Download presentation

Presentation is loading. Please wait.

Published byMartin Watson Modified over 2 years ago

1
Studying Uncertainty in Palaeoclimate Reconstruction SUPRaNet SUPRModels SUPR software Brian Huntley, Andrew Parnell Caitlin Buck, James Sweeney and many others Science Foundation Ireland Leverhulm Trust

2
Result: one pollen core in Ireland 95% of plausible scenarios have at least one “100 year +ve change” > 5 o C Mean Temp of Coldest Month

3
Climate over 100,000 years Greenland Ice Core 10,000 year intervals Oxygen isotope – proxy for Greenland temp Median smooth. Past 23000 years The long summer

4
Past 23000 years Climate over 100,000 years Greenland Ice Core 10,000 year intervals The long summer Int Panel on Climate Change WG1 2007 “During the last glacial period, abrupt regional warmings (probably up to 16 ◦ C within decades over Greenland) occurred repeatedly over the North Atlantic region”

5
Climate over 15,000 years Greenland Ice Core Younger Dryas Transition Holocene Ice dynamics? Ocean dynamics? What’s the probability of abrupt climate change?

6
Modelling Philosophy Climate is – Latent space-time stoch process C(s,t) All measurements are – Indirect, incomplete, with error – ‘Regionalised’ relative to some ‘support’ Uncertainty – Prob (Event) – Event needs explicitly defined function of C(s,t)

7
Proxy Data Collection Oak treeGISP iceSedimentPollen Thanks to Vincent Garreta

8
core samples mult. counts by taxa Pollen

9
Data

10
Data Issues Pollen 150 slices – 28 taxa (not species); many counts zero – Calibrated with modern data 8000 locations 14 C5 dates – worst uncertainties ± 2000 years Climate `smoothness’ – GISP data 100,000 years, as published

11
Model Issues Climate - Sedimentation - Veg response latent processes – Climate smooth (almost everywhere) – Sedimentation non decreasing – Veg response smooth Data generating process – Pollen – superimposed pres/abs & abundance – 14 C - Bcal Priors - Algorithms …….

12
SUPR-ambitions Principles – All sources of uncertainty – Models and modules – Communication Scientist to scientist to others Software Bclim Future SUPR tech stuff non-linear non-Gaussian multi-proxy space-time incl rapid change dating uncertainty mechanistic system models fully Bayesian fast software

13
Modelling Approach Latent processes – With defined stochastic properties – Involving explicit priors Conditional on ‘values’ of process(es) – Explicit stochastic models of – Forward Data Generating Processes – Combined via conditional independence – System Model

14
Modelling Approach Modular Algorithms – Sample paths, ensembles – Monte Carlo – Marginalisation to well defined random vars and events

15
Progress in Modelling Uncertainty Statistical models – Partially observed space-time stochastic processes – Bayesian inference Monte Carlo methods – Sample paths – Thinning, integrating Communication – Supplementary materials Modelled Uncertainty Does it change? In time? In space?

16
SUPR Info Proxy data: typically cores – Multiple proxies, cores; multivariate counts – Known location(s) in (2D) space – Known depths – unknown dates, some 14 C data – Calibration data – modern, imperfect System theory – Uniformitarian Hyp – Climate ‘smoothness’; Sedimention Rates ≥ 0 – Proxy Data Generating Processes

17
Chronology example

18
Bchron Models Sedimentation a latent process – Rates ≥ 0, piecewise const – Depth vs Time - piece-wise linear – Random change points (Poisson Process) – Random variation in rates (based on Gamma dist) 14 C Calibration curve latent process – ‘Smooth’ – in sense of Gaussian Process (Bcal) 14 C Lab data generation process – Gaussian errors

19
Bchron Algorithm Posterior – via Monte Carlo Samples Entire depth/time histories, jointly – Generate random piece-wise linear ‘curves’ – Retain only those that are ‘consistent’ with model of data generating system Inference – Key Parameter; shape par in Gamma dist – How much COULD rates vary?

20
20 Bivariate Gamma Renewal Process Comp Poisson Gamma wrt x ; x incs exponential Comp Poisson Gamma wrt y ; y incs exponential

21
21 Compound Poisson Gamma Process We take y = 1 for access to CPG and x > 2 for continuity wrt x Slope = Exp / Gamma = Exp x InvGamma infinite var if x > 2

22
22 Modelling with Bivariate Gamma Renewal Process Data assumed to be subset of renewal points Implicitly not small Marginalised wrt renewal pts Indep increments process Stochastic interpolation by simulation new y unknown x

23
23 Stochastic Interpolation Unit Square Monotone piece-wise linear CPG Process

24
24 Stochastic Interpolation Monotone piece-wise linear CPG Process

25
25 Stochastic Interpolation Monotone piece-wise linear CPG Process

26
26 Stochastic Interpolation Monotone piece-wise linear CPG Process

27
27 Stochastic Interpolation Monotone piece-wise linear CPG Process

28
28 Stochastic Interpolation Monotone piece-wise linear CPG Process

29
29 Stochastic Interpolation Density Known Depths Known age Calendar age

30
Data

31
Glendalough Time-Slice “Transfer-Function” via Modern Training Data Hypothesis Modern analogue Climate at Glendalough 8,000 yearsBP “like” Somewhere right now The present is a model for the past

32
Calibration ---------------------- ---------------------- c (t) y(t ) Modern (c, y ) pairs In space ---------------------- ---------------------- c(t) y(t ) Eg dendro Two time series Much c data missing Eg pollen One time series All c data missing Space for time substitution Over- lapping time series

33
Calibration Model Simple model of Pollen Data Generating Process ‘Response’ y depends smoothly on clim c Two aspects Presence/Absence Rel abundance if present Taxa not species Eg y i =0 probq(c) y i ~Poisson (λ(c)) prob1-q(c) Thus obsy i =0, y i =1very diff implications

34
One-slice-at-a time Slice j has count vector y j, depth d j Whence – separately - π(c j | y j ) and π(t j | d j ) ResponseChronmodule

35
Uncertainty one-layer-at-a-time Pollen => Uncertain Climate Depth => Uncertain depth But monotonicity Here showing 10 of 150 layers

36
Uncertainty one-layer-at-a-time

37
Uncertainty jointly Many potential climate histories are Consistent with ‘one-at-a-time Jointly inconsistent with Climate Theory Refine/subsample

38
Coherent Histories One-slice-at-a-time samples => {c(t 1 ), c(t 2 ),……c(t n )}

39
Coherent Histories One-slice-at-a-time samples => {c(t 1 ), c(t 2 ),……c(t n )}

40
Coherent Histories One-slice-at-a-time samples => {c(t 1 ), c(t 2 ),……c(t n )}

41
Coherent Histories One-slice-at-a-time samples => {c(t 1 ), c(t 2 ),……c(t n )}

42
GISP series (20 years)

43
Climate property? Non-overlapping (20 year?) averages are such that first differences are: adequately modelled as independent inadequately modelled by Normal dist adequately modelled by Normal Inv Gaussian – Closed form pdf – Infinitely divisible – Easily simulated, scale mixture of Gaussian dist

44
One joint (coherent) history

48
MTCO Reconstruction One layer at a time, showing temporal uncertainty Jointly, century resolution, allowing for temporal uncertainty Marginal time-slice: may not be unimodal

49
Rapid Change in GDD5 Identify 100 yr period with greatest change One history

50
Rapid Change in GDD5 One history Identify 100 yr period with greatest change

51
Rapid Change in GDD5 Study uncertainty in non linear functionals of past climate 1000 histories Identify 100 yr period with greatest change

52
Result: one pollen core in Ireland 95% of plausible scenarios have at least one 100 year +ve change > 5 o C Mean Temp of Coldest Month

53
Communication Scientist to scientist Exeter Workshop – Data Sets – With Uncertainty Associated with what precise support?

54
Modelling Approach Latent processes – With defined stochastic properties – Involving explicit priors Conditional on ‘values’ of process(es) – Explicit stochastic models of – Forward Data Generating Processes – Combined via conditional independence Modular Algorithms – Sample paths, ensembles – Monte Carlo – Marginalisation to well defined random vars and events

Similar presentations

OK

G. Cowan Lectures on Statistical Data Analysis Lecture 10 page 1 Statistical Data Analysis: Lecture 10 1Probability, Bayes’ theorem 2Random variables and.

G. Cowan Lectures on Statistical Data Analysis Lecture 10 page 1 Statistical Data Analysis: Lecture 10 1Probability, Bayes’ theorem 2Random variables and.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on health management information system Ppt on save environment Ppt on hybrid solar lighting system Ppt on ms powerpoint tutorial History of film editing ppt on ipad Ppt on climate of india Ppt on water scarcity in africa Ppt on topic health and medicine Ppt on changing face of london Ppt on modern olympic games