Seasonal Prediction of Sea Level Anomalies in the Western Pacific
Introduction
The PACCSAP project Seasonal prediction of sea level anomalies in the Western Pacific is focused on the development and verification of seasonal forecasts for sea level for Pacific Partner Countries. These forecasts are generated using the Australian Bureau of Meteorology's Predictive Ocean-Atmosphere Model for Australia (POAMA). This is a global ocean-atmosphere coupled ensemble seasonal forecast system developed jointly by the Australian Bureau of Meteorology (BoM) and the CSIRO Division of Marine and Atmospheric Research (CMAR).
The web portal delivers gridded forecasts and skill maps using a navigable map overlain with geospatial information. Sea level anomaly plume plots are also available for Partner Countries in PACCSAP.
Project background
Seasonal and inter-annual climate variability is acknowledged as a major factor in determining the vulnerability of many Pacific Island countries and Timor Leste to climate change. PACCSAP is working to improve the understanding of existing and future climatic changes and impacts in the Western Pacific region.
Seasonal sea level signals have significant amplitudes and can persist for many months. When superimposed with extreme sea level events from spring tides and/or storm surges these seasonal sea level signals can have the potential to exacerbate an already serious situation. The impacts of extreme sea levels include: the loss of amenities; the inhibition of primary production processes; loss of property; cultural resources and values; loss of tourism, recreation and transportation functionality; and increased risk of loss of life.
Project objectives
The new seasonal outlooks for sea level are based on the Predictive Ocean-Atmosphere Model for Australia (POAMA). The forecast system consists of a data assimilation system for the initialisation of the ocean, land and atmosphere and a coupled ocean–atmosphere model. The application of a dynamical physics-based model is extremely relevant in this region as there is no assumption of climate stationarity, thus enabling the prediction of unprecedented events in a changing climate.
This information is critical to Partner Countries in planning coastal development and safeguarding agricultural, marine and water resources. Pacific Island nations and Timor Leste experience high levels of seasonal sea level variability which, when combined with the effects of sea level rise and climate change, makes Pacific Island nations and Timor Leste particularly vulnerable to extreme sea level events. This project is working on developing a better understanding of seasonal sea level prediction, relevant skilful forecast products for the Western Pacific, and conducting in-country training on the use of these products.
Publications
A number of publications that assess the skill of POAMA in predicting seasonal sea level anomalies have been compiled.
- E. Miles, C. Spillman, J. Church and P. McIntosh “Seasonal Prediction of Global Sea level Anomalies using an Ocean-Atmosphere Dynamical Model”, Submitted to Climate Dynamics.
- E. Miles, C. Spillman, P. McIntosh, J. Church, A. Charles and R. de Wit “Seasonal Sea level predictions for the Western Pacific”, Submitted to 20th International Congress on Modelling and Simulation, Adelaide, Australia.
- P. McIntosh, J. Church, E. Miles, K. Ridgway, C. Spillman “Seasonal Prediction of Western Pacific Sea level using a Coupled Ocean-Atmosphere Dynamical Model”, In prep.
- E. Miles, A. Griesser and A. Charles “An Overview of the PACCSAP Seasonal Prediction Web Portals CAWCR Technical Report”, In prep.
Documentation
The Sea Level Outlooks Help Page explains how to use the product.
Further information and outcomes will be published in scientific articles and online. For model information see the POAMA website.
Related information
- Predictive Ocean-Atmosphere Model for Australia (POAMA)
- Sea level research at CSIRO
- CSIRO sea level satellite products
- PACCSAP Seasonal prediction of extreme ocean temperatures/coral bleaching
- ENSO Wrap-Up (BoM)
- POAMA climate model summary