Changes in Internal Variability Due to Global Warming

dc.contributor.advisorDelSole, Timothy M
dc.contributor.authorLaJoie, Emerson Nicole
dc.creatorLaJoie, Emerson Nicole
dc.date.accessioned2017-01-29T01:16:34Z
dc.date.available2017-01-29T01:16:34Z
dc.date.issued2016
dc.description.abstractExtreme weather events can have serious impacts on society, infrastructure, and human life. Evidence is growing that the frequency and intensity of extreme weather events will increase in response to rising greenhouse gas concentrations. However, a consensus has yet to be reached as to whether these changes can be explained by a simple shift in the underlying probability distribution, or by a change in shape of the distribution (namely variance) as well. Previous studies have investigated this question by aggregating data across space, but aggregation requires normalizing data in some way to allow data from different geographic regions to be combined into a single distribution. Unfortunately, subsequent studies showed that the normalization procedure introduces biases. This dissertation proposes a new methodology for quantifying changes in variance that is rigorous, multivariate, and invariant to linear transformation (and thus independent of normalization). The new methodology is applied to simulations from state-of-the-art climate models and reveals significant changes in seasonal- and annual-mean 2m temperature and precipitation in response to anthropogenic forcing. The models consistently predict decreases in temperature variance in regions of seasonal sea-ice formation and across the Southern Ocean by the end of the twenty-first century. While more than half the models also predict significant changes in variance over ENSO regions and the North Atlantic Ocean, the direction of this change is model dependent. Models also consistently predict widespread increases to precipitation variability, particularly in the tropics, extratropics, and polar latitudes. Some models predict more than a doubling in variance, raising questions about the adequacy of doubling uncertainty estimates to test robustness in detection and attribution studies.
dc.format.extent111 pages
dc.identifier.urihttps://hdl.handle.net/1920/10588
dc.language.isoen
dc.rightsCopyright 2016 Emerson Nicole LaJoie
dc.subjectClimate change
dc.subjectStatistics
dc.subjectClimate extremes
dc.subjectDiscriminant analysis
dc.subjectInternal variability
dc.subjectSea ice
dc.subjectVariability changes
dc.subjectWeather extremes
dc.titleChanges in Internal Variability Due to Global Warming
dc.typeDissertation
thesis.degree.disciplineClimate Dynamics
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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