RT2B: Making climate model projections usable for impact assessment

1. RT2B: Making climate model projections usable for impact assessment Clare Goodess c.goodess@uea.ac.uk ENSEMBLES WP6.2 Meeting Helsinki, 26 April 2007

2. http://www.meteo.unican.es/ensembles

3. Fabio Micale Iacopo Cerrani Giampiero Genovese ELECTRICITÉ DE FRANCE Laurent Dubus Marta Nogaj Downscale DEMETER and ENSEMBLES s2d hindcasts to get dailyprecip, radiation, wind speed, and maximum/minimum temperatures to make crop yield modeling. The goal is to compare the downscaled data to GCM outputs and to estimate seasonal predictability. Downscale DEMETER and ENSEMBLES s2d hindcasts to get dailymaximum and minimum temperatures to make electricity demand forecasts. The goal is to compare the downscaled data to GCM outputs. Local precipitation forecasts for hydropower production capacities. Collaboration with WP6.3 Users. Two ongoing research collaborations with s2d users.

4. Outline of RT2B approaches:D2B.1 & D2B.2 Preparing datasets RCM data server – D2B.3 Reanalysis – D2B.13 Observed – D2B.15 GCM-based – D2B.17 Developing/testing models Statistical: D2B.5, D2B.16 Dynamical: D2B.9, D2B.10 Issues and methods Ensemble averaging: D2B.6 Pattern scaling: D2B.7, D2B.25 Weighting: D2B.8 GCM-RCM matrix RCM quick-look: D2B.21 Interactions with users (RT6) Web-based downscaling service: D2B.4, D2B.19, D2B.23 Questionnaires Development of tools: D2B.18 Preliminary assessment: D2B.20 s2d statistical downscaling: D2B.12 s2d dynamical downscaling INM/RCA: MARS Modification of SDS methods for probabilistic framework: D2B.14 From month 30, the emphasis is on synthesis, application and scenario construction RT3: ERA@50/25: D3.1.4, D3.1.5 RCM weights: D3.2.2 RCM system: D3.3.1 RT1: Grand PDFs RT2A: stream 1 runs (s2d, ACC) RT5: gridded data set RT3: D2B.11: mo 31 25 km scenario runs: D2B.22 mo 36 Final RCM system: D3.3.2 GCM/RCM skill/biases: D3.4.1 Dynamical and statistical downscaling Probabilistic regional scenarios and tools Mo 36 on: RCM quick look analysis D2B.24: mo 40 Questions & issues Sources of uncertainty Reducing uncertainty Robustness of SDS (D2B.27) Synergistic use of SDS/DDS • Applications to case studies • Alps, Mediterranean (D2B.28)… • Storms, CWTs, blocking…. • Forestry, water…. Recommendations & guidance on methods for the construction of probabilistic regional climate scenarios: D2B.26 mo 42

5. RT3/RT2B RCM simulations • See table and news on RT3 website • Latest version of the matrix is in D3.3.1

6. Some RCM related issues • Good availability of ERA-40 based output • Officially now Dec 2007 for scenario runs • But some earlier? • Quick-look analysis (month 40?) • ‘Evaluated RCM-system for use in RT2B (choice of RCM-GCM combinations and preliminary RCM weights’) • D3.3.1; Mm3.3 • D3.2.2 – describes a set of preliminary weights (PRUDENCE) • Final weights will be based on ERA40@25 • Proposing to apply revised REA • ‘Could’ explore Tebaldi et al. Bayesian approach

7. Refinement of Reliability Ensemble Averaging (REA) method – Filippo Giorgi (D2B.6) W = F1 x F2 x F3 x F4 x F5 Inverse functions: F1 local mean T bias F2 local mean P bias F3 interannual T Std. Dev. bias F4 interannual P Coeffic. Var. bias Direct function: F5 correlation obs/sim SLP patterns

8. D2B.8 recommendations on weighting • Robust • Informed by processes/expert knowledge • Transparency • Seasonal, range of variables, IAV/trends etc • A common comprehensive/flexible scheme • But some users want ‘tailoring’ • Consultation with users • Avoid double counting • Compare weighted/unweighted Can weighting be used to improve ‘credibility’? Can ENSEMBLES develop a ‘seamless’ approach?

9. Need broader discussion of these weighting, credibility, reliability issues • Web forum posting (Jens/Linda/Clare) • Side event during IAMAS, Italy, 2-13 July • Next ENSEMBLES GA, November

10. Tailoring of ENSEMBLES regional climate scenario outputs to user needs: a questionnaire for users, stakeholders and scenario developers Scenario generator tools and outputs A scenario generator tool would process dynamically and/or statistically downscaled output for user-specified locations, variables and time periods in a fairly transparent manner – presenting probabilistic regional scenarios in the desired format(s). Would such a tool be useful to you: Definitely / maybe / no / don’t know The outputs could be presented in a number of different formats. Please indicate those that would be useful:

11. What regional data do users want? • Mainly ‘standard’ surface variables • Daily time series (some sub-daily) • 25/50 km and/or station scale • Indices: blocking, NAO, heatwaves, drought, flooding • Extremes: • Max 5-day rainfall, Max daily precipitation intensity • Heatwaves, Max wind gust • All kinds!, Will calculate own • Joint probabilities • Temperature and precipitation • Intense precipitation and wind • Temperature and wind • ??????

12. Will they/you get what they/you want? • Majority will use RCM ‘raw’ data • Willingness to use SDS data • All seem satisfied! (temporal scale)

13. What are preferred scenario formats? • PDFs and time series most popular • Interest in threshold exceedence • Also maps and joint probabilities • Some challenges & contradictions

14. What tools are available/needed? • Climate Explorer • Extremes in gridded data sets (D4.3.1) • STARDEX extremes software • General awareness of tools • Not many users (so far) • Support for better integration with regional scenarios • Scenario generator tools???????? • Lots of potential users for SDS portal

15. The CRANIUM methodology 100 x 30 yr runs Change in T & P 2071-2100 Weather generator RCM 1 39,000 Weather generator RCM 13 • Histograms, PDFs, CDFs etc • 10 UK case-study locations • Linkoeping, Karlstad • Saentis, Basel • Belgrade, Kaliningrad, Timisoara • 2080s – Medium-high scenario • 10 seasonal indices: • means • extremes • e.g., hot days, intense rainfall HIRHAM HIRHAM (ECHAM4) HadRM3P CHRM CLM REMO RCAO RCAO (ECHAM4) PROMES RegCM RACMO Arpege (HadCM3) Arpege (Arpege) 13 RCM runs from PRUDENCE http://prudence.dmi.dk/ 10 RCMs Forcing from 4 GCMs Most driven by HadAM3 All A2 (Medium-high) emissions

17. Outline of RT2B approaches:D2B.1 & D2B.2 Preparing datasets RCM data server – D2B.3 Reanalysis – D2B.13 Observed – D2B.15 GCM-based – D2B.17 Developing/testing models Statistical: D2B.5, D2B.16 Dynamical: D2B.9, D2B.10 Issues and methods Ensemble averaging: D2B.6 Pattern scaling: D2B.7, D2B.25 Weighting: D2B.8 GCM-RCM matrix RCM quick-look: D2B.21 Interactions with users (RT6) Web-based downscaling service: D2B.4, D2B.19, D2B.23 Questionnaires Development of tools: D2B.18 Preliminary assessment: D2B.20 s2d statistical downscaling: D2B.12 s2d dynamical downscaling INM/RCA: MARS Modification of SDS methods for probabilistic framework: D2B.14 From month 30, the emphasis is on synthesis, application and scenario construction RT3: ERA@50/25: D3.1.4, D3.1.5 RCM weights: D3.2.2 RCM system: D3.3.1 RT1: Grand PDFs RT2A: stream 1 runs (s2d, ACC) RT5: gridded data set RT3: D2B.11: mo 31 25 km scenario runs: D2B.22 mo 36 Final RCM system: D3.3.2 GCM/RCM skill/biases: D3.4.1 Dynamical and statistical downscaling Probabilistic regional scenarios and tools Mo 36 on: RCM quick look analysis D2B.24: mo 40 Questions & issues Sources of uncertainty Reducing uncertainty Robustness of SDS (D2B.27) Synergistic use of SDS/DDS • Applications to case studies • Alps, Mediterranean (D2B.28)… • Storms, CWTs, blocking…. • Forestry, water…. Recommendations & guidance on methods for the construction of probabilistic regional climate scenarios: D2B.26 mo 42