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Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U . S . N avy's A rctic C ap N owcast/ F orecast S ystem
Author(s) -
Hebert David A.,
Allard Richard A.,
Metzger E. Joseph,
Posey Pamela G.,
Preller Ruth H.,
Wallcraft Alan J.,
Phelps Michael W.,
Smedstad Ole Martin
Publication year - 2015
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1002/2015jc011283
Subject(s) - sea ice , arctic ice pack , buoy , climatology , environmental science , drift ice , sea ice concentration , arctic , antarctic sea ice , sea ice thickness , meteorology , oceanography , geology , geography
In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1–7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short‐term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.