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Building A Forecast System For Ocean and Ice Conditions Along the Northeast Passage and High Latitude Sea Route in the Arctic Ocean

Improving Arctic ice forecasts critical to coastal communities, shipping, and safe navigation

The GLERL-CIGLR Arctic-sea routes nowcast/forecast System (GCAS) products aim at providing detailed information to Arctic marine managers, decision makers, commercial shipping and energy exploration industries for their decision making, management of Arctic resources and environmental protection. A preliminary GCAS with a 25-km Coupled Ice-Ocean Model (CIOM/IcePOM) for 5-day forecast in the entire Arctic, with a nested 4-km high-resolution model in the Chukchi-East Siberian-Laptev-Kara (CELK) seas was implemented. The daily forecast models are forced by NCEP FV3GFS to predict ice and ocean conditions for the next 5 days.

Principal Investigator: Jia Wang |

Project Institution: Great Lakes Environmental Research Laboratory (GLERL) 

Partnerships: OAR/GLERL, University of Michigan

Award Period: 01 October 2021 – 30 September 2022

Data Access

Experimental Nowcast/Forecast Website

(a) Annual maximum ice cover (AMIC; %) from 1980 to 2020 (red line with black dots). The blue line with black dots indicates the sea ice concentration (SIC) averaged within November–December over the Bering Sea (black-dashed box in Fig. 1b). Gray solid lines and gray dashed lines indicate the means and standard deviations of AMIC in earlier (1980–97) and later (1998–2020) periods. The green line indicates the t-test p value of the separation year for periods before and after. Dotted and dotted–dashed lines indicate the 95% and 90% confidence levels, respectively, for determining the step change of AMIC based on the Wilcoxon rank sum test. (b) Sea ice concentration difference between the two periods (later minus earlier period). Dots indicate that the difference between the two periods reaches the 95% confidence level based on the t test. The black dashed box indicates the region for calculating the SIC that is shown as a blue line in Fig. 1a. The red box indicates the Great Lakes area for accumulated freezing degree days.

Featured Publication

Recently Amplified Interannual Variability of the Great Lakes Ice Cover in Response to Changing Teleconnections

September 8, 2022

Yu-Chun Lin, Ayumi Fujisaki-Manome, & Jia Wang

The interannual variability of the annual maximum ice cover (AMIC) of the Great Lakes is strongly influenced by large-scale atmospheric circulations that drive regional weather patterns. Based on statistical analyses from 1980 to 2020, we identify a reduced number of accumulated freezing degree days across the winter months in recent decades, a step-change decrease of AMIC after the winter of 1997/98, and an increased interannual variability of AMIC since 1993. Our analysis shows that AMIC is significantly correlated with El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Pacific–North American pattern (PNA) before the winter of 1997/98. After that, the AMIC is significantly correlated with the tropical–Northern Hemisphere pattern (TNH) and eastern Pacific oscillation (EPO).

Publications & References

Cai, Q., D. Beletsky, J. Wang, and R. Lei, 2021. Interannual and decadal variability of Arctic summer sea ice associated with atmospheric teleconnection patterns during 1850- 2017. J. Climate, DOI:
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Lin, Y-C, A. Fujisaki-Manome, and J. Wang, 2022. Recently Amplified Interannual Variability of Great Lakes Ice Cover and its Connection to Sea Ice over the Bering and Chukchi Seas. J. Climate, 35, 2683-2700, DOI: 10.1175/JCLI-D-21-0448.1
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Wang, J., A. Reiser*, A. Fujisaki-Manome, P. Chu, 2022. Development of Multi-Variable Regression Models for Hindcasting Arctic Summer Sea Ice Extent Using Six Teleconnection Patterns, NOAA Technical Memorandum-178, 105 pp. 139.
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Summers, T., J. Wang, Y-C Lin, A. Fujisaki-Manome, and P. Chu, 2022. Hindcast of Arctic September Ice Cover Using Teleconnection Forcings, NOAA Technical Memorandum-xxx, pp. 81

Hu, H, A Manome, and J. Wang, Modeling landfast ice in the Bering and Chukchi Seas using CICE6. IAHR International Ice Symposium, June 2022, Montreal, Canada

Wang, J., Modeling the ice-attenuated waves in the Great Lakes, NOAA’s Ocean and Coastal Community Modeling Workshop, Oct. 19-21, 2021 (virtual)

Wang, J., Great Lakes Environmental Research Laboratory’s Arctic Research and Applications, NOAA Alaska and Arctic Seminar Series (virtual), October 19, 2021 (recorded)