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Principles of climate models

In this article with accompanying video, Professor David Brayshaw explains the principles of climate modelling.

Raw prediction data is produced in a number of ways. To determine whether it’s fit for your particular purposes, you’ll need to know how the predictions are made. But first, it’s important to consider how weather and climate models work.

The Earth system contains four main components – the atmosphere, oceans, land, and cryosphere – and has two primary drivers of motion: differences in solar radiation (the equatorial regions are warmed more strongly by the Sun than the polar regions) and planetary rotation (corresponding to a linear speed of over 1,500 km/hour at the equator). These basic ingredients combine to create complex weather and climate phenomena which evolve naturally over timescales spanning hours to millennia.

Watch the video above which provides a brief introduction to how numerical models (also called General Circulation Models or GCMs) of weather and climate are constructed.

Numerical models are vital tools for understanding and predicting the behaviour of the climate system, including its response to man-made drivers such as greenhouse gas emissions. These models are firmly grounded in fundamental physics – equations of motion, radiative transfer and momentum exchange – and work by ‘evolving’ the state of the climate system on a timestep-by-timestep basis (see Step 2.2). The ability of these modelling systems to simulate emergent climate phenomena from basic physical principles has increased rapidly over recent years. And although modern systems remain far from perfect, they are capable of producing simulations with incredible fidelity.

Weather and climate data produced by numerical models are invaluable for helping inform, actionable and informative climate intelligence for business.

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Climate Intelligence: Using Climate Data to Improve Business Decision-Making

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