William R. Wieder

Uncertainty in land model projections remains high and the roles of parametric and structural uncertainty are difficult to disentangle. To compare parametric sensitivity across model structures we present two parameter perturbation ensembles using the Community Land Model (CLM) operating in satellite phenology mode. The ensembles contrast two vegetation modules: (a) the default CLM vegetation module and (b) the Functionally Assembled Terrestrial Ecosystem Simulator (CLM‐FATES). We perturbed over 300 parameters and quantified their effects on biophysical fluxes globally and across biomes. Most parameters have minimal impact on biophysical fluxes, with only a few substantially influencing results. While both models exhibit similar parameter sensitivity for some fluxes, CLM‐FATES shows larger spread in gross primary productivity (GPP), driven by strong sensitivity to carboxylation rate. CLM‐FATES also shows a weaker GPP response to soil hydrology parameters and exhibits higher water use efficiency (WUE). Cross‐model comparisons reveal similar sensitivities for some parameters (e.g., leaf dimension) but divergent responses to others (e.g., stomatal intercept), highlighting underlying structural differences. Differences in WUE and sensitivity to hydrology and stomatal conductance parameters underscore how model structure fundamentally alters parametric sensitivity. The data sets generated from these ensembles can be used to identify influential parameters and guide future calibration efforts.

A newly developed simulation tool, the National Center for Atmospheric Research–National Ecological Observatory Network (NCAR‐NEON) modeling framework, offers a promising opportunity to investigate parameter sensitivity in climate models. This is primarily due to its ultra‐efficient computational design and the availability of high‐quality NEON observational data. We conducted a Parameter Perturbation Ensemble (PPE) experiment using the Community Land Model version 5 (CLM5) at three NEON tower sites in the Southeastern U.S. We analyzed a subset of 30 parameters that influence carbon‐water cycle processes, running a total of 244,800 yrs of CLM5 simulations, which included 200 yrs of spin‐up and 4 yrs of production runs for each of the 400 parameter sets across three sites (3 sites × 400 parameter sets × 204 yrs). The best‐performing parameter set was identified by equally weighting normalized model performance for gross primary productivity (GPP) and evapotranspiration (ET). Joint calibration of both GPP and ET achieved the highest overall model performance (0.89), outperforming GPP‐only (0.75), ET‐only (0.78), and the default configuration (0.70) on a normalized scale from 0 to 1. Additionally, optimization produced a revised value for the hydraulic trait parameter psi50 that aligns more closely with trait‐based observations. When applied regionally at Ozarks Complex domain, the optimized point‐scale parameters reduced gross primary productivity overestimation by 39% and evapotranspiration underestimation by 10% compared to the default CLM5 configuration. Despite these improvements, challenges in upscaling remain due to uncertainties in input data and reference data sets, as well as the limited temporal and spatial coverage of NEON observations.

Warming temperatures are accelerating permafrost thaw and changing tundra vegetation, where woody shrubs are displacing sedges. Shrubs, such as Betula nana , and sedges, such as Eriophorum vaginatum , exhibit distinct life strategies including unique root-associated, or rhizosphere microbial communities. As permafrost thaws it unlocks previously unavailable carbon and nutrient sources resulting in deeper roots and a translocation of rhizosphere communities. Because permafrost microbial communities contain lower diversity and biomass than rhizosphere communities, the coalescence of rhizosphere and permafrost microbial communities could alter soil organic matter (SOM) degradation rates and increase greenhouse gas emissions. To identify metabolic strategies across distinct rhizosphere and permafrost microbial communities we conducted an isotope tracing incubation experiment. We inoculated thawed permafrost with shrub and sedge rhizosphere communities while adding exudates or water daily and compared this to an uninoculated control. After 46 days, we spiked samples with ¹⁸ O enriched water or ¹³ C enriched exudates and measured isotope incorporation into microbial DNA with quantitative stable isotope probing (qSIP). Our results indicate that exudate additions had little effect on uninoculated permafrost communities but the addition of exudates and rhizosphere inoculants had a compounding effect on respiration rates. We found that soils inoculated with shrub rhizosphere communities contained a mixture of exudate and SOM degraders while soils inoculated with sedge rhizosphere communities contained mainly SOM degraders. Finally, we found that individual microbial taxa exhibited maximum growth rates in one ‘environment’, which was a combination of microbial inoculant communities and exudate addition treatments. Our results reveal that microbial niches are strongly influenced by substrate preferences and community context, and suggest that a reduction in sedges and an expansion of shrubs may provide a mechanism by which permafrost carbon losses are mitigated through corresponding shifts in microbial communities and their substrate preferences.

Summary: Predicting shifts in species composition with global change remains challenging, but plant functional traits provide a key link to scale from plant to community and ecosystem levels. The extent to which functional trait shifts may mediate ecosystem response to climate change remains a critical question. We ran point‐scale Community Land Model (CLM) simulations with site‐specific functional trait and phenology observations to represent alpine tundra growth strategies. We validated our results with site observations and compared parameterized results to those using the default parameterization. We then quantified the relative contribution of plant functional trait shifts vs climate change scenarios (and the resulting phenological shifts) to uncertainty in future tundra ecosystem productivity outcomes. We found that using community‐specific functional traits and phenology observations significantly improved productivity estimates compared with overestimates in a default simulation. Uncertainty in potential plant trait shifts often had a larger effect on ecosystem productivity responses than uncertainty in the forced response from different climate change scenarios. These findings highlight the key role of functional traits in shaping vegetation responses to climate change and the value of incorporating site‐level measurements into land models to more accurately forecast climate change impacts on ecosystem function.

Uncertainty in land model projections remains persistently high and the relative roles of parametric and structural uncertainty are difficult to disentangle. To compare parametric sensitivity across model structures we present two parameter perturbation ensembles (PPEs) using the Community Land Model version 6.0 (CLM 6.0) both operating in satellite phenology (SP) mode. The ensembles contrast two vegetation modules: 1) the default CLM vegetation module and 2) the Functionally Assembled Terrestrial Ecosystem Simulator (CLM-FATES). We perturbed 204 CLM parameters and 137 FATES parameters spanning canopy, soil, and snow processes, and quantified their effects on biophysical fluxes globally and across biomes. Most parameters have minimal impact on global biophysical fluxes, with only a few substantially influencing model results. While CLM and CLM-FATES exhibit similar ensemble spread and parameter sensitivity for some fluxes, CLM-FATES shows substantially larger spread in gross primary production (GPP), driven primarily by strong sensitivity to top-of-canopy carboxylation rate (Vcmax). Compared to CLM, CLM-FATES also shows a weaker GPP response to soil hydrology parameters and exhibits higher water use efficiency. Cross-model comparisons reveal similar sensitivities for some parameters (e.g., leaf dimension) but divergent responses to others (e.g., stomatal slope and intercept), highlighting underlying structural differences between CLM and CLM-FATES. Differences in water use efficiency and sensitivity to hydrology- and stomatal conductance-related parameters underscore how model structural choice fundamentally alters parametric sensitivity. These results provide the first systematic framework for PPEs with CLM-FATES, and the datasets generated from these ensembles can be used to identify influential parameters and guide future calibration efforts.

Comprehensive land models are subject to significant parametric uncertainty, which can be hard to quantify due to the large number of parameters and high model computational costs. We constructed a large parameter perturbation ensemble (PPE) for the Community Land Model version 5.1 with biogeochemistry configuration (CLM5.1‐BGC). We performed more than 2,000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive data set, which can be used to identify the most influential parameters on a wide range of output variables globally, by biome, or by plant functional type. We found that parameter effects can exceed scenario effects and that a small number of parameters explains a large fraction of variance across our ensemble. The most important parameters can differ regionally and also based on the forcing scenario. The software infrastructure developed for this experiment has greatly reduced the human and computer time needed for CLM PPEs, which can facilitate routine investigation of parameter sensitivity and uncertainty, as well as automated calibration.

Litter decomposition is an important ecosystem process and global carbon flux that has been shown to be controlled by climate, litter quality, and microbial communities. Process‐based ecosystem models are used to predict responses of litter decomposition to climate change. While these models represent climate and litter quality effects on litter decomposition, they have yet to integrate empirical microbial community data into their parameterizations for predicting litter decomposition. To fill this gap, our research used a comprehensive leaf litterbag decomposition experiment at 10 temperate forest U.S. National Ecological Observatory Network (NEON) sites to calibrate (7 sites) and validate (3 sites) the MIcrobial‐MIneral Carbon Stabilization (MIMICS) model. MIMICS was calibrated to empirical decomposition rates and to their empirical drivers, including the microbial community (represented as the copiotroph‐to‐oligotroph ratio). We calibrate to empirical drivers, rather than solely rates or pool sizes, to improve the underlying drivers of modeled leaf litter decomposition. We then validated the calibrated model and evaluated the effects of calibration under climate change using the SSP 3–7.0 climate change scenario. We find that incorporating empirical drivers of litter decomposition provides similar, and sometimes better (in terms of goodness‐of‐fit metrics), predictions of leaf litter decomposition but with different underlying ecological dynamics. For some sites, calibration also increased climate change‐induced leaf litter mass loss by up to 5%, with implications for carbon cycle‐climate feedbacks. Our work also provides an example for integrating data on the relative abundance of bacterial functional groups into an ecosystem model using a novel calibration method to bridge empiricism and process‐based modeling, answering a call for the use of empirical microbial community data in process‐based ecosystem models. We highlight that incorporating mechanistic information into models, as done in this study, is important for improving confidence in model projections of ecological processes like litter decomposition under climate change.

Increasing fine root carbon (FRC) inputs into soils has been proposed as a solution to increasing soil organic carbon (SOC). However, FRC inputs can also enhance SOC loss through priming. Here, we tested the broad-scale relationships between SOC and FRC at 43 sites across the US National Ecological Observatory Network. We found that SOC and FRC stocks were positively related with an across-ecosystem slope of 7 ± 3 kg SOC m⁻² per kg FRC m⁻², but this relationship was driven by grasslands. Grasslands had double the across-ecosystem slope while forest FRC and SOC were unrelated. Furthermore, deep grassland soils primarily showed net SOC accrual relative to FRC input. Conversely, forests had high variability in whether FRC inputs were related to net SOC priming or accrual. We conclude that while FRC increases could lead to increased SOC in grasslands, especially at depth, the FRC-SOC relationship remains difficult to characterize in forests.