• We reconstructed time series of boreal tree growth with a biometric approach. • Aboveground tree growth was a minor and decoupled fraction of carbon input. • Partitioned estimates of tree carbon sink are valuable observational constraints. • Such observational constraints can be used for model validation and policy making. The boreal biome accounts for approximately one third of the terrestrial carbon (C) sink. However, estimates of its individual C pools remain uncertain. Here, focusing on the southern boreal forest, we quantified the magnitude and temporal dynamics of C allocation to aboveground tree growth at a mature black spruce ( Picea mariana )-dominated forest stand in Saskatchewan, Canada. We reconstructed aboveground tree biomass increments (AGBi) using a biometric approach, i.e., species-specific allometry combined with forest stand characteristics and tree ring widths collected with a C-oriented sampling design. We explored the links between boreal tree growth and ecosystem C input by comparing AGBi with eddy-covariance-derived ecosystem C fluxes from 1999 to 2015 and we synthesized our findings with a refined meta-analysis of published values of boreal forest C use efficiency (CUE). Mean AGBi at the study site was decoupled from ecosystem C input and equal to 71 ± 7 g C m –2 (1999–2015), which is only a minor fraction of gross ecosystem production (GEP; i.e., AGBi / GEP ≈ 9 %). Moreover, C allocation to AGBi remained stable over time (AGBi / GEP; –0.0001 yr –1 ; p -value=0.775), contrary to significant trends in GEP (+5.72 g C m –2 yr –2 ; p -value=0.02) and CUE (–0.0041 yr –1 , p -value=0.007). CUE was estimated as 0.50 ± 0.03 at the study area and 0.41 ± 0.12 across the reviewed boreal forests. These findings highlight the importance of belowground tree C investments, together with the substantial contribution of understory, ground cover and soil to the boreal forest C balance. Our quantitative insights into the dynamics of aboveground boreal tree C allocation offer additional observational constraints for terrestrial ecosystem models that are often biased in converting C input to biomass, and can guide forest-management strategies for mitigating carbon dioxide emissions.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.