Can we trust climate models?
This is the first in a series of profiles looking at
issues within climate science, also posted at Skeptical
Science.
Computer models are widely used within climate science. Models
allow scientists to simulate experiments that it would be
impossible to run in reality - particularly projecting future
climate change under different emissions scenarios. These
projections have demonstrated the importance of the actions taken
today on our future climate, with implications for the decisions
that society takes now about climate policy.
Many climate sceptics have however criticised computer models,
arguing that they are unreliable or that they have been
pre-programmed to come up with specific results. Physicist
Freeman Dyson
recently argued in the Independent that:
"…computer models are very good at
solving the equations of fluid dynamics but very bad at describing
the real world. The real world is full of things like clouds and
vegetation and soil and dust which the models describe very
poorly."
So what are climate models? And just how trustworthy are
they?
What are climate models?
Climate models are numerical representations of the Earth's
climate system. The numbers are generated using equations which
represent fundamental physical laws. These physical laws (described
in
this paper) are well established, and are replicated
effectively by climate models.
Major components of the atmosphere system such as the oceans,
land surface (including soil and vegetation) and ice/snow cover,
are represented by the current crop of models. The various
interactions and feedbacks between these components have also been
added, by using equations to represent physical, biological and
chemical processes known to occur within the system. This has
enabled the models to become more realistic representations of the
climate system. The figure below (taken from the
IPCC AR4 report, 2007) shows the evolution of these models over
the last 40 years.
Models range from the very simple to the hugely complex. For
example, 'earth system models of intermediate complexity' (
EMICs) are models consisting of relatively few components,
which can be used to focus on specific features of the climate. The
most complex climate models are known as 'atmospheric-oceanic
general circulation models' (
A-OGCMs) and were developed from early weather prediction
models.
A-OGCMs treat the earth as a 3D grid system, made up of
horizontal and vertical boxes. External influences, such as
incoming solar radiation and greenhouse gas levels, are specified,
and the model solves numerous equations to generate features of the
climate such as temperature, rainfall and clouds. The models are
run over a specified series of time-steps, and for a specified
period of time.
As the processing power of computers has increased, model
resolution has
hugely improved, allowing grids of many million boxes, and
using very small time-steps. However, A-OGCMs still have
considerably more skill for projecting large-scale rather than
small-scale phenomena.
IPCC model projections
As we have outlined in a previous blog, the
Intergovernmental Panel for Climate Change (IPCC) developed different potential
'
emissions scenarios' for greenhouse gases. These
emissions scenarios were then input to the A-OGCM models. Combining
the outputs of many different models allows the reliability of the
models to be assessed. The IPCC used
outputs from 23 different A-OGCMs, from 16 research groups to
come to their conclusions.
Can we trust climate models?
"All models are wrong, but some
are useful"
George E Box
There are sources of uncertainty in climate models. Some
processes in the climate system occur on such a small scale or are
so complex that they simply cannot be reproduced in the models. In
these instances modellers use a simplified version of the process
or estimate the overall impact of the process on the system, a
procedure called '
parameterisation'. When parameters cannot be measured,
they are calibrated or 'tuned', which means that the parameters are
optimised to produce the best simulation of real data.
These processes inevitably introduce a degree of error - this
can be assessed by
sensitivity studies (i.e. systematically changing the model
parameters to determine the effect of a specific parameter on model
output).
Other sources of potential error are less predictable or
quantifiable, for example simply not knowing what the next
scientific breakthrough will be, and how this will affect current
models.
The IPCC AR4 report evaluated the climate models used for their
projections, taking into account the limitations, errors and
assumptions associated with the models, and
found that:
"There is considerable confidence that
AOGCMs provide credible quantitative estimates of future climate
change, particularly at continental and larger scales."
This
confidence comes from the fact that the physical laws and
observations that form the basis of climate models are well
established, and have not been disproven, so we can be confident in
the underlying science of climate models.
Additionally, the models developed and run by different research
groups show essentially similar behaviour. Model inter-comparison
allows robust features of the models to be identified and errors to
be determined.
Models can successfully reproduce important, large-scale
features of the present and recent climate, including
temperature and
rainfall patterns. However, it must be noted that parameter
'tuning' accounts for some of the skill of models in reproducing
the current climate. Furthermore, models can reproduce the past
climate. For example simulations of broad regional climate features
of the Last Glacial Maximum (around 20,000 years ago) agree
well with the data from palaeoclimate records.
Climate models have successfully projected trends. For example,
model projections of sea level rise and temperature produced in the
IPCC Third Assessment Report (TAR - 2001) for 1990 - 2006 show good
agreement with subsequent observations over that period.
So it is a question of whether the understanding of the
uncertainties by the climate science community are sufficient to
justify confidence in model projections, and for us to base policy
on model projections. Whether we chose to accept or ignore model
projections is a risk. As Professor Peter Muller (University of
Hawaii) put it in an email to Carbon Brief:
"Not doing anything about the projected
climate change runs the risk that we will experience a catastrophic
climate change. Spending great efforts in avoiding global warming
runs the risk that we will divert precious resources to avoid a
climate change that perhaps would have never happened. People
differ in their assessment of these risks, depending on their
values, stakes, etc. To a large extent the discussion about global
warming is about these different risk assessments rather than about
the fairly broad consensus of the scientific community."
It should be noted that limits, assumptions and errors are
associated with any model, for example those routinely used in
aircraft or building design, and we are happy to accept the risk
that those models are wrong.
For more information about climate models: