About the Bureau's climate model: ACCESS–S

The model

The Bureau of Meteorology's climate forecast system for monthly, seasonal and longer-range climate outlooks is called the Australian Community Climate Earth-System Simulator – Seasonal (ACCESS–S). It is a state-of-the-art dynamical (physics-based) forecast modelling system, which uses ocean, atmosphere, ice and land observations to initiate outlooks for the season ahead. The ACCESS–S climate model is a collaboration between the Bureau of Meteorology and the UK Meteorological Office (UKMO).

The atmosphere and land model components of ACCESS–S operate at an approximate resolution of 60 km in the Australian region. At this resolution, the model is able to represent the markedly different climates of the Great Dividing Range and the eastern seaboard in Australia's east. Coarser resolution models, like the Bureau's previous dynamical model (POAMA – Predictive Ocean Atmosphere Model for Australia), aren't able to capture these differences as well.

The ocean model component of ACCESS–S operates at an approximate resolution of 25 km in the Australian region. At this resolution, the model can resolve small-scale currents and eddies.

Being a dynamical model, ACCESS–S is undergoing continuous research and development. Advances in the science of seasonal prediction, improvements in the observations and how they are fed into the model, as well as increases in supercomputing power are just some of the ways the model's accuracy will increase over time.

For more technical details on ACCESS–S, see: Hudson, D. et al, 2017: ACESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system. Journal of Southern Hemisphere Earth Systems Science, 67:3 132-159

ACCESS–S replaced POAMA in August 2018. POAMA, also a dynamical climate model, was used for official Bureau climate outlooks from May 2013 until ACCESS‐S was brought into operation. Prior to 2013, the Bureau used a statistical method to generate climate outlooks.

The outlooks

The Bureau uses output from ACCESS–S for several products including forecasts of the El NiƱo–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), as well as monthly to seasonal rainfall and temperature outlooks, and sea surface temperature (SST) anomaly forecasts for the Great Barrier Reef as an indicator of potential coral bleaching.

Rainfall and temperature outlooks

ACCESS–S produces rainfall and temperature outlooks for the months and season ahead. They are based on an ensemble of 99 forecasts (or scenarios) for the future climate. The variability of the outlooks among the 99 ensemble members gives an indication of the uncertainty in the future evolution of the climate system. For instance, if 80 of the 99 ensemble members suggest dry ahead, we can say there is about an 80% chance of dry conditions in the upcoming month/season.

The rainfall and temperature outlooks are provided as the probability of getting above median rainfall or temperature (maximum and minimum) for the months and season ahead. For rainfall, this outlook information can also be displayed as the probability of getting a particular rainfall amount.

El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) outlooks

Sea surface temperatures in specific regions of the Pacific and Indian oceans are monitored for signals that climate events, such as El Niño or La Niña, may be developing. These events can influence, or drive, Australian rainfall and temperature patterns. ACCESS–S produces temperature forecasts for these regions for the seasons ahead, to monitor the possible development of ENSO or IOD events.

Great Barrier Reef coral bleaching (spatial forecast and GBR index)

Persistent warmer than average sea temperatures are the primary cause of mass coral bleaching events. Forecast sea surface temperature anomalies (the difference in temperature from average levels) produced by ACCESS-S are used to inform the potential of coral bleaching events in the Great Barrier Reef. Seasonal forecasts from coupled dynamical models such as ACCESS-S can be used to detect anomalous SSTs several months in advance, allowing for proactive management responses.