbon) in the Arctic

troposphere can also be simulated by the model. They are largely from natural sources and have

different seasonal pattern, therefore further model validation could be conducted for each of

these components.

Here, the enhanced model has been used to assess the relative contributions of regional,

anthropogenic BC emissions to the abundance in the Arctic troposphere and deposition to the

Arctic surface. It can also be applied, in the near future, to study various challenging research

problems related to the climatic impact of aerosols. Optical parameters are computed and output

by GEM-AQ, which are ready to be used to estimate aerosol radiative forcing. As the improved

model is now able to properly reproduce the wintertime maximum of BC and SF aerosol in the

Arctic, it could be used to assess the radiative effects of Arctic haze, with reasonable

concentrations of other components reproduced by the model. By comparing the difference of

aerosol radiative forcing with and without Arctic haze, the importance of properly reproducing

Arctic haze could be addressed using the enhanced GEM-AQ.

The enhanced simulation tool can possibly be applied to study the inter-annual variation of

the Arctic aerosol in the future. Such an application, however, requires an aerosol emission

database reflecting the year-to-year variations. This is not possible with the current aerosol

emission dataset used in this study. With the implementation of a comprehensive emission

database reflecting the year-to-year variations, GEM-AQ could be used to investigate the relative

importance of changes in emission, atmospheric transport, aerosol removal efficiency, and so on

in governing the inter-annual variation of the Arctic aerosol.

- 150 -

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Appendices

Appendix A: GEM-AQ model installation

The hardware of current use is a commercial workstation Poweredege® 2800 from Dell®. A

summary of the hardware configuration is listed in Table 7. It is used to perform model

development, modification, code testing, and preliminary runs. Well-designed numerical

simulations will be conducted by IBM® supercomputer in Canadian Meteorological Centre.

The current GEM-AQ model is running only on a UNIX-like platform, with the support of

the ARMNLIB package, PGI compiler (for both FORTRAN and C), and MPI (Massage Passing

Interface). As designed, GEM-AQ makes use of a set of library functions, scripts and binaries

bundled under the ARMNLIB name. ARMNLIB package includes a software library (scientific

and high performance modules, scientific database, and interface routines) and a set of tools and

utilities for visualization (xrec and sigma), data field manipulation (pgsm), data file management

(editfst and editbrp), and source code management (etagere). The setup of the software

environment is summarized as follows. Some sort of testing is highly recommended after

performing each step.

Table A1. Hardware of Dell® Poweredge® 2800

Model CPUs (GHz) RAM (M) Hard drive (G) Network Adapter (M)

Poweredge 2800 2×3.0 2×1024 1×154 2×100

Step 1: Install the open source operating system Fedora core 3 with necessary utilities (i.e. vim

and Perl);

Step 2: Download and install both the install package of PGI compiler and the patches for Fedora

core 3;

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Step 3: Download and install MPICH 1.3.6 using proper environment variables to identify PGI

compilers to be used for MPICH compilation and use the specified libraries in stead of auto-

configure;

Step 4: Download and install ARMNLIB package and data files;

Step 5: Register ARMNLIB by obtaining license number from [email protected], and create

a license file $ARMNLIB/data/.LIC containing this number;

Step 6: Set up correct environment variables in the home directory of each user;

Step 7: Copy source codes for GEM version 3.1.1 and physics library version 4.0 to proper

directories.

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Appendix B: Procedure of running GEM-AQ

Before running GEM-AQ, it is necessary to set up the environment for GEM 3.1.1 by

conducting an ARMNLIB script r.sm.dot as follows:

. r.sm.dot gem 3.1.1

If the right information about the directories of GEM codes and physics package is given,

the environment of running GEM has been correctly set up. By using the étagère command

ouv_exp with the directories of GEM version 3.1.1 and physics package version 4.0, we can

generate a new experiment. The next step is to copy all the source codes for air quality modules

to the directory of newly opened experiment. It is necessary to create a Makefile by performing

r.make_exp command for use by compiler to compile source codes together with physics library.

Then we need to use linkit script to create links between current directory and the path of storage

of model output. Making GEM-AQ executables should be conducted to generate two executables,

one for pre-processor and the other for running the model. Before running GEM-AQ model,

configuration has to be made by editing a configuration file gem_setting.nml and an output file

outcfg.out. The horizontal resolution, for example, can be changed in the configuration file. The

physical and chemical properties to be output and the frequency of output can be configured in

outcfg.out. Executing pre-processor, main model and post-processor executables in sequence

completes the running of GEM-AQ model.

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Appendix C: Obtaining aerosol data from GEM-AQ output

Daily output data including global aerosol mass concentration, gaseous species and

meteorological information are stored in RPN standard file format. As aerosol data are contained

separately in 366 RPN standard files, extracting aerosol data into a single ASCII file for each

component in certain grid point (correspond to the site of measurement) becomes necessary.

The RPN standard file format was created in the late 1970’s to store 3D meteorological data

(now extended to include chemical data) with internal structure or relation. Each record in a

standard file has several non-ambiguous identifiers such as name, data type, vertical level and

time, which permit performing data queries with multiple conditions. Standard files were

designed to be accessed either sequentially or directly, the later approach allowing fast locating

and retrieval of queried data.

The RPN standard file format provides a programming interface (librmn.a) available for

high level programming in language C and FORTRAN. A FORTRAN program has been created

to open each RPN standard file in sequence, to perform a set of data queries to locate and

retrieve aerosol data of some chemical component for a certain grid point, and to store them in a

new ASCII file.

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Appendix D: Modified Dorling’s clustering algorithm

In Dorling’s algorithm, much of the computational effort is devoted to the repeated updating

of cluster mean trajectories. In order to assign one trajectory to its closest cluster, for example,

the Euclidean distances between a specific trajectory and all cluster mean trajectories have to be

computed and compared. The shortest Euclidean distance defines the most representative cluster.

Then the cluster mean trajectory of any newly updated group needs to be calculated and updated

again. This process involves the transformation of spherical coordinates into Cartesian

coordinates and tends to be inefficient. More importantly, the mean trajectory calculated from a

group of complex and slow-moving trajectories appears less representative, and can be

misleading. Thus using such slow-moving centroids in determining which cluster a trajectory

belongs to (as Dorling’s algorithm described) can affect the accuracy of clustering results and/or

increase the computational coast. Here, a new criterion of assigning trajectory to its closest

cluster is proposed. In the new clustering algorithm, the distance between a trajectory and a

cluster is computed as the average Euclidean distance between the trajectory to be assigned and

all the trajectories in that cluster. Thus, the improper use of a slow-moving centroid in

representing the center of a group of trajectories is avoided. Furthermore, the computational

overhead due to iterative computing of the cluster-mean trajectories and evaluating trajectory-to-

cluster distances is overcome. The new clustering algorithm based on Dorling’s algorithm is

detailed as follows:

1. Calculate the Euclidean distance between every possible pair of trajectories.

2. Start from single-member clusters.

3. Assign each of the real trajectories to the cluster that is closest in terms of the average distance

as previously defined. Update the total root mean squire deviation (RMSD). If RMSD decreases,

the assignment is accepted; if not, then the assignment is rejected.

4. Repeat step 3 until all real trajectories are correctly assigned and no more assignment is

required.

5. Calculate the final RMSD value with respect to the current cluster numbers.

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6. Find and merge those two closest clusters. Recalculate the RMSD value.

7. Repeat steps 3 to 6 until a single cluster containing all real trajectories forms.

The number of transport patterns best represent the types of distinct pathways were defined

by the clustering algorithm itself. As previously described, the total RMSD was calculated by

step 4 in the new algorithm. At the end of clustering, the percentage change in this value can be

plotted with respect to cluster number. Substantial change in such a plot indicates the merging of

clusters of trajectories that are significantly different in terms of the wind directions and speeds.

In this study, the change of 1.5% was assumed to be significant. So the cluster number before the

unacceptable merging of trajectories is determined as the optimal number of clusters.

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