**4. Conclusion**

78 Atmospheric Model Applications

Fig. 11 shows the distribution of observed P\* variance (contoured) and the mean U850 (shaded). The observed P\* maxima always follow the positive U850 or very weak zonal flow in both seasons. Latitudinal migration of mean U850 and P\* are more prominent over the west Pacific than over the Indian Ocean. The amplitude of variance is also larger over the west Pacific. The model, however, does not reproduce the observation well (Fig. 12). Variance of P\* seems to avoid the westerlies in both seasons. This is one of the most disturbing aspects of the simulated MJO in the NRCM. The larger values of P\* variance avoids the equatorial region in the simulation. During the boreal summer, the model reproduces spurious P\* variance over the eastern Pacific and in the SPCZ region (Fig. 12b) that is absent in the observation (Fig.11b). It seems that P\* variance follows the mean precipitation (Fig. 10), and not the mean westerlies. This indicates a lack of coupling

Fig. 11. Mean U850 (m sec-1, shaded) and variance of P\* (mm2 day-2, contoured) from the observation during the (a) boreal winter (DJFM), and (b) boreal spring (JJAS). Contour

between the convection and circulation in the model.

intervals are 2 mm2 day-2.

Fig. 12. Same as Fig. 11, but for the model.

A nested regional climate model (NRCM) is constructed at the NCAR based on Weather Research and Forecasting (WRF) model. This is also known as a tropical channel model (TCM), and is conceptually similar to the TCM developed at the University of Miami based on MM5. Both TCMs are useful tools to study the MJO dynamics and its initiation.

With the initial and lateral boundary conditions provided by a global reanalysis, the NRCM is integrated for several years. The simulated MJO statistics in the NRCM are not better than those found in the GCMs. This is less than satisfactory considering that the model is forced by time-varying reanalysis boundary conditions. Further diagnoses reveal that the error in the mean state is a reason for the poor MJO statistics in the simulation. For example, the MJO variance and the westerlies in the lower-troposphere are well collocated in the reanalysis, but not quite as well in the NRCM, particularly over the equatorial Indian Ocean where the initiation of the MJO events usually occur. The model also lacks precipitation in the equatorial Indian Ocean. The large error in the precipitation (through modifying the latent heating) must have inhibited any dynamical effects from the lateral boundaries from reaching the interior of the domain. Thus, the lateral boundary conditions couldn't participate effectively in simulating the mean conditions.

However, the multi-year simulation with large error in the mean state was able to capture two individual MJO events that were initiated by the extratropical influences (Ray et al., 2011a). In other words, the negative effect of mean state error can be overcome if there are extra dynamical influences, either from the meridional boundary conditions or initial conditions. Note that, it is not known to what extent the error in the mean state inhibits tropical variability, although it is likely to be model dependent.

The large error in the precipitation over the southern Indian Ocean was thought to be due to the interactions between tropical cyclones and the southern boundaries. To rectify this problem, southern boundaries were further moved to 45°S in another experiment. This simulation also has more vertical levels (55 levels instead of 35) and higher model top at 10 hPa level (instead of 50 hPa). However, this did not improve the result significantly, indicating potential problems with the model physics (Tulich et al., 2011; Murthi et al., 2011). Use of nested domains inside the model also did not improve the mean state (Ray et al., 2011).

In a regular regional model, the domain size is vital for the model mean state through the influence of boundary conditions. For example, a small domain may lead to very little "climate error" because the model is fundamentally controlled by its boundary conditions. On the other hand, the mean state in a global model would be less constrained. The NRCM lies between the regular regional model and the global model. Thus, climate drift in the NRCM simulation would not be noticeable in the smaller regional domains used by Gustafson and Weare (2004a, b) and Monier et al. (2009). How much error in the mean state is sufficient to prevent the initiation of an MJO in the model is not known; arguably, it is event dependent. Thus a systematic study for multiple MJO events including several "primary" (no prior MJO, Matthews, 2008) and "successive" (with prior MJO) events is needed to have a better idea of the effect of mean state on the MJO.

Is the poor skill of the NRCM to simulate MJO due to shortcomings from the cumulus parameterization (Park et al. 1990; Raymond and Torres, 1998; Wang and Schlesinger, 1999;

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Maloney and Hartmann, 2001)? Or do we need further increase in the model resolution (Hayashi and Golder, 1986; Gualdi et al., 1997; Grabowski and Moncrieff, 2001; Inness et al., 2001; Liess and Bengtsson, 2004)? The use the Betts-Miller-Janjic (Janjic, 1994) scheme did not improve the MJO simulation. Similarly, higher resolution nested domains inside the NRCM made minor differences. Further works need to be done to investigate the roles of cumulus parameterization and horizontal resolution on the simulated MJO.

The lack of MJO in the NRCM does not necessarily imply a lack of tropical-extratropical interaction. For example, if the observed source of perturbations that eventually initiate an MJO event is located inside the model domain, then the lateral boundary conditions may not be effective beyond the MJO predictability limit. As a result, the locations of the meridional boundaries of the NRCM are crucial for capturing the extratropical influences, if any, on MJO dynamics.

The NRCM is an atmosphere only model forced by the SST without true oceanic feedback. Therefore, it is difficult to rule out the role of coupled air-sea feedbacks in modulating the mean state (Hendon, 2000; Zheng et al. 2004; Vitart et al. 2007; Woolnough et al. 2007). Pegion and Kirtman (2008a, b) found that air-sea coupling was responsible for differences in the simulation of the MJO between the coupled and uncoupled models, specifically in terms of organization and propagation in the western Pacific. The role of intraseasonally varying SST was found to be important to the amplitude and propagation of the oscillation beyond the Maritime continent in their model. After removing the intraseasonally varying component in the SST and lateral boundary conditions in MM5, Gustafson and Weare (2004b) found only minor differences in the MJO simulation compared to the simulation forced with observed SST. Ray et al. (2009) also reported that use of constant SST did not influence the MJO initiation in the Indian Ocean. These results indicate that the MJO amplitude and propagation are influenced by the air-sea interactions whose effect is dominant over the Pacific.

In short, we have shown that the erroneous mean state may be responsible for poor MJO simulation in the model. Our results call for further research attention towards using the untapped potential of high-resolution models in the MJO simulation and forecasting.
