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Adaption of Paleoclimate Reconstructions for Interdisciplinary Research

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Title: Adaption of Paleoclimate Reconstructions for Interdisciplinary Research


1
Adaption of Paleoclimate Reconstructions for
Interdisciplinary Research
Oliver Timm International Pacific Research
Center, SOEST, University of Hawai'i at Manoa
2
Overview
  • The nature of the problem
  • We are confronted with chained reasoning,
    inferences and decision making in paleoclimate
    research, environmental studies
  • Basic concepts of paleoclimatic methods
  • Indirect evidence (proxies) for climatic
    conditions
  • Numerical simulations with climate models
  • Development of theoretical concepts of past
    climates
  • Specific examples
  • Reconstruction of El Nino- Southern Oscillation
    (ENSO)
  • Bayesian approach Reconstructing the probability
    of past El Nino/ La Nina events

Indirect evidence (proxies) for climatic
conditions
3
Chained reasoning A non-climatic textbook
example
  • Your house has a burglary alarm system.
  • You are at work.
  • You receive a call from your neighbor that the
    alarm went off.
  • What to do?
  • Stay at work and do not worry or go home and
    check your house.

On your way home you listen to radio an
earthquake hit your home area.
4
Chained reasoning Analog paleoclimate example
  • You have some prior understanding for relation
    ENSO- rain in Mexico
  • You find historical evidence for famine in Mexico
  • Reasoning Could El Nino by the cause?

Further research in archives reveals evidence for
turmoil
5
El Nino -Southern OscillationImpact on
paleoclimate proxies
Palmyra
Sea Surface Temperatures (SST) in colors
bluenegative, redpositive anomalies
Precipitation anomalies dashednegative,
solidpositive
6
Reasoning in Paleoclimate Reconstruction
El Nino-La Nina
ENSO
Global/large-scale climate
temperature
rainfall
regional/local scale
Proxy 2
Proxy 1
Proxy 3
geobiochemical/physical/documentary information
7
Paleoclimate ReconstructionA Simple Bayesian
Network
ENSO
Proxy m
Proxy 1
Proxy 2
Conditionally independent proxy information !
8
Short Review of standardreconstruction methods
  • Linear methods
  • Multiple linear regression
  • Principal Component Regression
  • Examples
  • Global (mean) temperature reconstructions (Mann
    et al. , 1998, 1999)
  • Stahle et al. ENSO index reconstruction
  • Non-linear methods
  • Non-linear regression models
  • Neural Networks

9
Multiple Linear Regressionvs Bayesian method
  • y(t) a0a1x1(t)a2x2(t) ... am(t) xm(t)e(t)
  • y(t) climate signal, the ENSO index, time
    dependent (t)
  • x1(t),x2(t) ... xm(t) proxy indices, time
    dependent (t)
  • e(t) noise (climate variability not explained by
    the model)
  • estimate the model a0 ... am parameters such
    that the
  • estimated climate signal is 'closest' to the
    true signal
  • represented
  • y(t) â0â1x1(t)â2x2(t) ... âm(t) xm(t)
  • Ey(t)-y(t)2 is minimized

10
Problems with linear regression model
y(t) a0a1x1(t)a2x2(t) ... am(t) xm(t)e(t)
  • Causality is inverted The proxies do not
    control ENSO!
  • We have indeed m equations of the following
    kind
  • x1(t) c1 b1y(t) n1(t)
  • x2(t) c2 b2y(t) n2(t)
  • ...
  • xm(t) cm bmy(t) nm(t)
  • The problem of 'multicollinearity'
  • Principal Component Regression

11
Linear regression / Bayesian Method
x1(t) c1 b1y(t) n1(t) x2(t) c2 b2y(t)
n2(t) ... xm(t) cm bmy(t) nm(t)
  • Probability of x1 given y P(x1y)
  • Probability of x2 given y P(x2y)
  • ...
  • Probability of xm given y P(xmy)

P(yx1,x2,xm)
y(t) a0a1x1(t)a2x2(t) ... am(t) xm(t)e(t)
y(t) climate signal, the ENSO index, time
dependent (t) x1(t),x2(t) ... xm(t) proxy
indices, time dependent (t) 
12
Example NINO3 index Palmyra Proxy
  • NINO3 index
  • Palmyra coral record ?18O(oxygen isotope
    concentration)
  • Time 1887-1991
  • Nov-Mar seasonal averages

Scatterplot
P(X,Y)
Bayes fundamental rule
P(X,Y)P(XY)P(Y) Probability of event X given
an event Y is equal to the joint probability of
event X and Y times the probability of event Y
13
Example NINO3 index Palmyra Proxy
  • NINO3 index
  • Palmyra coral record ?18O(oxygen isotope
    concentration)
  • Time 1882-1991
  • Nov-Mar seasonal averages

Scatterplot
P(X,Y)
P(Y)
14
Example NINO3 index Palmyra Proxy
Scatterplot
P(NINO3Palmyra)
Bayes fundamental rule P(XY)P(X,Y)/P(Y) P(NI
NO3Palmyra) P(NINO3,Palmyra)/P(Palmyra)
15
Example NINO3 index Palmyra Proxy
High probability
Low probability
NINO3 index
Maximum likelihood reconstruction
Linear Regression
16
Categorized index reconstruction
  • 105 years with pairs of NINO3 index and
    Palmyra proxy index
  • Question how to estimate the joint
    probability at 40x40 grid points.
  • Few categories 2D histogram on
  • 3x3 or 5x5 grid.


17
Combining three existing ENSO reconstructions
1) Stahle et al., 1998 network of
tree-ring data (Northern Mexico, the
southwestern U.S.A. , Indonesia) 2) D'Arrigo
et al., 2005 network of tree-ring
data (Northern Mexico, the southwestern U.S.A. ,
Indonesia) 3) Mann et al., 2000 global
multiproxy network
2
1
0
-1
-2
18
Training and Validation
Training period 1930-1979
Validation period 1887-1929
19
Categorized ENSO reconstruction
Training period 1930-1979
Validation Period
NINO3 index
Reconstructed Category
time
time
20
Categorized ENSO reconstruction
Reconstructed Category
time
time
21

Spliced Palmyra data (Cobb et al., 2003)
normalized and each segment detrended
22
Summary
1) Bayesian methods allow for the quantification
of uncertainties/likelihoods of the estimate 2)
Probablities estimates are the 'decision-makers'
- Hypothesis-Test - Cause-Effect studies 3)
Bayesian methods can provide the needed
information for hypothesis testing. 4) Bayesian
statistics can be useful to manage different
types of paleoclimate information.
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