A Hybrid Dynamical Approach for Seasonal Prediction of Sea-Level Anomalies: A Pilot Study for Charleston, South Carolina

Using Earth system models for seasonal sea-level prediction remains challenging due to model biases and initialization shocks. Here we present a hybrid dynamical approach for seasonal sea-level prediction to alleviate some of these issues. The approach is based on convolving atmospheric forcings with sea-level sensitivities to these forcings. The sensitivities are pre-computed by the adjoint model of the Estimating Circulation and Climate of the Ocean (ECCO) system. The forcings are a concatenation of ECCO forcings before prediction initialization and a 10-member predicted atmospheric forcing ensemble from the Community Climate System Model version 4 (CCSM4) after initialization, with offline forcing bias corrections applied using the observationally-constrained ECCO seasonal forcing climatology. As a pilot study, we conducted 12-month hindcasts from 1995 to 2016 in Charleston (United States East Coast). Our approach avoids drifts in CCSM4 sea-level predictions and beats seasonal climatology and damped persistence as predictors up to a 6-month lead time. The prediction skill comes from two factors: (a) ECCO forcings prior to prediction initialization influence sea level after initialization through delayed oceanic adjustments (e.g., coastally-trapped waves, open-ocean Rossby waves, and advection of steric anomalies) leading to skillful predictions beyond 2 months after initialization, and (b) the 10-member CCSM4 ensemble forcing predictions have relatively good skill at 1-2 months lead times. Our method is computationally efficient for operational sea-level prediction at specific locations and can attribute sea-level prediction skill and uncertainty to specific forcings or forcing from particular regions, thereby providing useful information to seasonal prediction centers for improving their prediction systems.

Automated summary

This study proposes a hybrid dynamical approach for seasonal sea-level prediction, which combines atmospheric forcings and sea-level sensitivities to make predictions. The approach achieves good prediction skill using pre-computed sensitivities and an ensemble forcing prediction. The method is computationally efficient and can provide useful information for improving seasonal prediction systems.