Multimodel Ensemble Sea Level Forecasts for Tropical Pacific Islands

• Published in: Journal of applied meteorology and climatology 2017-04, Vol.56 (4), p.849-862
• Main Authors: Widlansky, Matthew J., Marra, John J., Chowdhury, Md. Rashed, Stephens, Scott A., Miles, Elaine R., Fauchereau, Nicolas, Spillman, Claire M., Smith, Grant, Beard, Grant, Wells, Judith
• Format: Article
• Language: English
• Subjects: Anomalies; Atmospheric models; Climate; Climate change; Climate models; Climatic conditions; El Nino; El Nino phenomena; Ensemble forecasting; Global sea level; Greenhouse effect; Ice; Islands; La Nina; La Nina events; Low tide; Mean sea level; Monthly mean sea level; Ocean models; Ocean-atmosphere system; Oceans; Pacific Decadal Oscillation; Risk reduction; Sea level; Sea level anomalies; Sea level changes; Sea level fluctuations; Sea level forecasting; Sea level rise; Sea level variability; Shallow water; Southern Oscillation; Statistical analysis; Storms; Trade winds; Trends; Tropical climate
• ISSN: 1558-8424; 1558-8432
• DOI: 10.1175/jamc-d-16-0284.1

Sea level anomaly extremes impact tropical Pacific Ocean islands, often with too little warning to mitigate risks. With El Niño, such as the strong 2015/16 event, comes weaker trade winds and mean sea level drops exceeding 30 cm in the western Pacific that expose shallow-water ecosystems at low tides. Nearly opposite climate conditions accompany La Niña events, which cause sea level high stands (10–20 cm) and result in more frequent tide- and storm-related inundations that threaten coastlines. In the past, these effects have been exacerbated by decadal sea level variability, as well as continuing global sea level rise. Climate models, which are increasingly better able to simulate past and future evolutions of phenomena responsible for these extremes (i.e., El Niño–Southern Oscillation, Pacific decadal oscillation, and greenhouse warming), are also able to describe, or even directly simulate, associated sea level fluctuations. By compiling monthly sea level anomaly predictions from multiple statistical and dynamical (coupled ocean–atmosphere) models, which are typically skillful out to at least six months in the tropical Pacific, improved future outlooks are achieved. From this multimodel ensemble comes forecasts that are less prone to individual model errors and also uncertainty measurements achieved by comparing retrospective forecasts with the observed sea level. This framework delivers online a new real-time forecasting product of monthly mean sea level anomalies and will provide to the Pacific island community information that can be used to reduce impacts associated with sea level extremes.