Baltic Sea Science Conference, 20 March 2007 Climate simulations for the last millennium. Implications for the Baltic Sea Hans von Storch and Eduardo Zorita

Baltic Sea Science Conference, 20 March 2007

Climate simulations for the last millennium. Implications for the Baltic Sea

Hans von Storch and Eduardo Zorita

Institute for Coastal ResearchGKSS Research CenterGeesthacht, Germany


Overview

1. Experimental set-up2. Utility I: Testing validity of proxy derived

indicators.Testing MBH and Luterbacher temp’s

3. Utility II: Estimating the unobservable.Examples(1) extra-tropical storminess(2) Baltic Sea region conditions


ECHO-G simulations „Erik den Røde” (1000-1990)

and “Christoph Columbus” (1550-1990)

with estimated volcanic, GHG and solar forcing

Experimental set-up


1675-1710vs. 1550-1800

Reconstruction from historical evidence, from Luterbacher et al.

Late Maunder Minimum

Model-based reconstuction

Skill of simulation


Skill of simulation


Skill of simulation


Skill of simulation

Old statement:

Both, Erik den Røde and Christoph Columbus generate temperature variations considerably larger than standard reconstructions (Mann, Jones …).

The simulated temperature variations are of a similar range as derived from NH summer dendro-data and from terrestrial boreholes.


Conclusion, 1

1.Millennial simulations - efforts to simulate the response to estimated volcanic, GHG and solar forcing, 1000-2000.

2.Low-frequency variability in millennial simulations: > Mann, Jones, “hockeystick”, but ~ Esper, boreholes, (some) instrumental data


Testing validity of proxy-derived indicators

For the purpose of testing reconstruction methods, it does not really matter how „good“ the historical climate is reproduced by Erik den Røde.

The model data provide a laboratory to test MBH and Luterbacher’s methodologies.


Testing the MBH method

pseudo-proxies: grid point SAT plus white noisered: mimicking largest sample used in MBH

von Storch, H., E. Zorita, J.

Jones, Y. Dimitriev, F.

González-Rouco, and S. Tett,

2004: Reconstructing

past climate from noisy data,

Science 306, 679-682

and later comments and

responses




Storminess

New statement:

Hockey-stick curve is likely an under-estimate of low-frequency variability. The bulk of recent reconstructions show significantly larger variability than the hockeystick.



Testing the Luterbacher et al. (2004) reconstruction of European temperatures since 1500

Küttel, M., J. Luterbacher, E. Zorita, E. Xoplaki, N.

Riedwyl and H. Wanner, 2007: Testing a European

winter surface reconstruction in a surrogate climate.

Geophys. Res. Lett., in press


Applying the Luterbacher methodology to ECHO-G (top) and HadCM3 (bottom) simulation data, using the same decreasing network of proxy and instrumental data as available for Luterbacher.

Proxy data are degraded by white noise, instrumental data not.Dashed lines represent 90% confidence bands.


Conclusion

• Millennial simulation-data used to test methods for reconstructing historical temperature variations. • Randomized grid-point SAT (i.e. red noise added) is used as pseudo proxy.• MBH method, based on regression and inflation, suffers from significant under-estimation of low-frequency NH mean SAT. • Luterbacher’s approach works fine with sufficiently dense data networks; it shows loss of low-frequency variability, when the network becomes too thin.


Extratropical storminess

Developing hypotheses about the variability of climate variables


Estimates based upon repair costs for dikes in Hollandde Kraker, 1999

Very little evidence available

Number of yearly events with air pressure < 980 hPaLund and StockholmBärring and von Storch, 2004


Storminess

Fischer-Bruns, I., H. von Storch, F. González-Rouco and E. Zorita, 2005:

Modelling the variability of midlatitude storm activity on decadal to century time scales. Clim. Dyn. 25: 461-476

Studying the variability of extratropical storminess during hundredth of years


Storminess

Pre-industrial: 1550-1850 change from pre-industrial to industrial period 1850-2000


Storminess

Storm shift index defined as PCs of storm frequency EOFs

• North Atlantic• Mean near-

surface temperature (red/orange)

• storm frequency index (blue),

• storm shift index (green)

• 2 band of preindustrial conditions


Storminess - Conclusions

1. During historical times extra-tropical storminess is remarkably stationary with little variability.

2. During historical times, storminess and large-scale temperature variations are mostly decoupled.

3. There are indications for a poleward shift of the regions with high storm frequency on both hemispheres with future warming.


The Baltic Sea

Results for the Baltic Sea Region

- Overall development

- Maunder Minimum (downscaling)


The Baltic Sea

Gouirand, I., A. Moberg, and E.

Zorita, 2007: Climate

variability in Scandinavia for

the past millennium

simulated by an atmosphere-

ocean egenral circulation

model. Tellus 59A, 30-49

Low pass filtered Scandinavian temperatures

- Simulated by ECHO-G (black)- Reconstructed from proxies (grey)

- Uppsala temperature readings (dashed)

Proxies: tree rings

Proxies: ice break up

AMJJA

DJFM


Cold winters and springs, 1675-1710


Cold winters and springs, 1675-1710

Analysis of Columbus run, only.


Temperature conditions in Switzerland according to Pfister‘s classification (1999).


Simulated global 1675-1710 temperature anomaly

deMenocal et al.

(2000)

Ice conditions off Iceland (Koch, 1945)


The Baltic Sea

REMO model area; 0.5°x0.5° grid

ECHO-G grid

Dynamical Downscaling

„normal“: 1625-1656LMM : 1675-1705

Müller, B., 2004: Eine regionale Klimasimulation für Europa zur Zeit des späten Maunder Minimums 1675-1710, GKSS Report 2004/2


0

10

20

30

40

50

60

70

80

90

100

650 750 850 950 1050 1150 1250 1350 1450 1550 1650 1750 1850 1950

forest farmland greenland infrastructure water

Lower boundary conditions

are

a [

%]

Land-use

Sea ice coverageafter Koslowski (1999)

3.3.1692


REMO

Temperature differences LMM – non-LMM

Luterbacher


Mean difference of European air temperature during LMM and (pre-industrial) non-LMM.

LMM in Europe


Winter

-4

-3

-2

-1

0

1

2

5 10 15 20 25 30

Ano

mal

ie [K

]

REMO Luterbacher

Frühling

-4

-3

-2

-1

0

1

2

5 10 15 20 25 30

Anom

alie

[K]

Sommer

-4

-3

-2

-1

0

1

2

5 10 15 20 25 30

Anom

alie

[K]

Herbst

-4

-3

-2

-1

0

1

2

5 10 15 20 25 30

Anom

alie

[K]

REMO and Luterbacher

WinterWinter

FallFall

SpringSpring

SummerSummer

Seasonal Seasonal temperature temperature anomalies in anomalies in areas with areas with skill of skill of reconstruction reconstruction > 0.5> 0.5

Differences of Differences of ranked ranked seasonal seasonal means (i.e., means (i.e., 1= difference 1= difference of coldest of coldest season in LMM season in LMM and control and control season)season)


Precip anomalies LMM – non-LMM

Luterbacher[mm/season]

REMO


Overall conclusions

Multi-century simulations with state-of-the art GCMs are useful

for

… examining diagnostic (statistical) methods, incl. proxy assessments.

… deriving hypotheses about the free and forced variability in historical

times.



Storminess

Baltic Sea ice winter index after Koslowski (1998)

grey: raw index, red: 5 year mean, blue:20 year mean

Documents

Baltic Sea Science Conference, 20 March 2007 Climate simulations for the last millennium. Implications for the Baltic Sea Hans von Storch and Eduardo Zorita