MJO Simulation Diagnostics Application of Diagnostics to Models Part I

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MJO Simulation Diagnostics Application of
Diagnostics to ModelsPart I
Daehyun Kim, Duane E. Waliser, Kenneth R.
Sperber, Eric E. Maloney and In-Sik Kang on
behalf of US CLIVAR MJO Working
Group1 1http//www.usclivar.org/Organization/MJO_W
G.html
NCEP Wanqiu Wang PCMDI Kenneth R. Sperber
GFDL Bill Stern CSU Marat Khairoutdinov, David
A. Randall NASA/GSFC Myong-In Lee, Max J.
Suarez CAM3.5 Richard B. Neale SNU In-Sik
Kang
Acknowledge the following modeling groups
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The Madden-Julian Oscillation (MJO)
MJO Eastward propagating planetary scale wave
with 30-60 day period
Simulation of MJO Intercomparison studies

• Intercomparison studies using several
  contemporary GCMs
• - 1996 Slingo et al. (AMIP 15 models)
• - 2003 Waliser et al. (CLIVAR 10 models)
• - 2005 Sperber et al. (6
  coupled/uncoupled models)
• - 2006 Zhang et al. (8
  coupled/uncoupled models)
• - 2006 Lin et al. (IPCC/AR4 14 coupled models)

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The Madden-Julian Oscillation (MJO)
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The Madden-Julian Oscillation (MJO)
10oS-10oN Time-longitude Cross Section of
Precipitation during 1997
CMAP
CLIVAR monsoon AGCM intercomparison workshop
(Kang et al. 2001 Waliser et al. 2003)
Most of the models hardly simulate eastward
propagation
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The Madden-Julian Oscillation (MJO)
Coupled
Coupled
Coupled
Coupled
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The Madden-Julian Oscillation (MJO)
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The Madden-Julian Oscillation (MJO)

• MJO is still a harsh test for GCMs

• Tracking progress is difficult

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Application to climate models
Climate models
flux adjustment for heat and fresh water
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Application to climate models
How well the current climate models simulate MJO?

• Mean state
• Sub-Seasonal Variance
• Space/Time scale and propagation
• Spatial patterns

Boreal Winter time (Nov-Apr)
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Application to climate models
How well the current climate models simulate MJO?

• Mean state
• - Important variables for MJO simulation
• Variance
• Space/Time scale and propagation
• Spatial patterns

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Mean State Diagnostics
Unit m/s
Mean State U850
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Mean State Diagnostics
Unit mm/day
Mean State PRCP Bias
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Mean State Diagnostics Summary
better
Mean State Pattern Corr. vs. RMSE
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Application to climate models
How well the current climate models simulate MJO?

• Mean state
• Sub-Seasonal Variance
• - Magnitude and distribution
• Space/Time scale and propagation
• Spatial patterns

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Variance Map Diagnostics
Variance of 20-100 day filtered precipitation
(Nov-Apr)
Unit mm2/day2 muliplied by 100 Contour 60,
80
Variance Map PRCP
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Variance Map Diagnostics Summary
Magnitude and Pattern PRCP vs. U850
OBS
OBS
0-360E, 20S-20N
0-360E, 20S-20N
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PBL moisture convergence PRCP
Correlation between 925hPa moisture convergence
and PRCP
moisture convergence and precipitation are
20-100 day filtered only values larger than 0.2
are drawn
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Application to climate models
How well the current climate models simulate MJO?

• Mean state
• Sub-SeasonalVariance
• Space/Time scale and propagation
• - Distribution of power in wavenumber-frequency
  space
• Spatial patterns

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Frequency-wave spectra diagnostics
Equatorial space-time power spectrum (Nov-Apr)
Wavenumber
Unit mm2/day2 multiplied by 100
Wavenumber
Wavenumber
Frequency-Wave PRCP
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Frequency-wave spectra diagnostics
Equatorial (10N-10S) space-time power spectra
Wavenumber
Unit m2/s2 contour 6, 9, 12
Wavenumber
Wavenumber
Frequency-Wave U850
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Frequency-wave spectra diagnostics
East/West Ratio
Frequency-Wave PRCP
Wavnumber 1-3
ERA40
Frequency-Wave U850
GPCP
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Application to climate models
How well the current climate models simulate MJO?

• Mean state
• Sub-SeasonalVariance
• Space/Time scale and propagation
• Spatial patterns
• - Pattern of MJO modes

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EOF Diagnostics OLR vs. PRCP
U850
OBS
U850
ECHAM4/OPYC
U850
SPCAM
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EOF Diagnostics Summary
Pattern correlation of 1st EOF mode
OBS
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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
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Summary

• 1. Objectively developed standardized diagnostics
  for MJO simulation are applied to 3 coupled and 3
  uncoupled climate model simulations.
• 2. Applied diagnostics reasonably captured models
  characteristics related with MJO simulation.
• The biases in mean state is not consistent
  between variables and models
• Coupled models have stronger sub-seasonal
  variability than uncoupled models
• Spectral power of precipitation is generally
  concentrated on low-frequency part
• When models MJO is represented using dominant
  modes, one variable is not enough and
  precipitation is a tough one
• 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).

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Summary

• 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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Extended Study with the SNUGCM

• High-resolution experiments (up to 20 km)
• Dependence on Cumulus Paramterization
• - Trigger (Tokioka modification)
• - SAS vs McRAS parameterization

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High resolution modeling

• 200hPa velocity potential (1999)

The FIRST Ensemble case
OBS
20km
300km
SNUGCM
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High resolution modeling

• 200hPa velocity potential (1999)

ENSEMBLES in 20km High resolution !
ENS4
ENS5
ENS6
ENS2
ENS3
SNUGCM
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Dependence on Cumulus Parameterization
GPCP
Variance of 20-100 day filtered
precipitation SNUGCM All season 1997-2004
Control (SAS)
Convection is constrained (Tokioka 1988)
McRAS
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Variance Map Diagnostics
20-100 day filtered velocity potential (10S-10N)
2001
Control (SAS)
McRAS
NCEP Re
Convection is constrained
X106 m2s-1
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Coupled model simulation SST climatology
Strong constraint ? colder SST
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Thank you for your attention!Any questions?
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High resolution modeling

• Space-Time Spectrum

20 Km
100 Km
300 Km
SNUGCM
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Back up figures
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Mean State Diagnostics
Unit oC
Mean State SST
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Mean State Diagnostics
Unit oC
Mean State SST Bias
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Mean State Diagnostics
Unit m/s
Mean State U850 Bias
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Mean State Diagnostics
Unit mm/day
Mean State PRCP
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Mean State Diagnostics
Unit mm/day
Mean State PRCP Bias
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Mean State Diagnostics
Unit m/s
Mean State Wind shear (200-850)
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Variance Map Diagnostics
Variance of 20-100 day filtered precipitation
(Nov-Apr)
Variance Map PRCP Bias
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Frequency-wave spectra diagnostics
Equatorial space-time power spectrum (Nov-Apr)
Wavenumber
Wavenumber
Unit m2/s2 contour 60, 80
Wavenumber
Frequency-Wave U200
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Frequency-wave spectra diagnostics
Wavnumber 1-3
Frequency-Wave U850
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Lag - Correlation Diagnostics
U850 Lag-correlation diagnostics (Nov-Apr)
Shading 95 sig. Line 5m/s
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Frequency-wave spectra diagnostics
Equatorial space-time power spectrum (Nov-Apr)
Wavenumber
Unit mm2/day2 multiplied by 100
Wavenumber
Wavenumber
Frequency-Wave PRCP
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Frequency-wave spectra diagnostics
Equatorial space-time power spectrum (Nov-Apr)
Wavenumber
Unit mm2/day2 multiplied by 100
Wavenumber
Wavenumber
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Frequency-wave spectra diagnostics
Wavnumber 1-6
Frequency-Wave PRCP
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Lag - Correlation Diagnostics
PRCP Lag-correlation diagnostics (Nov-Apr)
Shading 95 sig. Line 5m/s
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EOF Diagnostics Percentage variance
Percentage variance of 1st and 2nd EOF mode
2nd mode is separated from 3rd mode by
Norths criteria (DOF number of day / 50)
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(No Transcript)
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EOF Diagnostics
OBS (ERA40)
ECHAM4/OPYC
SPCAM
EOF U850
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EOF Diagnostics
OBS (AVHRR)
ECHAM4/OPYC
SPCAM
EOF OLR
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EOF Diagnostics
OBS (GPCP)
ECHAM4/OPYC
SPCAM
EOF PRCP
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PBL moisture convergence PRCP
Correlation between 925hPa moisture convergence
and PRCP
Lag
Lag
Lag
20-100day filtered, 5S-5N averaged
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MJO Simulation Diagnostics
MJO Life cycle composite Specific humidity
NCEP1
ECHAM4/OPYC
MJO Life cycle composite Zonal wind