application of mjo simulation diagnostics to climate model simulations
DESCRIPTION
Application of MJO simulation diagnostics to climate model simulations. Authors. - PowerPoint PPT PresentationTRANSCRIPT
Application of Application of
MJO simulation diagnostics MJO simulation diagnostics to climate model simulationsto climate model simulations
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
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
Authors
Affiliations
Vitart et al. (2007)
VP200, ECMWF forecast31Dec92
01Feb93
MotivationMotivation
(Lin et al. 2006)
MJO Variance (eastward wavenumber 1-6, periods 30-70days)
* Only 2 models have comparable amplitude to OBS (IPCC AR4 14 models)
MotivationMotivation
MJO Simulation Diagnostics: http://climate.snu.ac.kr/mjo_diagnostics/index.htmMJO Simulation Diagnostics: http://climate.snu.ac.kr/mjo_diagnostics/index.htm
MJO Simulation Diagnostics - Web siteMJO Simulation Diagnostics - Web site
GeneralGeneralStrategyStrategy
&&DescriptionDescription
Calculation codes and example data - Needs feedback
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)
QuestionsQuestions
Climate models *: flux adjustment for heat and fresh water
US CLIVAR MJO WG modelsUS CLIVAR MJO WG models
Model HorizontalResolution
VerticalResolution (top level)
Cumulus parameterization Integration Reference
CFS- NCEP T62(1.8º)
64(0.2hPa)
Mass flux(Hong and Pan 1998)
20 years Wang et al. (2005)
ECHAM4/OPYC*- PCMDI
T42(2.8º)19
(10hPa)
Mass flux(Tiedtke 1989, adjustment closure
Nordeng 1994)20 years Sperber et al. (2005)
CM2.1- GFDL
2o lat x 2.5o lon
24(4.5hPa)
Mass flux(RAS;
Moorthi and Suarez 1992)20 years
Delworth et al. (2006)
SPCAM- CSU T42(2.8º)
26(3.5hPa)
Superparameterization (Khairoutdinov and
Randall 2003)
19 years01OCT1985-25SEP2005
Khairoutdinov et al. (2005)
GEOS5- NASA 1o lat x 1.25º lon
72(0.01hPa)
Mass flux(RAS;
Moorthi and Suarez 1992)
12 years01DEC1993-30NOV2005
To be documented
CAM3.5- NCAR 1.9o lat x 2.5o lon
26(2.2hPa)
Mass flux (Zhang and McFarlane 1995)
20 years01JAN1986-31DEC2005
Neale et al. (2007)
CAM3z- SIO T42(2.8º)
26(2.2hPa)
Mass flux (Zhang and McFarlane 1995)
15 years29JAN1980-23JUL1995
Zhang et al. (2005)
SNUAGCM- SNU T42(2.8º)
20(10hPa)
Mass flux (Numaguti et al. 1995)
8 years01JAN1997-31DEC2004
Lee et al. (2003)
Results: 20-100 day filtered varianceResults: 20-100 day filtered variance
Mass fluxAGCM
Super param.AGCM
Mass fluxCGCM
U850
Results: 20-100 day filtered varianceResults: 20-100 day filtered variance
Mass fluxAGCM
Super param.AGCM
CGCM
PRCP
Results: Space -Time power spectrumResults: Space -Time power spectrum
Nov-Apr
Shading: PRCP
Contour: U850
Results: Space -Time power spectrumResults: Space -Time power spectrum
Nov-Apr
Shading: PRCP
Contour: U850
Wavenumber 1 power spectra for 200hPa velocity potential
(Slingo et al. 1996)
OBS
* Spectral peak in 30-70 day period is NOT appeared in models
1996 : AMIP models1996 : AMIP models
Results: EOF 1Results: EOF 1stst mode (20-100day filtered) mode (20-100day filtered)
Nov-Apr
Shading: PRCP
Contour: U850
Results: EOF 1Results: EOF 1stst mode (20-100day filtered) mode (20-100day filtered)
Nov-Apr
Shading: PRCP
Contour: U850
InterpretationInterpretation
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?
PRCP - PBL convergence PRCP - PBL convergence
Correlation map between PRCP and 925hPa convergence
(20-100day filtered): initiation and strength
Mass fluxCGCM
Wavenumber-frequency spectrum
Maloney and Hartmann (2001)
CCM3.6+McRAS
CCM3.6 controlCMAP
CCM3.6+Hack
MJO signal
Observation CCM3.6 with McRAS
Lag Correlation between PRCP and convergence
Maloney (2002)
Unrealistic phase relationship instead of improved MJO variability
PRCP - PBL convergence PRCP - PBL convergence
Composite RH based on PRCPComposite RH based on PRCP
from Prof. David A. Randall’s presentation at MJO Workshop (Nov. 2007)
Warm Pool region(50E-180E, 15S-15N)
ERA40/GPCP
CAM
SPCAM
PRCP intensity
Pressure
Composite RHComposite RH based on PRCPbased on PRCP
Warm Pool region(50E-180E, 15S-15N)
PRCP intensity
Pressure
Conclusion & DiscussionsConclusion & 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
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.
Conclusion & DiscussionsConclusion & Discussions
Thank you!