Abstract
AbstractSkillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice‐edge error (IIEE), a user‐relevant verification metric defined as the area where the forecast and the “truth” disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often‐neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end‐user relevant forecasts.
Highlights
The ongoing retreat of sea ice opens new opportunities for human activities in the Arctic [Emmerson and Lahn, 2012]
The area where the forecast and the truth disagree on the ice concentration being above or below 15%, that is, the sum of all areas where the local sea ice extent is overestimated (O) or underestimated (U): integrated ice-edge error (IIEE) = O + U
The IIEE has a number of properties that make it a useful verification metric. (i) The IIEE is conceptually simple and straightforward to derive from modeled and observed gridded sea ice concentration data. (ii) Remote sensing data reveal the approximate ice-edge position for the past ∼35 years, enabling evaluation of sea ice forecasting systems with retrospective forecasts. (iii) The ice-edge position is an important characteristic of the sea ice cover and, the IIEE much more relevant to potential forecast users than just the sea
Summary
The ongoing retreat of sea ice opens new opportunities for human activities in the Arctic [Emmerson and Lahn, 2012]. Seasonal forecasts based on similar dynamical or simpler statistical models are increasingly being produced, and most of these forecasts are evaluated in the Sea Ice Prediction Network (SIPN) Sea Ice Outlook [Stroeve et al, 2014]. Despite these important recent developments, sea ice forecasts and their verification are still at an early stage. Given the importance of the ice-edge position, the mean distance between the forecasted and the true (observed) ice edge has been used as a verification metric by several groups [Melsom et al, 2011; Posey et al, 2015; Dukhovskoy et al, 2015].
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