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

TLDR

The paper reviews methods for partitioning net ecosystem exchange into gross ecosystem production and ecosystem respiration. The study proposes a generic algorithm that estimates short‑term temperature sensitivity of ecosystem respiration from eddy covariance data and fills data gaps using flux–meteorology covariance and temporal structure. The authors analyze 16 one‑year long eddy covariance data sets from boreal to Mediterranean sites, showing that using long‑term temperature response functions for nighttime respiration extrapolation biases daytime estimates, and develop an algorithm that derives short‑term sensitivity and fills gaps. They find that long‑term temperature sensitivities over‑ or under‑estimate daytime respiration by more than 25 % in summer active or passive ecosystems, respectively, and that the new algorithm reduces these biases but still leaves residual uncertainties.

Abstract

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.