Climate model ensembles are used to estimate
uncertainty in future projections, typically by interpreting
the ensemble distribution for a particular variable probabilistically.
There are, however, different ways to produce
climate model ensembles that yield different results, and
therefore different probabilities for a future change in a
variable. Perhaps equally importantly, there are different
approaches to interpreting the ensemble distribution that
lead to different conclusions. Here we use a reduced-resolution
climate system model to compare three common
ways to generate ensembles: initial conditions perturbation,
physical parameter perturbation, and structural changes.
Despite these three approaches conceptually representing
very different categories of uncertainty within a modelling
system, when comparing simulations to observations of
surface air temperature they can be very difficult to separate.
Using the twentieth century CMIP5 ensemble for
comparison, we show that initial conditions ensembles, in
theory representing internal variability, significantly
underestimate observed variance. Structural ensembles,
perhaps less surprisingly, exhibit over-dispersion in simulated
variance. We argue that future climate model
ensembles may need to include parameter or structural
perturbation members in addition to perturbed initial conditions
members to ensure that they sample uncertainty due
to internal variability more completely. We note that where
ensembles are over- or under-dispersive, such as for the
CMIP5 ensemble, estimates of uncertainty need to be
treated with care.
climate model ensembles, ensemble generation, ensemble uncertainty