Articles | Volume 22, issue 17
https://doi.org/10.5194/acp-22-11009-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-11009-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Aug 2022
Research article |
| 31 Aug 2022
| 31 Aug 2022
Satellite-based evaluation of AeroCom model bias in biomass burning regions
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the
Netherlands
Nick Schutgens
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the
Netherlands
Guido van der Werf
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the
Netherlands
Twan van Noije
Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Kostas Tsigaridis
Center for Climate Systems Research, Columbia University, 2880
Broadway, New York, NY, USA
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY, USA
Susanne E. Bauer
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY, USA
Center for Climate Systems Research, Columbia University, 2880
Broadway, New York, NY, USA
Tero Mielonen
Finnish Meteorological Institute, Kuopio, Finland
Alf Kirkevåg
Norwegian Meteorological Institute, Oslo, Norway
Øyvind Seland
Norwegian Meteorological Institute, Oslo, Norway
Harri Kokkola
Finnish Meteorological Institute, Kuopio, Finland
Ramiro Checa-Garcia
Laboratoire des Sciences du Climat et de l'Environnement, IPSL,
Gif-sur-Yvette, France
David Neubauer
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich,
Switzerland
Zak Kipling
European Centre for Medium-Range Weather Forecasts, Reading, UK
Hitoshi Matsui
Graduate School of Environmental Studies, Nagoya University, Nagoya,
Japan
Paul Ginoux
Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Toshihiko Takemura
Research Institute for Applied Mechanics, Kyushu University, Fukuoka,
Japan
Philippe Le Sager
Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Samuel Rémy
HYGEOS, Lille, France
Huisheng Bian
Joint Center for Earth Systems Technology, University of Maryland, Baltimore County (UMBC), Baltimore, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Mian Chin
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Kai Zhang
Pacific Northwest National Laboratory, Richland, WA, USA
Jialei Zhu
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin 300072, China
Svetlana G. Tsyro
Norwegian Meteorological Institute, Oslo, Norway
Gabriele Curci
Department of Physical and Chemical Sciences, University of L'Aquila,
L'Aquila, Italy
Center of Excellence in Telesensing of Environment and Model
Prediction of Severe Events (CETEMPS), University of L'Aquila, L'Aquila, Italy
Anna Protonotariou
Department of Physics, University of Athens, Athens, Greece
Ben Johnson
Met Office, Exeter, UK
Joyce E. Penner
Department of Climate and Space Sciences and Engineering, University
of Michigan, Ann Arbor, MI, USA
Nicolas Bellouin
Department of Meteorology, University of Reading, Reading, UK
Ragnhild B. Skeie
Center for International Climate and Environmental Research (CICERO), Oslo, Norway
Gunnar Myhre
Center for International Climate and Environmental Research (CICERO), Oslo, Norway
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Cited
7 citations as recorded by crossref.
- Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols Q. Zhong et al. 10.1126/sciadv.adi3568
- Fire–precipitation interactions amplify the quasi-biennial variability in fires over southern Mexico and Central America Y. Liu et al. 10.5194/acp-24-3115-2024
- Assimilation of POLDER observations to estimate aerosol emissions A. Tsikerdekis et al. 10.5194/acp-23-9495-2023
- Monitoring biomass burning aerosol transport using CALIOP observations and reanalysis models: a Canadian wildfire event in 2019 X. Shang et al. 10.5194/acp-24-1329-2024
- Large transboundary health impact of Arctic wildfire smoke B. Silver et al. 10.1038/s43247-024-01361-3
- Emissions Background, Climate, and Season Determine the Impacts of Past and Future Pandemic Lockdowns on Atmospheric Composition and Climate J. Hickman et al. 10.1029/2022EF002959
- Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires D. Robbins et al. 10.5194/amt-17-3279-2024
7 citations as recorded by crossref.
- Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols Q. Zhong et al. 10.1126/sciadv.adi3568
- Fire–precipitation interactions amplify the quasi-biennial variability in fires over southern Mexico and Central America Y. Liu et al. 10.5194/acp-24-3115-2024
- Assimilation of POLDER observations to estimate aerosol emissions A. Tsikerdekis et al. 10.5194/acp-23-9495-2023
- Monitoring biomass burning aerosol transport using CALIOP observations and reanalysis models: a Canadian wildfire event in 2019 X. Shang et al. 10.5194/acp-24-1329-2024
- Large transboundary health impact of Arctic wildfire smoke B. Silver et al. 10.1038/s43247-024-01361-3
- Emissions Background, Climate, and Season Determine the Impacts of Past and Future Pandemic Lockdowns on Atmospheric Composition and Climate J. Hickman et al. 10.1029/2022EF002959
- Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires D. Robbins et al. 10.5194/amt-17-3279-2024
Latest update: 29 Jun 2024
Short summary
Aerosol optical depth (AOD) errors for biomass burning aerosol (BBA) are evaluated in 18 global models against satellite datasets. Notwithstanding biases in satellite products, they allow model evaluations. We observe large and diverse model biases due to errors in BBA. Further interpretations of AOD diversities suggest large biases exist in key processes for BBA which require better constraining. These results can contribute to further model improvement and development.
Aerosol optical depth (AOD) errors for biomass burning aerosol (BBA) are evaluated in 18 global...
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