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Our understanding of sea ice and its role within Earth's climate system is underpinned by observation. Observations come in many forms, from qualitative records to quantitative data, and they have one key thing in common: they are made in situ. Direct measurements comprise most in situ observations; however, remote sensing technologies are also regularly used in situ to measure sea-ice physical properties. In this chapter, we provide an overview of in situ observations (including remote sensing) of sea ice from expeditions, drifting ice stations, autonomous platforms, and ongoing observation programs. We give a chronological account of sea-ice observations, highlighting the technological breakthroughs in sea-ice measurement techniques that have expanded observational capabilities. The chapter concludes with an outlook of future sea-ice observations and ways to bring observational and modeling efforts together to accelerate knowledge of the polar regions and Earth's climate. 

Integrating field data, remote satellite imagery, scientific analysis, and multimedia visual representation to document Arctic ice that is disappearing due to climate change, this artwork is the outcome of a four-year collaboration involving art, design, and polar science between artist Cy Keener, landscape researcher Justine Holzman, climatologist Ignatius Rigor, and scientist John Woods. With this work, Keener and Holzman’s goal is to make scientific data tangible, visceral, and experiential. They ask how artistic and creative practices can contribute to scientific endeavors while making scientific research visible to the public. 

Abstract This paper describes a remotely monitored buoy that, when deployed in open water prior to freeze up, permits scientists to monitor not only temperature with depth, and hence freeze up and sea ice thickness, but also the progression of sea ice development—e.g., the extent of cover at a given depth as it grows (solid fraction), the brine volume of the ice, and the salinity of the water just below, which is driven by brine expulsion. Microstructure and In situ Salinity and Temperature (MIST) buoys use sensor “ladders” that, in our prototypes, extend to 88 cm below the surface. We collected hourly measurements of surface air temperature and water temperature and electrical impedance every 3 cm to track the seasonal progression of sea ice growth in Elson Lagoon (Utqiaġvik, Alaska) over the 2017/18 ice growth season. The MIST buoy has the potential to collect detailed sea ice microstructural information over time and help scientists monitor all parts of the growth/melt cycle, including not only the freezing process but the effects of meteorological changes, changing snow cover, the interaction of meltwater, and drainage. Significance Statement There is a need to better understand how an increasing influx of freshwater, one part of a changing Arctic climate, will affect the development of sea ice. Current instruments can provide information on the growth rate, extent, and thickness of sea ice, but not direct observations of the structure of the ice during freeze up, something that is tied to salinity and local air and water temperature. A first deployment in Elson Lagoon in Utqiaġvik, Alaska, showed promising results; we observed fluctuations in ice temperatures in response to brief warmings in air temperature that resulted in changes in the conductivity, liquid fraction, and brine volume fraction within the ice. 

Our understanding of Arctic sea ice and its wide-ranging influence is deeply rooted in observation. Advancing technologies have profoundly improved our ability to observe Arctic sea ice, document its processes and properties, and describe atmosphere-ice-ocean interactions with unprecedented detail. Yet, our progress toward better understanding the Arctic sea ice system is mired by the stark disparities between observations that tend to be siloed by method, scientific discipline, and application. This article presents a review and philosophical design for observing sea ice and accelerating our understanding of the Arctic sea ice system. We give a brief history of Arctic sea ice observations and showcase the 2018 melt season within the context of five observational themes: spatial heterogeneity, temporal variability, cross-disciplinary science, scalability, and retrieval uncertainty. We synthesize buoy data, optical imagery, satellite retrievals, and airborne measurements to demonstrate how disparate data sets can be woven together to transcend issues of observational scale. The results show that there are limitations to interpreting any single data set alone. However, many of these limitations can be surmounted by combining observations that cross spatial and temporal scales. We conclude the article with pathways toward enhanced coordination across observational platforms in order to: (1) optimize the scientific, operational, and community return on observational investments, and (2) facilitate a richer understanding of Arctic sea ice and its role in the climate system. 

Both in situ and remote sensing observations of Arctic Ocean hydrography and circulation have improved dramatically in recent decades, and combining the two can yield the most complete picture of Arctic Ocean change. Recent results derived from classical hydrography and satellite ocean altimetry illustrate this synergy and also reveal a fundamental in situ sampling challenge. 

Abstract Arctic Ocean surface circulation change should not be viewed as the strength of the anticyclonic Beaufort Gyre. While the Beaufort Gyre is a dominant feature of average Arctic Ocean surface circulation, empirical orthogonal function analysis of dynamic height (1950–89) and satellite altimetry–derived dynamic ocean topography (2004–19) show the primary pattern of variability in its cyclonic mode is dominated by a depression of the sea surface and cyclonic surface circulation on the Russian side of the Arctic Ocean. Changes in surface circulation after Arctic Oscillation (AO) maxima in 1989 and 2007–08 and after an AO minimum in 2010 indicate the cyclonic mode is forced by the AO with a lag of about 1 year. Associated with a one standard deviation increase in the average AO starting in the early 1990s, Arctic Ocean surface circulation underwent a cyclonic shift evidenced by increased spatial-average vorticity. Under increased AO, the cyclonic mode complex also includes increased export of sea ice and near-surface freshwater, a changed path of Eurasian runoff, a freshened Beaufort Sea, and weakened cold halocline layer that insulates sea ice from Atlantic water heat, an impact compounded by increased Atlantic Water inflow and cyclonic circulation at depth. The cyclonic mode’s connection with the AO is important because the AO is a major global scale climate index predicted to increase with global warming. Given the present bias in concentration of in situ measurements in the Beaufort Gyre and Transpolar Drift, a coordinated effort should be made to better observe the cyclonic mode. 

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.