Report Series No.48: An Assessment of Uncertainties
in Climate Modelling at the
Regional Scale: The Development
of Probabilistic Based Climate Scenarios for Ireland. ISBN: 9781840953459.
Environmental Protection Agency, Johnstown Castle, Co. Wexford, Ireland.
Projected changes in future climate are inherently
uncertain. This uncertainty stems largely from the fact
that, even for a specified emissions scenario, global
climate model (GCM) simulations result in a range
of plausible scenarios being modelled. While most
models do agree that the globally averaged surface
temperature will increase due to increasing atmospheric
concentrations of greenhouse gases, there is a significant
divergence between models in both the spatial and
temporal projections of changes in precipitation. These
differences are most pronounced at the regional scale.
For example, differences are apparent in the magnitude
of projected temperature changes between GCMs; for
precipitation projections, both magnitude and direction
of change can vary between GCMs. Nonetheless,
regional scale climate information is necessary if robust
adaptation strategies are to be developed.
Until recently, the use of a single climate scenario
or climate trajectory was common in the literature.
However, reliance on the output from a single GCM
means there is significant potential for gross underor
over-estimation of the associated risks, which may
result in poor decision-making and increase the risk of
This report presents an overview of the uncertainties
that cascade or propagate through the climate modelling
framework – from emissions scenarios to subsequent
climate projections. It describes a methodology that
has been developed for quantifying such uncertainties
at the regional scale. Initially, a methodology adopted
from the dynamical modelling community was used
to ‘pattern scale’ previously downscaled emissions
scenarios for selected locations in Ireland. This enabled
the quantification of projected changes in temperature
and precipitation for the end of the present century
across four marker emissions scenarios.
In order to produce probabilistic-based scenarios of
temperature and precipitation for the selected station
locations, a Monte Carlo analysis was employed in
conjunction with three different estimates of future
warming. The projected changes in both temperature
and precipitation were found to display a considerable
spread in values. For example, winter temperature at
one location suggested an increase from between 0.6
and 6.6°C by the 2080s’ (2070–2099) period.
While the methodology outlined should enable the rapid
development of probabilistic climate projections, based
on a limited availability of downscaled climate scenarios,
caution needs to be expressed in the interpretation of
the results outlined in this report. While they provide a
basis for assessing the potential risks associated to be
quantified, at least one study has illustrated that details
of the level of risk are not independent of the methods
employed (New et al., 2007).
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