Publication | Open Access
Optimizing the Use of Carbonate Standards to Minimize Uncertainties in Clumped Isotope Data
63
Citations
32
References
2019
Year
EngineeringCarbonate StandardsEarth System ScienceClumped Isotope DataEarth ScienceCalibrationδ 47Carbon CycleAbsolute Reference FrameEmpirical Transfer FunctionIsotope AnalysisMarine GeologyBiogeochemistryGeochemical DataGeologyPaleoclimatologyIsotope GeochemistryStable Isotope ProbingGeochemistryEarth Sciences
Abstract Clumped isotopes provide a theory‐based proxy to reconstruct formation temperatures of carbonates. With the introduction of the empirical transfer function (ETF) and several carbonate standards, interlaboratory comparison has become achievable. Due to the rare occurrence of the multiply substituted isotopologues, the analytical precision of these measurements is very low. In order to improve precision, we investigate the optimal strategy for the conversion into the absolute reference frame using a simulation approach. We find that changing the relative proportions of the standards to include more standards that are closer to the unknown target Δ 47 value can greatly improve attainable precision. The inclusion of a hypothetical 4 °C standard results in only modest improvements in final temperature estimates for Earth surface temperature (0 and 40 °C) samples, indicating that the set of ETH‐1–3 standards is suitable for most applications. Full interpolation between two subsequent hypothetical standards, with Δ 47 values of CO 2 equilibrated at 1000 and 25 °C, results in modest improvements for samples with extreme Δ 47 values. With a more optimal distribution of standards it is possible to measure more sample replicates before the uncertainty derived from the ETF becomes limiting. We provide suggestions for the optimal distribution of standards for all target sample Δ 47 values and the R code to perform these simulations based on different laboratory settings. These optimizations can also be applied for ETFs using heated and equilibrated gases. We demonstrate numerically how optimizing the distribution and relative abundance of standards can increase measurement precision.
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