Public lands are a source of local pride, cultural significance, economic benefit, recreational opportunity, and ecological value. Extreme weather events such as floods pose a risk to the long-term preservation of these lands. For example, in November 2006 excessive rainfall fell atop an existing snowpack in Montana's Glacier National Park, producing significant snowmelt and near-record streamflows. The resulting floods damaged critical park infrastructure, including Going-to-the-Sun Road, resulting in multi-million dollar repair costs. Past studies have indicated that impacts from such "rain-on-snow" (ROS) flooding events have the potential to be even more severe in future climate scenarios. Therefore, it is critical for park managers and local communities to make necessary preparations to mitigate human, cultural, ecological, and economic costs. To help inform these key stakeholders, we employ a "storyline" approach, using models to re-create historical flooding events within a spectrum of future climate scenarios. A major goal of this research is to use our results as the basis of engagement with local communities to provide them with a range of plausible risks to their cherished public lands.

Earth System Models (ESMs) have notable biases on both regional and global spatial scales. Many of these biases are due, at least in part, to uncertainties in the parameterization of key subgrid-scale processes. I work with a climate process team (CPT) that seeks to understand which parameterized processes are impactful for regional and global ESM model output. We specifically focus on the parameterization of boundary layer (PBL) momentum flux. Using a computationally-efficient parameter sensitivity analysis, we identify a handful of input parameters in the PBL scheme that appreciably impact characteristics of the mean climate, including surface wind stress and cloud fraction. In this way, we explore parameter sensitivity for a variety of process-oriented output metrics in different regions and over different timescales. 

Atmospheric rivers (ARs) are long, plumelike corridors of high atmospheric water vapor transport, typically associated with extratropical cyclones. ARs are critical sources of seasonal precipitation, especially in the western U.S. Given the past, present, and future impacts of ARs, many studies have explored AR characteristics through the lens of both observations and models. To aid in these studies, various algorithms have been developed to objectively identify ARs in fields of water vapor transport. These algorithms typically employ criteria based on intensity and geometry. I contribute an existing AR detection algorithm in support of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). The goal of ARTMIP is to better understand uncertainties in observational and modeling studies due to the choice of AR detection algorithm.