Estimating the State of the Arctic Ocean using Existing and Emerging Technologies

Ocean climatology products, derived from in situ and remote observations, together with gap-filling techniques, have long been used by oceanographers, climate scientists, and modelers for a wide range of applications. However, rapid changes in the ocean, advances in computational capabilities, improvements in observational technologies, recent developments in numerical and Artificial Intelligence/Machine Learning (AI/ML) methods, and the growing availability of high-quality ocean reanalysis products provide an opportunity to reevaluate the strengths and limitations of ocean and sea ice climatologies. This is particularly important for polar regions. The Arctic is changing more rapidly than lower latitudes, making existing long-term mean climatologies obsolete or insufficient for representing the current state of the Arctic Ocean.

This workshop is motivated by the need to discuss and explore the ability of existing and emerging climatologies, along with other resources such as reanalyses, to represent the evolving state of the Arctic Ocean, including both seawater properties and sea ice. Another aspect to be discussed is the role of satellite observations and recently developed observational systems in providing information about the ocean state. The workshop will mainly focus on the representation of key physical variables in existing and newly developed products, including temperature, salinity, and velocity, as well as sea ice concentration, thickness, and snow depth.

Objectives

(1) Assess the reliability of existing ocean climatology products in representing the evolving state of the Arctic Ocean. 

(2) Evaluate the strengths and limitations of ocean climatologies, data-model synthesis products, and emerging approaches for applications including model validation, initialization, and forecasting.

(3) Explore approaches to reduce biases and uncertainties in Arctic Ocean climatologies, including methods that leverage information derived from remote sensing and novel observational platforms.

(4) Assess the current state of emerging technologies for estimating the Arctic Ocean state, including AI/ML-enhanced and downscaled techniques, as well as reconstruction of subsurface ocean fields from surface satellite observations.

Target Participants

The workshop will bring together scientists with expertise in polar observations and data analysis, research and operational ocean modeling, data assimilation, statistical analysis, and AI/ML. Attendees across career stages and identities are welcome. Participants will be encouraged to participate in leadership roles such as facilitating workshop discussions.

Workshop Format

The 3-day workshop will include plenary sessions with invited and contributed talks, poster sessions, and breakout group discussions. The goal of these discussions will be to identify the most important issues and recommend further actions or solutions to identified problems.

Outcomes

A workshop report will summarize key outcomes, including: assessment of the ability of existing polar ocean climatology datasets to robustly characterize the current state of the Arctic Ocean; suggested possible ways of reducing biases and uncertainties in the Arctic Ocean climatologies; and possibilities for using reanalysis and emerging technologies as a complement or an alternative source of information about the ocean state for specific applications such as model validation, initialization, and forecasting.

Scientific Organizing Committee

Dmitry Dukhovskoy, NOAA EMC (co-chair)
Wilbert Weijer, Los Alamos National Laboratory (co-chair)
An Nguyen, University of Texas Austin
Cecilia Peralta Ferriz, University of Washington
Michael Steele, University of Washington

Program Organizing Committee

Alyssa Johnson, US CLIVAR
Mike Patterson, US CLIVAR
Maggie Costley, UCAR CPAESS