On the choice of the driving temperature for eddy-covariance carbon dioxide flux partitioning

Abstract

Abstract. Networks that merge and harmonise eddycovariance 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 (R eco ). 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 R eco 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.

Improved light and temperature responses for light-use-efficiency-based GPP models

“…Furthermore, the choice of the driving variables to model R eco , e.g. air temperature or soil temperature, may be of importance (Lasslop et al, 2012). To date there has been no agreement on a general method to partition CO 2 fluxes.…”

Bulk partitioning the growing season net ecosystem exchange of CO<sub>2</sub> in Siberian tundra reveals the seasonality of its carbon sequestration strength

“…(Reichstein et al, 2005) as well as to fill measurement gaps in flux time series (Falge et al, 2001). In the simplest of such methods, the R eco term is often modelled as a function of temperature from nighttime conditions where P gross is assumed to be negligible (Falge et al, 2002), where the choice of air, soil, or surface temperature is made based on the correlation of each with nighttime NEE (Lasslop et al, 2012). This method has been widely applied at arctic sites, despite the seasonal near-absence of truly dark conditions (e.g.…”

Towards physiologically meaningful water‐use efficiency estimates from eddy covariance data

“…Figure suggests that ecosystem‐level G 1 is relatively robust to choices made on the partitioning approach, which is in line with the general good agreement between the two GPP products (Lasslop et al., ). Nonetheless, the high sensitivity of G 1 to GPP emphasizes the importance of correctly estimating GPP from EC data, which also relies on the use of a representative driving temperature for R eco (Lasslop et al., ). Also relevant for the direct comparison of g 1 at leaf and ecosystem levels are differences in the carbon uptake term used in Equations and : net photosynthesis ( A n ) at the leaf level and GPP at the canopy level.…”