An EC-Earth coupled atmosphere–ocean single-column model (AOSCM.v1_EC-Earth3) for studying coupled marine and polar processes12. October 2018
The Polar Amplification Model Intercomparison Project (PAMIP) contribution to CMIP625. March 2019
Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere–ocean–sea ice models. Although assimilation of sea-ice concentration
into these systems is commonplace, sea-ice thickness observations, being much less mature, are typically not assimilated.
However, many studies suggest that initialisation of winter sea-ice thickness could lead to improved prediction of
Arctic summer sea ice. Here, for the first time, we directly assess the impact of winter sea-ice thickness initialisation
on the skill of summer seasonal predictions by assimilating CryoSat-2 thickness data into the Met Office’s coupled seasonal
prediction system (GloSea). We show a significant improvement in predictive skill of Arctic sea-ice extent and ice-edge
location for forecasts of September Arctic sea ice made from the beginning of the melt season. The improvements in sea-ice cover lead to further improvement of near-surface air temperature and pressure fields across the region. A clear relationship between modelled winter thickness biases and summer extent errors is identified which supports the theory that Arctic winter thickness provides some predictive capability for summer ice extent, and further highlights the importance that modelled winter thickness biases can have on the evolution of forecast errors through the melt season.