Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3633-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3633-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Jun 2021
Model description paper |
| 17 Jun 2021
| 17 Jun 2021
A model for urban biogenic CO2 fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, UT, USA
now at: Division of Geological and Planetary Sciences, California
Institute of Technology, Pasadena, CA, USA
John C. Lin
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, UT, USA
Henrique F. Duarte
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, UT, USA
now at: Earth System Science Center, National Institute for
Space Research, São José dos Campos, Brazil
Vineet Yadav
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Nicholas C. Parazoo
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
Tomohiro Oda
Goddard Earth Sciences Technology and Research, Universities Space
Research Association, Columbia, MD, USA
Global Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, MD, USA
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD, USA
Eric A. Kort
Climate and Space Sciences and Engineering, University of Michigan,
Ann Arbor, MI, USA
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Kai Wu, Paul I. Palmer, Dien Wu, Denis Jouglet, Liang Feng, and Tom Oda
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We evaluate the theoretical ability of the upcoming MicroCarb satellite to estimate urban CO2 emissions over Paris and London. We explore the relative performance of alternative two-sweep and three-sweep city observing modes and take into account the impacts of cloud cover and urban biological CO2 fluxes. Our results find both the two-sweep and three-sweep observing modes are able to reduce prior flux errors by 20 %–40 % depending on the prevailing wind direction and cloud coverage.
Dien Wu, Junjie Liu, Paul O. Wennberg, Paul I. Palmer, Robert R. Nelson, Matthäus Kiel, and Annmarie Eldering
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Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
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Dustin Roten, John C. Lin, Lewis Kunik, Derek Mallia, Dien Wu, Tomohiro Oda, and Eric A. Kort
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Revised manuscript not accepted
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Russell Doughty, Thomas P. Kurosu, Nicholas Parazoo, Philipp Köhler, Yujie Wang, Ying Sun, and Christian Frankenberg
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Jennifer D. Hegarty, Karen E. Cady-Pereira, Vivienne H. Payne, Susan S. Kulawik, John R. Worden, Valentin Kantchev, Helen M. Worden, Kathryn McKain, Jasna V. Pittman, Róisín Commane, Bruce C. Daube Jr., and Eric A. Kort
Atmos. Meas. Tech., 15, 205–223, https://doi.org/10.5194/amt-15-205-2022, https://doi.org/10.5194/amt-15-205-2022, 2022
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Carbon monoxide (CO) is produced by combustion of substances such as fossil fuels and plays an important role in atmospheric pollution and climate. We evaluated estimates of atmospheric CO derived from outgoing radiation measurements of the Atmospheric Infrared Sounder (AIRS) on a satellite orbiting the Earth against CO measurements from aircraft to show that these satellite measurements are reliable for continuous global monitoring of atmospheric CO concentrations.
Eric J. Hintsa, Fred L. Moore, Dale F. Hurst, Geoff S. Dutton, Bradley D. Hall, J. David Nance, Ben R. Miller, Stephen A. Montzka, Laura P. Wolton, Audra McClure-Begley, James W. Elkins, Emrys G. Hall, Allen F. Jordan, Andrew W. Rollins, Troy D. Thornberry, Laurel A. Watts, Chelsea R. Thompson, Jeff Peischl, Ilann Bourgeois, Thomas B. Ryerson, Bruce C. Daube, Yenny Gonzalez Ramos, Roisin Commane, Gregory W. Santoni, Jasna V. Pittman, Steven C. Wofsy, Eric Kort, Glenn S. Diskin, and T. Paul Bui
Atmos. Meas. Tech., 14, 6795–6819, https://doi.org/10.5194/amt-14-6795-2021, https://doi.org/10.5194/amt-14-6795-2021, 2021
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We built UCATS to study atmospheric chemistry and transport. It has measured trace gases including CFCs, N2O, SF6, CH4, CO, and H2 with gas chromatography, as well as ozone and water vapor. UCATS has been part of missions to study the tropical tropopause; transport of air into the stratosphere; greenhouse gases, transport, and chemistry in the troposphere; and ozone chemistry, on both piloted and unmanned aircraft. Its design, capabilities, and some results are shown and described here.
Brad Weir, Lesley E. Ott, George J. Collatz, Stephan R. Kawa, Benjamin Poulter, Abhishek Chatterjee, Tomohiro Oda, and Steven Pawson
Atmos. Chem. Phys., 21, 9609–9628, https://doi.org/10.5194/acp-21-9609-2021, https://doi.org/10.5194/acp-21-9609-2021, 2021
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We present a collection of carbon surface fluxes, the Low-order Flux Inversion (LoFI), derived from satellite observations of the Earth's surface and calibrated to match long-term inventories and atmospheric and oceanic records. Simulations using LoFI reproduce background atmospheric carbon dioxide measurements with comparable skill to the leading surface flux products. Available both retrospectively and as a forecast, LoFI enables the study of the carbon cycle as it occurs.
Amy Hrdina, Jennifer G. Murphy, Anna Gannet Hallar, John C. Lin, Alexander Moravek, Ryan Bares, Ross C. Petersen, Alessandro Franchin, Ann M. Middlebrook, Lexie Goldberger, Ben H. Lee, Munkh Baasandorj, and Steven S. Brown
Atmos. Chem. Phys., 21, 8111–8126, https://doi.org/10.5194/acp-21-8111-2021, https://doi.org/10.5194/acp-21-8111-2021, 2021
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Wintertime air pollution in the Salt Lake Valley is primarily composed of ammonium nitrate, which is formed when gas-phase ammonia and nitric acid react. The major point in this work is that the chemical composition of snow tells a very different story to what we measured in the atmosphere. With the dust–sea salt cations observed in PM2.5 and particle sizing data, we can estimate how much nitric acid may be lost to dust–sea salt that is not accounted for and how much more PM2.5 this could form.
David R. Lyon, Benjamin Hmiel, Ritesh Gautam, Mark Omara, Katherine A. Roberts, Zachary R. Barkley, Kenneth J. Davis, Natasha L. Miles, Vanessa C. Monteiro, Scott J. Richardson, Stephen Conley, Mackenzie L. Smith, Daniel J. Jacob, Lu Shen, Daniel J. Varon, Aijun Deng, Xander Rudelis, Nikhil Sharma, Kyle T. Story, Adam R. Brandt, Mary Kang, Eric A. Kort, Anthony J. Marchese, and Steven P. Hamburg
Atmos. Chem. Phys., 21, 6605–6626, https://doi.org/10.5194/acp-21-6605-2021, https://doi.org/10.5194/acp-21-6605-2021, 2021
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The Permian Basin (USA) is the world’s largest oil field. We use tower- and aircraft-based approaches to measure how methane emissions in the Permian Basin changed throughout 2020. In early 2020, 3.3 % of the region’s gas was emitted; then in spring 2020, the loss rate temporarily dropped to 1.9 % as oil price crashed. We find this short-term reduction to be a result of reduced well development, less gas flaring, and fewer abnormal events despite minimal reductions in oil and gas production.
Caroline A. Famiglietti, T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, Stephanie G. Stettz, Yan Yang, Damien Bonal, A. Anthony Bloom, Mathew Williams, and Alexandra G. Konings
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Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
Christoph A. Keller, Mathew J. Evans, K. Emma Knowland, Christa A. Hasenkopf, Sruti Modekurty, Robert A. Lucchesi, Tomohiro Oda, Bruno B. Franca, Felipe C. Mandarino, M. Valeria Díaz Suárez, Robert G. Ryan, Luke H. Fakes, and Steven Pawson
Atmos. Chem. Phys., 21, 3555–3592, https://doi.org/10.5194/acp-21-3555-2021, https://doi.org/10.5194/acp-21-3555-2021, 2021
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This study combines surface observations and model simulations to quantify the impact of COVID-19 restrictions on air quality across the world. The presented methodology removes the confounding impacts of meteorology on air pollution. Our results indicate that surface concentrations of nitrogen dioxide, an important air pollutant emitted during the combustion of fossil fuels, declined by up to 60 % following the implementation of COVID-19 containment measures.
Junjie Liu, Latha Baskaran, Kevin Bowman, David Schimel, A. Anthony Bloom, Nicholas C. Parazoo, Tomohiro Oda, Dustin Carroll, Dimitris Menemenlis, Joanna Joiner, Roisin Commane, Bruce Daube, Lucianna V. Gatti, Kathryn McKain, John Miller, Britton B. Stephens, Colm Sweeney, and Steven Wofsy
Earth Syst. Sci. Data, 13, 299–330, https://doi.org/10.5194/essd-13-299-2021, https://doi.org/10.5194/essd-13-299-2021, 2021
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On average, the terrestrial biosphere carbon sink is equivalent to ~ 20 % of fossil fuel emissions. Understanding where and why the terrestrial biosphere absorbs carbon from the atmosphere is pivotal to any mitigation policy. Here we present a regionally resolved satellite-constrained net biosphere exchange (NBE) dataset with corresponding uncertainties between 2010–2018: CMS-Flux NBE 2020. The dataset provides a unique perspective on monitoring regional contributions to the CO2 growth rate.
Shamil Maksyutov, Tomohiro Oda, Makoto Saito, Rajesh Janardanan, Dmitry Belikov, Johannes W. Kaiser, Ruslan Zhuravlev, Alexander Ganshin, Vinu K. Valsala, Arlyn Andrews, Lukasz Chmura, Edward Dlugokencky, László Haszpra, Ray L. Langenfelds, Toshinobu Machida, Takakiyo Nakazawa, Michel Ramonet, Colm Sweeney, and Douglas Worthy
Atmos. Chem. Phys., 21, 1245–1266, https://doi.org/10.5194/acp-21-1245-2021, https://doi.org/10.5194/acp-21-1245-2021, 2021
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In order to improve the top-down estimation of the anthropogenic greenhouse gas emissions, a high-resolution inverse modelling technique was developed for applications to global transport modelling of carbon dioxide and other greenhouse gases. A coupled Eulerian–Lagrangian transport model and its adjoint are combined with surface fluxes at 0.1° resolution to provide high-resolution forward simulation and inverse modelling of surface fluxes accounting for signals from emission hot spots.
Xueying Yu, Dylan B. Millet, Kelley C. Wells, Daven K. Henze, Hansen Cao, Timothy J. Griffis, Eric A. Kort, Genevieve Plant, Malte J. Deventer, Randall K. Kolka, D. Tyler Roman, Kenneth J. Davis, Ankur R. Desai, Bianca C. Baier, Kathryn McKain, Alan C. Czarnetzki, and A. Anthony Bloom
Atmos. Chem. Phys., 21, 951–971, https://doi.org/10.5194/acp-21-951-2021, https://doi.org/10.5194/acp-21-951-2021, 2021
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Methane concentrations have doubled since 1750. The US Upper Midwest is a key region contributing to such trends, but sources are poorly understood. We collected and analyzed aircraft data to resolve spatial and timing biases in wetland and livestock emission estimates and uncover errors in inventory treatment of manure management. We highlight the importance of intensive agriculture for the regional and US methane budgets and the potential for methane mitigation through improved management.
Yuming Jin, Ralph F. Keeling, Eric J. Morgan, Eric Ray, Nicholas C. Parazoo, and Britton B. Stephens
Atmos. Chem. Phys., 21, 217–238, https://doi.org/10.5194/acp-21-217-2021, https://doi.org/10.5194/acp-21-217-2021, 2021
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We propose a new atmospheric coordinate (Mθe) based on equivalent potential temperature (θe) but with mass as the unit. This coordinate is useful in studying the spatial and temporal distribution of long-lived chemical tracers (CO2, CH4, O2 / N2, etc.) from sparse data, like airborne observation. Using this coordinate and sparse airborne observation (HIPPO and ATom), we resolve the Northern Hemisphere mass-weighted average CO2 seasonal cycle with high accuracy.
A. Anthony Bloom, Kevin W. Bowman, Junjie Liu, Alexandra G. Konings, John R. Worden, Nicholas C. Parazoo, Victoria Meyer, John T. Reager, Helen M. Worden, Zhe Jiang, Gregory R. Quetin, T. Luke Smallman, Jean-François Exbrayat, Yi Yin, Sassan S. Saatchi, Mathew Williams, and David S. Schimel
Biogeosciences, 17, 6393–6422, https://doi.org/10.5194/bg-17-6393-2020, https://doi.org/10.5194/bg-17-6393-2020, 2020
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We use a model of the 2001–2015 tropical land carbon cycle, with satellite measurements of land and atmospheric carbon, to disentangle lagged and concurrent effects (due to past and concurrent meteorological events, respectively) on annual land–atmosphere carbon exchanges. The variability of lagged effects explains most 2001–2015 inter-annual carbon flux variations. We conclude that concurrent and lagged effects need to be accurately resolved to better predict the world's land carbon sink.
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.
Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., Ren, C.,
Brorousse, O., Martilli, A., Neophytou, M., Mouzourides, P., Stewart, I.,
Hanna, A., Ng, E., Foley, M., Alexander, P., Aliaga, D., Niyogi, D.,
Shreevastava, A., Bhalachandran, P., Masson, V., Hidalgo, J., Fung, J.,
Andrade, M., Baklanov, A., Dai, W., Milcinski, G., Demuzere, M., Brunsell,
N., Pesaresi, M., Miao, S., Mu, Q., Chen, F., and Theeuwesits, N.: WUDAPT: An
urban weather, climate, and environmental modeling infrastructure for the
anthropocene, B. Am. Meteorol. Soc., 99, 1907–1924,
https://doi.org/10.1175/BAMS-D-16-0236.1, 2018.
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.
Crisp, D., Fisher, B. M., O'Dell, C., Frankenberg, C., Basilio, R., Bösch, H., Brown, L. R., Castano, R., Connor, B., Deutscher, N. M., Eldering, A., Griffith, D., Gunson, M., Kuze, A., Mandrake, L., McDuffie, J., Messerschmidt, J., Miller, C. E., Morino, I., Natraj, V., Notholt, J., O'Brien, D. M., Oyafuso, F., Polonsky, I., Robinson, J., Salawitch, R., Sherlock, V., Smyth, M., Suto, H., Taylor, T. E., Thompson, D. R., Wennberg, P. O., Wunch, D., and Yung, Y. L.: The ACOS CO2 retrieval algorithm – Part II: Global XCO2 data characterization, Atmos. Meas. Tech., 5, 687–707, https://doi.org/10.5194/amt-5-687-2012, 2012.
Davis, K. J., Deng, A., Lauvaux, T., Miles, N. L., Richardson, S. J., Sarmiento, D. P., Gurney, K. R., Hardesty, R. M., Bonin, T. A., Brewer, W. A., Lamb, B. K., Shepson, P. B., Harvey, R. M., Cambaliza, M. O., Sweeney, C., Turnbull, J. C., Whetstone, J., and Karion, A.: The Indianapolis Flux Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission measurements, Elementa, 5, 21, https://doi.org/10.1525/elementa.188, 2017.
Decina, S. M., Hutyra, L. R., Gately, C. K., Getson, J. M., Reinmann, A. B.,
Short Gianotti, A. G., and Templer, P. H.: Soil respiration contributes
substantially to urban carbon fluxes in the greater Boston area, Environ.
Pollut., 212, 433–439, https://doi.org/10.1016/j.envpol.2016.01.012, 2016.
Dietze, M. C., Vargas, R., Richardson, A. D., Stoy, P. C., Barr, A. G.,
Anderson, R. S., Arain, M. A., Baker, I. T., Black, T. A., Chen, J. M.,
Ciais, P., Flanagan, L. B., Gough, C. M., Grant, R. F., Hollinger, D.,
Izaurralde, R. C., Kucharik, C. J., Lafleur, P., Liu, S., Lokupitiya, E.,
Luo, Y., Munger, J. W., Peng, C., Poulter, B., Price, D. T., Ricciuto, D.
M., Riley, W. J., Sahoo, A. K., Schaefer, K., Suyker, A. E., Tian, H.,
Tonitto, C., Verbeeck, H., Verma, S. B., Wang, W., and Weng, E.:
Characterizing the performance of ecosystem models across time scales: A
spectral analysis of the North American Carbon Program site-level synthesis,
J. Geophys. Res.-Biogeo., 116, G04029, https://doi.org/10.1029/2011JG001661, 2011.
DiMiceli, C., Carroll, M., Sohlberg, R., Kim, D., Kelly, M., and Townshend, J.: MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD44B.006, 2020.
Duncanson, L., Neuenschwander, A., Hancock, S., Thomas, N., Fatoyinbo, T.,
Simard, M., Silva, C. A., Armston, J., Luthcke, S. B., Hofton, M., Kellner,
J. R., and Dubayah, R.: Biomass estimation from simulated GEDI, ICESat-2 and
NISAR across environmental gradients in Sonoma County, California, Remote
Sens. Environ., 242, 111779, https://doi.org/10.1016/j.rse.2020.111779, 2020.
Duveiller, G. and Cescatti, A.: Spatially downscaling sun-induced
chlorophyll fluorescence leads to an improved temporal correlation with
gross primary productivity, Remote Sens. Environ., 182, 72–89,
https://doi.org/10.1016/j.rse.2016.04.027, 2016.
Duveiller, G., Filipponi, F., Walther, S., Köhler, P., Frankenberg, C., Guanter, L., and Cescatti, A.: A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity, Earth Syst. Sci. Data, 12, 1101–1116, https://doi.org/10.5194/essd-12-1101-2020, 2020.
Eldering, A., Taylor, T. E., O'Dell, C. W., and Pavlick, R.: The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data, Atmos. Meas. Tech., 12, 2341–2370, https://doi.org/10.5194/amt-12-2341-2019, 2019.
Ellis, E. C. and Ramankutty, N.: Putting people in the map: Anthropogenic
biomes of the world, Front. Ecol. Environ., 6, 439–447,
https://doi.org/10.1890/070062, 2008.
Falbel, D., Allaire, J. J., and Chollet, F.: keras: R Interface to “Keras”
version 2.2.5.0, available at: https://keras.rstudio.com/index.html (last access: 14 April 2020), 2019.
Fasoli, B., Lin, J. C., Bowling, D. R., Mitchell, L., and Mendoza, D.: Simulating atmospheric tracer concentrations for spatially distributed receptors: updates to the Stochastic Time-Inverted Lagrangian Transport model's R interface (STILT-R version 2), Geosci. Model Dev., 11, 2813–2824, https://doi.org/10.5194/gmd-11-2813-2018, 2018.
Fisher, J. B., Sikka, M., Huntzinger, D. N., Schwalm, C., and Liu, J.: Technical note: 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange, Biogeosciences, 13, 4271–4277, https://doi.org/10.5194/bg-13-4271-2016, 2016.
Fisher, J. B., Lee, B., Purdy, A. J., Halverson, G. H., Dohlen, M. B.,
Cawse-Nicholson, K., Wang, A., Anderson, R. G., Aragon, B., Arain, M. A.,
Baldocchi, D. D., Baker, J. M., Barral, H., Bernacchi, C. J., Bernhofer, C.,
Biraud, S. C., Bohrer, G., Brunsell, N., Cappelaere, B., Castro-Contreras,
S., Chun, J., Conrad, B. J., Cremonese, E., Demarty, J., Desai, A. R., De
Ligne, A., Foltýnová, L., Goulden, M. L., Griffis, T. J.,
Grünwald, T., Johnson, M. S., Kang, M., Kelbe, D., Kowalska, N., Lim, J.
H., Maïnassara, I., McCabe, M. F., Missik, J. E. C., Mohanty, B. P.,
Moore, C. E., Morillas, L., Morrison, R., Munger, J. W., Posse, G.,
Richardson, A. D., Russell, E. S., Ryu, Y., Sanchez-Azofeifa, A., Schmidt,
M., Schwartz, E., Sharp, I., Šigut, L., Tang, Y., Hulley, G., Anderson,
M., Hain, C., French, A., Wood, E., and Hook, S.: ECOSTRESS: NASA's Next
Generation Mission to Measure Evapotranspiration From the International
Space Station, Water Resour. Res., 56, e2019WR026058, https://doi.org/10.1029/2019WR026058,
2020.
Frankenberg, C., Butz, A., and Toon, G. C.: Disentangling chlorophyll
fluorescence from atmospheric scattering effects in O2 A-band spectra
of reflected sun-light, Geophys. Res. Lett., 38, L03801,
https://doi.org/10.1029/2010GL045896, 2011.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover
Type Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land
Processes DAAC, https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Fritsch, S., Guenther, F., and Guenther, M. F: Package “neuralnet” version
1.44.2. The Comprehensive R Archive Network, available at: https://github.com/bips-hb/neuralnet (last access: 14 April 2020), 2016.
George, K., Ziska, L. H., Bunce, J. A., and Quebedeaux, B.: Elevated
atmospheric CO2 concentration and temperature across an urban-rural
transect, Atmos. Environ., 41, 7654–7665,
https://doi.org/10.1016/j.atmosenv.2007.08.018, 2007.
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A.,
Frankenberg, C., Huete, A. R., Zarco-Tejada, P., Lee, J.-E., Moran, M. S.,
Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D.,
Klumpp, K., Cescatti, A., Baker, J. M., and Griffis, T. J.: Global and
time-resolved monitoring of crop photosynthesis with chlorophyll
fluorescence, P. Natl. Acad. Sci. USA, 111, E1327–E1333,
https://doi.org/10.1073/pnas.1320008111, 2014.
Gurney, K. R., Mendoza, D. L., Zhou, Y., Fischer, M. L., Miller, C. C., Geethakumar, S., and de la Rue du Can, S.: High Resolution Fossil Fuel Combustion CO2 Emission Fluxes for the United States, Environ. Sci. Technol., 43, 5535–5541, https://doi.org/10.1021/es900806c, 2009.
Hao, D., Asrar, G. R., Zeng, Y., Zhu, Q., Wen, J., Xiao, Q., and Chen, M.: DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at
Hao, D., Chen, M., Asrar, G. R., Zeng, Y., Zhu, Q., Wen, J., and Xiao, Q: A
global DSCOVR/EPIC-based hourly/daily shortwave radiation/PAR dataset,
DataHub for Pacific Northwest National Laboratory,
https://doi.org/10.25584/1595069, 2020b.
Hardiman, B. S., Wang, J. A., Hutyra, L. R., Gately, C. K., Getson, J. M.
and Friedl, M. A.: Accounting for urban biogenic fluxes in regional carbon
budgets, Sci. Total Environ., 592, 366–372, 2017.
He, L., Magney, T., Dutta, D., Yin, Y., Köhler, P., Grossmann, K.,
Stutz, J., Dold, C., Hatfield, J., Guan, K., Peng, B., and Frankenberg, C.:
From the Ground to Space: Using Solar-Induced Chlorophyll Fluorescence to
Estimate Crop Productivity, Geophys. Res. Lett., 47, e2020GL087474,
https://doi.org/10.1029/2020GL087474, 2020.
Helm, L. T., Shi, H., Lerdau, M. T., and Yang, X.: Solar-induced chlorophyll
fluorescence and short-term photosynthetic response to drought, Ecol. Appl.,
30, e02101, https://doi.org/10.1002/eap.2101, 2020.
Hilton, T. W., Davis, K. J., and Keller, K.: Evaluating terrestrial CO2 flux diagnoses and uncertainties from a simple land surface model and its residuals, Biogeosciences, 11, 217–235, https://doi.org/10.5194/bg-11-217-2014, 2014.
Huntzinger, D. N., Schwalm, C., Michalak, A. M., Schaefer, K., King, A. W., Wei, Y., Jacobson, A., Liu, S., Cook, R. B., Post, W. M., Berthier, G., Hayes, D., Huang, M., Ito, A., Lei, H., Lu, C., Mao, J., Peng, C. H., Peng, S., Poulter, B., Riccuito, D., Shi, X., Tian, H., Wang, W., Zeng, N., Zhao, F., and Zhu, Q.: The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design, Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, 2013.
Hutyra, L. R., Duren, R., Gurney, K. R., Grimm, N., Kort, E. A., Larson, E.,
and Shrestha, G.: Urbanization and the carbon cycle: Current capabilities
and research outlook from the natural sciences perspective, Earth's Future,
2, 473–495, https://doi.org/10.1002/2014EF000255, 2014.
Johnson, T. D. and Belitz, K.: A remote sensing approach for estimating the
location and rate of urban irrigation in semi-arid climates, J. Hydrol.,
414–415, 86–98, https://doi.org/10.1016/j.jhydrol.2011.10.016, 2012.
Joiner, J., Guanter, L., Lindstrot, R., Voigt, M., Vasilkov, A. P., Middleton, E. M., Huemmrich, K. F., Yoshida, Y., and Frankenberg, C.: Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2, Atmos. Meas. Tech., 6, 2803–2823, https://doi.org/10.5194/amt-6-2803-2013, 2013.
Kettle, A. J., Kuhn, U., Von Hobe, M., Kesselmeier, J., and Andreae, M. O.:
Global budget of atmospheric carbonyl sulfide: Temporal and spatial
variations of the dominant sources and sinks, J. Geophys. Res.-Atmos.,
107, 4658, https://doi.org/10.1029/2002JD002187, 2002.
Knorr, W. and Heimann, M.: Uncertainties in global terrestrial biosphere
modelling, Part II: Global constraints for a process-based vegetation model,
Global Biogeochem. Cy., 15, 227–246, https://doi.org/10.1029/1998GB001060, 2001.
Köhler, P., Frankenberg, C., Magney, T. S., Guanter, L., Joiner, J., and
Landgraf, J.: Global retrievals of solar-induced chlorophyll fluorescence
with TROPOMI: First results and intersensor comparison to OCO-2, Geophys.
Res. Lett., 45, 10456–10463, https://doi.org/10.1029/2018GL079031, 2018.
Li, X. and Xiao, J.: A Global, 0.05-Degree Product of Solar-Induced
Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data,
Remote Sens., 11, 517, https://doi.org/10.3390/rs11050517, 2019a.
Li, X. and Xiao, J.: Mapping photosynthesis solely from solar-induced
chlorophyll fluorescence: A global, fine-resolution dataset of gross primary
production derived from OCO-2, Remote Sens., 11, 2563,
https://doi.org/10.3390/rs11212563,
2019b.
LI-COR Biosciences: EddyPro® (Version 4.1) [Computer software], Lincoln, NE, LI-COR, Inc, Infrastructure for Measurements of the European Carbon Cycle consortium, 2012.
Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis,
K. J., and Grainger, C. A.: A near-field tool for simulating the upstream
influence of atmospheric observations: The Stochastic Time-Inverted
Lagrangian Transport (STILT) model, J. Geophys. Res.-Atmos., 108, 4493, https://doi.org/10.1029/2002JD003161, 2003.
Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C.,
Grainger, C. A., Stephens, B. B., Bakwin, P. S., and Hollinger, D. Y.:
Measuring fluxes of trace gases at regional scales by Lagrangian
observations: Application to the CO2 Budget and Rectification Airborne
(COBRA) study, J. Geophys. Res.-Atmos., 109, 1–23,
https://doi.org/10.1029/2004JD004754, 2004.
Lin, J. C., Pejam, M. R., Chan, E., Wofsy, S. C., Gottlieb, E. W., Margolis,
H. A., and McCaughey, J. H.: Attributing uncertainties in simulated
biospheric carbon fluxes to different error sources, Global Biogeochem.
Cy., 25, GB2018, https://doi.org/10.1029/2010GB003884, 2011.
Luus, K. A., Commane, R., Parazoo, N. C., Benmergui, J., Euskirchen, E. S.,
Frankenberg, C., Joiner, J., Lindaas, J., Miller, C. E., Oechel, W. C.,
Zona, D., Wofsy, S., and Lin, J. C.: Tundra photosynthesis captured by
satellite-observed solar-induced chlorophyll fluorescence, Geophys. Res.
Lett., 44, 1564–1573, https://doi.org/10.1002/2016GL070842, 2017.
MacBean, N., Maignan, F., Bacour, C., Lewis, P., Peylin, P., Guanter, L.,
Köhler, P., Gómez-Dans, J., and Disney, M.: Strong constraint on
modelled global carbon uptake using solar-induced chlorophyll fluorescence
data, Sci. Rep., 8, 1–12, https://doi.org/10.1038/s41598-018-28697-z, 2018.
Magney, T. S., Frankenberg, C., Fisher, J. B., Sun, Y., North, G. B., Davis,
T. S., Kornfeld, A., and Siebke, K.: Connecting active to passive
fluorescence with photosynthesis: a method for evaluating remote sensing
measurements of Chl fluorescence, New Phytol., 215, 1594–1608,
https://doi.org/10.1111/nph.14662, 2017.
Magney, T. S., Bowling, D. R., Logan, B. A., Grossmann, K., Stutz, J.,
Blanken, P. D., Burns, S. P., Cheng, R., Garcia, M. A., Kohler, P., and
others: Mechanistic evidence for tracking the seasonality of photosynthesis
with solar-induced fluorescence, P. Natl. Acad. Sci. USA, 116,
11640–11645, 2019.
Magney, T. S., Barnes, M. L., and Yang, X.: On the co-variation of
chlorophyll fluorescence and photosynthesis across scales, Geophys. Res. Lett., 47, e2020GL091098,
https://doi.org/10.1029/2020GL091098, 2020.
Maguire, A. J., Eitel, J. U. H., Griffin, K. L., Magney, T. S., Long, R. A.,
Vierling, L. A., Schmiege, S. C., Jennewein, J. S., Weygint, W. A., Boelman,
N. T., and Bruner, S. G.: On the Functional Relationship Between Fluorescence
and Photochemical Yields in Complex Evergreen Needleleaf Canopies, Geophys.
Res. Lett., 47, e2020GL087858, https://doi.org/10.1029/2020GL087858, 2020.
Mahadevan, P., Wofsy, S. C., Matross, D. M., Xiao, X., Dunn, A. L., Lin, J.
C., Gerbig, C., Munger, J. W., Chow, V. Y., and Gottlieb, E. W.: A
satellite-based biosphere parameterization for net ecosystem CO2
exchange: Vegetation Photosynthesis and Respiration Model (VPRM), Global
Biogeochem. Cy., 22, GB2005, https://doi.org/10.1029/2006GB002735, 2008.
Marrs, J. K., Reblin, J. S., Logan, B. A., Allen, D. W., Reinmann, A. B.,
Bombard, D. M., Tabachnik, D., and Hutyra, L. R.: Solar-Induced Fluorescence
Does Not Track Photosynthetic Carbon Assimilation Following Induced Stomatal
Closure, Geophys. Res. Lett., 47, e2020GL087956, https://doi.org/10.1029/2020GL087956, 2020.
McRae, J. E. and Graedel, T. E.: Carbon dioxide in the urban atmosphere: dependencies and trends, J. Geophys. Res., 84, 5011–5017, https://doi.org/10.1029/JC084iC08p05011, 1979.
Meng, L., Mao, J., Zhou, Y., Richardson, A. D., Lee, X., Thornton, P. E.,
Ricciuto, D. M., Li, X., Dai, Y., Shi, X., and Jia, G.: Urban warming
advances spring phenology but reduces the response of phenology to
temperature in the conterminous United States, P. Natl. Acad. Sci. USA, 117, 4228–4233, https://doi.org/10.1073/pnas.1911117117, 2020.
Miao, G., Guan, K., Yang, X., Bernacchi, C. J., Berry, J. A., DeLucia, E.
H., Wu, J., Moore, C. E., Meacham, K., Cai, Y., Peng, B., Kimm, H., and
Masters, M. D.: Sun-Induced Chlorophyll Fluorescence, Photosynthesis, and
Light Use Efficiency of a Soybean Field from Seasonally Continuous
Measurements, J. Geophys. Res.-Biogeo., 123, 610–623,
https://doi.org/10.1002/2017JG004180, 2018.
Miller, J. B., Lehman, S. J., Verhulst, K. R., Miller, C. E., Duren, R. M.,
Yadav, V., Newman, S., and Sloop, C. D.: Large and seasonally varying
biospheric CO2 fluxes in the Los Angeles megacity revealed by
atmospheric radiocarbon, P. Natl. Acad. Sci. USA, 117,
26681–26687, https://doi.org/10.1073/pnas.2005253117, 2020.
Nassar, R., Napier-Linton, L., Gurney, K. R., Andres, R. J., Oda, T., Vogel,
F. R., and Deng, F.: Improving the temporal and spatial distribution of
CO2 emissions from global fossil fuel emission data sets, J. Geophys.
Res.-Atmos., 118, 917–933, https://doi.org/10.1029/2012JD018196, 2013.
Oda, T. and Maksyutov, S.: ODIAC Fossil Fuel CO2 Emissions Dataset
(Version name: ODIAC2019), Center for Global Environmental Research,
National Institute for Environmental Studies,
https://doi.org/10.17595/20170411.001, 2015.
Oda, T., Maksyutov, S., and Andres, R. J.: The Open-source Data Inventory for Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil fuel CO2 gridded emissions data product for tracer transport simulations and surface flux inversions, Earth Syst. Sci. Data, 10, 87–107, https://doi.org/10.5194/essd-10-87-2018, 2018.
Oda, T., Bun, R., Kinakh, V., Topylko, P., Halushchak, M., Marland, G.,
Lauvaux, T., Jonas, M., Maksyutov, S., Nahorski, Z., Lesiv, M., Danylo, O.,
and Horabik-Pyzel, J.: Errors and uncertainties in a gridded carbon dioxide
emissions inventory, Mitig. Adapt. Strateg. Glob. Chang., 24, 1007–1050,
https://doi.org/10.1007/s11027-019-09877-2, 2019.
Olsen, S. C. and Randerson, J. T.: Differences between surface and column
atmospheric CO2 and implications for carbon cycle research, J. Geophys.
Res.-Atmos., 109, D02301, https://doi.org/10.1029/2003JD003968, 2004.
Pastorello, G., Papale, D., Chu, H., Trotta, C., Agarwal, D., Canfora, E.,
Baldocchi, D., and Torn, M.: A New Data Set to Keep a Sharper Eye on Land-Air
Exchanges, Eos, Washington DC, August, https://doi.org/10.1029/2017eo071597, 2017.
Pataki, D. E., Alig, R. J., Fung, A. S., Golubiewski, N. E., Kennedy, C. A.,
Mcpherson, E. G., Nowak, D. J., Pouyat, R. V., and Lankao, P. R.: Urban
ecosystems and the North American carbon cycle, Glob. Change Biol., 12,
2092–2102, https://doi.org/10.1111/j.1365-2486.2006.01242.x, 2006.
Philip, S., Johnson, M. S., Potter, C., Genovesse, V., Baker, D. F., Haynes, K. D., Henze, D. K., Liu, J., and Poulter, B.: Prior biosphere model impact on global terrestrial CO2 fluxes estimated from OCO-2 retrievals, Atmos. Chem. Phys., 19, 13267–13287, https://doi.org/10.5194/acp-19-13267-2019, 2019.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding
for data-driven Earth system science, Nature, 566, 7743, https://doi.org/10.1038/s41586-019-0912-1,
2019.
Rolph, G., Stein, A., and Stunder, B.: Real-time environmental applications
and display system: READY, Environ. Model. Softw., 95, 210–228, 2017.
Rosenzweig, C., Solecki, W., Romero-Lankao, P., Mehrotra, S., Dhakal, S.,
and Ali Ibrahim, S. Climate Change and Cities: Second Assessment Report of
the Urban Climate Change Research Network, Cambridge University Press, available at: https://uccrn.ei.columbia.edu/arc3.2 (last access: 1 September 2020), 2018.
Santoro, M., Cartus, O., Mermoz, S., Bouvet, A., Le Toan, T., Carvalhais, N., Rozendaal, D., Herold, M., Avitabile, V., Quegan, S., Carreiras, J., Rauste, Y., Balzter, H., Schmullius, C., and Seifert, F. M.: GlobBiomass global above-ground biomass and growing stock volume datasets, available at: http://globbiomass.org/products/global-mapping (last access: 1 September 2020), 2018.
Sargent, M., Barrera, Y., Nehrkorn, T., Hutyra, L. R., Gately, C. K., Jones,
T., McKain, K., Sweeney, C., Hegarty, J., Hardiman, B., Wang, J. A., and Wofsy, S. C.:
Anthropogenic and biogenic CO2 fluxes in the Boston urban region, P.
Natl. Acad. Sci. USA, 115, 7491–7496, https://doi.org/10.1073/pnas.1803715115, 2018.
Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D., and DiMarzio, J.
P.: Overview of the ICESat mission, Geophys. Res. Lett., 32, 1–4,
https://doi.org/10.1029/2005GL024009, 2005.
Smith, I. A., Dearborn, V. K., and Hutyra, L. R.: Live fast, die young:
Accelerated growth, mortality, and turnover in street trees, PLoS One,
14,
e0215846, https://doi.org/10.1371/journal.pone.0215846, 2019.
Smith, W. K., Biederman, J. A., Scott, R. L., Moore, D. J. P., He, M.,
Kimball, J. S., Yan, D., Hudson, A., Barnes, M. L., MacBean, N., Fox, A. M.,
and Litvak, M. E.: Chlorophyll Fluorescence Better Captures Seasonal and
Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of
Southwestern North America, Geophys. Res. Lett., 45, 748–757,
https://doi.org/10.1002/2017GL075922, 2018.
Stavros, E. N., Schimel, D., Pavlick, R., Serbin, S., Swann, A., Duncanson,
L., Fisher, J. B., Fassnacht, F., Ustin, S., Dubayah, R., Schweiger, A., and
Wennberg, P.: ISS observations offer insights into plant function, Nat.
Ecol. Evol., 1, 0194, https://doi.org/10.1038/s41559-017-0194, 2017.
Stewart, I. D. and Oke, T. R.: Local climate zones for urban temperature
studies, B. Am. Meteorol. Soc., 93, 1879–1900,
https://doi.org/10.1175/BAMS-D-11-00019.1, 2012.
Sun, Y., Frankenberg, C., Wood, J. D., Schimel, D. S., Jung, M., Guanter,
L., Drewry, D. T., Verma, M., Porcar-Castell, A., Griffis, T. J., Gu, L.,
Magney, T. S., Kohler, P., Evans, B., and Yuen, K.: OCO-2 advances
photosynthesis observation from space via solar- induced chlorophyll
fluorescence, Science, 358, aam5747,
https://doi.org/10.1126/science.aam5747, 2017.
Thornton, P. E., Thornton, M. M., Mayer, B. W., Wei, Y., Devarakonda, R., Vose, R. S., and Cook, R. B.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1328, 2016.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016.
Turnbull, J. C., Sweeney, C., Karion, A., Newberger, T., Lehman, S. J.,
Tans, P. P., Davis, K. J., Lauvaux, T., Miles, N. L., Richardson, S. J.,
Cambaliza, M. O., Shepson, P. B., Gurney, K., Patarasuk, R., and Razlivanov,
I.: Toward quantification and source sector identification of fossil fuel
CO2 emissions from an urban area: Results from the INFLUX experiment,
J. Geophys. Res., 120, 292–312, https://doi.org/10.1002/2014JD022555, 2015.
Turner, A. J., Köhler, P., Magney, T. S., Frankenberg, C., Fung, I., and Cohen, R. C.: A double peak in the seasonality of California's photosynthesis as observed from space, Biogeosciences, 17, 405–422, https://doi.org/10.5194/bg-17-405-2020, 2020.
Turner, A. J., Köhler, P., Magney, T. S., Frankenberg, C., Fung, I., and Cohen, R. C.: Extreme events driving year-to-year differences in gross primary productivity across the US, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2021-49, in review, 2021.
Vahmani, P. and Hogue, T. S.: Incorporating an Urban Irrigation Module into
the Noah Land Surface Model Coupled with an Urban Canopy Model, J.
Hydrometeorol., 15, 1440–1456, https://doi.org/10.1175/jhm-d-13-0121.1, 2014.
van der Tol, C., Verhoef, W., Timmermans, J., Verhoef, A., and Su, Z.: An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance, Biogeosciences, 6, 3109–3129, https://doi.org/10.5194/bg-6-3109-2009, 2009.
Vasenev, V. and Kuzyakov, Y.: Urban soils as hot spots of anthropogenic
carbon accumulation: Review of stocks, mechanisms and driving factors, L.
Degrad. Dev., 29, 1607–1622, 2018.
Verma, M., Schimel, D., Evans, B., Frankenberg, C., Beringer, J., Drewry, D.
T., Magney, T., Marang, I., Hutley, L., Moore, C., and Eldering, A.: Effect
of environmental conditions on the relationship between solar-induced
fluorescence and gross primary productivity at an OzFlux grassland site, J.
Geophys. Res.-Biogeo., 122, 716–733, https://doi.org/10.1002/2016JG003580,
2017.
Wang, S., Ju, W., Peñuelas, J., Cescatti, A., Zhou, Y., Fu, Y., Huete,
A., Liu, M., and Zhang, Y.: Urban−rural gradients reveal joint control of
elevated CO2 and temperature on extended photosynthetic seasons, Nat.
Ecol. Evol., 3, 1076–1085, https://doi.org/10.1038/s41559-019-0931-1, 2019.
Wen, J., Köhler, P., Duveiller, G., Parazoo, N. C., Magney, T. S.,
Hooker, G., Yu, L., Chang, C. Y., and Sun, Y.: A framework for harmonizing
multiple satellite instruments to generate a long-term global high
spatial-resolution solar-induced chlorophyll fluorescence (SIF), Remote
Sens. Environ., 239, 111644,
https://doi.org/10.1016/j.rse.2020.111644, 2020.
White, E. D., Rigby, M., Lunt, M. F., Smallman, T. L., Comyn-Platt, E., Manning, A. J., Ganesan, A. L., O'Doherty, S., Stavert, A. R., Stanley, K., Williams, M., Levy, P., Ramonet, M., Forster, G. L., Manning, A. C., and Palmer, P. I.: Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network, Atmos. Chem. Phys., 19, 4345–4365, https://doi.org/10.5194/acp-19-4345-2019, 2019.
Wohlfahrt, G., Gerdel, K., Migliavacca, M., Rotenberg, E., Tatarinov, F.,
Müller, J., Hammerle, A., Julitta, T., Spielmann, F. M., and Yakir, D.:
Sun-induced fluorescence and gross primary productivity during a heat wave,
Sci. Rep., 8, 1–9, https://doi.org/10.1038/s41598-018-32602-z, 2018.
Wu, D.: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes
(SMUrFv1), Zenodo, https://doi.org/10.5281/zenodo.4018123, 2020.
Wu, D., Lin, J. C., Fasoli, B., Oda, T., Ye, X., Lauvaux, T., Yang, E. G., and Kort, E. A.: 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”), Geosci. Model Dev., 11, 4843–4871, https://doi.org/10.5194/gmd-11-4843-2018, 2018.
Wu, K.: Joint Estimation of Fossil Fuel and Biogenic CO2 Fluxes in an
Urban Environment, unpublished Doctor of Philosophy Dissertation, available at: https://etda.libraries.psu.edu/catalog/17434kzw151, last access: 1 September 2020.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo,
L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V.,
Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System project phase 2 (NLDAS-2): 1. Intercomparison and application of
model products, J. Geophys. Res.-Atmos., 117, D03109, https://doi.org/10.1029/2011JD016048,
2012.
Xiao, J., Davis, K. J., Urban, N. M., and Keller, K.: Uncertainty in model
parameters and regional carbon fluxes: A model-data fusion approach, Agr.
Forest Meteorol., 189–190, 175–186, https://doi.org/10.1016/j.agrformet.2014.01.022,
2014.
Yang, J., Chang, Y., and Yan, P.: Ranking the suitability of common urban
tree species for controlling PM2.5 pollution, Atmos. Pollut. Res., 6,
267–277, https://doi.org/10.5094/APR.2015.031, 2015.
Yang, K., Ryu, Y., Dechant, B., Berry, J. A., Hwang, Y., Jiang, C., Kang,
M., Kim, J., Kimm, H., Kornfeld, A., and Yang, X.: Sun-induced chlorophyll
fluorescence is more strongly related to absorbed light than to
photosynthesis at half-hourly resolution in a rice paddy, Remote Sens.
Environ., 216, 658–673, https://doi.org/10.1016/j.rse.2018.07.008, 2018.
Yang, X., Tang, J., Mustard, J. F., Lee, J. E., Rossini, M., Joiner, J.,
Munger, J. W., Kornfeld, A., and Richardson, A. D.: Solar-induced chlorophyll
fluorescence that correlates with canopy photosynthesis on diurnal and
seasonal scales in a temperate deciduous forest, Geophys. Res. Lett., 42,
2977–2987, https://doi.org/10.1002/2015GL063201, 2015.
Ye, X., Lauvaux, T., Kort, E. A., Oda, T., Feng, S., Lin, J. C., Yang, E. G.,
and Wu, D.: Constraining fossil fuel CO2 emissions from urban area
using OCO-2 observations of total column CO2, J. Geophys. Res.-Atmos., 125,
e2019JD030528, https://doi.org/10.1029/2019jd030528, 2020.
Yin, Y., Byrne, B., Liu, J., Wennberg, P. O., Davis, K. J., Magney, T., Kohler, P., He, L., Jeyaram, R., Humphrey, V., Gerken, T., Feng, S., Digangi, J. P., and Frankenberg, C.: Cropland
Carbon Uptake Delayed and Reduced by 2019 Midwest Floods, 1, e2019AV000140,
https://doi.org/10.1029/2019AV000140, 2020.
You, L., Wood, S., Wood-Sichra, U., and Wu, W.: Generating global crop
distribution maps: From census to grid, Agric. Syst., 127, 53–60,
https://doi.org/10.1016/j.agsy.2014.01.002, 2014.
Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S., and Gentine, P.: A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks, Biogeosciences, 15, 5779–5800, https://doi.org/10.5194/bg-15-5779-2018, 2018.
Zhao, Z., Peng, C., Yang, Q., Meng, F. R., Song, X., Chen, S., Epule, T. E.,
Li, P., and Zhu, Q.: Model prediction of biome-specific global soil
respiration from 1960 to 2012, Earth's Future, 5, 715–729,
https://doi.org/10.1002/2016EF000480, 2017.
Zuromski, L. M., Bowling, D. R., Köhler, P., Frankenberg, C., Goulden,
M. L., Blanken, P. D., and Lin, J. C.: Solar-Induced Fluorescence Detects
Interannual Variation in Gross Primary Production of Coniferous Forests in
the Western United States, Geophys. Res. Lett., 45, 7184–7193, 2018.
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.
A model (SMUrF) is presented that estimates biogenic CO2 fluxes over cities around the globe to...
