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

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 oceanatmosphere 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 ElNino/La Nina events are well represented by POAMA, although the forecast amplitude weakens with increasing lead-time.

High-Water Alerts from Coinciding High Astronomical Tide and High Mean Sea Level Anomaly in the Pacific Islands Region

“…MSLA varies slowly compared with the tide and decimating MSLA to peak high-tide makes negligible difference to the observed MSLA distribution, but it is convenient for data handling, as it reduces both high tide and MSLA time series to the same dimension. The dynamic model of Miles et al (2014) includes seasonal dynamic effects on MSLA. In this way the analysis methods are demonstrated without reference to gauge zero or local datum.…”

“…Changes in ocean temperature and salinity affect sea level through the associated changes in density and volume, and wind stress is a main driver of changes in mass and steric height through dynamic processes. Pacific Islands west of the date line show particularly high mean sea levels during La Niña periods (Becker et al 2012;Miles et al 2014), and during such periods the frequency and magnitude of high-tide-modulated flooding is much greater. Several studies have shown that low-frequency (ENSO scale and multidecadal) MSLA can be correlated to various climate indices (Becker et al 2012;Chowdhury et al 2007;Merrifield et al 2012;Miles et al 2014;Sasaki et al 2008).…”

“…These changes are directly related to coupled climate modes of variability, such as ENSO (Stammer et al 2013). Present examples in the Pacific Islands region are MSLA forecasts between 408S and 408N (Miles et al 2014) and 3-month seasonal outlooks at U.S.-affiliated Pacific Islands (Chowdhury et al 2007). The ENSO phenomenon has a strong impact on MSLA in the western Pacific Islands region with a tendency for lower mean sea levels during El Niño events and higher mean sea levels during La Niña events.…”

“…Several studies have shown that low-frequency (ENSO scale and multidecadal) MSLA can be correlated to various climate indices (Becker et al 2012;Chowdhury et al 2007;Merrifield et al 2012;Miles et al 2014;Sasaki et al 2008). Miles et al (2014) obtained statistically significant correlations at lead times of up to 7 months for both winter and summer seasons in the Pacific basin, observing relatively high predictive skill in this region due to the strength and predictability of ENSO, which delivers the dominant MSLA seasonal signal at both the global mean and regional scale. Pacific Islands west of the date line show particularly high mean sea levels during La Niña periods (Becker et al 2012;Miles et al 2014), and during such periods the frequency and magnitude of high-tide-modulated flooding is much greater.…”

Information for Australian impact and adaptation planning in response to sea-level rise

“…However, estimates are that the climate change sea-level signal, compared to the average over 1986 to 2005, will begin to emerge from this natural variability by 2030 off the east coast of Australia and 2040 off the west coast (Lyu et al, 2014) stressing the urgency for future planning and risk management to take account of the combined impact of SLR and natural variability signals. Seasonal sea-level predictions (up to nine months in advance) (Miles et al, 2014;McIntosh et al, 2015) may be a useful tool in helping to manage these risks.Any change in the El Nino Southern Oscillation or other modes of variability have the potential to impact Australian sea levels. However, the regional pattern of dynamic ocean sea-level change remains inadequately understood.…”