1 / 22

CERFACS/CNRS Climate Modelling and Global Change team

A multi-model ensemble approach to assessment of climate change impacts on the wind energy resources in France using a statistical downscaling method. Julien Najac , Laurent Terray. CERFACS/CNRS Climate Modelling and Global Change team.

kevyn-cobb
Download Presentation

CERFACS/CNRS Climate Modelling and Global Change team

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A multi-model ensemble approach to assessment of climate change impacts on the wind energy resources in France using a statistical downscaling method Julien Najac, Laurent Terray CERFACS/CNRS Climate Modelling and Global Change team Électricité De France

  2. Outline 1. Introduction 2. Method 3. Validation 4. Multi-model impact study 5. Conclusion 1. Introduction

  3. 1. Introduction Local physiographic features orography, land cover Large scale climate state (predictors) atmospheric pressure, geopotential, high level winds Define a statistical model that links predictands with predictors Local climate state (predictands) 10m wind, precipitation I. Statistical downscaling • Wind energy resources → local information needed • Climate change → global information available

  4. Outline 1. Introduction 2. Method 3. Validation 4. Multi-model impact study 5. Conclusion 2. Method

  5. 2. Method I. Data Learning period : 1974-2002 predictands : Daily mean UV10m observations 78 stations over France (Météo France) predictors : Daily mean UV850hPa ERA40 reanalysis Control period : 1961-2000 Daily mean UV850hPa 13 AOGCMs (IPCC AR4) A1B Scenario : 2046-2065 and 2081-2100 Daily mean UV850hPa 13 AOGCMs (IPCC AR4) 2 seasons Cold : October-March Warm : April-September

  6. 2. Method II. Learning period Classification of the days of the learning period in the [UV10m,UV850hPa] EOFs space NWT groups of days with close atmospheric dynamics features, called weather types Determination of the NWT UV850hPa weather type centroids Computation of the distances between days and the NWT UV850hPa weather type centroids Multiple linear regression U10Reg(day) = f (NWT distances day/centroids)

  7. 2. Method m/s m/s III. Weather types Composites Cold season SLP anomalies (hPa) UV850hPa(m/s) UV10m (m/s)

  8. 2. Method UV850hPa for any Day tout (cross-validation, IPCC AR4, …) Learning period Distances between Day tout and the centroids:dEucli(tout,centroid) Nearest weather type:WTP UV850hPa weather type centroids 10m wind speed reconstructed by regression:U10Reg(tout) Regression coefficients Observed days in : - the learning period - the same weather type WTp Nearest day (comparison of the U10Reg): tLP U10Down(tout) = U10Obs(tLP) IV. Reconstruction Cross-validation over the period 1974-2002 with ERA40 predictors described in a paper submitted to JGR

  9. Outline 1. Introduction 2. Method 3. Validation 4. Multi-model impact study 5. Conclusion 3. Validation

  10. 3. Validation I. Predictors Taylor diagram - UV850hPa mean states (IPCC vs ERA40) 1961-2000

  11. 3. Validation ERA40 IPCC AR4 II. Weather types Occurrence frequency of the UV850hPa weather types 1961-2000 Models dispersion

  12. 3. Validation Observations IPCC AR4 downscaling III. Downscaled 10m wind speed U10m probability density function 1961-2000

  13. 3. Validation Statistical significance (0.05) Observations IPCC AR4 downscaling III. Downscaled 10m wind speed Annual cycle of the monthly mean U10m 1961-2000

  14. Outline 1. Introduction 2. Method 3. Validation 4. Multi-model impact study 5. Conclusion 4. Multi-model impact study

  15. 4. Multi-model impact study WT1 WT4 WT2 WT5 WT1 WT2 WT3 WT4 WT5 WT6 I. Weather types Cold Season +8 % +11 % +11 % +11 % U10m composites observations 1961-2000 -13 % -15 % -10 % -13 % Occurrence frequency changes of the UV850hPa weather types 2046-2065 and 2081-2100

  16. 4. Multi-model impact study Statistical significance (0.05) Models dispersion II. U10 Multi-model mean U10m changes Cold season <U102046-2065 - U101961-2000>(%) <U102081-2100 - U101961-2000>(%)

  17. 4. Multi-model impact study WT1 WT3 WT2 WT4 WT5 WT1 WT2 WT3 WT4 WT5 WT6 I. Weather types Warm Season U10m composites observations 1961-2000 +12 % +20 % + 5 % +11 % - 9 % -12 % - 3 % -10 % - 9 % -17 % Occurrence frequency changes of the UV850hPa weather types 2046-2065 and 2081-2100

  18. 4. Multi-model impact study Statistical significance (0.05) Models dispersion II. U10 Multi-model mean U10m changes Warm season <U102046-2065 - U101961-2000>(%) <U102081-2100 - U101961-2000>(%)

  19. 4. Multi-model impact study Models dispersion IV. Wind power Multi-model mean wind power density changes <P102046-2065 - P101961-2000>(%) <P102081-2100 - P101961-2000>(%)

  20. Outline 1. Introduction 2. Method 3. Validation 4. Multi-model impact study 5. Conclusion 5. Conclusion

  21. 5. Conclusion The statistical downscaling method - efficient in reproducing wind climatological properties - enables physical interpretations thanks to the weather types The multi-model study - good agreement between the 13 IPCC AR4 AOGCMs - decrease of the wind power in the West and the South - increase of the wind power in the North Future work - coupling this downscaling method with a meso-scale model

  22. Questions ?

More Related