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.

Related authors

The Information Content of Dense Carbon Dioxide Measurements from Space: A High-Resolution Inversion Approach with Synthetic Data from the OCO-3 Instrument
Dustin Roten, John C. Lin, Lewis Kunik, Derek Mallia, Dien Wu, Tomohiro Oda, and Eric A. Kort
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-315,https://doi.org/10.5194/acp-2022-315, 2022
Preprint under review for ACP
Short summary
Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO2 and CO
Dien Wu, Junjie Liu, Paul O. Wennberg, Paul I. Palmer, Robert R. Nelson, Matthäus Kiel, and Annmarie Eldering
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-1029,https://doi.org/10.5194/acp-2021-1029, 2022
Preprint under review for ACP
Short summary
A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”)
Dien Wu, John C. Lin, Benjamin Fasoli, Tomohiro Oda, Xinxin Ye, Thomas Lauvaux, Emily G. Yang, and Eric A. Kort
Geosci. Model Dev., 11, 4843–4871, https://doi.org/10.5194/gmd-11-4843-2018,https://doi.org/10.5194/gmd-11-4843-2018, 2018
Short summary
Constraining fossil fuel CO2 emissions from urban area using OCO-2 observations of total column CO2
Xinxin Ye, Thomas Lauvaux, Eric A. Kort, Tomohiro Oda, Sha Feng, John C. Lin, Emily Yang, and Dien Wu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-1022,https://doi.org/10.5194/acp-2017-1022, 2017
Revised manuscript not accepted
Short summary
How can mountaintop CO2 observations be used to constrain regional carbon fluxes?
John C. Lin, Derek V. Mallia, Dien Wu, and Britton B. Stephens
Atmos. Chem. Phys., 17, 5561–5581, https://doi.org/10.5194/acp-17-5561-2017,https://doi.org/10.5194/acp-17-5561-2017, 2017
Short summary

Related subject area

Atmospheric sciences
Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1)
Jason E.​​​​​​​ Williams, Vincent Huijnen, Idir Bouarar, Mehdi Meziane, Timo Schreurs, Sophie Pelletier, Virginie Marécal, Beatrice Josse, and Johannes Flemming
Geosci. Model Dev., 15, 4657–4687, https://doi.org/10.5194/gmd-15-4657-2022,https://doi.org/10.5194/gmd-15-4657-2022, 2022
Short summary
Order of magnitude wall time improvement of variational methane inversions by physical parallelization: a demonstration using TM5-4DVAR
Sudhanshu Pandey, Sander Houweling, and Arjo Segers
Geosci. Model Dev., 15, 4555–4567, https://doi.org/10.5194/gmd-15-4555-2022,https://doi.org/10.5194/gmd-15-4555-2022, 2022
Short summary
Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the Weather Research and Forecasting (v4.1.3) model during the ICE-POP 2018 field campaign
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev., 15, 4529–4553, https://doi.org/10.5194/gmd-15-4529-2022,https://doi.org/10.5194/gmd-15-4529-2022, 2022
Short summary
A novel method for objective identification of 3-D potential vorticity anomalies
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Roderick van der Linden, Michael Maier-Gerber, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 15, 4447–4468, https://doi.org/10.5194/gmd-15-4447-2022,https://doi.org/10.5194/gmd-15-4447-2022, 2022
Short summary
Multiple same-level and telescoping nesting in GFDL's dynamical core
Joseph Mouallem, Lucas Harris, and Rusty Benson
Geosci. Model Dev., 15, 4355–4371, https://doi.org/10.5194/gmd-15-4355-2022,https://doi.org/10.5194/gmd-15-4355-2022, 2022
Short summary

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. 
Download
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.