Articles | Volume 25, issue 20
https://doi.org/10.5194/acp-25-13327-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-13327-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Oct 2025
Research article |
| 22 Oct 2025
| 22 Oct 2025
Application of PRIM for understanding patterns in carbon dioxide model-observation differences
School of Integrated Sciences, James Madison University, Harrisonburg, VA, USA
Kenneth J. Davis
Department of Meteorology and Atmospheric Sciences, Pennsylvania State University, University Park, PA, USA
Klaus Keller
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Pacific Northwest National Laboratory, Richland, WA, USA
Related authors
No articles found.
Edward J. Strobach, Elaine Jaross, Xinrong Ren, Joel Dreessen, Paul Walter, James S. Wang, Maurice Roots, Julia Famiglietti, Austin Turner, Phillip Stratton, Russell R. Dickerson, Kenneth J. Davis, and Daniel P. Lathrop
EGUsphere, https://doi.org/10.5194/egusphere-2026-6646, https://doi.org/10.5194/egusphere-2026-6646, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This research demonstrates Civil Air Patrol (CAP) aircraft as a viable solution for frequent surveys of greenhouse gases responsible for a warming climate. We showcase multisite evaluations of emissions at facilities such as landfills and natural gas compressor stations, and highlight coordination between facility operators and ground-based mobile measurements during a planned release. Since each state has a CAP wing, this approach could be expanded to 50 states.
Ye Liu, Huilin Huang, Troy M. Saltiel, Sha Feng, Andre Coleman, and Ann Almgren
EGUsphere, https://doi.org/10.5194/egusphere-2026-5430, https://doi.org/10.5194/egusphere-2026-5430, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
ERF-LSFIRE v1.0 is a new coupled fire–atmosphere modeling framework that links a C++/AMReX level-set surface-fire model with ERF. We document the model formulation and coupling strategy, verify the fire-spread implementation against analytical solutions, and demonstrate standalone, one-way, and two-way coupled simulations for two real wildfire events.
Yunsong Liu, Natasha L. Miles, Scott J. Richardson, Zachary R. Barkley, David O. Miller, Jonathan Kofler, Philip Handley, Stephen DeVogel, and Kenneth J. Davis
Atmos. Meas. Tech., 19, 965–981, https://doi.org/10.5194/amt-19-965-2026, https://doi.org/10.5194/amt-19-965-2026, 2026
Short summary
Short summary
This manuscript details laboratory and field-based testing of a tower-based methane and ethane measurement system to address the challenge of separating methane sources in oil and gas basins. We describe methods for managing water vapor, calibration, and estimating the various components of measurement uncertainty. With appropriate engineering and calibration, the instrument shows the capability to measure CH4 and C2H6 with sufficient stability to distinguish regional methane emission sources.
Jason P. Horne, Scott J. Richardson, Samantha L. Murphy, Helen C. Kenion, Bernd J. Haupt, Benjamin J. Ahlswede, Natasha L. Miles, and Kenneth J. Davis
Earth Syst. Sci. Data, 18, 823–843, https://doi.org/10.5194/essd-18-823-2026, https://doi.org/10.5194/essd-18-823-2026, 2026
Short summary
Short summary
We present data from a network of towers used to study the exchange of heat, water vapor, and carbon dioxide between the surface and atmosphere in and around the city of Indianapolis, IN, USA. We explain what was measured, how we checked data quality, and why these observations improve our overall understanding of the urban environment.
Jean-Francois Lamarque, Pierre Friedlingstein, Brian Osias, Steve Strongin, Venkatramani Balaji, Kevin W. Bowman, Josep G. Canadell, Philippe Ciais, Heidi Cullen, Kenneth J. Davis, Scott C. Doney, Kevin R. Gurney, Alicia R. Karspeck, Charles D. Koven, Galen McKinley, Glen P. Peters, Julia Pongratz, Britt Stephens, and Colm Sweeney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6457, https://doi.org/10.5194/egusphere-2025-6457, 2026
Short summary
Short summary
This Perspective highlights requirements to scale the carbon credit market and enable the growth in climate solutions funded through such market. The requirements are on the understanding of the value of the proposed carbon credit projects, and on the availability of a verification system. This verification becomes particularly relevant as the carbon credit market scales to significant impacts on CO2 (or other greenhouse gases), such that attribution to collective actions can be identified.
Matteo Puccioni, Sonia Wharton, Stephan F. J. De Wekker, Robert S. Arthur, Tianyi Li, Ye Liu, Sha Feng, Kyle Pressel, Raj K. Rai, Larry K. Berg, and Jerome D. Fast
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-215, https://doi.org/10.5194/wes-2025-215, 2025
Revised manuscript not accepted
Short summary
Short summary
A multi-wind Lidar campaign is conducted in a forest in the United States Southeast, a region featuring low wind resources due to the forest drag. Although the latter reduces the wind for hundreds of meters above ground, we found this effect to be negligible for heights above 8 times the tree height where the wind is dominated by local atmospheric events. This scenario opens to the development of taller turbines harvesting the wind farther away from the ground where the forest drag is minimal.
Bianca C. Baier, John B. Miller, Colm Sweeney, Scott J. Lehman, Chad Wolak, Joshua P. DiGangi, Yonghoon Choi, Kenneth Davis, Sha Feng, and Thomas Lauvaux
Atmos. Chem. Phys., 25, 10479–10497, https://doi.org/10.5194/acp-25-10479-2025, https://doi.org/10.5194/acp-25-10479-2025, 2025
Short summary
Short summary
CO2 radiocarbon content (Δ14CO2) is a unique tracer that helps to accurately quantify anthropogenic CO2 emitted into the atmosphere. Δ14CO2 measured in airborne flask samples is used to distinguish fossil versus biogenic CO2 sources. Mid-Atlantic US CO2 variability is found to be driven by the biosphere. Errors in modeled fossil fuel CO2 are evaluated using Δ14CO2 airborne data as an avenue to improving future regional models of atmospheric CO2 transport.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
Short summary
Short summary
The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
Short summary
Short summary
We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Zachary Barkley, Kenneth Davis, Natasha Miles, Scott Richardson, Aijun Deng, Benjamin Hmiel, David Lyon, and Thomas Lauvaux
Atmos. Chem. Phys., 23, 6127–6144, https://doi.org/10.5194/acp-23-6127-2023, https://doi.org/10.5194/acp-23-6127-2023, 2023
Short summary
Short summary
Using methane monitoring instruments attached to towers, we measure methane concentrations and quantify methane emissions coming from the Marcellus and Permian oil and gas basins. In the Marcellus, emissions were 3 times higher than the state inventory across the entire monitoring period. In the Permian, we see a sharp decline in emissions aligning with the onset of the COVID-19 pandemic. Tower observational networks can be utilized in other basins for long-term monitoring of emissions.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Rory A. Barton-Grimley, Amin R. Nehrir, Susan A. Kooi, James E. Collins, David B. Harper, Anthony Notari, Joseph Lee, Joshua P. DiGangi, Yonghoon Choi, and Kenneth J. Davis
Atmos. Meas. Tech., 15, 4623–4650, https://doi.org/10.5194/amt-15-4623-2022, https://doi.org/10.5194/amt-15-4623-2022, 2022
Short summary
Short summary
HALO is a multi-functional lidar that measures CH4 columns and profiles of H2O mixing ratio and aerosol/cloud optical properties. HALO supports carbon cycle, weather dynamics, and radiation science suborbital research and is a technology testbed for future space-based differential absorption lidar missions. In 2019 HALO collected CH4 columns and aerosol/cloud profiles during the ACT-America campaign. Here we assess HALO's CH4 accuracy and precision compared to co-located in situ observations.
Vanessa C. Monteiro, Natasha L. Miles, Scott J. Richardson, Zachary Barkley, Bernd J. Haupt, David Lyon, Benjamin Hmiel, and Kenneth J. Davis
Earth Syst. Sci. Data, 14, 2401–2417, https://doi.org/10.5194/essd-14-2401-2022, https://doi.org/10.5194/essd-14-2401-2022, 2022
Short summary
Short summary
We describe a network of five ground-based in situ towers, equipped to measure concentrations of methane, carbon dioxide, hydrogen sulfide, and the isotopic ratio of methane, in the Permian Basin, United States. The main goal is to use methane concentrations with atmospheric models to determine methane emissions from one of the Permian sub-basins. These datasets can improve emissions estimations, leading to best practices in the oil and natural gas industry, and policies for emissions reduction.
David F. Baker, Emily Bell, Kenneth J. Davis, Joel F. Campbell, Bing Lin, and Jeremy Dobler
Geosci. Model Dev., 15, 649–668, https://doi.org/10.5194/gmd-15-649-2022, https://doi.org/10.5194/gmd-15-649-2022, 2022
Short summary
Short summary
The OCO-2 satellite measures many closely spaced column-averaged CO2 values around its orbit. To give these data proper weight in flux inversions, their error correlations must be accounted for. Here we lay out a 1-D error model with correlations that die out exponentially along-track to do so. A correlation length scale of ∼20 km is derived from column CO2 measurements from an airborne lidar flown underneath OCO-2 for use in this model. The model's performance is compared to previous ones.
Cited articles
Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D.: Modelling CO2 weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019, 2019. a
Baier, B. C., Sweeney, C., Choi, Y., Davis, K. J., DiGangi, J. P., Feng, S., Fried, A., Halliday, H., Higgs, J., Lauvaux, T., Miller, B. R., Montzka, S. A., Newberger, T., Nowak, J. B., Patra, P., Richter, D., Walega, J., and Weibring, P.: Multispecies Assessment of Factors Influencing Regional CO2 and CH4 Enhancements during the Winter 2017 ACT-America Campaign, Journal of Geophysical Research: Atmospheres, 125, e2019JD031339, https://doi.org/10.1029/2019JD031339, 2020. a, b, c
Baker, D. F., Law, R. M., Gurney, K. R., Rayner, P., Peylin, P., Denning, A. S., Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fung, I. Y., Heimann, M., John, J., Maki, T., Maksyutov, S., Masarie, K., Prather, M., Pak, B., Taguchi, S., and Zhu, Z.: TransCom 3 Inversion Intercomparison: Impact of Transport Model Errors on the Interannual Variability of Regional CO2 Fluxes, 1988–2003, Global Biogeochemical Cycles, 20, 439, https://doi.org/10.1029/2004GB002439, 2006. a
Barnes, E. A., Parazoo, N., Orbe, C., and Denning, A. S.: Isentropic Transport and the Seasonal Cycle Amplitude of CO2, Journal of Geophysical Research: Atmospheres, 121, 8106–8124, https://doi.org/10.1002/2016JD025109, 2016. a
Bousquet, P., Ciais, P., Monfray, P., Balkansk1, Y., Ramonet, M., and Tans, P.: Influence of Two Atmospheric Transport Models on Inferring Sources and Sinks of Atmospheric CO2, Tellus B, 48, 568–582, https://doi.org/10.1034/j.1600-0889.1996.t01-2-00011.x, 1996. a
Bryant, B. P. and Lempert, R. J.: Thinking inside the Box: A Participatory, Computer-Assisted Approach to Scenario Discovery, Technological Forecasting and Social Change, 77, 34–49, https://doi.org/10.1016/j.techfore.2009.08.002, 2010. a, b, c
Campbell, J. F., Lin, B., Dobler, J., Pal, S., Davis, K., Obland, M. D., Erxleben, W., McGregor, D., O'Dell, C., Bell, E., Weir, B., Fan, T.-F., Kooi, S., Gordon, I., Corbett, A., and Kochanov, R.: Field Evaluation of Column CO 2 Retrievals From Intensity-Modulated Continuous-Wave Differential Absorption Lidar Measurements During the ACT-America Campaign, Earth and Space Science, 7, e2019EA000847, https://doi.org/10.1029/2019EA000847, 2020. a
Chan, D., Yuen, C. W., Higuchi, K., Shashkov, A., Liu, J., Chen, J., and Worthy, D.: On the CO2 Exchange between the Atmosphere and the Biosphere: The Role of Synoptic and Mesoscale Processes, Tellus B: Chemical and Physical Meteorology, 56, 194–212, https://doi.org/10.3402/tellusb.v56i3.16424, 2004. a
Chevallier, F., Feng, L., Bösch, H., Palmer, P. I., and Rayner, P. J.: On the Impact of Transport Model Errors for the Estimation of CO2 Surface Fluxes from GOSAT Observations, Geophysical Research Letters, 37, https://doi.org/10.1029/2010GL044652, 2010. a
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C, Le Quere, C., Myneni, R. B., Piao, S., and Thornton, P. E.: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK; New York, USA, 465–570, ISBN 978-1-107-05799-1, 2013. a
Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier, F., Liu, J., Deng, F., Feng, L., McKain, K., Chatterjee, A., Miller, J. B., Stephens, B. B., Eldering, A., Crisp, D., Schimel, D., Nassar, R., O'Dell, C. W., Oda, T., Sweeney, C., Palmer, P. I., and Jones, D. B. A.: The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network, Atmos. Chem. Phys., 19, 9797–9831, https://doi.org/10.5194/acp-19-9797-2019, 2019. a
Cui, Y. Y., Jacobson, A. R., Feng, S., Wesloh, D., Barkley, Z. R., Zhang, L., Gerken, T., Keller, K., Baker, D., and Davis, K. J.: Evaluation of CarbonTracker's Inverse Estimates of North American Net Ecosystem Exchange of CO2 From Different Observing Systems Using ACT-America Airborne Observations, Journal of Geophysical Research: Atmospheres, 126, e2020JD034 406, https://doi.org/10.1029/2020JD034406, 2021. a, b
Cui, Y. Y., Zhang, L., Jacobson, A. R., Johnson, M. S., Philip, S., Baker, D., Chevallier, F., Schuh, A. E., Liu, J., Crowell, S., Peiro, H. E., Deng, F., Basu, S., and Davis, K. J.: Evaluating Global Atmospheric Inversions of Terrestrial Net Ecosystem Exchange CO 2 Over North America on Seasonal and Sub-Continental Scales, Geophysical Research Letters, 49, e2022GL100147, https://doi.org/10.1029/2022GL100147, 2022. a, b
Davis, K., Obland, M., Lin, B., Lauvuax, T., O'Dell, C., Meadows, B., Browell, E., Crawford, J., DiGangi, J., Sweeney, C., McGill, M., Dobler, J., Barrik, J., and Nehrir, A.: ACT-America: L3 Merged In Situ Atmospheric Trace Gases and Flask Data, Eastern USA, ORNL Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/1593, 2018. a, b, c
Davis, K. J., Browell, E. V., Feng, S., Lauvaux, T., Obland, M. D., Pal, S., Baier, B. C., Baker, D. F., Baker, I. T., Barkley, Z. R., Bowman, K. W., Cui, Y. Y., Denning, A. S., DiGangi, J. P., Dobler, J. T., Fried, A., Gerken, T., Keller, K., Lin, B., Nehrir, A. R., Normile, C. P., O'Dell, C. W., Ott, L. E., Roiger, A., Schuh, A. E., Sweeney, C., Wei, Y., Weir, B., Xue, M., and Williams, C. A.: The Atmospheric Carbon and Transport (ACT) – America Mission, Bulletin of the American Meteorological Society, 1–54, https://doi.org/10.1175/BAMS-D-20-0300.1, 2021. a, b, c, d, e, f
Díaz-Isaac, L. I., Lauvaux, T., Davis, K. J., Miles, N. L., Richardson, S. J., Jacobson, A. R., and Andrews, A. E.: Model-Data Comparison of MCI Field Campaign Atmospheric CO2 Mole Fractions, Journal of Geophysical Research: Atmospheres, 119, 10536–10551, https://doi.org/10.1002/2014JD021593, 2014. a, b
Díaz-Isaac, L. I., Lauvaux, T., and Davis, K. J.: Impact of physical parameterizations and initial conditions on simulated atmospheric transport and CO2 mole fractions in the US Midwest, Atmos. Chem. Phys., 18, 14813–14835, https://doi.org/10.5194/acp-18-14813-2018, 2018. a
Fast, J. D., Gustafson, W. I., Easter, R. C., Zaveri, R. A., Barnard, J. C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of Ozone, Particulates, and Aerosol Direct Radiative Forcing in the Vicinity of Houston Using a Fully Coupled Meteorology-Chemistry-Aerosol Model, Journal of Geophysical Research: Atmospheres, 111, https://doi.org/10.1029/2005JD006721, 2006. a
Feng, S., Lauvaux, T., Davis, K. J., Keller, K., Zhou, Y., Williams, C., Schuh, A. E., Liu, J., and Baker, I.: Seasonal Characteristics of Model Uncertainties from Biogenic Fluxes, Transport, and Large-Scale Boundary Inflow in Atmospheric CO2 Simulations over North America, Journal of Geophysical Research: Atmospheres, 124, 14325–14346, https://doi.org/10.1029/2019JD031165, 2019a. a, b
Feng, S., Lauvaux, T., Keller, K., Davis, K. J., Rayner, P., Oda, T., and Gurney, K. R.: A Road Map for Improving the Treatment of Uncertainties in High-Resolution Regional Carbon Flux Inverse Estimates, Geophysical Research Letters, 46, 13431–13469, https://doi.org/10.1029/2019GL082987, 2019b. a
Feng, S., Lauvaux, T., Barkley, Z., Davis, K., Butler, M., Deng, A., Gaudet, B., and Stauffer, D.: Full WRF-Chem Output in Support of the NASA Atmospheric Carbon and Transport (ACT)-America Project (7/1/2016 – 7/31/2019), PSU Datacommons [data set], https://doi.org/10.26208/RQF5-Q142, 2020. a, b
Feng, S., Lauvaux, T., Williams, C. A., Davis, K. J., Zhou, Y., Baker, I., Barkley, Z. R., and Wesloh, D.: Joint CO2 Mole Fraction and Flux Analysis Confirms Missing Processes in CASA Terrestrial Carbon Uptake Over North America, Global Biogeochemical Cycles, 35, e2020GB006914, https://doi.org/10.1029/2020GB006914, 2021a. a
Feng, S., Lauvaux, T., Barkley, Z. R., Davis, K. J., Butler, M. P., Deng, A., Gaudet, B., and Stauffer, D.: ACT-America: WRF-Chem Baseline Simulations for North America, 2016–2019 (Version 1) ORNL Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/1884, 2021b. a
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Bakker, D. C. E., Hauck, J., Landschützer, P., Le Quéré, C., Luijkx, I. T., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Barbero, L., Bates, N. R., Becker, M., Bellouin, N., Decharme, B., Bopp, L., Brasika, I. B. M., Cadule, P., Chamberlain, M. A., Chandra, N., Chau, T.-T.-T., Chevallier, F., Chini, L. P., Cronin, M., Dou, X., Enyo, K., Evans, W., Falk, S., Feely, R. A., Feng, L., Ford, D. J., Gasser, T., Ghattas, J., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Joos, F., Kato, E., Keeling, R. F., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Lan, X., Lefèvre, N., Li, H., Liu, J., Liu, Z., Ma, L., Marland, G., Mayot, N., McGuire, P. C., McKinley, G. A., Meyer, G., Morgan, E. J., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K. M., Olsen, A., Omar, A. M., Ono, T., Paulsen, M., Pierrot, D., Pocock, K., Poulter, B., Powis, C. M., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Séférian, R., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tans, P. P., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., van Ooijen, E., Wanninkhof, R., Watanabe, M., Wimart-Rousseau, C., Yang, D., Yang, X., Yuan, W., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2023, Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, 2023. a
Friedman, J. H. and Fisher, N. I.: Bump Hunting in High-Dimensional Data, Statistics and Computing, 9, 123–143, https://doi.org/10.1023/A:1008894516817, 1999. a, b, c
Gaudet, B. J., Davis, K. J., Pal, S., Jacobson, A. R., Schuh, A., Lauvaux, T., Feng, S., and Browell, E. V.: Regional-Scale, Sector-Specific Evaluation of Global CO2 Inversion Models Using Aircraft Data From the ACT-America Project, Journal of Geophysical Research: Atmospheres, 126, e2020JD033623, https://doi.org/10.1029/2020JD033623, 2021. a
Gerken, T.: TobGerken/PRIM_Analysis_Gerken2025: v0.5, Zenodo [code], https://doi.org/10.5281/zenodo.14727932, 2025. a
Gourdji, S. M., Karion, A., Lopez-Coto, I., Ghosh, S., Mueller, K. L., Zhou, Y., Williams, C. A., Baker, I. T., Haynes, K. D., and Whetstone, J. R.: A Modified Vegetation Photosynthesis and Respiration Model (VPRM) for the Eastern USA and Canada, Evaluated With Comparison to Atmospheric Observations and Other Biospheric Models, Journal of Geophysical Research: Biogeosciences, 127, e2021JG006290, https://doi.org/10.1029/2021JG006290, 2022. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully Coupled “Online” Chemistry within the WRF Model, Atmospheric Environment, 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a
Hadka, D., Herman, J., Reed, P., and Keller, K.: An Open Source Framework for Many-Objective Robust Decision Making, Environmental Modelling & Software, 74, 114–129, https://doi.org/10.1016/j.envsoft.2015.07.014, 2015. a
Hu, L., Andrews, A. E., Thoning, K. W., Sweeney, C., Miller, J. B., Michalak, A. M., Dlugokencky, E., Tans, P. P., Shiga, Y. P., Mountain, M., Nehrkorn, T., Montzka, S. A., McKain, K., Kofler, J., Trudeau, M., Michel, S. E., Biraud, S. C., Fischer, M. L., Worthy, D. E. J., Vaughn, B. H., White, J. W. C., Yadav, V., Basu, S., and van der Velde, I. R.: Enhanced North American Carbon Uptake Associated with El Niño, Science Advances, 5, eaaw0076, https://doi.org/10.1126/sciadv.aaw0076, 2019. a
Hu, X.-M., Gourdji, S. M., Davis, K. J., Wang, Q., Zhang, Y., Xue, M., Feng, S., Moore, B., and Crowell, S. M. R.: Implementation of Improved Parameterization of Terrestrial Flux in WRF-VPRM Improves the Simulation of Nighttime CO2 Peaks and a Daytime CO2 Band Ahead of a Cold Front, Journal of Geophysical Research: Atmospheres, 126, e2020JD034362, https://doi.org/10.1029/2020JD034362, 2021. a, b, c
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. a
Huntzinger, D. N., Michalak, A. M., Schwalm, C., Ciais, P., King, A. W., Fang, Y., Schaefer, K., Wei, Y., Cook, R. B., Fisher, J. B., Hayes, D., Huang, M., Ito, A., Jain, A. K., Lei, H., Lu, C., Maignan, F., Mao, J., Parazoo, N., Peng, S., Poulter, B., Ricciuto, D., Shi, X., Tian, H., Wang, W., Zeng, N., and Zhao, F.: Uncertainty in the Response of Terrestrial Carbon Sink to Environmental Drivers Undermines Carbon-Climate Feedback Predictions, Sci. Rep., 7, 4765, https://doi.org/10.1038/s41598-017-03818-2, 2017. a
Hurwitz, M. D., Ricciuto, D. M., Bakwin, P. S., Davis, K. J., Wang, W., Yi, C., and Butler, M. P.: Transport of Carbon Dioxide in the Presence of Storm Systems over a Northern Wisconsin Forest, J. Atmos. Sci., 61, 607–618, https://doi.org/10.1175/1520-0469(2004)061<0607:TOCDIT>2.0.CO;2, 2004. a
Keppel-Aleks, G., Wennberg, P. O., and Schneider, T.: Sources of variations in total column carbon dioxide, Atmos. Chem. Phys., 11, 3581–3593, https://doi.org/10.5194/acp-11-3581-2011, 2011. a
Keppel-Aleks, G., Wennberg, P. O., Washenfelder, R. A., Wunch, D., Schneider, T., Toon, G. C., Andres, R. J., Blavier, J.-F., Connor, B., Davis, K. J., Desai, A. R., Messerschmidt, J., Notholt, J., Roehl, C. M., Sherlock, V., Stephens, B. B., Vay, S. A., and Wofsy, S. C.: The imprint of surface fluxes and transport on variations in total column carbon dioxide, Biogeosciences, 9, 875–891, https://doi.org/10.5194/bg-9-875-2012, 2012. a
Lauvaux, T. and Davis, K. J.: Planetary Boundary Layer Errors in Mesoscale Inversions of Column-Integrated CO2 Measurements, Journal of Geophysical Research: Atmospheres, 119, 490–508, https://doi.org/10.1002/2013JD020175, 2014. a, b
Michalak, A. M., Hirsch, A., Bruhwiler, L., Gurney, K. R., Peters, W., and Tans, P. P.: Maximum Likelihood Estimation of Covariance Parameters for Bayesian Atmospheric Trace Gas Surface Flux Inversions, Journal of Geophysical Research: Atmospheres, 110, https://doi.org/10.1029/2005JD005970, 2005. a
Pal, S., Davis, K. J., Lauvaux, T., Browell, E. V., Gaudet, B. J., Stauffer, D. R., Obland, M. D., Choi, Y., DiGangi, J. P., Feng, S., Lin, B., Miles, N. L., Pauly, R. M., Richardson, S. J., and Zhang, F.: Observations of Greenhouse Gas Changes across Summer Frontal Boundaries in the Eastern United States, Journal of Geophysical Research: Atmospheres, 125, e2019JD030526, https://doi.org/10.1029/2019JD030526, 2020. a, b, c, d
Parazoo, N. C., Denning, A. S., Kawa, S. R., Corbin, K. D., Lokupitiya, R. S., and Baker, I. T.: Mechanisms for synoptic variations of atmospheric CO2 in North America, South America and Europe, Atmos. Chem. Phys., 8, 7239–7254, https://doi.org/10.5194/acp-8-7239-2008, 2008. a, b, c, d
Parazoo, N. C., Denning, A. S., Berry, J. A., Wolf, A., Randall, D. A., Kawa, S. R., Pauluis, O., and Doney, S. C.: Moist Synoptic Transport of CO2 along the Mid-Latitude Storm Track, Geophysical Research Letters, 38, https://doi.org/10.1029/2011GL047238, 2011. a, b, c
Parazoo, N. C., Denning, A. S., Kawa, S. R., Pawson, S., and Lokupitiya, R.: CO2 flux estimation errors associated with moist atmospheric processes, Atmos. Chem. Phys., 12, 6405–6416, https://doi.org/10.5194/acp-12-6405-2012, 2012. a
Parazoo, N. C., Bowman, K. W., Baier, B. C., Liu, J., Lee, M., Kuai, L., Shiga, Y., Baker, I., Whelan, M. E., Feng, S., Krol, M., Sweeney, C., Runkle, B. R., Tajfar, E., and Davis, K. J.: Covariation of Airborne Biogenic Tracers (CO 2 , COS, and CO) Supports Stronger Than Expected Growing Season Photosynthetic Uptake in the Southeastern US, Global Biogeochemical Cycles, 35, e2021GB006956, https://doi.org/10.1029/2021GB006956, 2021. a, b
Peiro, H., Crowell, S., Schuh, A., Baker, D. F., O'Dell, C., Jacobson, A. R., Chevallier, F., Liu, J., Eldering, A., Crisp, D., Deng, F., Weir, B., Basu, S., Johnson, M. S., Philip, S., and Baker, I.: Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7, Atmos. Chem. Phys., 22, 1097–1130, https://doi.org/10.5194/acp-22-1097-2022, 2022. a
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, 2013. a, b
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen, F., Barlage, M. J., Yu, W., and Duda, M. G.: The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions, Bulletin of the American Meteorological Society, 98, 1717–1737, https://doi.org/10.1175/BAMS-D-15-00308.1, 2017. a
Samaddar, A., Feng, S., Lauvaux, T., Barkley, Z. R., Pal, S., and Davis, K. J.: Carbon Dioxide Distribution, Origins, and Transport Along a Frontal Boundary During Summer in Mid-Latitudes, Journal of Geophysical Research: Atmospheres, 126, e2020JD033118, https://doi.org/10.1029/2020JD033118, 2021. a
Schuh, A. E., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K., Chevallier, F., Crowell, S., Davis, K. J., Deng, F., Denning, S., Feng, L., Jones, D., Liu, J., and Palmer, P. I.: Quantifying the Impact of Atmospheric Transport Uncertainty on CO2 Surface Flux Estimates, Global Biogeochemical Cycles, 33, 484–500, https://doi.org/10.1029/2018GB006086, 2019. a, b
Stephens, B. B., Gurney, K. R., Tans, P. P., Sweeney, C., Peters, W., Bruhwiler, L., Ciais, P., Ramonet, M., Bousquet, P., Nakazawa, T., Aoki, S., Machida, T., Inoue, G., Vinnichenko, N., Lloyd, J., Jordan, A., Heimann, M., Shibistova, O., Langenfelds, R. L., Steele, L. P., Francey, R. J., and Denning, A. S.: Weak Northern and Strong Tropical Land Carbon Uptake from Vertical Profiles of Atmospheric CO2, Science, 316, 1732–1735, https://doi.org/10.1126/science.1137004, 2007. a, b
Sweeney, C., Karion, A., Wolter, S., Newberger, T., Guenther, D., Higgs, J. A., Andrews, A. E., Lang, P. M., Neff, D., Dlugokencky, E., Miller, J. B., Montzka, S. A., Miller, B. R., Masarie, K. A., Biraud, S. C., Novelli, P. C., Crotwell, M., Crotwell, A. M., Thoning, K., and Tans, P. P.: Seasonal Climatology of CO2 across North America from Aircraft Measurements in the NOAA/ESRL Global Greenhouse Gas Reference Network, Journal of Geophysical Research: Atmospheres, 120, 5155–5190, https://doi.org/10.1002/2014JD022591, 2015. a, b, c, d
Tarantola, A.: Inverse Problem Theory and Methods for Model Parameter Estimation, Society for Industrial and Applied Mathematics, Philadelphia, PA, ISBN 978-0-89871-572-9, 2005. a
Tarantola, A. and Valette, B.: Generalized Nonlinear Inverse Problems Solved Using the Least Squares Criterion, Reviews of Geophysics, 20, 219–232, https://doi.org/10.1029/RG020i002p00219, 1982. a
Wang, Q., Crowell, S. M. R., and Pal, S.: Atmospheric Variations in Summertime Column Integrated CO2 on Synoptic Scales Over the U.S., Journal of Geophysical Research: Atmospheres, 128, e2021JD036256, https://doi.org/10.1029/2021JD036256, 2023. a, b, c
Wei, Y., Shresha, R., Pal, S., Gerken, T., McNelis, J., Singh, D., Thornton, M., Boyer, A. G., Shook, M. A., Chen, G., Baier, B. C., Barkley, Z. R., Barrik, J., Bennet, J. R., Browell, E. V., Campbell, J. F., Campbell, L. J., Choi, Y., Collins, J., Dobler, J., Eckl, M., Feng, S., Fiehn, A., Fried, A., DiGangi, J. P., Barton-Gimley, R., Halliday, H., Klausner, T., Kooi, S., Kostinek, J., Lauvaux, T., Lin, B., McGill, M., Meadows, B., Nehrir, A. R., Nowak, J., Obland, M., O'Dell, C. W., Fao, R. M., Richter, D., Roiger, A., Sweeney, C., Walega, J., Weibring, P., Williams, C. A., Yang, M. M., Zhou, Y., and Davis, K. J.: The ACT-America Datasets: Description, Management and Delivery, Earth and Space Science, ESS2860, https://doi.org/10.1029/2020EA001634, 2021. a, b, c
Zhang, L., Davis, K. J., Schuh, A. E., Jacobson, A. R., Pal, S., Cui, Y. Y., Baker, D., Crowell, S., Chevallier, F., Remaud, M., Liu, J., Weir, B., Philip, S., Johnson, M. S., Deng, F., and Basu, S.: Multi-Season Evaluation of CO2 Weather in OCO-2 MIP Models, Journal of Geophysical Research: Atmospheres, 127, e2021JD035457, https://doi.org/10.1029/2021JD035457, 2022. a, b
Short summary
We apply the Patient Rule Induction Method (PRIM) technique to airborne CO2 and meteorological data to better understand atmospheric conditions and implications for carbon dioxide model-observation-mismatches. We found PRIM is capable of separating observations from different seasons and levels based on atmospheric conditions. Large magnitude carbon dioxide model-observation-differences were associated with non-typical atmospheric conditions, with implications for transport model evaluation.
We apply the Patient Rule Induction Method (PRIM) technique to airborne CO2 and meteorological...
Altmetrics
Final-revised paper
Preprint