The National Ecological Observatory Network (NEON) provides open-access measurements of stable isotope ratios in atmospheric water vapor (δ2H, δ18O) and carbon dioxide (δ13C) at different tower heights, as well as aggregated biweekly precipitation samples (δ2H, δ18O) across the United States. These measurements were used to create the NEON Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) dataset estimating precipitation (P; δ2H, δ18O), evapotranspiration (ET; δ2H, δ18O), and net ecosystem exchange (NEE; δ13C) isotope ratios. Statistically downscaled precipitation datasets were generated to be consistent with the estimated covariance between isotope ratios and precipitation amounts at daily time scales. Isotope ratios in ET and NEE fluxes were estimated using a mixing-model approach with calibrated NEON tower measurements. NEON-DICEE is publicly available on HydroShare and can be reproduced or modified to fit user specific applications or include additional NEON data records as they become available. The NEON-DICEE dataset can facilitate understanding of terrestrial ecosystem processes through their incorporation into environmental investigations that require daily δ2H, δ18O, and δ13C flux data.
Carbon fluxes in terrestrial ecosystems and their response to environmental change are a major source of uncertainty in the modern carbon cycle. The National Ecological Observatory Network (NEON) presents the opportunity to merge eddy covariance (EC)‐derived fluxes with CO2isotope ratio measurements to gain insights into carbon cycle processes. Collected continuously and consistently across >40 sites, NEON EC and isotope data facilitate novel integrative analyses. However, currently provisioned atmospheric isotope data are uncalibrated, greatly limiting ability to perform cross‐site analyses. Here, we present two approaches to calibrating NEON CO2isotope ratios, along with an R package to calibrate NEON data. We find that calibrating CO2isotopologues independently yields a lower
- PAR ID:
- 10445360
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 126
- Issue:
- 3
- ISSN:
- 2169-8953
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Stable isotope ratios of H (
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Rationale The simultaneous analysis of the three stable isotopes of oxygen—triple oxygen isotope analysis—has become an important analytical technique in natural sciences. Determination of the abundance of the rare17O isotope in CO2gas using magnetic sector isotope ratio mass spectrometry is complicated by the isobaric interference of17O by13C (13C16O16O and12C16O17O, both have mass 45 amu). A number of analytical techniques have been used to measure the17O/16O ratio of CO2gas. They either are time consuming and technically challenging or have limited precision. A rapid and precise alternative to the available analytical methods is desirable.
Methods We present the results of triple oxygen isotope analyses using an Aerodyne tunable infrared laser direct absorption spectroscopy (TILDAS) CO2analyzer configured for16O,17O, and18O combined with a custom gas inlet system. We evaluate the sensitivity of our results to a number of parameters. CO2samples with a wide range of δ18O values (from −9.28‰ to 39.56‰) were measured and compared to results using the well‐established fluorination‐gas source mass spectrometry method.
Results The TILDAS system has a precision (standard error, 2
σ ) of better than ±0.03‰ for δ18O and ±10 per meg for Δ′17O values, equivalent to the precision of previous analytical methods. Samples as small as 3 μmol CO2(equivalent to 300 μg CaCO3) can be analyzed with a total analysis time of ~30 min.Conclusions We have successfully developed an analytical technique for the simultaneous determination of the δ17O and δ18O values of CO2gas. The precision is equal to or better than that of existing techniques, with no additional chemical treatments required. Analysis time is rapid, and the system is easily automated so that large numbers of samples can be analyzed with minimal effort.
