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  • View in gallery

    The 20-yr DJF mean of zonally averaged precipitation (mm day−1) from (a) the CMT (thin solid), the CTL (dashed), and the CMAP data (thick solid) and (c) the difference between the CMT and CTL runs. (b), (d) The JJA mean.

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    The 20-yr DJF mean of zonally averaged high-level cloud amount (%) from (a) the CMT (thin solid), the CTL (dashed), and the ISCCP data (thick solid) and (c) the difference between the CMT and CTL runs. (b), (d) The JJA mean.

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    The 20-yr DJF mean of zonally averaged longwave cloud forcing (LWCF; W m−2) from (a) the CMT (thin solid), the CTL (dashed), and the CERES data (thick solid) and (c) the difference between the CMT and CTL runs. (b), (d) The JJA mean.

  • View in gallery

    The 20-yr DJF mean of zonally averaged vertical pressure velocity (mb day−1) from (a) the CMT run and(e) the NCEP reanalysis and (c) the difference between the CMT and CTL runs. (b), (d), (f) The JJA mean.

  • View in gallery

    The 20-yr DJF mean of zonally averaged meridional wind (m s−1) from (a) the CMT run and (c) the difference between the CMT and CTL runs. (b), (d) The JJA mean.

  • View in gallery

    Same as in Fig. 5 but for the zonal wind.

  • View in gallery

    The 20-yr DJF mean of zonally averaged (a) meridional component and (c) zonal component of apparent momentum source (0.1 m s−1 day−1). (b), (d) The JJA mean.

  • View in gallery

    The 20-yr DJF mean of zonally averaged convective heating (K day−1) from (a) the CMT and (b) CTL runs and (c) the difference between the CMT and CTL runs.

  • View in gallery

    Same as in Fig. 8 but for 20-yr JJA mean.

  • View in gallery

    The 15-yr (1979–93) mean of zonally averaged residuals (K day−1) of the heat budget of ECMWF reanalysis data (ERA) for (a) DJF and (b) JJA.

  • View in gallery

    The 20-yr DJF mean global distribution of (a) the surface wind stress vectors (N m−2) from the CMT run, (b) the difference between the CMT and CTL runs, and (c) the difference between the CTL run and NCEP reanalysis. Gray shades represent magnitudes of wind stress (N m−2).

  • View in gallery

    Same as in Fig. 11 but for the JJA mean.

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Coupling of Convective Momentum Transport with Convective Heating in Global Climate Simulations

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  • 1 Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa
  • | 2 Center for Atmospheric Sciences, Scripps Institution of Oceanography, La Jolla, California
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Abstract

The effects of convective momentum transport (CMT) on global climate simulations are examined in this study. Comparison between two sets of 20-yr (1979–98) integration using the NCAR Community Climate Model version 3 (CCM3) illustrates that the inclusion of CMT in the convection scheme systematically modifies the climate mean state over the equatorial region. The convective momentum tendencies slow down the equatorward flow at higher latitudes near the surface and weaken the equatorial convergence and convection. This reduces the convective heating and drying around the equator and produces an improved meridional distribution within the upward branch of the Hadley circulation. The major heating peak during the boreal winter is moved to south of the equator at about 10°S, which is closer to the heat budget residuals of the ECMWF reanalysis data. The responses of meridional wind to the reduced heating result in the secondary meridional circulation within the intertropical convergence zone.

Corresponding author address: Dr. Xiaoqing Wu, Iowa State University, 3010 Agronomy Hall, Ames, IA 50011. Email: wuxq@iastate.edu

Abstract

The effects of convective momentum transport (CMT) on global climate simulations are examined in this study. Comparison between two sets of 20-yr (1979–98) integration using the NCAR Community Climate Model version 3 (CCM3) illustrates that the inclusion of CMT in the convection scheme systematically modifies the climate mean state over the equatorial region. The convective momentum tendencies slow down the equatorward flow at higher latitudes near the surface and weaken the equatorial convergence and convection. This reduces the convective heating and drying around the equator and produces an improved meridional distribution within the upward branch of the Hadley circulation. The major heating peak during the boreal winter is moved to south of the equator at about 10°S, which is closer to the heat budget residuals of the ECMWF reanalysis data. The responses of meridional wind to the reduced heating result in the secondary meridional circulation within the intertropical convergence zone.

Corresponding author address: Dr. Xiaoqing Wu, Iowa State University, 3010 Agronomy Hall, Ames, IA 50011. Email: wuxq@iastate.edu

1. Introduction

Cloud system–related problems are at the heart of understanding global climate change and prediction. Convective clouds not only release latent heat from condensation and vertically redistribute heat and moisture but also transport momentum. Being an anisotropic quantity, convective momentum transport (CMT) is more intricate to parameterize than thermodynamic transports because of cloud-scale pressure gradients induced by organized convection. Unlike heat and moisture transports, reliable estimates of convective momentum transport are difficult to obtain from the momentum budget and relatively few attempts have been made (e.g., Stevens 1979; Sui et al. 1989; Wu and Yanai 1994; Carr and Bretherton 2001; Tung and Yanai 2002a, b). Consequently, the development and evaluation of CMT parameterization has been a challenging problem for a long time.

Recognizing the significant impact of cloud-induced pressure gradient on the in-cloud momentum and the CMT (e.g., Moncrieff 1981, 1992; LeMone 1983; LeMone et al. 1988; Schlesinger 1984; Flatau and Stevens 1987; Lafore et al. 1988; Caniaux et al. 1995), Zhang and Cho (1991) and Wu and Yanai (1994) theoretically formulated the cloud-scale pressure gradient in terms of large-scale vertical wind shear, cloud mass flux, and organization of convection in their CMT parameterizations. The diagnostic analyses showed that the schemes are able to reproduce the dominant features of observed momentum budget residuals, an improvement when compared to conventional CMT schemes (e.g., Ooyama 1971; Schneider and Lindzen 1976; Shapiro and Stevens 1980; Esbensen et al. 1987; Sui et al. 1989). Less-organized cumulus clouds act to decelerate environmental flow as friction, in which CMT tends to reduce vertical shear of horizontal wind. However, organized convection (e.g., squall line) can generate a cloud-scale horizontal pressure gradient that acts to accelerate the environmental flow, in which CMT tends to enhance vertical shear.

An alternative way to evaluate the parameterization of CMT is the use of cloud-resolving models (CRMs). The idea was first tested by Soong and Tao (1984) and Tao and Soong (1986), who estimated the convective momentum transports from two-dimensional (2D) and three-dimensional (3D) simulations of CRM. In these simulations, the large-scale forcing for the momentum equation is set to zero and integrations are made only for several hours to a day. The CRM-produced vertical transport of momentum in a tropical rainband was used to evaluate an earlier CMT scheme by Schneider and Lindzen (1976). Kershaw and Gregory (1997) and Gregory et al. (1997) adopted a similar approach to simulate the convective momentum transport in the midlatitude cold-air outbreak and tropical convection. The cloud-scale pressure gradient formulated based on the 3D simulations is similar to that theoretically derived by Wu and Yanai (1994).

Grabowski et al. (1996) proposed a new framework for the CRM simulation using objectively analyzed evolving large-scale forcing and condition. The domain-averaged wind field is relaxed to follow the observations. The evolution of nonsquall cluster, squall line to scattered convection observed during 1–7 September 1974 in Phase III of the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), is well simulated by both 2D and 3D CRMs (Wu and Moncrieff 1996; Grabowski et al. 1996, 1998). The month-long 2D-CRM simulations are also performed and extensively evaluated against the observations from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) and the Atmospheric Radiation Measurement Program (ARM; e.g., Wu and Moncrieff 2001). These simulations provided a good database for estimating the convective momentum transports. Mapes and Wu (2001) showed that the apparent momentum sources are broadly similar in the 7-day 2D and 3D simulations, but 3D produced more coherent structure in the vertical profiles. Physically speaking, the 3D framework certainly poses a more realistic setting for the development of cloud systems and therefore more realistic convective momentum transports.

The increasing understanding of CMT and its successful evaluation against the CRM simulations provide a physical basis for the impact study in general circulation models (GCMs). Diagnostic studies of GCM simulations have indicated the need of convective momentum transport processes for a better simulation of climate phenomena (e.g., Liang et al. 1997). Observational studies also show the relationship between convective momentum transport and tropical intraseasonal variability (e.g., Tung and Yanai 2002b). Several studies have incorporated the CMT effect in GCMs and have shown that the Hadley circulation is enhanced and the mean tropical circulation is closer to observations (Helfand 1979; Ose et al. 1989; Zhang and McFarlane 1995; Gregory et al. 1997). Recently, Wu et al. (2003) coupled Zhang and Cho’s CMT scheme with the Zhang and McFarlane (1995) convection scheme in the National Center for Atmospheric Research (NCAR) Community Climate Model, version 3 (CCM3). A 20-yr (1979–98) simulation is performed using the Atmospheric Model Intercomparison Project (AMIP) sea surface temperature (SST). They found that a secondary meridional circulation within the ascending branch of the Hadley circulation is induced by the inclusion of convective momentum transport in CCM3. This secondary circulation improves the simulation of seasonal migration of the intertropical convergence zone (ITCZ) precipitation maximum across the equator. However, the physical process that leads to the secondary circulation remains unknown.

The objective of this study is to understand the cause of the CMT-induced secondary meridional circulation by examining impacts of CMT on thermodynamic, dynamic, cloud, and radiation fields. The paper is organized as follows: section 2 briefly describes the CMT scheme, section 3 presents the AMIP simulation with the inclusion of CMT in the CCM3 and compares it with the standard CCM3 simulation, and section 4 gives the summary of results and conclusions.

2. Parameterization of convective momentum transport

Since the CCM3 uses the Zhang and McFarlane (1995) convection scheme, the Zhang and Cho (1991) CMT scheme is adopted in this study, which is briefly outlined here. Following the similar derivation of cumulus effects on the heat and moisture fields, the effects of convective momentum transports on the large-scale wind fields, that is, the apparent momentum sources, are expressed by
i1520-0469-64-4-1334-e1
i1520-0469-64-4-1334-e2
where Mc is the cumulus mass flux and is obtained from the convection scheme, ρ is the air density, and u and υ are the zonal and meridional components of large-scale wind fields, respectively. The in-cloud momentum components, uc and υc, are determined by
i1520-0469-64-4-1334-e3
i1520-0469-64-4-1334-e4
where σ is the fractional area covered by clouds and δ and ε are the mass detrainment and entrainment, respectively. The cloud-scale pressure gradients, that is, the last terms on Eqs. (3) and (4), are represented in terms of an assumed cloud-scale circulation and the interaction between convective updrafts/downdrafts and the vertical wind shear. The readers are referred to the paper for details.

Combining Eqs. (3) and (4) with Eqs. (1) and (2), respectively, the essential physical processes considered in the CMT scheme include (i) the vertical momentum advection due to subsidence of environmental air compensating cloud mass flux; (ii) the detrainment of excess momentum from clouds; and (iii) the convection-induced pressure gradient force (Zhang and Wu 2003). Wu et al. (2002) and Zhang and Wu (2003) applied the CRM simulations of GATE and TOGA COARE cloud systems to evaluate the Zhang and Cho CMT scheme. It was found that the scheme is able to reproduce the apparent momentum source and its evolution simulated by the CRM. Inclusion of cloud-scale pressure gradient force in the scheme has a large impact on the in-cloud momentum and the parameterized apparent momentum source, especially in the upper troposphere. This suggests that the dominant processes of convective momentum transport are captured by the scheme.

3. 20-yr AMIP simulations

Two simulations are performed with the only difference in the use of convective momentum transports. This ensures the difference between two simulations is purely due to the new physics and its interaction with the existing processes. The CCM3 runs with and without the CMT scheme are referred to as the CMT and control (CTL) runs, respectively, in the following presentation. The 20-yr simulations start from 1 September 1978 using the monthly observed SST from AMIP and run through the end of February 1999. The results will be presented in terms of the 20-yr mean of boreal winter December–February (DJF, from 1979 to 1998) and boreal summer June–August (JJA, from 1979 to 1998).

Figure 1 shows the 20-yr zonally averaged precipitation from the CMT and CTL runs and the differences for DJF and JJA. The differences between the two runs are mainly confined within 30°S to 30°N (Figs. 1c,d) where the precipitation is dominated by the convective processes, while the impact is small on the higher latitudes where the large-scale condensation is the main process for precipitation. For DJF, the peak of precipitation at 3°N produced by the CTL run is reduced in the CMT run, while the precipitation between 10° and 20°N is enhanced (Fig. 1c). A minimum precipitation around the equator is simulated by the CMT run (Fig. 1a). This corrects a long-standing bias present in the CCM3 (e.g., Hack et al. 1998) and also the new version of Community Atmosphere Model (CAM; Boville et al. 2006). It is also noted that the precipitation south of the equator around 10°S is increased in the CMT run but is still smaller than the CMAP analysis (Xie and Arkin 1998). For JJA, the peak of precipitation around 10°N remains about the same for both CTL and CMT runs (Fig. 1b), but the precipitation around the equator and 20°N is reduced by the inclusion of CMT (Fig. 1d).

The zonally averaged cloud amount and cloud radiative forcing are also affected by the inclusion of CMT in the convection scheme. Figure 2 shows the 20-yr mean high-level cloud amounts of the CMT and CTL runs and the differences for DJF and JJA, respectively. The largest reduction of cloud amounts is around the equator for both seasons, while the increases are found around 20°S for DJF and 25°N for JJA (Figs. 2c,d). The high-level cloud amounts produced by the CMT and CTL runs are still systematically much larger than those estimated by the International Satellite Cloud Climatology Project (ISCCP). The high-level cloud amount is tuned in the CCM3 to match the global radiation budget at the top of the atmosphere (TOA). Recently, Wu and Liang (2005) demonstrated that the improved treatment of cloud distribution in the radiation scheme and the improved vertical distribution of cloud water path based on the CRM simulations allow the use of more realistic cloud amounts as well as cloud water contents while producing TOA radiation budget closer to observations.

The changes in cloud amount lead to the modification of cloud radiative forcing as shown in Fig. 3, which compares the zonal means of longwave cloud forcing (LWCF) at TOA from the CMT and CTL runs with the observations by the National Aeronautics and Space Administration’s (NASA) Clouds and the Earth’s Radiant Energy System (CERES). The peak of LWCF just north of the equator during DJF is reduced by more than 6 W m−2 (Fig. 3c), which leads to an improved meridional distribution of LWCF in the CMT run with the peak around 10°S, closer to the observations (Fig. 3a). The JJA LWCF is also decreased more than 4 W m−2 around the equator (Fig. 3d), and the peak of LWCF around 10°N is narrowed and closer to the observations (Fig. 3b).

The systematic modification of convection and clouds around the equator is the consequence of the CMT-induced vertical velocity change. Figure 4 illustrates latitude–height distributions of vertical velocity from the CMT run and the National Centers for Environmental Prediction (NCEP) reanalysis and the difference between the CMT and CTL for DJF and JJA, respectively. Note that the vertical pressure velocity is negative for the upward motion. The CMT run decreases the upward vertical velocity around the equator and increases it around 10°S and 10°N for DJF (Fig. 4c) and JJA (Fig. 4d) from the CTL run. This results in an improved meridional distribution of vertical velocity closer to the NCEP reanalysis during DJF (Figs. 4a,e), which has a peak of vertical velocity around 10°S within the upward branch of Hadley circulation between 10°N and 20°S during DJF. The meridional distribution of vertical velocity is also improved during JJA when the upward branch of Hadley circulation shifts northward and lies between 20°N and 10°S. The reduced vertical velocity around the equator in the CMT run is closer to the reanalysis (Figs. 4b,f). While the meridional distribution of vertical velocity is improved by the CMT, it is also noted that the altitude of maximum vertical velocity in the CMT run is lower than that in the NCEP reanalysis for both DJF and JJA. This presents a problem that needs to be addressed by the convection scheme.

The impact of CMT on the vertical velocity can be further understood by analyzing the zonal mean of meridional and the zonal components of horizontal wind field. Figure 5 presents latitude–height distributions of meridional wind from the CMT run and the difference between the CMT and CTL runs. For DJF, northerly flow is enhanced from 700 to 400 mb and southerly flow is enhanced above 150 mb over the equator in association with the enhanced ascending motion at 10°S. In addition, there are more fine structures in the boundary layer and between 400 and 200 mb. The northerly and southerly to the north and south of the equator near the surface (Fig. 5a), respectively, are reduced in the CMT run. The difference of meridional wind (Fig. 5c) shows a northerly and southerly peak around 5°S and 5°N near the surface, respectively, and an opposite distribution between 400 and 200 mb. This results in a secondary circulation with upward motion at 10°S and downward motion on the equator, which in effect represents a shift of the Hadley circulation toward the Southern Hemisphere. For JJA, the inclusion of CMT also results in a divergence in the lower troposphere and a convergence in the upper troposphere (Fig. 5d), which reduces the upward vertical velocity in the CMT run at the equator and enhances the upward motion at 10°N. (Fig. 4d). Near 150 mb, the net effect of CMT is to strengthen the flow in both seasons. These suggest the complicated nonlinear interaction between the mean flow and CMT instead of a simple mixing process. Figure 6 shows zonally averaged zonal wind component in the CMT run and the difference between the CMT and CTL runs. The net impact of CMT on the zonal wind is relatively small with an enhanced westerly around the equator in the middle and lower troposphere for both DJF and JJA (Figs. 6c,d). The net effect of CMT tends to enhance the vertical shear of zonal wind in the middle and upper troposphere.

The above analysis shows that the effects of CMT on the Hadley circulation are complex. The upward branch of Hadley circulation occupies between 10°S and 20°N (Fig. 4b). The upward motion is enhanced by the inclusion of CMT around 10°N but reduced around the equator (Fig. 4d), which creates the secondary meridional circulation (Wu et al. 2003). The JJA simulation by Zhang and McFarlane (1995) in a different GCM showed similar results in terms of the enhanced Hadley circulation, as also found by Helfand (1979). However, in the boundary layer this study shows a much more complex structure of the CMT effect.

Figure 7 shows the zonally averaged apparent momentum source in the meridional and zonal directions for DJF and JJA. The meridional component of the momentum source (Fy) appears mainly in the lower troposphere below 600 mb for both DJF and JJA (Figs. 7a,b). It features a dipole pattern with a peak of negative tendency at 20°S and a peak of positive tendency at 15°N near the surface and an opposite pattern above, which acts to reduce the vertical shear of meridional wind in the lower troposphere and near the surface (Figs. 5a,b). This is consistent with the observational analysis by Tung and Yanai (2002a), who found that the 4-month mean of momentum budget residuals during TOGA COARE tends to decelerate the large-scale motion in both meridional and zonal directions. Near the equator, the zonal component of the momentum source (Fx) (Figs. 7c,d) shows negative tendencies near the surface, which tend to enhance the surface easterly, and positive tendencies between 600 and 850 mb, which tend to reduce the easterly in the lower troposphere. Small negative tendencies appear in the middle troposphere and a peak of positive tendency is shown at 200 mb. The JJA meridional and zonal apparent momentum sources in the CCM3 are generally similar to those in the Canadian Climate Centre (CCC) GCM (Zhang and McFarlane 1995), which used the same CMT scheme. Both studies showed that the CMT tendency is confined to the convective region in the lower troposphere. This is largely due to the environmental subsidence process, which depends on the cloud mass fluxes and vertical wind shear. One noticeable difference is a large positive CMT tendency occurring below 850 mb between 5° and 30°N in the CCM3 but only a small tendency in the CCC GCM, which could be due to the use of different boundary layer schemes in two models.

Comparison of Fig. 7a with Fig. 5c in DJF (Figs. 7b and 5d in JJA) shows that the pattern of changes in meridional wind is different from the pattern of CMT tendency. Apparently, the wind change is not a direct response to the CMT tendency. The analysis of momentum budget indicates that the changes of tendencies due to the boundary layer process, advection, and Coriolis acceleration (not shown) are small although not negligible compared to the CMT tendency. This suggests other processes are involved. The analysis in the rest of this section demonstrates that the inclusion of CMT in the convection scheme modifies the distribution of convective heating, which in turn results in change to the distribution of horizontal wind fields.

Figure 8 shows latitude–height distributions of convective heating produced by the Zhang and McFarlane convection scheme from the CMT and CTL runs for DJF and the difference between the two. The CMT runs demonstrate two distinct heating peaks with the dominant one at 10°S and the second relatively weaker one at 5°N (Fig. 8a). The meridional distribution of the heating in the CMT run is clearly different from that in the CTL run (Fig. 8b), which shows two heating peaks at 5°N and 5°S, respectively. The difference of heating profiles between the CMT and CTL runs (Fig. 8c) demonstrates a cooling peak around the equator. The convective heating and drying are coupled in the convection scheme; the difference between CMT and CTL runs shows less drying in the lower troposphere (not shown) consistent with the less heating in the middle and upper troposphere. The response of wind fields to the reduced heating rate produces a peak of southerly at 5°N and a peak of northerly at 5°S near the surface and an opposite distribution in the upper troposphere (Fig. 5c). This explains why the peak of meridional wind difference between the CMT and CTL near the surface (Fig. 5c) is not directly related to the peak of apparent momentum source (Fig. 7a).

In JJA, the latitude–height distributions of convective heating are similar for the CMT and CTL runs with a dominant peak at 10°N (Figs. 9a,b). But the heating at the equator is also reduced by the inclusion of the CMT as shown in Fig. 9c. Slightly differing from the DJF, the bottom of cooling is higher at about 900 mb, which leads to the peak of the response of meridional wind to the cooling rate at higher level than the DJF (Figs. 5c,d). This further supports that the CMT-induced secondary meridional circulation is due to the response of wind field to the change of convective heating.

The convective heating from the CCM3 simulations is compared with the heat budget residuals of the European Centre for Medium-Range Weather Forecasts (ECMWF) 15-yr Re-Analysis (ERA-15) data (Hung et al. 2004), which are shown in Fig. 10. Note that the budget residuals are averaged over 15 yr (1979–93) and include not only the contribution from the convective process but also other subgrid processes as well as the error contained in the reanalysis data (e.g., Yanai et al. 1973). In DJF, the qualitative comparison between Fig. 10a and Fig. 8 shows that both the CMT run and budget residual produce a major heating peak at the same latitude (10°S) but at the different levels (600 mb in the CMT run and 450 mb in the residual). In JJA, the CMT run has the peak of convective heating at 10°N (Fig. 9a), which is also consistent with the budget residuals (Fig. 10b). But as in DJF, the JJA heating peak of the CMT run and budget residuals are at 600 and 450 mb, respectively.

The above analyses illustrate the effects of coupling the CMT with the convection scheme on the zonally averaged meridional distribution of precipitation, cloud, and radiation fields. Another important effect of CMT is on the surface wind stress, which is the main force for driving the ocean currents through the exchange of momentum at the ocean surface. Although the CCM3 simulations are forced by the observed monthly SST from AMIP, the modification of surface wind stress by the inclusion of CMT should provide the indication if the CMT process can alter the SST simulation in the coupled models. Figure 11 shows the 20-yr-averaged global distribution of surface wind stress in DJF and the difference between the CMT and CTL runs. The difference between the CTL run and the NCEP reanalysis is also included for the comparison. Comparing Fig. 11b and Fig. 11c, the modification of wind stress is readily identified over the Indian Ocean, western Pacific, and North Pacific. The difference of wind stress between the CTL and NCEP is reduced by more than half. More importantly the divergence and convergence of the wind stress over these areas are largely altered by the coupling of the CMT and convective heating, which has potentially great impacts on the upwelling and downwelling in the equatorial ocean. In JJA, the inclusion of CMT also slightly reduces the difference of surface wind stress between the CTL and NCEP reanalysis in the equatorial region over the Indian Ocean and western Pacific (Fig. 12).

4. Concluding remarks

The CRM-validated parameterization scheme of convective momentum transport is implemented in the CCM3 to investigate its impacts on the global climate simulation. Two 20-yr integrations are conducted using the observed monthly SST from AMIP, one with the standard CCM3 code and the other with the addition of CMT scheme. The comparison of the two simulations demonstrates that the inclusion of CMT in the convection scheme systematically reduces the precipitation, high-level cloud amount, and longwave cloud radiative forcing over the equatorial region. The simulation with the CMT scheme improves the meridional distribution of convection and clouds within the ITCZ. The CMT-induced secondary meridional circulation is the consequence of the coupling of CMT with the convective heating. The convection-induced apparent momentum source tends to reduce the large-scale convergence of equatorward flow near the surface, which leads to less convective heating and drying over the equatorial region. The responses of wind field to the reduced heating then result in the secondary meridional circulation over the equatorial region. The strong impacts of this secondary circulation are clearly present in the global distribution of surface precipitation and wind stress. It remains to be seen if the impact of CMT on the global and regional climate simulations will be amplified by the feedback processes in the coupled atmosphere–ocean GCMs.

Since the representation of CMT in the convection scheme depends on the cloud mass flux and the vertical profile of large-scale wind fields, the intensity of convection-induced momentum tendency will vary as the intensity of convection changes. Recently, Zhang and Mu (2005) modified the closure assumption of the Zhang and McFarlane convection scheme and found it has strong effects on the tropical precipitation simulation. We are currently evaluating the performance of CMT scheme in the CCM3 simulations with the revised Zhang and McFarlane scheme. This should provide further insight into how the coupling of the CMT and the convection scheme interacts with the large-scale dynamical and thermodynamical processes. Finally, the improved understanding of the CMT process and its parameterization should also benefit the weather forecasting models. Recently, Han and Pan (2006) implemented the Wu and Yanai (1994) CMT scheme into the National Centers for Environmental Prediction’s (NCEP) operational Global Forecast System (GFS) and its nested Regional Spectral Model (RSM). They found that hurricane intensity forecasting is significantly improved and the forecast track error is reduced.

Acknowledgments

We thank Michio Yanai and Chih-Wen Hung for making the heat and moisture budgets of ERA data available for the comparison with the CCM3 simulations. Computing support by Daryl Herzmann is greatly appreciated. The presentation of the paper was improved by comments of two anonymous reviewers. This research was supported by the Biological and Environmental Research Program (BER), U.S. Department of Energy, Grants DE-FG02-04ER63868, DE-FG03-02ER63354, and DE-FG02-04ER63865.

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Fig. 1.
Fig. 1.

The 20-yr DJF mean of zonally averaged precipitation (mm day−1) from (a) the CMT (thin solid), the CTL (dashed), and the CMAP data (thick solid) and (c) the difference between the CMT and CTL runs. (b), (d) The JJA mean.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 2.
Fig. 2.

The 20-yr DJF mean of zonally averaged high-level cloud amount (%) from (a) the CMT (thin solid), the CTL (dashed), and the ISCCP data (thick solid) and (c) the difference between the CMT and CTL runs. (b), (d) The JJA mean.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 3.
Fig. 3.

The 20-yr DJF mean of zonally averaged longwave cloud forcing (LWCF; W m−2) from (a) the CMT (thin solid), the CTL (dashed), and the CERES data (thick solid) and (c) the difference between the CMT and CTL runs. (b), (d) The JJA mean.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 4.
Fig. 4.

The 20-yr DJF mean of zonally averaged vertical pressure velocity (mb day−1) from (a) the CMT run and(e) the NCEP reanalysis and (c) the difference between the CMT and CTL runs. (b), (d), (f) The JJA mean.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 5.
Fig. 5.

The 20-yr DJF mean of zonally averaged meridional wind (m s−1) from (a) the CMT run and (c) the difference between the CMT and CTL runs. (b), (d) The JJA mean.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 6.
Fig. 6.

Same as in Fig. 5 but for the zonal wind.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 7.
Fig. 7.

The 20-yr DJF mean of zonally averaged (a) meridional component and (c) zonal component of apparent momentum source (0.1 m s−1 day−1). (b), (d) The JJA mean.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 8.
Fig. 8.

The 20-yr DJF mean of zonally averaged convective heating (K day−1) from (a) the CMT and (b) CTL runs and (c) the difference between the CMT and CTL runs.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 9.
Fig. 9.

Same as in Fig. 8 but for 20-yr JJA mean.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 10.
Fig. 10.

The 15-yr (1979–93) mean of zonally averaged residuals (K day−1) of the heat budget of ECMWF reanalysis data (ERA) for (a) DJF and (b) JJA.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 11.
Fig. 11.

The 20-yr DJF mean global distribution of (a) the surface wind stress vectors (N m−2) from the CMT run, (b) the difference between the CMT and CTL runs, and (c) the difference between the CTL run and NCEP reanalysis. Gray shades represent magnitudes of wind stress (N m−2).

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

Fig. 12.
Fig. 12.

Same as in Fig. 11 but for the JJA mean.

Citation: Journal of the Atmospheric Sciences 64, 4; 10.1175/JAS3894.1

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