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ACP | Articles | Volume 20, issue 2
Atmos. Chem. Phys., 20, 969–994, 2020
https://doi.org/10.5194/acp-20-969-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 20, 969–994, 2020
https://doi.org/10.5194/acp-20-969-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 27 Jan 2020

Research article | 27 Jan 2020

Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

Xiaohua Pan et al.

Data sets

The quick fire emissions dataset (QFED) (https://portal.nccs.nasa.gov/datashare/iesa/aerosol/emissions/QFED/v2.4r6/) A. Darmenov and A. da Silva https://gmao.gsfc.nasa.gov/pubs/docs/Darmenov796.pdf

Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical (https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_aod.htm D. M. Giles, A. Sinyuk, M. G. Sorokin, J. S. Schafer, A. Smirnov, I. Slutsker, T. F. Eck, B. N. Holben, J. R. Lewis, J. R. Campbell, E. J. Welton, S. V. Korkin, and A. I. Lyapustin https://doi.org/10.5194/amt-12-169-2019

Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements (http://feer.gsfc.nasa.gov/data/emissions/) C. Ichoku and L. Ellison https://doi.org/10.5194/acp-14-6643-2014

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

Ability of multiangle remote sensing observations to identify and distinguish mineral dust types: Part 2. Sensitivity over dark water (https://eosweb.larc.nasa.gov/project/misr/mil3mae_table) O. V. Kalashnikova and R. A. Kahn https://doi.org/10.1029/2005JD006756

Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009) (https://daac.ornl.gov/VEGETATION/guides/global_fire_emissions_v3.1.html) G. R. van der Werf, J. T. Randerson, L. Giglio, G. J. Collatz, M. Mu, P. S. Kasibhatla, D. C. Morton, R. S. DeFries, Y. Jin, and T. T. van Leeuwen https://doi.org/10.5194/acp-10-11707-2010

Global fire emissions estimates during 1997–2016 (http://www.globalfiredata.org) G. R. van der Werf, J. T. Randerson, L. Giglio, T. T. van Leeuwen, Y. Chen, B. M. Rogers, M. Mu, M. J. E. van Marle, D. C. Morton, G. J. Collatz, R. J. Yokelson, and P. S. Kasibhatla https://doi.org/10.5194/essd-9-697-2017

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

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The differences between these six BB emission datasets are large. Our study found that (1) most current biomass burning (BB) aerosol emission datasets derived from satellite observations lead to the underestimation of aerosol optical depth (AOD) in this model in the biomass-burning-dominated regions and (2) it is important to accurately estimate both the magnitudes and spatial patterns of regional BB emissions in order for a model using these emissions to reproduce observed AOD levels.
The differences between these six BB emission datasets are large. Our study found that (1) most...
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