Analyses of Global Climate Variability in Data and In Models

将采用新的时间和空间数据分析方法来研究对流层和平流层的全球气候变异性。这些方法包括：线性判别分析(LDA)、综合均值差(CMD)投影、经验模式分解(EMD)和连续小波变换(CWT)。在对流层，更重要的是在地表，将研究十年时间尺度上的变暖和变冷模式，目的是将对太阳变化的变暖响应与由温室气体引起的变暖响应区分开来。将检查多个数据集，并使用现场测量来确认全球再分析的结果。政府间气候变化专门委员会第四次评估(IPCC AR4)储存库输出的全球气候模型(GCM)将以与观测数据相同的方式进行分析，以确认其中一些大气-海洋耦合模式能够正确模拟太阳周期强迫的响应，并利用这一结果来约束模拟的瞬变气候响应。在平流层，将提取厄尔尼诺-南方涛动(ENSO)、太阳周期和准两年振荡(QBO)对极地平流层的显著扰动，以便了解这些“外部”扰动如何相互作用。事实证明，LDA方法在提取这些扰动的空间模式并确定其统计意义方面很重要。连续小波变换可以用来客观地研究QBO周期和幅度的年代际调制，可能受太阳周期的影响。气候对太阳周期强迫的反应机制将通过数据和模型分析加以阐明，对其他自然外力的反应也将得到更好的量化。这些研究的更广泛影响在于它们对人为气候变化问题的应用。气候变化预测中的一个主要不确定性是地球对气候敏感的程度。参与IPCC AR4的大气-海洋耦合模式在其瞬变气候响应中跨越了很大的范围，但可以用来缩小不确定性的独立约束很少。这种不确定性导致了对全球变暖的大范围预测。这项工作的结果可以作为一个独立的观测约束，用于校准GCM预测瞬时变暖的能力。

Novel methods of temporal and spatial data analysis will be applied to study global climate variability in the troposphere and the stratosphere. These methods include: Linear Discriminant Analysis (LDA), Composite Mean Difference (CMD) Projection, Empirical Mode Decomposition (EMD) and the Continuous Wavelet Transform (CWT). In the troposphere, and more importantly at the surface, patterns of warming and cooling on decadal time scales will be studied, with the intent to separate the warming response to solar variations from that due to greenhouse gases. Multiple datasets will be examined, with in situ measurements used to confirm results from global reanalyses. Global climate model (GCM) output from the Intergovernmental Panel on Climate Change Fourth Assessment (IPCC AR4) repository will be analyzed in the same way as the observed data, to confirm that some of these coupled atmosphere-ocean models are capable of correctly simulating the response to solar-cycle forcing and to use this result to constrain modeled transient climate responses. In the stratosphere, statistically significant perturbations to the polar stratosphere from the El Nino-southern Oscillation (ENSO), from the solar cycle and from the Quasi-Biennial Oscillation (QBO) will be extracted, in order to understand how these "external" perturbations interact with each other. The LDA method has proven to be important in extracting the spatial patterns of these perturbations and establishing their statistical significance. CWT can be used to objectively study the decadal modulation of the period and amplitude of the QBO, possibly by the solar cycle. The mechanisms responsible for the climate response to solar-cycle forcing are to be elucidated through data and model analyses, and the responses to other natural external forcings better quantified. Broader impacts of these studies are in their application to the problem of anthropogenic climate change. A major uncertainty in the prediction of climate change is the magnitude of Earth's climate sensitivity. The coupled atmosphere-ocean models participating in IPCC AR4 span a large range in their transient climate response, but there are very few independent constraints that can be used to narrow the uncertainty. This uncertainty has led to a large range of predicted global warming. The results from this work can be used as an independent observational constraint for the calibration of GCMs in their ability to predict transient warming.