With the eddy covariance (EC) technique, net fluxes of carbon dioxide (CO₂) and other trace gases as well as water and energy fluxes can be measured at the ecosystem level. These flux measurements are a main source for understanding biosphere–atmosphere interactions and feedbacks through cross-site analysis, model–data integration, and upscaling. The raw fluxes measured with the EC technique require extensive and laborious data processing. While there are standard tools¹ available in an open-source environment for processing high-frequency (10 or 20 Hz) data into half-hourly quality-checked fluxes, there is a need for more usable and extensible tools for the subsequent post-processing steps. We tackled this need by developing the REddyProc package in the cross-platform language R that provides standard CO₂-focused post-processing routines for reading (half-)hourly data from different formats, estimating the u_* threshold, as well as gap-filling, flux-partitioning, and visualizing the results. In addition to basic processing, the functions are extensible and allow easier integration in extended analysis than current tools. New features include cross-year processing and a better treatment of uncertainties. A comparison of REddyProc routines with other state-of-the-art tools resulted in no significant differences in monthly and annual fluxes across sites. Lower uncertainty estimates of both u_* and resulting gap-filled fluxes by 50 % with the presented tool were achieved by an improved treatment of seasons during the bootstrap analysis. Higher estimates of uncertainty in daytime partitioning (about twice as high) resulted from a better accounting for the uncertainty in estimates of temperature sensitivity of respiration. The provided routines can be easily installed, configured, and used. Hence, the eddy covariance community will benefit from the REddyProc package, allowing easier integration of standard post-processing with extended analysis. ¹http://fluxnet.fluxdata.org/2017/10/10/toolbox-a-rolling-list-of-softwarepackages-for-flux-related-data-processing/, last access: 17 August 2018

Aim: To infer a forest carbon density map at 0.01° resolution from a radar remote sensing product for the estimation of carbon stocks in Northern Hemisphere boreal and temperate forests.Location: The study area extends from 30° N to 80° N, covering three forest biomes – temperate broadleaf and mixed forests (TBMF), temperate conifer forests (TCF) and boreal forests (BFT) – over three continents (North America, Europe and Asia).Methods: This study is based on a recently available growing stock volume (GSV) product retrieved from synthetic aperture radar data. Forest biomass and spatially explicit uncertainty estimates were derived from the GSV using existing databases of wood density and allometric relationships between biomass compartments (stem, branches, roots, foliage). We tested the resultant map against inventory‐based biomass data from Russia, Europe and the USA prior to making intercontinent and interbiome carbon stock comparisons.Results: Our derived carbon density map agrees well with inventory data at regional scales (r² = 0.70–0.90). While 40.7 ± 15.7 petagram of carbon (Pg C) are stored in BFT, TBMF and TCF contain 24.5 ± 9.4 Pg C and 14.5 ± 4.8 Pg C, respectively. In terms of carbon density, we found 6.21 ± 2.07 kg C m⁻² retained in TCF and 5.80 ± 2.21 kg C m⁻² in TBMF, whereas BFT have a mean carbon density of 4.00 ± 1.54 kg C m⁻². Indications of a higher carbon density in Europe compared with the other continents across each of the three biomes could not be proved to be significant.Main conclusions: The presented carbon density and corresponding uncertainty map give an insight into the spatial patterns of biomass and stand as a new benchmark to improve carbon cycle models and carbon monitoring systems. In total, we found 79.8 ± 29.9 Pg C stored in northern boreal and temperate forests, with Asian BFT accounting for 22.1 ± 8.3 Pg C.

The effect of temperature and the influence of fresh substrate addition on soil organic matter decomposition are two key factors we need to understand to forecast soil carbon dynamics under climate change and rising CO2 levels. Here we perform a laboratory incubation experiment to address the following questions: 1) Does the temperature sensitivity differ between freshly added organic matter and bulk soil carbon? 2) Does the addition of fresh organic matter stimulate the decomposition of soil organic matter (“priming effect”)? 3) If so, does this priming effect depend on temperature? In our study, we incubated sieved soil samples without and with two labelled plant litters with different 13C signals for 199 days. The incubations were performed with two diurnal temperature treatments (5–15 °C, 15–25 °C) in a flow-through soil incubation system. Soil CO2 production was continuously monitored with an infrared gas analyser, while the 13C signal was determined from gas samples. Phospholipid fatty acids (PLFA) were used to quantify microbial biomass. We observed that the instantaneous temperature sensitivity initially did not differ between the original and the amended soil. However in the amended treatment the temperature sensitivity slightly but significantly increased during the incubation time, as did the PLFA amount from microbial biomass. Further, we found that addition of fresh plant material increased the rate of decomposition of the original soil organic matter. On a relative basis, this stimulation was similar in the warm and cold treatments (46% and 52%, respectively). Overall our study contrasts the view of a simple physico-chemically derived substrate–temperature sensitivity relationship of decomposition. Our results rather request an explicit consideration of microbial processes such as growth and priming effects

Decomposition of soil organic matter (SOM) is limited by both the available substrate and the active decomposer community. The understanding of this colimitation strongly affects the understanding of feedbacks of soil carbon to global warming and its consequences. This study compares different formulations of soil organic matter (SOM) decomposition. We compiled formulations from literature into groups according to the representation of decomposer biomass on the SOM decomposition rate a) non-explicit (substrate only), b) linear, and c) non-linear. By varying the SOM decomposition equation in a basic simplified decomposition model, we analyzed the following questions. Is the priming effect represented? Under which conditions is SOM accumulation limited? And, how does steady state SOM stocks scale with amount of fresh organic matter (FOM) litter inputs? While formulations (a) did not represent the priming effect, with formulations (b) steady state SOM stocks were independent of amount of litter input. Further, with several formulations (c) there was an offset of SOM that was not decomposed when no fresh OM was supplied. The finding that a part of the SOM is not decomposed on exhaust of FOM supply supports the hypothesis of carbon stabilization in deep soil by the absence of energy-rich fresh organic matter. Different representations of colimitation of decomposition by substrate and decomposers in SOM decomposition models resulted in qualitatively different long-term behaviour. A collaborative effort by modellers and experimentalists is required to identify formulations that are more or less suitable to represent the most important drivers of long term carbon storage.

This study provides a comprehensive set of functions for predicting biomass for Common beech ( Fagus sylvatica L.) in Central Europe for all major tree compartments. The equations are based on data of stem, branch, timber, brushwood (wood with diameter below 5 or 7 cm), foliage, root, and total aboveground biomass of 443 trees from 13 studies. We used nonlinear mixed-effects models to assess the contribution of fixed effects (tree dimensions, site descriptors), random effects (grouping according to studies), and residual variance to the total variance and to obtain realistic estimates of uncertainty of biomass on an aggregated level. Candidate models differed in their basic form, the description of the variance, and inclusion of various combinations of additional fixed and random effects and were compared using the Akaike information criterion. Model performance increased most when accounting for between-study differences in the variability of biomass predictions. Further, performance increased with the inclusion of age, site index, and altitude as predictor variables. We show that neglecting variance partitioning and the fact that prediction errors of trees are not independent with respect to their predictor variables will lead to a significant underestimation of prediction variance.

Many projections of the soil carbon sink or source are based on kinetically defined carbon pool models. Para\\-meters of these models are often determined in a way that the steady state of the model matches observed carbon stocks. The underlying simplifying assumption is that observed carbon stocks are near equilibrium. This assumption is challenged by observations of very old soils that do still accumulate carbon. In this modelling study we explored the consequences of the case where soils are apart from equilibrium. Calculation of equilibrium states of soils that are currently accumulating small amounts of carbon were performed using the Yasso model. It was found that already very small current accumulation rates cause big changes in theoretical equilibrium stocks, which can virtually approach infinity. We conclude that soils that have been disturbed several centuries ago are not in equilibrium but in a transient state because of the slowly ongoing accumulation of the slowest pool. A first consequence is that model calibrations to current carbon stocks that assume equilibrium state, overestimate the decay rate of the slowest pool. A second consequence is that spin-up runs (simulations until equilibrium) overestimate stocks of recently disturbed sites. In order to account for these consequences, we propose a transient correction. This correction prescribes a lower decay rate of the slowest pool and accounts for disturbances in the past by decreasing the spin-up-run predicted stocks to match an independent estimate of current soil carbon stocks. Application of this transient correction at a Central European beech forest site with a typical disturbance history resulted in an additional carbon fixation of 5.7±1.5 tC/ha within 100 years. Carbon storage capacity of disturbed forest soils is potentially much higher than currently assumed. Simulations that do not adequately account for the transient state of soil carbon stocks neglect a considerable amount of current carbon accumulation.