The cycling of carbon (C) between the Earth surface and the atmosphere is controlled by biological and abiotic processes that regulate C storage in biogeochemical compartments and release to the atmosphere. This partitioning is quantified using various forms of C-use efficiency (CUE) – the ratio of C remaining in a system to C entering that system. Biological CUE is the fraction of C taken up allocated to biosynthesis. In soils and sediments, C storage depends also on abiotic processes, so the term C-storage efficiency (CSE) can be used. Here we first review and reconcile CUE and CSE definitions proposed for autotrophic and heterotrophic organisms and communities, food webs, whole ecosystems and watersheds, and soils and sediments using a common mathematical framework. Second, we identify general CUE patterns; for example, the actual CUE increases with improving growth conditions, and apparent CUE decreases with increasing turnover. We then synthesize > 5000 CUE estimates showing that CUE decreases with increasing biological and ecological organization – from unicellular to multicellular organisms and from individuals to ecosystems. We conclude that CUE is an emergent property of coupled biological–abiotic systems, and it should be regarded as a flexible and scale-dependent index of the capacity of a given system to effectively retain C.

Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer-term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the Arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and Arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal-to-decadal dynamics of vegetation greenness.

The use of dynamic global vegetation models (DGVMs) to estimate CO₂ emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (E_LUC^c) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and E_LUC^c. This method is applicable on the global and regional scale. The original DGVM estimates of E_LUC^c range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained E_LUC^c is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.

Turnover concepts in state‐of‐the‐art global vegetation models (GVMs) account for various processes, but are often highly simplified and may not include an adequate representation of the dominant processes that shape vegetation carbon turnover rates in real forest ecosystems at a large spatial scale. Here, we evaluate vegetation carbon turnover processes in GVMs participating in the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP, including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT) using estimates of vegetation carbon turnover rate (k) derived from a combination of remote sensing based products of biomass and net primary production (NPP). We find that current model limitations lead to considerable biases in the simulated biomass and in k (severe underestimations by all models except JeDi and VISIT compared to observation‐based average k), likely contributing to underestimation of positive feedbacks of the northern forest carbon balance to climate change caused by changes in forest mortality. A need for improved turnover concepts related to frost damage, drought, and insect outbreaks to better reproduce observation‐based spatial patterns in k is identified. As direct frost damage effects on mortality are usually not accounted for in these GVMs, simulated relationships between k and winter length in boreal forests are not consistent between different regions and strongly biased compared to the observation‐based relationships. Some models show a response of k to drought in temperate forests as a result of impacts of water availability on NPP, growth efficiency or carbon balance dependent mortality as well as soil or litter moisture effects on leaf turnover or fire. However, further direct drought effects such as carbon starvation (only in HYBRID4) or hydraulic failure are usually not taken into account by the investigated GVMs. While they are considered dominant large‐scale mortality agents, mortality mechanisms related to insects and pathogens are not explicitly treated in these models.

Vegetation carbon turnover processes in forest ecosystems and their dominant drivers are far from being understood at a broader scale. Many of these turnover processes act on long timescales and include a lateral dimension and thus can hardly be investigated by plot‐level studies alone. Making use of remote sensing‐based products of net primary production (NPP) and biomass, here we show that spatial gradients of carbon turnover rate (k) in Northern Hemisphere boreal and temperate forests are explained by different climate‐related processes depending on the ecosystem. k is related to frost damage effects and the trade‐off between growth and frost adaptation in boreal forests, while drought stress and climate effects on insects and pathogens can explain an elevated k in temperate forests. By identifying relevant processes underlying broadscale patterns in k, we provide the basis for a detailed exploration of these mechanisms in field studies, and ultimately the improvement of their representations in global vegetation models (GVMs).

The turnover time of terrestrial ecosystem carbon is an emergent ecosystem property that quantifies the strength of land surface on the global carbon cycle–climate feedback. However, observation- and modeling-based estimates of carbon turnover and its response to climate are still characterized by large uncertainties. In this study, by assessing the apparent whole ecosystem carbon turnover times (τ) as the ratio between carbon stocks and fluxes, we provide an update of this ecosystem level diagnostic and its associated uncertainties in high spatial resolution (0.083^∘) using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (C_soil), vegetation biomass (C_veg) and gross primary productivity (GPP). Using this new ensemble of data, we estimated the global median τ to be ${\mathrm{43}}_{-\mathrm{7}}^{+\mathrm{7}}$ yr (${\text{median}}_{-\text{difference to percentile 25}}^{+\text{difference to percentile 75}}$ ) when the full soil is considered, in contrast to limiting it to 1 m depth. Only considering the top 1 m of soil carbon in circumpolar regions (assuming maximum active layer depth is up to 1 m) yields a global median τ of ${\mathrm{37}}_{-\mathrm{6}}^{+\mathrm{3}}$ yr, which is longer than the previous estimates of ${\mathrm{23}}_{-\mathrm{4}}^{+\mathrm{7}}$ yr (Carvalhais et al., 2014). We show that the difference is mostly attributed to changes in global C_soil estimates. C_soil accounts for approximately 84 % of the total uncertainty in global τ estimates; GPP also contributes significantly (15 %), whereas C_veg contributes only marginally (less than 1 %) to the total uncertainty. The high uncertainty in C_soil is reflected in the large range across state-of-the-art data products, in which full-depth C_soil spans between 3362 and 4792 PgC. The uncertainty is especially high in circumpolar regions with an uncertainty of 50 % and a low spatial correlation between the different datasets ($\mathrm{0.2}<r<\mathrm{0.5}$ ) when compared to other regions ($\mathrm{0.6}<r<\mathrm{0.8}$ ). These uncertainties cast a shadow on current global estimates of τ in circumpolar regions, for which further geographical representativeness and clarification on variations in C_soil with soil depth are needed. Different GPP estimates contribute significantly to the uncertainties of τ mainly in semiarid and arid regions, whereas C_veg causes the uncertainties of τ in the subtropics and tropics. In spite of the large uncertainties, our findings reveal that the latitudinal gradients of τ are consistent across different datasets and soil depths. The current results show a strong ensemble agreement on the negative correlation between τ and temperature along latitude that is stronger in temperate zones (30–60^∘ N) than in the subtropical and tropical zones (30^∘ S–30^∘ N). Additionally, while the strength of the τ–precipitation correlation was dependent on the C_soil data source, the latitudinal gradients also agree among different ensemble members. Overall, and despite the large variation in τ, we identified robust features in the spatial patterns of τ that emerge beyond the differences stemming from the data-driven estimates of C_soil, C_veg and GPP. These robust patterns, and associated uncertainties, can be used to infer τ–climate relationships and for constraining contemporaneous behavior of Earth system models (ESMs), which could contribute to uncertainty reductions in future projections of the carbon cycle–climate feedback. The dataset of τ is openly available at https://doi.org/10.17871/bgitau.201911 (Fan et al., 2019).