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. 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. 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. [References: 53

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Aim The objectives of this study were to determine the relationships between climatic factors and litterfall in coniferous and broadleaf forests in Eurasia and to explore the difference in litterfall between coniferous and broadleaf forests as related to climate at a continental scale.Location We have used data from across Eurasia. MethodsThe relationships between litterfall and climatic factors were examined using linear regression analysis of a compilation of published data from coniferous and broadleaf forests in Eurasia. ResultsThe relationships between litterfall and climatic factors show that in the temperate, subtropical, and tropical areas, broadleaf forests had higher litterfall than coniferous ones, whilst the opposite was found for boreal forests. Combining all climatic zones, a multiple regression analysis using annual mean temperature (T) and annual precipitation (P) as independent variables gave an adjusted R 2 ( R 2 adj ) of 0.272 for total litterfall in coniferous forests ( n = 199, P < 0.001), 0.498 for broadleaf litterfall ( n = 240, P < 0.001), and 0.535 for combined coniferous and broadleaf litterfall ( n = 439, P < 0.001). The linear models for broadleaf stands have significantly higher coefficients for T and P than those for coniferous ones but the intercepts were similar. Thus, litterfall in broadleaf forests increased faster with T and P than that in coniferous forests. Further, a transformation of temperature and precipitation to relative units showed that a relative-unit change in T had a larger impact than P on total litterfall in broadleaf forests. The results indicate that at a continental scale, climatic controls over litterfall differ between coniferous and broadleaf forests.Conclusions A relative unit change in annual mean temperature has a greater effect on litterfall compared to the same change in annual precipitation across the Eurasian forests. Further, the higher response to T for broadleaf forests indicates a difference in climate control between coniferous and broadleaf forests at a continental scale, and consequently different litterfall responses to climate change. BIOSKETCHChunjiang Liu is a research scientist in the Ecology Centre, Christian-Albrechts University of Kiel. His research interests are focused on the environmental control of ecological processes in forest ecosystems at different geographical scales in the context of global change. The approaches used in his work include field studies, meta-analysis and theoretical simulation.

The timing of the commencement of photosynthesis (P*) in spring is an important determinant of growing‐season length and thus of the productivity of boreal forests. Although controlled experiments have shed light on environmental mechanisms triggering release from photoinhibition after winter, quantitative research for trees growing naturally in the field is scarce. In this study, we investigated the environmental cues initiating the spring recovery of boreal coniferous forest ecosystems under field conditions. We used meteorological data and above‐canopy eddy covariance measurements of the net ecosystem CO2 exchange (NEE) from five field stations located in northern and southern Finland, northern and southern Sweden, and central Siberia. The within‐ and intersite variability for P* was large, 30–60 days. Of the different climate variables examined, air temperature emerged as the best predictor for P* in spring. We also found that ‘soil thaw’, defined as the time when near‐surface soil temperature rapidly increases above 0°C, is not a useful criterion for P*. In one case, photosynthesis commenced 1.5 months before soil temperatures increased significantly above 0°C. At most sites, we were able to determine a threshold for air‐temperature‐related variables, the exceeding of which was required for P*. A 5‐day running‐average temperature (T5) produced the best predictions, but a developmental‐stage model (S) utilizing a modified temperature sum concept also worked well. But for both T5 and S, the threshold values varied from site to site, perhaps reflecting genetic differences among the stands or climate‐induced differences in the physiological state of trees in late winter/early spring. Only at the warmest site, in southern Sweden, could we obtain no threshold values for T5 or S that could predict P* reliably. This suggests that although air temperature appears to be a good predictor for P* at high latitudes, there may be no unifying ecophysiological relationship applicable across the entire boreal zone.