Box 3: Climate Models: How are they built and how are they applied?
Comprehensive climate models are based on physical laws represented by
mathematical equations that are solved using a three-dimensional grid
over the globe. For climate simulation, the major components of the climate
system must be represented in sub-models (atmosphere, ocean, land surface,
cryosphere and biosphere), along with the processes that go on within
and between them. Most results in this report are derived from the results
of models, which include some representation of all these components.
Global climate models in which the atmosphere and ocean components have
been coupled together are also known as Atmosphere-Ocean General Circulation
Models (AOGCMs). In the atmospheric module, for example, equations are
solved that describe the large-scale evolution of momentum, heat and moisture.
Similar equations are solved for the ocean. Currently, the resolution
of the atmospheric part of a typical model is about 250 km in the horizontal
and about 1 km in the vertical above the boundary layer. The resolution
of a typical ocean model is about 200 to 400 m in the vertical, with a
horizontal resolution of about 125 to 250 km. Equations are typically
solved for every half hour of a model integration. Many physical processes,
such as those related to clouds or ocean convection, take place on much
smaller spatial scales than the model grid and therefore cannot be modelled
and resolved explicitly. Their average effects are approximately included
in a simple way by taking advantage of physically based relationships
with the larger-scale variables. This technique is known as parametrization.
Box 3, Figure 1: The development of climate models over the
last 25 years showing how the different components are first developed
separately and later coupled into comprehensive climate models.
In order to make quantitative projections of future climate change, it
is necessary to use climate models that simulate all the important processes
governing the future evolution of the climate. Climate models have developed
over the past few decades as computing power has increased. During that
time, models of the main components, atmosphere, land, ocean and sea ice
have been developed separately and then gradually integrated. This coupling
of the various components is a difficult process. Most recently, sulphur
cycle components have been incorporated to represent the emissions of
sulphur and how they are oxidised to form aerosol particles. Currently
in progress, in a few models, is the coupling of the land carbon cycle
and the ocean carbon cycle. The atmospheric chemistry component currently
is modelled outside the main climate model. The ultimate aim is, of course,
to model as much as possible of the whole of the Earth’s climate
system so that all the components can interact and, thus, the predictions
of climate change will continuously take into account the effect of feedbacks
among components. The Figure above shows the past, present and possible
future evolution of climate models.
Some models offset errors and surface flux imbalances through “flux
adjustments”, which are empirically determined systematic adjustments
at the atmosphere-ocean interface held fixed in time in order to bring
the simulated climate closer to the observed state. A strategy has been
designed for carrying out climate experiments that removes much of the
effects of some model errors on results. What is often done is that first
a “control” climate simulation is run with the model. Then,
the climate change experiment simulation is run, for example, with increased
CO2 in the model atmosphere. Finally, the difference is taken to provide
an estimate of the change in climate due to the perturbation. The differencing
technique removes most of the effects of any artificial adjustments in
the model, as well as systematic errors that are common to both runs.
However, a comparison of different model results makes it apparent that
the nature of some errors still influences the outcome.
Many aspects of the Earth’s climate system are chaotic – its
evolution is sensitive to small perturbations in initial conditions. This
sensitivity limits predictability of the detailed evolution of weather
to about two weeks. However, predictability of climate is not so limited
because of the systematic influences on the atmosphere of the more slowly
varying components of the climate system. Nevertheless, to be able to
make reliable forecasts in the presence of both initial condition and
model uncertainty, it is desirable to repeat the prediction many times
from different perturbed initial states and using different global models.
These ensembles are the basis of probability forecasts of the climate
Comprehensive AOGCMs are very complex and take large computer resources
to run. To explore different scenarios of emissions of greenhouse gases
and the effects of assumptions or approximations in parameters in the
model more thoroughly, simpler models are also widely used. The simplifications
may include coarser resolution and simplified dynamics and physical processes.
Together, simple, intermediate, and comprehensive models form a “hierarchy
of climate models”, all of which are necessary to explore choices
made in parametrizations and assess the robustness of climate changes.