| 23 Oct 2025
Relationships between Arctic sea-ice concentration, temperature, and specific humidity in the lower troposphere during 1980–2021
Abstract. Understanding of the local effects of sea-ice concentration (SIC) variations on the Arctic atmosphere is a prerequisite for assessing the role of Arctic sea-ice decline in the climate system, including its influence on mid-latitudes. In this study, we analysed the relationships between SIC and both temperature and specific humidity at the surface and 2-m level, as well as at 950, 850, 750, and 600 hPa across the circumpolar Arctic. We applied linear ordinary-least-squares-regression analysis to detrended anomalies of monthly means of data from the NCEP/CFSR atmospheric reanalysis for 1980–2021. The results show the strongest correlations between SIC and temperature, as well as between SIC and specific humidity, in the marginal ice zone during the cold seasons (November–April) with the coefficient of determination (R2) around 0.6 at the surface and near-surface levels and around 0.3 at 950 and 850 hPa. During these cold seasons, SIC affects air temperature and specific humidity, while the effects of air temperature variations on SIC are limited. SIC correlates somewhat better with specific humidity than with temperature, which can be attributed to the exponential dependence of saturation specific humidity on temperature. In the Central Arctic, physical conditions are favourable for high R2 values, but low variability in SIC reduces the correlations. In contrast, in regions such as the northern Barents Sea, increased November–April SIC variability from 1980–2000 to 2001–2021 strengthens the correlations, even though surface heat and moisture fluxes become less sensitive to SIC in a warming climate. This finding suggests that statistical effects can outweigh the physical sensitivity in shaping observed relationships. During the warm seasons (May–October), high enough air temperatures reduce SIC, while the effect of SIC is small due to the surface temperature of the ice being close to that of the open ocean. The relationships between SIC and both temperature and specific humidity are generally weaker during these warm seasons with R2 at the surface and near-surface levels around 0.4 over the marginal ice zone during May–July and across the entire sea-ice zone during August–October. The role of wind speed and direction in the relationships between SIC and both temperature and specific humidity is discussed.
Summary: This study examines the leading relationships between sea ice concentration (SIC), temperature and specific humidity in the observational period, using NCEP reanalysis and statistical techniques. The authors find that there is a strong correlation between SIC with temperature and specific humidity, closest to the near surface in the marginal zones, where there is less multiyear ice and SIC is typically lower, with SIC driving the correlations. They find this relationship is less in the summer months, with the atmosphere having a small influence on SIC instead. They also find that there is less correlation in the central Arctic, as a result of statistical methodology.
Overall, the paper was well written with a good and clear experiment design. The science is of good quality, including statistical tests, and fits well within the scope of TC. Results would be of interest to anyone researching casual relationships between SIC and the atmosphere, and those seeking an understanding of the two way relationship between ice and atmosphere.
Suggestions: The physical mechanisms presented in this manuscript would seem obvious conclusions based on the physics, however, they represent a gap in the literature where these relationships have not been directly assessed. Sea ice is both a biproduct and barrier in ocean and atmosphere interaction in the poles. The difference in relationship between the seasons is a shift between the ocean and atmospheric interactions and forcings with sea ice. For this reason, it would be beneficial to include some additional analysis with SST.
The work seems to be a little limited by method, for example, the central Arctic correlations being impacted by variability. Is there a way this could be addressed or another method that could be used for this region?
There are some limitations with using reanalysis for statistical relationship work, in that reanalysis models are built on statistical relationships, leading to biases. This should be addressed within the text further.
An addition to this work, that would show these relationships clearer in a physical sense, rather than statistical, would be analysis of the turbulent heat flux, both sensible and latent, perhaps assessing the dominating flux in each season, further showing the direction of the relationship.