Author ORCID Identifier

https://orcid.org/0000-0002-8293-0206

Date of Award

2024

Document Type

Thesis (Ph.D.)

Department or Program

Ecology, Evolution, Environment and Society

First Advisor

Justin Mankin

Abstract

Detecting and attributing climate change to date is essential to understanding the harm global warming has caused and the threat it poses in the future. Yet detecting and attributing historical climate change and its attendant impacts is challenging owing to observational uncertainties in the historical record, the difficulty of isolating the forced response from unforced variability, and the methodological ambiguity on how to best map climate variability and change onto climate impacts. In this thesis, I use the study system of Northern Hemisphere snow to demonstrate how synthesizing a wide array of observational and climate model data with methods from statistics, machine learning, and detection and attribution can not only manage these sources of uncertainty, but leverage them into scientific insights and useful operational information. In my first chapter, I provide strong evidence for a previously-elusive anthropogenic fingerprint of climate change on long-term snowpack trends at both the hemispheric and river basin scales. I also identify a crucial nonlinear temperature sensitivity of snowpack that explains both why snow loss has only emerged in particular midlatitude regions and why it will accelerate with further warming. In my second chapter, I build this work, illustrating how the statistical properties of wintertime temperatures explain this nonlinearity, and suggest a future with a dramatically increasing risk of low-snow extremes, or snow droughts, in these temperature-sensitive regions. In my third chapter, I demonstrate how considering all attendant uncertainties around how these snow droughts are measured and defined can improve the ability to forecast their impacts. Finally, in my last chapter, I extend this work into a new domain, highlighting how many of these lessons drawn from studying snowpack can be applied to the study of record-breaking daily temperatures, helping us develop approaches to evaluate climate models in the presence of uncertainty. Combined, my dissertation not only provides new insights about fundamental science questions, but offers a highly generalizable set of practices that can be applied to the study of climate change and its impacts more widely.

Original Citation

Gottlieb, A. R., & Mankin, J. S. (2022). Observing, Measuring, and Assessing the Consequences of Snow Drought. Bulletin of the American Meteorological Society, 103(4), E1041–E1060. https://doi.org/10.1175/BAMS-D-20-0243.1

Gottlieb, A. R., & Mankin, J. S. (2024). Evidence of human influence on Northern Hemisphere snow loss. Nature, 625(7994), 293–300. https://doi.org/10.1038/s41586-023-06794-y

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