A model is a set of mathematical equations that represent a process. Thus,
a global climate model is a set of mathematical equations that represent the
interacting processes of the Earth System. These equations are tremendously
complex and are only able to be solved by a computer. Some simple models can
be run on a desktop computer, whereas the most complex ones can still only be
run using room-sized supercomputers.
There are two main uses of these climate models. The first is to
understand processes of the current and past climate system. A certain
process is changed in the model to see how it affects the model climate or the
model is run in the past to understand what processes contributed to climate
changes seen in the paleoclimate or historical record. The second is to try
to predict the future climate state as for the Intergovernmental Panel on Climate Change (IPCC)
Assessment Reports (ARs).
A Short History of the Development of Climate Models
Climate models have a lot in common with models that are used to predict
the weather. In fact, they both have the same roots. Numerical modeling of
the atmosphere had been envision as early as the early 20th Century. In 1904,
the Norwegian meteorologist Vilhelm Bjerknes first proposed the possibility of
the numerical prediction of weather if the initial state and the physical laws
were known accurately. Then, the English scientist Lewis Fry Richardson made
a weather prediction using equations describing the physics of the atmosphere
that he calculated by hand. In 1922, Richardson explained his forecast in his
book Weather Prediction by Numerical Process. Unfortunately, his
forecasts were horribly incorrect, because the observations that he used for
the initial conditions were not very reliable. Also, the equations he used
were too complex, allowing atmospheric waves of all kinds including sound waves.
These high frequency waves grew to be very large. Later in the 1930s, Carl
Gustav Rossby discovered this fatal mistake and reconfigured the equations to
filter out these high frequency waves (Washington and Parkinson 2005).
With the development of modern computers in the late 1940s, the idea of
direct numerical modeling of the atmosphere could be revisited. At Princeton
University's Institute for Advanced Studies, John von Neumann supervised the
construction of one of these early computers, and he realized the potential of
using it for weather forecasting. He subsequently established a team of
scientist led by Jule Charney to develop a numerical weather prediction model.
This team of scientists used Rossby's simplified equations (Washington and
Parkinson 2005). By this time, there were better data. What was missing from
the set of observational data at Richardson's time were data from above the
surface. By the 1940s, there were regular upper-air soundings made over land
(Weart 2011, http://www.aip.org/history/climate/index.htm).
However, the first models had to be two-dimensional and regional for weather
prediction purposes for the rudimentary computers of the time. Norman Phillips
at the University of Chicago took a step towards global climate modeling.
Inspired by his dishpan experiments of features that resembled weather in a
rotating pan of water, he developed a two-layer model on a cylinder instead of
a sphere that produced features that resembled a jet stream and weather systems
(Weart 2011, http://www.aip.org/history/climate/index.htm).
Encouraged by Phillips's results, Joseph Smagorinsky at the U.S. Weather
Bureau, the predecessor to the National
Weather Service, established a team to develop a general circulation model
(GCM), a global three-dimensional model of the atmosphere. A key member of
this team was Syukuro "Suki" Manabe. Smagorinsky and Manabe developed a
nine-layer model that was the first to include physical processes that are
explained in the next chapter as well as moisture fluxes from a global damp
surface (Weart 2011, http://www.aip.org/history/climate/index.htm).
This group grew to become the Geophysical Fluid
Dynamics Laboratory now housed at Princeton University.
Another group developing a GCM at about the same time was Yale Mintz's
group at the University of California-Los Angeles (UCLA). Mintz recruited Akio
Arakawa to help in the development of numerical schemes for a GCM. One of
those schemes was a staggered vertical grid to resolve complications that
develop when calculating all quantities at the same grid points. Together,
Mintz and Arakawa developed a two-layer model with separate land and ocean
surfaces (Weart 2011, http://www.aip.org/history/climate/index.htm).
With the advancement of computers, the GCMs became increasingly more complex
with the inclusion of more processes and even a return to the original
equations that Richardson used. Over the years, separate models began to be
developed for the oceans, land surface, and sea ice that were eventually
coupled to the atmospheric model for more accurate simulations of the whole
Earth system (or as close to the whole system as possible). Also, more and
more groups started developing their own GCMs first in the U.S. and then in
other countries, but many of the later models are really offshoots from earlier
models. A "family tree" of atmospheric GCMs can be found at http://www.aip.org/history/climate/xAGCMtree.htm.
The Difference Between Climate Modeling and Numerical Weather
Prediction
As we saw in the last section, numerical weather prediction and climate models
have much in common, and climate models are in fact derived in essence from
weather models. But everybody knows that weather prediction is notroiously
unreliable after a few days. Then, how can we expect to predict the climate
decades or centuries into the future? The reason is that climate prediction is
inherently different.
An example of the difference in forecasting the near-future versus the
distant future can be gleamed from the realm of economics. The direction of
the stock market is hard for economists to forecast on a few days in advance,
but everybody knows that the stock market will be higher 10 years from now.
The day-to-day variations in the stock market are based on a number of factors
that could change how the stock market reacts even if these factors change just
a little bit. On the other hand, the long-term upward trend of the stock
market is due to the inherent nature of the system (X. Zeng, personal
communication).
The same is true for weather versus climate. Weather is influenced by the
state of the atmosphere at any moment. Thus, a small perturbation in the
atmospheric state can have a dramatic effect on the weather. This is the
so-called "butterfly effect:" a butterfly beating its wings in
Beijing could produce a tornadic thunderstorm in Oklahoma or a major hurricane
that hits Miami. This is known as chaos. Chaos was first discovered by
Edward Lorenz who found that differing results were being produced by different
runs of a very simple model. It turns out that very slight changes in the
input to the model would radically change the output. This also occurs in more
complex models. Slight perturbations to the input data to a numerical weather
prediction model can produce different weather conditions. This is important,
because even though observations are greatly improved, there is still
uncertainties in those observations. Thus, many weather models are now run as
ensembles of runs with differing perturbations to the input observations. This
sensitivity to the initial conditions is known as an initial value problem.
However, climate is a boundary value problem. The small day-to-day weather
has little effect on the large scale climate. Instead, the climate is forced
by the state of the Earth system: the composition of the atmosphere, how much
solar radiation is received, the geographic distribution of the continents and
oceans, and so on. A small change in the input data set is not going to have
as dramatic of an effect on the results of a climate model as it would a
weather model. If the climate is in a stable state, that state would not be
changed nor would a trend be substantially different if the climate is
transitioning to a different state. An excellent example of this is the 20th
century trend in carbon dioxide as measured at Mauna Loa, Hawaii (http://en.wikipedia.org/wiki/File:Mauna_Loa_Carbon_Dioxide-en.svg).
The seasonal cycle in concentration can be seen in the data, but the long-term
average trend is constantly upward. Thus, we can have confidence in any trend
such as the temperature change after 100 or so years or the mean temperature
change per year, decade, or century that is predicted by a global climate model.
In fact, recent research has found that the model trends are most reliable for
very large horizontal and time scales (Sakaguchi et al. 2011).
The Ensemble of Climate Models
The international assessment of climate is done by the IPCC. Periodically the panel
puts out assessment reports which includes climate model predictions. The
climate models that were included in the latest assessment (Randall et al. 2007, Table 8.1, http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch8s8-2.html#table-8-1)
include models from groups from around the world. Together, these models can
be thought of as an "ensemble" of climate models.
These models are first assessed. Their simulated climate is compared to
observations and reanalyses, model hindcasts that have been continuously
constrained by observations. The group that heads this assessment is the
Program for Climate Model
Diagnosis and Interception (PCMDI) at Lawrence Livermore National
Laboratory. The group compares not only the fully coupled model results but
also the results from some of the individual components, most notably the
atmospheric model, and results from special situations and uses. Such a
comparison allows the models to be compared with each other on a level playing
field, because PCMDI prescribes how they are all to be forced to be included in
their comparison.
References
Randall, D. A., R. A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V.
Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R. J. Stouffer, A. Sumi, and K. E.
Taylor, 2007: Climate models and their evaluation in Climate Change
2007: The Physical Science Basis, Contribution of Working Group I to the
Fourth Assessment Report of the Intergovernmental Panel on Climate
Change. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B.
Averyt, M. Tignor, and H. L. Miller, eds. Cambridge Univ. Press: Cambridge,
UK.
Washington, W. M., and C. L. Parkinson, 2005: An Introduction
Three-Dimensional Climate Modeling. University Science Books:
Sausalito, Calif.
Weart, S. R., 2011: The Discovery of Global Warming. http://www.aip.org/history/climate/index.htm.