Short-term sea ice forecasts with the RASM-ESRL coupled model: A testbed for improving simulations of ocean-ice-atmosphere interactions in the marginal ice zone

Abstract:

The dramatic decrease of Arctic sea-ice has led to a new Arctic sea-ice paradigm and to increased commercial activity in the Arctic Ocean. NOAA’s mission to provide accurate and timely sea-ice forecasts, as explicitly outlined in the National Ocean Policy and the U.S. National Strategy for the Arctic Region, needs significant improvement across a range of time scales to improve safety for human activity. Unfortunately, the sea-ice evolution in the new Arctic involves the interaction of numerous physical processes in the atmosphere, ice, and ocean, some of which are not yet understood. These include atmospheric forcing of sea-ice movement through stress and stress deformation; atmospheric forcing of sea-ice melt and formation through energy fluxes; and ocean forcing of the atmosphere through new regions of seasonal heat release. Many of these interactions involve emerging complex processes that first need to be understood and then incorporated into forecast models in order to realize the goal of useful sea-ice forecasting.

The underlying hypothesis for this study is that errors in simulations of “fast” atmospheric processes significantly impact the forecast of seasonal sea-ice retreat in summer and its advance in autumn in the marginal ice zone (MIZ). We therefore focus on short-term (0-20 day) ice-floe movement, the freeze-up and melt-back processes in the MIZ, and the role of storms in modulating stress and heat fluxes. This study uses a coupled ocean-atmosphere-seaice forecast model as a testbed to investigate; whether ocean-sea ice-atmosphere coupling improves forecasts on subseasonal time scales, where systematic biases develop due to inadequate parameterizations (focusing on mixed-phase clouds and surface fluxes), how increased atmospheric resolution of synoptic features improves the forecasts, and how initialization of sea ice area and thickness and snow depth impacts the skill of the forecasts. Simulations are validated with measurements at pan-Arctic land sites, satellite data, and recent ocean field campaigns.