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With the improving skill of sea ice forecasts, verification methods are becoming increasingly important to inform model developers and users. In this study, the Modified Hausdorff Distance (MHD), the Spatial Probability Score (SPS), and a variation of the SPS are compared in order to assess their performances for evaluating the ice edge position in the new European Centre for Medium‐Range Weather Forecasts seasonal forecasts (SEAS5). On average, the SEAS5 forecasts outperform a climatological reference during about 3 weeks using the MHD, and during about 5 weeks using the SPS. Furthermore, our results show that the MHD is more sensitive than the SPS to the presence of isolated sea ice patches. Moreover, the variation of the SPS introduced here is not seasonally dependent (contrary to the original SPS) and can be interpreted as a distance error of the ice edge position, which is a potentially relevant information for end users. Key Points: Three verification methods for evaluating the ice edge position are compared in the context of seasonal forecastingVarious characteristics of the verification methods have been identified, which should help future forecast evaluationsPredictive skill of a new seasonal prediction system is assessed using various verification methods [ABSTRACT FROM AUTHOR]

The ice edge is a simple quantity in the form of a line that can be derived from a spatially varying sea ice concentration field. Due to its long history and relevance for operations in the Arctic, the position of the ice edge should be an essential element in any system that is designed to monitor or provide forecasts for the physical state of the Arctic Ocean and adjacent ocean regions. Like for all components of monitoring and forecast products, users need to complement information about the ice edge position with the expected accuracy of the data or model results. Such information is traditionally available as a set of metrics that provide a concentrated assessment of the information quality. In this study we provide a survey of metrics that are presently included in the product quality assessment of the CMEMS Arctic Marine Forecasting Center sea-ice edge position forecast. We show that when ice edge results from different products are compared, mismatching results for polynya and local freezing at the coasts of continents and archipelagos have a large impact on the quality assessment. Such situations, which occur regularly in the products we examine, have not previously properly been acknowledged when a set of metrics for the quality of ice edge position results have been constructed. We examine the quality of ice edge forecasts using a total of 17 metrics for the ice edge position. These metrics are analyzed in synthetic examples, in selected cases of actual forecasts, and for a full year of weekly forecast bulletins. Using necessity and simplicity of information as a guideline, we recommend using a set of four metrics that sheds light on the various aspects of product quality that we consider. Moreover, any user is expected to be interested in a limited part of the geographical domain, so metrics derived as domain-wide integrated quantities may be of limited value. Consequently, we recommend that metrics are also made available for appropriate set of subdomains. Furthermore, we find that the metrics' decorrelation time scales are much longer than the present forecast range. Hence our final recommendation is to include depictions of gridded mismatching of ice edge positions using maps for the integrated ice edge error. [ABSTRACT FROM AUTHOR]

Global coupled climate models are generally capable of reproducing the observed trends in the globally averaged atmospheric temperature. However, the global models do not perform as well on regional scales. Here, we present results from a 20-year, high-resolution ocean model experiment for the Atlantic and Arctic Oceans. The atmospheric forcing is taken from the final 20 years of a twentieth-century control run with a coupled atmosphere–ocean general circulation model. The ocean model results from the regional ocean model are validated using observations of hydrography from repeat cruises in the Barents Sea. Validation is performed for average quantities and for probability distributions in space and time. The validation results reveal that, though the regional model is forced by a coupled global model that has a noticeable sea ice bias in the Barents Sea, the hydrography and its variability are reproduced with an encouraging quality. We attribute this improvement to the realistic transport of warm, salty waters into the Barents Sea in the regional model. These lateral fluxes in the ocean are severely underestimated by the global model. The added value with the regional model that we have documented here lends hope to advance the quality of oceanic climate change impact studies. [ABSTRACT FROM AUTHOR]