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.

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.

Plant stomata mediate the fluxes of both carbon and water between the land and the atmosphere. The ratio between photosynthesis and stomatal conductance (gs), or intrinsic water-use efficiency (iWUE), can be directly inferred from leaf or tree-ring carbon isotope composition. In many Earth system models, iWUE is inversely proportional and controlled by a parameter (g1M) in the calculation of gs. Here we examine how iWUE perturbations, setting g1M to the 5th (low) and 95th (high) percentile for each plant type based on observations, influence photosynthesis using coupled Earth System model simulations. We find that while lower iWUE leads to reductions in photosynthesis nearly everywhere, higher iWUE had a photosynthetic response that is surprisingly regionally dependent. Higher iWUE increases photosynthesis in the Amazon and central North America, but decreases photosynthesis in boreal Canada under fixed atmospheric conditions. However, the photosynthetic response to higher iWUE in these regions unexpectedly reverses when the atmosphere dynamically responds due to spatially differing sensitivity to increases in temperature and vapor pressure deficit. iWUE also influences the photosynthetic response to atmospheric CO2, with higher and lower iWUE modifying the total global response to elevated 2x preindustrial CO2 by 6.4% and -9.6%, respectively. Our work demonstrates that assumptions about iWUE in Earth system models significantly affect photosynthesis and its response to climate. Further, the response of photosynthesis to iWUE depends on which components of the model are included, therefore studies of iWUE impacts on historical or future photosynthesis can not be generalized across model configurations.

Accurate simulation of terrestrial water storage (TWS), the integrated volume of water in soil, groundwater, snow, surface water and vegetation per unit area, is challenging. TWS is a critical but often overlooked model diagnostic that can  improve understanding of hydrological processes, water resource management, and  climate change impacts assessment. Observations of TWS anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellite and its follow-on (GRACE-FO) missions provide observational constraints on model-simulated TWS. The similarity between large spatial scales of GRACE TWS retrievals and global land models like the Community Land Model (CLM) facilitates  comparison of model-simulated and observed TWS anomalies as a function of input parameter values.  We demonstrate an approach to optimize CLM parameters for improved TWS simulation, using history matching with Evidential Deep Neural Networks (EDNN). History matching is a constraining technique that identifies plausible parameter sets by comparing model outputs to observations. To reduce the  computational expense of generating large perturbed parameter ensembles (PPEs) we use an emulator  EDNN that provides a probabilistic framework for representing uncertainty in the relationship between input parameters and TWS.  A key advantage of the EDNN approach is estimation of both epistemic and aleatoric uncertainty with the use of a single model, reducing the need to sample or train multiple ensembles. We applied the proposed methodology that utilizes history matching with EDNN to a real-world case study in which we  compared CLM simulations to observed TWS data (e.g., from the GRACE satellite mission) over the Contiguous United States (CONUS). The optimization process focuses on key parameters (e.g., dry surface layer, decay factor for fractional saturated area, and medlyn slope of conductance-photosynthesis relationship) within the CLM that govern water storage dynamics. The resulting  parameter sets  yield model simulations of TWS that agree with observations. Moreover, uncertainty estimates could improve TWS simulations, enabling more robust assessments of global water availability and hydrological changes.