Satellite-derived constraints on the effect of drought stress on biogenic isoprene emissions in the southeastern US

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  • Yuxuan Wang
  • Nan Lin
  • Wei Li
  • Alex Guenther
  • Joey C.Y. Lam
  • Amos P.K. Tai
  • Mark J. Potosnak
  • Seco, Roger

While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, large uncertainties remain with respect to isoprene emissions under stressed conditions. Here, we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the southeastern United States (SE US), a region of high isoprene emissions that is also prone to drought. OMI HCHO column density is found to be 6.7 % (mild drought) to 23.3 % (severe drought) higher than that under non-drought conditions. A global chemical transport model, GEOS-Chem, with version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGAN2.1) emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.1-1.5 times that of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γd_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) to 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γd_OMI the model predicts a nonlinear increasing trend in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better with respect to capturing the regional-scale drought-isoprene responses, as indicated by the near-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosol (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene-suppressing effect on oxidants, with an <1 ppb increase in O3 in high-isoprene regions and a 1-3 ppbv decrease in O3 in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.

Original languageEnglish
JournalAtmospheric Chemistry and Physics
Volume22
Issue number21
Pages (from-to)14189-14208
Number of pages20
ISSN1680-7316
DOIs
Publication statusPublished - 2022
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by the NASA Atmospheric Composition Modeling and Analysis Program (grant no. 80NSSC19K0986). The development of the ecophysiology module in GEOS-Chem has also been supported by the General Research Fund (grant no. 14306220) granted by the Hong Kong Research Grants Council. The authors thank NASA Langley Research Center for the OMI HCHO column data and the National Drought Mitigation Center for making and providing the USDM maps. Roger Seco was supported by grant nos. RYC2020-029216-I and CEX2018-000794-S, funded by MCIN/AEI/10.13039/501100011033, and by the European Social Fund "ESF Investing in your future". Financial support. This research has been supported by the National Aeronautics and Space Administration, Atmospheric Composition Modeling and Analysis Program (grant no. 80NSSC19K0986); the Research Grants Council, University Grants Committee (grant no. 14306220); and the Ministeriode Ciencia e Innovación and the European Social Fund (grant nos. RYC2020-029216-I and CEX2018-000794-S).

Funding Information:
This work was supported by the NASA Atmospheric Composition Modeling and Analysis Program (grant no. 80NSSC19K0986). The development of the ecophysiology module in GEOS-Chem has also been supported by the General Research Fund (grant no. 14306220) granted by the Hong Kong Research Grants Council. The authors thank NASA Langley Research Center for the OMI HCHO column data and the National Drought Mitigation Center for making and providing the USDM maps. Roger Seco was supported by grant nos. RYC2020-029216-I and CEX2018-000794-S, funded by MCIN/AEI/10.13039/501100011033, and by the European Social Fund “ESF Investing in your future”.

Funding Information:
This research has been supported by the National Aeronautics and Space Administration, Atmospheric Composition Modeling and Analysis Program (grant no. 80NSSC19K0986); the Research Grants Council, University Grants Committee (grant no. 14306220); and the Ministeriode Ciencia e Innovación and the European Social Fund (grant nos. RYC2020-029216-I and CEX2018-000794-S).

Publisher Copyright:
Copyright © 2022 Yuxuan Wang et al.

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