Abstract
Abstract. 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 (Reco). 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 Reco 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.
Highlights
IntroductionTo analyse changes in the observed net ecosystem exchange (NEE) with respect to the underlying processes photosynthesis and respiration, NEE is often partitioned into gross primary production (GPP) and ecosystem respiration (Reco)
Eddy-covariance measurements have contributed strongly to our understanding of ecosystem responses to climate with respect to water, carbon and energy fluxes (Law et al, 2002; Falge et al, 2002; Teuling et al, 2010; Mahecha et al, 2010, and many more).To analyse changes in the observed net ecosystem exchange (NEE) with respect to the underlying processes photosynthesis and respiration, NEE is often partitioned into gross primary production (GPP) and ecosystem respiration (Reco)
We analysed the uncertainty of GPP and Reco estimates caused by the choice between air or soil temperature observations using two commonly used flux partitioning algorithms
Summary
To analyse changes in the observed net ecosystem exchange (NEE) with respect to the underlying processes photosynthesis and respiration, NEE is often partitioned into gross primary production (GPP) and ecosystem respiration (Reco) This procedure is usually based on semi-empirical models of respiration, which use temperature as a driver. Eddy-covariance systems observe the flux above the canopy and, not at the time when the fluxes form, but rather delayed by the transport time from the location of respiration to the sensor (Phillips et al, 2011) This heterogeneity within the ecosystem influences the relationship between observed fluxes and temperature: it can appear as noise or hysteresis patterns may occur. Hysteresis can lead to systematic under- or overestimation in flux-partitioning algorithms that selectively fit only daytime or nighttime data and extrapolate the model in time
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