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

Abstract. Eddy covariance technique to measure CO 2 , water and energy fluxes between biosphere and atmosphere is widely spread and used in various regional networks. Currently more than 250 eddy covariance sites are active around the world measuring carbon exchange at high temporal resolution for different biomes and climatic conditions. In this paper a new standardized set of corrections is introduced and the uncertainties associated with these corrections are assessed for eight different forest sites in Europe with a total of 12 yearly datasets. The uncertainties introduced on the two components GPP (Gross Primary Production) and TER (Terrestrial Ecosystem Respiration) are also discussed and a quantitative analysis presented. Through a factorial analysis we find that generally, uncertainties by different corrections are additive without interactions and that the heuristic u * -correction introduces the largest uncertainty. The results show that a standardized data processing is needed for an effective comparison across biomes and for underpinning interannual variability. The methodology presented in this paper has also been integrated in the European database of the eddy covariance measurements.

[1] Photosynthesis and respiration impart distinct isotopic signatures to the atmosphere that are used to constrain global carbon source/sink estimates and partition ecosystem fluxes. Increasingly, the ''Keeling plot'' method is being used to determine the carbon isotope composition of ecosystem respiration (d 13 C R ) in order to better understand the processes controlling ecosystem isotope discrimination. In this paper we synthesize emergent patterns in d 13 C R by analyzing 146 Keeling plots constructed at 33 sites across North and South America. In order to interpret results from disparate studies, we discuss the assumptions underlying the Keeling plot method and recommend standardized methods for determining d 13 C R . These include the use of regression calculations that account for error in the x variable, and constraining estimates of d 13 C R to nighttime periods. We then recalculate d 13 C R uniformly for all sites. We found a high degree of temporal and spatial variability in C 3 ecosystems, with individual observations ranging from À19.0 to À32.6%. Mean C 3 ecosystem discrimination was 18.3%. Precipitation was a major driver of both temporal and spatial variability of d 13 C R , suggesting (1) a large influence of recently fixed carbon on ecosystem respiration and (2) a significant effect of previous climatic effects on d 13 C R . These results illustrate the importance of water availability as a key control on atmospheric 13 CO 2 and highlight the potential of d 13 C R as a useful tool for integrating environmental effects on dynamic canopy and ecosystem processes.

[1] Field-chamber measurements of soil respiration from 17 different forest and shrubland sites in Europe and North America were summarized and analyzed with the goal to develop a model describing seasonal, interannual and spatial variability of soil respiration as affected by water availability, temperature, and site properties. The analysis was performed at a daily and at a monthly time step. With the daily time step, the relative soil water content in the upper soil layer expressed as a fraction of field capacity was a good predictor of soil respiration at all sites. Among the site variables tested, those related to site productivity (e.g., leaf area index) correlated significantly with soil respiration, while carbon pool variables like standing biomass or the litter and soil carbon stocks did not show a clear relationship with soil respiration. Furthermore, it was evidenced that the effect of precipitation on soil respiration stretched beyond its direct effect via soil moisture. A general statistical nonlinear regression model was developed to describe soil respiration as dependent on soil temperature, soil water content, and site-specific maximum leaf area index. The model explained nearly two thirds of the temporal and intersite variability of soil respiration with a mean absolute error of 0.82 mmol m À2 s À1. The parameterized model exhibits the following principal properties: (1) At a relative amount of upper-layer soil water of 16% of field capacity, half-maximal soil respiration rates are reached. (2) The apparent temperature sensitivity of soil respiration measured as Q 10 varies between 1 and 5 depending on soil temperature and water content. (3) Soil respiration under reference moisture and temperature conditions is linearly related to maximum site leaf area index. At a monthly timescale, we employed the approach by Raich et al. [2002] that used monthly precipitation and air temperature to globally predict soil respiration (T&P model). While this model was able to explain some of the month-to-month variability of soil respiration, it failed to capture the intersite variability, regardless of whether the original or a new optimized model parameterization was used. In both cases, the residuals were strongly related to maximum site leaf area GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 17, NO. 4, 1104, doi:10.1029/2003GB002035, 2003 15 -1 index. Thus, for a monthly timescale, we developed a simple T&P&LAI model that includes leaf area index as an additional predictor of soil respiration. This extended but still simple model performed nearly as well as the more detailed time step model and explained 50% of the overall and 65% of the site-to-site variability. Consequently, better estimates of globally distributed soil respiration should be obtained with the new model driven by satellite estimates of leaf area index. Before application at the continental or global scale, this approach should be further tested in boreal, cold-temperate, and tropical biomes as well as for non-woody vegetation.INDEX TERMS: 1615 Global...

[1] Carbonyl sulfide (COS) is an atmospheric trace gas that participates in some key reactions of the carbon cycle and thus holds great promise for studies of carbon cycle processes. Global monitoring networks and atmospheric sampling programs provide concurrent data on COS and CO 2 concentrations in the free troposphere and atmospheric boundary layer over vegetated areas. Here we present a modeling framework for interpreting these data and illustrate what COS measurements might tell us about carbon cycle processes. We implemented mechanistic and empirical descriptions of leaf and soil COS uptake into a global carbon cycle model (SiB 3) to obtain new estimates of the COS land flux. We then introduced these revised boundary conditions to an atmospheric transport model (Parameterized Chemical Transport Model) to simulate the variations in the concentration of COS and CO 2 in the global atmosphere. To balance the threefold increase in the global vegetation sink relative to the previous baseline estimate, we propose a new ocean COS source. Using a simple inversion approach, we optimized the latitudinal distribution of this ocean source and found that it is concentrated in the tropics. The new model is capable of reproducing the seasonal variation in atmospheric concentration at most background atmospheric sites. The model also reproduces the observed large vertical gradients in COS between the boundary layer and free troposphere. Using a simulation experiment, we demonstrate that comparing drawdown of CO 2 with COS could provide additional constraints on differential responses of photosynthesis and respiration to environmental forcing. The separation of these two distinct processes is essential to understand the carbon cycle components for improved prediction of future responses of the terrestrial biosphere to changing environmental conditions.