The earliest and most basic numerical climate models are Energy Balance Models (EBMs). EBMs do not simulate the climate, but instead consider the balance between the energy entering the Earth’s atmosphere from the sun and the heat released back out to space.

A step along from EBMs are Radiative Convective Models, which simulate the transfer of energy through the height of the atmosphere – for example, by convection as warm air rises. Radiative Convective Models can calculate the temperature and humidity of different layers of the atmosphere. These models are typically 1D – only considering energy transport up through the atmosphere – but they can also be 2D.

The next level up are General Circulation Models (GCMs), also called Global Climate Models, which simulate the physics of the climate itself. This means they capture the flows of air and water in the atmosphere and/or the oceans, as well as the transfer of heat. Over time, scientists have gradually added in other aspects of the Earth system to GCMs. These would have once been simulated in standalone models, such as land hydrology, sea ice and land ice.   

The most recent subset of GCMs now incorporate biogeochemical cycles – the transfer of chemicals between living things and their environment – and how they interact with the climate system. These Earth System Models (ESMs) can simulate the carbon cycle, nitrogen cycle, atmospheric chemistry, ocean ecology and changes in vegetation and land use, which all affect how the climate responds to human-caused greenhouse gas emissions. They have vegetation that responds to temperature and rainfall and, in turn, changes uptake and release of carbon and other greenhouse gases to the atmosphere.

In the following video Prof Pete Smith, professor of soils & global change at the University of Aberdeen describes ESMs as “pimped” versions of GCMs:

There are also Regional Climate Models (RCMs) which do a similar job as GCMs, but for a limited area of the Earth. Because they cover a smaller area, RCMs can generally be run more quickly and at a higher resolution than GCMs. A model with a high resolution has smaller grid cells and therefore can produce climate information in greater detail for a specific area.

Finally, a subset of climate modelling involves Integrated Assessment Models (IAMs). These add aspects of society to a simple climate model, simulating how population, economic growth and energy use affect – and interact with – the physical climate.

IAMs produce scenarios of how greenhouse gas emissions may vary in future. Scientists can then run these scenarios through ESMs to generate climate change projections – providing information that can be used to inform climate and energy policies around the world.

In climate research, IAMs are typically used to project future greenhouse gas emissions and the benefits and costs of policy options that could be implemented to tackle them. For example, they are used to estimate the social cost of carbon – the monetary value of the impact, both positive and negative, of every additional tonne of CO2 that is emitted.

The infographic below shows how modellers have gradually incorporated individual model components into global coupled models over recent decades.



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