Rosie A. Fisher

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.

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.

Accurately predicting terrestrial ecosystem responses to climate change over long‐timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimization, focusing on carbon‐water‐vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterizing errors due to observation biases and model‐data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple data sets, and including the spin‐up and historical period in the assimilation window. We outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation data sets, harness ML advances, and enhance the predictive capabilities of LSMs.

Summary: Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land‐use changes, and are essential for projecting carbon cycling and large‐scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03–0.60, 0.18–0.70 and 0.35–1.10 kgCm ⁻² yr ⁻¹ for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates. This first multi‐model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.

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.

Accurately projecting the global carbon cycle requires Earth System Models (ESMs) to represent plant nutrient acquisition and associated carbon costs. However, most ESMs either neglect or oversimplify the role of mycorrhizal fungi, despite their key function in mediating plant-soil nutrient exchanges. Here, we integrate global satellite and field-based datasets on mycorrhizal distributions, soil nitrogen content, and carbon allocation to fungi to constrain nutrient acquisition costs in a land surface model, the Community Land Model (CLM). Incorporating these spatially explicit mycorrhizal costs reduces projected global carbon uptake by 15% over the 21st century. We find that mycorrhizal efficiency varies strongly by biome, with especially high carbon costs in boreal and semi-arid ecosystems. Model performance also improves by 5% in reproducing observed energy, water, and carbon fluxes. These results highlight the critical role of mycorrhizal symbioses in shaping ecosystem carbon balance and underscore the need to integrate fine-scale belowground data together with remote sensing into ESM frameworks for more accurate climate projections.

Complex Land Surface Models (LSMs) rely on a plethora of parameters. These parameters and the associated process formulations are often poorly constrained, which hampers reliable predictions of ecosystem dynamics and climate feedbacks. Robust and uncertainty‐aware parameter estimation with observations is complicated by, for example, the high dimensionality of the model parameter space and the computational cost of LSM simulations. Herein, we adapt a novel Bayesian data assimilation (DA) and machine learning framework termed “calibrate, emulate, sample” (CES) to infer parameters in a widely‐used LSM coupled with a demographic vegetation model (CLM‐FATES). First, an iterative ensemble Kalman smoother provides an initial estimate of the posterior distribution (“calibrate”). Subsequently, a machine‐learning‐based emulator is trained on the resulting model‐observation mismatches to predict outcomes for unseen parameter combinations (“emulate”). Finally, this emulator replaces CLM‐FATES simulations in an adaptive Markov Chain Monte Carlo approach enabling computationally feasible posterior sampling with enhanced uncertainty quantification (“sample”). We test our implementation with synthetic and real observations representing a boreal forest site in southern Finland. We estimate a total of six plant‐functional‐type‐specific photosynthetic parameters by assimilating evapotranspiration (ET) and gross primary production (GPP) flux data. CES provided the best estimates of the synthetic truth parameters when compared to data‐blind emulator sampling designs while all approaches reduced model‐observation errors compared to a default parameter simulation (GPP: % to %, ET: % to %). Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM‐FATES, and/or unknown errors in the data that are not accounted for.

Summary: Despite its importance for determining global carbon fluxes, leaf respiration remains poorly constrained in land surface models (LSMs). We tested the sensitivity of the Energy Exascale Earth System Model Land Model – Functionally Assembled Terrestrial Ecosystem Simulator (ELM‐FATES) to variation in the canopy gradients of leaf maintenance respiration ( R _dark ). We ran global and point simulations varying the canopy gradient of R _dark to explore the impacts on forest structure, composition, and carbon cycling. In global simulations, steeper canopy gradients of R _dark lead to increased understory survival and leaf biomass. Leaf area index (LAI) increased up to 77% in tropical regions compared with the default parameterization, improving alignment with remotely sensed benchmarks. Global vegetation carbon varied from 308 Pg C to 449 Pg C across the ensemble. In tropical forest simulations, steeper gradients of R _dark had a large impact on successional dynamics. Results show the importance of canopy gradients in leaf traits and fluxes for determining plant carbon budgets and emergent ecosystem properties such as competitive dynamics, LAI, and vegetation carbon. The high‐model sensitivity to canopy gradients in R _dark highlights the need for more observations of how leaf traits and fluxes vary along light micro‐environments to inform critical dynamics in LSMs.

The relationship between carbon dioxide emissions and their accumulation in the atmosphere is one of the most important elements of the function of the Earth system. Exertion of control over the terrestrial carbon budget, via afforestation, reforestation, bioenergy production and other methods to enhance land carbon storage (biochar, enhanced weathering) all imply a need to forecast and understand the dynamics of these carbon stores as they evolve in changing atmospheric CO2 and climatic conditions. The dynamics of the carbon cycle, however, are notably complex and require comprehension of models representing the functioning of numerous coupled systems which must produce predictions under these no-analog conditions, and so must necessarily embed process understanding to allow for meaningful extrapolation into the future. Models of the terrestrial biosphere, often embedded in Earth system models, thus contain advanced representations of a large set of processes that are known to impact ecosystem carbon storage. This complexity, however, has presented a substantial barrier to objective calibration using conventional statistical approaches, as the number of model parameters and the computational expense of the models means that comprehensive exploration of the parameter space is effectively unmanageable. Further, many ecosystem processes exhibit non-linear and threshold properties (notably, vegetation death, competitive interactions, fire thresholds) and thus are challenging for methods that assume linearity. Here we propose a method for decomposing the complexity of one such model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) that allows investigation, calibration and comprehension of individual parts of the system in isolation (driven by observed data fields). This ‘modular complexity’ approach allows the full complexity model to be run in a series of ‘modes’ that can operate as domain-specific models for, e.g. ecohydrology, community ecology, biogeochemistry etc. while also allowing the full complexity version to be used for higher order problems, such a predicting global vegetation dynamics under future climate scenarios. We describe a series of investigations using FATES that illustrate the potential for this model decomposition approach and discuss the potential for further application of this philosophy.

Accurately predicting terrestrial ecosystem responses to climate change is crucial for addressing global challenges. This relies on mechanistic modelling of ecosystem processes through Land Surface Models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimisation, focusing on carbon-water-vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterising errors due to observation biases and model-data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple datasets, and including the spin-up and historical period in the assimilation window. We then outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation datasets, harness machine learning advances, and enhance the predictive capabilities of LSMs.