28 Feb 2022
28 Feb 2022
Status: this preprint is currently under review for the journal ACP.

Satellite-based evaluation of AeroCom model bias in biomass burning regions

Qirui Zhong1, Nick Schutgens1, Guido van der Werf1, Twan van Noije2, Kostas Tsigaridis3,4, Susanne E. Bauer4,3, Tero Mielonen5, Alf Kirkevåg6, Øyvind Seland6, Harri Kokkola5, Ramiro Checa-Garcia7, David Neubauer8, Zak Kipling9, Hitoshi Matsui10, Paul Ginoux11, Toshihiko Takemura12, Philippe Le Sager2, Samuel Rémy13, Huisheng Bian14,15, Mian Chin15, Kai Zhang16, Jialei Zhu17, Svetlana G. Tsyro6, Gabriele Curci18,19, Anna Protonotariou20, Ben Johnson21, Joyce E. Penner22, Nicolas Bellouin23, Ragnhild B. Skeie24, and Gunnar Myhre24 Qirui Zhong et al.
  • 1Department of Earth Sciences, Vrije Universiteit, Amsterdam, The Netherlands
  • 2Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
  • 3Center for Climate Systems Research, Columbia University, 2880 Broadway, New York, NY 10025, USA
  • 4NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
  • 5Finnish Meteorological Institute, Kuopio, Finland
  • 6Norwegian Meteorological Institute, Oslo, Norway
  • 7Laboratoire des Sciences du Climat et de l'Environnement, IPSL, Gif-sur-Yvette, France
  • 8Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
  • 9European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 10Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
  • 11NOAA, Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
  • 12Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
  • 13HYGEOS, Lille, France
  • 14University of Maryland, Baltimore County (UMBC), Baltimore, MD, USA
  • 15NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 16Pacific Northwest National Laboratory, Richland, WA, USA
  • 17Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
  • 18Department of Physical and Chemical Sciences, University of L’Aquila, L’Aquila, Italy
  • 19Center of Excellence in Telesening of Environment and Model Prediction of Severe Events (CETEMPS), University of L’Aquila, L’Aquila (AQ), Italy
  • 20Department of Physics, University of Athens, Athens, Greece
  • 21Met Office, Exeter UK
  • 22Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA
  • 23Department of Meteorology, University of Reading, Reading, UK
  • 24Center for International Climate and Environmental Research-Oslo (CICERO), Oslo, Norway

Abstract. Global models are widely used to simulate biomass burning aerosols (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observation project (AeroCom) model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase III experiments (i.e., Biomass Burning Emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning on boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances (POLDER-GRASP) observations, we found that the modeled AOD values were biased by -93–152 %, with most models showing significant underestimations even for the state-of-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations of AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and mass extinction coefficient (MEC). We found that in the AEROCOM ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström Exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP observed AE suggested that the models tended to overestimate AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models comprise biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.

Qirui Zhong et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-96', Anonymous Referee #1, 30 Mar 2022
  • RC2: 'Comment on acp-2022-96', Anonymous Referee #2, 11 May 2022

Qirui Zhong et al.

Qirui Zhong et al.


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Short summary
Aerosol optical depth (AOD) errors for biomass burning aerosols (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 BB aerosols which require to be better constrained. These results can contribute to further model improvement and development.