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Making the case for an International Decade of Radiocarbon.

Timothy I EglintonHeather D GravenPeter A RaymondSusan E TrumboreLihini AluwihareEdouard BardSourish BasuPierre FriedlingsteinSamuel HammerJoanna LesterJonathan SandermanEdward A G SchuurCarlos A SierraHans-Arno SynalJocelyn C TurnbullLukas Wacker
Published in: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences (2023)
Radiocarbon ( 14 C) is a critical tool for understanding the global carbon cycle. During the Anthropocene, two new processes influenced 14 C in atmospheric, land and ocean carbon reservoirs. First, 14 C-free carbon derived from fossil fuel burning has diluted 14 C, at rates that have accelerated with time. Second, 'bomb' 14 C produced by atmospheric nuclear weapon tests in the mid-twentieth century provided a global isotope tracer that is used to constrain rates of air-sea gas exchange, carbon turnover, large-scale atmospheric and ocean transport, and other key C cycle processes. As we write, the 14 C/ 12 C ratio of atmospheric CO 2 is dropping below pre-industrial levels, and the rate of decline in the future will depend on global fossil fuel use and net exchange of bomb 14 C between the atmosphere, ocean and land. This milestone coincides with a rapid increase in 14 C measurement capacity worldwide. Leveraging future 14 C measurements to understand processes and test models requires coordinated international effort-a 'decade of radiocarbon' with multiple goals: (i) filling observational gaps using archives, (ii) building and sustaining observation networks to increase measurement density across carbon reservoirs, (iii) developing databases, synthesis and modelling tools and (iv) establishing metrics for identifying and verifying changes in carbon sources and sinks. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'.
Keyphrases
  • particulate matter
  • climate change
  • current status
  • computed tomography
  • wastewater treatment
  • machine learning
  • big data
  • artificial intelligence
  • deep learning
  • pet ct