ISSUES ON MODELING THE WARM SEASON CLIMATE IN SOUTH AMERICA

1. ISSUES ON MODELING THE WARM SEASON CLIMATE IN SOUTH AMERICA The South American Monsoon System (SAMS) The diurnal cycle in Amazonia The double ITCZ bias in GCMs C.R. Mechoso, H.-Y. Ma, I. Richter, G. Cazes-Boezio Department of Atmospheric and Oceanic Sciences Y. Xue Department of Geography University of California, Los Angeles, USA R. Terra, M. Mendina Institute of Fluid Mechanics University of the Republic, Uruguay CLARIS Implementation Meeting Bologna, Italy, July 7-9, 2005

2. SAMS - Divergence and Heating January - February Zonal Wavenumbers 2-6 T. -C Chen, J. Climate 2003

3. SAMS and NAMS Ascent to the east - Descent to the west

4. CMAP Precipitation and NCEP 850mb wind WARM SEASON - South America

5. Model Description • UCLA AGCM, version 7.1 • Resolution: high 2.5ºlon x 2ºlat x 29  levels low 5ºlon x 4ºlat x 15  levels • Harshvardhan (1987) radiation scheme; Fu anf Liou with aerosol scheme, SSiB • Prognostic version (Pan and Randall 1998) of the Arakawa-Schubert (1974) cumulus parameterization with convective downdrafts • The PBL top is a coordinate surface; a cloudy sublayer develops if this top is above condensation level (Deardorff 1972, Suarez et al.1983; Li et al. 1999, 2002). • Climatological monthly-mean SSTs prescribed

6. South America Low Resolution High Resolution

7. GPCP Evolution of SAMS TRMM AGCM (Observation: Courtesy Bill Lau)

8. Local time: 01 hr Local time: 09 hr Local time: 17 hr Local time: 03 hr Local time: 11 hr Local time: 19 hr Local time: 05 hr Local time: 13 hr Local time: 21 hr Local time: 07 hr Local time: 15 hr Local time: 23 hr 0 0.6 1.2 (mm/h) PERSIANN Diurnal Rainfall (DJF 2002)

9. Simulated Diurnal Cycle of Precipitation - January (UCLA AGCM v7.1H; 2.5x2x29)

10. Relative Phases of the Diurnal Cycle Amazonia Africa (5 lon x 4 lat x 15L)

11. Diurnal cycle in Manaus, Brazil, January UCLA AGCM - Revised PBL parameterization

12. Diurnal cycle at 60W, 10SShading corresponds to PBL clouds PBL Top midnight

13. Strong convective activity produces precipitation with a peak in the early afternoon The Diurnal Cycle: CRM (2D) Simulation In the afternoon, the convergence tends to spread over deeper layers, with the maximum still rising Highly concentrated turbulent moisture convergence near the top of the PBL, which rapidly deepens throughout themorning

14. AGCM Mean simulated precipitation in Amazonia Mean observed precipitation in Rondonia Observation Easterly regime Marengo et. al (2005)

15. Leading Mode of Variability In the Warm Season

16. Annual Coupled GCM Mean Sea Surface Temperatures Errors Obs CCSM CFS COLA Courtesy: Ben Kirtman

17. Annual Mean SST Simulated The recent revision of PBL parameterization in the UCLA AGCM has eliminated SST errors in subtropical stratocumulus regions. However, the “double ITCZ” bias persists! Observed (Reynolds) UCLA AGCM/MITogcm

18. “Double ITCZ” Bias Hypothesis 1: Poor heat transport by ocean eddied from upwelling regions - Insufficient OGCM resolution? Hypothesis 2: Poor simulation of the zonal circulation - Difficulties in the simulation of resolved and subgrid processes? Annual Mean SST Model Observation UCLA AGCM - MIT OGCM

19. Monsoon Sensitivity to Vegetation Processes Geopotential height (10 gpm) and stream lines at 200 hPa January-February 1988 NCEP AGCM; Triangular truncation (T-42); 18 Sigma layers (C) Two-layer soil model, (S1) Explicit vegetation model NOTE: The two schemes use the same initial soil moisture, monthly mean surface albedo, and surface roughness length Xue et al. (J. Climate 2005)

20. Sensitivity of monsoon evolution to vegetation processes 10-day mean precipitation [mm/day] OBS C1 S1 October December

21. The South American monsoon system… • …comprises an upper-level anticyclone/ low-level heat low structure; large-scale zone with ascent to the east and descent to the west over the ocean. Here stratocumulus clouds enhanced by subsidence and upwelling develop. • …shows intraseasonal variations that appear associated with continental-scale modes. In Amazonia these variations have been referred to as “westerly and easterly regimes” • …has interannual variations that appear influenced by synooptic systems from mid latitudes. • …tends to have stronger precipitation during El Niño events and weaker during La Niña. • …has low predictability, with weak ENSO impacts and importance of conditions at the land surface

22. Modeling Issues - SAMS Mean climatology: Onset, Role of land surface processes. Intraseasonal variability: Westerly and easterly regimes. Principal modes of variability PBL processes and simulation of the diurnal cycle. The eastern Tropical Pacific: Stratocumulus clouds and double ITCZ bias. Coastal modelling? The western Atlantic: SST anomalies and Brazil/Plata Basin rainfall Effects of aerosol?

23. OVERALL • GCMS (in their current framework) can be improved (in my lifetime) • Focus on processes rather than on particular fields • AGCM verification requires coupled to OGCM • 2. DIURNAL CYCLE • PBL behavior (vertical profiles of several quantities: Potential temp, moist static energy, total and liquid water vapor, turbulence….) • Interaction between PBL and free-atmosphere (entrainment at the PBL top, links with convection, downdrafts...) • 3. GENERAL CIRCULATION • Zonal circulation (how is it maintained? What processes are missing-badly represented?) • Convection and Radiation: Vertical distribution of heating (this is not independent of motion!) • 4. MONSOON SYSTEMS • Comprehensive approach (not one system, not just the updraft…) • Different time-space scale and interactions A few thoughts on Model Metrics