Thomas Laepple, Thomas Münch, Andrew Dolman, Maria Reschke Alfred Wegener Institute, Germany

DPG 2012, Berlin

How well do proxy system (data) models simulate real paleoclimate observations?

DAPS 29. May 2017, (apples, oranges and other figures are copied from Sylvia Dee‘s talk)

We need tests for proxy system models that are independent of climate models

simulated proxies observed proxies

Testing option: replicate proxy data

partly known climate

Compare the replicability of true and simulated data

EDML

Ice-core proxy system model test site

Münch et al., CP 2016

Signal of two shallow firn cores 500m distance

Observed replicability

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-55

-45

-35

depth (m)

d18O

(‰)

R=0.3

500m

Current ice PSM (e.g. Dee et al.,2015)

Measurement error, Time-uncertainty

Diffusion burial densification

dO18=f(T, …), altitude correction Precipitation weighting

Münch et al., CP 2016

Signal of two shallow firn cores 500m distance

Observed replicability

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-55

-45

-35

depth (m)

d18O

(‰)

R=0.3

500m

Horizontal structure

0 10 20 30 40

100

80

60

40

20

0

trench position [m]

dept

h [c

m]

−55

−50

−45

−40

−35

δ18O

[p

erm

il]

●● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Horizontal structure

Temporal structure of depositional noise, Münch et al., in prep

500m

Spatial structure of depositional noise, Münch et al., CP 2016

Stratigraphic noise model needed

Testing option: Design field experiments to test proxy system model components

Diffusion burial densification

Look at the archive again after several years

Münch et al., TCD 2017

Look at the archive again after several years

Münch et al., TCD 2017

•  Observed changes match the simulated changes

•  No evidence for other changes

unknown climate

PSM for proxy type 1 observed proxy type 1

PSM for proxy type 2 observed proxy type 2

Testing option: different proxies of the same physical parameter

corals and deep sediment cores

corals Uk37 Mg/Ca

Laepple&Huybers, PNAS 2014

proxy spectra in temperature units

period (years)

PS

D (S

ST

varia

nce/

df)

0.0001

0.001

0.01

0.1

3000 1000 500 100 50 10 5 2

Coral SSTUk37 SSTMg/Ca SST

model at proxy position

Period (years) Laepple&Huybers, PNAS 2014

Sediment PSM + inversion (spectral correction algorithm)

Validation Laepple and Huybers, EPSL 2013

https://bitbucket.org/ecus/sedproxy Dolman and Laepple in prep.

MgCa= f(T) Uk37 = f(T)

Seasonal export

bioturbation Sampling a finite #tests Cleaning Analytical error

Spectral filter Sclimate = (Sclimate_M/Sproxy_PSM) * Sproxy

^ ^

proxy spectra in temperature units

period (years)

PS

D (S

ST

varia

nce/

df)

0.0001

0.001

0.01

0.1

3000 1000 500 100 50 10 5 2

Coral SSTUk37 SSTMg/Ca SST

model at proxy position

Period (years)

corrected spectra

period (years)

PS

D (S

ST

varia

nce/

df)

0.0001

0.001

0.01

0.1

3000 1000 500 100 50 10 5 2

Coral SSTUk37 SST correctedMg/Ca SST corrected

model at proxy position

Uk37 SST rawMg/Ca SST raw

Period (years)

simulated proxies observed proxies

Testing option: same proxy from nearby sites

partly known climate (right spatial covariance structure)

Compare the spatial covariance structure in true and simulated data

empirical vs. proxy model based Signal/Noise ratio estimate -  Correlate pairs <5000km distance in model and proxy and

compare their covariance

Empirical estimate: S/NUk37 =1.3 (0.25-5) S/NMg/Ca =1.5 (0.20-5)

Proxy forward model: S/NUk37 =3 S/NMg/Ca =0.5

pUk37 <0.01

pMg/Ca <0.02

Laepple&Huybers, PNAS 14, supplement Reschke et al., in prep.

Conclusion

n  Essential to test and validate PSM’s to avoid interpreting or assimilating wormholes

n  Possibilities include replicate data, multiproxy data, the spatial covariance structure…

n  Likely that PSM are too optimistic (e.g. ice-cores)

n  Aim are PSM’s that are consistent with the process understanding as well as observational (paleo-)evidence

Are corals able to quantify SST variability?

-4.5 -4.0 -3.5 -3.0

-4.5

-4.0

-3.5

-3.0

2-10yr raw

log10 var coral SST

log1

0 va

r HA

DS

ST3

-4.5 -4.0 -3.5 -3.0

-4.5

-4.0

-3.5

-3.0

2-10yr both corrected

log10 var coral SST

log1

0 va

r HA

DS

ST

-5.5 -5.0 -4.5 -4.0 -3.5 -3.0

-5.5

-5.0

-4.5

-4.0

-3.5

-3.0

10-50yr raw

log10 var coral SST

log1

0 va

r HA

DS

ST3

-5.5 -5.0 -4.5 -4.0 -3.5 -3.0

-5.5

-5.0

-4.5

-4.0

-3.5

-3.0

10-50yr both corrected

log10 var coral SSTlo

g10

var H

AD

SS

T

Quinn 1998 dO18Dunbar 1994 dO18Asami 2005 dO18Hendy 2002 Sr/CaLinsley 2000 Sr/CaGoodkin 2008 Sr/CaCalvo et al., 2007 Sr/CaKilbourne et al., 2009 Sr/CaLinsley et al., 2006 Sr/CaSaenger 2009 growth rate

Calibration provided by the authors HadSST3 as provided

0.084 (mmol/mol SrCa)/C -0.23 permil δ18O/C, Mean replicate/uncertainty variability substracted from corals /ship data var(SST_coral)=var(coral)-var(replicate) var(SST_obs)=var(HadSST)-var(obsError)

Power spectra and sensitivity on proxy type

5 10 20 50 100 500

5e-05

5e-04

5e-03

5e-02

f (1/kyr)

PSD

all corals (10)only Sr/Ca (6)only dO18 (3)

5 10 20 50 100 500

5e-05

5e-04

5e-03

5e-02

f (1/kyr)

PSD

all corals, recalibratedall corals, authors calib

200 50 20 10 5 2 200 50 20 10 5 2

Period (years) Period (years)

known climate (e.g. instrumental observations)

simulated proxies observed proxies

Testing option 3: known climate, observed proxy

Compare time-series or power spectra

EDML Site, only densification

0.01 0.02 0.05 0.10

1e-02

1e+00

1e+02

f (1/cm)

PSD

EDML snow profilesAutomatic weather station AWS

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-10

05

1015

input timeseries

depth (m)

dO18

ano

m

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-10

-50

510

simulated

depth (m)

dO18

ano

m

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-10

-50

510

EDML data

depth (m)

dO18

ano

m

0.01 0.02 0.05 0.10

1e-02

1e+00

1e+02

f (1/cm)

PSD

EDML snow profilesAutomatic weather station AWSAWS+Diffusion

EDML Site… adding diffusion

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-10

05

1015

input timeseries

depth (m)

dO18

ano

m

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-10

-50

510

simulated

depth (m)

dO18

ano

m

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-10

-50

510

EDML data

depth (m)

dO18

ano

m

0.01 0.02 0.05 0.10

1e-02

1e+00

1e+02

f (1/cm)

PSD

EDML snow profilesAutomatic weather station AWSAWS+DiffusionAWS+aliasing/redistribution + Diffusion

EDML Site... Adding 80% conversion to white noise

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-15

-50

515

input timeseries

depth (m)

dO18

ano

m

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-10

-50

510

simulated

depth (m)

dO18

ano

m

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

-10

-50

510

EDML data

depth (m)

dO18

ano

m

Stratigraphic noise model needed

Test for the archive model: 2015 vs. 2013 trench data 1.) Estimated changes from

observations match the simulated changes 2.) No evidence for other changes

Münch et al., TCD 2017

Spectral filter Sxmodel/Symodel

Spectral correction algorithm (or sediment PSM + inversion)

synthetic climate records xmodel

S(f)=f-β,f<1/50yr

Bioturbation (2,10,20cm)

Sampling (as core)

+ measurement intrasample noise

σ

Synthetic proxy records ymodel

compare synthetic and real spectra

Estimate σ,β

Validation Laepple and Huybers, EPSL 2013

Validation https://bitbucket.org/ecus/sedproxy