Atmospheric Science Research Progress [1 ed.] 9781608766383, 9781604564396

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Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

ATMOSPHERIC SCIENCE RESEARCH PROGRESS

Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

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ATMOSPHERIC SCIENCE RESEARCH PROGRESS

Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

CHIH-HAO YANG EDITOR

Nova Science Publishers, Inc. New York

Copyright © 2009 by Nova Science Publishers, Inc.

All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter cover herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal, medical or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS.

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Library of Congress Cataloging-in-Publication Data Atmospheric science research progress / Chih-Hao Yang, editor. p. cm. ISBN 978-1-60876-638-3 (E-Book) 1. Atmosphere--Research. 2. Meteorology--Research. I. Yang, Chih-Hao. QC869.A86 2008 551.5--dc22 2008010235

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New York

CONTENTS Preface

vii

Short Communication An Overview of Cloud-to-Ground Lightning Research in Brazil in the Last Two Decades O. Pinto Chapter 1

Liquid-State Water Bimorphism in Cold Atmospheric Clouds Anatoly N. Nevzorov

Chapter 2

Urban and Industrial Influence on Rainfall in Southern and Western Australia E. Keith Bigg

59

Rate Constants of the Gas-Phase Reaction of Ozone with Organosulfides at Room Temperature Maofa Ge, Lin Du and Kun Wang

81

Global Atmospheric Changes from Aerosol Emissions: Why Is West Africa So Important? Okey. K. Nwofor

89

Chapter 3

Chapter 4

Chapter 5 Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

1

Chapter 6

Chapter 7

Decadal-to-Centennial Scale Climate-Carbon Cycle Interactions from Global Climate Models Simulations Forced by Anthropogenic Emissions Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko

15

105

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study in Center Taiwan Guor-Cheng Fang

131

Physical and Optical Properties of Columnar Aerosols: A Global Comparison from AERONET Observations Xingna Yu, Tiantao Cheng , Jianmin Chen and Yongfu Xu

167

vi Chapter 8

Chapter 9

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Index

Contents Variations in Aerosols and Greenhouse Gases in a Tropical Urban Environment, South India K. V. S. Badarinath, Shailesh Kumar Kharol, V. Krishna Prasad and K. Madhavi Latha Rainfall Initialization: Past, Present, and Future Lei-Ming Ma

183

209 219

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PREFACE Atmospheric science is an umbrella term for the study of the atmosphere, its processes, the effects other systems have on the atmosphere, and the effects of the atmosphere on these other systems. Meteorology includes atmospheric chemistry and atmospheric physics with a major focus on weather forecasting. Climatology is the study of atmospheric changes (both long and short-term) that define average climates and their change over time, due to both natural climate variability and anthropogenic climate variability. Atmospheric science has been extended to the field of planetary science and the study of the atmospheres of the planets of the solar system. This new book presents the latest research in the field from around the globe. Chapter 1 – In this chapter, the results of recent research of the properties and physicochemical nature of liquid water contained in droplets of subzero temperature clouds are stated. The study was initiated by hardly explainable anomalies connected with the liquid disperse phase in phase-mixed clouds. The work is based on the analysis of various experimental materials including author's unique data on the phase-disperse composition of cold atmospheric clouds, interpreted from fundamentals of the structure physical chemistry. It was found that the liquid disperse phase is a stable component of ice-containing clouds, which essentially differs in most properties from ordinary water constituting purely liquidwater clouds. Namely, it steadily remains at temperatures below – 40oC and is in condensation equilibrium with ice; its droplets consisting of H2O substance have a density as high as 2.1 g⋅cm–3, and so on. The typical presence of this water form in cold clouds is confirmed by the natural glory phenomenon. It is thoroughly proven that this specific form named A-water has a non-hydrogen-bonded intermolecular structure and belongs to the amorphous water heretofore known only as a laboratory low-temperature solid condensate, being its melt. The field measurements have not only discovered the natural existence of the amorphous water, but also specified its most important properties, inaccessible in laboratory conditions. As a part of the consideration of the nature of both metastable forms, supercooled ordinary liquid water and A-water, some important peculiarities of their internal freezing process are deduced based on their internal structure affecting in-cloud microphysical processes. The origin of both water forms and subsequent transformation of phase-disperse types of cold clouds are discussed. The conceptions evolved provide a comprehensive explanation of every obscure peculiarity of cold cloud composition, evolution processes and accompanying phenomena. Chapter 2 – Influences on precipitation of the particles on which cloud drops or ice crystals form are discussed. Emitted particles combined with oxidation of sulfur dioxide gas

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viii

Chih-Hao Yang

produced in large quantities by sulfide ore processing and coal-burning power stations are shown to lead to large increases in the former. A potential feedback process between rainfall and ice-forming particles is described which aids self-perpetuation of either wet or dry periods arising from other causes. Trends in precipitation over the 35 years from 1970-2004 have been examined for 2890 rainfall recording sites in about half of Australia. The aim was to see if any changes in rainfall could be attributed to urban or industrial sources of particulates and sulfur dioxide within broader scale changes of a meteorological origin. The spatial relation of large decreasing trends to known sources in the eastern states suggests that pollution may be partly or even wholly responsible. The widespread nature of the decreases is interpreted in terms of air trajectories from sources that result in a time-varying pool of high concentrations of particles inhibiting rain formation through coalescence. In Western Australia, the most conspicuous trends are in the inland and their extent suggests that the basic cause is a change in the atmospheric circulation in the tropics. However, trends there are largest in the vicinity of large iron ore mining operations and it is suggested that additional ice-forming particles in deep convective clouds may have increased the effects of a more widespread phenomenon. It is concluded that extensive aerosol cloud physics observations should be undertaken in all the affected regions to establish the extent to which rainfall changes are due to urban and industrial emissions and to reveal any remedial measures that could be applied. Chapter 3 – The atmospheric sulfur cycle has been the subject of intensive investigation for several decades because of the need to assess the contribution of anthropogenically produced sulfur to such problems as acid rain, visibility reduction, and climate modification. The atmospheric chemistry of sulfur-containing compounds is directly relevant to the formation of sulfur aerosol in marine air. Reduced organic sulfur compounds have been estimated to account for approximately 25% of the total global gaseous sulfur budget. Besides the predominant CH3SCH3 (dimethyl sulfide, DMS), other reduced sulfur compounds should also be estimated, such as C2H5SCH3 (ethylmethyl sulfide, EMS), n-C3H7SCH3 (npropylmethyl sulfide, PMS), and C2H5SC2H5 (diethyl sulfide, DES), of which there are only a few kinetics investigations. Based on our previous work of DMS and DES, we have measured the rate constants of the gas-phase reactions of ozone with EMS and PMS at room temperature in our self-made smog chamber. Experiments were conducted under supposedly pseudo-first-order decay conditions, keeping [sulfide]0 > 50[O3]0, but having different combinations of [sulfide]0 and [O3]0. Cyclohexane was added into the reactor to eliminate the effect of OH radicals. The rate constants of the gas-phase reactions of ozone with EMS and PMS were determined to be (1.12±0.18)×10-19 and (1.24±0.15)×10-19 cm-3 molecule-1 s-1, respectively. The authors’ results will enrich the kinetics data of atmospheric chemistry, and provide some useful information for evaluating the loss processes of reduced organic sulfur compounds. Chapter 4 – Changes in the global atmosphere have become highly pronounced since the last two decades or so. These changes are mostly precipitated by variations in the concentration of particulate and chemical species in the earth’s atmosphere at different time and space scales. In this paper, the West African region is considered as a major player in the processes that lead to the most significant changes noticed in the global weather and climate system expecially with regard to aerosol emissions. In this region there is a complex interaction between ecosystem processes, human factors arising from the region’s present stage of socio-economic development and a pre-existing and obviously complicated and

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Preface

ix

highly variable weather system giving rise to what may possibly be the world’s most significant aerosol region. Studying the present and unfolding aerosol emission scenarios is a key step towards understanding climate variability in West Africa. Such studies and others have been the preoccupation of several international collaborations. These efforts need to be stepped-up expecially with regard to imputes from local scientists and personnel. Chapter 5 – Simulations of the climate-carbon cycle interaction are discussed incomparison with observationally-based estimates for the global carbon cycle characteristics. Since the beginning of the industrial era, the storage of the carbon dioxide in the atmosphere is smaller than thecorresponding anthropogenic emissions. This is due to uptake of the atmospheric carbon dioxide to the terrestrial biota and ocean. Moreover, during the the 20th century, the sink of the carbon dioxide from the atmosphere to the terrestrial ecosystems became larger, due to CO2 fertilisation effect. However, in the 21st century, the global climate models with the carbon cycle project that interactions between climate and carbon cycle basically lead to the stronger growth of the carbon dioxide burden in the atmosphere. These interactions were in the focus of the Coupled Climate-Carbon Cycle Model Intercomparison Project. One of the basic outcome of this and related activities is positive climate--carbon cycle feedback leading to enhanced buildup of CO2 in the atmosphere due to response of climate and corresponding changes in the terrestrial and oceanic uptakes of carbon. In particular, the soil respiration growth overcompensates the increase of the net primary production and leads to the diminishing sink of the carbon dioxide from the atmosphere top the living biota and soil. In some models and emission scenarios the terrestrial biota even eventually becomes the source of the carbon dioxide. The parameter of the feedback between climate and carbon cycle changes non-monotonically in the 20th and 21st centuries, depicting characteristic periods of the emission growth and the respective climate response. On a century timescale, climate--carbon cycle feedback may saturate. The 20th century observations of the carbon cycle are insufficient to constrain future evolution of the coupled carbon--cycle system. Nevertheless, they are able to narrow the respective uncertainty range. This can be illustrated, in particular, by employing the Bayesian statistical treatment of the respective ensemble. However, for the most probable values of the governing parameters of the coupled system, climate--carbon cycle interactions enhance global warming in the 21st century by about 10% or even more under the SRES marker emission scenarios. Chapter 6 – Particulate matter (PM) is a major factor which affects the air quality of ambient environment in Taiwan region. The consecutive study of ambient air pollutants for different particulate seizes and their chemical compositions were conducted. Several character sampling sites were selected in this study. They are Taichung Harbor (TH) and WuChi traffic (WT) and Taichung airport (TA) sampling sites. As for TH and WT sampling sites, measured the concentrations of PM2.5 (PM with aerodynamic diameter −55 С о

Т ≈ −170 С Т = −30оС Т = −30оС Т = −30оС – Т < 0оС

Notes By [39] By [39]

10 ÷ 10 P

1

2,32 ± 0,17 g⋅cm−3 2,12 ± 0,15 g⋅cm−3 550 J⋅g−1 ± 20% 2290 J⋅g−1 ± 5% 1,81÷1,82 As for ice I

By [6]

−2

−1

2,3 2 2,4 2,5 2

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Notes: 1 Extrapolation of the experimental data of [39]. 2 The data obtained for the liquid-droplet fraction in ICCs [22,23]. 3 The value is derived from the refractive index by the Lorenz-Lorentz formula [8]. 4 The difference between evaporation heats of ice I and A-water. 5 The value for yellow light, determined from the glory angular size.

The most crucial feature to specify the role of A-water in the cloud ice genesis is its above-stated condensation equilibrium with ordinary crystalline ice. It is natural that this equilibrium should mean the identity in internal structures and consequently in phase states of experimentally detected "quasiliquid" layer enveloping the ice particle surface [12] and free (droplet-formed) A-water. Thereby the Fletcher's assumption [9] about the non-hydrogenbonded (amorphous) molecular structure of the said ice surface layer gains definite confirmation. In accordance with [9], the existence of this layer is a result of hydrogen bond breaking at the boundary of the proper ice structure where unused HBs induce a surface electric charge that attracts free polar H2O molecules. These molecules are concentrated as a film of the amorphous condensate and are oriented so that their total electric field would neutralize the field of the ice surface charge. At the same time, this "quasiliquid" film actually exhibits the properties of liquid [12]. Its thickness increase with the temperature compensates for violation of the ordered orientation of molecules due to their thermal motion. In general, the ice surface layer of the amorphous water forms an energetically and structurally optimum intermediate medium for mass exchange of ice with ambient vapor. Its consisting of A-water implies that this is the amorphous water rather than water-1 by [34] which constitutes a substance of an intermediate phase jump in vapor-to-ice transformation according to Ostwald's rule of step transition. By [41], the Ostwald's rule establishes that in any irreversible process, the state initially arises which is not the most stable and of the least

46

Anatoly N. Nevzorov

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free energy but the least stable and the most close to the initial state in free energy. In the given case, the initial and final water states are water vapor and crystalline ice, respectively. The independent existence of free A-water in the form of cloud droplets proves the capability of the Ostwald intermediate phase to remain indefinably long in metastable state. Only a droplet with the heterogeneous crystallization center within, either embedded in condensation nucleus or falling thereinto from outside, can transform into ice. Therefore, the stable existence of free A-water just as of water-1 occurs due to the incomparably smaller probability of a crystallization center presence in a microscopic condensation nucleus of each droplet than in a real substrate or vessel. It is significant that in contrast to water-1, with temperature lowering the probability of amorphous water freezing decreases [39]. Considered here were the A-water properties at negative temperatures. At positive temperatures, the partial pressure of saturated vapor over A-water lies above that over water-1 in conformity with the smooth extrapolation of the temperature dependence for ice [17]. This implies that at Т > 0оС the equilibrium existence of A-water droplets in atmospheric air containing water-1 condensation nuclei becomes impossible. At the same time, A-water is capable to stably exist at positive temperatures with no contact with air which can be easily ascertained by everybody. The suspension of A-water particles in water-1 forms as a result of ice melting and can be observed in a transparent (glass) vessel under strong lateral illumination. Their visibility to the naked eye is due to the difference of refractive indices, and evidences mutual insolubility of the two liquid forms of the same chemical substance. During the ice melting process, one can see a transparent film pieces exfoliating from ice and breaking into smaller fragments (Figure 15).

Figure 15. The suspended insoluble admixture of A-water in ice-melt water-1, observed under bright lateral illumination. The shape of admixture particles strongly differs from spherical; in all other respects, they behave just as consisting of a liquid heavier than water-1.

Liquid-State Water Bimorphism in Cold Atmospheric Clouds

47

The fact that the shape of A-water particles suspended in water-1 is very far from spherical can be presumably explained by the absence of the surface tension in the interphase barrier. Another peculiarity, their size dependence on mineralization of frozen water, is very likely the result of influence of electro-conductivity of A-water solution on the thickness of the ice surface layer. Large enough particles of A-water deposit with a noticeable velocity, thus demonstrating that their density appreciably exceeds the water-1 density; some of them can coalesce with others, thereby obviously revealing their liquid state. At the vessel bottom, they coalesce into a liquid layer capable of transforming again into a disperse admixture when the vessel contents mixed.

5. PECULIARITIES OF TWO-PHASE MICROSTRUCTURE OF ICE-CONTAINING CLOUDS The expansion of our knowledge of the variety, nature and properties of water phases in cold atmospheric clouds has considerably approached us to more adequate treatment of experimental data on their microphysical structure and to deeper understanding of processes therein proceeding.

5.1. Methodical Remarks In the section 3.2 devoted to ICC microstructure, we intentionally desisted from quantitative estimations of its parameters in view of further correction of measurements for actual properties of the liquid disperse phase consisting of A-water. Only two CMIS devices, LPS and CEP, both based on the effect of light beam attenuation by particles, are not sensitive to optical properties of cloud droplets. To estimate the true LWC value, WА, it is enough to multiply Wliq received from (3.6) by the ratio of the averaged evaporation heat of water-1 to that of A-water (see Section 3.3.1), i.e. Wliq,А = 4,7Wliq.

(5.1)

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Then TWC corrected for A-water content (AWC) will be correspondingly Wtot,A = Wliq,A + Wice.

(5.2)

The PPSA thresholds in terms of A-water droplet size, corresponding to the signal thresholds established for water-1 in accordance with Table 3.1, are listed in Table 5.1. Table 5.1. Particle size of PPSA counting channels corrected for A-water Сhannel number, i A-water droplet diameter, di (μm) Crystal effective dia., аi (μm)

1 12 20

2 19 33

3 31 53

4 46 80

5 69 120

р 40 ÷ 45 ~20

48

Anatoly N. Nevzorov

As for the rest, in the treatment of measurement data we used the same analytical means as stated in section 3. Taking into account that the main physical parameter influencing on the phase evolution of a water disperse system is its temperature, quantitative characteristics of cloud phase composition were determined against in situ temperature. For the complex data analysis, the cloud crossings with all CMIS devices operated were if necessary divided into the legs of ~5 to ~150 km length so that each leg is practically uniform in flight conditions and cloud parameter records. Such record sample was referred to a measurement case, and the data recorded were averaged over this for further use.

5.2. Types and Characteristics of Phase-Disperse Composition of Cold Clouds On the background of significant variability of values and proportions of the measured parameters of phase-mixed clouds, it is possible to classify them by three distinctly differing types of their phase-disperse structure [25,26,32]. Let us as before designate them as M1, M2 and M3 structure types differing as follows: −

−

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−

M1 type is for the "latent mixed" (as called in Section 3.2) clouds or cloud zones, where no ice crystal exceeding 20 μm in measured size were detected by PPSA, though the ice content Wice by the LWC–TWC probe was reliably above zero. The number concentrations of ice crystals can be indirectly estimated to come to 20 cm–3 or greater depending on their true sizes. No more definite information could be extracted here by our instrumental means but for the droplet effective diameter being typically 5 to 30 μm as in common liquid-water (warm) clouds in accordance with various numerous data; M2 type is for those opaque mixed clouds where the largest ice crystals exceeded 200 μm in size, and the extinction factor Е was contributed mainly by particles which sizes were beneath the PPSA measurement range by Table 5.1; M3 type is for the mixed clouds containing ice particles > 200 μm as in M2 type but whose extinction factor was noticeably less and mainly contributed by bigger particles of the PPSA or LPS measurement range.

The M2 and M3 cloud types are those which were united above under the term "icecontaining clouds (ICC)". It can be noticed that the listed cloud types replace by themselves the clouds commonly accepted as purely water, phase-mixed and purely ice, respectively. Their parametric difference is so explicit that in most cases they can be directly distinguished in visual records without their preliminary processing. It is characteristic that the intermediate structure between M1 and M2, wherein the largest crystals were between 20 and 200 μm, occurred within comparatively negligible time intervals. Also this to some extent concerns indefinites between M2 and M3 structures often alternating within the same cloud. Such peculiarity serves as certain sign of galloping transient processes from M1 to M2 and from M2 to M3 to be considered below.

Liquid-State Water Bimorphism in Cold Atmospheric Clouds

49

Table 5.2. Some averaged microphysical parameters of M2 and M3 type clouds Temperature interval (oC) Number of cases A-water content (AWC) (g⋅m−3) Ice water content (IWC) (g⋅m−3) Ratio AWC/TWC × 100% A-water droplet >12 μm concentration (l–1) Ice crystal >20 μm concentration (l−1)

M2 M3 M2 M3 M2 M3 M2 M3 M2 M3 M2 M3

−5...−15 55 5 0.42 0.19 0.031 0.17 93 53 203 435 45 192

−15...−25 11 20 0.29 0.18 0.024 0.12 92 61 162 248 63 196

−25...−35 10 58 0.060 0.088 0.006 0.022 91 80 193 231 127 202

−35...−45 – 38 – 0.050 – 0.012 – 81 – 299 – 259

−45...−55 – 19 – 0.028 – 0.007 – 80 – 582 – 380

The relative occurrences of all structure types are demonstrated in Figure 16 similar to Figure 1 against the local temperature. Also included here are clouds (zones) where no ice and no liquid were detected but their true phase affiliation remains questionable because of instrumental and methodical limitations.

Percent of occurence

100

Ice

80 60

M3

40

M2

20

M1 L

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0 -55

-45

-35

-25

-15

-5

Тemperature [оC] Figure 16. The smoothed temperature diagram of relative occurrence (between the curves) of types of phase-disperse structures of cold clouds. Here "Ice" and "L" signify the situations where no liquid and ice, respectively, were detected.

The summarized results of microphysical measurements in stratiform clouds of M2 and M3 types, corrected for the physical properties of A-water as in Section 5.1, are presented in Table 5.2 [26] and in Figures 17 and 18. All presented parameters exhibit a very wide scatter from cloud to cloud and inside the same cloud, whence the statistical uncertainty of the averages is a real factor.

50

Anatoly N. Nevzorov Water content (g.cm-3) 1 M2

0,1 M3

M2

M3

0,01

0,001 -50

-40

-30

-20

-10 0 Temperature (oC)

Figure 17. Temperature dependence of averaged phase components in clouds of M2 and M3 structural types. Solid lines are for AWC, dotted ones for IWC.

Average concentration [l-1]

1000 800

Droplets >12 um 5

600 400

19

Type M3

Crystals >20 um

Type M2 5

38 20

58

-20

-30

10 55

11

-10

-20

200 0

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-2,5

-30

-10

-40

-50

Mean of temperature interval [oC] Figure 18. Averaged concentrations of ice crystals with the sizes a> 20 microns and А-water droplets with d> 12 microns in clouds of M2 and M3 structural types at various temperatures. The figures over each bar are the numbers of the investigated clouds.

Nevertheless, as seen from the Table 5.2, the distinction between the selected cloud structure types M2 and M3 is pronounced enough in almost all averaged data, and most of all in ice particle concentration and IWC. The averaged number concentrations of A-water droplets exceeding 12 μm in diameter exhibit markedly less difference between both cloud types than ice particle concentrations. The concentrations of both kinds of particles with similar threshold sizes are of the same order in magnitude and little, if at all, depend on

Liquid-State Water Bimorphism in Cold Atmospheric Clouds

51

temperature. The share of A-water in the total condensed water content (TWC) is surprisingly high especially in M2 type clouds, and somewhat increases as temperature lowers in M3 type. It can be supposed that the last dependence results from the abovementioned decrease of Awater freezing probability at lower temperatures. As expected, the most distinct difference between M2 and M3 structures lies in droplet sizes. Figure 19 presents the cumulative probability functions of the lower bound of the droplet effective diameter, obtained by (3.10) in all clouds of M2 and M3 types separately. As follows from Table 5.1, M2 clouds consist in mass mainly of A-water, so that the lower bound of deff is close enough to its true value. Then the droplets responsible for most AWC varied herein between ~5 and ~70 μm in size, with a droplet majority lying beneath the PPSA range. In turn, as Figure 19 discloses, in M3 clouds most AWC contribution was always from droplets larger than 10 – 15 μm and exceeding 120 μm in more than 40% of cases. In Figure 20 droplet size spectra in the form of histograms of distribution density are shown. They have been constructed like described in Section 3.1.4 and shown in Figure 3, from the integration of individual spectra for the given structure type at the given temperature interval. It is indicative that the distribution modes in M2 type clouds lie to the left of 12 μm, whereas in M3 clouds they equally settle between 20 and 30 μm independently of temperature. Reasoning from the data of Figure 19, the right wings of the M3 cloud spectra were determined with some underestimation. It is essential that the "averaged" droplet spectra in Figure 20 conceal specific features of individual (local) spectra being of various widths and often bimodal. As to crystals size spectra, at a > 200 μm they are known to be close to exponent [16,17], and this dependence is assumed to keep down to the smallest sizes. Cumulative occurrence 100 80 M3

60

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40 M2 20 0 0

20

40

60

80

100

120

Minimum estimation for the effective diameter (mm) Figure 19. Cumulative occurence of the cases with the droplet effective diameters exceeding the abscissa values. The bottom and top curves are for structures M2 and M3, respectively.

52

Anatoly N. Nevzorov -1

-

Concentration density (l μm 16 12-19

14

M3

19-31

12

31-46

10

46-69

8 M2

6 4 2 0 -5 -15

-15 -25

-25 -35

-5 -15

-15 -25

-25 -35

-35 -55

o

Temperature interval ( C)

Figure 20. Size spectra of A-water droplets in ICC of M2 and M3 types at different temperatures, constructed of spectra united within each temperature interval. The numbers of cases are as given in Figure 18.

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5.3. Origin of Water Condensed Phases in Atmosphere Like supercooled water-1, A-water can originate at subzero temperatures only directly from the vapor condensation. Therefore, the permanent coexistence of free A-water with cloud ice implies that the processes of condensation and partial crystallization of A-water disperse phase necessarily play the significant role in formation of cloud phase composition. Moreover, the high and temperature-independent concentrations of ice crystals, close to those of A-water droplets (Table 5.1) and well exceeding those of the known water freezing nuclei (FN), give evidence that these processes are dominant in ICC formation, and that the primary nucleation of A-water occurs on alternative specific nuclei. The question arises about the nature of these A-water condensation nuclei (AWN) as well as about their origination. It is known that abundant atmospheric layers exist being free of cloud in spite of ice supersaturation. For simplest example, these are the layers beneath the bases of clouds of supercooled water-1. This implies that no active AWN are as a rule present in dry air. A real and most likely the only mechanism of ACN natural initiation was indirectly pointed to by Rosinski et al. [36] who have found that a supercooled water-1 droplet being evaporated can be immediately replaced by the newly formed ice crystal. In fact, as follows from all foregoing, the dehydrated residuals of water-1 droplets become primarily catalytic centers of condensation of A-water, and only a definite part of them containing ice-forming centers produce their crystallization. Such secondarily formed AWN are capable to be collectively generated within a supercooled water cloud, when relative humidity lowers to a degree sufficient for the smallest droplets to evaporate. Condition for this can occur near the cloud edges, or due to dry air entrainment into a cloud, or as the final of the cycle of wetting

Liquid-State Water Bimorphism in Cold Atmospheric Clouds

53

of non-hygroscopic nuclei at initially high vapor supersaturation, etc. In any case, a supercooled water-1 cloud is bound to acquire the "latent-mixed" structure M1 carrying Awater droplets and ice crystals most probable from the very beginning of its lifetime. The superhigh, as compared with the known FN, concentrations of ice particles in M1 type clouds [31] evidence that the ice forming mechanism via the evaporation of water-1 droplets is many orders more productive than the direct freezing of these droplets. Thus the formation of the disperse phases of atmospheric water begins from the vapor condensation into water-1 droplets. Then the following phase transformation processes occur in parallel: (i) the freezing of water-1 droplets with forming crystalline ice particles; (ii) the evaporation of water-1 droplets with forming A-water droplets; (iii) the freezing of A-water droplets with forming crystalline ice particles. The remaining liquid droplets of water-1 and A-water constitute the metastable liquid disperse phase where the water-1 fraction is devoid of condensation stability. Judging from the fact that the number concentrations of cloud ice crystals undergo no noticeable change around – 39oC, we postulate that at temperatures below – 39oC all phase forming processes are the same as just stated. Here water-1 serves as a momentary intermediate phase and exists until being warmed up to a suitable temperature by the heat released at its condensation.

5.4. Phase-Disperse Evolution of Cold Clouds Let us consider a developing stratiform cloud of M1 type. In spite of high ice supersaturation in the presence of water-1 droplets, the A-water and ice particles nucleated as before are initially growing extremely slowly in the molecular-diffusion growth mode. This mode keeps so long as the particles are small enough to be immobile relative to surrounding undisturbed air. There are two factors capable of hurrying mass exchange processes in a "latent-mixed" cloud [28]:

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−

−

The high-speed vapor emission from a droplet in process of its freezing, as described in Section 4.5. This phenomenon causes a temporary microscale air disturbance around the freezing droplet, which provokes the growth acceleration of particles falling into its disturbance zone. With the cloud droplets growing, their freezing becomes more frequent and disturbance zones therewith excited broaden out, that in turn increase the concentration of particles whose growth is accelerated as compared with the "quiet air" mode; The gravitational sedimentation of large enough particles. Their own growth is quickened by the air blowing effect; besides, their falling excites the microturbulence trail affecting other particles. With some part of particles sufficiently enlarging, this process becomes principal.

The combined action of the both processes leads to the acceleration of the rate of Bergeron – Findeisen re-condensation. This results in nonreversible enlarging of both ice particles and A-water droplets (until being broken) as well as in that the water-1 droplets first slow down in growth and then diminish in size by evaporation. When a sufficient number

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concentration of particles achieve critical sizes of order of 20 – 30 μm, this process proceeds to its avalanche-like stage that culminates in complete evaporation of water-1 droplets. Thus the "latent-mixed" M1 type cloud transforms into the manifest phase-mixed, or conventional ice-containing cloud (ICC) where the liquid disperse phase is substituted by A-water. A minute duration of the transient stage is expressed in its negligible occurrence as stated above. A cloud thereby formed may be considered, depending on the physical application involved in, as either a condensation-stable biphase cloud or a "quasi-ice" cloud wherein a part of potential ice remains in the form of a metastable transient substance. The two-phase microstructure of ICC evolves under the combined effect of a variety of factors such as: (i) a disbalance in the vapor saturation relative to ice, caused by air motions, air temperature trends, etc., (ii) a direct dependence of concentration of active ACN on vapor supersaturation, (iii) an inverse dependence of saturated vapor pressure on particle size, (iv) low condensation enthalpy of A-water, responsible for its smallest thermal resistance to both condensation and evaporation processes, and so on. The last factor suggests that the liquid A-water fraction serves as the most fast-acting regulator of the relative humidity in clouds, being the most sensitive in microstructure to its variations as observed in reality [32]. All the listed factors together provide quite certain explanation for the distinction between M2 and M3 cloud structure types. The M2 structure is formed under as high ice supersaturation of vapor as being sufficient for the activation a portion of poorly active condensation nuclei to produce the distinctive fraction of fine-sized particles. Subsequent vapor condensation occurs preferably on bigger particles; resulting fall of ice supersaturation causes the evaporation of the smallest particles, why the lower limit of droplet sizes increases. The further stage of M2 to M3 transition develops as a self-accelerated process, since with cloud particles enlarging, the saturation vapor pressure relatively to them diminishes and therefore the actual pressure increases in supersaturation. The cloud in that way transforms into the conceptually stable structure of M3 type wherein all droplets are large enough to provide, under given thermo-dynamical conditions, the best approach to equilibrium between the three phases including vapor. Generally, the M2 structure is primary in relation to M3, which is proved by lower average concentration of relatively large particles in M2 (Table 5.2, Figure 18). At adiabatic downturn of relative supersaturation (e.g. in descending air) the M2 to M3 transition is accelerated. When the humidity becomes below ice saturated at the given temperature, the cloud dissipates with quicker evaporation of А-water droplets beginning from the smallest ones. The instrumental indication Wliq = 0 referred to purely or almost ice composition of a cloud can serve as the indicator of the late stage of its dissipation. The described transformations are not in principle irreversible, i.e. do not exclude a possibility of return transitions between those structures most possible under updraft turbulent movements. The in-cloud mesoscale turbulence obviously promotes the intermittency of the structure types inside the same cloud. The most striking example of this phenomenon is represented by fiber-like visual structure of cirrus clouds of certain forms. For a long time, those fibers were explained in terms of fall-streaks of large ice crystals (virga). However, our measurements have shown that the particles larger than 200 μm make the least contribution to optical density of all ICC including cirrus clouds; on the contrary, the sharply prevailing contribution to the optical density of the opaque fibers is from the left wings of particle size

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spectra involving A-water and ice particles smaller than 12 and 20 μm in diameter, respectively. That is, the opaque fibers are zones of M2 structure, whereas the semitransparent background is of M3 type. Reasoning from all above, the streaks are formed in ascending air currents where the relative humidity achieved sufficient supersaturation to activate new portion of AWN for A-water nucleation. Thus the variously intricate pictures of cirrus clouds follow the air turbulence updrafts depicted by M2 type cloud streaks.

5.5. Explanation of Some Phenomena Connected to A-Water In the course of the investigation here performed, the explanations of "abnormal" features of cold clouds, as to how they seem from the current knowledge (Sections 2 and 3.2), have been offered. It was proved that all that features are related to in-cloud A-water. In this connection, the riddles of the celestial glory and melt water have been disclosed. The list of strange phenomena exhaustively explainable within the conception of A-water can be prolonged by phenomena routinely met. −

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That the solid ice is very slippery is accounted for by some authors in terms of a thinnest liquid film covering its surface. We join this explanation but being competent to add that this film consists of A-water. The liquid winter precipitations mostly mixed with ice particles, named snow with rain, freezing drizzle, etc. [2], contain liquid drops which in fact consist of A-water and are formed in clouds having totally negative temperatures, in defiance of the popular inverse opinion. Their deposition onto a surface cooled below 0оС causes the freezing of A-water just to the same ice as if this were ordinary water-1. Such precipitation can deposite on the ground at temperatures down to –60oC [2]. Often observable "explosive" effect of the glaciation of the top of a thick convective cloud (Cu cong) can be explained by the evaporation of its outer layer when contacting with wet underestimated air at T < 0oC. The supercooled water-1 droplets evaporated are instantly substituted by А-water droplets partly frozen as described earlier. In thereby perturbed air ice crystals very quickly grow. The fact that the specific heat of А-water evaporation is approximately five times less than that of ordinary water, leads to the corresponding underestimation of standard "hot wire" measurements of liquid water content in mixed clouds. With the permanent presence of A-water in cold, even in seemingly ice clouds, its total mass in atmospheric clouds turns out to be many times that assumed by the existing concepts for the ordinary water. Then, it follows from Table 4.1 that when freezing, A-water evolves about seven times as much energy as the same mass of ordinary water-1. Thus there is every reason to suggest that the mass freezing of cloud Awater is quite possible source of almost two orders greater, than commonly expected, energy of destructive processes associated with clouds, from local tornados to globalscale hurricanes.

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CONCLUSION The fundamental result obtained in this Chapter consists in the substantiation and development of essentially new concepts in the physics of subzero temperature clouds, or cold clouds (CC). The study was initiated by significant disagreements between field observations and aprioristic concepts in CC physics, and was conducted on basis of various experimental materials, first of all on our unique data on the phase-disperse structure of CC. These data not only supplement a series of such divergences but also contain crucial information for finding out their reasons. The physical interpretation of the data set has necessitated attracting modern knowledge on structural physical chemistry as well as on poorly studied phase states of water. As a result of the complex data analysis, the fact was established that the developed icecontaining clouds (ICC) simultaneously contain in comparable mass concentrations rather large liquid droplets consisting of a specific phase of water, А-water, distinguished in physical properties from ordinary water-1. It was found that the A-water is in condensation equilibrium with ice; keeps viability at T < –40oC; has the density ~2.1 g⋅cm–3, the refractive index ~1.8, and the evaporation or latent condensation heat ~550 J⋅g–1. This foundation met definite confirmation in adequate analysis of the natural phenomenon of on-cloud glory. The thorough comparison of A-water with the laboratory low-temperature amorphous water has shown that both belong to the same water polymorphous form, lacking of intermolecular hydrogen bonds intrinsic in crystalline ice and water-1. The existence of the amorphous water phase in the form of cloud droplets enabled us to specify and expand the list of its features now inaccessible in laboratory conditions. Some specific properties of both liquid forms of water were considered reasoning from the peculiarities of their molecular structure. The association of our results and other experimental results and conceptions, creates a basis for the development of new insight into the physical chemistry of water connected with its alternative phase states. It is proved that А-water like ordinary water-1 plays a role of a primary condensed phase in the ice origin and is able to remain in its own metastable. However, as distinct from water1, the A-water condensation nuclei are absent in dry air and arise in the atmosphere quite possibly by the only way of evaporation of water-1 droplets with immediate reactivation of their dehydrated nuclei. If a nucleus contains a crystallization center, thereon the nucleated Awater droplet freezes; otherwise it remains in a metastable liquid state. This evaporationreactivation mechanism is much more productive in cloud ice generation than the simple freezing of water-1 droplets, and thus provides the explanation of why the number concentration of cloud ice particles is extremely far from that of conventional ice-forming nuclei. The A-water properties have an impact on the features of the microphysical structure and evolution of cold clouds. On their basis, and guided by elementary physical concepts, ready explanations of every their real feature can be obtained. We consider that the most convincing confirmation of the A-water conception is in its representing a universal clue to understanding yet unsolved problems of the physics of cold clouds. These are anomalous stability of the mixed phase composition of layer-type clouds; "superhigh" concentrations of cloud ice crystals; origin of so-called supercooled rain, or "freezing drizzle", or of mixed precipitation; "quasiliquid" surface layer on ice, and other hardly explainable phenomena involved. The

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necessity disappears for a lot of speculative assumptions and the hypotheses is all dominating now in the physics of cold clouds. New results considerably change the present notions of optical, radiating, thermodynamic, chemical, and other applied properties of cold clouds as physical dispersive medium. By a rough estimate, the content of liquid А-water in the Earth atmosphere totals 1011 – 1012 tons. The presence of such plentiful free А-water in the atmosphere should draw attention to their role in accumulation, transformations and global transfer of atmospheric aerosol. It is easy to conjecture that the spherical particles discovered optically in stratospheric clouds as well as in cold cloudiness of some other planets can actually be A-water droplets.

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

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[12] [13] [14] [15] [16] [17] [18] [19] [20]

Angell C. A. Ann. Rev. Phys. Chem. 2004, 55, 559–583. Bezrukova N. A.; Jeck R. K.; Khalili M. F., et al. Atmos. Res. 2006, 82, 203–221. Borovikiv A. M. Doctoral thesis. CAO: Moscow, SU, 1969 (manuscript in Russian). Borovikiv A. M.; Gaivoronsky I. I.; Zak E. G., et al. Cloud physics. Gidrometeoizdat: Leningrad, SU, 1961 (in Russian). Cober, S. G.; Strapp J. W.; Isaac G. A. J. Appl. Meteor., 1996, 35, 2250 – 2260. Delsemme A. H.; Wenger A. Science, 1970, 167, 44–45. Deryagin B. V.; Churaev N. V. New properties of liquids. Nauka: Moscow, SU, 1971 (in Russian). Eisenberg D.; Kauzmann W. The structure and properties of water. Oxford Univ.: Oxford, GB, 1969. Fletcher, N. H. The chemical physics of ice. Cambridge Univ.: Cambridge, GB, 1970. Franks F. (ed.). Water and water solutions at temperatures below 0C. Naukova dumka: Kiev, SU, 1985 (in Russian). Hulst, van de, H. C. Light scattering by small particles. John Wiley: New York, US, 1957. Jellinek H. H. G. J. Colloid Interface Sci. 1967, 25 (2), 192–197. Korolev A. V.; Bailey M. P; Hallett J.; Isaac G. A. J. Appl. Met., 2004, 43, 612–622. Korolev A. V.; Strapp J. W.; Isaac G. A.; Nevzorov A. N. J. Atm. Oceanic Techn., 1998, 15(6), 1495–1510. Kosarev A. L.; Mazin I. P.; Nevzorov A. N.; Shugaev V.F. Optical density of clouds. Ed. Mazin I. P. Trudy CAO, 1976, issue 124, 168 pp. Kosarev A. L.; Mazin I. P.; Nevzorov A. N.; Shugaev V. F. In: Some problems of cloud physics. Gidrometeoizdat: Leningrad, SU, 1986, 160−186 (in Russian). Mazin I. P.; Khrgian A. Kh. (Eds.) Clouds and Cloudy Atmosphere. Handbook. Gidrometeoizdat: Leningrad, SU, 1989 (in Russian). Mazin, I. P.; Nevzorov A. N.; Shugaev V. F.; Korolev A. V. 11th Int. Conf. Clouds Precipitation, Montreal, Canada, 1992, 332−335. Mazin I. P., Shmeter S. M. Clouds, their structure and physics of formation. Gidrometeoizdat: Leningrad, SU, 1983 (in Russian). Mezrin, M. Yu., Mironova G. V. Trudy CAO, 1991, issue 178, 125−132 (in Russian).

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[21] Minnaert M. G. J. Light and color in nature. Nauka: Moscow, SU, 1969 (in Russian). [22] Nevzorov A. N. 11th Int. Conf. Clouds Precipitation. Montreal, Canada, 1992, 270– 273. [23] Nevzorov A. N. Meteorol. Gidrol., 1993, No. 1, 55–68 (Meteorol. Hydrol.). [24] Nevzorov A. N. 12th Int. Conf. Clouds Precipitation. Zurich, Switzerland, 1996, 124– 127. [25] Nevzorov A. N. WMO Workshop, Meas. Cloud Prop. Mexico City, Mexico, 1997, 173– 182. [26] Nevzorov A. N. 13th Int. Conf. Clouds Precipitation. Reno, Nevada, USA, 2000, 728– 731. [27] Nevzorov, A .N. Atmos. Res. 2006, 82, 367–378. [28] Nevzorov A. N. Izvestiya, Atmos. Oceanic Physics, 2006, Vol. 42, No. 6, 765–772 (Izv. Atmos. Oceanic Phys.). [29] Nevzorov A. N.; Petrov V. V.; Shugaev V. F. In: Active effects on hydrometerological processes. Gidrometeoizdat: Leningrad, SU, 1990, 571–576 (in Russian). [30] Nevzorov A. N.; Shugaev V. F. Trudy CAO, 1972, issue 101, 32−47. [31] Nevzorov A. N.; Shugaev V. F. Meteorol. Gidrol. 1992, No. 1, 84–92 (Meteorol. Hydrol.). [32] Nevzorov A. N.; Shugaev V. F. Meteorol. Gidrol. 1992, No. 8, 52–65 (Meteorol. Hydrol.). [33] Pershin S. M. Opt. Spectrosc. 2003, 95(4), 629–637. [34] Pruppacher H. R.; Klett J. D. Microphysics of clouds and precipitation. Reidel: Dordrecht, Ge, 1978. [35] Rangno A. L., Hobbs P. V. Q. J. R. Meteorol. Soc., 2005, 131, 639–673. [36] Rosinski J., Morgan G. J. Aerosol Sci. 1991, 22 (2), 123−133. [37] Sassen K., Todd J. C. J. Atm. Sci., 1988, 45, No. 8, 1357−1359. [38] Shifrin K. S. Introduction into Ocean Optics. Gidrometeoizdat: Leningrad, SU, 1983 (in Russian). [39] Skripov V. P., Koverda V. P. Spontaneous crystallization of supercooled liquids. Nauka: Moscow, SU, 1984 (in Russian). [40] Smith, R. S.; Kay B. D. Nature, 1999, 398, 788-791. [41] Urusov V. S. Theoretical crystallochemistry. Lomonosov State Univ.: Moscow, US, 1987 (in Russian). [42] Volkovitsky O. A., Pavlova L. N., Petrushin A. G. Optical properties of crystal clouds. Gidrometeoizdat: Leningrad, SU, 1984 (in Russian). [43] Zamorsky A. D. Atmospheric ice. Acad. Sci. USSR: Moscow, SU, 1955 (in Russian). [44] Zatsepina G. N. Physical properties and structure of water. Lomonosov State Univ.: Moscow, RF, 1998 (in Russian).

In: Atmospheric Science Research Progress Editor: Chih-Hao Yang

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Chapter 2

URBAN AND INDUSTRIAL INFLUENCE ON RAINFALL IN SOUTHERN AND WESTERN AUSTRALIA E. Keith Bigg∗ 12 Wills Ave. Castle Hill. NSW Australia

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ABSTRACT Influences on precipitation of the particles on which cloud drops or ice crystals form are discussed. Emitted particles combined with oxidation of sulfur dioxide gas produced in large quantities by sulfide ore processing and coal-burning power stations are shown to lead to large increases in the former. A potential feedback process between rainfall and ice-forming particles is described which aids self-perpetuation of either wet or dry periods arising from other causes. Trends in precipitation over the 35 years from 19702004 have been examined for 2890 rainfall recording sites in about half of Australia. The aim was to see if any changes in rainfall could be attributed to urban or industrial sources of particulates and sulfur dioxide within broader scale changes of a meteorological origin. The spatial relation of large decreasing trends to known sources in the eastern states suggests that pollution may be partly or even wholly responsible. The widespread nature of the decreases is interpreted in terms of air trajectories from sources that result in a time-varying pool of high concentrations of particles inhibiting rain formation through coalescence. In Western Australia, the most conspicuous trends are in the inland and their extent suggests that the basic cause is a change in the atmospheric circulation in the tropics. However, trends there are largest in the vicinity of large iron ore mining operations and it is suggested that additional ice-forming particles in deep convective clouds may have increased the effects of a more widespread phenomenon. It is concluded that extensive aerosol cloud physics observations should be undertaken in all the affected regions to establish the extent to which rainfall changes are due to urban and industrial emissions and to reveal any remedial measures that could be applied.

∗

E-mail: [email protected]

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1. INTRODUCTION Rosenfeld’s (2000) evidence from satellite pictures of the effects of urban or industrial pollution in South Australia and Victoria on cloud drop size distributions and his conclusion that this would result in a suppression of precipitation is important because of widespread and long-continuing droughts in eastern Australia However, Ayers (2005) questioned the basis of the conclusion, citing a study by Warner (1971) where pollution from sugar cane fires did not lead to a detectable reduction in rainfall in spite of an increase in cloud condensation nucleus (CCN) concentrations. The association between cloud drop size and pollution sources was questioned, while the affected clouds shown by Rosenfeld were said to be too thin to have produced significant rain. He therefore counseled caution in accepting the conclusion that rain would in general be reduced by increased concentrations of CCN. In a very comprehensive rebuttal Rosenfeld et al. (2006) showed that the cloud drop size reduction was real and was associated with specific sources. They pointed out that rainfall on a single day would provide no guide to the effects of pollution. Only the relationship between pollution sources and rainfall over a long period would be relevant. They cited numerous demonstrations of rainfall decreases associated with pollution in other parts of the world, e.g Givati and Rosenfeld, (2004), and Rosenfeld and Givati, (2006). Their conclusion was again that extended research into possible effects of aerosols on precipitation in Australia was urgently needed. There have been many studies of trends in rainfall around the world, but the emphasis here will be on trends within Australia and whether any relationship to cloud microphysical processes can be discerned. The IPCC fourth report (AR4, 2007) presents trends from 19012005 and 1979-2005 on a global scale with a 5º x 5º grid. In Australia the eastern half shows only very slight trends in either period, while there are large increases in the western half, with the exception of the southwest corner. In order to associate changes with urban or industrial emissions a much finer grid is needed, or contours of trends from individual gauges where there are few. Any cloud microphysical effects on rainfall associated with urban or industrial emissions will inevitably only be seen as a modulation of large-scale changes such as those thought to be due to greenhouse gas forcing. These are therefore briefly reviewed. Then the potential of particles and gas emissions to affect rainfall are described and the scales on which they might be effective considered. The population of cities and some large towns in Australia, their associated industries, large scale mining and smelting, have all increased enormously in the last thirty years. If there are any overall effects on rainfall they should be revealed by the relationship between the spatial distribution of temporal trends and the location of known potential sources of cloud modification, providing that changes due to general circulation operate on a broader scale. A similarity will not establish proof that rainfall has been modified by urban and industrial emissions but would suggest that Rosenfeld’s conclusions should be thoroughly investigated.

2. POSSIBLE LARGE-SCALE EFFECTS ON SPATIAL AND TEMPORAL DISTRIBUTION OF RAINFALL IN AUSTRALIA Pitman et al. (2004) showed that a climate model incorporating the effects of deforestation and changing land usage in southwestern Western Australia could predict the

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observed decrease in rainfall and also the observed increase further inland. Hope et al. (2006) found the decreasing trend in winter rainfall in the same region to be associated with a declining frequency of synoptic situations conducive to rainfall. Circulation changes due to greenhouse gas forcing were suggested as a cause and climate modeling supported this view. Rotstayn et al. (2007) found that climate models including the effects of Asian aerosol that increases atmospheric optical depth over northern Australia in summer predicted increasing rainfall and cloudiness in northwestern and northern Australia, in accordance with observation. If Asian aerosol was not included in the models, decreases were predicted. They therefore suggested that Asian dust might be responsible for the observed increases. A disturbing feature of these models is that both land usage change and Asian dust increases can lead to a prediction of inland rainfall increases, while land usage change and greenhouse gas forcing can successfully model the decreasing trends in rainfall in the southwest. It leads to a suspicion that the observed changes can be predicted with a variety of model inputs and that perhaps the real cause lies elsewhere. In addition to the large scale circulation changes invoked in these papers there are welldocumented shorter term events that affect the distribution of rainfall. Nicholls (2006) has described the detection and attribution of such changes and presents a map of trends in the Australian region from 1950-2005. Known influences include: the El Niño-Southern Oscillation (ENSO) e.g. McBride and Nicholls (1983), the “Indian Ocean dipole” (an oscillation in sea surface temperature in the Indian Ocean, e.g. Saji and Yamagata, 2003) and the Antarctic Oscillation or Southern Hemisphere Annular Mode (the difference in mean sea level pressure between latitudes 40°S and 65ºS, e.g. Hendon et al., 2007). The changes induced by the first two phenomena are inter-related and large scale circulation changes due to greenhouse gas forcing will probably cause some long-term variations in all of them. It is against this background of changes on a broad scale that any effects of urban or industrial pollution has to be detected. The latter are unlikely to exert an appreciable influence on the synoptic scale meteorology but could conceivably affect the probability of precipitation from potentially rain-bearing systems. We first have to decide what type of emissions from cities or industries might affect rainfall and then find how those emissions have changed with time.

3. GEOGRAPHICALLY FIXED SOURCES THAT MIGHT INFLUENCE RAINFALL 3.1. Dynamical Effects Climate and rainfall can be affected by changes in surface roughness, albedo, or heat capacity. Coutts et al. (2007) give an example of the first, while the “urban heat island” effect has long been studied. For example, Huff and Changnon (1973) considered that warm season rainfall was thereby enhanced within 100km from a city while Shepherd et al. (2002) using radar evaluation of rainfall near five US cities found the maximum increase in rainfall occurred 64km from the city centers but did not specify how far downwind the effect extended. In all such studies, including the present one, the net effect on rainfall will include both dynamical and cloud-microphysical effects.

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3.2. Airborne Particles That Affect Cloud Drop Production, Size Distribution and the Onset of Rain through Coalescence

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The concentration of particles on which cloud drops form (cloud condensation nuclei, CCN) and the concentration of supermicron particles, especially those that are hygroscopic, are known to have an important influence on rain production. Rain will form in clouds of sufficient depth and water content when some cloud drops are sufficiently large to sweep up their smaller neighbors. For a full discussion see e.g Pruppacher and Klett (1998). The efficiency of collection of a larger and smaller drop depends on the ratio of their diameters and in calm air falls off increasingly rapidly as the larger drop’s diameter decreases from 40µm. In turbulent air, collection efficiency is larger for a given drop size but is difficult to quantify. The size distribution of cloud drops depends on the concentration of CCN, the updraft velocity, turbulence and the time since condensation first occurred. Initially the spectrum is very narrow but broadens with time as a result of turbulence and entrainment of dry air and droplet collisions. The presence of unusually large hygroscopic particles reduces the time taken for drops to grow large enough to initiate coalescence. The probability of rain formation is reduced by increasing CCN concentrations because of the smaller drops that result but is enhanced by increasing concentrations of large hygroscopic particles. Hobbs et al. (1970) found CCN fluxes up to 1019s-1 from Kraft paper mills in Washington State USA and attributed an apparent increase in precipitation in their vicinity to the concentration of large hygroscopic sodium sulfate particles they emitted. Mather (1991) also observed increased rainfall from convective clouds in South Africa when a Kraft paper mill was constructed and used the observation as a basis for a very successful cloud seeding operating using large hygroscopic particles. The influence of a source of pollution on precipitation is therefore difficult to estimate in the absence of information on the size distribution and hygroscopic content of the emitted particles. Large directly emitted hygroscopic particles will be the first to be lost by wet deposition or sedimentation so that their tendency to counter rainfall decreases caused by added CCN will diminish with distance from the source. Where there are sufficient large hygroscopic particles to start the coalescence process, rain formation will be relatively insensitive to increased CCN concentrations. This is probably why Warner (1971) failed to find a decrease in rainfall associated with increased burning of sugar cane, because the cane is grown in coastal areas with onshore winds rich in large sea salt particles.

3.3. Effects on Rainfall Resulting from the Oxidation of SO2 Deposition of sulfuric acid from oxidation of sulfur dioxide (SO2) may totally alter the properties of otherwise inert particles. Levin et al. (1996) showed how deposition of sulfate on desert dusts influenced rain formation in the eastern Mediterranean and similar considerations would apply to SO2 sources in continental Australia. Van den Heever and Cotton (2007) have made the important point that the response of convective storms to urban pollution depends on the background concentrations of CCN. It probably applies also, but to a lesser extent, to stratiform clouds. In Australia thirty years ago Bigg and Turvey (1978) found average particle concentrations in the boundary layer away from specific sources to be about an order of magnitude lower than those reported in USSR, Europe and USA. Their airborne survey covered more than 100,000km of flight path within the boundary layer. Whether these

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low concentrations still apply to-day is doubtful –comprehensive recent measurements have not been made over the mainland. However, rainfall may in general be more sensitive to increases in CCN in Australia than in most northern hemisphere countries. Some power stations and sulfide-ore roasting processes are prolific sources of both particles and SO2. Evolution of the aerosol from them determines their ultimate effects on rainfall. Airborne observation of the emissions from an isolated copper and lead smelter at Mt.Isa in central north Queensland by Bigg and Turvey (1978) and Ayers et al. (1979) gave some information on this. Figure 1 is a transmission electron microscope photograph of particles collected within the particle plume 180km from the source. The specimens were coated in a vacuum with platinum atoms at an angle of 26º, yielding an artificial shadow twice as long as the height of the particle. A negative of the photograph then resembles an optical picture (shadows black) and reveals the three-dimensional structure of the particles. This photograph, in conjunction with others shown by Ayers et al. (1979) reveals some of the processes involved in the evolution of the aerosol from a concentrated source of fine particles and sulfur dioxide. The first process is revealed by the large electron-dense (white) particles which are aggregates of particles of 20-80nm diameter and probably consist of metal oxides or sulfides that have not completely decomposed. These are obviously derived from coagulation of the very numerous fine particles in the plume close to the source. The sulfuric acid component (halos of small droplets surrounding each aggregate particle) shows how oxidation of the high concentrations of sulfur dioxide changes particles that would not have an appreciable effect on rainfall production into active CCN that would.

Figure 1. Transmission electron microscope photograph of particles captured in the plume from an isolated copper and lead smelter at a distance of 180km from the source. The halos of liquid particles are composed of sulfuric acid and surround cores of aggregated 20-80nm electron-dense particles.

A further process seen in the Ayers et al. (1979) paper is that at a greater distance from the source the sulfuric acid has been largely converted to ammonium sulfate. Their paper also

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showed the application of a sulfate test that confirmed the identity of the droplet haloes. It estimated the flux of CCN active at a supersaturation of 0.5% to be a minimum of 5.1018s-1 in the plume at 500km from the source. High concentrations of gas phase sulfuric acid in the plume will probably lead to nucleation of new sulfuric acid particles as the plume becomes more dilute and these will help maintain high CCN concentrations to great distances. The lesson from these observations is that in cloud free conditions SO2 is likely to be one of the main sources of CCN concentrations at a large distance from the source, impairing the coalescence mechanism of rain formation if clouds subsequently form in this CCN-rich air. In the presence of non-precipitating cloud, aqueous oxidation of SO2 in cloud drops and recycling of evaporating drops will speed up the formation of CCN but will also reduce the number of potential condensation sites for the sulfuric acid through capture of the smaller particles by cloud drops. Precipitation near the source will be effective in reducing its effects at a distance through removal of particulates on which SO2 can condense. It is therefore necessary to distinguish between the effects of a source of SO2 on CCN concentrations at a large distance when fair weather anticyclonic conditions prevailed near the source and those during showery or rainy conditions. The effects of Melbourne (population ~3.106) and nearby industries on CCN concentration at a distance of about 300km in partly cloudy but non-precipitating conditions has often been documented at the Cape Grim Baseline Atmospheric Monitoring Station in northwest Tasmania that has operated continuously since April 1976. An example is available in the data set archives of the ACE-1 experiment in 1995. Trajectory analysis on November 17, 1995 showed CCN concentrations measured by J.Gras jumping from the background of about 80cm-3 to more than 600cm-3 at the time the air back-trajectory swept over the city.

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3.4. Potential Effects of Particles That Create Ice Crystals in Clouds Colder Than 0C (Ice Nuclei, IN) Ice formation in cold clouds influenced by pollution sources is a further potential influence on rainfall. In clean continental situations the formation of precipitation through the ice phase is usually more likely than through coalescence because of high cloud bases and low concentrations of large hygroscopic particles. However, there is an optimum concentration of IN in clouds colder than 0C for efficient precipitation production and sources that produce too many IN may actually suppress it. The situation is complicated by secondary ice crystal generation at temperatures near -5C when cloud drops >24µm diameter are present (Hallett and Mossop, 1975). Strong unpolluted maritime air flow regularly produces clouds with drops of this size and they can also be found in deep convective clouds anywhere. Loss of the large hygroscopic salt particles and added CCN from emitted particulates progressively reduce the chance of secondary ice formation in less vigorous clouds as the air moves inland. Mossop et al. (1972) observed a case where this occurred when the coast was about 400km upwind. A further possibility is that condensation of supersaturated gases such as tarry substances produced during combustion that may coat potential IN and also CCN, making them less likely to influence precipitation. Sources of IN were for many years a subject for debate, clay particles being most generally favored. However, on the Australian scene, Bigg and Miles (1964) found no correlation between IN active at -15C and terrestrial dusts. It later emerged that immersion in

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water or contact with a water drop was an important factor in determining the temperature at which a particle could nucleate ice and only production from supersaturated water vapor was used in their experiments. Clay particles will therefore not usually be active CCN unless they have ice nucleating bacterial debris attached, or when they contact cloud drops, or act as CCN due to associated hygroscopic material. Condensation of sulfuric acid derived from oxidation of SO2 can provide the hygroscopic component that will enhance the probability that they will act as CCN. The source and nature of IN active at -5C that initiate the Hallett-Mossop ice crystal multiplication referred to above was a mystery until Schnell and Vali (1972) found evidence that organic debris in leaf litter were extremely active in freezing water. It was then found (Vali et al. 1976) that this was due to the presence of very common worldwide plant living and terrestrial-living bacteria, notably Pseudomonas syringae, a small proportion of which could freeze water at temperatures warmer than 1C. The discovery has had commercial applications in prevention of frost injury to plants. The ice nucleating bacterial membranes derived from genetically modified E.Coli bacteria have also been used in snow-making machines on ski resorts. Hirano and Upper (2000) have reviewed the ice nucleating properties of several species which are due to ice nucleating genes with a large central region of repeated sequences of nucleotides. Ice proteins are thought to assemble in aggregates of various sizes in association with the outer membrane of bacterial cells. While viable airborne bacterial concentrations are typically of the order of a few tens m-3 the ice nucleating property is likely to remain on the membranes of dead cells so that IN concentrations of bacterial origin may be considerably larger.

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3.5. A Potential Positive Feedback Process between IN and Rain Bacterial concentrations can increase very rapidly as a result of rain and Lindemann and Upper (1985) found upward fluxes to the air also to be higher on days after rain than when the soil was dry. Multiplication and dispersal to the air in wet conditions presents the possibility of an interesting rainfall feedback effect. Bigg and Miles (1964) described IN concentrations measured at a temperature of -15C every day for 18 months at 19 sites covering the entire area of mainland Australia east of longitude 132ºE. Striking results were the increases in IN concentration associated with days having rainfall and the persistence of the high concentrations. In figure 2A a day with >25mm of rain and in figure 2B a day with >2.5mm of rain were designated “key days” (day 0) and the IN concentration and rainfall n days from a key day listed. IN and rain for all such events at day n were then totaled separately and shown in figure 2. Polynomial fits to the ice nuclei curves show how the concentrations reach a maximum two days after heavy rain (A) and about 5 days after the lighter rain (B). The IN concentrations rise before day 0 is not real but is due to the superposition procedure because of the tendency for rainfall to occur on several successive days. The important point is the asymmetry of the curves – IN take more than two weeks on average to return to the concentrations existing before rain. While the bacterial input to IN concentrations was unknown at the time, it is now clear that Figure 2 is consistent with the supposition that they account for a large proportion of the natural IN in Australia. Assuming that clouds are usually deficient in IN for optimum precipitation efficiency (the basis of glaciogenic cloud seeding)

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rain produces more IN leading to a greater precipitation efficiency – a positive feedback effect. On the other hand, prolonged dry conditions reduce bacterial populations, so that droughts, like wet periods, tend to be self-perpetuating, from this cause as well as from moisture and albedo changes.

Figure 2. Mean trends in ice nucleus concentrations at 19 Australian sites before and after (A) days with >25mm of rain, (B) days with >2.5mm of rain. Note the asymmetry which shows a persisting elevation of concentrations following rain.

3.6. Potential Influences of IN Produced by Mining and Ore Processing Operations on Rainfall Open-cut mining that involves the removal of overburden with explosives and stockpiles of fine ore particles that can become wind borne are potential sources of IN. In northwest Western Australia huge iron ore mining operations have developed during the last forty years.

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The most common ore is hematite and Mason and Maybank (1958) found hematite particles acted as ice nuclei at a temperature of -10C. The possibility that these might act as cloud seeding agents needs to be examined. Fukuta (1958) described the ice nucleating properties of a large number of metallic oxides and sulfides such as might be emitted from smelters. Those capable of forming ice at temperatures warmer than -10C included copper, silver and nickel oxides, and copper, lead and cobalt sulfides. Telford (1960) obtained direct evidence from aircraft surveys of the output of IN from a smelting operation. Whether particles from other industrial sources are effective as IN will depend on their size and what other material is associated with them. It is possible that their presence might reduce the impact on rainfall of added CCN through stimulation of the ice phase.

3.7. Population Trends and Pollution Mitigation

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During the last 50 years Australian population has doubled but it is the increase in use of fuel and expansion of industry and mining that have been spectacular, particularly in the last 30 years. Potential sources of aerosol should therefore show a continuous increase, but possibly more rapid than linear. Efforts have been made to reduce particulate emissions, but have been mainly directed towards removing particles larger than 10µm, classed as “ultragiant” in cloud physics terms. Removal of such large particles, especially those that are hygroscopic, is likely to enhance the tendency of the aerosol to reduce rainfall, although of course there are benefits such as improved visibility and less fallout. Precipitation washes particulates from the air, so that there is a possible feedback between the probability of precipitation and the potential of aerosol to influence precipitation, negative for large hygroscopic particles and positive for ordinary CCN. The net effect on rainfall of most sources of aerosol is therefore extremely complicated depending on the detailed properties of the emitted or evolved particles, distance from the source, intervening cloudiness and precipitation and the processes that lead to the precipitation. In the absence of relevant information on the detailed properties of the aerosol created by specific urban and industrial processes, the analysis of trends in rainfall with time and their spatial distribution in relation to sources may provide information on the importance of the net effects.

4. DATA AND ANALYSIS Daily rainfall data from all sites in the selected areas published by the Australian Bureau of Meteorology formed the basic data. The choice of starting and end points affects the absolute values of the trends. Starting in years of generally high rainfall and ending in years of low rainfall for example will bias the results towards a decreasing trend with time. Initially trends were calculated for an 80 year span; because of a large amount of missing data and the relatively small numbers of stations with such long records in some areas, this was reduced to 50 years. Many of the major potential aerosol sources were of more recent origin, or had increased their output in more recent years and a shorter period allowed a better geographical

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coverage of changes. A disadvantage of a long study period is that the probability of longterm changes due to changes in atmospheric circulation increases with time. A disadvantage of choosing a short period is that the effects of unusually severe storm tracks may not be smoothed out. The compromise period selected here was the 35 years from 1 January 1970 to 31 December 2004. All sites with records covering this entire period were first selected. Mean daily rainfalls were calculated for each year, rejecting years for which more than one month of data was missing. Sites with a total of more than 5 rejected years were omitted altogether. The Bureau has provided a quality control marker for each day’s rain. Those considered unsatisfactory were omitted because some of the entries were unrealistic. A least squares linear trend was then calculated for each accepted site and the change in rainfall between end points was expressed as a percentage of its overall mean daily rainfall for the 35 year period. Omission of a year may affect the trend substantially if rainfall in the area was unusually high or low in that year and accounts for some of the scatter in the results. Roughly 50% of sites (varying with area) had no missed data but even in these there was still a small proportion in which the trends were very different from that of their neighbors. It is speculated that small changes in the site or changes in exposure of the gauge might have caused such discrepancies.

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5. CHANGES IN RAINFALL IN SOUTHERN VICTORIA According to the National Pollutant Inventory (NPI) of the Department of Environment and Water Resources, the main sources of SO2 emissions in 2004 were the power stations in the Latrobe Valley near Traralgon about 160km east of Melbourne with combined emissions of about 100kt.yr-1 and at Anglesea (40kt.yr-1) about 110 km southwest form Melbourne. The former commenced coal mining in 1982 and electricity generation in 1984, with additional generating capacity in subsequent years to 1988. A second power station was added in 1993 and its output increased in 1996. Rosenfeld et al. (2006) considered that the reduced cloud drop sizes shown by satellite observations quite close to Melbourne and to the sources near Traralgon would lead to a reduction in rainfall. If so, it should show up in trends from 19702005. The Anglesea power station providing power for an aluminum smelter came on line in 1969. Dates of installation of its 8 power units are not available, so it is not clear how much it will have contributed to trends since 1970. The first group of rainfall measuring sites in a 220x450km west-east rectangle was chosen to look at changes relatively close to the Victorian sources specified by Rosenfeld et al. Any changes will be “near field” effects resulting from emitted particles, rather than from oxidation of the associated SO2. Shaw and King (1986) calculated the proportion of rain near Melbourne associated with each wind direction at 3000m, finding that directions from 180-315º accounted for 80% of the total, with maxima between 180-202º and 248-270º. We might therefore expect that any nearfield changes would extend towards the NNE or ENE from the sources. Figure 3 shows

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contours of percentage decreases in rain in 35 years, green representing 20%.

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Figure 3. Changes in rainfall from 1970 to 2005 expressed as a percentage of the mean daily rainfall at each site, near the city of Melbourne (M). Green is 20%. The power stations near Traralgon (T) in the Latrobe Valley and at Anglesea (A) are also marked with stars. The power stations burn brown coal and have large emissions of sulfur dioxide.

The location of the Anglesea power station (A), Melbourne city center (M) and the power stations near Traralgon (T) are marked with stars. A few sites having rainfall trends differing from others nearby can be seen. The differences are probably due to missing data or site changes but because they might be real have been left in. The contours to the north and northwest of Melbourne are not readily interpreted in terms of local potential effects on rain-bearing winds. The line of relatively large decreases encompassing the city and stretching to the northwest looks like an effect of the city but only a small proportion of rain is accompanied by SE winds. Decreases >20% to the north and east of the Latrobe Valley industrial area which includes Traralgon (T) are consistent with the relatively local effect of emitted particles, but an absence of reporting stations to its north due to the forested, mountainous and sparsely populated terrain means that its northern boundary is poorly defined. Fawcett (2004) concluded that the rainfall of Melbourne itself had decreased in recent years, while Stern et al. (2005) showed that there was a downward trend from 1970-2003, but not in earlier years. It is perhaps just coincidental that the start of the downward trends coincides with the establishment of the nearby Anglesea coal mine, power station and aluminum smelter. Figure 3 led to the suspicion that the decreases to the northwest of Melbourne could have originated in long distance transport from sources in South Australia, necessitating a similar study encompassing a much larger area.

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6. SOURCES OF SO2 IN SOUTH AUSTRALIA AND CHANGES IN RAINFALL TO THE EAST AND SOUTH

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The NPI shows that in 2005 by far the greatest source of SO2 in South Australia was the lead smelter at Port Pirie with an emission of 55kt.yr-1. It was identified by Rosenfeld (2000) as the cause of a plume of reduced cloud drop size revealed by satellite. The power station at Port Augusta burns sub-bituminous coal and produced 6.7kt.yr-1 of SO2, the Olympic Dam mining site 1.5kt yr-1 and the Whyalla steelworks 1.2 kt.yr-1. With the exception of the Olympic Dam site (outside the northern border of the Figure 4 map), these sites are identified by their initial letters. Also identified are Melbourne (M) and an aluminum smelter at Portland (P) to the west of Melbourne that appears to have caused a local decreasing trend. The Port Pirie smelter was already claimed to be the largest lead smelter in the world by 1934 but zinc, copper, silver and gold recovery and sulfuric acid manufacture and use have begun or expanded during the 1970-2004 period of this study. The Port Augusta power station was also in operation before 1970 but a second 400Mw station was built in 1984. Both Port Pirie and Port Augusta are therefore potentially capable of producing trends in rainfall from 1970 to 2004. The Whyalla steelworks were in operation before 1970 but there have been significant changes since then, such as additional coke ovens in 1980, flux pellet production beginning in 1981, a waste gas cleaning plant installed in 1998 and a dust catcher in 2001.

Figure 4. Changes in rainfall from 1970 to 2005 expressed as a percentage of the mean daily rainfall at each site, to the east (commonly downwind) from Adelaide and industrial sulfur dioxide sources to its north.

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The latter changes may have reversed earlier trends in aerosol production. The iron mines feeding this steelworks, about 80km to the west may also have produced aerosols that affect rainfall (see section 7.2). Possible effects on rainfall of the steelworks and mines and their changes with time are too complex to predict. Drawing detailed contours of rainfall change in 35 years in the area covered by Figure 4 provided a complex pattern which has been simplified by forming averages in each of nine contiguous 0.1ºx0.1º blocks over the whole area. The result is a smoothing of localized effects but also of random errors and still provides some fine resolution. In some of the semi-arid regions in the north of this diagram, interpolation had to be used for a few blocks because of absence of gauges with complete records. The decreases 80-500km to the east and southeast of Port Pirie (PP), Port Augusta (PA) and Whyalla (W) are spectacular, some exceeding 40% in 35 years. In terms of rainfall amount this is not great as annual rainfalls were only of the order of 300mm or less at the start of the period but the percentage change may make all the difference between survival and death for some vegetation or fauna. But was this source responsible, and were this and Adelaide’s emissions responsible for the decreases closer to Melbourne, or were these features simply a manifestation of climate change? This will be discussed further in section 8. Clearly the decreasing rainfall trends in the north of Figure 4 extend beyond the map, so that once again the study had to be extended to see if there was an origin for them. Unfortunately rainfall data are sparse in that region and the random errors in neighboring gauges cannot be reduced by averaging as they were in figure 4. Figure 5 uses contours spaced by 20% to reduce this problem. Decreasing trends greater than 20% appear to be sharply bounded to the west and to reduce more gradually to the east and north. There is a bulge in the contours around the Olympic Dam site (marked with a cross) suggesting that it might be a contributor to the decreases. A pilot plant started there in 1984 and full production began in 1987. Copper, uranium, gold and silver are the main products of the operation. In addition to the SO2 emissions from that site, the manufacture of sulfuric acid and its use in leaching uranium and residual copper from ores may lead to some new particle formation in the atmosphere from supersaturated vapors. The shape of the contours and the very large decreasing trends northeast from the known largest sources of SO2 at and near Port Pirie would be consistent with a strong cloud microphysical effect on post frontal rainfall that is associated with southwest winds. The distances involved are consistent with the observations made at Mt.Isa that were quoted in section 3.2. Since expansion of activities at Port Augusta, Port Pirie and Olympic Dam occurred from the mid to late 80s, a similar ratio of average daily rainfall to the east and west of the cross (Olympic Dam) in Figure 5 in 1969-1986 and 1987-2004 would imply that a large scale change in meteorology rather than industrial aerosol was responsible for the contours in that figure. Average daily rainfall was calculated for the two periods for the five sites shown in Figure 5 to the west of Olympic Dam having latitudes 30-32ºS and longitudes 135-136ºE and seven sites to the east between the same latitudes and longitudes 139-140ºE. Rainfall in the western group was 2% higher in the second period than in the first and in the eastern group was 17% lower, implying that added SO2 might have been responsible. The correlation coefficient between annual rainfalls of the two groups in the first period was 0.81 but in the second was only 0.65. It is still possible of course that meteorological rather than microphysical changes could account for this difference but it does make the latter cause

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seem more probable. An explanation that might account for the large area of substantial decreasing trends in rainfall and its shape seen in figures 4 and 5 is advanced in section 8.

Figure 5. Changes in rainfall from 1970 to 2005 expressed as a percentage of the mean daily rainfall at each site, to the east and northeast of major sources of sulfur dioxide in South Australia. The Olympic Dam mining and smelting site is marked with a cross.

7. WESTERN AUSTRALIA

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7.1. The Effects of the Industries Extending 200km to the South of Perth As described in section 2, climate models seem to indicate that the observed decrease in winter rain in Western Australia’s southwest can be accounted for by greenhouse gas forcing or changes in land usage while the increased summer rain in the tropical north can be due to circulation changes induced by Asian aerosols that reduce tropical insolation. However, there has been an enormous increase in industrial activity in the coastal region in the 200km strip south from Perth during the 1970-2004 period. Within the limits of Figure 6 the major sources operating during that period are power stations and an aluminum smelter near Bunbury and Collie, totaling 65kt.yr-1, and sundry sources in the Kwinana-Rockingham complex just south of Perth, totaling about 10kt.yr-1. The contours of figure 6 show a broad band having relatively slight decreasing trends of 0-10% bounded to the northeast and north by areas with increasing trends. Within this band are areas having 10-20% decreases and small patches with decreases greater than 20%. The band extending to the east and southeast from Collie suggests an effect of emitted particles near the source, but those more than 150km downwind would be more easily interpreted in terms of an effect of CCN produced by oxidation of SO2.

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Figure 6. Changes in rainfall from 1970 to 2005 expressed as a percentage of the mean daily rainfall at each site, to the west of Perth and the industries extending south near the coast.

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The area of 10-20% decreases embracing the Perth-Fremantle-Kwinana area also suggest an effect of emitted particulates while the isolated patch of 10-30% decreasing trends to its southeast could again be interpreted as an effect of CCN produced by oxidation of SO2. One problem with these interpretations is the area of 10-20% decreasing trends extending downwind from the town of Busselton (labeled “Buss” in figure 6). Although this is said to be the fastest growing town in the state, its population at the end of the period was only 20,000 and the NPI mentions no sources of pollution there. The NPI shows that much larger SO2 sources are located near Kalgoorlie, 200km to the east of the area shown in Figure 6 and just below the right corner of Figure 7. The absence of sufficient rainfall sites in the 1000km to their east precludes detection of any possible effects of these sources.

7.2. Rainfall Trends in the North of Western Australia The regions of increasing rainfall trends in the northeast of Figure 6 were followed further north to see if any sources could be established. The major activities of this very large region are the natural gas platforms to the east of the dashed line (top left), oil production at Barrow Island (BI), and iron ore mining. The latter is mainly hematite and open cut mining, crushing, dry processing, transport and shipping of the products represent very large operations. The two main centers are at Mt.Tom Price and Mt.Newman, located on the map with stars but there are other large mines in the vicinity. In 2004 at Tom Price alone 45Mt of

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ore were treated. The NPI lists 130kt of PM10 airborne dust particles produced by mining in Western Australia in 2004. Figure 7 shows contours of trends in rainfall at 20% intervals, the coarse divisions being necessary because there were only 253 satisfactory rainfall sites in this very large area, and because of the large range in trends.

Figure 7. Changes in rainfall from 1970 to 2005 expressed as a percentage of the mean daily rainfall at each site, in the northwest of Western Australia. The iron ore mining centers are marked with stars. Oil and gas operations lie between the dashed line and the coast and at Barrow Island (BI).

The map is terminated at longitude 123º because there are only two reporting stations between longitude 123ºE and 130ºE, at 25.0ºS, 128.3ºE and 31.7ºS, 128.9E. The contours roughly parallel the coast with mostly slight decreasing trends close to the coast, with the exception of a small region near the oil and gas operations where decreasing trends were greater than 20% at a few adjacent sites. The most conspicuous features however are the very large increasing trends inland. Within the >60% increase contour in the northeast corner only six rainfall sites with good records were available and all were within 150km of its western

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border. The eastern and northern boundaries of this contour are therefore unknown. The two stations near longitude 128ºE had increases of only about 40% so it does not extend as far as that. The area of increasing trends is consistent with the findings of Rotstayn et al. (2007). Whether there could be any contribution from local sources will be discussed in section 8.

8. INTERPRETATION OF THE RESULTS The net changes in rainfall over 35 years close to the three large cities in this study are not consistent or clear. Adelaide has fewer large emission sources nearby than either Melbourne or Perth and changes in rainfall within 50km of the centre are small. In Melbourne, the most important decreases are not in the direction that seems likely from the wind directions commonly associated with rain. Decreases near Perth may be more a result of the industrial complex quite close to the city in the Kwinana area than of urban pollution. The results suggest that the main changes occur at a distance of up to 500km from the largest sources. Either that is a result of large-scale circulation changes that affect the meteorology or it is due to SO2 oxidation to sulfuric acid transforming inactive particles from either urban or industrial sources into active CCN and inhibiting rain formation.

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8.1. The Cause of the Widespread Decreasing Trends in Figures 3-5 The rainfall data used in this study were the latest available when the work was started. Between January 2005 and October 2007 (the time of writing) drought in the area of decreasing trends has become generally worse. Trends calculated into 2007 would have mostly been steeper than those shown here and the situation has aroused strong concerns about global warming, generally believed to be the underlying cause. Taken together, figures 3-5 show an area of the order of 5.105km2 aligned roughly NWSE in which rainfall has been depleted by more than 20% in the 35 years from 1970 to 2004. Is this a manifestation of climate change, or is it due to a reduced efficiency of rain production because of increased CCN concentrations? The shape of this sharply bounded area and the surrounding regions having minor trends in rainfall make it seem unlikely that it resulted from a circulation change initiated by greenhouse gas forcing, so an explanation in terms of cloud microphysics was sought. The examples in section 3.2 showed that CCN concentrations can be greatly enhanced during precipitation-free transport. Such conditions usually occur at the leading edge of anticyclones with winds somewhere between SE and SW depending upon latitude and season. At first the SO2 and co-emitted particulates travel quickly but in the slack winds near the anticyclonic center progress slows down and stops. Under cloud-free skies the SO2 is oxidized to sulfuric acid and deposited on the particulates present. If the sulfuric acid gas is sufficiently supersaturated, nucleation of new particles can also occur but it depends also on the surface area of the pre-existing aerosol. The location of the various sources that have been listed above means on this basis that a large pool of CCN should form to the north of Melbourne near anticyclonic centers. Emissions from the Wollongong-Sydney-Newcastle and even Brisbane sources on the east

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coast can travel inland to the same area in similar circumstances. Frontal systems reaching the area as the anticyclone departs can at times produce potentially rain-bearing clouds and the high ambient CCN concentrations would then inhibit rain formation in them. The pre-frontal northwest winds will force the CCN to the southeast. Perhaps the band of decreases extending northwest from Melbourne are due to influences of those particles on pre-frontal rain. The accumulation of CCN during the period when an anticyclone is located over the center of the region of decreasing trends and the flushing that occurs as it moves east would lead to a pulsating concentration of CCN. Prolonged measurements would be required to detect this because anticyclones do not always follow similar tracks or have similar pressure gradients near their boundaries. Their centers are on average further north in winter than in summer. A five-year continuous study of CCN concentrations at a rural site (altitude 700m) at latitude 34.45ºS, longitude 150.4ºE was made from 1968-1973 by Twomey et al. (1978). It showed a maximum for NE winds coming from the Sydney metropolitan area of ~900cm-3 at 1% supersaturation, compared with 50[O3]0, but having different combinations of [sulfide]0 and [O3]0. Cyclohexane was added into the reactor to eliminate the effect of OH radicals. The rate constants of the gas-phase reactions of ozone with EMS and PMS were determined to be (1.12±0.18)×10-19 and (1.24±0.15)×10-19 cm-3 molecule-1 s-1, respectively. Our results will enrich the kinetics data of atmospheric chemistry, and provide some useful information for evaluating the loss processes of reduced organic sulfur compounds.

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INTRODUCTION Sulfur compounds play an important role in both the tropospheric and stratospheric budget of atmospheric gases [1,2]. The atmospheric chemistry of sulfur-containing compounds is directly relevant to the formation of sulfur aerosol in marine air [3,4]. Very recently, Barnes et al. [5] reviewed dimethyl sulfide (DMS), dimethyl sulfoxide (DMSO) and their oxidation in the atmosphere, which emphasized the importance of sulfur-containing compounds. Reduced organic sulfur compounds have been estimated to account for approximately 25% of the total global gaseous sulfur budget [3]. The species play an important role in the atmospheric sulfur cycle and it is important to understand their fate in the troposphere. The reduced organic sulfur compounds are mainly emitted from the sea surface, although anthropogenic emissions might also be of importance [6,7]. Among the reduced sulfur compounds released into the atmosphere, natural emissions of COS, H2S and DMS play a major role, but other species including ethylmethyl sulfide (C2H5SCH3, EMS), n-propylmethyl sulfide (n-C3H7SCH3, PMS), should also be considered. EMS was present in the samples of coffee aroma isolated from dry ground coffee as detected with direct mass spectrometric analysis [8]. PMS was found in the volatile organic compounds from garden waste with GC/MS system [9]. The homogeneous oxidation of these trace gases into sulfur dioxide is expected to involve ozone as oxidant as well as other reactive molecules and radicals present in the atmosphere. Very little, however, is known about the kinetics of these oxidation processes. As for the atmospheric chemistry of EMS, its reactions with OH [10], OD [11], NO3 [12], and Cl [13] have been reported. No other available kinetics data of EMS can be found in the literature. As for that of PMS, there is totally no kinetics work reported. These rate constants are badly needed for evaluating the atmospheric effects of EMS and PMS [14]. In the present study, we have determined the rate constants of ozone reactions with EMS and PMS under nearly real atmospheric conditions in our self-made apparatus of smog chamber. The atmospheric applications of these constants are also discussed.

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EXPERIMENTAL Absolute rate constants for the ozone reactions with EMS and PMS were determined at room temperature by monitoring the O3 decay rates in the presence of known concentrations of the reactant sulfide. The ozone concentrations were monitored as a function of time. Under these conditions, the reactions removing O3 are: O3 + wall → loss of O3

(1)

O3 + sulfide → products

(2)

and hence, -d[O3]/dt = (k1 + k2[sulfide])[O3] Ⅰ

Rate Constants of the Gas-Phase Reaction of Ozone with Organosulfides…

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The reactant concentrations were always greatly in excess of the initial O3 concentrations (i.e., [sulfide]/[O3]0 > 50) and equationⅠ may then be rearranged to yield

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-dln[O3]/dt = k1 + k2[sulfide] Ⅱ From the ozone decay rates, -dln[O3]/dt, measured at various sulfide concentrations and with a knowledge of the background O3 decay rate, k1, the rate constants k2 were obtained. Experiments were carried out in a 70 L FEP Teflon reaction chamber. At the two ends of the reactor, there is an inlet and an outlet made of Teflon, respectively, which are used for introducing reactants and sampling. The reactor and the analytical instruments are linked via Teflon tubes. Ozone and a known volume of the liquid sulfide were injected into the chamber with injectors. The concentrations of sulfides and cyclohexane in the entire chamber were calculated from the amount of organics introduced and the total volume of the reactor. It was observed that for both gaseous and liquid organics, these calculated concentrations agreed to within better than ±10% with the concentrations quantitatively measured by gas chromatography [15]. For a typical experiment, the experimental procedure is described as follows. At first, the reactor was purged with N2 (99.999%) until there was no detectable ozone and VOCs. Residual gas was pumped out, and a 70 L volume of N2 was introduced into the reactor. Ozone in O2 was introduced into the reactor by an injector. The accurate concentration of ozone was measured by the ozone analyzer thereafter. Cyclohexane was injected into the reactor, which was used to eliminate the effect of OH radicals generated in the subsequent reactions. The reactor was shaken fiercely to make it mixed fully. When it became gas completely, a certain amount of sulfide was injected into the reactor, and the reactor was shaken again. At the same time, time was recorded. During the experiment, ozone concentrations were measured every 30 min. All experiments were conducted at (301 ± 1) K and lasted for 3.5 ~4.5 h. When O3 reacts with sulfide, OH radicals will probably generate as a secondary radical [16]. Because OH radicals are very active, its presence will result in certain error of the rate constant for the reaction of sulfide and O3. In order to avoid the impacts of OH radicals, high concentrations of cyclohexane were added into the reactor to react with OH radicals. Cyclohexane cannot react with O3, so the results will be more accurate. Furthermore, during our experiments, the sulfide was in large excess of ozone, so the reactions between sulfide and secondary radicals might consume a relatively small amount of sulfide. The concentration of the sulfide changed less than 1% after four hours reaction, which was determined both at the beginning and ending of experiments by gas chromatography. Therefore, the concentration of the sulfide can still be considered as a constant. Ozone in O2 was produced by an ozone generator based on the silent electric discharge technique. The purity of the O2 used was 99.995%. Ethylmethyl sulfide (C2H5SCH3, EMS) and n-propylmethyl sulfide (n-C3H7SCH3, PMS) were purchased from Alfa Aesar and used without further purification. C6H12 (cyclohexane) in a purity of 99.5% was from Beijing Beihua Fine Chemicals Company. The instrument used for analyzing ozone concentrations is ozone analyzer (Model 49C, Thermo Electron Corporation). A gas chromatograph (Agilent 6820) equipped with a flame ionization detector (FID) was used for the analysis of the sulfides.

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RESULTS AND DISCUSSION

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The ozone losses were measured with and without cyclohexane in our previous work [17,18], which showed that 8.50×1014 molecule cm-3 of cyclohexane is enough for scavenging OH radicals in the experiments between DMS and DES with ozone. In this work, the concentration of the cyclohexane used was 2.13×1015 molecule cm-3, which was 2.5 times larger than that of the previous work. Therefore, we can assure the OH radicals were completely eliminated and they would have no effects on the determination of the rate constants between ozone and sulfides. In the present work, the loss rate of ozone was measured in a control experiment in the presence of 2.13×1015 molecule cm-3 of cyclohexane in the bath gas of pure N2. The wall decay loss profile is shown in Figure 1. As clearly seen, the ozone concentration-time profile is exponential. As for the mechanism of the reactions between ozone and sulfides, the reaction of DMS and ozone is initiated by the primary attack of ozone on the sulfide [16], which involves C-S bond scission, as evidenced by the SO2 chemiluminescence [19,20]. The major products of the reactions are H2CO, SO2, H2O and CO. OH radicals, SO radicals and H atoms are important intermediates in the DMS-O3 system [16]. The reactions between ozone and other sulfides should be similar. The results under different initial concentrations of ozone and sulfides in the bath gas of N2 are listed in Table 1. The initial concentrations of EMS were in a range of 4.75×1014 molecule cm-3 -14.25×1015 molecule cm-3, and those of PMS change from 4.05×1014 molecule cm-3 to 9.71×1015 molecule cm-3. Plots of ln([O3]0/[O3]) vs. time were constructed, where [O3]0 is the initial ozone concentration and [O3] is the ozone concentration at time t. The plots of the EMS-ozone reactions are shown in Figure 2.

Figure 1. Plot of ozone wall decay in the presence of 2.13×1015 molecule cm-3 of cyclohexane in the bath gas of N2.

Rate Constants of the Gas-Phase Reaction of Ozone with Organosulfides…

85

Figure 2. Plots of ln([O3]0/[O3]) against time for ozone decay (No. 1) and EMS reaction experiments (No. 2~5).

Linear regression of the data (unit-weighted least squares) yielded high correlation coefficients, near-zero intercepts and slopes, i.e. pseudo first-order rate constants, which were used to calculate the second-order reaction rate constants that are listed in Table 1. A good linearity in Figure 2 implies that equation Ⅰis correctly used for the sulfide-ozone reaction. Even if there are some secondary reactions, the influence of these reactions on the rate constant is very small. The data of the PMS-O3 reactions have been dealt with the same methods, and the results are also listed in Table 1.

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Table 1. Experimental result under different initial concentrations of ozone and sulfides

Wall decay

[sulfide]0 (×1014 molecule cm-3) 0

[O3]0 (×1012 molecule cm-3) 4.35

-dln[O3]/dt (×10-5 s-1) 0.618

k (×10-19 cm3 molecule-1 s-1) -

2

EMS

4.75

5.51

7.36

1.42

3

EMS

9.50

5.51

11.02

1.10

4

EMS

9.50

6.74

9.90

0.98

5

EMS

14.25

3.84

14.23

0.96

6

PMS

4.05

3.58

6.03

1.34

7

PMS

6.48

5.04

9.85

1.43

8

PMS

8.10

3.95

10.06

1.17

9

PMS

9.71

7.04

10.68

1.04

No.

sulfide

1

86

Maofa Ge, Lin Du and Kun Wang

Based on the experiments, the obtained wall decay rate is 6.18×10-6 s-1, which is ca. one order of magnitude lower than the pseudo first-order loss rates of ozone in the ozone-sulfidecyclohexane experiments. The corresponding half-life time of ozone loss is 31 h. As seen from the values of -dln[O3]/dt in Table 1, the effect of ozone wall decay cannot be neglected. When the EMS concentration is 4.75×1014 molecule cm-3, -dln[O3]/dt value is 7.36×10-5 s-1, in which the ozone wall decay accounts for 8%. Such an effect must be deducted from the experimental results. It can be known from the data in Table 1 that -dln[O3]/dt values increase linearly with the increasing concentration of EMS or PMS. According to equation Ⅱ, the rate constants of sulfides and ozone can be calculated from the data of wall decay rate, -dln[O3]/dt value, and [sulfide]0. The results are listed in Table 1, and these rate constants data can be averaged for EMS and PMS, respectively. Therefore, the rate constants for ozone reaction with EMS and PMS under room temperature are (1.12±0.18)×10-19 cm-3 molecule-1 s-1 and (1.24±0.15)×10-19 cm-3 molecule-1 s-1, respectively. At present, the available experimental information on the reactivity of sulfides with the different atmospheric oxidants is scarce. Thus, it is not possible to make an extended evaluation even on a qualitative basis of the relevance and dependence of the different contributions to the reactivity of sulfides. Clearly more experimental measurements are needed in order to increase the existing kinetic database on these reactions and to fully understand the reactivity changes among different sulfides. The rate constants available and estimated atmospheric lifetimes of similar sulfides can be compared and are listed in Table 2, including the reactions with OH radicals, Cl atoms, NO3 radicals and ozone. The lifetime of sulfides with respect to oxidation by OH radicals may be estimated using a 12-hour average concentration of OH radicals of 1.6×106 molecule cm-3 [21]. Although the global concentration of Cl atoms is likely to be small, significant concentrations are expected to be present in costal areas. Table 2. Rate constants and estimated atmospheric lifetime of sulfides with OH radicals, Cl atoms, NO3 radicals and O3 a CH3SCH3 OH τOH

1.7×10 b

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Cl τCl

1.1×10

τNO3

d

CH3SCH2Cl 2.5×10

-12 [26]

2.89 d -10 [25]

5.08 d

NO3

a

4.26 d 3.3×10

c

-12 [25]

1.0×10

0.51 h

O3

1.04×10

τO3 e

55.6 d

-19 [17]

8.5×10

-12 [10]

0.85 d -10 [26]

16.8 d -12 [25]

C2H5SCH3

-10 [13]

3.44 d -

-

-

-

-1

2.77×10 20.9 d

-1

-

-12 [28]

0.12 h -19

51.7 d 3

5.98×10 4.8×10

n-C3H7SCH3 -

-10 [13]

2.81 d

1.12×10

-

1.16×10

-11 [27]

0.62 d

4.88×10

-

C2H5SC2H5

-

-19 [18]

1.24×10-19 46.7 d

All the rate constants are in units of cm molecule s , and the temperature of these reactions is around 298 K (room temperature). b τOH = 1/(k[OH]), where k is the rate constant, [OH] = 1.6×106 molecule cm-3 [21], and d = days. c τCl = 1/(k[Cl]), where k is the rate constant, [Cl] = 6.9×103 molecule cm-3 [23], and d = days. d τNO3 = 1/(k[NO3]), where k is the rate constant, [NO3] = 5×108 molecule cm-3 [24], and h = hours. e τO3 = 1/(k[O3]), where k is the rate constant, [O3] = 2.0×1012 molecule cm-3 [23], and d = days.

Rate Constants of the Gas-Phase Reaction of Ozone with Organosulfides…

87

Wingenter et al. [22] predict a Cl atom concentration of 3.3×104 molecule cm-3 in marine air for the first 5 h after dawn. Using this value for the Cl atom concentration during the morning, an effective 24-hour concentration of Cl atom of (3.3×104×5/24) = 6.9×103 molecule cm-3 may be calculated [23]. The natural lifetime of sulfides with respect to OH radicals and Cl atoms in coastal areas would thus be calculated to be several days as shown in Table 2. The global concentration of NO3 radicals is estimated to be 5×108 molecule cm-3 [24], which is a 12-hour average concentration. The corresponding lifetimes with respect to NO3 radicals for DMS and PMS are within one hour. Similar calculations, employing a fairly high concentration of O3 of 2.0×1012 molecule cm-3 (80 ppb) [23], suggest that the lifetime of sulfides with respect to oxidation by O3 range from 20.9 days to 55.6 days. From the data in Table 2, we could draw the conclusion that reaction with OH radicals during the daytime and NO3 radicals during the nighttime are the dominating ways of chemical loss of sulfides. This study further confirms that under atmospheric conditions, the reaction of sulfides with ozone is too slow to represent an important oxidation pathway of sulfides in the atmosphere.

CONCLUSION With our self-made smog chamber, we determined the rate constants of ozone with EMS and PMS, respectively. These reactions are relatively slow, so the wall decay of ozone cannot be neglected. Under the condition of this work, the rate of ozone wall decay is 6.18×10-6 s-1, and the half-life time of ozone is 31 h. In order to eliminate the effect of OH radicals, cyclohexane was added into the reactor. The rate constants for ozone reaction with EMS and PMS derived from different concentrations of reactants under room temperature are determined to be (1.12±0.18)×10-19 and (1.24±0.15)×10-19 cm-3 molecule-1 s-1, respectively. Meanwhile, our results further confirm that the gas phase reaction of sulfides with O3 is too slow to represent a significant pathway for the oxidation of sulfides in the atmosphere.

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ACKNOWLEDGEMENTS This project was supported by Knowledge Innovation Program (Grant No. KZCX2-YW205) of the Chinese Academy of Sciences, the 973 program (No. 2006CB403701) and 863 program (No. 2006AA06A301) of Ministry of Science and Technology of China, and the National Natural Science Foundation of China (Contract No. 20577052, 20673123).

REFERENCES [1] [2] [3] [4] [5]

Nguyen, B. C.; Bonsang, B.; Gaudry, A. J. Geophys. Res. 1983, 88, 10903-10914. Chatfield, R. B.; Crutzen, P. J. J. Geophys. Res. 1984, 89, 7111-7132. Andreae, M. O.; Raemdonck, H. i 1983, 221, 744-747. Harvey, G. R.; Lang, R. F. Geophys. Res. Lett. 1986, 13, 49-51. Barnes, I.; Hjorth, J.; Mihalopoulos, N. Chem. Rev. 2006, 106, 940-975.

88 [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]

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[28]

Maofa Ge, Lin Du and Kun Wang Khoroshko, L. O.; Petrova, V. N.; Takhistov, V. V.; Viktorovskii, IV; Lahtiperä, M.; Paasivirta, J. Env. Sci. Pollut. Res. 2007, 14, 366–376. Kima, K.-H.; Jeona, E.-C.; Koob, Y.-S.; Im, M.-S.; Younc, Y.-H. Atmos. Environ. 2007, 41, 3829–3840. Merritt, Jr. C.; Bazinet, M. L.; Sullivan, J. H.; Robertson, D. H. J. Agr. Food Chem. 1963, 11, 152-155. Wilkins, K.; Larsen, K. Chemosphere 1996, 32, 2049-2055. Hynes, A. J.; Wine, P. H.; Semmes, D. H. J. Phys. Chem. 1986, 90, 4148-4156. Stickel, R. E.; Zhao, Z.; Wine, P. H. Chem. Phys. Lett. 1993, 212, 312-318. Jensen, N. R.; Hjorth, J.; Lohse, C.; Skov, H.; Restelli, G. Int. J. Chem. Kinet. 1992, 24, 839-850. Kinnison, D. J.; Mengon, W.; Kerr, J. A. J. Chem. Soc. Faraday Trans. 1996, 92, 369372. Yin, F. D.; Grosjean, D.; Seinfeld, J. J. Geophys. Res. 1986, 91, 14417-14438. Atkinson, R.; Aschmann, S. M. Int. J. Chem. Kinet. 1984, 16, 259-268. Martinez, R. I.; Herron, J. T. Int. J. Chem. Kinet. 1978, 10, 433-452. Du, L.; Xu, Y.; Ge, M.; Jia, L.; Yao, L.; Wang, W. Chem. Phys. Lett. 2007, 436, 36-40. Du, L.; Xu, Y.; Ge, M.; Jia, L. Atmos. Environ. 2007, 41, 7434–7439. Becker, K. H.; Inocencio, M. A.; Schurath, U. Int. J. Chem. Kinet. 1975, S1, 205-220. Akimoto, H.; Finlayson, B. J.; Pitts, J. N. Chem. Phys. Lett. 1971, 12, 199-201. Prinn, R. G.; Weiss, R. F.; Miller, B. R.; Huang, J.; Alyea, F. N.; Cunnold, D. M.; Fraser, P. J.; Hartley, D. E.; Simmonds, P. G. Science 1995, 269, 187–192. Wingenter, O. W.; Kubo, M. K.; Blake, N. J.; Smith Jr., T. W.; Blake, D. R.; Rowland, F. S. J. Geophys. Res. 1996, 101, 4331–4340. Canosa-Masa, C. E.; Duffya, J. M.; Kingb, M. D.; Thompsona, K. C. Atmos. Environ. 2002, 36, 2201–2205. Shu, Y. H.; Atkinson, R. J. Geophys. Res. 1995, 100, 7275–7281. Atkinson, R.; Baulch, D. L.; Cox, R. A.; Crowley, J. N.; Hampson, R. F.; Hynes, R. G.; Jenkin, M. E.; Rossi, M. J.; Troe, J. Atmos. Chem. Phys. 2004, 4, 1461-1738. Shallcross, D. E.; Vaughan, S.; Trease, D. R.; Canosa-Mas, C. E.; Ghosh, M. V.; Dyke, J. M.; Wayne, R. P. Atmos. Environ. 2006, 40, 6899-6904. Nielsen, O. J.; Sidebottom, H. W.; Nelson, L.; Rattigan, O.; Treacy, J. J.; O'Farrell, D. J. Int. J. Chem. Kinet. 1990, 22, 603-612. Daykin, E. P.; Wine, P. H. Int. J. Chem. Kinet. 1990, 22, 1083-1094.

In: Atmospheric Science Research Progress Editor: Chih-Hao Yang

ISBN 978-1-60456-439-6 © 2009 Nova Science Publishers, Inc.

Chapter 4

GLOBAL ATMOSPHERIC CHANGES FROM AEROSOL EMISSIONS: WHY IS WEST AFRICA SO IMPORTANT? Okey. K. Nwofor∗ Department of Physics, Imo State University, PMB 2000, Owerri, Nigeria

Reviewed by Theo. Chidiezie Chineke Atmospheric Physics Group, Department of Physics and Industrial Physics, Imo State University, PMB 2000, Owerri, Nigeria.

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ABSTRACT Changes in the global atmosphere have become highly pronounced since the last two decades or so. These changes are mostly precipitated by variations in the concentration of particulate and chemical species in the earth’s atmosphere at different time and space scales. In this paper, the West African region is considered as a major player in the processes that lead to the most significant changes noticed in the global weather and climate system expecially with regard to aerosol emissions. In this region there is a complex interaction between ecosystem processes, human factors arising from the region’s present stage of socio-economic development and a pre-existing and obviously complicated and highly variable weather system giving rise to what may possibly be the world’s most significant aerosol region. Studying the present and unfolding aerosol emission scenarios is a key step towards understanding climate variability in West Africa. Such studies and others have been the preoccupation of several international collaborations. These efforts need to be stepped-up expecially with regard to imputes from local scientists and personnel.

Keywords: Atmospheric aerosols, Climate change, West Africa, Sahara dust, Biomass burning. ∗

Email: [email protected]

90

Okey. K. Nwofor

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1. INTRODUCTION The earth’s atmosphere is a complicated system composed of a mixture of gases, including moisture and suspensions of liquid and solid particles called aerosols. These substances moderate the transmission of light and heat from the sun to the earth. Within the atmosphere, laws of fluid mechanics and thermodynamics govern the re-distribution of matter and energy. The resulting circulation is what gives rise to a highly variable global weather system and its long -term behavior i.e climate. Scientific evidence over the last two decades point to the fact that the global atmosphere and the associated climate could be changing in an unprecedented manner. The earth’s climate is of course inherently determined by the several interactions and feedback processes between the atmosphere, oceans and the land and these easily change due to several natural and anthropogenic factors. One of the greatest evidences perhaps of a changing climate is the increasing exponential trend (at least in the last 200years) in the mean annual temperature at the earth’s surface averaged over the entire globe (AMS, 2003).Coherent increases in surface temperatures with other parameters such as population, CO2 emissions, as well as the atmospheric CO2 concentrations have justifiably implicated human activities as the most probable cause of climate change (see Berger, 2000; Crutzen, 2002). These activities include primarily energy and land use; which give rise in addition to CO2 emissions, increasing atmospheric abundance of other green house gases as well as aerosols. The climate forcing action of greenhouse gases are today very well understood within the scientific community (IPCC, 2001), and these have a combined effect of increasing global warming. The climate forcing contributions of atmospheric aerosols on the other hand although poorly understood, is generally thought to give rise to positive forcing if black carbon aerosols are involved and cooling if sulfates, nitrates and soil dust are predominant in the aerosol population.(see also Hansen and Sato, 2001).In the stratosphere where there has been a global ozone decline ( ~ 0.6 % per year; at mid-latitudes and 0.2% per year at sub-tropics), sulfate and carbon aerosols are strongly thought to provide sites for ozone destroying heterogeneous chemistry (see also Schneider, 2002). The now very much discernible consequences on man of atmospheric changes through associated aerosol effects are very well documented and need not be repeated here. These are known to apply to the entire globe but with a scale of vulnerabilities that depend on location and on socio-economic development. Compared to most of the greenhouse gas emissions which arose traditionally from industrial activities in the mid-latitude regions of the world and added recently to by emissions from similar activities from Asia and the South Americas, global aerosol pollution is dominated by mineral dust emissions from desert and arid regions of the world and from biomass burning activities and these are mostly from Africa expecially West Africa. Although the entire African continent is of major significance in modulating global climate, due to its large contrasts in surface terrain and vegetation, there is perhaps no region where these contrasts and variations are more remarkable than in West Africa where land cover is perhaps the most rapidly decreasing in the world arising principally from growing population and poverty forming an increasingly gigantic reservoir of natural and anthropogenic aerosols. In this paper, the influence of the West African region in the global atmosphere is discussed on the basis of some available data with particular reference to:

Global Atmospheric Changes from Aerosol Emissions

91

a) The aerosol emission sources, intensities and temporal/spatial patterns, and ; b) The climatic effects of the rapidly changing land cover. These two factors are significant because they lead to internal/regional variability which transforms the global atmosphere through typical tropical circulation process.

2. WEST AFRICA: SOME GEOGRAPHICAL FACTS

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The region described as West Africa in this paper is located between longitudes 20o W and 20o E and latitudes 0o and 20o N. The climate of this region is essentially tropical but with significant spatial variations resulting in multiple climate zones that stretch from the Atlantic coast and progressively increasing in dryness towards the Sahara in the north. These different zones are distinguished mainly by differences in mean precipitation. The major divisions are the tropical rainforest (rainfall~ 2000-4000mm per-Annum (P/A)), the Sahel Savannah (rainfall~500-750 mm P/A) and the semi-arid/arid zones (rainfall < 500 mm P/A). (http://countrystudies.us/nigeria/33.htm). Humidity varies more gradually within these zones giving rise to the humid, moist sub-humid, dry sub-humid, semi arid, arid, and hyper arid zones. These major climatic zones are illustrated in the maps of Africa and West Africa shown respectively in figures 1a and 1b.

*1

Figure 1a. Map of Africa showing climatic zones as indicated in the legend. West Africa is seen to be traversed by many of this zones-aslo see fig 1b. Source; Granich S, Tiempo, Issue 59, April 2006.

92

Okey. K. Nwofor

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*2

Figure 1b. A map of a section of West Africa, showing the climatic zones in different resolution. The dashed box is the region described as West Africa in this paper (Adapted from the African Monsoon Multidisciplinary Analysis (AMMA) - http://www.amma-international.org. Figure 1. A section of Africa showing Local variations in rainfall are moderated by features.

(Figure 1*1 is adapted from Granich, 2006, showing the systematic evolution of aridity in West Africa from the Sahara in the north towards the coast; while Figure 2 *2 is reproduced from the African Monsoon Multidisciplinary Analysis (AMMA) website-http://www.ammainternational.org, showing the region described as West Africa in this paper-within the dashed box). Throughout the region, there are two seasons; the wet and the dry seasons. These are controlled by two air masses, a moist South Westerly (SW) that blows from the Atlantic ushering in rainfall from April through September and a dry continental North Easterly from the Sahara which brings the dry dusty harmattan weather around December and later the full

Global Atmospheric Changes from Aerosol Emissions

93

dry season till March of the succeeding year. Minor local variations occur as a result of effects of rivers, hills and vegetation. Temperatures are generally high within the region with marked seasonal as well as very pronounced diurnal variations. For instance at Lagos Nigeria (6o 27I N) in the coastal area, average highs and lows are 28oC and 23oC in the peak wet season (June/July) and 31oC and 23oC in January. Marked inter-annual variations in the climate of West Africa have occurred over the past 50 years. West Africa’s total population is put at ~ 220 million people, with more than half of this number living in Nigeria. (www.unf.edu/dept/flawi/-). At the present annual growth rate, the regional population is expected to double by 2025. (www.earthplatform.com/west/africa/countries). In the coastal and rain forest areas, where majority of the people reside, (unisdr.unbonn.org/ewpp/project_view.php?project), the major occupations are land cultivation, logging and fishing, while in the northern axis, the predominant occupations are land cultivation and cattle rearing. These forms of livelihood of the indigenous population are important as they affect the patterns of exploitation of forest resources in particular and future trends in the aerosol loading with severe consequences on the regional and global weather and climate.

3. NATURE OF WEST AFRICA’S AEROSOL EMISSIONS

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3.1. Preamble Satellite images according to Marticorena and Cairo, (2006) suggest that West Africa could be the world’s most significant source of aerosols as inferred from the density and persistence of satellite-captured plumes within this region. In fact the region has been considered to be a likely source of about half of the global mineral aerosols (Andrea, 1995). Dust aerosols in particular modify the earth’s radiation balance by scattering or absorbing solar and thermal radiation and contribute to cloud microphysics when hygroscopic material is attached. It is common knowledge that the effects of aerosols in comparison to those of greenhouse gases are much more pronounced within local and regional scales; for instance in a heavily polluted region in China, model studies of aerosol- climate forcing (Giorgi and Bi, 2002) yielded a spatially varying top of Atmosphere (TOA) radiative forcing ranges b/w -1 to 15W/m2 in summer from anthropogenic sulfates with fossil fuel emissions exerting between 0.5-2W/m2. The possible impacts of aerosols on the observed drought conditions in the Sahel have added to the regional significance of atmospheric aerosols in West Africa. According to the United Nations Environment Program, (UNEP, 1997), the Sahel region has in the past 25 years or so experienced about the most “substantial and sustained” reduction in global rainfall reductions amounting to ~ 2 standard deviation units over a century. Lrepert (2006) has shown that the effect of including anthropogenic greenhouse gases and aerosols simultaneously in climate models is to reduce evaporation and precipitation in the annual mean of global precipitation mean due to possible increase in residence time of moisture. At local and regional scales desert aerosols are implicated in drought occurrences in the Sahel (N’Tchayi et al, 1994;cyclehttp://www.agu.org/meetings/fm06/fm06-sessions/fm06_A32C.

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Okey. K. Nwofor

html) and there have been increasing evidence of rain suppression by desert dust aerosols (Rosenfield et al, 2001). In addition to regional radiative forcing, visibility changes and cloud microphysical effects which affect precipitation cycles can occur on a regional scale with tremendous influence on the global system. These local effects as already indicated can affect the large scale global circulation patterns. Increases in regional aerosol effects expecially in West Africa have the potential of increasing the unpredictability of global weather and climate as this interacts with a largely complicated pre-existing tropical weather and climate system (CLIVAR, 1999). Perturbation of the monsoon cycles expecially towards drought conditions has potentials of intensifying aerosol loading scenarios in the Sahara region. Changing land cover modify the planetary albedo and affects evapotranspiration and hence moisture/ heat budget and precipitation (Betts, 2004a). These affect the diurnal temperature ranges (DTR) which according to Dai et al, (1999), have shown world-wide decrease in the last 4-5 decades. In terms of some direct global effects, West African aerosols can be transported across to various continents. Kaufmann et al, (2004), have shown using the MODerate resolution Imaging Spectro-radiometer (MODIS) data that ~ 230 ± 80tg of dust are transported annually from Africa to the Atlantic alone. At locations in Europe, such as Lampadusa (35.5 o N; 12.6o E) large aerosol optical depths (AOD: 500nm > 0.5) and low values of Angstrom Exponent (AE) (AE< 0.5), suggestive of Sahara dust advected over the Mediterranean from Africa have been observed (Pace et al, 2005; also see Perrone et al, 2005).A major consequence on the global atmosphere is that reduction in available solar radiation impacts atmospheric chemistry since these affect photolysis rates and heterogeneous reactions (Martin et al, 2003; Crutzen, 1994; Crutzen and Zimmermann, 1991).

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3.2. Some Characteristics of Aerosols Loading in West Africa Studies of aerosol source strengths, emission trends and other temporal and spatial characteristics have provided some useful information regarding aerosols in West Africa: In most of these investigations, special prominence has been given to the coarse harmattarn dust aerosol which understandably produces the most serious and documented aerosol emission effects. (Pinker et al, 1994; Utah and Nggada, 1994; Okeke and Okoro, 2006, etc).Using techniques of Atomic Absorption Spectroscopy on harmattarn dust (Chiemeka et al , 2007) at Uturu a rain forest/Sahel transition site in Nigeria it has been shown that the harmattarn aerosols are highly enriched in metals such as potassium (~ 5.1mg/kg ), magnesium (~ 2.6mg/kg ), calcium (~ 102.7mg/kg), iron (~14.8mg/kg), zinc (~25.1mg/kg ), manganese (~4.0mg/kg ), and lead (~ 1.0mg/kg). The presence of these metallic species in the measured harmattarn aerosol samples could possibly imply that in addition to providing considerable reactive or catalytic sites for heterogeneous reactions, the harmattarn aerosol could also posses complicated scattering and absorptive properties associated with the various trace metals; expecially at different parts of the spectrum (Tegen et al, 1996). Most results on West Africa’s aerosol characteristics are from measurements conducted for very limited periods. Comprehensive long-term measurements needed for establishing important long-term trends have been hampered by the unavailability of suitable instruments and trained personnel. In the past ten years however this situation has improved with the institution of the National Aeronautics and Space Administration (NASA), AErosol RObotic NETwork (AERONET).This network of CIMEL sun photometers provides data on aerosol

Global Atmospheric Changes from Aerosol Emissions

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optical depth (AOD) and size information useful for validating satellite data (Holben, et al, 1998). In West Africa more than 12 AERONET sun photometer stations which include the station at Ilorin Nigeria are located and these have provided useful data for studying aerosol emission sources, types and patterns. The Ilorin site located in the sub-Sahel area of West Africa captures peculiar interactions between the annual southward and northward components of the intertropical Convergence Zone (ITCZ) which bring the dusty harmattarn weather in December through March (http://aeronet.gsfc.nasa.gov/climo/Ilorin.html. Some results using a six-year Ilorin AERONET data (1998-2003) have provided additional insight into the aerosol loading scenario in West Africa over a considerably prolonged period. 7

6

Wind Variance AOD(500nm)

5

4

3

2

1

0

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Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

*3Figure 2. Shows the typical yearly cycle of aerosol optical depths at a sub-Sahel site (Ilorin Nigeria).averaged over a six year period (1998-2003), plotted with the monthly wind speed variance over the average for 4 years. The AOD measurements are for the 500nm wavelength. It is clear from the figure that the aerosol flux for the site intensifies with the harmattarn wind. But although the harmattarn wind is highest by December/January each year, The aerosol flux (as indicated by the (AOD) maximizes by February when the harmattarn dust combines with farming season biomass burning .This is corroborated by the AE distribution which as an indication of size shows a bimodal character in the dry season reflecting the interaction of two aerosol size groups i.e. mineral dust and biomass smoke ; the schematic is shown in figures 3a and 3b ( see details on these conclusions in Nwofor et al, 2006).

96

Okey. K. Nwofor Gaussian distribution of wet-season Angstrom Exponenent (500-870nm) 45

40

35

frequency

30

25

20

15

10

5

0 -0.5

0

0.5

1

1.5

2

2.5

Angstrom Exponent

Figure 3a.

Gaussian distribution of dry-season Angstrom Exponent (500-870nm) 35 combined distribution large particle contribution

30

small particle contribution

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frequ ency

25

20

15

10

5

0 0

0.2

0.4

0.6

0.8

1

Angstrom Exponent

Figure 3b.

1.2

1.4

1.6

1.8

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In particular, the seasonal features of the Aerosol Optical Depth (AOD) in this sub-Sahel site (Ilorin, Nigeria) have been studied which have obviously linked the seasonal cycles i.e. the dry-season highs and wet-season lows to the monsoon cycle, which strongly and essentially incorporates wind regime and variance as strong particle dispersal factors (Nwofor, 2006; Nwofor and Chineke, 2007)-see Figure 2, which is a plot of monthly averages of AOD computed for the six year data (1998-2003) and wind speed variance over an annual mean for four years (2000-2003). The figure shows the influence of both the wind speed and the wind regime on aerosol flux. Although the AOD variation from the figure follows a discernable monsoon cycle, the bimodal character of the wind speed variance representing the monsoon SW (July) and the harmattarn NE (December/January) when co-related with the AOD variation provides an interesting result. The February AOD maximum is shown to continually occur for this site at a time when the harmattarn NE has subsided. This feature in the AOD pattern is most likely due to a combination of harmattarn dust, locally-raised dust and farming season biomass smoke (*3). Aerosol emissions from this region are therefore both natural and anthropogenic. Statistical analysis of AERONET AOD and Angstrom Exponent (AE) distribution has been used to infer sharp contrasts in seasonal characteristics of the aerosol loading (Nwofor et al, 2006).(see figure 4a and 4b). In the wet season, for instance, when the monsoon SW trade winds are active, the aerosol loading which is mainly from locally raised dust and emission from factories (gas flaring inclusive) traffic and refuse dumps, all of which have reduced wet season air borne presence as a consequence of wash out by rain introduce one major mode in the AE distribution, whereas in the dry season, the AE distribution is bimodal (Sahara dust and biomass smoke). Seasonal analysis of retrieved size distributions show that the wet-season particles size is most probably in the range 0.2 < r < 0.5 um .In the dry season, on the other hand when the NE trade winds cause an influx of Sahara dust superimposed on locally raised dust in the Sahel, and farming season biomass burning the size range is broader and far beyond 0.6um. From analysis of retrieved size data, the predominant aerosol shape is inferred to be mainly spheroids. A wide range of extinction efficiencies could be encountered in the region owing to the wide size range. Considerable light attenuation, which cover the entire solar wavelengths (Adeyewa and Balogun, 2003) occur in the dry season during the harmattarn period with characteristic early morning haze occasioned by moisture (from increased surface evapotranspiration) condensation on aerosol particles causing increased scattering efficiencies (see Bergin, 2000).The increased optical attenuation have pronounced effects on agriculture in the region (Chineke et al, 2005), health (see Nwofor, 2007) and visibility (N’tchayi et al, 1997; Utah and Nggada, 1994).These may also have pronounced impact on ground-based remote sensing of trace gases in the entire region (Nwofor and Chineke, 2006).

3.3. Patterns of Spatial Variability The possibilities of constructing spatial trends of aerosol loading within West Africa have been given reasonable academic priority with useful results (N’tchayi et al, 1994). In order to conduct studies of spatial trends objectively, integrated dynamic models that incorporate land surface, ocean and atmospheric processes as well as aerosol chemistry and monsoon

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dynamics would be required. Satellite images as well as ground-based data for instance of surface visibility (see Husar et al, 2000) are providing reasonable information on spatial distribution of aerosol loading loading in West Africa. In addition, synthesis of measurements of aerosol optical characteristics within the region have continued to provide interesting results that seem to be leading to very strong hypothesis regarding the spatial variability. A possible spatial character discussed by Utah (1995) and strongly reinforced by Nwofor (2006), based on a synthesis of AOD and AE measurements by Cerf (1976) at Ouagadougou (120 N)), Utah (1995 ), in Jos, Nigeria (9057iN) , Oluwafemi (1979 ), in Lagos Nigeria (6o27iN) and AERONET measurements at Ilorin, Nigeria (8o32i N) (Nwofor, 2006), shows that coarser particles (lower AE) are encountered as one moves towards the Sahara source. (See figure 4). The aerosol flux however as indicated by the AOD appears to increase as one moves from the Sahara towards the coast, showing the influence of transport in aerosol loading within the region; but the most intense aerosol flux seems to be around the sub-Sahel region (Ilorin), where wind blown Sahara dust combines with locally raised Sahel dust and farming season biomass smoke. This situation calls for further investigation. Variations of OD and Angstrom Exponents with lattitude

Angstrom(500nm820nm)

12

AOD-500nm

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la ttitu d e (D e g r e e s )

10

8

6

0

0.2

0.4

0.6

0.8

1

1.2

AOD(500nm) and Angstrom(500nm-800nm)

Figure 4. Spatial variations of AOD within West Africa synthesized from measurements made at same wavelengths for measurements conductions at locations with the indicated longitudes (nearest degree).

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4. THE LIKELIHOOD OF INCREASING EMISSIONS Consideration of the unfolding socio-economic scenario in West African countries points towards possible increases in traditional and new sources of aerosols. The new sources would most likely come from industrialization and urbanization which would increase trafficemitted aerosols like soot, and aerosols from refuse burning as discussed in Nwofor (2007). These trends would likely be similar in most parts of Africa. In terms of peculiarities however, the possible increases in traditional sources i.e. dust and biomass smoke are of more critical concern in West Africa and these are due to the twin process of desertification and deforestations.

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4.1. Nature of Desertification and Deforestation in West Africa The Sahara Desert, covering most of Africa north of the equator is the largest desert in the world. From north to south the Sahara is between 800 and 1,200 miles and is at least 3,000 miles (4,800 km) from east to west. On the west, the Sahara is bordered by the Atlantic Ocean and on the east by the Red Sea, and to the north are the Atlas Mountains and Mediterranean Sea. (http://library.thinkquest.org/16645/the_land/sahara_desert.shtml).;( http://library.thinkquest.org/16645/the_land/Sahara_desert.shtm). The Sahara is the natural reservoir of mineral dust aerosols in West Africa. Together with the much smaller Kalahari, the Sahara is responsible for over 230±80 tg of dust transported and deposited in different parts of the globe as reported by Kaufman et al, (2004). Present observations show that the Sahara is gradually encroaching into the non-desert areas of West Africa. The causes of this shift are both natural such as from changing rainfall and wind patterns and anthropogenic (i.e. deforestation from bush burning and over-grazing). Vegetation change has one of the most crucial effects on desertification; vegetation reduction increases the albedo of the soil and diminishes rainfall which reduces the land cover further. (See for example Nicholson and Coauthors, 1998). Reduced land cover implies that more surface area of desert dust is available for lifting. Increased drought conditions in addition cause loosening of the surface soil for enhanced uplift as well as improved residence time of the particles in the absence of the major sink which is rainfall. Unlike natural causes of desertification such as drought and wind, deforestation as a process leading to unprecedented land cover change is critical because it is becoming more intense and caused by socio-economic factors which although can be addressed have been allowed to intensify. Although climate is determined by the seasonal concentration in solar radiation, temperature, and precipitation which also determine the predominant vegetation, the reciprocal effect i.e. vegetation determining climate trends is perhaps becoming much more evident. According to the United Nations Food and Agricultural Organization (see Betts, 2004b) the rate of forest cover change of tropical forests relative to temperate zone forests is put at ~ -12.6 million hectare (ha) per year (yr), : +1.3 million ha/yr. Much of this depletion is taking place in West Africa, where over 90% of the original forest has been lost with only a small part of what remains qualifying as frontier forest. (http://www.afrol.com/features/10278).Since the felled forest is usually burnt; one often

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discovers that changing albedo from increasing aridity is coupled to increasing biomass burning from deforestation activities in West Africa. Global concern over biomass burning in parts of Africa has been largely precipitated by observations of biomass burning products in distant lands which were traced to Africa. Particular mention is the detection over the Atlantic of signals characteristic of biomass products and stratospheric ozone depletion over the Atlantic and residual high-concentration tropospheric ozone anomaly over south Atlantic as revealed by NASA Total Ozone Mapping Spectrometer (TOMS) which have been strongly linked to biomass burning in Africa and elsewhere (Swap et al, 2002). Atmospheric chemistry due to action of trace gases has been the greatest motivation in most biomass burning studies ahead of aerosols. Adams and Liu (2000) have attributed ~ 40% of the global CO2; ~ 38% of global tropospheric ozone increases and ~7% of total global particulate matter to biomass burning activities. Andrea and Merlet (2001) have articulated from different studies over 70 chemical species from biomass burning in Africa. The immense biomass burning activity in the West African tropical rainforest zone from bush burning and from grass land fires leads to high incidences of tropospheric ozone from photochemical production. (Cross et al, 1988; also Chineke, 2007). The West African rainforest compared to other frontier forests such as the Amazonians and the Congo basin is currently the most depleted-It sustains a greater population than the other two and therefore more deforested. Within West Africa several particles interact through vertical mixing facilitated by the temporally shifting Inter tropical convergence zone (ITCZ). The chemical action of the organic aerosols from biomass burning in West Africa are of immense importance as the circulation pattern inherent in this area easily lead to transport of aerosol loaded air and ozone depleting air into the atmosphere from where they enter the stratosphere (see Schneider, 2002). The socio-economic preconditions that intensify the emissions of anthropogenic aerosols in Nigeria which is Africa’s most populous country have been extensively discussed in Nwofor (2007).These conditions are also the same that result to biomass burning emissions of aerosols in West Africa and drastic changes in land cover. These are increasing population, poverty, illiteracy, crude agricultural practices and rapid urbanization. a) Over population; implies that there are more mouths to feed, more land to cultivate, more furniture to use, more fire wood , more animals to graze, etc. b) Poverty; intensifies over-exploitations of forest resources for food and income generation such as logging for firewood sale, and furniture. c) Illiteracy; the inability to appropriate scientific information related to need for reforestation and healthy agricultural practices d) Related to agriculture is the use of crude agricultural practice mainly by shifting cultivation and itinerant animal rearing which make much demand on forests resources. e) Rapid-urbanization results in clearing of more land for new settlements as urban areas expand.

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5. CONCLUDING REMARKS From the discussions that have followed in this paper, it is obvious that with many western countries taking giant steps and others being expected to, towards the reduction in industrial and traffic emissions through some actions with respect to the Kyoto protocol, West Africa could be about the most influential with respect to global atmospheric changes in the coming decades both in terms of both green house and aerosol emissions. The location, climate and unfolding socio-economic conditions (rising population, crude land use practices and possibly industrialization) as discussed in this paper are obvious predisposing factors towards phenomenal natural and anthropogenic emissions. Since the predictability of African regional climates on seasonal to inter-annual scales is dependent on several internal factors which must continuously be studied to get a better insight into the processes, possibilities of arriving at a better understanding of the problems require more cocoordinated approach. The efforts of study groups such as the climate research for Africa (CLIVAR) in this regard is commendable.CLIVAR in particular views the large Africa land mass on two sides of the equator as being significant to global circulation with regional anomalies such as from West Africa which is ~ 1/4th of Africa drastically affecting this. (CLIVAR, 1999). The prolonged drought over West African Savannah regions is now the most evident regional variation being studied under the auspices of CLIVAR. These drought conditions have been strongly linked with vegetation in the large-scale atmospheric circulation (Eltahir, 1996; Eltahir and Gong, 1996).Biomass burning studies in West Africa including among others the Dynamique et Chimie Atmospherique en Foret Equatoriale- Fire of savannah (DECAFEFOS) need to be intensified. –Other research goals which among others should include understanding the mechanism of natural and anthropogenic aerosol and trace gas emissions from West Africa- i.e. the sources and dispersion mechanisms need to be pursued. The scientific world seems to have understood part of the problem as indicated by the number of scientific studies on several aspects of the West African climate system for instance the NASA-GSFC-AERONET stations in West Africa are already providing very valuable data on aerosol optical climatology-. Much more however needs to be done in getting more people expecially from West Africa to use these data. Improved abilities in predicting and projecting land resource changes in Africa and elsewhere over the next four decades and their potential impacts on land resources and human livelihoods have been the motivation behind studies by AGRHYMET (http://www.agrhymet.ne), the Sahel Institute (INSAH – http://www.insah.org), the U.S. Geological Survey’s EROS DATA Center (http://www.usgs.gov), and the World Resource Institute (WRI – http://www.wri.org). Studies on the West African Monsoon (WAM), motivated by drought conditions from the 70s have been the objective of AMMA (See http://www.ofps.ucar.edu/amma/amma_summary. htm). Part of the AMMA idea which is very important is to understand aerosol microphysics and heterogeneous processes. (Vogel, 2005, personal communication).The emission scenarios described in this paper are cardinal to the AMMA objectives. These studies which are mainly geared towards understanding aspects of West African climate should also include studies of regional impacts and vulnerabilities, and framework for stability and adaptive capacities to changes. Improving general and expecially scientific literacy for the purpose of understating and appropriating scientific information involving

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West Africa is important for stepping up theses studies to more beneficial levels. A much greater number of local scientists are required to improve data generation and injection of much-needed local perspectives on the issues. Stemming hyper population trends and poverty are most critical to stabilizing and reversing the identified anthropogenic climate change trends.

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REFERENCES Adams F and Liu X, 2000: Characterization of biomass burning particles; in ERCA Vol 4: From weather forecasting to exploring the solar system, edited by Claude Boutron EDP Science Les Ulis, France, Pp 83-93. Adeyewa Z.D and Balogun E.E, 2003: Wavelength dependence of aerosol optical depth and the fit of the Angstrom law. Theoretical and Applied Climatology, 74, 105-122. AMS, (2003): Climate change research; issues for the atmospheric and related sciences. American Meteorological Society (AMS) Executive Summary. Bull. Amer. Met. Soc. 84,508-515. Andrea M.O 1995: Climatic effects of changing atmospheric aerosols level. World survey of climatology 16; future climates of the World, edited by A Henderson Sellers, Elsevier, Amsterdam, 341-392. Andréa M.O and Merlet P, 2001: Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles, Vol 15, No 4, 955-966. Bergin M.H, 2000: Aerosol radiative properties and their impacts; in ERCA Vol 4: From Weather Forecasting to Exploring the Solar System, edited by C.F Boutron. EDP. Les Ulis, France, pp 51 – 59. Berger A, 2000: Global warming; fact or fiction; in ERCA Vol 4: From Weather Forecasting to Exploring the Solar System, edited by C.F Boutron. EDP Science Les Ulis, Pp 25-39. Betts, R.A, 2004a: Vegetation-atmosphere interactions; an introduction. Lecture presented at the European Research Course on Atmospheres (ERCA), Grenoble France, January, 22, 2004. Betts R.A, 2004b: Forcing of climate by anthropogenic vegetation change. Lecture presented at the European Research Course on Atmospheres (ERCA), Grenoble France, January, 22, 2004. Cerf A, 1976: Atmospheric turbidity in West Africa. Proc. Symposium on radiation in the atmosphere, edited by H.J Bolle. Science Press Princeton, P 16-17. Chiemeka I.U, Oleka M.O, and Chineke T.C, 2007: Determination of aerosol metal composition and concentration during the 2004/2005 harmattarn season at Uturu, Nigeria. Adv.Sc. and Tech., Vol 3/4 (in press). Chineke T.C, 2007: The “missing link” in ground and TOMS satellite total ozone measurements in equatorial Africa. Adv. Sc and Tech.Vol 1, No1, 51-56. Chineke T.C, Ekenyem B, and Nwofor O.K (2005): Relation between global radiation and food production in a humid tropical climate of West Africa. Journ. of Central European Agriculture, Vol 6 (2005) No 2. CLIVAR, 1999: Climate Research for Africa. December 1999 WCRP Informal Report No. 16/1999. ICPO Publication Series No 2.

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Cross B., Delmas R., Nganga D., Clairac B and Fontan J, 1988: Seasonal trends of ozone in equatorial Africa: Experimental evidence of photochemical formation. Journ. Geophys. Res, 93, 8355-8366. Crutzen P.J, 1994: An overview of atmospheric chemistry. In ERCA Vol 1, Topics in atmospheric physics and chemistry, edited by C.F Boutron, EDP LES Ulis France, 63-88. Crutzen P. J, 2002: The anthropocene. Journal De Physique IV, Vol 12, Pr 10-1. Crutzen P.J and Zimmermann P.H (1991): The changing photochemistry of the troposphere Tellus 43, 136-157. Eltahir E.A, 1996: Role of vegetation in sustaining large-scale atmospheric circulation in the tropics. J. Geophys. Res, 101, No D2, 4255-4268. Eltahir E.A and Gong, 1996: Dynamics of wet and dry years in West Africa. J. Climate, 9, 1030-1042. Giorgi F and Bi X, 2002: Direct radiative forcing and regional climatic effects of anthropogenic aerosols over East Asia: A regional coupled climate-chemistry/aerosol model study. Jour. Geophys. Res 107, No D20, 4439, doi: 10.1029/2001JD001066, 2002. Granich, S (2006): Deserts and desertification, Tiempo Issue 59, Pg 9. Hanson J.E and Sato M, 2001: Trends in measured climate forcing agents. Proceedings National Academy of Sciences of the United States of America, December 18, 2001 Vol 18 No 26, 14778-14783. Holben B.N and Co-workers, 1998: AERONET – A federated instrument network and data archive for aerosol characteristics. Remote. Sensing and Environ. 66, 1 – 16, 1998. Husar R.B, Husar J.D and Martin L, 2000: Distribution of continental surface aerosol extinction based on visual range data. Atmos. Env.34, (9-30), 5067-5078. IPCC, (2001), Climate Change: The intergovernmental Panel on Climate change (IPCC), Third Assessment Report, Cambridge University Press, Cambridge and New York. Kaufman Y.J, Loren I, Remer L.A, Tanre D, Ginoux P, and Fan S, 2004: Dust transport and deposition observed from the Terra-MODIS spacecraft over the Atlantic Ocean. J. Geophys. Res. doi: 10.1029/2003JD004436. Lrepert, B.G, 2006: Effects of aerosols on the thermodynamics of the global water cyclehttp://www.agu.org/meetings/fm06/fm06-sessions/fm06_A32C.html. Marticorena, B and Cairo, F, 2006: EOP/LOP Aerosols Monitoring and Radiation (TT2b). AMMA International Implementation Plan-Version 2.0, Ch4, 2-15. Martin R.V, Jacob D.J, Yantosca R.M, Chin M, and Ginoux P, 2003: Global and regional decreases in troposphere oxidants from photochemical effects of aerosols. J. Geophys. Res.108, 4097, doi 10. 1029 / 200250002622,2003. Nicholson S.E and Co-authors, 1998: Desertification, drought and surface vegetation; an example from the West African Sahel. Bull. Amer.Meteorol. Soc 79, 815-829. N'tchayi G.M., Bertrand, J. J. ,Nicholson, S. E., 1997: The Diurnal and Seasonal Cycles of Wind-Borne Dust over Africa North of the Equator. Journal of Applied Meteorology, vol. 36, Issue 7, pp.868-882. N’Tchayi G.M, Bertrand J, Regrind M, and Baudet J, 1994: Temporal and spatial variation of atmospheric dust loading throughout West Africa over the last thirty years. Ann. Geophys,12,265-273. Nwofor O.K, 2006: Seasonality of Aerosol Optical Depth over Ilorin Nigeria. Unpublished PhD thesis, Department of Physics, Imo State University Owerri Nigeria.

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Nwofor O.K, and Chineke T.C, 2006: Radiance simulations of selected atmospheric species at different observer altitudes for ground-based FTIR spectroscopy: Implications for aerosol polluted sites in West Africa. Nigerian Journal of Physics, 18, No 2, 227-233. Nwofor O.K, and Chineke T.C, 2007: Mathematical representation of seasonal cycles of aerosol optical depths at Ilorin Nigeria using AERONET data. Global. Jour. Pure. Applied. Sc, 13, No 1, 285-293. Nwofor O.K, Chineke T.C and Pinker R.T (2006): Seasonal characteristics of spectral aerosol optical properties at a sub-Saharan site. Atmospheric Research, 85, 38-51. Nwofor O.K, 2007: Pondering a future of severe aerosol pollutions in Nigeria and the need for a monitoring network. Submitted to Int.J.Envir and Waste Mgt. Okeke F.N, and Okoro E.N, 2006: Measurements of aerosols parameters in Nsukka Nigeria. Nigerian Journal of Space Research 2, 37-46. Oluwafemi C.O, 1979: Preliminary solar spectro-photometric measurements of aerosol optical density at Lagos Nigeria. Atmos.Environment 13 1611-1615. Pace G, Di Sarra A, Meloni D, Monteleone F and Piacentino S, 2005:Observations of column aerosol optical properties at the ENEA remote station for climate observation at Lampedusa; influence of transport and classification of district aerosol types. Geophysical Research Abstracts Vol. 7, 06740, 2005. Perrone MR., Santesse M., Tafuro A.M., Holben B., and Smirnov A., 2005: Aerosol load characterisation over south-east Italy for one year of AERONET sun-photometer measurements. Atmos. Res. 75 Pp 111-133. Pinker R.T, Idemudia O and Aro T.O, 1994: Characteristics aerosol optical depths during the Harmattarn season in sub-Saharan Africa. Journal of Geophysical Research 21, 685. Rosenfield D, Rudich, y, and Lahav R, 2001: Desert dust suppressing precipitation – a possible desertification feedback loop. Proc. Nat. Acad. Sc 98, 5975-5980. Schneider, M, 2002: Continuous Observations of Atmospheric Trace Gases by Ground-based FTIR Spectroscopy at Izana Tenerife Island. Unpublished PhD Thesis, University of Karlsruhe Germany. Swap R.J, Annegarn H.J and Otter L, 2002: Southern African Regional Science Initiative (SAFARI, 2000): Summary of Science plan. South African Journal of Science 98, March/April, 2002. Tegen I, Lacis A, and Fung I, 1996: The influence of mineral aerosols from disturbed soil on the global radiation budget. Nature, 380, 419-507. UNEP 1997: IPCC Third Assessment Report. Working Group 2; Impacts, Adaptation and Vulnerability. Section 10.2.6. Utah E.U (1995): Aerosol optical density during the Harmattarn at Jos, Nigeria. Nig. Journ. of Phys. Vol.7. (1995) Pp 67 – 71. Utah E.U, and Ngadda AI, 1994: Visibility in the Jos harmattarn air and associated aerosol size and concentration. Nig. Journ. Phys, 6, 42-50.

In: Atmospheric Science Research Progress Editor: Chih-Hao Yang

ISBN 978-1-60456-439-6 c 2009 Nova Science Publishers, Inc.

Chapter 5

D ECADAL – TO –C ENTENNIAL S CALE C LIMATE –C ARBON C YCLE I NTERACTIONS FROM G LOBAL C LIMATE M ODELS S IMULATIONS F ORCED BY A NTHROPOGENIC E MISSIONS Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Pyzhevski, Moscow, Russia

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Abstract Simulations of the climate–carbon cycle interaction are discussed in comparison with observationally–based estimates for the global carbon cycle characteristics. Since the beginning of the industrial era, the storage of the carbon dioxide in the atmosphere is smaller than the corresponding anthropogenic emissions. This is due to uptake of the atmospheric carbon dioxide to the terrestrial biota and ocean. Moreover, during the the 20th century, the sink of the carbon dioxide from the atmosphere to the terrestrial ecosystems became larger, due to CO2 fertilisation effect. However, in the 21st century, the global climate models with the carbon cycle project that interactions between climate and carbon cycle basically lead to the stronger growth of the carbon dioxide burden in the atmosphere. These interactions were in the focus of the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4 MIP). One of the basic outcome of this and related activities is positive climate–carbon cycle feedback leading to enhanced buildup of CO2 in the atmosphere due to response of climate and corresponding changes in the terrestrial and oceanic uptakes of carbon. In particular, the soil respiration growth overcompensates the increase of the net primary production and leads to the diminishing sink of the carbon dioxide from the atmosphere top the living biota and soil. In some models and emission scenarios the terrestrial biota even eventually becomes the source of the carbon dioxide. The parameter of the feedback between climate and carbon cycle changes non-monotonically in the 20th and 21st centuries, depicting characteristic periods of the emission growth and the respective climate response. On a century timescale, climate–carbon cycle feedback may saturate. The 20th century observations of the carbon cycle are insufficient to constrain future evolution of the coupled carbon–cycle system. Nevertheless, they are able to narrow the respective uncertainty range. This can be illustrated, in particular, by employing the Bayesian statistical treatment of the respective ensemble. However, for

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Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko the most probable values of the governing parameters of the coupled system, climate– carbon cycle interactions enhance global warming in the 21st century by about 10% or even more under the SRES marker emission scenarios.

1.

Introduction

During the industrial era, anthropogenic emissions of carbon dioxide (basically related to to the fossil fuel, cement manufacture, and land use) led to the increase of the atmospheric concentration of carbon dioxide pCO2,a from 276 − 284 ppmv [55] in (these range is characteristic for the whole part of the last millenium before an industrialisation has started around 1750) to 379 ppmv in 2005 (as measured at the Mauna Loa Observatory) [57]. It is the carbon dioxide buildup in the atmosphere which is assumed to be basically responsible for the global warming of about 0.5 − 0.7 K as observed in the 20th century. Until recently, the most historical simulations and future projections of the climate changes with coupled climate models were perfromed with specified concentrations of greenhouse gases (CO2 , in particular) in the atmosphere [24, 46]. However, the corresponding anthropogenic emissions only partly stored in the atmosphere with the rest taken up by the oceans and by the terrestrial ecosystems. For example, according to [54], total (fossil fuel and land use) emissions for 1800–1994 amount 320 − 460 PgC. Only between one third and half of these emissions are stored in the atmosphere ( 165 ± 4 PgC). Oceanic uptake for this period has amounted 118 ± 19 PgC, and the terrstrial biosphere sink is estimated to be in the range 61 − 141 PgC [54]. The atmospheric buildup of the carbon dioxide for a given time interval is

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c0 ∆pCO2,a = Ec − Uoc − Ul ,

(1)

with c0 = 2.123 GtC/yr, Ec is cumulative anthropogenic CO2 emission for this time interval, and Uoc and Ul are corresponding oceanic and terrestrial uptakes of carbon. These fluxes, in turn, depend on the climate state and may be quite different for the future, anthropogenically forced, trajectory of the coupled climate–carbon cycle system. As a result, starting from the papers [10] and [16], a lot of climate modellers’ efforts are devoted to the simulations of the global carbon cycle, interactively coupled with the threedimensional climate models [5, 6, 18, 11, 30, 41, 19, 40, 48, 47, 12, 14, 13]. It was shown in those papers, that this interactive coupling changes the buildup of the carbon dioxide in the atmosphere in comparison to the hypothetical case, when the carbon cycle does not feel the climate changes. As a result, the so called climate–carbon cycle feedback (C3F) term has been introduced. This feedback can be measured, for instance, as a difference of the atmospheric CO2 concentration pCO2,a in some prechosen year under given carbon dioxide emission scenario between two sets of the numerical experiments. In the first set, the coupled (cpl) simulation is performed with the climate–carbon cycle model forced by the CO2 emissions. In the second set (uncoupled simulation, ucpl), the climate model is forced by the output of the carbon cycle model; in turn, the carbon cycle model is forced by the CO2 emissions, but not taking into account an influence of climate changes on the carbon cycle dynamics (or simply treating carbon dioxide as a radiatively non–active gas). The climate–carbon cycle feedback can be measured either in terms of the feedback

Decadal–to–Centennial Scale Climate–Carbon Cycle Interactions...

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ucpl f = ∆pCOcpl 2,a /∆pCO2,a .

(2)

parameter

or in terms of the feedback gain g = (f − 1)/f [16, 18]. Up to date, most coupled models implement only carbon cycle processes relevant to the decadal–to–centennial scale feedbacks between climate and carbon cycle. Among those processes are atmosphere–ocean exchange of carbon, terrestrail plant photosynthesis, their respiration and mortality, and soil respiration. The processes operating at much longer scales (e.g., silicate weathering) are not included in these models. This is consistent with typical length of simulations with such models ranging from decades up to a few millenia forced either by historical anthropogenic emissions or by anthropogenic emissions projected into the future. For the oceanic part, it is broadly accepted that the ocean exchanges the inorganic carbon with the atmosphere based simply on the solubility of the carbon in the sea water, while the oceanic chemistry can be treated as a background process [56]. A convenient and natural assumption employed in the most of the mentioned above simulations is the preindustrial climate–carbon cycle state was equilibrated, e.g., [58].

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In any simulation, the system inertia delays the climate response to early anthropogenic emissions of carbon dioxide. As a result, at the early part of simulations, coupled and uncouped runs should be almost indistinguishable between each other. In turn, f should be unity at initial time and close to unity slightly afterwards. If C3F is positive, f has to increase generally in the course of integration. However, as it will be shown below, its non–monotonic temporal behaviour of feedback parameter is possible due to temporal inhomogeneities of anthropogenic emissions and because of the near–saturated behaviour of the carbon dioxide absorption lines in the Earth’s atmosphere. The above–mentioned positive feedback between climate and carbon cycle is basically associated with an enhanced soil respiration in warmer climate (and to a smaller amount — to an enhanced plant respiration). This effect overwhelms the direct fertilisation effect of the carbon dioxide. However, the response of gross plant photosynthesis to climate changes is ambiguous. Starting from the present day climate, warmer temperature and increased precipitation generally lead to the enhanced plant photosynthesis [1, 27]. However, further climate changes may decrease this photosynthesis due to, e.g., the dieback of living biota [1, 27]. Furthermore, this effect can be regionally dependent; in particular, the strong dieback of the Amazonian forests is simulated [9, 28]. As will be shown below, response of the oceanic carbon cycle to climate changes is ambiguous as well. The goal of the present paper is to review recent efforts in modelling the interactions between climate and carbon cycle (Secs. 2. and 3.). In addition, based on simulations with climate model of intermediate complexity, variations in characteristics of C3F will be studied (Sec. 4.). In particular, it will be shown that climate–carbon cycle feedback eventually saturates. Finally (Sec. 5.), a structural uncertainty of the coupled system will be assessed by employing large ensemble with the same model of intermediate complexity.

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2.

Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko

Quantification of the Climate–Carbon Cycle feedback

In [18], a quantification of C3 F is suggested. Namely, for the uncoupled runs, oceanic and terrestrial uptakes are linearly related to the corresponding atmospheric buildups of CO2 Uxucpl = βx ∆pCOucpl 2,a ,

(3)

with x = oc, l. The corresponding uptakes in the coupled runs are linearly related to the cpl ∆pCOcpl 2,a and changes in the global annual surface air temperature ∆Tg cpl Uxcpl = βx ∆pCOcpl 2,a + γx ∆Tg ,

(4)

with β’s substituted from (3). From (1), (3), and (4), the resulting feedback gain reads [18, 17] g = −α (γl + γoc) / (1 + βl + βoc) (5) with transient temperature sensitivity α = ∆Tg /∆pCO2,a [18, 17]. One notes, that this definition of temperature sensitivity is different from the commonly applied climate sensitivity employing with logarithm of pCO2,a rather than with pCO2,a itself. In this, β’s figure the direct of pCO2,a on the terrestrial plants (e.i., fertilisation) and on the atmosphere–ocean CO2 exchange. In contrast,γ’s are relevant for the climate–carbon cycle feedback [13].

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3.

Coupled Climate–Carbon Cycle Intercomparison Project (C4 MIP)

Up to date, the most comprehensive intercomparison of the coupled climate– carbon cycle models is the Coupled Climate–Carbon Cycle Intercomparison Project (C4MIP) [17]. Eleven models were participating in this Project, among them seven general circulation models and four models of intermediate complexity. All models were forced by the historical emissions of carbon dioxide for 1860–2000 (according to [39] for the fossil fuel emissions and to [25] for the land use emissions) and by the SRES emission scenario A2 [24] for the 21st century. Changes in physical and biogeochemical properties of the vegetation were neglected. In the coupled C4 MIP simulations, pCOcpl 2,a in 2100 reaches 720 − 1020 ppmv depending on the model. All models exhibit positive C3F. However, qualitative characterteristics and underlying mechanisms differ drastically among the participating models. In particular, additional (due to carbon–climate cycle interactions) atmospheric buildup of carbon dioxide for the year 2100 varies between 20 ppmv and 200 ppmv (with six of eleven models falling into the range 50 − 100 ppmv). The simulated respective feedback gains are 0.04–0.31 (model average is 0.15, eight of eleven models simulate gains in the interval 0.10–0.20). Even larger spread is exhibited the particular components of the carbon budget are considered. To year 2100, terrestrial CO2 flux ranges among the models from the sink of 11 PgC/yr to the source of 6 PgC/yr. While the models agree between each other that oceans remain CO2 sink till the end of the 21st century, the simulations figure rather broad interval 4 − 10 PgC/yr for Foc in this time. In the intercomparison, a mutual compensation

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between oceanic and terrestrial fluxes of carbon is noted: the models with large oceanic sink of CO2 simulate the smaller corresponding terrestrial sink (or the higher respective terrestrail source). As a result, eight of eleven models relate C3F to changes in terrestrial carbon flux, while three attribute it to the ocean. In the coupled simulations, the airborne fraction of the anthropogenically emitted carbon dioxide ra to the end of the 21st century is in the range 0.42–0.71 (eight of eleven models fall into the range 0.47–0.60). The respective landborne fraction rl is between 0.01 and 0.45 (eight of eleven models simulate the range 0.14–0.27). In the coupled C4 MIP simulations, oceanborne fraction roc of the anthropogenic CO2 emissions is between 0.15 and 0.36 (with most models being in the range 0.20–0.26). Coefficients β’s of direct effect of CO2 change on carbon uptakes are positive for both oceanic and terrestrial fluxes indicating enhancement of both uptakes under growing pCO2,a. The simulated intervals of these coefficients βl = 0.2 − 2.8 PgC/ppmv (mean value is 1.35 PgC/ppmv, nine of eleven models fill the range 0.8 − 1.6 PgC/ppmv) and βoc = 0.9 − 1.5 PgC/ppmv (mean value is 1.13 PgC/ppmv), respectively. In contrast, coefficients γ’s of climate–carbon cycle feedback are negative for both terrestrial and oceanic fluxes pointing to supression of these fluxes under CO2–induced warming. The simulated ranges are γl = −(20 − 177) PgC/K and γoc = −(14 − 67) PgC/K, correspondingly, with ensemble averaged values γl = −79 PgC/K and γoc = −30 PgC/K, respectively. As a summary, current generation of the coupled climate–carbon cycle models converges in positive C3F, but attributes it to different mechanisms, either oceanic or terrestrial, and with a large diversity in quantitative characteristics of this feedback.

4.

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4.1.

IAP RAS Climate Model of Intermediate Complexity Description of the Model and Its Validation for the 20th Century Observations

The climate module of the Earth system model of intermediate complexity developed at the A.M. Obukhov Institute of Atmopsheric Physics RAS (IAP RAS CM) is described in [51, 21, 45, 46]. It includes modules for redistribution of the shortwave and longwave radiation, convection, cloud and precipitation formation. Large–scale atmospheric and oceanic dynamics (with the scales larger than those corresponding to the synoptic processes) are resolved explicitly. The synoptic–scale processes are treated as Gaussian ensembles. Sea ice in the IAP RAS CM is diagnosed from current surface air and sea surface temperatures. Soil hydrology in the model is prescribed and tuned to reproduce the observed climate. The IAP RAS CM horizontal resolution is 4.5 degrees latitude and 6.0 degrees longitude with 8 vertical layers in the atmosphere (up to 80 km) and 4 layers in the ocean. The IAP RAS CM simulates the present–day climate quite realistically [45, 46, 50]. The sensitivity of this model version to the doubling the carbon dioxide in the atmosphere is 2.2o C [46, 50]. It lies in the lower part of such estimates for the current generation of the climate models [24, 50]. Carbon uptake by the terrestrial vegetation–soil system is simulated with the use of

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Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko

zero–dimensional equations [58, 34, 36] dCv = N P P − L − D, dt dCs = L − Rs, dt

(6)

where Cv and Cs are carbon terrestrial vegetation and soil stocks, N P P is the terrestrial vegetation net primary productivity, L is litterfall, Rs is heterotrophic (soil) respiration. The terms on the right hand sides of Eqs. (6) depend on the corresponding carbon stocks and on the anomaly ∆Tg of globally averaged annual mean surface air temperature (GSAT) from the reference value (the latter will be specified later) [36] N P P = P − Rp, ∆T /∆T0

P = Apgf (pCO2,a)Cv(s)Q10,pg ∆T /∆T0

Rp = Ar Cv Q10,rg

,

(7)

L = Al Cv , ∆T /∆T0

Rs = As Cs Q10,sg

.

Here P is carbon production rate of photosynthesis, Rp is autotrophic (biota) respiration rate, Cv(s) is defined below, and Ap, Ar , Al , As are constants. The temperature ∆T /∆T

0 dependences of the carbon fluxes are proportional to Q10,xg , where ∆T0 = 10 K, Q10,x are constants, x = p, r, s [37]. While this approach performs unsatisfactorily for the model employing regional and/or seasonal resolution [37], it is considered to describe the climate dependences of the globally averaged annual mean carbon fluxes [53, 36] quite realistically. The reason for this is due to the relative smallness of ∆Tg for the climate change expected at decadal–to–centennial scales in comparison to the regional and seasonal variations of the surface air temperature. In Eqs. (7), gf is the fertilisation factor which is formulated according to the Michaelis–Menten law (see, e.g., [36])

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gf (pCO2,a) =

(

0

for pCO2,a < kc, otherwise,

pCO2,a −kc kM +pCO2,a −kc

where kM is the half–saturation point, kc is the compensation point (which is the threshold concentration of the carbon dioxide in the atmosphere, needed to start the photosynthesis). Cv(s) is the steady state living biomass, corrected for the agriculture harvesting. This variable is modelled using an additional prognostic equation [36]: dCv(s) = −kD D, dt

(8)

kD is constant. In the absense of land use emissions, total mass of carbon, stored in the living biomass would stay unchanged in this particular model. In the presense of the positive land use emissions, this mass decreases. To avoid it becomes negative, Cv,s is constrained not to change if it crosses zero level. The carbon flux to the ocean due to runoff is neglected,

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because its contribution is small, 0.5 − 1.0 GtC/yr [23]. Finally, the carbon uptake by the terrestrial vegetation–soil system is a difference Fl = dUl /dt = N P P − Rs .

(9)

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There is considerable uncertainty in the choice of the mentioned above Q10 ’s. In [36], based on a literature review, the following ”best guesses” were suggested: Q10,p = 1.50, Q10,r = 2.15, Q10,s = 2.40. In the current IAP RAS CM version, these values are adopted for the standard implementation of the zero–dimensional carbon cycle model. For kM , any of the values 150 ppmv [36] or 450 ppmv [15, 7] are adopted. Hereafter, these two versions are denoted as Fl150 and Fl450, respectively. The constant kc is set to 29 ppmv. For the constant kD , the value 0.27 is assigned [36]. The values of other model parameters are tuned to simulate the preindustrial carbon cycle state with pCO2,a,0 = 280 ppmv, Cs,0 = 1500 PgC, Cv,0 = 550 PgC, Cv(s),0 = 550 PgC, Rp,0 = 50 PgC/yr, L0 = 50 PgC/yr [23]. The adopted values = 11 yr, A−1 = 11 yr, A−1 = correspond to interannual–to–decadal time scales A−1 r s l −1 −1 (Al Clb,0/Cs,0 ) = 30 yr, Ap = (Al + Ar/gf (pCO2,a,0)) = 3.4 yr. Correspondingly, ∆Tg in the model is defined as an anomaly from the equilibrated preindustrial GSAT. In the simplest setting, the carbon uptake by the ocean is related linearly to the globally averaged annual mean SST Toc,g and to the atmospheric concentration of carbon dioxide pCO2,a [59, 18]: dUoc dpCO2,a dToc,g Foc = = uc − uT . (10) dt dt dt The values uc and uT are tuned to reproduce the observational estimations for the values of the oceanic carbon uptakes during 1980’s and 1990’s [24, 52, 26, 35] given the observed trends of GSAT [4] and the atmospheric concentration of the crabon dioxide [33] for the 20th century. As a result, the chosen values of the coefficients in Eq. (10) are uc = 1.31 PgC/ppmv, uT = 0.3 PgC/K. Hereafter, this version is denoted as FocL. Formulation of Foc in form (10) posesses a physically necessary condition to be zero in an equilibrium state. However, it could behave in a wrong way if emissions stop abruptly. After such emission stop, in reality, pCO2,a is expected to decrease due to continuing ocean uptake. In the model used here, in contrast, it would start to increase, because ocean would start to outgas CO2 . As a result, Eq. (10) is expected to be realistic only under continuing emissions of CO2 and can not be used in other circumstances. Motivated by this reasoning, another formulation of the oceanic uptake of CO2 has been implemented in the IAP RAS CM based on the nonlinear model [3] but with chemical constants computed as functions of temperature in accordance to [44]. In this, oceanic salinity is prescribed and, similar to [3], the dependence of evasion factor of partial pressure of CO2 between atmosphere and ocean is fitted by 



E(pCO2,a, Toc,g) = E0 E1(Toc,g)p2n + E2 (Toc,g)pn + E3(Toc,g)

(11)

with E0 = 10, pn = pCO2,a/290 ppmv, E1 = −2.19Tn2 + 3.73Tn − 1.57, E2 = 18.9Tn2 − 32.4Tn + 14.2, E3 = −19.8Tn2 + 35.8Tn − 15.8, Tn = Toc,g/300 K. Hereafter, this version is denoted as FocNL.

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Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko

Table 1. Basic characteristics of the simulated carbon cycle for the two IAP RAS CM versions, FocL–Fl150 and FocNL–Fl450, in comparion with recent observational estimates. Foc , 1980–1989, PgC/yr

FocL–Fl150 2.3

FocNL–Fl450 2.1

Foc , 1990–1999, PgC/yr

2.7

2.3

Uoc , 1860-1994, PgC Uoc , 1980-1999, PgC Fl , 1980–1989, PgC/yr

107 49 1.3

110 44 2.2

Fl , 1990–1999, PgC/yr

1.5

2.6

Ul , 1860-1994, PgC Ul , 1980-1999, PgC

85 31

110 48

obs. 1.9 ± 0.6 [24] 1.8 ± 0.8 [26] 1.8 ± 0.8 [57] 2.1 ± 0.7 [26] 1.9 ± 0.5 [49] 1.9 ± 0.6 [38] 1.8 ± 0.8 [26] 2.2 ± 0.4 [57] 118 ± 19 [54] 37 ± 8 [54] (−0.3) − (+3.8) [24] 0.3 − 4.0 [26] (−0.2) − (+3.4) [57] 1.6 − 4.8 [26] 1.2 ± 0.7 [49] 1.2 ± 0.8 [38] (−0.9) − (+4.3) [57] 61 − 141 [54] 39 ± 18 [54]

Finally, the atmosperic part of the carbon cycle is formulated based on the differential form of Eq. (1)

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c0 d∆pCO2,a/dt = E − Foc − Fl ,

(12)

with E standing for annual anthropogenic emissions of CO2 . For the standard settings of parameters, the IAP RAS CM forced by the historical fossil fuel and land use emissions [39, 25] generally reproduces basic observed characteristics of the carbon cycle, see Tab. 1 and Figs. 1–3. An improvement is noted in the oceanic part for the version FocNL–Fl450 in comparison to the older version FocL–Fl150. Both versions reproduce the observed variations of pCO2,a accurately enough, but underestimate (overestimate) pCO2,a slightly at the beginning (end) of this period. The absolute error does not exceeds 8 ppmv (Fig. 1). Such deviations are typical for the current generation of the coupled climate–carbon cycle models [10, 5, 11, 18, 30, 41, 40]. The globally averaged temperature change in the 20th century is reproduced with a reasonable accuracy as well (Figs. 4, 5).

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1100 1000 900 , ppmv

800

pCO

2,a

700 600 500 400 300 200 1900

1950

2000

2050

2100

Figure 1. Atmospheric concentration of CO2 simulated by the IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) in the coupled and uncoupled runs (solid and dash–dotted lines, respectively), for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly) compared with the values measured at the Mauna Loa Observatory [33] (black line).

5

4

Fl, PgC/yr

3

2

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1

0

−1 1900

1950

2000

2050

2100

Figure 2. Terrestrial uptakes of CO2 simulated by the IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) in the coupled and uncoupled runs (solid and dash–dotted lines, respectively), for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly) compared with uncertainty ranges figured in [26] for 1980’s and 1990’s (gray rectangles).

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Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko 12

10

Foc, PgC/yr

8

6

4

2

0 1900

1950

2000

2050

2100

Figure 3. Oceanic uptakes of CO2 simulated by the IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) in the coupled and uncoupled runs (solid and dash–dotted lines, respectively), for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly) compared with uncertainty ranges figured in [26] for 1980’s and 1990’s (gray rectangles).

3.5 3 2.5

∆ Tg, K

2 1.5 1

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0.5 0 −0.5 −1 1900

1950

2000

2050

2100

Figure 4. Anomaly (with respect to the preindustrial state) of the global annual surface air temperature simulated by the IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly) compared with the data from the University of East Anglia (Climate Research Unit) [4] (black line).

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115

3

ST , K/century

2.5

2

g

1.5

1

0.5

0 1900

1950

2000

2050

Figure 5. Linear trends of the globally averaged annual mean surface air temperature for the running 100–yr window centered at the year indicated at the x–axis (e.g., the value for 2050 represents 2001–2100). Shown are values simulated by the IAP RAS CM versions FocL– Fl150 and FocNL–Fl450 (thin and thick lines, respectively) for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly) and data from the University of East Anglia (Climate Research Unit) [4] (black line).

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4.2.

Climate–Carbon Cycle Interactions in 20th and 21st Centuries in IAP RAS CM

For the 21st century, experiments with the IAP RAS CM were performed under the anthropogenic CO2 emissions prescribed according to the SRES emission scenarios A2, A1B, B2, and B1 [24] (ordered here according to decrease of the cumulative emissions). The results are presented in Tab. 2 and plotted in Figs. 1–10. The atmospheric concentration of carbon dioxide in the coupled simulations grows to the late 21st century up to 615 − 875 ppmv in the version FocL–Fl150 and up to 652 − 1010 ppmv in the version FocNL–Fl450. The corresponding temperature increases relative to the preindustrial state are 2.4 − 3.4 K and 2.5−3.7 K. The climate–carbon cycle interactions increase the buildup of the atmnospheric carbon dioxide on 67 − 90 ppmv and 87 − 134 ppmv in the late 21st century for the versions FocL–Fl150 and FocNL–Fl450, respectively. This increase is stronger for the more agressive emissions scenarios. As a result, global warming is stronger in the coupled runs than in their uncoupled counterparts by 0.31 − 0.35 K for the version FocL–Fl150 and by 0.39 − 0.41 K for the version FocNL–Fl450. This additional warming may be further studied making use the linear trends of Tg computed for the running 100–yr window. These trends are systematically higher for the coupled than for the uncoupled runs. Generally, there is little difference between versions FocL–Fl150 and FocNL–Fl450. For scenario A2, trends of Tg increase monotonically during the whole course of integration. For scenarios A1B and B2, the trends become flatter to the end of simulations, and for the scenario B1 they even diminish during that period. This reflects the course of emissions during these scenarios leading to the respective changes in pCO2,a under different scenarios.

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Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko

Table 2. Key values simulated by the two versions of the IAP RAS CM (FocL–Fl150 and FocNL–Fl450) under marker SRES emission scenarios A2, A1B, B2, and B1: (i) atmospheric concentration of carbon dioxide pCO2,a in year 2100 for the coupled simulations together with difference between coupled and uncoupled simulations (in brackets); (ii) global warming ∆Tg in year 2100 relative to the equilibrated preindustrial state together with difference between coupled and uncoupled simulations (in brackets); (iii) value of climate–carbon cycle feedback parameter f in year 2100; (iv) airborne ra, landborne rl , and oceanborne roc fractions in year 2100 of anthropogenically emitted carbon dioxide for the coupled (uncoupled) simulations.

pCO2,a, ppmv ∆Tg , K f ra rl roc βl, PgC/ppmv γl, PgC/K βoc, PgC/ppmv

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γoc, PgC/K

FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450 FocL–Fl150 FocNL–Fl450

A2 875 (90) 1010 (134) 3.38 (0.31) 3.65 (0.41) 1.18 1.22 0.57 (0.48) 0.70 (0.57) 0.09 (0.25) 0.09 (0.24) 0.34 (0.29) 0.21 (0.19) 0.87 0.79 -124 -130 0.61 -18

A1B B2 762 (83) 669 (69) 854 (90) 721 (93) 3.05 (0.34) 2.65 (0.34) 3.22 (0.41) 2.73 (0.37) 1.21 1.22 1.26 1.27 0.56 (0.46) 0.55 (0.45) 0.66 (0.52) 0.62 (0.46) 0.10 (0.26) 0.12 (0.28) 0.11 (0.27) 0.13 (0.29) 0.34 (0.28) 0.33 (0.27) 0.23 (0.21) 0.25 (0.22) 1.06 1.21 0.98 1.15 -137 -142 -148 -154 1.3 (by construction) 0.70 0.81 -0.3 (by construction) -20 -20

B1 615 (67) 652 (87) 2.43 (0.35) 2.50 (0.39) 1.25 1.31 0.54 (0.43) 0.59 (0.49) 0.13 (0.31) 0.14 (0.32) 0.33 (0.26) 0.26 (0.23) 1.39 1.34 -157 -170 0.87 -21

The larger difference in the temperature and CO2 concentration responses between the coupled and uncoupled runs for the model version FocNL–Fl450 in comparison to the version FocL–Fl150 is due to nonlinearity of the oceanic sink of carbon from the atmosphere. Temperature rise suppresses Foc for the version FocNL stronger than for the version FocL. Despite of the different values for the half–saturation points, terrestrial carbon uptakes behave quite similarly between two model versions (see Fig. 2) with eventual decrease in the 21st century. In contrast, oceanic uptakes monotonically increase in the 21st century for the version FocL–Fl150 and eventually decrease for the version FocNL–Fl450 (Fig. 2). As a result, C3 F is attributed basically to the terrestrial pool for the former version, and both to terrestrial and oceanic pools for the latter version. For both feedbacks, however, typical time intervals may be determined by using on these uptakes. Till the mid 20th century, for both model versions, terrestrial uptakes of

Decadal–to–Centennial Scale Climate–Carbon Cycle Interactions...

117

1.35

1.3

f

1.25

1.2

1.15

1.1 1900

1950

2000

2050

2100

Figure 6. Parameter of the climate–carbon cycle feedback simulated by IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly). Growth of pCO2,a is computed with respect to year 1860.

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carbon differ only slightly between the coupled and uncoupled runs. For oceanic uptakes, this statement is valid even till the late 20th century. From this, one inferres that, in the 20th century, climate–carbon cycle feedback is not too strong. In this period, changes in Fl and Foc are basically due to CO2 –induced fertilisation of terrestrial productivity and due to increase in difference between partial pressures of carbon dioxide in surface air and surface oceanic water. In the IAP RAS CM coupled simulations, Fl peaks around year 2000 and Foc reach its maximum in the first half of the 21st century (the latter maximum is found only for the version FocNL–Fl450). In any model version, terrestrial uptakes in the coupled runs deviate strongly from their counterparts in the uncoupled runs for the most part of the 21st century. To a smaller extent, this statement is valid also for the oceanic uptakes in the second part of the 21st century. As a result, one concludes that, in the 21st century, climate–carbon cycle strongly affects the results of simulations. Temporal behaviour of the climate–carbon cycle interaction can be further quantified via temporal dependence of the respective parameter f (Eq. 2), see Fig. 6 [47, 14]. In the early industrial period, it is close to unity and grows monotonically till reaching the maximum in the middle of the 20th century. Afterwards, f slightly diminishes and attains shallow minimum in the early 1980’s. After that, the C3 F generally increases. The maximum of f in the mid 20th century is due to drastic increase of anthropogenic emissions in this period. It results in the enhanced buildup of CO2 in the atmosphere. This buildup is accompanied by diminished influence of the climate–carbon cycle interactions because of the climate system inertia and associated delay in climatic response. This diminished influence is reflected in the C3 F parameter decrease. However, the delay in the climate response becomes smaller in a few decades, and f starts to grow again. It is notable that evolution of the coupled system in terms of the C3F parameter is almost indistinguishable between the versions FocL–Fl150 and FocNL–Fl450 till the maximum in the middle

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Igor I. Mokhov, Alexey V. Eliseev and Andrey A. Karpenko

of the 20th century. Afterwards, f differs substantially among the model versions. In particular, shallow manimum in the late 20th century is even shallower in the latter model version. When system moves from maximum in the middle of the 20th century to shallow minimum in the early 1980’s, pCO2,a increases from about 310 ppmv to about 340 ppmv. Non–monotonic behaviour of f (as a function of pCO2,a which is, at a decadal time scale, a monotonic function of time) within this range of the atmospheric carbon dioxide concentration may be also seen in [19] (Figs. 1a,b there). While both model versions exhibit a general increase of f in the 21st century for most emission scenarios, they show slight decrease of this parameter in the late 21st century under the most agressive emission scenario A2. This decrease is more characteristic for the version FocL–Fl150 than for FocNL–Fl450 and may be termed a C3 F saturation. Physically, it is related to the nearly saturated absorption lines of CO2 in the atmosphere leading to the well–known logarithmic–type dependence of respective radiative forcing on pCO2,a [61]. As a consequence, when atmospheric carbon dioxide concentration is high, any additional atmospheric buildup of CO2 results in a relatively small additional raditive forcing. ucpl In particular, for the aggressive emission scenarios pCOcpl 2,a − pCO2,a does not lead to substantional additional radiative forcing in the presence of high values of pCO2,a and climate–carbon cycle feedback saturates [14]. In turn, given the length of integration, high concentrations of carbon dioxide in the atmosphere may be achieved only if the respective emission scenario is agressive enough. This is the reason why the saturation is exhibited only for the scenario A2 which is most agressive among those used here. However, if relatively moderate emissions scenarios (A1B, B2, and B1) are extended beyond year 2100, this saturation is expected to be visible for these scenarios as well. As a final note on the eventual saturation of the climate–carbon cycle feedback, one may distinguish its peculiarities in terms of feedback parameter, on one hand, and in terms of feedback gain, on the other one. In particular, an eventual C3F saturation is visible more clearly in terms f than in terms of g. This is because of dg/dt = f −2 df /dt and f attains higher values at the late part of the 21st century in comparison to its earlier part. In turn, it is a plausible reason why an eventual C3 F saturation is not clearly exhibited in the C4 MIP integrations where only temporal changes in g, not in f , were examined. Even in this, one C4 MIP model exhibits decrease of g in the late 21st century, and a few others show a slower decrease in the second part of this century in comparison to the first part (see Fig. 2b in [17]). Decadal–to–centennial variations in the climate–carbon cycle interaction may be understood via temporal behaviour of β’s and γ’s at these timescales. These coefficients were computed for the running 100–yr windows. In this computtaion, coefficient βl monotonically decreases with time for both model versions FocL–Fl150 and FocNL–Fl450. For the earliest available period 1860–1959, it amounts 1.5 − 1.6 PgC/ppmv. Then it decreases to 0.9 − 1.4 PgC/ppmv for the version FocL–Fl150 and to 0.8 − 1.3 PgC/ppmv for the version FocNL–Fl450 (with smaller values for the more agressive scenarios), see Tab. 2. Basically, temporal variations of this coefficient reflect interplay between the Michaelis–Menten law (as a function of pCO2,a) and dpCO2,a/dt. Similarly, in the version FocNL–Fl450, coefficient βoc decreases with time from about 1.5 PgC/ppmv in 1860–1959 to 0.6 − 0.9 PgC/ppmv in 2001–2100 (Tab. 2), again due to

Decadal–to–Centennial Scale Climate–Carbon Cycle Interactions...

119

−80 −100

γl, PgC/K

−120 −140 −160 −180 −200 −220 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

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Figure 7. Coefficients γl computed for the running 100–yr window centered at the year indicated at the x–axis (e.g., the value for 2050 represents 2001–2100). Shown are values simulated by the IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly). interplay between dpCO2,a/dt and the dependence of evasion factor on pCO2,a. In the version FocL–Fl150, the value of βoc ≡ 1.3 PgC/ppmv by construction. In contrast, temporal behaviour of γl differs markedly between the two model versions (Fig. 7). For the version FocL–Fl150, coefficient γl attains high values even during the earliest part of integration and then slightly diminishes. This decrease is plausibly not significant due to smallness of Tg in this period. For the version FocNL–Fl450, the value of γl is about −80 PgC/K in 1860–1959 and then increases by magnitude amounting −(130 − 170) PgC/K in 2001–2100 with smaller values for more aggressive scenarios in comparison to the less agressive ones (Tab. 2). The latter dependence on scenario is exhibited by the version FocL–Fl150 as well. Even stronger difference between two model versions on the decadal–to–centennial scale is exhibited by γoc, see Fig. 8. This coefficient is constant by construction for the version FocL–Fl150. For the version FocNL–Fl450, coefficient γoc steadily increases in magnitude. Its value for this model version is −6 PgC/K in 1860–1959 and −(18 − 21) PgC/K in 2001–2100 (see also Tab. 2). Difference in the climate–carbon cycle interactions is also reflected in temporal behaviour of landborne and oceanborne fractions ( rl and roc, respectively) of the anthropogenically emitted carbon dioxide, see Figs. 9 and 10. The former variable strongly decreases in the 20th–21st centuries. This decrease is stronger in the coupled runs in comparison to their uncoupled counterparts with a little difference between the versions FocL–Fl150 and FocNL–Fl450. The decrease of rl with time in uncoupled runs is due to enhanced fertilisation in the CO2 –enriched atmosphere. This results in higher NPP, larger litterfall, larger storage of carbon in soil, and, in turn, in higher soil respiration which overcompensates an increase of NPP. In contrast, roc differs drastically between the two model versions. For FocL–Fl150,

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−5

γo, PgC/K

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−15

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−25 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Figure 8. Coefficients γoc computed for the running 100–yr window centered at the year indicated at the x–axis (e.g., the value for 2050 represents 2001–2100). Shown are values simulated by the IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly).

0.45 0.4 0.35 0.3 r

l

0.25

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0.2 0.15 0.1 0.05 1900

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2100

Figure 9. Landborne fraction rl of anthropogenically emitted CO2 as simulated by IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly).

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0.3

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Figure 10. Oceanborne fraction roc of anthropogenically emitted CO2 as simulated by IAP RAS CM versions FocL–Fl150 and FocNL–Fl450 (thin and thick lines, respectively) for the emission scenarios A2, A1B, B2, and B1 (red, magenta, green, and blue curves, correspondingly). this variable generally increases during the course of integration, with higher increase exhibited in the coupled runs. For the version FocNL-Fl450, oceanborne fraction roc basically decreases with time. This difference reflects smallness of γl for the former version in comparison to the latter one.

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5.

Structural Uncertainty of Climate–Carbon Cycle Interaction

As it was mentioned in Introduction, there are uncertainties in the response of plant photosynthesis to warming temperatures. In addition, in [42], based on the results of the field measurements, it was noticed, that the nitrogen limitation of the organic matter decomposition in the soils may lead to the final decrease of the soil respiration under growing temperatures. The Q10’s for different processes are quite uncertain. In particular, in [36], based on the literature review, the following limits for these quantities were suggested: Q10,p = 1.1 − 2.66, Q10,r = 1.4 − 3.0, Q10,s = 1.3 − 3.8. In [29], the range for Q10,s was estimated as 1.4 − 2.8 based on the climate variations due to El Ni˜no events and volcano activity. While the generally adopted range of half–saturation point is 400−460 ppmv [15, 7], in some studies different values are adopted (e.g., kM = 150 ppmv in [36, 13, 14, 60]). The latter is caused basically either by simplifications employed in the terrestrial carbon cycle model (see the respective discussion in [13]) or by neglection of other anthropogenically induced effects (e.g., due to sulphate aerosols) in tuning the model to the 20th century observations. Moreover, intensity of climate–carbon cycle interations depend on the overall model’s sensitivity to greenhouse forcing [20]. Different fomulations of the coupled models lead to the diversity of the simulations with a striking example of such diversity exhibited in the C4MIP integrations.

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An objective approach to treat this diversity is to perform ensemble integrations with a climate model by perturbing different parameters of the carbon cycle in a systematic way. In this section, structural uncertainty due to terrestrial carbon cycle is explored for such ensemble integrations with Ithe AP RAS CM forced by the historical emissions for the 19th and 20th centuries and by the SRES A2 emissions for the 21st century. In this integrations, a subset of parameters Q10,p, Q10,r, Q10,s, kM and kD is perturbed. Namely, Q10,p was assigned one of four values (0.8, 1.0, 1.5, and 2.0). Q10,r was also varied between three values, 1.4, 2.15, and 3.0. The corresponding constant for soil respiration, Q10,s, was assigned one of the four values (1.0, 2.0, 2.4, and 3.0). The half saturation point kM was pertubed among the values 100 ppmv, 150 ppmv, 200 ppmv, and 450 ppmv. In these simulations, constants A’s in (7) are also changed in order to keep the realistic preindustrial state in the model. To test the model sensitivity to the choice of the constant kD regulating the living biomass with respect to the agricultural activity, this constant was also assigned one of the values 0 and 0.27. To test the uncertainty due to sensitivity of the model to the growth of the carbon dioxide in the atmosphere, simulated temperature anomalies entering the carbon cycle routine were multiplied by a constant factor in the course of integrations. This factor was assigned one of the values 0 (for the carbon cycle of the IAP RAS CM, it is analogous to the uncoupled simulations), 1.0 (the standard model setting), 1.7, and 2.0. One notes, that the latter two multiplication factors were choosed in order to denser populate the phase space of the model rather than to represent any particular climate model or ensemble of such models. All the model simulations were started from the preindustrial equilibrated model state. In every simulation, the first model year with the non–zero carbon dioxide emissions corresponds to the julian year 1859. Simulations end in the year corresponding to the julian year 2100. A subset of these simulations was considered earlier in [12, 13]. The sources of uncertainties different from the mentioned above, e.g., due to uncertainties in CO2 emissions, governing parameters of the model’s oceanic carbon cycle, influence of other external forcings (methane, ozone, sulphates, carbon aerosols, volcanic aerosols, etc) are not considered here. As characteristics of these forcings also associated with some uncertainty ranges, taking them into account result likely in a wider uncertainty ranges than those presented below (e.g., [2]). Further, in the present paper, the ensemble simulation is performed only for the version FocL–Fl150 without accounting for structural uncertainty between FocL–Fl150 and FocNL–Fl450. Even in this simplified case, the undertaken study presumes large computational burden. The cumulative length of the simulations performed in the present paper is 371,712 model years. Such long simulations are precluded currently for the state–of–the–art general circulation model due to technical reasons [8, 50]. This advocates the usage of the climate model of intermediate complexity for the purposes of the present study. If the performed simulations are subjected to the constraint of the maximum allowed deviation pCO2,a of the simulated pCO2,a from the observed values (measured at the Mauna Loa observatory during the second half of the 20th century) and/or to the constraint that simulated terrestrial and oceanic uptakes in 1980’s and 1990’s must be in the range published in [26], it is possible to rule out some of the simulations, performed in the present paper, and narrow the corresponding uncertainty range. The results of such analysis for values of pCO2,a in year 2100 are presented in Tab. 3.

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Table 3. Ranges of atmospheric CO2 concentration (ppmv) for different values of the allowed deviations pCO2,a of the simulated carbon dioxide concentration in the atmosphere from the Mauna Loa observations for 1959–2000, and for different constraints on the simulated uptakes.

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pCO2,a , ppmv none 10 5

none 496 . . . 1379 496 . . . 1241 496 . . . 1197

constrained uptakes Fl Foc 585 . . . 991 496 . . . 1379 705 . . . 988 496 . . . 1241 789 . . . 896 496 . . . 1197

Fl and Foc 585 . . . 991 705 . . . 988 789 . . . 896

As seen from this Table, constraining the simulations based on the 20th century observations narrows the respective uncertainty range substantially. If no constraint is imposed, the uncertainty range for pCO2,a(2100) is 496 − 1379 ppmv. If all above–mentioned characteristics are constrained (e.g., both Fl and Foc are in the range depicted in [26], and the tolerable deviation of the simulated atmospheric concentration of carbon dioxide from the Mauna Loa observations is pCO2,a = 2 ppmv), the corresponding range shrinks to 789 − 896 ppmv. Thus terrestrial uptakes of carbon constrain simulations more efficiently than the oceanic ones. This is possibly due to neglection of the structural uncertainty between versions FocL–Fl150 and FocNL–Fl450 because in the former version Foc changes only due to different evolutionary trajectories of pCO2,a . The sampled probability density functions (PDFs) P (pCO2,a) are non–symmetrical with respect to the modal values with a heavier right tail. Even heavier right tail is exhibited by the sampled probability density function P (ln pCO2,a) closely related to the PDF of the radiative forcing of CO2 . The latter also implies skewed PDF of the temperature response to the growth of pCO2,a with a larger probability for the response stronger than the modal one. An analysis of this ensemble simulation may be further performed by usage of the Bayesian averaging [32, 22]. The latter approach allows one to quantify the structural uncertainty of the simulation by weighting the members of such an ensemble with a smaller (larger) weights attached to less (more) realistic simulations. For this analysis, prior probability distribution functions for terrestrial and oceanic uptakes are chosen to be non– informative (i.e., uniform) [32] with a total range of variation corresponding to [26]. The respective prior function for pCO2,a is chosen to be Gaussian with a standard deviation ΣpCO2,a = 2 − 15 ppmv. The latter values are chosen subjectively in order to represent ”tolerable” deviation of simulated atmospheric concentration of CO2 from the Mauna Loa observations. The value of ΣpCO2,a does not affect the Bayesian ensemble mean too much, see Fig. 11. Moreover, in the presence of other constraints, the ensemble standard deviation of simulated pCO2,a, σpCO2,a , also shows only weak dependence on ΣpCO2,a . For instance, for year 2100, σpCO2,a increases from 46 ppmv at ΣpCO2,a = 2 ppmv to 61 ppmv at ΣpCO2,a = 15 ppmv. These values are much smaller than the value σpCO2,a = 182 ppmv which attained by simple, equally weighted, averaging of the members within this ensemble (see also Fig. 11). Interesting, the equally weighted ensemble mean is substantially higher than the respective Bayesian means.

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pCO

2,a

, ppmv

900 800 700 600 500 400 300 200 1900

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Figure 11. Uncertainty propagation for pCO2,a in the ensemble simulations with IAP RAS CM forced by the historical+SRES A2 emissions. Shown are ensemble means (lines) and uncertainty ranges (shaded areas) defined here as a doubled standard deviations for the equally weighted averaging (red) and for the Bayesian averaging with ΣpCO2,a = 8 ppmv and ΣpCO2,a = 15 ppmv (blue and green, correspondingly). See text for further details.

Irrespective of the approach to narrow the uncertainty within the ensemble (either selecting only realisitc simulations or employing Bayesian averaging) the resulting uncertainty is still substantial. It reflects relative smallness of the 20th century changes in comparison to those expected in the 21st century [13, 31]. In particular, in [31] it was stated that ”the observational record proves to be insufficient to tightly constrain carbon cycle processes or future feedback strength with implications for climate–carbon cycle model evaluation”. Moreover, in [43] the 20th century course of the carbon cycle characteristics was reproduced accurately with a model accounting only direct CO2 influence on Fl and Foc without considering respective climate feedbacks. The latter is consistent with [13] where it was shown that the changes in carbon cycle observed during the 20th century may be reproduced even by the model with a negative climate–carbon cycle feedback. This point is further illustrated in Fig. 12. In this figure, uncertainty range includes both positive and negative C3 F. This range becomes even wider during the course of integration. Nonetheless, positive feedback is more probable than the negative one, and the respective ucpl ensemble means of ∆pCOcpl 2,a −∆pCO2,a are positive. These ensemble means in year 2100 amount 136 ppmv for the equally–weighted averaging, 64 ppmv for Bayesian averaging with ΣpCO2,a = 15 ppmv, and 57 ppmv for Bayesian averaging with ΣpCO2,a = 8 ppmv. The last two means are statistically indistiguishable between each other taking into account ucpl ensemble standard deviations of ∆pCOcpl 2,a −∆pCO2,a amounting 107 ppmv and 98 ppmv, correspondingly. In addition, these two ensemble averages are within the most probable range derived from the C4MIP simulations, 50 − 100 ppmv (see 3.).

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500

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∆ pCOcpl −∆ pCOucpl, ppmv

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ucpl Figure 12. Uncertainty propagation for the C3F intensity ∆pCOcpl 2,a − ∆pCO2,a in the ensemble simulations with the IAP RAS CM forced by the historical+SRES A2 emissions. Shown are ensemble means (lines) and uncertainty ranges (shaded areas) defined here as a doubled standard deviations for the equally weighted averaging (red) and for the Bayesian averaging with ΣpCO2,a = 8 ppmv and ΣpCO2,a = 15 ppmv (blue and green, correspondingly). Black line depicts zero intensity of C3F. See text for further details.

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6.

Conclusion

At the decadal–to–sentennial scale, the climate–carbon cycle interaction is set basically by an interplay in responses of net primary productivity and soil respiration to climate changes, and by analogous response in the oceanic carbon uptake. The current generation of the coupled models converges at the positive sign of the resulting feedback ( C3F) with a typical enhancement of the global warming by about 10% in the 21st century. However, there is a large diversity among these models, with some of them attributing the climate– carbon cycle feedback to the terrestrial pools while the others trace C3F to the oceanic reservoir. Even larger diversity is found at regional level. Unfortunately, future evolution of the coupled system can not be effectively constrained by the 20th century observations due to relative smallness of the changes in the state of the coupled system in comparison to those expected in future. In particular, uncertainty ranges for the 20th century observations do not preclude zero–intensity or even negative climate– carbon cycle feedback. It is illustrated employing large ensemble of simulations with a climate model of intermediate comlexity IAP RAS CM. The members of this ensemble were stratified in accordance with a realism of their simulation of the carbon cycle characteristics observed in the 20th century. Bayesian ensemble averaging and/or selection of the ensemble members ”tolerably” deviating from the observations narrow the above–mentioned uncertainty range but still unable to rule out negative C3 F. However, the corresponding ensemble means lead to the positive climate–carbon cycle feedback. In the IAP RAS CM runs, characteristics of the climate–carbon cycle interaction undergo substantial changes during the 20th and, especially, during the 21st century. In particular, coefficients γl and γoc (computed for the running 100–yr window) basically increase

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in magnitude indicating a strengthening of C3F at a century timescale. This contrasts with diminishing direct influence of pCO2,a growth on terrestrial and oceanic uptakes of carbon as manifested in βl and βoc which both decrease with time. General strengthening of climate–carbon cycle interaction at a century timescale is also supported by general increase of the feedback parameter f during the course of integration. At a decadal timescale, however, deviations from this tendency are observed. During the 20th century, there are periods when climate–carbon cycle feedback either weakens or strengthens. This is related to the abrupt changes in the intensity of anthropogenic emissions and to the delay in response of the coupled system to these emissions. In addition, at a century timescale, an eventual saturation of C3 F is observed. Physically, it is related to the weak (logarithmic) dependence of the radiative forcing on pCO2,a . As a result, additional buildup of carbon dioxide in the atmosphere does not enlarge this forcing too much. As a result, additional (with respect to the simulation neglecting C3F) warming is small, and climate–carbon cycle feedback saturates. However, this saturation may not be expected during next few decades when C3F is expected to markedly modify the behaviour of the coupled system.

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[59] Thomas H. ; England M.H. ; Ittekkot V. , Geophys. Res. Lett., 2001, 28, 547–550. [60] Williamson M.S. ; Lenton T.M. ; Shepherd J.G. ; Edwards N.R. , Ecol. Mod., 2006, 198, 362–374. [61] Zuev V.E. ; Titov G.A. . Atmosphere Optics and Climate . Spektr, Tomsk, 1996. [in Russian].

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In: Atmospheric Science Research Progress Editor: Chih-Hao Yang

ISBN 978-1-60456-439-6 © 2009 Nova Science Publishers, Inc.

Chapter 6

CHARACTERISTIC OF ATMOSPHERIC PARTICULATES AND METALLIC ELEMENTS COMPOSITION STUDY IN CENTRAL TAIWAN Guor-Cheng Fang Air Toxic and Environmental Analysis Laboratory Hungkuang University, Sha-Lu, Taichung 433, Taiwan

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ABSTRACT Particulate matter (PM) is a major factor which affects the air quality of ambient environment in Taiwan region. The consecutive study of ambient air pollutants for different particulate seizes and their chemical compositions were conducted. Several character sampling sites were selected in this study. They are Taichung Harbor (TH) and WuChi traffic (WT) and Taichung airport (TA) sampling sites. As for TH and WT sampling sites, measured the concentrations of PM2.5 (PM with aerodynamic diameter autumn > summer. Based on a

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…139 comparison of average PM2.5–10 and PM2.5 concentrations, the concentration order during the sampling periods were as follows: spring > summer > winter > autumn. 200 180

Concentration (ug/m3)

160 140 120 100 80

TSP PM2.5 PM2.5-10

60 40 20 0 9.5

360

9.0

315 WS PWD

270

WS (m/s)

225 8.0 180 7.5 135 7.0

45

6.0 32

0 82

28

Temp. RH

80 78

26 76 24 74 22

RH (%)

Temp. (¢J)

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90

6.5

30

PWD (degree)

8.5

72 20 18 16

70 68

Figure 2. Variations of meteorological parameters and atmospheric particulates at TA sampling site.

140

Guor-Cheng Fang

TSP PM2.5 PM2.5-10

200

Y Data

150

100

50

0 32

80 Temp. RH

30

78

28

76

24 22

74

RH (%)

Temp. (¢J)

26

20 72

18 16

70

14 68 360 WS PWD

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WS (m/s)

10

315

9

270

8

225

7

180

6

135

5

90

4

45

3

PWD (degree)

12 11

0 rc h Ma

r il Ap

y e Ma Jun

t r r r r y y Jul ugus embe ctobe embe embe nuar A ept O Nov Dec Ja S

Month

Figure 3. Monthly variations of different meteorological parameters and atmospheric particulates at WT sampling site.

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…141

TH Sampling Site Figure 4 displays TSP, fine, coarse particulate concentrations and meteorological parameters such as temperature, relative humidity, and prevalent wind direction variations between March 2004 and January 2005 at TH sampling site.

200

TSP PM2.5 PM2.5-10

Y Data

150

100

50

0 24

90 Temp. RH 88

22

86

84 18 82

RH (%)

Temp. (¢J)

20

16 80 14

78

WS (m/s)

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13

76 360

WS PWD

315

12

270

11

225

10

180

9

135

8

90

7

45

PWD (degree)

12 14

Figure 4. Monthly variations of meteorological parameters and atmospheric particulates at TH sampling site.

142

Guor-Cheng Fang Measurement results obtained that TSP concentrations ranged from 101.32 to 186.55

µg/m3, and coarse particulate was 16.81－41.59 µg/m3. Furthermore, fine particulate

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concentrations ranged from 35.52 to 87.29 µg/m3. Additionally, average TSP concentration was 152.76±28.37 µg/m3, and coarse particulate 28.69±10.49 µg/m3. Moreover, average fine particulate concentration was 56.38±18.53 µg/m3 during sampling period. Besides, the average temperature, relative humidity were 19.42±3.18 and 82.73±4.03 %, respectively. Prevailing wind direction was principal blown from north and average wind speed was 9.16±1.74 m/sec during the sampling period at TH sampling site. Additionally, average concentrations of TSP in spring, summer, autumn and winter were 170.65±16.79, 113.47±23.13, 152.56±9.11 and 174.15±2.67 µg/m3, respectively. Average particulate concentrations of PM2.5 in spring, summer, autumn and winter were 51.50±7.93, 39.54±9.32, 39.65±6.53 and 50.53±3.88 µg/m3, respectively. Moreover, average particulate concentrations of PM2.5–10 in spring, summer, autumn and winter were 36.60±4.29, 21.19±5.79, 22.51±4.64, and 36.38±1.02 µg/m3, respectively. Based on comparisons of average TSP concentrations, the concentration order of sampling periods was as follows: winter > spring > autumn > summer. Additionally, based on a comparison of average coarse and fine particulate concentrations, the concentration order during sampling periods were as follows: spring > winter > autumn > summer. According to these analyses (TH and WT sampling sites), particulate concentrations (TSP, PM2.5–10 and PM2.5) in spring and winter were higher than those for other seasons for the WT and TH sampling sites. The primary reason was that the dust storms in China frequently occur in spring and winter, thereby increasing PM concentration in Taiwan. This experimental result was published by Fang et al. (2002). Moreover, the strong northeast monsoon has influenced Taiwan including Chinese dust storms in spring and winter and mean wind speeds in spring and winter are higher than those in other seasons.

3.1.1. Comparison of Average Atmospheric Particulates at This Three Sampling Sites Figure 5 schematizes average concentrations for atmospheric particulates at TA, WT, TH sampling sites during September to December sampling periods. Based on comparisons of average TSP and fine particulate concentrations at these three sampling sites, the concentration order during sampling periods was as follows: TH > WT > TA, and the average coarse particulate concentration order during sampling periods was as follows: TH > TA > WT. Therefore, TSP, fine and coarse particulate concentrations at TH sampling site were highest than those at other two sampling sites. Mean wind speed at TH sampling site higher than those at other sampling site was the main reason for this finding.

3.2. Metallic Elements in Atmospheric Particulates TA Sampling Site Figure 6 is a box-plot for those metallic elements concentrations in atmospheric particulates at TA sampling site during sampling period.

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…143 TSP-TH TSP-WT Coarse-TH Coarse-WT Fine-TH Fine-WT

Seasons

Winter

Autumn

Summer

Spring

10

100 3

Concentrations (ug/m )

Figure 5. Average seasonal concentrations of TSP, fine and coarse particulates at three sampling sites.

Concentrations (ng/m3)

Metallic elements in TSP

100

10

100

10

Metallic elements in coarse particulate 100

Concentrations (ng/m3)

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Concentrations (ng/m3)

Metallic elements in fine particulate

10

Figure 6. Box-plot of metallic elements for atmospheric particulates at TA sampling site.

144

Guor-Cheng Fang Regarding the concentrations of Fe, Mn, Cu, , Zn, Pb, Cr and Mg in TSP were ranged

193.06－561.67 ng/m3, 9.09－54.66 ng/m3, 26.73－129.1 ng/m3, 67.51－125 ng/m3, 21.68－55.84 ng/m3, 24－47.9 ng/m3 and 59.96－187.91 ng/m3, respectively, and the concentrations in fine particulates were ranged as follows: Fe, 173.59－488.89 ng/m3; Mn, 8.18－28.8 ng/m3; Cu, 8.83－26.6 ng/m3; Zn, 44.41－142.09 ng/m3; Pb, 16.36－38.82 ng/m3; Cr, 13.62－26 ng/m3 and Mg, 57.02－158.72 ng/m3. Moreover, Fe, Mn, Cu, Zn, Pb, Cr and Mg concentrations in coarse particulates were ranged 30.56－125.03 ng/m3, 4.87－15.43 ng/m3, 3.2－21.3 ng/m3, 20.54－64.4 ng/m3, 11.3－21.14 ng/m3, 5.7－18.8 ng/m3 and 12.1－102.1 ng/m3, respectively. The analytical results demonstrate that average metallic elements concentrations in TSP were as follows: Fe, 385.78±117.84 ng/m3; Mn, 34.28±12.77 ng/m3; Cu, 77.69±35.46 ng/m3; Zn, 90.4±19.72 ng/m3; Pb, 40.18±9.58 ng/m3; Cr, 34.03±6.12 ng/m3 and Mg, 119.21±39.81 ng/m3, and Fe, Mn, Cu, , Zn, Pb, Cr and Mg concentrations in fine particulates were 301.67±89.68 ng/m3, 16.32±5.65 ng/m3, 16.69±5.09 ng/m3, 82.99±26.71 ng/m3, 28.04±6.57 ng/m3, 19.86±3.8 ng/m3 and 110.22±29.95 ng/m3, respectively. Additionally, Fe, Mn, Cu, , Zn, Pb, Cr and Mg concentrations in coarse particulates were 67.07±29.74 ng/m3, 9.62±3.3 ng/m3, 10.45±4.59 ng/m3, 29.99±12.11 ng/m3, 16.15±2.88 ng/m3, 13.25±3.54 ng/m3 and 59.67±22.92 ng/m3, respectively. Base on above observations, the results reveal that three primary components of metallic elements in atmospheric particulates were Fe, Mg and Zn at TA sampling site during sampling period.

WT Sampling Site Figure 7 displays the distribution for Fe, Mg, Pb, Zn, Cr, Mn, and Cu concentrations at WT sampling site during sampling period. The concentrations in TSP were ranged as follows: Fe, 667－1755 ng/m3; Mg, 350－598 ng/m3; Pb, 19－102 ng/m3; Zn, 463－1297 ng/m3; Cr, 106－546 ng/m3; Mn, 11－82 ng/m3; Cu, 217－685 ng/m3 , and Fe, Mg, Pb, Zn, Cr, Mn, and Cu concentrations in fine particulates were ranged 168－517 ng/m3, 61－178 ng/m3, 16－56 Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

ng/m3, 217－622 ng/m3, 51－255 ng/m3, 11－41 ng/m3 and 94－285 ng/m3, respectively. Furthermore, Fe, Mg, Pb, Zn, Cr, Mn, and Cu concentrations in coarse particulates were ranged 529－1110 ng/m3, 103－394 ng/m3, 3－14 ng/m3, 113－244 ng/m3, 27－113 ng/m3, 11－31 ng/m3 and 60－201 ng/m3, respectively. The examination results obtain that average concentrations of Fe, Mg, Cr, Cu, Zn, Mn, and Pb were 1303±298 ng/m3, 478±98 ng/m3, 355±132 ng/m3, 522±143 ng/m3, 1081±277 ng/m3, 54±19 ng/m3 and 66±26 ng/m3 in TSP, respectively, and 839±188 ng/m3, 294±96 ng/m3, 94±29 ng/m3, 142±47 ng/m3, 198±43 ng/m3, 22±6 ng/m3 and 10±4 ng/m3 in coarse particulates, respectively. Additionally, average metallic element concentrations in fine particulates were as follows: Fe, 382±103 ng/m3; Mg, 144±32 ng/m3; Cr, 156±57 ng/m3; Cu, 225±58 ng/m3; Zn, 491±133 ng/m3; Mn, 29±9 ng/m3; and Pb, 39±12 ng/m3. According above

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…145 analytical results determine that metallic elements Fe and Zn were the primary components in TSP at WT sampling sites, and Fe and Mg were the predominant components in coarse and fine particulates. However, Zn and Fe were the predominant components in fine particulates at the WT sampling site during sampling period. Metallic elements in TSP

Concentrations (ng/m3)

1000

100

10

Concentrations (ng/m3)

Metallic elements in fine particulate

100

Concentrations (ng/m3)

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10 1000

Metallic elements in coarse particulate

100

10

Figure 7. Box-plot of metallic elements for atmospheric particulates at WT sampling site.

TH Sampling Site Metallic elements concentrations in atmospheric particulates at TH sampling site during sampling period was shown in figure 8 by a box-plot.

146

Guor-Cheng Fang Metallic elements in TSP

3

Concentrations (ng/m )

1000

100

Metallic elements in fine particulate

3

Concentrations (ng/m )

100

10

Metallic elements in coarse particulate

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3

Concentrations (ng/m )

1 1000

100

10

Figure 8. Box-plot of metallic elements for atmospheric particulates at TH sampling site.

The ranges of metallic elements concentrations were as follows: Fe, 817－1543 ng/m3; Mg, 449－745 ng/m3; Pb, 29－47 ng/m3; Zn, 589－927 ng/m3; Cr, 144－300 ng/m3; Mn, 66－110 ng/m3; Cu, 332－546 ng/m3 in TSP, and Fe, 140－330 ng/m3; Mg, 60－217 ng/m3; Pb, 2－8 ng/m3; Zn, 63－175 ng/m3; Cr, 30－53 ng/m3; Mn, 11－27 ng/m3; Cu, 76－103

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…147 ng/m3 in fine particulates. Additionally, the concentrations of Fe, Mg, Cr, Cu, Zn, Mn, and Pb were ranged 596－1103 ng/m3, 255－476 ng/m3, 39－68 ng/m3, 50－164 ng/m3, 74－201 ng/m3, 14－45 ng/m3 and 4－13 ng/m3, respectively, in coarse particulate. The measurement results demonstrate that average metallic element concentrations were as follows: Fe, 1151±236 ng/m3; Mg, 590±102 ng/m3; Cr, 235±43 ng/m3; Cu, 0.436±62 ng/m3; Zn, 793±104 ng/m3; Mn, 93±16 ng/m3; and Pb, 38±6 ng/m3 in TSP, and Fe, 253±67 ng/m3; Mg, 167±44 ng/m3; Cr, 43±7 ng/m3; Cu, 87±7 ng/m3; Zn, 134±37 ng/m3; Mn, 21±5 ng/m3; and Pb, 5±2 ng/m3 in fine particulates. Furthermore, average concentrations for Fe, Mg, Cr, Cu, Zn, Mn, and Pb were 825±155 ng/m3, 363±66 ng/m3, 58±8 ng/m3, 89±32 ng/m3, 146±39 ng/m3, 32±6 ng/m3 and 8±2 ng/m3 in coarse particulates, respectively. Judging from the above results, Fe and Zn were the main components in TSP, while Fe and Mg were the primary components in coarse and fine particulates at the TH sampling sites. Metallic elements concentrations in atmospheric particulates at three sampling site have been described in detail. These results lead to the conclusion that metallic elements of Fe, Mg, Zn were the predominant components in atmospheric particulates at these three sampling sites. According to previous studies (Allen et al., 2001; Kumar et al., 2001), Fe is a metallic element indicative of crustal, re-suspended dust and metal industry. In this study, the largest source of Fe may come from soil and re-suspended dust. In previous studies (Kumar et al., 2001; Shu et al., 2001), Pb was an effective index in traffic pollutant. However, Zheng et al. (2004) indicated that Pb cannot be considered as a traffic emission index as leaded gasoline has been phased out by comparing with lead isotope ratios. Notably, Zn has been considered a good maker for unleaded isotope vehicular emissions (Salvador et al., 2004). Hence, gasoline-powered vehicles were responsible for these results for fine particulates.

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3.3. Statistical Analysis 3.4.1. The Correlation between Atmospheric Particulates and Meteorological Parameters The relationship between atmospheric particulates and meteorological parameters at three sampling sites were estimated by using statically method of Pearson correlation. Furthermore, the simple and multiple regression equations were established to describe the relationship between atmospheric particulates and meteorological parameters. Table 2 presents the correlation coefficients between atmospheric particulates and meteorological parameters at TA sampling site. The correlation shows a significant negative relationship between coarse particulates and Temp. during sampling period. Furthermore, the relationships between coarse particulates and Temp. are also illustrated in Figure 9, and establish a regression equation for coarse particulates and Temp.. The regression equation for coarse particulates and Temp. during sampling period at TA sampling site is Coarse＝49.7525－1.0966 × Temp. The analytical results of Pearson correlation at WT sampling site are presented in Table 3. The correlations show a clear and strong relationship between atmospheric particulates and WS. Additionally, TSP and WS have been shown to be negative correlated with one another, and this similarity result was also obtained between WS and Temp.

148

Guor-Cheng Fang Coarse = 49.7525-1.0966* x; 0.95 Conf.I nt . 44

Coarse particulate concentrations ( ug/m 3)

42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 14

16

18

20

22

24

26

28

30

Temp. (℃)

Figure 9. The scatter-plot of coarse particulates and Temp. at TA sampling site.

Table 2. The person correlations of relationship between atmospheric particulates and meteorological parameters at TA sampling sites TA

TSP

Fine

Coarse

Fine

0.60

Coarse

0.38

0.49

Temp.

-0.45

-0.56

-0.68

RH

-0.52

-0.40

-0.02

Temp.

RH

WS

0.04

WS

0.41

0.49

0.39

-0.83

-0.06

PWD

-0.26

-0.21

-0.12

0.15

0.12

-0.41

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Note: italic and marked correlations are significant at p＜0.05.

Table 3. The person correlations of relationship between atmospheric particulates and meteorological parameters at WT sampling sites WT

TSP

Fine

0.77

Fine

Coarse

Temp.

RH

Coarse

0.67

0.80

Temp.

-0.97

-0.80

-0.63

RH

-0.15

0.22

0.11

0.02

WS

0.68

0.44

0.57

-0.61

-0.19

PWD

0.25

0.37

0.38

-0.40

0.67

Note: italic and marked correlations are significant at p＜0.05.

WS

0.09

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…149 Generally, the monsoon blows from north were cool and strong in Taiwan, and this phenomena description has been illustrated in Figure 2, previously. Thus, the negative relationship between WS and Temp. has been established and confirmed. Moreover, Figure 10 illustrates the corrections between TSP, Temp. and WS, a multiple regression equation for these three items has also established in this step and described as followed: TSP＝240.6308＋2.9777×WS－6.3666×Temp. Additionally, the corrections between coarse particulate and Temp.; fine particulate and Temp. are also illustrated in Figures 11 and 12. The analytical results concluded that regression equation for Temp. between coarse and fine particulate during sampling period can be presented as followed: Fine＝99.779－2.2649×Temp. Coarse＝51.1091－1.0856×Temp.

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TSP = 240.6308+ 2.9777* x-6.3666* y

TSP concentrations 180 160 140 120 100 80 60

Figure 10. The relationship between TSP, WS and Temp. at WT sampling site.

Table 4 presents the correlation coefficients between atmospheric particulates and meteorological parameters at TH sampling site. The observation results demonstrated that atmospheric particulates seemed to be closely connected to WS. Additionally, TSP and Temp. were also shown to be significantly related, and the relationship between WS and Temp. was the same as that of WT sampling site.

150

Guor-Cheng Fang

Table 4. The person correlations of relationship between atmospheric particulates and meteorological parameters at TH sampling sites TH

TSP

Fine

Coarse

Temp.

RH

Fine

0.73

Coarse

0.82

0.96

Temp.

-0.77

-0.57

-0.60

RH

-0.54

-0.16

-0.30

0.71

WS

0.74

0.83

0.73

-0.67

-0.09

PWD

0.18

0.29

0.38

-0.22

-0.10

WS

-0.01

Note: italic and marked correlations are significant at p＜0.05. Fine = 99.779-2.2649* x; 0.95 Conf.I nt . 75

Fine particulate concentrations ( ug/m3)

70 65 60 55 50 45 40 35 30 25 12

14

16

18

20

22

24

26

28

30

32

Temp. (℃)

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Figure 11. The scatter-plot of fine particulates and Temp. at WT sampling site.

Furthermore, Figure 13 illustrated the relationship between TSP, Temp. and WS, and a multiple regression equation was, thus, established as follow: TSP = 179.2496+6.5905×WS－4.4743×Temp. Besides, Figures 14 and 15 illustrate the correlations between coarse particulate and WS; fine particulate and WS, respectively. The analytical results obtain a regression equation for each correlation which can be presented as followed: Fine ＝-24.3011＋8.8044×WS Coarse＝-11.6222＋4.3997×WS

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…151 Coarse = 51.1091-1.0856* x; 0.95 Conf.I nt . 42

Coarse particulate concentrations ( ug/m3)

40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 12

14

16

18

20

22

24

26

28

30

32

Temp. (℃)

Figure 12. The scatter-plot of coarse particulates and Temp. at WT sampling site.

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TSP = 179.2496+ 6.5905* x-4.4743* y

TSP concentrations 200 180 160 140 120

Figure 13. The 3D surface plot of the corrections between TSP, Temp. and WS at TH sampling site.

152

Guor-Cheng Fang Fine = -24.3011+ 8.8044* x; 0.95 Conf.I nt .

Fine particulate concentrations ( ug/m 3)

90

80

70

60

50

40

30

6

7

8

9

10

11

12

13

14

13

14

WS (m /s)

Figure 14. The scatter-plot of coarse particulates and WS at TH sampling site.

Coarse = -11.6222+ 4.3997* x; 0.95 Conf.I nt .

Coarse particulate concentrations ( ug/m3)

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45

40

35

30

25

20

15

10

6

7

8

9

10

11

12

WS (m /s)

Figure 15. The scatter-plot of fine particulates and WS at TH sampling site.

According to above observations and analysis, the results reveal that WS and Temp. were the main factors which influenced atmospheric particulate concentrations, especially at TH sampling site. The possible proposed reason was that TH sampling site was close to the Taiwan western coast (about 100 m) compared to the rest of the other sampling sites.

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…153 Furthermore, TH sampling site has the smallest surface roughness. Thus, wind speed has the least factors, if there is any, to influence the above result.

3.4.2. Factor Analysis of Metallic Element Concentrations in Atmospheric Particulates The method of factor analysis was utilized to identify the possible sources for metallic elements. The eigenvalues extract by applied principal components with Varimax rotation to obtains greater factors. Once a factor has a great eigenvalue, it can explain more total variance, Kaiser ( 1974) ever suggested that we can obtained more total variance as long as we have eigenvalue ＞1. Additionally, factors extracted also observed from eigenvalue’s screeslope. Cattlell ( 1966) proposed that the steep slope which obtained from the turning point is the place that we need to extract. At TA sampling site, figure 16 illustrates the results for factor analysis of metallic element concentrations in TSP. The analytical results present that two factors eigenvalues ＞1 and explained about 71.53 % of total variance. Factor 1 explained 52.13 % of total variance and had high factor loading for Pb (0.91), Fe (0.87, Cr (0.72 and Zn (0.71), which identified the possible source for vehicle emission and incinerator, and factor 2 accounted for 18.4 % of total variance and identified the possible source for soil. Moreover, metallic element concentrations in coarse particulate were extracted two factors and the results are displayed in figure 17. The analytical results indicated that factor a explained 50.82 % of total variance and had high factor loading for Cr (0.88), Cu (0.82) and Fe (0.7), which identified the possible source for vehicle emission, and factor 2 explained 17.01 % of total variance and identified the possible source for soil and re-suspended dust. Additionally, figure 18 illustrates the results for factor analysis of metallic element concentrations in fine particulate.

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Three factors were extracted (eigenvalue ＞1) by factor analysis, and explained 85 % of total variance. Factor 1 accounted for 47.19 % of total variance and had high factor loading for Mn (0.93), Zn (0.87), Fe (0.85) and Pb (0.84) which identified the possible source for vehicular emission. Factor 2 explained 21.84 % of total variance and had high factor loading for Cr (0.91) which identified the possible source for coal combustion. Factor 3 explained for 16.48 % of total variance and had high factor loading for Mg (0.89) which identified the possible source for sea salt. At WT sampling site, figures 19, 20 and 21 show the results of factor analysis of metallic element concentrations in atmospheric particulates. As for TSP (Figure 19), two factors were extracted and explained 81.87 % of total variance. Factor 1 accounted for 71.87 of total variance, high factor loading of Zn (0.87), Pb (0.83), Cu (0.82) and Fe (0.79) were contributed by traffic emission. Factor 2 is relative to coal combustion and industrial process. Additionally, factor analysis of metallic element concentrations in coarse particulate were extracted two factors and accounted 82.36 % of total variance (Figure 20). High factor loading of Pb (0.93), Fe (0.92), Cu (0.78) and Mn (0.73) were contributed by traffic emission. High factor loading of Cr (0.94) and Zn (0.81) were observed on factor 2 and the possible source were industrial process and coal combustion. As for fine particulate, the results for factor analysis of metallic element concentrations are illustrated in figure 21. The analytical results show that high factor loadings of Zn (0.89), Pb (0.87), Cu (0.86), Mg (0.85) and Fe (0.7) on factor 1 and explained 69.55 % of total variance. This suggested that traffic vehicle

154

Guor-Cheng Fang

emissions were the main contributors. Factor 2 explained 13.1 % of total variance and had factor loading of Mn (0.94) which identified the possible source for metal industry. Fact or Loadings, Fact or 1 vs. Fact or 2 Rot at ion: Varimax raw Ext ract ion: Principal component s 1.0 Mg Cu

0.8 Factor 2 Eigenvalue: 1.29 Total variance: 18.4 % Origin: Soil

Fact or 2

0.6

Factor 1 Eigenvalue: 3.65 Total variance: 52.13 % Origin: Vehicle em ission & incinerator Pb

0.4

Zn 0.2 Fe Mn

0.0

Cr

-0.2 -0.2

0.0

0.2

0.4

0.6

0.8

1.0

Fact or 1

Figure 16. Factor analysis of metallic element concentrations in TSP at TA sampling site.

Fact or Loadings, Fact or 1 vs. Fact or 2 Rot at ion: Varimax raw Ext ract ion: Principal component s 1.0 0.8 0.6

Fact or 2

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0.4

Mn

Zn

Factor 2 Eigenvalue: 1.19 Total variance: 17.1 % Origin: soil and re-suspended

Fe Pb

Cr

0.2

Cu

0.0

Factor 1 Eigenvalue: 3.56 Total variance: 50.82 % Origin: Vehicle em ission

-0.2 -0.4 -0.6 0.2

Mg 0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Fact or 1

Figure 17. Factor analysis of metallic element concentrations in coarse particulate at TA sampling site.

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…155 Fact or Loadings, Fact or 1 vs. Fact or 2 vs. Fact or 3 Rot at ion: Varimax raw Ext ract ion: Principal component s

Factor 3 Eigenvalue: 1.15 Total variance: 16.48 % Origin: Sea salt

Mg

Pb Mn

Factor 1 Eigenvalue: 3.30 Total variance: 47.19 % Origin: Vehicular emission

Cr Cu Factor 2 Eigenvalue: 1.53 Total variance: 21.84 % Origin: Coal combustion

Fe Zn

Figure 18. Factor analysis of metallic element concentrations in fine particulate at TA sampling site. Plot of Eigenvalues 6.0 5.5 5.0 4.5 4.0

Val u e

3.5 3.0

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2.5 2.0 1.5 1.0 0.5 0.0

1

2

3

4

5

Number of Eigenvalues

Figure 19. (Continued).

6

7

156

Guor-Cheng Fang Fact or Loadings, Fact or 1 vs. Fact or 2 Rot at ion: Varimax raw Ext ract ion: Principal component s 1.0 Mn Fact or 2 Eigenvalue: 0.7 Tot al variance: 10 % Origin: Coal combustion & industrial process

0.9 0.8

Fact or 2

0.7

Cr Mg

0.6 Cu 0.5 0.4

Fact or 1 Fe Eigenvalue: 5.03 Tot al variance: 71.87 % Origin: Traffic emission

0.3 0.2 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Zn Pb 0.9

Fact or 1

Figure 19. The eigenvalue’s screeplot and factor analytical results for metallic element concentrations in TSP at WT sampling site.

Plot of Eigenvalues 5.5 5.0 4.5 4.0

Val u e

3.5 3.0 2.5

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2.0 1.5 1.0 0.5 0.0

1

2

3

4

5

Number of Eigenvalues

Figure 20. (Continued).

6

7

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…157 Fact or Loadings, Fact or 1 vs. Fact or 2 Rot at ion: Varimax raw Ext ract ion: Principal component s 1.0

Cr

0.9 Zn 0.8

Fact or 2

0.7 0.6

Fact or 2 Eigenvalue: 0.9 Tot al variance: 12.83 % Origin: Industrial process & coal combustion

Fact or 1 Eigenvalue: 4.87 Tot al variance: 69.53 % Origin: Traffic emission Cu

Mg

0.5

Mn

0.4 0.3

Fe Pb

0.2 0.1 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Fact or 1

Figure 20. The eigenvalue’s screeplot and factor analytical results for metallic element concentrations in coarse particulate at WT sampling site.

Plot of Eigenvalues 5.5 5.0 4.5 4.0

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Val u e

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

1

2

3

4

5

Number of Eigenvalues

Figure 21. (Continued).

6

7

158

Guor-Cheng Fang Fact or Loadings, Fact or 1 vs. Fact or 2 Rot at ion: Varimax raw Ext ract ion: Principal component s 1.0

Mn

0.8

Fact or 2 Eigenvalue: 0.91 Tot al variance: 13.06 % Origin: Metal industry

Cr

Fact or 2

0.6

Fact or 1 Eigenvalue: 4.87 Tot al variance: 69.55 % Origin: Traffic vehicle emissions Cu Mg

Fe

0.4

Zn

0.2

Pb

0.0

-0.2 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Fact or 1

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Figure 21. The eigenvalue’s screeplot and factor analytical results for metallic element concentrations in fine particulate at WT sampling site.

At TH sampling site, figure 22 illustrates the results for factor analysis of metallic element concentration in TSP. The analytical results show that two factors were extracted and explained 62.06 % of total variance. High factor loadings of Cu (0.88) and Cr (0.71) on factor 1 and explained 46.37 % of total variance. The sea salt was responsible for this result. Factor explained 15% of total variance and had high factor loading of Pb (0.92) and Zn (0.73) which the possible source for vehicular emission. As for coarse particulate, the results for factor analysis of metallic element concentrations are illustrated in figure 23. Clearly, three factors had been extracted by factor analysis. High factor loadings of Zn (0.9) and Mg (0.89) were on factor 1 and interpreted 48.35 % of total variance. The possible source for this finding was sea salt. Factor 2 accounted for 18.27 % of total variance and had high factor loading of Mn (0.94) which the possible source for crustal. Moreover, high factor loadings of Cr (0.81), Cu (0.74) and Pb (0.71) were on factor 3 and interpreted 14.35 % of total variance. The main source for this result was vehicular emission. In fine particulate, figure 24 shows the results for factor analysis of metallic element concentrations. Three factors eigenvalues ＞1 and explained about 76.25 % of total variance. High factor loadings of Mg ( 0.92), Zn (0.78) and Fe (0.72) were appeared on factor 1, and interpreted 39.17 % of total variance. This result suggested that sea salt was the main contributors. Factor 2 interpreted 20 % of total variance and had high factor loading of Cu (0.83) and Pb (0.76) which represented the major contributor was vehicular emission. Additionally, factor 3 explained 17.1 of total variance and had high factor loading of Mn (0.86) and Cr (0.72) which the possible source for coal combustion.

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…159 Fact or Loadings, Fact or 1 vs. Fact or 2 Rot at ion: Varimax raw Ext ract ion: Principal component s 1.0

0.8

Pb

Fact or 2 Eigenvalue: 1.1 Tot al variance: 15.69 % Origin: Vehicular emission

Zn

Fact or 2

0.6

Mn Fe

Mg

0.4 Cr 0.2 Fact or 1 Eigenvalue: 3.25 Tot al variance: 46.37 % Origin: Sea salt

0.0

-0.2 -0.2

0.0

0.2

0.4

0.6

Cu

0.8

1.0

Fact or 1

Figure 22. The results of factor analysis for metallic element concentrations in TSP particulate at TH sampling site.

Fact or Loadings, Fact or 1 vs. Fact or 2 vs. Fact or 3 Rot at ion: Varimax raw Ext ract ion: Principal component s

Fact or 3 Eigenvalue: 1 Tot al variance: 14.35 % Origin: Vehicular emission

Cr

Cu

Fact or 1 Eigenvalue: 3.38 Tot al variance: 48.35 % Origin: Sea salt

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Pb Mg Fe Mn Fact or 2 Eigenvalue: 1.28 Tot al variance: 18.27 % Origin: Crustal

Zn

Figure 23. The results of factor analysis for metallic element concentrations in coarse particulate at TH sampling site.

160

Guor-Cheng Fang Fact or Loadings, Fact or 1 vs. Fact or 2 vs. Fact or 3 Rot at ion: Varimax raw Ext ract ion: Principal component s

Fact or 3 Eigenvalue: 1.4 Tot al variance: 20 % Origin: Coal combust ion

Mn Cr Fe

Pb

Zn

Fact or 1 Eigenvalue: 2.74 Tot al variance: 39.17 % Origin: Sea salt

Mg Cu Fact or 2 Eigenvalue: 1.2 Tot al variance: 17.1 % Origin: Vhicular emission

Figure 24. The results of factor analysis for metallic element concentrations in fine particulate at TH sampling site.

Overall, vehicular emission was the main contributor for metallic element concentrations in atmospheric particulates, especially at TA and WT sampling sites. Generally, the high frequency of airplanes took off, landed plus expressed highway which surround this airport were the main pollution sources for TA sampling site. as for WT sampling site, high loading and major express highways which surround this region were the main pollution sources.

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Moreover，the main contributor for metallic element concentrations in atmospheric particulates at TH sampling site was sea salt. This is because that TH sampling sit is nearly Taiwan Strait (about 10 m). High wind speed which strengthen the effect of the deliver of sea salt. To sump up, the major pollution sources for metallic elements in atmospheric particulates were traffic and sea salts. Then followed by crustal elements and other sources around there regions in central Taiwan. Thus, how to improve the traffic problems has become the most important issue in this area.

CONCLUSION This study obtained the following conclusions. 1. Average TSP concentrations were 120.6±30.68 µg/m3, and coarse particulate concentrations were 24.45±7.83 µg/m3. Moreover, average fine particulate concentrations were 38.13±8.44 µg/m3 during sampling period (September to

Characteristic of Atmospheric Particulates and Metallic Elements Composition Study…161

2.

3.

4.

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5.

6.

December of 2005) at TA sampling site. Regarding atmospheric particulate concentrations at WT sampling site, the average TSP concentrations were 113.72±34.46 µg/m3, and coarse particulate concentrations were 25.99±8.18 µg/m3, respectively. Additionally, average fine particulate concentrations were 47.38±13.59 µg/m3 during March 2004 and January 2005 at WT sampling site. As for TH sampling site, average TSP concentrations were 152.76±28.37 µg/m3, and coarse particulate concentrations were 28.69±10.49 µg/m3. Moreover, average fine particulate concentrations were 56.38±18.53 µg/m3 between March 2004 and January 2005 at TH sampling site. Generally, atmospheric particulate concentrations (TSP, PM2.5–10 and PM2.5) in spring and winter were higher than those for other seasons at the WT and TH sampling sites. The primary reason was that the dust storms in China frequently occurred in spring and winter, thereby increasing PM concentrations in Taiwan. Moreover, the strong northeast monsoon which influenced Taiwan including Chinese dust storms in spring and winter and mean wind speeds in spring and winter are higher than those in other seasons. Comparison average atmospheric particulate concentrations during the same sampling months at these three sampling sites. The comparison results presented that average TSP and fine particulate concentrations at these three sampling sites, the concentration order during sampling periods was as follows: TH > WT > TA, and the average coarse particulate concentration order during sampling periods was as follows: TH > TA > WT. Therefore, TSP, fine and coarse particulate concentrations at TH sampling site were highest than those at other two sampling sites. Mean wind speed at TH sampling site higher than those at other sampling sites was the main reason for this finding. The investigation results revealed that three primary components of metallic elements in atmospheric particulates were Fe, Mg and Zn at TA sampling site. Furthermore, metallic elements Fe and Zn were the primary components on TSP at the TH and WT sampling sites, and Fe and Mg were the predominant components on coarse and fine particulates at the TH, and WT sampling sites. However, Zn and Fe were the predominant components on fine particulates at the WT sampling site. Statistical analytical results revealed that WS and Temp. were the main factors which influenced atmospheric particulate concentrations, especially at TH sampling site. The possible proposed reason was that TH sampling site was close to the Taiwan western coast (about 100 m) compared to the rest of the other sampling sites. Furthermore, TH sampling site has the smallest surface roughness. Thus, wind speed was the least factor, if there is any, to influence the above result. The major pollution sources for metallic elements in atmospheric particulates were traffic and sea salts. Then followed by crustal elements and other sources around there regions in central Taiwan. Thus, how to improve the traffic problems has become the most important issue in this area.

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ACKNOWLEDGMENTS The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC 96-2628-E-241-001MY3.

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In: Atmospheric Science Research Progress Editor: Chih-Hao Yang

ISBN 978-1-60456-439-6 © 2009 Nova Science Publishers, Inc.

Chapter 7

PHYSICAL AND OPTICAL PROPERTIES OF COLUMNAR AEROSOLS: A GLOBAL COMPARISON FROM AERONET OBSERVATIONS Xingna Yu1, Tiantao Cheng1 , Jianmin Chen1 and Yongfu Xu2 1

Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China 2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Reviewed by Renjian Zhang Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

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ABSTRACT A comparison of aerosol physical and optical properties in the atmosphere on a global scale was made on the basis of inversion products of AERONET measurements performed in the period of 1995-2006 at 25 typical sites. In general, aerosol optical thicknesses (AOT) were in higher levels over Asia, Africa and Middle East regions than those over Europe, North America, Atlantic, Caribbean and Pacific regions. The maximum of seasonal mean AOTs commonly occurred in spring and summer. Angström exponents decreased with increasing AOTs, and decreased to zero even negative when a heavy dust events broke out and thus dust outflow extended, especially in source regions of deserts. The monthly averages of Angström exponents similarly showed a minimum value among 12 months, for example, in April over Asia, in April or May over Europe and North America, and in May to July over Africa, Middle East and other oceanic islands. Volume particle size distributions were almost characterized by a tri-modal structure in logarithm normal feature with one accumulation and two coarse modes, however, the volume median radii of these modes fell in different size stages of 0.07-

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Xingna Yu, Tiantao Cheng , Jianmin Chen et al. 0.19, 1.3-2.5, 2.2-5.0 μm, and a slight variation in different regions. Asymmetry factor of particle scattering behaved a low sensitivity to wavelengths in all seasons, about 0.65 for Asia, Africa and Middle East regions, 0.69 for Pacific, Atlantic and Caribbean regions, and 0.60 for other regions.

Keywords: Aerosol properties; Dust event; Long-range transport.

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INTRODUCTION Aerosol is one important component of the earth atmosphere system, which plays an extremely important role both in the global climate change and in the biogeochemical cycle. Aerosol has strong impacts on the atmospheric radiation budget, but these effects still contain considerable uncertainties due to the poor understanding of aerosol properties and their spatial and temporal variation (IPCC, 2001). Ground-based sensing of aerosols is best suited to reliably and continuously derive detailed aerosol properties in key locations partly due to its wide angular and spectral measurements of solar and sky radiation. The Global Atmosphere Watch (GAW) of World Meteorological Organization (WMO), a coordinated network of observing stations, has already provided information on changes of the chemical composition and physical properties of the background atmosphere from all parts of the world. The Aerosol Robotic Network (AERONET), an automatic robotic sun and sky scanning measurement program, has grown rapidly through international federation since 1993 to over 100 sites worldwide so far (Holben et al., 2001). The Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) field campaign was conducted primarily in South Korea, Japan, China and adjacent oceanic regions to characterize the properties of Asian aerosols (Huebert et al., 2003). A sun and sky radiometer network based in East-Asia (SKYNET) was also established and conducted similar measurements as AERONET (Takamura et al., 2002). Characterization and temporal variation of aerosol properties over sources, continental areas and remote marine regions have been studied and published (Zhang et al., 2003; Tetsu et al., 2003; Mori et al., 2003; Xia et al., 2004; Murayama et al., 2001; Kim et al., 2005; Cheng et al., 2005; Zhuang et al., 2001). It is well known that aerosol properties vary with highly spatial and temporal evolution. In order to better understand the variation of particle properties in the atmospheric aerosol system, we need to investigate the climatology of aerosol properties and analyze their spatial variability on a global scale. The objective of this study is to characterize and compare the physical and optical properties of columnar aerosols using the inversion products of AERONET simultaneous measurements collected in Asia, Africa, Middle East, Europe, North America, Pacific, Atlantic and Caribbean regions. A long-term record of aerosol observations should provide a valuable data to deepen the understanding of aerosol properties worldwide.

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MATERIAL AND METHODS Large amounts of aerosol particles are annually emitted into the atmosphere from different sources, including sea salt, sulphate and nitrate, mineral dust, organic and black carbon, etc. During transport, some of the above aerosol particles undergo chemical modifications by oxidation of gaseous material such as SOx and NOx on their surface (Denterner et al., 1996), and by coagulation process to mix internally with other preexistent aerosols (Wurzler et al., 2000; Roth and Okada, 1998). And thus, these modifications change aerosol properties of size, shape, surface, optical and radiative factors, even their ability to be a condensation (Sokolik et al., 2001). So, it is important for observation sites in limited number to availably reveal the impacts of typical aerosol species from regional and remote sources, especially in mineral dust, and to reflect the features of complex regional mix of aerosols from natural and anthropogenic emission. Consecutive measurements of sun and sky radiance performed at 25 sites of the AERONET in 1995-2006 and its corresponding inversion products were in the interests of this study. The exact locations of all selected sites are given in Table 1. Mineral dust outflows originating from Asian continent commonly transport over long distance to the North Pacific and even to the North America (Duce, 1995). Moreover, fast economic development, large areas of desert, and intensive forest and agriculture fires in this region contribute one-fourth to one-third of the total global emissions of SO2, organic matter, soot and dust (Chin et al., 2003). Sites of Dunhuang, Yulin, Beijing, Gosan, Osaka, Midway_Island and Lanai are located along the line of Asian dust outflow from west to east, even including Missoula and San_Nicolas sites during severe dust storms. On the other hand, megacities of Beijing, Hong Kong and Osaka offer a good opportunity to compare the properties of anthropogenic aerosols from cites in different pollution features, and to examine the complex regional aerosol mix in East Asia. Also, mineral dust clouds from the Sahara cover large areas of tropical Atlantic and span the Atlantic to the Caribbean (Holben et al., 2001; Chiapello et al., 1999). Sites of Banizoumbou, Ouagadougou, Rome_Tor_Vergata, Lecce, Granada, Avignon, Cape_Verde, Bermuda and Barbados fall well in the outflow area of Saharan dust from the Sahara and its surrounding areas. GSFC, Mexico_City, Rome and Ouagadougou sites can be used to investigate the properties of anthropogenic aerosols emitted from typical cites in North America, Central America, Europe and Africa, respectively. Kanpur site is usually influenced by desert dust from Indian arid regions and local industrial emission, and Solar_Village and Bahrain sites in aerosol loading are dominate by dust from deserts in Middle East and local anthropogenic pollutant. All of measurements at above sites were made with sun and sky autonomous radiometer CE-318, which is a part of the AERONET. Sun and sky measurements were performed in seven spectral bands (340, 380, 440, 500, 670, 870 and 1020 nm), from which the aerosol properties such as optical depth, size distribution and single-scattering albedo etc. were derived (Dubovik et al., 2000). The solar extinction measurements were used to compute the aerosol optical depth at each wavelength except for 940 nm channel, and the sky radiance almucantar measurements in conjunction with the direct sun measurements were used to retrieve the aerosol size distributions (Dubovik et al., 2000; Nakajima et al., 1996). The uncertainties in the size distribution and refractive index values retrieved from AERONET

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data were discussed by Dubovik et al. (2000). Data sets used here were the data of level 2.0 fully cloud-screened, calibrated and verified by the method of Smirnov et al. (2000). Table 1. Location of AERONET sites selected for the present study on a global scale Region

Country

Site name

Latitude

Longitude

Asia

China

Dunhuang Yulin Beijing Hong_Kong Gosan Osaka Kanpur Solar_Village Bahrain Banizoumbou Ouagadougou

N 40.038º N 38.283º N 39.977º N 22.303º N 33.283º N 34.651º N 26.45º N 24.91º N 26.208º N 13.541º N 12.2º

E 94.794º E 109.72º E 116.38º E 114.18º E 126.17º E 135.59º E 80.346º E 46.41º E 50.609º E 2.665º W 1.4º

Elevation (m) 1300 1080 92 30 72 50 142 650 25 250 290

Rome Lecce Granada Avignon GSFC Missoula San_Nicolas Bratts_Lake Lanai Midway_Island Cape_Verde Bermuda Barbados Mexico_City

N 41.839º N 40.333º N 37.164º N 43.932º N 38.992º N 46.916º N 33.257º N 50.28º N 20.735º N 28.209º N 16.732º N 32.37º N 13.166º N 19.334º

E 12.647º E 18.1º E 3.61º E 4.878º W 76.849º W 114.08º W 119.49º W 104.7º W 156.92º W 177.38º W 22.935º W 64.696º W 59.5º W 99.182º

130 0 680 100 87 1028 133 586 20 20 60 10 0 2268

Middle East Africa Europe

North America

Pacific Atlantic Caribbean

Korea Japan India Saudi Arabia Bahrain Niger Burkina Faso Italy Spain France United States Canada Hawaii Midway Island Sal Island Bermuda Barbados Mexico

Acquisition period 2001 2001-2002 2001-2005 2006 2001-2005 2002-2004 2001-2005 1999-2002 1998-2000 1995-2003 1999-2005 2001-2004 2003-2004 2005 1999-2004 1995-2005 2000-2006 1998-2004 1999-2005 1997-2004 2001-2002 1996-2003 1996-2002 1999 1999-2005

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Aerosol Optical Thickness A comparison of monthly mean aerosol optical thickness (AOT) at 670 nm for the 25 selected sites is shown in Figure 1. The covering periods of all measurements are not coincident, some sites less than one year but others up to ten years. Multiyear average of AOTs is better to characterize an implicit climatology of aerosol loadings in seasonal cycle. Nevertheless, this comparison is still available to reveal the general magnitude and its seasonal variations of atmospheric aerosol loadings in different regions. The seasonal variability of AOTs was not entirely presented at Dunhuang, Hong_Kong, Yulin, Barbados and Granada sites because of the limit of data. As seen in Figure 1, the monthly averages of AOTs at Asian, African and Middle East sites were commonly higher than those at European, North American and oceanic (Pacific, Atlantic and Caribbean) sites in all months. This may be attributed to a stronger emission of

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aerosols such as dust, sulfate etc. from source regions, and a stronger contribution to solar light extinction.

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Figure 1. Monthly averages of aerosol optical thickness (AOT) at 670 nm for all selected sites.

The seasonal mean AOTs presented a maximum in spring at Dunhuang, Gosan, Midway_Island and Lanai sites, but in summer at Beijing, Yulin and Osaka sites (Figure 1a). Its secondary peak visibly occurred in spring and autumn only for Beijing site, and in winter and spring only for Osaka site. The highest monthly mean AOT of Beijing site 0.72 (June) was over 3 times larger than that of GSFC 0.22 (July) and Mexico_City 0.27 (May), even over 4 times larger than that of Rome 0.16 (June). Eck et al. (2005) reported that the maximum of monthly mean AOTs at 500 nm yielded 1.10 in June at Beijing site. Zhang et al. (2002) observed the value of highest monthly-mean AOT at 500 nm at Beijing site much lager suburban regions such as in Miyun (0.56 in June of 1998) and in Xinfeng (0.60 in April of 1998). The occurrence of extremely high values in AOT at Beijing site was mainly due to the combination of increasing dust particles that contained crustal elements and pollutant particles emitted from local sources, especially in spring (Zhuang et al., 2001). For Dunhuang site, the highest monthly-mean AOT (0.71, April) was related to large amounts of coarse particles emitted from local dust sources, which was roughly larger than the measurement (0.37) of 1999 (Xia et al., 2004) and lower than the average (1.16) of dusty days in 2001-2005 (Yu et al., 2006). The maximum of monthly mean AOTs (0.38, May) was about 4 times the minimum (0.1, October) at Gosan site. For the Pacific sites of Midway_Island and Lanai, the maximal monthly mean in AOT was up to levels of 0.08-0.1 (March-April), much higher than the multi-year average of 0.05-0.07, suggesting significant influences of Asian mineral dusts after a long-range transport in springtime (Shaw et al., 1980). These results revealed that

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though dry and wet deposition during transport, mineral dust particles from deserts and erosion areas in East Asia had strong impacts on the aerosol loadings over large areas of Asian dust outflow in winter and spring. However, in summer and autumn, the aerosol loadings over part of this region were still dominated by anthropogenic aerosols from local and remote pollutant emission, especially in urban regions, industrial regions and suburb surrounding city. At Banizoumbou and Ouagadougou sites, the prevailing aerosol types are dust from the Sahara and the semi-arid Sahelian region, and biomass burning aerosols (N’Tchayi et al., 1997). And, these monthly mean AOTs were high all year (>0.2) with primary peak in spring, and secondary peak in autumn (Figure 1b). For European sites of Rome, Lecce, Granada and Avignon, previous three sites in AOTs behaved a primary peak in summer and a secondary peak in spring, but last site was opposite to those (Figure 1c). This indicated that large amounts of dust particles transported from the Africa continent enhanced the atmospheric aerosol loadings in dusty periods, which exceeded the contributions of aerosol species emitted from local sources even exclusively in pollution periods. Bermuda site in AOTs showed a similar seasonal variation with Lanai, a maximum monthly average of 0.1 (April) and a multiyear average of 0.07 (Figure 1d). For Barbados site, the monthly mean AOTs exceeded 0.1 between May and August, Smirnov et al. (2000) also observed that AOTs at 870 and 440 nm increased from May to August and reached peaks in June or July. High AOTs occurred at Cape_Verde site from May to October, with a highest value of 0.39 in June, and Chiapello et al. (1999) also identified a seasonal variation of aerosol loadings over this region with a summer maximum using satellite TOMS data. These analyses demonstrated that mineral dust outflow originating from the Saharan and its surrounding semi-arid regions produced strong impacts on the aerosol loadings over large areas of tropical Atlantic, southern Europe and Caribbean. The highest monthly-mean AOT of Kanpur (0.56) occurred in May, and Singh et al. (2004) measured a maximum of 0.6 during the pre-monsoon season (March-May) which is dominated by a dust loading. High AOTs of Middle East sites usually occurred in spring and summer (Fig. 1b). For example, Solar_Village had a maximum of 0.39 in August and a secondary peak value of 0.36 in May. For Bahrain, the maximum monthly mean AOT of 0.43 occurred in July, and Smirnov et al. (2002) also reported that the period of March-July represented high aerosol loadings and the maximum reached 0.49 in July. This result was attributed to significance influences of mineral dust from Saudi Arabia, Iraqi, or southern Iran. For Hong_Kong, a megacity in southern border of Chinese mainland, its high AOTs occurred in spring, suggesting significance influences of aerosols (e.g. soot) from local sources and transported from neighboring regions. Sites of Mexico_City, GSFC and San_Nicolas presented high AOTs in spring. Holben et al. (2001) reported a highest monthlymean AOT of 0.48 (July) and a multi-year mean of 0.23 during 1993-1999 occurred at GSFC. Missoula and Bratts_Lake sites in AOTs had a first peak in August (0.10) and a secondary peak in April or May (0.07).

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Angström Exponent Angström exponent (α) is a good indicator of aerosol size, and its value varies depending on particle sizes, and increases equal to 4 corresponding to molecules and decreases to near zero or even negative for super-coarse particles. Monthly averages of Angström exponent for the 25 sites are shown in Figure 2, in which Angström exponent was computed from AOTs measured at 870 and 440 nm.

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Figure 2. Monthly averages of Angström exponents for all selected sites.

The monthly-mean α of urban sites were commonly higher than those sites in Asian source regions where airborne aerosol loadings were mainly affected by dust particles (Figure 2a). As a result of the influences of coarse particles, α was less than 0.4 at Dunhuang and Yulin sites in springtime, and about 0.6 at Kanpur site including August. The averages of α were larger than 1.0 in all months at Beijing, Hong_Kong, Gosan and Osaka sites, indicating aerosol loadings dominated by anthropogenic emission. However, Yu et al. (2006) measured the mean α of dusty days about 0.05 of 2001 at Dunhuang, 0.20 of 2002 at Yulin, and 0.42 of Beijing from 2001 to 2005. The α of Gosan derived from sunphotometer measurements were up to 0.38 in a heavy dust episode during ACE-Asia 2001 (Kim et al., 2005). Aoki and Fujiyoshi (2003) observed the multi-year mean α ranged from 0.99 to 1.09 at four sites of Japan based on sky radiometer measurements. These differences can be attributed to each contribution of coarse dust particles and fine pollution aerosols in aerosol loadings, especially between periods with and without dusty days. The monthly-mean α of African and Middle East sites were shown in Figure 2b. It was easily found that the mean α of Banizoumbou and Ouagadougou sites were less than 0.6 in all months, especially in spring and summer, and

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similar low values occurred in summer of Solar_Village and Bahrain sites. Masmoudi et al. (2003) measured that the daily-mean α varied from -0.1 to approximate 0.6 over Ouagadougou and Banizoumbou during April, May and June of 2001. For sites of Europe and North America, most of monthly mean α exceeded 1.0 except for San_Nicolas, and lower values commonly occurred in spring and autumn (Figure 2c). The mean α of GSFC which were larger than 1.7 in summer, autumn and winter, were in a good agreement with the measurements of Holben et al. (2001), indicating the predominance of fine pollution aerosol emitted from local automobiles. Missoula behaved a seasonal variation in α similar to Bratts_Lake, and Thulasiraman et al. (2002) also made a comparison of α between dust and dust-free episodes in April, with values of 0.40, 0.92 for Missoula and 0.69, 1.41 for Bratts_Lake, respectively. Rome, Lecce and Avignon had a moderate seasonal variation in α than other sites, with the yearly mean of 1.46-1.49. Also, one year measurement performed at Lecce presented that the average of α was about 1.59 in spring and summer due to the dominance of urban-industrial aerosols, and 0.7 in autumn and winter due to the contribution of maritime and/or polluted-maritime aerosols (Perrone et al. 2005). Under the extension of dust outflow from remote sources, the monthly mean α of Cape_Verde, Bermuda, Barbados, Midway_Island and Lanai sites were commonly lower than Mexico_City site over urban regions in severe air pollution, especially in spring for northern Pacific and in summer for tropical Atlantic (Figure 2d). The monthly mean α of Mexico_City site fell in a range of 1.38-1.68 mainly due to local industrial sources and automobile emissions.

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Volume Size Distribution The volume size distribution curves of aerosols between 0.05 and 15.0 μm showed a similar behavior at different AOTs. This has allowed us to plot average curves in different levels of AOTs, and this approach was used to characterize the urban, industrial, maritime, biomass burning and dust aerosols (Remer et al., 1998). The average volume size distributions of aerosols during winter and spring at 25 selected sites are presented in Figure 3, in which the curves have been averaged over all individual volume size distributions. It was clear that the aerosol volume size distributions commonly were in a tri-modal logarithm normal structure, one accumulation mode and two coarse modes. The accumulation mode was centered in radius 0.07-0.19 μm at Asian, African and Middle East sites, in radius 0.11 μm at European and North American sites, and in radius 0.07-0.14 μm at other sites. The first coarse mode was centered in radius 1.3-2.5 μm at Asian sites, in 1.0-2.2 μm at Pacific and Caribbean sites, and in 1.3-1.7 μm at other sites. The second coarse mode appeared with a median radius of 2.2-3.8 μm at Pacific and Caribbean sites, and a median radius of 2.9-5.0 μm at other sites. A pseudo-mode appeared with the median radius around 0.5 μm at Dunhuang, Yulin, Banizoumbou, Ouagadougou, Bahrain and Cape_Verde sites, whereas it was not notable at other sites. This additional mode may be possibly attributed to the local soil particles and was representative of the effects of regional climate (Patterson and Gillette, 1997). Figure 4 presents the average volume size distributions of aerosols for all sites during summer and autumn. There was also a tri-modal structure to describe the average size distribution of aerosols.

Physical and Optical Properties of Columnar Aerosols

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Figure 3. Mean volume size distributions of aerosols derived from sky radiance as a function of particle radius during winter and spring for all selected sites.

Figure 4. Mean volume size distributions of aerosols derived from sky radiance as a function of particle radius during summer and autumn for all selected sites.

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The accumulation mode appeared with the median radius of 0.11-0.19 μm at Asian sites, 0.11-0.14 μm at European and North American sites, and 0.07-0.14 μm at African, Middle East and other sites. The first coarse mode was commonly centered at radius of 1.7-2.2 μm for all sites. The second coarse mode appeared commonly with a median radius in 2.2-5.0 μm at Pacific, Atlantic and Caribbean sites, and in 3.8-5.0 μm at other sites. One pseudo-mode also appeared in radius around 0.5 μm at Banizoumbou and Ouagadougou sites. As in Figure 3 and Figure 4, the median radii of accumulation mode at sites near dust source regions were commonly lower than sites in dust downwind regions and urban regions. This could be explained by the increases of particle coagulation and the growth of hygroscopic particle after a long-range transport in downwind regions and urban regions (Eck et al., 2005). The amplitude of accumulation mode at urban sites was commonly larger than rural sites, suggesting the complexity of aerosol components significantly influenced by pollution emissions in metropolises. The accumulation mode was preponderant at Beijing, Gosan and Osaka in summer and autumn, and at European and North American sites in all seasons. It may be related to the deposition of most coarse particles by gravitational settling and their combination or mixture with sea salt during the dust long-range transport (Zhang et al., 2003). The coarse modes were dominant at Dunhuang, Yulin, Kanpur, Bahrain, Solar_Village, Cape_Verde, Banizoumbou and Ouagadougou sites due to the presence of dust particles with relative big size near dust source regions. These results were similar to the aerosol size distributions with three modes measured in other sites of dust source regions and Europe (Dubovik et al., 2002; Chiapello et al., 1999; Tanré et al., 2001; Masmoudi et al., 2003). Even so, we are still reminded of the existence of significant differences in aerosol size distributions due to the variability of sources and the traveling distance of particles away from sources (Sokolik et al., 1998).

Asymmetry Factor Asymmetry parameter reflects the asymmetrical distribution of particle dispersed radiation and is an average of the cosine of scattering direction angles. For a cloudless atmosphere asymmetry factor ranges from 0.1 in very clean conditions to 0.75 in polluted ones (Zege et al., 1991).

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Figure 5 shows the average asymmetry factors (ɡ ) for all sites at 440, 670, 870 and 1020 nm during winter and spring from 1995 to 2006. Figure 5a presents the average ɡ of seven Asian sites at above four wavelengths. For Hong_Kong and Kanpur sites, the average ɡ showed an entirely decreasing trend with wavelengths lengthening. Except for the decrease at 440-870 nm, ɡ showed a slightly increasing trend at 870-1020 nm for Yulin, Beijing, Gosan and Osaka sites. The averages of g were about 0.69 at 440 nm and 0.63 at 670-1020 nm for all Asian sites. The mean g showed a decreasing trend with wavelengths for Banizoumbou and Solar_Village sites, and decreased at 440-670 nm and then increased at 670-1020 nm for Ouagadougou and Bahrain sites (Figure 5b). The averages of ɡ were about 0.68 at 440 nm and 0.63 at 670-1020 nm at Middle East and African sites.

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Figure 5. The averages of aerosol asymmetry factors at 440, 670, 870 and 1020 nm during winter and spring for all selected sites.

Also, the average ɡ almost showed a decreasing trend with wavelengths at all European and North American sites (Figure 5c). The mean g of San_Nicolas at the four wavelengths was higher than that of other European and North American sites, with values of 0.68 and 0.60 respectively. The average ɡ at the four wavelengths of six oceanic sites was shown in Figure 5d. It is easily found that the averages of g at Mexico_City showed a sharp decreasing trend with wavelengths and were apparently lower than other sites at all four wavelengths. The average g commonly showed a low sensitivity to wavelengths at Barbados, Pacific and

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Caribbean sites, and almost decreased at 440-670 nm and then increased at 670-1020 nm. Figure 6 summarizes the averages of asymmetry factors at 440, 670, 870 and 1020 nm for all sites during summer and autumn. The average g commonly showed a decreasing trend with wavelengths at all Asian sites, with values of 0.70 at 440 nm and 0.62 at 670-1020 nm (Figure 6a). For African sites, also behaved a slight decreasing trend in 0.69 at 440 nm, and in 0.66 at 670-1020 nm. The average decreased with wavelength lengthening at Solar_Village site, but increased from 670 to 1020 nm at Bahrain site (Figure 6b). The averages of ɡ were about 0.67 at 440 nm and 0.63 at 670-1020 nm for African sites. The mean g was in 0.68 for first wavelength and 0.57 for latter three wavelengths at European and North American sites, with a decreasing trend (Figure 6c).

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Figure 6. The averages of asymmetry factors at 440, 670, 870 and 1020 nm during summer and autumn for all selected sites.

Figure 6d presents the averages of ɡ at four wavelengths for Pacific, Atlantic and Caribbean sites during summer and autumn. Similar to winter and spring, the ɡ

of

Mexico_City showed a sharp decreasing with wavelengths, about 0.69 at 440 nm and 0.59 at 670-1020 nm, apparently lower than other sites at the four wavelengths. Moreover, Barbados,

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Pacific and Caribbean sites showed a low sensitivity to wavelength variation, about 0.71 in ɡ at 440-1020 nm. In comparison Figure 5 with Figure 6, the averages of over Asian, African, Middle East, Pacific, Atlantic and Caribbean regions were commonly higher than those over European and North American regions in all seasons. The increase of asymmetry factor was possibly attributed to the higher contribution of coarse particles to scattering in airborne aerosol groups, even predominant in forward scattering. In general, the asymmetry factor at wavelengths 440-1020 nm, 0.65 over Asian, African and Middle East regions, 0.69 over Pacific, Atlantic and Caribbean regions, and 0.60 over European and North American regions, can be used in radiation calculation of climate models.

CONCLUSION The inversion products of solar and sky measurements of 25 AERONET sites have been utilized to characterize and compare aerosol physical and optical properties on a global scale

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from 1995 to 2006. In view of climate, the average AOT over Asia (0.39), Africa (0.43) and Middle East (0.24) regions was higher than over Europe (0.11), North America (0.06), Atlantic (0.13), Caribbean (0.14) and Pacific (0.06) regions. The maximum AOTs commonly occurred in spring or summer at all sites due to the presence of large amount of dust, smoke and urban-industrial aerosols. The Angström exponent decreased with increasing AOT, and decreased to zero or negative when the heavy dust events outbreak especially over source regions. The minimum monthly-mean Angström exponent occurred in April at Asian regions, in April or May at European and North American regions, and during May-July at other regions. The aerosol volume size distributions commonly showed a tri-modal structure, one accumulation mode and two coarse modes. In all regions, the accumulation mode was centered in radius of 0.07-0.19 μm, and the two coarse modes were concentrated in size of 1.3-2.5 μm, 2.2-5.0 μm respectively. The asymmetry factor showed a low sensitivity to wavelengths at all sites in all seasons, about 0.65 in Asia, Africa and Middle East regions, 0.69 in Pacific, Atlantic and Caribbean regions, and 0.60 over other regions.

ACKNOWLEDGEMENTS This work is supported by the National Natural Science Foundation of China (No. 40533017, 20377008, 40605001), the State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry of China (LAPC-KF-2006-05) and the Shanghai Tongji Gao Tingyao Environmental Science and Technology Development Foundation. We thank Holben B., Chatenet B., Wang P., Chen H., Kim Y., Sano I., Tanré D., Nichol J., Perrone M., Arboledas L., Gobbi G., Sappe G., McArthur B., Hao W., Frouin R., Abbadi N., Frouin R. for their efforts in establishing and maintaining sites of AERONET related to this investigation.

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In: Atmospheric Science Research Progress Editor: Chih-Hao Yang

ISBN 978-1-60456-439-6 © 2009 Nova Science Publishers, Inc.

Chapter 8

VARIATIONS IN AEROSOLS AND GREENHOUSE GASES IN A TROPICAL URBAN ENVIRONMENT, SOUTH INDIA K. V. S. Badarinath∗1, Shailesh Kumar Kharol1, V. Krishna Prasad2 and K. Madhavi Latha3 1

Atmospheric Science Section, National Remote Sensing Agency, Dept. of Space-Govt. of India, Balanagar, Hyderabad-500 037, India 2 Agroecosystem Management Program, The Ohio State University, USA 3 Department of Meteorology, University of Reading, Reading, UK

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ABSTRACT Aerosols and greenhouse gas emissions in urban areas constitute one of the major sources impacting radiation budget of the atmosphere. In this study, we report both the short term (diurnal variations) and long term (different years) variations in aerosols and some greenhouse gas emissions (GHG’s) over a typical urban environment, Hyderabad, south India. MICROTOPS-II sunphotometer has been used to measure aerosol optical depth (AOD) at different wavelengths viz., 380, 440, 500, 675, 870 and 1020nm in addition to columnar ozone and precipitable water content. Aerosol and UV measurements were also undertaken using Multifilter Rotating Shadowband Radiometer (MFRSR) and Ultraviolet MFRSR. Near real-time measurements of total, as well as size segregated mass concentration of near surface aerosols have been carried out using a ten channel Quartz Crystal Microbalance (QCM). Continuous and near-real-time measurements of the mass concentration of black carbon (BC) aerosol were carried out using an Aethalometer. In addition to BC, simultaneous measurements of CO and O3 have been carried out using portable CO and O3 analyzer. Also, long term variations in Aerosol Index (AI) were studied using Earth Probe (EP) total ozone mapping spectrometer (TOMS) satellite data that is a measure of a pure Rayleigh atmosphere. Results from these measurements clearly suggested spatial as well as temporal variations in aerosols and GHG’s. These variations were observed to be influenced by meteorological parameters as well as long distance transport. Integration of the above measurements with satellite data and back trajectory analysis using Lagrangian modeling suggested significant influence from biomass burning in addition to local sources. This ∗

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K. V. S. Badarinath, Shailesh Kumar Kharol, V. Krishna Prasad et al. study in overall provides a clear integration of ground-based measurements with satellite data and atmospheric modeling for characterizing aerosols and GHG’s in a typical tropical urban environment.

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INTRODUCTION Aerosols have an important effect on the Earth’s radiation budget both directly, by absorbing and scattering radiation, and indirectly, by altering the formation and precipitation efficiency of clouds (IPCC, 2001; Kaufman et al., 2002). The impact of aerosols on scattering and absorption of solar and terrestrial radiation depend strongly on the particle size and their optical properties (Charlson et al., 1992; Kaufman et al., 2002). Aerosols also affect cloud formation and thereby affect the radiation indirectly (Twomey, 1991; Kaufman et al., 2002). Sizes of aerosols and their surface properties are important for their effectiveness as cloud condensation nuclei (CCN). Some of the major sources of aerosols include urban pollution, biomass burning, dust etc., (Eck et al., 1999; Kaufman et al., 2002). For example, Eck et al. (1999) used the data from sunphotometers to derive optical thickness and particle sizes of dust, biomass burning and urban aerosols from aerosol robotic network (AERONET). They showed biomass burning and urban aerosols having high optical thickness and dominance in the accumulation mode (