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Results for “Artic Oceanography”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Baffin Bay Ice Drift and Export: 2002-2007

Multiyear estimates of sea ice drift in Baffin Bay and Davis Strait are derived for the first time from the 89 GHz channel of the AMSR-E instrument. Uncertainties in the drift estimates, assessed with Envisat ice motion, are approximately 2-3 km/day. A persistent atmospheric trough, between the coast of Greenland and Baffin Island, drives the prevailing southward drift pattern with average daily displacements in excess of 18-20 km during winter. Over the 5-year record, the ice export ranges between 360 and 675 x 10(exp 3) km(exp 2), with an average of 530 x 10(exp 3) km(exp 2). Sea ice area inflow from the Nares Strait, Lancaster Sound and Jones Sound potentially contribute up to a third of the net area outflow while ice production at the North Water Polynya contributes the balance. Rough estimates of annual volume export give approximately 500-800 km(exp 3). Comparatively, these are approximately 70% and approximately 30% of the annual area and Strait.

remote sensing↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗