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International audience; Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studiesthat demonstrate how EEO generate parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration, and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust toenvironmental change.

Greenhouse gas (GHG) research has traditionally required data collection and analysis using advanced and often expensive instruments, complex and proprietary software, and skilled technicians. Partly as a result, relatively little GHG research has been conducted in resource-constrained developing countries and a critical data gap exists in these regions. At the same time, these are the same countries and regions in which climate-change impacts will likely be strongest, and in which major science uncertainties are centered, given the importance of dryland and tropical systems to the global carbon cycle and climate. Increasingly, scientific communities have adopted appropriate technology and approach (AT&A) for GHG research, including low-cost and low-technology instruments, open source software and data, and participatory and networking-based research approaches. Adopting AT&A can mean acquiring data with fewer technical constraints and lower economic burden, and is thus a strategy for enhancing GHG research in developing countries. However, AT&A can be characterized by higher uncertainties; these can often be mitigated by carefully designing experimental set-up, providing clear protocols for data collection, and monitoring and validating the quality of obtained data. For implementing this approach in GHG research of developing countries, first, it is necessary to recognize the scientific and moral importance of AT&A. At the same time, new AT&A techniques should be identified and further developed. Finally, these processes should be promoted through training local staff and encouraged for wide use and further innovation in developing countries.

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions, including their seasonality, due to quasi-continuous and high temporal resolution of flux measurements, coincident measurements of carbon, water, and energy fluxes, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we 1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4- community-product/). FLUXNET-CH4 includes half-hourly and daily gap-filled and non gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we 2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally, because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands and because freshwater wetlands are a substantial source of total atmospheric CH4 emissions; and 3) provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions, but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20° S to 20° N) the spring onset of elevated CH4 emissions starts three days earlier, and the CH4 emission season lasts 4 days longer, for each degree C increase in mean annual air temperature. On average, the onset of increasing CH4 emissions lags soil warming by one month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling, and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). The FLUXNET-CH4 dataset provides an open-access resource for CH4 flux synthesis, has a range of applications, and is unique in that it includes coupled measurements of important CH4 drivers such as GPP and temperature. Although FLUXNET-CH4 could certainly be improved by adding more sites in tropical ecosystems and by increasing the number of site-years at existing sites, it is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4408468. Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/, and a complete list of the 79 individual site data DOIs is provided in Table 2 in the Data Availability section of this document., Earth System Science Data Discussions, ISSN:1866-3591

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system. publishedVersion

The ECOsystem Space Thermal Radiometer Experiment on the Space Station (ECOSTRESS) was launched to the International Space Station on June 29, 2018 by the National Aeronautics and Space Administration (NASA). The primary scientific focus of ECOSTRESS is on evapotranspiration (ET), which occurs as Level 3 (L3) latent heat flux (LE) data products. This data is generated from the Level 2 Earth Surface Temperature and Emissivity Product (L2_LSTE), along with the atmospheric and ancillary surface data. Here, we provide the first validation (Stage 1, preliminary) of the global product ECOSTRESS clear - sky ET (L3_ET_PT - JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against site measurements (instant clear sky / flyover time: r 2 = 0.88; overall bias = 8%; normalized root mean square error, RMSE = 6%) . ET uncertainty was generally constant across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), although temperate sites are over-represented. ECOSTRESS 70 m high spatial resolution improved correlations by 85% and RMSE by 62%, relative to 1 km pixels. This document serves as a reference for the accuracy of ECOSTRESS L3 ET and the Stage 1 validation status for subsequent science that continues to use this data. Fil: Fisher, Joshua B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Lee, Brian. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Purdy, Adam J. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Halverson, Gregory H. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Dohlen, Matthew B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Cawse - Nicholson, Kerry. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Wang, Audrey. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Anderson, Ray G. U.S. Department of Agriculture; Estados Unidos Fil: Aragon, Bruno. King Abdullah University of Science and Technology. Division of Biological and Environmental Science and Engineering. Hydrology, Agriculture and Land Observation Group (HALO); Arabia Saudita Fil: Arain, M. Alfat. McMaster University. School of Geography and Earth Sciences; Canadá Fil: Baldocchi, Dennis D. University of California, Berkeley. Department of Environmental Science, Policy and Management; Estados Unidos Fil: Baker, John M. U.S. Department of Agriculture; Estados Unidos Fil: Barral, Helene. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Bernacchi, Carl J. University of Illinois Urbana‐Champaign. Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology. Center for Advanced Bioenergy and Bioproducts Innovation; Estados Unidos. U.S. Department of Agriculture, Global Change and Photosynthesis Research Unit; Estados Unidos Fil: Bernhofer, Christian. Technische Universität Dresden, Institute of Hydrology and Meteorology; Alemania Fil: Biraud, Sebastien C. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Bohrer, Gil. Ohio State University. Department of Civil, Environmental and Geodetic Engineering; Estados Unidos Fil: Brunsell, Nathaniel. University of Kansas. Department of Geography and Atmospheric Science; Estados Unidos Fil: Cappelaere, Bernard. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Castro - Contreras, Saulo. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Chum, Junghwa. National Institute of Forest Science; Corea del Sur Fil: Conrad, Bryan. University of Kansas. Department of Geography and Atmospheric Science; Estados Unidos Fil: Cremonese, Edoardo. Environmental Protection Agency of Aosta Valley; Italia Fil: Demarty, Jerome. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Desai, Ankur R. University of Wisconsin‐Madison. Department of Atmospheric and Oceanic Sciences; Estados Unidos Fil: De Ligne, Anne. University of Liege. TERRA Teaching and Research Centre. Gembloux Agro‐Bio Tech; Bélgica Fil: Foltynova, Lenka. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Goulden, Michael L. University of California Irvine. Department of Earth System Science; Estados Unidos Fil: Griffis, Timothy J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos Fil: Grunwald, Thomas. Technische Universität Dresden, Institute of Hydrology and Meteorology; Alemania Fil: Johnson, Mark S. University of British Columbia. Department of Earth, Ocean, and Atmospheric Sciences; Canadá Fil: Kang, Minseok. National Center for AgroMeteorology; Corea del Sur Fil: Kelbe, Dave. Xerra Earth Observation Institute; Nueva Zelanda Fil: Kowalska, Natalia. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Jong - Hwan, Lim. National Institute of Forest Science; Corea del Sur Fil: Mainassara, Ibrahim. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: McCabe, Matthew F. King Abdullah University of Science and Technology. Division of Biological and Environmental Science and Engineering. Hydrology, Agriculture and Land Observation Group (HALO); Arabia Saudita Fil: Missik, Justine E.C. Washington State University. Department of Civil and Environmental Engineering. Laboratory for Atmospheric Research; Estados Unidos Fil: Mohanty, Binayak P. Texas A&M University. Texas Water Observatory; Estados Unidos Fil: Moore, Caitlin E. University of Illinois Urbana‐Champaign. Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology. Center for Advanced Bioenergy and Bioproducts Innovation; Estados Unidos Fil: Morillas, Laura. University of British Columbia. Department of Earth, Ocean, and Atmospheric Sciences; Canadá Fil: Morrison, Ross. Centre for Ecology and Hydrology; Reino Unido Fil: Munger, J. Willians. Harvard University. School of Engineering and Applied Sciences; Estados Unidos Fil: Posse, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Richardson, Andrew D. Northern Arizona University Center for Ecosystem Science and Society; Estados Unidos Fil: Russell, Eric. S. Washington State University. Department of Civil and Environmental Engineering. Laboratory for Atmospheric Research; Estados Unidos Fil: Ryu, Youngryel. Seoul National University . Department of Landscape Architecture and Rural Systems Engineering; Corea del Sur Fil: Sanchez - Azofeifa, Arturo. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Schmidt, Marius. IInstitute of Bio‐ and Geosciences: Agrosphere (IBG‐3) Forschungszentrum Jülich IBG‐3; Alemania Fil: Schwartz, Efrat. Weizmann Institute of Science. Department of Earth and Planetary Sciences, ; Israel Fil: Sharp, Iain. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Sigut, Ladislav. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Tang, Yao. Georgia Institute of Technology. School of Civil and Environmental Engineering; Estados Unidos Fil: Hulley, Glynn. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Anderson, Martha. U.S. Department of Agriculture‐Agricultural Research Service; Estados Unidos Fil: Hain, Christopher. NASA Marshall Space Flight Center; Estados Unidos Fil: French, Andrew. U.S. Department of Agriculture; Estados Unidos Fil: Wood, Eric. Princeton University. Department of Civil and Environmental Engineering; Estados Unidos Fil: Hook, Simón. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos

Changes in vegetation structure are expected to influence the redistribution of heat and moisture; however, how variations in the leaf area index (LAI) affect this global energy partitioning is not yet quantified. Here, we estimate that a unit change in LAI leads to 3.66 ± 0.45 and −3.26 ± 0.41 W m−2 in latent (LE) and sensible (H) fluxes, respectively, over the 1982–2016 period. Analysis of an ensemble of data-driven products shows that these sensitivities increase by about 20% over the observational period, prominently in regions with a limited water supply, probably because of an increased transpiration/evaporation ratio. Global greening has caused a decrease in the Bowen ratio (B = H/LE) of −0.010 ± 0.002 per decade, which is attributable to the increased evaporative surface. Such a direct LAI effect on energy fluxes is largely modulated by plant functional types (PFTs) and background climate conditions. Land surface models (LSMs) misrepresent this vegetation control, possibly due to underestimation of the biophysical responses to changes in the water availability and poor representation of LAI dynamics. Changes in the leaf area index alter the distribution of heat and moisture. The change in energy partitioning related to leaf area, increasing latent and decreasing sensible fluxes over the observational period 1982–2016, is moderated by plant functional type and background climate.

The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change.

Global vegetation greening trend stalled after the late 1990s due to increased atmospheric water demand., Atmospheric vapor pressure deficit (VPD) is a critical variable in determining plant photosynthesis. Synthesis of four global climate datasets reveals a sharp increase of VPD after the late 1990s. In response, the vegetation greening trend indicated by a satellite-derived vegetation index (GIMMS3g), which was evident before the late 1990s, was subsequently stalled or reversed. Terrestrial gross primary production derived from two satellite-based models (revised EC-LUE and MODIS) exhibits persistent and widespread decreases after the late 1990s due to increased VPD, which offset the positive CO2 fertilization effect. Six Earth system models have consistently projected continuous increases of VPD throughout the current century. Our results highlight that the impacts of VPD on vegetation growth should be adequately considered to assess ecosystem responses to future climate conditions.

International audience; Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution.

A growing literature is reporting on how the terrestrial carbon cycle is experiencing year-to-year variability because of climate anomalies and trends caused by global change. As CO2 concentration records in the atmosphere exceed 50 years and as satellite records reach over 30 years in length, we are becoming better able to address carbon cycle variability and trends. Here we review how variable the carbon cycle is, how large the trends in its gross and net fluxes are, and how well the signal can be separated from noise. We explore mechanisms that explain year-to-year variability and trends by deconstructing the global carbon budget. The CO2 concentration record is detecting a significant increase in the seasonal amplitude between 1958 and now. Inferential methods provide a variety of explanations for this result, but a conclusive attribution remains elusive. Scientists have reported that this trend is a consequence of the greening of the biosphere, stronger northern latitude photosynthesis, more photosynthesis by semi-arid ecosystems, agriculture and the green revolution, tropical temperature anomalies, or increased winter respiration. At the global scale, variability in the terrestrial carbon cycle can be due to changes in constituent fluxes, gross primary productivity, plant respiration and heterotrophic (microbial) respiration, and losses due to fire, land use change, soil erosion, or harvesting. It remains controversial whether or not there is a significant trend in global primary productivity (due to rising CO2, temperature, nitrogen deposition, changing land use, and preponderance of wet and dry regions). The degree to which year-to-year variability in temperature and precipitation anomalies affect global primary productivity also remains uncertain. For perspective, interannual variability in global gross primary productivity is relatively small (on the order of 2 Pg-C y-1) with respect to a large and uncertain background (123 +/- 4 Pg-C y-1), and detected trends in global primary productivity are even smaller (33 Tg-C y-2). Yet residual carbon balance methods infer that the terrestrial biosphere is experiencing a significant and growing carbon sink. Possible explanations for this large and growing net land sink include roles of land use change and greening of the land, regional enhancement of photosynthesis, and down regulation of plant and soil respiration with warming temperatures. Longer time series of variables needed to provide top-down and bottom-up assessments of the carbon cycle are needed to resolve these pressing and unresolved issues regarding how, why, and at what rates gross and net carbon fluxes are changing.

International audience; Interannual variability in biosphere-atmosphere exchange of CO2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.

a b s t r a c t The regular monitoring of evapotranspiration from satellites has been limited because of discontinu- ous temporal coverage, resulting in snapshots at a particular point in space and time. We developed a temporal upscaling scheme using satellite-derived instantaneous estimates of evapotranspiration to produce a daily-sum evapotranspiration averaged over an 8-day interval. We tested this scheme against measured evapotranspiration data from 34 eddy covariance flux towers covering seven plant functional types from boreal to tropical climatic zones. We found that the ratio of a half-hourly-sum of potential solar radiation (extraterrestrial solar irradiance on a plane parallel to the Earth's surface) between 10:00 hh and 14:00 hh to a daily-sum of potential solar radiation provides a robust scaling factor to convert a half-hourly measured evapotranspiration to an estimate of a daily-sum; the estimated and measured daily sum evapotranspiration showed strong linear relation (r 2 = 0.92) and small bias (−2.7%). By com- parison, assuming a constant evaporative fraction (the ratio of evapotranspiration to available energy) during the daytime, although commonly used for temporal upscaling, caused 13% underestimation of evapotranspiration on an annual scale. The proposed temporal upscaling scheme requires only latitude, longitude and time as input. Thus it will be useful for developing continuous evapotranspiration estimates in space and time, which will improve continuous monitoring of hydrological cycle from local to global scales.

International audience; We propose the Breathing Earth System Simulator (BESS), an upscaling approach to quantify global gross primary productivity and evapotranspiration using MODIS with a spatial resolution of 1-5 km and a temporal resolution of 8 days. This effort is novel because it is the first system that harmonizes and utilizes MODIS Atmosphere and Land products on the same projection and spatial resolution over the global land. This enabled us to use the MODIS Atmosphere products to calculate atmospheric radiative transfer for visual and near infrared radiation wave bands. Then we coupled atmospheric and canopy radiative transfer processes, with models that computed leaf photosynthesis, stomatal conductance and transpiration on the sunlit and shaded portions of the vegetation and soil. At the annual time step, the mass and energy fluxes derived from BESS showed strong linear relations with measurements of solar irradiance (r(2) = 0.95, relative bias: 8%), gross primary productivity (r(2) = 0.86, relative bias: 5%) and evapotranspiration (r(2) = 0.86, relative bias: 15%) in data from 33 flux towers that cover seven plant functional types across arctic to tropical climatic zones. A sensitivity analysis revealed that the gross primary productivity and evapotranspiration computed in BESS were most sensitive to leaf area index and solar irradiance, respectively. We quantified the mean global terrestrial estimates of gross primary productivity and evapotranpiration between 2001 and 2003 as 118 +/- 26 PgC yr(-1) and 500 +/- 104 mm yr(-1) (equivalent to 63,000 +/- 13,100 km(3) yr(-1)), respectively. BESS-derived gross primary productivity and evapotranspiration estimates were consistent with the estimates from independent machine-learning, data-driven products, but the process-oriented structure has the advantage of diagnosing sensitivity of mechanisms. The process-based BESS is able to offer gridded biophysical variables everywhere from local to the total global land scales with an 8-day interval over multiple years.