A global black carbon aerosol model

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 101, NO. D14, PAGES 19,395-19,409, AUGUST 27, 1996

A global black carbon aerosol model

William F. Cooke • and Julian J. N. Wilson Environment Institute, European Commission, Ispra, Italy

Abstract. A global inventory has been constructed for emissions of black carbon from fossil fuel combustion and biomass burning. This inventory has been implemented in a three- dimensional global transport model and run for 31 model months. Results for January and July have been compared with measurements taken from the literature. The modeled values of black carbon mass concentration compare within a factor of 2 in continental regions and some remote regions but are higher than measured values in other remote marine regions and in the upper troposphere. The disagreement in remote regions can be explained by the coarse grid scale of the model (10 ø x 10ø), the simplicity of the current deposition scheme, and possibly too much black carbon being available for transport, which would also account for the disagreement in the upper troposphere. The disagreement may also be due to problems associated with the measurement of black carbon. Emissions from this database appear to provide a reasonable estimate of the annual emissions of black carbon to the atmosphere. Biomass burning emissions amount to 5.98 Tg and that from fossil fuel amounts to 7.96 Tg. A local sensitivity analysis has been carded out and shows that black carbon has a lifetime between 6 and 10 days, depending on the transformation rate between hydrophobic and hydrophilic black carbon.

1. Introduction

The tropospheric aerosol burden has both a direct effect on the radiation balance through the backscattering and absorption of incoming solar radiation (and to a much lesser extent the absorption of infrared radiation) and an indirect effect through the influence on cloud optical properties and cloud lifetimes of the fraction of the aerosol population that is capable of acting as cloud condensation nuclei [Twomey, 1977]. A substantial fraction of the optically active submicrometer tropospheric aerosol is anthropogenic and a considerable effort has been devoted to both quantifying the radiative effects of tropospheric aerosols and the contribution from the anthropogenic fraction.

To date, studies of the radiative impact of anthropogenic aerosols have only considered the effects of sulphate aerosol derived from anthropogenic emissions of SO2 [Charlson et al., 1991, 1992; Kiehl and Brieglieb, 1993; Taylor and Penner, 1994]. However, both fossil fuel and biomass combustion, which are the principal sources of anthropogenic SO2, also generate direct emissions of carbonaceous aerosols of varying composition, ranging from elemental carbon to volatile organic compounds [Muhlbaier and Williams, 1982; Dod et al., 1985; Goldberg, 1985; Williams et al., 1989a,b; Cachier et al., 1990]. Optically, these aerosols also range from light absorbing to scattering according to their composition.

A significant fraction of carbonaceous aerosols, not just the elemental carbon fraction, are strongly light absorbing [Chylek et al.; 1984; Malta et al., 1994]. We refer to this

fraction as black carbon, and it is the fraction of carbonaceous aerosol which can be detected by techniques such as the aethalometer or by thermal decomposition at high temperatures. Whereas black carbon is believed to have an atmospheric residence time equal to or longer than that of sulphate [Ogren et al., 1984] and therefore similar to the timescale for typical synoptic scale weather features, organic carbon is more rapidly scavenged than black carbon [Cachier et al., 1991]. Consequently, black carbon has a proportionately greater effect on the radiation budget than the short-lived organic carbon. The carbonaceous component of the anthropogenic aerosol can therefore be expected to have a different radiative impact than the sulphate component.

There have been some assessments of the global impact of black carbon aerosols from biomass burning [Crutzen and Andreae; 1990; Catbier, 1992]. To date, however, there has been only one other published study of global transport of black carbon aerosols [Penner et al., 1993], although a study using a new biomass burning emission inventory is reported elsewhere in these proceedings [Liousse et al., this issue]. The former study presented two black carbon emission inventories, one calculated from fuel use data and one calculated from

estimated SO2 emissions, and used the second inventory in a global transport study.

We have therefore developed a global emission inventory for black carbon which considers emissions from both fossil

fuel combustion and biomass burning and implemented this in the global tracer transport model MOGUNTIA [Zimmermann, 1984; Zimmermann et al., 1989].

• Also at Department of Experimental Physics, University College, Galway, Ireland.

Copyright 1996 by the American Geophysical Union.

Paper number 96JD00671. 0148-0227/96/96JD-00671509.00

2. Construction of a Black Carbon Emission Inventory

Black carbon is produced by the pyrolysis of hydrocarbons. The two principal sources of black carbon emissions are therefore fossil fuel and biomass combustion. Black carbon

emissions for both sources were determined using published

19,395

19,396 COOKE AND WILSON: GLOBAL BLACK CARBON MODEL

emission factors and data sets. Not all the data necessary for such an approach were available, however, so in the absence of reliable data we have assumed emissions to be zero as, for example, in the case of biomass emissions from China. It is also probable that there are differences in combustion efficiencies and thus in emission factors between developed and less developed countries, but b•ause of lack of data, we have not taken this effect into account. We have also assumed

that both sources are entirely anthropogenic. Lightning- initiated fires could be considered a natural biomass burning source. However, in the absence of reliable data on the fraction of biomass fires that are lightning-induced, we have assumed that all emissions are anthropogenic. The resulting inventory can therefore be updated as additional black carbon emission factors and ancillary data are published.

2.1. Biomass Combustion

Routine burning of biomass is an important element of agricultural practice worldwide and is also an important fuel source in some areas of the world, although this is not explicitly considered in our current emission inventory. The principal biomass burning emission sources are the savanna and cerrado grasslands of Africa and South America, where the burning is carried out to stimulate grass growth, and the tropical forests in South America and East Asia, where the burning is carried out in clearing the forest. We also consider fires in extra-tropical forests, which are either carried out for forest management or are lightning-initiated, although they are far fewer in number and scale than the tropical forest fites. Global coverage of the inventory is acceptable. There are gaps where we have no published data on the vegetation burnt in particular countries, principally the southern states of the former USSR, the Middle East, and China.

The annual emissions of black carbon by biomass burning for any vegetation type is determined as follows:

BCE = A B tx 13 EF (1)

where BCE is the black carbon emission (gBC a'l), A is the area of vegetation burnt (m 2 a4), B is the biomass surface mass density (kgC m'2), tz is the above ground fraction of biomass, [3 is the fraction of tz which actually bums, and EF is the black carbon emission factor for the process. (gBC kg 4 C)

The spatial distribution and total area of forests and savannah have been taken from a 1ø x 1 ø resolution Goddard

Institute of Space Studies (GISS) surface-type data set which

classifies each grid element as one of 29 natural vegetation types (16 forest, 8 grass, and 5 shrubland), cultivated land, desert, ice, or water [Matthews, 1983]. A second GISS data set classifies each grid element according to country [Lerner et al., 1988]. Combining these two data sets with country- specific data on the areas of forests and grassland burnt per year gives the spatial distribution of A, the area of vegetation burnt per year. The area of grasslands burnt per annum is derived from Hao et al [1991 ], where they estimate that 75% of African and 50% of tropical American and Asian grasslands are burnt each year.

We have also compiled an inventory of the annual areas of forest burnt and this is summarized in Table 1 on a regional basis. For tropical forests the data refer to total deforestation and therefore include lightning-induced fires, land cleared for cultivation, fuelwood, and commercial logging. Commercial logging, however, contributes a small fraction to the total area of deforestation [Burgess, 1993] and has therefore been assumed to be zero. In addition, any fuelwood is assumed to be consumed in the gridbox where it was harvested. For extratropical forests the data refer to forest fires only; therefore fuelwood is an additional source in this region which is not included in the inventory. The areal extent of vegetation burnt in this inventory is representative of the mid-1980s.

Biomass densities, fractions of biomass above ground, fractions of above-ground biomass which bums, and emission factors for grassland and the different forest types have been taken from the literature and are summarized in Table 2.

Certain assumptions have been made for tz and [5 in Table 2 where values were not found in the literature. Seiler and

Crutzen [1980] give values of 0.81 and 0.73 for tz for a tropical rain forest and a coniferous temperate forest, respectively. Therefore a value of 0.75 was assumed for temperate-subpolar evergreen rain forest and for tropical- subtropical evergreen needle-leaved forest. Similarly evergreen sclerophyllous woodland is assumed to be similar to shrubland. For 13, Hao et al. [1991] give values of 0.3 and 0.4 for primary and secondary forests, and Dixon and Krankina [ 1993] give 0.05 to 0.25 as a range for boreal forest. Therefore intermediate values of 0.35 and 0.28 are taken for subtropical and polar forest types, respectively. Emission factors were taken from Andreae et al. [1988] for broad-leaved or deciduous forests (tropical-subtropical) and from Patterson et al. [1986] for evergreen forests (boreal) with an exception being made for a cold-deciduous forest, which was assumed

Table 1. Total Forest Area Burnt Per Annum Per Region Used in the Calculation of Biomass Burning Emissions

Region Forest Area Burnt, (km 2) References

Europe 4239

North America 21055

Central America 5015

South America 41770

Africa 13038

Asia and Oceania 101458

Muller [1992], Stocks and Barney [1981], Stocks [1991 ]

Muller [1992], Stocks and Barney [ 1981 ], Stocks [ 1991 ]

Houghton et al. [1987]

Barbier et al. [1991], Burgess [1993], Fearnside [1991 ], Houghton et al. [1987], Skole and Tucker [ 1993], Setzer and ?ereira [ 1991 ]

Barbier et al. [1991], Burgess [1993], Delmas et al. [1991], Houghton et al. [1987], Skole and Tucker [1993]

Barbier et al. [ 1991 ], Burgess [ 1993 ], Dixon and Krankina [ 1993], Houghton et al. [1987],Joshi [1991], Miiller [1992], Skole and Tucker [1993]

COOKE AND WILSON: GLOBAL BLACK CARBON MODEL 19,397

Table 2. Vegetation Types and Factors Used in the Consmlcfion of the Biomass Burning Black Carbon Emission Inventory

Vegetation type Biomass density a'b, B o• [• Emission Factor, EF

Tropical evergreen rain forest

Tropical-subtropical evergreen seasonal broad- leaved forest

Subtropical evergreen rainforest

Temperate-subpolar evergreen rainforest

Temperate evergreen seasonal broad-leaved forest, summer rain

Evergreen broad-leaved sclerophyllous forest, winter rain

Tropical-subtropical evergreen needle-leaved forest

Temperate-subpolar evergreen needle-leaved forest

Tropical-subtropical drought-decidous forest

Cold-deciduous forest with evergreens

Cold-deciduous forest without evergreens

Xeromorphic forest woodland

Evergreen broad-leaved sclerophyllous woodland

Evergreen needle-leaved woodland

Tropical subtropical drought-deciduous woodland

Cold-decidous woodland

Grassland

10 0.81 b 0.3 ½ 2.20

5 0.81 • 0.3 ½ 2.20

10 0.81 b 0.3 ½ 2.20

8.6 0.75 0.4 ½ 1 e

8 0.73 b 0.4 ½ 1 e

4.3 0.73 b 0.4 c 1 e

5.1 0.75 0.35 2.20

5.1 0.73 b 0.28

2 0.75 b 0.4 ½ 2.20

5.8 0.75 b 0.35 1.5

5.8 0.75 b 0.3 ½ 1.2

8 0.71 b 0.35 2.20

2 0.64 0.4 ½ 2.20

5 0.73 b 0.28 1 •

5 0.75 b 0.4 • 2.20

10 0.75 b 0.3 ½

1 0.65 b 0.83

Bolin et al. [1979]. Seiler and Crutzen [1980]. Hao et al. [1991 ]. Andreae et al. [1988]. Patterson et al. [1986].

to bum as a mixture of tropical and boreal forest. Annual emissions of black carbon on a 1 ø x 1 ø grid have been calculated using (1). This approach is illustrated in a flow diagram in Figure 1, where the shaded boxes indicate sources of information. The annual emissions of black carbon from

biomass burning on a 1 o x 1 o grid is shown in Figure 2. The seasonal distribution of the emissions from tropical

zones other than Africa is taken from Hao et al. [1991]. For Africa a seasonal distribution of fires based on satellite

observations of the burning seasons of November 1984 to October 1989 is used [Koffi et al., 1995, W.F. Cooke et al., Seasonality of vegetation fires in Africa from remote sensing data and application to a global chemistry model, submitted to J. Geophys. Res., 1995, hereinafter referred to as Cooke et al., submitted manuscript, 1995]. The African satellite data was used in preference to the data of Hao et al. [1991], as it is derived from direct observations of fires rather than indirect

observations of one of the secondary combustion products [Hao and Liu, 1994] or assumptions about agricultural practice [Hao et al., 1991]. Fires in the northern hemisphere boreal forests have been assumed tO occur uniformly from June to September, and other extratropical forests are assumed

to have a slight peak where 10-11% of the forest fires occur in each of the summer months.

2.2. Fossil Fuel Combustion

Annual black carbon emissions from combustion of each of

the fuel types considered have been calculated as follows:

BCE = F EF (2)

where F is the fuel consumption rate (kg a'l). We have taken fuel consumption rates from an energy statistics database compiled by the United Nations Statistical Division which contains data on production, trade, and intermediate and final consumption of primary and secondary conventional, nonconventional, and renewable energy sources for the period 1950-1991. The year 1984 was chosen as the reference year to calculate emissions.

Domestic fuel combustion is less efficient and therefore

dirtier than industrial or power production processes, with domestic emission factors of 10 times the power generation emission factor reported for coal combustion [Butcher and Ellenbecker, 1982; Bocola and Cirillo, 1989]. Therefore, in


29 vegetation types

29 vegetation types

Total Area

of grass or forest

per country

of vegetation type burnt

I Area of 1øxl ø

which is burnt

Amount of BC emitted per m 2 of vegetation

type burnt

I Black ca.robO.n o emitted I I xl

Figure 1. Tree diagram of method to generate biomass burning emissions. (Sources of information are shaded.)

order to calculate emissions, the fuel use sectors have been classified as domestic, industrial, and a "catchall" combined fuel use. No attempt has been made to account for differences in emission factors for developed and underdeveloped countries, as this would introduce too much uncertainty in the emission fields.

The fuel use database is compiled from returns from national statistical offices. Of the 54 primary and secondary energy sources included in the database, 23 have been used in

the calculation of black carbon emissions and are listed in

Table 3 along with the emission factors for the fuel use sectors mentioned above. Non black carbon producing energy sources (e.g., uranium, geothermal and hydroelectric power) and minor energy sources, for which emission factors were unobtainable (e.g., municipal wastes), have been excluded. Fuel production and trade data are more reliably reported than fuel consumption within a country; in fact, several countries do not report consumption of some fuels. We have therefore used the production and trade data to calculate total fuel usage within each country as follows:

Fuel usage = production + imports- exports

- stock changes - processing - losses

(3)

The total fuel usage is distributed between the consumption sectors in proportion to the reported fuel consumption distribution for each country for each of the fuels considered. Where there are no consumption data of a particular fuel for a country, the average fuel consumption distribution in one or more adjacent "proxy" countries is used, for example, the fuel consumption distribution of the former USSR is taken as the average for eastern Europe. For a fuel used in only a few countries, for example, peat, there may not be appropriate proxy countries, in which case the global average consumption distribution is used.

Thus the sectorized consumption for each of the 23 fuel types has been. determined for the 185 countries in the UNSTAT database. Emission factors (see Table 3) are then applied to each of the three usage categories to determine the amount of black carbon emitted per country per year. The resulting total black carbon emissions are distributed within each country in proportion to the population density, and the calculational method is again shown as a flow diagram in Figure 3. Results from this work have shown that hard coal, lignite (brown coal), and diesel fuel are the principal fossil fuel sources of black carbon emissions. The other main fossil

fuels, such as gasoline, fuel oils, and natural gas have much lower emission factors and thus lower emissions associated

Block carbon emissions from biomass burning per annum

Figure 2. Black carbon emissions (tonnes per 1 o x 1 o) from biomass burning.


Table 3. Fuel Types Used in the Construction of the Fossil Fuel Data Set Along With the Emission Factors Used for the Combined (C), Domestic (D), and Industrial (I') Emissions

Emission Factor (C, D, I), g kg 4

Solid Fuels

Hard coal 6,10,1

Lignite/brown coal 6,10,1 Peat 1,1,1

Lignite briquettes 1,1,1

Hard coal briquettes 1,1,1

Peat briquettes 1,1,1

Charcoal 1,1,1

Browl coal coke 1,1,1

Oil shale 1,1,1

Coke-oven coke 1,7.5,1

Gas coke 1,7.5,1

Liquid Fuels

Aviation gasoline 0.1 Gas-diesel oil 2,2,0.7 a'e•f

Jet fuel 1

Kerosene 0.3 •

Liquefied petroleum gas 0.06 a

Motor gasoline 0.1 g Residual fuel oil 0.02,0.08, 0.01

Gaseous Fuels

Natural gas 0.01,0.05,10 .4 a'e Blast furnace gas 6 x 10 Coke-oven gas 6 x 10 Gasworks gas 6 x 10 Refinery gas 6 x 10

Peat and the briquettes were assumed to have the same efficiency as charcoal.

• Bocola and Cirillo [1989]. b Butcher and Ellenbecker [1982]. ½ Cass et al. [1982]. a Barbella et al. [1988]. • Muhlbaier and Williams [1982]. f Williams et al. [1989b]. g Williams et al. [1989a].

with them. The annual emissions of black carbon from fossil

fuel on a 1 o x 1 o grid is shown in Figure 4. We assume that the emissions follow the same seasonal

trend as that used by Langner and Rodhe [ 1991 ] in their study of the global sulphur cycle; north of 30 ø N, emissions follow the seasonal trend for CO2 estimated by Rotty [1987], and south of this, emissions are distributed uniformly throughout the year. We realize that especially diesel vehicle emissions may not follow this trend. However, because of the lack of data on the seasonality of emissions, we have chosen to use

the same seasonality as previously used with the MOGUNTIA model.

2.3. Comparison With Other Estimates of Black Carbon Emissions

Table 4 compares the estimates made here with those of Penner et al. [1993], which were derived from SO2 emissions and the domestic coal, diesel, and wood burning sectors of an energy use database. Penner et al.'s [1993] SO2 emission derived total at 23.84 Tg is almost twice the total emission of 13.47 Tg presented here, while the energy use derived total of 12.61 Tg is approximately equal, despite being based on solely domestic coal use, diesel, and wood and bagasse burning data.

Comparing the geographical distribution of emissions, the SO2 derived emissions from Brazil, Africa, and Eastern Europe are lower than in both other inventories. This implies that either the BC-SO2 ratios used for these three regions should be higher, or that the SO2 emissions used are too low. Certainly the BC-SO2 ratios used for both Africa and Brazil are derived from urban observations and are unlikely to be representative of BC-SO2 ratios in the biomass burning areas [Ogren, 1982], which are the major sources for both regions. For Eastern Europe the difference may be due to uncertainties in the measurements of black carbon or overestimations in the

SO2 inventory. Much of the differences between the inventory presented here and the fuel use derived inventory of Penner et al. [1993] can be attributed to sources that are in one

trd Lig i I 1

I Di••] •ite [ 20 other fuel types ,wn •

180 other

countries

1

Production Consumption data data

Consumption IN data exists."?

I Y N

Cons= Prod ? Read proxy distribution

i i Domestic Industrial Combined

consumption consumption consumption

density per country

Black carbon oemitled løx 1

Figure 3. Tree diagram of method to distribute fossil fuel burning emissions. (Sources of information are shaded.)


Black carbon emissions from fossil fuel per annum

: -: !- :,.': '4 ,..•,,, ::•:•:•:½.•".,:.• ,, •, •: ............ ß ..... ',',;•.,'.•, ., .:.: ...... .• •: ..... . •'"K ;'.'•' • ":?,•' :':';". ' '",'•:::;':•:'•': ' ' ' ...... •:: •;q';;'•gZ•. , • ;-, "-}:-:.. :.>:.:.•,x.:,:.':'•-:.:.:.:.:•• .... ...... •; qex•g.'g• ........ .4::::: ' ', ::::::::::::::::::::::::::::: 6;:::'g:;;.• • .... • .,_ ..., .• ..., • .&c.•:•.; _;_;.;. ;..•_ •o.•. x;'•.. ',..'•:•[q::•::S:•.- ,• r• •-, -,- -- • -.

....... ' '.:•' , ..... ...,.??:::•i:':y,:-•::•'•%.' ........ •:•?•;•[ .,? .... ....... ::::::::::::::::::::::::::

................... • ....... •E::•:;•:;•:•.•½•:':g'• ß. • •- .'• •- • ...........

............... v... :.•:::::g: ::•:;:::$:::: f;{•. ...... ............... • ............. •',' ß - ....... Ionnos •t emi• , , , , i i i , , , i i , , i :.:.•:.:.•.:q •, , , l • ...... • _ • _,_ • _ r _ ,.., ....... • . • _, :'•:•:: :.'. /. .•. •.,

, , ................ •:•:•:•:• '• ............ r'- ,- •'- ß - • -,- , - , - ,- • - ß - ' ,- -, - • - • -, •:¾:•: ,- • - • -,- • - r - ,- - • •, - ß ................. f';'•:' ' ......... :-:-:. 5•0.0 ? ' :- ':- • -',- -',' • ', ......... • ......... •:::::• , ,. ,

L -I- -I- • -I- -I- J - L -I- -I- I - • -I- J • L -I- -I- I - L -I- J - • -I- -I- • - • -I- J - • -I- -I- ] - • -I- •- L . I

..................................... r.'.. • ". •0•.0

........................................................................... Figure 4. Black c•bon emissions (tun, es •r 1 • x 1 •) from fossil fuel combustion.

inventory and not the other; for example, industrial coal burning and grassland fires are in our inventory and not that of Penner et al. [1993 ], and wood and bagasse burning which is not explicitly included in our inventory is in that of Penner et al.. Both of Penner et al.'s [1993] inventories have proportionately higher emissions from the Asian countries than in our inventory, and in the fuel use derived inventory, this reflects differences in the assumed pattern of fuel consumption.

3. Atmospheric Transport, Transformation, and Removal of Black Carbon

Global atmospheric transport of black carbon is simulated using the MOGUNTIA global transport model [Zimmermann, 1984; Zimmermann et al., 1989]. MOGUNTIA is a Eulerian

model with 10 ø x 10 ø x 100 hPa resolution. Transport is described by climatological mean monthly windfields [0ort, 1983] and an eddy diffusion component derived from the standard deviation of the mean winds except subgrid-scale vertical exchange in convective clouds, which is treated separately. The principal synoptic variables are also described by monthly mean fields. The annual black carbon emission distribution has been implemented in the model and run for 31 model months using a 2-hour time step. Results are analyzed for the final January and July.

Elemental carbon is hydrophobic and chemically inert [Crutzen et al., 1984; Goldberg, 1985]. However, black carbon is only partly elemental carbon and is usually found internally mixed with other aerosols in samples from aged air parcels [Ogren, 1982]. It is therefore believed that any initial hydrophobic properties that black carbon aerosols may have

Table 4. Comparison With Penner et al. [1993] of Fossil Fuel and Biomass Black Carbon Emissions (Tg C yr -]) by Region

Country or Region Biomass Emissions Fossil Fuel Emissions Penner et al. [ 1993] SO2 Based Inventory

Penner eta]. [ 1993 ] Fuel Use Derived

Inventory

Norh America

Central America

Brazil

Rest of South America

China

Rest of Asia

Western Europe

Eastern Europe

Forn•er USSR

Oceania

Africa

Total

0.09

0.03

0.79

0.43

0

0.27

0.006

0.0006

0.11

0.04

4.21

5.98

1.27

0.01

0.06

0.04

1.10

0.85

1.09

1.56

1.55

0.10

0.34

7.97

2.31

0.06

0.44

1.20

3.66

4.96

2.46

1.34

5.6O

0.24

1.56

23.84

0.67

0.10

0.71

0.33

2.68

2.60

0.59

1.55

1.93

0.12

1.30

12.61


90N January

60N

3ON

EQ

3OS

60S

90S

180W 120W 60W 0 60E 120E 180E

90N July

60N

,]ON

30S

60S

90S

180W 120W 60W 0 601:' 1201:' 180E

BC concentration (ng m •)

Figure 5. Surface black carbon mass concentration (ng m '3) for (a) January and (b) July.

are lost as the aerosol ages. We represent this aging of the aerosol in the model by assuming that all black carbon is hydrophobic when emitted and therefore not subject to wet deposition, but it ages at an arbitrary rate of 7.1 x 10 -6 s 'l (equivalent to 5% per 2-hour time step) to a hydrophilic wet depositing form, which is removed in precipitation with the same efficiency as sulphate [Langner and Rodhe, 1991 ]. Both types of black carbon are assumed to have a dry deposition rate of 0.1 cm s 'l. The resulting mean globally averaged lifetime of the black carbon aerosol, at 7.85 days, appears reasonable [Ogren, 1982]. While we focus on the case of a 5% transformation rate, a sensitivity study discussed below also shows the variation of black carbon mass fields for other

transformation rates.

4. Results

4.1. Black Carbon Concentration in Air

Figures 5a and 5b show the surface concentration fields (ng m '3) for January and July respectively. The main fossil fuel burning regions of the northeast United States, Europe, and China stand out in both months along with the grassland burning regions of north central Africa in January and southern Africa and South America in July. The surface concentration fields calculated by Penner et al. [ 1993] agree reasonably well with Figures 5a and 5b in regions where

fossil fuel is used, although they are based on twice the emissions. We would therefore expect significant differences in either the black carbon concentrations predicted in the upper atmosphere by the two models or the deposition fields or both, neither of which are reported by Penner et al. [ 1993 ].

4.2. Comparison With Surface Level Air Concentration Measurements

Tables 5 and 6 compare measured and calculated black carbon mass concentrations in air for remote and continental

sites, respectively. Agreement between the continental sites is reasonable in most cases, being within a factor of 2 of the measured values. It should be noted that the point measurements reported here are typically for periods less than a month and are being compared with a monthly mean from a grid box of 10 ø x 10% which corresponds to an area of approximately 700,000 km 2 (at 45 ø latitude).

At many of the remote sites the modeled value of surface mass concentration is too high. The seasonal cycle at Bermuda appears to be too strong, although there are measurements for only these 2 months, while the modeled seasonal cycles are in closer agreement with the measurements at both Mace Head, Ireland, and Cape Grim, Tasmania, but are still overestimated. The seasonal variation of black carbon mass concentration in

air at Mace Head, the monthly means of the observations for 3 years (1990 - 1992), and the mean of the 3-years data are compared with the modeled monthly means in Figure 6. The model reproduces the seasonal trend observed at Mace Head, but modeled values are consistently higher than measured values. This may in part be due to the measurements being from the clean sector only, which the model is unable to reproduce, and also may in part be due to the uncertainty in the determination of the black carbon mass from the

aethalometer measurements. Seasonal variation of black

carbon mass concentration at Cape Grim using a thermal method has also been reported by Heintzenberg and Jacobsson [ 1991] and Heintzenberg [ 1992]. Their values, along with the modeled values, for Cape Grim are presented in Figure 7. The modeled values again overpredict the measured values, particularly in the austral summer of January to March. The overprediction between June and August is mainly due to biomass burning emissions in northern Australia. Agreement is reasonable for Ny •lesund, Spitsbergen, in January, but the predicted value is much higher than the measured value for July. Conversely the other Arctic site at Barrow, Alaska, agrees quite well for both months. At Amsterdam Island the model again overpredicts in both months. There is reasonable agreement between observations and modeled values at Mauna Loa, Hawaii while tha data at the South Pole agrees for January but is too high in the Antarctic winter. It should be noted, however, that there are uncertainties in the optical measuring methods used at several of the remote sites, as they measure total light absorption by the aerosol sample and infer a black carbon mass concentration from this. For example, the specific attenuation cross section• o used in aethalometer

measurements is fixed at 19 m 2 g-l, as recommended by the manufacturers, whereas Liousse et al. [1993] have found o values of 5 m 2 g-l for remote sites, including Mace Head. This implies that black carbon measurements at remote sites may be underestimated by almost a factor of 4. An additional complication is that the measurements from the remote sites


Table 5. Carbon Mass Concentration (ng m '3) Measured and Calculated for Various Remote Stations Around the World.

Measured Calculated

Station January July January July Method

Bermuda 30 a 40 a 103 17 T

Cape Grim 1.6 b 2.3 b 6.5 8 T Mace Head 68 ½ 17 ½ 72 44 A

Arctic Ny Alesund 70 a 3 a 255 67 IS 293 e

Arctic Barrow 206 e 80 t 248 63

52 t

314 t

Amsterdam Island 4 g 6 g 7.2 11 A

Mauna Loa 10 h 2.45 h 4.3 1 A •

5.1 • 10'

South Pole 1.4 • 0.3 J 0.9 2 A J

1.5 J

A, Aethalometer; T, Thermal decomposition; IS, Integrating sphere. Wolff et al. [1986]. Heintzenberg and Jacobsson [ 1991 ], Heintzenberg [1992]. Cooke [1993].

a Heintzenberg [1982]. Clarke [1989]. Penner et al. [1993 ]. Cachier et al. [1994]. Clarke et al. [1984]. Bodhaine et al. [1992]. Hansen et al. [1988].

only refer to clean sector observations. We have attempted to represent this in the analysis by taking the value for the adjacent grid element in the direction of the clean sector, but by the nature of the transport model this value will be influenced by transport from outside the clean sector, resulting in a higher modeled value than may be expected from measurements which have various controls to ensure

background air masses are being sampled. In this work,

measurements made at Mace Head used a while the measured values for Cape Grim were obtained by a thermal method.

Our analysis has shown that between 20 and 50% of the modeled July black carbon concentrations in air at Barrow and Ny •lesund is due to emissions from boreal forest fires. While this contribution improves the agreement between the model and observations from Barrow, the Ny •lesund observations

Table 6. Carbon Mass Concentrations (ng m -3) Measured and Calculated for Various Stations Around the World (Time of Year Not Specified)

Station Yearly measurement January July

Abastumani, Georgia 980 • 1027 706

Pacific Ocean, Japan 980 • 809 377

Oki Islands, Japan 520 • 933 446

Florida, USA 830 ½ 555 354

N. Carolina, USA 520 ½ 823 566

Ecuador 520 • 187 441

Manaus, Brazil 620 ½ 325 595

Penner et al. [1993 ]. Ohta and Okita [1984]. Andreae et al. [1984].


Mace Head, Ireland Hudson cruise

-{- Modeled momhly value

L / \ ,- .... I _1

• 80L / X •. M ac• Head rnonlhly .... "1

" ++++\ \ ++ + \ \ + ++++++

I I I I I I I I I I I

Jan Feb Mar Apr JUnMonlh Jul Aug Sep Oc• Nov Dec 70W SOW 50W Lon•lt•:le 30W 20W May 10W

Figure 6. Seasonal variation of measurements and modeled Figure 8. Measurements and modeled values for the Hudson values for Mace Head, Ireland. cruise.

are significantly overpredicted by the model. However, MOGUNTIA is unable to resolve regional transport effects within the Arctic, so the modeled results are very much an average over the whole region.

Observations of black carbon concentrations in air were

made on a cruise across the North Atlantic from Nova Scotia

to the Canary Islands and back aboard the R V Hudson [Van Dingenen et al., 1995] in September and October 1992. Modeled monthly averaged air concentrations for September and October are shown in Figure 8 along with the aethalometer measurements, using a o of 19 m 2 g4, from the cruise. Modeled and measured values agree reasonably well in all regions, and the trends in the observations are reproduced in the modeled concentration profiles. It should again be noted that the modeled values are monthly means for a 10 ø x 10 ø grid element, whereas the measurements are typically 6- to 12-hour means over a short section of the cruise track. We

would not therefore expect the model to be able to reproduce the high black carbon concentrations of greater than 1000 ng

m '3 observed in polluted air from Europe near the Canary Islands.

On a cruise from Hamburg, Germany, to Montevideo, Uruguay, during October and November 1980 aboard the F/S Meteor, Andreae [1983] measured black carbon mass concentration, and this data along with the modeled values are shown in Figure 9. There is good agreement between the model and measured values except for just south of the equator. In this region, there is strong convective activity predicted by the model which reduces the predicted mass concentration of black carbon and causes the October values

to fall to less than 10 ng m -3. It should be noted that the model values are monthly means of the gridbox in the direction of the back trajectories, as shown in Andreae [ 1983 ].

Finally, observations of light-absorbing carbon from the IMPROVE network in the United States for March 1988

through February 1991 [Malm et al., 1994] are compared with modeled data in Figure 10. Regional seasonal averages of the measured light absorbing carbon concentrations in air are

Cape Grim, Tasmania 12 I I I I I I I I I I I

.

_

-'t- Modeled momhly value _

10 O Cape Grim monthly average A A _

8 /% Cape Grim measurements _ _

• 4•. " tA Zi A •_ . / X .• - '•'

o/ I I I I I I I I I I I Jan Feb Mar Apr May Jun Jul Aug Sep O•1 Nov Dec

Month

Figure ?. Seasonal variation o• measurements and modeled values •or (:ape Grim, Tasmania.

10000

1000

100

10

40S

Meteor cruise ' ' ' I ' ' ' I ' ' ' I ' ' ' I ' ' '

-{- ¾ef, em' m•urm'nen•

• Octol:)4e modeled volul

•, Nevemb•' modeled velue

o

, , , I , , , I , , 0 I , 20S Eq 20N

Latitude

i I i , 40N SON

Figure 9. Measurements and modeled values for the Meteor cruise.


IMPROVE data 1000 ' ' ' I ' ' ' I ' ' ' I ' ' ' I ' '

I I

0 200 400 800 800 000 Measured BO conoentratlon (rig m')

Figure 10. Comparison of IMPROVE data with modeled data.

plotted against the modeled seasonal average for 18 of the 19 regions reported in that work. Washington D.C. has been omitted, as a model with 10 ø x 10 ø resolution will not be expected to calculate concentrations of the order of that found in urban areas. For most regions and seasons the agreement is within a factor of 2. The precision of the published data was 0.1 !xg m '3, which accounts for some of the scatter in Figure 10, and an apparent underprediction was found for Alaska ,and Hawaii where the model predicted 31-51 and 2-13 ng m '3, respectively, with reported measurements of 0.1 gg m '3.

4.3. Comparison With Upper Troposphere Air Concentration Measurements

One set of observations of black carbon in the upper atmosphere [Pueschel et al., 1992] found elemental black carbon concentrations of 0.5-7.0 ng m '3 in the northern hemisphere troposphere between 5.5 and 11 km compared with modeled predictions of 10-25 ng m '3. On the basis of this single set of measurements the model appears to be

January

100 _

300--

'•E 500- v

lOO! / j ...... S 30S EQ 3ON 60N 90N

July 10{3 ' ' m ' ' e ß . m . . m ' ' m , ,

b

300-

• 500-

700-

1000 f , m , m m m m m 90S 60S 50S EQ 5ON 60N 90N

Latitude

Figure ll. Zonai]y averaged black carbon mass concentration (ng m '3) for (a) January and (b) July.

overaccumulating black carbon in the upper troposphere. Conversely, at Mauna Loa (3400 m above sea level) the model is within a factor of 2 of the observed black carbon

concentrations. Zonally averaged black carbon mass concentration profiles are shown in figures 11a and l lb for January and July, respectively. This shows the vertical

Table 7. Comparison of Wet Deposition Measurements and Values as Predicted by the Model. Station Measurement, I. tg BC L 4 Modelled Value, I. tg BC L 4

5% Hydrophilic 2.5%

Lamto, Cot6 d'Ivoire (Jun-Oct) 69 (20-192) a 14-35 9-27 19-38

Enyele, Congo (Nov-Mar) 155 (75-258) a 94-853 80-818 101-785

Enyele, Congo (May-Oct) 45 (11-75) • 20-277 16-275 23-269

Paris 333 (27-1348) • 209-390 316-536 162-310

Seattle (Dec-Jan) 60 (28-130) b 31-34 27-34 38-39

Sweden (Apr Aug) 100 (20-600) b 392-1116 466-1586 316-852

Mace Head (Oct-Nov) 31 (9-94) • 83-97 126-135 73-86

The average and range of the measurements and range of modelled monthly wet deposition are shown. The different modelled values refer to cases where a 5% transformation rate, hydrophilic emissions and a 2.5% transformation rate were used in the model. a Ducret and Cachier [1992]. • Ogren et al. [1984].


gradient over,the source regions with mass concentrations decreasing to less than 100 ng m '3 by the 700 mbar level.

4.4. Comparison With Deposition Measurements

There are very few measurements of black carbon in rainfall to compare the model against, and these are given in Table 7 together with our modeled values for three cases. The first is where a transformation rate of 5% per 2-hour timestep is used. The agreement between this case and measurements will be discussed in this section, as we believe this transformation rate is the most reasonable. The other two

cases in Table 7 are for hydrophilic emissions and for a transformation rate of 2.5% per 2-hour timestep. The agreement between modeled and observed black carbon in rainfall is not as good as for black carbon air concentrations. This is however to be expected, as the subgrid scale spatial and temporal heterogeneity in rainfall is not represented in MOGUNTIA, and differences between the model and observations are not therefore conclusive evidence of incorrect

black carbon emissions in the inventories. In the sulphate version of MOGUNTIA, 15% of emissions are immediately removed to allow for dry deposition within the gridbox. This percentage was inferred from the Co-operative Programme for Monitoring and Evaluation of the Long-Range Transmission of Air Pollutants in Europe (EMEP) measurements of sulphate deposition over Europe. In this model, no immediate removal has been implemented, as there is no similar data for black carbon.

Of the four northern latitude sites, Paris is the only site with reasonable agreement between observed and modeled black carbon concentrations in rainfall. This site is within the major European source area, and emissions and deposition fields are relatively homogeneous within this region. Interpolating over the model grid should therefore give a better estimate than at, for example, Mace Head, where the steep gradients in the black carbon mass concentration and wet deposition fields at the continental edge may contribute to the overestimation of wet deposition. Similarly, Seatfie is an urban site but, unlike Paris, it is not within a major source region. Therefore local emissions, which are higher than the box average, are not resolved by the model and would contribute more to the wet deposition. The Seattle data set also consists of just five

African biomass burning sources are in reasonable agreement although the modeled values are somewhat lower than the measurements, but at Enyele, also near the African biomass burning sources, the model again consistently overpredicts. This may be due to the single cloud type used in the scavenging model, but at Enyele the situation is complicated by its proximity to the Intertropical Convergence Zone and the influence of the Atlantic monsoon. These effectively limit the transport of air from the biomass burning regions to the site but may not be resolved with sufficient detail by MOGUNTIA.

The rate of transformation of hydrophobic to hydrophilic black carbon has been varied and is discussed in more detail

in the next section. It could be expected that the slower the transformation of hydrophobic black carbon to hydrophilic black carbon is, the less wet deposition there will be. In fact, the model predicts more wet deposition in the tropics for the case where the transformation rate to hydrophilic black carbon is slowest. This may be explained by the meteorology. In the tropics, convective activity generates rainfall at greater heights and the hydrophobic black carbon is able to leave the boundary layer, and because of the lapse in time, aging will allow relatively more black carbon to be scavenged. In northern climates, rain is produced from stratiform-type clouds. Therefore the less hydrophilic black carbon has the opportunity to pass through the cloud and be transported to remote regions. The data from Seatfie shows more deposition for less hydrophilic black carbon, which is the opposite of that expected. It has not been determined whether this is due to convective activity in the model in this area.

4.5. Sensitivity Tests of Transformation Rate of Black Carbon

The local sensitivity of the surface black carbon mass concentration to the rate of transformation of hydrophobic to hydrophilic black carbon has been investigated. Three different cases are used to show the sensitivity of the modeled values to transformation rates from hydrophobic to hydrophilic black carbon. The first two cases are where 2.5 and 5% of

Amsterdam Island rainfall samples from rain events ranging in duration from 3 to 4o , , , , , , , , , , , 68.7 hours [Ogren et al., 1984], and if the 3-hour sample from

rainfall is omitted, the average wet deposition falls to I //• 3 mm ofL. • 46.5 gg which is in closer agreement with the predicted o• wet deposition. ao I s% ,,,•.,o•,,,,on ,,,. ] /

At Mace Head and Sweden the precipitation-scavenging zx I•tdrophlllo•mls•lons I ' ' scheme in MOGUNTIA may also contribute to the • m-u.,.•,.,.,,,,,, I overestimation by the model. Wet deposition in MOGUNTIA is simulated using the precipitation-scavenging model of

Junge and Gustafson [ 1957] in which precipitation scavenging in convective clouds is approximately an order of magnitude less efficient than in stratiform clouds. MOGUNTIA, however, treats precipitation scavenging independently of cloud type. Therefore the greater the fraction of total precipitation that is convective, the greater the likelihood of overprediction of wet removal. In both western o • • • • • • • • • • keland and Sweden a greater fraction of total low and d,, F.b II.r •r II.¾ dun dul Aug S•p O4 Nov IIor•h

midlevel clouds are cumulus or cumulonimbus (17 and 23%, Figure 12. Sensitivity of black carbon mass concentrations at respectively) than at Paris (13%) [Warren et al., 1986]. Amsterdam Island to variation of the transformation rate of

Measurements and modeled values at Lamto, near the hydrophobic to hydrophilic black carbon.


Ratio between 5• and •2..5•, cases f•or, danuary • ..... I ..... I ............ I ' ' ' ' ' a

1•3W 120W •3W 0 •0• 120• 1BOE 1•3W 120W •3W 0 •0• 120• 1BOE

Figure 13. Ratio of global black carbon fields for (a) 5% and 2.5% transformation rates for January, (b) 5% transformation rate and hydrophilic emissions for January, (c) 5% and 2.5% transformation rates for July, and (d) 5% transformation rate and hydrophilic emissions for July.

hydrophobic black carbon are converted to hydrophilic black carbon in a 2-hour timestep. The third case is where the emissions are treated as if they are initially hydrophilic, that is, effectively a 100% transformation rate. Figure 12 shows the modeled data for Amsterdam Island with measurements

for 1991 [Cachier et al., 1995]. As could be expected, higher values of black carbon mass concentration are found for lower transformation rates. The results for the 2.5% transformation

rate and the hydrophilic emission (100%) cases indicate a ratio of between 1.99 and 3.75 between the modeled mass

concentrations. The minimum difference is in August, and the maximal difference is in December. These differences are due

to less hydrophilic black carbon being available for wet deposition and therefore being transported further away from source regions. The bimodal structure in the modeled values can be attributed to the seasonality of the burning in southeastern Africa. The peak in the measurements in September has been attributed to biomass burning (H. Cachier, private communication, 1994) and the relatively greater lowering of the second peak with increasing transformation rate shows that air parcels arriving at Amsterdam Island have encountered more precipitation events

in November than in August. The discrepancy in the timing of the peak can be attributed to interannual variability Of biomass burning. Cooke et al. (submitted manuscript, 1995) have shown that the first peak of biomass burning in southeast Africa shifted from July in 1985 to September in 1988. No second peak has been seen in measurements, which would suggest that a 5% transformation rate is probably a lower limit or that the biomass burning in southeast Africa did not have a second peak in 1993. Physically, a 100% transformation rate is untenable, as elemental carbon, which forms at least some part of the black carbon, is hydrophobic. The transformation rate is probably a complex function of black carbon and sulphate concentrations. Global turnover times of black carbon for the three cases are 9.75, 7.85, and 5.57 days for the 2.5, 5, and 100% cases, respectively.

Figures 13a-13d show the ratio between the "standard" 5% transformation and the two other cases at the surface for January ,'red July. The contours show the black carbon mass concentrations of 25, 50, 75, 125, 150, 175, 200, and 300% of the standard 5% transformation case. As can be seen, the Pacific Ocean is the area most affected by the variation in the transformation rate. Black carbon mass concentrations can


vary from below 25% of the standard case in the case of hydrophilic emissions to more than 300% for the 2.5% transformation rate. It must be remembered that the mass

concentrations in these areas are quite low in the standard case (see Figure 5) but it is still a significant difference for the extreme cases. The Pacific Ocean can be expected to be the most affected by changes in scavenging, as it is the most remote from major source areas.

5. Conclusions

A global inventory of black carbon emissions has been constructed for fossil fuel and biomass burning sources by applying emission factors to estimates of fossil fuel consumption and biomass burning. Using this approach, we have calculated annual black carbon emissions of 7.96 Tg from fossil fuel consumption and 5.98 Tg from biomass burning.

The emission inventory has been implemented in the global transport model MOGUNTIA along with a simple model of the physical transformation of black carbon in the atmosphere from a hydrophobic form at the time of emission to a hydrophilic form, which is scavenged in precipitation with the same efficiency as sulphate.

We have compared the resulting air concentration and deposition fields with a number of sets of observations. The agreement between modeled and observed surface level concentrations of black carbon in air appears reasonable, although there is a tendency for the model to overpredict observations at remote sites. However, given the uncertainties in both the model and the observations, it is difficult to draw firm conclusions from this.

We have included boreal forest fires as a black carbon

source and have shown that it may contribute 20-50 % of the black carbon observed in the Arctic in July. This boreal forest source improves the agreement between the model and observations at Barrow but worsens it at the other Arctic site

Ny Sl, lesu nd. Comparisons of the modeled black carbon concentrations

in rainfall and concentrations in the upper troposphere show that the model is overpredicting both the atmospheric burden of black carbon and also the wet deposition, indicating either that the emissions are to{) high or that the model is overpredicting the fraction that is available for global transport. The deposition scheme here can be improved, and deposition near to the source of the emissions probably needs to be increased. Further work will also be carried out on the

sensitivity of the model to the emissions and deposition rate of black carbon. However, the emission database presented here appears to provide a reasonable estimate of the annual emissions of black carbon to the atmosphere.

A study has shown that varying the transformation rate of hydrophobic to hydrophilic black carbon gives differences of a factor of 3 in black carbon mass concentrations for very remote regions. This study also provides a lifetime range of black carbon between 6 and 10 days.

An important research need which should be highlighted is that of increased measurements outside of the United States

and western Europe. These measurements should include not only the mass concentration of black carbon at various sites but also the wet deposition of black carbon. Measurements of black carbon in the upper troposphere are equally desirable.

Acknowledgments. We would like to acknowledge the valuable comments of F. Raes and S.G. Jennings. This research has been undertaken as part of the European Commissions Environment and Climate Programme contract EV5V-CT92-0122 (SINDICATE). W. Cooke, as a grantholder, would like to acknowledge the sponsorship of the European Commission in this work.

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(Received November 28, 1994; revised October 30, 1995; accepted January 31, 1996.)

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A global black carbon aerosol model