The AmeriFlux data activity and data system: an evolving collection of data management techniques, tools, products and services

Abstract

Abstract. The Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL), USA has provided scientific data management support for the US Department of Energy and international climate change science since 1982. Among the many data archived and available from CDIAC are collections from long-term measurement projects. One current example is the AmeriFlux measurement network. AmeriFlux provides continuous measurements from forests, grasslands, wetlands, and croplands in North, Central, and South America and offers important insight about carbon cycling in terrestrial ecosystems. To successfully manage AmeriFlux data and support climate change research, CDIAC has designed flexible data systems using proven technologies and standards blended with new, evolving technologies and standards. The AmeriFlux data system, comprised primarily of a relational database, a PHPbased data interface and a FTP server, offers a broad suite of AmeriFlux data. The data interface allows users to query the AmeriFlux collection in a variety of ways and then subset, visualize and download the data. From the perspective of data stewardship, on the other hand, this system is designed for CDIAC to easily control database content, automate data movement, track data provenance, manage metadata content, and handle frequent additions and corrections. CDIAC and researchers in the flux community developed data submission guidelines to enhance the AmeriFlux data collection, enable automated data processing, and promote standardization across regional networks. Both continuous flux and meteorological data and irregular biological data collected at AmeriFlux sites are carefully scrutinized by CDIAC using established quality-control algorithms before the data are ingested into the AmeriFlux data system. Other tasks at CDIAC include reformatting and standardizing the diverse and heterogeneous datasets received from individual sites into a uniform and consistent network database, generating high-level derived products to meet the current demands from a broad user group, and developing new products in anticipation of future needs. In this paper, we share our approaches to meet the challenges of standardizing, archiving and delivering quality, well-documented AmeriFlux data worldwide to benefit others with similar challenges of handling diverse climate change data, to further heighten awareness and use of an outstanding ecological data resource, and to highlight expanded software engineering applications being used for climate change measurement data.

Partitioning Evapotranspiration into Green and Blue Water Sources in the Conterminous United States

“…We used level 4 latent heat flux data 36 from 21 eddy covariance FLUXNET sites (79 station-years) for the years 2001–2007 (see Fig. 7) obtained from the Oak Ridge National Laboratory’s AmeriFlux website (http://ameriflux.ornl.gov/).…”

“…7) obtained from the Oak Ridge National Laboratory’s AmeriFlux website (http://ameriflux.ornl.gov/). FLUXNET is a global network of micrometeorological flux measurement sites that measure the exchange of CO 2 , water vapor and energy between the biosphere and the atmosphere 36, 37 . The tower-measured monthly latent heat flux (LE, W/m 2 ) data were converted to ET (mm/month) using the proportionality parameter between energy and depth units of ET 38 aswhere λ is the latent heat of vaporization (2.45 MJ/kg); LE is comparable energy units of MJ/m 2 /day and ET in mm/day (i.e., 1 MJ/m 2 /day = 0.408 mm/day with a water density of 1000 kg/m 3 ).…”

Model‐data assimilation of multiple phenological observations to constrain and predict leaf area index

“…The cut-off values were chosen to be high enough to eliminate cloudy days. The observation uncertainty for PAR was approximated as having a coefficient of variation of ;10% from the AmeriFlux quality assurance measurement reports (Boden et al 2013). The compound uncertainty for a single tower LAI measurement is independently calculated for each time from the uncertainties of PAR, m, and k with the combined uncertainty formula…”

The NASA Carbon Airborne Flux Experiment (CARAFE): instrumentation and methodology

“…More recently, satellite measurements have been employed to provide increased sample density and coverage beyond that available from in situ measurements (Basu et al, 2013;Houweling et al, 2015). Bottom-up GHG flux estimates for terrestrial vegetation can be obtained from biogeophysical process models (Schaefer et al, 2008) and/or analysis of GHG flux observations, the latter typically from large tower networks such as AmeriFlux (Boden et al, 2013;Jung et al, 2012). Top-down and bottom-up flux estimates often compare poorly and models disagree among themselves in regional flux estimation, with uncertainties typically exceeding 100 % for continental-scale flux estimates Huntzinger et al, 2012).…”