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Understanding the Tropical Biases in GCMs:
Double-ITCZ, ENSO, MJO and Convectively Coupled
Equatorial Waves
The tropical biases: One of the main bottlenecks for climate modeling
The major difficulties for understanding and alleviating
these tropical biases
1. They all involve some forms of feedback, such as the
ocean-atmosphere feedback and the wave-heating
feedback, making it difficult to determine the real cause
of the bias;
2. The biases need to be traced back to specific model
characteristics, such as certain aspect of the physical
parameterizations, in order to provide useful guidance
on how to improve the model simulations.
How to attack the problem?
Difficult to understand the success of some schemes/
parameters
Difficult to try all combinations of schemes/parameters
Possible missing physics in all existing schemes
Simulations and Model Improvement
Predictions (Treatments)
Structure Analysis Feedback and Physical
(Symptoms) Relationship Analysis
(Mechanisms)
GCMs analyzed: 27 models including almost all the
major GCMs used for predictions and projections
• 22 IPCC AR4 coupled GCMs (IPCC Fourth Assessment Report to be
released in 2007; from PCMDI data archive)
• NCEP operational GFS and CFS (in collaboration with Wanqiu Wang of
NCEP)
• ECMWF model (from DEMETER archive)
• NASA GMAO GEOS5 GCM currently under development (in collaboration
with Siegfried Schubert, Max Suarez, Julio Bacmeister of NASA GMAO)
• GFDL next generation GCM currently under development (in collaboration
with Leo Donner of GFDL)
• Seoul National University GCM (in collaboration with Myong-In Lee of NASA
GMAO)
The double-ITCZ problem: Symptoms
(1) Excessive (insufficient) precipitation over much of tropics (equatorial
western Pacific); (2) Cold SST bias over much of tropics
Obs
NCAR
GFDL
Double-
ITCZ
From Lin (2006a) Shading: SST
Contours: precipitation
The double-ITCZ problem: Mechanisms
(1) Biases in AGCM’s
climatology initiate the
biases in the coupled
runs; (2)
Biases in ocean- SST gradient - trade wind (Bjerknes) feedback
atmosphere feedback (e.g. Bjerknes 1969, Neelin and Dijkstra 1995;
parameters amplify or Pierrehumbert 1995; Sun and Liu 1996; Jin 1996;
suppress the initial Clement et al. 1996; Liu 1997; Cai 2003)
problems.
SST - LHF feedback SST - SWF feedback
(e.g. Wallace 1992; (e.g. Ramanathan and
Liu et al 1994; Zhang Collins 1991)
et al. 1995)
Neelin and Dijkstra (1995) showed that any excessive positive
feedback (or insufficient negative feedback) tends to shift the
whole system westward, leading to a double-ITCZ pattern.
However, few previous studies have evaluated quantitatively
the feedback parameters in GCMs. From Lin (2006a)
The double-ITCZ problem: Mechanisms
(1) Excessive tropical precipitation in AGCMs leads to
enhanced Walker circulation and surface flux cooling
Precipitation Latent heat
flux
Excessive
Excessive
Surface Surface
zonal wind downward
stress shortwave
flux
Overly
strong Insufficient
Annual mean along the equator (5N-5S)
The double-ITCZ problem: Mechanisms
(2) Overly positive ocean-atmosphere feedback parameters
Bjerknes
Precip vs SST
x vs SST
Overly
positive
SST-LHF Qair vs SST
LHF vs SST
Overly
positive
SST-SWF Cld vs SST
SWF vs SST
Insufficiently
negative
Linear regression for 5N-5S averaged monthly data
The ENSO problem: Symptoms
(1) Large scatter in ENSO variance (2) Too-short ENSO period in
many models
Interannual
variance of SST
along the
equator (5N-5S)
CCSM3
Normalized
spectrum of
Nino3 SST
CCSM3
From Lin (2006b)
Existing ENSO theories
(6) Stochastic forcing theory (McWilliams and
Gent 1978, Lau 1985, Penland and Sardeshmukh
1995, Blanke et al. 1997, Kleeman and Moore
(1) Slow coupled mode theory
1997, Eckert and Latif 1997)
(Philander et al. 1984, Gill
1985, Hirst 1986, Neelin 1991,
Jin and Neelin 1993, Wang and
Weisberg 1996)
(2) Delayer oscillator theory
(Suarez and Schopf 1988,
Battisti and Hirst 1989)
(3) Advective-reflective
(4) Western Pacific oscillator theory (Picaut
oscillator theory et al 1997)
(Weisberg and Wang
1997)
Quasi-standing oscillation
within Pacific basin
triggered or forced by free
(5) Recharge oscillator theory oceanic waves
(Jin 1997a,b)
From Lin (2006c)
A new observation-based mechanism for ENSO:
The coupled wave oscillator (Lin 2006c,d)
ENSO amplitude and
period are determined by
circum-equatorial
coupled equatorial
waves, and their
interactions with the off-
equatorial Rossby waves
The ENSO Problem: Mechanism
Incorrect representation of the coupled wave oscillator
Too-fast Realistic
phase speed phase speed
SSH SSH
x x
CCSM3 ENSO MPI ENSO
Period=2.5 yrs Period=4 yrs
The MJO and CCEW problems: Symptoms
Only half of the models have the waves, but usually with too weak
variances and too fast phase speeds
Obs
GFDL
NCAR
The MJO and CCEW problems: Symptoms
The problem is especially severe for MJO, with very weak variance, no
coherent eastward propagation, and no significant spectral peak
All season
Asian summer
monsoon
CCSM3
Spectrum of precipitation at 0N85E
North American
monsoon
West African
monsoon (Lin et al. 2006a,b,c,
Lin 2007)
The MJO and CCEW problems: Mechanisms
Vertical heating profile
In collaboration w/ Leo Donner
Stratiform heating
In collaboration w/ Myong-In Lee
Moisture pre-conditioning Column-integrated
diabatic heating has six
In collaboration w/ Ping Liu major components
Shallow/midtop convection (Mean state and higher-
frequency modes affect
the MJO through the
nonlinear terms)
In collaboration w/ Myong-In Lee
Radiation feedback
Model resolution
IPCC runs In collaboration w/
Air-sea coupling Wanqiu Wang
The MJO and CCEW problems: Treatments
Moisture trigger often significantly enhances the variances of
CCEWs, and sometimes slows down the phase speeds
No convection
Strong trigger
Weak trigger
No trigger
Effect on MJO is not monotonic Lin, Lee. Kim, Kang (2006d)
The MJO and CCEW problems: Treatments
Moisture trigger significantly enhances the fraction of large-scale
precipitation
No convection
Strong trigger
Weak trigger
No trigger
Lin, Lee, Kim, Kang (2006d)
Recommendation: A model development strategy for
alleviating the tropical biases
Difficult to understand the success of some schemes/
parameters
Difficult to try all combinations of schemes/parameters
Possible missing physics in all existing schemes
Simulations and Model Improvement
Predictions (Treatments)
Structure Analysis Feedback and Physical
(Symptoms) Relationship Analysis
(Mechanisms)
Understand the reasons of past successes/failures
Save time and computer resources in testing parameters
Know the directions of future improvements
