1 CNRS, Centre National de Recherches Météorologiques (CNRM / Météo-France, Toulouse)

1. AMMA Land surface Model Intercomparison Project Aaron Boone1, Patricia deRosnay2, and the ALMIP Working Group 1 CNRS, Centre National de Recherches Météorologiques (CNRM / Météo-France, Toulouse) 2 CNRS, Centre d’Etudes de la BIOsphere (CESBIO, Toulouse, France)

2. AMMA = African Monsoon Multidisciplinary Analysis • Goal: • Obtain a better understanding of the West African Monsoon (WAM) on daily to inter-annual timescales. AMMA relies on: • the use of extensive observations, notably multi-sensor remote sensing products, • ii) a multi-year field campaign over west Africa, and • iii) a coordinated modeling strategy at different spatial and temporal scales. * See the AMMA Special Issue of GEWEX News, Feb., 2006

3. Importance of the Continental Land surface to the WAM • Surface processes which influence the atmosphere and hydrology: • Significant Meridional Gradients: • - vegetation • - soil moisture • Large spatial variability: • - surface runoff, infiltration, flooded areas • - surface fluxes

4. Break various components of complex coupled system into managable portions which can then provide insight into various processes: 1st step Force LSMs in Offline mode ALMIP Science Questions: • No one scheme perfect: examine ensemble of models (currently 12): Response of different schemes to scale change • Develop a mult-model climatology of « realistic » high resolution soil moisture, surface fluxes, water and energy budget • See what improvements needed in SVATs to simulate processes particular to African climate and surface • Examine how simple SVATs can simulate spatial distribution and inter-annual variability of vegetation GSWP SnowMIP

5. Model acronym Institute Contact

6. Land Surface Model Soil-Vegetation Parameters • ECOCLIMAP – global 1x1 km2, decadal • Operatioal NWP, Mesoscale research • Large annual variability (« mirrors » rainfall) • Single annual cycle Precipitation (mm day-1) 2004 ECOCLIMAP LAI (m2 m-2) month

7. ECMWF Forecast Product: • Lowest model level atmospheric variables  Tair, Qair, Wind, PSurf • Vertically integrated fluxes  SWdown, LWdown, Rainf, CRainf • Merged Forcing: • ECMWF + Satellite based

8. LAND-SAF Downwelling radiative flux products SWdown: • MSG Data: 0.6µm, 0.8µm, 1.6µm • Solar and View Angles • Land/Sea Mask • Cloud Mask (SAF-NWC software) • Total Column Water Vapour (ECMWF) • Ozone Content (Climatology) • Land Surface Albedo: Static Map, later AL product • [Visibility -> Aerosol Optical Thickness] • AMMA-SAT  0.05 deg., 30 min. • Began July, 2005 B. Geiger

9. SAF Radiative flux products: • SWdown, LWdown radiative fluxes • MSG (coherent with AMMA-SAT precip.) • OSI-SAF  0.10 deg., 3h 2004 • LAND-SAF 0.05 deg., 30min 2005+ Same tendency as OSI-SAF: monsoon further N than in ECMWF Combine with EPSAT: up through 2006

10. MSG Channels SRTM Digital Elevation Model Neural Network 2A25 TRMM precipitation Radar data AMMA-SAT Rainfall product: EPSAT-SG(Estimation des Pluies par SATellite – Seconde Génération)F. Chopin, M. Dubois, LMD, Paris, France GPCP1dd rainfall images (Igpcp) Rainfall probability images (Pr) Equation 1 Potential rainfall intensity images (Ip) Equation 2 Equation 2 Estimated Rainfall Intensity at time t during day d and position a: Final product resolutions: Space resolution : 3 kms Time resolution : 15 minutes EPSAT-SG rainfall est. (Ie)

11. Monthly Average Precipitation Rates (mm day-1) 2004 June July Aug. Sep. EPSAT ECMWF RFE (CPC) Recent Study (Jobard et al. 2007): EPSAT best statistical results 2004-6  AGRHYMET Sahelian rain obs A Boone CNRS/CNRM

12. Experiments: Exp. Name: Resolution Time period • Analysis and intercomparison  2004-2005 (2006) • Repeat Above using « Interactive Vegetation » option

13. Total Evapotranspiration 2004 (JJAS) in mm day-1 Total Evapotranspiration 2004 (JJAS) in mm day-1 • Exp1 (ECMWF Precip) • Large inter-model differences • Exp1 (ECMWF Precip) • Large inter-model differences

14. Total Evapotranspiration 2004 (JJAS) in mm day-1 Total Evapotranspiration 2004 (JJAS) in mm day-1 • Exp1 (ECMWF Precip) • Large inter-model differences • Exp2 (ECMWF Precip) • Large inter-model differences

15. Evapotranspiration Difference: • Exp2 (Satellite-based forcing) – Exp1 (NWP-based) • Northward displacement of active precipitation • 50 to 100% increase in Sahel: region with possibly largest atmospheric feedback

16. Exp2 2004: Large inter-model scatter  quantity atmopshere « feels » • Meridional gradient of Evap: • Exp 2 vs Exp 1 • Northward shift by several 100 kms • Implications for NWP model initialization (e.g. ECMWF)

17. Exp2 JJAS 2004 All-SVATs Coefficient of Variation: Inter-model scatter Large in Sahel  water stress, low average Evap (where coupling is important ?) Large in Forested regions  Canopy Interception differences important Product USERS  give some estimate of inter-model variability Dominant land class:

18. Exp2 2004 JJAS Multi-Model AVG • Water budget terms (mm day-1) DelSoilMoist Evap Runoff Rainf

19. Exp2 2004 JJAS Multi-Model AVG • Water budget terms (mm day-1) DelSoilMoist/Rainf Evap/Rainf Runoff/Rainf Rainf

20. Gourma, Mali • September • Significant inter-annual vegetation variability • Can LSMs model this? 09/84 09/89 09/86 09/92 09/87 09/00 * Photos courtesy of CESBIO

21. Inputs: Outputs: Interactive mode Biomass (LAI) Vegetation/Soil Parameters Turbulent and Radiative fluxes (CO2) Atmospheric Forcing ISBA-Ags Runoff Irrigation Ocean/Sea Rivers, Lakes… Pashtuchak, Peyrillé, Plante, and Boone

22. Coupled (atmosphere) mode Interactive mode Biomass (LAI) Vegetation/Soil Parameters Turbulent and Radiative fluxes (CO2) Atmospheric Forcing ISBA-Ags Runoff Irrigation Ocean/Sea Rivers, Lakes… Peyrillé and Boone

23. Exp 1 Exp 2 • Correlations between 0.8 and 1.0 over a large part of Sahel (where vegetation signal strongest) • Exp2 (satellite-based) precipitation  improved LAI prediction (and enhanced Evap) • But… • Montly LAI misses rapid greenup • Forest regions: MODIS quality questionable AND ISBA-Ags needs work! • Care must be taken in fully coupled mode!

24. Perspectives: • ALMIP ongoing: 2006 Forcing to be released and runs using updated forcing (new EPSAT algorithm) to be submitted this summer (Exp2b) • Complete regional merged and mesoscale atmospheric forcing data (2004): extend through 2006 • Regional scale evaluation data (forcing and for simulations)  comparison with AMSR-E, ALMIP-MEB, TRIP 0.5 deg, GRACE… • 2007-8 Mesoscale Forcing  Evaluation of processes using SOP, EOP data • 1st step towards ALDAS • Collaboration with WAMME (Lau, Xue), AMMA-CROSS (Guichard, Hourdin, et al) • Parallel actions at CNRM: • Simulate land surface fluxes, soil moisture, vegetation at various scales • Compare simulated soil moisture to ARPEGE/assimilation • NWP/Mesoscale model initialization: CNRM

25. Thank you for Your attention… And to the ALMIP Working Group: Anton Beljaars, ECMWF Aaron Boone, CNRM Jan Polcher, LMD Chris Taylor, CEH Phil Harris, CEH Inge Sandholt, U. Copenhagen Anette Norgaard, U. Copenhagen Patricia deRosnay, CESBIO Eric Mougin, CESBIO Laurent Kergoat, CESBIO Agnes Ducharne, UTMC Tristan Orgeval, LMD Yonkang Xue, UCLA Isabelle Poccard-Leclercq, U. Nantes Christine Delire, ISE Bertrand Decharme, CETP Catherine Ottle, CETP By M. Nuret, Dakar, Senegal

27. EXTRA….

28. 2 Database • Land Parameters: ECOCLIMAP • Single annual cycle (decad) • 12 classes  255 types • Effective or multi-tile schemes • 1 km, global • Applications: • used at Météo-France • to be used at ECMWF (0.5 deg.)

29. Determination of baseline or « minimum » physics (eg. Stomatal resistance) • Importance of sub-grid hydrology • Importance of soil moisture (on surface energy balance: HAPEX MOBILHY) • Better numerical schemes • Chance for extensive validation • Ability of schemes to simulate carbon cycle • Innovations in Sub-grid cold-season processes • Impact of changing scale • Ability of such models to simulate regional / large scale hydrology

30. 2 Database Atmospheric Forcing: Data for running an LSM in « offline » mode Spatial scales for the atmospheric forcing datasets

31. 2 Database Forcing Data: Evaluation Data:

32. 3 ALMIP description Model acronym Institute Contact * Contact us if you wish to participate…

33. 2 Database Vadidation/Evaluation of atmospheric forcing? • AMSR-E • Microwave sensor • « Surface » soil moisture • 50 km, 2 passes daily • Flagged data below 5-10 N • Difference: July – June (averages) Difference: July-June

34. 2 Database Vadidation/Evaluation of atmospheric forcing? • AMSR-E • Microwave sensor • « Surface » soil moisture • 50 km, 2 passes daily • Flagged data below 5-10 N • Difference: July – June (averages) Difference: July-June

35. 3 ALMIP description Global Soil Wetness Project 2 GSWP • International Land Surface Model Intercomparison Project – Dirmeyer et al. • 15 LSMs (NWP, GCM, Hydrology…) • Control Exp. Forcing: • - ISLSCP parameters • - NCEP – hybridized, gauge-corrections • Global scale, 1 deg resolution • Forcing: 3h, 1982-1995 • Evapotranspiration (monthly: mm day-1) • 10-year average: 1986-1995 • Significant differences!!! 

36. 1 Overview 1948-2000: ECMWF-FC Merged ECMWF-FC, AMMA SAT/PRECIP, EOP-SOP NCC-DB (LMD) Forcing ECOCLIMAP, MODIS Parameters Long term Observation Period (LOP) Enhanced Observing Period (EOP) Special Observing Periods (SOP) 2 1 3 0 2007 2003 2001 2005 2010 2006 On going: - AMMA LSM DB for 2001-2005--- (CNRM) - Analysis of the models sensitivity to the forcing NCC-DB (LMD)

37. 1 Overview • Influence of soil moisture « memory » on the atmosphere • Improve regional scale hydrological processes • (sub-grid parameterizations, infiltration, flood planes) • Determine key processes  vegetation functioning (African ecosystems) • Extend vegetation processes to the regional scale • Use a multi-scale approach • Develop a surface flux and soil moisture « climatology »  used to study coupling with atmosphere, improve assimilation systems… • Intercomparison Project (ALMIP) over region (CESBIO and CNRM)

38. EPSAT-SG(Estimation des Pluies par SATellite – Seconde Génération) MSG Channels SRTM Digital Elevation Model Neural Network 2A25 TRMM precipitation Radar data Equation 1 Rainfall probability images (Pr) GPCP1dd rainfall images (Igpcp) Ais a disc of about 125kms radius cA is the centre of A dis the considered day T is the period [d-15days,d+15days] dt1 corresponds to 1 day dt2 corresponds to 15 minute da corresponds to 1 MSG pixel Equation 1 Potential rainfall intensity images (Ip) Equation 2 Equation 2 Estimated Rainfall Intensity at time t during day d and position a: Final product resolutions: Space resolution : 3 kms Time resolution : 15 minutes EPSAT-SG rainfall estimation (Ie)

39. ECMWF Forecast Precipitation: Hovmoller plots

40. 3 ALMIP description • 15 LSSs, 1 deg. res. • Evap (average 10 years, -20E to 30 E longitude) • Inter-LSS Meridional gradient highly variable GSWP

41. 4 Some Results • Simulated Offline fluxes: Evapotranspiration ECMWF forcing Merged forcing Evap: Merged - ECMWF

42. Forcing: Evaluation:

43. 2 Database Merged AMMA-SAT, SAF and ECMWF Forcing : • Coherence between OSI-SAF and AMMA-SAT data • e.g. Time series: • Summer 2004 • Downwelling radiative fluxes • precipitation Time (DoY)

47. Regional Scale Domain (0.5deg): Meso-Super Sites: Gourma, Mali Oueme, Benin Niamey, Niger CATCH window

48. Interactive mode Biomass (LAI) Vegetation/Soil Parameters Pashtuchak, Peyrillé, Plante, and Boone Turbulent and Radiative fluxes (CO2) Atmospheric Forcing ISBA-Ags Runoff Irrigation LAI Exp1 2005 LAI Exp2 2005 MODIS (2005)