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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 Research GKSS Research Center Geesthacht, Germany. Baltic Sea Science Conference, 20 March 2007. Overview. Experimental set-up

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Baltic Sea Science Conference, 20 March 2007

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  1. Climate simulations for the last millennium. Implications for the Baltic Sea Hans von Storch and Eduardo Zorita Institute for Coastal ResearchGKSS Research CenterGeesthacht, Germany Baltic Sea Science Conference, 20 March 2007

  2. Overview • Experimental set-up • Utility I: Testing validity of proxy derived indicators.Testing MBH and Luterbacher temp’s • Utility II: Estimating the unobservable.Examples(1) extra-tropical storminess(2) Baltic Sea region conditions Baltic Sea Science Conference, 20 March 2007

  3. Experimental set-up ECHO-G simulations „Erik den Røde” (1000-1990) and “Christoph Columbus” (1550-1990) with estimated volcanic, GHG and solar forcing Baltic Sea Science Conference, 20 March 2007

  4. Skill of simulation Late Maunder Minimum Reconstruction from historical evidence, from Luterbacher et al. Model-based reconstuction 1675-1710vs. 1550-1800 Baltic Sea Science Conference, 20 March 2007

  5. Skill of simulation Baltic Sea Science Conference, 20 March 2007

  6. Skill of simulation Baltic Sea Science Conference, 20 March 2007

  7. 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. Baltic Sea Science Conference, 20 March 2007

  8. Conclusion, 1 • Millennial simulations - efforts to simulate the response to estimated volcanic, GHG and solar forcing, 1000-2000. • Low-frequency variability in millennial simulations: > Mann, Jones, “hockeystick”, but ~ Esper, boreholes, (some) instrumental data Baltic Sea Science Conference, 20 March 2007

  9. 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. Baltic Sea Science Conference, 20 March 2007

  10. Testing the MBH method 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 pseudo-proxies: grid point SAT plus white noise red: mimicking largest sample used in MBH Baltic Sea Science Conference, 20 March 2007

  11. Testing validity of proxy-derived indicators Baltic Sea Science Conference, 20 March 2007

  12. 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 hockey- stick. Baltic Sea Science Conference, 20 March 2007

  13. Testing validity of proxy-derived indicators 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 Baltic Sea Science Conference, 20 March 2007

  14. 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. Baltic Sea Science Conference, 20 March 2007

  15. 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. Baltic Sea Science Conference, 20 March 2007

  16. Developing hypotheses about the variability of climate variables Extratropical storminess Number of yearly events with air pressure < 980 hPa Lund and Stockholm Bärring and von Storch, 2004 Estimates based upon repair costs for dikes in Hollandde Kraker, 1999 Very little evidence available Baltic Sea Science Conference, 20 March 2007 Baltic Sea Science Conference, 20 March 2007

  17. Storminess Studying the variability of extratropical storminess during hundredth of years 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 Baltic Sea Science Conference, 20 March 2007

  18. Storminess Pre-industrial: 1550-1850 change from pre-industrial to industrial period 1850-2000 Baltic Sea Science Conference, 20 March 2007

  19. Storminess • North Atlantic • Mean near-surface temperature (red/orange) • storm frequency index (blue), • storm shift index (green) • 2 band of preindustrial conditions Storm shift index defined as PCs of storm frequency EOFs Baltic Sea Science Conference, 20 March 2007

  20. Storminess - Conclusions • During historical times extra-tropical storminess is remarkably stationary with little variability. • During historical times, storminess and large-scale temperature variations are mostly decoupled. • There are indications for a poleward shift of the regions with high storm frequency on both hemispheres with future warming. Baltic Sea Science Conference, 20 March 2007

  21. The Baltic Sea • Results for the Baltic Sea Region • Overall development • Maunder Minimum (downscaling) Baltic Sea Science Conference, 20 March 2007

  22. 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 AMJJA Proxies: tree rings • Low pass filtered Scandinavian temperatures • Simulated by ECHO-G (black)- Reconstructed from proxies (grey) • Uppsala temperature readings (dashed) Proxies: ice break up DJFM Baltic Sea Science Conference, 20 March 2007

  23. Late Maunder Minimum Cold winters and springs, 1675-1710 Analysis of Columbus run, only.

  24. Late Maunder Minimum Temperature conditions in Switzerland according to Pfister‘s classification (1999). Baltic Sea Science Conference, 20 March 2007

  25. Ice conditions off Iceland (Koch, 1945) deMenocal et al. (2000) Simulated global 1675-1710 temperature anomaly

  26. The Baltic Sea „normal“: 1625-1656LMM : 1675-1705 ECHO-G grid Müller, B., 2004: Eine regionale Klimasimulation für Europa zur Zeit des späten Maunder Minimums 1675-1710, GKSS Report 2004/2 Dynamical Downscaling REMO model area; 0.5°x0.5° grid Baltic Sea Science Conference, 20 March 2007

  27. Lower boundary conditions 3.3.1692 Land-use area [%] Sea ice coverageafter Koslowski (1999) Baltic Sea Science Conference, 20 March 2007

  28. Temperature differences LMM – non-LMM REMO Luterbacher Baltic Sea Science Conference, 20 March 2007

  29. LMM in Europe Mean difference of European air temperature during LMM and (pre-industrial) non-LMM. Baltic Sea Science Conference, 20 March 2007

  30. Winter Spring Seasonal temperature anomalies in areas with skill of reconstruction > 0.5 Differences of ranked seasonal means (i.e., 1= difference of coldest season in LMM and control season) Summer Fall REMO and Luterbacher Baltic Sea Science Conference, 20 March 2007

  31. Precip anomalies LMM – non-LMM REMO Luterbacher[mm/season] Baltic Sea Science Conference, 20 March 2007

  32. 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. Baltic Sea Science Conference, 20 March 2007

  33. Baltic Sea Science Conference, 20 March 2007

  34. Storminess Baltic Sea Science Conference, 20 March 2007

  35. Baltic Sea ice winter index after Koslowski (1998)grey: raw index, red: 5 year mean, blue:20 year mean

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