the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Aug 2025
EC-Earth- and ERA5-driven ensemble hindcasts with the fully coupled ice-sheet–ocean–sea ice–atmosphere–land circum-Antarctic model PARASO
Abstract. The origins of recent and ongoing Antarctic climate trends are topic of debate, partly because trends and variability can originate from both the Antarctic climate system itself as well as from the mid-latitudes. Furthermore, we lack observations for a detailed analysis of these effects. Here, we use the regional ice sheet-ocean-sea ice-atmosphere-land circum-Antarctic model PARASO to produce four hindcasts of the Antarctic climate over the 1985–2014 period. The first is a control simulation, forced by atmospheric and oceanic reanalyses (ERA5 and ORAS5), which realistically reproduces the pre-2017 increase in Antarctic Sea Ice Extent (SIE) and Surface Mass Balance (SMB) of the Antarctic Ice Sheet. In contrast, the other three hindcasts, driven by EC-Earth historical simulations, simulate a declining SIE and increasing SMB over the same period, a behaviour consistent with biases seen in many global climate models, suggesting that biases in these models may be due to misrepresented lower-latitude dynamics or poleward transports. While both ERA5- and EC-Earth-driven simulations reproduce a dipole in sea ice concentration trends—positive in the east and negative in the west—the magnitude differs. The larger negative trend in the West in the EC-Earth-driven simulations feature a stronger intensification and displaced Amundsen Sea Low, enhancing northerly winds, moisture and heat flux between the Ross and Amundsen Sea. In turn, the different trends in SIE between the ERA5 driven and EC-Earth driven hindcasts result in opposing trends for moisture transport towards Antarctica and precipitation. By comparing the agreement between the three EC-Earth driven hindcasts, a small imprint of internal climate variability was found over the Southern Ocean, whereas this imprint over the continent is much stronger. Nonetheless, all EC-Earth driven simulations exhibit a robust positive SMB trend, indicating a link with sea ice decline or with large-scale advection shared across ensemble members.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-2889', Anonymous Referee #1, 25 Oct 2025
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RC2: 'Comment on egusphere-2025-2889', Christoph Kittel, 03 Mar 2026
Sauerland et al. present reconstructions of recent Antarctic climate using an almost fully coupled modelling framework. This is an interesting and valuable study, particularly given the well-known difficulty of running a fully coupled model without significant drift. From my understanding, the main message of the manuscript is that the atmospheric driving largely explains the simulated sea-ice trends, including the contrast with the opposite trends typically produced by GCMs. In this context, the improved physics of PARASO does not appear to substantially improve the representation of sea-ice trends compared to the driving GCM itself. It would therefore be helpful to show the direct results of the GCM (P21L460) in order to better assess the added value of the coupled framework.
Most of the simulated sea-ice trends and associated changes are attributed to atmospheric processes. However, the manuscript does not discuss potential changes at the oceanic boundaries. Overall, the manuscript is comprehensive, but it would benefit from a simplification of the text to better highlight the main storyline. This is particularly noticeable in Section 4.1 on sea ice. For example, parts of the caption of Figure 3 repeat information that is already obvious (e.g., stating that ice shelves are land). The manuscript would also gain clarity from a more structured development of the results and clearer links between the different analyses so that the central narrative emerges more distinctly.
I understand the rationale behind the debiasing of EC-Earth; however, the correction is applied at levels very close to the surface. This raises the question of whether the correction is dynamically consistent across the full atmospheric column. For instance, moisture advection typically occurs at levels well above the surface (e.g., around 850 hPa). If the correction is not vertically consistent, could it alter the dynamical forcing from EC-Earth or affect the structure of atmospheric circulation patterns? In that respect, could ERA5 be considered more reliable than EC-Earth even in the stratosphere, given that data assimilation is still performed at those altitudes and may constrain large-scale atmospheric properties more consistently?
Finally, although the authors provide a detailed evaluation of their configurations, the manuscript does not sufficiently discuss the implications of the identified biases for the simulations themselves. I would suggest including a comparison with in situ station data for atmospheric variables particularly wind rather than relying primarily on comparisons with ERA5 over the ice sheet, especially for the reanalysis-driven simulations. While this is not the most critical issue, it would strengthen the robustness of the evaluation.
See also the remaining minor comments.
P2 L29 , however. Correct the . location.
P3 L66 : The authors could try to rewrite the sentence to avoid the repition of « boundary and initial conditions ».
P3L80 : The authors can remove « in the boundary conditions »P4 L112: Is this only related to your spectific set up or to a real absence of ice surface change over 30yr ? Is the model fully coupled then ? For me it’s not the case as there is no coupling of the ice sheet towards the atmosphere. (also for P20L430: I’d better use nearly fully-coupled model)
P5L114: You can probably remove the two forcings occurences here. I’d also suggest to remove « regarding the forcing » P5 L121. I think there are enough (too much) “forcing” occurences in that paragraph so it’s clear you are talking about that.
P5L124: Does this change of forcing frequency have an impact on the ocean model results ?P6L172 : Consider to replace « snow mantle » by snowpack or snowpack properties (and then list wich properties you are initializing)
P6L189 : in instead of In.
P10 L239-251: I’d suggest the author to move these paragraphes at the end of the method section as they also explain where you did the evaluation, and especially the Figure 3 should be placed before the results of the evaluation and the becoming Fig 1.
P12 L282 – P13 L284: Isn't that a bit too simplistic to justify the decrease in sea ice in this region?
Figure 6 could be placed in Appendix as it is not essential for the main story.
P13 L286: I’d suggest to cut the sentence in two:
To examine the possible impact of different warming rates applied at the boundaries of the regional model (from ERA5 or EC-Earth), we compare the yearly average changes in air temperature and specific humidity. These averages are calculated along the boundary of the evaluation domain (i.e., the outermost grid cells of the evaluation domain and the innermost grid cells of the buffer zone) for the ERA5- and EC-Earth-driven hindcasts (Fig. 6). I understand you want to give important information (what is the boundary of the evaluation domain), but this often makes sentences quite confusing, with many elements in a single long sentence.P13 L296 : initially instead of Initially.
P13 L301 : ERA5-driven to be consistent.
P14 L305 : I suggest to seperate this part in two: one for ERA5, the other one for EC-Earth simulations, adding some connectors such as In contrast, but also avoiding the repetition of « a cooling trend ». This observation applies to many passages in the paper where the authors could simplify their sentences by avoiding repetitions of this kind and linking the same elements to shorten the sentences.
In the ERA5-driven hindcast, the cooling at the lateral boundaries mainly results in cooling over the Southern Ocean. The model simulates a cooling trend of −0.11 K per decade at 2 m and −0.15 K per decade at 700 hPa (Table 2). In general, the 2 m and 700 hPa temperature trends are consistent in both sign and significance, although their magnitudes differ slightly. This cooling is consistent with the simulated increase in sea ice extent (SIE) in the ERA5-driven hindcast, in agreement with previous findings (e.g., Comiso et al., 2017). No significant temperature trend is found over the Antarctic continent.
In contrast, the EC-Earth-driven hindcasts simulate warming over the Southern Ocean, with a mean 2 m air temperature trend of +0.30 K per decade (Table 2). They also produce positive 2 m temperature trends over Antarctica, although with larger inter-member variability (ranging from +0.14 to +0.27 K per decade) and lower robustness of the trend estimates (e.g., the trend is not statistically significant for member FC02). The higher variability over the continent compared to the ocean highlights the strong contribution of internal variability to multi-decadal temperature trends over Antarctica, consistent with the weakly significant trends reported in observations (Turner et al., 2016, 2020).
Figure 8: Yellow and green stripes are really difficult to see. Consider to add them on Figure 3 and removing on Fig8.
Table 3: How is surface mass balance computed ? From what I understand it is P-E, but this is a simplified definition and not the real surface mass balance.
P16 334-338 : surprising is not really suited for scientific publication. Furthermore, this is another example of long sentences with too much less important information. I suggest to rewrite these sentences.Because saturation water vapour pressure decreases with temperature, colder conditions are generally expected to reduce evaporation and limit moisture transport from the warmer mid-latitudes toward the continent, ultimately leading to lower precipitation. Nevertheless, the ERA5-driven hindcast indicates an increase in precipitation over the Southern Ocean of 196.9 Gt yr⁻¹ per decade (Table 3), despite the simulated cooling (Table 2). Over the continent, however, both temperature and precipitation trends remain insignificant.
The authors show a reduction in humidity at 700 Hpa, but this is not the only level that brings humidity. I think it would be better to show the integrated humidity transport across the entire air column and according to wind direction in Figure 6 (so MT boundary). This would help to better illustrate this “surprising” result, i.e., an increase in precipitation while decreasing in temperature.
P17L361: Remove surprising (also P19L389). To what extent could this apparent contrast be related to your non-uniform EC Earth bias on the air column?Figure 10 : Caption (a) ERA5 and (b) EC-Earth- driven hindcasts to be consistent with the remaining part of the manuscript but also reducing the lenght of the caption. In the figure, B should not be hindcats but EC-Earth if you set A to ERA5. For the rest, I'm not sure I understand the caption in relation to the graph (decrease where I see an increase according to the figures in the figure). Moisture transport (SMB) is also inadequate.
P19L397-398: rewrite the sentence and remove this is thanks (twice). P19 L397: Is it really better than EC-Earth? Although I agree with the authors, I don’t think they demonstrate this improvment in your paper. There are also stand alone NEMO simulation that can correctly reproduce the sea ice trend (Goose et al., 2024) so the authors cannot use this to justify the potential improvment in PARASO. Furthermore, as you mention, when forced by EC-Earth,P ARASO cannot reproduce the correct trend.
P20L405 : I am wandering what is the effect from ocean boundaries condition compared to the atmospheric. The authors never mention changes in the ocean boundaries.
P20L415 : The formulation suggesting that the chain of processes “reaches the SMB” is conceptually unclear. SMB is a diagnostic quantity resulting from multiple processes, rather than an endpoint that can be “reached.” Please rephrase for clarity.
Finally, I would like to apologize to the authors and editor for my delay in submitting this review.Citation: https://doi.org/10.5194/egusphere-2025-2889-RC2
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- 1
A review of the paper
EC-Earth- and ERA5-driven ensemble hindcasts with the fully coupled ice-sheet–ocean–sea ice–atmosphere–land circum-Antarctic model PARASO
By Florian Sauerland and co-authors
The paper of Florian and co-authors concerns a very important problem – to what degree can we trust to the GCMs when we apply them to simulate the climate round the Antarctica. The clear answer which follows from their study is “no, we cannot”. And this is the main conclusion of the research.
In other words, the authors aim to focus on pit-falls of the global climate models which purpose is to predict the possible future of the Antarctic climate. This is a crusial challenge – to separate imprints of the anthropogenic climate forcing from the climate natural variability. If the modern climate models fail to reproduce Antarctic climate in the nearest past, can we rely on their predictions? The problem is aggravated by the limit amount of the historical observations on the region.
It is also true that the polar regions are significantly different in many aspects from the other regions of the globe. This is the reason for the obvious mismatches between observations (or the reanalysis data) and modeling results.
These mismatches are clearly demonstrated by collating results of the numerical simulations in which the model PARASO is forced with the reanalysis and with the GCM simulations (sea ice extent, 2m air temperature etc.). Since the GCMs demonstrate not only the “wrong” figures but even opposite trends in climatic variables for the historic periods, one become rather sceptical about fasibility of their application to predict the climate of the Antarctica.
The manusript is well structured and clearly written (except some particular details mentioned below). The abstract provide a clear summary of the paper. Authors provide appropriate supplementary materials which illustrate the text. In overall, the study can make a valuable contribution to our understanding and modeling the climate dynamics in the polar regions. In this view the paper clearly addresses very important scientific questions within the scope of ESD.
There are several major remarks:
Other cpmments (some of them intersect with the above ones)
Line 15: “internal climate variability”. What is it? According to the WMO definition climate is measured by 30-yr period
Line 63: daily-weekly time step to couple an ice sheet to other components of a system. Such small steps seem to be excessively small. A comment here would be pertinent.
Line 67: three experiments can hardly be called an “ensemble”. This is just only three numerical experiments with sligtly different perturbed boundary conditions of the same model. Of course, no statistics can be ruled out of the results, which is the essence of the ensemble approach in climate modeling.
Lines 112-113: I see a contradiction here. In case surface changes of the Antarctic Ice Sheet (AIS) are negligible during a 30-yr numerical experiment (and it is in the real world except may be the processes on the boundary ice sheet-ice shelf), why the model is called “fully coupled” in the context of the AIS? I suppose that the actual geometry of the AIS is used, isn’t it?
Line 181: 20-yr or 20-kyr? 20 years seems a rather short period of relaxation, no?
Lines 210 and 211: Correct sea name is “BellingShausen”
Figure 1: More illustrative would be to demonstrate also absolute and relative differences in ice extent. I think the conclusions would be less optimistic: good coincidence only in November-December period.
Lines 213-221: Discussion on the sea-ice extent simulations. The same note as to Fig. 1. I think, a short discussion will be plausible on how the mismatches in sea ice extent affect the overall simulation.
Lines 242-245: “Since all EC-Earth driven experiments share quasi-identical initial states, all of those simulations are affected by similar forcing signals (due to greenhouse gases etc.) and model drift, while the timing of the internal variability may vary. Differences in long-term trends should therefore be the result of internal climate variability, generated outside and/or inside the domain.” Not necessarily. These trends can aslo indicate the sensitivity of the model to the initial conditions or to different setup of FC00, FC01 and FC02. Isn’t it?
Section 4.1 (also line 309): What is “pluri-decadal” in the context of this paper? Ten days or ten years? In the second case statistics has no sence. In the text, trends over 30 years (30 septembers and 30 marches) are described (i.e. in Table 1). Then the term “decadal” is really confusing.
Line 267: “ensemble averaged”: does it mean an avearge over three values?
Line 267-268: The follwing is not clear: “The EC-Earth hindcasts themselves are not showing a negative SIE trend (Mann-Kendall test for the ensemble averaged September SIE: p = 0.972”. In the figure 4 one can clearly see a negative trend in EC-Earth hindcasts – from ~17.5 mln. sq. km to ~15 mln. sq. km over 30 years (meaning over 30 septermbers). Shown in Table 1 treds are statistically significant …
Line 288: “bufferzone”: may be “buffer zone”?
Figures 7 and 8: Shown are anomalies, not absolute values, aren’t they? Then the colour scales and figure captions must be changed to avoid confusion.
Figure 8: The color scale is indicated as 700 mb but in the figure caption is written 850 mb. What is true?
Table 3: It is not clear over which decade averaging is carried out. The indicated period is 1985-2014. Are the trends estimated over annually averaged values? Isn’t the period of ten years (ten values) is long enough to figure out a realistic trend? By what means the trends were estimated?
Figure 9: Evaporation and precipitation TRENDS
Line 357: “After all, precipitation that does not have a local source has to have an origin somewhere else” – This is not entirely correct if the internal part of the Antarctca is considered. Here the precipitation has another (local) mechanism and is formed at the upper boundary of the inversion layer. Anyway, the related part of the text must be clarified in case anything else was supposed.
Table 4. The same note as for the Table 3. What decade is taken? Or the average among 3 decades is indicated? What do the authors mean under “decadal trend”?
Line 425: “the slow ocean variability”: the ice sheet is much less variable compared to the ocean, may be except the gronding line migration.
Lines 427-428: “With our approach where we have three hindcasts driven by EC-Earth, we can better differentiate internal variability from interannual variability”. The arguments in support of this statement seem not to be very strong. What was clearly demonstarted is that the GCM output driven model PARASO failed to reproduce some crusial regional climate features compared to the case when PARASO was forced by the reanalysis. By itself, this would be a very important conclusion. It is also not clear what “internal” variability means.