Impact of Ocean, Sea Ice or Atmosphere Initialization on Seasonal Prediction of Regional Antarctic Sea Ice

Abstract

Dynamical modeling is widely utilized for Antarctic sea ice prediction. However, the relative impact of initializing different model components remains unclear. We compare three sets of hindcasts of the Norwegian Climate Prediction Model (NorCPM), which are initialized by ocean, ocean/sea-ice, or atmosphere data and referred to as the OCN, OCNICE, and ATM hindcasts hereafter. The seasonal cycle of sea ice extent (SIE) in the ATM reanalysis shows a slightly better agreement with observations than the OCN and OCNICE reanalyzes. The trends of sea ice concentration (SIC) in the OCN and OCNICE reanalyzes compare well to observations, but the ATM reanalysis is poor over the western Antarctic. The OCNICE reanalysis yields the most accurate estimation of sea ice variability, while the OCN and ATM reanalyzes are comparable. Evaluation of the hindcasts reveals the predictive skill varies with region and season. Austral winter SIE of the western Antarctic can be skillfully predicted 12 months ahead, while the predictive skill in the eastern Antarctic is low. Austral winter SIE predictability can be largely attributed to high sea surface temperature predictability, thanks to skillful initialization of ocean heat content. The ATM hindcast from July or October performs best due to the effective initialization of sea-ice thickness, which enhances prediction skills until early austral summer via its long memory. Meanwhile, the stratosphere-troposphere coupling contributes to the prediction of springtime. The comparable skill between the OCN and OCNICE hindcasts implies limited benefits from SIC data on prediction when using ocean data.