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Corbin, K. D.; Denning, A. S.; Lokupitiya, E. Y.; Schuh, A. E.; Miles, N. L.; Davis, K, J,; Richardson, S.; Baker, I. T. (2011)
Publisher: Tellus B
Journal: Tellus B
Languages: English
Types: Article
Subjects:
Human conversion of natural ecosystems to croplands modifies not only the exchange of water and energy between the surface and the atmosphere, but also carbon fluxes. To investigate the impacts of crops on carbon fluxes and resulting atmospheric CO2 concentrations in the mid-continent region of the United States, we coupled a crop-specific phenology and physiology scheme for corn, soybean and wheat to the coupled ecosystem–atmosphere model SiB3–RAMS. Using SiBcrop–RAMS improved carbon fluxes at the local scale and had regional impacts, decreasing the spring uptake and increasing the summer uptake over the mid-continent. The altered fluxes changed the mid-continent atmospheric CO2 concentration field at 120 m compared to simulations without crops: concentrations increased in May and decreased >20 ppm during July and August, summer diurnal cycle amplitudes increased, synoptic variability correlations improved and the gradient across the mid-continent region increased. These effects combined to reduce the squared differences between the model and high-precision tower CO2 concentrations by 20%. Synoptic transport of the large-scale N–S gradient caused significant day-to-day variability in concentration differences measured between the towers. This simulation study shows that carbon exchange between crops and the atmosphere significantly impacts regional CO2 fluxes and concentrations.DOI: 10.1111/j.1600-0889.2010.00485.x
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    • Andrews, A. E., Kofler, J. D., Bakwin, P. S., Zhao, C. and Tans, P. 2009. Carbon dioxide and carbon monoxide dry air mole fractions from the NOAA ESRL tall tower network, 1992-2009. Version: 2009-05-12, ftp://ftp.cmdl.noaa.gov/ccg/towers/.
    • Baker, I. T. and Denning, A. S. 2008. SiB3 modeled global 1-degree hourly biosphere-atmosphere carbon flux, 1998-2006. In: Data set. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, TN, USA, http://daac.ornl.gov/.
    • Baldocchi, D., Falge, E., Gu, L. H., Olson, R., Hollinger, D. and coauthors. 2001. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. B. Am. Meteor. Soc. 82, 2415-2434.
    • Betts, R. A. 2005. Integrated approaches to climate-crop modelling: needs and challenges. Phil. Trans. R. Soc. B 360, 2049-2065, doi:10/1098/rstb.2005.1739.
    • Bonan, G. B. 1997. Effects of land use on the climate of the United States. Climatic Change 37, 449-486.
    • Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W. and coauthors. 2007. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biol. 13, 679-706, doi:10.1111/j.1365-2486.2006.01305.x.
    • Copeland, J. H., Pielke, R. A. and Kittel, T. G. F. 1996. Potential climatic impacts of vegetation change: a regional modeling study. J. Geophys. Res.-Atmos 101(D3), 7409-7418.
    • Corbin, K. D., Denning, A. S., Lu, L., Wang, J.-W. and Baker, I. T. 2008. Using a high resolution coupled ecosystem-atmosphere model to estimate representation errors in inversions of satellite CO2 retrievals. J. Geophys. Res.-Atmos 113(D02301), doi:10.1029/2007JD008716.
    • Crosson, E. R. 2008. A cavity ring-down analyzer for measuring atmospheric levels of methane, carbon dioxide, and water vapor. Appl. Phys. B 92(3), 403-408.
    • Denning, A. S., Nicholls, M., Prihodko, L., Baker, I. T., Vidale, P.-L. and co-authors. 2003. Simulated variations in atmospheric CO2 over a Wisconsin forest using a coupled ecosystem-atmosphere model. Global Change Biol. 9, 1241-1250.
    • De Noblet-Ducoudre, N., Gervois, S., Ciais, P., Viovy, N., Brisson, N. and co-authors. 2004. Coupling the Soil-Vegetation-AtmosphereTransfer Scheme ORCHIDEE to the agronomy model STICS to study the influence of croplands on the European carbon and water budgets. Agronomie 24, 397-407, doi:10.1051/agro:2004038.
    • Energy Information Administration. 2007. Emissions of greenhouse gases. Report DOE/EIA-0573. Office of Integrated Analysis and Forecasting. U.S. Department of Energy, Washington, DC, 64.
    • Frietas, S. R., Longo, K. M., Silva Dias, M. A. F., Silva Dias, P. L., Chatfield, R. and co-authors. 2005. Monitoring the transport of biomass burning emissions in South America. Environ. Fluid Mech. 5(1-2), 135-167.
    • Gervois, S., de Noblet-Ducoudre, N., Viovy, N. and Ciais, P. 2004. Including croplands in a global biosphere model: methodology and evaluation at specific sites. Earth Interact. 8(16), 1-25.
    • Griffis, T. J., Sargent, S. D., Baker, J. M., Lee, X., Tanner, B. D. and coauthors. 2008. Direct measurement of biosphere-atmosphere isotopic CO2 exchange using the eddy covariance technique. J. Geophys. Res.- Atmos. 113(D08304), doi:10.1029/2007JD009297.
    • Gurney, K. R., Mendoza, D. L., Zhou, Y., Fischer, M. L., Miller, C. C. and co-authors. 2009. High resolution fossil fuel combustion CO2 emission fluxes for the United States. Environ. Sci. Technol. 43, doi:10.1021/es900806c.
    • Houghton, R. A. 2003. Revised estimates of the annual net flux of carbon to the atmosphere from changes in land use and land management 1850-2000. Tellus 55B, 378-390.
    • International Geosphere Biosphere Programme (IGBP). 2000. Global soil data products CD-ROM (IGBP-DIS). In: Global Soil Data Task. Data and Information System, Potsdam, Germany. Available from Oak Ridge National Laboratory Distributive Active Archive Center, Oak Ridge, TN, USA, http://www.daac.ornl.gov.
    • Janssens, I. A., Freibauer, A., Ciais, P., Smith, P., Nabuurs, G.-J. and co-authors. 2003. Europe's terrestrial biosphere absorbs 7 to 12% of European anthropogenic CO2 emissions. Science 300(5625), 1538- 1542.
    • Lawrence, D. M. and Slingo, J. M. 2004. An annual cycle of vegetation in a GCM. Part II: Global impacts on climate and hydrology. Climate Dyn. 22, 107-122, doi:10.1007/s00382-003-0367-8.
    • Lokupitiya, E., Breidt, F. J., Lokupitiya, R., Williams, S. and Paustian, K. 2007. Deriving comprehensive county-level crop yield and area data for U.S. cropland. Agron. J., 99(3), 673-681.
    • Lokupitiya, E., Denning, A. S., Paustian, K., Baker, I. T., Schaefer, K. and co-authors. 2009. Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeosciences 6, 969-986.
    • Lu, L., Denning, A. S., da Silva-Dias, M. A., da Silva-Dias, P., Longo, M. and co-authors. 2005. Mesoscale circulations and atmospheric CO2 variations in the Tapajos Region, Para, Brazil. J. Geophys. Res.-Atmos. 110(D21102), doi:10.1029/2004JD005757.
    • Matamala, R., Jastrow, D. J., Miller, R. M. and Garten, C. 2008. Temporal changes in the distribution of C and N stocks in a restored tallgrass prairie in the U.S. Midwest. Ecol. Appl. 18, 1470-1488.
    • Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C. and co-authors. 2006. North American Regional Reanalysis. Bull. Am. Meteorol. Soc. 87(3), 343-360.
    • Nicholls, M. E., Denning, A. S., Prihodko, L., Vidale, P.-L., Baker, I. T. and co-authors. 2004. A multiple-scale simulation of variations in atmospheric carbon dioxide using a coupled biosphere-atmospheric model. J. Geophys. Res.-Atmos. 109(D18117), doi:10.1029/2003JD004482.
    • Ogle, S., Davis, K. J., Andrews, A., Gurney, K. R., West, T. and co-authors. 2006. Mid-continent intensive campaign of the North America Carbon Program. Sci. Plan., http://www.nacarbon.org/ nacp/mci.html.
    • Osborne, T. M., Lawrence, D. M., Challinor, A. J., Slingo, J. M. and Wheeler, T. R. 2007. Development and assessment of a coupled cropclimate model. Global Change Biol. 13, 169-183, doi:10.1111/j.1365- 2486.2006.01274.x.
    • Parazoo, N. C., Denning, A. S., Kawa, S. R., Corbin, K. D., Lokupitiya, R. S. and co-authors. 2008. Mechanisms for synoptic variations of atmospheric CO2 in North America, South America, and Europe. Atmos. Chem. Phys. 8, 7239-7254.
    • Ramankutty, N. and Foley, J. A. 1998. Characterizing patterns of global land use: an analysis of global croplands data. Global Biogeochem. Cycles 12(4), 667-685.
    • Richardson, S. J., Miles, N. L., Davis, K. J., Crosson, E., Van Pelt, A. D. and co-authors. 2009. EOS Trans. AGU 90(52), Fall Meet. Suppl., Abstract B51E-0343.
    • Verma, S. B., Dobermann, A., Cassman, K. G., Walters, D. T., Knops, J. M. and co-authors. 2005. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agric. Forest. Met. 131(1-2), 77-96.
    • Vleeshouwers, L. M. and Verhagen, A. 2002. Carbon emission and sequestration by agricultural land use: a model study for Europe. Global Change Biol. 8, 519-530.
    • Wang, J.-W., Denning, A. S., Lu, L., Baker, I. T., Corbin, K. D. and co-authors. 2007. Observations and simulations of synoptic, regional, and local variations in atmospheric CO2. J. Geophys. Res.-Atmos. 112(D04108), doi:10.1029/2006JD007410.
    • Xiao, J. F., Zhuang, Q. L., Baldocchi, D. D., Law, B. E., Richardson, A. D. and co-authors. 2008. Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data. Agric. Forest Met. 148(11), 1827-1847.
    • Zhao, M., Heinsch, F. A., Nemani, R. R. and Running, S. W. 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. Rem. Sens. Environ. 95, 164-176.
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