Maqola

On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm

Markus ReichsteinDepartment of Forest Science and Environment, University of Tuscia, 01100 Viterbo, Italy,Eva FalgeDepartment of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany,Dennis BaldocchiDepartment of Environmental Science, Policy and Management, Ecosystem Science Division, University of California, 151 Hilgard Hall #3110, Berkeley, CA 94720-3110, USA,Dario PapaleDepartment of Forest Science and Environment, University of Tuscia, 01100 Viterbo, Italy,Marc AubinetUnité de Physique, Faculté des Sciences Agronomiques de Gembloux, B-50 30 Gembloux, Belgium,Paul BerbigierINRA–EPHYSE, BP 81, F33883 Villenave d'Ornon Cedex, France,Christian BernhoferTechnische Universität Dresden, IHM-Meteorologie, Pienner Strasse 9, 01737 Tharandt, Germany,Nina BuchmannInstitute of Plant Sciences, ETH Zürich, Universitätsstrasse 2, 8092 Zürich, Switzerland,Tagir G. GilmanovDepartment of Biology/Microbiology AgH 310, Box 2207B South Dakota State University Brookings, SD 57007-2142, USA,André GranierINRA, Unité d'Ecophysiologie Forestière, F-54280 Champenoux, France,Thomas GrünwaldTechnische Universität Dresden, IHM-Meteorologie, Pienner Strasse 9, 01737 Tharandt, Germany,K. HavránkováHannu IlvesniemiFinnish Forest Research Institute, Jokiniemenkuja 1, FIN-01300 Vantaa, Finland,Dalibor JanoušAlexander KnohlDepartment of Environmental Science, Policy and Management, Ecosystem Science Division, University of California, 151 Hilgard Hall #3110, Berkeley, CA 94720-3110, USA,Tuomas LaurilaFinnish Meteorological Institute, PO Box 503, FIN-00101 Helsinki, Finland,Annalea LohilaFinnish Meteorological Institute, PO Box 503, FIN-00101 Helsinki, Finland,Denis LoustauINRA-Ecophysiologie, Domaine de l'Hermitage-Pierroton, 69, route d'Arcachon, 33610 Cestas, France,Gioṙgio MatteucciTilden P. MeyersNOAA/Atmospheric Turbulence and Diffusion, PO Box 2456 Oak Ridge, TN 37830, USA,F. MigliettaIBIMET, P.le delle Cascine, 18, 50144 Firenze, Italy,Jean‐Marc OurcivalDREAM Unit, Centre d'Ecologie Fonctionelle et Evolutive, CNRS, 1919 route de Mende, Montpellier, France,Jukka PumpanenDepartment of Forest Ecology, PO Box 27, University of Helsinki, FIN-00014 Helsinki, Finland,Serge RambalDREAM Unit, Centre d'Ecologie Fonctionelle et Evolutive, CNRS, 1919 route de Mende, Montpellier, France,Eyal RotenbergDepartment of Environmental Sciences and Energy Research, Weizmann Institute of Science, PO Box 26, Rehovot 76100, Israel,María José SanzCEAM, Parque Tecnológico, c/Charles H. Darwin 14, 46980 Paterna (Valencia), Spain,John TenhunenDepartment of Plant Ecology, University of Bayreuth, D-95440 Bayreuth, Germany,Günther SeufertFrancesco Primo VaccariIBIMET, P.le delle Cascine, 18, 50144 Firenze, Italy,Timo VesalaDepartment of Physical Sciences, University of Helsinki, FIN-00014 Helsinki, FinlandDan YakirDepartment of Environmental Sciences and Energy Research, Weizmann Institute of Science, PO Box 26, Rehovot 76100, Israel,Riccardo ValentiniDepartment of Forest Science and Environment, University of Tuscia, 01100 Viterbo, Italy,

2005en

ABI

Annotatsiya

Abstract This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration ( R eco ). In particular, we analyse the effect of the extrapolation of night‐time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long‐term data sets. For this analysis, we used 16 one‐year‐long data sets of carbon dioxide exchange measurements from European and US‐American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems. We show that the temperature sensitivity of R eco , derived from long‐term (annual) data sets, does not reflect the short‐term temperature sensitivity that is effective when extrapolating from night‐ to daytime. Specifically, in summer active ecosystems the long‐term temperature sensitivity exceeds the short‐term sensitivity. Thus, in those ecosystems, the application of a long‐term temperature sensitivity to the extrapolation of respiration from night to day leads to a systematic overestimation of ecosystem respiration from half‐hourly to annual time‐scales, which can reach >25% for an annual budget and which consequently affects estimates of GEP. Conversely, in summer passive (Mediterranean) ecosystems, the long‐term temperature sensitivity is lower than the short‐term temperature sensitivity resulting in underestimation of annual sums of respiration. We introduce a new generic algorithm that derives a short‐term temperature sensitivity of R eco from eddy covariance data that applies this to the extrapolation from night‐ to daytime, and that further performs a filling of data gaps that exploits both, the covariance between fluxes and meteorological drivers and the temporal structure of the fluxes. While this algorithm should give less biased estimates of GEP and R eco , we discuss the remaining biases and recommend that eddy covariance measurements are still backed by ancillary flux measurements that can reduce the uncertainties inherent in the eddy covariance data.

Hali tarjima qilinmagan

Identifikatorlar

DOI: 10.1111/j.1365-2486.2005.001002.x

Iqtiboslar va manbalar

2 ta iqtibos0 ta foydalanilgan manba

Koʻrsatkichlar — AkademScholar