Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exiting the Arctic Ocean does so through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated summer (July–August) time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison of this time series with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years at the end of the Transpolar Drift. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate for the impact processes, such as Atlantification, have on sea ice thickness in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea-ice-covered Arctic.

The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm 2 (VASIA2) algorithms were used as well as the database of visual ice observations accumulated in the course of 15 Arctic expeditions. The comparison was performed in line with the SIC gradation (in tenths) into very open (1–3), open (4–6), close (7–8), very close and compact (9–10,10) ice, separately for summer and winter seasons. On average, in summer NT underestimates SIC by 0.4 tenth as compared to ship observations, while ASI and VASIA2 by 0.3 tenth. All three algorithms overestimate total SIC in regions of very open ice and underestimate it in regions of close, very close, and compact ice. The maximum average errors are typical of open ice regions that are most common in marginal ice zones. In winter, NT and ASI also underestimate SIC on average by 0.4 and 0.8 tenths, respectively, while VASIA2, on the contrary, overestimates by 0.2 tenth against the ship data, however, for open and close ice the average errors are significantly higher than in summer. In the paper, we also estimate the impact of ice melt stage and presence of new ice and nilas on SIC derived from NT, ASI, and VASIA2.

The SSM/I data processed by the NASA Team algorithm are used to compare the total ice concentration obtained from the visual shipborne observations with satellite images. A comparison of the satellite images with the shipborne data shows significant differences with the shipborne data observed onboard ice-breakers during 15 scientific expeditions to the Barents, Kara, Laptev, and East Siberian seas. The most pronounced differences are observed in the ice edge regions. They cause errors in the estimates of the total ice concentration and ice extent, which makes difficult to use them in various practical and scientific tasks. Generally, the methods of remote sensing (RS) underestimate the real sea ice concentration: the average error is on the order of 10% in both winter and in summer. A statistical analysis of the comparison of two sources of information was carried out separately for the total ice concentration for summer and winter data taking into account the new ice and without it. During the summer period in the area of open ice, the SSM/I data over-estimate the total ice concentration by 0.5–1, but in close ice they underestimate it on average by 2 grades. If the new ice is subtracted from the total ice concentration obtained onboard the icebreakers, the total error decreases to −3.4%. In the winter period in the region of rare ice, the SSM/I data overestimate the total ice concentration by 1–2 grades, but in close ice this difference is as high as 2, like in summer. New ice in winter is determined better by remote sensing methods than in summer; hence, its exclusion from the total ice concentration does not lead to a decrease in the average error.

In the period of intense ice melting, algorithms retrieving sea-ice concentration from satellite microwave radiometry (SMR) data may fail to detect vast regions of floating ice. Late-stage melting is characterized by an abundance of melt ponds on ice, resulting in considerable underestimation of sea-ice concentration. Also, during melting, ice concentration decreases and ice breccias disintegrate; therefore the size of ice floes decreases. In winter, at river mouths in the shelf seas, ice is formed with a heavy load of terrigenous sediments carried by the rivers. A dirty ice surface becomes visible in summer when snow cover melts off. SMR techniques fail to adequately determine dirty ice concentration. The paper considers the impact of ice melt, concentration, floe size, and dirtiness on the determination of sea ice extent in the Arctic in summer using the ARTIST Sea Ice (ASI) algorithm. Our study shows that the portion of ice cover left undetected by the SMR technique may range in different years 5.7 to 23.3% of the total Arctic sea ice extent.

In this paper we compare the sea ice concentration data obtained by satellite microwave radiometry using the NASA Team, ASI, and VASIA2 algorithms and by special visual ship observations. A database of visual observations of the sea ice concentration obtained during 15 expeditions in the Arctic was used. The comparison for various concentration gradations (very open, open, close, very close, and compact ice) was carried out separately for summer and winter periods. On average, during the summer period, the NASA Team algorithm underestimates the sea ice concentration by 1 point, while the ASI and VASIA2 algorithms underestimate by 0.5 points when compared to ship observations. All three algorithms overestimate the total concentration in the zones of very open ice and underestimate it in the zones of close, very close, and compact ice. The maximal average errors are characteristic of open ice that is most often observed in ice edge zones. In winter, the average error for all algorithms is no more than 1 point when compared with ship observations. However, in very open and close, the average error in winter is significantly higher than that in summer. To assess the effect of melting processes on the concentration values obtained by satellite microwave radiometry, we used ship data on ice melt stage. The average error at the maximal area of melt ponds on the ice surface reaches –2.9 points for the NASA Team algorithm, –2.8 points for the ASI algorithm, and –5.0 points for the VASIA2 algorithm. The results will be useful for determining the extent of ice-cover degradation in the Arctic Ocean observed in recent years.

Sea-ice concentrations retrieved from satellite microwave radiometry data are influenced by many natural factors. In the work we consider sea-ice contamination. The values of sea-ice concentration derived with the use of the ASI, TUD, OSI-401-b, BT, NT, and CDR algorithms are analyzed on the example of fast ice in the East Siberian Sea. These algorithms are chosen because they are available for open access and are commonly used in scientific research. All of the algorithms return lower ice concentrations in contaminated ice as compared to clean ice during the active ice-melt period, from late May to early July. The ASI and TUD are the two most sensitive to sea-ice contamination, which is due to the high-frequency channels, 85–91 GHz, used in them.