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.

Tropospheric O3 damage to plants significantly affects global vegetation productivity, yet accurately predicting this damage remains challenging. This study develops a parameterization to globally predict ozone damage by integrating a combination of factors: cumulative uptake of ozone (CUO), O3 concentration, stomatal conductance (Gs), and total exposure time. We compiled experimental data from over 200 peer-reviewed publications spanning 50 years, focusing on the responses of various crops and trees to chronic ozone exposure. Our analysis reveals that while CUO alone has a weak relationship with changes in photosynthesis and stomatal conductance under O3 stress, combining CUO with O3 concentration, Gs, and total exposure time significantly strengthens the predictive power. This combined approach was validated across diverse categories from experimental data, including plant types, tree age, exposure systems, types of control air, rooting environments, and ozone concentration bins. We found photosynthesis exhibited a weaker response relationship than stomatal conductance, indicative of underlying responses to O3 stress that could not be captured by the variables and methods used in this study. Our results underscore the complexity of predicting O3 damage and highlight the importance of synthesizing multiple predictors. Future research should incorporate other environmental stressors, e.g., heat, drought, and elevated CO2 levels, to enhance the accuracy of O3 damage models. This study provides a significant advancement in incorporating O3 damage parameterization for global crop and land surface modeling.

Satellite data reveal widespread changes in Earth’s vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO2 emissions during the 1959-2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models collectively referred to as the TRENDY ensemble and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well TRENDY models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference datasets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.