BG Biogeosciences BG Biogeosciences 1726-4189 Copernicus Publications Göttingen, Germany 10.5194/bg-9-5243-2012 On the choice of the driving temperature for eddy-covariance carbon dioxide flux partitioning Lasslop G. https://orcid.org/0000-0001-9939-1459 1 9 Migliavacca M. 2 Bohrer G. https://orcid.org/0000-0002-9209-9540 3 Reichstein M. 1 Bahn M. https://orcid.org/0000-0001-7482-9776 4 Ibrom A. https://orcid.org/0000-0002-1341-921X 5 Jacobs C. https://orcid.org/0000-0003-4079-9067 6 Kolari P. 7 Papale D. https://orcid.org/0000-0001-5170-8648 8 Vesala T. 7 Wohlfahrt G. https://orcid.org/0000-0003-3080-6702 4 Cescatti A. https://orcid.org/0000-0002-2769-2591 2 Max Planck Institute for Biogeochemistry, Biogeochemical Model-Data Integration Group, Jena, Germany European Commission, Joint Research Centre, Institute for Environment and Sustainability, Climate Risk Management Unit, Ispra, Italy Department of Civil, Environmental & Geodetic Engineering, The Ohio State University, OH, USA Institute of Ecology, University of Innsbruck, Innsbruck, Austria Centre for Ecosystems and Environmental Sustainability, Department of Chemical and Biochemical, Technical University of Denmark (DTU), Denmark Wageningen UR, Alterra, Wageningen, The Netherlands Department of Forest Sciences, University of Helsinki, Finland Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), Univ. of Tuscia, Viterbo, Italy now at: Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany 18 12 2012 9 12 5243 5259 Copyright: © 2012 G. Lasslop et al. 2012 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://bg.copernicus.org/articles/9/5243/2012/bg-9-5243-2012.html The full text article is available as a PDF file from https://bg.copernicus.org/articles/9/5243/2012/bg-9-5243-2012.pdf

Networks that merge and harmonise eddy-covariance measurements from many different parts of the world have become an important observational resource for ecosystem science. Empirical algorithms have been developed which combine direct observations of the net ecosystem exchange of carbon dioxide with simple empirical models to disentangle photosynthetic (GPP) and respiratory fluxes (<i>R</i><sub>eco</sub>). The increasing use of these estimates for the analysis of climate sensitivities, model evaluation and calibration demands a thorough understanding of assumptions in the analysis process and the resulting uncertainties of the partitioned fluxes. The semi-empirical models used in flux partitioning algorithms require temperature observations as input, but as respiration takes place in many parts of an ecosystem, it is unclear which temperature input – air, surface, bole, or soil at a specific depth – should be used. This choice is a source of uncertainty and potential biases. In this study, we analysed the correlation between different temperature observations and nighttime NEE (which equals nighttime respiration) across FLUXNET sites to understand the potential of the different temperature observations as input for the flux partitioning model. We found that the differences in the correlation between different temperature data streams and nighttime NEE are small and depend on the selection of sites. We investigated the effects of the choice of the temperature data by running two flux partitioning algorithms with air and soil temperature. We found the time lag (phase shift) between air and soil temperatures explains the differences in the GPP and <i>R</i><sub>eco</sub> estimates when using either air or soil temperatures for flux partitioning. The impact of the source of temperature data on other derived ecosystem parameters was estimated, and the strongest impact was found for the temperature sensitivity. Overall, this study suggests that the choice between soil or air temperature must be made on site-by-site basis by analysing the correlation between temperature and nighttime NEE. We recommend using an ensemble of estimates based on different temperature observations to account for the uncertainty due to the choice of temperature and to assure the robustness of the temporal patterns of the derived variables.

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