Evaluating atmospheric CO₂ effects on gross primary productivity and net ecosystem exchanges of terrestrial ecosystems in the conterminous United States using the AmeriFlux data and an artificial neural network approach

Journal Article · Thu Jan 21 00:00:00 EST 2016 · Agricultural and Forest Meteorology

DOI:https://doi.org/10.1016/j.agrformet.2016.01.007· OSTI ID:1327619

Liu, Shaoqing ^[1]; Zhuang, Qianlai ^[2]; He, Yujie ^[1]; Noormets, Asko ^[3]; Chen, Jiquan ^[4]; Gu, Lianhong ^[5]

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1. Purdue Univ., West Lafayette, IN (United States). Department of Earth, Atmospheric, and Planetary Sciences
2. Purdue Univ., West Lafayette, IN (United States). Department of Earth, Atmospheric, and Planetary Sciences and Department of Agronomy
3. North Carolina State Univ., Raleigh, NC (United States). Department of Forestry and Environmental Resources and Southern Global Change Program
4. Michigan State Univ., East Lansing, MI (United States). CGCEO/Geography
5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division

Quantitative understanding of regional gross primary productivity (GPP) and net ecosystem exchanges (NEE) and their responses to environmental changes are critical to quantifying the feedbacks of ecosystems to the global climate system. Numerous studies have used the eddy flux data to upscale the eddy covariance derived carbon fluxes from stand scales to regional and global scales. However, few studies incorporated atmospheric carbon dioxide (CO₂) concentrations into those extrapolations. In this study, we consider the effect of atmospheric CO₂ using an artificial neural network (ANN) approach to upscale the AmeriFlux tower of NEE and the derived GPP to the conterminous United States. Two ANN models incorporating remote sensing variables at an 8-day time step were developed. One included CO₂ as an explanatory variable and the other did not. The models were first trained, validated using eddy flux data, and then extrapolated to the region at a 0.05° × 0.05° (latitude × longitude) resolution from 2001 to 2006. We found that both models performed well in simulating site-level carbon fluxes. The spatially-averaged annual GPP with and without considering the atmospheric CO₂ were 789 and 788 g C m^-2 yr^-1, respectively (for NEE, the values were -112 and -109 g C m^-2 yr^-1, respectively). Model predictions were comparable with previous published results and MODIS GPP products. However, the difference in GPP between the two models exhibited a great spatial and seasonal variability, with an annual difference of 200 g C m^-2 yr^-1. Further analysis suggested that air temperature played an important role in determining the atmospheric CO₂ effects on carbon fluxes. In addition, the simulation that did not consider atmospheric CO₂ failed to detect ecosystem responses to droughts in part of the US in 2006. In conclusion, we suggest that the spatially and temporally varied atmospheric CO₂ concentrations should be factored into carbon quantification when scaling eddy flux data to a region.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: AC05-00OR22725; FG02-08ER64599
• OSTI ID: 1327619
• Journal Information: Agricultural and Forest Meteorology, Vol. 220; ISSN 0168-1923
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Gross primary production
Net ecosystem change
Eddy flux tower
CO2
Artificial neural network