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

Drainage has turned peatlands from a carbon sink into one of the world's largest greenhouse gas (GHG) sources from cultivated soils. We analyzed a unique data set (12 peatlands, 48 sites and 122 annual budgets) of mainly unpublished GHG emissions from grasslands on bog and fen peat as well as other soils rich in soil organic carbon (SOC) in Germany. Emissions and environmental variables were measured with identical methods. Site-averaged GHG budgets were surprisingly variable (29.2 ± 17.4 t CO -eq. ha yr ) and partially higher than all published data and the IPCC default emission factors for GHG inventories. Generally, CO (27.7 ± 17.3 t CO ha yr ) dominated the GHG budget. Nitrous oxide (2.3 ± 2.4 kg N O-N ha yr ) and methane emissions (30.8 ± 69.8 kg CH -C ha yr ) were lower than expected except for CH emissions from nutrient-poor acidic sites. At single peatlands, CO emissions clearly increased with deeper mean water table depth (WTD), but there was no general dependency of CO on WTD for the complete data set. Thus, regionalization of CO emissions by WTD only will remain uncertain. WTD dynamics explained some of the differences between peatlands as sites which became very dry during summer showed lower emissions. We introduced the aerated nitrogen stock (N ) as a variable combining soil nitrogen stocks with WTD. CO increased with N across peatlands. Soils with comparatively low SOC concentrations showed as high CO emissions as true peat soils because N was similar. N O emissions were controlled by the WTD dynamics and the nitrogen content of the topsoil. CH emissions can be well described by WTD and ponding duration during summer. Our results can help both to improve GHG emission reporting and to prioritize and plan emission reduction measures for peat and similar soils at different scales.

The static chamber method (non-flow-through-non-steady-state chambers) is the most common method to measure fluxes of methane (CH4) from soils. Laboratory comparisons to quantify errors resulting from chamber design, operation and flux calculation methods are rare. We tested fifteen chambers against four flux levels (FL) ranging from 200 to 2300 mu g CH4 M-2 II-1. The measurements were conducted on a calibration tank using three quartz sand types with soil porosities of 53% (dry fine sand, S1), 47% (dry coarse sand, S2), and 33% (wetted fine sand, S3). The chambers tested ranged from 0.06 to 1.8 m in height, and 0.02 to 0.195 m(3) in volume, 7 of them were equipped with a fan, and 1 with a vent-tube. We applied linear and exponential flux calculation methods to the chamber data and compared these chamber fluxes to the reference fluxes from the calibration tank. The chambers underestimated the reference fluxes by on average 33% by the linear flux calculation method (R-Iin), whereas the chamber fluxes calculated by the exponential flux calculation method (R-exp) did not significantly differ from the reference fluxes (p <0.05). The flux under- or overestimations were chamber specific and independent of flux level. Increasing chamber height, area and volume significantly reduced the flux underestimation (p <0.05). Also, the use of non-linear flux calculation method significantly improved the flux estimation; however, simultaneously the uncertainty in the fluxes was increased. We provide correction factors, which can be used to correct the under- or overestimation of the fluxes by the chambers in the experiment. (C) 2012 Elsevier B.V. All rights reserved

A laboratory experiment was conducted with two types of closed static chambers to estimate the effects of chamber placement, manual headspace sampling and headspace mixing on methane (CH 4 ) fluxes. Chamber fluxes were compared to a known reference flux in a chamber calibration system. The measurements were conducted with three types of soils (coarse dry, fine dry and fine wet quarts sand) at five flux levels ranging from 60 to 2000 μg CH 4 m −2 h −1 . We found that the placement of a non-vented chamber disturbed the initial CH 4 concentration development within the chamber headspace for 10 to 30 s. Excluding this short period from the flux calculation resulted in a lower flux estimate (mean±SE) of 126±26 μg CH 4 m −2 h −1 compared to 134±26 μg CH 4 m −2 h −1 if data from time zero of the enclosure were included. We also found that in non-mixed chambers (no fan mixing) the gas sampling by syringes or gas bottles disturbed the development of CH 4 concentration during the enclosure. Furthermore, flux estimates in non-mixed chambers were significantly underestimated (on average 36%) compared to the measured reference fluxes. However, the use of fans to constantly mix the chamber headspace during enclosure significantly improved the goodnessof-fit of the regression analysis used to calculate the flux and further eliminated the disturbance of the manual sampling on the concentration development. We recommend that chambers should be vented during the placement of the chamber, and that fans are used as an integrated part of static chambers while headspace mixing with syringes should be avoided.

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Abstract. Nitrous oxide emissions from a network of agricultural experiments in Europe were used to explore the relative importance of site and management controls of emissions. At each site, a selection of management interventions were compared within replicated experimental designs in plot-based experiments. Arable experiments were conducted at Beano in Italy, El Encin in Spain, Foulum in Denmark, Logården in Sweden, Maulde in Belgium, Paulinenaue in Germany, and Tulloch in the UK. Grassland experiments were conducted at Crichton, Nafferton and Peaknaze in the UK, Gödöllö in Hungary, Rzecin in Poland, Zarnekow in Germany and Theix in France. Nitrous oxide emissions were measured at each site over a period of at least two years using static chambers. Emissions varied widely between sites and as a result of manipulation treatments. Average site emissions (throughout the study period) varied between 0.04 and 21.21 kg N2O-N ha−1 yr−1, with the largest fluxes and variability associated with the grassland sites. Total nitrogen addition was found to be the single most important determinant of emissions, accounting for 15% of the variance (using linear regression) in the data from the arable sites (p < 0.0001), and 77% in the grassland sites. The annual emissions from arable sites were significantly greater than those that would be predicted by IPCC default emission factors. Variability of N2O emissions within sites that occurred as a result of manipulation treatments was greater than that resulting from site-to-site and year-to-year variation, highlighting the importance of management interventions in contributing to greenhouse gas mitigation.

Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) 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 CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach was 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 CO2 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 CO2 flux measurements (total number: 1764) conducted at three peatland sites in Finland and a tundra site in Siberia. The flux measurements were performed using transparent chambers on vegetated surfaces and opaque chambers on bare peat surfaces. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 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) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. CO2 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 and even lower for longer closure times. The degree of underestimation increased with increasing CO2 flux strength and is dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 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.