Science Issues and Research Needs for AMY and IMS

1. Science Issues and Research Needs for AMY and IMS Bin Wang AMY08 International Workshop Beijing 4-23 to 4--25 2007 Acknowledgements: CLIVAR/AAMP

2. A Message from Shukla 4-21-2007 Nearly half of the world population is affected by monsoon. I can notthink of a bigger challenge than to understand, model, and ultimatelypredict monsoon variations at ALL space and time scales. GEWEX: Diurnal-Intarseasonal, Land CLIVAR: Intraseasonal-Interdecdal, Ocean Monsoon study provides a cross cutting theme (for GEWEX , CLIVAR, and CliC) and a focus for CP and ACC.

3. Science Issues • Monsoon Modeling • Monsoon Prediction/Predictability • Monsoon Intraseasonal oscillation (MISO) • IDV and ACC

4. 1. Monsoon Modeling Issues • What determines the structure and dynamics of the annual cycle (AC) and diurnal cycle (DC) of the coupled atmosphere-ocean-land system? • What are the major weaknesses of the climate models in simulation of the AC and DC? • Do models getting DC and AC right will improve the modeling of low-frequency variability (intraseasonal to interannual)?

5. AGCMs simulate climatology poorly over the WNP heat source region Kang et al. 2004, Cli Dyn Wang et al. 2004, Cli Dyn

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. 2. IAV & Predictability/Prediction Questions • What is the current accuracy of and how to improve the dynamic monsoon seasonal predictions? • How predictable is the monsoon interannual variability (IAV)? Understanding of the roles of the tropical-extratropical teleconnection, atmosphere-land interaction, monsoon-warm pool ocean interaction, and Tibetan Plateau changes.

8. 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

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

10. Two-tier 5-AGCM MME hindcast of JJA rainfall (21 yrs) Pattern Correlation Coefficient 5-AGCM EM hindcast skill (21Yr) • Two-tier system was unable to predict ASM rainfall. • TTS tends to yield positive SST-rainfall correlations in SM region that are at odds with observation (negative). • Treating monsoon as a slave to prescribed SST results in the failure. OBS SST-rainfall correlation Model SST-rainfall correlation (Wang et al. 2005) Wang et al. 2005

11. 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%

12. 3. ISV and Predictability • What are critical processes causing Monsoon ISV, in particular, the roles of multi-scale interaction? • To what extent the MISV is predictable? • What are major challenges to modeling and predict MJO and MISV?

13. Satellite Observed Boreal Summer ISO (1998-2005) Numbers: four phases, phase interval: 8 days Wang et al. 2006 • Northward propagation in Bay of Bengal(Yasunari 1979, 1980, Sikka and Gadgel 1980) and northwestward propagation in WNP(Nitta 1987) • Formation of NW-SE tilted anomaly rain band(Maloney and Hartmann 1998,Annamalai and Slingo 2001, Kemball-Cook and Wang 2001, Lawrence and Webster 2002,Waliser et al. 2003) • Initiation in the western EIO (60-70E)(Wang, Webster and Teng ‘05) • Seesaw between BOB and ENP and between EEIO and WNP.

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

15. 4. IDV and Future Change Issues • What are the major modes of interdecadal variation of the monsoon system? • How will monsoon system change in a global warming environment? • What are sub-seasonal to interannual factors that influence extreme events? • What is the sensitivity of the monsoon to external and anthropogenic climate forcing?

16. Global Monsoon accounts for 80% of the Annual Variation Global Monsoon Domain Rainfall MPI=Annual range/ annual mean Annual range= Abs(MJJAS-NDJFM) Thresholds for monsoon domain: a) Annual range >300mm b) MPI>0.5

17. 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.

18. Future Scenarios for Summer Monsoon Rainfall and Annual Temperature over South Asia under A2 Scenario • The general conclusion that emerges of the diagnostics of the IPCC AR4 simulations: Asian summer monsoon rainfall is likely to be enhanced. From Kumar et al.

19. Key Monsoon Issues • 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?

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

21. observation Understanding Modeling Prediction Phenomena Validate models Calibrate Satellite Initial conditions

22. 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 validate and improve 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.

23. 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.

24. 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.

25. 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.

26. Regional focus:Field campaign/regional processes • Focusing on Maritime continent-SEA and adjacent warm pool oceans • Understanding Atmosphere-land-ocean interaction: • Address processes over the MC, western boundar currents, ITF • Observations to validate model parameterization: Surface fluxes, PBL and cloud • Diurnal cycle and MJO

27. How important is land-sea contrast and orography in Controlling monsoon AC? Chang et al. 2006

28. A Proposed APCC and CLIVAR Project to ConductHigh Resolution Climate Model Simulations of Recent Hurricane and Typhoon Activity: The Impact of SSTs and the Madden Julian Oscillation Sieg Schubert Project Overview • Description: A coordinated international project to carry out and analyze high-resolution simulations of tropical storm activity with a number of state-of-the-art global climate models • Issues to be addressed: the mechanisms by which SSTs control tropical storm activity on inter-annual and longer time scales, the modulation of that activity by the Madden Julian Oscillation on sub-seasonal time scales, and the sensitivity to model physics and resolution. • Approach: case studies of selected years with highly unusual tropical storm activity, including model runs with specified SST, an anomaly mixed layer ocean, and fully coupled models. • Resource/sponsorship requests: sponsorship from APCC and U.S. and international CLIVAR; funding for a workshop in the fall of 2007 ($50K), and for maintaining a central data repository (estimated at about $100K).

29. Expected Outcomes • Improved understanding of the physical mechanisms controlling major changes in tropical storm activity on subseasonal, seasonal and longer time scales (role of SST, role of MJO/ISO) • An assessment of the ability of current climate models, when run at high resolution, to simulate hurricanes and changes in hurricane activity, including an assessment of sensitivity to model resolution and physics • An assessment of the predictable of major changes in hurricane activity (linked to prediction of MJO and S-I ocean variability)

30. Atmospheric Model • High resolution runs- minimum 1/2 degree or better (should be sufficient to produce realistic hurricane structures). • Low resolution AMIP runs - standard climate resolution (~ 2-3 degrees). • Focus is on global models (uncoupled and coupled) but also invite participants interested in carrying out runs with high resolution regional models. MJO/ISO Experiments • Table 2: Summary of proposed forecast experiments to assess impact of MJO. Model resolution should be at the equivalent of ½ degree or higher. The SSTs are either specified or predicted. The atmosphere, and for coupled runs, the ocean, is initialized at different phases of the MJO. Multiple ensemble members (say 5) are encouraged, including those to assess the impact of model formulation on the simulation of the MJO.

31. Regional climate modeling and predictability study Takehiko Satomura Kyoto University Japan

32. Difficulties in regional climate modeling in tropics • MJO • Tropical cyclones • Diurnal cycle

33. Efforts to coordinate regional climate modeling activity • Coordination of less-than-seasonal-time-scale intercomparison of RCMs • Japan-China-Korea climate model improvement project (A3 foresight program (proposed))

34. Targets Multi-time-scale: from diurnal cycle via ISO to ENSO Find processes reducing prediction scores Methods Process study MME (GCMs and RCMs) Area E & SE Asia (including Maritime Continent) Data IOP of GAME IOP of MAHASRI: AMY Tasks remained: How are models compared? How are ensemble perturbations specified? Are MJO or Kelvin waves unstable modes? Possible collaboration between CLIVAR & MAHASRI through RCM intercomparison study

35. Possible participating models in Japan • MRI spectral coupling RCM (NHM) • RAMS-Utsukuba • Tohoku Univ. Non-Hydro. Model • MM5 (original version) and others…?

36. A3 foresight program • PIs • Japan (S. Miyahara, Kyushu Univ.) • China (R. Lu, IAP) • Korea (Y. Noh, Yonsei Univ.) • Objects • analysis of variability and feedbacks of the atmosphere-ocean-land-vegetation system • model intercomparison and improvement by using the global and regional climate models owned by the three countries • predictability of climate variability and climate change study induced by anthropogenic factors, especially focusing on East Asian climate

37. Modeling/Prediction (AAMP input) • Coordinate CGCM/RCM Process study on MJO/ MISO (MC-SEA): AAMP/MAHASRI, CIMS, MAIRS • 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)

38. Coordination of monsoon modeling with MAHASRI/ CEOPII • 1st pan--WCRP Monsoon Modelling Workshop for key studies of the diurnal cycle over both land and ocean. • Coordinated GCM/RCM Process study on MJO/ MISO and monsoon onset of SEASM. • Develop Multi-model ensemble Regional Climate prediction (Downscaling system) experiment in collaboration with MAHASRI and APCC. • Develop land surface data base for GCM MME hindcast Experimenst (CEOPII)

39. Thanks

40. 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

41. 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

42. The increasing trend in Seoul JJAS precipitation and extreme venets reflect a trend in large scale East Asian monsoon rain belt, which appears to be related to strong trends in northern Indian ocean SST.

43. 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.

44. ISO Potential Predictability Air-Sea Coupling Extends the Predictability of Monsoon Intraseasonal Oscillation ATM Forecast Error CPL Forecast Error Signal ATM: 17 days, CPL: 24 days Fu et al. 2006

45. 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

46. Performance on Annual mean & Annual Cycle Linkage to Seasonal prediction skill Pattern Correlation Skill over the Global Tropics (0-360E, 30S-30N) Precipitation Performance on Annual Cycle and its Linkage with Seasonal Prediction skill Annual Mean Precipitation The models’ performance in simulating and forecasting seasonal mean states is closely related to the models’ capability in predicting seasonal anomalies.

47. Factors determining the IAV • Remote forcing from El Nino/La Nina • Monsoon-warm pool ocean Interaction --Equatorial Bjeckness positive feedback (IOD/IOZM) (Webster et al. 1999, Saji et al. 1999) --Off-equatorial Rossby Wave-SST feedback either positive or negative, depending on background annual cycle (Wang et al. 2000) --Negative feedback by monsoon-induced anomalies(Webster et al. 2002, Loschnigg et al. 2003, Lau and Nath 2000). --Memories of ocean mixed layer/land (Meehl 1994, 1997) • Regulation of the annual cycle (indirect role of continent) --Regulation of the monsoon-ocean interaction(Nicholls 1983) --Modify monsoon response to remote ENSO(Wang et al. 2003)

48. Performance of MMEs in Hindcast Global Temperature Temporal Correlation Skill of 2m Air Temperature JJA DJF • MME seasonal prediction with 1-month lead time using 17 climate models which participate in CliPAS and DEMETER

49. Model Descriptions of CliPAS System APCC/CliPAS Tier-1 Models APCC/CliPAS Tier-2 Models