The presence of sea ice leads in the sea ice cover represents a key feature in polar regions by controlling the heat exchange between the relatively warm ocean and cold atmosphere due to increased fluxes of turbulent sensible and latent heat. Sea ice leads contribute to the sea ice production and are sources for the formation of dense water which affects the ocean circulation. Atmospheric and ocean models strongly rely on observational data to describe the respective state of the sea ice since numerical models are not able to produce sea ice leads explicitly. For the Arctic, some lead datasets are available, but for the Antarctic, no such data yet exist. Our study presents a new algorithm with which leads are automatically identified in satellite thermal infrared images. A variety of lead metrics is used to distinguish between true leads and detection artefacts with the use of fuzzy logic. We evaluate the outputs and provide pixel-wise uncertainties. Our data yield daily sea ice lead maps at a resolution of 1 km² for the winter months November– April 2002/03–2018/19 (Arctic) and April–September 2003–2019 (Antarctic), respectively. The long-term average of the lead frequency distributions show distinct features related to bathymetric structures in both hemispheres.

Unmanned aerial systems (UAS) fill a gap in high-resolution observations of meteorological parameters on small scales in the atmospheric boundary layer (ABL). Especially in the remote polar areas, there is a strong need for such detailed observations with different research foci. In this study, three systems are presented which have been adapted to the particular needs for operating in harsh polar environments: The fixed-wing aircraft M ${}^2$ AV with a mass of 6 kg, the quadrocopter ALICE with a mass of 19 kg, and the fixed-wing aircraft ALADINA with a mass of almost 25 kg. For all three systems, their particular modifications for polar operations are documented, in particular the insulation and heating requirements for low temperatures. Each system has completed meteorological observations under challenging conditions, including take-off and landing on the ice surface, low temperatures (down to −28 ${}^{\circ }$ C), icing, and, for the quadrocopter, under the impact of the rotor downwash. The influence on the measured parameters is addressed here in the form of numerical simulations and spectral data analysis. Furthermore, results from several case studies are discussed: With the M ${}^2$ AV, low-level flights above leads in Antarctic sea ice were performed to study the impact of areas of open water within ice surfaces on the ABL, and a comparison with simulations was performed. ALICE was used to study the small-scale structure and short-term variability of the ABL during a cruise of RV Polarstern to the 79 ${}^{\circ }$ N glacier in Greenland. With ALADINA, aerosol measurements of different size classes were performed in Ny-Ålesund, Svalbard, in highly complex terrain. In particular, very small, freshly formed particles are difficult to monitor and require the active control of temperature inside the instruments. The main aim of the article is to demonstrate the potential of UAS for ABL studies in polar environments, and to provide practical advice for future research activities with similar systems.

Sea ice is of substantial importance for the Southern Ocean as it insulates the relatively warm ocean from the cold atmosphere. Due to mechanical stress induced by wind and ocean currents sea‐ice leads occur, which are characterized by open water and thin ice causing an increase of energy and moisture fluxes between ocean and atmosphere. Furthermore, they contribute to the ice production and provide a habitat for animals. Thus, it is important to gain information about the temporal and spatial distribution of leads on a circum‐Antarctic scale. So far, no operational data set exists which provides such information. We use thermal satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive the predominant lead patterns for 2003‐2018, April‐September. This study provides first results for the long‐term average lead frequencies in the Southern Ocean and discusses possible links to ocean currents, tides, and the bathymetry.

In this paper, we present a characterization of Antarctic sea ice based on the classification of annual sea ice concentration (SIC) data from 1979 to 2018. A clustering algorithm was applied to provide a climatological description of significant annual cycles of SIC and their spatial distribution around the Southern Ocean. Based on these classification results, we investigate the variability of SIC cycles on decadal and inter‐annual time scales. First, we discuss significant spatial shifts of SIC cycles during 1979–1998 and 1999–2018. In the Weddell Sea and in large parts of the Ross Sea, we observed higher SIC during the summer season, and an extension of sea ice cover in winter compared to the long‐term average. Second, we introduce the Climatological Sea Ice Anomaly Index (CSIAI), which is an annual measure for year‐round sea ice anomalies of the Southern Ocean and its regional sub‐sectors. By relating selected years of significant sea ice conditions (1981, 2007 and 2014) with atmospheric influences, we demonstrate that the CSIAI is very useful for assessing inter‐annular Antarctic SIC variability. Positive and negative sea ice anomalies can be qualitatively explained by atmospheric circulation anomalies in the years 1981 and 2007. However, in 2014, the year with the largest observed sea ice extent in our time series, we found that this positive sea ice anomaly was surprisingly not associated with a stationary and inter‐seasonally persistent pattern of circulation anomaly. This suggests that sub‐seasonal to seasonal circulation anomalies and ocean‐related processes favored the formation of the sea ice maximum in 2014. With this study we provide additional information on the long‐term annual SIC variability around Antarctica. Furthermore, our classification approach and its results have potential for application in the evaluation of sea ice model results.