Natural and anthropogenic disturbances act as important drivers of tree mortality, shaping the structure, composition and biomass distribution of forests. Disturbance regimes may emerge from different characteristics of disturbance events over time and space. We design a model- based experiment to investigate the links between disturbance regimes at the landscape scale and spatial features of biomass patterns. The effects on biomass of a wide range of disturbance regimes are simulated by varying three different parameters, i.e. μ (probability scale), α (clustering degree), and β (intensity slope) that shape the extent, frequency, and intensity of disturbance events, respectively. A simple dynamic carbon cycle model is used to simulate 200 years of plant biomass dynamics in response to circa +2000 different disturbance regimes, depending on the different combinations of μ, α, and β. Each parameter combination yields a spatially explicit estimate of plant biomass for which sixteen synthesis statistics are estimated on the spatial distributions of biomass, including information-based and texture features. Based on a multi-output regression approach we link these synthesis statistics with additional gross primary production (GPP) constraints to retrieve the three disturbance parameters. In doing so we evaluate the confidence in inferring disturbance regimes from spatial distributions of biomass. Our results show that all three parameters can be confidently retrieved. The Nash-Sutcliffe efficiency for the prediction of the μ, α, and β is 97.3%, 96.6%, and 97.9%, respectively. A feature importance analysis reveals that the distribution statistics dominate the prediction of μ and β, while features quantifying texture have a stronger connection with α. Overall, this study clarifies the association between biomass patterns emerging from different underlying disturbance regimes, while overcoming the previously found equifinality between mortality rates and total biomass. Given the links between decadal vegetation dynamics and the uncertainties in the role of terrestrial ecosystems in the global biogeochemical cycles, a better understanding and the quantification of disturbance regimes would improve our current understanding of controls and feedback at the biosphere-atmosphere interface in the current Earth system models.

The spatial contribution to the global land-atmosphere carbon dioxide (CO\textsubscript{2}) exchange is crucial in understanding and projecting the global carbon cycle, yet different studies diverge on the dominant regions. Informing land models with observational data is a promising way to reduce the parameter and structural uncertainties and advance our understanding. Here, we develop a parsimonious diagnostic process-based model of land carbon cycles, constraining parameters with observation-based products. We compare CO\textsubscript{2} flux estimates from our model with observational constraints and Trends in Net Land-Atmosphere Carbon Exchange (TRENDY) model ensemble to show that our model reasonably reproduces the seasonality of net ecosystem exchange (NEE) and GPP and interannual variability (IAV) of NEE. Finally, we use the developed model, TRENDY models, and observational constraints to attribute variability in global NEE and gross primary productivity (GPP) to regional variability. The attribution analysis confirms the dominance of Northern temperate and boreal regions in the seasonality of CO\textsubscript{2} fluxes. Regarding NEE IAV, we identify a significant contribution from tropical savanna regions as previously perceived. Furthermore, we highlight that tropical humid regions are also identified as at least equally relevant contributors as semi-arid regions. At the same time, the largest uncertainty among ensemble members of NEE constraint and TRENDY models in the tropical humid regions underscore the necessity of better process understanding and more observations in these regions. Overall, our study identifies tropical humid regions as key regions for global land-atmosphere CO\textsubscript{2} exchanges and the inter-model spread of its modeling.

The productivity of terrestrial vegetation is determined by a multitude of drivers between the land surface and atmosphere. Water availability is critical for vegetation productivity, but the vertical dimension of soil moisture has been largely overlooked. Here, we analyze dominant controls of global vegetation productivity represented by sun-induced fluorescence and spectral vegetation indices at the half-monthly time scale. We apply random forests to predict anomalies of vegetation productivity from a comprehensive set of hydro-meteorological variables including multi-layer soil moisture and quantify the variable importance. Dominant hydro-meteorological controls generally vary with latitudes: temperature in higher latitudes, solar radiation in lower latitudes, and soil moisture from sub-surface layers in between. We find that including vertically resolved soil moisture allows a better understanding of vegetation productivity and reveals a broader water-related control. This is found especially for semiarid regions, illustrating the global relevance of deep(er) rooting systems as an adaptation to water limitation.