The Precipitating Cloud Population of the Madden – Julian Oscillation over the Indian and West Pac...
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The Precipitating Cloud Population of the Madden – Julian Oscillation over the Indian and West Pacific Oceans. Hannah C. Barnes. Dynamics Seminar 24 January 2013, University of Washington, Seattle, Washington. What is the Madden-Julian Oscillation?. Episodic convective burst Along equator

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Hannah c barnes

The Precipitating Cloud Population of the Madden – Julian Oscillation over the Indian and West Pacific Oceans

Hannah C. Barnes

Dynamics Seminar

24 January 2013, University of Washington, Seattle, Washington


What is the madden julian oscillation

What is the Madden-Julian Oscillation?

  • Episodic convective burst

  • Along equator

  • Indian Ocean -> dateline

  • 30-90 day period

  • Boreal Winter

  • Deep baroclinic circulation

Madden and Julian 1972


Mjo in indian and west pacific oceans

MJO in Indian and West Pacific Oceans

1

  • Wheeler and Hendon Index (2004)

  • EOF analysis of 200 and 850 hPa zonal winds and OLR

  • Two PC time series

    • 8 phases

    • Significant MJO

2

3

4

5

6

7

8

OLR and 850 hPa Winds


Importance of the mjo

Importance of the MJO

Lin et al. 2006


Mjo and pacific northwest

MJO and Pacific Northwest

  • More precipitation, floods when convection near dateline

Floods in Western Washington

(Bond and Vecchi 2003)

Precipitation rate anomalies


Mjo structure

MJO Structure

  • Convectively coupled Kelvin and Rossby waves

  • Eastward ~ 5 ms-1

  • Air-sea interaction

Rossby Wave

Kelvin Wave

Rui and Wang 1990


Hannah c barnes

Tropical Cloud Population

MESOSCALE CONVECTIVE SYSTEMS (MCSs)

Houze et al. 1980


Importance of cloud population

Importance of Cloud Population

Satellite

obs.

  • Models unrealistic without including shallow convection (Zhang and Song 2009)

  • MJO sensitive to deep and shallow heating (Haertel et al. 2008)

Entire population

No shallow

Zhang and Song 2009


Objectives

Objectives

  • Variability of precipitating clouds in MJO using TRMM Precipitation Radar

  • Associated humidity and wind shear


Trmm satellite instrumentation

TRMM Satellite Instrumentation

λ= 2 cm

Important! PR measures 3D structure of radar echoes

Kummerow et al, 1998


Data and methodology

Data and Methodology

  • TRMM PR

    • 2A23 (rain type classification)

    • 2A25 (attenuated corrected reflectivity)

  • ERA-interim reanalysis

  • 1999 – 2011, October – February, Wheeler and Hendon Index > 1

  • Bootstrapping

TRMM orbit


Geographic regions

Geographic Regions

Central Indian Ocean

SoutheastWest Pacific


Trmm pr identification

TRMM PR Identification

Identify each contiguous 3D echo objectseen by TRMM PR

Convective component

Stratiform component

Extreme characteristic

Contiguous stratiform echowith horizontal area > 50 000 km2

“Broad stratiform region (BSR)”

Extreme characteristic

Contiguous 3D volume ofconvective echo > 30 dBZ

Top height > 8 km

“Deep convective core (DCC)”

Horizontal area > 800 km2

“Wide convective core (WCC)”

“Isolated shallow echo (ISE)”

Echo top > 1 km below freezing level and separate from deeper convection

Houze et al, 2007, Romatschke et al. 2010, Rasmussen and Houze 2011, Zuluaga and Houze 2013


Central indian ocean cio analysis

Central Indian Ocean (CIO) Analysis


The mjo in the cio

The MJO in the CIO

1

Transition to Active

2

Active

3

Transition to Suppressed

4

5

Suppressed

6

7

8


Isolated shallow echoes

Isolated Shallow Echoes

10N

0

% Pixels

10S

90E

60E

MJO Phase

%

Frequency (%)

20 samples (blue), average (black), and 99% confidence interval (red)


Deep convective cores

Deep Convective Cores

% Pixels

0.025

MJO Phase

%

Frequency (%)

20 samples (blue), average (black), and 99% confidence interval (red)


Broad stratiform regions

Broad Stratiform Regions

% Pixels

MJO Phase

%

Frequency (%)

20 samples (blue), average (black), and 99% confidence interval (red)


Mjo precipitating cloud population

MJO Precipitating Cloud Population

  • All cloud types vary significantly

    • ISE suppressed, 2 phases after active

    • DCC, WCC, and BSR simultaneous active

  • Areal variability - BSR dominate

  • Number variability

    • ISE dominate

    • WCC > DCC > BSR

x104

%

ISE

DCC

WCC

BSR

x10^4

250

MJO Phase

MJO Phase


Large scale relative humidity

Large-Scale Relative Humidity

Number

Frequency (%)

4

Pressure (hPa)

8

2-3

1

MJO Phase

ISE

DCC

WCC

BSR

Relative Humidity (%)

Solid lines = active, dashed = suppressed


Large scale 1000 750 hpa shear

Large-Scale 1000-750 hPa Shear

Shading = shear magnitude

ms-1

Frequency (%)

Number

ISE

DCC

WCC

BSR

MJO Phase


Hannah c barnes

Strong Low-Level Shear Favors Development with Locally Stronger Surface Convergence

Stratiform Region

Convective Core

Houze et al. 1989


Hannah c barnes

Strong Low-Level Shear Favors Development with Locally Stronger Surface Convergence

C

Houze et al. 1989


Large scale 750 200 hpa shear

Large-Scale 750-200 hPa Shear

Shading = shear magnitude

ms-1

Frequency (%)

ISE

DCC

WCC

BSR

MJO Phase


Hannah c barnes

Very Strong Upper-Level Shear Separates Stratiform from Convective Moisture

Houze et al. 1989


Hannah c barnes

Very Strong Upper-Level Shear Separates Stratiform from Convective Moisture

Houze et al. 1989


Hannah c barnes

Very Strong Upper-Level Shear Separates Stratiform from Convective Moisture

Houze et al. 1989


Precipitating cloud population and large scale atmospheric conditions

Precipitating Cloud Population and Large-Scale Atmospheric Conditions


Southeast west pacific sewp analysis

Southeast West Pacific (SEWP) Analysis


Mjo in the sewp

MJO in the SEWP

1

Suppressed

2

3

Transition to Active

4

5

Active

6

Transition to Suppressed

7

8


Isolated shallow echoes1

Isolated Shallow Echoes

10N

0

% Pixels

10S

140E

170E

MJO Phase

%

Frequency (%)

20 samples (blue), average (black), and 99% confidence interval (red)


Deep convective cores1

Deep Convective Cores

% Pixels

MJO Phase

%

Frequency

20 samples (blue), average (black), and 99% confidence interval (red)


Broad stratiform regions1

Broad Stratiform Regions

% Pixels

MJO Phase

%

Frequency (%)

20 samples (blue), average (black), and 99% confidence interval (red)


Mjo precipitating cloud population1

MJO Precipitating Cloud Population

  • All cloud types significantly vary

    • ISE suppressed, 3 phases before active

    • BSR one phase before DCC and WCC

  • Areal variability - BSR dominate

  • Number variability

    • ISE dominate

    • DCC > WCC > BSR

%

x10^4

Area

Number

Number

ISE

DCC

WCC

BSR

MJO Phase

MJO Phase


Large scale relative humidity1

Large-Scale Relative Humidity

Frequency (%)

Number

Pressure (hPa)

MJO Phase

6-7

4-5

ISE

DCC

WCC

BSR

Relative Humidity (%)

Solid lines = active, dashed = suppressed


Large scale 1000 750 hpa shear1

Large-Scale 1000-750 hPa Shear

Number

ISE

DCC

WCC

BSR

Frequency

(%)

MJO Phase

Shading = shear magnitude

ms-1


Large scale 750 200 hpa shear1

Large-Scale 750-200 hPa Shear

ISE

DCC

WCC

BSR

Frequency

(%)

MJO Phase

Shading = shear magnitude

ms-1


Precipitating cloud population and large scale atmospheric conditions1

Precipitating Cloud Population and Large-Scale Atmospheric Conditions


Conclusions precipitating cloud population

Conclusions Precipitating Cloud Population

  • Precipitating cloud population varies significantly

    • Areal variability – BSR dominate

    • Number variability

      • ISE dominate

      • DCC & WCC > BSR


Conclusions precipitating cloud population and large scale atmosphere

Conclusions:Precipitating Cloud Population and Large-Scale Atmosphere

  • RH leads then positive feedback with the deep convection

  • Strong low-level shear -> strong surface convergence

  • Very strong upper-level shear -> stratiform torn from convective source


Future work

Future Work

  • Kinematics and microphysics

    • 11 rain events, Zuluaga and Houze (2013)

    • Compare kinematics to TOGA COARE

    • Expand with microphysical data

    • Relate storm structure to large-scale

  • Modeling???

(Kingsmill and Houze 1999a)


Acknowledgements

Acknowledgements

  • Bob Houze

  • Committee

    • Rob Wood and Mike Wallace

  • Beth Tully

  • Houze group

  • 626 Officemates

  • Grads 2010

  • Family

  • Funding

    • DOE DE-SC0001164 / ER-64752 and DE-SC0008452

    • DYNAMO – NSF AGS-1059611

    • PMM-NASA Grant NNX10AH70.


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