Calibrated Probabilistic Forecasts of Arctic Sea Ice Concentration

Abstract

Seasonal forecasts of Arctic sea ice using dynamical models are inherently uncertain and so are best communicated in terms of probabilities. Here, we describe novel statistical postprocessing methodologies intended to improve ensemble-based probabilistic forecasts of local sea ice concentration (SIC). The first of these improvements is the application of the parametric zero- and one-inflated beta (BEINF) probability distribution, suitable for doubly bounded variables such as SIC, for obtaining a smoothed forecast probability distribution. The second improvement is the introduction of a novel calibration technique, termed trend-adjusted quantile mapping (TAQM), that explicitly takes into account SIC trends and is applied using the BEINF distribution. We demonstrate these methods using a set of 10-member ensemble SIC hindcasts from the Third Generation Canadian Climate Coupled Global Climate Model (CanCM3) over the period 1981–2017. Though fitting ensemble SIC hindcasts to the BEINF distribution consistently improves probabilistic hindcast skill relative to a simpler “count based” probability approach in perfect model experiments, it does not itself correct model biases that may reduce this improvement when verifying against observations. The TAQM calibration technique is effective at removing SIC biases present in CanCM3 and improving forecast reliability. Over the recent 2000–17 period, TAQM-calibrated SIC hindcasts show improved skill relative to uncalibrated hindcasts. Compared against a climatological reference forecast adjusted for the trend, TAQM-calibrated hindcasts show widespread skill, particularly in September, even at 3–4-month lead times.