palaeoclimate reconstruction: modelling temporal uncertainty many collaborators: stats:...
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Palaeoclimate Reconstruction:Modelling Temporal Uncertainty
Many collaborators:Stats: Bhattacharya, Gelfand, Salter-Townshend, Parnell, Whiley, Wilson, others
Botany: Allen, Huntley, Mitchell,
Support: SFI and previously by Enterprise Ireland and PRTLI
Glendalough Co Wicklow
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Sunday Times
“Gobsmacking”says Haslett
“Not what I said”says Haslett
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Courtesy of Sunday Times graphics dept
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Reconstruction of GDD5
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coresamples
mult. counts by taxa
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Changing pollen composition
10,000 BP
Use Matts pollen diagramObserved pollen proportions vs 14C y BP
Pollen composition changes Climate changesRecent Shallow
Ancient Deep
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ScienceChanging pollen composition in carefully
selected sites• Reflects changing vegetation, which• Reflects changing climate
– GDD5 Growing Deg Days > 5o
– MTCO Mean Temp Coldest Monthwhence
• Can reconstruct climate quantitatively• Can reduce uncertainty about past climate
GDD5 eg Avg temp on four successive days
5 8 4 10
Excess over 5 0 3 0 5
Thus GDD5 = 8
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Data
• Pollen Data – multivariate counts – 14 distinguishable taxa– 115 samples at Sluggan Moss, Lough Neagh– 115 depths of which 32 radiocarbon dated– Climate unknown (2D GDD5, MTCO)
• Modern data – 7815 modern sites – Counts known – surface pollen (depth = 0)– Climate known
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Statistical Tasks
Aspects• Pollen response to climate
– Use modern data– Transfer functions/response surfaces– Climate one sample at a time
• Climate smoothness in time– Climate history – Greenland ice cores– Dating uncertainties
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Statistical Tasks
Given • Modern data (pm,cm) (7815 records); and• Fossil data (pf,?) at one depth
seek post dist π(cf | pm,cm ,pf) at that depth
Additionally, given • ‘Climate smoothness’; and• Fossil data (pf,?, d) at 115 depths• Radiocarbon dating info at 32 depths
seek post dist π(cf | pm,cm ,pf) entire climate history
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GlendaloughModern Training Data
• Data on modern pollen compositions pm
7815 sites in Eur/ N. America
• Modern climate cm known.
• Hence relationship
π(pm | cm)
• Adopt for fossil data
π(pf | cf)
Sluggan Moss
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Physical and (2D) climate spaces
Climate space grid
Glendalough
An extreme climate?
Impossible climates?Unknown climates?
Growing Deg Day > 5oMea
n T
emp
Col
dest
Mon
th
7815 data locations - grey points.Computational grid - black points
7815 data locations
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Pollen response to (2D) climate
•π(p | c) pdfp 14 dim comp vectc 2 dim climate (here)
•Latent Gaussian proc•mixture of multinomials•zero inflation•MCMC
MT
CO
GDD5Small change in climate c Small change in vegetation p = p(c) smooth multivariate function
7815 data locations - grey points.Computational grid - black points
Two stage implementation
1 MCMC creates/stores many realisations of p(c)
2(a) Draw one at random
2(b) MCMC Climate recon
2(c) Repeat (a,b)
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Reconstruction of GDD5
Here depth to radiocarbon age presumedknown
Later addressdatinguncertainty
Note dates in Radio-carbon YBP
One sample at a time
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Post Dist cf given pf
Given vector of counts at given depth, whence pf
Findπ(GDD5f,MTCOf | pf) by MCMC for each depth
Here concentrate on GDD5
eg depth 10 m
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Differential Response to 1D Climate
0 500 1000 1500 2000 2500
C being 1D Climate
A
B
Inverse relationship
• Model taxon productivity response to ‘climate’• Multi-modal climate posteriors natural• Toy example; two taxa one climate dimension
Prop of A high
Post prob C given A high
0 500 1000 1500 2000 2500
Prop of A low
0 500 1000 1500 2000 2500
Post prob C given A low
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Implied Climate Histories
Pointwiserealisations of climate
Climateapparently
volatile
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Implied Climate Histories
Pointwiserealisations of climate
Climateapparently
volatile
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Implied Climate Histories
Pointwiserealisations of climate
Climateapparently
volatile
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Implied Climate Histories
Pointwiserealisations of climate
Climateapparently
volatile
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Implied Climate Histories
Pointwiserealisations of climate
Climateapparently
volatile
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Climate Smoothness
• Climate changes δi = c(timei) - c(timei-1)– Mostly small/sometimes large “smooth”– Depends on increments |timei - timei-1|– Reject (most) volatile climates
• Issues– How smooth?
• Greenland ice cores
– Uncertainty in 14C dating?• Random chronology
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Temporal uncertainty
• 115 samples at Sluggan MossFor 115 : core depths di
For 32 : reported 14C ages yi ± σi
• Seek θi true calendar age θi all di
“chronology model”– r(θ) 14C calibration curve
• yi ~ N( r(θi), σi2) (outliers, so long tails)
• r(θ)~ N( μ(θ), σ2(θ)) prior
– Piecewise constant sedimentation rate• Gaussian random walk
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ChronologyGiven complete knowledge of sedimentation history, age may be determined from depth
Calendar age θ
d = depth of accumulated sediment
ButOnly know 14C age at some depthsSeek realisations of sediment history,conditional on dataPrior: Gaussian random walk with driftconstrained to be monotonePiecewise const iid sedimentation rate
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Temporal uncertainty:single dated sample
Lab report3180 ± 30
Implied post dist
Schematic of Bayesian 14C calibration curveBuck
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Temporal uncertainty:all dated samples
Prior:Discrete time (20 year intervals)Random Walk with drift (monotone)
• Draw random θi | yi σi each of 32 di
– Order constraint θi > θk if di > dk
• Stoch. interpolation to undated samples– Sample θm (undated)| θi (dated), all depths
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Draw set of random dates for 14C dated samples
x
x
x
x
Calendar age θ
Depth d
Realisations of order constrained radio-carbon dates
drift
drift
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Complete random chronology
x
x
x
x
Realisations of order constrained stochastic chronology, conditional on radio-carbon derived dates
Monotone random walk with drift
Depth d
Calendar age θ
Given set of depths
Realisation of a set of calendar dates
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Climate Smoothness
• Climate changes δi = c(timei) - c(timei-1)
– Mostly small/sometimes large
– Depends on increments |timei - timei-1|
• Prior for smoothness rejection of histories with large |δi |
implicit smoothing / borrowing strength
• Issues– Prior for δi long tail random walk
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Climate over 100,000 yearsGreenland Ice Core
Temporal structure for climate (20 yr. resolution)Frequent small changes, occasional large changes
Ice Core data time series Greenland Ice Core Data10,000 year intervals
Irish study periodOxygen isotope – proxy for Greenland temp
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Climate over 100,000 yearsGreenland Ice Core
-6
-4
-2
0
2
4
6
-4 -3 -2 -1 0 1 2 3 4 5
Normal prob plot
First diffs
First Differences
-4
-2
0
2
4
0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000
Years BP
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Climate Smoothness
Ice Core data time series Greenland Ice Core Data10,000 year intervals
Long-tailed Random Walk Prior
Model δ = c(t) - c(t-20) as iid NIGNormal Inverse Gamma Random Walk
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Sampling Climate Histories
Given – Realisation of pollen response surfaces– Sample pollen at each of 115 depths– Realisation of complete chronology
• 115 dates given 14C dates for 32 samples
– Model for climate smoothness
Sample realisations of climate at 115 dates
Sample climate history every 20 years
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Modelled Climate Histories
ClimateSmoothmostly
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Modelled Climate Histories
ClimateSmoothmostly
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Modelled Climate Histories
ClimateSmoothmostly
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Modelled Climate Histories
ClimateSmoothmostly
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Modelled Climate Histories
ClimateSmoothmostly
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Better Reconstruction of GDD5
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Reconstruction of GDD5
Note dates in Radio-carbon YBP
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Monte Carlo ModulesResp Surface
Randomset of surfaces
Modern dataClimate and pollen
Random set of 115 dates
Depths andradiocarbon
dates
Dating
RandomClimate History
length 115
Temporal Stochastic
Smoothness
Stochastic Interpolation
Random Climate History 12,600y by
20y step
Summaries
Fossil Pollen
Point wise Recon-
struction
Randompoint-wisehistories
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Next Stages
• Multiple sites– Joint reconstruction of two sites– Probable synchronicity of climate change
• Borrow more strength– for dates, for climate smoothness
– Joint reconstruction of many sites in space
• More climate dimensions and taxa– Many high dim response surfaces
• Other proxies, covariates• Confront General Circ. Models
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Methodological Issues
• MCMC - the way forward?– Speed and convergence– Approximations esp for response surfaces
– Model checking and model choice
• Technical issues– Zero inflation– Fast high-dim non-parametric smoothing– Long tailed space-time prior for climate– Latent (mixtures of) Gaussian processes