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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.

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]

Fires are a key component of the global carbon cycle and humans are changing their characteristics. Fire emission monitoring is important to keep track of those changes and TROPOMI satellite observations of tropospheric nitrogen dioxide, carbon monoxide and the absorbing aerosol index can be used to quantify and verify the accuracy and precision of global wildfire emission estimates on a daily basis. Here we use TROPOMI observations to evaluate a new fire emission database based on Global Fire Atlas input for the Sense4Fire project (GFA20 S4F) and from the Copernicus Atmosphere Monitoring (CAMS) Global Fire Assimilation System (GFAS) for a number of test regions worldwide representative of the most important wildfire type environments. The main focus is on Amazon and Cerrado biomes (tropical rain forests and deforestation) during August-September 2020, but analyses are also made for a region in sub-Saharan Africa (savannah) as well as two regions in Siberia (steppe and boreal forests/tundra). GFA-S4F and GFAS fire emissions are used as input for global atmospheric composition model simulations based on IFS-COMPO, i.e. an extension of ECMWF's Integrated Forecasting System (IFS) for simulating atmospheric composition. Comparing the model output with the TROPOMI observations then provides an indirect check on the realism of these emission estimates. Furthermore, for tropospheric nitrogen dioxide the IFS-COMPO model simulations are also used to estimate the model sensitivity of tropospheric nitrogen dioxide columns with respect to fire emission changes. This local relationship is used to optimize the fire NOx emissions directly using the TROPOMI nitrogen dioxide observations. The results reveal that for small fires emission nitrogen dioxide estimates are realistic on average albeit with a large spread, i.e. for individual fires emissions can be significantly under or overestimated regardless of emission database. However, for large fires nitrogen dioxide emissions are systematically and largely overestimated in all four regions. The overestimation can be an order of magnitude or even more. For area total nitrogen dioxide emissions this "large fire bias" is of minor importance, i.e. total nitrogen dioxide emissions are dominated by small fires. The GFA-S4F emission estimates were characterized by a larger positive bias for large fire NO2 emission cases compared to GFAS. The source of this bias is not well understood. With optimized NO2 emissions by direct adjustment of emission using TROPOMI nitrogen dioxide observations the large positive bias can efficiently be resolved. Combined with an update of soil NOx emissions - causing too low background NOx levels - a fairly good agreement between IFS-COMPO and TROPOMI was reached. Carbon monoxide was generally underestimated using GFAS emission (~50% on average for the selected regions). Updating carbon monoxide emissions over the Amazon region by incorporating more Sentinel satellite data (GFA-S4F) did reduce this fire CO bias significantly (to ~25% on average). Overall, the results show that TROPOMI data allows for systematically identifying uncertainties and errors in satellite-data based fire emissions. The results also suggest that the use of dynamic emission factors may further improve satellite based global emissions inventories. In addition, the results also highlight that the use of TROPOMI data could be much more detailed and refined towards assessing individual fires on a daily basis for better understanding fire dynamics and to improve and diversify fire emission factors. [ABSTRACT FROM AUTHOR]

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.

Climate change is shifting the phenological cycles of plants.sup.1, thereby altering the functioning of ecosystems, which in turn induces feedbacks to the climate system.sup.2. In northern (north of 30° N) ecosystems, warmer springs lead generally to an earlier onset of the growing season.sup.3,4 and increased ecosystem productivity early in the season.sup.5. In situ.sup.6 and regional.sup.7-9 studies also provide evidence for lagged effects of spring warmth on plant productivity during the subsequent summer and autumn. However, our current understanding of these lagged effects, including their direction (beneficial or adverse) and geographic distribution, is still very limited. Here we analyse satellite, field-based and modelled data for the period 1982-2011 and show that there are widespread and contrasting lagged productivity responses to spring warmth across northern ecosystems. On the basis of the observational data, we find that roughly 15 per cent of the total study area of about 41 million square kilometres exhibits adverse lagged effects and that roughly 5 per cent of the total study area exhibits beneficial lagged effects. By contrast, current-generation terrestrial carbon-cycle models predict much lower areal fractions of adverse lagged effects (ranging from 1 to 14 per cent) and much higher areal fractions of beneficial lagged effects (ranging from 9 to 54 per cent). We find that elevation and seasonal precipitation patterns largely dictate the geographic pattern and direction of the lagged effects. Inadequate consideration in current models of the effects of the seasonal build-up of water stress on seasonal vegetation growth may therefore be able to explain the differences that we found between our observation-constrained estimates and the model-constrained estimates of lagged effects associated with spring warming. Overall, our results suggest that for many northern ecosystems the benefits of warmer springs on growing-season ecosystem productivity are effectively compensated for by the accumulation of seasonal water deficits, despite the fact that northern ecosystems are thought to be largely temperature- and radiation-limited.sup.10.Widespread but contrasting delayed responses of ecosystem productivity to spring warmth across northern ecosystems is inferred from satellite data, with higher areal fractions of adverse effects than beneficial effects., Author(s): Wolfgang Buermann [sup.1] [sup.2] , Matthias Forkel [sup.3] , Michael O'Sullivan [sup.1] , Stephen Sitch [sup.4] , Pierre Friedlingstein [sup.5] , Vanessa Haverd [sup.6] , Atul K. Jain [sup.7] [...]

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]