%0 Journal Article %1 jung_global_2011 %A Jung, Martin %A Reichstein, Markus %A Margolis, Hank A. %A Cescatti, Alessandro %A Richardson, Andrew D. %A Arain, M. Altaf %A Arneth, Almut %A Bernhofer, Christian %A Bonal, Damien %A Chen, Jiquan %A Gianelle, Damiano %A Gobron, Nadine %A Kiely, Gerald %A Kutsch, Werner %A Lasslop, Gitta %A Law, Beverly E. %A Lindroth, Anders %A Merbold, Lutz %A Montagnani, Leonardo %A Moors, Eddy J. %A Papale, Dario %A Sottocornola, Matteo %A Vaccari, Francesco %A Williams, Christopher %D 2011 %J Journal of Geophysical Research: Biogeosciences %K FLUXNET, carbon, ecomodelling energy, ensembles, interannnual model tree upscaling variability, %N G3 %R 10.1029/2010JG001566 %T Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations %U https://onlinelibrary.wiley.com/doi/abs/10.1029/2010JG001566 %V 116 %X 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.

@article{jung_global_2011, 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.}, added-at = {2023-07-31T08:05:54.000+0200}, author = {Jung, Martin and Reichstein, Markus and Margolis, Hank A. and Cescatti, Alessandro and Richardson, Andrew D. and Arain, M. Altaf and Arneth, Almut and Bernhofer, Christian and Bonal, Damien and Chen, Jiquan and Gianelle, Damiano and Gobron, Nadine and Kiely, Gerald and Kutsch, Werner and Lasslop, Gitta and Law, Beverly E. and Lindroth, Anders and Merbold, Lutz and Montagnani, Leonardo and Moors, Eddy J. and Papale, Dario and Sottocornola, Matteo and Vaccari, Francesco and Williams, Christopher}, biburl = {https://www.bibsonomy.org/bibtex/2aa05e81cd6267a8a60ff3de6ebaa1fca/jascal_panetzky}, doi = {10.1029/2010JG001566}, file = {Full Text PDF:/Users/pascal/Zotero/storage/ISQCGGRH/Jung et al. - 2011 - Global patterns of land-atmosphere fluxes of carbo.pdf:application/pdf}, interhash = {bed279475ff29bb1a6424be94347e71e}, intrahash = {aa05e81cd6267a8a60ff3de6ebaa1fca}, issn = {2156-2202}, journal = {Journal of Geophysical Research: Biogeosciences}, keywords = {FLUXNET, carbon, ecomodelling energy, ensembles, interannnual model tree upscaling variability,}, language = {en}, note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2010JG001566}, number = {G3}, timestamp = {2023-07-31T08:07:14.000+0200}, title = {Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2010JG001566}, urldate = {2023-07-26}, volume = 116, year = 2011 }