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Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr−1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr−1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×103 ± 0.9×103 km3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

While the growth rate of atmospheric CO2 mole fractions can be measured with high accuracy, there are still large uncertainties in its attribution to specific regions and diverse anthropogenic and natural sources and sinks. A major source of uncertainty is the net flux of carbon dioxide from the biosphere to the atmosphere, the net ecosystem exchange (NEE). There are two major approaches to quantifying NEE: top-down approaches that typically use atmospheric inversions and bottom-up estimates that rely on process-based or data-driven models or inventories. Both top-down and bottom-up approaches have known strengths and limitations. Atmospheric inversions (e.g., those used in global carbon budgets) produce estimates of NEE that are consistent with the atmospheric CO2 growth rate at regional and global scales but are highly uncertain at smaller scales. Bottom-up data-driven models based on eddy-covariance measurements (e.g., FLUXCOM) match local observations of NEE and their spatial variability but have difficulty in accurately upscaling to a reliable global estimate. In this study, we propose combining the two approaches to produce global NEE estimates, with the goal of capitalizing on each approach's strengths and mitigating their limitations. We do this by constraining the data-driven FLUXCOM model with regional estimates of NEE derived from an ensemble of atmospheric inversions from the Global Carbon Budget 2021. To do this, we need to overcome a series of scientific and technical challenges when combining information about diverse physical variables, which are influenced by different processes at different spatial and temporal scales. We design a modeling structure that optimizes NEE by considering both the model's performance at the in situ level, based on eddy-covariance measurements, and at the level of large regions, based on atmospheric inversion estimates of NEE and their uncertainty. This resulting “dual-constraint” data-driven flux model improves on information based on single constraints (either top down or bottom up), producing robust locally resolved and globally consistent NEE spatio-temporal fields. Compared to reference estimates of the global land sink from the literature, e.g., Global Carbon Budgets, our double-constraint inferred global NEE shows a considerably smaller bias in global and tropical NEE compared to the underlying bottom-up data-driven model estimates (i.e., single constraint). The mean seasonality of our double-constraint inferred global NEE is also more consistent with the Global Carbon Budget and atmospheric inversions. At the same time, our model allows for more robustly spatially resolved NEE. The improved performance of the double-constraint model across spatial and temporal scales demonstrates the potential for adding a top-down constraint to a bottom-up data-driven flux model.

Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. Factors such as the duration, timing, and intensity of extreme events influence the magnitude of impacts on ecosystem processes such as gross primary production (GPP), i.e., the ecosystem uptake of CO2. Preceding soil moisture depletion may exacerbate these impacts. However, some vegetation types may be more resilient to climate extremes than others. This effect is insufficiently understood at the global scale and is the focus of this study. Using a global upscaled product of GPP that scales up in situ land CO2 flux observations with global satellite remote sensing, we study the impact of climate extremes at the global scale. We find that GPP in grasslands and agricultural areas is generally reduced during heat and drought events. However, we also find that forests, if considered globally, appear in general to not be particularly sensitive to droughts and heat events that occurred during the analyzed period or even show increased GPP values during these events. On the one hand, normal-to-increased GPP values are in many cases plausible, e.g., when conditions prior to the event have been particularly positive. On the other hand, however, normal-to-increased GPP values in forests may also reflect a lack of sensitivity in current remote-sensing-derived GPP products to the effects of droughts and heatwaves. The overall picture calls for a differentiated consideration of different land cover types in the assessments of risks of climate extremes for ecosystem functioning.

FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R2>0.94 at 1∘ spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2∼0.75) than with GPP from DGVMs (mean global pixel-wise R2∼0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2>0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R2=0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.

Climate variables carry signatures of variability at multiple timescales. How these modes of variability are reflected in the state of the terrestrial biosphere is still not quantified or discussed at the global scale. Here, we set out to gain a global understanding of the relevance of different modes of variability in vegetation greenness and its covariability with climate. We used >30 years of remote sensing records of the normalized difference vegetation index (NDVI) to characterize biosphere variability across timescales from submonthly oscillations to decadal trends using discrete Fourier decomposition. Climate data of air temperature (Tair) and precipitation (Prec) were used to characterize atmosphere–biosphere covariability at each timescale. Our results show that short-term (intra-annual) and longer-term (interannual and longer) modes of variability make regionally highly important contributions to NDVI variability: short-term oscillations focus in the tropics where they shape 27 % of NDVI variability. Longer-term oscillations shape 9 % of NDVI variability, dominantly in semiarid shrublands. Assessing dominant timescales of vegetation–climate covariation, a natural surface classification emerges which captures patterns not represented by conventional classifications, especially in the tropics. Finally, we find that correlations between variables can differ and even invert signs across timescales. For southern Africa for example, correlation between NDVI and Tair is positive for the seasonal signal but negative for short-term and longer-term oscillations, indicating that both short- and long-term temperature anomalies can induce stress on vegetation dynamics. Such contrasting correlations between timescales exist for 15 % of vegetated areas for NDVI with Tair and 27 % with Prec, indicating global relevance of scale-specific climate sensitivities. Our analysis provides a detailed picture of vegetation–climate covariability globally, characterizing ecosystems by their intrinsic modes of temporal variability. We find that (i) correlations of NDVI with climate can differ between scales, (ii) nondominant subsignals in climate variables may dominate the biospheric response, and (iii) possible links may exist between short-term and longer-term scales. These heterogeneous ecosystem responses on different timescales may depend on climate zone and vegetation type, and they are to date not well understood and do not always correspond to transitions in dominant vegetation types. These scale dependencies can be a benchmark for vegetation model evaluation and for comparing remote sensing products.

Combined droughts and heatwaves are among those compound extreme events that induce severe impacts on the terrestrial biosphere and human health. A record breaking hot and dry compound event hit western Russia in summer 2010 (Russian heatwave, RHW). Events of this kind are relevant from a hydrometeorological perspective, but are also interesting from a biospheric point of view because of their impacts on ecosystems, e.g., reductions in the terrestrial carbon storage. Integrating both perspectives might facilitate our knowledge about the RHW. We revisit the RHW from both a biospheric and a hydrometeorological perspective. We apply a recently developed multivariate anomaly detection approach to a set of hydrometeorological variables, and then to multiple biospheric variables relevant to describe the RHW. One main finding is that the extreme event identified in the hydrometeorological variables leads to multidirectional responses in biospheric variables, e.g., positive and negative anomalies in gross primary production (GPP). In particular, the region of reduced summer ecosystem production does not match the area identified as extreme in the hydrometeorological variables. The reason is that forest-dominated ecosystems in the higher latitudes respond with unusually high productivity to the RHW. Furthermore, the RHW was preceded by an anomalously warm spring, which leads annually integrated to a partial compensation of 54 % (36 % in the preceding spring, 18 % in summer) of the reduced GPP in southern agriculturally dominated ecosystems. Our results show that an ecosystem-specific and multivariate perspective on extreme events can reveal multiple facets of extreme events by simultaneously integrating several data streams irrespective of impact direction and the variables' domain. Our study exemplifies the need for robust multivariate analytic approaches to detect extreme events in both hydrometeorological conditions and associated biosphere responses to fully characterize the effects of extremes, including possible compensatory effects in space and time.

Interactions between the biosphere and the atmosphere can be well characterized by fluxes between the two. In particular, carbon and energy fluxes play a major role in understanding biogeochemical processes on an ecosystem level or global scale. However, the fluxes can only be measured at individual sites, e.g., by eddy covariance towers, and an upscaling of these local observations is required to analyze global patterns. Previous work focused on upscaling monthly, 8-day, or daily average values, and global maps for each flux have been provided accordingly. In this paper, we raise the upscaling of carbon and energy fluxes between land and atmosphere to the next level by increasing the temporal resolution to subdaily timescales. We provide continuous half-hourly fluxes for the period from 2001 to 2014 at 0.5° spatial resolution, which allows for analyzing diurnal cycles globally. The data set contains four fluxes: gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LE), and sensible heat (H). We propose two prediction approaches for the diurnal cycles based on large-scale regression models and compare them in extensive cross-validation experiments using different sets of predictor variables. We analyze the results for a set of FLUXNET tower sites showing the suitability of our approaches for this upscaling task. Finally, we have selected one approach to calculate the global half-hourly data products based on predictor variables from remote sensing and meteorology at daily resolution as well as half-hourly potential radiation. In addition, we provide a derived product that only contains monthly average diurnal cycles, which is a lightweight version in terms of data storage that still allows studying the important characteristics of diurnal patterns globally. We recommend to primarily use these monthly average diurnal cycles, because they are less affected by the impacts of day-to-day variation, observation noise, and short-term fluctuations on subdaily timescales compared to the full half-hourly flux products. The global half-hourly data products are available at https://doi.org/10.17871/BACI.224.

Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the eight largest (typically very large) extreme events; but only a ≥ 50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere.

Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface–atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface–atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean ± 1 SD: 0.8 ± 0.1 PgC yr−1, positive numbers are sources to the atmosphere), Russia (0.1 ± 0.4 PgC yr−1), East Asia (1.6 ± 0.3 PgC yr−1), South Asia (0.3 ± 0.1 PgC yr−1), Australia (0.2 ± 0.3 PgC yr−1), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of −5.4 ± 2.0 PgC yr−1. By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001–2010 indicates that the true value of NCE is a net CO2 source of 4.3 ± 0.1 PgC yr−1. This mismatch of nearly 10 PgC yr−1 highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO2 data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water–atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over- or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed.

Several recent wind power estimates suggest that this renewable energy resource can meet all of the current and future global energy demand with little impact on the atmosphere. These estimates are calculated using observed wind speeds in combination with specifications of wind turbine size and density to quantify the extractable wind power. However, this approach neglects the effects of momentum extraction by the turbines on the atmospheric flow that would have effects outside the turbine wake. Here we show with a simple momentum balance model of the atmospheric boundary layer that this common methodology to derive wind power potentials requires unrealistically high increases in the generation of kinetic energy by the atmosphere. This increase by an order of magnitude is needed to ensure momentum conservation in the atmospheric boundary layer. In the context of this simple model, we then compare the effect of three different assumptions regarding the boundary conditions at the top of the boundary layer, with prescribed hub height velocity, momentum transport, or kinetic energy transfer into the boundary layer. We then use simulations with an atmospheric general circulation model that explicitly simulate generation of kinetic energy with momentum conservation. These simulations show that the assumption of prescribed momentum import into the atmospheric boundary layer yields the most realistic behavior of the simple model, while the assumption of prescribed hub height velocity can clearly be disregarded. We also show that the assumptions yield similar estimates for extracted wind power when less than 10% of the kinetic energy flux in the boundary layer is extracted by the turbines. We conclude that the common method significantly overestimates wind power potentials by an order of magnitude in the limit of high wind power extraction. Ultimately, environmental constraints set the upper limit on wind power potential at larger scales rather than detailed engineering specifications of wind turbine design and placement.

Jet streams are regions of sustained high wind speeds in the upper atmosphere and are seen by some as a substantial renewable energy resource. However, jet streams are nearly geostrophic flow, that is, they result from the balance between the pressure gradient and Coriolis force in the near absence of friction. Therefore, jet stream motion is associated with very small generation rates of kinetic energy to maintain the high wind velocities, and it is this generation rate that will ultimately limit the potential use of jet streams as a renewable energy resource. Here we estimate the maximum limit of jet stream wind power by considering extraction of kinetic energy as a term in the free energy balance of kinetic energy that describes the generation, depletion, and extraction of kinetic energy. We use this balance as the basis to quantify the maximum limit of how much kinetic energy can be extracted sustainably from the jet streams of the global atmosphere as well as the potential climatic impacts of its use. We first use a simple thought experiment of geostrophic flow to demonstrate why the high wind velocities of the jet streams are not associated with a high potential for renewable energy generation. We then use an atmospheric general circulation model to estimate that the maximum sustainable extraction from jet streams of the global atmosphere is about 7.5 TW. This estimate is about 200-times less than previous estimates and is due to the fact that the common expression for instantaneous wind power 12 ρv3 merely characterizes the transport of kinetic energy by the flow, but not the generation rate of kinetic energy. We also find that when maximum wind power is extracted from the jet streams, it results in significant climatic impacts due to a substantial increase of heat transport across the jet streams in the upper atmosphere. This results in upper atmospheric temperature differences of >20 °C, greater atmospheric stability, substantial reduction in synoptic activity, and substantial differences in surface climate. We conclude that jet stream wind power does not have the potential to become a significant source of renewable energy.

Life has significantly altered the Earth's atmosphere, oceans and crust. To what extent has it also affected interior geological processes? To address this question, three models of geological processes are formulated: mantle convection, continental crust uplift and erosion and oceanic crust recycling. These processes are characterised as non-equilibrium thermodynamic systems. Their states of disequilibrium are maintained by the power generated from the dissipation of energy from the interior of the Earth. Altering the thickness of continental crust via weathering and erosion affects the upper mantle temperature which leads to changes in rates of oceanic crust recycling and consequently rates of outgassing of carbon dioxide into the atmosphere. Estimates for the power generated by various elements in the Earth system are shown. This includes, inter alia, surface life generation of 264 TW of power, much greater than those of geological processes such as mantle convection at 12 TW. This high power results from life's ability to harvest energy directly from the sun. Life need only utilise a small fraction of the generated free chemical energy for geochemical transformations at the surface, such as affecting rates of weathering and erosion of continental rocks, in order to affect interior, geological processes. Consequently when assessing the effects of life on Earth, and potentially any planet with a significant biosphere, dynamical models may be required that better capture the coupled nature of biologically-mediated surface and interior processes.

The availability of wind power for renewable energy extraction is ultimately limited by how much kinetic energy is generated by natural processes within the Earth system and by fundamental limits of how much of the wind power can be extracted. Here we use these considerations to provide a maximum estimate of wind power availability over land. We use several different methods. First, we outline the processes associated with wind power generation and extraction with a simple power transfer hierarchy based on the assumption that available wind power will not geographically vary with increased extraction for an estimate of 68 TW. Second, we set up a simple momentum balance model to estimate maximum extractability which we then apply to reanalysis climate data, yielding an estimate of 21 TW. Third, we perform general circulation model simulations in which we extract different amounts of momentum from the atmospheric boundary layer to obtain a maximum estimate of how much power can be extracted, yielding 18–34 TW. These three methods consistently yield maximum estimates in the range of 18–68 TW and are notably less than recent estimates that claim abundant wind power availability. Furthermore, we show with the general circulation model simulations that some climatic effects at maximum wind power extraction are similar in magnitude to those associated with a doubling of atmospheric CO2. We conclude that in order to understand fundamental limits to renewable energy resources, as well as the impacts of their utilization, it is imperative to use a "top-down" thermodynamic Earth system perspective, rather than the more common "bottom-up" engineering approach.

No abstract available.