Abstract
Abstract. As a consequence of a diminishing sea ice cover in the Arctic, activity is on the rise. The position of the sea ice edge, which is generally taken to define the extent of the ice cover, changes in response to dynamic and thermodynamic processes. Forecasts for sea ice expansion on synoptic timescales due to an advancing ice edge will provide information that can be of significance for open ocean operations in polar regions. However, the value of this information depends on the quality of the forecasts. Here, we present methods for examining the quality of forecasted sea ice expansion on sub-seasonal timescales and the geographic location where the largest expansions are expected from the forecast results. The algorithm is simple to implement, and an examination of 2 years of model results and accompanying observations demonstrates the usefulness of the analysis.
Highlights
Due to climate change the sea ice extent is in decline in the Arctic (Parkinson, 2014)
Goessling et al (2016) introduced the integrated ice edge error (IIEE), a quantity for describing mismatching sea ice extents from two data sets to analyze the predictability of the sea ice edge
In the previous section we focused on metrics which describe the displacement of a single sea ice edge
Summary
Due to climate change the sea ice extent is in decline in the Arctic (Parkinson, 2014). Data sets for monitoring and forecasting sea ice conditions are receiving growing attention. The past years have seen a flurry of activity related to assessing the quality of sea ice data sets. Melsom et al (2019) took advantage of the IIEE in their examination of various metrics for the assessment of the quality of forecasts for the sea ice edge position. Methods for examining the quality of probabilistic results for sea ice conditions have been introduced by Goessling and Jung (2018) and Palerme et al (2019). Cheng et al (2020) examined the accuracy of a visually estimated ice concentration monitoring product
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