Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations

被引:1032
作者
Jung, Martin [1 ]
Reichstein, Markus [1 ]
Margolis, Hank A. [2 ]
Cescatti, Alessandro [3 ]
Richardson, Andrew D. [4 ]
Arain, M. Altaf [5 ,6 ]
Arneth, Almut [7 ,8 ]
Bernhofer, Christian [9 ]
Bonal, Damien [10 ]
Chen, Jiquan [11 ]
Gianelle, Damiano [12 ]
Gobron, Nadine [13 ]
Kiely, Gerald [14 ]
Kutsch, Werner [15 ]
Lasslop, Gitta [1 ]
Law, Beverly E. [16 ]
Lindroth, Anders [7 ]
Merbold, Lutz [17 ]
Montagnani, Leonardo [18 ,19 ]
Moors, Eddy J. [20 ]
Papale, Dario [21 ]
Sottocornola, Matteo [12 ]
Vaccari, Francesco [22 ]
Williams, Christopher [23 ]
机构
[1] Max Planck Inst Biogeochem, Model Data Integrat Grp, Jena, Germany
[2] Univ Laval, Ctr Etud Foret, Fac Foresterie Geog & Geomat, Quebec City, PQ G1V 0A6, Canada
[3] Commiss European Communities, Joint Res Ctr, Climate Change & Air Qual Unit, Inst Environm & Sustainabil, I-21020 Ispra, Italy
[4] Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA
[5] McMaster Univ, Sch Geog & Earth Sci, Hamilton, ON L8S 4K1, Canada
[6] McMaster Univ, McMaster Ctr Climate Change, Hamilton, ON L8S 4K1, Canada
[7] Lund Univ, Div Phys Geog & Ecosyst Anal, Dept Earth & Ecosyst Sci, SE-22362 Lund, Sweden
[8] Karlsruhe Inst Technol, Inst Meteorol, Garmisch Partenkirchen, Germany
[9] Tech Univ Dresden, Dept Meteorol, Inst Hydrol & Meteorol, D-01062 Dresden, Germany
[10] INRA, UMR INRA UHP Ecol & Ecophysiol Forestiere 1137, F-54280 Champenoux, France
[11] Univ Toledo, Dept Environm Sci, Toledo, OH 43606 USA
[12] Fdn Edmund Mach, Environm & Nat Resources Area, Res & Innovat Ctr, IASMA, I-38100 Trento, Italy
[13] Commiss European Communities, Joint Res Ctr, Global Environm Monitoring Unit, Inst Environm & Sustainabil, I-21020 Ispra, Italy
[14] Univ Coll Cork, HYDROMET, Civil & Environm Engn Dept, Cork, Ireland
[15] Johann Heinrich von Thunen Inst, Inst Agrarrelevante Klimaforsch, D-38116 Braunschweig, Germany
[16] Oregon State Univ, Dept Forest Ecosyst & Soc, Corvallis, OR 97331 USA
[17] ETH, Grassland Sci Grp, Inst Plant Anim & Agroecosyst Sci, CH-8092 Zurich, Switzerland
[18] Forest Serv & Agcy Environm, I-39100 Bolzano, Italy
[19] Free Univ Bolzano, Fac Sci & Technol, Bolzano, Italy
[20] Wageningen Univ, Alterra Wageningen UR, NL-6700 AA Wageningen, Netherlands
[21] Univ Tuscia, Dept Forest Environm & Resources, I-01100 Viterbo, Italy
[22] CNR, Inst Biometeorol, I-50144 Florence, Italy
[23] Clark Univ, Grad Sch Geog, Worcester, MA 01610 USA
基金
美国国家科学基金会; 加拿大自然科学与工程研究理事会;
关键词
NET ECOSYSTEM EXCHANGE; ENERGY-BALANCE CLOSURE; PRIMARY PRODUCTIVITY; MODEL; CLIMATE; UNCERTAINTY; RESPIRATION; SENSITIVITY; VEGETATION; DYNAMICS;
D O I
10.1029/2010JG001566
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 degrees x 0.5 degrees 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 x 10(18) yr(-1)), H (164 +/- 15 J x 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.
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页数:16
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