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Biogeosciences An interactive open-access journal of the European Geosciences Union
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Volume 4, issue 5
Biogeosciences, 4, 743–758, 2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Greenhouse gases in the Northern Hemisphere

Biogeosciences, 4, 743–758, 2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  13 Sep 2007

13 Sep 2007

Characterizing ecosystem-atmosphere interactions from short to interannual time scales

M. D. Mahecha1, M. Reichstein1, H. Lange2, N. Carvalhais3, C. Bernhofer4, T. Grünwald4, D. Papale5, and G. Seufert6 M. D. Mahecha et al.
  • 1Max-Planck-Institut für Biogeochemie, P.O. Box 10 01 64, 07701 Jena, Germany
  • 2Norsk Institutt for Skog og Landskap, P.O. Box 115, 1431 Aas, Norway
  • 3Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
  • 4Institut für Hydrologie und Meteorologie, Technische Universität Dresden, 01062 Dresden, Germany
  • 5DISAFRI, Università degli Studi della Tuscia, Via Camillo de Lellis, snc – 01100, Viterbo, Italy
  • 6Climate Change Unit, European Commission – Joint Research Centre, Via E. Fermi 1, 21020 Ispra, Italy

Abstract. Characterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series currently comprise an observation period of up to one decade. In this study, we explored whether the observation period is already sufficient to detect cross-relationships between the variables beyond the annual cycle, as they are expected from comparable studies in climatology.

We investigated the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an effective signal to noise separation.

We found that most observations (Net Ecosystem Exchange, NEE, Gross Primary Productivity, GPP, Ecosystem Respiration, Reco, Vapor Pressure Deficit, VPD, Latent Heat, LE, Sensible Heat, H, Wind Speed, u, and Precipitation, P) were influenced significantly by low-frequency components (interannual variability). Furthermore, we extracted a set of nontrivial relationships and found clear seasonal hysteresis effects except for the interrelation of NEE with Global Radiation (Rg).

SSA provides a new tool for the investigation of these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing, since it can be applied as a novel gap filling approach relying on the temporal correlation structure of the time series structure only.

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