Seasonal Forecasting Skill of Sea‐Level Anomalies in a Multi‐Model Prediction Framework

Abstract

Sea-level variability increasingly contributes to coastal impacts such as flooding, erosion, and damage to infrastructure or ecosystems due to saltwater inundation (

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

“…This is higher than the ACC values of the damped‐persistence prediction model and the three dynamical prediction models presented in Figure 5 of Long et al. (2021) for month 0 near Charleston, visually determined to be about 0.3–0.4. For 6‐month lead time, the ACC values from our hybrid predictions are approximately 0.2 (ECCO‐CCSM4) and 0.3 (ECCO‐DynP).…”

“…For 6-month lead time, the ACC values from our hybrid predictions are approximately 0.2 (ECCO-CCSM4) and 0.3 (ECCO-DynP). In comparison, the ACC values at 6-month lead time near Charleston from Figures 5 and 6 in Long et al (2021) are in the range of 0-0.1. Shin and Newman (2021) found that their Linear Inverse Model has higher skill for the US East Coast with an ACC value of about 0.6 or higher near Charleston, which is higher than the NMME average.…”

“…Such a difference may result in sea‐level drifts in the prediction models. These drifts are routinely accounted for by subtracting the lead‐time dependent climatology (Long et al., 2021). However when the drift is not constant or linear, fully accounting for this drift becomes more challenging.…”

Toward Regional Marine Ecological Forecasting Using Global Climate Model Predictions From Subseasonal to Decadal Timescales: Bottlenecks and Recommendations

“…GCM prediction skill should be examined for various oceanic variables that are useful for biological forecasts, because skillful ocean predictions of quantities that drive biological models are needed to achieve skillful ecological forecasts. To date, oceanic prediction skill has been examined mainly for SST (e.g., Becker et al, 2014;Doi et al, 2019;Hervieux et al, 2019) including marine heatwaves (Jacox et al, 2022), sea-surface height (e.g., Widlansky et al, 2017;Long et al, 2021;Shin and Newman, 2021;Amaya et al, 2022), and upper-layer temperatures (e.g., Yeager et al, 2018;Doi et al, 2020), because these variables are important in describing physical climate variability and relatively easy to evaluate with observation-based products. However, for biological predictions, other variables (e.g., mixed-layer depth, upwelling, salinity, bottom temperature, vertical profiles of temperature and density) can also be important, as they impact nutrient availability and the habitat of marine species, and they may be associated with a higher degree of predictability (e.g., Siedlecki et al, 2016;Capotondi et al, 2019a).…”

“…In this case, users may employ statistical downscaling rather than dynamical downscaling. A promising future extension of this workflow is to use multiple GCM outputs (Figure 1C) because a multi-model ensemble can better capture reality than a single model due to the reduction of model-specific errors, as found for SST (Hervieux et al, 2019;Yati and Minobe, 2021) and for sea-surface height (Widlansky et al, 2017;Long et al, 2021). Furthermore, the reduction of model-specific errors can lead to a better estimation of prediction uncertainty, which can be useful for applications using predictions.…”