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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 up-scaling framework, FLUXCOM-X. The X-BASE products comprise of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GP P) as well as 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 P g 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 bio-climatic conditions. However, X-BASE global net ecosystem exchange shows a very low inter-annual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 P gC · yr−1 for the same period, X-BASE GP P is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7x103 ± 0.9x103 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 are 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 up-scaling 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. [ABSTRACT FROM AUTHOR]

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 needleleaf 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 needleleaf 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. While warming increases ecosystem respiration, it 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. [ABSTRACT FROM AUTHOR]

Global collections of synthesized flux tower data such as FLUXNET have accelerated scientific progress beyond the eddy covariance community. However, remaining data issues in FLUXNET data pose challenges for users, particularly for multi-site synthesis and modeling activities. Here we present complementary consistency flags (C2F) for flux tower data, which rely on multiple indications of inconsistency among variables, along with a methodology to detect discontinuities in time series. The C2F relates to carbon and energy fluxes as well as to core meteorological variables, and consists of: (1) flags for daily data values, (2) flags for entire site variables, (3) flags at time stamps that mark large discontinuities in the time series. The flagging is primarily based on combining outlier scores from a set of predefined relationships among variables. The methodology to detect break points in the time series is based on a non-parametric test for the difference of distributions of model residuals. Applying C2F to the FLUXNET 2015 dataset reveals that: (1) Among the considered variables, gross primary productivity and ecosystem respiration data were flagged most frequently, in particular during rain pulses under dry and hot conditions. This information is useful for modelling and analysing ecohydrological responses. (2) there are elevated flagging frequencies for radiation variables (shortwave, photosynthetically active, and net). This information can improve the interpretation and modelling of ecosystem fluxes with respect to issues in the driver. (3) The majority of long-term sites show temporal discontinuities in the time series of latent energy, net ecosystem exchange, and radiation variables. This should be useful for carefully assessing the results on interannual variations and trends of ecosystem fluxes. The C2F methodology is flexible for customizing, and allows for varying the desired strictness of consistency. We discuss the limitations of the approach that can present starting points for future improvements. [ABSTRACT FROM AUTHOR]

Droughts affect terrestrial ecosystems directly and concurrently, and can additionally induce lagged effects in subsequent seasons and years. Such legacy effects of drought on vegetation growth and state have been widely studied in tree-ring records and satellite-based vegetation greenness, while legacies on ecosystem carbon fluxes are still poorly quantified and understood. Here, we focus on two ecosystem monitoring sites in central Germany with similar climate but characterized by different species and age structures. Using eddy-covariance measurements, we detect legacies on gross primary productivity (GPP) by calculating the difference between random-forest model estimates of potential GPP and observed GPP. Our results showed that at both sites, droughts caused significant legacy effects on GPP at seasonal and annual time scales which were partly explained by reduced leaf development. The GPP reduction due to drought legacy effects is of comparable magnitude to the concurrent drought effects, but differed between two neighbouring forests with divergent species and age structures. The methodology proposed here allows quantifying the temporal dynamics of legacy effects at the sub-seasonal scale and separating legacy effects from model uncertainties. Application of the methodology at a larger range of sites will help quantify whether the identified lag effects are general and on which factors they may depend. [ABSTRACT FROM AUTHOR]

The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at several hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, and Terrestrial Ecosystem Research Network (TERN) / OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the training and validation of ecosystem models. However, insufficient quality, frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions drawn from them. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40_ off-nadir. We offer to the community pre-processed Earth observation data in a radius of 2 km around 338 flux sites based on the MCD43A4/A2, MxD11A1 MODIS products and Landsat collection 1 Tier1 and Tier2 products. The data sets we provide can widely facilitate the integration of activities in the fields of eddy-covariance, remote sensing and modelling. [ABSTRACT FROM AUTHOR]

Vegetation plays a vital role in the Earth system by sequestering carbon, producing food and oxygen, and providing evaporative cooling. Vegetation productivity extremes have multi-faceted implications, for example on crop yields or the atmospheric CO[sub 2] concentration. Here, we focus on productivity extremes as possible impacts of coinciding, potentially extreme hydrometeorological anomalies. Using monthly global satellite-based Sun-induced chlorophyll fluorescence data as a proxy for vegetation productivity from 2007 - 2015, we show that vegetation productivity extremes are related to hydrometeorological hazards as characterized through ERA5-Land reanalysis data in approximately 50% of our global study area. For the latter, we are considering sufficiently vegetated and cloud-free regions; and we refer to hydrometeorological hazards as water or energy related extremes inducing productivity extremes. The relevance of the different hazard types varies in space; temperature-related hazards dominate at higher latitudes with cold spells contributing to productivity minima and heat waves supporting productivity maxima, while water-related hazards are relevant in the (sub)tropics with droughts being associated with productivity minima and wet spells with the maxima. Next to single hazards also compound events such as joint droughts and heat waves or joint wet and cold spells play a role, particularly in dry and hot regions. Further, we detect regions where energy control transitions to water control between maxima and minima of vegetation productivity. Therefore, these areas represent hot spots of land-atmosphere coupling where vegetation efficiently translates soil moisture dynamics into surface fluxes such that the land affects near-surface weather. Overall, our results contribute to pinpoint how potential future changes in temperature and precipitation could propagate to shifting vegetation productivity extremes and related ecosystem services. [ABSTRACT FROM AUTHOR]

Understanding the dependencies of the terrestrial carbon and water cycle is a prerequisite to anticipate their behaviour under climate change conditions. However, terrestrial ecosystems and the atmosphere interact via a multitude of variables, time-and space scales. Additionally the interactions might differ among vegetation types or climatic regions. Today, novel algorithms aim to disentangle the causal structure behind such interaction from empirical data. Visualising the estimated structure in networks, the nodes represent relevant meteorological determinants and land-surface fluxes, and the links dependencies among them possibly including their lag and strength. Here we show that biosphere–atmosphere interactions are strongly shaped by meteorological conditions. For example, we find that temperate and high latitude ecosystems during peak productivity exhibit very similar biosphere–atmosphere interaction networks as tropical forests. In times of anomalous conditions like drought though, both ecosystems behave more like Mediterranean ecosystems during their dry season. Our results demonstrate that ecosystems from different climate or vegetation types have similar biosphere–atmosphere interactions if their meteorological conditions are similar. We anticipate our analysis to foster the use of network approaches as they allow a more comprehensive understanding of the state of ecosystem functioning. Long term or even irreversible changes in network structure are rare and thus can be indicators of fundamental functional ecosystem shifts. [ABSTRACT FROM AUTHOR]

Remote Sensing (RS) has traditionally provided estimates of key biophysical properties controlling light interaction with the canopy (e.g., chlorophyll content (Cab) or leaf area index (LAI)). However, recent and upcoming developments in hyperspectral RS are expected to lead to a new generation of products such as vegetation functional traits that control leaf carbon and water gas exchange. This information is pivotal to improve our understanding and capability to predict biosphere-atmosphere fluxes at global scale. Yet, the retrieval of key functional traits such as maximum carboxylation rate (Vcmax) or the Ball-Berry stomatal sensitivity parameter (m) remains challenging, as they only have a weak and indirect influence on optical reflectance factors. Recently, the assimilation of different observations in coupled soil-vegetation-atmosphere transfer (SVAT) and radiative transfer models (RTM) is allowing Vcmax and m estimates; notably using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model. In this work we assess the potential of airborne and satellite emulated hyperspectral imagery jointly with eddy covariance (EC) data for the retrieval of functional traits. Specifically, we made use of time series of gross primary production (GPP) and thermal irradiance measured with net radiometers, together with 17 hyperspectral airborne images. The potential of satellite-borne sensors was tested with emulated EnMAP imagery from the airborne data. EnMAP was selected because of the availability of the emulator, and because is one of the foreseen hyperspectral satellite missions expected to contribute to a new generation of RS products. We estimated ecosystem functional traits by inverting the senSCOPE model, a novel version of SCOPE adapted to represent partly senescent canopies. The experiment takes place in a Mediterranean tree-grass ecosystem subject of a large scale manipulation experiment with nitrogen and nitrogen plus phosphorus, monitored by three EC towers. Parameter estimates and predicted fluxes were evaluated using both ground observations and pattern-oriented model evaluation approach. The method developed in this study provided robust estimates of functional and biophysical parameters for both airborne and synthetic EnMAP datasets. Cab and Vcmax estimates followed observed relationships with leaf nitrogen concentration; whereas m and predicted underlying water use efficiency showed expected relationships with discrimination of 13C isotope in leaves. Results prove that the inversion of coupled RTM-SVAT models against a combination of hyperspectral imagery (e.g., EnMAP), and time series of GPP and thermal irradiance provides reliable estimates of key functional parameters of vegetation that are robust to several sources of uncertainty. The forthcoming satellite hyperspectral missions combined with ecosystem station networks (e.g. Integrated Carbon Observation System (ICOS), NEON, FLUXNET, etc...), offers unique possibilities to characterize the spatiotemporal distribution of functional parameters relevant for terrestrial biosphere modeling. [ABSTRACT FROM AUTHOR]

In the last decades terrestrial ecosystems have reabsorbed on average more than one quarter of anthropogenic emissions (Le Quéré et al., 2018). However, this large carbon sink is modulated by climate and is therefore highly variable in time and space. The magnitude and temporal changes of the sensitivity of terrestrial CO2 fluxes to climate drivers are key factors to determine future atmospheric CO2 concentration and climate trajectories. In the literature there is so far a strong focus on the climatic controls of inter-annual variability, while less is known about the key drivers of the sub-annual variability of the fluxes. This latter temporal scale is relevant to assess which climatic drivers dominate the seasonality of the fluxes and to understand which factors limit the net ecosystem CO2 exchange. Here, we investigated the global sensitivity of terrestrial CO2 fluxes to three key climate drivers (i.e. global radiation, temperature and soil water content) from weekly to seasonal temporal scales, in order to explore the short-term interdependence between climate and the terrestrial carbon budget. We observed that the CO2 exchange over most of the land surface is controlled by temperature during the carbon uptake period, while radiation is the most widespread dominant climate driver during the carbon release period. As expected, soil water content plays a key role in arid regions of the southern hemisphere. Looking at the decadal trend of these sensitivities we observed that the importance of radiation as a driver is increasing over time, while we observed a decrease in sensitivity to temperature in Eurasia. Overall, we show that the temporal variation of the fluxes due to a specific driver is dominated by the temporal changes in ecosystem sensitivity rather than to the temporal variability of the driver itself. Ultimately this analysis shows that the response of the ecosystem to climate drivers is significantly changing both in space and in time, with potential repercussion on the future terrestrial CO2 sink and therefore on the role that land may play in climate mitigation. [ABSTRACT FROM AUTHOR]

Quantifying responses of vegetation phenology to climate variability is a key prerequisite to predict shifts in how ecosystem dynamics due to climate change. So far, many studies have focused on responses of classical phenological events (e.g. budburst or flowering) to climatic variability for individual species. Comparatively little is known on physio-phenological events such as the timing of the maximum gross primary production (DOYGPPmax). However, understanding this type of physio-phenological phenomena is an essential element in predicting the response of the terrestrial carbon cycle to climate variability. In this study, we aim to understand how DOYGPPmax depends on climate drivers across 52 eddy-covariance (EC) sites in the FLUXNET network for different regions of the world. Most phenological studies rely on linear methods that cannot be generalized across both hemispheres and therefore do not allow for deriving general rules that can be applied for future predictions. Here we explore a new class of circular-linear (here called circular) regression approach that may show a path ahead. Circular regression allows relating circular variables (in our case phenological events) to linear predictor variables (e.g. climate conditions). As a proof of concept, we compare the performance of linear and circular regression to recover original coefficients of a predefined circular model on artificial and EC data. We then quantify the sensitivity of DOYGPPmax to air temperature, short-wave incoming radiation, precipitation and vapor pressure deficit using circular regressions. Finally, we evaluate the predictive power of the regression models for different vegetation types. Our results show that the DOYGPPmax of each FLUXNET site has a unique signature of climatic sensitivities. Overall radiation and temperature are the most relevant controlling factors of DOYGPPmax across sites. The circular approach gives us new insights at the site level. In a Mediterranean shrub-land, for instance, we find that the two growing seasons are controlled by different climatic factors. Although the sensitivity of the DOYGPPmax to the climate drivers is very site specific, it is possible to extrapolate the circular regression model across vegetation types. From a methodological point of view, our results reveal that circular regression is a robust alternative to conventional phenological analytic frameworks. In particular global analyses can benefit, where phase shifts play a role or double peaked growing seasons may occur. [ABSTRACT FROM AUTHOR]

FLUXNET assembles 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 in order to obtain global-scale estimates of biosphere-atmosphere exchange from the flux tower network. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVM), we provide here 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 datasets, and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent among FLUXCOM and DGVM ensembles (R² > 0.94 at 1° spatial resolution) while the majority of DGVMs are outside the FLUXCOM range for 70% of the land surface. 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 of 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 of 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 uses also meteorological inputs shows a strong co-variation of interannual patterns with inversions (R² = 0.88 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 gC m² yr-1. 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. [ABSTRACT FROM AUTHOR]

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models. [ABSTRACT FROM AUTHOR]

Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. Our ability to partition evapotranspiration (ET) into E and T is limited at the ecosystem scale, which renders the validation of satellite data and land surface models incomplete. Here, we review current progress in partitioning E and T, and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques provide additional opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. Many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency (EWUE) exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but the conditions under which this assumption holds require further analysis. Another critical assumption for many ET partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T frequently exceeds 95 % of ET from some ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T, yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon-water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water flux and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle. [ABSTRACT FROM AUTHOR]

Mediterranean grasslands are highly seasonal and co-limited by water and nutrients. In such systems little is known about root dynamics which may depend on plant habit and environment as well seasonal water shortages and site fertility. This latter factor is affected by the presence of scattered trees and site management including grazing, as well as chronic nitrogen deposition, which may lead to N:P imbalance. In this study we combined observations from minirhizotrons collected in a Mediterranean tree-grass ecosystem (Spanish Dehesa), with root measurements from direct soil cores and ingrowth cores, and above-ground biomass to investigate seasonal root dynamics and root:shoot ratios. We investigated responses to soil fertility, using a nutrient manipulation (N / NP additions) and microhabitats effects between open pasture and under tree canopy locations. Root dynamics over time were compared with indices of above-ground growth drawn from proximal remote sensing (Normalised Difference Vegetation Index and Green Chromatic Coordinate derived from near-infrared enabled digital repeat photography). Results show distinct differences in root dynamics and biomass between treatments and microhabitats. Root biomass was higher with N additions, but not with NP additions in early spring, but by the end of the growing season root biomass had increased with NP in open pastures but not higher than N alone. In contrast, root length density (RLD) in pastures responded stronger to the NP than N only treatment, while beneath trees RLD tended to be higher with only N. Even though root biomass increased, root:shoot ratio decreased under nutrient treatments.We interpret these differences as a shift in community structure and/or root traits under changing stoichiometry and altered nutrient limitations. The timing of maximum root cover, as assessed by the minirhizotrons, did not match with above-ground phenology indicators at the site as root growth was low during autumn despite the "greening up" of the ecosystem. In other periods, roots responded quickly to rain events on the scale of days, matching changes in above-ground indices. Our results highlight the need for high resolution sampling to increase understanding of root dynamics in such systems, linkage with shifts in community structure and traits, and targeting of appropriate periods of the year for in-depth campaigns. [ABSTRACT FROM AUTHOR]

The ratio of CO2 efflux to O2 influx (ARQ, apparent respiratory quotient) in tree stems is expected to be 1.0 for carbohydrates, the main substrate supporting stem respiration. In previous studies of stem fluxes, ARQ values below 1.0 were observed and hypothesized to indicate retention of respired carbon within the stem. Here, we demonstrate that stem ARQ < 1.0 values are common across 85 tropical, temperate, and Mediterranean forest trees from 9 different species. Mean ARQ values per species per site ranged from 0.39 to 0.78, with an overall mean of 0.59. Assuming that O2 uptake provides a measure of in situ stem respiration (due to the low solubility of O2), the overall mean indicates that on average 41 % of CO2 respired in stems is not emitted from the local stem surface. The instantaneous ARQ did not vary with sap flow. ARQ values of incubated stem cores were similar to those measured in stem chambers on intact trees. We therefore conclude that dissolution of CO2 in the xylem sap and transport away from the site of respiration cannot explain the low ARQ values. We suggest to examine refixation of respired CO2 in biosynthesis reactions as possible mechanism for low ARQ values. [ABSTRACT FROM AUTHOR]

With the eddy-covariance (EC) technique, net fluxes of carbon dioxide (CO2) and other greenhouse gases as well as water and energy fluxes can be measured at the ecosystem level. These flux measurements are a main source for understanding biosphere-atmosphere interactions and feedbacks by cross-site analysis, model-data integration, and up-scaling. The raw fluxes measured with the EC technique require an extensive and laborious data processing. While there are standard tools available in open source environment for processing high-frequency (10 or 20 Hz) data into half-hourly quality checked fluxes, there is a need for more usable and extensible tools for the subsequent post-processing steps. We tackled this need by developing the REddyProc package in the cross-platform language R that provides standard CO2-focused post-processing routines for reading (half-)hourly data from different formats, estimating the uStar threshold, gap-filling, flux-partitioning, and visualizing the results. In addition to basic processing, the functions are extensible and allow easier integration in extended analysis than current tools. New features include cross year processing and a better treatment of uncertainties. A comparison of REddyProc routines with other state-of the art tools resulted in no significant differences in monthly and annual fluxes across sites. Lower uncertainty estimates of both uStar and resulting gap-filled fluxes with the presented tool was achieved by an improved treatment of seasons during the bootstrap analysis. Higher estimates of uncertainty in day-time partitioning resulted from a better accounting of the uncertainty in estimates of temperature sensitivity of respiration. The provided routines can be easily installed, configured, used, and integrated with further analysis. Hence the eddy covariance community will benefit from using the provided package, allowing easier integration of standard post-processing with extended analysis. [ABSTRACT FROM AUTHOR]

Understanding of terrestrial carbon and water cycles is currently hampered by an uncertainty in how to capture the large variety of plant responses to drought across climates, ecological strategies, and environments. In FLUXNET, the global network of CO2 and H2O flux observations, many sites do not uniformly report the ancillary variables needed to study drought response physiology such as soil moisture, sap flux, or species composition. In this sense, the use of diurnal energy, water, and carbon flux patterns to derive clues on ecosystem water limitation responses at a daily resolution could prove valuable, if nothing less than a benchmark to test current hypotheses. To this end, we propose two data-driven indicators derived directly from the eddy covariance data and based on theorized physiological responses to hydraulic and non-stomatal limitations. Hydraulic limitations (i.e. intra-plant limitations to water movement) are proxied using the relative diurnal centroid (C*ET), which measures the degree to which the flux of evapotranspiration (ET) is shifted toward the morning. Non-stomatal limitations (e.g. inhibitions of biochemical reactions, Rubisco activity, and/or mesophyll conductance) are characterized by the Diurnal Water:Carbon Index (DWCI), which measures the degree of coupling between ET and gross primary productivity (GPP) within each day. Globally, we found indications of hydraulic limitations in the form of significantly high frequencies of morning shifted days in dry/Mediterranean climates and savanna/evergreen plant functional types (PFT), whereas high frequencies of decoupling were dominated by dry climates and grassland/savanna PFTs indicating a prevalence of non-stomatal limitations in these ecosystems. Overall, both the diurnal centroid and DWCI were associated with high net radiation and low latent energy typical of drought. Using three water use efficiency (WUE) models, we found the mean differences between expected and observed WUE to be -0.14 to 0.56 µmol/mmol and -0.52 to -0.64 µmol/mmol for decoupled and morning shifted days respectively compared to mean differences -1.41 to -1.43 µmol/mmol in dry conditions. These results suggest that morning shifts/hydraulic responses are associated with an increase in WUE whereas decoupling/non-stomatal limitations are not. [ABSTRACT FROM AUTHOR]

Extreme climatic events, such as droughts and heat stress induce anomalies in ecosystem-atmosphere CO2 fluxes, such as gross primary production (GPP) and ecosystem respiration (Reco), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and Reco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme event impacts on GPP, Reco, and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30 year time period. We then used FLUXNET eddy-covariance flux measurements to estimate the CO2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they down-regulated GPP, resulting in a moderate reduction of the ecosystem's carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and Reco, and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of Reco over time turned from an initial increase to a down-regulation after about two weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies, but as a novelty generalizes these findings at the global scale. Specifically, we find that the different response functions of the two antipodal land-atmosphere fluxes GPP and Reco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing. [ABSTRACT FROM AUTHOR]

Extreme hydrometeorological conditions typically impact ecophysiological processes of terrestrial vegetation. 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 if the density of existing ecological in-situ networks is sufficient for analyzing the impact of extreme events, or 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 network, 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 largest 8 (typically very large) extreme events; but only a ≥ 50 % chance of capturing the largest 39 events. These finding are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation issues 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 theoretic considerations, we find that today's systematic network designs (i.e. NEON) reliably detects the largest extremes. But the extreme event detection rates are not higher than they would be achieved by randomly designed networks. Spatiotemporal expansions of ecological in-situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor their impacts in the terrestrial biosphere. [ABSTRACT FROM AUTHOR]