The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

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Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO 2 ) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO 2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO 2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO 2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO 2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO 2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO 2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO 2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evoCorrespondence to: L. Kutzbach (kutzbach@uni-greifswald.de) lution in the chamber headspace and estimation of the initial CO 2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO 2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO 2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO 2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO 2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO 2 balances than in the individual fluxes. To avoid serious bias of CO 2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

The carbon budgets of the atmosphere and terrestrial ecosystems are closely coupled by vertical gas exchange fluxes. Uncertainties remain with respect to high latitude ecosystems and the processes driving their temporally and spatially highly variable methane (CH4) exchange. Problems associated with scaling plot measurements to larger areas in heterogeneous environments are addressed based on intensive field studies on two nested spatial scales in Northern Siberia. CH4 fluxes on the microsite scale (0.1–100 m2) were measured in the Lena River Delta from July through September 2006 by closed chambers and were compared with simultaneous ecosystem scale (104–106 m2) flux measurements by the eddy covariance (EC) method. Closed chamber measurements were conducted almost daily on 15 plots in four differently developed polygon centers and on a polygon rim. Controls on CH4 emission were identified by stepwise multiple regression. In contrast to relatively low ecosystem-scale fluxes controlled mainly by near-surface turbulence, fluxes on the microsite scale were almost an order of magnitude higher at the wet polygon centers and near zero at the drier polygon rim and high-center polygon. Microsite scale CH4 fluxes varied strongly even within the same microsites. The only statistically significant control on chamber-based fluxes was surface temperature calculated using the Stefan–Boltzmann equation in the wet polygon centers, whereas no significant control was found for the low emissions from the dry sites. The comparison with the EC measurements reveals differences in controls and the seasonal dynamics between the two measurement scales, which may have consequences for scaling and process-based models. Despite those differences, closed chamber measurements from within the EC footprint could be scaled by an area-weighting approach of landcover classes based on high-resolution imagery to match the total ecosystem-scale emission. Our nested sampling design allowed for checking scaling results against measurements and to identify potentially missed sources or sinks