The seasonal prediction system at CMCC: a focus on Africa

1. The seasonal prediction system at CMCC: a focus on Africa Stefano Materia*, Andrea Borrelli, Alessio Bellucci, Silvio Gualdi, Antonio Navarra * stefano.materia@cmcc.it Centro Euro Mediterraneo sui Cambiamenti Climatici SERC (Climate Service Division) - Bologna (Italy)

2. CMCC aims within Climafrica Validation of the Seasonal Forecast System (SFS) in use at CMCC at a seasonal and decadal time-scale. Assessment of the improvements due to atmosphere initialization. Evaluation of feedbacks and coupling between climate variability and land-surface in Sub-Saharan region. Assessment of the role of land surface initialization, both for seasonal and decadal timescales, through new observational datasets. Predictability of the West African Monsoon EGU Vienna 22-27/04/2012 ClimAfrica is an international project co-funded by the European Union under the 7th Framework Programme. It aims at developing improved climate predictions on seasonal to decadal climatic scales, and producing up-to-date tools to better understand and predict climate change in Sub-Saharan Africa for the next 10-20 years, analyzing the expected impacts on water and agriculture and proposing adaptation strategies tailored to the African context. The ClimAfrica consortium comprises 18 institutions: 9 from Europe, 8 from Africa, and one international.

3. Coupled Model component Off line Initialization Tools Ocean initial condition production T & S - OI assimilation SOFA 3.0 (De Mey and Benkiran 2002) Bellucci, Masina, Di Pietro & Navarra, 2007. MWR Coupling Daily No flux adjustment The concept of Seasonal Prediction System: not only a fully coupled model Radiative forcings GHGs & SO4 • A fully coupled model for climate projection is constituted by different components which represent the different elements of the Earth system Atmospheric IC from ERA-Interim Reanalysis Land Surface SILVA (Alessandri 2006, 2007) Atmosphere ECHAM5 (T63 ≈ 1.87°x1.87°) (Roeckner et al 1996, 2003) Spectral & Time interpolation INTERA (Kirchner, 2001) Coupler OASIS3 (Valcke et al, 2000) Sea Ice LIM (ORCA2) (Timmerman et al, 2005) Ocean OPA 8.2 (ORCA2) (Madec et al, 1998)

4. Coupled Model component Off line Initialization Tools Ocean initial condition production T & S - OI assimilation SOFA 3.0 (De Mey and Benkiran 2002) Bellucci, Masina, Di Pietro & Navarra, 2007. MWR Coupling Daily No flux adjustment The CMCC-Seasonal Prediction System (CMCC-SPS) Radiative forcings GHGs & SO4 Atmospheric IC from ERA-Interim Reanalysis Land Surface SILVA (Alessandri 2006, 2007) Atmosphere ECHAM5 (T63 ≈ 1.87°x1.87°) (Roeckner et al 1996, 2003) Spectral & Time interpolation INTERA (Kirchner, 2001) Coupler OASIS3 (Valcke et al, 2000) Sea Ice LIM (ORCA2) (Timmerman et al, 2005) Ocean OPA 8.2 (ORCA2) (Madec et al, 1998) Specification of initial conditions allow the model to evolve from a state which is the “reality” measured at the time of the forecast. The ocean and, at a much lesser extent, the atmosphere, have a memory which affect the response of the model

5. Seasonal Forecast: experiment setup OFF LINE interpolated Atmosphere IC from Operational analysis Day lag every 12 hours -5 0 -5 0 -5 0 -5 0 Time INITIALIZED COUPLED RUNS 1° Feb start date 1° May start date 1° Aug start date Time 1° Nov start date OFF LINE assimilated OCEAN ANALYSIS • 6-month-integration hindcasts from 1989 to 2010 • 12 start dates per year, 1 per month • 9 ensemble members for each start date

6. Predictability is generally higher on the ocean than on land (red areas indicate larger association between model results and observations), and in the Tropics than at mid latitudes. In a few areas the predictability is high during the whole year ISO (Intra-Seasonal Oscillation) experiments computed with CMCC-SPS

7. The focus on Africa makes clear that the skill of the model in terms of precipitation is still poor in the area, although some good result is visible in the south-east during boreal winter, and in Gulf of Guinea region in spring and summer. Temperature skill is good in NW tropical Atlantic and generally in the equatorial region.

8. Predictability of the West African Monsoon The model intercepts the interannual variability of Monsoon winds. Correlation coefficient between model and observations is 0.66 WAMI = u850hPa - u200hPa Fontaine et al., 1995 J.Clim

9. Precipitation predictability Precipitation (mm/day) (May start date, lead 1- JJA) This bias partly affects precipitation, which turn out to be too weak and to penetrate too much inland. The onset of the Monsoon occurs between the end of June and beginning of July, when there’s a jump in the ITCZ between 5°and 10°N

10. The CMCC-SPS seasonal forecast • Quasi-monthly product • Still a scientific exercise (not operational) • It gives updates about actual situation, verification versus the latest season, and (obviously) the forecast for the next one • Available upon request

11. Verification: surface temperature in the last spring (MAM) CMCC-SPS NCEP reanalysis

12. Global Seasonal Forecast (SON) (lead 3 only) • CMCC-SPS is now facing a porting to a new and more powerful machine. The porting is now ready and the next set of seasonal forecast will be computed with the start date of September. This summer then, only the June start date was prepared, and SON represents a lead 3

13. Global Seasonal Forecast (SON) (lead 3 only) • Our very early forecast predicts a dry SON season in north-eastern Africa, while wet conditions will occur in western Sahel and the equatorial countries

14. Next steps

15. Initialization of Land Surface The dynamical mechanisms described lead to a strong coupling between the land surface and atmosphere over western/central Africa (Patricola and Cook, 2010 Clim Dyn). Small perturbation in the land surface condition can propagate vertically and impact regional atmospheric circulation (Steiner et al., 2009 Clim Dyn). The “soil moisture memory” (Koster et al., 2006) can be as long as 6 months/1 year, affecting climate at a seasonal time scale. Predictability of the West African Monsoon EGU Vienna 22-27/04/2012 LAND SURFACE: 3rd element initialized (together with ATMO and OCE) Observational datasets of soil moisture and leaf area index, will be implemented to provide enhanced seasonal to decadal forecast.

16. Conclusions • CMCC has a functioning Seasonal Prediction System that works on the seasonal time scale • Verifications show that the system has skill representing the mean climate and predicting the main patterns of the following season • A quasi-monthly bulletin is prodiced, and it is available available upon request (not online yet). • There are good results at a regional scale as well, although some characteristics in a few areas of the world turned out to be poorly predictable. • The initialization of land surface will be starting soon, and we expect major improvements.

17. Thank you

18. Heat wave 2003

19. Minimum temperature Day time temperature