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.

A long-standing challenge in studying the global carbon cycle has been understanding the factors controlling inter–annual variation (IAV) of carbon fluxes related to vegetation photosynthesis and respiration, and improving their representations in existing biogeochemical models. Here, we compared an optimality-based mechanistic model and a semi-empirical light use efficiency model to understand how current models can be improved to simulate IAV of gross primary production (GPP). Both models simulated hourly GPP and were parameterized for (1) each site–year, (2) each site with an additional constraint on IAV (CostIAV), (3) each site, (4) each plant–functional type, and (5) globally. This was followed by forward runs using calibrated parameters, and model evaluations at different temporal scales across 198 eddy covariance sites. Both models performed better on hourly scale than annual scale for most sites. Specifically, the mechanistic model substantially improved when drought stress was explicitly included. Most of the variability in model performances was due to model types and parameterization strategies. The semi-empirical model produced statistically better hourly simulations than the mechanistic model, and site–year parameterization yielded better annual performance for both models. Annual model performance did not improve even when parameterized using CostIAV. Furthermore, both models underestimated the peaks of diurnal GPP in each site–year, suggesting that improving predictions of peaks could produce a comparatively better annual model performance. GPP of forests were better simulated than grassland or savanna sites by both models. Our findings reveal current model deficiencies in representing IAV of carbon fluxes and guide improvements in further model development.

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO₂ fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO₂ fertilization effect from other long-term drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO₂ fertilization effect on GPP and daily maximum net ecosystem production (NEPmax) using eddy covariance records across 38 extratropical forest sites. We find the median stimulation rate of GPP and NEPmax to be 16.4 ± 4% and 17.2 ± 4% per 100 ppm increase in atmospheric CO₂ across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process-based land surface models. We acknowledge that additional factors, including nitrogen deposition and land management, may impact plant productivity, potentially confounding the attribution to the CO₂ fertilization effect. Assuming these site-specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO₂ fertilization effect. However, disentanglement of these long-term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long-term CO₂ fertilization across multiple eddy covariance sites. Such observation-based quantification is key to constraining the long-standing uncertainties in the land carbon cycle under rising CO₂ concentrations.

Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well-being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and management may put this buffering capacity at risk. Here, we summarise the evidence that reductions in biodiversity can destabilise the functioning of ecosystems facing climate extremes. We then explore if impaired ecosystem functioning could, in turn, exacerbate climate extremes. We argue that only a comprehensive approach, incorporating both ecological and hydrometeorological perspectives, enables to understand and predict the entire feedback system between altered biodiversity and climate extremes. This ambition, however, requires a reformulation of current research priorities to emphasise the bidirectional effects that link ecology and atmospheric processes.

Vegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modelled by Land Surface Models (LSMs), which are an essential part of numerical weather prediction and data assimilation. However, most current LSMs implemented specifically in weather forecasting systems use climatological vegetation indices, and land use/land cover datasets in these models are often outdated. In this study, we update land surface data in the ECMWF land surface modelling system ECLand using Earth observation-based time varying leaf area index and land use/land cover data, and evaluate the impact of vegetation dynamics on model performance. The performance of the simulated latent heat flux and soil moisture is then evaluated against global gridded observation-based datasets. Updating the vegetation information does not always yield better model performances because the model’s parameters are adapted to the previously employed land surface information. Therefore we recalibrate key soil and vegetation-related parameters at individual grid cells to adjust the model parameterizations to the new land surface information. This substantially improves model performance and demonstrates the benefits of updated vegetation information. Interestingly, we find that a regional parameter calibration outperforms a globally uniform adjustment of parameters, indicating that parameters should sufficiently reflect spatial variability in the land surface. Our results highlight that newly available Earth-observation products of vegetation dynamics and land cover changes can improve land surface model performances, which in turn can contribute to more accurate weather forecasts.

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.

Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed sprouting leaves, with significantly different leaf traits compared to mature and old leaves, are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. Thus, the temporal dynamics of canopy GCC can be explained by leaf spectral properties and leaf age. Air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the new leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits thus can better estimate of water fluxes such as canopy conductance.

Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modelling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasise limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the “inverse texture effect”’ on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation–water interactions from regional to continental scales.

The process of evapotranspiration transfers water vapour from vegetation and soil surfaces to the atmosphere, the so-called latent heat flux (𝑄 LE), and thus crucially modulates Earth’s energy, water, and carbon cycles. Vegetation controls 𝑄 LE through regulating the leaf stomata (i.e., surface resistance 𝑟 s) and through altering surface roughness (aerodynamic resistance 𝑟 a). Estimating 𝑟 s and 𝑟 a across different vegetation types proves to be a key challenge in predicting 𝑄 LE. Here, we propose a hybrid modeling approach (i.e., combining mechanistic modeling and machine learning) for 𝑄 LE where neural networks independently learn the resistances from observations as intermediate variables. In our hybrid modeling setup, we make use of the Penman-Monteith equation based on the Big Leaf theory in conjunction with multi-year flux measurements across different forest and grassland sites from the FLUXNET database. We follow two conceptually different strategies to constrain the hybrid model to control for equifinality arising when estimating the two resistances simultaneously. One strategy is to impose an a priori constraint on 𝑟 a based on our mechanistic understanding (theory-driven strategy), while the other strategy makes use of more observational data and adds a constraint in predicting 𝑟 a through multi-task learning of the latent as well as the sensible heat flux (𝑄 H ; data-driven strategy). Our results show that all hybrid models exhibit a fairly high predictive skill for the target variables with 𝑅 2 = 0.82-0.89 for grasslands and 𝑅 2 = 0.70-0.80 for forests sites at the mean diurnal scale. The predictions of 𝑟 s and 𝑟 a show physical consistency across the two regularized hybrid models, but are physically implausible in the under-constrained hybrid model. The hybrid models are robust in reproducing consistent results for energy fluxes and resistances across different scales (diurnal, seasonal, interannual), reflecting their ability to learn the physical dependence of the target variables on the meteorological inputs. As a next step, we propose to test these heavily observation-informed parameterizations derived through hybrid modeling as a substitute for overly simple ad hoc formulations in Earth system models.

Next to precipitation, secondary water sources emerging from shallow groundwater and lateral redistribution of soil moisture, together with soil properties modulating their accessibility are highly important in water-limited ecosystems. However, effects of these land-associated secondary inputs are not well known over large domains given the mismatch of spatial scales of processes. Here, we quantify the role of land properties on the spatial variations of seasonal decay rate of vegetation cover over water-limited regions of Africa, using machine learning. Over the study domain, 17 % of these variations are directly attributed to land properties, and 16 % are attributed to interaction effects of land properties with climate and vegetation. Locally, total land attributed variations account for more than 60 % in hotspots with different land properties like shallow groundwater, complex topography, and favourable soil properties. Our findings lend empirical evidence for the importance of local-scale secondary water inputs over large domains.

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.

Elevated atmospheric CO2 (eCO2) influences the carbon assimilation rate and stomatal conductance of plants, and thereby can affect the global cycles of carbon and water. However, the extent to which these physiological effects of eCO2 influence the land-atmosphere exchange of carbon and water is uncertain. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We use a comprehensive process-based land surface model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) to simulate the leaf-level effects of increasing atmospheric CO2 concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO2 effects. The signal in gross primary production (GPP) emerges at relatively low eCO2 (Δ[CO2] ~ 20 ppm) where the leaf area index (LAI) is relatively high. Compared to GPP, the eCO2 effect causing reduced 28 transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (Δ[CO2] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO2 is detectable earlier for variables of the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass- dominated ecosystems. Our results provide a step towards when and where we expect to detect physiological CO2 effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.