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Towards Understanding the Role of Natural Variability in Climate Change

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December 9, 2016
Jingyuan Li
Hosted by David Thompson (advisor), Elizabeth Barnes, Daniel Cooley (Statistics)

Abstract

Natural 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 has been through large ensembles of climate models. This thesis uses 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 also compare various methods for calculating the effects of circulation dynamics on surface temperature. Methods are compared using the control run and then applied to the large ensemble. Our results show that the temperature fit from the principal components of sea level pressure explains the most variance in the temperature field. Dynamical adjustment also narrows the uncertainty among the large ensemble and brings the realizations closer to the forced signal.