Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice. (4th April 2019)

• Record Type: Journal Article
• Authors: Gerland, Sebastian; Barber, David; Meier, Walt; Mundy, Christopher J; Holland, Marika; Kern, Stefan; Li, Zhijun; Michel, Christine; Perovich, Donald K; Tamura, Takeshi
• Is Part Of: Environmental research letters. Volume 14:Number 4(2019:Apr.)
• Publication Date: 2019-04-04
• Subjects: sea ice -- arctic -- climate -- observation gaps -- knowledge gaps; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/ab09b3
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: 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 sea-ice 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 long-term observational time series due to sensor failure or cuts in observation programmes.