ACCESS-S2 is a major upgrade to the Australian Bureau of Meteorology's multi-week to seasonal prediction system. It was made operational in October 2021, replacing ACCESS-S1. The focus of the upgrade is the addition of a new weakly coupled data assimilation system to provide initial conditions for atmosphere, ocean, land and ice fields. The model is based on the UK Met Office GloSea5-GC2 seasonal prediction system and is unchanged from ACCESS-S1, aside from minor corrections and enhancements. The performance of the assimilation system and the skill of the seasonal and multi-week forecasts have been assessed and compared to ACCESS-S1. There are improvements in the ACCESS-S2 initial conditions compared to ACCESS-S1, particularly for soil moisture and aspects of the ocean, notably the ocean currents. More realistic soil moisture initialisation has led to increased skill for forecasts over Australia, especially those of maximum temperature. The ACCESS-S2 system is shown to have increased skill of El Nino-Southern Oscillation forecasts over ACCESS-S1 during the challenging autumn forecast period. Analysis suggests that ACCESS-S2 will deliver improved operational forecast accuracy in comparison to ACCESS-S1. Assessments of the operational forecasts are underway. ACCESS-S2 represents another step forward in the development of seasonal forecast systems at the Bureau of Meteorology. However, key rainfall and sea surface temperature biases in ACCESS-S1 remain in ACCESS-S2, indicating where future efforts should be focused.
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.
Sea-level variability increasingly contributes to coastal impacts such as flooding, erosion, and damage to infrastructure or ecosystems due to saltwater inundation (
Long term datasets can be used to create climatologies and underpin return period analysis for engineering design (Ewans and Jonathan, 2020). Observations from in situ instrumentation such as wave buoys and radars can provide accurate data at high temporal resolutions; however, they are sparsely located in the southern hemisphere. Satellite altimeter wave data are
A study was conducted to assess the effect of a Selective Traffic Enforcement Program on seat belt usage. The program consisted of increased enforcement of the seat belt legislation and publication of this enforcement. It was predicted that during the program, there 1 would be increases in seat belt usage and subjective probability of being apprehended in the experimental city, whereas there would be no-changes in these variables in the control city. Seat belt usage (N = 23,910) and telephone (N = 1,738) surveys were conducted before, during, 1 month after, and 6 months after the program in both cities. In the experimental city observed belt usage increased from 58% to 80% during the program, dropping to 70% 6 months after the program. Reported belt use and subjective probability also increased during the program. In the control city an increase in subjective probability was observed, but there was no increase in belt usage noted, The role of subjective probability as a mediator of the effect of enforcement on driver behavior is discussed.
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