1. High resolution simulation of August 1 AMMA case: impact of soil moisture initial state on the PBL dynamics and comparison with observations. S. Bastin (1), C. Taylor (2), A. Boone (3) (1) LATMOS/IPSL (CNRS/UPMC/UVSQ), Paris, France (2) CEH, Wallingford, UK (3) CNRM/Meteo-France, Toulouse, France With contributions/help from: D. Bou Karam, D. Parker, N. Asencio, J. Escobar, P. LeMoigne Questions: Which are the vertical and horizontal extents of the effects of surface heterogeneities? Is there a threshold (intensity of gradient/U or /h PBL ) ? How long do effects persist? Are parameterizations of these effects in models OK, i.e. is a good surface and synoptic forcing enough to reproduce observations? 1 August 2006: First step to answer these questions

2. Polarisation ratio anomalies from TRMM Spatial resolution ~ 50 km Storm track Flight track 1000 km Synoptic situation of 1 August case Courtesy: Chris Taylor Flight over storm track 18 hours later 31 July @ 17 UTC: Convective system over Niger/Mali Niamey

3. Storm track: Note within-storm variability due to individual convective cells Observed PBL temperature PBL temperature according to ECMWF forecast model Land surface temperature anomaly (satellite) PBL gradient due to vegetation feature Spectral analysis shows that LSTA and PBL temperature are correlated up to scales of ~ 5 km. Amplitude of effects is variable. From Taylor et al., GRL, 2007 Summary of data analysis (1) Which resolution is necessary for modelling studies?

4. Wind at 170 m Convergence Divergence Convergence Surface gradients are strong enough to generate thermally- induced circulations. Land surface temperature anomaly Summary of data analysis (2) What are the spatial (vertical) extent and life cycle of these circulations?

5. Version MASDEV4_7_2 2 domains: 12 et 3 km 57 vertical levels MESONH Simulations ECM Simulation Initialised at 00Z on 1 August 2006. Surface model = ISBA SWI Simulation - Atmos. 3D fields: ECMWF (~1°) - Surface fields: ECMWF (~1°) - Atmos. 3D fields: ECMWF (~1°) - Surface fields: T soil and soil moisture from ALMIP simulations (~50 km) (off-line mode of ISBA) Soil moisture at 0600 UTC No soil moisture variability in the area of interest Strong soil moisture variability before sunrise BAe flight track

6. Comparisons MESONH/OBS (1330 UTC) SWI2: more humidity in the 2 nd and 3rd soil layers than in SWI1 Dropsondes (~ 15 UTC) Temperature bias

7. Wind difference 10 m wind difference: REF-SWI1 ~200 m AGL wind difference: REF-SWI1 More wind divergence in SWI1 than in ECM over the moist soil : in agreement with obs

8. Time evolution of temperature Simulation ECMSimulation SWI T2- 1000 UTC T2- 1300 UTC Cold pool persists after soil moisture evaporation in the model

9. Model outputs at 12Z Problem of temperature bias MNH/ECMMNH/ALMIP Model outputs at 1330 UTC Midday ECMWF analysis Simulation chauffe trop 12 UTC 1330 UTC

10. Tests with other analyses, other model (1) MESONH/ ECMWF MESONH/ ARPEGE WRF/NCEP WITHOUT ALMIPWITH ALMIP Nouvelles simulations: - MesoNH forcé par ARPEGE (avec et sans ALMIP) - MesoNH forcé par ECMWF sans aérosols (non montré ici, très peu de différences) - WRF forcé par NCEP (avec et sans ALMIP) - WRF forcé par ECMWF (sans ALMIP + tests sur patchs humides en cours)

11. Problem of temperature bias MNH-ECM (13 UTC) MNH-ARP (13 UTC) WRF-ECM (13 UTC) WRF-NCEP (13 UTC) ECMWF midday ana. NCEP midday ana. BAE observations (1330)

12. Problem of temperature bias Midday ECMWF analysis Midday NCEP analysis NCEP warmer than ECMWF in the northern part

13. ECM (13 UTC) ECM/ALMIP (13 UTC) ARP (13 UTC) ARP/ALMIP (13 UTC) WRF-NCEP (13 UTC) WRF-NCEP/ALMIP ECMWF midday ana. ARPEGE midday ana. BAE observations (1330) - ALMIP has a strong impact on the PBL thermodynamics in the 3 couples of simulations. - The impact on the wind can be locally strong: to be analized - None of the simulations is able to reproduce Q variability. Tests with other analyses, other model (2)

14. Conclusion and perspectives Technical problems:  Impact of ALMIP is considerable  Temperature bias: aerosols, clouds or dynamics?  Resolution of ALMIP probably not high enough to answer the question of scales of correlation Fig. Variability of surface T (shading), T2m (blue), T at 170m AGL (green) and T 925hPa (red) along track in simulation SWI1. Scientific questions:  For this case, surface strongly influences temperature variability while humidity variability is influenced by other processes. Analyse transport of air masses to better evaluate the relative importance of advection VERSUS local effects in observations and in the model.  Effects of surface are not visible within the whole PBL and persistence of the effects of surface after evaporation : is the method of correlation always pertinent?  Run ‘semidealized simulations with artificial modifications of SM to test the scales of correlation, life cycle of circulation…