Celebrating the Monsoon Bangalore, India 7-25 2007 Climate Modeling of Monsoon Bin Wang

1. Celebrating the Monsoon Bangalore, India 7-25 2007 Climate Modeling of Monsoon Bin Wang Department of Meteorology and IPRC, SOEST University of Hawaii, Honolulu, HI 96822, USA

2. Dynamic model is ultimate tool for climate prediction (Probabilistic forecast) • MME depends on good models. Improvement of models is of central importance for improvement of climate forecast

3. Coordinated evaluation of climate models • MIPs: AMIP, CMIP, S-MIP, PMIP,….. • IPCC models: 20C,… • Climate prediction models: DEMETER, APCC/CliPAS Use of forecast type experiments to evaluate models and study climate sensitivities should be encouraged. Identify common errors Set the priority for future field studies

4. Gaps: First Pan-WCRP workshop (2005) • 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: need 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 modelling of ISO and ENSO-monsoon relationship • Regional foci: processes over the Maritime Continent, Pacific cold tongue and western boundary currents, and Indonesian through flow.

5. What is best strategy for improving climate modeling? • Seamless evaluation with a set of good metrics Diurnal cycle, Annual cycle, Intraseasonal variation, Interannual variability,…. • Global constraints: Energy and water balance in coupled climate models

6. DC and AC 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)?

7. Why Care about the diurnal cycle • Initial model errors are often indicative of the systematic climate biases • Diurnal cycle provides a effective varification of the model physical parameterization:surface fluxes, planetary boundary layer and cloud. • Diurnal cycle is most relevant to monsoon modeling

8. Large diurnal cycle is associated with monsoon 【 Diurnal Range (Pmax-Pmin) map 】3B42 1998-2006 JJA DJF

9. :PC1 :PC2 Most complex diurnal cycle is located in monsoon regions 【 Annual mean Diurnal Precipitation 3B42 】 PCs EOF1 (62 %) EOF2 (27 %) • Oceanic • Continental • Coastal Kikuchi and Wang (2007)

10. 【 Phase propagation in the coastal regime (1) 】EEOF analysis (1) (a) S. Asia (46, 36%) (b) America (44, 37%) (c) W. Africa (50, 36%) 09 (12) LST 12 (15) LST 15 (18) LST 18 (21) LST 21 (00) LST

11. 【 Phase propagation in the coastal regime (2) 】EEOF analysis (f) MDGSCR (51, 36%) (d) MC (49, 39%) (e) S. America (45, 37%) 09 (12) LST 12 (15) LST 15 (18) LST 18 (21) LST 21 (00) LST

12. Global distribution of diurnal rainfall peak Model produces diurnal rainfall peak 2-3 hour too early Lau and Kim (2007: JMSJ Special Issue)

13. Standard Deviation of Climatological pentad mean precipitation (May-Sept.) Longitudinal distribution of SD averaged for 10-20N All models simulate larger-than-observed amplitudes of climatological seasonal variations of the Indian summer monsoon but underestimate the amplitudes in the western Pacific. Kang et al. 2002

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

15. Global precipitation climatology Metrics

16. JJAS minus DJFM MVEOF1 AM minus ON MVEOF2

17. Upper panel Shading: Annual range (AR) in unit mm/day AR=local summer minus local winter Local summer: MJJAS in NH and NDJFM in SH Black contour: global monsoon domain two criteria: AR > 250 mm Local summer/annual total rainfall > 50% green line: ITCZ (JJA)/blue line: ITCZ (DJF) the maximum rainfall at each longitude between 30S-30N Lower panel annual mean rainfall in unit mm/day

18. 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.

19. 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.

20. MJO Simulation AMIP intercomparison: Slingo et al. (1996); Lau et al. (1996). Individual model studies Lau and Lau, 1986; Park et al., 1990; Slingo and Madden, 1991; Wang and Schlesinger, 1999; Hendon, 2000; Inness et al., 2001; Maloney and Hartmann, 2001; Maloney, 2002; Wu et al., 2002; Inness and Slingo, 2003; Inness et al., 2003; Lee et al., 2003; Liess and Bengtsson, 2003; Liess et al., 2003; Waliser et al., 2003a; ECMWF, 2004. Prominent shortcomings: A tendency for weak variability; propagate too fast; sensitivity to mean state conditions – particularly in the Indian and western Pacific Ocean; a less than ideal representation of the modulation by the annual cycle; improper phase relationships between convection and surface heat flux components.

21. ISV Variance is too small • MJO variance does not come from pronounced spectral peak but from over reddened spectrum: too strong persistence of equatorial precipitation (13/14)

22. Satellite Observed Boreal Summer ISO (1998-2005) Numbers: four phases, phase interval: 8 days • 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.

23. Problems that tend to be unique to the boreal summer ISO simulations: • Sperber et al. (2001): 7 models. • The models usually fail to project the subseasonal modes onto the seasonal mean anomalies • Waliser et al. (2003): 10 AGCMs • Most problematic feature is the overall lack of variability in the equatorial Indian Ocean. • Most of the model ISO patterns did exhibit some form of northward propagation. But they often show a southwest-northeast tilt rather than the observed northwest-southwest tilt. • The fidelity of a model to represent N.H. summer versus winter ISV appears to be strongly linked. • Double ITCZ; inadequate global teleconnection;Lack of ocean coupling (Fu et al. 2003, Zheng et al. 2004)

24. ISV prediction skill in DEMETER and CliPAS models • Predictability of unfiltered daily precipitation (signal/noise) • Indian Ocean (60˚E~100˚E, 10˚S~20˚N) and • (b) WNP (120˚E-140˚E, EQ-20˚N). Nearly all models show drop of forecast skill after about a week in the summer monsoon regions. Confirmed by anomaly correlation.

25. 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

26. Interannual variations of the boreal summer intraseasonal variability predicted by ten atmosphere-ocean coupled models Hye-Mi Kim and In-Sik Kang School of Earth and Environmental Science, Seoul National University, Seoul, Korea Bin Wang and June-Yi Lee Department of Meteorology and International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii, USA

27. Climatological ISV activity The observed ISV activity exhibits its largest values over the WNP and Indian monsoon regions. Model tend to underestimate the variability in these regions and over estimate the variability in equatorial WP.

28. Interannual standard deviations of the boreal summer ISV activity Notable interannual variation of ISV activity is over the WNP in observation. Most models have the largest variability over the central tropical Pacific and exhibit a wide range of variability in spatial patterns that differ from observation.

29. EOF modes of the observed and model composite IAV of ISV activity Although models have large systematic biases in spatial pattern of dominant variability, the leading EOF modes of the ISV activity in the models are closely linked to the models’ ENSO, which is a feature that resembles the observed ISV and ENSO relationship.

30. Scatter plot of predictability on summer mean precipitation (horizontal axis) and ISV activity (vertical axis) over the Asian monsoon region (40˚-180˚E and 20˚S-30˚N).

31. Findings • Predictability of the IAV of ISO activity is positively correlated with models’ predictability of IAV of the seasonal mean rainfall. • The ENSO-induced easterly vertical shear anomalies in the western and central tropical Pacific, where the summer mean vertical wind shear is weak, result in ENSO-related changes of ISV activity in both observation and models. • The predictability of ISV activity can be improved since the model errors are systematic and related to the external SST condition.

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

33. ISV Dynamics • What are major modes of the Monsoon ISV (Boreal summer ISO)? • What is the typical multi-scale structure of ISO? What is the 3-D structure of thermodynamic fields of ISO? • How are organized convections linked to large scale forcing? What are triggers for organized convective events in general? • How do we get a complete theoretical framework for describing characteristics of MJO? Are the multi-scale structure/interaction important for ISO and how? • What is the role of mesoscale systems in determining the heating profile (convective/stratiform) and how does this impact the evolution of ISO? How to get them right?

34. Cloud and rainfall profile during genesis process Height (km) (a) Phase 1 (b) Phase 2 (c) Phase 3 mm/hour mm/hour mm/hour Composite vertical profiles of precipitation rate anomaliesmeasured bythe TRMM precipitation radar (TRMM/2A25) over the equatorial Indian ocean (5ºS-5ºN, 60º-70ºE) where ISO initiates. The composite was made for 28 ISO events. Each phase spends about four days. Orange and green color represent convective and stratiform rain rate, respectively. During the initiation of the convective anomalies (phase 2 and 3), the stratiform and convective rains have comparable rates; the prevailing cloud type experiences a trimodal evolution from shallow (phase 1) to deep convection (phase 2), and finally to anvil and extended stratiform clouds (phase 3). These information is critical for validation of numerical models. From Wang et al. (2006).

35. MJO (MISO) • How should we evaluate our models and measure ISO predictability and prediction skill? US-CLIVAR MJO working group: Metrics • What is the current level of performances and common problems in the models? How to correct these systematic errors? • How does the errors in simulating ISO impacts simulations of the interannual variability? • To what extent the MISV is predictable? • What roles does atmosphere-ocean-land interaction play in MISV?

36. Interannual Variability Sperber and Palmer (1996): 32 models from AMIP results. Asian Monsoon variability is not well simulated. The Webster and Yang index (wind shear) is better simulated than the all-India rainfall. IAV is better simulated in models which are able to generate a better climatology. After model revision, simulation of the interannual variability was significantly improved (Sperber et al., 1999). Wang et al. (2004): 10 AGCMs Wang et al. (2005): 5 two-tier model 21-year hindcast Wang et al. (2007) 10 CGCM (one-tier system 21-year hindcast DEMETER and APCC/CliPAS models)

40. A fundamental Challenge in Climate Prediction Wang et al. 2005 State-of-the-art AGCMs, when forced by observed SST, are unable to simulate Asian-Pacific summer monsoon rainfall (Fig. a). The models tend to yield positive SST-rainfall correlations in the summer monsoon region (Fig. c) that are at odds with observation (Fig.b). Treating monsoon as a slave to prescribed SST results in the models’ failure, which suggests inadequacy of the tier-2 climate prediction system. a. 5-AGCM ensemble hindcast skill b. OBS SST-rainfall correlation c. Model SST-rainfall correlation

41. El Nino region(10oS-5oN, 80oW-180oW) WNP (5-30oN, 110-150oE) Asian-Pacific MNS (5-30oN, 70-150oE) Area averaged correlation coefficients (skills)

42. Simultatious Rainfall Leads SST by 1-month SST leads Rainfall by 1-month Observed Rainfall-SST correlation (1979-2002) Wang et al. 2005

43. Interannual variation • How accurate do coupled climate models predict major modes of interannual variability of A-AM? • What roles does atmosphere-land interaction play? • How predictable is the continental monsoon interannual variability (IAV)? • How to improve seasonal prediction in continental monsoon regions?

44. Comparison of Spatial Patterns

45. MME capture ENSO-MNS relation PC time series MMEs are highly correlated with CMAP PC Spectra: MMEs underestimate QB Peak and total variances S-EOF1 concurs with ENSO SEOF2 leads ENSO by 1 year

46. Comparison of MME forecast with Reanalyses The MME beats two re-analysesin capturing both the spatial patterns and temporal evolutions of the two leading modes

47. Discussion • The difficulty in current numerical simulation of the annual cycle in the East Asian summer monsoon is rooted in the relatively weak external forcing. • The difficulty in current numerical simulation of the interannual variability in the Indian summer monsoon is rooted in the relatively weak internal forcing within the coupled climate system and monsoon chaos. • The difficulty in modeling of the MJO and ISV is primarily due to deficiencies in atmospheric internal dynamics: handing convective interaction with large scale dynamics (parameterization problem) and in handling multi-scale interaction.

48. Improving model physical parameterization • Monsoon processes are sensitive to: • Cumulous parameterization • Cloud-radiation interaction • Aerosol impacts • Coupled model bias • Waliser (2005): When a model does exhibit a relatively good MJO/ISO, we can at best only give vague or plausible explanations for its relative success. This inhibits the extension of individual model successes to other more ISV-challenged models. • Ccollaboration among various modeling groups (large-scale modelers, meso-scale modelers, and cloud modelers • Strategy for validation of the models’ representation of the physical processes.

49. Atmosphere-land interaction Effects of the soil moisture and snow cover over the Eurasian continent on Asian monsoon variability. Yasunari (1991), Dirmeyer(1999): the land surface condition in Spring has an impact on the following summer monsoon. Shen et al. (1998) have investigated the impact of the Eurasian snowfall and concluded that it plays a part but does not overwhelm the SST-impact.

50. Effects of model resolution Orography is better represented as the resolution is increased and the rainfall associated with orography is better represented in higher resolution models. High resolution models may be able resolve better Mei-Yu front. Kawatani and Takahashi (2003), Sumi et al. (2004) demonstrated that the Baiu front can be well simulated by increasing the horizontal and vertical resolutions.