Introduction to proxy system modeling€¦ · Introduction to proxy system modeling Kevin...
20
Introduction to proxy system modeling Kevin Anchukaitis University of Arizona Suz Tolwinski-Ward AIR Worldwide Diane Thompson Boston University Michael Evans University of Maryland
Transcript of Introduction to proxy system modeling€¦ · Introduction to proxy system modeling Kevin...
Introduction to proxy system modeling
Kevin AnchukaitisUniversity of Arizona
Suz Tolwinski-WardAIR Worldwide
Diane ThompsonBoston University
Michael EvansUniversity of Maryland
‘The best material model of a cat is another, or
preferably the same, cat.’Arturo Rosenblueth and Norbert Wiener
Under- vs. Over-specified Models
after Dean Urban, Duke University
Under- vs. Over-specified Models
after Dean Urban, Duke University
Trying to get here
‘A good forward model requires not only a good understanding of the key processes that are responsible for generating the signal (proxy
climate record) in the archive, but it also needs appropriate sources of estimates for the parameters and a reasonable understanding of
various levels of uncertainties related to the raw proxy data.’
Hughes and Ammann 2009
ENVIRONMENT SENSOR ARCHIVE OBSERVATION
SENSOR MODEL ARCHIVE MODEL OBSERVATION MODEL
RECONSTRUCTION
CALIBRATION
Evans, Tolwinski-Ward, Thompson, Anchukaitis, 2013based on SUPRAnet (UK) Working Group
ENVIRONMENT SENSOR ARCHIVE OBSERVATION
SENSOR MODEL ARCHIVE MODEL OBSERVATION MODEL
RECONSTRUCTION
CALIBRATION
TEMPERATUREPRECIPITATIONSOIL MOISTURE
SUNLIGHTSNOW WOOD
TREE
RING WIDTHMXD
EARLYWOODLATEWOOD
ISOTOPES
Evans, Tolwinski-W
ard, Thompson, Anchukaitis, 2013
�18Ocoral
= f(T, S)
Application: Observational network design
Comboul et al (2015), using Thompson et al (2011); Other examples: Bradley (1996), Evans et al (1998), Evans (2007)
�18Ocoral
= f(T, S)
Application: model/data comparison
Thompson et al, 2011, 2012, 2013 after Brown et al (2008) and Meehl et al (2007)
Application: model/data comparisonTRW = f(T, P,�)
Anchukaitis et al. 2012
Application: model/data comparisonTRW = f(T, P,�)
10−3
10−2
10−1
10−1
100
FREQUENCY (cycles/year)
NO
RM
ALIZ
ED
PO
WE
R
PSEUDOPROXYMODEL PDSI
Anchukaitis et al. in preparation
Application: paleoclimate inference & interpretation
Schmidt 1999
�18Oforaminifera
= f(T, S, �18Osw
)
Application: paleoclimate inference & interpretationTRW = f(T, P,�)
1955 1960 1965 1970 1975 1980−3
−2
−1
0
1
2
YEAR
WHITE MOUNTAINS (WM1), METHUSELAH WALK
A
r = 0.75, p = 0.00
SIMULATEDREAL
100 200 3000
0.2
0.4
0.6
0.8
1
DAY OF YEAR
GROWTH RATES
C
GrGrWGrT
100 200 3000
0.5
1
1.5
2
2.5
3
DAY OF YEAR
ENVIRONMENT
BSNOW/1000(mm)
TRANS(mm/d)SM(v/v)
Anchukaitis, Evans, Hughes
Application: paleoclimate inference & interpretation�18O
cellulose
= f(T, �18Orain
, �18Ovapor
,%RH)
60oE 80
oE 100oE 120
oE 140o
E 160oE
18oS
0o
18oN
36oN
54oN
r(δ18Ocellulose
,NINO3.4 SSTA
−1 0 1
Anchukaitis and LeGrande
Tolwinski-Ward et al. 2014
X
TRW = f(T, P,�)
Application: paleoclimate reconstruction constraint
Future work: more proxy systems models!
Proxy systems may be multivariate, nonlinear, spectral climate filters.
Use of “intermediate-class” proxy system models permits:
Paleo-observational network design/hypothesis testing.
Subtle interpretation of the environmental response.
Robust intercomparison of paleodata and climate model output.
Scientifically-defensible weak constraints for paleoreconstructions.
email: [email protected]
