Open AccessGlobal patterns of land‐atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations
Author(s) -
Jung Martin,
Reichstein Markus,
Margolis Hank A.,
Cescatti Alessandro,
Richardson Andrew D.,
Arain M. Altaf,
Arneth Almut,
Bernhofer Christian,
Bonal Damien,
Chen Jiquan,
Gianelle Damiano,
Gobron Nadine,
Kiely Gerald,
Kutsch Werner,
Lasslop Gitta,
Law Beverly E.,
Lindroth Anders,
Merbold Lutz,
Montagnani Leonardo,
Moors Eddy J.,
Papale Dario,
Sottocornola Matteo,
Vaccari Francesco,
Williams Christopher
Publication year - 2011
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2010jg001566
Subject(s) - eddy covariance , fluxnet , environmental science , latent heat , atmospheric sciences , climatology , satellite , ecosystem respiration , carbon cycle , sensible heat , atmosphere (unit) , primary production , water cycle , ecosystem , meteorology , geography , ecology , geology , biology , aerospace engineering , engineering
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 × 10 18 yr −1 ), H (164 ± 15 J × 10 18 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.