Research Needs for AMY 2008-2009: CLIVAR/AAMP perspective

1. Research Needs for AMY 2008-2009: CLIVAR/AAMP perspective Bin Wang Second AMY08 International Workshop 9-3-4 2007, Bali Acknowledgements: CLIVAR/AAMP

2. CLIVAR/A-AMP Co-Chair: Bin Wang and Harry Hendon Cobin Fu, In-Sik Kang, Jay McCreary, Holger Meinke, Rajeevan, Takehiko Satomura, Andrews Schiller, Julia Slingo, Ken Sperber, Peter Webster

3. Gaps: First Pan-WCRP workshop • Global Phenomena: diurnal cycle annual cycle, intraseasonal oscillation, atmospheric moisture distribution and transport aerosol-monsoon-cloud interaction • Model processes: surface fluxes, planetary boundary layer and cloud. • Land surface: better observations of land surface conditions, roles of atmosphere-land coupling in developing monsoon precipitation, • Ocean: improve (and sustain) observations; importance of air-sea interaction and ocean processes in modeling of ISO and ENSO-monsoon relationship • Regional foci: processes over the Maritime Continent, Pacific cold tongue and western boundary currents, and Indonesian through flow.

4. Year of coordinated Observing, modeling and Forecasting:Addressing the Challenge ofOrganized Tropical Convection This proposed activity arose out of a recommendation by the THORPEX/WCRP/ICTP Workshop on Organisation and Maintenance of Tropical Convection andthe MJO, held in Trieste in March 2006. It was presented at the WCRP/CLIVAR SSG Meeting in Buenos Aires in April 2006. Based on positive feedback from the WCRP Director and the SSG, the SSG asked that the proposal be developed in cooperation with THORPEX, GEWEX, CEOP, AAMP, WOAP, WMP, etc. If implemented in 2008, this initiative could be a WCRP contribution to the UN Year of Planet Earth* and compliment IPY.

5. Key Issues AAMP is addressing • What determines the structure and dynamics of the annual cycle (AC) and diurnal cycle (DC) of the coupled atmosphere-ocean-land system? How to remedy the major weaknesses of climate models in simulation of the AC and DC? • How predictable is the monsoon interannual variability (IAV)? How to improve the dynamic monsoon seasonal predictions? • What cause monsoon Intraseasonal Variability (ISV)? How to overcome the major challenges to modeling and predict monsoon ISV? • What are the major modes of interdecadal variation of the monsoon system? • How and why will monsoon system change in a global warming environment? • What is priority for future field and modeling studies and for improving observing and modeling strategy of the monsoon system?

6. Modeling/prediction of Global Monsoon Domain Number of Model The monsoon precipitation index (shaded) and monsoon domain (contoured) captured by (a) CMAP and (b) the one-month lead MME prediction. (c) The number of model which simulates MPI over than 0.5 at each grid point.

7. Precipitation Wet Dry Dry Dry Wet Dry Dry Wet Dry Wet Dry Wet Wet Dry Dry Wet * Impact of El-Nino on Global Climate from NOAA (based on Ropelewski and Halpert (1987), Halpert and Ropelewski (1992), and Rasmusson and Carpenter (1982) Performance of MMEs in Hindcast Global Precipitation Temporal Correlation Skill of Precipitation

8. Forecast Skills of the Leading Modes of AA-M Asian-Australian Monsoon Predictability S-EOF of Seasonal Mean Precipitation Anomalies The First Mode: 30% The Second Mode: 13%

9. Hot places of land surface feedback Koster et al. 2004

10. Need to understand Multi-Scale Interrelation In Monsoon ISO Slingo2006: THORPEX/WCRP Workshop report

11. Global Monsoon Changes (1948-2004) Wang and Ding 2006, GRL Annual Mean Precipitation In the last 56 years global land monsoon shows a weakening trend. However, in the last 25 years, Oceanic monsoon rainfall increases while land monsoon unchanged.

12. Monsoon Research Needs • Observation • Modeling • Prediction • Future changes

13. Observation • Field campaign for observing specific phenomena: e.g., organization of convection, multi-scale structure of ISV. (Monsoon trough and Maritime Continent) • Supper station for validating and improving models • Provide ground truth for calibrating Satellite measurements. • Promote integrated usage of satellite observations to study , e.g., 3-D structure and multi-scale interaction in ISV. • Improve long-term monitoring network in tropical IO-WP and maritime Asia. • Improve and develop new reanalysis datasets that use new satellite observations, e.g., land data assimilation, ocean data assimilation.

14. Modeling • Design monsoon metrics for assessing model performance and identify key modeling issues. Provide one-stop data source for cross-panel use. • Develop effective strategy for improving model Physics. • Determine directions for developing next generation climate models. High resolution modeling • Encouraging use of forecast type experiments to evaluate models and study climate sensitivities. • Use large-domain CR or CSR simulation to provide surrogate data for studying convective organization, and mulit-scale interaction.

15. Prediction • Better understand physical basis for seasonal prediction and ways to predict uncertainties of the prediction. • Improve representation of slow coupled physics. • Improve initialization scheme and initial conditions in ocean and land surface. • Develop new strategy and methodology for sub-seasonal monsoon prediction. • Design metrics for objective, quantitative assessing predictability and prediction skill. Improve MME prediction system.

16. Assess Future Changes • Coordinate IPCC AR4 monsoon assessment to address how and why AA-M system will change in a global climate change environment. • Role of the monsoon-aerosol interaction and land use in future monsoon change. • Use MME approach to study the sensitivity of the monsoon to external and anthropogenic climate forcing. • Coordinate MME experiments to investigate sub-seasonal to interannual factors that influence extreme events, such as TC. • Determine coherent structure and dynamics of the global monsoon system on Dec/Cen time scales and their linkage to ocean.

17. Modeling/Prediction (AAMP) • Coordinate CGCM/RCM Process study on MJO/ MISO (MC-SEA): AAMP/MAHASRI, CIMS • Develop Multi-model ensemble Regional Climate prediction experiment with CGCM, RCM, GLACE in collaboration with MAHASRI, APCC, and MAIRS to determine impacts of the land surface data assimilation, land surface processes, and land-atmosphere interaction on monsoon seasonal prediction • Coordinated experiment on high resolution climate model simulation of hurricane/Typhoon activity. (NASA/GMAO: Sieg Schubert)

18. Thanks

19. AAMP-MAHASRI :Coordinated GCM/RCM Process study onMonsoon ISO and onset (SEA+MC) • Integration of observation and modelling, Meteorology and Hydrology • Domain: MC+SEA (70-150, 15S-40N)—a critical region for monsoon ISO influence • Phenomenon and Issues: ISO, and its interaction with diurnal cycle, meso-scale and synoptic scale regulation. Onset of monsoon (summer and winter); impacts of Tibetan Plateau land surface processes • Design: Driving field, Output, validation strategy and Data,… • Participating model groups: both AGCM and RCM, each 4-5

20. MME Downscaling Seasonal Prediction Experiment Develop effective strategy and methodology for RCM downscaling Assess the added values of RCM MME downscaling Determine the predictability of monsoon precipitation Large scale driving: 10 CGCM from DEMETER and APCC/CliPAS models