scale interactions in organized tropical convection george n. kiladis physical sciences division...
TRANSCRIPT
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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
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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
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OBSERVATIONS OF WAVES WITHIN THE MJOTime–longitude diagram of CLAUS Tb (5S–equator), February 1987
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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
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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
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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
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Theoretical Dispersion Relationships for Shallow Water Modes on Eq. β Plane
Frequency
Zonal Wavenumber
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Theoretical Dispersion Relationships for Shallow Water Modes on Eq. β Plane
Kelvin
Inertio-Gravity
Equatorial Rossby
Frequency
Zonal Wavenumber
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Kelvin Wave Theoretical Structure
Wind, Pressure (contours), Divergence, blue negative
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Mixed Rossby-Gravity Wave Theoretical Structure
Wind, Pressure (contours), Divergence, red negative
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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
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OLR power spectrum, 15ºS-15ºN, 1979–2001 (Symmetric)
from Wheeler and Kiladis, 1999
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OLR power spectrum, 15ºS-15ºN, 1979–2001 (Symmetric)
from Wheeler and Kiladis, 1999
Eastward Power
Westward Power
1.25 Days
96 Days
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OLR power spectrum, 15ºS-15ºN, 1979–2001 (Antisymmetric)
from Wheeler and Kiladis, 1999
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OLR background spectrum, 15ºS-15ºN, 1979–2001
from Wheeler and Kiladis, 1999
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fromWheeler and Kiladis, 1999
OLR power spectrum, 1979–2001 (Symmetric)
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fromWheeler and Kiladis, 1999
OLR power spectrum, 1979–2001 (Symmetric)
Kelvin
Westward Inertio-Gravity
Equatorial Rossby
Madden-Julian Oscillation
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fromWheeler and Kiladis, 1999
OLR power spectrum, 1979–2001 (Antisymmetric)
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fromWheeler and Kiladis, 1999
OLR power spectrum, 1979–2001 (Antisymmetric)
Mixed Rossby-Gravity
Eastward Inertio-Gravity
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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)
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OBSERVATIONS OF KELVIN AND MRG WAVESTime–longitude diagram of CLAUS Tb (2.5S–7.5N), May 1987
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1998 Brightness Temperature 5ºS-5º N
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Kelvin Wave Theoretical Structure
Wind, Pressure (contours), Divergence, blue negative
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OLR power spectrum, 1979–2001 (Symmetric)
fromWheeler and Kiladis, 1999
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Regression Models
Simple Linear Model:
y = ax + b
where: x= predictor (filtered OLR)y= predictand (OLR, circulation)
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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
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OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004
Day-6
Geopotential Height (contours 2 m)
Wind (vectors, largest around 5 m
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004
Day-5
Geopotential Height (contours 2 m)
Wind (vectors, largest around 5 m
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004
Day-4
Geopotential Height (contours 2 m)
Wind (vectors, largest around 5 m
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004
Day-3
Geopotential Height (contours 2 m)
Wind (vectors, largest around 5 m
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004
Day-2
Geopotential Height (contours 2 m)
Wind (vectors, largest around 5 m
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004
Day-1
Geopotential Height (contours 2 m)
Wind (vectors, largest around 5 m
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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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
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OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004
Day+1
Geopotential Height (contours 2 m)
Wind (vectors, largest around 5 m
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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OLR and 1000 hPa Flow Regressed against Kelvin-filtered OLR (scaled -20 W m2) at 10N, 150W for June-Aug. 1979-2004
Day+2
Geopotential Height (contours 2 m)
Wind (vectors, largest around 5 m
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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Mixed Rossby-Gravity Wave Theoretical Structure
Wind, Pressure (contours), Divergence, red negative
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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
s-1)OLR (shading starts at +/- 6 W s-
2), negative blue
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C W C W
Direction of Motion
Temperature Structure of a Dry Kelvin Wave
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C W C W
Direction of Motion
Temperature Structure of a Dry Kelvin Wave
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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
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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
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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
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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
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Temperature at Majuro (7N, 171E) Regressed against MRG-filtered OLR
(scaled -40 W m2) for 1979-1999
OLR (top, Wm-2)Temperature (contours, .1 °C),
red positive
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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
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Haertel and Kiladis 2004
Wave Motion
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Haertel and Kiladis 2004
Wave Motion
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Haertel and Kiladis 2004
Wave Motion
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Haertel and Kiladis 2004
Wave Motion
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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
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Temperature at Honiara (10S, 160.0E) Regressed against MJO-filtered OLR (scaled -40 W m2) for
1979-1999
OLR (top, Wm-2)Temperature (contours, .1 °C),
red positive
OLR
Pressure(hPa)
from Kiladis et al. 2005
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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)
from Kiladis et al. 2005
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Q1 Regressed against MJO-filtered OLR over the IFA during COARE
from Kiladis et al. 2005
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Morphology of a Tropical Mesoscale Convective Complex in the eastern Atlantic during GATE
(from Zipser et al. 1981)Storm Motion
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Observed Kelvin wave morphology (from Straub and Kiladis 2003)
Wave Motion
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Two day (WIG) wave cloud morphology (from Takayabu et al. 1996)
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from Morita et al., 2006
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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
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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)
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Rainfall Power Spectra, IPCC AR4 Intercomparison 15S-15N, (Symmetric)
from Lin et al., 2006
Observations
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Rainfall Power Spectra, IPCC AR4 Intercomparison 15S-15N, (Symmetric)
from Lin et al., 2006
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Rainfall Spectra/Backgr, IPCC AR4 Intercomparison 15S-15N, (Symmetric)
from Lin et al., 2006
Observations
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from Lin et al., 2006
Rainfall Spectra/Backgr, IPCC AR4 Intercomparison 15S-15N, (Symmetric)
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Rainfall Spectra/Backgr., IPCC AR4 Intercomparison 15S-15N, (Antisymm.)
from Lin et al., 2006
Observations
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Rainfall Spectra/Backgr., IPCC AR4 Intercomparison 15S-15N, (Antisymm.)
from Lin et al., 2006
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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?