Application of MJO simulation diagnostics to climate model simulations

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!