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Understanding the impacts of climate extremes on the carbon cycle is important for quantifying the carbon-cycle climate feedback and highly relevant to climate change assessments. Climate extremes and fires can have severe regional effects, but a spatially explicit global impact assessment is still lacking. Here, we directly quantify spatiotemporal contiguous extreme anomalies in four global data sets of gross primary production (GPP) over the last 30 years. We find that positive and negative GPP extremes occurring on 7% of the spatiotemporal domain explain 78% of the global interannual variation in GPP and a significant fraction of variation in the net carbon flux. The largest thousand negative GPP extremes during 1982?2011 (4.3% of the data) account for a decrease in photosynthetic carbon uptake of about 3.5?Pg?C?yr?1, with most events being attributable to water scarcity. The results imply that it is essential to understand the nature and causes of extremes to understand current and future GPP variability.

On October 29, 2019, the Vaia storm hits the North-Eastern regions of Italy by wind gusts exceeding 200 km h-1. The forests in these regions have been seriously damaged. This contribution illustrates the methodology adopted in the emergency phase to estimate forest damages caused by Vaia storm, both in terms of damaged forest areas and growing stock volume of fallen trees. 494 Municipalities registered forest damages caused by Vaia, destroyed or intensely damaged forest stands amounted to about 42.500 ha, spread in Trentino Alto Adige, Veneto, Friuli Venezia Giulia, Lombardy and, only marginally, Piedmont and Valle d’Aosta. The growing stock volume of fallen trees was about 8.5 millions m3.

Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solarinduced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicate the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as a bridge between EC measurements and remote-sensing data. In situ spectral measurements have already been conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of these measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global estimates of GPP over terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

In this paper we explore the skill of hyperspectral reflectance measurements and vegetation indices (VIs) derived from these in estimating carbon dioxide (CO2) fluxes of grasslands. Hyperspectral reflectance data, CO2 fluxes and biophysical parameters were measured at three grassland sites located in European mountain regions using standardized protocols. The relationships between CO2 fluxes, ecophysiological variables, traditional VIs and VIs derived using all two-band combinations of wavelengths available from the whole hyperspectral data space were analysed.We found that VIs derived from hyperspectral data generally explained a large fraction of the variability in the investigated dependent variables but differed in their ability to estimate midday and daily average CO2 fluxes and various derived ecophysiological parameters. Relationships between VIs and CO2 fluxes and ecophysiological parameters were site-specific, likely due to differences in soils, vegetation parameters and environmental conditions. Chlorophyll and water-content-related VIs explained the largest fraction of variability in most of the dependent variables. Band selection based on a combination of a genetic algorithm with random forests (GA-rF) confirmed that it is difficult to select a universal band region suitable across the investigated ecosystems. Our findings have major implications for upscaling terrestrial CO2 fluxes to larger regions and for remote- and proximal-sensing sampling and analysis strategies and call for more cross-site synthesis studies linking ground-based spectral reflectance with ecosystem-scale CO2 fluxes. [ABSTRACT FROM AUTHOR]

Climate extremes can affect the functioning of terrestrial ecosystems, for instance via a reduction of the photosynthetic capacity or alterations of respiratory processes. Yet the dominant regional and seasonal effects of hydrometeorological extremes are still not well documented and in the focus of this paper. Specifically, we quantify and characterize the role of large spatiotemporal extreme events in gross primary production (GPP) as triggers of continental anomalies. We also investigate seasonal dynamics of extreme impacts on continental GPP anomalies. We find that the 50 largest positive extremes (i.e., statistically unusual increases in carbon uptake rates) and negative extremes (i.e., statistically unusual decreases in carbon uptake rates) on each continent can explain most of the continental variation in GPP, which is in line with previous results obtained at the global scale. We show that negative extremes are larger than positive ones and demonstrate that this asymmetry is particularly strong in South America and Europe. Our analysis indicates that the overall impacts and the spatial extents of GPP extremes are power-law distributed with exponents that vary little across continents. Moreover, we show that on all continents and for all data sets the spatial extents play a more important role for the overall impact of GPP extremes compared to the durations or maximal GPP. An analysis of possible causes across continents indicates that most negative extremes in GPP can be attributed clearly to water scarcity, whereas extreme temperatures play a secondary role. However, for Europe, South America and Oceania we also identify fire as an important driver. Our findings are consistent with remote sensing products. An independent validation against a literature survey on specific extreme events supports our results to a large extent. [ABSTRACT FROM AUTHOR]

Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the effectiveness with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependence on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well. [ABSTRACT FROM AUTHOR]

Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the efficiency with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependance on viewing and illumination geometry can be overcome and a single PRIbased model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at sitelevel, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well. [ABSTRACT FROM AUTHOR]

We estimated the long-term carbon balance [net biome production (NBP)] of European (EU-25) croplands and its component fluxes, over the last two decades. Net primary production (NPP) estimates, from different data sources ranged between 490 and 846 gC m−2 yr−1, and mostly reflect uncertainties in allocation, and in cropland area when using yield statistics. Inventories of soil C change over arable lands may be the most reliable source of information on NBP, but inventories lack full and harmonized coverage of EU-25. From a compilation of inventories we infer a mean loss of soil C amounting to 17 g m−2 yr−1. In addition, three process-based models, driven by historical climate and evolving agricultural technology, estimate a small sink of 15 g C m−2 yr−1 or a small source of 7.6 g C m−2 yr−1. Neither the soil C inventory data, nor the process model results support the previous European-scale NBP estimate by Janssens and colleagues of a large soil C loss of 90 ± 50 gC m−2 yr−1. Discrepancy between measured and modeled NBP is caused by erosion which is not inventoried, and the burning of harvest residues which is not modeled. When correcting the inventory NBP for the erosion flux, and the modeled NBP for agricultural fire losses, the discrepancy is reduced, and cropland NBP ranges between −8.3 ± 13 and −13 ± 33 g C m−2 yr−1 from the mean of the models and inventories, respectively. The mean nitrous oxide (N2O) flux estimates ranges between 32 and 37 g C Eq m−2 yr−1, which nearly doubles the CO2 losses. European croplands act as small CH4 sink of 3.3 g C Eq m−2 yr−1. Considering ecosystem CO2, N2O and CH4 fluxes provides for the net greenhouse gas balance a net source of 42–47 g C Eq m−2 yr−1. Intensifying agriculture in Eastern Europe to the same level Western Europe amounts is expected to result in a near doubling of the N2O emissions in Eastern Europe. N2O emissions will then become the main source of concern for the impact of European agriculture on climate. [ABSTRACT FROM AUTHOR]

Quantification of ecosystem carbon pools is a fundamental requirement for estimating carbon fluxes and for addressing the dynamics and responses of the terrestrial carbon cycle to environmental drivers. The initial estimates of carbon pools in terrestrial carbon cycle models often rely on the ecosystem steady state assumption, leading to initial equilibrium conditions. In this study, we investigate how trends and inter-annual variability of net ecosystem fluxes are affected by initial non-steady state conditions. Further, we examine how modeled ecosystem responses induced exclusively by the model drivers can be separated from the initial conditions. For this, the Carnegie- Ames-Stanford Approach (CASA) model is optimized at set of European eddy covariance sites, which support the parameterization of regional simulations of ecosystem fluxes for the Iberian Peninsula, between 1982 and 2006. The presented analysis stands on a credible model performance for a set of sites, that well represent the plant functional types and selected descriptors of climate and phenology present in the Iberian region - except for a limited northwestern area. The effects of initial conditions on inter-annual variability and on trends, results mostly from the recovery of pools to equilibrium conditions; which control most of the inter-annual variability (IAV) and both the magnitude and sign of most of the trends. However, by removing the time series of pure model recovery from the time series of the overall fluxes, we are able to retrieve estimates of inter-annual variability and trends in net ecosystem fluxes that are quasi-independent from the initial conditions. This approach reduced the sensitivity of the net fluxes to initial conditions from 47% and 174% to -3% and 7%, for strong initial sink and source conditions, respectively. With the aim to identify and improve understanding of the component fluxes that drive the observed trends, the net ecosystem production (NEP) trends are decomposed into net primary production (NPP) and heterotrophic respiration (RH) trends. The majority (∼97%) of the positive trends in NEP is observed in regions where both NPP and RH fluxes show significant increases, although the magnitude of NPP trends is higher. Analogously, ∼83% of the negative trends in NEP are also associated with negative trends in NPP. The spatial patterns of NPP trends are mainly explained by the trends in fAPAR (r=0.79) and are only marginally explained by trends in temperature and water stress scalars (r=0.10 and r=0.25, respectively). Further, we observe the significant role of substrate availability (r=0.25) and temperature (r=0.23) in explaining the spatial patterns of trends in RH. These results highlight the role of primary production in driving ecosystem fluxes. Overall, our study illustrates an approach for removing the confounding effects of initial conditions and emphasizes the need to decompose the ecosystem fluxes into its components and drivers for more mechanistic interpretations of modeling results. We expect that our results are not only specific for the CASA model since it incorporates concepts of ecosystem functioning and modeling assumptions common to biogeochemical models. A direct implication of these results is the ability of this approach to detect climate and phenology induced trends regardless of the initial conditions. [ABSTRACT FROM AUTHOR]

There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking) LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) — we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: (1) to determine appropriate use of current data and to explore the information gained in using longer time series; (2) to avoid confounding effects of missing process representation on parameter estimation; (3) to assimilate more data types, including those from earth observation; (4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and (5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts. [ABSTRACT FROM AUTHOR]

Globally, the year 2003 is associated with one of the largest atmospheric CO2 rises on record. In the same year, Europe experienced an anomalously strong flux of CO2 from the land to the atmosphere associated with an exceptionally dry and hot summer in Western and Central Europe. In this study we analyze the magnitude of this carbon flux anomaly and key driving ecosystem processes using simulations of seven terrestrial ecosystem models of different complexity and types (process-oriented and diagnostic). We address the following questions: (1) how large were deviations in the net European carbon flux in 2003 relative to a short-term baseline (1998-2002) and to longer-term variations in annual fluxes (1980 to 2005), (2) which regions exhibited the largest shift 10 in carbon fluxes during the growing season 2003, and (3) which processes controlled the carbon balance anomaly . In Western and Central Europe, the anomaly in net ecosystem productivity (NEP) over growing season 2003 was outside the 1σ bound of the carbon flux anomalies for 1980-2005. The estimated growing season anomaly ranged between -29 and -196 TgC for Western Europe and between 13 and -94 TgC for Central Europe depending on the model used. All models responded to a dipole pattern of the climate anomaly in 2003. In Western and Central Europe NEP was reduced due to heat and drought. Over Western Russia NEP was decreased in response to lower than normal temperatures and high precipitation. While models agree on changes in simulated NEP and gross primary productivity anomalies in 2003 over Western and Central Europe, models diverge in the estimates of anomalies in ecosystem respiration. Except for two process models which simulate respiration increase, most models simulated a decrease in ecosystem respiration in 2003. The diagnostic models showed a weaker decrease in ecosystem respiration than the process-oriented models. Based on the multi-model simulations we estimated the total carbon flux anomaly over the 2003 growing season in Europe to range between -0.02 and -0.27 PgC relative to the net flux in 1998-2002. [ABSTRACT FROM AUTHOR]

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data., (© 2023 The Author(s).)

The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

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., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2023.)

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Hydropower emits less carbon dioxide than fossil fuels but the lower albedo of hydropower reservoirs compared to terrestrial landscapes results in a positive radiative forcing offsetting some of the negative radiative forcing by hydroelectricity generation. The cumulative effect of this lower albedo has not been quantified. Here we show, by quantifying the difference in remotely sensed albedo between globally distributed hydropower reservoirs and their surrounding landscape, that 19 % of all investigated hydropower plants required 40 years and more for the negative radiative forcing from the fossil fuel displacement to offset the albedo effect. The length of these break-even times depends on the specific combination of climatic and environmental constraints, power plant design characteristics and country-specific electricity carbon intensities. We conclude that future hydropower plants need to minimize the albedo penalty in order to make a meaningful contribution towards limiting global warming., Competing Interests: Competing interests The authors declare no competing interests.

The modification of the surface radiation and energy balance in urban areas causes the temperatures in these areas to exceed those of the surrounding countryside. It has thus been suggested that urban environments may serve as field laboratories for studying the effects of a warming climate on biota in a space-for-time substitution. Here we investigated changes in the timing of plant phenology and temperature across study sites that differed in the degree of urbanization using publicly available pan-European datasets for the period 1981-2010. We found a significant advancement in the phenological phases of leaf development, flowering and fruiting with higher degrees of urbanization, whereas a significant delay was observed for phenological phases of leaf senescence. In addition to these phenological changes, an increase in air temperature with higher degrees of urbanization was observed. This increase was largest during the periods of leaf development, flowering and fruiting and smallest during the period of leaf senescence. On the basis of these results, we show that the apparent temperature sensitivity of phenological phases to urban warming is either significantly dampened (leaf development, flowering and fruiting) or reversed (leaf senescence) compared with the temperature sensitivity inferred from temporal changes in phenology and temperature. We conclude that gradients in urbanization represent a poor analogue for the temporal changes in plant phenology, apparently owing to confounding factors associated with urbanization.

Model parameterization and validation of earth-atmosphere interactions are generally performed using a single timescale (e.g., nearly instantaneous, daily, and annual), although both delayed responses and hysteretic effects have been widely recognized. The lack of consideration of these effects hampers our capability to represent them in empirical- or process-based models. Here we explore, using an apple orchard ecosystem in the North of Italy as a simplified case study, how the considered timescale impacts the relative importance of the single environmental variables in explaining observed net ecosystem exchange (NEE) and evapotranspiration (ET). Using 6 years of eddy covariance and meteorological information as input data, we found a decay of the relative importance of the modeling capability of photosynthetically active radiation in explaining both NEE and ET moving from half-hourly to seasonal timescale and an increase in the relative importance of air temperature ( T ) and VPD. Satellite NDVI, used as proxy of leaf development, added little improvement to overall modeling capability. Increasing the timescale, the number of variables needed for parameterization decreased (from 5 to 1), while the proportion of variance explained by the model increased ( r 2 from 0.56-0.78 to 0.85-0.90 for NEE and ET respectively). The wavelet coherence and the phase analyses showed that the two variables that increased their relative importance when the scale increased ( T , VPD) were not in phase at the correlation peak of both ET and NEE. This phase shift in the time domain corresponds to a hysteretic response in the meteorological variables domain. This work confirms that the model parameterization should be performed using parameters calculated at the appropriate scale. It suggests that in managed ecosystems, where the interannual variability is minimized by the agronomic practices, the use of timescales large enough to include hysteretic and delayed responses reduces the number of required input variables and improves their explanatory capacity.

In complex, sloping terrain, horizontal measurements of net radiation are not reflective of the radiative energy available for the conductive and convective heat exchange of the underlying surface. Using data from a grassland site on a mountain slope characterised by spatial heterogeneity in inclination and aspect, we tested the hypothesis that a correction of the horizontal net radiation measurements which accounts for the individual footprint contributions of the various surfaces to the measured sensible and latent heat eddy covariance fluxes will yield more realistic slope-parallel net radiation estimates compared to a correction based on the average inclination and aspect of the footprint. Our main result is that both approaches led to clear, but very similar improvements in the phase between available energy and the sum of the latent and sensible heat fluxes. As a consequence the variance in the sum of latent and sensible heat flux explained by available radiation improved by >10 %, while energy balance closure improved only slightly. This is shown to be mainly due to the average inclination and aspect corresponding largely with the inclination and aspect of the main flux source area in combination with a limited sensitivity of the slope correction to small angular differences in, particularly, inclination and aspect. We conclude with a discussion of limitations of the present approach and future research directions.

Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (RECO) in deciduous forests. Previous studies showed that empirical RECO models can be substantially improved by considering the biotic dependency of RECO on the short-term productivity (e.g., daily gross primary production, GPP) in addition to the well-known environmental controls of temperature and water availability. Here, we use a model-data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECO at 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data-oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled RECO when compared to a version of the model that does not consider the physiological phenology. The reduction of the model-observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model-error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECO in deciduous forests by including the phenological cycle of the canopy., (© 2014 John Wiley & Sons Ltd.)

More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land−a key diagnostic criterion of the effects of climate change and variability−remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.

We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an 'extra' day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.

Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.