Analyzing the impact of CryoSat-2 ice thickness initialization on seasonal Arctic Sea Ice prediction

• Published in: Annals of Glaciology
• Main Authors: Richard Allard, E. Joseph Metzger, Neil Barton, Li Li, Nathan Kurtz, Michael Phelps, Deborah Franklin, Ole Martin Smedstad, Julia Crout, Pamela Posey
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press 2020
• Subjects: Coupled modeling system; CryoSat-2; ice thickness; sea ice extent; time-lagged ensembles; Meteorology. Climatology; QC851-999; Arctic; Annals of Glaciology; Beaufort Sea; Sea ice
• Online Access: https://doi.org/10.1017/aog.2020.15; https://doaj.org/article/219136e6ece240a99ce4de38bf9452ee

Twin 5-month seasonal forecast experiments are performed to predict the September 2018 mean and minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M km2 (5.27 M km2) vs the ensemble forecasts initialized with CS2 ice thickness that had an error of 0.40 M km2 (4.99 M km2), a 43% reduction in error. The September mean integrated ice edge error shows a 18% improvement for the Pan-Arctic with the CS2 data vs the control forecasts. Comparison against upward looking sonar ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of forecasts show ice free conditions as observed at moorings A and D.