This paper studies the climate/d18O response at the LIG with the focus on the Arctic and Antarctic regions. The authors used 4 isotope-enabled climate models under 127 climate forcing, and one model with a long hosing simulating the H11 event. The major conclusion seems to be the model response is too small relative to the ice core observations in both temperatura and d18O, except perhaps the H3000-overshoot. It seems to me this paper has two major points. First,  there is a systematic model-data inconsistency, with the model of less signal than in observation. Second, the H11 events indeed tends to reduce the model-data inconsistency, and therefore may be an important factor in the real world LIG resposne.  It is a useful paper that summrizes the current state-of-the-art modeling of climate/d18O on LIG. Nevertheless, I think the paper can be further improved before publications.

Major concern:

Comparison with LGM: The LIG model-data comparsion will be better compared in the context of LGM model-data comparison of the same models. (I assume these models have done the LGM experiments before LIG experiments). Are all models has the similar inconsistency with observations? This LGM comparison has two advantages. First, observational data should have more uncertainty at the LIG than at LGM, while the model uncertainty is the same at LIG and LGM.  Second, LGM clearly has no influence of H events (because it is well separated from H1 and H2). So, if all the models also have less signals in temperature/d18O at LGM than in observations, it is more likely that the model-data inconsistency is caused by the model deficiency. Otherwise, H11 may be a more important factor in reconciling the model-data discrepancy.

Minor concerns:

• The strategy to use cross-model T-d18O slope is no guarantee the model slope is more correct than real world. Even if the slope is the same as in real world, the small signal in both temperatura and d18O are consistent with the data-model inconsistency anyway. It is an interesting try, but does not give much information.

• The major result is model-data inconsistency, not just in the Arctic and Antarctic, but also for the global mean temperatura. The paper title, however, implies a bias in observation.

• The paper should address the inversion layer problem more explicitly. This is potentially serious in the Antactica and has been shown recently to disrupt the T-d18O slope dramatically, at least, at LGM (Liu et al. (2023).

Liu, Z. et al., 2023: Reconstructing past Antarctic temperature using present seasonal d18O-inversion layer temperature: Unified Slope Equations and application. J. Clim., 36, 2933-2957,

Summary of Sime et al.

Sime et al. use an isotope enabled model ensemble to estimate the δ¹⁸O-temperature slope during the Last Interglacial (LIG). The authors explore model decencies, impacts of the seasonality of precipitation and sea ice anomalies. Apart from the PMIP protocol LIG simulations, Sime et al. explore a H11 overshoot scenario, which, unlike the PMIP simulations, explains most of the amplitude of LIG ice core isotope anomalies in both Greenland and Antarctica. Combining all simulations the conclusion is an estimated LIG temperature anomaly with about half the amplitude for Greenland compared to previous studies.

General comments.

I find that the overshoot scenario part in this study to be the most interesting and novel aspect, and I wish the authors would focus more, if not entirely on this topic. The standard simulations presented in this work mainly reflect the discrepancies between simulations and ice core data during LIG found by previous studies. The rationale behind the overshoot scenario could be explained more in depth, expanding on the section L56-66. While the model setup and the use of multiple models could be tested more in future studies, I think the overshoot scenario is a viable explanation for a large part (80%?) of the discrepancy between models and ice core data, unless there are other reasons that the authors do not consider this an option? The remaining part of the simulated isotope signal could be related to vegetation feedbacks (Claussen et al., 2006; Schugers et al., 2007; Nikolova et al., 2013), and uncertainties in ice sheet configuration (e.g., Otto-Bliesner et al., 2006).

It is a bit odd to me to combine all simulations to estimate the δ¹⁸O-temperature slope as we now know, and I’m sure that the authors also know, that the slope varies with climate change. So, if the PMIP scenario simulations miss the amplitude of the climate change, they will likely miss the climate-dependent impacts on the slope.

Furthermore, I think that the introduction is missing a general discussion of the δ¹⁸O-temperature slope. For example, the regional variation of the temporal slope can be explained by Boyle’s mechanism (Boyle 1997, further explored by Guan et al. 2016). In short, the site temperature is more variable than source temperature due to polar amplification, which result in the temporal being slope most often flatter than the spatial slope. Polar amplification is observed to be strong in the Arctic compared to the Antarctic, which partly explains the flatter temporal slope of the δ¹⁸O-temperature relation in Greenland compared to Antarctica. Due to the impact of obliquity on the meridional temperature gradient the slope is therefore also modulated by the obliquity (Kindler et al., 2014)

In addition, the literature covered in the introduction and discussion is biased towards research focused on Antarctica, while many of this study’s main conclusions concern Greenland. Processes the impact the slope in Greenland and Antarctica are different, for example, due to the very different geographical land-ocean distribution.

For the seasonal aspects the authors focus on the annual cycle of precipitation and the sea-ice anomalies. This is of course important, but seasonality are other things than precipitation and sea ice, and they work differently for Antarctica and Greenland. See list below in detailed comments.

In summary, what I would wish the for the authors to do is to recast this study along the lines of Lagged response to meltwater explains model-data discrepancy for the Last Interglacial. The PMIP runs can be used to show that no matter how you slice it, the standard runs fail to capture the ice core anomalies (presuming that the anomalies are not all due to ice sheet configuration, which is reasonable (Johnsen & Vinther, 2007; NEEM members, 2013)). All in all, a lot of the existing manuscript can be kept, but the focus shifted to the H11 run.

As mentioned above, I think lumping together all runs to calculate the δ¹⁸O-temperature slope is not the best approach. The PMIP and H11 runs show different slopes (judging by the standard error Tables A5 and A6), something which could be explored more.

My suggestion requires substantial revisions and some analysis. In its present form I don’t see a clear message from the paper, and I can’t recommend publication without major revisions.

Detailed comments.

L9: “… capture around half of the warming in the Arctic… “. Do they really capture half of the warming? Only in Antarctica.

L76: “The most important control on δ¹⁸O in precipitation in polar regions… “

From my point of view this topic is about the δ¹⁸O -temperature slope, but this is not discussed in depth in the introduction. With all the other controls acting on the δ¹⁸O can the site temperature be isolated as the most important? In case of a Rayleigh-distillation scenario, then, yes, but changes during LIG are far more complex.

L87: “…include all the factors that affect the δ¹⁸O …” I think the details should be written out here without the reader having to go through the papers and guess what you mean. This lack of details is to a certain extent symptomatic for the introduction.

Below I list of issues with this part of the introduction:

• There is no citation for supporting the claim of site temperature control on δ¹⁸O, and there are several more factors than presently listed.
• The papers cited are papers biased towards studies on Antarctica.
• The source-site gradient control on δ¹⁸O is not discussed (Boyle, 1997; Kindler er al., 2014; Guan el al., 2016).
• Temperature control on Greenland δ¹⁸O is weak for summer (Vinther et al., 2010; Sjolte et al., 2014).
• Intermittency of precipitation impacts the signal recorded in δ¹⁸O (Münch et al., 2020)
• Evaporative fluxes and continental vapor recycling impacts the δ¹⁸O-temperature slope (Werner et al., 2001; Sjolte et al., 2014).
• Explain isotopic enrichment of evaporation in the Arctic when the ambient air is depleted (Lee et al., 2008).

References.

Boyle, E. A. (1997), Cool tropical temperatures shift the global δ¹⁸O-T relationship: An explanation for the ice core δ¹⁸O-borehole thermometry conflict?, Geophys. Res. Lett.,  24, 273–276, doi:10.1029/97GL00081.

Claussen, M.,  J. Fohlmeister,  A. Ganopolski, and  V. Brovkin (2006),  Vegetation dynamics amplifies precessional forcing, Geophys. Res. Lett.,  33, L09709, doi:10.1029/2006GL026111.

Guan, J.,  Z. Liu,  X. Wen,  E. Brady,  D. Noone,  J. Zhu, and  J. Han (2016),  Understanding the temporal slope of the temperature-water isotope relation during the deglaciation using isoCAM3: The slope equation, J. Geophys. Res. Atmos.,  121,  10,342–10,354, doi:10.1002/2016JD024955.

Johnsen S. , Vinther B ., Edited by Elias S. A . Ice core records – Greenland stable isotopes . Encyclopedia of Quaternary Science . 2007 ; Oxford : Elsevier . 1250 – 1258

Kindler, P., Guillevic, M., Baumgartner, M., Schwander, J., Landais, A., and Leuenberger, M.: Temperature reconstruction from 10 to 120 kyr b2k from the NGRIP ice core, Clim. Past, 10, 887–902, https://doi.org/10.5194/cp-10-887-2014, 2014.

Lee, J.-E.,  I. Fung,  D. J. DePaolo, and  B. Otto-Bliesner (2008),  Water isotopes during the Last Glacial Maximum: New general circulation model calculations, J. Geophys. Res.,  113, D19109, doi:10.1029/2008JD009859.

Münch, T., Werner, M., and Laepple, T.: How precipitation intermittency sets an optimal sampling distance for temperature reconstructions from Antarctic ice cores, Clim. Past, 17, 1587–1605, https://doi.org/10.5194/cp-17-1587-2021, 2021.

NEEM community members: Eemian interglacial reconstructed from a Greenland folded ice core, Nature, 493, https://doi.org/10.1038/nature11789, 2013.

Nikolova, I., Yin, Q., Berger, A., Singh, U. K., and Karami, M. P.: The last interglacial (Eemian) climate simulated by LOVECLIM and CCSM3, Clim. Past, 9, 1789–1806, https://doi.org/10.5194/cp-9-1789-2013, 2013.

Otto-Bliesner B., Marsha S., Overpeck J., Miller G., Hu A., co-authors . Simulating arctic climate warmth and icefield retreat in the last interglaciation . Science . 2006 ; 311 ( 5768 ): 1751 – 1753 

Schurgers G. , Mikolajewicz U. , Groeger M. , Maier-Reimer E. , Vizcaino M. , co-authors . The effect of land surface changes on Eemian climate . Clim. Dynam . 2007 ; 29 ( 4 ): 357 – 373

Sjolte, J., Hoffmann, G. and Johnsen, S.J. (2014) ‘Modelling the response of stable water isotopes in Greenland precipitation to orbital configurations of the previous interglacial’, Tellus B: Chemical and Physical Meteorology, 66(1), p. 22872. Available at: https://doi.org/10.3402/tellusb.v66.22872.

Vinther, B.M., P.D. Jones, K.R. Briffa, H.B. Clausen, K.K. Andersen, D. Dahl-Jensen, S.J. Johnsen, Climatic signals in multiple highly resolved stable isotope records from Greenland, Quaternary Science Reviews,Volume 29, Issues 3–4, 2010, Pages 522-538, ISSN 0277-3791, https://doi.org/10.1016/j.quascirev.2009.11.002.

Werner, M., Heimann, M., & Hoffmann, G. (2001). Isotopic composition and origin of polar precipitation in present and glacial climate simulations. Tellus B: Chemical and Physical Meteorology, 53(1), 53–71. https://doi.org/10.3402/tellusb.v53i1.16539