Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations
Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations
复制标题
利用大型大气环流模型模拟，量化极端事件频率变化对二氧化碳加倍影响的不确定性
DOI：
10.1007/s00382-005-0097-1
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发表时间：
2006-04-01
期刊：
影响因子：
4.6
通讯作者：
Webb, MJ
中科院分区：
文献类型：
--
作者：
Barnett, DN;Brown, SJ;Webb, MJ
我们讨论了每日极端地表气温和降水事件的平衡变化，以响应加倍的大气CO2，模拟53个版本的HadSM 3，包括HadAM 3大气环流模式（GCM）耦合到一个混合层海洋的合奏。由于其规模和设计，集合，采样的不确定性所产生的参数化的大气物理过程和自然变异的影响，提供了第一个机会，量化的鲁棒性预测的变化，在极端的GCM模拟。通过计算2 x CO2模拟中固定阈值相对于1 x CO2模拟的重复频率来量化极端值的变化。该相对频率的集合平均值提供了对预期变化的最佳估计，而集合中的值范围提供了对相关不确定性的度量。例如，当极端阈值被定义为1 × CO2分布的第99百分位数时，全球平均总体平均相对频率的极端温暖日数在1月为20天，在7月为28天，这意味着在目前的天气条件下，每百天发生一次的事件通常会在2 × CO2条件下发生20-30天。然而，相对频率的集合范围与集合平均值的幅度相似，表明增加幅度存在相当大的不确定性。响应于加倍的CO2的相对频率随着用于定义极端事件的阈值降低而变小。对于一个变量（7月日最高温度），我们研究了这种模拟的变化与阈值，表明它可以很好地再现通过假设响应CO2倍增的特点是简单地作为一个均匀的高斯分布的移位。尽管如此，二氧化碳加倍确实会导致温度和降水量日分布形状的变化，但这些变化对极端事件相对频率的影响通常对降水量更大。例如，大约五分之一的地球仪显示出时间平均降水量的总体平均减少，同时极端潮湿日的频率增加。极端降水量的总体变化幅度（相对于总体变化平均值）通常大于极端气温，表明降水量变化的不确定性更大。在全球平均水平下，在2x CO2条件下，极端潮湿的日子预计将增加一倍。我们还考虑了极端季节的变化，发现在2 × CO2下，极端温暖或潮湿季节的频率模拟增加几乎处处大于每日极端事件的相应增加。每日极端事件频率的小幅增加是由每日天气变化的影响所解释的，与季节性变化相比，每日分布的方差膨胀。
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 x CO2 simulation relative to the 1 x 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 x 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 x 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 x CO2 conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm or wet seasons under 2 x 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.