Systematic differences in future 20 year temperature extremes in AR4 model projections over Australia as a function of model skill
Systematic differences in future 20 year temperature extremes in AR4 model projections over Australia as a function of model skill
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AR4 模型预测澳大利亚未来 20 年极端温度的系统差异作为模型技能的函数
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
S. Sisson
中科院分区：
文献类型：
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作者：
S. Perkins;A. Pitman;S. Sisson
参与政府间气候变化专门委员会第四次评估报告（AR4）的气候模型对极端温度的预测在澳大利亚进行了区域性审查。最低和最高极端温度定义为使用极值理论计算的 20 年返回值。模型评估的三种方法，即基于均值的方法、基于分布的方法（通过概率密度函数 (PDF)）和基于极端的方法（通过 PDF 的尾部），用于将每日模型数据与 20 年期间不同气候区域的每日观测数据进行比较。为每个区域创建由每个技能衡量标准确定的“更好”和“较差”模型组成的模型集合。将它们与全模型集合进行比较，以检查更熟练的集合预测 2046-2065 年和 2081-2100 年 A2（高排放）情景中极端温度的差异。如果使用任何一种基于分布的评估方法来区分模型，则对于最高温度和最低温度，技能较高的模型预计 20 年回报值的增幅小于全模型集合。对于某些区域，较好和较差整体范围的 90% 置信区间不重叠，表明预测在统计上存在显着差异。我们表明，基于平均值的评估与两种基于分布的评估方法产生的结果不太一致。我们得出的结论是，根据不同的技能指标，特定的 AR4 模型在澳大利亚大多数地区表现相对较差，导致 20 年极端温度的预计增长偏向于更高的值。我们还认为，模拟平均气候的性能是衡量气候模型能力的不可靠指标，用于选择预测澳大利亚极端变化的模型。版权所有 © 2012 英国皇家气象学会
The projection of temperature extremes by climate models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) are examined regionally over Australia. Minimum and maximum temperature extremes are defined as the 20 year return value calculated using extreme value theory. Three measures of model evaluation, a means‐based, a distribution‐based [via probability density functions (PDFs)] and an extreme‐based (via the tails of PDFs) method, are used to compare daily model data to observed daily data over various climatic regions for a 20 year period. Model ensembles consisting of the ‘better’ and ‘poorer’ models determined by each measure of skill are created for each region. These are compared with an all‐model ensemble to examine the difference in more skilled ensemble projections of temperature extremes in the A2 (high emissions) scenario for 2046‐2065 and 2081‐2100. If either of the distribution‐based evaluation methods were used to distinguish models, the higher skilled models projected smaller increases in the 20 year return values than the all‐model ensemble for both maximum temperature and minimum temperature. For some regions, the 90% confidence intervals of the better and poorer ensemble ranges did not overlap, indicating that projections are statistically significantly different. We show that the means‐based evaluation produces less consistent results to the two distribution‐based evaluation methods. We conclude that specific AR4 models, shown to be relatively poor over most regions of Australia by different skill metrics, bias the projected increase in the 20 year temperature extremes towards higher values. We also suggest that performance in simulating the mean climate is an unreliable measure of climate model capacity used to select models for projecting changes in extremes over Australia. Copyright © 2012 Royal Meteorological Society
影响因子：
4.9
作者：
D. Klocke;R. Pincus;J. Quaas
通讯作者：
D. Klocke;R. Pincus;J. Quaas