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A 368-Year Maximum Temperature Reconstruction Based on Tree Ring Data in Northwest Sichuan Plateau (NWSP), China” by Liangjun Zhu Et Al

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A 368-Year Maximum Temperature Reconstruction Based on Tree Ring Data in Northwest Sichuan Plateau (NWSP), China” by Liangjun Zhu Et Al Interactive comment on “A 368-year maximum temperature reconstruction based on tree ring data in northwest Sichuan Plateau (NWSP), China” by Liangjun Zhu et al. Liangjun Zhu et al. 5 [email protected] and [email protected] Response to Anonymous Referee #1: Thank you for your constructive comments on our manuscript, especially for correcting those grammatical mistakes. All comments are very valuable and helpful for revising and improving our MS, as well as the important guiding significance to 10 our researches. We have studied your comments carefully and have made correction. Major comments: 1. In the past 368 years, you identified seven short cold periods and three long warm periods (two long and one short). Could you explain why it appeared like this? Response: Comment accepted. Thanks, we have explained this phenomenon. Those cold or warm periods identified from our 15 reconstructions might be driven by solar and volcanic forcing. The occurrence of the cold or warm periods were nearly consistent with the solar and volcanic forcing. For the detailed information, please look up the lines 23-33, page 9. 2. You detected different significant periodicities of temperature variations in the past 368 years. Only for this, you thought the temperature variations could be driven by ENSO, PDO, AMO and solar activity, which may not accurate. Please give more 20 evidences if possible. Response: Comment accepted. Thanks, in addition to the MTM analysis, correlation analysis between our reconstruction and those large-scale atmospheric circulations indices also revealed the significantly correlations between our reconstruction and the ENSO, PDO, AMO and solar activity (see Table 3). On this basis, we added the teleconnection analysis between our reconstruction and SSTs. The RLST variables significantly correlated with the SSTs changes, especially the west and 25 equatorial Pacific as well as the north Atlantic Oceans (Fig. 9), which further verified the driving of large scale climate (e.g. ENSO, PDO and AMO) on regional temperature from another angle (for details, please see the lines 11-15, page 11). In addition, we also added the superposed epoch analysis of volcanic eruption and discussed the influence of volcanic forcing on temperature variations (Fig. 10; for details, please see the lines 24-34, pages 11) 30 Table 3 Correlation of the smoothed (11-yr moving averaged) temperature reconstruction and other large-scale climate system cycles. 1 Month AMO (n=156) PDO (n=112) MEI (n=62) PMAY -0.15 -0.43** -0.43** PJUN -0.19* -0.26** -0.42** PJUL -0.19* -0.27** -0.32* PAUG -0.17* -0.27** -0.27* PSEP -0.17* -0.24* -0.27* POCT -0.07 -0.19* -0.29* PNOV -0.05 -0.21* -0.26* PDEC -0.07 -0.21* -0.28* JAN -0.00 -0.34** -0.28* FEB -0.13 -0.29** -0.27* MAR -0.13 -0.39** -0.38** APR -0.13 -0.39** -0.41** MAY -0.18* -0.46** -0.43** JUN -0.22** -0.31** -0.42** JUL -0.22** -0.30** -0.31* AUG -0.21** -0.30** -0.28* AMO: Atlantic Multidecadal Oscillation; PDO: Pacific Decadal Oscillation; MEI: Multivariate ENSO Index * p < 0.05, ** p < 0.01 Fig. 9 Spatial correlations between estimated temperatures and monthly OISSTs at the global scale. The spatial correlation 5 was carried out for months (July-August) covering a time span from 1982 to 2012. 2 21 1.2 A B 1.0 20 0.8 ) ) 0.6 95% CONF INT ℃ ℃ 19 0.4 0.2 18 Temperature ( 0.0 Mean anomaly ( -0.2 17 1601 1642 1816 1763 1836 -0.4 1810 1992 16 -0.6 -6 -5 -4 -3 -2 -1 0 1 2 3 4 -6 -5 -4 -3 -2 -1 0 1 2 3 4 Years relative to volcanic events Years relative to volcanic events Fig. 10 The influence of volcanic forcing on temperature variations. Temperature change around the volcanic eruptions (A) and results of the superposed epoch analysis (SEA) for volcanic cooling (B). 5 3. References in text of the manuscript should be listed in chronological order. Response: Comment accepted. Thanks, we have listed references in text of our manuscript in chronological order. 4. It would be better if this manuscript is fluent by a native speaker again. Response: Comment accepted. Thanks, we have invited a specialist (native-English speaker) made a job of language revision, 10 in order to improve the ability of English expression. Other detailed comments: Line 11, Page 1: replace “for” with “in” Line 12, Page 1…… Line 4, page 21: replace “for NWSP” with “in NWSP”, “C” with “(C)”, and “for Kathmandu” with “in Kathmandu”. Response: Comment accepted. Thanks, we have corrected those mistakes in our MS according to your comments. For the 15 detailed information, please look up the new manuscript. Once again, thank you very much for your comments and suggestions. Best Regards, 20 Liangjun Zhu, on behalf of all co-authors 3 Response to Anonymous Referee #2: Thank you for your constructive comments on our manuscript. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied your comments carefully and have made correction. 5 1. Comments: The manuscript will benefit from a last check by a native speaker. However, readability will improve quite a lot following the careful language check done by reviewer 1. Response: Comment accepted. Thanks for this constructive comment. We have invited a specialist (native-English speaker) made a job of language revision, in order to improve the ability of English expression. 10 2. General and minor comments: 1) The only potential weakness that I see on the paper refers to the chronology replication. Only 16 trees compose the chronology. This makes me wonder if the EPS has been calculated taking into consideration that more than one core per tree have been included in the chronology. It is not the same to calculate the EPS for an “n” of 32 than 16. On the other hand, I 15 wonder if the few samples reaching back 1650 are cores from the same or different trees. How many trees are covering the part of the chronology with the lowest replication? Response: Comment accepted. Thanks, we have recalculated the EPS of the chronology. EPS has an overall mean of 0.9, well above the generally accepted 0.85 cutoff except for a brief period in the 1690s when it falls to just above 0.8 (the Rbar decline to 0.37). The lower EPS and Rbar values Ca.1695 (Fig. 2) seem to result from suppressed growth during this cool period in 20 more mature trees and somewhat erratic juvenile growth in the trees entering the chronology about this time (D'Arrigo et al. 2001). To extend the length of the chronology, we thus consider this chronology to be most reliable over the past 368 years (AD 1646-2013), which corresponds to a minimum sample depth of 5 cores (four trees) for our chronology (Fig. 2). In order to avoid confusing the reader, we have added some related description about the replication and EPS of the chronology in the text. Please see lines 9-16, page 4 in the text. 4 1.6 35 1.4 A 30 25 1.2 20 1.0 15 0.8 10 No. of cores Tree-ring index n ≥ 5 Tree-ring index 0.6 5 No. of cores 0.4 0 1.0 B 0.8 EPS = 0.8 0.6 0.4 EPS / Rbar Rbar 0.2 EPS 0.0 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 Year Fig. 2 The tree-ring chronology of Picea purpurea at timberline in Chali. A, Standard chronology and sample size changing with time and B, Rbar and EPS (see text for explanation) plotted for 50-year windows with 25-year overlap for the standard chronology. The vertical dashed line represents the cut-off point when the n ≥ 5 and EPS > 0.8. The horizontal red dashed 5 line represents the theoretical 0.8 threshold. 2) The authors compared the newly developed reconstruction with pre-existing temperature reconstructions. The correlation values obtained between the different reconstructions are not very high, but they are significant due the high number of observations. The interesting point of comparing different reconstructions is more related to medium and low-frequency 10 climate variations than interannual. Hence, I would suggest to filter the reconstructions and discuss the similarities among them in terms of decadal to multidecadal variations. Moving correlations will definitely help to visualize and describe the periods of agreement and disagreement. Response: Comment accepted. Thanks, we have filtered our and other reconstructions with 11- (medium-frequency) and 61- yr (low-frequency) moving averaged, and have visually described the similarities between them using the moving correlations. 15 Our reconstruction temperatures on the whole showed consistency with the near reconstruction in decadal (11-yr moving averaged) variations and the far away reconstruction multidecadal (61-yr moving averaged) variations, although some potentially differences remain (see lines 9-16, page 9 in the text; Fig. 8). However, those decadal or multidecadal differences between them might be due to differences in season, species and the different standardization approach applied on tree-ring data as well as those inherent differences in climate variables and its driven mechanisms (see lines 16-23, page 5 in the text). 5 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 Correlation coefficient-0.6 K11 N11 -0.8 K61 N61 -1.0 1576-16751601-17001626-17251651-17501676-17751701-18001726-18251751-18501776-18751801-19001826-19251851-19501876-19751901-2000 Year Fig.
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