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Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change. The rapid changes in Arctic sea ice have been widely concerned. However, the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series. In this study, the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data. The results indicated that Arctic sea ice was declining at a remarkably high rate of −5.4 × 104 km2/a in sea ice area (SIA) and −2.2 cm/a in sea ice thickness (SIT) during 1979 to 2020, and the reduction of SIA and SIT was the largest in summer and the smallest in winter. Spatially, compared with other sub-regions, SIA showed a sharper declining trend in the Barents Sea, Kara Sea, and East Siberian Sea, while SIT presented a larger downward trend in the northern Canadian Archipelago, northern Greenland, and the East Siberian Sea. Regarding to the seasonal trend of sea ice on sub-region scale, the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons, especially in summer and winter, i.e., the sub-regions linked to the open ocean exhibited a higher decline rate in winter; however, the other sub-regions blocked by the coastlines presented a greater decline rate in summer. For SIT, the sub-regions such as the Beaufort Sea, East Siberian Sea, Chukchi Sea, Central Arctic, and Canadian Archipelago always showed a higher downward rate in all seasons. Furthermore, a striking freeze-thaw asymmetry of Arctic sea ice was also detected. Comparing sea ice changes in different dimensions, sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension. The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period, and the rate of sea ice melting was 0.3 × 104 km2/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions, respectively. Moreover, there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004, respectively, in the horizontal/vertical dimension. [ABSTRACT FROM AUTHOR]

Arctic sea ice export is important for the redistribution of freshwater and sea ice mass. Here, we use the sea ice thickness, sea ice velocity, and sea ice concentration (SIC) to estimate the exported sea ice volume through the Fram Strait from 2011 to 2018. We further analyse the contributions of the sea ice thickness, velocity and concentration to sea ice volume export. Then, the relationships between atmospheric circulation indices (Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Arctic Dipole (AD)) and the sea ice volume export are discussed. Finally, we analyse the impact of wind-driven oceanic circulation indices (Ekman transport (ET)) on the sea ice volume export. The sea ice volume export rapidly increases in winter and decreases in spring. The exported sea ice volume in winter is likely to exceed that in spring in the future. Among sea ice thickness, velocity and SIC, the greatest contribution to sea ice export comes from the ice velocity. The exported sea ice volume through the zonal gate of the Fram Strait (which contributes 97% to the total sea ice volume export of the Fram Strait) is much higher than that through the meridional gate (3%) because the sea ice flowing out of the zonal gate has the characteristics of a high thickness (mainly thicker than 1 m), a high velocity (mainly faster than 0.06 m/s) and a high concentration (mainly higher than 80%). The AD and ET explain 53.86% and 38.37% of the variation in sea ice volume export, respectively. [ABSTRACT FROM AUTHOR]

The Arctic Oscillation (AO) has important effects on the sea ice change in terms of the dynamic and thermodynamic processes. However, while the dynamic processes of AO have been widely explored, the thermodynamic processes of AO need to be further discussed. In this paper, we use the fifth state-of-the-art reanalysis at European Centre for Medium-Range Weather Forecasts (ERA5) from 1979 to 2020 to investigate the relationship between AO and the surface springtime longwave (LW) cloud radiative forcing (CRF), summertime shortwave (SW) CRF in the Arctic region (65°–90°N). In addition, the contribution of CRF induced by AO to the sea ice change is also discussed. Results indicate that the positive (negative) anomalies of springtime LW CRF and summertime SW CRF are generally detected over the Arctic Ocean during the enhanced positive (negative) AO phase in spring and summer, respectively. Meanwhile, while the LW (SW) CRF generally has a positive correlation with AO index (AOI) in spring (summer) over the entire Arctic Ocean, this correlation is statistically significant over 70°–85°N and 120°W–90°E (i.e., region of interest (ROI)) in both seasons. Moreover, the response of CRF to the atmospheric conditions varies in spring and summer. We also find that the positive springtime (summertime) AOI tends to decrease (increase) the sea ice in September, and this phenomenon is especially prominent over the ROI. The sensitivity study among sea ice extent, CRF and AOI further reveals that decreases (increases) in September sea ice over the ROI are partly attributed to the springtime LW (summertime SW) CRF during the positive AOI. The present study provides a new pattern of AO affecting sea ice change via cloud radiative effects, which might benefit the sea ice forecast improvement. [ABSTRACT FROM AUTHOR]

Information on the Arctic sea ice climate indicators is crucial to business strategic planning and climate monitoring. Data on the evolvement of the Arctic sea ice and decadal trends of phenology factors during melt season are necessary for climate prediction under global warming. Previous studies on Arctic sea ice phenology did not involve melt ponds that dramatically lower the ice surface albedo and tremendously affect the process of sea ice surface melt. Temporal means and trends of the Arctic sea ice phenology from 1982 to 2017 were examined based on satellite-derived sea ice concentration and albedo measurements. Moreover, the timing of ice ponding and two periods corresponding to it were newly proposed as key stages in the melt season. Therefore, four timings, i.e., date of snow and ice surface melt onset (MO), date of pond onset (PO), date of sea ice opening (DOO), and date of sea ice retreat (DOR); and three durations, i.e., melt pond formation period (MPFP, i.e., MO—PO), melt pond extension period (MPEP, i.e., PO—DOR), and seasonal loss of ice period (SLIP, i.e., DOO—DOR), were used. PO ranged from late April in the peripheral seas to late June in the central Arctic Ocean in Bootstrap results, whereas the pan-Arctic was observed nearly 4 days later in NASA Team results. Significant negative trends were presented in the MPEP in the Hudson Bay, the Baffin Bay, the Greenland Sea, the Kara and Barents seas in both results, indicating that the Arctic sea ice undergoes a quick transition from ice to open water, thereby extending the melt season year to year. The high correlation coefficient between MO and PO, MPFP illustrated that MO predominates the process of pond formation. [ABSTRACT FROM AUTHOR]

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from −3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer. [ABSTRACT FROM AUTHOR]