Changes in Internal Variability Due to Global Warming

• Main Author: LaJoie, Emerson Nicole
• Other Authors: DelSole, Timothy M
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: 2017
• Subjects: Climate change; Statistics; climate extremes; discriminant analysis; internal variability; sea ice; variability changes; weather extremes; Southern Ocean; North Atlantic; Sea ice
• Online Access: http://hdl.handle.net/1920/10588

Extreme 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.