Sea ice volume integrates the effects of large-scale changes underway in the Arctic. During the past 50 years, Arctic sea ice has thinned dramatically, consistent with the observed reduction in ice area and Arctic warming. Sea ice thickness is also important for predictions, providing a source of memory that can give rise to sea ice area predictability on seasonal to interannual timescales.
Tracking changes in sea ice thickness is important for a variety of research questions, but there are considerable difficulties in observing and simulating this property of the ice pack. Advancing the science requires a better integration of the different approaches to understanding ice thickness conditions and their changes over time. Specifically, there is a need to bring together in situ observations, remote sensing, and modeling related to the study of sea ice thickness.
To advance these goals, some 30 attendees representing 15 institutions attended a workshop on sea ice thickness hosted by the National Center for Atmospheric Research. The overall objectives included bringing observational and modeling scientists together, learning about the opportunities and challenges associated with ice thickness measurement and simulation, and finding ways to better integrate sea ice research and communities moving forward.
The meeting included overview talks on in situ observations, remote sensing, and modeling of sea ice thickness. The meeting also featured application-oriented talks on data assimilation, sea ice predictability, and satellite simulators.
Many of the presentations and discussions at the workshop covered recent innovations in sea ice research, including the use of ice mass balance buoys to constrain ice thickness changes over multiple locations and years. Participants also discussed uncertainties associated with sea ice thickness and its measurement capabilities, such as the spatial representation of in situ observations and potential sources of error in remotely sensed observations.
The workshop highlighted the need for improved ice thickness data sets and model simulations, and participants discussed how future advances could be integrated. For example, snow strongly influences sea ice mass budgets and is a major source of uncertainty when converting remotely sensed freeboard (above-water) measurements of the ice and snow surface to ice thickness estimates. Therefore, researchers need an enhanced understanding of snow conditions on sea ice to improve ice observation and modeling.
Meeting participants agreed that better methods, such as data assimilation and satellite simulators within modeling systems, should be used to enhance comparisons between observed and model-simulated ice thickness. They also identified the ongoing need for better communication about measurement uncertainties and modeling capabilities.
We thank the National Science Foundation Arctic System Science Program for funding this workshop.
—David Anthony Bailey (email: [email protected]) and Marika Holland, National Center for Atmospheric Research, Boulder, Colo.
Correction, 26 June 2017: The byline has been updated to include a missing coauthor.