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Uncertainty in Predictions of the Climate Response to
Rising Levels of Greenhouse Gases
Supplementary Discussion
The PC Based GCM
The climateprediction.net experiment has only been possible using a distributed
computing methodology and it was therefore necessary to implement the model on
personal computers. To provide the greatest possible participation base19 it was decided
to focus initially on the Windows operating system, although in July 2004 a new version
was released which also runs on Macs and Linux machines . Limiting the overall time
taken by each simulation was crucial to maintain participant interest, and thus ensure
that results were obtained. For this reason the model was implemented in 32 bit
precision, roughly doubling the rate of simulation. Supplementary figures 1a-1d
demonstrate that the unperturbed model used in this project compares as well with
observations as does the supercomputer based, “standard”, version of HadSM330. The
observations used for these comparisons were i) 1.5m temperature from the CRU
datasets1 and ii) precipitation from Xie-Arkins2.
Example Perturbed Model Versions
The control climate of the low and high sensitivity models used in figure 3 can be
seen in supplementary figures 1e-1h to compare similarly well with observations as
does the unperturbed model. Of course the perturbed models have not been through any
tuning procedures through which it may be possible to improve their simulation of
observations.
Cooling Simulations
There is a well understood mechanism for models with a mixed layer ocean to
produce dramatic, unphysical cooling. The process begins in the eastern tropical Pacific.
During the calibration phase there is a mean level of low cloud over the region and
consequently a negative cloud radiative forcing. The heat flux convergence field is
negative here, mimicking the cooling effect of upwelling colder ocean water. During a
subsequent phase the regional amount of cloud may become greater than was typical in
the calibration phase. As a result the amount of short wave radiation penetrating to the
surface is reduced and the sea surface temperatures fall. This can lead to further lowlevel cloud and a positive feedback mechanism, ultimately cooling the whole planet.
This runaway effect is an unphysical consequence of using a mixed layer ocean; it could
happen at any point in the control or double CO2 phases. Some model versions may be
more susceptible but any model versions with sufficient variability, potentially
including the unperturbed model, could produce it given sufficiently long simulations.
The climateprediction.net simulations highlight this generic problem of mixed
layer models, as a consequence of three factors: i) the total number of model years is far
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greater than in any previous experiment so the effect is simply more likely to occur, ii)
some perturbed model versions may be more susceptible due to increased variability,
and iii) the experimental design requires that all model versions use a 15 year
calibration phase – for model versions with higher than standard variability it may be
necessary to increase the length of this phase to reduce the likelihood of encountering
the problem.
Of the simulations removed from the analysis due to a drift in the control, many
exhibit only slow drifts in global mean temperature; a result either of an insufficiently
long calibration or a spurious deduction as a consequence of natural variability in the
control. The remaining ones appear to be a result of the above described effect. Its
consequences are seen in the surface temperature field which show a dramatic cooling
in the eastern equatorial pacific. Supplementary figure 2 shows the difference in the
surface temperature field between the control and calibration phases for (a) a stable
simulation and (b) an unstable simulation showing this effect. These unstable
simulations are removed from the analysis by the requirement that the drift in Tg be less
than 0.02 K/year in the last eight years of the control (see Methods – Data Quality). It is
impractical to assess these fields by eye for all simulations in our grand ensemble but
such manual verification confirms that none of the simulations which pass the quality
and stability checks and have sensitivity greater than 8.5K, exhibit this problem.
Six simulations have stable controls but show a negative drift in the double CO2
phase. These are also a result of the above described effect as can be seen in
supplementary figure 3; in the simulation shown the 8 year mean surface temperature in
the double CO2 phase shows a strong cooling in the tropical pacific with temperatures
down to 27K below the calibration phase. We consider it justified, therefore, to remove
them from the current analysis. However, it will be important not to omit such
parameter combinations from future experiments using models with dynamic oceans
since they would not exhibit the same problem.
Sensitivity to Perturbations
The detailed impact of different parameter perturbations, both individually and
simultaneously, will be the subject of further research. However, figure 2 shows the
impact of sub-sampling model versions by omitting those in which each of two
parameters are perturbed. Supplementary figure 4 complements this plot by illustrating
the effect of omitting those model versions in which each of the remaining four
parameters are perturbed.
References:
s1. New, M., Hulme, M. & Jones, P. Representing twentieth-century space-time
climate variability. Part I: Development of a 1961-90 mean monthly terrestrial
climatology. J. Clim. 12,829-856 (1999).
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s2. Xie, P. & Arkin P. A. Global precipitation: A 17-year monthly analysis based on
gauge observations, satellite estimates and numerical model outputs. Bull. Am.
Met. Soc. 78,2539-2558 (1997).