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 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 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 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 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. 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. 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. The degree of underestimation increased with increasing CO₂ flux strength and was 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.

This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH ₄ ) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH ₄ flux measurements globally, initial results comparing CH ₄ fluxes across the sites, and future research directions and needs. Annual estimates of net CH ₄ fluxes across sites ranged from −0.2 ± 0.02 g C m ^–2 yr ^–1 for an upland forest site to 114.9 ± 13.4 g C m ^–2 yr ^–1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m ^–2 yr ^–1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH ₄ flux across wetland sites globally. Water table position was positively correlated with annual CH ₄ emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH ₄ fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH ₄ estimates due to gap-filling and random errors were on average ±1.6 g C m ^–2 yr ^–1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH ₄ flux database, the controls on ecosystem CH ₄ fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH ₄ emissions.

The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2 kPa, peatland ET exceeds forest ET by up to 30%. Future (2091–2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections.