Author
Stefan Metzger
Other affiliations: Battelle Memorial Institute, University of Colorado Boulder, University of Bayreuth ...read more
Bio: Stefan Metzger is an academic researcher from National Ecological Observatory Network. The author has contributed to research in topics: Eddy covariance & Flux footprint. The author has an hindex of 17, co-authored 62 publications receiving 819 citations. Previous affiliations of Stefan Metzger include Battelle Memorial Institute & University of Colorado Boulder.
Topics: Eddy covariance, Flux footprint, Atmosphere, Permafrost, Flux
Papers
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Lawrence Berkeley National Laboratory1, National University of Singapore2, Stanford University3, University of Wisconsin-Madison4, National Ecological Observatory Network5, Oak Ridge National Laboratory6, McMaster University7, University of Nebraska–Lincoln8, University of California, Berkeley9, Agricultural Research Service10, University of British Columbia11, University of Colorado Boulder12, Ohio State University13, University of Florida14, University of Guelph15, University of Kansas16, Michigan State University17, Pacific Northwest National Laboratory18, United States Department of Agriculture19, University of New Mexico20, National Research Council21, Marine Biological Laboratory22, University of Alberta23, Virginia Commonwealth University24, University of Minnesota25, Université de Montréal26, Dalhousie University27, Carleton University28, Shinshu University29, Japan Agency for Marine-Earth Science and Technology30, Northern Arizona University31, Oregon State University32, Yale University33, Washington State University34, Harvard University35, Texas A&M University36, Indiana University37, Florida International University38, San Diego State University39, California State University, East Bay40, Wayne State University41, University of Sydney42, Wilfrid Laurier University43, University of Alabama44, Environment Canada45, United States Geological Survey46, Argonne National Laboratory47, Osaka Prefecture University48, University of Delaware49, University of Missouri50, University of Sheffield51
TL;DR: In this article, the authors evaluate the representativeness of flux footprints and evaluate potential biases as a consequence of the footprint-to-target-area mismatch, which can be used as a guide to identify site-periods suitable for specific applications.
137 citations
TL;DR: In this article, the authors characterized the sensible (H ) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River catchment, Inner Mongolia, China.
Abstract: The goal of this study is to characterize the sensible ( H ) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River catchment, Inner Mongolia, China. Eddy-covariance flux measurements at 50–100 m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90 m of the flux measurements. For a total of 8446 flux observations during 12 flights, MODIS land surface temperature (LST) and enhanced vegetation index (EVI) in each flux footprint are determined. Boosted regression trees are then used to infer an environmental response function (ERF) between all flux observations ( H , LE) and biophysical (LST, EVI) and meteorological drivers. Numerical tests show that ERF predictions covering the entire Xilin River catchment (a3670 km 2 ) are accurate to ≤18% (1 σ). The predictions are then summarized for each land cover type, providing individual estimates of source strength (36 W m −2 H −2 , 46 W m −2 −2 ) and spatial variability (11 W m −2 H −2 , 14 W m −2 LE −2 ) to a precision of ≤5%. Lastly, ERF predictions of land cover specific Bowen ratios are compared between subsequent flights at different locations in the Xilin River catchment. Agreement of the land cover specific Bowen ratios to within 12 p 9% emphasizes the robustness of the presented approach. This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.
79 citations
Helmholtz Centre for Environmental Research - UFZ1, University of Liège2, Karlsruhe Institute of Technology3, University of Antwerp4, Finnish Meteorological Institute5, University of Nebraska–Lincoln6, University of Edinburgh7, Forschungszentrum Jülich8, Institut national de la recherche agronomique9, Technical University of Denmark10, University of Reims Champagne-Ardenne11, Lund University12, Max Planck Society13, University of Granada14, University of Helsinki15, University of Wisconsin-Madison16, Academy of Sciences of the Czech Republic17, Swedish University of Agricultural Sciences18, University of Göttingen19, University of Innsbruck20
TL;DR: In this article, the authors describe the standards the ICOS ecosystem station network has adopted for GHG flux measurements with respect to the setup of instrumentation on towers to maximize measurement precision and accuracy.
Abstract: The Integrated Carbon Observation System Research Infrastructure aims to provide long-Term, continuous observations of sources and sinks of greenhouse gases such as carbon dioxide, methane, nitrous oxide, and water vapour. At ICOS ecosystem stations, the principal technique for measurements of ecosystem-Atmosphere exchange of GHGs is the eddy-covariance technique. The establishment and setup of an eddy-covariance tower have to be carefully reasoned to ensure high quality flux measurements being representative of the investigated ecosystem and comparable to measurements at other stations. To fulfill the requirements needed for flux determination with the eddy-covariance technique, variations in GHG concentrations have to be measured at high frequency, simultaneously with the wind velocity, in order to fully capture turbulent fluctuations. This requires the use of high-frequency gas analysers and ultrasonic anemometers. In addition, to analyse flux data with respect to environmental conditions but also to enable corrections in the post-processing procedures, it is necessary to measure additional abiotic variables in close vicinity to the flux measurements. Here we describe the standards the ICOS ecosystem station network has adopted for GHG flux measurements with respect to the setup of instrumentation on towers to maximize measurement precision and accuracy while allowing for flexibility in order to observe specific ecosystem features. (Less)
66 citations
TL;DR: In this paper, a new approach was developed to project turbulent flux maps at regional scale and hourly temporal resolution using environmental response functions (ERFs), which is based on an approach employed in airborne flux observations, and relates turbulent flux observations to meteorological forcings and surface properties across the flux footprint.
64 citations
Academy of Sciences of the Czech Republic1, University of Antwerp2, Institut national de la recherche agronomique3, University of Liège4, Finnish Meteorological Institute5, National Ecological Observatory Network6, Trinity College, Dublin7, Université Paris-Saclay8, Lawrence Berkeley National Laboratory9, Australian National University10, ETH Zurich11, University of Nebraska–Lincoln12, University of Toulouse13, University of Edinburgh14, ARPA-E15, Stockholm University16, LI-COR Biosciences17, University of Orléans18, Forschungszentrum Jülich19, Swedish University of Agricultural Sciences20, Dresden University of Technology21, Icos22, Karlsruhe Institute of Technology23, Technical University of Denmark24, University of Reims Champagne-Ardenne25, University of Gothenburg26, Lund University27, Max Planck Society28, University of Granada29, Autonomous University of Barcelona30, International Livestock Research Institute31, Central Maine Community College32, University of Wisconsin-Madison33, University College Dublin34, University of Padua35, University of Eastern Finland36, University of Göttingen37, Natural Environment Research Council38, Université de Montréal39, Swiss Federal Institute for Forest, Snow and Landscape Research40, Université catholique de Louvain41, University of Innsbruck42
TL;DR: The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans.
Abstract: Research infrastructures play a key role in launching a new generation of integrated long-Term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-Access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value. (Less)
64 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
01 Dec 2012
Abstract: We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.
948 citations
TL;DR: In this article, a two-dimensional footprint model for flux footprint prediction is proposed. But it is not suitable for application to long time series, due to their high computational demands.
Abstract: . Flux footprint models are often used for interpretation of flux tower measurements, to estimate position and size of surface source areas, and the relative contribution of passive scalar sources to measured fluxes. Accurate knowledge of footprints is of crucial importance for any upscaling exercises from single site flux measurements to local or regional scale. Hence, footprint models are ultimately also of considerable importance for improved greenhouse gas budgeting. With increasing numbers of flux towers within large monitoring networks such as FluxNet, ICOS (Integrated Carbon Observation System), NEON (National Ecological Observatory Network), or AmeriFlux, and with increasing temporal range of observations from such towers (of the order of decades) and availability of airborne flux measurements, there has been an increasing demand for reliable footprint estimation. Even though several sophisticated footprint models have been developed in recent years, most are still not suitable for application to long time series, due to their high computational demands. Existing fast footprint models, on the other hand, are based on surface layer theory and hence are of restricted validity for real-case applications. To remedy such shortcomings, we present the two-dimensional parameterisation for Flux Footprint Prediction (FFP), based on a novel scaling approach for the crosswind distribution of the flux footprint and on an improved version of the footprint parameterisation of Kljun et al. (2004b). Compared to the latter, FFP now provides not only the extent but also the width and shape of footprint estimates, and explicit consideration of the effects of the surface roughness length. The footprint parameterisation has been developed and evaluated using simulations of the backward Lagrangian stochastic particle dispersion model LPDM-B (Kljun et al., 2002). Like LPDM-B, the parameterisation is valid for a broad range of boundary layer conditions and measurement heights over the entire planetary boundary layer. Thus, it can provide footprint estimates for a wide range of real-case applications. The new footprint parameterisation requires input that can be easily determined from, for example, flux tower measurements or airborne flux data. FFP can be applied to data of long-term monitoring programmes as well as be used for quick footprint estimates in the field, or for designing new sites.
524 citations
TL;DR: The REddyProc package as discussed by the authors provides standard CO2-focused post-processing routines for reading (half-)hourly data from different formats, estimating the u* threshold, as well as gap-filling, flux-partitioning, and visualizing the results.
Abstract: . With the eddy covariance (EC) technique, net fluxes of carbon dioxide (CO2) and other trace gases as well as water and energy fluxes can be measured at the ecosystem level. These flux measurements are a main source for understanding biosphere–atmosphere interactions and feedbacks through cross-site analysis, model–data integration, and upscaling. The raw fluxes measured with the EC technique require extensive and laborious data processing. While there are standard tools1 available in an open-source environment for processing high-frequency (10 or 20 Hz) data into half-hourly quality-checked fluxes, there is a need for more usable and extensible tools for the subsequent post-processing steps. We tackled this need by developing the REddyProc package in the cross-platform language R that provides standard CO2-focused post-processing routines for reading (half-)hourly data from different formats, estimating the u* threshold, as well as gap-filling, flux-partitioning, and visualizing the results. In addition to basic processing, the functions are extensible and allow easier integration in extended analysis than current tools. New features include cross-year processing and a better treatment of uncertainties. A comparison of REddyProc routines with other state-of-the-art tools resulted in no significant differences in monthly and annual fluxes across sites. Lower uncertainty estimates of both u* and resulting gap-filled fluxes by 50 % with the presented tool were achieved by an improved treatment of seasons during the bootstrap analysis. Higher estimates of uncertainty in daytime partitioning (about twice as high) resulted from a better accounting for the uncertainty in estimates of temperature sensitivity of respiration. The provided routines can be easily installed, configured, and used. Hence, the eddy covariance community will benefit from the REddyProc package, allowing easier integration of standard post-processing with extended analysis. 1 http://fluxnet.fluxdata.org/2017/10/10/toolbox-a-rolling-list-of-softwarepackages-for-flux-related-data-processing/ , last access: 17 August 2018
479 citations
01 Dec 2012
TL;DR: In this paper, the magnitude and evolution of parameters that characterize feedbacks in the coupled carbon-climate system are compared across nine Earth system models (ESMs), based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1.
Abstract: The magnitude and evolution of parameters that characterize feedbacks in the coupled carbon–climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1. These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO2 fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO2 concentration and to changes in temperature and other climate variables. The carbon–concentration and carbon–climate feedback parameters characterize the response of the CO2 flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon–concentration feedback to...
454 citations