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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- RC1: 'Comment on egusphere-2025-2889', Anonymous Referee #1, 25 Oct 2025 reply
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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.