the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Control of the temperature signal in Antarctic proxies by snowfall dynamics
Aymeric P. M. Servettaz
Cécile Agosta
Christoph Kittel
Anaïs J. Orsi
Abstract. Antarctica, the coldest and driest continent, is home to the largest ice sheet, whose mass is predominantly recharged by snowfall. A common feature of polar regions is the warming associated with snowfall, as moist oceanic air and cloud cover increase the surface temperature. Consequently, snow accumulated onto the ice sheet is deposited under unusually warm conditions. Here we use a polar-oriented regional atmospheric model to study the statistical difference between average and snowfall-weighted temperatures. During snowfall, the warm anomaly scales with snowfall amount, with strongest sensitivity at low accumulation sites. Heavier snowfall in winter contributes to cool the annual snowfall-weighted temperature, but this effect is overwritten by the event-scale warming associated with precipitating atmospheric systems, which particularly contrast with the extremely cold conditions in winter. Consequently, the seasonal range of snowfall-weighted temperature is reduced by 20 %. On the other hand, annual snowfall-weighted temperature shows 80 % more interannual variability than annual temperature, due to irregularity of snowfall occurrence and their associated temperature anomaly. Disturbance in apparent annual temperature cycle and interannual variability have important consequences for the interpretation of water isotopes in precipitation, which are deposited with snowfall and commonly used for paleo-temperature reconstructions from ice cores.
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Aymeric P. M. Servettaz et al.
Status: open (until 05 Oct 2023)
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RC1: 'Comment on egusphere-2023-1903', Anonymous Referee #1, 30 Aug 2023
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Review of
Control of the temperature signal in Antarctic proxies by snowfall dynamics
by Aymeric P. M. Servettaz
Summary
This paper uses 1979-2020 daily total snowfall accumulation and average temperature from a regional climate model to study the Antarctic temperature bias associated with the atmospheric conditions during snowfall. This is a relevant quantity for the reconstruction of the Antarctic climate using firn/ice cores. The paper analysis is simple, yet very well written, and the figures are of excellent quality. My comments are relatively minor.Major comments
In the introduction, it could be made more clear that it is the temperature at the elevation of precipitation formation (the condensation temperature) that is imprinted in the snow, and not near-surface or surface temperature. This temperature is then often regressed onto average surface temperature (from 10 m snow temperatures) to make the coupling of the isotopic signal to the surface.l. 69: "extensively evaluated for its representation of Antarctic surface mass balance and temperature". This is true, but e.g. Mottram and others (2022) show that MAR3.10 appears to be significantly above-average wet in the East Antarctic region west of the Ross ice shelf, also one of the delta_T hotspots in Fig. 2. Moreover, the model is not evaluated for the key variables used in this paper, i.e., the timing of precipitation. Any comments?
Figure 3: Consider including standard deviation in the temperature curves and precipitation bars, to indicate the temporal variability on which these averages are based. This also supports the statement about temperature variability in winter in l. 154.
Minor and textual comments
Please use 'higher' and 'lower' temperatures rather than 'warmer' and 'colder/cooler' temperatures throughout; I realize it is a rearguard battle but hey, that is the privilege of the reviewer!l. 38: "ablation-redeposition and sublimation-condensation" These combinations are not necessarily mutually exclusive. Dis you mean "erosion/sublimation and deposition cycles"?
l. 50: stronger -> morel. 53: hot -> warmer
l. 78: evaporation -> sublimation
l. 91: "...surface air temperature" Although widely used, this is an ambiguous phrase. Please simply use '2 m air temperature' or 'near-surface air temperature'.
l. 95: Surface temperature -> 2 m air temperature
l. 116: warmer -> higher (see above)
l. 18: temperature anomaly -> average temperature anomaly
l. 151: This formulation could be condensed to: " emerges from the stronger near-surface horizontal and vertical temperature gradients..."
l. 202: "snowfall-weighted δ18O " Do you mean oxygen isotopes in atmospheric water vapor? Please clarify.
l. 218: warm -> positive
l. 220: The trend of temperature increase -> The slope of temperature increase as a function of accumulation amount
l. 223: "Snowfall-weighted climate normal temperature " This is unclear, please reformulate or clarify.
Citation: https://doi.org/10.5194/egusphere-2023-1903-RC1 -
RC2: 'Review of Servettaz et al. for TC (egusphere-2023-1903)', Anonymous Referee #2, 29 Sep 2023
reply
General comment
Servettaz et al. investigated the warm bias in Antarctica during snow precipitation events. Consideration of this aspect is particularly important for paleoclimate reconstructions using stable isotopes of water measured in firn and ice cores, which record temperature only during snowfall. For their study, Servettaz et al. used modeled snowfall and near-surface air temperature (i.e., temperature at 2 m) from the MAR regional climate model for the period 1979-2020. The analysis and idea are simple (in a good way) and very well written, making the article easy to read and follow. The difference between "true" mean temperature and mean temperature during snowfall only is increasingly discussed in the paleoclimate and ice core communities, and this article represents an important additional contribution to that discussion. I therefore recommend publication of this study in The Cryosphere after addressing the minor points detailed below.
Major comments (but minor revisions)
- Stable isotopes of water are mentioned in the article from the second sentence and throughout the rest of the paragraph, with more detailed descriptions of the processes controlling isotopic signals in Antarctic firn and ice cores. I think it’s a little bit too harsh and too specific considering the main topic of this paper, even if the findings of this study have important implications for the paleoclimate reconstructions using stable water isotopes in Antarctic ice cores. To make it simple, I think the two first paragraphs could be swapped (with some adaptation). Moreover, it would make a smoother transition with the 3rd paragraph.
- One of the most interesting findings concerns the greater inter-annual variability of snowfall-weighted temperature compared with annual temperature. Could you try to establish a link with an index of internal climate variability such as the Southern Annular Mode (SAM)? For example, Kino et al (2021) have shown the impact of SAM on the water isotope temperature record at Fuji Dome, through changes in atmospheric circulation.
- 2m air temperature is used for analysis. Could you explain in a few sentences the differences you would expect if condensation temperature were used instead?
Minor technical comments:
- Line 27: reduced by 20% compared to what?
- Line 44: “are known to increase the surface temperature”. I agree with the comment of the first reviewer about higher and lower temperatures (and not warmer and cooler temperatures).
- Line 53: “incorporating warm air”
- Line 82: which fields of MAR are nudged to ERA5 (U and V winds?)? Please give some more details. Moreover, the proper reference to ERA5 reanalyses is Hersbach et al. (2020).
- Line 116: “is statistically higher than”
- Line 120: “similar patterns are modeled”
- Line 152: remove “the” before “T reaches”.
- Lines 188-191: Other studies before weighted the d18O and temperature by daily variations of precipitation (and not by monthly variations only) to study the isotope-temperature temporal relationships, like in Werner et al. (2018).
- Lines 203-205: Exactly!
- Line 220: “The slope of temperature increase”
References
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803.
Kino, K., Okazaki, A., Cauquoin, A., & Yoshimura, K. (2021). Contribution of the southern annular mode to variations in water isotopes of daily precipitation at dome Fuji, east Antarctica. Journal of Geophysical Research Atmospheres, 126(23). https://doi.org/10.1029/2021jd035397.
Werner, M., Jouzel, J., Masson-Delmotte, V., & Lohmann, G. (2018). Reconciling glacial Antarctic water stable isotopes with ice sheet topography and the isotopic paleothermometer. Nature Communications, 9(1), 3537. https://doi.org/10.1038/s41467-018-05430-y.
Citation: https://doi.org/10.5194/egusphere-2023-1903-RC2 - Stable isotopes of water are mentioned in the article from the second sentence and throughout the rest of the paragraph, with more detailed descriptions of the processes controlling isotopic signals in Antarctic firn and ice cores. I think it’s a little bit too harsh and too specific considering the main topic of this paper, even if the findings of this study have important implications for the paleoclimate reconstructions using stable water isotopes in Antarctic ice cores. To make it simple, I think the two first paragraphs could be swapped (with some adaptation). Moreover, it would make a smoother transition with the 3rd paragraph.
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RC3: 'Comment on egusphere-2023-1903', Anonymous Referee #3, 29 Sep 2023
reply
Review of Controls on the temperature signal in Antarctic proxies by snowfall dynamics.
This manuscript investigates how snowfall intermittency over Antarctica acts to bias a precipitation-record of temperature. It is based on the output from the regional MAR model, driven by the ERA5 reanalysis. The structure, figures, and writing are mostly very good, and the topic is suitable for the Cryosphere and the special issue. Overall, I support its publication here.
However, there is one major issue, that will require some effort to fix. This is the patchy referencing. This patchiness also leads directly to a variety of problems with the manuscript, including the introduction, methods/approach description, lack of comparison with previous results, and the conclusions. Fixing these issues requires that the authors read, compare to and cite papers that have previously dealt with the topic of how daily to interannual snowfall intermittency over Antarctica acts to bias the precipitation-record of temperature, or as they term it ‘controls on the temperature signal in Antarctic proxies by snowfall dynamics’. Changes are needed throughout the manuscript to deal with this issue.Major comment (i) The missing literature on precipitation intermittency:
The key previous papers, and some of the results are:
1. Sime, Louise C. , Tindall, Julia C., Wolff, Eric W., Connolley, William M., Valdes, Paul J.. (2008) Antarctic isotopic thermometer during a CO2 forced warming event. Journal of Geophysical Research, 113. 16 pp. doi:10.1029/2008JD010395
This provides a decomposition method for studying how snowfall intermittency over Antarctica acts to bias a precipitation-record of temperature. It breaks it down into the components that the authors require, to that inter-annual, seasonal and synoptic (daily) affects on precipitation-weighted temperature are seperated. This paper also provide results that the authors can, and should, compare their MAR results against.In 2.2 it would be very useful to see the authors directly apply the Sime et al. (2008) decomposition to MAR, given provides a method to split intermittency biasing effects into daily, seasonal, and inter-annual components. Note this method requires only simple bandpass filtering of daily P and T output and will be easy to apply to the authors daily output.
2. Sime, Louise , Wolff, Eric, Oliver, K.I.C., Tindall, J.C.. (2009) Evidence for warmer interglacials in East Antarctic ice cores. Nature, 462. 342-346. 10.1038/nature08564
This paper provides detailed analysis of GCM experiments, showing trends in covariance between surface temperature and precipitation throughout the modelled warming, and how they affect ice core δ18O record. It shows that seasonal and synoptic T-P covariance changes have a limited impact on the geographical patterns associated with warming, but are nevertheless sufficient to explain the climate dependence of the δ18O against T relationship – and its site dependence. HadCM3 results show that warmer climates are associated with a larger proportion of precipitation in cold seasons over Dome C and Vostok. Note where the authors ask for in their concluding section for applications, as to why this P-T biasing is important, this is perhaps the most obvious example. Together 1 and 2 together show precisely how and why the effect of precipitation-weighting at daily-to-interannual frequencies can matter for Antarctic ice core science.3. Masson-Delmotte, V., Buiron, D., Ekaykin, A., Frezzotti, M., Gallee, H., Jouzel, J., Krinner, G., Landais, A., Motoyama, H., Oerter, H., Pol, K., Pollard, D., Ritz, C., Schlosser, E., Sime, Louise C. , Sodemann, H., Stenni, B., Uemura, R., Vimeux, F.. (2011) A comparison of the present and last interglacial periods in six Antarctic ice cores. Climate of the Past, 7. 397-423. 10.5194/cp-7-397-2011
Figure 3 from this paper shows all inter-annual, seasonal and synoptic (daily) affects on precipitation-weighted Antarctic temperature for ERA40. The authors should really compare their results to these older ERA40, to see if their newer model results show changes.Application of these methods for virtual cores. Should also be read and cited.
4. Sime, Louise C. , Marshall, Gareth J. , Mulvaney, Robert , Thomas, Elizabeth R. . (2009) Interpreting temperature information from ice cores along the Antarctic Peninsula: ERA40 analysis. Geophysical Research Letters, 36. 5 pp. 10.1029/2009GL038982
5. And see also: Sime Louise , Lang, Nicola, Thomas, Elizabeth , Benton, Ailsa, Mulvaney, Robert . (2011) On high-resolution sampling of short ice cores: dating and temperature information recovery from Antarctic Peninsula virtual cores. Journal of Geophysical Research, 116. 17 pp. 10.1029/2011JD015894Applications of precipitation-weighting methods and analysis for Peninsula ice cores. Once the authors have read these papers, it would also be a useful exercise if they check for citations of these works, to also insure they haven’t similarly missed a lot of important more recent papers too.
Check also incase the work of Thomas Laepple’s group is similarly of value to this work.Major comment (ii) The importance of surface versus condensation temperature:
When discussing the difference and importance of surface versus condensation temperature do also read: Z Liu, C He, M Yan, C Buizert, BL Otto-Bliesner, F Lu, C Zeng (2023) Reconstruction of Past Antarctic Temperature Using Present Seasonal δ 18 O–Inversion Layer Temperature: Unified Slope… Journal of Climate 36 (9), 2933-2957 and modify 2.2 accordingly.Minor comments:
Introduction – needs to be fairly substantially modified in the light of the above.
Line 124 – please compare with the equivalent numbers from previous HadCM3 and ERA40 results in the 2008 and 2011 papers.
Line 167 – add calculations also for the inter-annual terms using MAR-ERA5 output.
3.3 needs quite a lot of rewriting to acknowledge that whilst previous authors have calculated the daily biasing effects – and have shown these to be largest - nevertheless the most terms that changes the most with climate is generally the seasonal, rather than the daily/synoptic biasing terms. On this, do also read and consider referencing: Holloway, Max D. , Sime, Louise C. , Singarayer, Joy S., Tindall, Julia C., Bunch, Pete, Valdes, Paul J.. (2016) Antarctic last interglacial isotope peak in response to sea ice retreat not ice-sheet collapse. Nature Communications, 7. 9 pp. doi:10.1038/ncomms12293. Text can be modified to reflect that this paper also shows the primacy of seasonal (change with climate) effects. The 2008, 2009 and 2011 papers, noted above, methods and results should also accounted for during rewriting.Citation: https://doi.org/10.5194/egusphere-2023-1903-RC3
Aymeric P. M. Servettaz et al.
Aymeric P. M. Servettaz et al.
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