**2.3 Data**

Model validation uses observations and reanalyses data. They include: National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) Reanalysis (Kalnay et al., 1996) winds and the merged analysis of precipitation (CMAP; Xie and Arkin, 1997).

The initial and boundary conditions of the NRCM are from the NCEP-NCAR reanalysis. The SSTs are from Atmospheric Model Intercomparison Project (AMIP; 1° x 1°, 6-hourly; Taylor et al., 2000). For brevity, both reanalysis and CMAP precipitation will be referred to as "observations".

### **3. Results**

The simulated mean state is described first, followed by the MJO and how it has been affected by the model mean state.

#### **3.1 Mean state**

The mean state of the model is compared with the observation with respect to P and U850 (Fig. 1). The main error in the model precipitation is over the equatorial Indian and west Pacific Ocean and over the South Pacific Convergence Zone (SPCZ), where the variance of the MJO related precipitation is maximum (Zhang and Dong, 2005). This is the first indication that the simulated MJO may be affected by the mean state. The model precipitation seems to move further from the equator with much higher values over the southern Indian Ocean and north of maritime continent. Most of this error comes during the northern winter. On the other hand, simulated U850 is somewhat stronger than those of reanalysis over the Indian Ocean and the eastern and central Pacific. The model overestimates winds at 200 hPa in the equatorial Indian and west Pacific Ocean also (not shown). The simulation captures the winds quite well over the west African monsoon region, where the lack of precipitation in the model is obvious. Easterlies at 850 hPa are stronger over the southern Indian Ocean, where there is error in precipitation as well.

Mean State and the MJO in a High Resolution Nested Regional Climate Model 73

Fig. 2. Longitudinal distribution of (a) Precipitation (mm/day) and (b) U850 (m/s), averaged over 10°S-10°N from the observation/reanalysis (red) and NRCM (green).

Fig. 3. Latitudinal distribution of (a) Precipitation (mm/day) and (b) U850 (m/s), averaged

over 0°-360° from the observation/reanalysis (red) and NRCM (green).

Fig. 1. Annual mean rainfall (shaded, mm/day) and U850 (contoured, m/s) during 1996- 2000 from the (a) observation/reanalysis, and (b) NRCM. Zero contours are thickened.

Overall, the precipitation is underestimated over the equatorial (10°S-10°N) Indian Ocean, and is slightly overestimated over the west Pacific (Fig. 2a). However, U850 is overestimated over the Indian Ocean and is slightly underestimated over the west Pacific (Fig. 2b). The results indicate a possible lack of coupling between the winds and precipitation in the model compared to observations.

To further explore the simulated mean state, we show latitudinal distributions of P and U850 in Fig. 3. Precipitation is underestimated close to the equator (10°S-10°N), however, the model overestimates precipitation between 10°-30° latitudes, particularly in the southern hemisphere. The northern ITCZ is shifted towards the higher latitude. As expected, simulated winds are almost same as that of the reanalysis near the boundaries (Fig. 3b). Although there are large differences between the simulated and observed U850 over the equatorial Indian and west Pacific (Fig. 2b), the zonally averaged U850 match well due to the cancellation of errors (Fig. 3b).

Fig. 1. Annual mean rainfall (shaded, mm/day) and U850 (contoured, m/s) during 1996- 2000 from the (a) observation/reanalysis, and (b) NRCM. Zero contours are thickened.

model compared to observations.

the cancellation of errors (Fig. 3b).

Overall, the precipitation is underestimated over the equatorial (10°S-10°N) Indian Ocean, and is slightly overestimated over the west Pacific (Fig. 2a). However, U850 is overestimated over the Indian Ocean and is slightly underestimated over the west Pacific (Fig. 2b). The results indicate a possible lack of coupling between the winds and precipitation in the

To further explore the simulated mean state, we show latitudinal distributions of P and U850 in Fig. 3. Precipitation is underestimated close to the equator (10°S-10°N), however, the model overestimates precipitation between 10°-30° latitudes, particularly in the southern hemisphere. The northern ITCZ is shifted towards the higher latitude. As expected, simulated winds are almost same as that of the reanalysis near the boundaries (Fig. 3b). Although there are large differences between the simulated and observed U850 over the equatorial Indian and west Pacific (Fig. 2b), the zonally averaged U850 match well due to

Fig. 2. Longitudinal distribution of (a) Precipitation (mm/day) and (b) U850 (m/s), averaged over 10°S-10°N from the observation/reanalysis (red) and NRCM (green).

Fig. 3. Latitudinal distribution of (a) Precipitation (mm/day) and (b) U850 (m/s), averaged over 0°-360° from the observation/reanalysis (red) and NRCM (green).

Mean State and the MJO in a High Resolution Nested Regional Climate Model 75

Fig. 5. (left) Variance of U850 averaged over 15°S-15°N during (a) all season, (b) boreal winter (DJFM), and (c) boreal summer (JJAS). Right panels are averaged over 5°S-5°N.

Fig. 6. (left) Variance of P averaged over 15°S-15°N during (a) all season, (b) boreal winter,

and (c) boreal summer. Right panels are averaged over 5°S-5°N.

#### **3.2 MJO**

A space-time spectrum analysis is performed on the filtered time series of U850 and P to compare the eastward and westward propagating intraseasonal (20-90 day) signal (Fig. 4). A necessary criterion for the MJO is the dominance of the eastward propagating power over its westward propagating counterpart at the intraseasonal and planetary scales. In the observations (Fig. 4, left), the eastward spectral power dominates its westward counterpart at the MJO space and time scales, but not quite so in the simulation (Fig. 4, right), particularly for P (Fig. 4d). The simulated MJO signal in P (Fig. 4d) is much weaker than that in U850 (Fig. 4c) in comparison to the observation. This discrepancy indicates a lack of physical-dynamical coherence in the NRCM simulation. This is consistent with the mean U850 and P in Fig. 1. The results are similar using other variables.

Fig. 4. Time-Space spectra for (a) U850 and (b) precipitation from the observation. The right panels are for the model. Zonal wavenumber 1, and frequency 0.1 (50 days), represent the dominant MJO scales. All are averaged over 10°S-10°N.

To further explore the MJO in the NRCM, the longitudinal variation of the MJO variance of U850 and P are shown in Fig. 5. For U850, over the Indian Ocean, the variance is underestimated, particularly near the equator (5°N-5°S, Fig. 5, right). However, when a larger area is considered (15°S-15°N, Fig. 5, left), the differences between the observation and the model become smaller. Over the west Pacific, however, the model overestimates the MJO variance in U850. For P, the MJO variance is greatly underestimated over the Indian Ocean (Fig. 6), particularly over the western Indian Ocean, where most MJO initiation occurs. This is consistent with the lack of precipitation over the equatorial Indian and west Pacific Ocean as shown in Fig. 1.

It is natural to enquire how the MJO simulation in the NRCM compares with those in GCM simulations. A quick comparison with GCM simulations reveals that the MJO in the NRCM is not better than those in GCMs. This is less than satisfactory considering that the model is forced by time-varying reanalysis boundary conditions. As a result, we further diagnoss the role of the mean state in the simulated MJO statistics.

A space-time spectrum analysis is performed on the filtered time series of U850 and P to compare the eastward and westward propagating intraseasonal (20-90 day) signal (Fig. 4). A necessary criterion for the MJO is the dominance of the eastward propagating power over its westward propagating counterpart at the intraseasonal and planetary scales. In the observations (Fig. 4, left), the eastward spectral power dominates its westward counterpart at the MJO space and time scales, but not quite so in the simulation (Fig. 4, right), particularly for P (Fig. 4d). The simulated MJO signal in P (Fig. 4d) is much weaker than that in U850 (Fig. 4c) in comparison to the observation. This discrepancy indicates a lack of physical-dynamical coherence in the NRCM simulation. This is consistent with the mean

Fig. 4. Time-Space spectra for (a) U850 and (b) precipitation from the observation. The right panels are for the model. Zonal wavenumber 1, and frequency 0.1 (50 days), represent the

To further explore the MJO in the NRCM, the longitudinal variation of the MJO variance of U850 and P are shown in Fig. 5. For U850, over the Indian Ocean, the variance is underestimated, particularly near the equator (5°N-5°S, Fig. 5, right). However, when a larger area is considered (15°S-15°N, Fig. 5, left), the differences between the observation and the model become smaller. Over the west Pacific, however, the model overestimates the MJO variance in U850. For P, the MJO variance is greatly underestimated over the Indian Ocean (Fig. 6), particularly over the western Indian Ocean, where most MJO initiation occurs. This is consistent with the lack of precipitation over the equatorial Indian and west

It is natural to enquire how the MJO simulation in the NRCM compares with those in GCM simulations. A quick comparison with GCM simulations reveals that the MJO in the NRCM is not better than those in GCMs. This is less than satisfactory considering that the model is forced by time-varying reanalysis boundary conditions. As a result, we further diagnoss the

U850 and P in Fig. 1. The results are similar using other variables.

dominant MJO scales. All are averaged over 10°S-10°N.

role of the mean state in the simulated MJO statistics.

Pacific Ocean as shown in Fig. 1.

**3.2 MJO** 

Fig. 5. (left) Variance of U850 averaged over 15°S-15°N during (a) all season, (b) boreal winter (DJFM), and (c) boreal summer (JJAS). Right panels are averaged over 5°S-5°N.

Fig. 6. (left) Variance of P averaged over 15°S-15°N during (a) all season, (b) boreal winter, and (c) boreal summer. Right panels are averaged over 5°S-5°N.

Mean State and the MJO in a High Resolution Nested Regional Climate Model 77

Fig. 9 shows the role of the observed mean precipitation on the P\* variance. During the boreal winter (Fig. 9a), the P\* variance is over the southern hemisphere with three peaks, one over the Indian Ocean, and the other two over the west Pacific. The P\* variance is always very well collocated with the stronger mean precipitation. This cannot be said for the NRCM simulation (Fig. 10a), in particular, the P\* variance seems to avoid the equator. During the summer, the observed P\* variance is in the northern hemisphere (Fig. 9b), however the model produces spurious variance in the SPCZ region and the eastern Pacific. Note that the P\* variance is very small over the equatorial Indian Ocean due to the lack of precipitation in that region in the model. This is consistent with the mean annual precipitation (Fig. 1) and the spectrum (Fig. 4) indicating the role of the mean state on the simulated MJO. Next, we describe how the P\* variance is affected by the mean distribution

Fig. 9. Mean P (mm day-1, shaded) and variance of P\* (mm2 day-2, contoured) from the observation (CMAP) during the (a) boreal winter (DJFM), and (b) boreal spring (JJAS).

of U850.

Contour intervals are 2 mm2 day-2.

Fig. 10. Same as Fig. 9, but for the model.

#### **3.3 Role of the mean state on the MJO**

Role of the mean state on the simulated MJO is described with respect to U850 and Precipitation. The MJO is represented by the variances of U850\* and P\*. Figs. 7 and 8 show the role of mean U850 on the U850\* variance from the observation and model, respectively. The MJO variance (contoured) and the westerlies (yellow hues) are reasonably collocated in the reanalysis (Fig. 7), but not quite as well in the NRCM (Fig. 8), particularly over the equatorial Indian Ocean. During the boreal winter, simulated westerlies and the MJO variance (Fig. 8a) are stronger and located further from the equator compared to the reanalysis (Fig. 7a). This is the season when the MJO is strongest (Zhang and Dong, 2004). During the boreal summer, the observed variance of the MJO is located north of the equator (Fig. 7b). The simulated variance during the summer in the northern Indian Ocean is greatly reduced in the simulation (Fig. 8b).

Fig. 7. Mean U850 (m s-1, shaded) and variance of U850\* (m2 s-2, contoured) from the NCEP-NCAR reanalysis during the (a) boreal winter (DJFM), and (b) boreal spring (JJAS). Contour intervals are 1 m2 s-2.

Fig. 8. Same as Fig. 7, but for the model.

Role of the mean state on the simulated MJO is described with respect to U850 and Precipitation. The MJO is represented by the variances of U850\* and P\*. Figs. 7 and 8 show the role of mean U850 on the U850\* variance from the observation and model, respectively. The MJO variance (contoured) and the westerlies (yellow hues) are reasonably collocated in the reanalysis (Fig. 7), but not quite as well in the NRCM (Fig. 8), particularly over the equatorial Indian Ocean. During the boreal winter, simulated westerlies and the MJO variance (Fig. 8a) are stronger and located further from the equator compared to the reanalysis (Fig. 7a). This is the season when the MJO is strongest (Zhang and Dong, 2004). During the boreal summer, the observed variance of the MJO is located north of the equator (Fig. 7b). The simulated variance during the summer in the northern Indian Ocean is greatly

Fig. 7. Mean U850 (m s-1, shaded) and variance of U850\* (m2 s-2, contoured) from the NCEP-NCAR reanalysis during the (a) boreal winter (DJFM), and (b) boreal spring (JJAS). Contour

**3.3 Role of the mean state on the MJO** 

reduced in the simulation (Fig. 8b).

intervals are 1 m2 s-2.

Fig. 8. Same as Fig. 7, but for the model.

Fig. 9 shows the role of the observed mean precipitation on the P\* variance. During the boreal winter (Fig. 9a), the P\* variance is over the southern hemisphere with three peaks, one over the Indian Ocean, and the other two over the west Pacific. The P\* variance is always very well collocated with the stronger mean precipitation. This cannot be said for the NRCM simulation (Fig. 10a), in particular, the P\* variance seems to avoid the equator. During the summer, the observed P\* variance is in the northern hemisphere (Fig. 9b), however the model produces spurious variance in the SPCZ region and the eastern Pacific. Note that the P\* variance is very small over the equatorial Indian Ocean due to the lack of precipitation in that region in the model. This is consistent with the mean annual precipitation (Fig. 1) and the spectrum (Fig. 4) indicating the role of the mean state on the simulated MJO. Next, we describe how the P\* variance is affected by the mean distribution of U850.

Fig. 9. Mean P (mm day-1, shaded) and variance of P\* (mm2 day-2, contoured) from the observation (CMAP) during the (a) boreal winter (DJFM), and (b) boreal spring (JJAS). Contour intervals are 2 mm2 day-2.

Fig. 10. Same as Fig. 9, but for the model.

Mean State and the MJO in a High Resolution Nested Regional Climate Model 79

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

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

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

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

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

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;

nested domains inside the model also did not improve the mean state (Ray et al., 2011).

on MM5. Both TCMs are useful tools to study the MJO dynamics and its initiation.

participate effectively in simulating the mean conditions.

tropical variability, although it is likely to be model dependent.

needed to have a better idea of the effect of mean state on the MJO.

**4. Conclusion** 

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 between the convection and circulation in the model.

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 intervals are 2 mm2 day-2.

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