The lack of energy balance closure in Eddy-Covariance (EC) measurements is a well-known, still unresolved challenge in micrometeorology, with energy balance closure (EBC) rates typically ranging between 60% and 80%. While numerous hypotheses have been proposed to explain this imbalance, the relative contributions of neglected energy storage terms, data quality and flux processing options remain insufficiently disentangled. Using standardized ICOS and NEON datasets, we show that a significant portion of the observed energy imbalance can be attributed to overlooked or inconsistently handled energy components and turbulent flux quality control. Using data drawn from 84 sites, we show that comprehensive energy accounting-including soil heat flux, storage terms (soil, air, biomass), photosynthetic energy demand, and strict quality filtering of turbulent fluxes-improved EBC by 16% on average, with site-specific gains up to 40%. However, we also identify a persistent residual imbalance that is unlikely to be resolved through methodological refinements or additional measurements alone, pointing to fundamental physical processes that are not accounted for in the standard measurement and processing. We argue that this unresolved imbalance should be explicitly acknowledged and bounded, rather than implicitly absorbed into correction schemes, and we outline practical guidance for diagnosing and interpreting EBC in standardized flux networks. This perspective evaluates methodological advances and residual uncertainties, providing an actionable framework for the appropriate use of EC energy fluxes in carbon, water, and climate research.

Accurate estimation of Gross Primary Productivity (GPP) for European winter wheat is critical for assessing regional food security and understanding land-atmosphere carbon exchange. Light Use Efficiency (LUE) models are widely applied in natural ecosystems, but their performance in dynamic agricultural landscapes, particularly for key crops like winter wheat, remains underexplored. To bridge this gap, we developed IB-WSE-LUE (INRAE-BORDEAUX-water stress enhanced-light use efficiency), a novel GPP model specifically tailored for winter wheat. This model leverages high-resolution Sentinel-2 satellite data and comprehensively integrates key environmental stress factors, enabling GPP simulation at an unprecedented 10-meter spatial resolution. We compared IB-WSE-LUE against thirteen established GPP models, using both tower-based meteorological data and the ERA5 reanalysis dataset (the latter ensuring broader applicability across large scales without reliance on extensive in-situ measurements). Validation demonstrated IB-WSE-LUE's superior performance, achieving average R2 improvements of 11.9% (with tower data) and 8.8% (with ERA5 dataset) in daily GPP simulations for European winter wheat. Furthermore, IB-WSE-LUE more accurately captured spatial, seasonal, and interannual GPP variations and significantly reduced the common underestimation at high GPP levels observed in other models. Its robust performance extended to drought and high-temperature conditions, demonstrating that water stress exerts a stronger influence on winter wheat GPP than temperature stress, a feature accurately captured by our model. This study provides a robust, high-resolution, and spatially transferable framework for accurately monitoring and predicting winter wheat GPP across large agricultural regions, offering key insights for food security assessments and improved agricultural land management in a changing climate.

Restrictions in the soil water availability can strongly impact crop productivity. The increasing frequency and severity of drought events, as a result of global warming, has made the assessment of drought stress effects on vegetation of utmost importance for meeting humanity's agricultural production needs. Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) provide a basis for new approaches to directly assess crop water status, since SIF is closely related to photosynthesis and, thus, to early plant physiological processes triggered by limitations in the water supply. This study provides new insights into the effect of varying levels of plant available water (PAW) in the soil on SIF emissions. We used several SIF datasets acquired with the high-performance airborne imaging spectrometer HyPlant during five subsequent vegetation periods (2018, 2019, 2020, 2021 and 2022), each having a different precipitation regime. We normalized the SIF maps for the underlying effects of canopy structure, calculated SIF emission efficiency (eSIF) and selected various crop fields including sugar beet, wheat and potato. Maps of eSIF were compared with spatial PAW patterns, which were derived from a forward soil infiltration model. Our results show positive correlation between eSIF and PAW in rainfed sugar beet fields at early growing stage, which remained consistent when accounting for variations in the leaf area index (LAI). This suggests that eSIF variations in sugar beet reflect the spatial reduction of photosynthesis caused by reduced PAW. In irrigated potato fields, conversely, no eSIF-PAW correlations were found. This indicates the absence of leaf-level water stress in these well-irrigated fields. In rainfed winter wheat fields that were already in a late developmental stage, the variations in the SIF signal were dominated by locally different ripening, i.e., chlorophyll degradation, and therefore not representative of changing PAW. With this study, we could demonstrate that normalized airborne SIF measurements are related to the functional water stress response in different crops. This study supports future investigations on the development of SIF-based tools for the improvement of water management in agriculture.

Accurate estimation of agroecosystem carbon fluxes is essential for assessing cropland sustainability and climate resilience. This study integrates Leaf Area Index (LAI) retrieval from Radiative Transfer Model (RTM) inversion into AgroC, an agroecosystem model, from Unmanned Aerial System (UAS) platform to enhance carbon fluxes estimates, including Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and Total Ecosystem Respiration (TER). By replacing the internally developed LAI in the AgroC model with interpolated LAI time series derived from UAS, improved spatiotemporal representativeness of agroecosystem carbon fluxes is observed under both the Farquhar-von Caemmerer-Berry (FvCB) and the Light Use Efficiency (LUE) photosynthesis approaches. Temporally, the highest GPP accuracy was achieved by the AgroCFvCB model integrated with UASderived LAI (RMSE = 3.19 gC m-2 d-1, KGE = 0.89), while the best NEE estimation was obtained with the AgroCLUE model integrated with UAS-derived LAI (RMSE = 2.10 gC m-2 d-1, KGE = 0.89). Spatially, the superior performance of the AgroCFvCB model in integrating UAS-derived LAI enabled high-resolution (1 m) mapping of GPP and NEE, effectively capturing within-field spatial variations in a winter wheat field. The daily Pearson correlation coefficient (r) overtime ranged from 0.16 in non-vegetated areas to 0.94 in vegetated zones for GPP, and up to 0.88 for NEE. Despite the advantages taking physical basis in RTM inversion for LAI retrieval and biochemical constraints considered in FvCB approach, the limitation in TER improvement requires further investigation to refine RTM-AgroC coupling for cropland carbon fluxes modelling using UAS platforms.

Global warming increases ecosystem respiration (ER), creating a positive carbon-climate feedback. Thermal acclimation, the direct responses of biological communities to reduce the effects of temperature changes on respiration rates, is a critical mechanism that compensates for warming-induced ER increases and dampens this positive feedback. However, the extent and effects of this mechanism across diverse ecosystems remain unclear. By analyzing CO2 flux data from 93 eddy covariance sites worldwide, we observed thermal acclimation at 84 % of the sites. If sustained, thermal acclimation could reduce projected warming-induced nighttime ER increases by at least 25 % across most climate zones by 2041-2060. Strong thermal acclimation is particularly evident in ecosystems at high elevation, with low-carbon-content soils, and within tundra, semi-arid, and warm-summer Mediterranean climates, supporting the hypothesis that extreme environments favor the evolution of greater acclimation potential. Moreover, ecosystems with dense vegetation and high productivity such as humid tropical and subtropical forests generally exhibit strong thermal acclimation, suggesting that regions with substantial CO2 uptake may continue to serve as strong carbon sinks. Conversely, some ecosystems in cold continental climates show signs of enhancing thermal responses, the opposite of thermal acclimation, which could exacerbate carbon losses as climate warms. Our study underscores the widespread yet climate-specific patterns of thermal acclimation in global terrestrial ER, emphasizing the need to incorporate these patterns into Earth System Models for more accurate carbon-climate feedback projections.

Continuous information on soil water content (SWC) and plant development is crucial for environmental monitoring, agricultural management, and beyond. Cosmic-ray neutron sensors (CRNS), widely used to estimate SWC, also have the potential to monitor field-scale variations in vegetation properties. In this study, a CRNS measured both epithermal (EN) and thermal (TN) neutron intensities over a 10-year period at an ICOS Class 1 ecosystem station in Selhausen (Germany). Compared to nearby point-scale sensors, the CRNS provided more representative SWC estimates within the monitoring area of the adjacent eddy covariance (EC) station. A general co-development was observed between TN and gross primary productivity (GPP), but differences during senescence and desiccation suggest that factors beyond plant water content can influence TN. An extensive dataset of plant height (PH), leaf area index (LAI), and dry above-ground biomass (AGB) was used to evaluate the ability of N to monitor plant development. TN was found to be more closely related to vegetation dynamics than to changes in SWC. CRNS estimations of PH, LAI, and AGB yielded relatively good agreement with reference data (RMSE of 0.13 m, 1.01 m2/m2, and 0.27 kg/m2, respectively). The RMSE obtained with a leave-one-out cross validation generally confirmed these findings. Although CRNS estimates generally had lower accuracy than traditional methods, they have the key advantages of being continuous, non-invasive, and non-laborious. Combined with simultaneous estimation of SWC at a relevant spatial scale, CRNS becomes a particularly interesting tool among long-term monitoring platforms with further potential in modelling, remote sensing, and decision-making in agriculture.

Abstract. Compared to drought and heat waves, the impact of winter warming on forest CO2 fluxes has been less studied, despite its significant relevance in colder regions with higher soil carbon content. Our objective was to test the effect of the exceptionally warm winter of 2020 on the winter CO2 budget of cold-adapted evergreen needleleaf forests across Europe and identify the contribution of climate factors to changes in winter CO2 fluxes. Our hypothesis was that warming in winter leads to higher emissions across colder sites due to increased ecosystem respiration. To test this hypothesis, we used 98 site-year eddy covariance measurements across 14 evergreen needleleaf forests (ENFs) distributed from the north to the south of Europe (from Sweden to Italy). We used a data-driven approach to quantify the effect of radiation, air temperature, and soil temperature on changes in CO2 fluxes during the warm winter of 2020. Our results showed that warming in winter decreased forest net ecosystem productivity (NEP) significantly across most sites. The contribution of climate variables to CO2 fluxes varied across the sites: in southern regions with warmer mean temperatures, radiation had a greater influence on NEP. Conversely, at colder sites, air temperature played a more critical role in affecting NEP. During the warm winter of 2020, colder regions experienced larger air temperature anomalies compared to the other sites; however we did not observe a significantly larger increase at colder sites due to winter warming. The varying responses of NEP across different sites highlight the complex interactions between climate variables such as air temperature, soil temperature, and radiation. These findings underscore the importance of integrating winter warming effects to more accurately predict the impacts of climate change on forest carbon dynamics.

In September 2013, 8.6 hectares of a 70-year old Norway spruce (picea abies) monoculture were cleared in the newly founded national park Eifel (Germany) and left to spontaneous regrowth of the expected deciduous forest matching the site’s climate and soil conditions. The site is part of the 38.5 hectare experimental catchment “Wüstebach” (50° 30’N, 6° 19’E, 595 to 630 m a.s.l.), one of the core investigation sites of TERENO (TERrestrial ENvironmental Observatories, https://www.tereno.net). Most of the rest of the catchment is still covered by the original spruce monoculture. Its energy and matter exchange with the atmosphere, most notably of CO2, is monitored by an ICOS associated eddy-covariance station (DE-RuW) since 2010. In 2013 after the partial deforestation, a second flux station was installed near the centre of the clearcut. Due to an overpressure of game (boar and deer) in the area, 2 hectares of the central clearcut area are protected against grazing by a fence. CO2 budget and albedo results from the first four growing periods after the clearcut were presented by Ney et al. in 2019 (https://doi.org/10.1016/j.agrformet.2019.04.009). Here, we will give an update covering the first ten growing periods after deforestation (2014-2023). Most notably, regrowing vegetation on the initially almost bare clearcut turned it from a source back into a sink of atmospheric CO2 eight years after the deforestation. We will give an overview on how flux components (soil) respiration and gross primary productivity, season length and peak fluxes contributed to the difference between the spruce forest and the early and recent stages of the regrowing forest. For the last eight years, we recorded the species, height and partly the diameter of all spontaneous regrowing trees in the deforested area in a 10 m corridor both inside and outside the fence. Regrowth was strongly dominated by rowan (sorbus aucuparia, >1200 trees), a pioneer species propagated through their berries by birds that was present with at least one adult tree already before the deforestation beside further trees in distant surroundings. The next two important species were spruce and birch (betula pendula), whose seeds are propagated by wind. Rowan and birch grew in height approximately twice as fast as spruce. The presence of the protective fence affected all species, especially rowan, which grew more than twice as fast on the inside of the fence.

Continuous information on plant traits such as plant height, leaf area index (LAI), and above-ground biomass (AGB) is important in the study of plant growth and such information can help farmers achieve better yields while reducing agricultural inputs, e.g. through more efficient water use. Knowledge on plant traits is also key to further test and develop crop and land surface models. Cosmic-ray neutron sensors (CRNS) have primarily been used to determine soil moisture. Recently, Jakobi et al. (2022) found that thermal neutrons can be used to monitor aboveground biomass (ABG) and that the variations in measured thermal neutron intensity may also depend on the vegetation biomass and structure. However, different soil properties of the test sites may have influenced the results (e.g. related to differences in soil chemistry). In this follow-up study, a single agricultural field was investigated over a long measurement period (2015-2023) to avoid site-specific effects on the CRNS measurements. This new dataset contains different crop rotations with repetitions of the same crop and continuous measurements of plant height instead of sporadic biomass measurements. Based on this data, we developed regression models that take into account plant structure to predict traits (i.e. plant height and LAI) from observed thermal neutron intensity.The annual regression models for plant height provided generally high R²-values (0.86 on average), with the highest values found for potato and winter wheat. An aggregation by crop type of the different seasons resulted in a slight reduction of the R² to 0.84 for winter wheat (3 seasons), 0.68 for sugar beet (2 seasons), and 0.75 for potato (2 seasons). The slope values of these regressions were distinctly different, thus supporting the assumption that the relationship between plant traits and thermal neutron intensity depends on vegetation structure. The root mean square error (RMSE) of the plant height predicted with thermal neutrons were 12 cm for winter wheat and 14 cm for both sugar beet and potato. In addition, we tested a prediction of LAI based on thermal neutrons. For this, we used a regression model that predicts LAI based on plant height (R²: 0.78). Using this model, we were able to predict the LAI for a period of 5 years with LAI observation data with an RMSE of 1.23 m/m, which is still within the uncertainty range of radiation-based LAI methods (Fang et al., 2019). Independent validation was performed also against spatio-temporal LiDAR-based plant height and multispectral-based LAI measurements, each averaged for the CRNS footprint area. Our results demonstrate the potential of cosmic-ray neutron sensing for continuous monitoring of plant traits at the field scale. LiteratureFang, H., F. Baret, S. Plummer and G. Schaepman‐Strub (2019): An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics 57(3): 739-799. DOI: 10.1002/hyp.11274Jakobi, J., J.A. Huisman, H. Fuchs, H. Vereecken and H. Bogena (2022): Potential of Thermal Neutrons to Correct Cosmic-Ray Neutron Soil Moisture Content Measurements for Dynamic Biomass Effects. Water Resour. Res. 58(8): e2022WR031972. DOI: 10.1029/2022WR031972

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 degrees) 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 74.7x10(3) +/- 0.9x10(3) km(3) 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.

Management practices that increase the surface albedo of cultivated land could mitigate climate change, with similar effectiveness to practices that reduce greenhouse gas emissions or favor natural CO 2 sequestration. Yet, the efficiency of such practices is barely quantified. In this study, we quantified the impacts of seven different management practices on the surface albedo of winter wheat fields (nitrogen fertilizer, herbicide, fungicide, sowing, harvest, tillage, and crop residues) by analyzing observed daily albedo dynamics from eight European flux-tower sites with interpretable machine learning. We found that management practices have significant influences on surface albedo dynamics compared with climate and soil conditions. The nitrogen fertilizer application has the largest effect among the seven practices as it increases surface albedo by 0.015 ± 0.004 during the first two months after application, corresponding to a radiative forcing of −4.39 ± 1.22 W m −2 . Herbicide induces a modest albedo decrease of 0.005 ± 0.002 over 150 d after application by killing weeds in the fallow period only, resulting in a magnitude of radiative forcing of 1.33 ± 1.06 W m −2 which is higher than radiative forcing of other practices in the same period. The substantial temporal evolution of the albedo impacts of management practices increases uncertainties in the estimated albedo-mediated climate impacts of management practices. Although these albedo effects are smaller than published estimates of the greenhouse gas-mediated biogeochemical practices, they are nevertheless significant and should thus be accounted for in climate impact assessments.

In Europe, the heterogeneous features of crop systems with majority of small to medium sized agricultural holdings, and diversity of crop rotations, require high-resolution information to estimate cropland Net Ecosystem Exchange (NEE) and its two main components of Gross Ecosystem Exchange (GEE) and the Ecosystem Respiration (RECO). In this context, this paper presents an assimilation of high-resolution Sentinel-2 indices with eddy covariance measurements at selected European cropland flux sites in a new modified version of Vegetation Photosynthesis Respiration Model (VPRM). VRPM is a data-driven model simulating CO2 fluxes previously applied using satellite-derived vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study proposes a modification of the VPRM by including an explicit soil moisture stress function to the GEE and changing the equation of RECO. It also compares the model results driven by S2 indices instead of MODIS. The parameters of the VPRM model are calibrated using eddy-covariance data. All possible parameters optimization scenarios include the use of the initial version vs. the proposed modified VPRM, S2, or MODIS vegetation indices, and finally the choice of calibrating a single set of parameters against observations from all crop types, a set of parameters per crop type, or one set of parameters per site. Then, we focus the analysis on the improvement of the model with distinct parameters for different crop types vs. parameters optimized without distinction of crop types. Our main findings are: (1) the superiority of S2 vegetation indices over MODIS for cropland CO2 fluxes simulations, leading to a root mean squared error (RMSE) for NEE of less than 3.5 μmolm-2s-1 with S2 compared to 5 μmolm-2s-1 with MODIS (2) better performances of the modified VPRM version leading to a significant improvement of RECO, and (3) better performances when the parameters are optimized per crop-type instead of for all crop types lumped together, with lower RMSE and Akaike information criterion (AIC), despite a larger number of parameters. Associated with the availability of crop-type land cover maps, the use of S2 data and crop-type modified VPRM parameterization presented in this study, provide a step forward for upscaling cropland carbon fluxes at European scale.

Abstract. Relative to drought and heat waves, the effect of winter warming on forest CO2 fluxes during the dormant season has less been investigated, despite its relevance for net CO2 uptake in colder regions with higher carbon content in soils. Our objective was to test the effect of the exceptionally warm winter in 2020 on the winter CO2 budget of cold-adapted evergreen needle-leaf forests across Europe, and identify the contribution of soil and air temperature to changes in winter CO2 fluxes in response to warming. Our hypothesis was that warming in winter leads to higher emissions across colder sites due to increased ecosystem respiration. To test this hypothesis, we used 98 site-year eddy covariance measurements across 14 evergreen needle-leaf forests (ENFs) distributed from north to south of Europe (from Sweden to Italy). We used a data-driven approach to quantify the effect of air and soil temperature on changes in net ecosystem productivity (NEP) during the warm winter of 2020. Our results showed that the impact of warming was different across sites, as in the lower altitude and lower latitude sites positive soil temperature anomalies were larger, while positive air temperature anomalies were larger in the northern latitude and high-altitude sites. Warming in winter led to a divergent response across the sites. Out of 14 sites only in 3 sites net ecosystem productivity declined in winter significantly in response to warming. In addition, we observed that in the colder sites daytime NEP (that is dominated by photosynthesis) declined with warming of the air in winter, whereas in the warmer sites daytime NEP increased with warming of the soil. This shows that warming of the air – if not translated into a direct warming of the soil– might not trigger productivity in winter if the soil within the rooting zone remains frozen. Forests within the same plant functional type category can exhibit differing reactions to winter warming and to predict their responses accurately it is crucial to account for variations in local climate, physiology, and structure simultaneously.

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evap-oration 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 underesti-mation is highly site-specific. We then propose a machine learning based method to correct for this underesti-mation, 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.

Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Data and Code for 'Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects' by Graf et al. (Communications Earth and Environment)

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) - the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015-2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O3) concentration remained relatively unchanged at the research sites, making it unlikely that O3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1‐km 2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km 2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse‐grained air temperature estimates from ERA5‐Land (an atmospheric reanalysis by the European Centre for Medium‐Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome‐specific offsets emphasize that the projected impacts of climate and climate change on near‐surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil‐related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Flux measurements over heterogeneous surfaces with growing vegetation and a limited fetch are a difficult task, as measurement heights that are too high or too low above the canopy adversely affect results. The aim of this study is to assess implications from measurement height in regard to low-pass filtering, footprint representativeness, and energy balance closure for a clear-cut site with regrowing vegetation of varying height. For this, measurements from two open-path eddy-covariance systems at different heights are compared over the course of one growing season. Particular attention is paid to low-pass-filtering corrections, for which five different methods are compared. Results indicate significant differences between fluxes from the upper and lower systems, which likely result from footprint differences and an insufficient spectral correction for the lower system. Different low-pass-filtering corrections add an uncertainty of 3.4% (7.0%) to CO 2 fluxes and 1.4% (3.0%) to H 2 O fluxes for the upper (lower) system, also leading to considerable differences in cumulative fluxes. Despite limitations in the analysis, which include the difficulty of applying a footprint model at this study site and the likely influence of advection on the total exchange, the analysis confirms that information about the choice of spectral correction method and measurement-height changes are critical for interpreting data at complex sites.