Biodiversity experiments have shown that species loss reduces ecosystem functioning in grassland. To test whether this result can be extrapolated to forests, the main contributors to terrestrial primary productivity, requires large-scale experiments. We manipulated tree species richness by planting more than 150,000 trees in plots with 1 to 16 species. Simulating multiple extinction scenarios, we found that richness strongly increased stand-level productivity. After 8 years, 16-species mixtures had accumulated over twice the amount of carbon found in average monocultures and similar amounts as those of two commercial monocultures. Species richness effects were strongly associated with functional and phylogenetic diversity. A shrub addition treatment reduced tree productivity, but this reduction was smaller at high shrub species richness. Our results encourage multispecies afforestation strategies to restore biodiversity and mitigate climate change.

Forest ecosystems are an integral component of the global carbon cycle as they take up and release large amounts of C over short time periods (C flux) or accumulate it over longer time periods (C stock). However, there remains uncertainty about whether and in which direction C fluxes and in particular C stocks may differ between forests of high versus low species richness. Based on a comprehensive dataset derived from field-based measurements, we tested the effect of species richness (3-20 tree species) and stand age (22-116 years) on six compartments of above- and below-ground C stocks and four components of C fluxes in subtropical forests in southeast China. Across forest stands, total C stock was 149 ± 12 Mg ha with richness explaining 28.5% and age explaining 29.4% of variation in this measure. Species-rich stands had higher C stocks and fluxes than stands with low richness; and, in addition, old stands had higher C stocks than young ones. Overall, for each additional tree species, the total C stock increased by 6.4%. Our results provide comprehensive evidence for diversity-mediated above- and below-ground C sequestration in species-rich subtropical forests in southeast China. Therefore, afforestation policies in this region and elsewhere should consider a change from the current focus on monocultures to multi-species plantations to increase C fixation and thus slow increasing atmospheric CO concentrations and global warming.

Abstract. The role of soil microorganisms in regulating soil organic matter (SOM) decomposition is of primary importance in the carbon cycle, in particular in the context of global change. Modeling soil microbial community dynamics to simulate its impact on soil gaseous carbon (C) emissions and nitrogen (N) mineralization at large spatial scales is a recent research field with the potential to improve predictions of SOM responses to global climate change. In this study we present a SOM model called ORCHIMIC, which utilizes input data that are consistent with those of global vegetation models. ORCHIMIC simulates the decomposition of SOM by explicitly accounting for enzyme production and distinguishing three different microbial functional groups: fresh organic matter (FOM) specialists, SOM specialists, and generalists, while also implicitly accounting for microbes that do not produce extracellular enzymes, i.e., cheaters. ORCHIMIC and two other organic matter decomposition models, CENTURY (based on first-order kinetics and representative of the structure of most current global soil carbon models) and PRIM (with FOM accelerating the decomposition rate of SOM), were calibrated to reproduce the observed respiration fluxes of FOM and SOM from the incubation experiments of Blagodatskaya et al. (2014). Among the three models, ORCHIMIC was the only one that effectively captured both the temporal dynamics of the respiratory fluxes and the magnitude of the priming effect observed during the incubation experiment. ORCHIMIC also effectively reproduced the temporal dynamics of microbial biomass. We then applied different idealized changes to the model input data, i.e., a 5 K stepwise increase of temperature and/or a doubling of plant litter inputs. Under 5 K warming conditions, ORCHIMIC predicted a 0.002 K−1 decrease in the C use efficiency (defined as the ratio of C allocated to microbial growth to the sum of C allocated to growth and respiration) and a 3 % loss of SOC. Under the double litter input scenario, ORCHIMIC predicted a doubling of microbial biomass, while SOC stock increased by less than 1 % due to the priming effect. This limited increase in SOC stock contrasted with the proportional increase in SOC stock as modeled by the conventional SOC decomposition model (CENTURY), which can not reproduce the priming effect. If temperature increased by 5 K and litter input was doubled, ORCHIMIC predicted almost the same loss of SOC as when only temperature was increased. These tests suggest that the responses of SOC stock to warming and increasing input may differ considerably from those simulated by conventional SOC decomposition models when microbial dynamics are included. The next step is to incorporate the ORCHIMIC model into a global vegetation model to perform simulations for representative sites and future scenarios.

Realistic representation of vegetation's response to drought is important for understanding terrestrial carbon cycling. We evaluated nine Earth system models from the historical experiment of the Coupled Model Intercomparison Project Phase 5 for the response of gross primary productivity (GPP) and leaf area index (LAI) to hydrological anomalies. Hydrological anomalies were characterized by the standardized precipitation index (SPI) and surface soil moisture anomalies (SMA). GPP and LAI in models were on average more responsive to SPI than in observations revealed through several indicators. First, we find higher mean correlations between global annual anomalies of GPP and SPI in models than observations. Second, the maximum correlation between GPP and SPI across 1–24 month time scales is higher in models than observations. And finally, we found stronger excursions of GPP to extreme dry or wet events. Similar to GPP, LAI responded more to SPI in models than observations. The over‐response of models is smaller if evaluated based on SMA instead of SPI. LAI responses to SMA are inconsistent among models, showing both higher and lower LAI when soil moisture is reduced. The time scale of maximum correlation is shorter in models than the observation for GPP, and the markedly different response time scales among models for LAI indicate gaps in understanding how variability of water availability affects foliar cover. The discrepancy of responses derived from SPI and SMA among models, and between models and observations, calls for improvement in understanding the dynamics of plant‐available water in addition to how vegetation responds to these anomalies.

Aims Litterfall, as an important link between aboveground and belowground processes, plays a key role in forest ecosystems. Here, we test for effects of tree species richness on litter production and litter quality in subtropical forest. The study further encompasses a factorial gradient of secondary succession that resulted from human exploitation. Given that a large percentage of subtropical forests are in secondary successional stages, understanding the role of biodiversity on forest re-growth after disturbance appears critical. Methods From January 2009 to December 2014, we monitored forest litterfall in 27 Comparative Study Plots that spanned a gradient of tree species richness (3-20 species) and secondary successional ages ( 20 to 120 years) in Gutianshan Natural Nature Reserve, Zhejiang Province, China. The experiment is part of the biodiversity-ecosystem functioning research platform 'BEF-China'. Tree litterfall was collected in monthly intervals using litter traps. Samples were separated into leaf and non-leaf components. Leaf litter was further sorted into dominant and other species. Community level monthly leaf litter C and N contents were analysed through a full year. General linear mixed-effects models were applied to test for effects of tree species richness and successional age on litter quantity and leaf litter C/N. Important Findings Litterfall increased with species richness among and within successional age and this effect was consistent across years. Successionally older stands had higher litterfall and this effect was related to increased tree species richness. However, species richness did not change the intra-and inter-annual temporal stability of litterfall. Increasing tree species richness increased leaf litter quality (decreased C/N), while successional age had no effect. Our study indicates that more diverse forest stands produce more leaf litter and that this litter has higher N concentrations, which could promote forest growth through accelerated nutrient re-cycling.

Large uncertainties exist in predicting responses of wetland methane (CH4) fluxes to future climate change. However, sources of the uncertainty have not been clearly identified despite the fact that methane production and emission processes have been extensively explored. In this study, we took advantage of manual CH4 flux measurements under ambient environment from 2011 to 2014 at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site and developed a data‐informed process‐based methane module. The module was incorporated into the Terrestrial ECOsystem (TECO) model before its parameters were constrained with multiple years of methane flux data for forecasting CH4 emission under five warming and two elevated CO2 treatments at SPRUCE. We found that 9°C warming treatments significantly increased methane emission by approximately 400%, and elevated CO2 treatments stimulated methane emission by 10.4%–23.6% in comparison with ambient conditions. The relative contribution of plant‐mediated transport to methane emission decreased from 96% at the control to 92% at the 9°C warming, largely to compensate for an increase in ebullition. The uncertainty in plant‐mediated transportation and ebullition increased with warming and contributed to the overall changes of emissions uncertainties. At the same time, our modeling results indicated a significant increase in the emitted CH4:CO2 ratio. This result, together with the larger warming potential of CH4, will lead to a strong positive feedback from terrestrial ecosystems to climate warming. The model‐data fusion approach used in this study enabled parameter estimation and uncertainty quantification for forecasting methane fluxes.

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Abstract. In support of the Global Stocktake of the Paris Agreement on Climate change, this study presents a comprehensive framework to process the results of atmospheric inversions in order to make them suitable for evaluating UNFCCC national inventories of land-use carbon dioxide (CO2) emissions and removals, corresponding to the Land Use, Land Use Change and Forestry and waste sectors. We also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, and anthropogenic nitrous oxide (N2O) emissions from inversions. To compare inversions with national reports, we compiled a new global harmonized database of national emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by Non-Annex I countries, given by National Communications and Biennial Update Reports. The method to reconcile inversions with inventories is applied to selected large countries covering 78 % of the global land carbon uptake for CO2, as well as emissions and removals in the land use, land use change and forestry sector, and top-emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products, and the role of carbon uptake in unmanaged lands, both not accounted for by the rules of inventories. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in-situ network and those using satellite CH4 retrievals, and a tendency for inversions to diagnose higher CH4 emissions estimates than reported by inventories. In particular, oil and gas extracting countries in Central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to either a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, or to the existence of natural sources intertwined with managed lands, or to an under-estimation of N2O emission factors for direct agricultural soil emissions. The advantage of inversions is that they provide insights on seasonal and interannual greenhouse gas fluxes anomalies, e.g. during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges.