Statistical approaches and tools for IntCal20 · Poster Overview Summary of IntCal20: Aims, Data...
Transcript of Statistical approaches and tools for IntCal20 · Poster Overview Summary of IntCal20: Aims, Data...
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Statistical approaches and tools for IntCal20
Christopher Bronk Ramsey, Tim Heaton, Maarten Blaauw, Paul Blackwell, Paula Reimer, Ron Reimer,
and Marian Scott
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• High frequency solar Miyake-type events• Records with uncertainty in calendar age• Floating tree-ring sequences (eg late glacial)• Reservoir and dead-carbon effects
Some key challenges
New approach• Bayesian splines• Ability to deal with Geophysical
constraints• Posterior information generated• More rapid code to run
Updated tools• Calib• OxCal• Bacon• IntChron (INTIMATE)
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Generates multiple curve realisations
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Miyake events
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Merging of timescales
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Marine and speleothem data
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Additional information
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Statistical approaches and tools for IntCal20
Christopher Bronk Ramsey, Tim Heaton, Maarten Blaauw, PaulBlackwell, Paula Reimer, Ron Reimer and Marian Scott
IntCal Statistics Group
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 1 / 19
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Poster Overview
Summary of IntCal20: Aims, Data and Challenges;
New statistical method of Bayesian spline regression;
Incorporating unique features in the data:
Tree rings (ca. 0 � 14000 cal BP) — blocking, keeping detail,Miyake events;
Further back in time (ca. 14000 � 55000 cal BP) — uncertaincalendar ages; reservoir/dcf effect; heavy tails.
Updates to Bacon, Calib and OxCal.
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 2 / 19
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Idea of radiocarbon calibration: IntCal
Proportion of atmospheric 14C fluctuatedsignificantly over timeNeed to adjust "radiocarbon dates" viacalibration curveIntCal20 curve provides historic estimate of14C from 0 – 55,000 cal BPFind all calendar ages ✓ consistent withobserved radiocarbon age X
This is an inverse problem so Bayesian statistics is natural
⇡(✓|X ) / f (X |✓)⇡(✓)
where f (X |✓) is likelihood of observing 14C determination X if it camefrom calendar year ✓, given by the calibration curve.
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 3 / 19
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IntCal20 Component Datasets
IntCal20 based on > 12,900 14C determinations where calendar age ✓is known (either exactly or estimated). Data split into two categories:
Back to 14,190 cal BP:Dendrochronologically dated trees - many annual measurements
Further back (up to 55,000 cal BP):Speleothems e.g. Hulu Cave (Cheng et al., 2018)Corals e.g. Barbados, Tahiti (Bard et al., 1990)Macrofossils e.g. Lake Suigetsu (Bronk Ramsey et al., 2012)Forams e.g. Cariaco Basin (Hughen et al., 2004)Floating 14C tree-ring sequences — Bølling-Allerød (Adolphi et al.,2017) and SH kauri (Turney et al., 2010)
Need to combine all these datasets together
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 4 / 19
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Key data challenges
Tree-rings a mix of annual and multi-year 14C measurementsVariable data density and sharp Miyake-type eventsUncertainty on calendar ages of some 14C determinationsFloating tree-ring sequences (with relative but no absolutecalendar ages)Indirect measurements of atmospheric 14C — speleothems andmarine samplesPotential over-dispersion in observed 14C samples (additionalsources of variation)
We’ll discuss those in red.
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 5 / 19
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A new statistical methodology
IntCal20 uses new methodology:Quicker to update curves (permit more investigation);Still rigorous, captures uniqueness of the data;Ideally Bayesian (consistent with calibration).
We selected:Bayesian splines;Flexible to incorporate unique features of the data;Provides posterior information of independent interest.
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 6 / 19
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Bayesian Splines
Bayesian Methods:Prior — attempts to capture beliefs, penalizes roughness in �14CObserved data — provides a likelihood to combine with priorbased on closeness to 14C samplesCombine into posterior — updated beliefs in light of observed data
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 7 / 19
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Bayesian Splines: Posterior
Use MCMC — outputs lots of plausible curves we summarise
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 8 / 19
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Unique aspects of IntCal data
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 9 / 19
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Challenges: Variable Data Density and
Miyake-type events
Can choose more knots where need more detail e.g. Miyake events
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 10 / 19
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Challenges: Merging timescales I
Errors-in-variablesNot all calendar ages of older determinations are known exactlye.g. varve counting, wiggle-matching, palaeoclimate tie-pointing,floating tree-ring sequences;We only observe (Xi ,Ti), where Ti noisy observations of truecalendar age ✓i :
Xi = µ(✓i) + ✏i can’t just be simplified to Xi = µ(✓i) + ✏i
Ti = ✓i + ⌘i Ti = ✓i
Some timescales need registering/merging;Hope that:
If multiple records show same features then keep;Features seen only in one record are likely noise so smoothed.
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 11 / 19
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Challenges: Merging timescales II
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 12 / 19
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Challenges: Indirect Measurements
Marine and speleothem determinations do not directly measureatmospheric 14CReservoir ages and DCF:
Xi = µ(✓i) + Rj(✓i) + ✏i
where R(✓) is term specific to set j .Marine Reservoir Ages — estimated via a OGCM with coastal shiftDead carbon fractions — varying around an unknown meanIncorporated similarly to errors-in-variables (but Up-Down 14Cshift as opposed to L-R ✓ shift)
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 13 / 19
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Challenges: Estimating MRAs
Create preliminary 14C curve based uponHulu-cave onlyUse as input to 3D LSG OGCM (Butzin etal., 2020)Provide first-order approximation to MRAsfor each datasetApply constant coastal shifts and addvariation to make consistent with overlapwith atmospheric trees
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10 20 30 40 50
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Delta 14 Plot
T
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setno = 124setno = 127setno = 128
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Calendar Age (cal BP)
LSG
OG
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Mar
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ervo
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usin
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S sc
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14C
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econ
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Open−ocean site nearest to BarbadosOpen−ocean site nearest to Iberian MarginOpen−ocean site nearest to KiritimatiOpen−ocean site nearest to MururoaOpen−ocean site nearest to PapuaOpen−ocean site nearest to TahitiOpen−ocean site nearest to VanuatuOpen−ocean site nearest to Pakistan MarginOpen−ocean site nearest to CariacoLSG global−average (50°S − 50°N)
Cariaco is a unique case dealt with differently
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 14 / 19
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Outputs and Implications
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 15 / 19
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IntCal20: Estimated �14C
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Delta 14 Plot
postcalage
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ocean: uvCariacoocean: vCariacoocean: Iberianocean: Pakistancoral: Bardcoral: Cutlercoral: Durandcoral: Fairbanksspeleo: Beckspeleo: Hoffmannspeleo: Hululake: Suigetsutree: Adolphi Atree: Adolphi Btree: Adolphi Ctree: Kauritree: Mangawhai
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C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 16 / 19
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IntCal20: Realisations and Internal Calibration
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 17 / 19
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IntCal20: User Differences and Benefits
Higher annual detail — will increase multimodality in calibratedage estimatesPotential use of realisations to include more information on curvecurrently lost in summarisationSee our other talk
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 18 / 19
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Conclusion
IntCal20 has new methodology based on Bayesian splines;Runs much more quickly;More flexible and can investigate modelling choices;Provides output of potential further interest.
C Bronk Ramsey (IntCal Statistics Group) EGU 2020 3rd May 2020 19 / 19