Abstract
Abstract. The increasing volume and spatio-temporal resolution of satellite-derived ice velocity data have created new exploratory opportunities for the quantitative analysis of glacier dynamics. One potential technique, proper orthogonal decomposition (POD), also known as empirical orthogonal functions, has proven to be a powerful and flexible technique for revealing coherent structures in a wide variety of environmental flows. In this study we investigate the applicability of POD to an openly available TanDEM-X/TerraSAR-X-derived ice velocity dataset from Sermeq Kujalleq (Jakobshavn Isbræ), Greenland. We find three dominant modes with annual periodicity that we argue are explained by glaciological processes. The primary dominant mode is interpreted as relating to the stress reconfiguration at the glacier terminus, known to be an important control on the glacier's dynamics. The second and third largest modes together relate to the development of the spatially heterogenous glacier hydrological system and are primarily driven by the pressurisation and efficiency of the subglacial hydrological system. During the melt season, variations in the velocity shown in these two subsidiary modes are explained by the drainage of nearby supraglacial melt ponds, as identified with a Google Earth Engine Moderate Resolution Imaging Spectroradiometer (MODIS) dynamic thresholding technique. By isolating statistical structures within velocity datasets and through their comparison to glaciological theory and complementary datasets, POD indicates which glaciological processes are responsible for the changing bulk velocity signal, as observed from space. With the proliferation of optical- and radar-derived velocity products (e.g. MEaSUREs, ESA CCI, PROMICE), we suggest POD, and potentially other modal decomposition techniques, will become increasingly useful in future studies of ice dynamics.
Highlights
The surface flow of glaciers is one of the most important measurements to assess the health of the cryosphere and is critical to understand its mass balance and changing dynamics
This study acts as a proof of concept, building on the work described by Mair et al (2002), that the eigen-decomposition of ice velocity can detect the influence of changes in glacial hydrology on ice dynamics
Our study area within SK-JI was chosen owing to the excellent and openly available ice velocity dataset, but it is arguably not the ideal place to test the applicability of proper orthogonal decomposition (POD) to ice velocity due to the dominance of stress patterns created by changing terminus geometry on the overall seasonal velocity, as outlined by previous work (Joughin et al, 2020a; Lemos et al, 2018; Bondzio et al, 2017)
Summary
The surface flow of glaciers is one of the most important measurements to assess the health of the cryosphere and is critical to understand its mass balance and changing dynamics. It is an emergent property resulting from the complex interaction of numerous processes occurring on a variety of spatial and temporal scales. POD may have value in glaciology because it exactly describes a series of snapshots from a flow field with the product of ranked spatially orthogonal “modes” of spatial weighting and one-dimensional “temporal” coefficients (eigenvectors). In this paper we explore the applicability of this eigen-decomposition technique to a series of colocated satellite-derived ice velocity images for the first time. We use openly available ice velocity datasets and focus on the excep-
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