5.3. kelvin wave in general circulation models katherine straub
TRANSCRIPT
Zonal wavenumber-frequency power spectrum of tropical OLR
data, 1979-2001
This plot shows the spectral power in observed tropical OLR that exists above a smoothed red noise background spectrum.
The solid lines are dispersion curves for wave modes with equivalent depths of 8, 25, and 90 m, or Kelvin wave phase speeds of 9, 16, and 30 m s-1.
Based on Wheeler and Kiladis (1999), Journal of the Atmospheric Sciences
Kelvin
n=1 WIG
n=1 ER
MJO
Zonal wavenumber-frequency power spectrum of tropical
precipitation data, 1998-2007
This plot shows the spectral power in observed tropical precipitation (TRMM 3G68) that exists above a smoothed red noise background spectrum.
Kelvin waves are still present at the same range of shallow equivalent depths.
Kelvin
n=1 WIG
n=1 ER
MJO
Very similar to Cho et al. (2004), Journal of Climate
Do global models have Kelvin waves?
• Data: Output from 21 global models run for the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP)– “Climate of the 20th Century” model runs
(1961-2000) are analyzed for Kelvin waves– Wavenumber-frequency power spectrum of
precipitation is calculated for each model• This study is similar to Lin et al. (2006), but with the
goal of studying Kelvin waves rather than intraseasonal variability
21 models analyzed for Kelvin waves
Name Abbreviation
Model(s)
Bjerknes Center for Climate Research, Norway BCCR BCM2.0
Canadian Centre for Climate Modelling and Analysis, Canada
CCCM CGCM3.1, T63CGCM3.1, T47
CCSR/NIES/FRCGC, Japan CCSR MIROC3.2, medium resolution
CSIRO Atmospheric Research, Australia CSIRO Mk3.0Mk3.5
INGV, National Institute of Geophysics and Volcanology, Italy
INGV ECHAM4.6
Institute for Numerical Mathematics, Russia INM INMCM3.0
IPSL/LMD/LSCE, France IPSL CM4V1
LASG, Institute of Atmospheric Physics, China IAP FGOALS1.0_g
Max Planck Institute for Meteorology, Germany MPI ECHAM5/MPI
Meteo-France, Centre National de Recherches Meteorologiques, France
CNRM CM3
Meteorological Institute of the University of Bonn, Germany
MIUB ECHO-G
Meteorological Research Institute, Japan MRI CGCM2.3.2a
NASA Goddard Institute for Space Studies, USA GISS AOM C4x3E20/HYCOME20/Russell
National Center for Atmospheric Research, USA NCAR CCSM3.0PCM1
NOAA Geophysical Fluid Dynamics Laboratory, USA GFDL CM2.0CM2.1
Example: Model with strong KW variability
precipitation averaged 5S-5N
Straight lines represent equivalent depths of 8, 25, and 90 m, or KW
phase speeds of 9, 16, and 30 m s-1
Example: Model with no KW variability
Straight lines represent equivalent depths of 8, 25, and 90 m, or KW
phase speeds of 9, 16, and 30 m s-1 precipitation averaged 5S-5N
Rainfall Power Spectra, IPCC AR4 Intercomparison 15S-15N, (Symmetric)
from Lin et al., 2006
Observations
Rainfall Spectra/Backgr, IPCC AR4 Intercomparison 15S-15N, (Symmetric)
from Lin et al., 2006
Observations
Models with KW variability
• Of the 21 models analyzed, 8 have reasonable-looking KW spectra:– CCSR, Japan (MIROC)– GISS-AOM, USA– GISS-EH, USA– GISS-ER, USA– IPSL, France– MIUB, Germany (ECHO)– MPI, Germany (ECHAM5)– MRI, Japan
What do model KWs look like?
• How do model KWs compare to observations?
• Does the existence of a “good” KW spectral signature ensure the existence of realistic-looking waves?
Filters used to isolate KWs in precipitation datasets
Faster filter used for 3 GISS, IPSL, MRI (equivalent depths
12-150 m)
Slower filter used for CCSR, MIUB, MPI (equivalent depths
4-60 m)
Models with realistic KW distributions (MJJAS)
OLR - observations
CCSR, Japan
MIUB, Germany
MPI, Germany
Models with less realistic KW distributions
OLR - observations
GISS-AOM, USA
GISS-EH, USA
GISS-ER, USA
KW structure analysis: Methodology
• Regress 40 years of daily 3-D model grids (1961-2000) onto KW filtered precipitation data at point of maximum variance during NH summer (MJJAS)
Precipitation scale and propagation speed: PAC
Observations Models
CCSR
12 m s-1
MPI
11 m s-1
MIUB
11 m s-1
14 m s-1
OLR
MRI
21 m s-1
Precipitation scale and propagation speed: PAC
Observations
Models
GISS-AOM
20 m s-1
GISS-ER
14 m s-1
GISS-EH
22 m s-1
14 m s-1
OLR
What do observed KWs look like?
• OLR centered to north of equator, along ITCZ• Dynamical signals centered on equator• Winds are primarily zonal• Convergence to east of low OLR• Westerlies in phase with low OLR
OLR (red: increased cloudiness); ECMWF 1000-hPa u, v (vectors), z (contours)
What do model KWs look like?
CCSR
MIUB
MPI
Precipitation (shading); 1000-hPa u, v (vectors); SLP (contours)
What do model KWs look like?
GISS-AOM
Precipitation (shading); 1000-hPa u, v (vectors); SLP (contours)
GISS-EH
GISS-ER
Observed KWs: Upper troposphere
• Divergence collocated with/to the west of lowest OLR
• Zonal winds near equator• Rotational circulations off of equator
OLR (shading); ECMWF 200-hPa u, v (vectors), streamfunction (contours)
H L
H L
Model KWs: Upper troposphere
CCSR
MIUB
MPI
Precipitation (shading); 200-hPa u, v (vectors); streamfunction (contours)
H L
L
H L
LH
H L
H L
Model KWs: Upper troposphere
Precipitation (shading); 200-hPa u, v (vectors); streamfunction (contours)
MRI
L
L
H
Model KWs: Upper troposphere
Precipitation (shading); 200-hPa u, v (vectors); streamfunction (contours)
GISS-AOM
GISS-EH
GISS-ER
LH
L H
L
LHL
Conclusions
• Of 21 models analyzed, 3 reasonably simulate convectively coupled Kelvin waves– Common features:
• Slow phase speed• Maximum wave activity in Pacific ITCZ, equatorial Indian
Ocean• Realistic amplitude of SLP anomalies relative to
precipitation• Upper-level rotational signals in both hemispheres• Second vertical mode temperature structure• Significant cooling and drying following precipitation
• The existence of a reasonable-looking precipitation spectrum does not guarantee the existence of reasonable-looking Kelvin waves
Summary and Final comments
• KWs described by shallow water theory (Matsuno, 1966).
• KWs couple the dynamical circulations to regions of enhanced tropical cloudiness and rainfall.
• Convectively coupled KWs are ubiquitous in observational data of the tropical atmosphere:
• The western Pacific (Straub and Kiladis 2002)• The Atlantic ITCZ (Wang and Fu 2007)• Africa (Mounier et al. 2007; Mekonnen et al. 2008; Nguyen and
Duvel 2008)• The Indian Ocean (Roundy 2008)• South America (Liebmann et al. 2009)
Summary and Final comments• The coupled signal of a KW moves eastward at 10-20 m/s along the
ITCZ, with a zonal wavelength of 3000-6000 km.
• Wind are primarily zonal near the equator.
• Geopotential height and zonal wind are in phase at the surface.
• Surface convergence and increased low-level moisture lead the enhanced cloudiness and precipitation in the wave by 1/8 to ¼ wavelength.
• Upper-tropospheric divergence is in phase with high cloudiness and precipitation.
• The large-scale eastward-moving envelope of cloudiness typically consists to smaller-scale, westward-moving cloud clusters.
• The predominant mode of cloudiness in the wave tends to progress from shallow to deep convective to stratiform clouds.
Summary and Final comments
• Kiladis et al. (2009) suggest the possibility of a unified theory for convectively coupled equatorial waves (CCEWs) for their dynamics and coupling mechanism.
• GCMs typically found deficient in simulating CCEWs (Lin et al. 2006).
• Given KW has the strongest spectral peak, and the importance of CCEWs in explaining the observed variability of tropical rainfall, it is of interest to fully understand and explore their representation in GCMs.
Summary and Final comments
• From 21 GCMs, less than half contain and spectral peak in precipitation in the KW band.
• From these with spectral peak, only 3 reasonably simulate the geographical distribution and 3D structure of the waves.
• The most commonality among these 3 models is the convective parameterization:
• Tiedtke (1989) modified by Nordeng (1994) in MPI and MIUB• Pan and Randall (1998) in CCSR
• Suggest that a model parameterization plays a crucial role in its ability to organize tropical convection into wave-like disturbances.