Ensemble forecast experiments of summertime sea ice in the Arctic Ocean using the TOPAZ4 ice-ocean data assimilation system

Abstract

Precise information on sea ice thickness (SIT) and its prediction at medium-range (2-week) timescale is crucial for the safe maritime navigation in the Arctic Ocean. In this study, we investigate the sensitivity of medium-range prediction skill of summertime SIT distribution in the Arctic marginal seas to atmospheric forecast data, using the 51-member ECMWF operational ensemble prediction system (EPS). For a synoptic-scale cyclone event occurred in July 5–6, 2015, two-week probabilistic forecast experiments were conducted with the TOPAZ4 ice-ocean forecast system, starting on 1st July. The ensemble correlation analysis between the forecast SIT and the meteorological parameters shows that the forecast error of SIT distribution is sensitive to the sea ice drift speed until 1-week, indicating that realistic sea ice drift improves the sea ice thickness prediction. On the other hand, beyond 1 week lead, the forecast error of SIT distribution is more sensitive to surface heat flux rather than sea ice drift. The surface heat flux signal is confined to the sea ice edge region, where the shortwave radiation flux is related to the SIT change through the sea ice melting process. The shortwave radiation flux in the sea ice edge is mostly determined by the sea ice distribution, suggesting that the skillful prediction of sea ice distribution, which is largely affected by synoptic-scale disturbance, at shorter lead times indirectly affects the medium-range forecast skill. A comparison of different ensemble perturbation techniques shows that the prediction skill is better at shorter lead times (up to 1 week), when using an atmospheric EPS rather than the random perturbations used in the operational forecast system, but the random perturbations are advantageous beyond 1 week. Thus, the application of the EPS to an ice-ocean coupled forecast system leads to a more precise sea ice prediction on medium-range timescale, which we expect to become of practical use for the optimum shipping route in the Arctic Ocean.