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FluxNet
About: FluxNet is a research topic. Over the lifetime, 465 publications have been published within this topic receiving 47348 citations.
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University of California, Berkeley1, University of Bayreuth2, Oak Ridge National Laboratory3, United States Department of Agriculture4, University of Montana5, Oregon State University6, Dresden University of Technology7, Duke University8, Yale University9, University of Edinburgh10, National Oceanic and Atmospheric Administration11, Harvard University12, San Diego State University13, Technical University of Denmark14, Indiana University15, Tuscia University16, University of Nebraska–Lincoln17, University of Helsinki18
TL;DR: The FLUXNET project as mentioned in this paper is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere.
Abstract: FLUXNET is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. At present over 140 sites are operating on a long-term and continuous basis. Vegetation under study includes temperate conifer and broadleaved (deciduous and evergreen) forests, tropical and boreal forests, crops, grasslands, chaparral, wetlands, and tundra. Sites exist on five continents and their latitudinal distribution ranges from 70°N to 30°S. FLUXNET has several primary functions. First, it provides infrastructure for compiling, archiving, and distributing carbon, water, and energy flux measurement, and meteorological, plant, and soil data to the science community. (Data and site information are available online at the FLUXNET Web site, http://www-eosdis.ornl.gov/FLUXNET/.) Second, the project supports calibration and flux intercomparison activities. This activity ensures that data from the regional networks are intercomparable. And third, FLUXNET supports the synthesis, discussion, and communication of ideas and data by supporting project scientists, workshops, and visiting scientists. The overarching goal is to provide information for validating computations of net primary productivity, evaporation, and energy absorption that are being generated by sensors mounted on the NASA Terra satellite. Data being compiled by FLUXNET are being used to quantify and compare magnitudes and dynamics of annual ecosystem carbon and water balances, to quantify the response of stand-scale carbon dioxide and water vapor flux densities to controlling biotic and abiotic factors, and to validate a hierarchy of soil–plant–atmosphere trace gas exchange models. Findings so far include 1) net CO 2 exchange of temperate broadleaved forests increases by about 5.7 g C m −2 day −1 for each additional day that the growing season is extended; 2) the sensitivity of net ecosystem CO 2 exchange to sunlight doubles if the sky is cloudy rather than clear; 3) the spectrum of CO 2 flux density exhibits peaks at timescales of days, weeks, and years, and a spectral gap exists at the month timescale; 4) the optimal temperature of net CO 2 exchange varies with mean summer temperature; and 5) stand age affects carbon dioxide and water vapor flux densities.
3,162 citations
09 Jun 2001
3,014 citations
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Potsdam Institute for Climate Impact Research1, University of Bayreuth2, University of California, Berkeley3, Institut national de la recherche agronomique4, Dresden University of Technology5, ETH Zurich6, Max Planck Society7, South Dakota State University8, Academy of Sciences of the Czech Republic9, Finnish Forest Research Institute10, Finnish Meteorological Institute11, Oak Ridge National Laboratory12, Centre national de la recherche scientifique13, University of Helsinki14, Weizmann Institute of Science15
TL;DR: In this paper, the authors analyse the effect of extrapolation of night-time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long-term data sets.
Abstract: This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration (Reco). In particular, we analyse the effect of the extrapolation of night-time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long-term data sets. For this analysis, we used 16 one-year-long data sets of carbon dioxide exchange measurements from European and US-American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems. We show that the temperature sensitivity of Reco, derived from long-term (annual) data sets, does not reflect the short-term temperature sensitivity that is effective when extrapolating from night- to daytime. Specifically, in summer active ecosystems the long
2,881 citations
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TL;DR: The eddy covariance method is most accurate when the atmospheric conditions (wind, temperature, humidity, CO2) are steady, the underlying vegetation is homogeneous and it is situated on flat terrain for an extended distance upwind as discussed by the authors.
Abstract: The eddy covariance technique ascertains the exchange rate of CO2 across the interface between the atmosphere and a plant canopy by measuring the covariance between fluctuations in vertical wind velocity and CO2 mixing ratio. Two decades ago, the method was employed to study CO2 exchange of agricultural crops under ideal conditions during short field campaigns. During the past decade the eddy covariance method has emerged as an important tool for evaluating fluxes of carbon dioxide between terrestrial ecosystems and the atmosphere over the course of a year, and more. At present, the method is being applied in a nearly continuous mode to study carbon dioxide and water vapor exchange at over a hundred and eighty field sites, worldwide. The objective of this review is to assess the eddy covariance method as it is being applied by the global change community on increasingly longer time scales and over less than ideal surfaces.
The eddy covariance method is most accurate when the atmospheric conditions (wind, temperature, humidity, CO2) are steady, the underlying vegetation is homogeneous and it is situated on flat terrain for an extended distance upwind. When the eddy covariance method is applied over natural and complex landscapes or during atmospheric conditions that vary with time, the quantification of CO2 exchange between the biosphere and atmosphere must include measurements of atmospheric storage, flux divergence and advection.
Averaging CO2 flux measurements over long periods (days to year) reduces random sampling error to relatively small values. Unfortunately, data gaps are inevitable when constructing long data records. Data gaps are generally filled with values produced from statistical and empirical models to produce daily and annual sums of CO2 exchange. Filling data gaps with empirical estimates do not introduce significant bias errors because the empirical algorithms are derived from large statistical populations. On the other hand, flux measurement errors can be biased at night when winds are light and intermittent. Nighttime bias errors tend to produce an underestimate in the measurement of ecosystem respiration.
Despite the sources of errors associated with long-term eddy flux measurements, many investigators are producing defensible estimates of annual carbon exchange. When measurements come from nearly ideal sites the error bound on the net annual exchange of CO2 is less than ±50 g C m−2 yr−1. Additional confidence in long-term measurements is growing because investigators are producing values of net ecosystem productivity that are converging with independent values produced by measuring changes in biomass and soil carbon, as long as the biomass inventory studies are conducted over multiple years.
2,210 citations
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Max Planck Society1, Centre national de la recherche scientifique2, McMaster University3, University of California, Berkeley4, National Center for Atmospheric Research5, Potsdam Institute for Climate Impact Research6, Lund University7, University of Sheffield8, University of Antwerp9, Laval University10, Centro Agronómico Tropical de Investigación y Enseñanza11, Wageningen University and Research Centre12, Clark University13
TL;DR: Estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate–carbon cycle process models.
Abstract: Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.
2,081 citations