Seasonal prediction of global sea level anomalies using an ocean–atmosphere dynamical model

Elaine R. Miles,Claire M. Spillman,Peter C. McIntosh,John A. Church

doi:10.1007/s00382-013-2039-7

Abstract

Advanced warning of extreme sea level events is an invaluable tool for coastal communities, allowing the implementation of management policies and strategies to minimise loss of life and infrastructure damage. This study is an initial attempt to apply a dynamical coupled ocean–atmosphere model to the prediction of seasonal sea level anomalies (SLA) globally for up to 7 months in advance. We assess the ability of the Australian Bureau of Meteorology’s operational seasonal dynamical forecast system, the Predictive Ocean Atmosphere Model for Australia (POAMA), to predict seasonal SLA, using gridded satellite altimeter observation-based analyses over the period 1993–2010 and model reanalysis over 1981–2010. Hindcasts from POAMA are based on a 33-member ensemble of seasonal forecasts that are initialised once per month for the period 1981–2010. Our results show POAMA demonstrates high skill in the equatorial Pacific basin and consistently exhibits more skill globally than a forecast based on persistence. Model predictability estimates indicate there is scope for improvement in the higher latitudes and in the Atlantic and Southern Oceans. Most characteristics of the asymmetric SLA fields generated by El-Nino/La Nina events are well represented by POAMA, although the forecast amplitude weakens with increasing lead-time.

Highlights

Sea level rise is expected to be one of the most profound consequences of climate change and has been identified by the Intergovernmental Panel on Climate Change (IPCC) as a serious problem threatening a large percentage of the earth’s coasts, atolls, estuaries and river deltas (Nicholls et al 2007; McGranahan et al 2007)
We present an initial attempt to investigate the skill of seasonal sea level anomalies (SLA) forecasts created by the dynamical coupled ocean–atmosphere multi-model ensemble global system Predictive Ocean Atmosphere Model for Australia (POAMA)
We have identified the capabilities and deficiencies of POAMA in predicting SLA which will underpin the validity of real-time forecasts

Summary

Introduction

Sea level rise is expected to be one of the most profound consequences of climate change and has been identified by the Intergovernmental Panel on Climate Change (IPCC) as a serious problem threatening a large percentage of the earth’s coasts, atolls, estuaries and river deltas (Nicholls et al 2007; McGranahan et al 2007). In addition to the input from the increasing global trend, extreme sea level events are influenced by changes in mean sea level associated with intra-seasonal to interannual climate processes such as the El Nino/Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the Madden-Julian Oscillation (MJO). These sea level signals have significant amplitudes, can persist for many months and have the capability to exacerbate extreme sea levels from spring tides and/or storm surges. 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 (Nicholls et al 2007)

Methods

Results

Conclusion