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Climate Change Scenarios for the CH2011 Initiative

PRECLIM (part 2). Climate Change Scenarios for the CH2011 Initiative. Andreas Fischer, Andreas Weigel, Mark Liniger, Christoph Buser, Christof Appenzeller. NCCR WP2 Meeting, 5 October 2010, Zurich. based on 21 ENSEMBLES RCM simulations

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Climate Change Scenarios for the CH2011 Initiative

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  1. PRECLIM (part 2) Climate Change Scenarios for the CH2011 Initiative Andreas Fischer, Andreas Weigel, Mark Liniger, Christoph Buser, Christof Appenzeller NCCR WP2 Meeting, 5 October 2010, Zurich

  2. based on 21 ENSEMBLES RCM simulations • Probabilistic Scenarios of Temperature and Precipitation for Northeastern, Western, and Southern Switzerland • Collaboration between MeteoSwiss, ETH, ART, OcCC, NCCR-Climate, C2SM • Spring 2011 „CH2011“ report ENSEMBLES Final Report (2009) Swiss Climate Scenarios „CH2050“ report • Probabilistic Scenarios of Temperature and Precipitation for Northern and Southern Switzerland • based on PRUDENCE RCM simulations OcCC (2007)

  3. PDF Derivation of Probablistic Scenarios Modelled Climate Change Signals Bayes Algorithm (Buser, Künsch, Lüthi, Wild, Schär, 2009) ?  Assumptions transparent

  4. Bayesian Multi-Model Combination (Buser et al., 2009) Obs NOW Models NOW „Obs“ FUTURE Models FUTURE Prior p(x) Posterior p(x|data) P(x|data)  p(x) * p(data|x) Gibbs Sampler Likelihood p(data|x)

  5. Application of Algorithm within CH2011 Different considerations: • Estimation of Projection Uncertainty (σ2β) • Role of Internal Variability • Independent Model Data

  6. (1) GCM Uncertainty (2) RCM Uncertainty 8 different GCMs HadCM3Q0 ECHAM Smoothing of timeseries by polynomial fit (Hawkins & Sutton, 2009) 1. Estimating Projection Uncertainty Assumption: Projection Uncertainty is fully sampled by range of available model simulations

  7. 2. Internal Variability • As a pre-processing step we remove internal variability from time-series • Calculate posterior distributions with Bayes Algorithm • Add internal variability to posterior distribution of μ

  8. 2. Internal Variability Summer Temperature over CHNE (Model: ETHZ – HadCM3Q0) 4th order polynomial fit 30-yr Running Mean (Hawkins and Sutton, 2009)

  9. 2. Internal Variability Summer Temperature over CHNE (Model: ETHZ – HadCM3Q0) 4th order polynomial fit 30-yr Running Mean (Hawkins and Sutton, 2009)

  10. ECHAM Average all RCMs driven by the same GCM HadCM3Q0 3. Independent Model Data DJF Temperature 1980-2009 (AL) ECHAM HadQ0 HadQ3 HQ16 ARP. BCM ECHAM HadQ0 HadQ3 HQ16 ARP. BCM

  11. Probabilistic Climate Change Scenarios Northeastern Switzerland Reference Period 1980 - 2009 Orography of Switzerland

  12. 2035 Swiss Climate Scenario (A1B) 2060 2085 Temperature (K) Chains averaged according to GCM Individual GCM-RCM chains

  13. 2035 Swiss Climate Scenario (A1B) 2060 2085 Precipitation (%) Temperature (K) Chains averaged according to GCM Individual GCM-RCM chains

  14. 2035 Swiss Climate Scenario (A1B) 2060 2085 Precipitation (%) Temperature (K) CH2011 Chains averaged according to GCM Individual GCM-RCM chains

  15. Conclusions The Bayes Algorithm of Buser et al. (2009) is a transparent tool for generating probabilistic climate change scenarios An objective method to estimate projection uncertainty as a prior assumption has been proposed. The internal Variability is subtracted from the timeseries as a pragmatic solution. The probabilistic climate change scenarios for Northeastern Switzerland show a continous increase in temperature over the 21st century. For precipitation only in summer a signal in the second half of the century is detectable.

  16. HIRHAM (DMI) HIRHAM (Met.No) BCM RCA (SMHI) CLM (ETHZ) PROMES (UCLM) RRCM (VMGO) Standard sens. HadRM3 (Met Office) HadCM3 HIRHAM (Met.No) RCA3 (C4I) High sens. HadRM3 (Met Office) HadRM3 (Met Office) SRES A1B Low sens. RCA (SMHI) REMO (MPI) HIRHAM (DMI) ECHAM5 RACMO (KNMI) RCA (SMHI) REGCM3(ICTP) ALADIN v1 (CNRM) ALADIN v2 (CNRM) ARPEGE HIRHAM (DMI) CGCM3 CRCM (OURANOS) IPSL CLM (GKSS) 21 RCM–GCM–chains Final Report (2009)

  17. Swiss Climate Scenarios: Precipitation 2035 2060 2085 Points: 5/6 GCMs agree on sign JJA Precipitation Change [%]

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