Calibration of subseasonal sea‐ice forecasts using ensemble model output statistics and observational uncertainty

Abstract

AbstractIn response to a growing demand for improved sea‐ice forecast guidance at shorter timescales and higher spatial resolutions, this study investigates the predictive skill of daily subseasonal sea‐ice forecasts from two state‐of‐the‐art prediction systems: SEAS5 from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the Global Ensemble Prediction System (GEPS) of Environment and Climate Change Canada (ECCC). Based on hindcast records from 1998–2017, we find that probabilistic forecasts of sea‐ice concentration (SIC) throughout the marginal ice zone of the Canadian Arctic in June and November are no more skillful than simple benchmark forecasts based on climatology and damped persistence. At short lead times, the lack of skill arises from overconfident ensemble spread and errors in forecast initial conditions. At longer lead times, the development of model drift also plays a role. To improve the forecasts, we develop the nonhomogeneous censored Gaussian regression for SIC (NCGR‐sic) calibration procedure, which uses the forecast ensemble mean and ensemble standard deviation (both locally and from neighboring locations) as predictors in an ensemble model output statistics framework. Importantly, NCGR‐sic incorporates observational uncertainty directly during model parameter fitting, leading to enhanced calibration. NCGR‐sic improves the spatial probability score by , on average, half of which we infer results from the removal of climatological bias and half from the improvement of forecast uncertainty. Calibrated forecasts from SEAS5 (GEPS) exceed the skill of climatology throughout the marginal ice zone over at least the first 33 (26) days in June and the first 32 (15) days in November. Generally, better skill occurs outside the Canadian Archipelago and for low‐ to mid‐range SIC compared with high SIC. While calibrated forecasts also outperform damped persistence after the first 3–10 days, we postulate that initial condition inconsistencies must be resolved in order to obtain skill at shorter timescales. Finally, the robustness of NCGR‐sic is demonstrated by effectively improving an operational forecast from June 2020.