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Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions

Author

Listed:
  • Qirui Zhong

    (Vrije Universiteit Amsterdam)

  • Nick Schutgens

    (Vrije Universiteit Amsterdam)

  • Guido R. Werf

    (Vrije Universiteit Amsterdam)

  • Twan Noije

    (Royal Netherlands Meteorological Institute)

  • Susanne E. Bauer

    (NASA Goddard Institute for Space Studies
    Columbia University)

  • Kostas Tsigaridis

    (NASA Goddard Institute for Space Studies
    Columbia University)

  • Tero Mielonen

    (Finnish Meteorological Institute)

  • Ramiro Checa-Garcia

    (Laboratoire des Sciences du Climat et de l’Environnement, IPSL
    University of Natural Resources and Life Sciences)

  • David Neubauer

    (ETH Zurich)

  • Zak Kipling

    (European Centre for Medium-Range Weather Forecasts)

  • Alf Kirkevåg

    (Norwegian Meteorological Institute)

  • Dirk J. L. Olivié

    (Norwegian Meteorological Institute)

  • Harri Kokkola

    (Finnish Meteorological Institute)

  • Hitoshi Matsui

    (Nagoya University)

  • Paul Ginoux

    (NOAA, Geophysical Fluid Dynamics Laboratory)

  • Toshihiko Takemura

    (Kyushu University)

  • Philippe Sager

    (Royal Netherlands Meteorological Institute)

  • Samuel Rémy

    (HYGEOS)

  • Huisheng Bian

    (University of Maryland, Baltimore County (UMBC)
    NASA Goddard Space Flight Center)

  • Mian Chin

    (NASA Goddard Space Flight Center)

Abstract

Biomass burning (BB) is a major source of aerosols that remain the most uncertain components of the global radiative forcing. Current global models have great difficulty matching observed aerosol optical depth (AOD) over BB regions. A common solution to address modelled AOD biases is scaling BB emissions. Using the relationship from an ensemble of aerosol models and satellite observations, we show that the bias in aerosol modelling results primarily from incorrect lifetimes and underestimated mass extinction coefficients. In turn, these biases seem to be related to incorrect precipitation and underestimated particle sizes. We further show that boosting BB emissions to correct AOD biases over the source region causes an overestimation of AOD in the outflow from Africa by 48%, leading to a double warming effect compared with when biases are simultaneously addressed for both aforementioned factors. Such deviations are particularly concerning in a warming future with increasing emissions from fires.

Suggested Citation

  • Qirui Zhong & Nick Schutgens & Guido R. Werf & Twan Noije & Susanne E. Bauer & Kostas Tsigaridis & Tero Mielonen & Ramiro Checa-Garcia & David Neubauer & Zak Kipling & Alf Kirkevåg & Dirk J. L. Olivié, 2022. "Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33680-4
    DOI: 10.1038/s41467-022-33680-4
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    References listed on IDEAS

    as
    1. Hitoshi Matsui & Douglas S. Hamilton & Natalie M. Mahowald, 2018. "Black carbon radiative effects highly sensitive to emitted particle size when resolving mixing-state diversity," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    2. Claire Granier & Bertrand Bessagnet & Tami Bond & Ariela D’Angiola & Hugo Denier van der Gon & Gregory Frost & Angelika Heil & Johannes Kaiser & Stefan Kinne & Zbigniew Klimont & Silvia Kloster & Jean, 2011. "Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period," Climatic Change, Springer, vol. 109(1), pages 163-190, November.
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