Disentangling the Multiple Sources of Large-Scale Variability in Australian Wintertime Precipitation
Disentangling the Multiple Sources of Large-Scale Variability in Australian Wintertime Precipitation
复制标题
解开澳大利亚冬季降水大规模变化的多重来源
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Sherwood
中科院分区：
文献类型：
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作者：
P. Maher;S. Sherwood
降水受多种大尺度自然过程的影响。这些大规模降水的“驱动因素”中有许多并非相互独立，这使归因变得复杂。此外，尚不清楚自然年际驱动因素本身是否可以解释观测到的长期降水趋势，或解释气候模型中看到的全球变暖的预测降水变化。分离的主要年际驱动程序，可能会出现在较长的时间尺度上，如向极环流转变或增加比湿度的过程，是必不可少的降水变率的一个更好的理解，并作出长期predictions.In这项研究中，一个客观的方法来解开多个来源的大尺度变化适用于澳大利亚降水。这种方法使用多变量线性独立模型，涉及多个线性回归，以产生一个部分相关矩阵，直接链接变量使用显着性阈值。
AbstractPrecipitation is influenced by multiple large-scale natural processes. Many of these large-scale precipitation “drivers” are not independent of one another, which complicates attribution. Moreover, it is unclear whether natural interannual drivers alone can explain the observed longer-term precipitation trends or account for projected precipitation changes with global warming seen in climate models. Separating the main interannual drivers from processes that may prevail on longer time scales, such as a poleward circulation shift or increased specific humidity, is essential for an improved understanding of precipitation variability and for making longer-term predictions.In this study, an objective approach to disentangle multiple sources of large-scale variability is applied to Australian precipitation. This approach uses a multivariate linear independence model, involving multiple linear regressions to produce a partial correlation matrix, which directly links variables using significance threshol...