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We demonstrate that substrate uptake kinetics in any consumer–substrate network subject to the total quasi-steady-state assumption can be formulated as an equilibrium chemistry (EC) problem. If the consumer-substrate complexes equilibrate much faster than other metabolic processes, then the relationships between consumers, substrates, and consumer-substrate complexes are in quasi-equilibrium and the change of a given total substrate (free plus consumer-bounded) is determined by the degradation of all its consumer-substrate complexes. In this EC formulation, the corresponding equilibrium reaction constants are the conventional Michaelis–Menten (MM) substrate affinity constants. When all of the elements in a given network are either consumer or substrate (but not both), we derived a first-order accurate EC approximation (ECA). The ECA kinetics is compatible with almost every existing extension of MM kinetics. In particular, for microbial organic matter decomposition modeling, ECA kinetics explicitly predicts a specific microbe's uptake for a specific substrate as a function of the microbe's affinity for the substrate, other microbes' affinity for the substrate, and the shielding effect on substrate uptake by environmental factors, such as mineral surface adsorption. By taking the EC solution as a reference, we evaluated MM and ECA kinetics for their abilities to represent several differently configured enzyme-substrate reaction networks. In applying the ECA and MM kinetics to microbial models of different complexities, we found (i) both the ECA and MM kinetics accurately reproduced the EC solution when multiple microbes are competing for a single substrate; (ii) ECA outperformed MM kinetics in reproducing the EC solution when a single microbe is feeding on multiple substrates; (iii) the MM kinetics failed, while the ECA kinetics succeeded, in reproducing the EC solution when multiple consumers (i.e., microbes and mineral surfaces) were competing for multiple substrates. We then applied the EC and ECA kinetics to a guild based C-only microbial litter decomposition model and found that both approaches successfully simulated the commonly observed (i) two-phase temporal evolution of the decomposition dynamics; (ii) final asymptotic convergence of the lignocellulose index to a constant that depends on initial litter chemistry and microbial community structure; and (iii) microbial biomass proportion of total organic biomass (litter plus microbes). In contrast, the MM kinetics failed to realistically predict these metrics. We therefore conclude that the ECA kinetics are more robust than the MM kinetics in representing complex microbial, C substrate, and mineral surface interactions. Finally, we discuss how these concepts can be applied to other consumer–substrate networks.

Global wetlands are believed to be climate sensitive , and are the largest natural emitters of methane (CH 4). Increased wetland CH 4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH 4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH 4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO 2) forcing datasets. The WETCHIMP experiments Published by Copernicus Publications on behalf of the European Geosciences Union. 754 J. R. Melton et al.: WETCHIMP conclusions were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO 2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prog-nostically, while other models relied on remotely sensed in-undation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH 4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH 4 emissions, but large variation between the models remains. For annual global CH 4 emissions, the models vary by ±40 % of the all-model mean (190 Tg CH 4 yr −1). Second, all models show a strong positive response to increased atmospheric CO 2 concentrations (857 ppm) in both CH 4 emissions and wetland area. In response to increasing global temperatures (+3.4 • C globally spatially uniform), on average, the models decreased wetland area and CH 4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH 4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5 •). This limitation severely restricts our ability to model global wetland CH 4 emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH 4 emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH 4 emission models, even after uncertainties in wetland areas are accounted for.

The direction and magnitude of soil organic carbon (SOC) changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5). Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1–296 kg C m−2, 2–166 kg m−2, and 0–232 kg m−2, respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were found to be land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth system models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31%), followed by discontinuous (28%), isolated (24.3%), and sporadic (23.6%) permafrost areas. Our high-resolution mapping of soil carbon stock reveals the potential vulnerability of high-latitude soil carbon and can be used as a basis for future studies of anthropogenic and climatic perturbations.

The fate of currently frozen permafrost carbon as high-latitude climate warms remains highly uncertain and existing models give widely varying estimates of the permafrost carbon-climate feedback. This uncertainty is due to many factors, including the role that permafrost thaw-induced transitions in soil hydrologic conditions will have on organic matter decomposition rates and the proportion of aerobic to anaerobic respiration. Large-scale permafrost thaw, as predicted by the Community Land Model (CLM) under an unmitigated greenhouse gas emissions scenario, results in significant soil drying due to increased drainage following permafrost thaw, even though permafrost domain water inputs are projected to rise (net precipitation minus evaporation >0). CLM predicts that drier soil conditions will accelerate organic matter decomposition, with concomitant increases in carbon dioxide (CO2) emissions. Soil drying, however, strongly suppresses growth in methane (CH4) emissions. Considering the global warming potential (GWP) of CO2 and CH4 emissions together, soil drying weakens the CLM projected GWP associated with carbon fluxes from the permafrost zone by more than 50% compared to a non-drying case. This high sensitivity to hydrologic change highlights the need for better understanding and modeling of landscape-scale changes in soil moisture conditions in response to permafrost thaw in order to more accurately assess the potential magnitude of the permafrost carbon-climate feedback.

Soils are a crucial component of the Earth system; they comprise a large portion of terrestrial carbon stocks, mediate the supply and demand of nutrients, and influence the overall response of terrestrial ecosystems to perturbations. In this paper, we develop a new soil biogeochemistry model for the Community Land Model, version 4 (CLM4). The new model includes a vertical dimension to carbon (C) and nitrogen (N) pools and transformations, a more realistic treatment of mineral N pools, flexible treatment of the dynamics of decomposing carbon, and a radiocarbon (14 C) tracer. We describe the model structure, compare it with site-level and global observations, and discuss the overall effect of the revised soil model on Community Land Model (CLM) carbon dynamics. Site-level comparisons to radiocarbon and bulk soil C observations support the idea that soil C turnover is reduced at depth beyond what is expected from environmental controls for temperature, moisture, and oxygen that are considered in the model. In better agreement with observations, the revised soil model predicts substantially more and older soil C, particularly at high latitudes, where it resolves a permafrost soil C pool. In addition, the 20th century-C dynamics of the model are more realistic than those of the baseline model, with more terrestrial C uptake over the 20th century due to reduced N downregulation and longer turnover times for decomposing C.

Soil is a complex system where biotic (e.g., plant roots, micro-organisms) and abiotic (e.g., mineral surfaces) consumers compete for resources necessary for life (e.g., nitrogen, phosphorus). This competition is ecologically significant, since it regulates the dynamics of soil nutrients and controls aboveground plant productivity. Here we develop, calibrate and test a nutrient competition model that accounts for multiple soil nutrients interacting with multiple biotic and abiotic consumers. As applied here for tropical forests, the Nutrient COMpetition model (N-COM) includes three primary soil nutrients (NH4+, NO3− and POx; representing the sum of PO43−, HPO42− and H2PO4−) and five potential competitors (plant roots, decomposing microbes, nitrifiers, denitrifiers and mineral surfaces). The competition is formulated with a quasi-steady-state chemical equilibrium approximation to account for substrate (multiple substrates share one consumer) and consumer (multiple consumers compete for one substrate) effects. N-COM successfully reproduced observed soil heterotrophic respiration, N2O emissions, free phosphorus, sorbed phosphorus and NH4+ pools at a tropical forest site (Tapajos). The overall model uncertainty was moderately well constrained. Our sensitivity analysis revealed that soil nutrient competition was primarily regulated by consumer–substrate affinity rather than environmental factors such as soil temperature or soil moisture. Our results also imply that under strong nutrient limitation, relative competitiveness depends strongly on the competitor functional traits (affinity and nutrient carrier enzyme abundance). We then applied the N-COM model to analyze field nitrogen and phosphorus perturbation experiments in two tropical forest sites (in Hawaii and Puerto Rico) not used in model development or calibration. Under soil inorganic nitrogen and phosphorus elevated conditions, the model accurately replicated the experimentally observed competition among nutrient consumers. Although we used as many observations as we could obtain, more nutrient addition experiments in tropical systems would greatly benefit model testing and calibration. In summary, the N-COM model provides an ecologically consistent representation of nutrient competition appropriate for land BGC models integrated in Earth System Models.

Terrestrial carbon–climate feedbacks depend on two large and opposing fluxes—soil organic matter decomposition and photosynthesis—that are tightly regulated by nutrients1,2. Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 represented nutrient dynamics poorly1,3, rendering predictions of twenty-first century carbon–climate feedbacks highly uncertain. Here, we use a new land model to quantify the effects of observed plant nutrient uptake mechanisms missing in most other ESMs. In particular, we estimate the global role of root nutrient competition with microbes and abiotic processes during periods without photosynthesis. Nitrogen and phosphorus uptake during these periods account for 45 and 43%, respectively, of annual uptake, with large latitudinal variation. Globally, night-time nutrient uptake dominates this signal. Simulations show that ignoring this plant uptake, as is done when applying an instantaneous relative demand approach, leads to large positive biases in annual nitrogen leaching (96%) and N2O emissions (44%). This N2O emission bias has a GWP equivalent of ~2.4 PgCO2 yr−1, which is substantial compared to the current terrestrial CO2 sink. Such large biases will lead to predictions of overly open terrestrial nutrient cycles and lower carbon sequestration capacity. Both factors imply over-prediction of positive terrestrial feedbacks with climate in current ESMs.

The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data set with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

To better understand sources of uncertainty in projections of terrestrial carbon cycle feedbacks, we present an approach to separate the controls on modeled carbon changes. We separate carbon changes into four categories using a linearized, equilibrium approach: those arising from changed inputs (productivity-driven changes), and outputs (turnover-driven changes), of both the live and dead carbon pools. Using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations for five models, we find that changes to the live pools are primarily explained by productivity-driven changes, with only one model showing large compensating changes to live carbon turnover times. For dead carbon pools, the situation is more complex as all models predict a large reduction in turnover times in response to increases in productivity. This response arises from the common representation of a broad spectrum of decomposition turnover times via a multi-pool approach, in which flux-weighted turnover times are faster than mass-weighted turnover times. This leads to a shift in the distribution of carbon among dead pools in response to changes in inputs, and therefore a transient but long-lived reduction in turnover times. Since this behavior, a reduction in inferred turnover times resulting from an increase in inputs, is superficially similar to priming processes, but occurring without the mechanisms responsible for priming, we call the phenomenon \"false priming\", and show that it masks much of the intrinsic changes to dead carbon turnover times as a result of changing climate. These patterns hold across the fully coupled, biogeochemically coupled, and radiatively coupled 1 % yr−1 increasing CO2 experiments. We disaggregate inter-model uncertainty in the globally integrated equilibrium carbon responses to initial turnover times, initial productivity, fractional changes in turnover, and fractional changes in productivity. For both the live and dead carbon pools, inter-model spread in carbon changes arising from initial conditions is dominated by model disagreement on turnover times, whereas inter-model spread in carbon changes from fractional changes to these terms is dominated by model disagreement on changes to productivity in response to both warming and CO2 fertilization. However, the lack of changing turnover time control on carbon responses, for both live and dead carbon pools, in response to the imposed forcings may arise from a common lack of process representation behind changing turnover times (e.g., allocation and mortality for live carbon; permafrost, microbial dynamics, and mineral stabilization for dead carbon), rather than a true estimate of the importance of these processes.

Wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of methane emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This risk is particularly relevant at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over the next 100 years. However, global models disagree as to the magnitude and spatial distribution of emissions, due to uncertainties in wetland area and emissions per unit area and a scarcity of in situ observations. Recent intensive field campaigns across the West Siberian Lowland (WSL) make this an ideal region over which to assess the performance of large-scale process-based wetland models in a high-latitude environment. Here we present the results of a follow-up to the Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP), focused on the West Siberian Lowland (WETCHIMP-WSL). We assessed 21 models and 5 inversions over this domain in terms of total CH4 emissions, simulated wetland areas, and CH4 fluxes per unit wetland area and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite surface water products. We found that (a) despite the large scatter of individual estimates, 12-year mean estimates of annual total emissions over the WSL from forward models (5.34 ± 0.54 Tg CH4 yr−1), inversions (6.06 ± 1.22 Tg CH4 yr−1), and in situ observations (3.91 ± 1.29 Tg CH4 yr−1) largely agreed; (b) forward models using surface water products alone to estimate wetland areas suffered from severe biases in CH4 emissions; (c) the interannual time series of models that lacked either soil thermal physics appropriate to the high latitudes or realistic emissions from unsaturated peatlands tended to be dominated by a single environmental driver (inundation or air temperature), unlike those of inversions and more sophisticated forward models; (d) differences in biogeochemical schemes across models had relatively smaller influence over performance; and (e) multiyear or multidecade observational records are crucial for evaluating models' responses to long-term climate change.