JungReichsteinMargolisEtAl2011

Reference

Jung, M., Reichstein, M., Margolis, H.A., Cescatti, A., Richardson, A.D., Arain, M.A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D., Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B.E., Lindroth, A., Merbold, L., Montagnani, L., Moors, E.J., Papale, D., Sottocornola, M., Vaccari, F., Williams, C. (2011) Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. Journal of Geophysical Research: Biogeosciences, 116(3):G00J07. (Scopus )

Abstract

We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5° × 0.5° spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 1018 yr-1), H (164 ± 15 J × 1018 yr-1), and GPP (119 6 Pg C yr-1) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr-1) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere. Copyright 2011 by the American Geophysical Union.

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@ARTICLE { JungReichsteinMargolisEtAl2011,
    AUTHOR = { Jung, M. and Reichstein, M. and Margolis, H.A. and Cescatti, A. and Richardson, A.D. and Arain, M.A. and Arneth, A. and Bernhofer, C. and Bonal, D. and Chen, J. and Gianelle, D. and Gobron, N. and Kiely, G. and Kutsch, W. and Lasslop, G. and Law, B.E. and Lindroth, A. and Merbold, L. and Montagnani, L. and Moors, E.J. and Papale, D. and Sottocornola, M. and Vaccari, F. and Williams, C. },
    TITLE = { Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations },
    JOURNAL = { Journal of Geophysical Research: Biogeosciences },
    YEAR = { 2011 },
    VOLUME = { 116 },
    PAGES = { G00J07 },
    NUMBER = { 3 },
    ABSTRACT = { We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5° × 0.5° spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 1018 yr-1), H (164 ± 15 J × 1018 yr-1), and GPP (119 6 Pg C yr-1) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr-1) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere. Copyright 2011 by the American Geophysical Union. },
    COMMENT = { Export Date: 26 September 2011 Source: Scopus Art. No.: G00J07 doi: 10.1029/2010JG001566 },
    ISSN = { 01480227 (ISSN) },
    OWNER = { Luc },
    TIMESTAMP = { 2011.09.26 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-80052534797&partnerID=40&md5=d7f137985587f251f1a0913bc8fe8984 },
}

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