the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Sep 2025
Impact of the temperature-cloud phase relationship on the simulated Arctic warming during the last interglacial
Abstract. The Arctic during the last interglacial period (LIG) was considered warmer than today. While a recent proxy-based study suggests the disappearance of summer sea ice in the Arctic at the LIG, many climate models fail to capture this feature. It is thus essential to investigate sources of uncertainty in numerical models. The current study examines the impact of the temperature-cloud phase relationship. Sensitivity studies are conducted for the first time to explore the potential importance of this relationship in simulating the LIG climate. Two different cloud parameter sets are used for an atmosphere-ocean general circulation model with and without the dynamic vegetation feedback. The model with cloud parametrization permitting liquid water at a lower temperature and a larger fraction of supercooled liquid water at the same temperature simulates a warmer preindustrial (PI) climate, larger annual mean Arctic warming at the LIG, and substantially reduced sea ice cover during summer at the LIG. It is demonstrated that the low-level clouds play a crucial role in controlling the Arctic response via the greenhouse effect. The result indicates the importance of the temperature-cloud phase relationship in simulating the Arctic climate at the LIG. It also highlights the importance of accurately simulating modern sea ice thickness and representing the processes that affect the fraction of supercooled liquid water in clouds.
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RC1: 'Comment on egusphere-2025-4109', Anonymous Referee #1, 02 Oct 2025
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AC1: 'Reply on RC1', Masakazu Yoshimori, 14 Oct 2025
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Thank you very much for taking the time to carefully review the manuscript. We reply to the individual points below regarding the revision.
1. Impact of Model Biases and the Validity of Proxy Comparison
As we described these biases in the text, we believe they are not negligible; thus, we agree that care must be exercised when interpreting and drawing implications from our results. We will provide further discussion on this issue. We note that sea ice concentration, notably in the Barents and Kara Seas, improved with the cloud parameter C, rather than A, as illustrated in the comparison with the HadISST2 data. The simulated present-day sea ice thickness has not yet received sufficient attention in the interpretation of paleoclimate modeling results, perhaps due to the lack of a long-term thickness observation dataset. Our results suggest that it may affect the model-data comparison. Our intention is not to declare that our simulation is successful, but rather to present cases in which the representation of present-day sea ice thickness and the temperature dependency of cloud phase, both controlled by the cloud parameterization here, affect the LIG Arctic simulations and can also be a contributing factor, or at least a factor to be concerned about, to the model spread.
There are a couple of other reasons why we did not claim which model version is more realistic (more appropriate wording may have been “more correct”): 1) While the temperature dependency of cloud phase in the parameter set C is designed to be closer to modern satellite observations in the previous study (Sherriff-Tadano, 2020), there is no guarantee that the same relation holds at the LIG environment, when the aerosol distribution is likely different from that of the present-day. Thus, we intended to emphasize that the aim of the study has a nature of sensitivity experiments; 2) the agreement of surface temperature difference of LIG and PI between the model and proxies is still insufficient (Fig. 8), as commonly seen in other models; and 3) there is some disagreement in the reconstruction of summer sea ice cover between the two datasets we used. We will clarify these points in the revision.
2. Initial Condition Sensitivity
The model was integrated for at least 2,000 years, a sufficiently long period to reach a quasi-equilibrium. Therefore, the results are not sensitive to the choice of imposed initial conditions, as our study does not focus on the transient response. The experiment protocol follows the PMIP4 LIG experiment. In the real world, the LIG does not strictly represent the equilibrium climate, and the complications arising from its transient nature require investigation in separate studies, including the effect of freshwater fluxes. We consider that the initial conditions are not a critical element of the interpretation of our conclusion.
3. Overemphasis on Annual Mean Temperature
The seasonal summary of temperature, as well as surface radiative and heat fluxes in the Arctic region, is presented in the form of bar graphs throughout the paper. Our intention is not to emphasize the annual mean temperature; we will consider adding polarized seasonal maps of Fig. 2 in the supplementary materials in response to the referee’s suggestion.
4. Oversimplified Explanation for Polar Asymmetry
We agree that the complete response of Southern Ocean cooling near Antarctica is subject to feedback through changes in atmospheric circulation, moisture transport, and other factors. However, we argue that these factors are secondary for two reasons: 1) the difference between the two model versions in PI simulations is the cloud parameters, and thus the cloud parameterization is the cause of the difference (though not necessarily the dominant contributor to the complete response); and more importantly 2) the energy balance analysis at the surface clearly shows year-round cloud longwave warming and the dominant cloud shortwave cooling effect during summer near the Antarctica, compared to other terms. We will discuss the suggested potential feedback factors and clarify these points.
5. The attribution of autumn/winter warming
We agree with the referee’s comment. Indeed, our interpretation, as stated in Section 5.4, is: “As discussed above, the LWP and low-level cloud amount increases in LIG simulations are likely associated with decreased sea ice cover from PI simulations. As the reduction in sea ice cover is much larger in ΔLIGvC than in ΔLIGvA, a larger cloud response occurs in ΔLIGvC than in ΔLIGvA.” We state that both a larger reduction in sea ice cover and stronger cloud feedback in the model with cloud parameter set C are constructively responsible for the different response from the model with cloud parameter set A. We will reconsider the title of Sect. 5.4, which may have been misleading, and clarify the writing.
Citation: https://doi.org/10.5194/egusphere-2025-4109-AC1
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AC1: 'Reply on RC1', Masakazu Yoshimori, 14 Oct 2025
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RC2: 'Comment on egusphere-2025-4109', Anonymous Referee #2, 08 Oct 2025
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4109/egusphere-2025-4109-RC2-supplement.pdf
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AC3: 'Reply on RC2', Masakazu Yoshimori, 24 Oct 2025
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Thank you very much for taking the time to review the manuscript carefully and for your many valuable comments, including suggestions for additional literature.
Cloud parameterisations
We will add the relevant variables for additional model performance checks. Historical simulations are not available for all model versions, as is sometimes the case for the designated paleoclimate simulations. We plan to discuss some of the differences between the historical and preindustrial simulations in the revised manuscript.Fixed-angle calendar
There is no perfect or universal way to adjust the calendar for comparing past and present, and we share the view that the paleoclimate modeling community should establish several different robust methodologies for specific purposes. Our feedback analysis aims to explain the differences in surface temperature for each month, independently of the other months, using the calendar definition employed in many previous studies. This is useful for comparing with summer proxies, as the referee suggested, and for interpreting the response of the same season. As the referee correctly points out, the time integration over a year under the fixed-angle calendar does not equal the annual average; thus, the interpretation of the analysis for annual mean values is not apparent. The annual mean surface temperature difference can be interpreted using the annual mean of the additional fixed-date calendar feedback analysis. We note that no calendar adjustments are applied before computing the presented annual mean values, and thus, they are not affected by the calendar definition. Sea ice minima are simulated in September, and the comparison with corresponding proxies is not expected to be impacted significantly. The main conclusion remains unchanged when calendars are defined at the data analysis stage. Nevertheless, the point raised by the referee requires attention that the community has overlooked, particularly in the context of ocean heat or ice mass budget analysis to explain their tendencies. We plan to discuss these points with the available data in the revised manuscript.Observations
Thank you very much for suggesting the specific summer reconstruction dataset. This is very helpful for further comparison.Marine core data
Thank you very much for clarifying the reliability and reinterpretation of published data. We also appreciate your guidelines for reviewing the current understanding of the LIG Arctic sea ice state. The descriptions provided in the comment regarding the latest interpretation are invaluable.Citation: https://doi.org/10.5194/egusphere-2025-4109-AC3
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AC3: 'Reply on RC2', Masakazu Yoshimori, 24 Oct 2025
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RC3: 'Comment on egusphere-2025-4109', Anonymous Referee #3, 10 Oct 2025
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Review comment on "Impact of the temperature-cloud phase relationship on the simulated Arctic warming during the last interglacial" by Arima et al.
This manuscript addresses a source of uncertainty in climate simulations for the LIG Arctic by performing in total 4 model simulations using different cloud parameters and models versions with and without dynamic vegetation. This study exaimines the impact of cloud phase representation and shows an important influence of SLF parameterization on LIG Arctic warming and sea ice cover. A parameter set (socalled C) which permits liquid water at lower temperatures and higher SLF can lead to a warmer PI Arctic, larger LIG Arctic warming, and substantially reduced LIG summer sea ice. This aligns with proxy records suggesting a nearly ice-free LIG Arctic summer, addressing a longstanding gap between models, which often fail to simulate ice-free conditions, and paleo observations. This study also present surface energy balance diagnostics which highlight the importance of LW CRE particularly in autumn and winter. Overall I find the manuscript is clear, methodologically thorough and can make a valuable contribution. The following are my suggestions, mostly minor.
1 The introduction outlines cloud phase’s general role in Arctic climate, but lacks explicit connection to the LIG’s unique orbital-driven insolation pattern. So, why is temperature-cloud phase relationship particularly important for resolving LIG Arctic climate ...than the MH, for example?
2 Table 2 and 3 could be merged together while adding two columns called “model” and “vegetation” (either dynamic or fixed).
3 Line 94-100, the texts explains LPJ-DGVM’s vegetation dynamics but does not say about how dynamic vegetation might interact with cloud phase. So why you choose two models with and without dynamic vegetation?
4 I think your results can be enhanced by adding a positive feedback between seaice thickness and cloud. For example, thinner PI sea ice in C may lead to more LIG sea ice melt, which increases surface moisture fluxes, drives more low-level clouds and thus higher LW CRE, and further reduces sea ice.
5 Fig 4:should make the caption more detailed. I guess that each colored segment’s vertical length corresponds to its contribution to surface temperature change, with positive contributions above the 0 line and negative ones below. The caption should explicitly state this to avoid guesswork. The same for Fig. 9.
6 The authors show that both model versions (A and C) exhibit biases, including a warm bias over North America, insufficient Arctic sea ice along the North American coast, and uncertainties in sea ice thickness simulations due to polar Fourier filtering. How these biases might influence the interpretation of LIG results. For example, the warm bias over North America could affect atmospheric circulation patterns that interact with Arctic clouds (e.g., moisture transport to the Arctic). Could this bias amplify or dampen the cloud phase effect on LIG Arctic warming? Might be better to add a discussion on this.
7 Line 341: The phrase "the difference between ΔLIGvC (=LIGvC – PIvC) and ΔLIGvA (=LIGvA – PIvA)" is redundant. Try to just simplify to "the difference between ΔLIGvC and ΔLIGvA" .
Citation: https://doi.org/10.5194/egusphere-2025-4109-RC3 -
AC2: 'Reply on RC3', Masakazu Yoshimori, 19 Oct 2025
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Thank you very much for taking the time to carefully review the manuscript. We reply to the individual points below regarding the revision.
1.
Although we have not examined the sensitivity of the mid-Holocene (MH) Arctic simulation to the cloud phase representation of the model, we expect that the representation is particularly important for the LIG. The summer insolation anomaly from today is larger at the LIG than the MH because perihelion was close to the summer solstice at the LIG (rather than the autumnal equinox at the MH), and the reconstruction suggests a warmer Arctic at the LIG. As shown in the manuscript, sea ice cover and cloud cover changes are closely related. Thus, we expect that the warmer Arctic makes the cloud phase feedback more critical. We will add this point to the revised manuscript.2.
We will merge the two tables so that readers can easily grasp an overview of the experiments.3.
To solely demonstrate the effect of cloud phase representation differences, it may be sufficient to use only the model with dynamic vegetation, as we believe that dynamic vegetation feedback is vital for realistic simulations. We also examined the sensitivity of cloud phase representation using the model without dynamic vegetation because most PMIP models that conducted the LIG simulation in previous studies did not include dynamic vegetation feedback. There was a concern that the difference in simulated LIG climate state (with and without vegetation feedback) may result in differing sensitivity to the cloud parameterization. Thus, we aim to examine whether the effect of cloud representation on the simulated LIG climate is robust and point out the potential of explaining a portion of the spread in the simulated LIG climate among many models, even without the dynamic vegetation feedback. We will clarify this motivation in the experimental design of the revised manuscript.4.
Thank you for highlighting this important process. This was stated briefly and may be implicit in the text. We will make the statement more explicit in the revised manuscript.5.
Thank you for pointing this out. We will improve the corresponding captions.6.
This is an important point, which the first referee also raised. We will add the detailed discussion on this uncertainty. Please see the reply to comment #1 of the first referee.7.
We will change the phrase as suggested.Citation: https://doi.org/10.5194/egusphere-2025-4109-AC2
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AC2: 'Reply on RC3', Masakazu Yoshimori, 19 Oct 2025
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RC4: 'Comment on egusphere-2025-4109', Anonymous Referee #4, 14 Oct 2025
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Please refer to my evaluation in the attached document.
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AC4: 'Reply on RC4', Masakazu Yoshimori, 24 Oct 2025
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Thank you very much for taking the time to carefully review the manuscript. Thank you also for the encouraging and helpful comments. They surely help improve the presentation and readability of the manuscript, and we will do our best to incorporate your suggestions in the revised manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-4109-AC4
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AC4: 'Reply on RC4', Masakazu Yoshimori, 24 Oct 2025
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This study presents investigation into the role of cloud phase parameterization in simulating the Arctic climate during LIG. The topic is highly relevant, given the persistent model-data discrepancy regarding the magnitude of Arctic warming and summer sea ice extent during this period. The authors convincingly demonstrate that a parameter set allowing for more supercooled liquid water can lead to a significantly warmer Arctic and a closer match to proxy-based summer sea ice reconstructions. While the findings are intriguing and the experimental design is robust in its sensitivity framework, several key aspects of the argumentation require clarification and a more nuanced discussion to strengthen the paper's conclusions.
1 Impact of Model Biases and the Validity of Proxy Comparison
It seems there are significant model biases in the PI control simulations, notably a warm bias over North America and an unrealistic sea ice thickness distribution near Canada and Greenland. If the model's description of the modern Arctic climate is flawed, the feedback processes it simulates (e.g., sea ice albedo, sea ice clouds) may also be inaccurate. The fact that the PI simulations using parameter set A (Figure 6a) appear to be closer to observed sea ice concentrations. The authors should clearly explain how the modern biases may affect their interpretation of the LIG results and the generalizability of their findings. In addition, the authors admit they cannot claim
which model versions is more realistic. Then is it necessary to compare the LIGvC and LIGvA with the proxy reconstruction?
It is necessary to discuss whether the improved agreement with the LIG proxy parameters stems from more realistic physical processes or from accidental compensation of model errors.
2 Initial Condition Sensitivity
The central finding—that a warmer, thinner sea-ice initial state (PIvC) leads to an ice-free LIG Arctic—raises the critical question of whether the cloud-phase process would be sufficiently powerful to generate a similar outcome if the model were initialized from a more realistic, colder glacial state. Is the result demonstrating a unique, enhanced physical response to LIG forcing, or is it primarily a consequence of sensitivity to the initial state? The manuscript does not sufficiently disentangle these two effects.
3 Overemphasis on Annual Mean Temperature
The analysis of surface air temperature changes focuses heavily on annual means (e.g., Fig. 2). However, the crucial processes for sea ice evolution—melting in summer and freezing in autumn—are inherently seasonal. The key mechanism proposed, the liquid cloud longwave feedback, is explicitly shown to be a autumn/winter phenomenon. Therefore, the annual mean temperature is a less informative metric for the core thesis than a detailed seasonal analysis.
The authors should place greater emphasis on the seasonal progression of temperature and energy budget changes when discussing the causes of sea ice loss. Seasonal (e.g., JJA, SON) temperature and radiation anomaly maps could be prioritized in the main text to directly link the forcing to the response.
4 Oversimplified Explanation for Polar Asymmetry
The explanation for the asymmetric temperature response between the Arctic (warming) and Antarctic (cooling) to the cloud parameter change is attributed solely to the difference in summer climatological temperatures. This explanation is plausible but likely incomplete. The discussion of this asymmetry should be expanded to acknowledge the potential role of these large-scale dynamical factors, even if a full analysis is beyond the paper's scope. atmospheric circulation, moisture transport,
5 The attribution of autumn/winter warming
The attribution of autumn/winter warming primarily to the cloud parameterization requires refinement. A compelling alternative explanation is the summer remnant effect whereby the larger summer sea-ice loss in LIGvC creates a warmer, moister autumn lower atmosphere, passively leading to more clouds and warming. While the AGCM experiments provide some evidence for an active role of the cloud phase, the coupled model result is likely a combination of this passive response and the active cloud-phase amplification. The authors should clearly distinguish between these two effects and present the autumn/winter warming as a result of a positive feedback loop where the cloud parameterization acts as a powerful amplifier of the conditions created by prior sea-ice loss.