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Application of MJO simulation diagnostics to climate model simulations

Application of MJO simulation diagnostics to climate model simulations. Authors.

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Application of MJO simulation diagnostics to climate model simulations

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  1. Application of MJO simulation diagnostics to climate model simulations Authors Daehyun Kim1 , D. E. Waliser2, K. R. Sperber3 , L Donner4, J. Gottschalck5, H. H. Hendon6, W. Higgins5, I.-S. Kang1, E. D. Maloney7, M. W. Moncrieff8, S. Schubert9, W. Stern4, F. Vitart10 , B. Wang11, W. Wang5, K. M. Weickmann12, M. C. Wheeler6, S. Woolnough13,C. Zhang14, M. Khairoutdinov15, M.-I. Lee9, R. Neale8, D. Randall7, M. Suarez9, and G. Zhang16 Affiliations 1SEES/Seoul National University, Korea, 2JPL/California Institute of Technology, USA, 3PCMDI/Lawrence Livermore National Laboratory, USA, 4 GFDL/NOAA, USA, 5Climate Prediction Center/NCEP/NOAA, USA, 6Bureau of Meteorology Research Center, Australia, 7Colorado State University, USA 8National Center for Atmospheric Research, USA, 9Goddard Space Flight Center/NASA, USA 10European Centre for Medium-Range Weather Forecasts, UK, 11IPRC/University of Hawaii, USA, 12Climate Diagnostics Center/NOAA, USA, 13Univertisy of Reading, UK, 14RSMAS/University of Miami, USA, 15Stony Brook University, USA, 16Scripps Institution of Oceanography, USA

  2. Motivation 31Dec92 VP200, ECMWF forecast 01Feb93 Vitart et al. (2007)

  3. MJO Variance(eastward wavenumber 1-6, periods 30-70days) (Lin et al. 2006) * Only 2 models have comparable amplitude to OBS (IPCC AR4 14 models) Motivation

  4. MJO Simulation Diagnostics - Web site MJO Simulation Diagnostics: http://climate.snu.ac.kr/mjo_diagnostics/index.htm General Strategy & Description Calculation codes and example data - Needs feedback

  5. Questions & Points 1. How well the current climate models simulate MJO? Large-scale circulation vs. Convection (850hPa zonal wind) (Precipitation) 2. What are the shortcomings of the models (models’ convection)? PBL convergence - PRCP Relative Humidity – PRCP (Trigger function) Questions

  6. US CLIVAR MJO WG models Climate models *: flux adjustment for heat and fresh water

  7. Results: 20-100 day filtered variance U850 Mass flux AGCM Super param. AGCM Mass flux CGCM

  8. Results: 20-100 day filtered variance PRCP Mass flux AGCM Super param. AGCM CGCM

  9. Results: Space -Time power spectrum Nov-Apr Shading: PRCP Contour: U850

  10. Results: Space -Time power spectrum Nov-Apr Shading: PRCP Contour: U850

  11. 1996 : AMIP models Wavenumber 1 power spectra for 200hPa velocity potential OBS (Slingo et al. 1996) * Spectral peak in 30-70 day period is NOT appeared in models

  12. Results: EOF 1st mode (20-100day filtered) Nov-Apr Shading: PRCP Contour: U850

  13. Results: EOF 1st mode (20-100day filtered) Nov-Apr Shading: PRCP Contour: U850

  14. Interpretation MJO signal in large-scale circulation (850hPa zonal wind) MJO signal in convection (precipitation) Improper relationship between them? Are they maintained in different way from observation?

  15. PRCP - PBL convergence Mass flux CGCM Correlation map between PRCP and 925hPa convergence (20-100day filtered): initiation and strength

  16. Lag Correlation between PRCP and convergence Wavenumber-frequency spectrum CCM3.6 control CMAP Observation CCM3.6 with McRAS CCM3.6+Hack CCM3.6+McRAS MJO signal Maloney (2002) Maloney and Hartmann (2001) PRCP - PBL convergence Unrealistic phase relationship instead of improved MJO variability

  17. Composite RH based on PRCP Pressure ERA40/GPCP SPCAM PRCP intensity Warm Pool region (50E-180E, 15S-15N) from Prof. David A. Randall’s presentation at MJO Workshop (Nov. 2007) CAM

  18. Composite RHbased on PRCP Pressure PRCP intensity Warm Pool region (50E-180E, 15S-15N)

  19. Conclusion & Discussions • 1. Standardized diagnostics are objectively developed by MJO working group for MJO simulation of climate model simulations(J. Climate, to be submitted). • website: http://www.usclivar.org/Organization/MJO_WG.html • 2. As a baseline of future studies, developed diagnostics are applied to 3 coupled and 5 uncoupled climate model simulations. • 3. The applied diagnostics reasonably captured models characteristics related with MJO simulation. • Model’s sub-seasonal variability strongly depends on the detail implementation of convection scheme • The current state-of-the-art climate models can reproduce eastward propagation of lower level zonal wind

  20. Conclusion & Discussions • 3. Overall comparisons reveal that ECHAM4/OPYC and SPCAM have relatively better skill among the models. ECHAM4/OPYC produces very reasonable mean state with flux adjustment process. Convection is represented in more explicit manner in SPCAM (superparameterization). • 4. MJO signal in 850hPa zonal wind is generally better than that of precipitation in terms of i) variance ii) peaks in spectra and iii) eastward propagation. • 5. Diabatic heating (rainfall) is more difficult variable to simulate than large scale circulation field although heating and circulation are closely linked together. It will be tracked from this study what change or development can overcome this paradox.

  21. Thank you!

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