Seasonal Forecasting Skill of Sea‐Level Anomalies in a Multi‐Model Prediction Framework. Issue 6 (1st June 2021) - Long, Xiaoyu; Widlansky, Matthew J.; Spillman, Claire M.; Kumar, Arun; Balmaseda, Magdalena; Thompson, Philip R.; Chikamoto, Yoshimitsu; Smith, Grant A.; Huang, Bohua; Shin, Chul‐Su; Merrifield, Mark A.; Sweet, William V.; et al.
Abstract: Coastal high water level events are increasing in frequency and severity as global sea‐levels rise, and are exposing coastlines to risks of flooding. Yet, operational seasonal forecasts of sea‐level anomalies are not made for most coastal regions. Advancements in forecasting climate variability using coupled ocean‐atmosphere global models provide the opportunity to predict the likelihood of future high water events several months in advance. However, the skill of these models to forecast seasonal sea‐level anomalies has not been fully assessed, especially in a multi‐model framework. Here, we construct a 10‐model ensemble of retrospective forecasts with future lead times of up to 11 months. We compare predicted sea levels from bias‐corrected forecasts with 20 years of observations from satellite‐based altimetry and shore‐based tide gauges. Forecast skill, as measured by anomaly correlation, tends to be highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along some continental coasts. For most locations, multi‐model averaging produces forecast skill that is comparable to or better than the best performing individual model. We find that the most skillful predictions typically come from forecast systems with more accurate initializations of sea level, which is generally achieved by assimilating altimetry data. Having relatively higher horizontal resolution in the ocean is also beneficial, as such models seem to better capture dynamical processes necessary for successful forecasts. The multi‐model assessment suggests that skillful seasonal sea‐level forecasts are possible in many, though not all, parts of the global ocean. Plain Language Summary: We assess 10 global climate forecasting systems to predict monthly and seasonal anomalies of local sea levels up to a year into the future. We find that skillful seasonal sea‐level forecasts are possible in many parts of the global ocean. Forecast skill is generally highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along continental coasts. For most locations, multi‐model averaging improves the forecast skill, compared to considering the models individually. Overall, the most skillful predictions are from forecasting systems with more accurate initializations of sea level and higher horizontal resolutions of the ocean. Key Points: Prediction skill of seasonal sea‐level anomalies up to a year in the future is assessed in 10 global climate forecasting systems Skillful seasonal sea‐level forecasts are found in the tropics, whereas the skill is lower in higher latitudes and along continental coasts The most skillful predictions are from models with more accurate initializations of sea level and higher resolutions of the ocean