A Short Note on Marine Reservoir Age Simulations Used in IntCal20

Butzin, Martin; Heaton, Timothy J; Köhler, Peter; Lohmann, Gerrit

doi:10.1017/rdc.2020.9

Cited by 48 publications

(71 citation statements)

References 41 publications

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“…Together, these features enable more accurate estimation of both the global marine calibration curve and the globally averaged MRA, especially beyond the Holocene. We show that the results BICYCLE provides for individual runs are almost indistinguishable from the three dimensional Large Scale Geostrophic Ocean General Circulation Model (LSG OGCM) (Butzin et al 2017(Butzin et al , 2020 and that BICYCLE's model output agrees well with pre-bomb data based on seawater (Bard 1988) and marine shells (Reimer and Reimer 2001;Toggweiler et al 2019).…”

Section: The Marine20 Calibration Curvementioning

confidence: 80%

“…To achieve this, a preliminary Δ 14 C history is estimated based upon Hulu Cave speleothems (Southon et al 2012;Cheng et al 2018). This preliminary Δ 14 C curve is used to drive the LSG OGCM and provide prior, first-order, estimates of regional MRAs (Butzin et al 2020 in this issue) for each IntCal20 marine dataset, with the exception of the Cariaco Basin which is adjusted adaptively during curve construction (Hughen and Heaton 2020 in this issue). The adjusted marine data are then combined with speleothems, macrofossils from Lake Suigetsu, and floating tree-ring sequences to constitute a mixed, atmospheric and atmospheric-adjusted, dataset extending from ca.…”

Section: The Marine20 Calibration Curvementioning

confidence: 99%

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Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP)

et al. 2020

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The concentration of radiocarbon (14C) differs between ocean and atmosphere. Radiocarbon determinations from samples which obtained their 14C in the marine environment therefore need a marine-specific calibration curve and cannot be calibrated directly against the atmospheric-based IntCal20 curve. This paper presents Marine20, an update to the internationally agreed marine radiocarbon age calibration curve that provides a non-polar global-average marine record of radiocarbon from 0–55 cal kBP and serves as a baseline for regional oceanic variation. Marine20 is intended for calibration of marine radiocarbon samples from non-polar regions; it is not suitable for calibration in polar regions where variability in sea ice extent, ocean upwelling and air-sea gas exchange may have caused larger changes to concentrations of marine radiocarbon. The Marine20 curve is based upon 500 simulations with an ocean/atmosphere/biosphere box-model of the global carbon cycle that has been forced by posterior realizations of our Northern Hemispheric atmospheric IntCal20 14C curve and reconstructed changes in CO2 obtained from ice core data. These forcings enable us to incorporate carbon cycle dynamics and temporal changes in the atmospheric 14C level. The box-model simulations of the global-average marine radiocarbon reservoir age are similar to those of a more complex three-dimensional ocean general circulation model. However, simplicity and speed of the box model allow us to use a Monte Carlo approach to rigorously propagate the uncertainty in both the historic concentration of atmospheric 14C and other key parameters of the carbon cycle through to our final Marine20 calibration curve. This robust propagation of uncertainty is fundamental to providing reliable precision for the radiocarbon age calibration of marine based samples. We make a first step towards deconvolving the contributions of different processes to the total uncertainty; discuss the main differences of Marine20 from the previous age calibration curve Marine13; and identify the limitations of our approach together with key areas for further work. The updated values for ΔR, the regional marine radiocarbon reservoir age corrections required to calibrate against Marine20, can be found at the data base http://calib.org/marine/.

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Section: The Marine20 Calibration Curvementioning

confidence: 80%

Section: The Marine20 Calibration Curvementioning

confidence: 99%

Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP)

et al. 2020

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“…For IntCal20 it was decided to apply MRA corrections that vary both spatially and temporally. We use MRA fields calculated by means of an enhanced version of the Hamburg Large Scale Geostrophic Ocean General Circulation Model (LSG OGCM) (see Butzin et al 2020 in this issue, and further references therein) driven by a preliminary atmospheric Δ 14 C reconstruction based on the Hulu Cave data alone. This preliminary atmospheric, Hulu-Cave-based, Δ 14 C estimate was obtained with the same Bayesian spline procedure used for IntCal20.…”

Section: Marine 14 C Reservoir Agesmentioning

confidence: 99%

The IntCal20 Northern Hemisphere Radiocarbon Age Calibration Curve (0–55 cal kBP)

et al. 2020

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Radiocarbon (14C) ages cannot provide absolutely dated chronologies for archaeological or paleoenvironmental studies directly but must be converted to calendar age equivalents using a calibration curve compensating for fluctuations in atmospheric 14C concentration. Although calibration curves are constructed from independently dated archives, they invariably require revision as new data become available and our understanding of the Earth system improves. In this volume the international 14C calibration curves for both the Northern and Southern Hemispheres, as well as for the ocean surface layer, have been updated to include a wealth of new data and extended to 55,000 cal BP. Based on tree rings, IntCal20 now extends as a fully atmospheric record to ca. 13,900 cal BP. For the older part of the timescale, IntCal20 comprises statistically integrated evidence from floating tree-ring chronologies, lacustrine and marine sediments, speleothems, and corals. We utilized improved evaluation of the timescales and location variable 14C offsets from the atmosphere (reservoir age, dead carbon fraction) for each dataset. New statistical methods have refined the structure of the calibration curves while maintaining a robust treatment of uncertainties in the 14C ages, the calendar ages and other corrections. The inclusion of modeled marine reservoir ages derived from a three-dimensional ocean circulation model has allowed us to apply more appropriate reservoir corrections to the marine 14C data rather than the previous use of constant regional offsets from the atmosphere. Here we provide an overview of the new and revised datasets and the associated methods used for the construction of the IntCal20 curve and explore potential regional offsets for tree-ring data. We discuss the main differences with respect to the previous calibration curve, IntCal13, and some of the implications for archaeology and geosciences ranging from the recent past to the time of the extinction of the Neanderthals.

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“…Initial MRA estimates for each of our locations were obtained by creating a preliminary atmospheric calibration curve using the same Bayesian spline methodology but based only on the Hulu Cave record (Southon et al 2012;Cheng et al 2018). This Hulu-based curve was then used as a forcing for an enhanced Hamburg Large Scale Geostrophic (LSG) ocean general circulation model to provide estimates K of MRA at each given location (see Butzin et al 2020 in this issue, for details). Due to coastal effects, these LSG estimates were considered to define the shape of the MRA, i.e.…”

Section: Offsets: Reservoir Ages and Dead Carbon Fractionsmentioning

confidence: 99%

“…Here K are our location-specific MRA estimates obtained from the LSG model in its "GS" scenario (Butzin et al 2020 in this issue) forced by a preliminary estimate of atmospheric Δ 14 C using the same Bayesian spline and errors-in-variables approach as IntCal20 but based only upon data from Hulu Cave. To complete our offset model, we are required to specify K , the variability in MRA/dcf offset from one calendar year to the next beyond that already present in the LSG/constant dcf model.…”

Section: Priors On Marine and Dead Carbon Fraction Offsetsmentioning

confidence: 99%

The IntCal20 Approach to Radiocarbon Calibration Curve Construction: A New Methodology Using Bayesian Splines and Errors-in-Variables

et al. 2020

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To create a reliable radiocarbon calibration curve, one needs not only high-quality data but also a robust statistical methodology. The unique aspects of much of the calibration data provide considerable modeling challenges and require a made-to-measure approach to curve construction that accurately represents and adapts to these individualities, bringing the data together into a single curve. For IntCal20, the statistical methodology has undergone a complete redesign, from the random walk used in IntCal04, IntCal09 and IntCal13, to an approach based upon Bayesian splines with errors-in-variables. The new spline approach is still fitted using Markov Chain Monte Carlo (MCMC) but offers considerable advantages over the previous random walk, including faster and more reliable curve construction together with greatly increased flexibility and detail in modeling choices. This paper describes the new methodology together with the tailored modifications required to integrate the various datasets. For an end-user, the key changes include the recognition and estimation of potential over-dispersion in 14C determinations, and its consequences on calibration which we address through the provision of predictive intervals on the curve; improvements to the modeling of rapid 14C excursions and reservoir ages/dead carbon fractions; and modifications made to, hopefully, ensure better mixing of the MCMC which consequently increase confidence in the estimated curve.

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A Short Note on Marine Reservoir Age Simulations Used in IntCal20

Cited by 48 publications

References 41 publications

Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP)

Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP)

The IntCal20 Northern Hemisphere Radiocarbon Age Calibration Curve (0–55 cal kBP)

The IntCal20 Approach to Radiocarbon Calibration Curve Construction: A New Methodology Using Bayesian Splines and Errors-in-Variables

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