Soil moisture supply and atmospheric demand for water independently limit-and profoundly a ect-vegetation productivity and water use during periods of hydrologic stress [1][2][3][4] . Disentangling the impact of these two drivers on ecosystem carbon and water cycling is di cult because they are often correlated, and experimental tools for manipulating atmospheric demand in the field are lacking. Consequently, the role of atmospheric demand is often not adequately factored into experiments or represented in models 5-7 . Here we show that atmospheric demand limits surface conductance and evapotranspiration to a greater extent than soil moisture in many biomes, including mesic forests that are of particular importance to the terrestrial carbon sink 8,9 . Further, using projections from ten general circulation models, we show that climate change will increase the importance of atmospheric constraints to carbon and water fluxes in all ecosystems. Consequently, atmospheric demand will become increasingly important for vegetation function, accounting for >70% of growing season limitation to surface conductance in mesic temperate forests. Our results suggest that failure to consider the limiting role of atmospheric demand in experimental designs, simulation models and land management strategies will lead to incorrect projections of ecosystem responses to future climate conditions. Ecosystem moisture stress is often characterized by changes in soil water availability 10,11 . Declining soil moisture impedes the movement of water to evaporating sites at the soil or leaf surface 12 , reducing the surface conductance to water vapour (G S )-a key determinant of carbon and water cycling-and thereby evapotranspiration (ET). However, atmospheric demand for water, which is directly related to the atmospheric vapour pressure deficit (VPD), also affects G S and ET. Plants close their stomata to prevent excessive water loss when VPD is high [13][14][15][16] and thus, increases in VPD during periods of hydrologic stress represent an independent constraint on plant carbon uptake and water use in ecosystems.While the plant physiological community has long recognized the critical role of VPD in determining plant functioning, VPD is often overlooked in many fields of hydrologic and climate science. For example, precipitation manipulation experiments are frequently used to draw conclusions about ecosystem response to drought stress, even though VPD is unaffected by precipitation manipulation 10 . Some terrestrial ecosystem and ecohydrological models do not permit stomatal conductance to vary with atmospheric demand 5,11 . Many models designed to capture these impacts rely on empirical parameterizations for soil moisture and VPD stress that promote compensating effects and model equifinality 5 , and/or use relative humidity instead of VPD as the primary driver, with significant consequences for projections of

The measured net ecosystem exchange (NEE) of CO₂ between the ecosystem and the atmosphere reflects the balance between gross CO₂ assimilation [gross primary production (GPP)] and ecosystem respiration (R-eco). For understanding the mechanistic responses of ecosystem processes to environmental change it is important to separate these two flux components. Two approaches are conventionally used: (1) respiration measurements made at night are extrapolated to the daytime or (2) light-response curves are fit to daytime NEE measurements and respiration is estimated from the intercept of the ordinate, which avoids the use of potentially problematic nighttime data. We demonstrate that this approach is subject to biases if the effect of vapor pressure deficit (VPD) modifying the light response is not included. We introduce an algorithm for NEE partitioning that uses a hyperbolic light response curve fit to daytime NEE, modified to account for the temperature sensitivity of respiration and the VPD limitation of photosynthesis. Including the VPD dependency strongly improved the model's ability to reproduce the asymmetric diurnal cycle during periods with high VPD, and enhances the reliability of R-eco estimates given that the reduction of GPP by VPD may be otherwise incorrectly attributed to higher R-eco. Results from this improved algorithm are compared against estimates based on the conventional nighttime approach. The comparison demonstrates that the uncertainty arising from systematic errors dominates the overall uncertainty of annual sums (median absolute deviation of GPP: 47 g C m(-2) yr(-1)), while errors arising from the random error (median absolute deviation: similar to 2 g C m(-2) yr(-1)) are negligible. Despite site-specific differences between the methods, overall patterns remain robust, adding confidence to statistical studies based on the FLUXNET database. In particular, we show that the strong correlation between GPP and R-eco is not spurious but holds true when quasi-independent, i.e. daytime and nighttime based estimates are compared

The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO 2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA's Data Assimilation Office's (DAO) and tower-based meteorology is 28%, indicating that NASA's DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values

The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements.

Measured surface-atmosphere fluxes of energy (sensible heat, H, and latent heat, LE) and CO 2 (FCO 2 ) represent the ''true'' flux plus or minus potential random and systematic measurement errors. Here, we use data from seven sites in the AmeriFlux network, including five forested sites (two of which include ''tall tower'' instrumentation), one grassland site, and one agricultural site, to conduct a cross-site analysis of random flux error. Quantification of this uncertainty is a prerequisite to model-data synthesis (data assimilation) and for defining confidence intervals on annual sums of net ecosystem exchange or making statistically valid comparisons between measurements and model predictions.We differenced paired observations (separated by exactly 24 h, under similar environmental conditions) to infer the characteristics of the random error in measured fluxes. Random flux error more closely follows a double-exponential (Laplace), rather than a normal (Gaussian), distribution, and increase as a linear function of the magnitude of the flux for all three scalar fluxes. Across sites, variation in the random error follows consistent and robust patterns in relation to environmental variables. For example, seasonal differences in the random error for H are small, in contrast to both LE and FCO 2 , for which the random errors are roughly three-fold larger at the peak of the growing season compared to the dormant season. Random errors also generally scale with R n (H and LE) and PPFD (FCO 2 ). For FCO 2 (but not H or LE), the random error decreases with increasing wind speed. Data from two sites suggest that FCO 2 random error may be slightly smaller when a closed-path, rather than open-path, gas analyzer is used.

Land management and land-cover change have impacts of similar magnitude on surface temperature" (2014). Papers in Natural Resources. 554.

The energy balance at most surface-atmosphere flux research sites remains unclosed. The mechanisms underlying the discrepancy between measured energy inputs and outputs across the global FLUXNET tower network are still under debate. Recent reviews have identified exchange processes and turbulent motions at large spatial and temporal scales in heterogeneous landscapes as the primary cause of the lack of energy balance closure at some intensively-researched sites, while unmeasured storage terms cannot be ruled out as a dominant contributor to the lack of energy balance closure at many other sites. We analyzed energy balance closure across 173 ecosystems in the FLUXNET database and explored the relationship between energy balance closure and landscape heterogeneity using MODIS products and GLOBEstat elevation data. Energy balance closure per research site (CEB,s) averaged 0.84 ± 0.20, with best average closures in evergreen broadleaf forests and savannas (0.91–0.94) and worst average closures in crops, deciduous broadleaf forests, mixed forests and wetlands (0.70–0.78). Half-hourly or hourly energy balance closure on a percent basis increased with friction velocity (u*) and was highest on average under near-neutral atmospheric conditions. CEB,s was significantly related to mean precipitation, gross primary productivity and landscape-level enhanced vegetation index (EVI) from MODIS, and the variability in elevation, MODIS plant functional type, and MODIS EVI. A linear model including landscape-level variability in both EVI and elevation, mean precipitation, and an interaction term between EVI variability and precipitation had the lowest Akaike’s information criterion value. CEB,s in landscapes with uniform plant functional type approached 0.9 and CEB,s in landscapes with uniform EVI approached 1. These results suggest that landscape-level heterogeneity in vegetation and topography cannot be ignored as a contributor to incomplete energy balance closure at the flux network level, although net radiation measurements, biological energy assimilation, unmeasured storage terms, and the importance of good practice including site selection when making flux measurements should not be discounted. Our results suggest that future research should focus on the quantitative mechanistic relationships between energy balance closure and landscape-scale heterogeneity, and the consequences of mesoscale circulations for surface-atmosphere exchange measurements

Abstract. 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 independentCorrespondence to: M. Williams (mat.williams@ed.ac.uk) 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.Published by Copernicus Publications on behalf of the European Geosciences Union.