Joshua B. Fisher

Amazon forests are a key but poorly understood component of the global carbon cycle. If, as anticipated, they dry this century, they might accelerate climate change through carbon losses and changed surface energy balances. We used records from multiple long-term monitoring plots across Amazonia to assess forest responses to the intense 2005 drought, a possible analog of future events. Affected forest lost biomass, reversing a large long-term carbon sink, with the greatest impacts observed where the dry season was unusually intense. Relative to pre-2005 conditions, forest subjected to a 100-millimeter increase in water deficit lost 5.3 megagrams of aboveground biomass of carbon per hectare. The drought had a total biomass carbon impact of 1.2 to 1.6 petagrams (1.2 x 10(15) to 1.6 x 10(15) grams). Amazon forests therefore appear vulnerable to increasing moisture stress, with the potential for large carbon losses to exert feedback on climate change.

[1] Data from spaceborne light detection and ranging (lidar) opens the possibility to map forest vertical structure globally. We present a wall-to-wall, global map of canopy height at 1-km spatial resolution, using 2005 data from the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite). A challenge in the use of GLAS data for global vegetation studies is the sparse coverage of lidar shots (mean = 121 data points/degree2 for the L3C campaign). However, GLAS-derived canopy height (RH100) values were highly correlated with other, more spatially dense, ancillary variables available globally, which allowed us to model global RH100 from forest type, tree cover, elevation, and climatology maps. The difference between the model predicted RH100 and footprint level lidar-derived RH100 values showed that error increased in closed broadleaved forests such as the Amazon, underscoring the challenges in mapping tall (>40 m) canopies. The resulting map was validated with field measurements from 66 FLUXNET sites. The modeled RH100 versus in situ canopy height error (RMSE = 6.1 m, R2 = 0.5; or, RMSE = 4.4 m, R2 = 0.7 without 7 outliers) is conservative as it also includes measurement uncertainty and sub pixel variability within the 1-km pixels. Our results were compared against a recently published canopy height map. We found our values to be in general taller and more strongly correlated with FLUXNET data. Our map reveals a global latitudinal gradient in canopy height, increasing towards the equator, as well as coarse forest disturbance patterns.

Numerous models of evapotranspiration have been published that range in data-driven complexity, but global estimates require a model that does not depend on intensive field measurements. The Priestley–Taylor model is relatively simple, and has proven to be remarkably accurate and theoretically robust for estimates of potential evapotranspiration. Building on recent advances in ecophysiological theory that allow detection of multiple stresses on plant function using biophysical remote sensing metrics, we developed a bio-meteorological approach for translating Priestley–Taylor estimates of potential evapotranspiration into rates of actual evapotranspiration. Five model inputs are required: net radiation (Rn), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), maximum air temperature (Tmax), and water vapor pressure (ea). Our model requires no calibration, tuning or spin-ups. The model is tested and validated against eddy covariance measurements (FLUXNET) from a wide range of climates and plant functional types—grassland, crop, and deciduous broadleaf, evergreen broadleaf, and evergreen needleleaf forests. The model-to-measurement r2 was 0.90 (RMS = 16 mm/month or 28%) for all 16 FLUXNET sites across 2 years (most recent data release). Global estimates of evapotranspiration at a temporal resolution of monthly and a spatial resolution of 1° during the years 1986–1993 were determined using globally consistent datasets from the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP-II) and the Advanced Very High Resolution Spectroradiometer (AVHRR). Our model resulted in improved prediction of evapotranspiration across water-limited sites, and showed spatial and temporal differences in evapotranspiration globally, regionally and latitudinally.

Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate depends critically on knowing where, when and how carbon dioxide is exchanged between the land and atmosphere. Terrestrial gross primary production (GPP) constitutes the largest flux component in the global carbon budget, however significant uncertainties remain in GPP estimates and its seasonality. Empirically, we show that global spaceborne observations of solar induced chlorophyll fluorescence – occurring during photosynthesis – exhibit a strong linear correlation with GPP. We found that the fluorescence emission even without any additional climatic or model information has the same or better predictive skill in estimating GPP as those derived from traditional remotely-sensed vegetation indices using ancillary data and model assumptions. In boreal summer the generally strong linear correlation between fluorescence and GPP models weakens, attributable to discrepancies in savannas/croplands (18–48% higher fluorescence-based GPP derived by simple linear scaling), and high-latitude needleleaf forests (28–32% lower fluorescence). Our results demonstrate that retrievals of chlorophyll fluorescence provide direct global observational constraints for GPP and open an entirely new viewpoint on the global carbon cycle. We anticipate that global fluorescence data in combination with consolidated plant physiological fluorescence models will be a step-change in carbon cycle research and enable an unprecedented robustness in the understanding of the current and future carbon cycle.

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Abstract The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space‐based perspective, necessary to advance them.

A global analysis of gross primary productivity reveals that drought recovery is driven by climate and carbon cycling, with recovery longest in the tropics and high northern latitudes, and with impacts increasing over the twentieth century. Droughts affect ecosystem carbon storage, but the factors influencing ecosystem recovery from droughts remain poorly understood. This study finds that gross primary productivity recovery times from droughts are strongly associated with climate and carbon cycle dynamics and to a much lesser extent with biodiversity and carbon dioxide fertilization. The authors find that recovery time is longest in the tropics and high northern latitudes. They also observe that the area of ecosystems under active recovery and the length of recovery time have increased globally over the twentieth century. If future droughts become more frequent this may lead to a chronic state of incomplete recovery with adverse consequences for the land carbon sink. Drought, a recurring phenomenon with major impacts on both human and natural systems1,2,3, is the most widespread climatic extreme that negatively affects the land carbon sink2,4. Although twentieth-century trends in drought regimes are ambiguous5,6,7, across many regions more frequent and severe droughts are expected in the twenty-first century3,7,8,9. Recovery time—how long an ecosystem requires to revert to its pre-drought functional state—is a critical metric of drought impact. Yet the factors influencing drought recovery and its spatiotemporal patterns at the global scale are largely unknown. Here we analyse three independent datasets of gross primary productivity and show that, across diverse ecosystems, drought recovery times are strongly associated with climate and carbon cycle dynamics, with biodiversity and CO2 fertilization as secondary factors. Our analysis also provides two key insights into the spatiotemporal patterns of drought recovery time: first, that recovery is longest in the tropics and high northern latitudes (both vulnerable areas of Earth’s climate system10) and second, that drought impacts11 (assessed using the area of ecosystems actively recovering and time to recovery) have increased over the twentieth century. If droughts become more frequent, as expected, the time between droughts may become shorter than drought recovery time, leading to permanently damaged ecosystems and widespread degradation of the land carbon sink.

*The rich ecology of tropical forests is intimately tied to their moisture status. Multi-site syntheses can provide a macro-scale view of these linkages and their susceptibility to changing climates. Here, we report pan-tropical and regional-scale analyses of tree vulnerability to drought. *We assembled available data on tropical forest tree stem mortality before, during, and after recent drought events, from 119 monitoring plots in 10 countries concentrated in Amazonia and Borneo. *In most sites, larger trees are disproportionately at risk. At least within Amazonia, low wood density trees are also at greater risk of drought-associated mortality, independent of size. For comparable drought intensities, trees in Borneo are more vulnerable than trees in the Amazon. There is some evidence for lagged impacts of drought, with mortality rates remaining elevated 2 yr after the meteorological event is over. *These findings indicate that repeated droughts would shift the functional composition of tropical forests toward smaller, denser-wooded trees. At very high drought intensities, the linear relationship between tree mortality and moisture stress apparently breaks down, suggesting the existence of moisture stress thresholds beyond which some tropical forests would suffer catastrophic tree mortality.

Feedbacks from the terrestrial carbon cycle significantly affect future climate change. The CO2 concentration dependence of global terrestrial carbon storage is one of the largest and most uncertain feedbacks. Theory predicts the CO2 effect should have a tropical maximum, but a large terrestrial sink has been contradicted by analyses of atmospheric CO2 that do not show large tropical uptake. Our results, however, show significant tropical uptake and, combining tropical and extratropical fluxes, suggest that up to 60% of the present-day terrestrial sink is caused by increasing atmospheric CO2. This conclusion is consistent with a validated subset of atmospheric analyses, but uncertainty remains. Improved model diagnostics and new space-based observations can reduce the uncertainty of tropical and temperate zone carbon flux estimates. This analysis supports a significant feedback to future atmospheric CO2 concentrations from carbon uptake in terrestrial ecosystems caused by rising atmospheric CO2 concentrations. This feedback will have substantial tropical contributions, but the magnitude of future carbon uptake by tropical forests also depends on how they respond to climate change and requires their protection from deforestation.

Terrestrial ecosystem and carbon cycle feedbacks will significantly impact future climate, but their responses are highly uncertain. Models and tipping point analyses suggest the tropics and arctic/boreal zone carbon-climate feedbacks could be disproportionately large. In situ observations in those regions are sparse, resulting in high uncertainties in carbon fluxes and fluxes. Key parameters controlling ecosystem carbon responses, such as plant traits, are also sparsely observed in the tropics, with the most diverse biome on the planet treated as a single type in models. We analyzed the spatial distribution of in situ data for carbon fluxes, stocks and plant traits globally and also evaluated the potential of remote sensing to observe these quantities. New satellite data products go beyond indices of greenness and can address spatial sampling gaps for specific ecosystem properties and parameters. Because environmental conditions and access limit in situ observations in tropical and arctic/boreal environments, use of space-based techniques can reduce sampling bias and uncertainty about tipping point feedbacks to climate. To reliably detect change and develop the understanding of ecosystems needed for prediction, significantly, more data are required in critical regions. This need can best be met with a strategic combination of remote and in situ data, with satellite observations providing the dense sampling in space and time required to characterize the heterogeneity of ecosystem structure and function.

It is commonly assumed that transpiration does not occur at night because leaf stomata are closed in the dark. We tested this assumption across a diversity of ecosystems and woody plant species by various methods to explore the circumstances when this assumption is false. Our primary goals were: (1) to evaluate the nature and magnitude of nighttime transpiration, En, or stomatal conductance, gn; and (2) to seek potential generalizations about where and when it occurs. Sap-flow, porometry and stable isotope tracer measurements were made on 18 tree and eight shrub species from seven ecosystem types. Coupled with environmental data, our findings revealed that most of these species transpired at night. For some species and circumstances, nighttime leaf water loss constituted a significant fraction of total daily water use. Our evidence shows that En or gn can occur in all but one shrub species across the systems we investigated. However, under conditions of high nighttime evaporative demand or low soil water availability, stomata were closed and En or gn approached zero in eleven tree and seven shrub species. When soil water was available, En or gn was measurable in these same species demonstrating plasticity for En or gn. We detected En or gn in both trees and shrubs, and values were highest in plants from sites with higher soil water contents and in plants from ecosystems that were less prone to atmospheric or soil water deficits. Irrespective of plant or ecosystem type, many species showed En or gn when soil water deficits were slight or non-existent, or immediately after rainfall events that followed a period of soil water deficit. The strongest relationship was between En or gn and warm, low humidity and (or) windy (> 0.8 m s−1) nights when the vapor pressure deficit remained high (> 0.2 kPa in wet sites, > 0.7 kPa in dry sites). Why En or gn occurs likely varies with species and ecosystem type; however, our data support four plausible explanations: (1) it may facilitate carbon fixation earlier in the day because stomata are already open; (2) it may enhance nutrient supply to distal parts of the crown when these nutrients are most available (in wet soils) and transport is rapid; (3) it may allow for the delivery of dissolved O2 via the parenchyma to woody tissue sinks; or (4) it may occur simply because of leaky cuticles in older leaves or when stomata cannot close fully because of obstructions from stomatal (waxy) plugs, leaf endophytes or asymmetrical guard cells (all non-adaptive reasons). We discuss the methodological, ecophysiological, and theoretical implications of the occurrence of En or gn for investigations at a variety of scales.

Three process based models are used to estimate terrestrial heat fluxes and evapotranspiration (ET) at the global scale: a single source energy budget model, a Penman–Monteith based approach, and a Priestley–Taylor based approach. All models adjust the surface resistances or provide ecophysiological constraints to account for changing environmental factors. Evaporation (or sublimation) over snow-covered regions is calculated consistently for all models using a modified Penman equation. Instantaneous fluxes of latent heat computed at the time of satellite overpass are linearly scaled to the equivalent daily evapotranspiration using the computed evaporative fraction and the day-time net radiation. A constant fraction (10% of daytime evaporation) is used to account for the night time evaporation. Interception losses are computed using a simple water budget model. We produce daily evapotranspiration and sensible heat flux for the global land surface at 5 km spatial resolution for the period 2003–2006. With the exception of wind and surface pressure, all model inputs and forcings are obtained from satellite remote sensing. Satellite-based inputs and model outputs were first carefully evaluated at the site scale on a monthly-mean basis, then as a four-year mean against a climatological estimate of ET over 26 major basins, and finally in terms of a latitudinal profile on an annual basis. Intercomparison of the monthly model estimates of latent and sensible heat fluxes with 12 eddy-covariance towers across the U.S. yielded mean correlation of 0.57 and 0.54, respectively. Satellite-based meteorological datasets of 2 m temperature (0.83), humidity (0.70), incident shortwave radiation (0.64), incident longwave radiation (0.67) were found to agree well at the tower scale, while estimates of wind speed correlated poorly (0.17). Comparisons of the four year mean annual ET for 26 global river basins and global latitudinal profiles with a climatologically estimated ET resulted in a Kendall's τ > 0.70. The seasonal cycle over the continents is well represented in the Hovmöeller plots and the suppression of ET during major droughts in Europe, Australia and the Amazon are well picked up. This study provides the first ever moderate resolution estimates of ET on a global scale using only remote sensing based inputs and forcings, and furthermore the first ever multi-model comparison of process-based remote sensing estimates using the same inputs.

The global terrestrial carbon sink offsets one-third of the world's fossil fuel emissions, but the strength of this sink is highly sensitive to large-scale extreme events. In 2012, the contiguous United States experienced exceptionally warm temperatures and the most severe drought since the Dust Bowl era of the 1930s, resulting in substantial economic damage. It is crucial to understand the dynamics of such events because warmer temperatures and a higher prevalence of drought are projected in a changing climate. Here, we combine an extensive network of direct ecosystem flux measurements with satellite remote sensing and atmospheric inverse modeling to quantify the impact of the warmer spring and summer drought on biosphere-atmosphere carbon and water exchange in 2012. We consistently find that earlier vegetation activity increased spring carbon uptake and compensated for the reduced uptake during the summer drought, which mitigated the impact on net annual carbon uptake. The early phenological development in the Eastern Temperate Forests played a major role for the continental-scale carbon balance in 2012. The warm spring also depleted soil water resources earlier, and thus exacerbated water limitations during summer. Our results show that the detrimental effects of severe summer drought on ecosystem carbon storage can be mitigated by warming-induced increases in spring carbon uptake. However, the results also suggest that the positive carbon cycle effect of warm spring enhances water limitations and can increase summer heating through biosphere-atmosphere feedbacks.

Abstract [1] A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993–1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/Mosaic/Noah). An intercomparison of the global latent heat flux (Qle) annual means shows a spread of ∼20 W m−2 (all-product global average of ∼45 W m−2). A similar spread is observed for the sensible (Qh) and net radiative (Rn) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Qle and Qh absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Qle differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Qle and Rn were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Geophysical Research LettersVolume 38, Issue 6 Hydrology and Land Surface StudiesFree Access Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations B. Mueller, B. Mueller [email protected] Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandSearch for more papers by this authorS. I. Seneviratne, S. I. Seneviratne Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandSearch for more papers by this authorC. Jimenez, C. Jimenez LERMA, Observatoire de Paris, Paris, FranceSearch for more papers by this authorT. Corti, T. Corti Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland Now at Center for Climate Systems Modeling, ETH Zurich, Zurich, Switzerland.Search for more papers by this authorM. Hirschi, M. Hirschi Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland MeteoSwiss, Zurich, SwitzerlandSearch for more papers by this authorG. Balsamo, G. Balsamo European Centre for Medium-Range Weather Forecasts, Reading, UKSearch for more papers by this authorP. Ciais, P. Ciais LSCE, UMR CEA-CNRS, Gif-sur-Yvette, FranceSearch for more papers by this authorP. Dirmeyer, P. Dirmeyer Center for Ocean-Land-Atmosphere Studies, Calverton, Maryland, USASearch for more papers by this authorJ. B. Fisher, J. B. Fisher NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USASearch for more papers by this authorZ. Guo, Z. Guo Center for Ocean-Land-Atmosphere Studies, Calverton, Maryland, USASearch for more papers by this authorM. Jung, M. Jung Max Planck Institute for Biogeochemistry, Jena, GermanySearch for more papers by this authorF. Maignan, F. Maignan LSCE, UMR CEA-CNRS, Gif-sur-Yvette, FranceSearch for more papers by this authorM. F. McCabe, M. F. McCabe School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales, AustraliaSearch for more papers by this authorR. Reichle, R. Reichle NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorM. Reichstein, M. Reichstein Max Planck Institute for Biogeochemistry, Jena, GermanySearch for more papers by this authorM. Rodell, M. Rodell NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorJ. Sheffield, J. Sheffield Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USASearch for more papers by this authorA. J. Teuling, A. J. Teuling Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, NetherlandsSearch for more papers by this authorK. Wang, K. Wang Department of Geography, University of Maryland, College Park, Maryland, USASearch for more papers by this authorE. F. Wood, E. F. Wood Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USASearch for more papers by this authorY. Zhang, Y. Zhang CSIRO Land and Water, Canberra, ACT, AustraliaSearch for more papers by this author B. Mueller, B. Mueller [email protected] Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandSearch for more papers by this authorS. I. Seneviratne, S. I. Seneviratne Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandSearch for more papers by this authorC. Jimenez, C. Jimenez LERMA, Observatoire de Paris, Paris, FranceSearch for more papers by this authorT. Corti, T. Corti Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland Now at Center for Climate Systems Modeling, ETH Zurich, Zurich, Switzerland.Search for more papers by this authorM. Hirschi, M. Hirschi Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland MeteoSwiss, Zurich, SwitzerlandSearch for more papers by this authorG. Balsamo, G. Balsamo European Centre for Medium-Range Weather Forecasts, Reading, UKSearch for more papers by this authorP. Ciais, P. Ciais LSCE, UMR CEA-CNRS, Gif-sur-Yvette, FranceSearch for more papers by this authorP. Dirmeyer, P. Dirmeyer Center for Ocean-Land-Atmosphere Studies, Calverton, Maryland, USASearch for more papers by this authorJ. B. Fisher, J. B. Fisher NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USASearch for more papers by this authorZ. Guo, Z. Guo Center for Ocean-Land-Atmosphere Studies, Calverton, Maryland, USASearch for more papers by this authorM. Jung, M. Jung Max Planck Institute for Biogeochemistry, Jena, GermanySearch for more papers by this authorF. Maignan, F. Maignan LSCE, UMR CEA-CNRS, Gif-sur-Yvette, FranceSearch for more papers by this authorM. F. McCabe, M. F. McCabe School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales, AustraliaSearch for more papers by this authorR. Reichle, R. Reichle NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorM. Reichstein, M. Reichstein Max Planck Institute for Biogeochemistry, Jena, GermanySearch for more papers by this authorM. Rodell, M. Rodell NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorJ. Sheffield, J. Sheffield Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USASearch for more papers by this authorA. J. Teuling, A. J. Teuling Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, NetherlandsSearch for more papers by this authorK. Wang, K. Wang Department of Geography, University of Maryland, College Park, Maryland, USASearch for more papers by this authorE. F. Wood, E. F. Wood Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USASearch for more papers by this authorY. Zhang, Y. Zhang CSIRO Land and Water, Canberra, ACT, AustraliaSearch for more papers by this author First published: 18 March 2011 https://doi.org/10.1029/2010GL046230Citations: 291AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract [1] Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite-based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddy-covariance flux measurements, and atmospheric water balance datasets. The LandFlux-EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations-based products. Though differently constrained with observations, the analyzed reference datasets display similar large-scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter-product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations. 1. Introduction [2] Land evapotranspiration (ET) is a common component in the water and energy cycles, and provides a link between the surface and the atmosphere. Accurate global-scale estimates of ET are critical for better understanding climate and hydrological interactions. Local scale ET observations are available from the FLUXNET project [Baldocchi et al., 2001]. However, dense global coverage by such point measurements is not feasible and the representativeness of point-scale in-situ measurements for larger areas is a subject of active research. [3] To address this limitation, several alternative global multi-year ET datasets have been derived in recent years. These datasets include satellite-based estimates, land surface models driven with observations-based forcing, reanalysis data products, estimates based on empirical upscaling of point observations, and atmospheric water balance estimates. The LandFlux-EVAL project (see http://www.iac.ethz.ch/url/research/LandFlux-EVAL) aims at evaluating and comparing these currently available ET datasets. The effort forms a key component of the Global Energy and Water Cycle Experiment (GEWEX) LandFlux initiative, a GEWEX Radiation Panel program that seeks to develop a consistent and high-quality global ET dataset for climate studies. Knowledge of the uncertainties in available ET products is a prerequisite for their use in many applications, in particular for the evaluation of climate-change projections [e.g., Boé and Terray, 2008; Seneviratne et al., 2010]. We provide here an analysis of 30 observations-based multi-year global ET datasets for the 1989–1995 time period, focusing on inter-product spread in various river basins. In addition, we analyze ET in 11 coupled atmosphere-ocean-land GCMs from the IPCC Fourth Assessment Report (AR4). A complementary analysis for a three-year period (1993–1995) by Jimenez et al. [2011] focuses on sensible and latent heat fluxes in a subset of twelve satellite-based, LSM and reanalysis datasets. 2. Data and Methods [4] The analyzed datasets are subdivided into four categories (Table 1). In the ‘diagnostic datasets’ category, we include datasets that specifically derive ET from combinations of observations or observations-based estimates, together with relatively simple or empirically-derived formulations. The remaining categories provide ET estimates as a byproduct. The second category includes LSM products driven with observations-based surface meteorological data, while the third includes several atmospheric reanalyses. These first three categories are referred to collectively as ‘reference datasets’ in the context of assessing the IPCC AR4 estimates. IPCC AR4 simulations from 11 GCMs form the fourth category. An overview of the datasets can be found in Table 1. For a detailed description, the reader is referred to the auxiliary material. Table 1. Overview of Employed ET Datasets Category Dataset Reference Information Observations-Based Datasets Diagnostic datasets UCB Fisher et al. [2008] Priestley-Taylor, ISLSCP-II (SRB, CRU, AVHRR) MAUNI Wang and Liang [2008] Empirical, calibrated with Ameriflux, ISLSCP-II (SRB, CRU, AVHRR) PRUNI Sheffield et al. [2010] Penman-Monteith ET, ISCCP, AVHRR MPI Jung et al. [2010] Empirical upscaling of FLUXNET, CRU, GPCC, AVHRR CSIRO Zhang et al. [2010] Penman-Monteith-Leuning ET AWB Mueller et al. [2010] Atmospheric water balance (GPCP, ERA-Interim) Land Surface Models GSWP LSMs: GS-COLA, GS-NOAH, GS-NSIPP, GS-VISA, GS-ISBA, GS-BUCK, GS-CLMTOP, GS-HYSSIB, GS-LAD, GS-MOSAIC, GS-MOSES2, GS-SIBUC, GS-SWAP Dirmeyer et al. [2006] 13 GSWP LSM simulations, forced with ISLSCP-II and/or reanalysis data GLDAS LSMs: GL-NOAH, GL-CLM, GL-MOSAIC Rodell et al. [2004] EI-ORCH Krinner et al. [2005] ORCHIDEE LSM with ERA-Interim forcing CRU-ORCH ORCHIDEE LSM with CRU-NCEP forcing VIC Sheffield and Wood [2007] LSM Reanalyses ERA-INT Dee and Uppala [2008] ERA-Interim Reanalysis MERRA Bosilovich [2008] Reanalysis M-LAND (R. Reichle et al., Assessment and enhancement of MERRA land surface hydrology estimates, submitted to Journal of Climate, 2010) MERRA-Land Reanalysis NCEP Kalnay et al. [1996] Reanalysis JRA25 Onogi et al. [2007] Reanalysis Category Reference Datasets Reference Statistics (Mean, IQR, Standard Deviation) of Ensemble of Single Observations-Based Datasets (30 in Total) Global Climate Models IPCC AR4 ECHAM5, INMSM, IPSL, HADGEM, NCAR, HADCM, MRI, GISS, MIROC-MED, CCCMA, GFDL Meehl et al. [2007] AR4 simulations (20c3m) from 11 global climate models [5] The subdivision of the datasets in the first three categories is somewhat arbitrary, since they are all based to some degree on observations and modeling assumptions. Thus, it cannot be inferred a priori that one category of datasets may be closer to actual ET. In addition, several datasets are not independent, since they use common calibration or forcing datasets, and/or common model assumptions (ET parametrization). [6] The analyses are performed for the common period 1989–1995. The calculation of the interquartile ranges (IQR) and standard deviations presented below are based on the categories (see Table 1), giving each dataset equal weight. Only land pixels that are common to all datasets (excluding Greenland and the Sahara, where ET values are generally low) are considered for the analyses. 3. Results and Discussion Annual Means and Global Patterns [7] Figure 1a displays the mean annual land ET values of each analyzed dataset, as well as the means and the standard deviations within each category. The values are around 1.59 ± 0.19 mm/d (46 ± 5 W m−2), a value close to the reanalyses estimates given by Trenberth et al. [2009] for two different time periods. The standard deviation of the IPCC AR4 simulations (0.16 mm/d or 4.6 W m−2) is lower than those of the reference datasets (standard deviations ranging from 0.17 to 0.19 mm/d or 4.9 to 5.6 W m−2). The standard deviation of the GSWP LSMs is still smaller (0.12 mm/d or 3.6 W m−2) than that of the IPCC AR4 simulations. Figure 1Open in figure viewerPowerPoint Mean global land ET values for each dataset (a) with mean and standard deviation for each category (numbers). Mean, relative interquartile range (IQR) and difference of mean to mean of reference datasets (Ref.) of the (b–d) diagnostic datasets, (e–g) LSMs, (h–j) reanalyses, and (k–m) IPCC AR4 simulations. (n) Mean and (o) relative IQR of the reference datasets and (p) difference of relative IQRs IPCC AR4 to reference datasets. Hatched areas in Figures 1d, 1g, 1j, and 1m show a nominal 5%-significance level as heuristic descriptive indicator (Wilcoxon Rank-Sum test). [8] Global patterns of ET for 1989–1995 are displayed in Figures 1b–1p. The mean values of the four categories (first column) reveal high congruence (for example high ET in the tropics, and lower ET in higher latitudes), and nearly no regions with significant differences (5% Level, Wilcoxon Rank-Sum Test) are found in respective comparisons with the mean of all reference datasets (third column), except for the IPCC AR4 category. In this category, the ET values compare well with the reference datasets in many regions (Figures 1k–1o), but ET values are significantly lower in the IPCC AR4 simulations in India and South America, and significantly higher in semi-arid regions such as western Australia, western China and the western USA. Overall, the IPCC AR4 simulations appear to underestimate ET gradients within continents (e.g., in North and South America, in Asia north and south of the Himalaya, and in Australia), which could be related to the generally coarse resolution of the models. [9] The relative IQR (IQR divided by the median, second column) of the LSMs is lower than those of the other categories in Australia and in tropical regions, probably because many of the LSMs share a common forcing (GSWP, GLDAS), but higher in, e.g., most of Europe. The IQR of the diagnostic datasets is, compared to the other reference datasets, high in, e.g., Australia, and southern and central Africa, but much smaller in Europe. [10] The IPCC AR4 simulations display higher inter-model deviations than the reference datasets in semi-arid regions such as Australia, India, South Africa, and parts of the Tibetan plateau (Figures 1l and 1o). Accordingly, the IQR of the models in these regions (Figure 1p) is much higher. On the other hand, some regions show markedly less inter-model spread across the IPCC AR4 simulations than would be expected based on the uncertainties inferred from the reference datasets (e.g., tropical Africa, East Asia, central Europe, eastern USA). Thus, climate models may share common biases in these regions, either related to biases in forcing (precipitation, clouds, radiation) or in the representation of land hydrology. Basin-Scale Analysis [11] Multi-year ET values of all analyzed datasets are displayed in Figure 2 as the deviation from the reference datasets' mean for selected basins (Mississippi, Amazon, central European basins, Volga, Nile, Changjiang, Murray-Darling). The catchment definitions from Hirschi et al. [2006] are used for the computation (see Figure 2, bottom). Plots for individual seasons (May to June (MAM), June to August (JJA), September to November (SON), and December to February (DJF)) are provided in the auxiliary material. Datasets are sorted into the four categories (separate bars). Additionally, ET estimated from the difference between precipitation (P) derived from the Global Precipitation Climatology Project (GPCP) and runoff (R) from local measurements is shown for multi-year means (ET = P − R is not generally valid for shorter time scales) in the Mississippi, central European, Volga, Changjiang and Murray-Darling basins. The P − R values can be seen as a long-term constraint on ET (indicated with red lines where available), although multi-year anomalies of terrestrial water storage cannot be excluded in some regions. Overall, the P − R values are found to be close to the reference datasets in the Mississippi, central European and Murray-Darling basins. Figure 2Open in figure viewerPowerPoint (top) Deviations of each dataset from the reference datasets' mean (displayed on the bottom as ‘ref’) over 1989–1995 (multi-year means) for the selected river basins (1) Amazon, (2) Mississippi, (3) Central European basins, (4) Volga, (5) Changjiang, (6) Nile, (7) Murray-Darling. The datasets are grouped into diagnostic datasets (Diagn), LSMs (LSMs), reanalyses (Rean) and IPCC AR4 simulations (IPCC). P-R values are marked with red stars and dashed lines. (bottom right) Location of river basins. [12] The absolute intra-category spreads are largest in the Amazon basin, where the highest ET rates occur. The second largest spreads are found in the Murray-Darling basin during SON and DJF, most pronounced in the IPCC AR4 simulations (see auxiliary material). Comparing the four dataset categories, the intra-category spreads are similar. However, the values can differ largely between basins. In the Changjiang basin for example, the reanalyses and IPCC AR4 simulations display notably higher ET rates than the other dataset categories (up to 0.75 mm/d on average during MAM; see auxiliary material). The intra-category spreads of the IPCC AR4 simulations are much larger than the other categories in the semi-arid Nile and Murray-Darling basins. ET is water (precipitation) limited in these regions, and since the calculation of ET in the IPCC AR4 simulations is based on modeled precipitation (as compared to observed precipitation in the case of reference datasets), the high variability of ET may be partly explained by the large uncertainties in modeled precipitation. [13] Despite overall similarities of the ET values within these analyzed dataset categories, individual datasets stand out in some regions and seasons. For example, during MAM and in the annual mean, the NCEP reanalysis exhibits above average ET values in the Mississippi, central European, Volga and the Amazon basins. The GFDL IPCC simulation stands out in the Amazon basin during SON (auxiliary material). Note that outliers among the reference datasets are not necessarily erroneous. Indeed, congruence across ET datasets may be induced by the use of common data forcing or model algorithms, rather than the correct representation of ET, as several of the considered products are not independent (see next section). Cluster Analysis [14] In order to study the inter-relationship between the individual datasets, a hierarchical cluster analysis of the multi-year mean ET values is performed (Figure 3). The cluster analysis sorts the datasets into groups in a way that the degree of association between two datasets belonging to the same group is maximal. The criterion used for our analysis is the Euclidean distance between datasets on each land grid cell. Datasets in the same branch of the cluster tree share similar global patterns. The strongest dataset cluster is built by the GSWP simulations (with GS-COLA being the only GSWP model outside the cluster). Most of the IPCC models also form a common branch in the cluster tree. However, the diagnostic datasets and reanalyses are separated into two different main branches of the cluster tree. This indicates that these datasets, although based on observations, exhibit distinct spatial patterns. All the reanalysis datasets are constrained by different exogenous data and some of them are on different main branches of the tree. Note also that simulations using the same model but a different forcing (Mosaic, driven with both GSWP and GLDAS forcing) are separated into two main branches. These findings suggest that forcing can be critical for the resulting ET patterns. Figure 3Open in figure viewerPowerPoint Hierarchical cluster analysis of global ET values, averaged over 1989–1995, using Euclidean distance matrix. Diagnostic datasets (red), LSMs (green), reanalyses (yellow) and IPCC AR4 simulations (grey). 4. Conclusions [15] This study provides an overview and evaluation of 41 global land ET datasets for the 1989–1995 time period. Comparing IPCC AR4 GCM simulations with datasets which include some observational information (reference datasets), similarities can be found regarding their global patterns and level of uncertainty (interquartile ranges) in most regions. In their global average, the IPCC AR4 simulations show a smaller spread than the categories and groups that are partly based on observations, except for LSMs from the GSWP, which are driven with common forcing data. In addition, climate models display narrower inter-model range than the reference datasets in some regions, which may suggest shared biases. However the uncertainty of the observational datasets prevents evaluation of the magnitude of this bias. [16] To reduce uncertainty in ET estimates, besides improving ET models, further collection of ‘ground truth’ observations to validate and force the models continues to be essential, especially in data-poor regions. More refined analyses may allow a reduction of the uncertainty range in observations-based ET products, by identifying whether given outliers can be excluded based on physical considerations [e.g., McCabe et al., 2008]. Such analyses should nonetheless also consider the lack of independence among certain products, which may lead to an underestimation of ET uncertainty. This is well illustrated by the analysis of the GSWP simulations, which, e.g., form a strong cluster in the cluster analysis performed for global ET values of all datasets. Further analyses of the datasets collected as part of the LandFlux-EVAL project will allow addressing some of these questions. Acknowledgments [17] The GPCP precipitation data were provided by NASA GSFC's Laboratory for Atmospheres. NCEP reanalysis data were retrieved from www.esrl.noaa.gov/psd. The JRA-25 data are from the Japan Meteorological Agency and the Central Research Institute of Electric Power Industry. We acknowledge the Global Modeling and Assimilation Office and the GES DISC for the dissemination of MERRA. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling for making available the WCRP CMIP3 dataset. Runoff data were obtained from the GRDC in Koblenz (Germany), the U.S. Geological Survey, the South Australian Surface Water Archive and the Changjiang Water Resource Commission in Wuhan, China. [18] Paolo D'Odorico thanks two anonymous reviewers. Supporting Information Auxiliary material for this article contains more information on the evapotranspiration datasets used in our study, as well as some further analyses of the datasets for different seasons. Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right-click and select “Save Target As…” (PC) or CTRL-click and select “Download Link to Disk” (Mac). Additional file information is provided in the readme.txt. Filename Description grl27784-sup-0001-readme.txtplain text document, 5.3 KB readme.txt grl27784-sup-0002-txts01.txtplain text document, 7.8 KB Text S1. Description of the ET datasets used in this study. grl27784-sup-0003-fs01.pdfPDF document, 60.5 KB Figure S1. Deviations of each dataset from the reference datasets' mean over 1989–1995 for the selected river basins. grl27784-sup-0004-fs02.pdfPDF document, 64.8 KB Figure S2. Mean ET of the diagnostic datasets. grl27784-sup-0005-fs03.pdfPDF document, 70.5 KB Figure S3. Relative interquartile range of ET in the diagnostic datasets. grl27784-sup-0006-fs04.pdfPDF document, 65.3 KB Figure S4. Difference of mean ET of the diagnostic datasets to mean of reference datasets. grl27784-sup-0007-fs05.pdfPDF document, 62.1 KB Figure S5. Mean ET of the land-surface models. grl27784-sup-0008-fs06.pdfPDF document, 65.4 KB Figure S6. Relative IQR of ET in the LSMs. grl27784-sup-0009-fs07.pdfPDF document, 64.5 KB Figure S7. Difference of mean ET of the LSMs to mean of reference datasets. grl27784-sup-0010-fs08.pdfPDF document, 62.6 KB Figure S8. Mean ET of the reanalyses. grl27784-sup-0011-fs09.pdfPDF document, 70.1 KB Figure S9. Relative IQR of ET in the reanalyses. grl27784-sup-0012-fs10.pdfPDF document, 66.4 KB Figure S10. Difference of mean ET of the reanalyses to mean of reference datasets. grl27784-sup-0013-fs11.pdfPDF document, 62.1 KB Figure S11. Mean ET of the IPCC AR4 simulations. grl27784-sup-0014-fs12.pdfPDF document, 64.6 KB Figure S12. Relative IQR of ET in the IPCC AR4 simulations. grl27784-sup-0015-fs13.pdfPDF document, 68.5 KB Figure S13. Difference of mean ET of the IPCC AR4 simulations to mean of reference datasets. grl27784-sup-0016-fs14.pdfPDF document, 64 KB Figure S14. Mean ET of the reference datasets. grl27784-sup-0017-fs15.pdfPDF document, 67.1 KB Figure S15. Relative IQR of ET in the reference datasets. grl27784-sup-0018-fs16.pdfPDF document, 68.7 KB Figure S16. Difference of relative IQRs IPCC AR4 to reference datasets. grl27784-sup-0019-t01.txtplain text document, 1.7 KB Tab-delimited Table 1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. References Baldocchi, D., et al. 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Abstract. Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr−2 in our merged product) followed by a significant decrease in this trend (−0.18 mm yr−2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr−1 (1.35 mm d−1) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr−1 (34 406 km3 yr−1) for a total land area of 130 922 000 km2. Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.

New PhytologistVolume 229, Issue 5 p. 2413-2445 Tansley reviewFree Access Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2 Anthony P. Walker, Corresponding Author Anthony P. Walker walkerap@ornl.gov orcid.org/0000-0003-0557-5594 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USA Author for correspondence: Anthony P. Walker Email:walkerap@ornl.govSearch for more papers by this authorMartin G. De Kauwe, Martin G. De Kauwe orcid.org/0000-0002-3399-9098 ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052 Australia Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052 Australia Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW, 2052 AustraliaSearch for more papers by this authorAna Bastos, Ana Bastos orcid.org/0000-0002-7368-7806 Ludwig Maximilians University of Munich, Luisenstr. 37, Munich, 80333 GermanySearch for more papers by this authorSoumaya Belmecheri, Soumaya Belmecheri orcid.org/0000-0003-1258-2741 Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721 USASearch for more papers by this authorKaterina Georgiou, Katerina Georgiou orcid.org/0000-0002-2819-3292 Department of Earth System Science, Stanford University, Stanford, CA, 94305 USASearch for more papers by this authorRalph F. Keeling, Ralph F. Keeling orcid.org/0000-0002-9749-2253 Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, 92093 USASearch for more papers by this authorSean M. McMahon, Sean M. McMahon orcid.org/0000-0001-8302-6908 Smithsonian Environmental Research Center, Edgewater, MD, 21037 USASearch for more papers by this authorBelinda E. Medlyn, Belinda E. Medlyn orcid.org/0000-0001-5728-9827 Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751 AustraliaSearch for more papers by this authorDavid J. P. Moore, David J. P. Moore orcid.org/0000-0002-6462-3288 School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ, 85721 USASearch for more papers by this authorRichard J. Norby, Richard J. Norby orcid.org/0000-0002-0238-9828 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorSönke Zaehle, Sönke Zaehle orcid.org/0000-0001-5602-7956 Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745 GermanySearch for more papers by this authorKristina J. Anderson-Teixeira, Kristina J. Anderson-Teixeira orcid.org/0000-0001-8461-9713 Conservation Ecology Center, Smithsonian Conservation Biology Institute, MRC 5535, Front Royal, VA, 22630 USA Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama City, PanamaSearch for more papers by this authorGiovanna Battipaglia, Giovanna Battipaglia orcid.org/0000-0003-1741-3509 Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Università della Campania, Caserta, 81100 ItalySearch for more papers by this authorRoel J. W. 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Hungate, Bruce A. Hungate orcid.org/0000-0002-7337-1887 Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011 USASearch for more papers by this authorColleen M. Iversen, Colleen M. Iversen orcid.org/0000-0001-8293-3450 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorFortunat Joos, Fortunat Joos orcid.org/0000-0002-9483-6030 Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Sidlerstr. 5, Bern, CH-3012 SwitzerlandSearch for more papers by this authorMingkai Jiang, Mingkai Jiang orcid.org/0000-0002-9982-9518 Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751 AustraliaSearch for more papers by this authorTrevor F. Keenan, Trevor F. Keenan orcid.org/0000-0002-3347-0258 Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, 94720 USA Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley, CA, 94720 USASearch for more papers by this authorJürgen Knauer, Jürgen Knauer orcid.org/0000-0002-4947-7067 CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601 AustraliaSearch for more papers by this authorChristian Körner, Christian Körner orcid.org/0000-0001-7768-7638 Department of Environmental Sciences, Botany, University of Basel, Basel, 4056 SwitzerlandSearch for more papers by this authorVictor O. Leshyk, Victor O. Leshyk Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011 USASearch for more papers by this authorSebastian Leuzinger, Sebastian Leuzinger orcid.org/0000-0001-9306-5281 School of Science, Auckland University of Technology, Auckland, 1142 New ZealandSearch for more papers by this authorYao Liu, Yao Liu orcid.org/0000-0003-2783-3291 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorNatasha MacBean, Natasha MacBean orcid.org/0000-0001-6797-4836 Department of Geography, Indiana University, Bloomington, IN, 47405 USASearch for more papers by this authorYadvinder Malhi, Yadvinder Malhi orcid.org/0000-0002-3503-4783 School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY UKSearch for more papers by this authorTim R. McVicar, Tim R. McVicar orcid.org/0000-0002-0877-8285 CSIRO Land and Water, GPO Box 1700, Canberra, ACT, 2601 Australia Australian Research Council Centre of Excellence for Climate Extremes, 142 Mills Rd, Australian National University, Canberra, ACT, 2601 AustraliaSearch for more papers by this authorJosep Penuelas, Josep Penuelas orcid.org/0000-0002-7215-0150 CSIC, Global Ecology CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, 08193 Spain CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, 08193 SpainSearch for more papers by this authorJulia Pongratz, Julia Pongratz orcid.org/0000-0003-0372-3960 Ludwig Maximilians University of Munich, Luisenstr. 37, Munich, 80333 Germany Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, GermanySearch for more papers by this authorA. Shafer Powell, A. Shafer Powell orcid.org/0000-0002-9622-0061 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorTerhi Riutta, Terhi Riutta School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY UKSearch for more papers by this authorManon E. B. Sabot, Manon E. B. Sabot orcid.org/0000-0002-9440-4553 ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052 Australia Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052 Australia Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW, 2052 AustraliaSearch for more papers by this authorJuergen Schleucher, Juergen Schleucher orcid.org/0000-0002-4815-3466 Department of Medical Biochemistry & Biophysics, Umeå University, Umea, 901 87 SwedenSearch for more papers by this authorStephen Sitch, Stephen Sitch orcid.org/0000-0003-1821-8561 College of Life and Environmental Sciences, University of Exeter, Exeter, Laver Building EX4 4QF UKSearch for more papers by this authorWilliam K. Smith, William K. Smith orcid.org/0000-0002-5785-6489 School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ, 85721 USASearch for more papers by this authorBenjamin Sulman, Benjamin Sulman orcid.org/0000-0002-3265-6691 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorBenton Taylor, Benton Taylor orcid.org/0000-0002-9834-9192 Smithsonian Environmental Research Center, Edgewater, MD, 21037 USASearch for more papers by this authorCésar Terrer, César Terrer orcid.org/0000-0002-5479-3486 Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550 USASearch for more papers by this authorMargaret S. Torn, Margaret S. Torn orcid.org/0000-0002-8174-0099 Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley, CA, 94720 USASearch for more papers by this authorKathleen K. Treseder, Kathleen K. Treseder orcid.org/0000-0003-2847-6935 Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697 USASearch for more papers by this authorAnna T. Trugman, Anna T. Trugman orcid.org/0000-0002-7903-9711 Department of Geography, 1832 Ellison Hall, Santa Barbara, CA, 93016 USASearch for more papers by this authorSusan E. Trumbore, Susan E. Trumbore orcid.org/0000-0003-3885-6202 Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745 GermanySearch for more papers by this authorPhillip J. van Mantgem, Phillip J. van Mantgem orcid.org/0000-0002-3068-9422 U.S. Geological Survey, Western Ecological Research Center, Arcata, CA, 95521 USASearch for more papers by this authorSteve L. Voelker, Steve L. Voelker orcid.org/0000-0002-0110-3381 Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY, 13210 USASearch for more papers by this authorMary E. Whelan, Mary E. Whelan orcid.org/0000-0002-2067-1835 Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ, 08901 USASearch for more papers by this authorPieter A. Zuidema, Pieter A. Zuidema orcid.org/0000-0001-8100-1168 Forest Ecology and Forest Management group, Wageningen University, PO Box 47, Wageningen, 6700 AA the NetherlandsSearch for more papers by this author Anthony P. Walker, Corresponding Author Anthony P. Walker walkerap@ornl.gov orcid.org/0000-0003-0557-5594 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USA Author for correspondence: Anthony P. Walker Email:walkerap@ornl.govSearch for more papers by this authorMartin G. De Kauwe, Martin G. De Kauwe orcid.org/0000-0002-3399-9098 ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052 Australia Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052 Australia Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW, 2052 AustraliaSearch for more papers by this authorAna Bastos, Ana Bastos orcid.org/0000-0002-7368-7806 Ludwig Maximilians University of Munich, Luisenstr. 37, Munich, 80333 GermanySearch for more papers by this authorSoumaya Belmecheri, Soumaya Belmecheri orcid.org/0000-0003-1258-2741 Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721 USASearch for more papers by this authorKaterina Georgiou, Katerina Georgiou orcid.org/0000-0002-2819-3292 Department of Earth System Science, Stanford University, Stanford, CA, 94305 USASearch for more papers by this authorRalph F. Keeling, Ralph F. Keeling orcid.org/0000-0002-9749-2253 Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, 92093 USASearch for more papers by this authorSean M. McMahon, Sean M. McMahon orcid.org/0000-0001-8302-6908 Smithsonian Environmental Research Center, Edgewater, MD, 21037 USASearch for more papers by this authorBelinda E. Medlyn, Belinda E. Medlyn orcid.org/0000-0001-5728-9827 Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751 AustraliaSearch for more papers by this authorDavid J. P. Moore, David J. P. Moore orcid.org/0000-0002-6462-3288 School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ, 85721 USASearch for more papers by this authorRichard J. Norby, Richard J. Norby orcid.org/0000-0002-0238-9828 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorSönke Zaehle, Sönke Zaehle orcid.org/0000-0001-5602-7956 Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745 GermanySearch for more papers by this authorKristina J. Anderson-Teixeira, Kristina J. Anderson-Teixeira orcid.org/0000-0001-8461-9713 Conservation Ecology Center, Smithsonian Conservation Biology Institute, MRC 5535, Front Royal, VA, 22630 USA Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama City, PanamaSearch for more papers by this authorGiovanna Battipaglia, Giovanna Battipaglia orcid.org/0000-0003-1741-3509 Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Università della Campania, Caserta, 81100 ItalySearch for more papers by this authorRoel J. W. Brienen, Roel J. W. Brienen orcid.org/0000-0002-5397-5755 School of Geography, University of Leeds, Leeds, LS6 9JT UKSearch for more papers by this authorKristine G. Cabugao, Kristine G. Cabugao Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorMaxime Cailleret, Maxime Cailleret orcid.org/0000-0001-6561-1943 INRAE, UMR RECOVER, Aix-Marseille Université, 3275 route de Cézanne, Aix-en-Provence Cedex 5, 13182 France Swiss Federal Institute for Forest Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, SwitzerlandSearch for more papers by this authorElliott Campbell, Elliott Campbell Department of Geography, University of California Santa Barbara, Santa Barbara, CA, 93106 USASearch for more papers by this authorJosep G. Canadell, Josep G. Canadell orcid.org/0000-0002-8788-3218 CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601 AustraliaSearch for more papers by this authorPhilippe Ciais, Philippe Ciais orcid.org/0000-0001-8560-4943 Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, F-91191 FranceSearch for more papers by this authorMatthew E. Craig, Matthew E. Craig orcid.org/0000-0002-8890-7920 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorDavid S. Ellsworth, David S. Ellsworth orcid.org/0000-0002-9699-2272 Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751 AustraliaSearch for more papers by this authorGraham D. Farquhar, Graham D. Farquhar orcid.org/0000-0002-7065-1971 Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT, 2601 AustraliaSearch for more papers by this authorSimone Fatichi, Simone Fatichi orcid.org/0000-0003-1361-6659 Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore, 117576 Singapore Institute of Environmental Engineering, ETH Zurich, Stefano-Franscini Platz 5, Zurich, 8093 SwitzerlandSearch for more papers by this authorJoshua B. Fisher, Joshua B. Fisher orcid.org/0000-0003-4734-9085 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA, 91109 USASearch for more papers by this authorDavid C. Frank, David C. Frank Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721 USASearch for more papers by this authorHeather Graven, Heather Graven orcid.org/0000-0003-3934-2502 Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2AZ UKSearch for more papers by this authorLianhong Gu, Lianhong Gu orcid.org/0000-0001-5756-8738 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorVanessa Haverd, Vanessa Haverd orcid.org/0000-0003-4359-5895 CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601 AustraliaSearch for more papers by this authorKelly Heilman, Kelly Heilman orcid.org/0000-0001-5932-1317 Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721 USASearch for more papers by this authorMartin Heimann, Martin Heimann orcid.org/0000-0001-6296-5113 Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745 GermanySearch for more papers by this authorBruce A. Hungate, Bruce A. Hungate orcid.org/0000-0002-7337-1887 Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011 USASearch for more papers by this authorColleen M. Iversen, Colleen M. Iversen orcid.org/0000-0001-8293-3450 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorFortunat Joos, Fortunat Joos orcid.org/0000-0002-9483-6030 Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Sidlerstr. 5, Bern, CH-3012 SwitzerlandSearch for more papers by this authorMingkai Jiang, Mingkai Jiang orcid.org/0000-0002-9982-9518 Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751 AustraliaSearch for more papers by this authorTrevor F. Keenan, Trevor F. Keenan orcid.org/0000-0002-3347-0258 Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, 94720 USA Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley, CA, 94720 USASearch for more papers by this authorJürgen Knauer, Jürgen Knauer orcid.org/0000-0002-4947-7067 CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601 AustraliaSearch for more papers by this authorChristian Körner, Christian Körner orcid.org/0000-0001-7768-7638 Department of Environmental Sciences, Botany, University of Basel, Basel, 4056 SwitzerlandSearch for more papers by this authorVictor O. Leshyk, Victor O. Leshyk Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011 USASearch for more papers by this authorSebastian Leuzinger, Sebastian Leuzinger orcid.org/0000-0001-9306-5281 School of Science, Auckland University of Technology, Auckland, 1142 New ZealandSearch for more papers by this authorYao Liu, Yao Liu orcid.org/0000-0003-2783-3291 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorNatasha MacBean, Natasha MacBean orcid.org/0000-0001-6797-4836 Department of Geography, Indiana University, Bloomington, IN, 47405 USASearch for more papers by this authorYadvinder Malhi, Yadvinder Malhi orcid.org/0000-0002-3503-4783 School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY UKSearch for more papers by this authorTim R. McVicar, Tim R. McVicar orcid.org/0000-0002-0877-8285 CSIRO Land and Water, GPO Box 1700, Canberra, ACT, 2601 Australia Australian Research Council Centre of Excellence for Climate Extremes, 142 Mills Rd, Australian National University, Canberra, ACT, 2601 AustraliaSearch for more papers by this authorJosep Penuelas, Josep Penuelas orcid.org/0000-0002-7215-0150 CSIC, Global Ecology CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, 08193 Spain CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, 08193 SpainSearch for more papers by this authorJulia Pongratz, Julia Pongratz orcid.org/0000-0003-0372-3960 Ludwig Maximilians University of Munich, Luisenstr. 37, Munich, 80333 Germany Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, GermanySearch for more papers by this authorA. Shafer Powell, A. Shafer Powell orcid.org/0000-0002-9622-0061 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorTerhi Riutta, Terhi Riutta School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY UKSearch for more papers by this authorManon E. B. Sabot, Manon E. B. Sabot orcid.org/0000-0002-9440-4553 ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052 Australia Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052 Australia Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW, 2052 AustraliaSearch for more papers by this authorJuergen Schleucher, Juergen Schleucher orcid.org/0000-0002-4815-3466 Department of Medical Biochemistry & Biophysics, Umeå University, Umea, 901 87 SwedenSearch for more papers by this authorStephen Sitch, Stephen Sitch orcid.org/0000-0003-1821-8561 College of Life and Environmental Sciences, University of Exeter, Exeter, Laver Building EX4 4QF UKSearch for more papers by this authorWilliam K. Smith, William K. Smith orcid.org/0000-0002-5785-6489 School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ, 85721 USASearch for more papers by this authorBenjamin Sulman, Benjamin Sulman orcid.org/0000-0002-3265-6691 Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this authorBenton Taylor, Benton Taylor orcid.org/0000-0002-9834-9192 Smithsonian Environmental Research Center, Edgewater, MD, 21037 USASearch for more papers by this authorCésar Terrer, César Terrer orcid.org/0000-0002-5479-3486 Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550 USASearch for more papers by this authorMargaret S. Torn, Margaret S. Torn orcid.org/0000-0002-8174-0099 Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley, CA, 94720 USASearch for more papers by this authorKathleen K. Treseder, Kathleen K. Treseder orcid.org/0000-0003-2847-6935 Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697 USASearch for more papers by this authorAnna T. Trugman, Anna T. Trugman orcid.org/0000-0002-7903-9711 Department of Geography, 1832 Ellison Hall, Santa Barbara, CA, 93016 USASearch for more papers by this authorSusan E. Trumbore, Susan E. Trumbore orcid.org/0000-0003-3885-6202 Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745 GermanySearch for more papers by this authorPhillip J. van Mantgem, Phillip J. van Mantgem orcid.org/0000-0002-3068-9422 U.S. Geological Survey, Western Ecological Research Center, Arcata, CA, 95521 USASearch for more papers by this authorSteve L. Voelker, Steve L. Voelker orcid.org/0000-0002-0110-3381 Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY, 13210 USASearch for more papers by this authorMary E. Whelan, Mary E. Whelan orcid.org/0000-0002-2067-1835 Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ, 08901 USASearch for more papers by this authorPieter A. Zuidema, Pieter A. Zuidema orcid.org/0000-0001-8100-1168 Forest Ecology and Forest Management group, Wageningen University, PO Box 47, Wageningen, 6700 AA the NetherlandsSearch for more papers by this author First published: 12 August 2020 https://doi.org/10.1111/nph.16866Citations: 45 See also the Commentary on this article by Way et al., 229: 2383–2385. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary Atmospheric carbon dioxide concentration ([CO2]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO2] increase and thus climate change. However, ecosystem CO2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO2]-driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO2] (iCO2) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industrial times. Established theory, supported by experiments, indicates that iCO2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO2 responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO2, albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change. Video summary Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2 by Walker et al. Contents Summary 2414 I. Introduction 2415 II. Theory – a hierarchy of mechanism 2416 III. The evidence 2422 IV.

It is unclear to what extent seasonal water stress impacts on plant productivity over Amazonia. Using new Greenhouse gases Observing SATellite (GOSAT) satellite measurements of sun-induced chlorophyll fluorescence, we show that midday fluorescence varies with water availability, both of which decrease in the dry season over Amazonian regions with substantial dry season length, suggesting a parallel decrease in gross primary production (GPP). Using additional SeaWinds Scatterometer onboard QuikSCAT satellite measurements of canopy water content, we found a concomitant decrease in daily storage of canopy water content within branches and leaves during the dry season, supporting our conclusion. A large part ( r 2 = 0.75) of the variance in observed monthly midday fluorescence from GOSAT is explained by water stress over moderately stressed evergreen forests over Amazonia, which is reproduced by model simulations that include a full physiological representation of photosynthesis and fluorescence. The strong relationship between GOSAT and model fluorescence ( r 2 = 0.79) was obtained using a fixed leaf area index, indicating that GPP changes are more related to environmental conditions than chlorophyll contents. When the dry season extended to drought in 2010 over Amazonia, midday basin-wide GPP was reduced by 15 per cent compared with 2009.

Abstract. Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

Terrestrial ecosystems remove about 30 per cent of the carbon dioxide (CO2) emitted by human activities each year1, yet the persistence of this carbon sink depends partly on how plant biomass and soil organic carbon (SOC) stocks respond to future increases in atmospheric CO2 (refs. 2,3). Although plant biomass often increases in elevated CO2 (eCO2) experiments4-6, SOC has been observed to increase, remain unchanged or even decline7. The mechanisms that drive this variation across experiments remain poorly understood, creating uncertainty in climate projections8,9. Here we synthesized data from 108 eCO2 experiments and found that the effect of eCO2 on SOC stocks is best explained by a negative relationship with plant biomass: when plant biomass is strongly stimulated by eCO2, SOC storage declines; conversely, when biomass is weakly stimulated, SOC storage increases. This trade-off appears to be related to plant nutrient acquisition, in which plants increase their biomass by mining the soil for nutrients, which decreases SOC storage. We found that, overall, SOC stocks increase with eCO2 in grasslands (8 ± 2 per cent) but not in forests (0 ± 2 per cent), even though plant biomass in grasslands increase less (9 ± 3 per cent) than in forests (23 ± 2 per cent). Ecosystem models do not reproduce this trade-off, which implies that projections of SOC may need to be revised.

ABSTRACT Aim Many macroecological analyses are based on analyses of climatological data, within which evapotranspiration estimates are of central importance. In this paper we evaluate and review the use of evapotranspiration models and data in studies of geographical ecology to test the likely sensitivity of the analyses to variation in the performance of different metrics of potential evapotranspiration. Location Analyses are based on: (1) a latitudinal transect of sites (FLUXNET) for 11 different land‐cover types; and (2) globally gridded data. Methods First, we review the fundamental concepts of evapotranspiration, outline basic evapotranspiration models and describe methods with which to measure evapotranspiration. Next, we compare three different types of potential evapotranspiration models – a temperature‐based (Thornthwaite type), a radiation‐based (Priestley–Taylor) and a combination (Penman–Monteith) model – for 11 different land‐cover types. Finally, we compare these models at continental and global scales. Results At some sites the models differ by less than 7%, but generally the difference was greater than 25% across most sites. The temperature‐based model estimated 20–30% less than the radiation‐based and combination models averaged across all sites. The combination model often gave the highest estimates (22% higher than the radiation‐based model averaged across all sites). For continental and global averages, the potential evapotranspiration was very similar across all models. However, the difference in individual pixels was often larger than 150 mm year −1 between models. Main conclusions The choice of evapotranspiration model and input data is likely to have a bearing on model fits and predictions when used in analyses of species richness and related phenomena at geographical scales of analysis. To assist those undertaking such analyses, we provide a guide to selecting an appropriate evapotranspiration model.

Elevated CO2 (eCO2) experiments provide critical information to quantify the effects of rising CO2 on vegetation1–6. Many eCO2 experiments suggest that nutrient limitations modulate the local magnitude of the eCO2 effect on plant biomass1,3,5, but the global extent of these limitations has not been empirically quantified, complicating projections of the capacity of plants to take up CO27,8. Here, we present a data-driven global quantification of the eCO2 effect on biomass based on 138 eCO2 experiments. The strength of CO2 fertilization is primarily driven by nitrogen (N) in ~65% of global vegetation and by phosphorus (P) in ~25% of global vegetation, with N- or P-limitation modulated by mycorrhizal association. Our approach suggests that CO2 levels expected by 2100 can potentially enhance plant biomass by 12 ± 3% above current values, equivalent to 59 ± 13 PgC. The future effect of eCO2 we derive from experiments is geographically consistent with past changes in greenness9, but is considerably lower than the past effect derived from models10. If borne out, our results suggest that the stimulatory effect of CO2 on carbon storage could slow considerably this century. Our research provides an empirical estimate of the biomass sensitivity to eCO2 that may help to constrain climate projections. Elevated CO2 increases plant biomass, providing a negative feedback on global warming. Nutrient availability was found to drive the magnitude of this effect for the majority of vegetation globally, and analyses indicated that CO2 will continue to fertilize plant growth in the next century.

Abstract. The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman–Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERA-Interim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.

Summary 1. Plant–soil interactions play a central role in the biogeochemical carbon (C), nitrogen (N) and hydrological cycles. In the context of global environmental change, they are important both in modulating the impact of climate change and in regulating the feedback of greenhouse gas emissions (CO 2 , CH 4 and N 2 O) to the climate system. 2. Dynamic global vegetation models (DGVMs) represent the most advanced tools available to predict the impacts of global change on terrestrial ecosystem functions and to examine their feedbacks to climate change. The accurate representation of plant–soil interactions in these models is crucial to improving predictions of the effects of climate change on a global scale. 3. In this paper, we describe the general structure of DGVMs that use plant functional types (PFTs) classifications as a means to integrate plant–soil interactions and illustrate how models have been developed to improve the simulation of: (a) soil carbon dynamics, (b) nitrogen cycling, (c) drought impacts and (d) vegetation dynamics. For each of these, we discuss some recent advances and identify knowledge gaps. 4. We identify three ongoing challenges, requiring collaboration between the global modelling community and process ecologists. First, the need for a critical evaluation of the representation of plant–soil processes in global models; second, the need to supply and integrate knowledge into global models; third, the testing of global model simulations against large‐scale multifactor experiments and data from observatory gradients. 5. Synthesis . This paper reviews how plant–soil interactions are represented in DGVMs that use PFTs and illustrates some model developments. We also identify areas of ecological understanding and experimentation needed to reduce uncertainty in future carbon coupled climate change predictions.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level-3 (L3) latent heat flux (LE) data products. These data are generated from the Level-2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear-sky ET product (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 the site measurements (clear-sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized root-mean-square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70-m-high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1-km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.

Evapotranspiration (ET) is a key component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. Several satellite-based ET models have been developed for extrapolating local observations to regional and global scales, but recent studies have shown large model uncertainties in ET simulations. In this study, we compared eight ET models, including five empirical and three process-based models, with the objective of providing a reference for choosing and improving methods. The results showed that the eight models explained between 61 and 80% of the variability in ET at 23 eddy covariance towers in China and adjacent regions. The mean annual ET for all of China varied from 535 to 852 mm yr− 1 among the models. The interannual variability of yearly ET varied significantly between models during 1982–2009 because of different model structures and the dominant environmental factors employed. Our evaluation results showed that the parameters of the empirical methods may have different combination because the environmental factors of ET are not independent. Although the three process-based models showed high model performance across the validation sites, there were substantial differences among them in the temporal and spatial patterns of ET, the dominant environment factors and the energy partitioning schemes. The disagreement among current ET models highlights the need for further improvements and validation, which can be achieved by investigating model structures and examining the ET component estimates and the critical model parameters.

Because of China's large size, satellite observations are necessary for estimation of the land surface latent heat flux (LE). We describe here a satellite-driven Priestley–Taylor (PT)-based algorithm constrained by the Normalized Difference Vegetation Index (NDVI) and Apparent Thermal Inertia (ATI) derived from temperature change over time. We compare to the satellite-driven PT-based approach, PT-JPL, and validate both models using data collected from 16 eddy covariance flux towers in China. Like PT-JPL, our proposed algorithm avoids the computational complexities of aerodynamic resistance parameters. We run the algorithms with monthly Moderate Resolution Imaging Spectroradiometer (MODIS) products (0.05° resolution), including albedo, Land Surface Temperature (LST), surface emissivity, and NDVI; and, Insolation from the Japan Aerospace Exploration Agency (JAXA). We find good agreement between our estimates of monthly LE and field-measured LE, with respective Root Mean Square Error (RMSE) and bias differences of 12.5 W m−2 and −6.4 W m−2. As compared with PT-JPL, our proposed algorithm has higher correlations with ground-measurements. Between 2001 and 2010, LE shows generally negative trends in most regions of China, though positive LE trends occur over 39% of the region, primarily in Northeast, North and South China. Our results indicate that the variations of terrestrial LE are responding to large-scale droughts and afforestation caused by human activity with direct links to terrestrial energy exchange, both spatially and temporally.

The annual peak growth of vegetation is critical in characterizing the capacity of terrestrial ecosystem productivity and shaping the seasonality of atmospheric CO2 concentrations. The recent greening of global lands suggests an increasing trend of terrestrial vegetation growth, but whether or not the peak growth has been globally enhanced still remains unclear. Here, we use two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in annual peak vegetation growth (that is, GPPmax and NDVImax). We demonstrate that the peak in the growth of global vegetation has been linearly increasing during the past three decades. About 65% of the NDVImax variation is evenly explained by expanding croplands (21%), rising CO2 (22%) and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend is substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which demonstrate that croplands have a higher photosynthetic capacity than other vegetation types. The large contribution of CO2 is also supported by a meta-analysis of 466 manipulative experiments and 15 terrestrial biosphere models. Furthermore, we show that the contribution of GPPmax to the change in annual GPP is less in the tropics than in other regions. These multiple lines of evidence reveal an increasing trend in the peak growth of global vegetation. The findings highlight the important roles of agricultural intensification and atmospheric changes in reshaping the seasonality of global vegetation growth. Combining two global datasets, the authors show that peak vegetation growth has been increasing linearly for the past 30 years, with similar proportions of NDVI variation attributable to expanding croplands, rising CO2 and intensifying nitrogen deposition.

This study evaluates the large-scale seasonal phenology and physiology of vegetation over northern high latitude forests (40°–55°N) during spring and fall by using remote sensing of solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI) and observation-based estimate of gross primary productivity (GPP) from 2009 to 2011. Based on GPP phenology estimation in GPP, the growing season determined by SIF time-series is shorter in length than the growing season length determined solely using NDVI. This is mainly due to the extended period of high NDVI values, as compared to SIF, by about 46 days (± 11 days), indicating a large-scale seasonal decoupling of physiological activity and changes in greenness in the fall. In addition to phenological timing, mean seasonal NDVI and SIF have different responses to temperature changes throughout the growing season. We observed that both NDVI and SIF linearly increased with temperature increases throughout the spring. However, in the fall, although NDVI linearly responded to temperature increases, SIF and GPP did not linearly increase with temperature increases, implying a seasonal hysteresis of SIF and GPP in response to temperature changes across boreal ecosystems throughout their growing season. Seasonal hysteresis of vegetation at large-scales is consistent with the known phenomena that light limits boreal forest ecosystem productivity in the fall. Our results suggest that continuing measurements from satellite remote sensing of both SIF and NDVI can help to understand the differences between, and information carried by, seasonal variations vegetation structure and greenness and physiology at large-scales across the critical boreal regions.

The land surface comprises the smallest areal fraction of the Earth system's major components (e.g., versus atmosphere or ocean with cryosphere). As such, how is it that some of the largest sources of uncertainty in future climate projections are found in the terrestrial biosphere? This uncertainty stems from how the terrestrial biosphere is modeled with respect to the myriad of biogeochemical, physical, and dynamic processes represented (or not) in numerous models that contribute to projections of Earth's future. Here, we provide an overview of the processes included in terrestrial biosphere models (TBMs), including various approaches to representing any one given process, as well as the processes that are missing and/or uncertain. We complement this with a comprehensive review of individual TBMs, marking the differences, uniqueness, and recent and planned developments. To conclude, we summarize the latest results in benchmarking activities, particularly as linked to recent model intercomparison projects, and outline a path forward to reducing uncertainty in the contribution of the terrestrial biosphere to global atmospheric change.

Vegetation impacts on ecosystem functioning are mediated by mycorrhizas, plant-fungal associations formed by most plant species. Ecosystems dominated by distinct mycorrhizal types differ strongly in their biogeochemistry. Quantitative analyses of mycorrhizal impacts on ecosystem functioning are hindered by the scarcity of information on mycorrhizal distributions. Here we present global, high-resolution maps of vegetation biomass distribution by dominant mycorrhizal associations. Arbuscular, ectomycorrhizal, and ericoid mycorrhizal vegetation store, respectively, 241 ± 15, 100 ± 17, and 7 ± 1.8 GT carbon in aboveground biomass, whereas non-mycorrhizal vegetation stores 29 ± 5.5 GT carbon. Soil carbon stocks in both topsoil and subsoil are positively related to the community-level biomass fraction of ectomycorrhizal plants, though the strength of this relationship varies across biomes. We show that human-induced transformations of Earth's ecosystems have reduced ectomycorrhizal vegetation, with potential ramifications to terrestrial carbon stocks. Our work provides a benchmark for spatially explicit and globally quantitative assessments of mycorrhizal impacts on ecosystem functioning and biogeochemical cycling.

Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earth's carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but diverge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar-induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7-8 Pg C yr(-1) from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr(-1) ) and enhanced GPP in tropical forests (~3.7 Pg C yr(-1) ). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak-to-trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40-70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well-suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.

Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.

Abstract. The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower (R2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Abstract. 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.

Accurate estimation of global evapotranspiration (ET) is essential to understand water cycle and land-atmosphere feedbacks in the Earth system. Satellite-driven ET models provide global estimates, but many of the ET algorithms have been designed independently of soil moisture observations. As water for ET is sourced from the soil, incorporating soil moisture into global remote sensing algorithms of ET should, in theory, improve performance, especially in water-limited regions. This paper presents an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit daily surface soil moisture control on soil evaporation and canopy transpiration. The updated algorithm is evaluated using 14 AmeriFlux eddy covariance towers co-located with COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture observations. The new PT-JPLSM model shows reduced errors and increased explanation of variance, with the greatest improvements in water-limited regions. Soil moisture incorporation into soil evaporation improves ET estimates by reducing bias and RMSE by 29.9% and 22.7% respectively, while soil moisture incorporation into transpiration improves ET estimates by reducing bias by 30.2%, RMSE by 16.9%. We apply the algorithm globally using soil moisture observations from the Soil Moisture Active Passive Mission (SMAP). These new global estimates of ET show reduced error at finer spatial resolutions and provide a rich dataset to evaluate land surface and climate models, vegetation response to changes in water availability and environmental conditions, and anthropogenic perturbations to the water cycle.

Abstract The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data‐driven irrigation management strategies, and expanding incentive‐driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field‐scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community‐driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well‐established satellite‐based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web‐based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite‐driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT‐JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

Water availability plays a critical role in shaping terrestrial ecosystems, particularly in low- and mid-latitude regions. The sensitivity of vegetation growth to precipitation strongly regulates global vegetation dynamics and their responses to drought, yet sensitivity changes in response to climate change remain poorly understood. Here we use long-term satellite observations combined with a dynamic statistical learning approach to examine changes in the sensitivity of vegetation greenness to precipitation over the past four decades. We observe a robust increase in precipitation sensitivity (0.624% yr-1) for drylands, and a decrease (-0.618% yr-1) for wet regions. Using model simulations, we show that the contrasting trends between dry and wet regions are caused by elevated atmospheric CO2 (eCO2). eCO2 universally decreases the precipitation sensitivity by reducing leaf-level transpiration, particularly in wet regions. However, in drylands, this leaf-level transpiration reduction is overridden at the canopy scale by a large proportional increase in leaf area. The increased sensitivity for global drylands implies a potential decrease in ecosystem stability and greater impacts of droughts in these vulnerable ecosystems under continued global change.

Land-surface temperature retrieved from thermal infrared (TIR) remote sensing has proven to be a valuable constraint in surface energy balance models for estimating evapotranspiration (ET). For optimal utility in agricultural water management applications, frequent thermal imaging (<4-day revisit) at sub-field (100 m or less) spatial resolution is desired. While, the current suite of Landsat satellites (7 and 8) provides the required spatial resolution, the 8-day combined revisit can be inadequate to capture rapid changes in surface moisture status or crop phenology, particularly in areas of persistent cloud cover. The new ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission, with an average 4-day revisit interval and nominal 70-m resolution, provides a valuable research platform for augmenting Landsat TIR sampling and for investigating TIR-based ET mapping mission requirements more broadly. This study investigates the interoperability of Landsat and ECOSTRESS imaging for developing ET image timeseries with high spatial (30-m) and temporal (daily) resolution. A data fusion algorithm is used to fuse Landsat and ECOSTRESS ET retrievals at 30 m with daily 500-m retrievals using TIR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) over target agricultural sites spanning the United States.The added value of the combined multi-source dataset is quantified in comparison with daily flux tower observations collected within these target domains. In addition, we investigate ET model performance as a function of ECOSTRESS view angle, overpass time, and time separation between TIR and Landsat visible to shortwave infrared (VSWIR) data acquisitions used to generate land-surface temperature, leaf area index, and albedo inputs to the surface energy balance model. The results demonstrate the value of the higher temporal sampling provided by ECOSTRESS, especially in areas that are frequently impacted by cloud cover. Limiting usage to ECOSTRESS scenes collected between 9:00 a.m. to 5:00 p.m. and nadir viewing angles <20° yielded daily (24-h) ET retrievals of comparable quality to the well-tested Landsat baseline. We also discuss challenges in using land-surface temperature from a thermal free-flyer system for ET retrieval, which may have ramifications for future TIR water-use mapping missions.

Abstract Roots promote the formation of slow‐cycling soil carbon (C), yet we have a limited understanding of the magnitude and controls on this flux. We hypothesised arbuscular mycorrhizal (AM)‐ and ectomycorrhizal (ECM)‐associated trees would exhibit differences in root‐derived C accumulation in the soil, and that much of this C would be transferred into mineral‐associated pools. We installed δ 13 C‐enriched ingrowth cores across mycorrhizal gradients in six Eastern U.S. forests ( n = 54 plots). Overall, root‐derived C was 54% greater in AM versus ECM‐dominated plots. This resulted in nearly twice as much root‐derived C in putatively slow‐cycling mineral‐associated pools in AM compared to ECM plots. Given that our estimates of root‐derived inputs were often equal to or greater than leaf litter inputs, our results suggest that variation in root‐derived soil C accumulation due to tree mycorrhizal dominance may be a key control of soil C dynamics in forests.

Abstract Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r 2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.

At the leaf scale, it is a long-held assumption that stomata close at night in the absence of light, causing transpiration to decrease to zero. Energy balance models and evapotranspiration equations often rely on net radiation as an upper bound, and some models reduce evapotranspiration to zero at night when there is no solar radiation. Emerging research is showing, however, that transpiration can occur throughout the night in a variety of vegetation types and biomes. At the ecosystem scale, eddy covariance measurements have provided extensive data on latent heat flux for a multitude of ecosystem types globally. Nighttime eddy covariance measurements, however, are generally unreliable because of low turbulence. If significant nighttime water loss occurs, eddy flux towers may be missing key information on latent heat flux. We installed and measured rates of sap flow by the heat ratio method (Burgess et al. 2001) at two AmeriFlux (part of FLUXNET) sites in California. The heat ratio method allows measurement and quantification of low rates of sap flow, including negative rates (i.e., hydraulic lift). We measured sap flow in five Pinus ponderosa Dougl. ex Laws. trees and three Arctostaphylos manzanita Parry and two Ceanothus cordulatus A. Kellog shrubs in the Sierra Nevada Mountains, and in five Quercus douglasii Hook and Arn. trees at an oak savanna in the Central Valley of California. Nocturnal sap flow was observed in all species, and significant nighttime water loss was observed in both species of trees. Vapor pressure deficit and air temperature were both well correlated with nighttime transpiration; the influence of wind speed on nighttime transpiration was insignificant at both sites. We distinguished between storage-tissue refilling and water loss based on data from Year 2005, and calculated the percentage by which nighttime transpiration was underestimated by eddy covariance measurements at both sites.

Abstract Tropical vegetation is a major source of global land surface evapotranspiration, and can thus play a major role in global hydrological cycles and global atmospheric circulation. Accurate prediction of tropical evapotranspiration is critical to our understanding of these processes under changing climate. We examined the controls on evapotranspiration in tropical vegetation at 21 pan‐tropical eddy covariance sites, conducted a comprehensive and systematic evaluation of 13 evapotranspiration models at these sites, and assessed the ability to scale up model estimates of evapotranspiration for the test region of Amazonia. Net radiation was the strongest determinant of evapotranspiration (mean evaporative fraction was 0.72) and explained 87% of the variance in monthly evapotranspiration across the sites. Vapor pressure deficit was the strongest residual predictor (14%), followed by normalized difference vegetation index (9%), precipitation (6%) and wind speed (4%). The radiation‐based evapotranspiration models performed best overall for three reasons: (1) the vegetation was largely decoupled from atmospheric turbulent transfer (calculated from Ω decoupling factor), especially at the wetter sites; (2) the resistance‐based models were hindered by difficulty in consistently characterizing canopy (and stomatal) resistance in the highly diverse vegetation; (3) the temperature‐based models inadequately captured the variability in tropical evapotranspiration. We evaluated the potential to predict regional evapotranspiration for one test region: Amazonia. We estimated an Amazonia‐wide evapotranspiration of 1370 mm yr −1 , but this value is dependent on assumptions about energy balance closure for the tropical eddy covariance sites; a lower value (1096 mm yr −1 ) is considered in discussion on the use of flux data to validate and interpolate models.

Nitrogen (N) generally limits plant growth and controls biosphere responses to climate change. We introduce a new mathematical model of plant N acquisition, called Fixation and Uptake of Nitrogen (FUN), based on active and passive soil N uptake, leaf N retranslocation, and biological N fixation. This model is unified under the theoretical framework of carbon (C) cost economics, or resource optimization. FUN specifies C allocated to N acquisition as well as remaining C for growth, or N‐limitation to growth. We test the model with data from a wide range of sites (observed versus predicted N uptake r 2 is 0.89, and RMSE is 0.003 kg N m −2 ·yr −1 ). Four model tests are performed: (1) fixers versus nonfixers under primary succession; (2) response to N fertilization; (3) response to CO 2 fertilization; and (4) changes in vegetation C from potential soil N trajectories for five DGVMs (HYLAND, LPJ, ORCHIDEE, SDGVM, and TRIFFID) under four IPCC scenarios. Nonfixers surpass the productivity of fixers after ∼150–180 years in this scenario. FUN replicates the N uptake response in the experimental N fertilization from a modeled N fertilization. However, FUN cannot replicate the N uptake response in the experimental CO 2 fertilization from a modeled CO 2 fertilization; nonetheless, the correct response is obtained when differences in root biomass are included. Finally, N‐limitation decreases biomass by 50 Pg C on average globally for the DGVMs. We propose this model as being suitable for inclusion in the new generation of Earth system models that aim to describe the global N cycle.

Evapotranspiration, a major component in terrestrial water balance and net primary productivity models, is difficult to measure and predict. This study compared five models of potential evapotranspiration (PET) applied to a ponderosa pine forest ecosystem at an AmeriFlux site in Northern California. The AmeriFlux sites are research forests across the United States, Canada, Brazil, and Costa Rica with instruments on towers that measure carbon, water, and energy fluxes into and out of the ecosystems. The evapotranspiration models ranged from simple temperature and solar radiation-driven equations to physically-based combination approaches and included reference surface and surface cover-dependent algorithms. For each evapotranspiration model, results were compared against mean daily latent heat from half-hourly measurements recorded on a tower above the forest canopy. All models calculate potential evapotranspiration (assuming well-watered soils at field capacity), rather than actual evapotranspiration (based on soil moisture limitations), and thus overpredicted values from the dry summer seasons of 1997 and 1998. A soil moisture function was integrated to estimate actual evapotranspiration, resulting in improved accuracy in model simulations. A modified Priestley–Taylor model performed well given its relative simplicity.

The future of tropical forests has become one of the iconic issues in climate-change science. A number of studies that have explored this subject have tended to focus on the output from one or a few climate models, which work at low spatial resolution, whereas society and conservation-relevant assessment of potential impacts requires a finer scale. This study focuses on the role of climate on the current and future distribution of humid tropical forests (HTFs). We first characterize their contemporary climatological niche using annual rainfall and maximum climatological water stress, which also adequately describe the current distribution of other biomes within the tropics. As a first-order approximation of the potential extent of HTFs in future climate regimes defined by global warming of 2 ° C and 4 ° C, we investigate changes in the niche through a combination of climate-change anomaly patterns and higher resolution (5 km) maps of current climatology. The climate anomalies are derived using data from 17 coupled Atmosphere–Ocean General Circulation Models (AOGCMs) used in the Fourth Assessment of the Intergovernmental Panel for Climate Change. Our results confirm some risk of forest retreat, especially in eastern Amazonia, Central America and parts of Africa, but also indicate a potential for expansion in other regions, for example around the Congo Basin. The finer spatial scale enabled the depiction of potential resilient and vulnerable zones with practically useful detail. We further refine these estimates by considering the impact of new environmental regimes on plant water demand using the UK Met Office land-surface scheme (of the HadCM3 AOGCM). The CO 2 -related reduction in plant water demand lowers the risk of die-back and can lead to possible niche expansion in many regions. The analysis presented here focuses primarily on hydrological determinants of HTF extent. We conclude by discussing the role of other factors, notably the physiological effects of higher temperature.

Abstract Accurate estimation of the satellite‐based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite‐based global terrestrial LE estimation by merging five process‐based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote‐sensing‐based Penman‐Monteith LE algorithm, the Priestley‐Taylor‐based LE algorithm, the modified satellite‐based Priestley‐Taylor LE algorithm, and the semi‐empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process‐based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process‐based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower‐specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower‐specific meteorology decreased by more than 5 W/m 2 for crop and grass sites, and by more than 6 W/m 2 for forest, shrub, and savanna sites. The average coefficients of determination ( R 2 ) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO‐MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long‐term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles.

Most vegetation is limited in productivity by nutrient availability, but the magnitude of limitation globally is not known. Nutrient limitation is directly relevant not only to ecology and agriculture, but also to the global carbon cycle by regulating how much atmospheric CO 2 the terrestrial biosphere can sequester. We attempt to identify total nutrient limitation in terrestrial plant productivity globally using ecophysiological theory and new developments in remote sensing for evapotranspiration and plant productivity. Our map of nutrient limitation qualitatively reproduces known regional nutrient gradients (e.g., across Amazonia), highlights differences in nutrient addition to croplands (e.g., between “developed” and “developing” countries), identifies the role of nutrients on the distribution of major biomes (e.g., tree line migration in boreal North America), and compares similarly to a ground‐based test along the Long Substrate Age Gradient in Hawaii, U.S.A. (e.g., foliar and soil nutrients, litter decomposition). Nonetheless, challenges in representing light and water use efficiencies, disturbance, and comparison to ground data with multiple interacting nutrients provide avenues for further progress on refining such a global map. Global average reduction in terrestrial plant productivity was within 16–28%, depending on treatment of disturbance; these values can be compared to global carbon cycle model estimates of carbon uptake reduction with nutrient cycle inclusion.

Accurate projections of the future land carbon (C) sink by terrestrial biosphere models depend on how nutrient constraints on net primary production are represented. While nutrient limitation is nearly universal, current models do not have a C cost for plant nutrient acquisition. Also missing are symbiotic mycorrhizal fungi, which can consume up to 20% of net primary production and supply up to 50% of a plant's nitrogen (N) uptake. Here we integrate simultaneous uptake and mycorrhizae into a cutting-edge plant N model—Fixation and Uptake of Nitrogen (FUN)—that can be coupled into terrestrial biosphere models. The C cost of N acquisition varies as a function of mycorrhizal type, with plants that support arbuscular mycorrhizae benefiting when N is relatively abundant and plants that support ectomycorrhizae benefiting when N is strongly limiting. Across six temperate forested sites (representing arbuscular mycorrhizal- and ectomycorrhizal-dominated stands and 176 site years), including multipath resistance improved the partitioning of N uptake between aboveground and belowground sources. Integrating mycorrhizae led to further improvements in predictions of N uptake from soil (R2 = 0.69 increased to R2 = 0.96) and from senescing leaves (R2 = 0.29 increased to R2 = 0.73) relative to the original model. On average, 5% and 9% of net primary production in arbuscular mycorrhizal- and ectomycorrhizal-dominated forests, respectively, was needed to support mycorrhizal-mediated acquisition of N. To the extent that resource constraints to net primary production are governed by similar trade-offs across all terrestrial ecosystems, integrating these improvements to FUN into terrestrial biosphere models should enhance predictions of the future land C sink.

Plants typically expend a significant portion of their available carbon (C) on nutrient acquisition - C that could otherwise support growth. However, given that most global terrestrial biosphere models (TBMs) do not include the C cost of nutrient acquisition, these models fail to represent current and future constraints to the land C sink. Here, we integrated a plant productivity-optimized nutrient acquisition model - the Fixation and Uptake of Nitrogen Model - into one of the most widely used TBMs, the Community Land Model. Global plant nitrogen (N) uptake is dynamically simulated in the coupled model based on the C costs of N acquisition from mycorrhizal roots, nonmycorrhizal roots, N-fixing microbes, and retranslocation (from senescing leaves). We find that at the global scale, plants spend 2.4 Pg C yr(-1) to acquire 1.0 Pg N yr(-1) , and that the C cost of N acquisition leads to a downregulation of global net primary production (NPP) by 13%. Mycorrhizal uptake represented the dominant pathway by which N is acquired, accounting for ~66% of the N uptake by plants. Notably, roots associating with arbuscular mycorrhizal (AM) fungi - generally considered for their role in phosphorus (P) acquisition - are estimated to be the primary source of global plant N uptake owing to the dominance of AM-associated plants in mid- and low-latitude biomes. Overall, our coupled model improves the representations of NPP downregulation globally and generates spatially explicit patterns of belowground C allocation, soil N uptake, and N retranslocation at the global scale. Such model improvements are critical for predicting how plant responses to altered N availability (owing to N deposition, rising atmospheric CO2 , and warming temperatures) may impact the land C sink.

Abstract. We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.

We examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982 to 2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increasing trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO2 ranked second in these models after the predominant climatic influences, and yielded decreasing trends in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increasing nitrogen deposition slightly amplified global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.

Abstract. We have examined the utility of retrieved column-averaged, dry-air mole fractions of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT) for quantifying monthly, regional flux estimates of CO2, using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system. We focused on assessing the potential impact of biases in the GOSAT CO2 data on the regional flux estimates. Using different screening and bias correction approaches, we selected three different subsets of the GOSAT XCO2 data for the 4D-Var inversion analyses, and found that the inferred global fluxes were consistent across the three XCO2 inversions. However, the GOSAT observational coverage was a challenge for the regional flux estimates. In the northern extratropics, the inversions were more sensitive to North American fluxes than to European and Asian fluxes due to the lack of observations over Eurasia in winter and over eastern and southern Asia in summer. The regional flux estimates were also sensitive to the treatment of the residual bias in the GOSAT XCO2 data. The largest differences obtained were for temperate North America and temperate South America, for which the largest spread between the inversions was 1.02 and 0.96 Pg C, respectively. In the case of temperate North America, one inversion suggested a strong source, whereas the second and third XCO2 inversions produced a weak and strong sink, respectively. Despite the discrepancies in the regional flux estimates between the three XCO2 inversions, the a posteriori CO2 distributions were in good agreement (with a mean difference between the three inversions of typically less than 0.5 ppm) with independent data from the Total Carbon Column Observing Network (TCCON), the surface flask network, and from the HIAPER Pole-to-Pole Observations (HIPPO) aircraft campaign. The discrepancy in the regional flux estimates from the different inversions, despite the agreement of the global flux estimates suggests the need for additional work to determine the minimum spatial scales at which we can reliably quantify the fluxes using GOSAT XCO2. The fact that the a posteriori CO2 from the different inversions were in good agreement with the independent data although the regional flux estimates differed significantly, suggests that innovative ways of exploiting existing data sets, and possibly additional observations, are needed to better evaluate the inferred regional flux estimates.

Remote sensing of vegetation function and traits has advanced significantly over the past half-century in the capacity to retrieve useful plant biochemical, physiological and structural quantities across a range of spatial and temporal scales. However, the translation of remote sensing signals into meaningful descriptors of vegetation function and traits is still associated with large uncertainties due to complex interactions between leaf, canopy, and atmospheric mediums, and significant challenges in the treatment of confounding factors in spectrum-trait relations. This editorial provides (1) a background on major advances in the remote sensing of vegetation, (2) a detailed timeline and description of relevant historical and planned satellite missions, and (3) an outline of remaining challenges, upcoming opportunities and key research objectives to be tackled. The introduction sets the stage for thirteen Special Issue papers here that focus on novel approaches for exploiting current and future advancements in remote sensor technologies. The described enhancements in spectral, spatial and temporal resolution and radiometric performance provide exciting opportunities to significantly advance the ability to accurately monitor and model the state and function of vegetation canopies at multiple scales on a timely basis.

Evapotranspiration (E) in the Amazon connects forest function and regional climate via its role in precipitation recycling However, the mechanisms regulating water supply to vegetation and its demand for water remain poorly understood, especially during periods of seasonal water deficits In this study, we address two main questions: First, how do mechanisms of water supply (indicated by rooting depth and groundwater) and vegetation water demand (indicated by stomatal conductance and intrinsic water use efficiency) control evapotranspiration (E) along broad gradients of climate and vegetation from equatorial Amazonia to Cerrado, and second, how do these inferred mechanisms of supply and demand compare to those employed by a suite of ecosystem models? We used a network of eddy covariance towers in Brazil coupled with ancillary measurements to address these questions With respect to the magnitude and seasonality of E, models have much improved in equatorial tropical forests by eliminating most dry season water limitation, diverge in performance in transitional forests where seasonal water deficits are greater, and mostly capture the observed seasonal depressions in E at Cerrado However, many models depended universally on either deep roots or groundwater to mitigate dry season water deficits, the relative importance of which we found does not vary as a simple function of climate or vegetation In addition, canopy stomatal conductance (gs) regulates dry season vegetation demand for water at all except the wettest sites even as the seasonal cycle of E follows that of net radiation In contrast, some models simulated no seasonality in gs, even while matching the observed seasonal cycle of E. We suggest that canopy dynamics mediated by leaf phenology may play a significant role in such seasonality, a process poorly represented in models Model bias in gs and E, in turn, was related to biases arising from the simulated light response (gross primary productivity, GPP) or the intrinsic water use efficiency of photosynthesis (iWUE). We identified deficiencies in models which would not otherwise be apparent based on a simple comparison of simulated and observed rates of E. While some deficiencies can be remedied by parameter tuning, in most models they highlight the need for continued process development of belowground hydrology and in particular, the biological processes of root dynamics and leaf phenology, which via their controls on E, mediate vegetation-climate feedbacks in the tropics.

In 2018 technologies on the International Space Station will provide ∼1 year of synchronous observations of ecosystem composition, structure and function. We discuss these instruments and how they can be used to constrain global models and improve our understanding of the current state of terrestrial ecosystems. Author Correction (05 September 2017)

Abstract Evapotranspiration ET is a critical water, energy, and climate variable, and recent work has been published comparing different global products. These comparisons have been difficult to interpret, however, because in most studies the evapotranspiration products were derived from models forced by different input data. Some studies have analyzed the uncertainty in regional evapotranspiration estimates from choice of forcings. Still others have analyzed how multiple models vary with choice of net radiation forcing data. However, no analysis has been conducted to determine the uncertainty in global evapotranspiration estimates attributable to each class of input forcing datasets. Here, one of these models [Priestly–Taylor JPL (PT-JPL)] is run with 19 different combinations of forcing data. These data include three net radiation products (SRB, CERES, and ISCCP), three meteorological datasets [CRU, Atmospheric Infrared Sounder (AIRS) Aqua, and MERRA], and three vegetation index products [MODIS; Global Inventory Modeling and Mapping Studies (GIMMS); and Fourier-Adjusted, Sensor and Solar Zenith Angle Corrected, Interpolated, Reconstructed (FASIR)]. The choice in forcing data produces an average range in global monthly evapotranspiration of 10.6 W m−2 (~20% of global mean evapotranspiration), with net radiation driving the majority of the difference. Annual average terrestrial ET varied by an average of 8 W m−2, depending on choice of forcings. The analysis shows that the greatest disagreement between input forcings arises from choice of net radiation dataset. In particular, ISCCP data, which are frequently used in global studies, differed widely from the other radiation products examined and resulted in dramatically different estimates of global terrestrial ET.

Terrestrial evapotranspiration (ET) for each plant functional type (PFT) is a key variable for linking the energy, water and carbon cycles of the atmosphere, hydrosphere and biosphere. Process-based algorithms have been widely used to estimate global terrestrial ET, yet each ET individual algorithm has exhibited large uncertainties. In this study, the support vector machine (SVM) method was introduced to improve global terrestrial ET estimation by integrating three process-based ET algorithms: MOD16, PT-JPL and SEMI-PM. At 200 FLUXNET flux tower sites, we evaluated the performance of the SVM method and others, including the Bayesian model averaging (BMA) method and the general regression neural networks (GRNNs) method together with three process-based ET algorithms. We found that the SVM method was superior to all other methods we evaluated. The validation results showed that compared with the individual algorithms, the SVM method driven by tower-specific (Modern Era Retrospective Analysis for Research and Applications, MERRA) meteorological data reduced the root mean square error (RMSE) by approximately 0.20 (0.15) mm/day for most forest sites and 0.30 (0.20) mm/day for most crop and grass sites and improved the squared correlation coefficient (R2) by approximately 0.10 (0.08) (95% confidence) for most flux tower sites. The water balance of basins and the global terrestrial ET calculation analysis also demonstrated that the regional and global estimates of the SVM-merged ET were reliable. The SVM method provides a powerful tool for improving global ET estimation to characterize the long-term spatiotemporal variations of the global terrestrial water budget.

Abstract. Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (σ) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0 ± 9.2 kg C m−2), then gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), net ecosystem exchange (NEE) (−0.01 ± 0.19 kg C m−2 yr−1), and CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region.

Recent advances in the retrieval of Chl fluorescence from space using passive methods (solar-induced Chl fluorescence, SIF) promise improved mapping of plant photosynthesis globally. However, unresolved issues related to the spatial, spectral, and temporal dynamics of vegetation fluorescence complicate our ability to interpret SIF measurements. We developed an instrument to measure leaf-level gas exchange simultaneously with pulse-amplitude modulation (PAM) and spectrally resolved fluorescence over the same field of view - allowing us to investigate the relationships between active and passive fluorescence with photosynthesis. Strongly correlated, slope-dependent relationships were observed between measured spectra across all wavelengths (Fλ , 670-850 nm) and PAM fluorescence parameters under a range of actinic light intensities (steady-state fluorescence yields, Ft ) and saturation pulses (maximal fluorescence yields, Fm ). Our results suggest that this method can accurately reproduce the full Chl emission spectra - capturing the spectral dynamics associated with changes in the yields of fluorescence, photochemical (ΦPSII), and nonphotochemical quenching (NPQ). We discuss how this method may establish a link between photosynthetic capacity and the mechanistic drivers of wavelength-specific fluorescence emission during changes in environmental conditions (light, temperature, humidity). Our emphasis is on future research directions linking spectral fluorescence to photosynthesis, ΦPSII, and NPQ.

Satellite based retrievals of evapotranspiration (ET) are widely used for assessments of global and regional scale surface fluxes. However, the partitioning of the estimated ET between soil evaporation, transpiration, and canopy interception regularly shows strong divergence between models, and to date, remains largely unvalidated. To examine this problem, this paper considers three algorithms: the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MODIS), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL), and the Global Land Evaporation Amsterdam Model (GLEAM). Surface flux estimates from these three models, obtained via the WACMOS-ET initiative, are compared against a comprehensive collection of field studies, spanning a wide range of climates and land cover types. Overall, we find errors between estimates of field and remote sensing-based soil evaporation (RMSD = 90–114%, r2 = 0.14–0.25, N = 35), interception (RMSD = 62–181%, r2 = 0.39–0.85, N = 13), and transpiration (RMSD = 54–114%, r2 = 0.33–0.55, N = 35) are relatively large compared to the combined estimates of total ET (RMSD = 35–49%, r2 = 0.61–0.75, N = 35). Errors in modeled ET components are compared between land cover types, field methods, and precipitation regimes. Modeled estimates of soil evaporation were found to have significant deviations from observed values across all three models, while the characterization of vegetation effects also influences errors in all three components. Improvements in these estimates, and other satellite based partitioning estimates are likely to lead to better understanding of the movement of water through the soil-plant-water continuum.

Accurate estimation of the terrestrial latent heat flux (LE) for each plant functional type (PFT) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley–Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (Ta), relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FLUXNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 4.3 W/m2 (for 6 deciduous needleleaf forest sites) to 18.1 W/m2 (for 34 crop sites) and with a R2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05°by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for different PFTs, and all driving forces of the algorithm can be acquired from satellite and meteorological observations.

Amazon forests exert a major influence on the global carbon cycle, but quantifying the impact is complicated by diverse landscapes and sparse data. Here we examine seasonal carbon balance in southern Amazonia using new measurements of column‐averaged dry air mole fraction of CO 2 (XCO 2 ) and solar induced chlorophyll fluorescence (SIF) from the Greenhouse Gases Observing Satellite (GOSAT) from July 2009 to December 2010. SIF, which reflects gross primary production (GPP), is used to disentangle the photosynthetic component of land‐atmosphere carbon exchange. We find that tropical transitional forests in southern Amazonia exhibit a pattern of low XCO 2 during the wet season and high XCO 2 in the dry season that is robust to retrieval methodology and with seasonal amplitude double that of cerrado ecosystems to the east (4 ppm versus 2 ppm), including enhanced dilution of 2.5 ppm in the wet season. Concomitant measurements of SIF, which are inversely correlated with XCO 2 in southern Amazonia (r = −0.53, p &lt; 0.001), indicate that the enhanced variability is driven by seasonal changes in GPP due to coupling of strong vertical mixing with seasonal changes in underlying carbon exchange. This finding is supported by forward simulations of the Goddard Chemistry Transport Model (GEOS‐Chem) which show that local carbon uptake in the wet season and loss in the dry season due to emissions by ecosystem respiration and biomass burning produces best agreement with observed XCO 2 . We conclude that GOSAT provides critical measurements of carbon exchange in southern Amazonia, but more samples are needed to examine moist Amazon forests farther north.

Abstract. Although plant photosynthetic capacity as determined by the maximum carboxylation rate (i.e., Vc, max25) and the maximum electron transport rate (i.e., Jmax25) at a reference temperature (generally 25 °C) is known to vary considerably in space and time in response to environmental conditions, it is typically parameterized in Earth system models (ESMs) with tabulated values associated with plant functional types. In this study, we have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA) to predict photosynthetic capacity at the global scale under different environmental conditions. We adopt an optimality hypothesis to nitrogen allocation among light capture, electron transport, carboxylation and respiration. The LUNA model is able to reasonably capture the measured spatial and temporal patterns of photosynthetic capacity as it explains ∼ 55 % of the global variation in observed values of Vc, max25 and ∼ 65 % of the variation in the observed values of Jmax25. Model simulations with LUNA under current and future climate conditions demonstrate that modeled values of Vc, max25 are most affected in high-latitude regions under future climates. ESMs that relate the values of Vc, max25 or Jmax25 to plant functional types only are likely to substantially overestimate future global photosynthesis.

The use of Penman–Monteith (PM) equation in thermal remote sensing based surface energy balance modeling is not prevalent due to the unavailability of any direct method to integrate thermal data into the PM equation and due to the lack of physical models expressing the surface (or stomatal) and boundary layer conductances (gS and gB) as a function of surface temperature. Here we demonstrate a new method that physically integrates the radiometric surface temperature (TS) into the PM equation for estimating the terrestrial surface energy balance fluxes (sensible heat, H and latent heat, λE). The method combines satellite TS data with standard energy balance closure models in order to derive a hybrid closure that does not require the specification of surface to atmosphere conductance terms. We call this the Surface Temperature Initiated Closure (STIC), which is formed by the simultaneous solution of four state equations. Taking advantage of the psychrometric relationship between temperature and vapor pressure, the present method also estimates the near surface moisture availability (M) from TS, air temperature (TA) and relative humidity (RH), thereby being capable of decomposing λE into evaporation (λEE) and transpiration (λET). STIC is driven with TS, TA, RH, net radiation (RN), and ground heat flux (G). TS measurements from both MODIS Terra (MOD11A2) and Aqua (MYD11A2) were used in conjunction with FLUXNET RN, G, TA, RH, λE and H measurements corresponding to the MODIS equatorial crossing time. The performance of STIC has been evaluated in comparison to the eddy covariance measurements of λE and H at 30 sites that cover a broad range of biomes and climates. We found a RMSE of 37.79 (11%) (with MODIS Terra TS) and 44.27 W m− 2 (15%) (with MODIS Aqua TS) in λE estimates, while the RMSE was 37.74 (9%) (with Terra) and 44.72 W m− 2 (8%) (with Aqua) in H. STIC could efficiently capture the λE dynamics during the dry down period in the semi-arid landscapes where λE is strongly governed by the subsurface soil moisture and where the majority of other λE models generally show poor results. Sensitivity analysis revealed a high sensitivity of both the fluxes to the uncertainties in TS. A realistic response and modest relationship was also found when partitioned λE components (λEE and λET) were compared to the observed soil moisture and rainfall. This is the first study to report the physical integration of TS into the PM equation and finding analytical solution of the physical (gB) and physiological conductances (gS). The performance of STIC over diverse biomes and climates points to its potential to benefit future NASA and NOAA missions having thermal sensors, such as HyspIRI, GeoSTAR and GOES-R for mapping multi-scale λE and drought.

Abstract Novel satellite measurements of solar‐induced chlorophyll fluorescence (SIF) can improve our understanding of global photosynthesis; however, little is known about how to interpret the controls on its spectral variability. To address this, we disentangle simultaneous drivers of fluorescence spectra by coupling active and passive fluorescence measurements with photosynthesis. We show empirical and mechanistic evidence for where, why, and to what extent leaf fluorescence spectra change. Three distinct components explain more than 95% of the variance in leaf fluorescence spectra under both steady‐state and changing illumination conditions. A single spectral shape of fluorescence explains 84% of the variance across a wide range of species. The magnitude of this shape responds to absorbed light and photosynthetic up/down regulation; meanwhile, chlorophyll concentration and nonphotochemical quenching control 9% and 3% of the remaining spectral variance, respectively. The spectral shape of fluorescence is remarkably stable where most current satellite retrievals occur (“far‐red,” &gt;740nm), and dynamic downregulation of photosynthesis reduces fluorescence magnitude similarly across the 670‐ to 850‐nm range. We conduct an exploratory analysis of hourly red and far‐red canopy SIF in soybean, which shows a subtle change in red:far‐red fluorescence coincident with photosynthetic downregulation but is overshadowed by longer‐term changes in canopy chlorophyll and structure. Based on our leaf and canopy analysis, caution should be taken when attributing large changes in the spectral shape of remotely sensed SIF to plant stress, particularly if data acquisition is temporally sparse. Ultimately, changes in SIF magnitude at wavelengths greater than 740 nm alone may prove sufficient for tracking photosynthetic dynamics.

The Soil Moisture Active Passive (SMAP) mission provides a global surface soil moisture (SM) product at 36-km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications, there are also a lot of applications which would benefit from a higher resolution SM product. The SMAP radiometer-based SM product was downscaled to 1 km using Moderate Resolution Imaging Spectroradiometer (MODIS) data and validated against airborne data from the Passive Active L-band System instrument. The downscaling approach uses MODIS land surface temperature and normalized difference vegetation index to construct soil evaporative efficiency, which is used to downscale the SMAP SM. The algorithm was applied to one SMAP pixel during the SMAP Validation Experiment 2015 (SMAPVEX15) in a semiarid study area for validation of the approach. SMAPVEX15 offers a unique data set for testing SM downscaling algorithms. The results indicated reasonable skill (root-mean-square difference of 0.053 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for 1-km resolution and 0.037 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for 3-km resolution) in resolving high-resolution SM features within the coarse-scale pixel. The success benefits from the fact that the surface temperature in this region is controlled by soil evaporation, the topographical variation within the chosen pixel area is relatively moderate, and the vegetation density is relatively low over most parts of the pixel. The analysis showed that the combination of the SMAP and MODIS data under these conditions can result in a high-resolution SM product with an accuracy suitable for many applications.

Abstract Future projections of land carbon uptake in Earth System Models are affected by land surface model responses to both CO 2 and nitrogen fertilization. The Community Land Model, Version 5 (CLM5), contains a suite of modifications to carbon and nitrogen cycle representation. Globally, the CLM5 has a larger CO 2 response and smaller nitrogen response than its predecessors. To improve our understanding of the controls over the fertilization responses of the new model, we assess sensitivity to eight parameters pertinent to the cycling of carbon and nitrogen by vegetation, both under present‐day conditions and with CO 2 and nitrogen fertilization. The impact of fertilization varies with both model parameters and with the balance of limiting factors (water, temperature, nutrients, and light) in the pre‐fertilization model state. The model parameters that impact the pre‐fertilization state are in general not the same as those that control fertilization responses, meaning that goodness of fit to present‐day conditions does not necessarily imply a constraint on future transient projections. Where pre‐fertilization state has low leaf area, fertilization‐induced increases in leaf production amplify the model response to the initial fertilization via further increases in photosynthesis. Model responses to CO 2 and N fertilization are strongly impacted by how much plant communities can increase their rates of nitrogen fixation and also directly affected by costs of N extraction from soil and stoichiometric flexibility. Illustration of how parametric flexibility impacts model outputs should help inform the interpretation of carbon‐climate feedbacks estimated by in fully coupled Earth system model simulations.

Abstract Surface temperatures are mechanistically linked to vegetation biophysical and physiological processes. Although remote sensing in the thermal infrared (TIR) domain can offer novel insights into the impacts of changing surface temperatures on vegetation, the transformative potential of remote sensing for plant ecology has not yet been realized. Remotely sensed surface temperatures can be used to derive stomatal behaviour and identify stressful environmental conditions in near‐real time. Plant species, traits and structural characteristics can be evaluated with high spectral resolution TIR emissivity. Beyond canopy scales, thermal remote sensing can enhance the inferences obtained from manipulative experiments and empirical evidence, providing unique insight into shifts in species ranges and phenology with changing climate conditions. Scaling leaf traits, canopy structure and regional patterns require an integrated understanding of both process and technology. Theory linking surface temperatures to vegetation dynamics is summarized from an energy balance perspective. We outline scaling considerations including the impacts of morphology on leaf energy balance, canopy structure influences on convective heat exchange and potential confounding impacts of non‐vegetated surfaces. Synthesis . We introduce a unifying framework to link leaf to globe through thermal remote sensing. Recent and emerging advances in sensors, data availability and analytics, together with synergies between TIR remote sensing and other data sources, present a timely opportunity for ecologists to advance our understanding of plant physiology, ecology and biogeography with thermal remote sensing.

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

More accurate and process-based satellite evapotranspiration (ET) estimation for the Tibetan Plateau (TP)—the Third Pole of the world—have long been of major interest in hydrometeorology. Combining recent advances in satellite-based ET mechanistic algorithms and data-oriented methods allows ET hybrid modeling by coupling physical constraints with machine learning (ML). Specifically, we developed two hybrid models, a surface conductance-based ML model (ML-Gs) and a soil evaporation-based ML model (ML-Es), to estimate regional ET on the TP. These hybrid models have biophysical framework, under which one of the parameters or components is modeled using ML. Hybrid models make ML complementary to the process-based ET framework, which to find an optimal junction between well physical mechanism and high model performance. The daily ET estimates were evaluated at 28 eddy covariance flux tower sites, as well as by comparison with two process-based ET algorithms (a Penman–Monteith-based ET-PM algorithm and a Priestley-Taylor-based ET-PT algorithm) and a data-oriented pure ML method. The hybrid models decreased the root-mean-square-error (RMSE) of two physical algorithms (1.11 mm/day for ET-PM, 1.09 mm/day for ET-PT) to 0.50 mm/day, and increased the Kling-Gupta efficiency (KGE) (0.35 for ET-PM, 0.36 for ET-PT) to 0.92. Our hybrid models also showed improved performance (KGE of 0.65) than pure ML (KGE of 0.62) at data-sparse regions as well as for the responses to extreme weather events. It indicates that our approach does not only boost the ET simulation accuracy, but also improve the physical understanding of ML-based ET estimation. More importantly, ML-Es focuses on the ET components on the TP and is more well-defined than ML-Gs. An innovation of our approach is that for data-sparse regions and extreme cases, the more robust physical mechanism was coupled, the better generalization performance of hybrid model could achieve. The spatiotemporal ET patterns based on our hybrid models were consistent with the variations in local climatic regions and could provide critical information on the understanding of hydrological processes under the global and regional climate changes.

Although comprehensive evaluation of different types of global terrestrial evapotranspiration (ET) products has been conducted, the satellite remote sensing techniques have prompted the development of several available global ET products, warranting a reassessment as products continue to evolve. Recently, we produced the long-term Global LAnd Surface Satellite (GLASS) ET product, but there is a lack of comparison and evaluation with other ET products and EC observations on a global scale. In this study, we evaluated the accuracy and uncertainty of seven satellite-based (GLASS-AVHRR, GLASS-MODIS, BESS, FLUXCOM, GLEAM, MOD16, and PML_V2) and two reanalysis (ERA5 and MERRA2) global terrestrial ET products at multiple scales for selecting the most suitable ET products and developing large-scale ET models. At the point scale, their accuracy was evaluated through direct comparison with in situ observations from 230 global flux towers. The results indicate that no single ET product can provide the most accurate ET estimates for all land cover types, although GLASS-MODIS [coefficient of determination (R2) of 0.51, Kling–Gupta efficiency (KGE) of 0.68] and FLUXCOM [R2 of 0.51, KGE of 0.66] outperform the seven other products [0.54 =< KGE =< 0.66, 0.35 =< R2 =< 0.50] at all sites. At the basin scale, the accuracy of ET products was assessed through 36 large river basins. The R2 values between all ET products and the water balance-derived ET (WBET) are >0.88, while the accuracies of the nine ET products differ in some sense. The three-cornered hat (TCH) method and comparison analysis are applied to assess the uncertainty of nine ET products at the pixel level. The TCH outputs reveal that GLASS-AVHRR and GLASS-MODIS are the two products with the lowest relative uncertainty, while MERRA2 has the largest relative uncertainty. Our results imply that there is no single ET product performing best in all respects. The selection of ET products for scientific research should consider their performance differences in spatial scale as well as the influence of land cover and climate conditions.

Abstract Background: The forests of western Amazonia are known to be more dynamic that the better-studied forests of eastern Amazonia, but there has been no comprehensive description of the carbon cycle of a western Amazonian forest. Aims: We present the carbon budget of two forest plots in Tambopata in south-eastern Peru, western Amazonia. In particular, we present, for the first time, the seasonal variation in the detailed carbon budget of a tropical forest. Methods: We measured the major components of net primary production (NPP) and total autotrophic respiration over 3–6 years. Results: The NPP for the two plots was 15.1 ± 0.8 and 14.2 ± 1.0 Mg C ha−1 year−1, the gross primary productivity (GPP) was 35.5 ± 3.6 and 34.5 ± 3.5 Mg C ha−1 year−1, and the carbon use efficiency (CUE) was 0.42 ± 0.05 and 0.41 ± 0.05. NPP and CUE showed a large degree of seasonality. Conclusions: The two plots were similar in carbon cycling characteristics despite the different soils, the most notable difference being high allocation of NPP to canopy and low allocation to fine roots in the Holocene floodplain plot. The timing of the minima in the wet–dry transition suggests they are driven by phenological rhythms rather than being driven directly by water stress. When compared with results from forests on infertile forests in humid lowland eastern Amazonia, the plots have slightly higher GPP, but similar patterns of CUE and carbon allocation. Keywords: allocationGPPherbivoryNPPphenologyseasonalitysoil respirationstem respirationtropical forestswestern Amazonia Acknowledgements This work is a product of the RAINFOR, ABERG and GEM research consortia, and was funded by grants from the Gordon and Betty Moore Foundation to the Amazon Forest Inventory Network (RAINFOR) and the Andes Biodiversity and Ecosystems Research Group (ABERG), and two grants to YM from the UK Natural Environment Research Council (Grants NE/D01025X/1, NE/D014174/1), one to PM (NE/F002149/1), and the NERC AMAZONICA consortium grant. YM is supported by the Jackson Foundation, the Oxford Martin School and a European Research Council Advanced Investigator Grant. We thank the Explorer's Inn (Tambopata) for the hosting of the project and the continuous logistical support provided, and INRENA for permits to work in the Tambopata Reserve. We also thank Eric Cosio, Eliana Esparza and Joana Ricardo for facilitating research permits and equipment shipment in Peru.

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

Abstract We explored the potential of Landsat Enhanced Thematic Mapper (ETM+) imagery to quantify the expansion of planted oil palm area and changes in aboveground biomass (AGB) in plantation and forest in Sabah, Malaysian Borneo, from 2000 to 2008. For comparison, a classification layer derived from an Advanced Land-Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) Fine Beam Dual (FBD)-polarized mosaic from 2008 was used for change detection analysis. Field-measured AGB values from 85 ha of forest and oil palm plantation plots were compared with 12 vegetation indices (VIs) and four spectral mixture analysis (SMA) derivatives. Correlations against indices using optical data were higher for oil palm biomass than for forest biomass. Change detection analysis of forest conversion to oil palm plantation was performed for areas designated as protected areas, commercial forest reserve and areas with no forest-use designation. This analysis found an increase in oil palm area of 38% (1450 km2) and a total decrease in forest area of 13.1% (1900 km2) for the whole study area from 2000 to 2008. The greatest area of forest loss was in areas not designated as forest reserve by the Sabah Forestry Department, although some oil palm expansion was detected in both commercial and protected areas. Using derived equations for biomass, we estimated that 46.6 Tg of carbon dioxide equivalents (CO2e) were released in these three forest designations or 53.4 Tg CO2e for the entire study area due to forest conversion to oil palm. These results are presented as relevant for on-going efforts to remotely monitor the carbon emission implications of forest loss as part of the United Nations Framework Convention on Climate Change's (UNFCCC's) proposed mechanism, Reduced Emissions from Deforestation and Degradation (REDD). Acknowledgements We would like to thank Przemyslaw Zelazowski for the use of his LandCor atmospheric correction algorithm and the helpful suggestions of Heiko Balzter, Julia McMorrow and two anonymous reviewers.

Background: The slopes of the eastern Andes harbour some of the highest biodiversity on Earth and a high proportion of endemic species. However, there have been only a few and limited descriptions of carbon budgets in tropical montane forest regions. Aims: We present the first comprehensive data on the production, allocation and cycling of carbon for two high elevation (ca. 3000 m) tropical montane cloud forest plots in the Kosñipata Valley, Peruvian Andes. Methods: We measured the main components and seasonal variation of net primary productivity (NPP), autotrophic (R a) and heterotrophic (R h) respiration to estimate gross primary productivity (GPP) and carbon use efficiency (CUE) in two 1-ha plots. Results: NPP for the two plots was estimated to be 7.05 ± 0.39 and 8.04 ± 0.47 Mg C ha−1 year−1, GPP to be 22.33 ± 2.23 and 26.82 ± 2.97 Mg C ha−1 year−1 and CUE was 0.32 ± 0.04 and 0.30 ± 0.04. Conclusions: We found strong seasonality in NPP and moderate seasonality of R a, suggesting that forest NPP is driven by changes in photosynthesis and highlighting the importance of variation in solar radiation. Our findings imply that trees invest more in biomass production in the cooler season with lower solar radiation and more in maintenance during the warmer and high solar radiation period.

Abstract Water use efficiency (WUE), defined as the ratio of gross primary productivity and evapotranspiration at the ecosystem scale, is a critical variable linking the carbon and water cycles. Incorporating a dependency on vapor pressure deficit, apparent underlying WUE (uWUE) provides a better indicator of how terrestrial ecosystems respond to environmental changes than other WUE formulations. Here we used 20th century simulations from four terrestrial biosphere models to develop a novel variance decomposition method. With this method, we attributed variations in apparent uWUE to both the trend and interannual variation of environmental drivers. The secular increase in atmospheric CO 2 explained a clear majority of total variation (66 ± 32%: mean ± one standard deviation), followed by positive trends in nitrogen deposition and climate, as well as a negative trend in land use change. In contrast, interannual variation was mostly driven by interannual climate variability. To analyze the mechanism of the CO 2 effect, we partitioned the apparent uWUE into the transpiration ratio (transpiration over evapotranspiration) and potential uWUE. The relative increase in potential uWUE parallels that of CO 2 , but this direct CO 2 effect was offset by 20 ± 4% by changes in ecosystem structure, that is, leaf area index for different vegetation types. However, the decrease in transpiration due to stomatal closure with rising CO 2 was reduced by 84% by an increase in leaf area index, resulting in small changes in the transpiration ratio. CO 2 concentration thus plays a dominant role in driving apparent uWUE variations over time, but its role differs for the two constituent components: potential uWUE and transpiration.

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions. .

NASA has launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE). While the initial phases focus on field and airborne data collection, early integration with modeling activities is important to benefit future modeling syntheses. We compiled feedback from ecosystem modeling teams on key data needs, which encompass carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance dynamics. A suite of variables was identified as part of this activity with a critical requirement that they are collected concurrently and representatively over space and time. Individual projects in ABoVE may not capture all these needs, and thus there is both demand and opportunity for the augmentation of field observations, and synthesis of the observations that are collected, to ensure that science questions and integrated modeling activities are successfully implemented.

Evapotranspiration (ET) is critical to terrestrial ecosystems as it links the water, carbon, and surface energy exchanges. Numerous ET models were developed for the ET estimations, but there are large model uncertainties. In this study, a Bayesian Model Averaging (BMA) method was used to merge eight satellite-based models, including five empirical and three process-based models, for improving the accuracy of ET estimates. At twenty-three eddy covariance flux towers, we examined the model performance on all possible combinations of eight models and found that an ensemble with four models (BMA_Best) showed the best model performance. The BMA_Best method can outperform the best of eight models, and the Kling–Gupta efficiency (KGE) value increased by 4% compared with the model with the highest KGE, and decreased RMSE by 4%. Although the correlation coefficient of BMA_Best is less than the best single model, the bias of BMA_Best is the smallest compared with the eight models. Moreover, based on the water balance principle over the river basin scale, the validation indicated the BMA_Best estimates can explain 86% variations. In general, the results showed BMA estimates will be very useful for future studies to characterize the regional water availability over long-time series.

Abstract Uncertainty in ground heat flux ( G ) means that evaluation of the other terms in the surface energy balance (e.g., latent and sensible heat fluxes ( LE and H )) remains problematic. Algorithms that calculate LE and H require available energy, the difference between net radiation, R NET , and G . There are a wide range of approaches to model G for large‐scale applications, with a subsequent wide range of estimates and accuracies. We provide the largest review of these methods to date ( N = 6), evaluating modeled G against measured G from 88 FLUXNET sites. The instantaneous midday variability in G is best captured by models forced with net radiation, while models forced by temperature show the least error at both instantaneous and daily time scales. We produce global decadal data sets of G to illustrate regional and seasonal sensitivities, as well as uncertainty. Global model mean midmorning instantaneous G is highest during September, October, and November at 63.42 (±16.84) Wm −2 , while over December, January, and February G is lowest at 53.86 (±18.09) Wm −2 but shows greater intermodel uncertainty. Results from this work have the potential to improve evapotranspiration estimates and guide appropriate G model selection and development for various land uses.

Abstract Recognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period (2003–09). Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available.

Clarifying how increased atmospheric CO2 concentration (eCO2) contributes to accelerated land carbon sequestration remains important since this process is the largest negative feedback in the coupled carbon–climate system. Here, we constrain the sensitivity of the terrestrial carbon sink to eCO2 over the temperate Northern Hemisphere for the past five decades, using 12 terrestrial ecosystem models and data from seven CO2 enrichment experiments. This constraint uses the heuristic finding that the northern temperate carbon sink sensitivity to eCO2 is linearly related to the site-scale sensitivity across the models. The emerging data-constrained eCO2 sensitivity is 0.64 ± 0.28 PgC yr−1 per hundred ppm of eCO2. Extrapolating worldwide, this northern temperate sensitivity projects the global terrestrial carbon sink to increase by 3.5 ± 1.9 PgC yr−1 for an increase in CO2 of 100 ppm. This value suggests that CO2 fertilization alone explains most of the observed increase in global land carbon sink since the 1960s. More CO2 enrichment experiments, particularly in boreal, arctic and tropical ecosystems, are required to explain further the responsible processes. The northern temperate carbon sink is estimated to increase by 0.64 PgC each year for each increase in atmospheric CO2 concentrations by 100 ppm, suggests an analysis of data from field experiments at 7 sites constraints.

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.

Accurate estimation of gross primary production (GPP), the amount of carbon absorbed by plants via photosynthesis, is of great importance for understanding ecosystem functions, carbon cycling, and climate-carbon feedbacks. Remote sensing has been widely used to quantify GPP at regional to global scales. However, polar-orbiting satellites (e.g., Landsat, Sentinel, Terra, Aqua, Suomi NPP, JPSS, OCO-2) lack the capability to examine the diurnal cycles of GPP because they observe the Earth's surface at the same time of day. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), launched in June 2018, observes the land surface temperature (LST) at different times of day with high spatial resolution (70 m × 70 m) from the International Space Station (ISS). Here, we made use of ECOSTRESS data to predict instantaneous GPP with high spatial resolution for different times of day using a data-driven approach based on machine learning. The predictive GPP model used instantaneous ECOSTRESS LST observations along with the daily enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), land cover type from the National Land Cover Database (NCLD), and instantaneous meteorological data from the ERA5 reanalysis dataset. Our model estimated instantaneous GPP across 56 flux tower sites fairly well (R2 = 0.88, Root Mean Squared Error (RMSE) = 2.42 μmol CO2 m−2 s−1). The instantaneous GPP estimates driven by ECOSTRESS LST captured the diurnal variations of tower GPP for different biomes. We then produced multiple high resolution ECOSTRESS GPP maps for the central and northern California. We found distinct changes in GPP at different times of day (e.g., higher in late morning, peak around noon, approaching zero at dusk), and clear differences in productivity across landscapes (e.g., savannas, croplands, grasslands, and forests) for different times of day. ECOSTRESS GPP also captured the seasonal variations in the diurnal cycling of photosynthesis. This study demonstrates the feasibility of using ECOSTRESS data for producing instantaneous GPP (i.e., GPP for the acquisition time of the ECOSTRESS data) for different times of day. The ECOSTRESS GPP can shed light on how plant photosynthesis and water use vary over the course of the diurnal cycle and inform agricultural management and future improvement of terrestrial biosphere/land surface models.

Accurate estimates of the spatiotemporal distribution of evapotranspiration (ET) are essential for understanding terrestrial energy, carbon and water cycles. Station-based observations are limited for their spatial coverage whereas satellite-derived ET products exhibit large discrepancies and uncertainties. Here we presented a Deep Neural Networks based Merging ET (DNN-MET) framework that combines information from satellite-derived ET products, eddy covariance (EC) observations and ancillary surface properties to improve the representation of the spatiotemporal distribution of ET, especially in data-sparse regions. DNN-MET was implemented over the Heihe River Basin (HRB) from 2008 to 2015, and the performance of DNN-MET and eight input state-of-the-art satellite-derived ET products (i.e., MOD16, ET-SEMI, ET-JPL, ET-MS, ET-HF, GLEAM, ETMonitor and EB-ET) was evaluated against observations from 19 EC flux tower sites. The results showed that DNN-MET improved ET estimates over HRB, and decreased the RMSE by 0.13 to 1.02 mm/day (14%-56%) when compared with eight products. DNN-MET also yielded superior performance compared to the products derived by other merging methods (i.e., Random Forest, Bayesian model averaging and a simple averaging method). When DNN-MET was validated for data-scarce regions, its performance remained better even when the training samples were decreased to 20% of the available EC sites. An innovation of our approach is by building a multivariate merging model with ancillary surface properties, DNN-MET incorporated geographical proximity effects and spatial autocorrelations into merging procedure, which can be used as a “spatial knowledge engine” to improve ET predictions. The approach can be readily and effectively applied elsewhere to improve the spatiotemporal representation of various hydrometeorological variables.

Abstract Spectrally mixed pixels are the rule, not the exception, in decameter terrestrial imaging. By definition, the reflectance spectrum of a mixed pixel is a function of more than one generative process. Physically based surface biology and geology retrievals must therefore isolate the component of interest from a myriad of unrelated processes, each occurring with unknown presence and abundance across the hundreds of square meters sampled by each pixel. Foliar traits, for example, must be isolated from canopy structure and substrate composition. In many cases, these unrelated processes can dominate overall variance of spatially integrated reflectance. We propose a new approach to isolate low‐variance spectral signatures in mixed pixels. The aggregate effects of (high‐variance) spatial mixing processes within each pixel are modeled by treating the observed reflectance as a linear mixture of a small set of generic endmember spectra. Spatial mixing effects are removed by computing the (low‐variance) difference between the modeled and observed spectra, named the Mixture Residual (MR). The MR, a residual reflectance spectrum that is presumed to carry the subtler and variable signals of interest, is then leveraged as a source of signal. We illustrate the approach using three independent collections of reflectance spectra: synthetic composites computed from field measurements, NEON AOP airborne image compilations, and DESIS satellite data. The MR is found to discriminate between land cover versus plant trait signals, and to accentuate subtle absorption features. Mean band‐to‐band correlations within the visible, NIR, and SWIR wavebands decrease from 0.97, 0.94, and 0.97 to 0.95, 0.04, and 0.31 (respectively). The number of dimensions required to explain 99% of image variance increases from 4 to 13. We focus on vegetation as an illustrative example, but note that the concept can be extended to other classes of spectra and used as an input to other algorithms.

Abstract Estimating the impacts of climate change on the global carbon cycle relies on projections from Earth system models (ESMs). While ESMs currently project large warming in the high northern latitudes, the magnitude and sign of the future carbon balance of Arctic-Boreal ecosystems are highly uncertain. The new generation of increased complexity ESMs in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) is intended to improve future climate projections. Here, we benchmark the Coupled Model Intercomparison Project (CMIP) 5 and 6 (8 CMIP5 members and 12 CMIP6 members) with the International Land Model Benchmarking (ILAMB) tool over the region of NASA’s Arctic-Boreal vulnerability experiment (ABoVE) in North America. We show that the projected average net biome production (NBP) in 2100 from CMIP6 is higher than that from CMIP5 in the ABoVE domain, despite the model spread being slightly narrower. Overall, CMIP6 shows better agreement with contemporary observed carbon cycle variables (photosynthesis, respiration, biomass) than CMIP5, except for soil carbon and turnover time. Although both CMIP ensemble members project the ABoVE domain will remain a carbon sink by the end of the 21st century, the sink strength in CMIP6 increases with CO 2 emissions. CMIP5 and CMIP6 ensembles indicate a tipping point defined here as a negative inflection point in the NBP curve by 2050–2080 independently of the shared socioeconomic pathway (SSP) for CMIP6 or representative concentration pathway (RCP) for CMIP5. The model ensembles therefore suggest that, if the carbon sink strength keeps declining throughout the 21st century, the Arctic-Boreal ecosystems in North America may become a carbon source over the next century.

This paper examines the spatial point pattern of industrial toxic substances and the associated environmental justice implications in the San Francisco Bay Area, California, USA. Using a spatial analysis method called Ripley's K we assess environmental justice across multiple spatial scales, and we verify and quantify the West Oakland neighborhood as an environmental justice site as designated by the US Environmental Protection Agency. Further, we integrate the ISCST3 air dispersion model with Geographic Information Systems (GIS) to identify the number of people potentially affected by a particular facility, and engage the problem of non-point sources of diesel emissions with an analysis of the street network.

Evapotranspiration models allow climate modelers to describe surface–atmosphere interactions, ecologists to understand the impact that global temperature change and increased radiation budgets will have on ecosystems, and farmers to decide how much irrigation to give their crops. Physically based algorithms for estimating evapotranspiration must manage a trade-off between physical realism and the difficulty of parameterizing key inputs, namely resistance factors associated with water vapor transport through the canopy and turbulent transport of water vapor from the canopy to ambient air. In this study we calculate predicted evapotranspiration at 42 AmeriFlux sites using two types of dedicated evapotranspiration models—one using physical resistances from the Penman–Monteith equation (Monteith, 1965) (Mu et al., 2007, Mu et al., 2011) and another based on the Priestley–Taylor (1972) equation, substituting functional constraints for resistances (Fisher et al., 2008). We analyze the structure of the residual series with respect to various meteorological and biophysical inputs, specifically Jarvis and McNaughton's (1986) decoupling coefficient, Ω, which is designed to represent the degree of control that plant stomata versus atmospheric demand and net radiation exercise over transpiration. We find that vegetation indices, magnitude of daytime fluxes, and bulk canopy resistance (rc)—which largely drives Ω—are strong predictors of patterns in model bias for all flux products. Though our analysis suggests a consistently negative relationship between Ω and mean predicted error for all evapotranspiration models, we found that vegetation indices and flux magnitudes were the most significant drivers of model error. Before addressing error associated with canopy resistance and Ω, refinements to existing models should focus on correcting biases with respect to flux magnitudes and canopy indices. We suggest a dual-model approach for backsolving rc (rather than estimating it from lookup tables and canopy indices), and increased attention to water availability, which largely drives stomatal opening and closure.

A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere–Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol.

Abstract. We present results from a study of canopy-atmosphere fluxes of carbon dioxide from 2007 to 2009 above a site in Wytham Woods, an ancient temperate broadleaved deciduous forest in southern England. Gap-filled net ecosystem exchange (NEE) data were partitioned into gross primary productivity (GPP) and ecosystem respiration (Re) and analysed on daily, monthly and annual timescales. Over the continuous 24 month study period annual GPP was estimated to be 21.1 Mg C ha−1 yr−1 and Re to be 19.8 Mg C ha−1 yr−1; net ecosystem productivity (NEP) was 1.2 Mg C ha−1 yr−1. These estimates were compared with independent bottom-up estimates derived from net primary productivity (NPP) and flux chamber measurements recorded at a plot within the flux footprint in 2008 (GPP = 26.5 ± 6.8 Mg C ha−1 yr−1, Re = 24.8 ± 6.8 Mg C ha−1 yr−1, biomass increment = ~1.7 Mg C ha−1 yr−1). Over the two years the difference in seasonal NEP was predominantly caused by changes in ecosystem respiration, whereas GPP remained similar for equivalent months in different years. Although solar radiation was the largest influence on daily values of CO2 fluxes (R2 = 0.53 for the summer months for a linear regression), variation in Re appeared to be driven by temperature. Our findings suggest that this ancient woodland site is currently a substantial sink for carbon, resulting from continued growth that is probably a legacy of past management practices abandoned over 40 years ago. Our GPP and Re values are generally higher than other broadleaved temperate deciduous woodlands and may represent the influence of the UK's maritime climate, or the particular species composition of this site. The carbon sink value of Wytham Woods supports the protection and management of temperate deciduous woodlands (including those managed for conservation rather than silvicultural objectives) as a strategy to mitigate atmospheric carbon dioxide increases.