A predicted ocean
We are one year into the UN Decade of Ocean Science for Sustainable Development. Scientists around the world are being challenged to develop ‘the science we need for the ocean we want’. One aim of this decade is A Predicted Ocean. Better predictions will bring benefits to many areas. In particular, ocean economies, coastal resilience, and our understanding of the ocean-climate nexus. CSIRO scientists have been sharpening their tools on this challenge.
To forecast the ocean for tomorrow or a hundred years hence, we need to understand what state it is in today. This is not an easy task. The ocean is a chaotic and energetic stratified fluid on a spinning planet with cascades of energy across space-time scales. This three-dimensional system also interacts with the atmosphere and sea ice and is affected by the sun, moon, rivers, and the seafloor.
Gathering ocean observation data
The first step to understand the ocean is to gather as many observations as we can. It is hard for humans to sample the salty depths, particularly in remote and forbidding seas such as the Southern Ocean. We use robots and satellites. A large fleet of drifting robots called Argo helps us measure ocean temperature and salinity from the surface to 2000 m depth. When each Argo float surfaces every 10th day it transmits its measurements via satellite. The data is then shared with the global user community.
Meanwhile, satellites orbit at around 1000 km above the earth’s surface measuring sea surface height. These altimeters measure height by emitting a radar pulse and measuring the time it takes to go down to the ocean surface and back again. Altimeter data are then adjusted for waves, tides, water vapour and many other things. The complicated corrections need extremely accurate positioning of the satellite. Satellites also measure sea surface temperature. Maps of surface temperature reveal detailed information about currents, fronts, and eddies. CSIRO scientists combine these satellite observations with the Argo data to build a picture of the ocean state.
But the ocean is vast, and if you combine all these measurements together it covers only a small percentage of the ocean. As brilliant as these observations are, there are still places where we don’t know what is going on. How do you fill in the gaps?
Plugging the gaps for better prediction
We create an ‘analysis’. The forecast from yesterday’s ocean is adjusted with the most recent observations from today, in a process known as data assimilation. The result is then double-checked against the typical conditions for this time of year. The model physics ensures realistic ocean features, while the data assimilation makes sure that those features appear in the model at the place and time that they actually occurred.
For over 15 years, CSIRO scientists, together with the Bureau of Meteorology under the Bluelink partnership, have been refining their method of creating an analysis. As ocean science develops, we test new model options on the National Computational Infrastructure (NCI) supercomputers. These could be improvements in the fundamental physics or new types of ocean observations. Every few years, the team runs a new ‘reanalysis’ that goes back to the 1990s. It applies the latest analysis techniques to all the ocean observations received between then and now. In this way, we create our best estimate of the daily ocean state for the past few decades.
The result is known as the Bluelink ReANalysis, or BRAN. NCI supercomputers have recently finished crunching out BRAN2020, with up-to-date observations and model physics. Published results confirm that this version is a big step forward in global ocean science. BRAN2020 data is freely available and used by oceanographers worldwide.
Video: Animation of daily Sea Surface Temperature from BRAN2020 in the greater Australasian region from July 2017 to June 2021. The trains of black chevrons indicate the position of surface drifters.
The ocean dataset
BRAN is a versatile ocean dataset. It provides information about seasonal trends, regional dynamics, ocean extremes, and climatology. The dataset is also a reference for the prediction of ocean states in the long term. As the UN Ocean Decade recognises, these are critical for sustainable development on a global scale.
Ocean prediction gives advance warning of changing ocean conditions. This, in turn, enables informed choices to be made. CSIRO’s lead role in developing the BRAN techniques, and providing BRAN data, ensures we understand the past, present, and future ocean better than ever before.