Improving the routine analysis of siderite for δ¹³C and δ¹⁸O in environmental change research

Abstract

Rationale The carbon (δ13C) and oxygen (δ18O) isotope composition of siderite (FeCO3) is used widely to understand and quantify geochemical processes to reconstruct past climate and environmental change. However, few laboratories follow precisely the same protocol for the preparation and analysis of siderite‐bearing materials, which combined with the absence of international reference materials and mineral‐specific acid fractionation factors, leads potentially to significant differences in isotope data generated by individual laboratories. Here we examine procedures for the isotope analysis of siderite and discuss factors potentially contributing to inconsistencies in sample measurement data. Methods Isotope analysis of siderite is first assessed using similar versions of the classical off‐line, sealed vessel acid digestion method by comparing data sets obtained from intercomparison materials measured at two participating laboratories. We then compare data from the classical method against those generated using an automated preparation technique using data produced from an independent set of test materials. Results Measurement of siderite δ13C is generally both repeatable and reproducible, but measurement of δ18O may be subject to large (~1‰), method‐dependent bias for siderite reacted at differing temperatures (70°C and 100°C) under classical and automated CO2 preparation conditions. The potential for poor oxygen isotope measurement reproducibility is amplified by local differences in sample preparation protocols and procedures used to calibrate measurement data to international reference scales. Conclusions We offer suggestions for improving the repeatability and reproducibility of δ13C and δ18O analysis on siderite. The challenge of producing consistent isotope data from siderite can only be resolved by ensuring the availability of siderite reference materials to facilitate identical treatment as a basis for minimising method‐dependent contributions to data inconsistency between laboratories.