|Parker et al., Review: A data-model approach to interpreting speleothem oxygen isotope records from monsoon regions on orbital timescales|
The manuscript has improved considerably with the revisions in response to the two previous reviewers. It now reads more easily for those not familiar with the analytical approaches used. The authors utilize SISAL proxy data to define monsoon regions with similar speleothem d18O responses and then interrogate isotope enabled models to explain the patterns in the context of climate variables. The findings lend strong support to the evolving consensus that there is significant variability in the speleothem d18O response of the various monsoon systems and variability in the components of the climate system that drive the regional speleothem d18O signals (source area variability, dynamics along the moisture path, precipitation…).
Most of the comments below are largely along the lines of clarifications. Two can be considered more important, including the discussion of glacial-interglacial variability and the definition of source areas, both of which, in my opinion, can be addressed at the discretion of the authors; I recommend publication with minor revision.
I recommend dropping ‘….on orbital timescales’ from the title. The three short-duration time slices MH, LIG, and LGM explored here don’t address the range of dynamics of orbital-scale variability expressed in paleoclimate records. More importantly, dropping these three words makes the title broader, better reflecting the idea that this approach can be applied to any time-scale of variability.
“Differences in speleothem δ18O between the mid-Holocene and Last Interglacial in the East Asian and Indian monsoons are small, despite the larger summer insolation values during the Last Interglacial.”
Might this be due to the fact that CO2 is more similar in the mid-Holocene and present, implicating an internal radiative forcing versus an external insolation forcing? CO2 as a driver for East Asian monsoon variability is supported by most (all?) non-speleothem-based East Asian summer monsoon proxies examined at orbital time scales. See, for example, the Pleistocene loess proxies.
Paragraph on isotope-enabled modeling (lines 93-105).
Jalihal et al., 2019 is a useful reference here as well (https://doi.org/10.5194/cp-15-449-2019).
2.6 Multiple regression analysis
“We investigate the drivers of regional δ18Oprecip, and by extension δ18Ospel, through the Holocene using multiple linear regression (MLR) of annual precipitation-weighted mean δ18Oprecip anomalies and climate variables from GISS model E-R. Climate variables were chosen to represent the four potential large-scale drivers of regional changes in the speleothem δ18O records. Specifically, we use changes in mean precipitation and precipitation recycling over the monsoon regions, and changes in mean surface air temperature and surface wind direction over the moisture source regions. Whereas the influence of changes in precipitation, recycling and temperature are relatively direct measures, the change in surface wind direction over the moisture source region is used as an index of potential changes in the moisture source region and transport pathway.”
The three climate variables chosen are useful (and if recycling had not been presented, reviewers would have requested it). Nevertheless, I wonder if Including some variable that monitors changes in tropical deep convective precipitation (e.g. ISM) versus subtropical precipitation that is frontal in nature (e.g. EAM) could be useful in the MRL as well.
All climate variables were extracted for the summer months, defined as May to September (MJJAS) for northern hemisphere regions and November to March (NDJFM) for southern hemisphere regions (Wang and Ding, 2008).
This appears to be in conflict with the assumption (line 231-232) that “(i) precipitation-weighted mean annual δ18Oprecip is equivalent to mean annual drip-water δ18O (Yonge et al., 1985)”. Please clarify; if the proxy reflects mean annual conditions, why compare to summer – season climate variables?
3.2 Regional interglacial-glacial differences
“To investigate the causes of glacial-interglacial shifts in δ18O, we compare simulated and observed regional δ18O signals during the LIG, LGM and MH with shifts in climate variables (precipitation and temperature). Only the ISM, EAM and IAM regions have sufficient speleothem data (i.e. at least one record from every time period) to allow comparisons across the MH, LGM and LIG (Fig. 3) and have similar shifts in observed δ18Ospel and simulated δ18Oprecip. T. ….. The most positive δ18Ospel anomalies in all three regions occur at the LGM, with more negative anomalies for the MH and LIG.”
A hallmark of the EAM orbital-scale composite speleothem record from the Yangtze River Valley is that it has no 100-kyr glacial-interglacial variance in the spectrum; it’s virtually all precession. Discussion of termination occurrences in Cheng et al., is in the context of one every 4 or 5 precession cycles. Hence, this section (and associated figure 3 and discussion), is interpreting EAM precession-scale variance as glacial-interglacial (100-kyr) variance. In contrast, the ISM record from Xiaobialong (XBL) contains “real (100-ky)” glacial-intgerglacial variability and the associated publication (Cai et al., 15) discusses it in the context of global ice volume and sea level change. In this context, the proxy-model comparison result that “The glacial-interglacial changes in δ18Oprecip are consistent with the simulated temperature and precipitation changes, with warmer and wetter conditions during interglacials and cooler and drier conditions during the LGM in all three regions’’ may be valid for the ISM but not the EAM. The EAM differences between values at the time of the LGM and the times of the MH and LIG may be better interpreted in the context of precession-band differences in model results (precession maxima vs precession minima) instead of LGM vs MH or LIG. I guess the point is that it’s odd to discuss glacial-interglacial differences for records that have no 100-kyr variance.
The ISM source region seems too small if it includes only the red box encompassing the Arabian Sea; Indian summer monsoon moisture is sourced on the order of 50% from monsoon lows and depressions originating in the Bay of Bengal and tracking NW into India. Please clarify if the Bay of Bengal is or is not included as possible source region for the ISM and if not, explain why.
3.4 Multiple regression analysis of Holocene δ18Oprecip
“The global model for the Holocene (1 to 9ka) δ18Oprecip trends has a pseudo-R2 of 0.80 and shows statistically significant relationships between the anomalies in δ18Oprecip and anomalies in regional precipitation, temperature and surface wind direction (Table 3).”
The term ‘global model’ is not defined anywhere in the paper and, hence, confusing. Please clarify what model is used and if the results apply to the entire globe (pole to pole) or all monsoon regions combined…
Why are the predictor variables summer mean values whereas the d18Oprecip anomalies are precipitation weighted annual average values? Please clarify why summer mean d18Oprecip is not used.
“We have also shown that there is little difference in the isotopic values between the MH and the LIG in the ISM and EAM regions, which is also observed in individual speleothem records (Kathayat et al., 2016; Wang et al., 2008). Given that the increase in summer insolation is much larger during the LIG than the MH, this finding indicates that other factors play a role in modulating the monsoon response to insolation forcing and may reflect the importance of global constraints on the externally-forced expansion of the tropical circulation (Biasutti et al., 2018).”
What about greenhouse gasses as a possible explanation? Is the radiative forcing LIG to MH more similar compared to that of insolation forcing?