Reviewer: 1 Comments to the Author This study suggested that the AMO modulates the relationship between the ISMR and ASMR, making this relationship weaker during the period 1932-1966. This conclusion is very interesting, and the results shown in the paper may be helpful for a further understanding of the remote impacts of the AMO. However, I found there are some problems, mainly on the presentation, in the current version, which are listed below. Reply Thanks for your valuable comments. We have revised manuscript carefully based on your comments and detailed responses are presented below. 1. The correlation between 10-m zonal winds and wind speeds shown in Figure 2 seems to serve no purpose. It is surely be positive for westerlies and negative for easterlies. It seems to me that Figure S1, for both SON and DJF, can be merged into Figure 2. 2. How to draw Figures 7 and 9? The method should be given in more details. Is the AMO index same as that shown in Fig. 1? AMO is an interdecadal variation, and thus it would be hard to interpret the regression onto the AMO index during each sub-period. It seems to me that the anomalies for each sub-period from the 1903-1992 averages, rather than the regression onto the AMO index, can be simply used to illustrate possible impact of the AMO. 3. Figure 8 is not clear, especially for the geopotential anomalies. And in this figure it is hard to see “the upper level wave flux upstream of Australia can be traced back to a geopotential anomaly center in the Atlantic region”, and “During sub-period 2, we can see a relatively concentrated and strong wave flux over the north Australia as compared to the sub-period 3”, as argued by the authors. 4. There are many bugs in the manuscript. The authors should carefully revise the whole manuscript. Reviewer: 2 Comments to the Author The paper sets out to explore the reason why the inter-monsoon connection between Indian and Australian summer monsoons weakened during the 1932-1966 period, and suggests that the AMO plays a potential role in weakening this connection, via abating the impacts of ENSO on the ASMR. I find that the results of the paper and their interpretation interesting and convincing. However, the paper need some revisions before it can be considered for publication. The detailed comments are listed in the following. 1. As illustrated by authors, the positive AMO is related with the enhanced ASMR, and decreases the ENSO’s effect. Both of the ISM and ASMR have a negative relationship with ENSO. Many studies have suggested that there is a positive connection between the AMO and the ISM. Authors should give some analysis and interpretations about the relationships among the AMO, ISM and ENSO. And please show the ISM and ASMR indices in figure 1. I think it helps us understand this question better. 2. Data quality is the limitation of the paper. Authors should check the results from another data sets, such as ERA-20C. 3. The AMO has got into its positive phase after the mid-1990s. What about the relationship between the ISM and ASMR during this period? 4. Line 169, the term “spatial correlation” is not accurate. 5. Line 209-210, “Similarly, we can observe positive and negative wind anomalies in Fig. 6a;”. I don’t think they are similar. 6. Figure 8 is not clear. Please redraw this figure and show the results of the Northern Hemisphere.
Abstractcomphwe comprehensively evaluated the Advanced Research Weather Research and Forecasting (WRF-ARW; WRFV3.7) model skill in simulating the track,landfall and intensity of the tropical cyclone Hudhud that formed over the northeastern Bay of Bengal during 7th-14th October 2014 sensitivity to the physics parameterizations and initial conditions. Kain-Fritsch cumulus parameterization has shown relatively superior performance when compared to other available schemes, similarly YSU PBL physics and Ferrier (new eta) micro physics parameterizations has shown the superior performance, In addition to this we also evaluated the model skill sensitivity to the initial conditions in 24 hour interval. Overall model able to simulate the cyclone reasonably in terms of track, rainfall and intensity. The error/bias of these variables increased with the increasing lead time.