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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2020-301
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-301
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 07 Oct 2020

Submitted as: model description paper | 07 Oct 2020

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This preprint is currently under review for the journal GMD.

A Model for Urban Biogenic CO2 Fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)

Dien Wu1,a, John C. Lin1, Henrique F. Duarte1,b, Vineet Yadav2, Nicholas C. Parazoo2, Tomohiro Oda3,4,5, and Eric A. Kort6 Dien Wu et al.
  • 1Department of Atmospheric Sciences, University of Utah, Salt Lake City, USA
  • 2NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA
  • 3Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, USA
  • 4Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, USA
  • 5Department of Atmospheric and Oceanic Science, University of Maryland, College Park, USA
  • 6Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, USA
  • anow at: Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, USA
  • bnow at: Earth System Science Center, National Institute for Space Research, São José dos Campos, Brazil

Abstract. When estimating fossil fuel carbon dioxide (FFCO2) emissions from observed CO2 concentrations, the accuracy can be hampered by biogenic carbon exchanges during the growing season even for urban areas where strong fossil fuel emissions are found. While biogenic carbon fluxes have been studied extensively across natural vegetation types, biogenic carbon fluxes within an urban area have been challenging to quantify due to limited observations and differences between urban versus rural regions. Here we developed a simple model representation, i.e., Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes ("SMUrF"), that estimates the gross primary production (GPP) and ecosystem respiration (Reco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance flux data, and additional remote sensing-based products, and developed algorithms to gap fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) are extracted from SMUrF and evaluated against 1) non-gapfilled measurements at 67 eddy-covariance (EC) sites from FLUXNET during 2010–2014 (r > 0.7 for most data-rich biomes), 2) independent observations at two urban vegetation and two crop EC sites over Indianapolis from Aug 2017 to Dec 2018 (r = 0.75), and 3) an urban biospheric model based on fine-grained land cover classification within Los Angeles (r = 0.83). Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 emissions over 40 cities and addressed the urban-rural contrast regarding both the magnitude and timing of CO2 fluxes. By examining a few summertime satellite tracks over four cities, we found that the urban-rural gradient in column CO2 (XCO2) anomalies due to NEE can sometimes reach ~ 0.5 ppm and be close to XCO2 enhancements due to FFCO2 emissions. With rapid advances in space-based measurements and increased sampling of SIF and CO2 measurements over urban areas, SMUrF can be useful for informing the biogenic CO2 fluxes over highly vegetated regions during the growing season.

Dien Wu et al.

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Dien Wu et al.

Model code and software

A Model for Urban Biogenic CO2 Fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF) Dien Wu https://doi.org/10.5281/zenodo.4018123

Dien Wu et al.

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Latest update: 23 Oct 2020
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Short summary
A model (SMUrF) is presented that estimates biogenic CO2 fluxes over cities around the globe, to separate out biogenic fluxes from anthropogenic emissions. The model leverages satellite-based Solar-Induced Fluorescence data and a machine learning technique. We evaluate the biogenic fluxes against flux observations and show contrasts between biogenic vs. anthropogenic fluxes over 40 cities, revealing urban-rural flux gradients, diurnal cycles, and the resulting imprints on atmospheric column CO2.
A model (SMUrF) is presented that estimates biogenic CO2 fluxes over cities around the globe, to...
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