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Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.

Humid tropical forests play a dominant role in the functioning of Earth but are under increasing threat from changes in land use and climate. How forest vulnerability varies across space and time and what level of stress forests can tolerate before facing a tipping point are poorly understood. Here, we develop a tropical forest vulnerability index (TFVI) to detect and evaluate the vulnerability of global tropical forests to threats across space and time. We show that climate change together with land-use change have slowed the recovery rate of forest carbon cycling. Temporal autocorrelation, as an indicator of this slow recovery, increases substantially for above-ground biomass, gross primary production, and evapotranspiration when climate stress reaches a critical level. Forests in the Americas exhibit extensive vulnerability to these stressors, while in Africa, forests show relative resilience to climate, and in Asia reveal more vulnerability to land use and fragmentation. TFVI can systematically track the response of tropical forests to multiple stressors and provide early-warning signals for regions undergoing critical transitions.

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

The response of the terrestrial carbon cycle to climate change is among the largest uncertainties affecting future climate change projections. The feedback between the terrestrial carbon cycle and climate is partly determined by changes in the turnover time of carbon in land ecosystems, which in turn is an ecosystem property that emerges from the interplay between climate, soil and vegetation type. Here we present a global, spatially explicit and observation-based assessment of whole-ecosystem carbon turnover times that combines new estimates of vegetation and soil organic carbon stocks and fluxes. We find that the overall mean global carbon turnover time is 23(+7)(-4) years (95 per cent confidence interval). On average, carbon resides in the vegetation and soil near the Equator for a shorter time than at latitudes north of 75° north (mean turnover times of 15 and 255 years, respectively). We identify a clear dependence of the turnover time on temperature, as expected from our present understanding of temperature controls on ecosystem dynamics. Surprisingly, our analysis also reveals a similarly strong association between turnover time and precipitation. Moreover, we find that the ecosystem carbon turnover times simulated by state-of-the-art coupled climate/carbon-cycle models vary widely and that numerical simulations, on average, tend to underestimate the global carbon turnover time by 36 per cent. The models show stronger spatial relationships with temperature than do observation-based estimates, but generally do not reproduce the strong relationships with precipitation and predict faster carbon turnover in many semi-arid regions. Our findings suggest that future climate/carbon-cycle feedbacks may depend more strongly on changes in the hydrological cycle than is expected at present and is considered in Earth system models.

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. In this study, we design a model‐based experiment to investigate the links between disturbance regimes and spatial biomass patterns. First, the effects of disturbance events on biomass patterns are simulated using a simple dynamic carbon cycle model based on different disturbance regime attributes, which are characterized via three parameters: μ (probability scale), α (clustering degree), and β (intensity slope). 856,800 dynamically stable biomass patterns were then simulated using combined disturbance regime, primary productivity, and background mortality. As independent variables, we use biomass synthesis statistics from simulated biomass patterns to retrieve three disturbance regime 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. Biomass histogram statistics primarily dominate the prediction of μ and β, while texture features have a more substantial influence on α. Overall, these results demonstrate the association between biomass patterns and disturbance regimes. Given the increasing availability of Earth observation of biomass, our findings open a new avenue to understand better 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 present Earth system models. Plain Language Summary: Forest dynamics are shaped by different disturbances, which are challenging to monitor and predict. Identifying individual disturbance occurrences and their impact on forest carbon stocks (biomass) is complex. However, our study deciphers the characteristics of disturbance occurrence, that is, disturbance regime, from biomass pattern. We characterized this regime across three dimensions: extent (μ), frequency (α), and intensity (β). Through a 200‐year landscape experiment, we explored the synthetic dynamically stable biomass under different disturbance regimes. Statistical features from biomass simulations revealed distinct spatial patterns, forming a connection between these patterns and the disturbance regime parameters via machine learning. Notably, specific biomass pattern statistics influence distinct disturbance regime parameters: μ and β are linked to histogram stats, while α is tied to texture statistics. This approach establishes a framework to diagnose disturbance regimes from biomass patterns, offering a way to incorporate these regimes into Earth system models. Key Points: We investigate the link between disturbance regimes and spatial patterns of aboveground biomass emerging from diverse primary productivityThe proposed framework allows for inferring disturbance probability, size and intensity from spatial features in aboveground biomassDisturbance regimes from high‐res Earth observations can enhance carbon cycle dynamics prediction from interannual to longer time scales [ABSTRACT FROM AUTHOR]

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning. Complex Earth system challenges can be addressed by incorporating spatial and temporal context into machine learning, especially via deep learning, and further by combining with physical models into hybrid models., Author(s): Markus Reichstein [sup.1] [sup.2] , Gustau Camps-Valls [sup.3] , Bjorn Stevens [sup.4] , Martin Jung [sup.1] , Joachim Denzler [sup.2] [sup.5] , Nuno Carvalhais [sup.1] [sup.6] , Prabhat [sup.7] [...]

Author(s): Karl-Heinz Erb (corresponding author) [1]; Thomas Kastner [1, 2]; Christoph Plutzar [1, 3]; Anna Liza S. Bais [1]; Nuno Carvalhais [4, 5]; Tamara Fetzel [1]; Simone Gingrich [1]; Helmut [...]

Wood density is a crucial variable linked to mechanical, physiological, and ecological properties. In this study, we analyzed an extensive dataset of over 48,000 wood density samples collected from 2,920 trees. Our aim was to explore variations in wood density, at both inter-tree and intra-tree levels, along with the factors contributing to these variations. Intertree variations reveal significant differences in wood density among eight dominant species, highlighting their role in shaping wood density. As tree species exhibit specific spatial distributions associated with microhabitats, we anticipated a link between wood density distribution and microhabitat. Using a feature selection approach and random forest model, we identified six predictors, including satellite-based vegetation indexes, topographic variables, and soil sand content, capable of predicting 91% of spatial wood density variations. The Normalized Difference Vegetation Index (NDVI) positively representing the amount of carbon within trees correlated with wood density, while the Normalized Difference Water Index (NDWI), reflecting water content, and soil sand content showed negative associations. Geomorphons and soil sand context provided insights into wood density variations and specific landforms. Lower wood density values were linked to landforms with low geomorphons (summit, ridge, or shoulder), whereas higher wood density was found in landforms with high geomorphons (valley, depression, or hollow areas). Furthermore, our study highlighted the importance of considering intra-tree variation, a facet often overlooked in previous research. Interestingly, the magnitude of intra-tree variation is comparable to, and in some species even exceeds, that of inter-tree variations. The intra-tree wood density samples display significant differences both vertically along the height and radially from the center to the bark zones of trees. These variations are influenced by tree growing strategy, living conditions, and physiological structure. In summary, our research delved into the multifaceted features of wood density, shedding light on critical aspects of this fundamental variable. [ABSTRACT FROM AUTHOR]

Terrestrial water storage (TWS) is an integrative hydrological state that is key for our understanding of the global water cycle. The TWS observation from the GRACE missions has, therefore, been instrumental in the calibration and validation of hydrological models and understanding the variations in the hydrological storage. The models, however, still show significant uncertainties in reproducing observed TWS variations, especially for the interannual variability (IAV) at the global scale. Here, we diagnose the regions dominating the variance in globally integrated TWS IAV and the sources of the errors in two data-driven hydrological models that were calibrated against global TWS, snow water equivalent, evapotranspiration, and runoff data. We used (1) a parsimonious process-based hydrological model, the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework and (2) a machine learning, physically based hybrid hydrological model (H2M) that combines a dynamic neural network with a water balance concept. While both models agree with the Gravity Recovery and Climate Experiment (GRACE) that global TWS IAV is largely driven by the semi-arid regions of southern Africa, the Indian subcontinent and northern Australia, and the humid regions of northern South America and the Mekong River basin, the models still show errors such as the overestimation of the observed magnitude of TWS IAV at the global scale. Our analysis identifies modeling error hotspots of the global TWS IAV, mostly in the tropical regions including the Amazon, sub-Saharan regions, and Southeast Asia, indicating that the regions that dominate global TWS IAV are not necessarily the same as those that dominate the error in global TWS IAV. Excluding those error hotspot regions in the global integration yields large improvements in the simulated global TWS IAV, which implies that model improvements can focus on improving processes in these hotspot regions. Further analysis indicates that error hotspot regions are associated with lateral flow dynamics, including both sub-pixel moisture convergence and across-pixel lateral river flow, or with interactions between surface processes and groundwater. The association of model deficiencies with land processes that delay the TWS variation could, in part, explain why the models cannot represent the observed lagged response of TWS IAV to precipitation IAV in hotspot regions that manifest as errors in global TWS IAV. Our approach presents a general avenue to better diagnose model simulation errors for global data streams to guide efficient and focused model development for regions and processes that matter the most. [ABSTRACT FROM AUTHOR]