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Seasonal Forecasting Skill of Sea‐Level Anomalies in a Multi‐Model Prediction Framework
Author(s) -
Long Xiaoyu,
Widlansky Matthew J.,
Spillman Claire M.,
Kumar Arun,
Balmaseda Magdalena,
Thompson Philip R.,
Chikamoto Yoshimitsu,
Smith Grant A.,
Huang Bohua,
Shin ChulSu,
Merrifield Mark A.,
Sweet William V.,
Leuliette Eric,
Annamalai H. S.,
Marra John J.,
Mitchum Gary
Publication year - 2021
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1029/2020jc017060
Subject(s) - tide gauge , climatology , forecast skill , anomaly (physics) , environmental science , sea level , shore , coastal flood , altimeter , latitude , data assimilation , meteorology , oceanography , climate change , geology , geography , sea level rise , geodesy , physics , condensed matter physics
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.