Toward Process Resolving Modeling of the Arctic Marginal Ice Zone

Wieslaw Maslowski1, Robert Osinski2, Dominic DiMaggio1, Andrew Roberts1 and Jaclyn L Clement Kinney1, (1)Naval Postgraduate School, Monterey, CA, United States, (2)Institute of Oceanology Polish Academy of Sciences, Sopot, Poland
Abstract:
The Regional Arctic System Model (RASM) has been developed to better understand the past and present operation of Arctic System at process scale and to predict its change at time scales from days to decades. It is a limited-area, fully coupled ice-ocean-atmosphere-land model that includes the Weather Research and Forecasting (WRF) model, the LANL Parallel Ocean Program (POP) and Community Ice Model (CICE) and the Variable Infiltration Capacity (VIC) land hydrology model, as well as a streamflow routing (RVIC) model to transport the freshwater flux from the land surface to the Arctic Ocean. All RASM components are coupled at high frequency (currently at 20-minute intervals) to allow realistic representation of inertial interactions among the model components. The model domain covers the entire Northern Hemisphere marine cryosphere, terrestrial drainage to the Arctic Ocean and its major inflow and outflow pathways, with optimal extension into the North Pacific / Atlantic to model the passage of cyclones into the Arctic. By default RASM is configured at an eddy-permitting resolution of 1/12° (or ~9km) for the ice-ocean and 50 km for the atmosphere-land model components. In addition, we have recently developed, analyzed and will present results from a 1/48° (or ~2.4km) grid configuration for the ice-ocean model components.

Model results are presented from both fully coupled and a subset of RASM, where the atmospheric and land components are replaced with prescribed realistic atmospheric reanalysis data. Selected physical processes and resulting feedbacks in the Arctic marginal ice zone (MIZ) will be discussed to emphasize the need for high model resolution and fine-tuning of many present parameterizations of sub-grid physical processes when changing model spatial resolution. We also investigate sensitivity of simulated sea ice states to scale dependence of model parameters controlling ocean and sea ice dynamics, thermodynamics, and their coupling.