Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3633–3661, 2021
https://doi.org/10.5194/gmd-14-3633-2021
Geosci. Model Dev., 14, 3633–3661, 2021
https://doi.org/10.5194/gmd-14-3633-2021

Model description paper 17 Jun 2021

Model description paper | 17 Jun 2021

A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)

Dien Wu et al.

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Cited articles

Chen, J., Zhao, F., Zeng, N. and Oda, T.: Comparing a global high-resolution downscaled fossil fuel ­- CO2 emission dataset to local inventory-based estimates over 14 global cities, Carbon Balance Manag., 15, 1–15, https://doi.org/10.1186/s13021-020-00146-3, 2020. 
Coleman, R. W.: Southern California 60-cm Urban Land Cover Classification, Mendeley Data, V1, https://doi.org/10.17632/zykyrtg36g.1, 2020. 
Coleman, R. W., Stavros, E. N., Yadav, V., and Parazoo, N.: A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity, Remote Sensing, 12, 2399, https://doi.org/10.3390/rs12152399, 2020. 
Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), available at: https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 14 April 2020), https://doi.org/10.24381/cds.bd0915c6, 2017. 
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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 and anthropogenic fluxes over cities, revealing urban–rural flux gradients, diurnal cycles, and the resulting imprints on atmospheric-column CO2.