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We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre-existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant-soil dynamics for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth system models.

Key uncertainties in terrestrial carbon cycle projections revolve around the timing, direction, and magnitude of the carbon cycle feedback to climate change. This is especially true in carbon-rich Arctic ecosystems, where permafrost soils contain roughly one third of the world's soil carbon stocks, which are likely vulnerable to loss. Using an ensemble of soil biogeochemical models that reflect recent changes in the conceptual understanding of factors responsible for soil carbon persistence, we quantify potential soil carbon responses under two representative climate change scenarios. Our results illustrate that models disagree on the sign and magnitude of global soil changes through 2100, with disagreements primarily driven by divergent responses of Arctic systems. These results largely reflect different assumptions about the nature of soil carbon persistence and vulnerabilities, underscoring the challenges associated with setting allowable greenhouse gas emission targets that will limit global warming to 1.5°C.

Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon-nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and-the newly developed-5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO2) enrichment with meta-analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET-MTE observations. Simulations with N and CO2 enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections.

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0-100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20th century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

Future projections of land carbon uptake in Earth System Models are affected by land surface model responses to both CO2 and nitrogen fertilization. The Community Land Model, Version 5 (CLM5), contains a suite of modifications to carbon and nitrogen cycle representation. Globally, the CLM5 has a larger CO2 response and smaller nitrogen response than its predecessors. To improve our understanding of the controls over the fertilization responses of the new model, we assess sensitivity to eight parameters pertinent to the cycling of carbon and nitrogen by vegetation, both under present‐day conditions and with CO2 and nitrogen fertilization. The impact of fertilization varies with both model parameters and with the balance of limiting factors (water, temperature, nutrients, and light) in the pre‐fertilization model state. The model parameters that impact the pre‐fertilization state are in general not the same as those that control fertilization responses, meaning that goodness of fit to present‐day conditions does not necessarily imply a constraint on future transient projections. Where pre‐fertilization state has low leaf area, fertilization‐induced increases in leaf production amplify the model response to the initial fertilization via further increases in photosynthesis. Model responses to CO2 and N fertilization are strongly impacted by how much plant communities can increase their rates of nitrogen fixation and also directly affected by costs of N extraction from soil and stoichiometric flexibility. Illustration of how parametric flexibility impacts model outputs should help inform the interpretation of carbon‐climate feedbacks estimated by in fully coupled Earth system model simulations. Key Points: The Community Land Model, Version 5, contains numerous modifications to representations of the vegetation carbon and nitrogen cyclesThe control state, and responses to CO2 and nitrogen, are sensitive to parameter choice, and to baseline water and nitrogen limitationParameters controlling nitrogen fixation rates dominate fertilization responses, highlighting a need for greater scrutiny of this process [ABSTRACT FROM AUTHOR]

The increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework. Key Point: The ILAMB benchmarking system broadly compares models to observational data sets and provides a synthesis of overall performance [ABSTRACT FROM AUTHOR]

Measurements of the stable carbon isotope ratio (Δ13C) on annual tree rings offer new opportunities to evaluate mechanisms of variations in photosynthesis and stomatal conductance under changing CO2 and climate, especially in conjunction with process-based biogeochemical model simulations. The isotopic discrimination is indicative of the ratio between the CO2 partial pressure in the intercellular cavities and the atmosphere (ci / ca) and of the ratio of assimilation to stomatal conductance, termed intrinsic water-use efficiency (iWUE). We performed isotope-enabled simulations over the industrial period with the land biosphere module (CLM4.5) of the Community Earth System Model and the LPX-Bern dynamic global vegetation model. Results for C3 tree species show good agreement with a global compilation of Δ13C measurements on leaves, though modeled 13C discrimination by C3 trees is smaller in arid regions than measured. A compilation of seventy-six tree-ring records, mainly from Europe, boreal Asia, and western North America, suggest on average small 20th-century changes in isotopic discrimination and an increase in iWUE of about 27% since 1900. LPX-Bern results match these century-scale reconstructions, supporting the idea that the physiology of stomata has evolved to optimize trade-offs between carbon gain by assimilation and water loss. In contrast, CLM4.5 simulates an increase in discrimination and in turn a change in iWUE that is almost twice as large as revealed by the tree-ring data. Factorial simulations show that these changes are mainly in response to rising atmospheric CO2. The results suggest that the down-regulation of ci / ca and of photosynthesis by nitrogen limitation is possibly too strong in the standard setup of CLM4.5 or there may be more fundamental problems associated with the prescribed relationship between conductance and assimilation. [ABSTRACT FROM AUTHOR]

Wetlands are the largest global natural methane (CH4) source, and emissions between 50 and 70° N latitude contribute 10-30% to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with siteto regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May-September) CarbonTracker Alaskan regional-level CH4 predictions and sitelevel observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October-April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, coldseason CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in highlatitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements. [ABSTRACT FROM AUTHOR]