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Statistical problems in climate change detection and attribution

Statistical problems in climate change detection and attribution. Andreas Hense, Meteorologisches Institut Universität Bonn. Overview. Introduction The detection problem The attribution problem The Bayesian view Summary and Conclusion. Yes or No ?. Random Variations?. Detection.

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Statistical problems in climate change detection and attribution

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  1. Statistical problems in climate change detection and attribution Andreas Hense, Meteorologisches Institut Universität Bonn Andreas Hense, Universität Bonn

  2. Overview • Introduction • The detection problem • The attribution problem • The Bayesian view • Summary and Conclusion Andreas Hense, Universität Bonn

  3. Yes or No ? Random Variations? Detection Andreas Hense, Universität Bonn

  4. Yes or No ? Attribution Andreas Hense, Universität Bonn

  5. The detection problem Null Hypothesis H0 : Random Natural Variability Alternative Hypothesis HA : No natural Variability ... and a testvariable to measure the climate change Andreas Hense, Universität Bonn

  6. Probability for testvariable in case of H0 < 0.05 ... 0.01 Rejection of H0 Andreas Hense, Universität Bonn

  7. The testvariable • Collect the information from field data • Collect natural variability information • „multivariate statistics“ • data vector d • covariance matrix S • optimize change analysis • „optimal fingerprint“ • fingerprint vector g Andreas Hense, Universität Bonn

  8. The testvariable • Data and fingerprint are Gaussian variables • data = fingerprint if distance | d - g | small • Mahalanobis distance D² natural measure Andreas Hense, Universität Bonn

  9. Amplitude of modeled change Amplitude of observed change Hasselmann‘s optimal fingerprint: similarity measure Andreas Hense, Universität Bonn

  10. Andreas Hense, Universität Bonn

  11. A detection experiment (Paeth and Hense, 2001) Observation time Simulation time Andreas Hense, Universität Bonn

  12. The attribution problem • Assumption for detection • climate change g is constant • no variability in climate change scenario • Assume a climate change ensemble • defines an Alternative - Hypothesis HA • Only possible by climate modelling Andreas Hense, Universität Bonn

  13. The attribution problem Random climate variations : Control run Null Hypothesis ensemble H0 Climate Change: Greenhouse gase scenario Alternative Hypothesis ensemble HA Andreas Hense, Universität Bonn

  14. The misclassification Reality OK Decision OK Andreas Hense, Universität Bonn

  15. The attribution problem • Optimal classification • Minimize the cost of misclassification • Bayes-Decision • Classical discrimination analysis Andreas Hense, Universität Bonn

  16. The Attribution problem • Bayes Decision with least costs is given if • observation part of Control if prob(obs | control) > prob(obs | scenario) • observation part of scenario if prob(obs | control) < prob(obs | scenario) Andreas Hense, Universität Bonn

  17. The attribution problem Andreas Hense, Universität Bonn

  18. The Bayesian View • Sir Thomas Bayes 1763 • allows you to start with what you already believe (in climate change) • to see how new information changes your confidence in that belief Andreas Hense, Universität Bonn

  19. The Bayesian view Less weight More weight The Climate Sceptics Equal weight Equal weight The Uninformed Less weight More weight The Environmentalist Andreas Hense, Universität Bonn

  20. A Bayesian attribution experiment • ECHAM3/LSG 1880-1979 Control • ECHAM3/LSG in 2000 Scenario • NCEP Reanalysis Data 1958-1999 Observations • Northern hemisphere area averages • near surface (2m) Temperature • 70 hPa Temperature • joint work with Seung-Ki Min, Heiko Paeth and Won-Tae Kwon Andreas Hense, Universität Bonn

  21. A Bayesian Attribution experiment The Uninformed Andreas Hense, Universität Bonn

  22. A Bayesian attribution experiment The Environmentalist The Climate Sceptics Andreas Hense, Universität Bonn

  23. Summary and Conclusion • Climate change detection and attribution are classical statistical prodecures • detection: Mahalanobis distance • attribution: discriminant analysis • attribution: internal variability in climate change scenario through ensemble simulations • Bayesian statistics unified view Andreas Hense, Universität Bonn

  24. Summary and Conclusion • Application to ECHAM3/LSG Ensemble and NCEP Reanalysis data • Northern Hemisphere area averaged temperatures (2m and 70 hPa) • 1995-1999 increasing classification into ECHAM3/LSG in model year 2000 • weak evidence and 10% to 15% misclassification risk • Missing processes in climate change simulation? Andreas Hense, Universität Bonn

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