the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Exploring Holocene temperature trends and a potential summer bias in simulations and reconstructions
Christian Wirths
Elisa Ziegler
Kira Rehfeld
Abstract. Proxy-based reconstructions and climate model simulations of surface temperature trends during the Holocene disagree: While reconstructions show a cooling during the mid- and late Holocene, climate models show a continuous warming – a contradiction known as the Holocene temperature conundrum. Despite extensive research, the reason for the disagreement remains unclear. Both, missing processes in the models as well as biases in the proxies and the resulting reconstructions are possible sources of the conundrum. Here we compare our TransEBM v1.2 climate simulation as well as additional climate models of different complexity and Holocene temperature trends from the Temperature12k dataset (Kaufman et al., 2020b), with regards to model-data and model-model agreement. We show that models of all complexities disagree with mid-Holocene temperature trends in reconstructions and that this disagreement is almost independent of proxy and archive type. While, models show the highest agreement with summer temperature trends in reconstructions, our study shows that a trivial summer bias in proxies is not sufficient to explain the conundrum. Further effort to disentangle seasonal biases in proxies and the testing of potential misrepresentations in climate models, like anthropogenic land-use, in form of sensitivity experiments are needed to resolve the Holocene conundrum.
Christian Wirths et al.
Status: open (until 01 May 2023)
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RC1: 'Comment on egusphere-2023-86', Anonymous Referee #1, 13 Mar 2023
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The manuscript compares the temperature trends simulated by climate models of various complexities with paleo data over the Holocene. Many explanations have been suggested to explain the disagreements between models and data, the authors addressing specifically the impact of potential seasonal biases. They describe in details the seasonal and spatial distribution of the trends in the selected models and in data. This description is very clear. The paper is well written and easy to follow. I thus have no minor comment or suggestion to improve the presentation of the manuscript. However, there are two major points to consider in a revised version of the text.
1/ The added value of the study is not clearly explained and the authors should insist more on this in the conclusion, which is very short in the current version of the manuscript. The first paragraph of the conclusion summarizes the description of the trends presented in the previous sections. The second (and final) paragraph starts by a quite mild sentence: ‘Regarding the Holocene conundrum, it follows that a simple seasonal proxy bias is unlikely as a full explanation’ and then present some general suggestions for improvements or new studies. The fact that seasonal proxy biases might play a role but could not explain the full model-data disagreement is already around for some time (see the recent review of Kaufmann and Broadman, 2023) and the authors should explain more clearly the new contribution they bring to the debate.
2/ The authors analyze relatively old simulations that have been discussed in several studies. The selected data base has also already been used in model-data comparisons. A new simulation is included (TransEBM) but it has in general a lower agreement than the other ones with observations (see for instance Figure 5). This new simulation might be helpful to understand some of the characteristics of the other models but this is not developed in the current version of the manuscript. Furthermore, the set of selected experiments is not designed to test hypotheses, such as the potential role of vegetation or of the volcanic forcing for instance, as done in some other studies. Several transient Holocene have been performed recently. Some only cover parts of the Holocene or might not be publicly available but a larger set of experiments would provide additional information for the discussion (see for instance Gravgaard Askjær et al. 2022, in particular their Figure 3).
References:
Gravgaard Askjær T., Q.Zhang, F. Schenk, F.Charpentier Ljungqvist, Z. Lu, C. M. Brierley, P. O. Hopcroft, J. Jungclaus, X. Shi, G. Lohmann, W. Sun, J. Liu, P. Braconnot, B.L. Otto-Bliesner , Z. Wu, Q. Yin, Y. Kang, H. Yang, 2022. Multi-centennial Holocene climate variability in proxy records and transient model simulations. Quat. Sci. Rev. https://doi.org/10.1016/j.quascirev.2022.107801
Kaufman D.S. and E. Broadman, 2023. Revisiting the Holocene global temperature conundrum. Nature 614, 425-435 . https://doi.org/10.1038/s41586-022-05536-w
Citation: https://doi.org/10.5194/egusphere-2023-86-RC1 -
RC2: 'Comment on egusphere-2023-86', Anonymous Referee #2, 22 Mar 2023
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Summary
Wirths et al. summarise a comparison of simulations and reconstructions of temperature trends over three phases of the Holocene. They confirm important differences between the warming seen in models and the cooling reconstructed in temp12k. They conclude that a simple seasonal bias in the temp12k proxy records is unlikely to be able to explain the differences with the model runs.
Main comments:
A main assumption within this work is that the seasonal and annual reconstructions extracted from temp12k are able to separately resolve the seasons. I am no expert in this dataset, but this seems unlikely to be the case. In fact if we can take the separate seasonal records at face value then we can immediately conclude that the seasonal bias is not the answer to the ‘Holocene temperature conundrum’. My best guess would be that the annual and summer reconstructions are probably both somewhat a mixture of several seasons, consistent with line 144: “reconstructions tend to show similar patterns in annual and summer temperature trends over all periods”.
In the abstract it is stated that: “our study shows that a trivial summer bias in proxies is not sufficient to explain the conundrum”. I believe this is based on the comparison of summer simulations with annual records but it’s not clear because, apart from the caveat around the records mentioned above, the modelled summer signal would be unlikely to resemble a summer-biased annual record. It seems more likely that it might look like a weighted combination of two seasons. Given this, I think the main finding around seasonality could benefit from futher elaboration.
Minor comments:
It’s not always clear how the present study’s use of the EBM adds to the existing debate. Instead the results seem to highlight where the EBM is significantly different from the other models and these already span a fairly large gradient of complexity. Perhaps one conclusion that could be strengthened is that a model like TransEBM is not greatly informative for this type of problem where seasonal differences are large?
The MPI-ESM simulations by Bader et al (2020) show a cooling mode as discussed in your introduction. It would be good to clarify here whether the simulation with MPI-ESM analysed here shows a similar result as it does not look to be the case from figure 1 or B3. If this is not the case, does this support your hypothesis about ocean spin up temperature being important or is there some other reason that can be identified?
It seems like a major difference between TransEBM and the other 3D models arises in the polar regions. Could you elaborate on this?
Line 67: “Sea-ice extent is linearly interpolated between the Last Glacial Maximum (LGM) and present-day states given in Zhuang et al. (2017).”
Line : “The sea-ice was interpolated using the same method. For sea-ice, the distributions given by Zhuang et al. (2017) were used as fix points for present-day and LGM conditions.”
Related to the point above, the role that sea-ice plays in the EBM is not clear from the sentences above. I suggest you add a paragraph briefly summarising TransEBM itself (in addition to forcings) to the Appendix.
Line 91: Could you spell out in more detail how you extract a seasonal reconstruction from temp12k?
Is JJA the best choice of season for the southern hemisphere? Related to this, can you replace summer with northern hemisphere summer (JJA) in the rest of the text.
Line 129: expect for TransEBM which shows a high latitude cooling and warming over the North Atlantic and the North American
East Coast.
Can you discuss why TransEBM has hte opposite sign over the ice-sheet?
Line 228: Another relevant reference here is Dallmeyer et al (2022): https://doi.org/10.1038/s41467-022-33646-6.
This is a bit of a minor point, but to me the figures would make more sense if ordered the panels in the modelling figures (3 & 4) from low complexity to high? Maybe TransEBM, LOVECLIM, CCSM3, MPI-ESM (going by number of levels for example)?
Technical corrections:
Line 15: perhaps spell out cmp - compare?
Line 42: “a feature of” perhaps better as “an artifact of”?
Erb et al 2022 is now published.
Ziegler, E. and Rehfeld, K this reference is incomplete.
Citation: https://doi.org/10.5194/egusphere-2023-86-RC2
Christian Wirths et al.
Christian Wirths et al.
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