Evaluating atmospheric CO2 inversions at multiple scales over a highly inventoried agricultural landscape
Abstract
An intensive regional research campaign was conducted by the N orth A merican Carbon Program (NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the M idwestern U nited S tates. Forty‐five different associated projects were conducted across five US agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO 2 mixing ratio data to estimate CO 2 flux over the major croplands of the U nited S tates by comparing the results to an inventory of CO 2 fluxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom‐up inventory flux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three different inversion systems, representing spatial scales varying from high resolution mesoscale (PSU), to continental (CSU) and global (CarbonTracker), coupled to different transport models and optimization techniques. The inversion‐based mean CO 2‐C sink estimates were generally slightly larger, 8–20% for PSU, 10–20% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region‐wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO 2 fluxes at sub‐regional scales with a relatively high density of CO 2 observations and adequate information on atmospheric transport in the region.
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Top-down modeling utilizing a network of tower-based observations of CO2 concentrations was used to verify bottom-up inventory modeling in the largely agricultural region of the central United States.
“This 'topdown' modeling, utilizing a network of tower-based observations of CO 2 concentrations, was used to verify 'bottom-up' inventory modeling based on observed management activities, in the largely agricultural region of the central United States 83,84”
From Climate-smart soils (2016) · cites this paper as [83]
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- Authors
- Schuh, A. E., Lauvaux, T., West, T. O., Denning, A. S., Davis, K. J., Miles, N., Richardson, S., Uliasz, M., Lokupitiya, E., Cooley, D., Andrews, A., & Ogle, S.
- Affiliations
- Cooperative Institute for Research in the Atmosphere (US)
- Pennsylvania State University (US)
- Joint Global Change Research Institute (US)
- Department of Atmospheric Sciences Colorado State University Fort Collins CO USA
- Colorado State University (US)
- University of Colombo (LK)
- NOAA Earth System Research Laboratory (US)
- Corresponding author
- Schuh, Andrew E.
- ORCID
- Venue
- Global Change Biology
- Publisher
- Wiley
- Published
- 2013-03-12
- Volume
- 19
- Issue
- 5
- Pages
- 1424-1439
- ISSN
- 1354-1013 (ISSN-L)
- 1365-2486
- OpenAlex
- W2121476603
- Cited by
- 85
- References
- 66
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 5 · 2023: 7 · 2022: 4 · 2021: 13 · 2020: 7 · 2019: 5 · 2018: 6 · 2017: 8 · 2016: 5 · 2015: 8 · 2014: 7 · 2013: 4
- FWCI
- 6.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Global Change Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Oceanic and Atmospheric Administration
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric and Environmental Gas Dynamics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Keywords
- atmospheric CO2 inversion, carbon flux, carbon sink, North American Carbon Program, CarbonTracker, spatial scale, atmospheric transport, Midwestern United States
- MeSH terms
- Air Pollutants, Carbon Dioxide, Zea mays, Environmental Monitoring, Models, Theoretical, Glycine max, Midwestern United States, Crops, Agricultural
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