Quantifying uncertainties and trends in the climate change trajectory

• Main Author: Lickley, Megan Jeramaz.
• Other Authors: Susan Solomon., Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences., Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
• Format: Thesis
• Language: English
• Published: Massachusetts Institute of Technology 2020
• Subjects: Earth; Atmospheric; and Planetary Sciences; Arctic; Indian; Climate change
• Online Access: https://hdl.handle.net/1721.1/127143

Thesis: Ph. D. in Climate Science, Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 159-172). The characterization of climate change depends on the location and rate of change while its impacts on nature and society also depend on vulnerabilities. This thesis contributes to the quantification of uncertainties, drivers, the spatial variability, and impacts of the climate change trajectory. Results of this work have evolved using a range of data science techniques that combine observations and Earth models aimed at informing adaptation and mitigation policies. In the first chapter, the drivers, timing, and impacts of aridity change over the 21st century are assessed using an ensemble of general circulation models (GCMs) together with population statistics. Results indicate that drier regions are projected to dry earlier, more severely and to a greater extent than humid regions, a result driven by differential changes in precipitation across aridity zones. Impacts are exacerbated as arid regions (such as the Mediterranean etc.) are more populated and experiencing much higher population growth than humid regions (which includes the Arctic). Under an unconstrained emissions scenario, GCMs project that most of humanity will live in a more arid climate by the end of the 21st century. For the second chapter, the southern African rainfall (SAR) response to sea surface temperature (SST) anomalies in the Indian Ocean, Atlantic Ocean and Niño 3.4 region is examined. This is done using observations and three large ensembles of GCMs run over the 20th and 21st century. Some previous studies suggested that the Indian Ocean dominated changes in SAR. In this chapter, Niño 3.4 SSTs are found to be most strongly correlated with SAR, while correlations between SAR and the Indian Ocean are dominated by their respective responses to Niño 3.4. GCMs project that this relationship ...