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Presentation Transcript
slide1

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

slide2

Motivation

31Dec92

VP200, ECMWF forecast

01Feb93

Vitart et al. (2007)

slide3

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

slide4

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

slide5

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

slide6

US CLIVAR MJO WG models

Climate models

*: flux adjustment for heat and fresh water

slide7

Results: 20-100 day filtered variance

U850

Mass flux

AGCM

Super param.

AGCM

Mass flux

CGCM

slide8

Results: 20-100 day filtered variance

PRCP

Mass flux

AGCM

Super param.

AGCM

CGCM

slide9

Results: Space -Time power spectrum

Nov-Apr

Shading: PRCP

Contour: U850

slide10

Results: Space -Time power spectrum

Nov-Apr

Shading: PRCP

Contour: U850

slide11

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

slide12

Results: EOF 1st mode (20-100day filtered)

Nov-Apr

Shading: PRCP

Contour: U850

slide13

Results: EOF 1st mode (20-100day filtered)

Nov-Apr

Shading: PRCP

Contour: U850

slide14

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?

slide15

PRCP - PBL convergence

Mass flux

CGCM

Correlation map between PRCP and 925hPa convergence

(20-100day filtered): initiation and strength

slide16

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

slide17

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

slide18

Composite RHbased on PRCP

Pressure

PRCP intensity

Warm Pool region

(50E-180E, 15S-15N)

slide19

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
slide20

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.
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