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 vesion 5.1 with biogeochemistry configuration (CLM5.1-BGC). We performed more than 2000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive dataset, 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.

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 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: -10% to -30%, ET: -4% to -6%). Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality and insufficient experiment complexity.

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.

We present a representation of nitrogen and phosphorus cycling in the vegetation demography model the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), within the Energy Exascale Earth System (E3SM) land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. The model tracks nutrient uptake, losses via turnover from both live plants and mortality into soil decomposition, and allocation during tissue growth for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake is governed by fine root biomass, and plants vary in their fine root carbon 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 (ESMs).