Using Search Algorithms and Probabilistic Graphical Models to Understand the Influence of Atmospheric Circulation on Western US Drought

Abstract:

This work explores a new approach to quantify cool-season mid-latitude circulation dynamics as they relate western US streamflow variability and drought. This information is used to probabilistically associate patterns of synoptic atmospheric circulation with spatial patterns of drought in western US streamflow. Cool-season storms transport moisture from the Pacific Ocean and are a primary source for western US streamflow. Studies overthe past several decades have emphasized that the western US hydroclimate is influenced by the intensity and phasing of ocean and atmosphere dynamics and teleconnections, such as ENSO and North Pacific variability. These complex interactions are realized in atmospheric circulation along the west coast of North America. The region’s atmospheric circulation can encourage a preferential flow in winter storm tracks from the Pacific, and thus influence the moisture conditions of a given river basin over the course of the cool season. These dynamics have traditionally been measured with atmospheric indices based on values from fixed points in space or principal component loadings. This study uses collective search agents to quantify the position and intensity of potentially non-stationary atmosphere features in climate reanalysis datasets, relative to regional hydrology. Results underline the spatio-temporal relationship between semi-permanent atmosphere characteristics and naturalized streamflow from major river basins of the western US. A probabilistic graphical model quantifies this relationship while accounting for uncertainty from noisy climate processes, and eventually, limitations from dataset length. This creates probabilities for semi-permanent atmosphere features which we hope to associate with extreme droughts of the paleo record, based on our understanding of atmosphere-streamflow relations observed in the instrumental record.