Abstract
Abstract. 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 initialization 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 initialization 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.
Highlights
Introduction and motivationArctic sea ice is one of the most rapidly, and visibly, changing components of the global climate system
Being prior to the launch of CryoSat-2 satellite (CS2), this change does not have any impact on the results of our study but we include all years available from CTRL-HC in Fig. 4 to build a picture of the skill of the CTRL-HC predictions made without sea ice thickness initialization
We have used nudging techniques to test the impact that initializing sea ice thickness using CryoSat-2 (CS2) measurements could have on Met Office seasonal predictions of September sea ice extent
Summary
Arctic sea ice is one of the most rapidly, and visibly, changing components of the global climate system. The ocean and sea-ice components of GloSea are initialized each day using the Forecast Ocean Assimilation Model (FOAM) operational ocean–sea-ice analysis of Blockley et al (2014, 2015). There have been several recent studies that have sought to improve the representation of Arctic sea-ice thickness in analyses and short-range forecasts using satellite thickness products derived from Soil Moisture and Ocean Salinity (SMOS) brightness temperatures and/or from CryoSat-2 (hereafter CS2) radar freeboard measurements Such studies have generally focused on assimilation of thickness using ensemble techniques into short-range, externally forced, ocean–sea-ice models in the Topaz system (Xie et al, 2016) or using MITgcm (Yang et al, 2014; Mu et al, 2018).
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