Analyses of Global Climate Variability in Data and In Models

<p>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.</p>