Tree-ring oxygen isotope based inferences on winter and summer moisture dynamics over the glacier valleys of Central Himalaya

Singh, Nilendu; Shekhar, Mayank; Parida, Bikash Ranjan; Gupta, Anil K.; Sain, Kalachand; Rai, Santosh K.; Bräuning, Achim; Sharma, Vikram; Tiwari, Reet Kamal

doi:https://doi.org/10.5194/cp-2021-53

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https://doi.org/10.5194/cp-2021-53

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28 May 2021

| 28 May 2021

Status: this preprint has been withdrawn by the authors.

Tree-ring oxygen isotope based inferences on winter and summer moisture dynamics over the glacier valleys of Central Himalaya

Nilendu Singh, Mayank Shekhar, Bikash Ranjan Parida, Anil K. Gupta, Kalachand Sain, Santosh K. Rai, Achim Bräuning, Vikram Sharma, and Reet Kamal Tiwari

Abstract. Accelerated glacier mass loss is primarily attributed to greenhouse-induced warming, but land–climate interaction has increasingly been recognized as an important forcing at the regional-local scale. However, the related effects on the Himalayan glaciers are less explored but believed to be an important factor regulating spatial heterogeneity. This study aims to present a multi-decadal approximation on hydroclimate and glacier interaction over the western central Himalaya (WCH). Three highly coherent, multi-species, tree-ring δ¹⁸O site-chronologies from WCH were used to derive regional changes in atmospheric humidity (atmospheric moisture content: AMC) since the last four centuries. Coherency analyses between AMC and glacier mass balance (GMB: tree-ring δ¹³C-derived) indicate an abrupt phase-shift since the 1960s within a common record of 273 years. To ascertain the cause of phase-shift, annual AMC was disintegrated into seasonal-scale, utilizing δ¹⁸O record of deciduous species. Seasonal (winter: October–March; & summer-accumulation season: April–September) decomposition results reveal that winter-westerlies rather than summer precipitation from Indian summer monsoon (ISM) govern the ice-mass variability in WCH. Decadal coherency between summer-season AMC and GMB remained relatively stable since the mid-20th century, despite a decline in central Himalayan summer precipitation (tree-ring δ¹⁸O records). We hypothesize that excess water vapor brought to the atmosphere through increase in pre-monsoon precipitation and greening-mediated increase in evapotranspiration might have been recycled through the summer season to compensate for the ISM part of precipitation. However, isotope-enabled ecophysiological models and measurements would be able to strengthen this hypothesis. In addition, high-resolution radiative forcing and glacier valley-scale vegetation trend analyses point towards a probable influence of greening on GMB. Results indicate that attribution of ice-mass to large-scale dynamics is likely to be modulated by local vegetation changes. We contend that glacier-climate models fed with these feedback processes could reliably improve the projections.

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Nilendu Singh, Mayank Shekhar, Bikash Ranjan Parida, Anil K. Gupta, Kalachand Sain, Santosh K. Rai, Achim Bräuning, Vikram Sharma, and Reet Kamal Tiwari

Interactive discussion

Status: closed

RC1: 'Comment on cp-2021-53', Anonymous Referee #1, 04 Jul 2021

Review: Tree-ring oxygen isotope based inferences on winter and summer moisture dynamics over the glacier valleys of Central Himalaya by Singh et al.

The authors use previously published d18O of tree-ring chronologies from central Himalaya to reconstruct past variations in the annual and seasonal atmospheric moisture content (AMC). The decadal coherency between summer-season AMC and previously published d13C-derived glacier mass balance data was observed despite a decline in summer precipitation since the mid-20^th century. The authors attribute this to an increase in pre-monsoon precipitation and enhanced vapor recycling through an increase in evapotranspiration. This is a novel approach. However, various issues need to be addressed.

The GMB is affected by precipitation as well as temperature variability. It is surprising to note that the manuscript ignores the discussion regarding temperature variability (seasonal and inter-annual) in the region and its likely role in influencing GMB. This is one of the weak points of this work. Also, discussion regarding the correlation between the AMC and precipitation is missing.

There appears to be a contradiction in the authors’ claim: it is claimed that the winter-westerlies rather than summer precipitation govern the ice-mass variability (line 22-23) but subsequently, the authors attribute GMB to increase in the pre-monsoon precipitation and to decrease in summer precipitation. Comparison of Figures 3b and 4a clearly shows the correlation between the summer AMC and GMB.

There is a serious issue with the main claim made in the manuscript, “Decadal coherency between summer-season AMC and GMB remained relatively stable since the mid-20^th century, despite a decline in central Himalayan summer precipitation” (lines 23,24) and “However, it is intriguing that despite a strong decline in summer precipitation since the mid-20^th century…, correlations remained stable (Fig. 4c)” (lines 332,333). The correlation between summer-season AMC and GMB (Fig. 4c) appears to be statistically insignificant. Therefore, the claim regarding stability/instability of the correlation is not valid. 95% p-value bands are needed in figures 3c, 4c and 4d. Further, figure 4a does not corroborate the assertion made in line 333: “… a strong decline in summer precipitation since the mid-20^th century (Fig. 2; Fig. 4a)”. If the authors meant to say that the summer AMC showed a strong decreasing trend during the 20^th century, then for that period there should be a positive correlation between summer AMC (Fig. 4a) and GBM (Fig. 3b). In that context, the ‘intriguing’ observation (line 332) merely reflects the fact that the AMC and GMB are positively correlated and the correlation can remain stable despite the decrease in the summer season’s AMC.

Post-1960, annual AMC shows a positive correlation with GMB (Fig. 3c). For the same period, the summer season’s AMC does not show any correlation with GMB (Fig. 4c) and the winter season’s AMC shows a negative correlation with GMB (Fig. 4d, which again appears to be statistically insignificant!). This requires explanation.

Comparison of Figure 3b and 4a suggests a good positive correlation between low-frequency variations of the summer season’s AMC and GMB which contradicts the claim made at line 358. Therefore, the interpretations can vary if the length of the period of observation is changed.

Although the manuscript describes the correlation between AMC and GMB, it does not describe the causal link between the two. The meaning of positive and negative correlations (figures 3c, 4c and 4d) is not clear. Further, if AMC and GMB have a causal relationship, shouldn’t it be reflected at the interannual scale as well? For example, the amplitude of inter-annual in AMC (fig 3a) is much higher than that in the 11-yr/51-yr moving trends. Given the annual nature of vapor recycling, the authors should demonstrate that the large variations in AMC do control the GMB variability. It would be also useful to give the statistics of correlation (for example, split period calibration-verification) between the summer AMC and GMB. The correlations of the low-frequency variations between AMC and GMB do not necessarily establish the causal relationship. Again, why the revival of winter westerlies-driven moisture influx leads to a negative correlation between winter season's AMC and GMB (lines from 356 to 358) (i.e. a higher winter season’s moisture influx leads to a decline in GMB) is not clear.

The manuscript claims to present a new reconstruction of the winter season’s AMC. Although referred at line 279, it is not clear how the AMC during the winter season was estimated. I assume it is the difference between the annual and summer seasons’ AMC. There are problems with this approach. d18O of the broadleaf species is affected by the winter conditions as well (figure S2c). There are also issues with the carryover of photosynthates from a season to the next season. The best way to prove the validity of this approach is to demonstrate the statistics of the correlation between the estimated and actual wintertime AMC on the inter-annual scale.

Isotopic compositions, especially d-excess, of the seasonal precipitations and glacier ice can be used to show that pre-monsoon rain and evapo-transpired water is contributing to the glaciers. I am not sure about how much of the recycled moisture goes to the glaciers and associated dynamical constraints.

Line 355. ….AMC and GMB correlations remained negative (Fig. 4d). Figure 4d does not demonstrate this.

Line 19: it is not clear what a ‘phase-shift’ indicates here.

Line 97: not clear how this was done.

Lines 231-234: if the relative contributions from westerlies and the Bay of Bengal branch of ISM is changing significantly at the tree-ring sampling sites, using mean d18O to reconstruct past summer and winter influx has limitations. Can the relative contributions of these moisture sources explain the 8 to 10 permil variations in d18O?

Line 246-247: d18O of the atmospheric moisture is one of the parameters that control d18O of the tree ring. It is not the main factor.

Figure 3. If the 51-year moving correlation is shown, (a) and (b) should also show 51-yr moving averages.

Figure 3. “Dark lines in (a) and (b)……”

Figure 4. Add 95% p-value bands and error bars to the figures. If the 51-year moving correlation is shown, (a) and (b) should also show 51-yr moving averages.

Line 355. The negative correlation mentioned here is not seen in the relevant figure (4d)

Line 356. ..1920s and 1960s…wrong range. The drop is at 1950 to 1980 during which the correlation is negative.

Citation: https://doi.org/10.5194/cp-2021-53-RC1
RC2:
'Comment on cp-2021-53', Anonymous Referee #2, 21 Sep 2021
Based on tree-ring oxygen isotope records, Singh et al. reconstructed the moisture dynamics in the central Himalayas. Subsequently, they linked the summer and winter moisture dynamics with the glacier mass balance during the past 273 years. Overall, the topic seems interesting and suitable for the readerships of this journal. However, I have four major concerns about the current manuscript. 1) Why didn’t the authors choose the atmospheric moisture content data from MERRA-2 instead of other datasets? It’s better to provide some reasons. 2) The reliability of the atmospheric moisture content reconstruction is questionable. The authors only showed the correlations between tree-ring oxygen isotopes and atmospheric moisture content, and didn’t present any details for their interannual comparisons, which made the reconstruction questionable. 3) There are lot of the citation problems. For example, in line 40, the authors cited 10 papers in only one sentences. If the authors cited too many papers in one sentence, readers or reviewers will question whether authors had read these cited papers or not. For example, Thompson et al. (2000) interpreted the Dasuopuo ice core oxygen isotopes as a temperature record in the raw paper, while the authors treated Dasuopuo ice core oxygen isotopes as a hydroclimate proxy in the citation. 4) The part “3.5 Vegetation greening and radiative balance analyses” didn’t seem to relate to the topic of this manuscript. I am not sure why the authors put this part in this manuscript. Detailed comments and suggestions are as follows:

Line 65 “A set of information pertaining to the vegetation greening trends in the valleys, decadal changes in seasonal vegetation pattern, biophysical aspects and radiative fluxes were accessed using high-resolution satellite and CERES (Cloud and Earth Radiation Energy System) datasets.”. After reading the whole manuscript, I didn’t understand why authors used satellite data to support their conclusions.

Line 105ï¼for the Table 1, revise “Annual phenology” to “forest type”

Line 110, the authors used the atmospheric moisture content data from MERRA-2 and soil moisture data NOAA. Two datasets are from different reanalysis products. I suggest that authors used both atmospheric moisture content and soil moisture only from the same product (MERRA-2 or NOAA). Besides, there are too many reanalysis datasets, the authors should explain why they chose the MERRA-2. Have the reliability of the selected data been tested or not?

Line 245 “Earlier studies (Baker et al., 2016) also found that in warm-moist tropical rainforest, δ 245 18O values are controlled by basin-intrinsic ecophysiological processes.”. Baker et al. (2016) found δ¹⁸O_TRin Bolivia are indeed controlled by basin-intrinsic processes, with rainout over the basin the most important factor. This is totally different from how authors have cited in this manuscript. Therefore, I don’t think this paper (Baker et al., 2016) has been cited corrected.

Line 250, the authors only provide the correlations, and there are too limited information for the reconstructions. Interannual comparison for the observed and reconstructed data is required.

Line 260, “Paleoclimatic evidence such as tree-ring δ¹⁸O chronologies (Singh et al., 2019; Sano et al., 2012, 2013, 2017; Xu et al., 2018), speleothems (Kotlia et al., 2012; Liang et al., 2015) and ice-core records from the central Himalaya (Thompson et al., 2000; Kaspari et al., 2008) show that regional hydroclimate shifted towards a drier phase since the mid-19th century,”. Originally, Thompson et al. (2000) interpreted the Dasuopu ice core oxygen isotopes as a proxy of temperature instead of hydroclimate (e.g., precipitation amount). This is inconsistent with how authors have cited in this manuscript.

Line 325, I don’t understand the differences between Figure 4c and Figure 4d based on the current caption.

Line 365, “Isotopic studies and modelling experiments indicated a high moisture recycling rates and an enhancement in positive trend in recent decades (An et al., 2017; Dirmeyer et al., 2009; Keys et al., 2016; Harding et al., 2013; Tuinenburg et al., 2012).” The authors didn’t mention where these cited studies come from. These references are cited in a rough way. Did the cited studies support your statement?

Lines 520 and 675, pay attention to the symbols in the reference list (e.g., δ¹⁸O). Please check the reference list.
Citation: https://doi.org/10.5194/cp-2021-53-RC2

Interactive discussion

Status: closed

RC1: 'Comment on cp-2021-53', Anonymous Referee #1, 04 Jul 2021

Review: Tree-ring oxygen isotope based inferences on winter and summer moisture dynamics over the glacier valleys of Central Himalaya by Singh et al.

The authors use previously published d18O of tree-ring chronologies from central Himalaya to reconstruct past variations in the annual and seasonal atmospheric moisture content (AMC). The decadal coherency between summer-season AMC and previously published d13C-derived glacier mass balance data was observed despite a decline in summer precipitation since the mid-20^th century. The authors attribute this to an increase in pre-monsoon precipitation and enhanced vapor recycling through an increase in evapotranspiration. This is a novel approach. However, various issues need to be addressed.

The GMB is affected by precipitation as well as temperature variability. It is surprising to note that the manuscript ignores the discussion regarding temperature variability (seasonal and inter-annual) in the region and its likely role in influencing GMB. This is one of the weak points of this work. Also, discussion regarding the correlation between the AMC and precipitation is missing.

There appears to be a contradiction in the authors’ claim: it is claimed that the winter-westerlies rather than summer precipitation govern the ice-mass variability (line 22-23) but subsequently, the authors attribute GMB to increase in the pre-monsoon precipitation and to decrease in summer precipitation. Comparison of Figures 3b and 4a clearly shows the correlation between the summer AMC and GMB.

There is a serious issue with the main claim made in the manuscript, “Decadal coherency between summer-season AMC and GMB remained relatively stable since the mid-20^th century, despite a decline in central Himalayan summer precipitation” (lines 23,24) and “However, it is intriguing that despite a strong decline in summer precipitation since the mid-20^th century…, correlations remained stable (Fig. 4c)” (lines 332,333). The correlation between summer-season AMC and GMB (Fig. 4c) appears to be statistically insignificant. Therefore, the claim regarding stability/instability of the correlation is not valid. 95% p-value bands are needed in figures 3c, 4c and 4d. Further, figure 4a does not corroborate the assertion made in line 333: “… a strong decline in summer precipitation since the mid-20^th century (Fig. 2; Fig. 4a)”. If the authors meant to say that the summer AMC showed a strong decreasing trend during the 20^th century, then for that period there should be a positive correlation between summer AMC (Fig. 4a) and GBM (Fig. 3b). In that context, the ‘intriguing’ observation (line 332) merely reflects the fact that the AMC and GMB are positively correlated and the correlation can remain stable despite the decrease in the summer season’s AMC.

Post-1960, annual AMC shows a positive correlation with GMB (Fig. 3c). For the same period, the summer season’s AMC does not show any correlation with GMB (Fig. 4c) and the winter season’s AMC shows a negative correlation with GMB (Fig. 4d, which again appears to be statistically insignificant!). This requires explanation.

Comparison of Figure 3b and 4a suggests a good positive correlation between low-frequency variations of the summer season’s AMC and GMB which contradicts the claim made at line 358. Therefore, the interpretations can vary if the length of the period of observation is changed.

Although the manuscript describes the correlation between AMC and GMB, it does not describe the causal link between the two. The meaning of positive and negative correlations (figures 3c, 4c and 4d) is not clear. Further, if AMC and GMB have a causal relationship, shouldn’t it be reflected at the interannual scale as well? For example, the amplitude of inter-annual in AMC (fig 3a) is much higher than that in the 11-yr/51-yr moving trends. Given the annual nature of vapor recycling, the authors should demonstrate that the large variations in AMC do control the GMB variability. It would be also useful to give the statistics of correlation (for example, split period calibration-verification) between the summer AMC and GMB. The correlations of the low-frequency variations between AMC and GMB do not necessarily establish the causal relationship. Again, why the revival of winter westerlies-driven moisture influx leads to a negative correlation between winter season's AMC and GMB (lines from 356 to 358) (i.e. a higher winter season’s moisture influx leads to a decline in GMB) is not clear.

The manuscript claims to present a new reconstruction of the winter season’s AMC. Although referred at line 279, it is not clear how the AMC during the winter season was estimated. I assume it is the difference between the annual and summer seasons’ AMC. There are problems with this approach. d18O of the broadleaf species is affected by the winter conditions as well (figure S2c). There are also issues with the carryover of photosynthates from a season to the next season. The best way to prove the validity of this approach is to demonstrate the statistics of the correlation between the estimated and actual wintertime AMC on the inter-annual scale.

Isotopic compositions, especially d-excess, of the seasonal precipitations and glacier ice can be used to show that pre-monsoon rain and evapo-transpired water is contributing to the glaciers. I am not sure about how much of the recycled moisture goes to the glaciers and associated dynamical constraints.

Line 355. ….AMC and GMB correlations remained negative (Fig. 4d). Figure 4d does not demonstrate this.

Line 19: it is not clear what a ‘phase-shift’ indicates here.

Line 97: not clear how this was done.

Lines 231-234: if the relative contributions from westerlies and the Bay of Bengal branch of ISM is changing significantly at the tree-ring sampling sites, using mean d18O to reconstruct past summer and winter influx has limitations. Can the relative contributions of these moisture sources explain the 8 to 10 permil variations in d18O?

Line 246-247: d18O of the atmospheric moisture is one of the parameters that control d18O of the tree ring. It is not the main factor.

Figure 3. If the 51-year moving correlation is shown, (a) and (b) should also show 51-yr moving averages.

Figure 3. “Dark lines in (a) and (b)……”

Figure 4. Add 95% p-value bands and error bars to the figures. If the 51-year moving correlation is shown, (a) and (b) should also show 51-yr moving averages.

Line 355. The negative correlation mentioned here is not seen in the relevant figure (4d)

Line 356. ..1920s and 1960s…wrong range. The drop is at 1950 to 1980 during which the correlation is negative.

Citation: https://doi.org/10.5194/cp-2021-53-RC1
RC2:
'Comment on cp-2021-53', Anonymous Referee #2, 21 Sep 2021
Based on tree-ring oxygen isotope records, Singh et al. reconstructed the moisture dynamics in the central Himalayas. Subsequently, they linked the summer and winter moisture dynamics with the glacier mass balance during the past 273 years. Overall, the topic seems interesting and suitable for the readerships of this journal. However, I have four major concerns about the current manuscript. 1) Why didn’t the authors choose the atmospheric moisture content data from MERRA-2 instead of other datasets? It’s better to provide some reasons. 2) The reliability of the atmospheric moisture content reconstruction is questionable. The authors only showed the correlations between tree-ring oxygen isotopes and atmospheric moisture content, and didn’t present any details for their interannual comparisons, which made the reconstruction questionable. 3) There are lot of the citation problems. For example, in line 40, the authors cited 10 papers in only one sentences. If the authors cited too many papers in one sentence, readers or reviewers will question whether authors had read these cited papers or not. For example, Thompson et al. (2000) interpreted the Dasuopuo ice core oxygen isotopes as a temperature record in the raw paper, while the authors treated Dasuopuo ice core oxygen isotopes as a hydroclimate proxy in the citation. 4) The part “3.5 Vegetation greening and radiative balance analyses” didn’t seem to relate to the topic of this manuscript. I am not sure why the authors put this part in this manuscript. Detailed comments and suggestions are as follows:

Line 65 “A set of information pertaining to the vegetation greening trends in the valleys, decadal changes in seasonal vegetation pattern, biophysical aspects and radiative fluxes were accessed using high-resolution satellite and CERES (Cloud and Earth Radiation Energy System) datasets.”. After reading the whole manuscript, I didn’t understand why authors used satellite data to support their conclusions.

Line 105ï¼for the Table 1, revise “Annual phenology” to “forest type”

Line 110, the authors used the atmospheric moisture content data from MERRA-2 and soil moisture data NOAA. Two datasets are from different reanalysis products. I suggest that authors used both atmospheric moisture content and soil moisture only from the same product (MERRA-2 or NOAA). Besides, there are too many reanalysis datasets, the authors should explain why they chose the MERRA-2. Have the reliability of the selected data been tested or not?

Line 245 “Earlier studies (Baker et al., 2016) also found that in warm-moist tropical rainforest, δ 245 18O values are controlled by basin-intrinsic ecophysiological processes.”. Baker et al. (2016) found δ¹⁸O_TRin Bolivia are indeed controlled by basin-intrinsic processes, with rainout over the basin the most important factor. This is totally different from how authors have cited in this manuscript. Therefore, I don’t think this paper (Baker et al., 2016) has been cited corrected.

Line 250, the authors only provide the correlations, and there are too limited information for the reconstructions. Interannual comparison for the observed and reconstructed data is required.

Line 260, “Paleoclimatic evidence such as tree-ring δ¹⁸O chronologies (Singh et al., 2019; Sano et al., 2012, 2013, 2017; Xu et al., 2018), speleothems (Kotlia et al., 2012; Liang et al., 2015) and ice-core records from the central Himalaya (Thompson et al., 2000; Kaspari et al., 2008) show that regional hydroclimate shifted towards a drier phase since the mid-19th century,”. Originally, Thompson et al. (2000) interpreted the Dasuopu ice core oxygen isotopes as a proxy of temperature instead of hydroclimate (e.g., precipitation amount). This is inconsistent with how authors have cited in this manuscript.

Line 325, I don’t understand the differences between Figure 4c and Figure 4d based on the current caption.

Line 365, “Isotopic studies and modelling experiments indicated a high moisture recycling rates and an enhancement in positive trend in recent decades (An et al., 2017; Dirmeyer et al., 2009; Keys et al., 2016; Harding et al., 2013; Tuinenburg et al., 2012).” The authors didn’t mention where these cited studies come from. These references are cited in a rough way. Did the cited studies support your statement?

Lines 520 and 675, pay attention to the symbols in the reference list (e.g., δ¹⁸O). Please check the reference list.
Citation: https://doi.org/10.5194/cp-2021-53-RC2

Nilendu Singh, Mayank Shekhar, Bikash Ranjan Parida, Anil K. Gupta, Kalachand Sain, Santosh K. Rai, Achim Bräuning, Vikram Sharma, and Reet Kamal Tiwari

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https://doi.org/10.5194/cp-2021-53-supplement

Nilendu Singh, Mayank Shekhar, Bikash Ranjan Parida, Anil K. Gupta, Kalachand Sain, Santosh K. Rai, Achim Bräuning, Vikram Sharma, and Reet Kamal Tiwari

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Tree-ring isotope records from central Himalaya provided a basis for century-scale approximation on hydroclimate and glacier interaction. Multi-species isotopic coherencies specify an abrupt phase-shift since the 1960s and the governing role of winter-westerlies in regional ice-mass variability. Radiative forcing and glacier valley-scale vegetation trend analyses indicate that attribution of ice-mass to large-scale dynamics is likely to be modulated by local vegetation changes.


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