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Soils store large quantities of carbon in the subsoil (below 0.2 m depth) that is generally old and believed to be stabilized over centuries to millennia, which suggests that subsoil carbon sequestration (CS) can be used as a strategy for climate change mitigation. In this article, we review the main biophysical processes that contribute to carbon storage in subsoil and the main mathematical models used to represent these processes. Our guiding objective is to review whether a process understanding of soil carbon movement in the vertical profile can help us to assess carbon storage and persistence at timescales relevant for climate change mitigation. Bioturbation, liquid phase transport, belowground carbon inputs, mineral association, and microbial activity are the main processes contributing to the formation of soil carbon profiles, and these processes are represented in models using the diffusion–advection–reaction paradigm. Based on simulation examples and measurements from carbon and radiocarbon profiles across biomes, we found that advective and diffusive transport may only play a secondary role in the formation of soil carbon profiles. The difference between vertical root inputs and decomposition seems to play a primary role in determining the shape of carbon change with depth. Using the transit time of carbon to assess the timescales of carbon storage of new inputs, we show that only small quantities of new carbon inputs travel through the profile and can be stabilized for time horizons longer than 50 years, implying that activities that promote CS in the subsoil must take into consideration the very small quantities that can be stabilized in the long term. [ABSTRACT FROM AUTHOR]

Plant–soil interactions, such as the coupling of plants' below-ground biomass allocation with soil organic matter (SOM) decomposition, nutrient release and plant uptake, are essential to understand the response of carbon (C) cycling to global changes. However, these processes are poorly represented in the current terrestrial biosphere models owing to the simple first-order approach of SOM cycling and the ignorance of variations within a soil profile. While the emerging microbially explicit soil organic C models can better describe C formation and turnover, at present, they lack a full coupling to the nitrogen (N) and phosphorus (P) cycles with the soil profile. Here we present a new SOM model – the Jena Soil Model (JSM) – which is microbially explicit, vertically resolved and integrated with the N and P cycles. To account for the effects of nutrient availability and litter quality on decomposition, JSM includes the representation of enzyme allocation to different depolymerisation sources based on the microbial adaptation approach as well as of nutrient acquisition competition based on the equilibrium chemistry approximation approach. Herein, we present the model structure and basic features of model performance in a beech forest in Germany. The model reproduced the main SOM stocks and microbial biomass as well as their vertical patterns in the soil profile. We further tested the sensitivity of the model to parameterisation and showed that JSM is generally sensitive to changes in microbial stoichiometry and processes. [ABSTRACT FROM AUTHOR]

Describing the coupling of nitrogen (N), phosphorus (P), and carbon (C) cycles of land ecosystems requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. These efficiencies are studied by the Soil Enzyme Steady Allocation Model (SESAM) at the decadal scale. The model assumes that the soil microbial communities and their element use efficiencies develop towards an optimum where the growth of the entire community is maximized. Specifically, SESAM approximated this growth optimization by allocating resources to several SOM-degrading enzymes proportional to the revenue of these enzymes, called the Relative approach. However, a rigorous mathematical treatment of this approximation has been lacking so far. Therefore, in this study we derive explicit formulas of enzyme allocation that maximize the total return from enzymatic processing, called the Optimal approach. Further, we derive another heuristic approach that prescribes the change of allocation without the need of deriving a formulation for the optimal allocation, called the Derivative approach. When comparing predictions across these approaches, we found that the Relative approach was a special case of the Optimal approach valid at sufficiently high microbial biomass. However, at low microbial biomass, it overestimated allocation to the enzymes having lower revenues compared to the Optimal approach. The Derivative-based allocation closely tracked the Optimal allocation. These findings increase our confidence in conclusions drawn from SESAM studies. Moreover, the new developments extend the range of conditions at which valid conclusions can be drawn. Further, based on these findings we formulated the constrained enzyme hypothesis. This hypothesis provides a complementary explanation why some substrates in soil are preserved over decades, although they are often decomposed within a few years in incubation experiments. This study shows how optimality considerations lead to simplified models, new insights, and new hypotheses. This is another step in deriving a simple representation of an adaptive microbial community, which is required for coupled stoichiometric C–N–P dynamic models that are aimed to study decadal processes beyond the ecosystem scale. [ABSTRACT FROM AUTHOR]

Describing the coupling of nitrogen (N), phosphorus (P), and carbon (C) cycles of land ecosystems requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. These efficiencies are studied by the soil enzyme steady allocation model (SESAM) at decadal scale. The model assumes that the soil microbial communities and their element use efficiencies develop towards an optimum where the growth of the entire community is maximized. Specifically, SESAM approximated this growth optimization by allocating resources to several SOM degrading enzymes pro- portional to the revenue of these enzymes, called the Relative approach. However, a rigorous mathematical treatment of this approximation has been lacking so far. Therefore, in this study we derive explicit formulas of enzyme allocation that maximize total return from enzymatic processing, called the Optimal approach. Further, we derive another heuristic approach that prescribes the change of allocation without the need of deriving a formulation for the optimal allocation, called the Derivative approach. When comparing pre- dictions across these approaches, we found that the Relative approach was a special case of the Optimal approach valid at sufficiently high microbial biomass. However, at low microbial biomass, it overestimated allocation to the enzymes having lower revenues compared to the Optimal approach. The Derivative-based allocation closely tracked the Optimal allocation. The model finding that the Relative approach was a special case of the more rigorous Optimal approach together with observing the same patterns across optimization approaches increases our confidence into conclusions drawn from SESAM studies. Moreover, the new developments extend the range of conditions at which valid conclusions can be drawn. The new model finding that a smaller set of enzyme types was expressed at low microbial biomass led us to formulate the constrained enzyme hypothesis, which provides a complementary explanation why some substrates in soil are preserved over decades although often being decomposed within a few years in incubation experiments. This study shows how optimality considerations lead to simplified models, new insights and new hypotheses. It is another step in deriving a simple representation of an adaptive micro- bial community, which is required for coupled stoichiometric CNP dynamic models that are aimed to study decadal processes beyond ecosystem scale. [ABSTRACT FROM AUTHOR]

Profiles of soil organic carbon (SOC) are often characterized by a steep increase of 14C age with depth, often leading to subsoil 14C ages of more than 1000 years. These observations have generally been reproduced in SOC models by introducing a SOC pool that decomposes on the time-scale of millennia. The overemphasis of chemical recalcitrance as the major factor for the persistence of SOC was able to provide a mechanistic justification for these very low decomposition rates. The emerging view on SOC persistence, however, stresses that apart from molecular structure a multitude of mechanisms can lead to the long-term persistence of organic carbon in soils. These mechanisms, however, have not been incorporated into most models. Consequently, we developed the SOC profile model COMISSION which simulates vertically resolved SOC concentrations based on representations of microbial interactions, sorption to minerals, and vertical transport. We calibrated COMISSION using published concentrations of SOC, microbial biomass and mineral-associated OC (MOC), and in addition, 14C contents of SOC and MOC of a Haplic Podzol profile in North-Eastern Bavaria, Germany. In order to elucidate the contribution of the implemented processes to the 14C age in different parts of the profile, we performed model-experiments in which we switched off the limitation of SOC decomposition by microbes, sorptive stabilization on soil minerals, and dissolved OC (DOC) transport. By splitting all model pools into directly litter-derived carbon and microbe-derived organic carbon, we investigated the contribution of repeated microbial recycling to 14C ages throughout the profile. The model-experiments for this site lead to the following implications: Without rejuvenation by DOC transport, SOC in the subsoil would be on average 1700 14C years older. Across the profile, SOC from microbial recycling is on average 1400 14C years older than litter-derived SOC. Without microbial limitation of depolymerization, SOC in the subsoil would be on average 610 14C years younger. Sorptive stabilization is responsible for relatively high 14C ages in the topsoil. The model-experiments further indicate that the high SOC concentrations in the Bh horizon are caused by the interplay between sorptive stabilization and microbial dynamics. Overall, the model-experiments demonstrate that the high 14C ages are not solely caused by slow turnover of a single pool, but that the increase of 14C ages along a soil profile up to ages >1000 years is the result of different mechanisms contributing to the overall persistence of SOC. The dominant reasons for the persistence of SOC are stabilization processes, followed by repeated microbial processing of SOC. [ABSTRACT FROM AUTHOR]

We used bomb-radiocarbon and raw minirhizotron lifetimes of fine roots (< 0.5 mm in diameter) in the organic layer of Norway spruce ( Picea abies) forests in southern Sweden to test if different models are able to reconcile the apparently contradicting turnover time estimates from both techniques., We present a framework based on survival functions that is able to jointly model bomb-radiocarbon and minirhizotron data. At the same time we integrate prior knowledge about biases of both techniques - the classification of dead roots in minirhizotrons and the use of carbon reserves to grow new roots., Two-pool models, either in parallel or in serial setting, were able to reconcile the bomb-radiocarbon and minirhizotron data. These models yielded a mean residence time of 3.80 ± 0.16 yr (mean ± SD). On average 60 ± 2% of fine roots turned over within 0.75 ± 0.10 yr, while the rest was turning over within 8.4 ± 0.2 yr. Bomb-radiocarbon and minirhizotron data alone give a biased estimate of fine-root turnover., The two-pool models allow a mechanistic interpretation for the coexistence of fast- and slow-cycling roots - suberization and branching for the serial-two-pool model and branching due to ectomycorrhizal fungi-root interactions for the parallel-two-pool model. [ABSTRACT FROM AUTHOR]

The turnover of fine-roots is a crucial component for the input of C to the soil. The amount of root litter input is depending on estimates of turnover times from different techniques. Turnover times from fine-root cameras (minirhizotrons) often yield 75% higher root litter input estimates than turnover times estimated with the bomb-radiocarbon signature of fine roots. We introduce a generic framework for the analysis of fine-root 14C with different survival functions. So far, mostly an exponential function has been used to estimate the turnover time and mean age of fine roots. In the context of the introduced survival function framework we clarify the terms turnover time, mean residence time, mean longevity, and mean age, which are commonly used in studies of root turnover. Using a unique time series of fine-root 14C ( Fröberg 2012), we test if survival functions other than the exponential function are better in accordance with turnover-time estimates commonly found with other methods. A survival function that corresponds to a two-pool model was best in agreement with minirhizotron-based estimates (mean residence time of 1.9 y). We argue that using fine-root 14C and minirhizotron time-to-death data together would give the best constraints on fine-root turnover. At the same time this could allow quantifying systematic biases inherent to both techniques. [ABSTRACT FROM AUTHOR]

Here, we report site-to-site variability and 12-14 year trends of dissolved organic carbon (DOC) from organic layers and mineral soils of 22 forests in Bavaria, Germany. DOC concentrations in the organic layer were negatively correlated with mean annual precipitation and elevation whereas air temperature had a positive effect on DOC concentrations. DOC fluxes in subsoils increased by 3 kg ha yr per 100 mm precipitation or per 100 m elevation. The highest DOC concentrations were found under pine stands with mor humus. Average DOC concentrations in organic layer leachates followed the order: pine>oak>spruce>beech. However, the order was different for mean DOC fluxes (spruce>pine>oak>beech) because of varying precipitation regimes among the forest types. In 12 of 22 sites, DOC concentrations of organic layer leachates significantly increased by 0.5 to 3.1 mg C L yr during the sampling period. The increase in DOC concentration coincided with decreasing sulfate concentration, indicating that sulfate concentration is an important driver of DOC solubility in the organic layer of these forest sites. In contrast to the organic layer, DOC concentrations below 60 cm mineral soil depth decreased by <0.1-0.4 mg C L yr at eight sites. The negative DOC trends were attributed to (i) increasing adsorption of DOC by mineral surfaces resulting from desorption of sulfate and (ii) increasing decay of DOC resulting from decreasing stabilization of DOC by organo-Al complexes. Trends of DOC fluxes from organic layers were consistent with those of DOC concentrations although trends were only significant at seven sites. DOC fluxes in the subsoil were with few exceptions small and trends were generally not significant. Our results suggest that enhanced mobilization of DOC in forest floors contributed to the increase of DOC in surface waters while mineral horizons did not contribute to increasing DOC export of forest soils. [ABSTRACT FROM AUTHOR]

During the last decade, a paradigmatic shift regarding which processes determine the persistence of soil organic matter (SOM) took place. The interaction between microbial decomposition and association of organic matter with the soil mineral matrix has been identified as a focal point for understanding the formation of stable SOM. Using an improved version of the vertically resolved SOM model COMISSION (Ahrens et al., 2015), this paper investigates the effect of a maximum sorption capacity (Q max) for mineral-associated organic matter (MAOM) formation and its interaction with microbial processes, such as microbial decomposition and microbial necromass production. We define and estimate the maximum sorption capacity Q max with quantile regressions between mineral-associated organic carbon (MAOC) and the clay plus silt (<20 μm) content. In the COMISSION v2.0 model, plant- and microbial-derived dissolved organic matter (DOM) and dead microbial cell walls can sorb to mineral surfaces up to Q max. MAOC can only be decomposed by microorganisms after desorption. We calibrated the COMISSION v2.0 model with data from ten different sites with widely varying textures and Q max values. COMISSION v2.0 was able to fit the MAOC and SOC depth profiles, as well as the respective 14C gradients with soil depth across these sites. Using the generic set of parameters retrieved in the multi-site calibration, we conducted model experiments to isolate the effects of varying Q max , point-of-entry of litter inputs, and soil temperature. Across the ten sites, the combination of depolymerization limitation of microorganisms due to substrate scarcity in the subsoil and the size of Q max explain 14C depth gradients in OC. • The vertically explicit SOC model COMISSION was used to study 14C depth gradients. • A texture-based maximum sorption capacity was introduced to the COMISSION model. • One universal parameter set was calibrated to 14C and SOC data from ten sites. • Energy limitation and sorption capacity explain millennial 14C ages in the subsoil. [ABSTRACT FROM AUTHOR]

Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions. [ABSTRACT FROM AUTHOR]

The research on soil organic matter dynamics has seen a paradigmatic shift regarding which processes determine the persistence of soil organic matter in soils. The interaction between microbial decomposition and association of organic matter with the soil mineral matrix has been identified as pivotal for understanding the formation of soil organic matter. Yet, the stabilization of organic matter on soil minerals is currently not explicitly considered in the soil modules of earth system models. Using a vertically integrated SOM model (COMISSION, Ahrens et al. (2015), Soil Biology and Biochemistry, 88, 390-402), we investigate the effect of a limited organo-mineral association capacity and its interaction with microbial processes, such as microbial decomposition and microbial necromass production.We defined a maximum organo-mineral association capacity based on quantile regressions between the observed organo-mineral fraction and the clay + silt content. With the COMISSION model we model organo-mineral associations equivalent to a dynamic Langmuir sorption model. We suggest that plant- and microbe-derived dissolved organic matter and dead microbial cell walls can form organo-mineral associations that are protected from microbial decomposition. With this global modelling study, we show that the inclusion of organo-mineral associations provides a mechanism for long residence times of organic matter in soils. Globally, at ESM grid cell level (200 km) COMISSION captures patterns of soil organic carbon and apparent radiocarbon ages in the top- and subsoil. While COMISSION reproduces mean radiocarbon profiles across the most dominant soil orders of the compiled database, missing processes such as pelo- and cryoturbation or mineralogical control in Oxisols and Andisols explain mismatches between modelled and observed radiocarbon profiles for certain soil orders. [ABSTRACT FROM AUTHOR]

The article is a response to a commentary by Xiao et al. that questioned the conclusions of a study by Tao et al. on the role of microbial carbon use efficiency (CUE) in global soil organic carbon (SOC) storage. Tao et al. used a model to evaluate the relative importance of microbial CUE and other processes in determining SOC storage. They found that microbial CUE was more important than non-microbial carbon transfer and decomposition coefficients in determining SOC storage at a global scale. However, the authors acknowledge that more empirical evidence and mechanistic modeling studies are needed to establish causal relationships between CUE and SOC. They also emphasize the importance of incorporating process-based models with big data to gain a better understanding of global SOC storage. The study was funded by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship and the European Research Council. [Extracted from the article]

In order to understand the coupling of carbon (C) and nitrogen (N) cycles, it is necessary to understand C and N-use efficiencies of microbial soil organic matter (SOM) decomposition. While important controls of those efficiencies by microbial community adaptations have been shown at the scale of a soil pore, an abstract simplified representation of community adaptations is needed at ecosystem scale. Therefore we developed the soil enzyme allocation model (SEAM), which takes a holistic, partly optimality based approach to describe C and N dynamics at the spatial scale of an ecosystem and time-scales of years and longer. We explicitly modelled community adaptation strategies of resource allocation to extracellular enzymes and enzyme limitations on SOM decomposition. Using SEAM, we explored whether alternative strategy-hypotheses can have strong effects on SOM and inorganic N cycling. Results from prototypical simulations and a calibration to observations of an intensive pasture site showed that the so-called revenue enzyme allocation strategy was most viable. This strategy accounts for microbial adaptations to both, stoichiometry and amount of different SOM resources, and supported the largest microbial biomass under a wide range of conditions. Predictions of the holistic SEAM model were qualitatively similar to precitions of the SYMPHONY model, which explicitly represents competing microbial guilds. With adaptive enzyme allocation under conditions of high C/N ratio of litter inputs, N that was formerly locked in slowly degrading SOM pools was made accessible, whereas with high N inputs, N was sequestered in SOM and protected from leaching. The findings imply that it is important for ecosystem scale models to account for adaptation of C and N use efficiencies in order to represent C-N couplings. The combination of stoichiometry and optimality principles is a promising route to yield simple formulations of such adaptations at community level suitable for incorporation into land surface models. [ABSTRACT FROM AUTHOR]

The Australian continent contributes substantially to the year-to-year variability of the global terrestrial carbon dioxide (CO2) sink. However, the scarcity of in situ observations in remote areas prevents the deciphering of processes that force the CO2 flux variability. In this study, by examining atmospheric CO2 measurements from satellites in the period 2009–2018, we find recurrent end-of-dry-season CO2 pulses over the Australian continent. These pulses largely control the year-to-year variability of Australia’s CO2 balance. They cause two to three times larger seasonal variations compared with previous top-down inversions and bottom-up estimates. The pulses occur shortly after the onset of rainfall and are driven by enhanced soil respiration preceding photosynthetic uptake in Australia’s semiarid regions. The suggested continental-scale relevance of soil-rewetting processes has substantial implications for our understanding and modeling of global climate–carbon cycle feedbacks. [ABSTRACT FROM AUTHOR]

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 (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble of data, we estimated the global median τ to be 43-7+7 yr (median-difference to percentile 25+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 37-6+3 yr , which is longer than the previous estimates of 23-4+7 yr (Carvalhais et al., 2014). We show that the difference is mostly attributed to changes in global Csoil estimates. Csoil accounts for approximately 84 % of the total uncertainty in global τ estimates; GPP also contributes significantly (15 %), whereas Cveg contributes only marginally (less than 1 %) to the total uncertainty. The high uncertainty in Csoil is reflected in the large range across state-of-the-art data products, in which full-depth Csoil 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 (0.2

Accelerated soil erosion has become a pervasive feature on landscapes around the world and is recognized to have substantial implications for land productivity, downstream water quality, and biogeochemical cycles. However, the scarcity of global syntheses that consider long-term processes has limited our understanding of the timing, the amplitude, and the extent of soil erosion over millennial time scales. As such, we lack the ability to make predictions about the responses of soil erosion to long-term climate and land cover changes. Here, we reconstruct sedimentation rates for 632 lakes based on chronologies constrained by 3,980 calibrated 14C ages to assess the relative changes in lake-watershed erosion rates over the last 12,000 y. Estimated soil erosion dynamics were then complemented with land cover reconstructions inferred from 43,669 pollen samples and with climate time series from the Max Planck Institute Earth System Model. Our results show that a significant portion of the Earth surface shifted to human-driven soil erosion rate already 4,000 y ago. In particular, inferred soil erosion rates increased in 35% of the watersheds, and most of these sites showed a decrease in the proportion of arboreal pollen, which would be expected with land clearance. Further analysis revealed that land cover change was the main driver of inferred soil erosion in 70% of all studied watersheds. This study suggests that soil erosion has been altering terrestrial and aquatic ecosystems for millennia, leading to carbon (C) losses that could have ultimately induced feedbacks on the climate system. [ABSTRACT FROM AUTHOR]

Over the past decades, our understanding of soil organic carbon (SOC) cycling has evolved to one where its persistence is mainly due to physical and chemical protection mechanisms, while soil microbes are now acknowledged as being the main precursor of stabilized OC. In line with this understanding, microbial-driven SOC models that explicitly account for these mechanisms have been developed during the past years. While 14C has been widely used to calibrate and validate SOC models, stable carbon isotopes (δ13C) have to date not been used for the calibration of a mechanistic SOC model. The change in these isotopic ratios is, however, caused by fundamentally different mechanisms: the Δ14C value of SOC provides an indication of how long C has been in the soil, while the δ13C value provides information about the intensity with which this C has been processed by soil microorganisms. If the δ13C value of SOC can be mechanistically simulated, this might improve confidence in model calibration and the modeled residence time of SOC substantially.Therefore, we developed a microbial-driven SOC model that simulates depth profiles of total SOC, δ13C and Δ14C. The novelty of this model is that it separates SOC processes in the rhizosphere (with fast OC cycling due to high C inputs and limited protection mechanisms) and the mineral soil (with slow OC cycling due to the protection by organo-mineral interactions). Furthermore, the physical protection of OC in aggregates is simulated while microbial residues are stabilized on mineral surfaces. The simulated depth profiles of δ13C reflect the increasing contribution of microbial residues to SOC with depth, while this isotopic signal is influenced by heterotrophic CO2 assimilation by microorganisms. The model has been used to simulate depth profiles of OC, δ13C and Δ14C of stable landscape locations under forest in western Europe. [ABSTRACT FROM AUTHOR]

The response of the terrestrial carbon cycle to climate change is among the largest uncertainties affecting future climate change projections. The feedback between the terrestrial carbon cycle and climate is partly determined by changes in the turnover time of carbon in land ecosystems, which in turn is an ecosystem property that emerges from the interplay between climate, soil and vegetation type. Here we present a global, spatially explicit and observation-based assessment of whole-ecosystem carbon turnover times that combines new estimates of vegetation and soil organic carbon stocks and fluxes. We find that the overall mean global carbon turnover time is years (95 per cent confidence interval). On average, carbon resides in the vegetation and soil near the Equator for a shorter time than at latitudes north of 75° north (mean turnover times of 15 and 255 years, respectively). We identify a clear dependence of the turnover time on temperature, as expected from our present understanding of temperature controls on ecosystem dynamics. Surprisingly, our analysis also reveals a similarly strong association between turnover time and precipitation. Moreover, we find that the ecosystem carbon turnover times simulated by state-of-the-art coupled climate/carbon-cycle models vary widely and that numerical simulations, on average, tend to underestimate the global carbon turnover time by 36 per cent. The models show stronger spatial relationships with temperature than do observation-based estimates, but generally do not reproduce the strong relationships with precipitation and predict faster carbon turnover in many semi-arid regions. Our findings suggest that future climate/carbon-cycle feedbacks may depend more strongly on changes in the hydrological cycle than is expected at present and is considered in Earth system models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "Luo, Y.P. et al., Using Near-Infrared Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree-Grass Ecosystems. Remote Sens. 2018, 10, 1293" which appeared on March 15, 2019 in this issue.

Partitioning evapotranspiration (ET) into its constitutive components is essential for linking plant-water use (T) and carbon assimilation (GPP) interactions to environmental variability. In this contribution we present a new approach for partitioning eddy covariance (EC) ET measurements based on model-data integration. The method enables FLUXNET data users to partitioning ET fluxes as well as to deriving relevant ecosystem functional properties. The algorithm was successfully evaluated against independent data collected with lysimeter measurements and benchmarked with other existing state of the art micrometeorological approaches. Firstly, an optimal "internal" leaf-to-ambient CO2 concentration (χ) – a key aspect of plant-water use efficiency (WUE; defined as GPP/T) – was predicted theoretically in the light of stomatal optimality theory. Within a big-leaf representation accounting for the influence of boundary layer conductance, estimated χ provided a means to investigate stomatal behavior (gs) in accordance with the EC GPP estimates. With the objective of accounting for the coordinated effect of soil moisture on χ and gs, a novel optimization approach was implemented to develop solutions for an optimal gs model that maximizes WUE. The gs model offered the backbone on which optimal T estimates were derived. The evaporation from soil and/or other wet surfaces (Es) was then calculated as a residual between the observed ET and modeled T. The proposed method was applied to long-term EC measurements collected above a Mediterranean tree-grass ecosystem. Estimated Es agreed with independent lysimeter measurements during the dry periods when evaporation from rainfall or dewfall interception are neglected (MAE = 0.49 mmol m-2 s-1, slope = 0.95, r = 0.69). They also agreed with other partitioning methods derived from similarity theory and conditional sampling applied to turbulence measurements, which appeared to be highly sensitive to different χ parameterization schemes. In particular, we showed that T was overestimated by 30% when χ was assumed constant. A surprising result was the large contribution of Es to the total ET during the wet period (approx. 70%), as well as the dry period (approx. 40%). Our results confirmed that a first-principle representation of χ provides a means for predicting T in the absence of water stress. However, a seasonal χ pattern was characterized in response to soil dryness. Therefore, further theoretical frameworks detailing the interplay of χ and gs in carbon assimilation for conditions with varying water availability are required in dynamic ecosystem models, which make exciting future inquiry. The robustness of the results provides a new perspective on ET partitioning of EC fluxes and can be utilized across a wide range of climates and biomes. [ABSTRACT FROM AUTHOR]

Although soils are a globally important carbon reservoir, there are large uncertainties in thedecadal response of this carbon to a changing climate. Here we explore the dynamics ofcarbon respired from soils, which is rapidly cycling and likely to be most vulnerable toenvironmental change on decadal timescales. We synthesize heterotrophic respiration datafrom incubation studies, which measure the radiocarbon content of the respired CO2. Thesemeasurements are an indicator of the age of carbon leaving the system and correspondingly,the transit time of carbon in soils. The compiled dataset spans a complete latitudinal gradient,from the arctic to the tropics, allowing us to evaluate the extent to which temperature andmoisture drive differences in the age of respired carbon. Across sites, the transittime for CO2 respired from shallow soils ranges from two years to a decade. Wealso find respired 14C concentrations are closer to the atmosphere at warmer sites,indicating faster transit times with increasing mean annual temperature. Finally, wepresent ways in which this dataset can be used to constrain rates of active carboncycling in global models, since models are increasingly providing predictions ofthe age of respired carbon. To demonstrate this approach, we compare our datasetagainst the COMISSION model and the E3SM land model (ELM-ECA). A betterunderstanding of the age of respired carbon globally will improve our ability topredict the response of actively cycled soil C to disturbance on decadal timescales. [ABSTRACT FROM AUTHOR]

Increasing CO2 concentrations and future climate change will affect the ability of ecosystemsto sequester carbon due to increasing nutrient limitation, in particular nitrogen (N), which isthe key limiting nutrient in many terrestrial ecosystems. However, the N deficit is difficult, ifnot impossible, to quantify simply from the ecosystem monitoring data. One of the mostcommon tools used is the N stable isotope composition, either at natural abundance, wherethe isotopic fractionation of different processes can indicate N limitation, or asadded tracer in manipulative experiments to trace the fate of N in the ecosystem.Despite the wealth of data, terrestrial biosphere models do not often include thecapacity to include 15N in their predictions, making it difficult to use the availableinformation. We have developed a new terrestrial biosphere model QUINCY (QUantifying Interactionsbetween terrestrial Nutrient CYcles and the climate system) which includes fully coupledcarbon, nutrient and water cycles, as well as an improved representation of plant and soilphysiological processes. Most importantly, we include a 15N tracer to all soil and vegetationpools that allows us to evaluate our model against observations directly. Here, wepresent the first results from a land surface model which includes explicit isotopetracers. First, we explore the trends in foliar 15N during the last century at over 400 forest sitesworldwide. The observed trends of foliar 15N show a decline during the last decades implyingincreasing N deficiency in the ecosystems but attributing the driver of these changes is notstraightforward simply from observations. Our model is able to replicate the observed trendand we show that it is caused by the CO2 fertilisation effect, rather than changes in climateand N deposition. Next, we test the model at two manipulative experimental sites, the Free-Air Carbondioxide Enrichment (FACE) experiment at Oak Ridge National Laboratory (ORNL),where measurements of natural abundance 15N allow an in-depth exploration ofN limitation under elevated CO2, and the 15N enrichment experiment at Cornell,where the 15N tracer allows us to explore fate of added N in soil and vegetationpools. These three case studies showcase the utility of a terrestrial biosphere model withintegrated isotope tracers to not only predict the observed state of the ecosystem but alsoexplore the fundamental drivers and internal ecosystem processes. [ABSTRACT FROM AUTHOR]

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP. [ABSTRACT FROM AUTHOR]