Geoscience Reference
In-Depth Information
Models
There is a whole hierarchy of climate models, from relatively simple box models to ex-
tremely complex three-dimensional GCMs. Each has a role in examining and furthering
our understanding of the global climate system. However, it is the complex three-dimen-
sional general circulation models that are used to predict future global climate. These com-
prehensive climate models are based on physical laws represented by mathematical equa-
tions, which are solved using a three-dimensional grid over the globe. To obtain the most
realistic simulations, all the major parts of the climate system must be represented in sub-
models, including atmosphere, ocean, land surface (topography), cryosphere, and bio-
sphere, as well as the processes that go on within them and between them. Most global cli-
mate models have at least some representation of each of these components. Models that
couple together both the ocean and atmosphere components are called atmosphere-ocean
general circulation models (AOGCMs).
Over the last 30 years there has been a huge improvement in climate models. This has been
due to our increased knowledge of the climate system but also because of the nearly expo-
nential growth in computer power. There has been a massive improvement in spatial resol-
ution of the models from the very first IPCC report in 1990 to the latest in 2013. The cur-
rent generation of AOGCMs have multiple layers in the atmosphere, land, and ocean and a
spatial resolution greater than one point every 100 km by 100 km (see
Figure 12
). Equa-
tions are typically solved for every simulated 'half-hour' of a model run. Many physical
processes, such as cloud and ocean convection, of course take place on a much smaller
scale than the model can resolve. Therefore, the effects of small-scale processes have to be
lumped together, which is referred to as 'parameterization'. Many of these parameteriza-
tions are, however, checked with separate 'small-scale-process models' to validate the scal-
ing up of these smaller influences. The reason that the spatial scale is limited is that com-
prehensive AOGCMs are very complex and use a huge amount of computer time to run. At
the moment, much of the improvement in computer processing power that has occurred
over the last decade has been used to improve the representation of the global climate sys-
tem by coupling more models directly into the GCMs. The very latest models or 'climate
simulators', as some groups are now referring to them, include much better representations
of atmospheric chemistry, clouds, aerosol processes, and the carbon cycle, including land
vegetation feedbacks. But the biggest unknown in the models is not the physics, it is the es-
timation of future global GHG emissions over the next 90 years. This includes many vari-
ables, such as the global economy, global and regional population growth, development of
technology, energy use and intensity, political agreements, and personal lifestyles. Hence
