Advancing Process-Based Nonstationary Analysis of Climate Extremes: Modeling, Uncertainty Assessment and Multivariate Attribution

Extreme weather events are inherent in climate variability and they can cause ecosystem alterations, infrastructure damages, suspension of food supply chains, and loss of lives. Today's highly populated and urbanized society is more vulnerable than ever to natural hazards and their disruptive consequences. Projected population growth and changes in climate variability are expected to exacerbate the societal and economic impact of climatic extremes. The scientific community, global organizations, and other stakeholders have all recognized the urgency of improving our understanding of both natural and human-induced climate variability. The overarching goal of this thesis is to advance the current methods for nonstationary analysis of climatic extremes and their attributions. Here we propose a methodological framework for investigating hydroclimatic extremes over time and in response to a physical driver/covariate. The Process-based Nonstationary Extreme Value Analysis (ProNEVA) framework is unique in that it allows for incorporating a physical component into traditional frequency analysis techniques to account for observed or process-based changes in the variable of interest. The model can be used for both stationary and nonstationary analyses of extremes and includes a Graphical User Interface (GUI) for easier implementation. We then shed light on the uncertainty inherent in the estimation of climatic extremes. We propose a generalized approach for including uncertainty information in the recurrence interval of extremes. The approach offers insights on how information about extremes should be interpreted by planners and decision-makers under conditions of uncertainty. Using the method developed in this thesis, we show how extreme precipitation is expected to change in the future. We also highlight the importance of merging information from observations and climate model simulations for risk assessment purposes. Finally, we outline a methodological framework for attribution of changes in multiple extremes or multiple features of an extreme event. We show the potential of copula functions for attribution of changes in both the magnitude and the dependence structure between characteristics of a natural phenomenon or, more generally, between two dependent variables