Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations

• Published in: Climate dynamics Vol. 26; no. 5; pp. 489 - 511
• Main Authors: BARNETT, David N, BROWN, Simon J, MURPHY, James M, SEXTON, David M. H, WEBB, Mark J
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer 01.04.2006; Berlin Springer Nature B.V
• Subjects: Air temperature; Atmospheric circulation; Carbon dioxide; Climatology. Bioclimatology. Climate change; Earth, ocean, space; Exact sciences and technology; External geophysics; General circulation models; Meteorology; Rainy season; Surface temperature; Ensemble simulation; Uncertainty; General circulation models; atmospheric precipitation; Carbon dioxide; daily variations; Surface temperature; digital simulation; troposphere; greenhouse gas; Dynamical climatology; Ocean-atmosphere model; climate modification; Atmospheric temperature; extreme value; global change; warming; Air pollution; seasonal variations
• ISSN: 0930-7575; 1432-0894
• DOI: 10.1007/s00382-005-0097-1

We discuss equilibrium changes in daily extreme surface air temperature and precipitation events in response to doubled atmospheric CO^sub 2^, 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 × CO^sub 2^ simulation relative to the 1 × CO^sub 2^ 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 × CO^sub 2^ 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 × CO^sub 2^ 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 CO^sub 2^ 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 CO^sub 2^ to be characterised simply as a uniform shift of a Gaussian distribution. Nevertheless, doubling CO^sub 2^ 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 × CO^sub 2^ conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm or wet seasons under 2 × CO^sub 2^ 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.[PUBLICATION ABSTRACT]