1. June 16th, 2009 Christian Pagé, CERFACS Laurent Terray, CERFACS - URA 1875 Julien Boé, U California Christophe Cassou, CERFACS - URA 1875 Weather typing approach for seasonal forecasts? HEPEX09 - COST731 Workshop - Toulouse - 15-19 June 2009

2. 1. Motivations Difficult to forecast precipitation adequately at long range and at monthly/seasonal timescales Even more at higher spatial resolution (hydrological applications) Numerical Models and Ensemble Forecast Systems have more abilities to forecast Large-Scale Circulation than fine-scale local variables at these timescales Downscaling techniques based on statistical relationships between the Large-Scale Circulation and local scale fields have proven significant abilities in climate sciences (Boe and Terray, 2007) Weather-typing approach A sort of extended analog methodology with dynamical and local variable constraints Can process a large number of simulations, such as large ensemble forecasts systems of atmospheric and/or hydrological models (low CPU cost)  Monthly/Seasonal forecasts applications?

3. 3 Downscaling 2. Background Local fields (precipitations, temperature) Local geographic characteristics (topography, rugosity) Large-Scale Circulation Statistical downscaling Build a statistical model linking the large-scale circulation and local precipitation Statistical Downscaling From Global OR Regional Models! (e.g. ARPEGE)

4. 4 Classification 3. Methodology Daily Mean Sea-Level Pressure Clusters group #1 Clusters group #2 Cluster composite: Average of the variable which is classified within a group Each cluster is defined by: - its composite - the days’ distribution within the cluster Classification: main concepts as in Boe and Terray (2007) statistical downscaling methodology Composite Based on Michelangeli et al, 1995 Precipitation observations are used in the classification learning phase (multi-variate): discriminant Temperature (model AND observations) is also used when selecting analog day Distances to all clusters (inter- types) are also considered Pictures by Julien Najac, Cerfacs

5. 5 Weather types 3. Methodology NCEP MSLP anomalies (hPa) Weather types examples Winter Methodology produces Weather types discriminant for precipitation Related precipitation anomalies from Météo-France 8-km mesoscale analysis SAFRAN (%)

6. 6 3. Methodology Validation Weather types occurrence validation 1950-1999

7. 7 3. Methodology Validation Downscaled NCEP reanalysis vs SAFRAN analysis Downscaled ARPEGE V4 vs NCEP reanalysis 1981-2005 Validation Period Annual total mean precipitation 1981-2005 Differences in %

8. 8 3. Methodology Validation Precipitation Time Tendencies Validation => Seasonal Cumulated Precipitation (NDJFM) reconstructed by multiple regression using weather types occurrence and clusters’ distances Correlation observation /reconstruction 1900/2000 1 point=1 station, color: latitude => blue=south, red=north Time Tendencies Pr 1951-2000 observation vs reconstruction

9. 9 The Météo-France SIM model for hydrological simulations (Habets et al., 2008) SAFRAN : meteorological parameters: mesoscale analysis at 8-km resolution ISBA : water flux and ground surface energy fluxes (evaporation, snow, runoff, water infiltration) MODCOU : hydrological model (river flows) Daily river flows Latent Sensible Snow Atmosphere Source: Météo-France 3. Methodology Validation Habets, F., et al. (2008), The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, J. Geophys. Res., 113, D06113, doi:10.1029/2007JD008548.

10. 10 3. Methodology Validation River flow Validation using the SIM hydrometeorological model Winter Mean OBS NCEP (0.85) SAFRAN (0.97) 20101960 500 0 Precipitation and other meteorological variables reconstructed at 8-km using: NCEP reanalysis data (Large-Scale Circulation and Temperature) Statistical downscaling methodology (SAFRAN analysis used for analog daily data) Good agreement of downscaled NCEP data vs SAFRAN and observations SIM simulations by Eric Martin, Météo-France

11. Could this kind of statistical downscaling weather typing methodology be used for Monthly/Seasonal forecasts? Predictability of Weather Regimes at Monthly/Seasonal scales Very preliminary and exploratory studies have already been done (Chabot et al., 2008, 2009) 4 Standard weather regimes, large North Atlantic Domain Many questions still to be addressed ! Weather types Are some weather types more predictable than others at monthly/seasonal scale ? Increase in predictability ? If yes, what would be the forcings responsible for the most predictable weather types ? Which region and large-scale variable(s) to use ? How many weather types to use ? Some questions should be explored by doing a hindcast experiment 11 4. Perspectives

12. 12 Thanks for your attention! Christian Pagé, CERFACS christian.page@cerfacs.fr Laurent Terray, CERFACS - URA1875 Julien Boé, U California Christophe Cassou, CERFACS - URA1875 HEPEX09 - COST731 Workshop - Toulouse - 15-19 June 2009

13. 13 4. Monthly/Seasonal Methodology Facts BUT! Numerical models have forecasts performances at monthly timescales which are much better than at seasonal timescales (4 weeks lead time) Ridge A previous preliminary and exploratory study (Chabot et al., 2008) showed that: Weather regimes predictability at seasonal timescales is low Except when strong oceanic forcing (ENSO, Tropical Atlantic) This study used: Geopotential Height at 500 hPa (Z500) for Large-Scale Circulation classification (tendencies problems) A Large North Atlantic Domain Four Standard Weather Types Blocking

14. 14 4. Monthly/Seasonal Methodology Facts A monthly extension to the Chabot et al., 2008 study shows (Chabot et al., 2009) : Good predictability for weather types anomaly sign (60 to 80 % of correct forecasts) Percentage of correct forecasts for the most probable weather type Percentage of correct forecasts for the least probable weather type days 30

15. 15 3. Methodology Validation Flow Validation Winter Mean OBS NCEP (0.85) SAFRAN (0.97) Annual Cycle OBS NCEP ARPEGE-VR CDF OBS NCEP ARPEGE-VR Jan Dec 0 1 ARIEGE (Foix) LOIRE(Blois) SEINE (Poses) VIENNE (Ingrandes) 0 2500 0 0 0 0 0 1200 2500 250 150800 20101960 500 0