The hydrologic cycle is significantly impacted by climate change, affecting both human societies and ecosystem health. Climate change-driven changes in the hydrological cycle can be subtle and difficult to identify, due to noise arising from natural climate variability and uncertainties in the tools used to understand this forced change, such as climate models. This dissertation investigates these changes across different scales, to discern the effects of climate change amidst inherent variability and modeling challenges. It explores the complex interplay between climate change, climate variability, and their impacts on the hydrological cycle, with a particular focus on precipitation patterns, wildfires, and droughts. Central to this research is the application of machine learning (ML) techniques to dissect and understand these phenomena on various spatial and temporal scales.
The first main research theme focuses on enhancing the understanding of the climate change signal within historical climate model simulations and future projections of precipitation on a global scale. In Chapter 2, utilizing ML methods and state-of-the-art climate model simulations, we identify the anthropogenic fingerprints in historical records of annual maximum daily precipitation across four observational and seven reanalysis datasets. Chapter 3 is dedicated to understanding future changes in precipitation patterns and reducing the uncertainty of these projections. We introduce an emergent constraint on the tropical atmospheric overturning circulation, a major contributor to model uncertainty in future precipitation patterns. Using ML, we estimate the observational estimates of this emergent constraint, yielding a constrained distribution of the future change in circulation (from -1.41±1.06 %/K to -2.20±0.93 %/K). Through this approach, we provide constrained spatial patterns of future precipitation changes, important for impact assessments.
The second main theme focuses on the regional and ecosystem-scale impacts of climate change and climate variability, through changes in the hydrological cycle. In Chapter 4, we focus on the historical fire season onset timing in 13 of California’s ecoregions, showing that onset is primarily controlled by climate variability and change via altering fuel moisture. Through this mechanistic knowledge, we quantify the contribution of climate change to the advancing trend in onset. We use an ML-based dynamical adjustment technique to separate the effects of climate change and variability on climatic drivers. We show that climate change has contributed to an advancement of onset by 5-55 days during the 1992-2020 period, across 11 out of 13 ecosystems. Chapter 5 focuses on the impact of droughts on California’s forests, particularly the severe 2012-2015 drought, which led to massive tree die-offs in the Sierra Nevada forests. We investigate the vulnerability of forests to drought, with a special interest in why certain areas were more affected and in the drought resistance of southern versus northern Sierra forests. Utilizing remote sensing and climate data, the research identifies a drought sensitivity timescale and examines the interaction between this timescale and drought severity to understand the spatial and temporal patterns of tree mortality. ML is employed to analyze factors contributing to tree mortality, revealing that forests in the Northern Sierras would be susceptible if the drought severity were spatially uniform. The study also explores potential future impacts of climate change on drought severity and forest vulnerability, using global climate simulations to predict changes in drought patterns and their effects on forest die-offs.
There has been a vast uptake of machine learning methods in climate science research over the last few years. The variety of data-driven approaches used throughout this thesis highlights the wide range of ML applications for understanding hydroclimate change and its impacts. These applications pave the way for future implementation of data-driven methods in the climate sciences, especially in separating the impacts of climate change from internal variability.
