Title: Adaption of Paleoclimate Reconstructions for Interdisciplinary Research
1Adaption of Paleoclimate Reconstructions for
Interdisciplinary Research
Oliver Timm International Pacific Research
Center, SOEST, University of Hawai'i at Manoa
2Overview
- 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
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5El Nino -Southern OscillationImpact on
paleoclimate proxies
Palmyra
Sea Surface Temperatures (SST) in colors
bluenegative, redpositive anomalies
Precipitation anomalies dashednegative,
solidpositive
6Reasoning 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
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8Short 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
9Multiple 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
-
-
10Problems 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
11Linear 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)
12Example 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
13Example 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)
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15Example NINO3 index Palmyra Proxy
High probability
Low probability
NINO3 index
Maximum likelihood reconstruction
Linear Regression
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17Combining 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
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0
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18Training and Validation
Training period 1930-1979
Validation period 1887-1929
19Categorized ENSO reconstruction
Training period 1930-1979
Validation Period
NINO3 index
Reconstructed Category
time
time
20Categorized ENSO reconstruction
Reconstructed Category
time
time
21 Spliced Palmyra data (Cobb et al., 2003)
normalized and each segment detrended
22Summary
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.