**4.2 Numerical simulations of Typhoon Choi-Wan in 2009**

The discussion in this subsection focuses on the mature phase of Typhoon Choi-Wan in 2009, which passed near the moored buoy of the NOAA Kuroshio Extension Observatory (KEO) located at 32.3ºN, 144.5ºE in the North Pacific recirculation gyre south of the Kuroshio Extension (Bond et al., 2011). The JMA global spectral model with a grid spacing of 20 km predicted the intensity of Choi-Wan to be much stronger in its mature phase than indicated by JMA best-track analysis, although the JMA track prediction was accurate.

This study investigated the effect of NHM parameters (SSC, atmospheric and oceanic environmental conditions, horizontal resolution, and cloud physics) on the intensity and track of simulated Choi-Wan. TC intensity is measured in terms of MCP throughout subsection 4.2.

#### **4.2.1 The impact of atmospheric environment on simulations**

CPs obtained using GA for initial atmospheric and lateral-boundary conditions are lower than those obtained using JCDAS (Fig. 15). Simulated CP deepens in the early part of all simulations, but the period of deepening for JCDAS conditions is longer than that for GA conditions. The minimum simulated CP is maintained for almost 24 h for GA conditions, whereas the trend of CP for JCDAS conditions shows a sudden reversal after reaching its minimum (Fig. 15).

Track predictions indicate slower speeds of passage of simulated Choi-Wan than that of JMA best-track analysis (Fig. 16). Interestingly, the simulated tracks using GA conditions are close to the JMA best track, whereas those using JCDAS conditions clearly show a westward bias north of 25ºN (Fig. 16).

(c)

(d)

suppression of the intensification process. The negative processes thus lead to weakening of adiabatic heating in the upper troposphere and contribute to vortex Rossby wave activity.

The discussion in this subsection focuses on the mature phase of Typhoon Choi-Wan in 2009, which passed near the moored buoy of the NOAA Kuroshio Extension Observatory (KEO) located at 32.3ºN, 144.5ºE in the North Pacific recirculation gyre south of the Kuroshio Extension (Bond et al., 2011). The JMA global spectral model with a grid spacing of 20 km predicted the intensity of Choi-Wan to be much stronger in its mature phase than indicated by JMA best-track analysis, although the JMA track prediction was accurate.

This study investigated the effect of NHM parameters (SSC, atmospheric and oceanic environmental conditions, horizontal resolution, and cloud physics) on the intensity and track of simulated Choi-Wan. TC intensity is measured in terms of MCP throughout subsection 4.2.

CPs obtained using GA for initial atmospheric and lateral-boundary conditions are lower than those obtained using JCDAS (Fig. 15). Simulated CP deepens in the early part of all simulations, but the period of deepening for JCDAS conditions is longer than that for GA conditions. The minimum simulated CP is maintained for almost 24 h for GA conditions, whereas the trend of

Track predictions indicate slower speeds of passage of simulated Choi-Wan than that of JMA best-track analysis (Fig. 16). Interestingly, the simulated tracks using GA conditions are close to the JMA best track, whereas those using JCDAS conditions clearly show a westward

CP for JCDAS conditions shows a sudden reversal after reaching its minimum (Fig. 15).

Fig. 14. As in Fig. 12, but for experiment OC with *md* = 17.5.

(a)

(b)

**4.2 Numerical simulations of Typhoon Choi-Wan in 2009** 

**4.2.1 The impact of atmospheric environment on simulations** 

bias north of 25ºN (Fig. 16).

Fig. 15. Evolution of CPs for various simulations of Typhoon Choi-Wan in 2009 from 00:00 UTC 17 September and JMA best-track CP. Model specifications are (a) 6 km horizontal grid spacing and GA, (b) 6 km horizontal grid spacing and JCDAS, (c) 12 km horizontal grid spacing and GA and (d) 12 km horizontal grid spacing and JCDAS. Results shown include those obtained by both the NHM and NHM-wave-ocean coupled model in combination with the oceanic reanalysis dataset with a horizontal grid spacing of 0.1º or 0.5º (see Table 2).

Fig. 16. Various simulated tracks for Typhoon Choi-Wan in 2009 from 00:00 UTC 17 September and the JMA best track. Model specifications are (a) 6 km horizontal grid spacing and GA, (b) 6 km horizontal grid spacing and JCDAS, (c) 12 km horizontal grid spacing and GA and (d) 12 km horizontal grid spacing and JCDAS. Results shown include those obtained by both the NHM and NHM-wave-ocean coupled model in combination with the oceanic reanalysis dataset with a horizontal grid spacing of 0.1º or 0.5º (see Table 2).

Effect of the Ocean on TC 63

Fig. 19. Horizontal distributions of initial SST derived from oceanic reanalysis data with horizontal grid spacing of (a) 0.5º and (b) 0.1º. Contours show initial sea-level pressure.

**4.2.3 The impact of horizontal resolution on simulations** 

(a) (b)

predictions (Fig. 16).

resolution.

environment.

These results indicate that the impacts of these two pre-existing oceanic environment datasets on the evolution of simulated CP are different, but the difference is considerably smaller than that obtained using GA and JCDAS atmospheric environment data (Fig. 15). The pre-existing oceanic environment dataset used has no noticeable effect on track

Horizontal resolutions of 6 and 12 km show marked differences in the simulated evolution of CP when GA data are used for initial and lateral-boundary atmospheric conditions, but little difference when using JCDAS data (Fig. 15). Horizontal resolution has no impact on track prediction, as is also the case for choice of pre-existing oceanic environment (Fig. 16). Differences of simulations of the structure of Choi-wan at the mature phase for horizontal resolutions of 6 and 12 km are investigated by considering the simulated horizontal distribution of PV at 1500 m altitude (Fig. 20) and that of hourly precipitation at the surface (Fig. 21). Each of the horizontal distributions of PV (Fig. 20) shows horizontal annular distributions about the center of the simulated Choi-Wan. PV is greatest for simulation C6G5I, but the PVs of simulations C6J5I and C12J5I are similar, indicating that PV near the center of the simulated Choi-Wan is not necessarily dependent on horizontal

However, the PV distributions of simulations C6G5I and C6J5I show more detailed structure, such as mesovortices within the spiral bands, than those of simulations C12G5I and C12J5I. Differences are notable in the size of horizontal annular distributions about the center of the simulated Choi-Wan among the four simulations shown in Fig. 20. For example, simulation C6G5I produces a small, compact PV pattern, whereas that of simulation C12J5I is considerably larger. These results indicate that the size of the simulated Choi-Wan is influenced by horizontal resolution as well as by the pre-existing atmospheric

The horizontal distributions of initial atmospheric pressure at 5850 m altitude (near the 500 hPa level) for GA and JCDAS initial conditions show differences in both magnitude and contour patterns for Choi-Wan and the subtropical high to the northeast (Fig. 17). Moreover, the horizontal distributions of initial relative humidity at 5850 m altitude from GA and JCDAS data are clearly different, particularly around the center of Choi-Wan and along the cold front (Fig. 18). These differences of the initial atmospheric environment affect simulations of both the intensity and track of Choi-wan. These impacts on the simulations of intensity affect not only the value of CP but also its evolution.

Fig. 17. Horizontal distributions of initial pressure at 5850 m altitude. Atmospheric initial and boundary conditions are from (a) GA and (b) JCDAS.

Fig. 18. As in Fig. 17, but for relative humidity.

#### **4.2.2 The Impact of oceanic environment on simulations**

The horizontal distributions of initial SST obtained from oceanic reanalysis data with horizontal grid spacings of 0.5 º and 0.1º are clearly different around the center of Choi-Wan and in the Kuroshio Extension area. The data with a horizontal grid spacing of 0.1º resolve both the Kuroshio meander and warm-eddy structure around the Kuroshio Extension (Fig. 19). Both datasets reproduce the latitudinal SST front along 35ºN, indicating that synoptic oceanic features are almost the same for both SST fields.

The horizontal distributions of initial atmospheric pressure at 5850 m altitude (near the 500 hPa level) for GA and JCDAS initial conditions show differences in both magnitude and contour patterns for Choi-Wan and the subtropical high to the northeast (Fig. 17). Moreover, the horizontal distributions of initial relative humidity at 5850 m altitude from GA and JCDAS data are clearly different, particularly around the center of Choi-Wan and along the cold front (Fig. 18). These differences of the initial atmospheric environment affect simulations of both the intensity and track of Choi-wan. These impacts on the simulations of

Fig. 17. Horizontal distributions of initial pressure at 5850 m altitude. Atmospheric initial

The horizontal distributions of initial SST obtained from oceanic reanalysis data with horizontal grid spacings of 0.5 º and 0.1º are clearly different around the center of Choi-Wan and in the Kuroshio Extension area. The data with a horizontal grid spacing of 0.1º resolve both the Kuroshio meander and warm-eddy structure around the Kuroshio Extension (Fig. 19). Both datasets reproduce the latitudinal SST front along 35ºN, indicating that synoptic

intensity affect not only the value of CP but also its evolution.

(a) (b)

and boundary conditions are from (a) GA and (b) JCDAS.

(a) (b)

Fig. 18. As in Fig. 17, but for relative humidity.

**4.2.2 The Impact of oceanic environment on simulations** 

oceanic features are almost the same for both SST fields.

Fig. 19. Horizontal distributions of initial SST derived from oceanic reanalysis data with horizontal grid spacing of (a) 0.5º and (b) 0.1º. Contours show initial sea-level pressure.

These results indicate that the impacts of these two pre-existing oceanic environment datasets on the evolution of simulated CP are different, but the difference is considerably smaller than that obtained using GA and JCDAS atmospheric environment data (Fig. 15). The pre-existing oceanic environment dataset used has no noticeable effect on track predictions (Fig. 16).

#### **4.2.3 The impact of horizontal resolution on simulations**

Horizontal resolutions of 6 and 12 km show marked differences in the simulated evolution of CP when GA data are used for initial and lateral-boundary atmospheric conditions, but little difference when using JCDAS data (Fig. 15). Horizontal resolution has no impact on track prediction, as is also the case for choice of pre-existing oceanic environment (Fig. 16).

Differences of simulations of the structure of Choi-wan at the mature phase for horizontal resolutions of 6 and 12 km are investigated by considering the simulated horizontal distribution of PV at 1500 m altitude (Fig. 20) and that of hourly precipitation at the surface (Fig. 21). Each of the horizontal distributions of PV (Fig. 20) shows horizontal annular distributions about the center of the simulated Choi-Wan. PV is greatest for simulation C6G5I, but the PVs of simulations C6J5I and C12J5I are similar, indicating that PV near the center of the simulated Choi-Wan is not necessarily dependent on horizontal resolution.

However, the PV distributions of simulations C6G5I and C6J5I show more detailed structure, such as mesovortices within the spiral bands, than those of simulations C12G5I and C12J5I. Differences are notable in the size of horizontal annular distributions about the center of the simulated Choi-Wan among the four simulations shown in Fig. 20. For example, simulation C6G5I produces a small, compact PV pattern, whereas that of simulation C12J5I is considerably larger. These results indicate that the size of the simulated Choi-Wan is influenced by horizontal resolution as well as by the pre-existing atmospheric environment.

Effect of the Ocean on TC 65

The simulations of the horizontal distributions of hourly precipitation each show a wavenumber-1 pattern, except for simulation C6G5I, which shows a concentric eyewall (Fig. 21). Formation of the concentric eyewall is dependent on the intensity of the simulated Choi-Wan (Fig. 15). Indeed, the eye of Choi-Wan in simulation C6G5I is much smaller than in the other simulations (Figs. 20 and 21). The wave-number-1 patterns of simulated precipitation show heavy precipitation in the left upper quadrant of simulated Choi-Wan, where simulated PV is also high (Fig. 20). The National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission Microwave Imager (TRMM/TMI) data at 4:43 UTC on 19 September 2009 clearly shows a wave-number-1 pattern (Fig. 22), although the heavy rain to the north of Choi-Wan does not match the simulations shown in Fig. 21. The eye of Choi-Wan in TRMM/TMI data is nearly 1º in diameter, which is comparable with the C12G5I simulation (Figs. 20c and 21c), although TRMM/TMI data show more debris of the inner eyewall east of the eye. It should be noted that TRMM/TMI data come with certain uncertainty so that the horizontal distributions of hourly precipitation should be validated using observed *in situ* rain gauge data. However, it is difficult to obtain the data when a

Fig. 22. Horizontal distribution of rain rate (inches h-1) from TRMM/TMI data at 4:43 UTC on 19 September 2009 (http://www.nrlmry.navy.mil/TC.html). The center position is 29.0ºN, 141.4ºE, with a central pressure of 967 hPa and maximum wind speed of 75 knots.

The impacts of ice cloud, snow and graupel on the intensity and track predictions of simulated Choi-wan are investigated using the NHM and NHM-wave-ocean coupled model, but without the cold-rain cloud physics associated with ice cloud, snow and graupel (Fig. 23). Under these simulation conditions, total water around Choi-Wan is lower than that obtained in previous simulations, particularly in the rain shield on the northern side of the simulated Choi-Wan, and simulated CP deepens to some extent (Fig. 23). Interestingly, the simulated tracks are shifted eastward when compared with simulations that include coldrain physics (Fig. 23). The simulated tracks agree better to JMA best-track positions, except that the simulated speed of passage of Choi-Wan along its track is slower than JMA estimates (Fig. 23). These simulations suggest that cloud physics has considerable impact on

typhoon exists in the open ocean.

**4.2.4 The impact of cloud physics on simulations** 

simulations of the intensity and track of Choi-Wan.

Fig. 20. Horizontal distributions of PV at 1500 m altitude at 60 h (12:00 UTC on 19 September 2009) for simulations (a) C6G5I, (b) C6J5I, (c) C12G5I, and (d) C12J5I.

Fig. 21. Horizontal distribution of precipitation (mm h-1) at the surface at 60 h (12:00 UTC on 19 September 2009) for simulations (a) C6G5I, (b) C6J5I, (c) C12G5I, and (d) C12J5I.

Fig. 20. Horizontal distributions of PV at 1500 m altitude at 60 h (12:00 UTC on 19 September

Fig. 21. Horizontal distribution of precipitation (mm h-1) at the surface at 60 h (12:00 UTC on

19 September 2009) for simulations (a) C6G5I, (b) C6J5I, (c) C12G5I, and (d) C12J5I.

2009) for simulations (a) C6G5I, (b) C6J5I, (c) C12G5I, and (d) C12J5I.

(a) (c)

(b) (d)

(a) (c)

(b) (d)

The simulations of the horizontal distributions of hourly precipitation each show a wavenumber-1 pattern, except for simulation C6G5I, which shows a concentric eyewall (Fig. 21). Formation of the concentric eyewall is dependent on the intensity of the simulated Choi-Wan (Fig. 15). Indeed, the eye of Choi-Wan in simulation C6G5I is much smaller than in the other simulations (Figs. 20 and 21). The wave-number-1 patterns of simulated precipitation show heavy precipitation in the left upper quadrant of simulated Choi-Wan, where simulated PV is also high (Fig. 20). The National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission Microwave Imager (TRMM/TMI) data at 4:43 UTC on 19 September 2009 clearly shows a wave-number-1 pattern (Fig. 22), although the heavy rain to the north of Choi-Wan does not match the simulations shown in Fig. 21. The eye of Choi-Wan in TRMM/TMI data is nearly 1º in diameter, which is comparable with the C12G5I simulation (Figs. 20c and 21c), although TRMM/TMI data show more debris of the inner eyewall east of the eye. It should be noted that TRMM/TMI data come with certain uncertainty so that the horizontal distributions of hourly precipitation should be validated using observed *in situ* rain gauge data. However, it is difficult to obtain the data when a typhoon exists in the open ocean.

Fig. 22. Horizontal distribution of rain rate (inches h-1) from TRMM/TMI data at 4:43 UTC on 19 September 2009 (http://www.nrlmry.navy.mil/TC.html). The center position is 29.0ºN, 141.4ºE, with a central pressure of 967 hPa and maximum wind speed of 75 knots.

#### **4.2.4 The impact of cloud physics on simulations**

The impacts of ice cloud, snow and graupel on the intensity and track predictions of simulated Choi-wan are investigated using the NHM and NHM-wave-ocean coupled model, but without the cold-rain cloud physics associated with ice cloud, snow and graupel (Fig. 23). Under these simulation conditions, total water around Choi-Wan is lower than that obtained in previous simulations, particularly in the rain shield on the northern side of the simulated Choi-Wan, and simulated CP deepens to some extent (Fig. 23). Interestingly, the simulated tracks are shifted eastward when compared with simulations that include coldrain physics (Fig. 23). The simulated tracks agree better to JMA best-track positions, except that the simulated speed of passage of Choi-Wan along its track is slower than JMA estimates (Fig. 23). These simulations suggest that cloud physics has considerable impact on simulations of the intensity and track of Choi-Wan.

Effect of the Ocean on TC 67

evolution is thus sensitive to vertical turbulent mixing in the oceanic mixed layer caused by breaking surface waves. In contrast, the effects of processes in the surface-boundary and planetary-boundary layers are found in the relationship between MCP and MWS, although the impact of these on TC intensity and intensification is smaller than that of vortex-induced SSC. After formation of the annular ring within the inner core of the vortex, TC intensity is less sensitive to vortex-induced SSC. Budget analysis of AAM averaged over a 120 km x 120 km square at the center of the computational domain shows that vortex-induced SSC is effective in decreasing AAM due to vertical advection and stretching. This decrease leads to decay of the acceleration of vortex intensification and affects adiabatic heating processes in

Numerical simulations of Choi-Wan show that the effects of Choi-Wan-induced SSC and pre-existing oceanic conditions on simulations of TC track and intensity are smaller than those of pre-existing atmospheric conditions and cloud physics. Fine horizontal resolution of model parameters provides excessive simulations of TC intensity for the mature and decaying phases. Even though the excessive simulations of TC intensity are reduced by the effect of Choi-Wan-induced SSC, the improvement may have led to the simulation of excessive SSC induced by the passage of Choi-Wan. These results suggest that a model parameter, horizontal resolution of 1 to 2 km, is not always needed for TC simulations at the mature and decaying phases, or that both the atmosphere-wave-ocean coupled model and

This work was supported by the Japan Society for the Promotion of Science (JSPS) through the Grant-in-Aid for Scientific Research (C) (22540454) and by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) under Grant-in-Aid for

Bender, M. & Ginis, I. (2000). Real-time simulation of hurricane-ocean interaction. *Monthly* 

Bond, N. A.; Cronin, M. F.; Sabine, C.; Kawai, Y.; Ichikawa, H.; Freitag, P. & Ronnholm, K.

Deardorff, J. W. (1980). Stratocumulus-capped mixed layers derived from a threedimensional model. *Boundary-Layer Meteorology*, Vol. 18, pp.495-527. Deardorff, J. W. (1983). A multi-limit mixed-layer entrainment formulation. *Journal of* 

Donelan, M. A.; Haus, B. K.; Reul, N.; Plant, W. J.; Stiassnie, M.; Graber, H. C.; Brown, O. B.

very high winds. *Geophysical Research Letters*, Vol.31, L18306.

(2011). Upper ocean response to Typhoon Choi-Wan as measured by the Kuroshio Extension Observatory mooring. *Journal of Geophysical Research*, Vol. 116, C02031. Chen, S. S.; Price, J. F.; Zhao, W.; Donelan, D. A. & Walsh, E. J. (2007). The CBLAST-

Hurricane program and the next-generation fully coupled atmosphere-wave-ocean models for hurricane research and prediction. *Bulletin of American Meteorological* 

& Saltzman, E. S. (2004). On the limiting aerodynamic roughness of the ocean in

Scientific Research on Innovative Areas #2205 (in proposed research area 23106505).

atmospheric reanalysis data will require further development.

*Weather Review*, Vol.128, pp. 917-946.

*Physical Oceanography*, Vol.13, pp. 988-1002.

*Society*, Vol. 88, pp.311-317.

the upper troposphere.

**7. Acknowledgment** 

**8. References** 

Fig. 23. Left panel is the same as Fig. 15 and right panel is the same as Fig. 16, except that the results shown here were obtained by using the NHM or NHM-wave-ocean coupled model, but without the physics of ice cloud, snow and graupel.

### **5. Discussion**

The results of this study show that the effect of the ocean on TCs differs according to the phase of the TC, particularly during the intensification and mature or decaying phases. To accurately reproduce the intensification of a TC, the numerical model must resolve innercore dynamics, such as the development of mesovortices and the vortex merger events. The results obtained here are consistent with those of previous studies that show that 1 to 2 km horizontal resolution is required (e.g. Wang & Wu, 2004; Chen et al., 2007). This study also shows that vortex-induced SSC is effective in suppressing the intensification of a TC. The effect of SSC is not instantaneous but accumulated over time within the inner core of the vortex (Wada & Usui, 2007; Wada, 2009).

However, the use of fine horizontal resolution for model parameters produces excessively strong TC intensity during the mature and decaying phases. Numerical simulations obtained using the JMANHM-ocean coupled model with 24-km horizontal resolution produce reasonable simulations of CP (not shown) that are comparable with JMA best-track estimates around the KEO moored buoy. During the mature phase, the effects of both vortex-induced SSC and pre-existing oceanic environment on a TC are smaller than the effects of pre-existing atmospheric environment and cloud physics. What is clear from this study is that the final TC intensity at the mature phase is not dependent simply on SST directly below the vortex and horizontal resolution, but is determined by the background effects of the evolution of CP and SST. Therefore, for accurate predictions of a TC, careful attention must be paid to model specifications other than SST and horizontal resolution.

#### **6. Conclusion**

This chapter describes the interactions between TCs and the ocean on the basis of numerical experiments using a nonhydrostatic atmosphere model coupled with a third generation wave model and a multi-layer ocean model developed jointly by NPD and MRI in JMA. The results of idealized numerical experiments and numerical simulations of Typhoon Choi-Wan in 2009 allow the following conclusion to be drawn.

The evolution of CP of a vortex can be divided into four phases: the spin-up, slow intensification, rapid intensification, and mature phases. Vortex-induced SSC is effective in suppressing TC intensification from the spin-up phase to the rapid intensification phase. CP

Fig. 23. Left panel is the same as Fig. 15 and right panel is the same as Fig. 16, except that the results shown here were obtained by using the NHM or NHM-wave-ocean coupled model,

The results of this study show that the effect of the ocean on TCs differs according to the phase of the TC, particularly during the intensification and mature or decaying phases. To accurately reproduce the intensification of a TC, the numerical model must resolve innercore dynamics, such as the development of mesovortices and the vortex merger events. The results obtained here are consistent with those of previous studies that show that 1 to 2 km horizontal resolution is required (e.g. Wang & Wu, 2004; Chen et al., 2007). This study also shows that vortex-induced SSC is effective in suppressing the intensification of a TC. The effect of SSC is not instantaneous but accumulated over time within the inner core of the

However, the use of fine horizontal resolution for model parameters produces excessively strong TC intensity during the mature and decaying phases. Numerical simulations obtained using the JMANHM-ocean coupled model with 24-km horizontal resolution produce reasonable simulations of CP (not shown) that are comparable with JMA best-track estimates around the KEO moored buoy. During the mature phase, the effects of both vortex-induced SSC and pre-existing oceanic environment on a TC are smaller than the effects of pre-existing atmospheric environment and cloud physics. What is clear from this study is that the final TC intensity at the mature phase is not dependent simply on SST directly below the vortex and horizontal resolution, but is determined by the background effects of the evolution of CP and SST. Therefore, for accurate predictions of a TC, careful attention must be paid to model specifications other than SST and horizontal resolution.

This chapter describes the interactions between TCs and the ocean on the basis of numerical experiments using a nonhydrostatic atmosphere model coupled with a third generation wave model and a multi-layer ocean model developed jointly by NPD and MRI in JMA. The results of idealized numerical experiments and numerical simulations of Typhoon Choi-

The evolution of CP of a vortex can be divided into four phases: the spin-up, slow intensification, rapid intensification, and mature phases. Vortex-induced SSC is effective in suppressing TC intensification from the spin-up phase to the rapid intensification phase. CP

but without the physics of ice cloud, snow and graupel.

Wan in 2009 allow the following conclusion to be drawn.

vortex (Wada & Usui, 2007; Wada, 2009).

**5. Discussion** 

**6. Conclusion** 

evolution is thus sensitive to vertical turbulent mixing in the oceanic mixed layer caused by breaking surface waves. In contrast, the effects of processes in the surface-boundary and planetary-boundary layers are found in the relationship between MCP and MWS, although the impact of these on TC intensity and intensification is smaller than that of vortex-induced SSC. After formation of the annular ring within the inner core of the vortex, TC intensity is less sensitive to vortex-induced SSC. Budget analysis of AAM averaged over a 120 km x 120 km square at the center of the computational domain shows that vortex-induced SSC is effective in decreasing AAM due to vertical advection and stretching. This decrease leads to decay of the acceleration of vortex intensification and affects adiabatic heating processes in the upper troposphere.

Numerical simulations of Choi-Wan show that the effects of Choi-Wan-induced SSC and pre-existing oceanic conditions on simulations of TC track and intensity are smaller than those of pre-existing atmospheric conditions and cloud physics. Fine horizontal resolution of model parameters provides excessive simulations of TC intensity for the mature and decaying phases. Even though the excessive simulations of TC intensity are reduced by the effect of Choi-Wan-induced SSC, the improvement may have led to the simulation of excessive SSC induced by the passage of Choi-Wan. These results suggest that a model parameter, horizontal resolution of 1 to 2 km, is not always needed for TC simulations at the mature and decaying phases, or that both the atmosphere-wave-ocean coupled model and atmospheric reanalysis data will require further development.

#### **7. Acknowledgment**

This work was supported by the Japan Society for the Promotion of Science (JSPS) through the Grant-in-Aid for Scientific Research (C) (22540454) and by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) under Grant-in-Aid for Scientific Research on Innovative Areas #2205 (in proposed research area 23106505).

#### **8. References**


**4** 

Pallav Ray

*USA* 

**Mean State and the MJO in a** 

*International Pacific Research Center (IPRC), University of Hawaii* 

**High Resolution Nested Regional Climate Model** 

The Madden-Julian oscillation (MJO, Madden and Julian, 1971, 1972) is a dominant feature of intraseasonal (20-90 day) variability in the tropics. According to the classic view, the MJO begins as a positive convective anomaly in the equatorial western Indian Ocean. It then propagates eastward toward the maritime continent where convection weakens until the MJO reaches the west Pacific where the convection strengthens again. Convective coupling diminishes in the eastern Pacific in the presence of cooler sea surface temperature (SST), but the wind component in the upper troposphere may propagate eastward as free waves at about 12-15 m s-1, much faster than the MJO propagation speed of 5 m s-1 (Knutson et al., 1986). Global circumnavigation associated with the MJO can also be noticed in the uppertropospheric divergent wind (e.g., Krishnamurti et al., 1985; Knutson and Weickman, 1987) and moisture fields (Kikuchi and Takayabu, 2003), but is difficult to detect in parameters

The MJO has been found to influence a number of features in the tropics including the Indian summer monsoon (e.g., Yasunari, 1979), Australian monsoon (e.g., Hendon and Liebmann, 1990), tropical storms (e.g., Liebmann et al., 1994), and the initiation of El Nino events (e.g., Lau and Chan, 1985). However, the influence of the MJO is not limited to the tropics. The MJO affects the global medium and extended range weather forecasts (e.g., Jones and Schemn, 2000) and modulates the global angular momentum (e.g., Weickmann et al., 1997). This tropics-extratropics interaction produced by the MJO affects the skill of the northern hemisphere weather forecasts (Ferranti et al., 1990). The long periodicity of the MJO convection relates it with the predictability on seasonal time scales. As a result, longer-

There have been considerable advancements in understanding the different aspects of the MJO using observation, theory and numerical modeling. However, an accurate MJO simulation using numerical models remains an extremely difficult task due to a number of model deficiencies (Lin et al., 2006; Zhang et al., 2006; Kim et al., 2009). One such deficiency is the model's inability in capturing the correct mean state. The role of the mean state on the MJO was previously explored using GCMs (e.g., Slingo et al., 1996; Inness et al., 2003; Maloney and Hartmann, 2001; Ajayamohan and Goswami, 2007; Maloney, 2009), observations (Zhang and Dong, 2004), and model-observation comparison (Zhang et al., 2006). It is found that the realistic distributions of precipitation, lower-tropospheric zonal

range forecasts could be improved if the MJO can be predicted.

**1. Introduction** 

closer to the surface.

