Data assimilation in glaciology

Abstract This chapter addresses an inverse problem in glaciology, namely how to infer a climatic scenario (i.e. how to reconstruct past polar temperature) from ice volume records. Ice volume observations are available from oceanic records, giving information about sea level, and therefore about the...

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Main Authors: Bonan, B., Nodet, M., Ozenda, O., Ritz, C.
Format: Book Part
Language:English
Published: Oxford University PressOxford 2014
Subjects:
Online Access:http://dx.doi.org/10.1093/acprof:oso/9780198723844.003.0025
https://academic.oup.com/book/chapter-pdf/45168910/acprof-9780198723844-chapter-25.pdf
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spelling croxfordunivpr:10.1093/acprof:oso/9780198723844.003.0025 2024-06-09T07:46:48+00:00 Data assimilation in glaciology Bonan, B. Nodet, M. Ozenda, O. Ritz, C. 2014 http://dx.doi.org/10.1093/acprof:oso/9780198723844.003.0025 https://academic.oup.com/book/chapter-pdf/45168910/acprof-9780198723844-chapter-25.pdf en eng Oxford University PressOxford Advanced Data Assimilation for Geosciences page 577-584 ISBN 0198723849 9780198723844 9780191791185 book-chapter 2014 croxfordunivpr https://doi.org/10.1093/acprof:oso/9780198723844.003.0025 2024-05-10T13:17:09Z Abstract This chapter addresses an inverse problem in glaciology, namely how to infer a climatic scenario (i.e. how to reconstruct past polar temperature) from ice volume records. Ice volume observations are available from oceanic records, giving information about sea level, and therefore about the amount of water stored in ice sheets, ice caps, and glaciers. The link between the climatic scenario and the ice sheet volume is complex. The temperature affects the ice mass budget (accumulating minus melting ice). Then, the ice mass budget is a key factor in ice dynamics, which is quite complex in itself (non-Newtonian fluid and nonlinear rheology). The few previous studies of this subject have used fairly simple inverse methods. The idea of the approach described in this chapter is to explore, with idealized twin experiments, the ability of the adjoint method to solve the inverse problem of reconstructing past temperature given all available observations. Book Part Ice Sheet Oxford University Press
institution Open Polar
collection Oxford University Press
op_collection_id croxfordunivpr
language English
description Abstract This chapter addresses an inverse problem in glaciology, namely how to infer a climatic scenario (i.e. how to reconstruct past polar temperature) from ice volume records. Ice volume observations are available from oceanic records, giving information about sea level, and therefore about the amount of water stored in ice sheets, ice caps, and glaciers. The link between the climatic scenario and the ice sheet volume is complex. The temperature affects the ice mass budget (accumulating minus melting ice). Then, the ice mass budget is a key factor in ice dynamics, which is quite complex in itself (non-Newtonian fluid and nonlinear rheology). The few previous studies of this subject have used fairly simple inverse methods. The idea of the approach described in this chapter is to explore, with idealized twin experiments, the ability of the adjoint method to solve the inverse problem of reconstructing past temperature given all available observations.
format Book Part
author Bonan, B.
Nodet, M.
Ozenda, O.
Ritz, C.
spellingShingle Bonan, B.
Nodet, M.
Ozenda, O.
Ritz, C.
Data assimilation in glaciology
author_facet Bonan, B.
Nodet, M.
Ozenda, O.
Ritz, C.
author_sort Bonan, B.
title Data assimilation in glaciology
title_short Data assimilation in glaciology
title_full Data assimilation in glaciology
title_fullStr Data assimilation in glaciology
title_full_unstemmed Data assimilation in glaciology
title_sort data assimilation in glaciology
publisher Oxford University PressOxford
publishDate 2014
url http://dx.doi.org/10.1093/acprof:oso/9780198723844.003.0025
https://academic.oup.com/book/chapter-pdf/45168910/acprof-9780198723844-chapter-25.pdf
genre Ice Sheet
genre_facet Ice Sheet
op_source Advanced Data Assimilation for Geosciences
page 577-584
ISBN 0198723849 9780198723844 9780191791185
op_doi https://doi.org/10.1093/acprof:oso/9780198723844.003.0025
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