The Great Paradox of Indian Monsoon Failure (Unraveling The Mystery of Indian Monsoon Failure During El Niño)

Transcription

1 The Great Paradox of Indian Monsoon Failure (Unraveling The Mystery of Indian Monsoon Failure During El Niño) K. Krishna Kumar, B. Rajagopalan, M. Hoerling, G. Bates and M. Cane Point-by-point response to reviewers comments Review 1 The paper attempts to resolve the paradox of why severe droughts in India (more than 2 s.d. deficit of All India rainfall) are accompanied by El Niños while major El Niño events do not always produce severe droughts. The authors find that the spatial pattern of sea surface temperature (SST) anomalies over the Pacific associated with El Niños that produce severe Indian droughts is significantly different from that associated with El Niños that do not produce Indian droughts. By conducting sensitivity experiments with a general circulation model (namely, the CCM3), they show that the atmospheric response to difference in SST pattern can explain why some El Niños produce monsoon droughts while others do not. This is a good study that provides a new insight on the physical mechanism of how El Niños influence the Indian monsoon. However, there are a couple of lacunae in the paper related partly to the robustness of the main result and partly to breath of implication of the result. I shall be happy to recommend the paper for publication in Science subject to a revision that incorporates satisfactory clarification of the following major and minor points. We appreciate the reviewer s recommendation to accept the manuscript. Below, we respond to your specific comments, and have made the revisions that further strengthen the key points of the paper in particular by illustrating the reproducibility of results within different climate models. Major Comments: 1. How robust is the result that different shades of El Niños with different east-west distribution of SST anomalies produce different precipitation response over Indian monsoon region? Could it be affected by systematic bias of this particular model? Therefore, the authors need to show that this result is not model dependent. It is learned that the authors have already done similar experiments with two other models. They should include results of these experiments to establish the robustness of the major result presented in this paper. The set of original experiments, using NCAR-CCM3, have been repeated using the new GFDL and NCEP climate models. The former is the atmospheric version of their AR4 model, and the latter is the new operational seasonal forecast model of NOAA. As shown in the attached Figures R1, R2 and R3, all reproduce the relation between Indian monsoon rainfall and the ENSO SST patterns. to those derived from the NCAR climate model as reported in the original manuscript. The paper has been revised to indicate this

2 robustness, and supplementary documentation of the model reproducibility is now included. 2.The study proposed (page 4, para 2) that ' two hypotheses are examined to understand this ambiguity in the El Niño- Indian monsoon relationship'. One hypothesis is that it is due to the fact that the Indian monsoon is an inherently noisy system while the other hypothesis is that the response over the Indian monsoon region is sensitive to details of east-west structure of the tropical Pacific SST. While the paper provides evidence to support the second hypothesis, it does not provide any discussion or comment on the first hypothesis, namely the role of the internal low frequency noise. A major discussion is required on this issue. There are several points related to this question that need to be clarified: (2.1)While it is shown here that a certain type of SST pattern can lead to a major Indian drought while some other SST patterns do not, what is the probability of having a major Indian drought in the absence of any of these SST anomalies? The authors should comment on this from their long control run (or control runs of the other models). Whilst the paper emphasizes the Indian monsoon mean rainfall response (i.e., signal) related to two different El Niño forcings, it also illustrates the uncertainty of each signal (i.e., the inherent noise). In particular, the PDFs of Fig. 4 display both the signal, as indicated by the shift in means, and the inherent noise, as indicated by the spread of each PDF. As you state, these confirm the presence of different signals, and thereby support the second hypothesis. Nonetheless, the responses to the different SSTs are also shown to be probabilistic, owing to omnipresent internal variability. We have revised the paper by providing new text that speaks more clearly to these two factors for Indian monsoon variability. State it as: The inherent internal variability means that a given year may violate the expected relationship between monsoon rainfall and central Pacific SST, but the shifts in the mean rainfall is highly significant. (2.2)The probability distribution function (PDF) of monsoon rainfall due to a given SST pattern is a Gaussian (Fig.4C), the width of which indicates the amplitude of internal interannual variability (IAV) of monsoon simulated by the model. Given this uncertainty, how significant is the shift of the mean of the PDF due to difference in SST forcing (bottom panel of Fig.4C)? Since different models may have different internal IAV of monsoon, does the shift of the PDF as a result of response of different SST remains significant in other models? Again, the authors may be able to answer these questions from results of the two other models. The mean shift of the PDFs in Fig. 4c is significant at 95%. Further, as indicated in the attached figure R1, a similar mean shift and significance thereof, occur when repeating the experiments using two additional climate models. This information is now made available in the supplementary material, and we have added text indicating the reproducibility among models in the revised manuscript. While R3 is different, but still separates +EOF2 from EOF2; it just no longer separates it from the mean as strongly. Minor comments

3 3. While several factors influence monsoon droughts, the study addresses only influence of El Niño on monsoon drought. In fact it does not address the question why a large number of monsoon droughts are not related to El Niño. Therefore, the title of the paper is misleading as it gives the the impression that the paradox is related to whole spectrum of monsoon failures. An appropriate title may be ' The Great Paradox of Indian Monsoon - El Niño Relationship' This point is well taken and our focus in this research is on the link with ENSO, which provides predictability. There are moderate drought years not associated with ENSO, and these may be consistent with the internal variability, or other aspects of ocean influences not focused upon herein. We have changed the paper s title accordingly to Unraveling The Mystery of Indian Monsoon Failure During El Niño, to better reflect its core message. 4.An in-depth analysis has not been made in the paper to understand how different SST pattern lead to difference in response over the Indian region. The representation of the Walker circulation by the velocity potential at 200 hpa is rather inadequate. Representation of the Walker circulation from the winds themselves may be better. Also, the authors may like to comment on the extra-tropical teleconnection ( Goswami and Xavier, GRL, VOL. 32, L18717, doi: /2005gl023216, 2005 ) through which El Niño could influence the Indian monsoon. As the precipitaion distribution over the tropical Pacific is quite different during the two shades of El Niño, the stationary waves they set up will also be quite different and hence could lead to a significantly different tropospheric temperature (temperature averaged between 200 and 600 hpa, TT) gradient over the monsoon region during June-September (JJAS). The role of extra-tropical teleconnection cannot be ruled out and the results presented in the reference above are interesting. And we fully support further investigation on this issue both data analysis and modeling experiments. Nonetheless, it is clear from the 133-yr Indian monsoon rainfall record that ENSO is the dominant forcing in terms of producing severe droughts. Regarding the mechanism by which the two flavors of ENSO yield different impacts on the Indian region, we have offered only one possible candidate - namely a shift in the Walker Cell branches. We have added language to the text that speaks to other plausible candidate processes, and we agree that further in-depth research on various mechanisms is called for. Review 2 This paper analyzes the difference between strong (> 1 std) El Niño events, stratified according to whether drought (ISMR rainfall < -1 std) occurred over India, using records The resulting SST difference pattern is argued to resemble a linear combination of the 2 leading empirical orthogonal functions (EOFs) of monthly tropical Pacific SST anomalies. An ensemble of atmospheric GCM runs forced with this EOFreconstructed SST anomaly pattern yields a pattern of divergent mass circulation with similarities to the one seen to differentiate the IMSR during El Niño years. The paper thus argues that the SST differences between ENSO events are responsible for the

4 differences in their impact on Indian rainfall. While the results are interesting, I find the modeling approach flawed because of the use of SST EOF patterns to force the GCM. The results would be considerably more straightforward to interpret if the SST difference field in Fig. 2A were used to force the GCM instead. The SST anomaly patterns in Figs 2A (obs) and 4A (EOFs) are actually quite different in detail, which makes the interpretation of the similarities/differences in precipitation and circulation between Figs 2B and 4B unnecessarily difficult. Although the results seem promising, they are thus inconclusive. We have confirmed the robustness of our simulations by analyzing a 16-member ensemble of CCM3-POGA runs that span These utilize the complete field of observed Pacific Oceans SSTs between 25N-25S. We have constructed the ensemble rainfall composites for the years identified as "severe drought" and "drought free" from the scatter plot of Fig. 1. The SST difference between these two is then essentially the map shown in Fig. 2A. The PDFs of the 16 separate composites (corresponding to each of the available POGA realizations) for these severe drought and drought free years are shown in the attached Fig R4. The mean rainfall between these two groups is statistically significant at 90% confidence. A distinct bias toward drier outcome based on the composite of years with greater central Pacific SST warming is evident. The spatial plot of the 16-member averaged rainfall differences between these composites is provided in Fig. R5, and are strikingly similar to the rainfall difference composites in observed (Fig. 1) and in the idealized model experiments (Fig. 4). Specific Comments: 1. Title: It seems scarcely a "great paradox" that drought has "always" been accompanied by El Niño, while El Niño does not always produce drought. In fact, rainfall deficits apparently do occur in the absence of El Niño, viz. "roughly half of the deficits/excess in ISMR have occurred in non-enso years: of the 22 (19) strongly dry (wet) years during , only 11 (8) were associated with El Niño (La Niña)" (Gadgil et al., 2004, GRL). Thus the association seems somewhat definition-dependent. Similarly, it seems a bit of an overstatement to assert that the "conventional wisdom" is that El Niño causes a "monolithic failure" of the Indian monsoon, since it is quite well known that there is considerable spread in the evolution and timing of ENSO events. We appreciate your comment, and have modified the title to Unraveling The Mystery of Indian Monsoon Failure During El Niño to, better reflect this paper s core message. 2. P.3, ln.-7: Even in most statistical forecast tools, I doubt that ENSO is the only predictor used (eg the IMD models). Dynamical forecast models (ie GCMs) are not based explicitly on ENSO. Almost all the predictors used in the statistical models are some form of ENSO. It is true that GCM-based predictions, using for example coupled models, permit an impact on the

5 Indian monsoon from the global field of SST anomalies. Nonetheless, there is the basic premise that the suitability of a dynamical model for such purposes hinges critically on the realism of its ENSO-monsoon correlation. 3. The physical magnitudes of 1-sigma departures of rainfall and SST should be included in the caption of Fig. 1, and the rationale for equating drought with a 1-sigma rainfall anomaly clarified. We used 1-sigma of the standardized anomolies. The climatological average of All India monsoon rainfall is 850cm and its standard deviation is 84cm. The +/1-sigma threshold of the standardized anomalies correspond to about +/- 1-standard deviation departure from the mean. This definition amounts to the Indian Meteorological Department s definition of normal, drought and flood which are based on +/- 10% of average rainfall. We have clarified this point in the revised paper. 4. P. 4, ln.-5: One needs to specify the location of these changes in ascent and rainfall. Although mass continuity requires increased descent, its spatial distribution is not straightforward. We agree. We have added an enlarged discussion of plausible physical mechanisms for the Indian monsoon sensitivity to El Niño-like forcing. 5. P. 5, ln.-7: The relationship between velocity potential and mass divergence needs to be explained, together with their dynamical relationship to precipitation. The velocity potential is a effectively a spatially smoothed representation of the mass convergence and divergence, emphasizing the planetary wave components of the mass divergence that is likely of greatest relevance to seasonal mean monsoon scales. We have added clarifying language to the revised paper. 6. P. 5, ln. -4: Details of statistical significance levels and degrees of freedom should be included for proper interpretation of Fig. 2B. We used a standard two sample t-test and the degrees of freedome is 5. The colored regions are statistically significant at 90% confidence level. Please see Materials and Methods #2. 7. Fig. 3: The EOF analysis of Pacific SST appears to include all months. The seasonal evolution of individual ENSO events (tending to peak at the end of the calendar year) may be key to impacts on the Indian summer monsoon. The second EOF of SST has often been equated with the transition phase of ENSO, and thus might be relatively more important to the connection with the monsoon. Thus, experiments with linear combination of 2-sigma excursions of the PCs need to be carefully interpreted. Your point is well taken. The physical interpretation of the second EOF -- especially as perhaps representing a distinct physical mode of the coupled system -- is debatable.

6 We prefer to view the linear combination of the two leading patterns as a concise phase space for describing inter-event differences in the oceanic expressions of El Niño. 8. Fig. 4: There are rather large differences in precipitation between the model (Fig. 4B) and observations (Fig. 2B), and it is not clear how far these can be accounted for by the differences in the SST fields. GCM experiments using the anomaly pattern in Fig. 2A may help. Do the 2-sigma linear combination of SST EOFs match the outlier years in Fig. 1? The differences could be due to sample size differences. Figure 2B is based on handful (3~5) of years from the post 1979 data while Figure 4B is based on several ensembles.

7 Figure R1: The PDFs of Indian monsoon rainfall corresponding to control (green) and forced experiments (ii) and (iii) with 2SD imposed SST anomalies, from the CCM3 model. Same as top and bottom panels of Figure 4 C

8 Figure R2: Same as Figure R1 but from the AM2 model

9 Figure R3 Same as Figure R1 but from the GFS model

10 Figure R4 The PDFs of Indian monsoon rainfall corresponding to severe drought (red) and drought free (blue) years of POGA simulations from CCM3 model.

11 Figure R5 Composite rainfall difference map between severe drought and drought free years of the POGA simulations.