Broadly, my research concerns the ocean’s role in the global carbon cycle, and in particular the complex interplay between climate, ocean circulation, and ocean biogeochemistry. Understanding and modeling these interactions is one of the fundamental challenges in science, and the key to unraveling the human impact on Earth’s climate.
Climate models are some of the most complex pieces of software ever written, able to simulate a vast number of different parts of the overall system, such as the atmosphere or ocean. Many have been developed by hundreds of scientists over decades and are constantly being added to and refined. They can run to over a million lines of computer code — tens of thousands of printed pages.
An initial condition with minimal"drift" is essential to accurately simulate the effects of human-made factors on the climate. But thanks to the ocean and other sluggish components this can take several months even on large supercomputers.
That is true up to a point. The problem is that the different subdomains need to"know" what is happening in adjoining ones, which requires transmitting information between chips. That is much slower than the speed with which modern chips can perform arithmetic calculations, what computer scientists call"bandwidth limitation." There are therefore diminishing returns from throwing more computing power at the problem. Ocean models especially suffer from such poor"scaling.
Among other applications, it is widely used by chemists and material scientists to calculate the structure of atoms and molecules, a problem that happens to take up more than half the world's supercomputing resources. Developing a new algorithm is the easy part. Getting others to use it is often the bigger challenge. It is therefore promising that the UK Met Office and other climate modeling centers are trying it out.
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