The Tropical Asian-Pacific Climate Simulated in a Hybrid

Coupled General Circulation Model (HcGCM)

Xiouhua Fu*a, Bin Wanga,b, Tim Lia,b , and Fei-fei Jinc

aIPRC, SOEST, University of Hawaii, Honolulu, Hawaii

bDepartment of Meteorology, SOEST, University of Hawaii, Honolulu, Hawaii

cDepartment of Meteorology, Florida State University, 404 Love Building, Tallahassee,

Florida

Journal of Climate

Revised on February 20, 2005

Corresponding author address: Dr. Xiouhua Joshua Fu (E-mail: xfu@hawaii.edu),

International Pacific Research Center (IPRC), SOEST, University of Hawaii at Manoa,

1680 East West Road, POST Bldg., 4th Floor, Honolulu, HI 96822

ABSTRACT

The first hybrid coupled general circulation model (HcGCM) that exercises full

atmosphere-ocean coupling is described and evaluated. This coupled model comprises an

ECHAM-4 AGCM with an intermediate tropical upper ocean model. In this study, the

first 40-year (after 5-year spin-up) model simulation has been analyzed and compared

with available long-term observations and analysis products.

Overall, the model simulations of the climatology and variability in the tropical

Asian-Pacific sector (including Indo-Pacific mean SST and its annual cycle, Asian-

Australian monsoon, tropical intraseasonal oscillations (TISO) and ENSO) are fairly

realistic and comparable with those state-of-the-art coupled GCMs. The model bias in

mean SST is within 1oC in most of the ocean domain except in the southeast Pacific,

where the model suffers a warm-bias syndrome present in many coupled GCMs. The

simulated TISO exhibits significant seasonality as in the observations with eastward-

propagating MJO favored in boreal winter and northward-propagating mode over the

Indian and western Pacific regions in boreal summer. The model ENSO has dominant

periods about 2 years and 5 years. It also shows significant annual phase locking with the

minimum and maximum variance, respectively, in boreal spring and late fall.

The intention to develop a hybrid coupled GCM, in which the ocean component

only considers the upper ocean dynamics and thermodynamics, is to capture the major

intraseasonal-to-interannual climate variability in the tropical Asian-Pacific sector. The

encouraging results presented here suggest that this framework is a pragmatic approach in

representing the present-day tropical climatology and its climate variability.

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1. Introduction

In the past two decades, under the auspices of the TOGA (Tropical Ocean-Global

Atmosphere) program and the follow-on CLIVAR (Climate Variability and

Predictability) program, many atmosphere-ocean coupled models have been developed to

improve our understanding of the nature and the predictability of the tropical Pacific and

global climate variability (Meehl et al. 2000; Latif et al. 2001; Davey et al. 2002). In the

TOGA decade (1985-1994), the coupled models are primarily designed to simulate and

predict the El Nino-Southern Oscillation (ENSO) and the associated tropical and extra-

tropical climate variability. The early inter-comparisons of coupled models (Neelin et al.

1992; Mechoso et al. 1995) found that many coupled GCMs (CGCMs) are able to

produce a realistic warm-pool/cold tongue configuration and reasonable zonal SST

gradient along the equatorial central Pacific. However, the interannual variability in these

models ranged from very weak to moderate. Among those coupled models, ENSO

simulated by the Cane-Zebiak intermediate model looks most realistic (Zebiak and Cane

1987). Even today, the Cane-Zebiak model is still one of the best coupled models

(including CGCMs) in terms of the ENSO simulation and prediction (Latif et al. 2001;

Chen 2004). Nevertheless, the CGCMs still had substantial biases from the observed

state. The simulated equatorial cold tongue generally tends to be too strong, too narrow,

and extends too far west. SSTs in the southeast Pacific are generally too warm, which is

accompanied by a fictitious double inter-tropical convergence zone (ITCZ). The CGCMs

also have a variety of problems in simulating the seasonal cycle of the equatorial SST in

the eastern Pacific (e. g., a too-weak annual harmonic but a too-strong semiannual

harmonic). To summarize the development of coupled models during the TOGA decade,

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Delecluse et al. (1998) concluded that substantial progress was made in representation of

the tropical mean state and climate variability through the synergic efforts of the

observational, theoretical, and hierarchal modeling studies (McPhaden et al. 1998; Neelin

et al. 1998; Latif et al. 1998). However, several general systematic errors in both mean

state and seasonal cycle have yet to be eliminated, especially in the east Pacific.

Two recent CGCM inter-comparison projects, ENSIP (the El Nino simulation

intercomparison project, Latif et al. 2001) and STOIC (a study of coupled model

climatology and variability in tropical ocean regions, Davey et al. 2002), also revealed

that there remains substantial potential for further model improvement. Latif et al. (2001),

through comparing the performance of 24 CGCMs in the tropical Pacific, indicated that

almost all models (even those employing flux correction) still have considerable

problems in simulating the SST climatology. Only a few of the coupled models

realistically simulate the ENSO in terms of gross equatorial SST anomalies (e.g.,

amplitude and annual phase locking). In particular, many models overestimate the

variability in the western equatorial Pacific and underestimate the SST variability in the

east, which may be associated with the extension of the model cold tongue too far

westward. Davey et al. (2002) further found that the interannual variability (both SST

and zonal wind stress) is commonly too weak in the models. Most models have difficulty

in reproducing the observed Pacific ‘horseshoe’ pattern of negative SST correlations with

interannual Nino-3 SST anomalies, and the observed Indo-Pacific lag correlations. Both

inter-comparison projects confirm that improving the simulations of the tropical Pacific

climatology and ENSO remains a continuing challenge for the coupled-model

community.

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In this paper, we present a Hybrid coupled GCM (HcGCM) newly developed at

the International Pacific Research Center (IPRC), University of Hawaii. This model

couples the ECHAM-4 AGCM (Roeckner et al. 1996) with an intermediate ocean model

(Wang et al. 1995; Fu and Wang 2001). Active air-sea coupling is in the tropical Indo-

Pacific region only1. In contrast to other anomalous hybrid coupled GCMs (e.g.,

Alexander 1992; Kirtman and Zebiak 1997; Yeh et al. 2004), this hybrid coupled model

exercises full coupling. The model was designed to simulate the annual mean, annual

cycle, and interannual variability within one framework. The model simulations of the

climate and variability in the tropical Asian-Pacific sector are very encouraging even

compared to those well-known, state-of-the-art coupled GCMs (e.g., SINTEX CGCM,

Gualdi et al. 2003). The main objective of this study is to validate the model simulations

of the climatology and intraseasonal-to-interannual variability in the tropical Asian-

Pacific sector with available observations, thus establishing a baseline for evaluating

future improvements and for comparison with other models.

Because our focus is the tropical Asian-Pacific climate, we will validate the

model simulations of tropical Pacific climate along with the Asian-Australian monsoon

and tropical intraseasonal oscillations (TISO). In literature, a few AGCM inter-

comparison projects have been conducted to focus on the Asian summer monsoon

(Gadgil and Sajani 1998; Kang et al. 2002) and the TISO (Slingo et al. 1996; Waliser et

al. 2003). We will also refer to some results from those inter-comparison projects when

we validate the relevant simulations from this hybrid-coupled model.

1 IPRC’s mission is to understand the nature and predictability of climate variability and regional aspects of global environmental change in the Asian-Pacific sector (http://iprc.soest.hawaii.edu).

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The remaining parts of the paper are organized as follows. The model and the data

used to validate the model are described in section 2. In section 3, we validate the model

climatology (both the annual mean and annual cycle) in the tropical Asian-Pacific sector.

In section 4, the simulated TISO, both the northward-propagating ISO in boreal summer

and eastward-propagating MJO in boreal winter, are compared to the available

observations. The interannual variability of the tropical Pacific is evaluated in section 5.

Finally, we summarize our main results and discuss the pathways to further improve the

model in section 6.

2. The Hybrid coupled GCM (HcGCM)

a. The atmospheric component ECHAM-4

The atmospheric model used in this study is the ECHAM-4 general circulation

model, which has been documented in detail by Roeckner et al. (1996). A brief

description is given here for the convenience of readers. We used the T30 version (the

corresponding horizontal resolution is about 3.75o) in this study instead of the standard

T42 version, because it produces similar results but requires fewer computational

resources (Stendel and Roeckner 1998). The model has 19 layers extending from the

surface to 10 hPa. Its land surface scheme is a modified bucket model with improved

parameterization of rainfall-runoff (Dumenil and Todini 1992). The surface fluxes of

momentum, heat, water vapor, and cloud water are based on the Monin-Obukhov

similarity theory. The vertical diffusion in the model is computed with a high-order

closure scheme depending on the turbulent kinetic energy. Gravity wave drag associated

with orographic gravity waves is simulated after Miller et al. (1989). The mass flux

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scheme of Tiedtke (1989) for deep, shallow, and mid-level convection has been used with

CAPE closure (Nordeng 1994). The radiation scheme is a modified version of the

European Center for Medium-Range Weather Forecasts (ECMWF) scheme. Two- and

six-band intervals are used in the solar and terrestrial part of the spectrum, respectively.

b. The updated intermediate ocean model

The ocean component of this hybrid coupled model is a tropical upper ocean

model with intermediate complexity. It was originally developed by Wang et al. (1995)

and later improved by Fu and Wang (2001). The ocean model comprises a mixed layer,

in which the temperature and velocity are vertically uniform, and a thermocline layer in

which temperature decreases linearly from the mixed layer base to the thermocline base.

Both layers have variable depths. The deep ocean beneath the thermocline base is

motionless with a constant reference temperature. This ocean model combines the mixed-

layer physics of Gaspar (1988) and the upper ocean dynamics of McCreary and Yu

(1992). It reasonably simulates the annual cycles of sea surface temperature, upper ocean

currents, and mixed layer depth in the tropical Pacific (Wang and Fu 2001; Fu and Wang

2001).

In this study, the parameterization of the entrained water temperature has been

updated. In our previous studies, the entrained water temperature is parameterized in

terms of the vertical temperature gradient between the mixed layer and the deep inert

layer. The weakness of this parameterization is that the mixed-layer temperature is not

sensitive to the changes of thermocline depth. Therefore, the SST interannual variability

that is strongly associated with thermocline feedback (Zebiak and Cane 1987) is very

weak in our early versions of coupled models (Fu and Wang 2001; Fu et al. 2002).

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Following the footsteps of other intermediate ocean models (Zebiak and Cane 1987;

Seager et al. 1988; McCreary et al. 1993; Jin 1996, 1998), we have parameterized the

entrained water temperature as an explicit function of thermocline depth, very similar to

the one used in Zebiak and Cane (1987) and Jin (1996). In this study, the ocean model is

active in the tropical Indian and Pacific Oceans (from 30oS to 30oN) with realistic but

simplified coastal boundaries of the Oceans. It is feasible to further extend the ocean

domain to the tropical Atlantic Ocean and mid-latitude region (Lu et al. 1998). The

horizontal resolution of the model is 0.5o longitude by 0.5o latitude, which requires an

approximate time interval of 3 h. No-flux conditions for temperature and free-slip

conditions for velocities are applied at the coastal boundaries.

c. The coupling procedure

The ECHAM-4 was coupled with the intermediate ocean model in the tropical

Indian and Pacific Oceans without heat flux correction (except that the SSTs in the north-

south open boundaries have been relaxed to the observations as in Fu and Wang 2001).

Beyond the coupling regions, the underlying sea surface temperature is specified as the

climatological monthly mean SST from the AMIP II experiment (Taylor et al. 2000). In

the active air-sea coupling domain, atmospheric component exchanges information with

ocean component once per day. The atmosphere provides daily mean surface winds and

heat fluxes to the ocean model. The latter send daily mean SST back to the former. The

coupled model is integrated with seasonally varying solar radiation forcing. The initial

atmospheric field is a restart file on January 1 from previous long-term integration. The

initial ocean field is the steady state in January after a ten-year integration of the ocean

model forced with observed climatological surface winds and heat fluxes. The spin-up

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period for the coupled model is 5 years. The output from the next 40 years’ integration

was used in the following analyses.

d. The data used to validate the model

Several long-term datasets either from the observations or from the analysis and

reanalysis have been used in this study to validate the model simulations. The datasets

include the Hadley Center monthly-mean SST from 1901 to 2000 (GISST, Rayner et al.

1998); Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP)

pentad-mean rainfall from 1979 to 2000 (Xie and Arkin 1997); daily–mean winds of

ECMWF analysis from 1991 to 2000; and monthly-mean sea level pressure from NCEP

reanalysis from 1961 to 2000 (Kalnay et al. 1996).

e. Other ECHAM4 family coupled GCMs

It is worthwhile to mention that there are three other CGCMs that also used

ECHAM-4 as their atmospheric component. Two coupled versions were developed at

Max Planck Institute, Germany: 1) ECHO-G, which coupled ECHAM-4 with HOPE

ocean general circulation model (Frey et al. 1997; Guilyardi et al. 2004); 2) ECHAM-4 is

also coupled to OPYC3 general circulation model with annual-mean heat flux correction

(Bacher et al. 1998); 3) SINTEX CGCM (Gualdi et al. 2003) couples the ECHAM-4 with

the ORCA ocean general circulation model. Through comparing the results from different

atmospheric GCMs (or from the same GCM with different resolutions) coupled to one

ocean model, Guilyardi et al. (2004) have suggested the important role of the atmospheric

component in setting up ENSO characteristics. On the other hand, coupling one AGCM

to different ocean models may give us a clue about how the ocean component will affect

the behaviors of coupled systems. In our following analyses, we will briefly refer to the

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papers describing other ECHAM-4 family CGCMs on relevant simulations. More detail

comparisons of those models are deferred to future studies2.

3. The Model Climatology

a. In the tropical Asian-Pacific sector

During the 40-year integration, the coupled model doesn’t exhibit apparent

climate drift though no explicit heat flux correction is applied. The simulated annual-

mean SST (averaged in 40-years) agrees with the observations (averaged in 100-years)

quite well (Fig. 1). The model bias is within 1oC in most tropical Indo-Pacific regions

except in the southeast Pacific, particularly close to the Peruvian coast, where the model

SST is too high (Fig. 1c). This warm bias is associated with the fictitious eastward

extension of the south Pacific convergence zone (SPCZ) warm water. This problem is a

common syndrome for most state-of-the-art CGCMs (Davey et al. 2002; Frey et al. 1997;

Gordon et al. 2000; Meehl et al. 2001; Guilyardi et al. 2003; Wang et al. 2004, among

others). It is believed that this problem originates primarily from deficiencies in

atmospheric models due to underestimate of stratocumulus and/or along-shore winds

(Philander et al. 1996; Schneider et al. 1997; Mechoso et al. 2000), and probably

amplified by local air-sea coupling (Li and Philander 1996).

Figure 2a-d shows the climatological zonal wind shear (850 hPa-200 hPa) and

precipitation from the observations and the coupled model. We assessed the model wind

shear here, because it is suggested to be an important factor to initiate and steer the

intraseasonal variability in the Indo-western Pacific region (Wang and Xie 1996; Jiang et

2 We are now collaborating with SINTEX-F group at Frontier Research Center of Japan to diagnose their model output and search the ways to further improve the model.

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al. 2004). In boreal summer, the major observed rainfall systems (Fig. 2a) are associated

with the Asian summer monsoon, ITCZ and the eastern North Pacific summer monsoon

(ENPSM, Murakami et al. 1992). Strong easterly shear is observed in the northern Indian

Ocean and the western North Pacific (WNP) associated with the monsoon rainbelt around

15oN (Fig. 2a). The coupled model well captures this easterly shear (Fig. 2b) with a

maximum of about 30 m s-1 located in the northwest Indian Ocean. In the eastern Pacific,

the model tends to overestimate the easterly shear associated with the ENPSM even

though the rainfall is a little underestimated. The ITCZ rainfall in the central Pacific

(between 160oW and 120oW) is weaker than the observed. This bias also exists in the

stand-alone ECHAM-4 GCM (Roeckner et al. 1996). This rainfall bias may be associated

with the westerly bias in the upper troposphere (Fig. 4b). The erroneous westerly duct in

the model favors intrusion of the subtropical dry (and cold) air into the equatorial region

and tends to suppress the model ITCZ convection in the central Pacific (Mapes and

Zuidema 1996; Yoneyama and Parsons, 1999; Tompkins 2001).

In the Asian-western Pacific region, the model captures the major rainfall

systems, for example, strong rainfall at the eastern Arabian Sea and the Bay of Bengal

(Figs. 2a-b). The simulated maximum rainfall in the Arabian Sea shifts too far away from

the Indian western coast probably owing to the coarse resolution (see also Gualdi et al.

2003 and Rajendran et al. 2004). The observed rainbelt just south of the equatorial

Indian Ocean is reproduced but with a weaker intensity. This rainbelt may play an

important role in initiating the dominant northward propagating ISO in the Indian Ocean

(Waliser et al. 2003; Fu and Wang 2004b), but was missed by many AGCMs (Kang et al.

2002). The observed rainbelt in the South China Sea and the WNP is captured but with a

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different orientation compared to the observations (Figs. 2a-b). A rainy center is observed

in the SPCZ but the model only shows a hint there. Overall, the simulated rainfall pattern

associated with Asian summer monsoon is very similar with that in the SINTEX CGCM

(Gualdi et al. 2003) and is much better than the simulations with stand-alone ECHAM-4

GCM (Roeckner et al. 1996) and many other AGCMs (Kang et al. 2002). This result

supports our previous finding that warm-pool air-sea coupling significantly improves the

simulation of mean Asian summer monsoon (Fu et al. 2002).

In boreal winter, major convective zones move to the Southern Hemisphere (Figs.

2c-d) following the seasonal migration of solar radiation. The major rainbelt extends

from the southern Indian Ocean to the SPCZ, with a tail (the Pacific ITCZ) remaining in

the Northern Hemisphere. The observed easterly shear centers over the maritime

continent. The overall rainfall and wind shear patterns are well simulated by the model

(Fig. 2d). However, there are two systematic errors in the simulation: the northern ITCZ

is too weak and the SPCZ extends too far eastward. Most likely, both errors are

associated with the warm bias in the southeast Pacific (Fig. 1c).

Figures 3a-d compare the climatology of 1000-hPa winds from the ECMWF

analysis and the model. In boreal summer (Figs. 3a-b), the monsoonal flows associated

with the Asian summer monsoon in the Indian Ocean are reasonably captured. In the

tropical Pacific, the simulated northeast trades are more realistic than the southeast trades.

The latter are too strong in the western South Pacific (between 150oE and 150oW), which

may be related to the weaker SPCZ rainfall in the model (Fig. 2a). In boreal winter (Figs.

3c-d), the overall flow patterns are similar between the model and the observations except

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that the strong northeast trades in the model penetrate too far equatorward and the south

Pacific convergence zone extends too far eastward (Fig. 3d).

The upper troposphere (200-hPa) zonal winds from the ECMWF analysis and

coupled model are compared in Figs. 4a-d. In boreal summer, the easterlies associated

with the Asian-western Pacific summer monsoon are reproduced but with smaller

amplitude, particularly in the northern Indian Ocean (Fig. 4b). On the other hand, the

easterlies in the eastern equatorial Pacific are slightly too strong. As mentioned before, an

erroneous westerly duct is produced in the equatorial central Pacific. In boreal winter, the

observed easterlies associated with the Australian monsoon (Fig. 4c) are much weaker

than their summer counterparts (Fig. 4a). The simulated easterly center locates over the

maritime continent as in the observations but with slightly smaller amplitude (Fig. 4d).

b. In the equatorial Indo-Pacific region

Davey et al. (2002) evaluated the global equatorial SSTs and zonal wind stresses

simulated in 15 CGCMs (without heat flux correction). They found that most CGCMs

(11 of 15) have significant cold bias in the western-central equatorial Pacific. At the same

time, easterly winds are considerably overestimated in the western Pacific, but

underestimated in the central Pacific (figures 1 and 2 in Davey et al. 2002). The causes of

these systematic errors are probably associated with the misrepresentations of oceanic

mixing (Li et al. 2001), adjacent monsoon systems (Fu 2005), and local atmosphere-

ocean feedback (Li and Philander 1996).

We have compared the mean SSTs and zonal winds in the equatorial Indo-Pacific

Oceans from this hybrid coupled model with the observations (Figs. 5a-b). The model

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SST bias (Fig. 5a) is very small in most equatorial regions except in the eastern end of the

Pacific (east of 120oW), where simulated SST is too warm with a bias about 2oC. For this

hybrid coupled model, the warm bias primarily originates from the underestimated

stratocumulus in the atmospheric component (figure not shown). The simulated zonal

winds (Fig. 5b) also show a reasonable agreement with the observations except in the

western Pacific, where the model easterlies are slightly too strong.

In the deep tropics, the downward solar radiation at the top of atmosphere has a

dominant semiannual cycle. However, the observed SST in the equatorial eastern-central

Pacific shows a peculiar annual cycle (Fig. 6a) with highest and lowest SST, respectively,

in spring and fall. Many state-of-the-art CGCMs have various problems in reasonably

simulating this feature (Mechoso et al. 1995; Latif et al. 2001). Some models tend to

produce a dominant semiannual cycle in the equatorial eastern Pacific. The failure to

simulate a reasonable annual cycle may result in an unrealistic annual phase locking of

the model El Nino (Guilyardi et al. 2004). This hybrid coupled model produces a

significant SST annual cycle in the eastern Pacific (Fig. 6b) even though the simulated

phase lagged the observations by about 1 month and the amplitude is somewhat reduced.

Compared to other ECHAM-4 family CGCMs (Frey et al. 1997; Gualdi et al. 2003;

Guilyardi et al. 2004), the annual cycle seems better represented in this hybrid coupled

model. The reason is likely related to the introduction of an explicit mixed-layer in the

intermediate ocean model (Wang et al. 1995; Fu and Wang 2001). In the western Pacific,

the observed semiannual cycle is also well reproduced by the model. In the Indian Ocean,

the model captures the annual cycle in the eastern basin and the semiannual cycle in the

western basin. However, the observed strong summer cooling near the Somali coast is

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underestimated, suggesting the coastal upwelling is not well represented. Higher

horizontal resolutions in both the atmosphere and ocean models are probably needed to

mitigate this bias.

Figure 7 compares the annual cycles of the equatorial zonal winds between the

observations and the model. The major observed features are captured in the model: the

annual cycle in the eastern Pacific, the westerly in both winter and spring along with the

easterly in summer over the western Pacific, and semiannual cycle in the Indian Ocean.

The simulation, however, shows a few obvious biases, particularly in the western Pacific,

such as a stronger westerly in boreal spring and a weaker westerly in boreal winter.

4. The Tropical Intraseasonal Oscillations (TISO)

The atmosphere-ocean variability (e.g., precipitation, low-level winds, surface

heat fluxes and SST) associated with the TISO has its strongest signal in the tropical

Indo-Pacific sector even though its impacts could spread around the world. The TISO

significantly regulates the onset and retreat of Asian-Australian monsoons (Yasunari

1980), tropical storm activity (Maloney and Hartmann 2000), and ENSO evolution

(McPhaden and Yu 1999), even the subseasonal rainfall variability over Americas (Mo

2000; Jones 2000). This intraseasonal mode is first revealed by Madden and Julian (1971)

as an eastward propagating planetary-scale zonal wind oscillation with a period of about

40-50 days (popularly termed as Madden-Julian Oscillation or MJO). Many follow-up

studies have found that the eastward propagating MJO prevails primarily in boreal

winter. In boreal summer, the dominant intraseasonal mode propagates northeastward

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from the equatorial Indian Ocean to East Asia (Yasunari 1979; Lau and Chan 1986;

Wang and Rui 1990).

As revealed by several GCM inter-comparison projects (Slingo et al. 1996;

Sperber et al. 1997; Waliser et al. 2003), most current GCMs have considerable problems

in realistically representing the TISO. Many recent studies have suggested that air-sea

coupling significantly improves the simulations of the TISO in terms of its intensity,

convection-SST phase relationship, propagation and seasonality (Flatau et al. 1997;

Waliser et al. 1999; Inness and Slingo 2003; Fu et al. 2003). Fu and Wang (2004a, b)

further demonstrated that two different TISO solutions, respectively, exist in an air-sea

coupled system and a forced atmosphere-only system. The solution from the coupled

system resembles the observations more than that from the atmosphere-only system. In

this section, the TISO simulated in this hybrid coupled GCM is assessed briefly. We

focus on the seasonal variations of the TISO, such as the spatial patterns of rainfall

variability, eastward-propagating MJO in boreal winter and northward-propagating ISO

in boreal summer.

Figures 8a-d show the standard deviations of filtered rainfall (with period of 20-

90 days retained), which are used to represent the intensity of the TISO, from the CMAP

observations and the coupled model. In boreal winter (Fig. 8a), major rainfall variances

associated with TISO activities are observed in the southern Indian Ocean, SPCZ, ITCZs

and South America. The model captures almost all these activities with the intensity

overestimated in the maritime continent, southern Indian Ocean and SPCZ (Fig. 8b). The

simulation in the ITCZ is weaker than the observations, probably a consequence of the

underestimated mean rainfall in this area (Figs. 2c-d). During boreal summer (Fig. 8c),

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major rainfall variability shifts to the Northern Hemisphere following the seasonal march

of mean rainfall. The action centers appear in the equatorial and northern Indian Ocean,

South China Sea, the WNP and ITCZs. They are well collocated with the mean summer

rainfall (Fig. 2a). As in the model mean (Fig. 2b), the orientation of maximum TISO

intensity in the WNP is not in line with the observations (Figs. 8c-d). In the eastern

Pacific ITCZ, a relatively isolated TISO center occurs in association with the ENPSM

(Maloney and Esbensen 2003). In general, the coupled model captures all the action

centers that appeared in the observations but with slightly larger amplitude (Fig. 8d).

Compared to the simulations of all 10 AGCMs that participated in the CLIVAR/Asian-

Australian monsoon inter-comparison project (Waliser et al. 2003), the simulations of

this hybrid coupled model are relatively better in terms of both the spatial pattern and

amplitude of the TISO rainfall variability. The space-time evolutions of rainfall, surface

winds and SST associated with boreal-summer ISO (Fu and Wang 2004b) and MJO

(figure not shown) are also quite similar with the observations.

A limited-domain wavenumber-frequency spectral analysis is used to summarize

the spatio-temporal characteristics of the TISO rainfall in the Asian-Pacific sector. The

advantages and usefulness of this method have been substantiated by several previous

studies (Teng and Wang 2003; Fu et al. 2003). In boreal winter (NDJFMA), this

wavenumber-frequency analysis is applied in zonal direction between 40oE and 140oW to

extract the eastward-westward propagating modes. In boreal summer (MJJASO), the

analysis is applied in meridional direction between 10oS and 30oN to extract the

northward-southward propagating modes.

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Figures 9a-b compare the wavenumber-frequency spectra of rainfall (averaged

between 10oS and 5oN) associated with the eastward-westward propagating intraseasonal

modes in boreal winter from the CMAP observations and the coupled model. The results

from the observations and the model are, respectively, 22-year (1979-2000) mean and 39-

year mean. In the observations (Fig. 9a), eastward propagating disturbances

overwhelmingly dominate their westward counterparts. The maximum spectrum

corresponds to the MJO mode discovered by Madden and Julian (1971) with a period of

50 days and a wavelength of 200 degrees in longitude. The associated eastward

propagating speed is about 5 m s-1. The major characteristics of the observed MJO (e.g.,

period, wavelength (or speed), and intensity) seem well simulated by this model (Fig.

9b). On the other hand, there are also several biases in the simulation, for example, too

strong westward propagating disturbances and too much variance with shorter periods

and smaller spatial scales.

During boreal summer, northward propagating TISO dominates in the Indian

and western Pacific Oceans (Lau and Chan 1986; Wang and Rui 1990; Fu and Wang

2004b). Our previous studies have focused over the Indian Ocean (Fu et al. 2003; Fu and

Wang 2004a). Here, we shift our attention to the western Pacific. Figures 10a, b compare

the wavenumber-frequency spectra of rainfall variability averaged between 120oE and

150oE from the CMAP observations and the coupled model. The TISO characteristics in

the simulation resemble those in the observations. The observed maximum spectrum

corresponds to an oscillation of a period about 40-50 days and a wavelength of 40

degrees in latitude. The corresponding northward propagating speed is about 1 m s-1. In

the simulation (Fig. 10b), the dominant period is about 30-50 days with both northward

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and southward variances slightly larger than the observations. The model also tends to

produce more variability in shorter time scales and smaller spatial scales.

The above analyses indicate that the observed seasonal variations of the TISO

have been largely captured by this hybrid coupled model. The spatial patterns of the

TISO intensity (Fig. 8) and mean rainfall (Fig. 2) are highly correlated with each other in

both the observations and the simulation. This coincidence probably suggests that the

better simulation of mean rainfall is the pre-requirement for the better simulation of

TISO. Additional analyses of 10 AGCMs’ outputs from the CLIVAR/monsoon inter-

comparison project (Kang et al. 2002) showed that none of them are able to reasonably

capture both the dominant northward propagation in summer and eastward propagation in

winter (figure not shown). It is very encouraging to see that this hybrid coupled model is

capable of representing this significant seasonality of TISO with some realism.

5. The ENSO Variability

The most significant interannual variability in the tropical Asian-Pacific region is

the variability associated with ENSO. Though considerable improvements of the

simulation and prediction of ENSO have been made during the past twenty years (Latif et

al. 1998; Chen et al. 2004), current coupled models still need to be improved with regard

to realistically representing ENSO (Latif et al. 2001; Davey et al. 2002; Wang et al.

2004). In this section, the ENSO variability simulated by this hybrid coupled GCM is

evaluated.

First, we compare the spatial patterns of SST standard deviation in the tropical

Asian-Pacific sector between the observations and the model (Figs. 11a-b). The coupled

19

model produces significant SST variability in the central-eastern equatorial Pacific as in

the observations. The observations have two maximum variance centers: one near the

Peruvian coast and the other in the eastern equatorial Pacific (~110oW). The model well

locates the first maximum center, but the second center is shifted 40 degrees west of the

observed one. This is a common error presented in many other coupled GCMs (e.g., Frey

et al. 1997; Knutson and Manabe 1998), probably associated with too much westward

extension of the cold tongue in mean state.

The simulated time series of SST anomaly in the Nino-3.4 region (Fig. 12b) has a

somewhat similar evolution as in the observations during the period of 1912-1951 (Fig.

12a). As in the observations, the simulated SST time series indicates considerable

irregularity. For example, before the warm event that peaks in 1930 in the observations

(Fig. 12a), there is no significant cold event preceding it. Similar warm events (years 34

and 37) appear in the simulation. The model also produces significant biennial variations

during years 16-20 as in the observations during the period of 1922-1926. The elongated

warm event (years 27-30) with embedded annual variability in the simulation also finds a

resemblance in the observations from 1939 to 1941. With a longer simulation (~ 85

years), a power spectrum analysis indicates that the time series of SST anomaly in the

Nino-3.4 region has two peaks with periods of about 2 years and 5 years, respectively

(figure not shown).

The model ENSO shows reasonable phase locking with annual cycle (Fig. 13). The

observed SST standard deviation in the Nino-3.4 region is minimum (~ 0.45oC) in late

spring and maximum (~ 0.65oC) in winter. The simulated SST standard deviation has

considerable annual variations as in the observations. The minimum (maximum) in the

20

model occurs one month (two months) earlier than that in the observations, with the

annual range slightly larger. Compared to the results from those coupled GCMs

participating in the ENSIP project (Latif et al. 2001), the simulated ENSO annual phase

locking in this model is better than most of them. The reason is probably related to the

reasonable simulation of the climatology in the central-eastern Pacific. For example, the

ENSO simulated in SINTEX CGCM (Guilyardi et al. 2004), which also used ECHAM-4

as its atmospheric component, has no apparent annual phase locking possibly due to the

systematic errors in the simulated climatology. After improving the model climatology,

the SINTEX-F at Frontier Research Center of Japan shows much better simulation of

ENSO (Luo et al. 2005).

Although the strongest SST signal associated with ENSO is in the equatorial

eastern Pacific, its impacts actually spread around the world. Figures 14a-b compare the

global sea-level-pressure (SLP) teleconnection patterns correlated with the Nino-3.4

indices from the NCEP reanalysis (surrogate of the observations) and the coupled model.

The model captures almost all the large-scale teleconnection features presented in the

observations, particularly the see-saw patterns between the tropical Pacific Ocean and

Indian Ocean. Compared to the observations (Fig. 14a), the simulated pattern shifts

slightly westward due to the flaw in SST anomaly pattern (Fig. 11). The teleconnection

with the North American continent is also reproduced with the simulated positive SLP

center slightly locating westward of the reanalysis. El Nino in the Pacific also acts to

increase the SLP in the equatorial Atlantic (Fig. 14a). The model tends to exaggerate this

connection.

21

6. Discussion and Summary

b. Discussion

Many previous theoretical studies have suggested that the tropical Pacific

climatology (e.g., Dijkstra and Neelin 1995; Jin 1996) and annual-to-interannual

variability (e.g., Zebiak and Cane 1987; Chang and Philander 1994; Xie 1994; Li and

Philander 1996) can be reasonably explained with shallow-water-type atmosphere-ocean

coupled models. In these intermediate coupled systems, the major processes involved are

the stationary atmospheric response to underlying SST forcing (or related atmospheric

heating) and upper ocean dynamics and thermodynamics. In this study, we further tested

the above hypothesis through coupling an AGCM with an intermediate ocean model

without heat flux correction or using anomaly coupling. The results presented here tend

to support the notion that the present-day climatology and its major climate variability in

the tropical Asian-Pacific sector can be reasonably simulated with a hybrid coupled

GCM, in which the ocean component only considers the upper ocean dynamics and

thermodynamics. Although encouraging results have been obtained with this model, we

believe that there is still room to improve it for both the atmosphere component and

ocean component. Further model improvements will focus on the following two aspects.

First, we will try to improve the stratocumulus’ simulation in the atmospheric

model, which probably leads to significant mitigation of the warm bias near the Peruvian

coast in the coupled model (Fig. 1c). Recent numerical studies with regional atmospheric

models (Wang et al. 2004; McCaa and Bretherton 2004) have shown that the

stratocumulus in the southeastern Pacific is very sensitive to the cumulus

parameterization schemes and model resolutions. Because both studies have proven that

22

the stratocumulus can be reasonably represented with a mass flux scheme, it is optimistic

that ECHAM-4 AGCM (also using a mass flux scheme) can be improved through better

validation of the cumulus parameterization scheme and increase of model resolution.

Second, the parameterization of entrained water temperature in the ocean model can be

further improved. Because of the coarse vertical resolution of intermediate ocean models,

they cannot produce an entrainment temperature as do ocean general circulation models

(Gent and Cane 1989). The success of intermediate ocean models largely depends on the

parameterization of entrained water temperature. The usefulness of this approach has

been supported by many previous studies (Zebiak and Cane 1987; Seager et al. 1988;

McCreary et al. 1993; Wang et al. 1995; Jin 1998). The results presented in this study

further support that this framework is a pragmatic approach to represent the

thermodynamics and dynamics of upper tropical oceans. As suggested by previous

studies (Perigaud and Dewitte 1996; Zhang and Zebiak 2003), the parameterization of

entrained water temperature can be further improved through validating it with available

ocean observational data or reanalysis products. We are also aware that ultimate

improvement of fully coupled GCMs needs to better represent various mixing processes

in the ocean GCMs. Our current effort to develop a hybrid coupled GCM is an approach

complementary to full CGCMs.

b. Summary

In this study, A hybrid coupled GCM (HcGCM) has been developed. It showed

reasonable skill to reproduce both the climatology and intraseasonal-to-interannual

variability in the tropical Asian-Pacific sector. This hybrid coupled GCM combined the

23

ECHAM-4 GCM (Roeckner et al. 1996) with an intermediate ocean model developed at

University of Hawaii (Wang et al. 1995; McCreary and Yu 1992; Fu and Wang 2001).

In this study, the first 40-year (after 5-year spin-up) integration has been analyzed

and validated with available observations and compared to other state-of-the-art coupled

GCMs. Overall, the model simulations of the climatology and variability in the tropical

Asian-Pacific sector (e.g., Indo-Pacific mean SST and its annual cycle, Asian-Australian

monsoon, tropical intraseasonal oscillations and ENSO) are quite reasonable and

comparable with many sophisticated CGCMs (Latif et al. 2001; Davey et al. 2002;

Gualdi, et al. 2003).

The mean SST difference between the model and observations is within 1oC in

most of the tropical Indo-Pacific Oceans (Fig. 1c). The cold bias problem of the

equatorial western-central Pacific that troubled many CGCMs is only minor in this

model. It suggests that the ‘climatological Bjerknes atmosphere-ocean feedback’

mechanism (Dijkstra and Neelin 1995; Jin 1996), which is critical to configure the warm-

pool/cold tongue along the equatorial Pacific, has been reasonably represented. However,

the model also suffers a warm-bias syndrome in the southeast Pacific as do many other

CGCMs (Davey et al. 2002). The primary reason is that the atmospheric model

considerably underestimates the stratocumulus in this region. The simulated seasonal

cycle in the eastern Pacific resembles the observations with a dominant annual harmonic

(Fig. 6). However, the amplitude is slightly underestimated with a phase delayed about 1-

2 months.

The Asian summer monsoon and tropical intraseasonal variability are also well

simulated in this hybrid coupled model as in most other ECHAM-4 family coupled

24

models (e.g., Gualdi et al. 2003; Sperber et al. 2003). The spatial patterns of summer-

mean rainfall (Fig. 2) and its intraseasonal standard deviation (Fig. 8) in the model are

much closer to the observations than all 10 AGCMs participating in the CLIVAR/Asian-

Australian monsoon inter-comparison project (Kang et al. 2002; Waliser et al. 2003). The

simulated tropical intraseasonal oscillations (TISO) exhibit significant seasonality as in

the observations. In boreal winter (NDJFMA), the TISO action centers are primarily

located in the southeast Indian Ocean and Pacific SPCZ region (Fig. 8b). The dominant

ISO mode is the eastward-propagating MJO (Fig. 9). In boreal summer (MJJASO), major

TISO activities shift to the Northern Hemisphere (Fig. 8d), with dominant northward-

propagating mode in the Indian and western Pacific regions (Fig. 10).

The simulated ENSO is comparable to the observed one in terms of the variance

and frequency. The model ENSO has two dominant periods about 2 years and 5 years.

The ENSO variance shows enough meridional expansion, but the location of the

maximum shifts slightly too westward. The simulated ENSO indicates reasonable annual

phase locking with minimum and maximum variances, respectively, in boreal spring and

late fall. This hybrid coupled model also reasonably captures the global teleconnection of

ENSO, including the remote impacts to the Indian Ocean sector and North America (Fig.

15).

We have conducted a two-hundred-year integration with this hybrid coupled

model. Preliminary analysis of this long integration shows almost same results (e.g., SST

climatology and annual cycle; ENSO pattern, period, and annual phase locking et al.) as

obtained with 40-year integration. We also validated model ENSO with SODA POP v1.2

reanalysis (provided by Benjamin Giese, personal communication). The associated space-

25

time evolutions of surface winds and thermocline depth between the model and SODA

reanalysis are very similar. This result will be reported elsewhere.

Acknowledgments. This work was supported by NASA Earth Science Program, NOAA

PACS Program, and NSF Climate Dynamics Program and by the Japan Agency for

Marine-Earth Science and Technology (JAMSTEC) through its sponsorship of the IPRC.

XF would like to thank Julian P. McCreary for helpful discussions and Diane Henderson

for editing the manuscript. This paper is SOEST contribution number xxxx and IPRC

contribution number yyyy.

26

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37

Figure Captions

Figure 1. Annual-mean SST from the observations (100-year mean from GISST) (a),

from the hybrid coupled model (40-year mean) (b), and model bias ((b)-(a)). Contour

interval is 1oC.

Figure 2. Seasonal-mean zonal wind vertical shear (850 hPa-200 hPa) and rainfall in

boreal summer (JJAS) from the observations (a), from the model (b); and in boreal winter

(DJFM) from the observations (c) and the model (d). Shadings are for rainfall (mm day-1)

and contours are for vertical shear (m s-1).

Figure 3. 1000-hPa wind vector and wind speed (contours) in boreal summer (JJAS) from

the observations (a) and the model (b); and in boreal winter (DJFM) from the

observations (c) and the model (d). Contour interval is 2 m s-1 (larger than 6 m s-1 are

shaded).

Figure 4. 200-hPa zonal wind speed in boreal summer (JJAS) from the observations (a)

and the model (b); and in boreal winter (DJFM) from the observations (c) and the model

(d). Contour interval is 5 m s-1 (larger than 40 m s-1 are shaded).

Figure 5. Annual means of (a) SSTs (oC) and (b) surface zonal winds (m s-1) from the

observations and the model along the equatorial Indo-Pacific Oceans.

Figure 6. Annual cycles of SSTs along the equatorial Indo-Pacific Oceans from the

observations (a) and the model (b). Contour interval is 0.5oC (positive values are shaded).

38

Figure 7. Annual cycles of surface zonal winds along the equatorial Indo-Pacific Oceans

from the observations (a) and the model (b). Contour interval is 0.5 m s-1 (positive values

are shaded).

Figure 8. Rainfall standard deviations associated with tropical intraseasonal oscillations

(with periods between 20-90 days) in boreal winter (NDJFMA) from the observations (a)

and the model (b); and in boreal summer (MJJASO) from the observations (c) and the

model (d). Contour interval is 1 mm day-1 (larger than 2 are shaded).

Figure 9. Wavenumber-frequency spectra of rainfall associated with westward-eastward

propagating disturbances in boreal winter (NDJFMA) averaged between 10oS and 5oN

from the observations (a) and the model (b). Contour interval is 3 (mm day-1)2.

Figure 10. Wavenumber-frequency spectra of rainfall associated with southward-

northward propagating disturbances in boreal summer (MJJASO) averaged between

120oE and 150oE from the observations (a) and the model (b). Contour interval is 3 (mm

day-1)2.

Figure 11. Standard deviation of SST anomalies in Indo-Pacific sector from the

observations (a) and the model (b). Contour interval is 0.1oC (larger than 0.2 are shaded).

Figure 12. Time series of Nino-3.4 SST anomalies (oC) from the observations (a) and the

model (b).

Figure 13. Seasonal cycles of SST internannual standard deviations (oC) at Nino-3.4

region from the observations (a) and the model (b).

39

Figure 14. Maps of correlation of the Nino-3.4 SST anomaly time series with global sea-

level pressure anomaly from the NCEP reanalysis (a) and the model (b). Correlation

coefficients larger than 0.6 or smaller than -0.6 are shaded.

40

Figure 1. Annual-mean SST from the observations (100-year mean from GISST) (a),

from the hybrid coupled model (40-year mean) (b), and model bias ((b)-(a)). Contour

interval is 1oC.

41

Figure 2. Seasonal-mean zonal wind vertical shear (200 hPa-850 hPa) and rainfall in

boreal summer (JJAS) from the observations (a), from the model (b); and in boreal winter

(DJFM) from the observations (c) and the model (d). Shadings are for rainfall (mm day-1)

and contours are for vertical shear (m s-1).

42

Figure 3. 1000-hPa wind vector and wind speed (contours) in boreal summer (JJAS) from

the observations (a) and the model (b); and in boreal winter (DJFM) from the

observations (c) and the model (d). Contour interval is 2 m s-1 (larger than 6 m s-1 are

shaded).

43

Figure 4. 200-hPa zonal wind speed in boreal summer (JJAS) from the observations (a)

and the model (b); and in boreal winter (DJFM) from the observations (c) and the model

(d). Contour interval is 5 m s-1 (larger than 40 m s-1 are shaded).

44

Figure 5. Annual means of (a) SSTs (oC) and (b) surface zonal winds (m s-1) from the

observations and the model along the equatorial Indo-Pacific Oceans.

45

Figure 6. Annual cycles of SSTs along the equatorial Indo-Pacific Oceans from the

observations (a) and the model (b). Contour interval is 0.5oC (positive values are shaded).

46

Figure 7. Annual cycles of surface zonal winds along the equatorial Indo-Pacific Oceans

from the observations (a) and the model (b). Contour interval is 0.5 m s-1 (positive values

are shaded).

47

Figure 8. Rainfall standard deviations associated with tropical intraseasonal oscillations

(with periods between 20-90 days) in boreal winter (NDJFMA) from the observations (a)

and the model (b); and in boreal summer (MJJASO) from the observations (c) and the

model (d). Contour interval is 1 mm day-1 (larger than 2 are shaded).

48

Figure 9. Wavenumber-frequency spectra of rainfall associated with westward-eastward

propagating disturbances in boreal winter (NDJFMA) averaged between 10oS and 5oN

from the observations (a) and the model (b). Contour interval is 3 (mm day-1)2.

49

Figure 10. Wavenumber-frequency spectra of rainfall associated with southward-

northward propagating disturbances in boreal summer (MJJASO) averaged between

120oE and 150oE from the observations (a) and the model (b). Contour interval is 3 (mm

day-1)2.

50

Figure 11. Standard deviation of SST anomalies in Indo-Pacific sector from the

observations (a) and the model (b). Contour interval is 0.1oC (larger than 0.2 are shaded).

51

Figure 12. Time series of Nino-3.4 SST anomalies (oC) from the observations (a) and the

model (b).

52

Figure 13. Seasonal cycles of SST internannual standard deviations (oC) at Nino-3.4

region from the observations (a) and the model (b).

53

Figure 14. Maps of correlation of the Nino-3.4 SST anomaly time series with global sea-

level pressure anomaly from the NCEP reanalysis (a) and the model (b). Correlation

coefficients larger than 0.6 or smaller than -0.6 are shaded.

54