the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 Mar 2026
Impact of meteorological and oceanographic conditions on the state of sea ice in Hornsund, Svalbard over 23 years
Abstract. The full archive of historical SAR imagery at ~50 m resolution (Envisat ASAR, RADARSAT-2 and Sentinel-1) capturing the Hornsund fjord, Svalbard was used to create an unprecedented set of near-daily binary ice/open water maps over the fjord area for 23 seasons (2002–2025). We observe a general trend with the sea ice season shortening by 2.3 days yr−1, and a gradual decrease in average ice coverage, particularly in the main basin of Hornsund (−1.6 % yr−1). The interannual ice condition variability was strongly related to the autumn (October–December) and/or winter (January–March) air temperatures. The length of the sea ice season was shortened by 19.5 days for every 1 °C increase in mean autumn air temperature (R2 = 0.61, p < 0.05). Air temperature remained under the freezing point for over 90 days before landfast ice freeze-up. Drift ice was present in the fjord before the freeze-up with an average coverage exceeding 20 % 40 days and 26 % one day before the landfast ice onset. The landfast ice season break-up period was characterised by a lack of drift ice, and positive air temperatures for over a month. The day of landfast ice freeze-up and break-up overlapped with an average sea ice thickness of 0.33 m and 0.57 m as derived from thermodynamic terms, respectively, suggesting the importance of other processes.
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Status: open (until 27 Jun 2026)
- RC1: 'Comment on egusphere-2026-1656', Anonymous Referee #1, 29 May 2026 reply
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RC2: 'Comment on egusphere-2026-1656', Anonymous Referee #2, 30 May 2026
reply
Review on “Impact of meteorological and oceanographic conditions on the state of sea ice in Hornsund, Svalbard over 23 years”
Sea ice within Arctic fjords remains relatively quiescent owing to topographic sheltering, with limited exposure to external oceanic thermal and dynamical forcing. Serving as a valuable indicator of local climate variability, it bears notable implications for phenological research. Unraveling the seasonal evolution and long-term trends of sea-ice coverage and landfast ice inside fjords is critical to advancing studies on fjord ecosystems and coastal erosion.
Based on high-resolution nearly daily SAR images spanning 23 ice seasons, this study identifies key sea-ice phenological metrics for Hornsund Fjord, Svalbard, including freeze onset, breakup date, sea ice coverage, landfast ice extent and ice duration. It further reveals how environmental drivers such as local air temperature, sea surface temperature and wind fields modulate these critical sea ice parameters. This study is worthy of publication. Nevertheless, several deficiencies remain regarding the methodological details and manuscript presentation; therefore, major revision is recommended prior to reconsideration for publication.
General Comments
- The research motivation of this manuscript should be more focused on the climatic, physical and ecological settings of fjords, as some background information concerning the pan-Arctic Ocean is not fully applicable to fjord environments.
- When compared with previous studies, the discussion ought to center more on the phenomena and physical mechanisms uncovered by the long-term time series and high-resolution (daily, 50-m) dataset.
- For the linkage between sea ice and local meteorological as well as oceanic conditions, temporal resolution can be refined to pinpoint the time windows within which each sea ice parameter correlates most closely with specific atmospheric or oceanic drivers. Such refinement helps quantify the sensitivity of individual sea ice metrics to climatic and oceanic variability over distinct periods.
- Additional comparison against existing phenological studies of sea ice in other Arctic fjords is recommended.
- It is recommended to add comparisons with published findings on variations in other climatic, oceanic and environmental factors within Hornsund Fjord over a comparable study period.
Specific Comments:
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Line 16 “The length of the sea ice season was shortened by 19.5 days for every 1°C increase in mean autumn air temperature”: You may identify the meteorological factors through a specificperiod (not just seasonal) most closely correlated with ice season duration (as well as other parameters) based on moving average methods (see Lei et al., 2012), enabling more accurate characterization of the physical linkage between climate change and sea ice variables.
Lei et al., Changes in ice-season characteristics of a European Arctic lake from 1964 to 2008, Climatic Change, 2012.
- Line 19 “with an average coverage exceeding 20% 40 days and 26% one day”: Please provide the complete expression.
- Line 149 -153: This paragraph describing wind climate appears somewhat disjointed; it is recommended to shorten or remove it.
- The constraints on variable T in Equations 1 and 2 are unnecessary. Certain periods with temperatures above the freezing point should be included when calculating freezing degree-days; otherwise, their interfering effects on sea ice growth would be overlooked. The same logic applies to the calculation of Thawingdegree-days.
- Equations 3 and 4 describe the analytical model for thermodynamic sea ice growth, whose key assumptions and limitations need to be highlighted:1) Snow cover effects are neglected; 2) Oceanic heat flux is not incorporated (this flux is generally relatively large in fjord regions); 3) Vertical ice temperature profile is assumed to linearly, and the air temperature equals the ice surface temperature, etc. Potential impacts of these assumptions on estimation results also need discussion.
- though the relationships are weak and statistically insignificant (Fig. 3aand other text or figures: Statistically insignificant trends or correlations are not recommended for presentation.
- Figure 6: For ice seasons with large deviations from linear regression, their anomalous features and potential causes deserve further discussion.
- Wind exerts complicated effects on ice formation. Increased wind speed can accelerate ocean heat loss and favor ice freeze-up; meanwhile, stronger winds frequently fracture thin ice and hinder the development of stable ice cover or landfast ice. Intensifiedice deformation by the enhanced wind is commonly observed in sea ice across the central Arctic Ocean. It is therefore necessary to further distinguish between onshore-offshore wind effects and wind overall effects, as onshore winds facilitate stable seaice formation.
- Line 447 “but the difference between FDD over consecutive days with T < 1.8°C (79°C days) and the ϑ over the entire period from the first day with T < 1.8°C”: The value here should be −1.8.
- Some cited references are unnecessary. For instance, De Steur et al. (2023) reported a correlation between sea ice extent in the Fram Strait and water temperature observed at depths ranging from 55 m to 2440 m, yet their physical mechanisms differ fundamentally from those addressed in your study.Many other such examples can be found.
Citation: https://doi.org/10.5194/egusphere-2026-1656-RC2
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- 1
This manuscript presents a comprehensive dataset on sea ice cover, atmospheric data and SST in Hornsund, do a regression analysis between sea ice cover etc. and the other data, and also formulates a model for predicting ice thickness. The idea is very good, but I have some problems with the presentation being far too detailed in the result section. Much of the text is repeating what is already shown in figures. This makes the manuscript hard to read. A more serious matter is whether the prediction model for ice thickness is made correctly (data shown in Table 4 with implications for Figure 10). Read details below following Line 448 and Table 4. II suggest the authors carefully examines the integration (cumulation) they have done compared to Su and Wang (2012), and do a revision of the manuscript.
These, and additional comments, following the manuscript chronologically:
Lines 104-105: wrong unit for salinity. From the numbers I see that salinity is Practical salinity which is defined by having no unit. In 2010 TEOS10 became the new standard with Absolute salinity having unit g/kg, but in this standard, salinity values are higher.
Line 162: Larsen et al., 2006 is not in reference list
Line 168: NPI, 2014 is not in reference list
Line 246: If you find space, it would be good to indicate position 77N, 15E in the map, Fig.1.
Line 260: according to Table 4, freezing point Tf = -1.8 deg C. Please indicate here.
Line 315: Is ref Fig.4 meant to be Fig. 3b?
Figure 4: are the figures showing the mean distributions over the years 2002-2025, please specify.
Figure 5; add seasons to the figure legend or above each column of plots.
Lines 390-297: all text here is also shown on Fig.6. Consider not giving the details.
Line 409: Fig. 7 does not say anything about how many predictors were included in the regression model, so explain why you show the figure.
Line 410-425: perhaps use ‘correlation’ instead of ‘relationship’, it is less emotional.
Table 3: Tell if these regression formulas are the ones used as predictions in Figure 7.
Line 448: Temperature should be below -1.8 centigrades (freezing temperature).
Line 448 and Table 4: Additionally, why are you counting consecutive days? Equation 1 for FDD does not mention that the days must be consecutive. Do you only estimate FDD when cold days follow in a row? And how can the term defined in Equation 3 be larger than FDD when, according to Equation 3, FDD is the largest number it can have..? You must in that case done integration for a different period for Equation 3 than equation 1. It is crucial for Figure 10 that you do the integrations correctly. Your reference Su and Wang (2012) calls eq. 1 and 2 ‘cumulative FDD’ and ‘cumulative SDD’ They are added during the same periods of time, FDD only when air temperature is below SST freezing temperature (-1.8 centigrades in your case), SDD only when air temperature is above 0 centigrades, and zero value is added to both of them when air temperature is between -1.8 to 0 centigrades.
Table 4: explain which days are ‘on the day’.
Line 462: I presume Fig. 14b should be Fig. 8b
Line 676: the publishing year should be 2018.