April 2002 Andreas Hense, Universität Bonn1 Statistical problems in climate change detection and attribution Andreas Hense, Meteorologisches Institut Universität.

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Presentation transcript:

April 2002 Andreas Hense, Universität Bonn1 Statistical problems in climate change detection and attribution Andreas Hense, Meteorologisches Institut Universität Bonn

April 2002 Andreas Hense, Universität Bonn2 Overview Introduction The detection problem The attribution problem The Bayesian view Summary and Conclusion

April 2002 Andreas Hense, Universität Bonn3 Yes or No ? Detection Random Variations?

April 2002 Andreas Hense, Universität Bonn4 Yes or No ? Attribution

April 2002 Andreas Hense, Universität Bonn5 The detection problem Null Hypothesis H 0 : Random Natural Variability Alternative Hypothesis H A : No natural Variability... and a testvariable to measure the climate change

April 2002 Andreas Hense, Universität Bonn6 Probability for testvariable in case of H 0 < Rejection of H 0

April 2002 Andreas Hense, Universität Bonn7 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

April 2002 Andreas Hense, Universität Bonn8 The testvariable Data and fingerprint are Gaussian variables data = fingerprint if distance | d - g | small Mahalanobis distance D² natural measure

April 2002 Andreas Hense, Universität Bonn9 Amplitude of modeled change Amplitude of observed change Hasselmann‘s optimal fingerprint: similarity measure

April 2002 Andreas Hense, Universität Bonn10

April 2002 Andreas Hense, Universität Bonn11 A detection experiment (Paeth and Hense, 2001) Simulation time Observation time

April 2002 Andreas Hense, Universität Bonn12 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 H A Only possible by climate modelling

April 2002 Andreas Hense, Universität Bonn13 The attribution problem Random climate variations : Control run Climate Change: Greenhouse gase scenario Null Hypothesis ensemble Alternative Hypothesis ensemble HAHA H0H0

April 2002 Andreas Hense, Universität Bonn14 The misclassification Reality Decision OK

April 2002 Andreas Hense, Universität Bonn15 The attribution problem Optimal classification Minimize the cost of misclassification Bayes-Decision Classical discrimination analysis

April 2002 Andreas Hense, Universität Bonn16 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)

April 2002 Andreas Hense, Universität Bonn17 The attribution problem

April 2002 Andreas Hense, Universität Bonn18 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

April 2002 Andreas Hense, Universität Bonn19 The Bayesian view More weight Less weight The Climate Sceptics Equal weight The Uninformed More weight Less weight The Environmentalist

April 2002 Andreas Hense, Universität Bonn20 A Bayesian attribution experiment ECHAM3/LSG Control ECHAM3/LSG in 2000 Scenario NCEP Reanalysis Data Observations Northern hemisphere area averages –near surface (2m) Temperature –70 hPa Temperature joint work with Seung-Ki Min, Heiko Paeth and Won-Tae Kwon

April 2002 Andreas Hense, Universität Bonn21 A Bayesian Attribution experiment The Uninformed

April 2002 Andreas Hense, Universität Bonn22 A Bayesian attribution experiment The Environmentalist The Climate Sceptics

April 2002 Andreas Hense, Universität Bonn23 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

April 2002 Andreas Hense, Universität Bonn24 Summary and Conclusion Application to ECHAM3/LSG Ensemble and NCEP Reanalysis data Northern Hemisphere area averaged temperatures (2m and 70 hPa) – increasing classification into ECHAM3/LSG in model year 2000 –weak evidence and 10% to 15% misclassification risk Missing processes in climate change simulation?