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Characterizing ecosystem-atmosphere interactions from short to interannual time scales

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons62472

Mahecha,  M. D.
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons62524

Reichstein,  M.
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons62352

Carvalhais,  N.
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Zitation

Mahecha, M. D., Reichstein, M., Lange, H., Carvalhais, N., Bernhofer, C., Grunwald, T., et al. (2007). Characterizing ecosystem-atmosphere interactions from short to interannual time scales. Biogeosciences, 4(5), 743-758. doi:10.5194/bg-4-743-2007.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-D56A-6
Zusammenfassung
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, R-eco, 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 (R-g). 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.