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Climatic effects of biomass burning

Susan Marshall," John A. Taylor)' Robert .I. O g l e s b y ) Jay W. Larson? t David J. Erickson ~"

"l./NC-Clmrlotte, Charlotte, NC, USA ~'Australian National University. Canberra. Australia

~Purdue Unixersity, West l,afayette, IN. USA 'JAusualian National University, Canberra. Australia

~NU\R, Boulder, ('O, I,!SA

Abstract

We have made a series of general circulation model IGCM) and chemical transport model (CTMI simulations to examine further the consequences of biomass burning. We lake a three-pronged approach to this stud\: ( 1 ) examine only the direct short-,aa,~e forcing of the smoke clouds by imposing smoke 'clouds" in 1110 model, (2) examine the indirec! effect el bionlass smoke b', allowing the biomass smoke lo modify lhe optical properties of exisling clouds, reducing the effective (mass) droplel radius, and (3i examine the downstream effect by advecting biomass smoke using semi-Lagrangian transport m the GCM. Preliminary results from the GCM simulations suggest strong local cooling,; (2 4'(7 or more) where biomass burning occurs ~ith somewhat icduced regional coolings. Initial results of the transport of smoke by the Australian National University CTM !ANU-CTM) sh<w the bJomass smoke to remain lairly localized with the advection of smoke extending lhc effects dmvnwind. Copyright :c 19~)6 |:.l~exier Science 1 ld

Kevu'ords: Climate: biomass burning: climate models

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Program title: NCAR y Cli Model, 2 Developers: , for ~ C_~Rml Division

) Contact address: !~:)x 3~ CO 80307 First available: ~,, 1992; available May I~

: C R A Y :or a d v ~ w (IBM, SUN~ ~ I , E)

Web ~ ~ URL: ~

1. In troduct ion

Biomass burning occurs as a result of natural causes or through the actions of humans. Deforestat ion prac- tices in the tropics and sub-l,ropics play a large role in the global total of b iomass burned. Forest fires (natural and an thropogenica l ly -mduced) in both henri- spheres appear to provide an important source of smoke. The smoke produced by bio |nass burning in the southern hemisphere may be a signilicanl, source of c l imat ica l ly-ac t ive part icles as lossil fuel e |mss ions are much lower l,han in the northern hemisphere.

The smoke plu |nes l,hal, result from biomass burning

53

;ire dramat ica l ly obvious on visible wavelengih satell i te images (nearly indis t inguishable from low. and mid- level clouds), but are virtually absent on infrared wave- length images. This is because the part iculates s t rongly Jeflecl, shor t -wave lsolar) radial,ion, but are largely transparent to infrared Iterrestrial) radiation. These biomass smoke plumes should therefore have a net cool ing effecl on the system. Robock 11991) finds thai the presence of smoke plume,, from forest fires in North Amer ica produced observed surface temperatures '~' to 6°C cooler than forecast. Pennel ~,t ~l/. (1991, i992) found that the radiat ive effects of smoke plumes t an approach that of a doubl ing of ( ' O , {though of

54 S. Marshall et al./Climatic effects of biomass burning

opposite sign). This suggests that prolonged biomass burning events (such as for tropical deforestation) may therefore locally mask the effects of increased CO2.

This cooling effect would have the largest effect on regions or seasons that have relatively little natural cloud cover, i.e. the dry season. This would be the time when the climate would be most sensitive to the short-wave effect of the smoke clouds. Observations suggest that biomass burning smoke clouds are most prevalent during the dry season and therefore, should have a noticeable impact on the climate of the region. The dry season for the northern hemisphere tropics and sub-tropics occurs from December through March and, for the southern hemisphere tropics and sub- tropics, occurs from July through October, reaching a peak in South America in October.

The biomass smoke also should produce an indirect effect on the radiative balance of the region. These smoke particles are effective cloud condensation nuclei and can result in a decrease in the effective cloud droplet size. This indirect effect would then result in an increase in cloud reflectivity that may be on the order of the radiative effect of the direct cooling (Penner et al., 1992). This indirect cooling effect of the smoke released from deforestation has, however, received little attention.

We are making a series of general circulation model (GCM) simulations with the National Center for Atmospheric Research (NCAR) Community Climate Model, version 2 (CCM2) in order to examine further the consequences of biomass burning, with emphasis on the direct and indirect short-wave effects of the smoke clouds as well as the transport of these smoke plumes outside of the source region and its effect on the regional climate.,

2. Sources of biomass smoke

We use a seasonally varying source function, developed to predict the flux of trace gases from biomass burning by Taylor and Zimmerman (1991), to estimate the spatial and temporal distribution of smoke clouds arising from biomass burning. The biomass burning source function has a spatial resolution of 2.5 ° (latitude) × 2.5 ° (longitude) and produces monthly mean estimates of the flux of trace gases to the atmos- phere. The source function was developed using the land cover classes of Wilson and Henderson-Sellers (1985), at a 1 ° × 1 ° resolution, to identify the extent of biomass burning.

The seasonal distribution of the flux of trace gases and particulates to the atmosphere was then apportioned inversely with respect to the amount of precipitation. This leads to high fluxes during the dry season in the tropics and nearly zero during the wet season. The model derived estimates of the variation in the extent and seasonality of biomass burning have been con-

firmed qualitatively by satellite observations. The pre- cipitation climatology of Shea (1986) at 2 .5°x 2.5 ° resolution was used to derive the seasonal variation in biomass burning. In the long-term we hope to couple a model of ecosystem net primary productivity with CCM2. This will then be used to estimate the amount of fuel available for biomass burning in a manner consistent with the climate produced by CCM2 and will allow the feedbacks between biomass burning, climate and ecosystem productivity to be assessed. Results of the current model of the flux of trace gases and particulates from biomass burning (areas of biomass smoke shown in Fig. 1) indicate that more than half of the total emissions occur in the southern hemisphere.

3. Model ing strategy

We have proposed a three-pronged strategy to inves- tigate the climatic impact of biomass smoke using global climate models; (1) Examining only the direct short-wave forcing of the smoke clouds by imposing 'clouds' in the model whenever biomass smoke was predicted by the Taylor and Zimmerman model (1991). These smoke 'clouds' are only allowed to interact with the short-wave radiation scheme in the CCM2. (2) Allow the smoke to modify the optical properties of any existing cloud, i.e. model the sensitivity of the regions to the indirect effect of biomass smoke. (3) Advection of the biomass smoke within the model to assess any downwind effects.

All climate model simulations use the National Center for Atmospheric Research's Community Climate Model, version 2 (NCAR CCM2). The CCM2 is a global, pseudo-spectral T42 18-layer hybrid coordinate model with the top of the model atmosphere occurring at approximately 2.9 mb. The spectral transform grid has an effective horizontal resolution of 2.8°× 2.8 °. Shortwave properties of water clouds are parameterized following Slingo (1989). A comprehensive account of the routines in CCM2 are described in Hack et al. (1993).

3.1. Direct effect: Cloud cover fract ion

Data of gases and particulates from biomass bums for Brazil and the Congo suggest that the vertical extent of the biomass plumes is concentrated between 1 and 4 km above the surface (Andreae, 1991). As our first guess of the climatic effects of introducing a short-wave cloud into the region, we simply add 'clouds' to the model troposphere in layers 3-7 of the model. This corresponds approximately to the mid- tropospheric levels in the model atmosphere. The optical thickness of the smoke clouds is scaled by the relative amount of smoke produced by the Taylor and Zimmerman source function. The monthly values of

S. Marshall et al./Climatic effects of biomass burning 55

biomass smoke emission are assumed to represent mid- month values and are linearly interpolated by the GCM to daily values.

We determine the smoke cloud fraction between 0 to 0.30 (or 0-30% fractional grid space coverage in the model) dependent upon the mass flux of smoke (that is, the largest monthly mass flux of smoke would result in a 30% coverage cloud at that model grid). This technique results in a global field of smoke clouds for January and July as indicated in Fig. 1. The scaling factor (in this case, 0.30) is a tunable parameter in this simulation as the 'true' values are poorly known. We chose 0.30 as this corresponds to the maximum cloud fraction for convective clouds in the model.

At each level where the smoke clouds are assumed to occur, the biomass smoke cloud fraction is then combined with the model water vapor cloud fraction to produce a total grid cloud cover fraction,

fold = (1--fH2Ocld) X fsmoke + fH2Ocld ( l )

where fc~d is the total (water vapor cloud plus biomass smoke) cloud fraction, fmocta is the water vapor cloud fraction and f,.moke is the biomass smoke cloud fraction (as determined from the source function of Taylor and Zimmerman).

The biomass 'clouds' affect only the short-wave radiation calculation of the model through the cloud optical depth and in every other way but fractional coverage, are treated as water vapor clouds.

3.2. Indirect effect: Cloud droplet radius

(CCN) (Rogers et al., 1991). The result is a reduction in the effective (mass) droplet radius, thereby changing the shortwave optical properties of the existing clouds (see Woods et al., 1991).

In step 2, we examine the indirect effect by reducing the mean cloud droplet radius of the existing clouds in regions of biomass burning. NCAR CCM2 pre- scribes the mean effective (mass) cloud droplet radius to be 10 micrometers everywhere. We reduced the droplet radii to 5 micrometers everywhere there is biomass smoke as determined from the source function of Taylor and Zimmerman. This produces 'brighter' clouds in the regions of biomass burning.

3.3. Transport of smoke particles

We have used the Australian National University chemical transport model (ANU-CTM), a global three- dimensional Lagrangian tracer transport model to inves- tigate the dispersion of CO2 from biomass burning. The ANU CTM has 2.5°× 2.5 ° resolution and uses European Centre for Medium Range Weather Fore- casting (ECMWF) wind fields as boundary conditions. This model was run for a 2-year simulation to make an initial assessment of the extent of transport of the biomass smoke. The ANU CTM simulation used the surface forcing of biomass burning described in Section 2 and a background level of 350 ppm CO2. No other source or sink processes were operating other than the biomass burning source. The total source of biomass carbon was set at 2 GtC per year and the distributions were for the model surface layer only.

Aerosols emitted by the biomass burning are gener- ally small (in the range from 0.1 to 0.2 micrometers), and highly effective as cloud condensation nuclei

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Fig. 1. (a) January and (b) July biomass smoke cloud fraction. Contours show the 0.01 and 0.I0 smoke cloud fractions. Regions with cloud fractions greater than 0.10 are shaded.

4. Results and discussion

4.1. Direct: Increasing cloud fraction

We have run the CCM2 for several seasons using the 12-month data of Taylor and Zimmerman to introduce biomass smoke into the model atmosphere as described above. We expect the primary effect of the biomass smoke clouds to be a reduction in surface temperature due to shading of the surface. Averaging the results of the simulation for January and July, we find localized coolings of up to 6°C in the regions of biomass smoke (Fig. 2). The seasonality of the biomass emissions is also evident in the surface temperature differences, indicating strongest cooling in the northern hemisphere in January (2-6°C) and in the southern hemisphere in July (2-4°C). Note that the African Sahel shows the largest response to the imposed biomass smoke cloud. This area is naturally one of the driest in the model and normally has a low cloud cover fraction. Imposing even small increases in cloud cover will be demonstrated by a large response in the climate. Other regions, such as the Congo in July, show a smaller response to increased cloud fraction,

56 S. Marshall et al./Climatic effects of biomass burning

(a) January "

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but these regions generally have a higher natural cloud fraction.

A secondary, but basic effect of the biomass smoke clouds is on the short-wave heating of the atmospheric column. The dual effect of cooling the lower tropo- sphere and warming the upper troposphere would sta- bilize the atmospheric column and affect the dynamics. Figure 3 shows the January temperature profile for a model grid-point in Sahelian Africa (model grid cent-

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ered at: 26.5°S, 56.3°W). This grid location had an imposed biomass smoke cloud fraction of 0.14. Clearly evident in Fig. 3 is the cooling that occurs below the smoke cloud layers at this location. Less clear is the sign of any distinct warming occurring within the biomass smoke cloud layer.

4.2. Indirect: Decreasing droplet radius

In the above investigation we have not considered the impact of the smoke particles as a source of cloud condensation nuclei in the southern hemisphere. These effects may be at least as significant as the direct effects of biomass burning. Figure 4 shows the surface temperature difference plots of the BIOMASS-CON- TROL run for the decreased cloud droplet size exper- iment. Reducing the droplet radius will result in a brighter cloud (more reflectivity) and should decrease temperature within and below the cloud, similar to the previous experiment. This effect is clearly seen over regions of the African Sahel during January with sur- face cooling of 5-6°C in the areas of the greatest smoke mass. A temperature difference profile (not shown) indicates this cooling to be confined to the layers within and below the biomass smoke cloud lay- ers.

4.3. Three-dimensional modeling

The results of the ANU CTM transport simulation (Fig. 5) show the biomass smoke to remain fairly localized as strong gradients are evident in the vertical. The major impact beyond the source regions occurs

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Fig. 3. Profile of January air temperature differences (°C) for grid location in Sahelian Africa, centered on 26.5S, 56.3E. Area of biomass smoke cloud is shaded.

Fig. 4. (a) January and (b) July surface temperature differ- ences (BIOMASS-CONTROL) for experiment with reduced effective cloud droplet radius. Contour interval is I°C.

S. Marshall et al./Climafic effects of biomass burning 57

ANU-CTM Biomams C02 ppm (a) .lanuary

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Fig. 5. (a) January and (b) July ANU-CTM CO2 flux. Contour interval is 2 ppm.

over the South Atlantic with biomass burning CO2 being transported from Africa and South America. These results illustrate that the smoke will disperse rapidly both vertically and horizontally, which means that the direct effects of biomass burning smoke will predominately occur in the region where burning takes place. However, some smoke will eventually be trans- ported over the southern ocean where indirect effects may prove to be highly significant. These modeling results also indicate that the greatest impact from the transport of biomass smoke will most likely occur over the southern Atlantic. We hope to eventually use the semi-Lagrangian transport capabilities of the NCAR CCM3 to allow for the model wind fields to move the smoke particles.

to a completely interactive smoke-radiation model simulation, we will first take advantage of some pre- vious runs of the CCM2 in which CO2 has been advected. Global 3-D numerical simulations of the atmospheric CO2 cycle were performed using a variety of boundary forcings at T42 resolution and using a time step of 20 minutes. Air-sea exchange biomass burning, fossil fuel combustion and monthly varying exchanges with the terrestrial biosphere are included in the model run. The amplitude of the seasonal cycle of atmospheric CO2 varies from less than 1 ppm over southern hemispheric ocean regions to 45 ppm over the active areas of terrestrial biosphere exchange. The 3-D CO2 fields were output from the above simulation and are provided to us as daily values. We will use these values to impose a 3-D biomass CO2 'smoke' cloud in the model.

Second, we will then use the 2-D biomass source field (from Taylor and Zimmerman) as an initial bound- ary condition to the model and use the semi-Lagrangian transport of the CCM3 to advect the biomass smoke spatially and through time. This will give us a represen- tation of the extent of the biomass cloud in the vertical and horizontal and allow for feedbacks between the biomass smoke and the climate of the model.

Acknowledgements

This research has been supported in part by the National Oceanic and Atmospheric Administration under grant NA36GP0396 to the University of North Carolina at Charlotte and by the Department of Energy grant DE-FG02-85ER14144 to W. Prell, J. Kutzbach, R. J. Oglesby and T. Webb and by computing support from the NCAR Scientific Computing Division.

References

5. Future work

We plan to combine the direct and indirect effects of the biomass smoke in two experiments: (1) increase the cloud fraction and decrease the effective cloud droplet size (essentially combine 3.1 and 3.2) and (2) change the optical properties of the smoke cloud to those of smoke, based on the findings of Ogren (1995) and Penner et al. (1994; 1992).

The CCM3, described above, uses a semi-Lagrangian transport (SLT) method (Williamson and Rasch, 1989) for advecting water vapor and chemical constituents. The NCAR CCM3 Semi-Lagrangian Transport (SLT) has been adapted for use in studying the global carbon cycle. This provides us with the capability to transport biomass smoke through the model atmosphere. We plan to test the effect of advected CO2 in the model atmosphere in two experiments using the CCM3. Prior

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