1. Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Climate Change Scenarios for the CH2011 Initiative NCCR WP2 Meeting, 5 October 2010, Zurich Andreas Fischer, Andreas Weigel, Mark Liniger, Christoph Buser, Christof Appenzeller PRECLIM (part 2)

2. 2 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Swiss Climate Scenarios Probabilistic Scenarios of Temperature and Precipitation for Northern and Southern Switzerland based on PRUDENCE RCM simulations OcCC (2007) „CH2050“ report 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 ENSEMBLES Final Report (2009) „CH2011“ report

3. 3 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Derivation of Probablistic Scenarios Modelled Climate Change Signals PDF ? Bayes Algorithm (Buser, Künsch, Lüthi, Wild, Schär, 2009)  Assumptions transparent

4. 4 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Bayesian Multi-Model Combination (Buser et al., 2009) Prior p(x) Posterior p(x|data) Obs NOW Models NOW Models FUTURE „Obs“ FUTURE Likelihood p(data|x) P(x|data)  p(x) * p(data|x) Gibbs Sampler

5. 5 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Application of Algorithm within CH2011 1.Estimation of Projection Uncertainty (σ 2  β ) 2.Role of Internal Variability 3.Independent Model Data Different considerations:

6. 6 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch 1. Estimating Projection Uncertainty Assumption: Projection Uncertainty is fully sampled by range of available model simulations ECHAM HadCM3Q0 (2) RCM Uncertainty 8 different GCMs (1) GCM Uncertainty Smoothing of timeseries by polynomial fit (Hawkins & Sutton, 2009)

7. 7 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch 2. Internal Variability (1) As a pre-processing step we remove internal variability from time-series (2) Calculate posterior distributions with Bayes Algorithm (3) Add internal variability to posterior distribution of  μ

8. 8 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch 30-yr Running Mean 4th order polynomial fit (Hawkins and Sutton, 2009) Summer Temperature over CHNE (Model: ETHZ – HadCM3Q0) 2. Internal Variability

9. 9 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch 30-yr Running Mean 4th order polynomial fit (Hawkins and Sutton, 2009) Summer Temperature over CHNE (Model: ETHZ – HadCM3Q0) 2. Internal Variability

10. 10 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch ECHAM HadCM3Q0 3. Independent Model Data ECHAMHadQ0HadQ3HQ16ARP.BCM ECHAM HadQ0 HadQ3 HQ16 ARP. BCM DJF Temperature 1980-2009 (AL) Average all RCMs driven by the same GCM

11. 11 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Probabilistic Climate Change Scenarios Orography of Switzerland Reference Period 1980 - 2009 Northeastern Switzerland

12. 12 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Temperature (K) Swiss Climate Scenario (A1B) Chains averaged according to GCM Individual GCM-RCM chains 2035 2060 2085

13. 13 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Temperature (K) Precipitation (%) Swiss Climate Scenario (A1B) Chains averaged according to GCM Individual GCM-RCM chains 2035 2060 2085

14. 14 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Temperature (K) Precipitation (%) Swiss Climate Scenario (A1B) Chains averaged according to GCM Individual GCM-RCM chains 2035 2060 2085 CH2011

15. 15 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch 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. 16 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch 21 RCM–GCM–chains HadCM3 HIRHAM (Met.No) REMO (MPI) SRES A1B ECHAM5 Low sens. High sens. Standard sens. ARPEGE CGCM3 BCM RCA (SMHI) HadRM3 (Met Office) RCA (SMHI) HadRM3 (Met Office) RCA3 (C4I) CLM (ETHZ) PROMES (UCLM) HIRHAM (DMI) RACMO (KNMI) RCA (SMHI) ALADIN v1 (CNRM) HIRHAM (DMI) REGCM3(ICTP) CRCM (OURANOS) RRCM (VMGO) IPSLCLM (GKSS) HadRM3 (Met Office) ALADIN v2 (CNRM) HIRHAM (Met.No) HIRHAM (DMI) Final Report (2009)

17. 17 Climate Services, NCCR WP2 Meeting | 5 October 2010 andreas.fischer@meteoswiss.ch Swiss Climate Scenarios: Precipitation JJA Precipitation Change [%] 20602085 2035 Points: 5/6 GCMs agree on sign