Near‐surface air temperatures close to 0°C were observed in situ over sea ice in the central Arctic during the last three winter seasons. Here we use in situ winter (December–March) temperature observations, such as those from Soviet North Pole drifting stations and ocean buoys, to determine how common Arctic winter warming events are. Observations of winter warming events exist over most of the Arctic Basin. Temperatures exceeding −5°C were observed during >30% of winters from 1954 to 2010 by North Pole drifting stations or ocean buoys. Using the ERA‐Interim record (1979–2016), we show that the North Pole (NP) region typically experiences 10 warming events (T2m > −10°C) per winter, compared with only five in the Pacific Central Arctic (PCA). There is a positive trend in the overall duration of winter warming events for both the NP region (4.25 days/decade) and PCA (1.16 days/decade), due to an increased number of events of longer duration.

We present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.

The National Aeronautics and Space Administration (NASA)’s Operation IceBridge (OIB) was a 13‐year (2009–2021) airborne mission to survey land and sea ice across the Arctic, Antarctic, and Alaska. Here, we review OIB’s goals, instruments, campaigns, key scientific results, and implications for future investigations of the cryosphere. OIB’s primary goal was to use airborne laser altimetry to bridge the gap in fine‐resolution elevation measurements of ice from space between the conclusion of NASA’s Ice, Cloud, and land Elevation Satellite (ICESat; 2003–2009) and its follow‐on, ICESat‐2 (launched 2018). Additional scientific requirements were intended to contextualize observed elevation changes using a multisensor suite of radar sounders, gravimeters, magnetometers, and cameras. Using 15 different aircraft, OIB conducted 968 science flights, of which 42% were repeat surveys of land ice, 42% were surveys of previously unmapped terrain across the Greenland and Antarctic ice sheets, Arctic ice caps, and Alaskan glaciers, and 16% were surveys of sea ice. The combination of an expansive instrument suite and breadth of surveys enabled numerous fundamental advances in our understanding of the Earth’s cryosphere. For land ice, OIB dramatically improved knowledge of interannual outlet‐glacier variability, ice‐sheet, and outlet‐glacier thicknesses, snowfall rates on ice sheets, fjord and sub‐ice‐shelf bathymetry, and ice‐sheet hydrology. Unanticipated discoveries included a reliable method for constraining the thickness within difficult‐to‐sound incised troughs beneath ice sheets, the extent of the firn aquifer within the Greenland Ice Sheet, the vulnerability of many Greenland and Antarctic outlet glaciers to ocean‐driven melting at their grounding zones, and the dominance of surface‐melt‐driven mass loss of Alaskan glaciers. For sea ice, OIB significantly advanced our understanding of spatiotemporal variability in sea ice freeboard and its snow cover, especially through combined analysis of fine‐resolution altimetry, visible imagery, and snow radar measurements of the overlying snow thickness. Such analyses led to the unanticipated discovery of an interdecadal decrease in snow thickness on Arctic sea ice and numerous opportunities to validate sea ice freeboards from satellite radar altimetry. While many of its data sets have yet to be fully explored, OIB’s scientific legacy has already demonstrated the value of sustained investment in reliable airborne platforms, airborne instrument development, interagency and international collaboration, and open and rapid data access to advance our understanding of Earth’s remote polar regions and their role in the Earth system.

ESA's CryoSat‐2 has transformed the way we monitor Arctic sea ice, providing routine measurements of the ice thickness with near basin‐wide coverage. Past studies have shown that uncertainties in the sea ice thickness retrievals can be introduced at several steps of the processing chain, for instance, in the estimation of snow depth, and snow and sea ice densities. Here, we apply a new physical model to CryoSat‐2, which further reveals sea ice surface roughness as a key overlooked feature of the conventional retrieval process. High‐resolution airborne observations demonstrate that snow and sea ice surface topography can be better characterized by a lognormal distribution, which varies based on the ice age and surface roughness within a CryoSat‐2 footprint, than a Gaussian distribution. Based on these observations, we perform a set of simulations for the CryoSat‐2 echo waveform over “virtual” sea ice surfaces with a range of roughness and radar backscattering configurations. By accounting for the variable roughness, our new lognormal retracker produces sea ice freeboards that compare well with those derived from NASA's Operation IceBridge airborne data and extends the capability of CryoSat‐2 to profile the thinnest/smoothest sea ice and thickest/roughest ice. Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first‐year ice and 30% over multiyear ice, representing one of the principal sources of pan‐Arctic sea ice thickness uncertainty.

National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System, is now providing routine, very high‐resolution, surface elevation data across the globe, including the Arctic and Southern oceans. In this study, we demonstrate our new processing chain for converting the along‐track ICESat‐2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion, we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model. The coarse resolution (~100 km) snow data are redistributed onto the high‐resolution (approximately 30–100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA's Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from European Space Agency's CryoSat‐2 satellite mission, with ICESat‐2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19 February to 21 March) 2008 ice thickness estimates using the same input assumptions, which show an ~0.37 m or ~20% thinning across an inner Arctic Ocean domain in this 11‐year time period.

We present the first winter season of surface height and sea ice freeboards of the Arctic Ocean from the new ICESat‐2 (IS‐2) mission. The Advanced Topographic Laser Altimeter System (ATLAS) onboard has six photon‐counting beams for surface profiling with a 10‐kHz pulse‐rate (inter‐pulse distance ~0.7m) and footprints of ~17m. Geolocated heights assigned to individual photons scattered from the surface allow significant flexibility in the construction of height distributions used in surface finding. For IS‐2 sea ice products, a fixed 150‐photon aggregate is used to control height precision and obtain better along‐track resolution over high reflectance surfaces. Quasi‐specular returns in openings as narrow as ~27m, crucial for freeboard calculations, are resolved. The fixed photon aggregate results in unique variable along‐track resolutions and non‐uniform sampling (17m×27m to 17m×200m for the strong beams) of the surface. The six profiling beams – three pairs separated by 3.3 km with a strong and weak beam in each pair – provide correlated statistics at regional length scales for assessment of beam‐to‐beam retrieval consistency and accuracy. Analysis shows along‐track height precisions of ~2cm, and agreement in the monthly freeboard distributions across the strong beams to 1‐2cm. In this paper, we describe briefly the approaches used in surface height and freeboard retrievals from ATLAS photon clouds, and detail the key features of these along‐track sea ice products, focusing on the first release of data collected over the Arctic Ocean, which spans the period between October 14, 2018 – the start of data collection – and the end of March 2019.