Scale Interactions in Organized Tropical Convection George N. Kiladis Physical Sciences Division...

Scale Interactions in Organized Tropical Convection

Scale Interactions in Organized Tropical Convection

George N. Kiladis

Physical Sciences DivisionESRL, NOAA

George N. Kiladis

Physical Sciences DivisionESRL, NOAA

Why Study Tropical Convective Variability?

Why Study Tropical Convective Variability?

Tropical Convection acts as a primary “heat engine” for the atmospheric circulation

Variability in tropical convection has global scale impacts over a variety of time scales

Convection is coupled to the ocean within the tropics>Sea surface temperature has a strong influence>Atmospheric disturbances influence SST

Tropical Convection acts as a primary “heat engine” for the atmospheric circulation

Variability in tropical convection has global scale impacts over a variety of time scales

Convection is coupled to the ocean within the tropics>Sea surface temperature has a strong influence>Atmospheric disturbances influence SST

OBSERVATIONS OF WAVES WITHIN THE MJOTime–longitude diagram of CLAUS Tb (5S–equator), February 1987

The Madden-Julian Oscillation (MJO)The Madden-Julian Oscillation (MJO)

Discovered by Rol Madden and Paul Julian at NCAR in 1971

Characterized by an envelope of convection ~10,000 km wide moving eastward at around 5 m/s

Most active over regions of high sea surface temperature (> 27 C) Can have a profound impact on the extratropical circulation

Is poorly represented in general circulation models, if at all

Composed of a variety of higher frequency, smaller scale disturbances

Discovered by Rol Madden and Paul Julian at NCAR in 1971

Characterized by an envelope of convection ~10,000 km wide moving eastward at around 5 m/s

Most active over regions of high sea surface temperature (> 27 C) Can have a profound impact on the extratropical circulation

Is poorly represented in general circulation models, if at all

Composed of a variety of higher frequency, smaller scale disturbances

Shallow Water System (Matsuno, 1966)

€

∂u∂t

−βyv+∂φ

∂x= 0

∂v

∂t+βyu +

∂φ

∂y= 0

∂φ

∂t+ gh

∂u

∂x+∂v

∂y

⎛

⎝ ⎜

⎞

⎠ ⎟= 0

Shallow Water System (Matsuno, 1966)

€

h

€

gh

whereis the meridional gradient of f at the eq is theequivalent depth is the gravitywave speed

€

f = βy

€

β =2Ω /a

Theoretical Dispersion Relationships for Shallow Water Modes on Eq. β Plane

Frequency

Zonal Wavenumber

Theoretical Dispersion Relationships for Shallow Water Modes on Eq. β Plane

Kelvin

Inertio-Gravity

Equatorial Rossby

Frequency

Zonal Wavenumber

Kelvin Wave Theoretical Structure

Wind, Pressure (contours), Divergence, blue negative

Mixed Rossby-Gravity Wave Theoretical Structure

Wind, Pressure (contours), Divergence, red negative

Wavenumber-Frequency Spectral Analyis

Decompose into Symmetric and Antisymmetric Fields about the Equator

Complex Fourier Transform into wavenumber space at each latitude

FFT of each wavenumber into frequency space

Average the Power for each wavenumber/frequency by latitude

Determine “background” spectrum by smoothing raw spectra

Divide raw spectra by background spectra to determine signals standing above the background

OLR power spectrum, 15ºS-15ºN, 1979–2001 (Symmetric)

from Wheeler and Kiladis, 1999

OLR power spectrum, 15ºS-15ºN, 1979–2001 (Symmetric)


Eastward Power

Westward Power

1.25 Days

96 Days

OLR power spectrum, 15ºS-15ºN, 1979–2001 (Antisymmetric)


OLR background spectrum, 15ºS-15ºN, 1979–2001


fromWheeler and Kiladis, 1999

OLR power spectrum, 1979–2001 (Symmetric)



Kelvin

Westward Inertio-Gravity

Equatorial Rossby

Madden-Julian Oscillation


OLR power spectrum, 1979–2001 (Antisymmetric)


OLR power spectrum, 1979–2001 (Antisymmetric)

Mixed Rossby-Gravity

Eastward Inertio-Gravity

OBSERVATIONS OF KELVIN WAVES AND THE MJOTime–longitude diagram of CLAUS Tb (2.5S–7.5N), January–April 1987

Kelvinwaves

(15 m s-1)

MJO(5 m s-1)

OBSERVATIONS OF KELVIN AND MRG WAVESTime–longitude diagram of CLAUS Tb (2.5S–7.5N), May 1987

1998 Brightness Temperature 5ºS-5º N

Kelvin Wave Theoretical Structure

Wind, Pressure (contours), Divergence, blue negative



Regression Models

Simple Linear Model:

y = ax + b

where: x= predictor (filtered OLR)y= predictand (OLR, circulation)

OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004

Day 0

Geopotential Height (contours 2 m)

Wind (vectors, largest around 5 m

s-1)OLR (shading starts at +/- 6 W s-

2), negative blue


Day-6




2), negative blue


Day-5




2), negative blue


Day-4




2), negative blue


Day-3




2), negative blue


Day-2




2), negative blue


Day-1




2), negative blue


Day 0




2), negative blue


Day+1




2), negative blue


Day+2




2), negative blue

Mixed Rossby-Gravity Wave Theoretical Structure

Wind, Pressure (contours), Divergence, red negative

OLR and 850 hPa Flow Regressed against MRG-filtered OLR (scaled -40 W m2) at 7.5 N,

172.5E, 1979-2004

Day-1Streamfunction (contours 2 X 105

m2 s-1)Wind (vectors, largest around 2 m


2), negative blue

C W C W

Direction of Motion

Temperature Structure of a Dry Kelvin Wave

C W C W

Direction of Motion

Temperature Structure of a Dry Kelvin Wave

Temperature at Majuro (7N, 171E) Regressed against Kelvin-filtered OLR

(scaled -40 W m2) for 1979-1999

OLR (top, Wm-2)Temperature (contours, .1 °C),

red positive

from Straub and Kiladis 2002

Zonal Wind at Majuro (7N, 171E) Regressed against Kelvin-filtered OLR

(scaled -40 W m2) for 1979-1999

OLR (top, Wm-2)Zonal Wind (contours, .25 m s-

1), red positive

from Straub and Kiladis 2002

Specific Humidity at Majuro (7N, 171E) Regressed against Kelvin-filtered OLR

(scaled -40 W m2) for 1979-1999

from Straub and Kiladis 2002OLR (top, Wm-2)

Specific Humidity (contours, 1 X 10-1 g kg-1), red positive

Meridional Wind at Majuro (7N, 171E) Regressed against MRG-filtered OLR

(scaled -40 W m2) for 1979-1999

OLR (top, Wm-2)Meridional Wind (contours, .25 m s-1), red

positive

Temperature at Majuro (7N, 171E) Regressed against MRG-filtered OLR

(scaled -40 W m2) for 1979-1999


red positive

Specific Humidity at Majuro (7N, 171E) Regressed against MRG-filtered OLR

(scaled -40 W m2) for 1979-1999

OLR (top, Wm-2)Specific Humidity (contours, 1 X 10-1 g

kg-1), red positive

Haertel and Kiladis 2004

Wave Motion


Wave Motion


Wave Motion


Wave Motion

Zonal Wind at Honiara (10S, 160E) Regressed against MJO-filtered OLR (scaled -40 W m2) for

1979-1999

OLR (top, Wm-2)U Wind (contours, .5 m s-1),

red positive

OLR

Pressure(hPa)

from Kiladis et al. 2005

Temperature at Honiara (10S, 160.0E) Regressed against MJO-filtered OLR (scaled -40 W m2) for

1979-1999


red positive

OLR

Pressure(hPa)


Specific Humidity at Truk (7.5N, 152.5E) Regressed against MJO-filtered OLR (scaled -40 W

m2) for 1979-1999

OLR (top, Wm-2)Specific Humidity (contours, 1 X 10-1 g

kg-1), red positive

OLR

Pressure(hPa)


Q1 Regressed against MJO-filtered OLR over the IFA during COARE


Morphology of a Tropical Mesoscale Convective Complex in the eastern Atlantic during GATE

(from Zipser et al. 1981)Storm Motion

Observed Kelvin wave morphology (from Straub and Kiladis 2003)

Wave Motion

Two day (WIG) wave cloud morphology (from Takayabu et al. 1996)

from Morita et al., 2006

Equatorial Wave MorphologyEquatorial Wave Morphology

All waves examined have broadly self-similar vertical structures in terms of their dynamical fields (temperature, wind, pressure, diabatic heating)

Cloud morphology is consistent with a progression of shallow to deep convection, followed by stratiform precipitation

Suggests a fundamental interaction between wave dynamics and convection across a wide range of scales

All waves examined have broadly self-similar vertical structures in terms of their dynamical fields (temperature, wind, pressure, diabatic heating)

Cloud morphology is consistent with a progression of shallow to deep convection, followed by stratiform precipitation

Suggests a fundamental interaction between wave dynamics and convection across a wide range of scales

Convection in General Circulation Models

Convection in General Circulation Models

Question: How well do GCMs do in characterizing intraseasonal tropical convective variability?

Jialin Lin et al. (2006) applied identical space-time spectral techniques to observed and modeled tropical precipitation

Models used are the 14 coupled ocean-atmosphere GCMs used for intercomparison in the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC)

Question: How well do GCMs do in characterizing intraseasonal tropical convective variability?

Jialin Lin et al. (2006) applied identical space-time spectral techniques to observed and modeled tropical precipitation

Models used are the 14 coupled ocean-atmosphere GCMs used for intercomparison in the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC)

Rainfall Power Spectra, IPCC AR4 Intercomparison 15S-15N, (Symmetric)

from Lin et al., 2006

Observations

Rainfall Power Spectra, IPCC AR4 Intercomparison 15S-15N, (Symmetric)


Rainfall Spectra/Backgr, IPCC AR4 Intercomparison 15S-15N, (Symmetric)


Observations


Rainfall Spectra/Backgr, IPCC AR4 Intercomparison 15S-15N, (Symmetric)

Rainfall Spectra/Backgr., IPCC AR4 Intercomparison 15S-15N, (Antisymm.)


Observations

Rainfall Spectra/Backgr., IPCC AR4 Intercomparison 15S-15N, (Antisymm.)


Outstanding IssuesOutstanding Issues

General Circulation Models do a relatively poor job in correctly simulating variability in tropical convection (but not necessarily its mean state)

Is this due to the misrepresentation of convection itself, or its coupling to the large scale (or both)?

Is convection even parameterizable in models?

Improvements in the representation of tropical convection will lead to improvements in medium-range weather forecasts in mid-latitudes (and perhaps to ENSO) What is the impact of poor tropical variability in GCMs on climate change scenarios?

General Circulation Models do a relatively poor job in correctly simulating variability in tropical convection (but not necessarily its mean state)

Is this due to the misrepresentation of convection itself, or its coupling to the large scale (or both)?

Is convection even parameterizable in models?

Improvements in the representation of tropical convection will lead to improvements in medium-range weather forecasts in mid-latitudes (and perhaps to ENSO) What is the impact of poor tropical variability in GCMs on climate change scenarios?

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Scale Interactions in Organized Tropical Convection George N. Kiladis Physical Sciences Division...

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