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RT2B: Making climate model projections usable for impact assessment Clare Goodess ENSEMBLES WP6.2 Meeting Helsinki, 26 April 2007.

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Presentation on theme: "RT2B: Making climate model projections usable for impact assessment Clare Goodess ENSEMBLES WP6.2 Meeting Helsinki, 26 April 2007."— Presentation transcript:

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 AI met group http://www.meteo.unican.es/ 2 http://www.meteo.unican.es/ensembles

3 AI met group http://www.meteo.unican.es/ 3 Collaboration with WP6.3 Users. Fabio Micale Iacopo Cerrani Giampiero Genovese Downscale DEMETER and ENSEMBLES s2d hindcasts to get daily precip, 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. Two ongoing research collaborations with s2d users. Downscale DEMETER and ENSEMBLES s2d hindcasts to get daily maximum 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. ELECTRICITÉ DE FRANCE Laurent Dubus Marta Nogaj

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 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 Final RCM system: D3.3.2 GCM/RCM skill/biases: D3.4.1 RT3: 25 km scenario runs: D2B.22 mo 36 RCM quick look analysis D2B.24: mo 40 D2B.11: mo 31 Dynamical and statistical downscaling Probabilistic regional scenarios and tools 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 s2d dynamical downscaling INM/RCA: MARS Questions & issues Sources of uncertainty Reducing uncertainty Robustness of SDS (D2B.27) Synergistic use of SDS/DDS RT1: Grand PDFs RT2A: stream 1 runs (s2d, ACC) RT5: gridded data set Mo 36 on:

5 RT3/RT2B RCM simulations See table and news on RT3 website Latest version of the matrix is in D3.3.1 Global model Regional model METO- HC MPIMETIPSLCNRMNERSCTotal number METO-HC1950-2100 2 MPIMET1950-21001950-2050*2 CNRM1950-20502 DMI1950-21001950-2050*2 ETH1950-20501 KNMI1950-20501 ICTP1950-20501 SMHI1950-20501950-2050*2 UCLM1950-20501 C4I1950-20501 GKSS**1950-2050*1 Met.No**1950-2050*1 CHMI**1950-2050*1 Total (1950-2050)4623217

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 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 / dont know The outputs could be presented in a number of different formats. Please indicate those that would be useful: Probability density functionsDefinitely / maybe / no / dont know Cumulative density functionsDefinitely / maybe / no / dont know Percentile values (e.g., 10 th, 50 th, 90 th )Definitely / maybe / no / dont know Probability of exceeding specified threshold(s)Definitely / maybe / no / dont know Response surfacesDefinitely / maybe / no / dont know MapsDefinitely / maybe / no / dont know Time seriesDefinitely / maybe / no / dont know Joint probabilities (give examples of variables if possible) Definitely / maybe / no / dont know Tailoring of ENSEMBLES regional climate scenario outputs to user needs: a questionnaire for users, stakeholders and scenario developers

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 RCM 1 RCM 13 Weather generator Change in T & P 2071-2100 The CRANIUM methodology Weather generator 100 x 30 yr runs 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 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 39,000

16

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 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 Final RCM system: D3.3.2 GCM/RCM skill/biases: D3.4.1 RT3: 25 km scenario runs: D2B.22 mo 36 RCM quick look analysis D2B.24: mo 40 D2B.11: mo 31 Dynamical and statistical downscaling Probabilistic regional scenarios and tools 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 s2d dynamical downscaling INM/RCA: MARS Questions & issues Sources of uncertainty Reducing uncertainty Robustness of SDS (D2B.27) Synergistic use of SDS/DDS RT1: Grand PDFs RT2A: stream 1 runs (s2d, ACC) RT5: gridded data set Mo 36 on:

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