An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018

Abstract

The improved and updated Coupled Arctic Prediction System (CAPS) is evaluated using a set of Pan-Arctic prediction experiments for the year 2018. CAPS is built on the Weather Research and Forecasting model (WRF), the Regional Ocean Modeling System (ROMS), the Community Ice CodE (CICE), and a data assimilation based on the local error subspace transform Kalman filter. We analyze physical processes linking improved and changed physical parameterizations in WRF, ROMS, and CICE to changes in the simulated Arctic sea ice state. Our results show that the improved convection and boundary layer schemes in WRF result in an improved simulation of downward radiative fluxes and near-surface air temperature, which influences the predicted ice thickness. The changed tracer advection and vertical mixing schemes in ROMS reduce the bias in sea surface temperature and change ocean temperature and salinity structure in the surface layer, leading to improved evolution of the predicted ice extent (particularly correcting the late ice recovery issue in the previous CAPS). The improved sea ice thermodynamics in CICE have noticeable influences on the predicted ice thickness. The updated CAPS can better predict the evolution of Arctic sea ice during the melting season compared with its predecessor, though the prediction still has some biases at the regional scale. We further show that the updated CAPS can remain skillful beyond the melting season, which may have a potential value for stakeholders to make decisions for socioeconomic activities in the Arctic.