Towards understanding the role of natural variability in climate change

dc.contributor.authorLi, Jingyuan, author
dc.contributor.authorThompson, David W. J., advisor
dc.contributor.authorBarnes, Elizabeth A., committee member
dc.contributor.authorCooley, Daniel, committee member
dc.description2017 Fall.
dc.descriptionIncludes bibliographical references.
dc.description.abstractNatural variability plays a large role in determining surface climate on local and regional scales. Understanding the role of natural variability is crucial for accurately assessing and attributing climate trends, both past and future. One successful way to examine the role of natural variability in climate change has been through large ensembles of climate models. This thesis uses one such large ensemble (the NCAR CESM-LE) to test various methods used to quantify natural variability in the context of climate change. We first introduce a simple analytic expression for calculating the lead time required for a linear trend to emerge in a Gaussian first order autoregressive process. The expression is derived from the standard error of the regression and is tested using the CESM-LE. It is shown to provide a robust estimate of the point in time when the forced signal of climate change has emerged from the natural variability of the climate system with a predetermined level of statistical confidence. The expression provides a novel analytic tool for estimating the time of emergence of anthropogenic climate change and its associated regional climate impacts from either observed or modeled estimates of natural variability and trends. We next compare and analyze various methods for calculating the effects of internal circulation dynamics on surface temperature. Dynamical adjustment seeks to separate out dynamical contribution to temperature trends, thus reducing the amplitude of natural variability that obscures the signal of anthropogenic forcing. Three specific methods used in the climate literature are examined: principal component analysis (PCR), maximum covariance analysis (MCA), and constructed circulation analogs. An assessment of these methods are given with their respective results from the CESM control run and large ensemble.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.publisherColorado State University. Libraries
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dc.titleTowards understanding the role of natural variability in climate change
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