The MJO problem in GCMs: What are the missing physics?

1. The MJO problem in GCMs:What are the missing physics? Jia-Lin Lin1, Brian E. Mapes2, George N. Kiladis3, Klaus M. Weickmann1, Minghua Zhang5, Kenneth R. Sperber4, Matthew Newman1, Wuyin Lin5, Matthew Wheeler6, Siegfried D. Schubert7, Anthony Del Genio8, Leo J. Donner9, Seita Emori10, Jean-Francois Gueremy11, Frederic Hourdin12, Philip J. Rasch13, Erich Roeckner14, John F. Scinocca15 1NOAA-CIRES Climate Diagnostics Center, Boulder, CO, 2RSMAS, University of Miami, Miami, FL, 3NOAA Aeronomy Laboratory, Boulder, CO, 4PCMDI, Lawrence Livermore National Laboratory, Livermore, CA, 5State University of New York, Stony Brook, NY, 6BMRC, Melbourne, Australia, 7NASA GSFC Global Modeling and Assimulation Office, Greenbelt, MD, 8NASA Goddard Institute for Space Studies, New York, NY, 9NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, 10National Institute for Environmental Studies, Ibaraki, Japan, 11Meteo-France CNRM, Paris, France, 12Laboratoire de Meteorologie Dynamique, Universite de Paris, Paris, France, 13National Center for Atmospheric Research, Boulder, CO, 14Max Planck Institute for Meteorology, Hamburg, Germany, 15Canadian Centre for Climate Modeling & Analysis, Victoria, Canada

2. 1. Introduction: The MJO and its teleconnections

3. 2. Motivation: The MJO problem– A longstanding, major tropical bias in GCMs Pioneering studies in 1980s (Hayashi and Golder 1986, 1988, Hayashi and Sumi 1986, Lau et al. 1988) Eastward Kelvin-Rossby or Kelvin waves but with too fast phase speeds (10-18 m/s) AMIP models in early 1990s (Slingo et al. 1996): Simulated signals are generally too weak and too fast • Models in late 1990s (Schubert et al. 2002, Waliser et al. 2003) • More models are getting something in the way of an MJO. • But when a model does exhibit a relatively good MJO, 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 MJO-challenged models. • Moreover, it is often the case that stated successes do not stand up to a great deal of detailed scrutiny. • Latest models participating in the IPCC Fourth Assessment Report (AR4) to be released in 2007 (Lin et al. 2005)

4. Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: Convective signals (Lin et al. 2005) Participating models: GFDL-CM2.0, GFDL-CM2.1, NCAR-CCSM3, NCAR-PCM, GISS-AOM, GISS-ER, MIROC3.2-hires, MIROC3.2-medres, MRI-CGCM2.3.2, CCC-CGCM3.1-T47, ECHAM/MPI-OM, IPSL-CM4, CNRM-CM3, CNRM-CM3-AMIP A new generation of climate models Before conducting the extended simulations for IPCC AR4, many of the modeling centers applied an overhaul to their physical schemes to incorporate the state-of-the-art research results.

5. Questions (1)How well do the IPCC AR4 models simulate the convectively coupled equatorial waves, especially the MJO? (2) Is there any systematic bias that is important for the MJO simulation?

6. Data • Each model: 8 years of daily precipitation from the Climate of the 20th Century (20C3M) experiment • Observation: 8 years of daily precipitation from GPI and GPCP 1DD

7. Method • Identification of the dominant intraseasonal modes Space-time spectral analysis (Wheeler and Kiladis 1999) Raw and Raw/Background, symmetric and antisymmetric • Isolating the MJO mode Definition of the MJO: eastward wavenumbers 1-6, 30-70 day mode The MJO is also compared with its westward counterpart: westward wavenumbers 1-6, 30-70 day mode

8. Climatological precipitation along the equatorial belt (15N-15S): Reasonably simulated over warm pool

9. Climatological precipitation on the equator (5N-5S): Some models have double-ITCZ problem

10. Total intraseasonal (2-128 day) variance (15N-15S): Variances in most models are smaller than in observations

11. Total intraseasonal (2-128 day) variance (5N-5S): Variances in most models are smaller than in observations

12. Raw space-time spectra (15N-15S symmetric):Variances in most models are too weak and too red Obs

13. Raw/background spectra (15N-15S symmetric):Many models have Kelvin waves, some have ER, WIG waves. But their phase speeds are too fast – too large equivalent depth Obs Dominant modes: MJO, Kelvin, ER, WIG Dispersion curves correspond to equivalent depth 8, 12, 25, 50, 90m. Larger depth –faster phase speed. All modes: 25 m.

14. Raw/background spectra (15N-15S antisymmetric):Many models have MRG-EIG waves. But their phase speeds are too fast – too large equivalent depth Obs Dominant modes: MRG, EIG Dispersion curves correspond to equivalent depth 8, 12, 25, 50, 90m. Both modes: 25m.

15. An interesting result Within a given model, equivalent depth is the same for all different equatorial waves.This is indicative of similar physical processes linking the convection and large-scale disturbances within each model.

16. Variance of the MJO mode (eastward wavenumbers 1-6, 30-70 day):MJO Variance in most models is smaller than in observations, but approaches the observed value in two models (MPI,CNRM)

17. Variance of the westward counterpart of the MJO(westward wavenumbers 1-6, 30-70 day):In many models, the eastward MJO variance is significantly larger than its westward counterpart

18. Propagation of the 30-70 day precipitation anomaly: Models with eastward MJO variance much stronger than its westward counterpart show clear eastward propagation Obs The three thick lines correspond to phase speed of 3, 7, and 15 m/s.

19. Raw spectra of eastward wavenumbers 1-6 at 0N85E:The MJO variance in most models does not come from a pronounced spectral peak, but from a too red spectrum. The only model with a prominent spectral peak is CNRM.

20. Normalized spectra of eastward wavenumbers 1-6 at 0N85E:Highlight the models with small variance

21. Theoretical first-order linear Markov process: A too red spectrum suggests a too strong persistence Spectrum Auto-correlation

22. Auto-correlation of precipitation at 0N85E:Most models do have a too strong persistence, which is consistent with their too red spectra

23. Summary of IPCC AR4 model evaluation:Two encouraging results • Many of the models have signals of convectively coupled waves, with Kelvin and MRG-EIG waves especially prominent. • The eastward MJO precipitation variance in many models is significantly larger than its westward counterpart, and even approaches the observed value in two models.

24. Summary of IPCC AR4 model evaluation: Two common biases • The MJO variances in many models do not come from a pronounced spectral peak, but from part of a too red spectrum (i.e., too red “background noise” ), which in turn are associated with a too strong persistence of precipitation. • The equivalent depths for all equatorial waves are too large, which is indicative of a too strong “effective static stability” and thus too weak wave-heating feedback.

25. Ongoing works 1. Dynamical signals and 3D wave structure Analyzing the daily 3D upper air data • Budget analysis and feedback analysis Calculating the heat and moisture budgets for all models and analyzing the wave-heating feedback Future works: Apply these diagnostics to NCEP GFS/CFS

26. 3. Hypothesis • Because the MJO problem is a common problem in many GCMs, our hypothesis is: • The MJO problem is caused by some missing physics in current GCMs. • (1) Missing physics associated with too red background noise • Missing physics associated with too weak wave-heating feedback

27. (1) Missing physics associated with too red background noise (too strong persistence of precip):Why is the persistence of precip weak in observation? Self-suppression processes in tropical deep convection

28. Convective downdrafts and Mesoscale downdrafts Convective updrafts Mesoscale updrafts Mesoscale downdrafts Convective downdrafts Zipser (1977), modified by Houze (1993)

29. Convective downdrafts and mesoscale downdrafts significantly affect the post-convection sounding “Onion” sounding: (similar to trade wind region in EP) Lower troposphere: drier, warmer Boundary layer: drier, cooler Pre-convection Post-convection Zipser (1977)

30. Self-suppression processes in tropical deep convection are missing in many GCMs  Missing physics II: Mesoscale downdrafts Missing physics I:  Convective downdrafts Missing physics III: Control of deep convection by lower troposphere moisture

31. Deep convection schemes in the 14 IPCC AR4 models

32. Control of deep convection by lower troposphere moisture is also missing in many models • All schemes are mass flux scheme using an ensemble of: • Entraining plumes • (e.g. Arakawa and Schubert 1974) or • (2) Buoyancy sorting parcels • (e.g. Emanuel 1991) • A common problem in many schemes: including undiluted or weakly diluted members, and therefore are not sensitive to lower troposphere moisture. • Solution in a couple of schemes: • Include only strongly diluted members (e.g. Tokioka et al. 1988, Tiedke 1989) • Add explicit RH trigger (e.g. Emori et al. 2001)

33. (2) Missing physics associated with wave-heating feedback Vertical heating profile Missing physics IV: Stratiform heating profile Missing physics V:  Shallow convective momentum transport Column-integrated diabatic heating has six major components (Mean state and higher-frequency modes affect the MJO through the nonlinear terms)

34. Missing physics IV: Stratiform precipitation and stratiform heating profile Stratiform precipitation has 3 characteristics: 1. Contributes significantly to total precipitation (>40%); 2. Lags convective precipitation by several hours; 3. Associated with upper-level heating and low-level cooling, making total heating profile top-heavy. Heating Divergence From Houze (1997)

35. Doppler Radar Climatology Project(Mapes and Lin 2005) • 7 experiments -- covering almost all precipitation centers • Simultaneous measurements of convection and circulation • for a region with the size of a GCM grid (~200*200 km) • hourly datasets • more than 20 days long for each experiment

36. Composite life cycle of deep convection for one experiment (EPIC): precip and divergence Stratiform Convective Convective Stratiform Stratiform precip provides more than 50% of total precip

37. Corresponding heating profile Top-heavy heating

38. Composite lifecycle of deep convection for all experiments: Stratiform precip and heating are important for all precipitation centers From Mapes and Lin (2005)

39. Stratiform precipitation and heating profile are missing in almost all GCMs MJO anomaly Observation 6 GCMs Observation - Model From Lin et al. (2004a)

40. Theoretical results • Top-heavy heating profile tends to amplify all intraseasonal modes (e.g. Cho and Pendlebury 1997). • Time-mean top-heavy heating profile can make the MJO highly viscous, and thus enhance wave-heating feedback in the MJO (Lin et al. 2004b). • Time-lag between stratiform precipitation and large-scale forcing may damp short waves, and favor long waves, which may enhance the MJO (Emanuel 1993, Cho et al. 1994).

41. Missing physics V: Shallow convective momentum transport Mechanical damping rate in observed MJO estimated from 15 years of NCEP/NCAR and ECMWF reanalysis data (Lin et al. 2004b) Strong mechanical damping above PBL 2 day 10 day 940 Over the warm pool region, PBL top is generally below 940 mb.

42. Theoretical model results: A thick frictional layer tends to amplify the MJO (Wang and Li 1994)

43. Summary: Missing physics in GCMs which are likely important for the MJO • Convective downdrafts (saturated and unsaturated) 2. Mesoscale updrafts/downdrafts • Control of deep convection by lower troposphere moisture • Shallow convective momentum transport Others? (e.g. other mechanical damping, gustiness, radiation)

44. It would be interesting to install these missing physics into GCMs and test their effects on the MJO simulation