Atmospheric rivers: a complexity science approach to understanding their dynamics and impacts

Atmospheric rivers (ARs) are long, narrow, and transient corridors of intense moisture transport in the lower atmosphere that play a pivotal role in Earth’s water cycle. They contribute significantly to the freshwater supply of the midlatitudes and drive precipitation and wind regimes in many coastal regions. However, ARs are also responsible for extreme weather events and natural disasters. Despite their importance, AR science is still a young field, facing challenges such as uncertainties in detection and tracking methodologies, regional biases that limit global analyses, and an incomplete understanding of AR dynamics and impacts. To address these knowledge gaps, this dissertation pioneers the study of ARs through a complexity science approach.<br>The first study investigates the dynamics and far-reaching impacts of ARs that penetrate inland after making landfall along the West Coast of North America. Employing nonlinear time series analysis and climate networks, I uncover a cascade of heavy precipitation events that originates along the coast and propagates to central and eastern Canada. This cascade is driven by intense, long-lasting, late-summer ARs making landfall in British Columbia. Combining these findings with composite analysis of meteorological anomalies, I further reveal the typical synoptic-scale evolution of these inland-penetrating ARs. This work highlights the potential of complexity science to unravel hidden spatiotemporal dynamics and impacts of ARs, offering new insights into AR forecasting and risk-mitigation.<br>The second study advances the attribution of precipitation-induced landslides (PILs) to land-falling ARs. By combining stochastic climate theory, probabilistic causation, and nonlinear time series analysis, I develop a multi-step attribution framework to assess the causal relation between land-falling ARs, precipitation, and PILs along the West Coast of North America. Results show that AR-induced precipitation is the primary cause of PILs, with 86% of events occurring after AR-attributed precipitation. Classifying ARs as landslide-triggering or non-triggering events, I find that the causal relation is dominated by intense, long-lasting ARs. Additionally, I uncover that individual ARs and sequences of ARs, known as AR families, contribute equally to the occurrence of PILs in the region. This work provides crucial insights to improve landslide forecasting and disaster mitigation while advancing the mathematical rigor of attribution in AR science.<br>The third study expands the investigation to a global scale by introducing PIKART, a novel and comprehensive catalog of ARs spanning from 1940 to 2023, with a high spatiotemporal resolution of 0.5° and 6 hours. This dataset enhances AR identification through innovations in detection, tracking, and classification methods. Analyses of the PIKART catalog reveal (i) new hotspots of AR genesis, inland penetration, and termination, (ii) continental regions newly identified as exposed to considerable AR impacts, and (iii) significant historical trends such as the poleward shift of southern hemispheric ARs and a global intensification of AR moisture transport. These findings not only advance our understanding of ARs on regional and global scales but also provide a robust resource for future studies on AR dynamics and impacts, especially through a complexity science approach.<br>While this dissertation significantly advances AR science, it also identifies open challenges, such as understanding (i) the physical mechanisms underlying correlation patterns, (ii) land-atmosphere interactions leading to AR-induced disasters, and (iii) AR-like features in the tropics. Addressing these challenges will require further methodological advancements and interdisciplinary collaboration. By laying a robust theoretical and methodological foundation, this work opens new pathways for studying ARs as interacting elements of the Earth's climate system and paves the way for future research on AR dynamics, impacts, and prediction.

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