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

Lighting technologies for plant growth are improving rapidly, providing numerous options for supplemental lighting in greenhouses. Here we report the photosynthetic (400–700 nm) photon efficiency and photon distribution pattern of two double-ended HPS fixtures, five mogul-base HPS fixtures, ten LED fixtures, three ceramic metal halide fixtures, and two fluorescent fixtures. The two most efficient LED and the two most efficient double-ended HPS fixtures had nearly identical efficiencies at 1.66 to 1.70 micromoles per joule. These four fixtures represent a dramatic improvement over the 1.02 micromoles per joule efficiency of the mogul-base HPS fixtures that are in common use. The best ceramic metal halide and fluorescent fixtures had efficiencies of 1.46 and 0.95 micromoles per joule, respectively. We also calculated the initial capital cost of fixtures per photon delivered and determined that LED fixtures cost five to ten times more than HPS fixtures. The five-year electric plus fixture cost per mole of photons is thus 2.3 times higher for LED fixtures, due to high capital costs. Compared to electric costs, our analysis indicates that the long-term maintenance costs are small for both technologies. If widely spaced benches are a necessary part of a production system, the unique ability of LED fixtures to efficiently focus photons on specific areas can be used to improve the photon capture by plant canopies. Our analysis demonstrates, however, that the cost per photon delivered is higher in these systems, regardless of fixture category. The lowest lighting system costs are realized when an efficient fixture is coupled with effective canopy photon capture.

Evaporation (E) and transpiration (T ) respond differently to ongoing changes in climate, atmospheric composition, and land use. It is difficult to partition ecosystem-scale evapotranspiration (ET) measurements into E and T , which makes it difficult to validate satellite data and land surface models. 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 create new opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. For example, many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency 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 more analysis is necessary to determine the conditions for which this assumption holds. Another critical assumption for many 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 can exceed 95 % of ET from certain 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 fluxes and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.

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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 35 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 40 Biogeosciences Discuss., https://doi.

Plant transpiration (T), biologically controlled movement of water from soil to atmosphere, currently lacks sufficient estimates in space and time to characterize global ecohydrology. Here we describe the Transpiration Estimation Algorithm (TEA), which uses both the signals of gross primary productivity and evapotranspiration (ET) to estimate temporal patterns of water use efficiency (WUE, i.e., the ratio between gross primary productivity and T) from which T is calculated. The method first isolates periods when T is most likely to dominate ET. Then, a Random Forest Regressor is trained on WUE within the filtered periods and can thus estimate WUE and T at every time step. Performance of the method is validated using terrestrial biosphere model output as synthetic flux data sets, that is, flux data where WUE dynamics are encoded in the model structure and T is known. TEA reproduced temporal patterns of T with modeling efficiencies above 0.8 for all three models: JSBACH, MuSICA, and CASTANEA. Algorithm output is robust to data set noise but shows some sensitivity to sites and model structures with relatively constant evaporation levels, overestimating values of T while still capturing temporal patterns. The ability to capture between-site variability in the fraction of T to total ET varied by model, with root-mean-square error values between algorithm predicted and modeled T∕ET ranging from 3% to 15% depending on the model. TEA provides a widely applicable method for estimating WUE while requiring minimal data and/or knowledge on physiology which can complement and inform the current understanding of underlying processes. Plain Language SummaryWhile it is widely known that plants need water to survive, exactly how much water plants in an ecosystem use is hard to quantify. However, many places have been measuring how much total water leaves an ecosystem, both the water plants use directly and the water that simply evaporates from the soil or the surfaces of leaves, using eddy covariance towers. These eddy covariance towers also measure the coming and going of carbon, such as the total amount of carbon taken up by photosynthesis. Here we present the idea that by using the signals from both photosynthesis and total water losses together, we can capture the water signal related to plants, namely, transpiration, using an algorithm called Transpiration Estimation Algorithm (TEA). To verify that TEA is working the way we expect, we test it out using artificial ecosystem simulations where transpiration and photosynthesis come from mathematical models. By thoroughly testing TEA, we have a better idea of how it will work in a real world situation, hopefully opening the door for a better understanding on how much water ecosystems are using and how it might affect our changing planet.

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.