Analysis of sea ice concentration and thickness over Barents Sea using standard logistic curve model

Abstract

As marginal, the Barents Sea plays a major role in the process of Atlantification, and large seasonal variability in sea ice is observed over the region. Current sea ice concentration and thickness obtained from satellite help one understand the variation in sea ice is seasonal. During summer, the concentration and thickness of sea ice are seen to fall, and during winters, it is seen to rise. In order to understand the difference in these variabilities and to analyse the future state of sea ice, a standard logistic curve model is considered. The standard logistic curve model is applied to sea ice parameters during summer and winter to quantify the sea ice growth and decay processes over the Barents Sea.The model yields predicted values based on the adjustment parameter (b) used.Results show that the predicted sea ice concentration performs well with the satellite sea ice concentration values. The model is run on the timeframe grouped into two, with each set having an average of ten years from 2000-2020. For the decay process, the fitted sea ice concentration decay curves derived from the standard logistic curve model are in good agreement with the observed data for the two timelines, with r2 = 0.88 and 0.87, respectively. Similarly, for the growth process, the relevant fitted decay curves derived from the standard logistic curve model are also in good agreement with the observed data during the above different time periods withr2= 0.80 and 0.78, respectively. Further, the model is implied to sea ice thickness, and the result obtained by the logistic curve model is found to be consistent with the satellite sea ice thickness with r2 = 0.75 for the years 2011–2020. Particularly, both the rapid sea ice increase pattern during the growth process and the remarkable decrease pattern during the decay process are successfully characterized by the corresponding fitted curves. The introduction of calculated adjustment parameters into the model helps in accurately determining the sea ice variables, which brings us closer to conservation tools that mitigate therisks associated with rapid sea ice loss.