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Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice

Environmental Research Letters 2019 42 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Sebastian Gerland Sebastian Gerland David G. Barber, Walter N. Meier, Sebastian Gerland C. J. Mundy, Marika M. Holland, Stefan Kern, Zhijun Li, Christine Michel, Donald K. Perovich, Takeshi Tamura, Sebastian Gerland

Summary

This review maps current gaps in our understanding of Arctic sea ice—its structure, biology, and role in climate—and highlights priorities for future observation and research. While not directly about microplastics, Arctic sea ice is known to trap and release microplastics, making better ice monitoring relevant.

While Arctic sea ice is changing, new observation methods are developed and process understanding improves, whereas gaps in observations and understanding evolve. Some previous gaps are filled, while others remain, or come up new. Knowing about the status of observation and knowledge gaps is important for interpreting observation and research results, interpretation and use of key climate indicators, and for research and observation planning. This paper deals with identifying some of the important current gaps connected to Arctic sea ice and related climate indicators, including their role and functions in the sea ice and climate systems. Subtopics that are discussed here include Arctic seaice extent, concentration, and thickness, sea-ice thermodynamics, age and dynamic processes, and biological implications of changing sea ice. Among crucial gaps are few in situ observations during the winter season, limited observational data on snow and ice thickness from the Arctic Basin, and wide gaps in biological rate measurements in or under sea ice. There is a need to develop or improve analyzes and products of remote sensing, especially for new sensors and technology such as remotely operated vehicles. Potential gaps in observations are inevitably associated with interruptions in longterm observational time series due to sensor failure or cuts in observation programmes.

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