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Abstract Despite progress in continental United States (CONUS) tornado forecasting, operational seasonal outlooks remain challenging due to intrinsic predictability limits. While previous studies link the El Niño–Southern Oscillation (ENSO) to spring tornado frequency, relying on single tropical indices misses crucial regional variability. Here, we present a novel hybrid dynamical-statistical framework that introduces a paradigm for long-lead predictability.
Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation.
Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan-Arctic sea-ice extent (SIE). In this work, we move towards stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system.
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