The Influence of Ice Coverage, Calving, and Melt on Underwater Ambient Sound in a Glacierized Fjord

Abstract Noise from calving icebergs, cracking ice, and melting ice dominates the underwater soundscape of glacierized fjords creating one of the loudest recorded ambient ocean environments. While progress has been made toward identifying and describing individual sound sources—including the automatic detection of calving and quantification of ice‐mass loss—the relative contributions of multiple, simultaneous processes, and how these contributions evolve over time, remain underexplored, limiting robust interpretation of ice‐ocean interactions. Here, we show that unsupervised machine learning separates a series of recordings captured over 8 months into five dominant sound profiles related to glacier activity. We deployed an array of hydrophones approximately 400 m from the terminus of Xeitl Sít’ (LeConte Glacier) in Southeast Alaska and recorded sound regularly between October 2016 and May 2017. Using the k‐means clustering algorithm, we cluster spectral shapes of 10,440 background acoustic spectra, defined as the 25th‐percentile spectral level of each recording. We identify five distinct acoustic clusters and relate their temporal occurrence to environmental time series including ice movement, meteorology, and oceanographic data. We further link spectral shapes to known glacier sources such as calving and ice melt. Our analysis reveals that these clusters correspond more closely with glacier and ice‐mélange activity than with other environmental variables, confirming the dominance of glacier behavior on fjord soundscapes. This research demonstrates the effectiveness of clustering passive acoustic data and provides a framework for analyzing large, complex acoustic data sets of undersampled environments—such as glacierized fjords—to guide interpretation and track changes in dominant environmental processes.