Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Default user image.

Anders Lindroth

Professor

Default user image.

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

Författare

  • Martin Jung
  • Markus Reichstein
  • Hank A. Margolis
  • Alessandro Cescatti
  • Andrew D. Richardson
  • M. Altaf Arain
  • Almut Arneth
  • Christian Bernhofer
  • Damien Bonal
  • Jiquan Chen
  • Damiano Gianelle
  • Nadine Gobron
  • Gerald Kiely
  • Werner Kutsch
  • Gitta Lasslop
  • Beverly E. Law
  • Anders Lindroth
  • Lutz Merbold
  • Leonardo Montagnani
  • Eddy J. Moors
  • Dario Papale
  • Matteo Sottocornola
  • Francesco Vaccari
  • Christopher Williams

Summary, in English

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.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • eSSENCE: The e-Science Collaboration
  • MERGE: ModElling the Regional and Global Earth system
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publiceringsår

2011

Språk

Engelska

Sidor

00-07

Publikation/Tidskrift/Serie

Journal of Geophysical Research

Volym

116

Dokumenttyp

Artikel i tidskrift

Förlag

Wiley-Blackwell

Ämne

  • Physical Geography

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 2156-2202