Multimodel Ensemble Sea Level Forecasts for Tropical Pacific Islands

Abstract

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.

Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

“…Second, the models used for projections do not start from an observed state, and hence, the representation of interannual variability is not expected to be in phase across the multimodel ensemble, which means that averaging across many climate models in developing SLR projections tends to cancel out the interannual variability (e.g., McInnes et al, ). High‐resolution climate simulations initialized with observed states can provide better short‐term predictions including a wider range of the full seasonal to interannual variability, but forecasting skills vary from region to region depending on the extent to which the dominant modes of variability are captured in the model or not (McIntosh et al, ; Miles et al, ; Roberts et al, ; Widlansky et al, ). For example, the skill of forecasts for sea levels is low in extratropical regions because of the inherent ocean variability and because of the limited predictability of wind and atmospheric variability in these regions (Roberts et al, ).…”

Sea Level Change and Coastal Climate Services: The Way Forward

“…As a result, the National Oceanic and Atmospheric Administration (NOAA) has recently provided experimental annual flood predictions that consider past trends, and in some locations, interannual variability with the El Niño Southern Oscillation [45]. This allows for statistical-dynamical tidal-flood [46,47] and sea level anomaly [48] predictions, enabling uses to improve readiness for current and future flooding. These products complement mid-and longer-term climate services such as NOAA's SLR Viewer web mapping tool [49], that support community decision making around infrastructure plans and designs that consider performance and reliability for local relative SLR up to 100 years in the future.…”

A Statistical Model for Frequency of Coastal Flooding in Honolulu, Hawaii, During the 21st Century

“…Harmonics were calculated from the NTDE as a whole, rather than performing the calculations year by year. While there are typically some variations between different tidal prediction products, such as from the National Oceanic and Atmospheric Administration (NOAA) Center for Operational Oceanographic Products and Services, we found our procedure to describe well the observed tidal oscillations at Honolulu and for other locations with sufficiently long records (Widlansky et al, ).…”