Here, we present results from the most comprehensive compilation of Holocene peat soil properties with associated carbon and nitrogen accumulation rates for northern peatlands. Our database consists of 268 peat cores from 215 sites located north of 45°N. It encompasses regions within which peat carbon data have only recently become available, such as the West Siberia Lowlands, the Hudson Bay Lowlands, Kamchatka in Far East Russia, and the Tibetan Plateau. For all northern peatlands, carbon content in organic matter was estimated at 42 ± 3% (standard deviation) for Sphagnum peat, 51 ± 2% for non- Sphagnum peat, and at 49 ± 2% overall. Dry bulk density averaged 0.12 ± 0.07 g/cm³, organic matter bulk density averaged 0.11 ± 0.05 g/cm³, and total carbon content in peat averaged 47 ± 6%. In general, large differences were found between Sphagnum and non- Sphagnum peat types in terms of peat properties. Time-weighted peat carbon accumulation rates averaged 23 ± 2 (standard error of mean) g C/m²/yr during the Holocene on the basis of 151 peat cores from 127 sites, with the highest rates of carbon accumulation (25–28 g C/m²/yr) recorded during the early Holocene when the climate was warmer than the present. Furthermore, we estimate the northern peatland carbon and nitrogen pools at 436 and 10 gigatons, respectively. The database is publicly available at https://peatlands.lehigh.edu .

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.

Peatlands are a major terrestrial carbon store and a persistent natural carbon sink during the Holocene, but there is considerable uncertainty over the fate of peatland carbon in a changing climate. It is generally assumed that higher temperatures will increase peat decay, causing a positive feedback to climate warming and contributing to the global positive carbon cycle feedback. Here we use a new extensive database of peat profiles across northern high latitudes to examine spatial and temporal patterns of carbon accumulation over the past millennium. Opposite to expectations, our results indicate a small negative carbon cycle feedback from past changes in the long-term accumulation rates of northern peatlands. Total carbon accumulated over the last 1000 yr is linearly related to contemporary growing season length and photosynthetically active radiation, suggesting that variability in net primary productivity is more important than decomposition in determining long-term carbon accumulation. Furthermore, northern peatland carbon sequestration rate declined over the climate transition from the Medieval Climate Anomaly (MCA) to the Little Ice Age (LIA), probably because of lower LIA temperatures combined with increased cloudiness suppressing net primary productivity. Other factors including changing moisture status, peatland distribution, fire, nitrogen deposition, permafrost thaw and methane emissions will also influence future peatland carbon cycle feedbacks, but our data suggest that the carbon sequestration rate could increase over many areas of northern peatlands in a warmer future.