ENSEMBLES. ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 1

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1 ENSEMBLES ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 1

2 Setting the scene for the third project aim: Maximising the results by linking the ensemble prediction system to a range of applications Clare Goodess University of East Anglia ENSEMBLES ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 2

3 Two methods of impact assessment using outputs from the EPS Tim Carter, SYKE & RT6 ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 3

4 Tailoring of ENSEMBLES regional climate scenario outputs to user needs: a questionnaire for users What are preferred output formats? Time series most popular Also PDFs & threshold exceedences Some interest in maps and joint probabilities Climate response surfaces Some challenges & contradictions e.g., extremes Impact response surfaces But users don t just want data also guidance and tools ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 4

5 ENSEMBLES regional scenarios portal ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 5

6 ENSEMBLES data archives ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 6

7 The ENSEMBLES Downscaling Portal A user-friendly Web tool for GCM post-processing and downscaling José Manuel Gutiérrez A.S. Cofiño, D. San-Martín, C. Sordo, J. Fernández, D. Frías, M.A. Rodríguez, S. Herrera, R. Ancell, M. Pons, B. Orfila, E. Díez Santander Met Group & AEMET ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 7

8 The ENSEMBLES Downscaling Portal Connecting end-users with data providers (CERA, DMI, ECMWF) in a Web-based transparent way to undertake regional impact studies (statistical downscaling). ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 8

9 GCM Data Availability. Seasonal & ACC Seasonal Predictions: DEMETER. Multi-model seasonal prediction experiment including seven models ran for six months four times a year using 9 different perturbed initial conditions (9 members). ENSEMBLES Stream 2 multi-model experiment. Climate Change Projections: Daily worldwide datasets obtained from different sources: CERA (ENSEMBLES repository) PCMDI Local Providers. Some examples: CERA_MPI-ECHAM5, including 20th century data ( ) and scenarios A1B, B1, and A2 ( ). CNRM-CM3 (local provider), including 20th century ( ) and scenarios A1B, B1, and A2 ( ). ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 9

10 Portal activities within ENSEMBLES The downscaling portal has been in operations for the last three years: The portal has been used in different Impact studies done by RT6 endusers (see e.g. Marta Nogaj EDF presentation at 11). But it has also contributed to other activities: Assessing the robustness of statistical downscalig methods. Producing ensembles of local scenarios with different SD methods. Assessing the skill of multi-model seasonal predictions in Europe conditioned to ENSO events. ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 10

11 Future... The ENSEMBLES downscaling portal will continue in operation after the end of the ENSEMLBES project partially supported by other national and European initiatives: QWeCI Quantifying Weather and Climate Impacts on Health in Developing Countries FUME (Forest fires under climate, social and economic changes in Europe, the Mediterranean and other fire affected areas of the world). METAFOR (Common Metadata for Climate Modelling Digital Repositories) Spanish program for regional scenarios. Full integration with GRID technologies to provide more resources for new users. Collaboration with EGEE, EGI, SSC-ES, EELA and others initiatives. ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 11

12 Facilitating the use of downscaled outputs some examples Assessments of robustness of information RCM changes Stationarity of statistical downscaling Validation and evaluation (process-based) Weighting schemes for dynamical & statistical downscaling sensitivity analyses Construction of PDFs & other probabilistic outputs ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 12

13 Robustness of temperature change projections for Europe Annual temperature change Inter-model standard deviation Climate-change signal ( minus ) for annual temperature ( C) for the multi-model mean of 16 ENSEMBLES RCMs. A1B scenario. (Philip Lorenz, MPI) ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 13

14 Robustness of precipitation change projections for Europe Annual precipitation change Number of models showing increase Climate-change signal ( minus ) for annual precipitation total (%) for the multi-model mean of 16 ENSEMBLES RCMs. A1B scenario. (Philip Lorenz, MPI) ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 14

15 Robustness of direction of change in seasonal precipitation Michel Déqué MétéoFrance Location of model grid points with a significant increase (light grey) or decrease (dark grey) in total rainfall at (compared with ) for winter (left) and summer (right). A weather regime approach is used to complete the ENSEMBLES GCM/RCM matrix. A1B emissions scenario. ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 15

16 For SDS: Regional weighting metrics SDS model performance Reproduction of trends/climate states Performance of driving model predictors Stability of relationships Correction for multiplicity of SDS models For RCMs f1: large scale circulation based on a weather regime classification f2: meso-scale signal based on seasonal temperature and precipitation analysis f3: probability density distribution match of daily and monthly temperature and precipitation analysis f4: extremes in terms of reoccurrence periods for temperature and precipitation How sensitive are projections & f5: trend analysis for temperature impacts to weighting? f6: representation of the annual Are the weights relevant to users? cycle in temperature and precipitation Do they reflect physical processes? What about GCM weighting? ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 16

17 Sensitivity to weighting WEIGHT #1 (performance) * #2 (reproduction of trend) WEIGHT #1 (performance) NO WEIGHT PDFs of daily Tmax from 39 SDS models, winter, Strážnice, CZ. Black bars: weights of individual SDS models. Red line: estimated PDF. Bottom: without weighting; centre: weighted by criterium (i); top: weighted by the product of criteria (i) and (ii). (Radan Huth, CAS) PDFs of Tmean change for Heathrow ( minus , A1B scenario) from CRU WG perturbed by RCM change factors. Red curve: unweighted. Black curve: weighted using RT3 product-based scheme. Coloured vertical bars: changes calculated directly from the 17 RCMs. (David Lister & Clare Goodess, UEA) ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 17

18 Some examples of PDFs All these PDFs are conditional (A1B, assumptions.) And subjective do not sample full uncertainty range ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 18

19 Some of the ENSEMBLES case studies ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 19

20 RT6 regional impacts studies ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 20

21 A new impact feedback?! ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 21

22 Applications outside ENSEMBLES CIRCE RCM outputs provide wider uncertainty framework for new coupled A-O RCM simulations for Mediterranean ClimateCost main source of European RCM scenario data for bottom-up economic assessments. E1 runs will also be used. ClarisLPB drawing on methods, e.g., gridding observed data, model metrics.... So ENSEMBLES provides a major knowledge and data legacy for future applications users! ENSEMBLES Final Symposium, November 2009, Met Office Exeter Page 22