Subseasonal-to-Seasonal Forecast Skill in the California Current System and Its Connection to Coastal Kelvin Waves

Accurate dynamical forecasts of ocean variables in the California Current System (CCS) are essential decision support tools for advancing ecosystem-based marine resource management. However, model and dynamical uncertainties present a significant challenge when attempting to incorporate these forecasts into a formal decision-making process. To provide guidance on the reliability of dynamical forecasts, previous studies have suggested that deterministic climate processes associated with atmospheric or oceanic teleconnections may provide opportunities for enhanced forecast skill. Recent computational advances have led to the availability of subseasonal-to-seasonal (S2S) forecasts of key oceanic variables such as sea surface height (SSH), which may be leveraged to identify such forecast opportunities. In this study, we conduct a S2S forecast skill assessment of SSH anomalies in the CCS using an ensemble of 46-day reforecasts made by the European Center for Medium-range Weather Forecasting (ECMWF) model for the period 2000-2018. We find that the ECMWF model consistently produces skillful dynamical forecasts of SSH, particularly in both the southern and northern CCS at leads of 4-7 weeks. Using a high-resolution ocean reanalysis, we develop a new index designed to characterize the location and intensity of coastally trapped waves propagating through the CCS. We then show that the S2S dynamical forecasts have enhanced skill in forecasts of SSH in weeks 4-7 when initialized with strong or extreme coastally trapped wave conditions, explaining 30-40% more SSH variance than the corresponding persistence forecast.

Automated summary

Accurate dynamical forecasts of ocean variables in the California Current System (CCS) are essential for ecosystem-based marine resource management. In this study, we evaluate the skill of subseasonal-to-seasonal (S2S) predictions of sea surface height (SSH) anomalies in the CCS using the ECMWF model. We find that the model consistently produces skillful forecasts, particularly in the southern and northern CCS at leads of 4-7 weeks. We also develop an index to characterize coastally trapped waves in the CCS and show that the S2S forecasts have enhanced skill when initialized with strong or extreme wave conditions.