Spatial Damped Anomaly Persistence of the sea-ice edge as a benchmark
for dynamical forecast systems
Abstract
Accelerated loss of the sea-ice cover and increased human activities in
the Arctic emphasize the need for skillful prediction of sea-ice
conditions at sub-seasonal to seasonal (S2S) time scales. To assess the
quality of predictions, dynamical forecast systems can be benchmarked
against reference forecasts based on present and past observations of
the ice edge. However, the simplest types of reference forecasts
–persistence of the present state and climatology– do not exploit the
observations optimally and thus lead to an overestimation of forecast
skill. For spatial objects such as the ice-edge location, the
development of damped-persistence forecasts that combine persistence and
climatology in a meaningful way poses a challenge. We have developed a
probabilistic reference forecast method that combines the
climatologically derived probability of ice presence with initial
anomalies of the ice-edge location, both derived from satellite sea-ice
concentration data. No other observations, such as sea-surface
temperature or sea-ice thickness, are used. We have tested and optimized
the method based on minimization of the Spatial Probability Score. The
resulting Spatial Damped Anomaly Persistence forecasts clearly
outperform both simple persistence and climatology at sub-seasonal
timescales. The benchmark is thus about as skilful as the
best-performing dynamical forecast system in the S2S database. Despite
using only sea-ice concentration observations, the method provides a
challenging benchmark to assess the added value of dynamical forecast
systems.