Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations |
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| Authors: | David N Barnett Simon J Brown James M Murphy David M H Sexton Mark J Webb |
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| Institution: | (1) Met Office, Hadley Centre for Climate Prediction and Research, FitzRoy Road, Exeter, Devon, EX1 3PB, UK |
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| Abstract: | We discuss equilibrium changes in daily extreme surface air temperature and precipitation events in response to doubled atmospheric
CO2, simulated in an ensemble of 53 versions of HadSM3, consisting of the HadAM3 atmospheric general circulation model (GCM)
coupled to a mixed layer ocean. By virtue of its size and design, the ensemble, which samples uncertainty arising from the
parameterisation of atmospheric physical processes and the effects of natural variability, provides a first opportunity to
quantify the robustness of predictions of changes in extremes obtained from GCM simulations. Changes in extremes are quantified
by calculating the frequency of exceedance of a fixed threshold in the 2 × CO2 simulation relative to the 1 × CO2 simulation. The ensemble-mean value of this relative frequency provides a best estimate of the expected change while the
range of values across the ensemble provides a measure of the associated uncertainty. For example, when the extreme threshold
is defined as the 99th percentile of the 1 × CO2 distribution, the global-mean ensemble-mean relative frequency of extremely warm days is found to be 20 in January, and 28
in July, implying that events occurring on one day per hundred under present day conditions would typically occur on 20–30 days
per hundred under 2 × CO2 conditons. However the ensemble range in the relative frequency is of similar magnitude to the ensemble-mean value, indicating
considerable uncertainty in the magnitude of the increase. The relative frequencies in response to doubled CO2 become smaller as the threshold used to define the extreme event is reduced. For one variable (July maximum daily temperature)
we investigate this simulated variation with threshold, showing that it can be quite well reproduced by assuming the response
to doubling CO2 to be characterised simply as a uniform shift of a Gaussian distribution. Nevertheless, doubling CO2 does lead to changes in the shape of the daily distributions for both temperature and precipitation, but the effect of these
changes on the relative frequency of extreme events is generally larger for precipitation. For example, around one-fifth of
the globe exhibits ensemble-mean decreases in time-averaged precipitation accompanied by increases in the frequency of extremely
wet days. The ensemble range of changes in precipitation extremes (relative to the ensemble mean of the changes) is typically
larger than for temperature extremes, indicating greater uncertainty in the precipitation changes. In the global average,
extremely wet days are predicted to become twice as common under 2 × CO2 conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm
or wet seasons under 2 × CO2 are almost everywhere greater than the corresponding increase in daily extremes. The smaller increases in the frequency of
daily extremes is explained by the influence of day-to-day weather variability which inflates the variance of daily distributions
compared to their seasonal counterparts. |
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| Keywords: | Climate change Extreme events Uncertainty Ensemble General circulation model Parametrisation Perturbed physics |
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