Climate is a large-scale phenomenon
that emerges from complicated interactions among small-scale physical systems. Yet despite
the phenomenon's complexity, climate models have demonstrated some impressive successes.
Climate
projections made with sophisticated computer codes have informed the world's policymakers
about the potential dangers of anthropogenic interference with Earth's climate system. Those
codes purport to model a large part of the system. But what physics goes into the models, how are the
models evaluated, and how reliable are they?
The task climate modelers have set for
themselves is to take their knowledge of the local interactions of air masses, water, energy, and
momentum and from that knowledge explain the climate system's large-scale features, variability,
and response to external pressures, or "forcings." That is a formidable task, and though far from
complete, the results so far have been surprisingly successful. Thus, climatologists have some
confidence that theirs isn't a foolhardy endeavor.
Climate modeling derives
from efforts first formulated in the 1920s to numerically predict the weather. However, it wasn't
until the 1960s that electronic computers were able to meet the extensive numerical demands of
even a minimal description of weather systems. Since then, ever more components have been added
to climate models—land, oceans, sea ice, and more recently, interactive atmospheric aerosols,
atmospheric chemistry, and representations of the carbon cycle. Indeed, a significant part of
the interdisciplinary work needed to understand climate change is being driven by climate model
development. Today's models are flexible tools that can answer a wide range of questions, but at
a price: They can be almost as difficult to analyze and understand as the real world.
Basic physics, emergent behavior
The physics
in climate models can be divided into three categories. The first includes fundamental principles
such as the conservation of energy, momentum, and mass, and processes, such as those of orbital
mechanics, that can be calculated from fundamental principles. The second includes physics that
is well known in theory, but that in practice must be approximated due to discretization of continuous
equations. Examples include the transfer of radiation through the atmosphere and the Navier–Stokes
equations of fluid motion. The third category contains empirically known physics such as formulas
for evaporation as a function of wind speed and humidity.
For the latter two categories, modelers
often develop parameterizations that attempt to capture the fundamental phenomenology of a small-scale
process. For instance, the average cloudiness over a 100-km2 grid box is not cleanly
related to the average humidity over the box. Nonetheless, as the average humidity increases,
average cloudiness will also increase. That monotonic relationship could be the basis for a parameterization,
though current schemes are significantly more complex than my example.
Given the nature of parameterizations
among other features, a climate model depends on several expert judgment calls. Thus, each model
will have its own unique details. However, much of the large-scale behavior projected by climate
models is robust in that it does not depend significantly on the specifics of parameterization
and spatial representation.
The most interesting behavior
of the climate system is emergent. That is, the large-scale phenomena are not obvious functions
of the small-scale physics but result from the complexity of the system. For instance, no formula
describes the Intertropical Convergence Zone of tropical rainfall, which arises through a combination
of the seasonal cycle of solar radiation, the properties of moist convection, Earth's rotation,
and so on. Emergent qualities make climate modeling fundamentally different from numerically
solving tricky equations.
Climate modeling is also
fundamentally different from weather forecasting. Weather concerns an initial value problem:
Given today's situation, what will tomorrow bring? Weather is chaotic; imperceptible differences
in the initial state of the atmosphere lead to radically different conditions in a week or so. Climate
is instead a boundary value problem—a statistical description of the mean state and variability
of a system, not an individual path through phase space. Current climate models yield stable and
nonchaotic climates, which implies that questions regarding the sensitivity of climate to, say,
an increase in greenhouse gases are well posed and can be justifiably asked of the models. Conceivably,
though, as more components—complicated biological systems and fully dynamic ice-sheets,
for example—are incorporated, the range of possible feedbacks will increase, and chaotic
climates might ensue.
Testing climate models
Model assessment
occurs on two distinct levels—the small scale at which one evaluates the specifics of a parameterization
and the large scale at which predicted emergent features can be tested. The primary test bed is the
climate of the present era, particularly since 1979, when significant satellite data started
to become readily available.
The 1991 eruption of Mount Pinatubo
provided a good laboratory for model testing (see the figure). Not only was the subsequent global
cooling of about 0.5 °C accurately forecast soon after the eruption, but the radiative, water-vapor,
and dynamical feedbacks included in the models were quantitatively verified.
More than a dozen facilities
worldwide develop climate models, whose ability to simulate the current climate has improved measurably over the past 20 years. Interestingly,
the average across all models almost invariably outperforms any single model, which shows that
the errors in the simulations are surprisingly unbiased. Significant biases common to most models
do exist, however—for instance, in patterns of tropical precipitation.
Climate modelers are particularly
interested in testing the variability of their models. Some variability is intrinsic, but modelers
also study variability caused by changes in external forcings, such as in Earth's orbit or in solar
activity. Those studies are complicated by incomplete observations, the nature of satellite
data, uncertainties in the forcings, and other issues.
The most comprehensive
comparison of models ever conducted is now under way using simulations that were performed in 2004
and 2005 for the Intergovernmental Panel on Climate Change. Those simulations for the 20th century
and beyond are being examined by hundreds of independent teams who will assess the robustness of
the results and help illuminate persistent problems.
Many challenging climate
questions remain unanswered. Examples include how climate conditions influence El Niño;
how responses can be predicted at the regional scale; and how simulations of rare, extreme events
such as hurricanes and heat waves can be validated. Such issues may require better encapsulations
of, for example, the turbulent behavior of the near-surface atmosphere, the effects of ocean eddies,
or the microphysics of clouds and aerosols. The implementation of more sophisticated parameterizations
and the ongoing increases in resolution as computer resources increase suggest that models will
continue to improve. However, many results, such as the warming effect of increasing greenhouse
gases that was first demonstrated in much simpler models decades ago, have proved extremely robust.
Climate models are unmatched
in their ability to quantify otherwise qualitative hypotheses and generate new ideas that can
be tested against observations. The models are far from perfect, but they have successfully captured
fundamental aspects of air, ocean, and sea-ice circulations and their variability. They are therefore
useful tools for estimating the consequences of humankind's ongoing and audacious planetary
experiment.
Gavin Schmidt is a research
scientist at the NASA Goddard Institute for Space Studies (http://www.giss.nasa.gov) in New
York.
Additional resources
Intergovernmental Panel on Climate Change, Climate Change 2001: The Scientific Basis, [LINK].
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