Fractal geometry of melt ponds: Modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces

honors thesis College of Science Mathematics Kenneth M. Golden During the late spring, most of the Arctic Ocean is covered by sea ice with a layer of snow on top. As the snow and sea ice begin to melt, water collects on the surface to form melt ponds. As melting progresses, sparse, disconnected pond...

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Main Author: Bowen, Brady
Format: Text
Language:English
Published: 2016
Subjects:
Online Access:https://collections.lib.utah.edu/ark:/87278/s6p59xdd
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spelling ftunivutah:oai:collections.lib.utah.edu:ir_htoa/1378475 2023-05-15T14:59:19+02:00 Fractal geometry of melt ponds: Modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces Bowen, Brady 2016-04 application/pdf https://collections.lib.utah.edu/ark:/87278/s6p59xdd eng eng https://collections.lib.utah.edu/ark:/87278/s6p59xdd (c) Brady Bowen Melt pond geometry Text 2016 ftunivutah 2021-09-30T17:12:47Z honors thesis College of Science Mathematics Kenneth M. Golden During the late spring, most of the Arctic Ocean is covered by sea ice with a layer of snow on top. As the snow and sea ice begin to melt, water collects on the surface to form melt ponds. As melting progresses, sparse, disconnected ponds coalesce to form complex, self-similar structures which are connected over large length scales. The shapes undergo a transition in fractal dimension from 1 to about 2 around a critical length scale of 100 square meters, as found previously from area-perimeter data. Melt pond geometry depends strongly on sea ice and snow topography. Here we construct a rather simple model of melt pond boundaries as the intersection of a horizontal plane, representing the water level, with a random surface representing the topography. We show that an autoregressive class of anisotropic random Fourier surfaces provides topographies that yield the observed fractal dimension transition, with the ponds evolving and growing as the plane rises. The results are compared with a partial differential equation model of melt pond evolution that includes much of the physics of the system. Properties of the shift in fractal dimension, such as its amplitude, phase and rate, are shown to depend on the surface anisotropy and autocorrelation length scales in the models. Melting-driven differences between the two models are highlighted. Text Arctic Arctic Ocean Sea ice The University of Utah: J. Willard Marriott Digital Library Arctic Arctic Ocean
institution Open Polar
collection The University of Utah: J. Willard Marriott Digital Library
op_collection_id ftunivutah
language English
topic Melt pond geometry
spellingShingle Melt pond geometry
Bowen, Brady
Fractal geometry of melt ponds: Modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces
topic_facet Melt pond geometry
description honors thesis College of Science Mathematics Kenneth M. Golden During the late spring, most of the Arctic Ocean is covered by sea ice with a layer of snow on top. As the snow and sea ice begin to melt, water collects on the surface to form melt ponds. As melting progresses, sparse, disconnected ponds coalesce to form complex, self-similar structures which are connected over large length scales. The shapes undergo a transition in fractal dimension from 1 to about 2 around a critical length scale of 100 square meters, as found previously from area-perimeter data. Melt pond geometry depends strongly on sea ice and snow topography. Here we construct a rather simple model of melt pond boundaries as the intersection of a horizontal plane, representing the water level, with a random surface representing the topography. We show that an autoregressive class of anisotropic random Fourier surfaces provides topographies that yield the observed fractal dimension transition, with the ponds evolving and growing as the plane rises. The results are compared with a partial differential equation model of melt pond evolution that includes much of the physics of the system. Properties of the shift in fractal dimension, such as its amplitude, phase and rate, are shown to depend on the surface anisotropy and autocorrelation length scales in the models. Melting-driven differences between the two models are highlighted.
format Text
author Bowen, Brady
author_facet Bowen, Brady
author_sort Bowen, Brady
title Fractal geometry of melt ponds: Modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces
title_short Fractal geometry of melt ponds: Modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces
title_full Fractal geometry of melt ponds: Modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces
title_fullStr Fractal geometry of melt ponds: Modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces
title_full_unstemmed Fractal geometry of melt ponds: Modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces
title_sort fractal geometry of melt ponds: modeling the fractal geometry of arctic melt ponds using the level sets of random surfaces
publishDate 2016
url https://collections.lib.utah.edu/ark:/87278/s6p59xdd
geographic Arctic
Arctic Ocean
geographic_facet Arctic
Arctic Ocean
genre Arctic
Arctic Ocean
Sea ice
genre_facet Arctic
Arctic Ocean
Sea ice
op_relation https://collections.lib.utah.edu/ark:/87278/s6p59xdd
op_rights (c) Brady Bowen
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