Assessment of fire emission inventories during the South American Biomass Burning Analysis (SAMBBA) experiment

Copernicus GmbH - Tập 16 Số 11 - Trang 6961-6975
Gabriel Pereira1, Ricardo Almeida de Siqueira2, Nilton E. Rosário3, K. Longo2,4, Saulo R. Freitas2,4, Francielle S. Cardozo1, Johannes W. Kaiser5, Martin J. Wooster6,7
1Department of Geoscience, Federal University of Sao Joao del-Rei (UFSJ), Sao Joao del-Rei, Brazil
2Center for Weather Forecast and Climate Studies, National Institute for Space Research (INPE), Cachoeira Paulista, Brazil
3Environmental Sciences Department, São Paulo Federal University (UNIFESP), Diadema, São Paulo, Brazil
4now at: Global Modeling and Assimilation Office, NASA Goddard Space Flight Center and USRA/GESTAR, Greenbelt, MD, USA
5Max Planck Institute for Chemistry (MPIC), Mainz, Germany
6Department of Geography, King's College London (KCL), London, UK
7NERC National Centre for Earth Observation (NCEO), Leicester, UK

Tóm tắt

Abstract. Fires associated with land use and land cover changes release large amounts of aerosols and trace gases into the atmosphere. Although several inventories of biomass burning emissions cover Brazil, there are still considerable uncertainties and differences among them. While most fire emission inventories utilize the parameters of burned area, vegetation fuel load, emission factors, and other parameters to estimate the biomass burned and its associated emissions, several more recent inventories apply an alternative method based on fire radiative power (FRP) observations to estimate the amount of biomass burned and the corresponding emissions of trace gases and aerosols. The Brazilian Biomass Burning Emission Model (3BEM) and the Fire Inventory from NCAR (FINN) are examples of the first, while the Brazilian Biomass Burning Emission Model with FRP assimilation (3BEM_FRP) and the Global Fire Assimilation System (GFAS) are examples of the latter. These four biomass burning emission inventories were used during the South American Biomass Burning Analysis (SAMBBA) field campaign. This paper analyzes and inter-compared them, focusing on eight regions in Brazil and the time period of 1 September–31 October 2012. Aerosol optical thickness (AOT550 nm) derived from measurements made by the Moderate Resolution Imaging Spectroradiometer (MODIS) operating on board the Terra and Aqua satellites is also applied to assess the inventories' consistency. The daily area-averaged pyrogenic carbon monoxide (CO) emission estimates exhibit significant linear correlations (r, p  >  0.05 level, Student t test) between 3BEM and FINN and between 3BEM_ FRP and GFAS, with values of 0.86 and 0.85, respectively. These results indicate that emission estimates in this region derived via similar methods tend to agree with one other. However, they differ more from the estimates derived via the alternative approach. The evaluation of MODIS AOT550 nm indicates that model simulation driven by 3BEM and FINN typically underestimate the smoke particle loading in the eastern region of Amazon forest, while 3BEM_FRP estimations to the area tend to overestimate fire emissions. The daily regional CO emission fluxes from 3BEM and FINN have linear correlation coefficients of 0.75–0.92, with typically 20–30 % higher emission fluxes in FINN. The daily regional CO emission fluxes from 3BEM_FRP and GFAS show linear correlation coefficients between 0.82 and 0.90, with a particularly strong correlation near the arc of deforestation in the Amazon rainforest. In this region, GFAS has a tendency to present higher CO emissions than 3BEM_FRP, while 3BEM_FRP yields more emissions in the area of soybean expansion east of the Amazon forest. Atmospheric aerosol optical thickness is simulated by using the emission inventories with two operational atmospheric chemistry transport models: the IFS from Monitoring Atmospheric Composition and Climate (MACC) and the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modelling System (CCATT-BRAMS). Evaluation against MODIS observations shows a good representation of the general patterns of the AOT550 nm time series. However, the aerosol emissions from fires with particularly high biomass consumption still lead to an underestimation of the atmospheric aerosol load in both models.

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Tài liệu tham khảo

Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, https://doi.org/10.5194/acp-11-4039-2011, 2011.

Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from biomass burning, Global Biogeochem. Cy., 15, 955–966, 2001.

Andreae, M., Rosenfeld, D., Artaxo, P., Costa, A., Frank, G., Longo, K. M., and Silva Dias, M. A. F.: Smoking rain clouds over the Amazon, Science, 303, 1342–1345, 2004.

Baldassarre, G., Pozzoli, L., Schmidt, C. C., Unal, A., Kindap, T., Menzel, W. P., Whitburn, S., Coheur, P.-F., Kavgaci, A., and Kaiser, J. W.: Using SEVIRI fire observations to drive smoke plumes in the CMAQ air quality model: a case study over Antalya in 2008, Atmos. Chem. Phys., 15, 8539–8558, https://doi.org/10.5194/acp-15-8539-2015, 2015.

Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., D'Antonio, C. M., DeFries, R. S., Doyle, J. C., Harrison, S. P., Johnston, F. H., Keeley, J. E., Krawchuk, M. A., Kull, C. A., Marston, J. B., Moritz, M. A., Prentice, I. C., Roos, C. I., Scott, A. C., Swetnam, T. W., van der Werf, G. R., and Pyne, S. J.: Fire in the Earth System, Science, 324, 481–484, 2009.

Bustamante, M. M. C., Roitman, I., Aide, T. M., Alencar, A., Anderson, L., Aragão, L., Asner, G. P., Barlow, J., Berenguer, E., Chambers, J., Costa, M. H., Fanin, T., Ferreira, L. G., Ferreira, J. N., Keller, M., Magnusson, W. E., Morales, L., Morton, D., Ometto, J. P. H. B., Palace, M., Peres, C., Silvério, D., Trumbore, S., and Vieira, I. C. G.: Towards an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity, Glob. Change Biol., 1, 92–109, https://doi.org/10.1111/gcb.13087, 2016.

Cardozo, F. S., Pereira, G., Shimabukuro, Y. E., and Moraes, E. C.: Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest, Remote Sens., 6, 8002–8025, 2014.

Chuvieco, E., Cocero, D., Riano, D., Martinc, P., Martínez-Vegac, J., Rivad, J., and Pérez, F.: Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating, Remote Sens. Environ., 92, 322–331, 2004.

Christian, T. J., Kleiss, B., Yokelson, R. J., Holzinger, R., Crutzen, P. J., Hao, W. M., Saharjo, B. H., and Ward, D. E.: Comprehensive laboratory measurements of biomass-burning emissions: 1. Emissions from Indonesian, African, and other fuels, J. Geophys. Res., 108, 4719, https://doi.org/10.1029/2003JD003704, 2003.

Crutzen, P. J. and Andreae, M. O.: Biomass burning in the tropics: Impact on atmospheric chemistry and biogeochemical cycles, Science, 250, 1669–1678, 1990.

De Santis, A., Asner, G. P., Vaughan, P. J., and Knapp, D. E.: Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery, Remote Sens. Environ., 114, 1535–1545, https://doi.org/10.1016/j.rse.2010.02.008, 2010.

Efron, B.: The jackknife, the bootstrap, and other resampling plans, Society of Industrial and Applied Mathematics, CBMS-NSF Monographs, No. 38, Philadelphia, PA, 1982.

Ellicott, E., Vermote, E., Giglio, L., and Roberts, G.: Estimating biomass consumed from fire using MODIS FRE, Geophys. Res. Lett., 36, L13401, https://doi.org/10.1029/2009GL038581, 2009.

Fearnside, P. M.: Global warming and tropical land use change: Greenhouse gas emissions from biomass burning, decomposition and soils in forest conversion, shifting cultivation and secondary vegetation, Climatic Change, 46, 115–158, 2000.

Freeborn, P. H., Wooster, M. J., and Roberts, G.: Addressing the spatiotemporal sampling design of MODIS to provide estimates of the fire radiative energy emitted from Africa, Remote Sens. Environ, 115, 475–498, 2011.

Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., and Kasibhatla, P.: Global estimation of burned area using MODIS active fire observations, Atmos. Chem. Phys., 6, 957–974, https://doi.org/10.5194/acp-6-957-2006, 2006.

Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.

Heil, A., Kaiser, J. W., van der Werf, G. R., Wooster, M. J., Schultz, M. G., and Dernier van der Gon, H.: Assessment of the real-time fire emissions (GFASv0) by MACC, Tech. Memo. 628, ECMWF, Reading, UK, 2010.

Houghton, R. A., Lawrence, K. T., Hackler, J. L., and Brown, S.: The spatial distribution of forest biomass in the Brazilian Amazon: A comparison of estimates, Glob. Change Biol., 7, 731–746, 2001.

Ichoku, C. and Kaufman, Y. J.: A method to derive smoke emission rates from MODIS fire radiative energy measurements, IEEE T. Geosci. Remote Sens., 43, 2636–2649, 2005.

Justice, C. O., Giglio, B., Korontzi, S., Owens, J., Morisette, J. T., Roy, D. P., Descloitres, J., Alleaume, S., Petitcolin, F., and Kaufman, Y.: The MODIS fire products, Remote Sens. Environ, 83, 244–262, 2002.

Kaiser, J. W., Suttie, M., Flemming, J., Morcrette, J.-J., Boucher, O., and Schultz, M. G.: Global real-time fire emission estimates based on space-borne fire radiative power observations, AIP Conf. Proc., 1100, 645–648, 2009.

Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012.

Kaufman, J. B., Cummings, D. L., Ward, D. E., and Babbitt, R.: Fire in the Brazilian Amazon: Biomass, nutrient pools, and losses in slashed primary forests, Oecologia, 104, 397–408, 1995.

Kaufman, Y. J. and Tanré, D.: Algorithm for Remote Sensing of Tropospheric Aerosols from MODIS, MODIS Algorithm Theoretical Basis Document, Product ID: MOD04, revised 26 October 1998, available at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod02.pdf (last access: 30 October 2015), 1998.

Kumar, S. S., Roy, D. P., Boschetti, L., and Kremens, R.: Exploiting the power law distribution properties of satellite fire radiative power retrievals: A method to estimate fire radiative energy and biomass burned from sparse satellite observations, J. Geophys. Res., 116, D19303, https://doi.org/10.1029/2011JD015676, 2011.

Longo, K. M., Freitas, S. R., Andreae, M. O., Setzer, A., Prins, E., and Artaxo, P.: The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) – Part 2: Model sensitivity to the biomass burning inventories, Atmos. Chem. Phys., 10, 5785–5795, https://doi.org/10.5194/acp-10-5785-2010, 2010.

Mao, Y. H., Li, Q. B., Chen, D., Zhang, L., Hao, W.-M., and Liou, K.-N.: Top-down estimates of biomass burning emissions of black carbon in the Western United States, Atmos. Chem. Phys., 14, 7195–7211, https://doi.org/10.5194/acp-14-7195-2014, 2014.

Olson, J. S., Watts, J. A., and Allison, L. J.: Major world ecosystem complexes ranked by carbon in live vegetation: A database, available at: http://cdiac. esd.ornl.gov/ndps/ndp017.html (last access: 7 August 2010), 2000.

Pereira, G., Freitas, S. R., Moraes, E. C., Ferreira, N. J., Shimabukuro, Y. E., Rao, V. B., and Longo, K. M.: Estimating trace gas and aerosol emissions over South America: Relationship between fire radiative energy released and aerosol optical depth observations, Atmos. Environ., 43, 6388–6397, 2009.

Peterson, D., Wang, J., Ichoku, C., Hyer, E., and Ambrosia, V.: A sub-pixel-based calculation of fire radiative power from MODIS observations: 1: Algorithm development and initial assessment, Remote Sens. Environ., 129, 262–279, https://doi.org/10.1016/j.rse.2012.10.036, 2012.

Prins, E. M., Felz, J. M., Menzel, W. P., and Ward, D. E.: An overview of GOES-8 diurnal fire and smoke results for SCAR-B and 1995 fire season in South America, J. Geophys. Res., 103, 31821–31825, 1998.

Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M., and Morton, D. C.: Global burned area and biomass burning emissions from small fires, J. Geophys. Res., 117, G04012, https://doi.org/10.1029/2012JG002128, 2012.

Roberts, G., Wooster, M. J., Perry, G. L. W., Drake, N., Rebelo, L. M., and Dipotso, F.: Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI imagery, J. Geophys. Res.-Atmos., 110, D21111, https://doi.org/10.1029/2005JD006018, 2005.

Rosário, N. E., Longo, K. M., Freitas, S. R., Yamasoe, M. A., and Fonseca, R. M.: Modeling the South American regional smoke plume: aerosol optical depth variability and surface shortwave flux perturbation, Atmos. Chem. Phys., 13, 2923–2938, https://doi.org/10.5194/acp-13-2923-2013, 2013.

Schroeder, W., Csiszar, I., and Morisette, J.: Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon, Remote Sens. Environ., 112, 456–470, 2008.

Seiler, W. and Crutzen, P. J.: Estimates of gross and net fluxes of carbon between the biosphere and atmosphere from biomass burning, Climatic Change, 2, 207–247, 1980.

Sestini, M., Reimer, E., Valeriano, D., Alvalá, R., Mello, E., Chan, C., and Nobre, C.: Mapa de cobertura da terra da Amazônia legal para uso em modelos meteorológicos, in: Simpósio Brasileiro de Sensoriamento Remoto (SBSR), 11, Belo Horizonte, Anais, São José dos Campos: INPE, 2003, Artigos, 2901–2906, CD-ROM, On-line, ISBN 85-17-00018-8, 2003.

Setzer, A. W., Pereira Jr., A. C., and Pereira, M. C.: Satellite studies of biomass burning in Amazonia: some practical aspects, Remote Sens. Rev., 10, 91–103, 1994.

Shimabukuro, Y. E., Pereira, G., Cardozo, F. S., Stockler, R., Freitas, S. R., and Coura, S. M. C.: Biomass burning emission estimation in Amazon tropical forest, in: Domingo Alcaraz Segura; Carlos Marcelo Di Bella; Julieta Veronica Straschnoy (Org.), Earth Observation of Ecosystem Services, 1st Edn., Oxford, UK, Taylor & Francis, 1, 112–130, 2013.

Val Martin, M., Logan, J. A., Kahn, R. A., Leung, F.-Y., Nelson, D. L., and Diner, D. J.: Smoke injection heights from fires in North America: analysis of 5 years of satellite observations, Atmos. Chem. Phys., 10, 1491–1510, https://doi.org/10.5194/acp-10-1491-2010, 2010.

van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010.

Vermote, E., Ellicott, E., Dubovik, O., Lapyonok, T., Chin, M., Giglio, L., and Roberts, G. J.: An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power, J. Geophys. Res., 114, 205–227, 2009.

Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.

Wooster, M. J., Zhukov, B., and Oertel, D.: Fire radiative energy for quantitative study of biomass burning: Derivation from the BIRD experimental satellite and comparison to MODIS fire products, Remote Sens. Environ, 86, 83–107, 2003.

Wooster, M. J., Roberts, G., and Perry, G. L. W.: Retrieval of biomass combustion rates and totals from fire radiative power observations: Calibration relationships between biomass consumption and fire radiative energy release, J. Geophys. Res., 110, D24311, https://doi.org/10.1029/2005JD006318, 2005.

Xu, W., Wooster, M., Roberts, G., and Freeborn, P.: New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America, Remote Sens. Environ., 114, 1876–1895, 2010.

Yebra, M., Chuvieco, E., and Riano, D.: Estimation of live fuel moisture content from MODIS images for fire risk assessment, Agr. Forest Meteorol., 148, 523–536, 2009.