Natural and anthropogenic disturbances are important drivers of tree mortality, shaping the structure, composition, and biomass distribution of forest ecosystems. Differences in disturbance regimes, characterized by the frequency, extent, and intensity of disturbance events, result in structurally different landscapes. Characterizing different disturbance regimes through landscape-scale forest structure provides a unique perspective for diagnosing the impacts and potential carbon-climate feedbacks from terrestrial ecosystems. In this study, we design a model-based experiment to investigate the links between disturbance regimes and spatial biomass patterns. We generate over 850 thousand biomass patterns, from 2,142 combinations of μ, α, and β under different primary productivity and background mortality scenarios. We characterize the emergent biomass patterns via synthesis statistics, including central tendency statistics; different moments of the distribution; information-based and texture features. We further follow a multi-output regression approach that takes the biomass synthesis statistics and gross primary production (GPP) as independent variables to retrieve the three disturbance regimes parameters. Results show confident inversion of all three “true” disturbance parameters, with Nash-Sutcliffe efficiency of  94.8% for μ, 94.9% for α, and 97.1% for β. Overall, these results demonstrate the association between biomass patterns and disturbance statistics that emerge from different underlying disturbance regimes. By doing so, it overcomes the known issue of equifinality between mortality rates and total biomass. Given the increasing availability of Earth observation of biomass, our findings open a new avenue to better understand and parameterize disturbance regimes and their links with vegetation dynamics under climate change. Ultimately, at a large scale, this approach 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.