Response to Reviewer’s Comments

Reviewer A

We sincerely thank this reviewer for insightful comments that helped in significantly improving the manuscript. Below are our point-by-point responses to the ‘specific comments’. 

GENERAL COMMENTS

We modified Figures 8, 10 and 11. In that, the composite maps (Figures 8, 10 and 11) are shown as composite differences with statistically significant regions (using the standard t-test) at 90% confidence levels shaded in gray. This strengthens our hypothesis and arguments that we make in the paper. Furthermore, it addresses the issue of “unsubstantiated speculation” mentioned by the reviewer in the “General Comments” part of the review. Given the small sample size we urge caution in interpreting the statistical significance – nonetheless, the t-test adds strength to the conclusions.  

SPECIFIC COMMENTS

1. “P. 7 line 7: “girded” should be “gridded”

This has been changed.

2. “P. 8 second paragraph: As in my previous comments, the discussion here still has some problems. First of all, what is the correlation coefficient in Fig. 4a? How much variance in the rainfall can be explained by the temperature? Also, as commented by the other reviewer, even if such a relationship is statistically significant, it is quite far stretched that temperatures in MAM can cause differences in rainfall in SON. Where does the memory reside? What is the physics? If the correlation in Fig. 4b is not statistically significant (in fact it is obvious from the scatterplot that the two do not have a significant relationship), how can the authors attribute a physical meaning to this “non-result”?

Firstly, the correlation between MAM surface temperature over Thailand and the ASO (monsoon seasonal) rainfall (Fig. 4a) is 0.37 which is statistically significant at 95% confidence level – we have mentioned this in the text.

Secondly, if we take the entire pre-monsoon season (March – July) mean surface temperature, it’s correlation with ASO rainfall is 0.3 (which is statistically significant at 90% confidence level) – see figure below. 

The main point of Figure 4a is to show that pre-monsoon surface temperatures and consequently, the land-sea gradient that it develops has an influence on the monsoon rainfall. In that, a stronger land temperature can lead to a strong land-sea gradient and consequently, more rainfall – thus, the positive correlation.

This land-sea gradient is the conventional wisdom of any monsoon climatology. However, the land region that is responsible for setting up of the land-sea gradient is debatable. Some have suggested, for the Indian monsoon case, that it should encompass most of Eurasia as opposed to just the Indian subcontinent (which seems intuitive).

We chose MAM temperatures in this research because it is the season with the highest climatological temperature (see Figure 3).  

With regards to Figure 4b we do mention in the text that the correlation is not statistically significant. An inverse relationship between the monsoon seasonal surface temperature and rainfall is to be expected (i.e. more rainfall leads to surface cooling and vice-versa). However, the monsoon seasonal temperature exhibits an increasing trend, especially in the post-1970 period – see Figure below. This is also corroborated in IPCC (2001). This increasing trend counters the inverse relationship and consequently, a weak correlation. We provided Figure 4b just for the sake of completeness and we have included the above point in the text.

3. “P 9-10 section 3.2: A linear trend is probably not the best fit to the time series in Figs. 5a and b. The temperatures and rainfall decreased till the mid 1970s and then climbed up again before dropping towards the second half of 1990s. Thus, a higher-order polynomial (3rd or 4th order) might actually be better. I suggest that the authors should attempt to make such a fit and compare the amount of variance explained with that of a linear fit. The objective of the study is to examine interdecadal variability, but such variability does not necessarily have to be linear. Again, any trend in Fig. 5c is not obvious and certainly not statistically significant. How can the authors claim “an increasing trend in monsoon seasonal temperatures”? What is important here is the consistency with the IPCC report, as discussed in the last part of the section.”

As suggested we have superimposed local polynomial (a nonparametric method) smoothers along with the linear regression lines in Figure 5. The local polynomial fits clearly show the decadal variability (as pointed out by the reviewer). 

We do not mention that the trend in Figure 5c is statistically significant. We only point to a general increase in monsoon seasonal temperatures (Figure 5c) that appears to be consistent with the general decrease in monsoon precipitation (Figure 5b). Our main summary is that these trends are consistent with the IPCC observations – as mentioned by the reviewer, with whom we agree on this.  

4. “P. 11 line9: Are you sure they are “identical”?

Perhaps, identical is a bit strong. But certainly, they are “very similar”.  We have changed the wording to this.

5. “P. 11 lines 14-15: insert the explanation given in the response to reviewers’ comments here, i.e. how you choose the three highest and lowest”

We have done this.

6. ”P. 11 lines 2-5 from bottom: What is the evidence of this statement?”

With the recent Figure 8, where we show the composite difference between Wet and Dry years along with the t-test the above sentences (the entire paragraph) have been changed. The nonlinearity aspect mentioned in the above lines has been removed. 

7. “P. 12 lines 7-8 from bottom: I don’t see much difference in the SST anomaly patterns in Fig. 10 in terms of the westward extension of the warm anomaly. The zero contour in both (a) and (b) extend to about 160E. The main difference lies in the magnitude of the maximum anomalies between the dateline and 90W. This difference reflects the choice of the years – 1982 and 1997 had the largest anomalies among all the years in the sample.”

The new Figure 10 shows significant positive anomalous SST in the El Nino years of post-1980 well to the east of the dateline. We agree with the reviewer that 1982 and 1997 events play a big role, which is our point. 

8. “Fig. 5: If the ordinate in all the plots represents the standardized anomalies, they should be dimensionless, or in units of standard deviation.”

We thank the reviewer for pointing out this mistake. It has been corrected – i.e. we removed the units from the plots, since they are standardized anomalies.