Dynamical Origins of Warm-Season Precipitation Extremes over the Northern Extratropics: A Multiscale Diagnostic and Modeling Study
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You, Zhenyu
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Abstract
Extreme precipitation events are increasing in frequency and intensity under climate change, causing on the order of hundreds of billions of US dollars in global annual economic losses. While many studies have focused on the thermodynamic drivers of these extremes, regional variability of extreme precipitation during the boreal warm season is often governed by complex multiscale atmospheric-flow interactions. These interactions range from planetary-scale forcing to regional meso- and micro-scale processes, and they remain inadequately understood and poorly represented in models. This thesis addresses these challenges by investigating the large-scale dynamical origins of warm-season precipitation extremes across the northern extratropics, with a particular focus on Mesoscale Convective Systems (MCSs) over the central United States and precipitation extremes at the margins of the Asian summer monsoon. Through a combination of observational analysis, model diagnosis, and idealized modeling, this work develops a hierarchical framework to disentangle the roles of large-scale forcing, synoptic variability, and upscale feedback in shaping the distribution and variability of regional extremes, with the ultimate goal of improving their simulation and prediction in climate models.
The first part of the study (Chapter 2) presents a comprehensive diagnostic analysis of MCSs over the U.S. Great Plains during boreal spring from 2000 to 2020. Using hierarchical clustering analysis, the study identifies five large-scale upper-tropospheric circulation patterns associated with MCS genesis. These clusters fall into two dynamical categories: “remotely forced” patterns such as downstream-propagating storm track eddies and “locally excited” patterns driven by regional instability. Among them, Cluster 2, linked with Pacific storm track disturbances, is the dominant contributor to the recent upward trend in MCS frequency. The phase transition of the Pacific Decadal Oscillation (PDO) emerges as a key climate mode contributing to this trend. This chapter also evaluates the performance of the NOAA GFDL AM4 model in simulating MCSs. While the model underestimates MCS frequency and shifts MCS activity eastward compared to observation, it successfully captures the observed large-scale forcing patterns of MCSs. The locational bias in MCS activity is ultimately traced to deficiencies in both seasonal mean circulation and synoptic biases, including weakened Great Plains low-level jets (LLJs) and misplaced surface fronts. The analysis shows that biases in the mean state and transient processes jointly contribute to the eastward shift of MCS activity in the model.
The second part of the study (Chapter 3) is a dynamical investigation of how MCS heating provides upscale feedback to large-scale atmospheric circulations. Using an idealized two-layer quasi-geostrophic (QG) model with empirical heating parameterizations, the chapter shows that latent heating from MCSs leads to stronger storm tracks, increased downstream eddy activity, and better organized synoptic wave propagation, as demonstrated through nonmodal instability (optimal mode) analysis. These effects also improve the representation of circumglobal teleconnection structures. For example, including MCS heating strengthens the feedback of North Atlantic synoptic eddies on the background Atlantic jet, with implications for the development of low- frequency modes such as the Northern Annular Mode (NAM). Although based on a highly simplified model, the study demonstrates the planetary-scale significance of MCS heating in modulating springtime extratropical multiscale variability.
The third part of the study (Chapter 4) combines methodologies from the previous two chapters, integrating multiscale diagnosis and dynamical analysis using idealized models to identify the fundamental causes of trends and variability in regional extremes. The focus is placed upon summertime extreme precipitation in Northeast China and Pakistan, two monsoon-margin zones with increasing vulnerability to extreme precipitation and flooding. Using clustering and a barotropic vorticity model, the study identifies distinct Rossby wave pathways associated with extreme precipitation in Northeast China. It then applies optimal mode analysis to background flows from two different periods (1982-2002 and 2003-2023) to assess how changes in the mean flow affect wave excitation and propagation and thus the occurrence of precipitation extremes. The results show that in the recent two decades the atmospheric waveguides feature an extension over Eurasia, strengthening within the Asian jet, and weakening at its flanks. These structural changes align with an increased frequency of a particular pathway of wave propagation. Further analysis confirms that optimal modes under the recent mean flow resemble this pathway of preferred wave propagation while correlations with other pathways (clusters) decrease. This provides compelling evidence that evolving seasonal mean flows are changing the frequency of regional precipitation extremes through their influence on the excitation and propagation of atmospheric disturbances.
The final chapter summarizes the findings and highlights the importance of understanding scale interactions in the climate system. Across all chapters, the thesis builds a multiscale framework that connects planetary-scale climate modes, synoptic forcing, and local processes to the genesis, modulation, and impact of regional extremes. It highlights how idealized models, when combined with observations, can provide fundamental insights into the mechanisms governing trends and variability of precipitation extremes. By linking biases in Earth System Models to their deficiencies in representing multiscale interactions, the thesis offers pathways for improving weather predictions and climate projections. This thesis advances our understanding of warm-season extreme precipitation by identifying the key dynamical processes that control their variability and trend. Ultimately, these insights contribute to better adaptation strategies and more reliable assessments of future climate and weather risks.
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Date
2025-08-15
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Dissertation (PhD)