the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The role of upper ocean heat content in the regional variability of Arctic sea ice at sub-seasonal time scales
Elena Bianco
Doroteaciro Iovino
Simona Masina
Stefano Materia
Paolo Ruggieri
Abstract. In recent decades, the Arctic Ocean has undergone changes associated with enhanced poleward inflow of Atlantic and Pacific waters and increased heat flux exchange with the atmosphere in seasonally ice-free regions. The associated changes in upper ocean heat content can alter the exchange of energy at the ocean-ice interface. Yet, the role of ocean heat content in modulating Arctic sea ice variability is poorly documented, particularly at regional scale. We analyze ocean heat transports and surface heat fluxes between 1980–2021 using two eddy-permitting global ocean reanalyses, C-GLORSv5 and ORAS5, to assess the surface energy budget of the Arctic Ocean and its regional seas. We then assess the role of upper ocean heat content, computed in the surface mixed layer (Qml) and in the 0–300 m layer (Q300), as a sub-seasonal precursor of sea ice variability by means of lag correlations. Our results reveal that in the Pacific Arctic regions, sea ice variability in autumn is linked with Qml anomalies leading by 1 to 3 months, and this relationship has strengthened in the Laptev and East Siberian seas during 2001–2021 relative to 1980–2000, primarily due to reduced surface heat loss since the mid-2000s. Q300 anomalies act as a precursor for wintertime sea ice variability in the Barents and Kara seas, with considerable strengthening and expansion of this link from 1980–2000 and 2001–2021 in both reanalyses. Our results highlight the role played by upper ocean heat content in modulating the interannual variability of Arctic sea ice at sub-seasonal timescales. Heat stored in the ocean has important implications for the predictability of sea ice, calling for improvements in forecast initialization and focus upon regional predictions in the Arctic region.
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Elena Bianco et al.
Status: open (until 08 Nov 2023)
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RC1: 'Comment on egusphere-2023-1406', Anonymous Referee #1, 30 Sep 2023
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This manuscript is written in good grammar and contains technically good
quality figures. The analysis appears generally valid, and lagged correlation
values greater than 0.7 (explaining more than 50% of the variance) indicate
significant influences of Q on SIC at sub-seasonal scales, which is
interesting.It is not clear what new the results contain in terms of mutual significance of
physical processes identified, the study seems to confirm earlier results, and
therefore the novelty aspect is low, although it claims to shed light on the
regional mechanisms (line 276). This aspect could possibly be elevated by
adding more specific regional interpretations of the roles of physical
processes, following the example presented in lines 309-312, but at a more
advanced level. Particularly, the Arctic warming, linked to sea-ice
variability, is a result on multiple other processes than the ice-albedo feedback,
such as the atmospheric ones related to large-scale weather patterns, winds,
precipitation and air temperature, and riverine heat influx. What is the role
of processes other than OHT on sub-seasonal sea-ice variability? Additionally,
now only the sea-ice variability in terms of SIC is explored, while the
thinning of ice could be linked to SSHF. Also, sea-ice dynamics, not only
thermodynamics, could be regionally important, as the Arctic sea-ice volume
declines. In this context, adding a reanalysis product with sea-ice model
having a sub-grid-scale sea-ice thickness distribution parametrisation would be
valuable.A considerable issue is that the study is based on two very similar ocean
reanalysis products only, both driven by ERA-Interim atmospheric forcing. But
ocean reanalyses are known to vary a lot in the Arctic, see for example Uotila
et al. (2019) Figure 8. Related to this, the ERA-Interim sea-ice
parametrisation is not particularly realistic (Batrak et al. 2019), that affects
its surface energy balance and further the variables used to drive ocean
models. Other more contemporary atmospheric reanalyses and ocean reanalyses
driven by them might do better, which would be interesting to know.Importantly, the robustness of the quantitative results remains questionable
before including more reanalyses, or similar data, and therefore must be tested
with other products, such as TOPAZ, NCEP CFSR, ECCO2, GLORYS, NEMO-EnkF and
PIOMAS, before the publication. Many reanalyses have output publicly available.
In particular, the NCEP CFSR based comparison would be valuable as it is a
coupled atmosphere-ocean one, with the ocean able to influence the atmosphere.
Findings from this extended analysis could then be compared to the ones by
Mayer et al. (2019) in discussion.AW is mentioned many times as the source of OHT but apparently without
consideration of its depth. In some parts of the Arctic Ocean AW is lies
deeper under a strong halocline and its influence on upper ocean Q and SIC
variability may remain limited. This should be taken into account when
interpreting the results but has not been mentioned.After adequately addressing these issues, the manuscript will contribute to
changing our scientific understanding of the Arctic sea-ice variability on
sub-seasonal scales and become an important source for scientific community.Specific points:
- line 46. and some other places: do not italize units, in this case W m**-2.
- line 48. the Arctic Ocean
- line 133. Is mentioned that the diffusive heat transport is not taken into account. Does it mean that AW is not diffusing heat to upper ocean, which could be a significant shortcoming?
- line 145. from the surface to seasonally-varying ...
- line 149. Did you consider setting Tf to depend on salinity? In the Arctic Ocean some regions have a rather low salinities and therefore low Tf and possibly significantly lower Q than with Tf=-1.8 degC.
- line 168. Figure 2e and 2f show annual trends, but seasonal trends would be better to show, and in general seasonal summer/winter analyses where relevant instead of the annual one. This is because SSHF represents ocean cooling in winter but warming in summer. The annual analysis becomes especially problematic when showing flux anomalies/trends, where a negative (positive) anomaly/trend could be related to either a downward flux becoming stronger (weaker) downward, or an upward flux becoming less (more) upward. The same issue exists at least in Figure 3.
- Figure 2. Stippling denotes 95% significance, but what is confusing is that even zero trends are often stippled, for example in Figure 2c. Such significance testing is meaningless and should not be used.
- There are quite striking differences between C-GLORS and ORAS5 Q300 in Figure 3b and 3c, which has not been mentioned and reasons discussed. Also, instead of anomalies would be better to plot absolute Q values to reveal the real differences between the reanalyses. It would also be good to add the basin averaged sea-ice thickness time series to show its relationship to Q:s.
- lines 207-208. The expression 'satisfaction of physical constraints' is unclear and should be clarified.
- lines 215-216. Links between the OIHF sharp drops and SIC decline are not clear in Figure 5. For example in Figure 5f the SIC decline starts years earlier than the OIHF sharp drop. So, the statement looks rather speculative.
- lines 220-223. This text repeats Figure 6 caption and should be removed.
- line 227. Specify the freezing season months. Mentioning the AW annual maximum indicates the AW seasonal variability, add a suitable literature reference here.
- line 231. considerably more negative
- Figure 6. As said in text, the empty boxes represent non-significant correlations (line 223). That information is better in the figure caption.
- line 245. lag time, and correlations are
- line 252. This process should be explained more in detail in terms of physics. How is the SIC prediction skill reduction associated with Qml during the earlier period and the MLD shoaling during the later period?
- line 259. the statistically non-significant OHT trend. Supposedly you think that the positive OHT trend is physically significant because it is mentioned, right?
- lines 264-265. Qml is not shown in Figure 8.
- Figure 7 presents a region of positive correlation in the Beaufort Sea in 2001-2021 that looks interesting. What physics explains that and why does it emerge in 2001-2021 compared to 1980-2000?
- line 302. Did you calculate lag correlations longer than 3 three months? Would be interesting to see how correlations decrease with lag time providing information on characteristic time scales.
- lines 309-312. This explanation is plausible, safe and presented multiple times before (for example Fox-Kemper et al. 2021, Chapter 9.3.1.1). It is regrettably also almost the only regional physical mechanism presented in the paper.
- line 324. The authors have decided to exclude stratification and halocline, although they form an essential component to understand the results.
- line 326-327. Perhaps worth mentioning, but this appears almost trivial.
Literature:Batrak, Y. and Müller, M.: On the warm bias in atmospheric reanalyses induced
by the missing snow over Arctic sea-ice, Nat Commun, 10, 4170,
https://doi.org/10.1038/s41467-019-11975-3, 2019.Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L.
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Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021:
Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical
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Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362,
doi:10.1017/9781009157896.011.Citation: https://doi.org/10.5194/egusphere-2023-1406-RC1
Elena Bianco et al.
Elena Bianco et al.
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