Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach
Essential to understanding sea level change and its causes during the last interglacial (LIG) is the quantification of uncertainties. In order to estimate the uncertainties, we develop a statistical framework for the comparison of palaeoclimatic sea level index points and GIA model predictions. For...
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ftunivmaynooth:oai:mural.maynoothuniversity.ie:12286 2023-05-15T16:41:08+02:00 Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach Düsterhus, André Tamisiea, Mark E. Jevrejeva, Svetlana 2016-08-01 text https://mural.maynoothuniversity.ie/12286/ https://mural.maynoothuniversity.ie/12286/1/Duesterhus_Estimating_2016.pdf en eng Oxford University Press https://mural.maynoothuniversity.ie/12286/1/Duesterhus_Estimating_2016.pdf Düsterhus, André and Tamisiea, Mark E. and Jevrejeva, Svetlana (2016) Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach. Geophysical Journal International, 206 (2). pp. 900-920. ISSN 1365-246X Article PeerReviewed 2016 ftunivmaynooth 2022-06-13T18:47:18Z Essential to understanding sea level change and its causes during the last interglacial (LIG) is the quantification of uncertainties. In order to estimate the uncertainties, we develop a statistical framework for the comparison of palaeoclimatic sea level index points and GIA model predictions. For the investigation of uncertainties, as well as to generate better model predictions, we implement a massive ensemble approach by applying a data assimilation scheme based on particle filter methods. The different runs are distinguished through varying ice sheet reconstructions based on oxygen-isotope curves and different parameter selections within the GIA model. This framework has several advantages over earlier work, such as the ability to examine either the contribution of individual observations to the results or the probability of specific input parameters. This exploration of input parameters and data leads to a larger range of estimates than previously published work. We illustrate how the assumptions that enter into the statistical analysis, such as the existence of outliers in the observational database or the initial ice volume history, can introduce large variations to the estimate of the maximum highstand. Thus, caution is required to avoid overinterpreting results. We conclude that there are reasonable doubts whether the data sets previously used in statistical analyses are able to tightly constrain the value of maximum highstand during the LIG. Article in Journal/Newspaper Ice Sheet Maynooth University ePrints and eTheses Archive (National University of Ireland) |
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Open Polar |
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Maynooth University ePrints and eTheses Archive (National University of Ireland) |
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ftunivmaynooth |
language |
English |
description |
Essential to understanding sea level change and its causes during the last interglacial (LIG) is the quantification of uncertainties. In order to estimate the uncertainties, we develop a statistical framework for the comparison of palaeoclimatic sea level index points and GIA model predictions. For the investigation of uncertainties, as well as to generate better model predictions, we implement a massive ensemble approach by applying a data assimilation scheme based on particle filter methods. The different runs are distinguished through varying ice sheet reconstructions based on oxygen-isotope curves and different parameter selections within the GIA model. This framework has several advantages over earlier work, such as the ability to examine either the contribution of individual observations to the results or the probability of specific input parameters. This exploration of input parameters and data leads to a larger range of estimates than previously published work. We illustrate how the assumptions that enter into the statistical analysis, such as the existence of outliers in the observational database or the initial ice volume history, can introduce large variations to the estimate of the maximum highstand. Thus, caution is required to avoid overinterpreting results. We conclude that there are reasonable doubts whether the data sets previously used in statistical analyses are able to tightly constrain the value of maximum highstand during the LIG. |
format |
Article in Journal/Newspaper |
author |
Düsterhus, André Tamisiea, Mark E. Jevrejeva, Svetlana |
spellingShingle |
Düsterhus, André Tamisiea, Mark E. Jevrejeva, Svetlana Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach |
author_facet |
Düsterhus, André Tamisiea, Mark E. Jevrejeva, Svetlana |
author_sort |
Düsterhus, André |
title |
Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach |
title_short |
Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach |
title_full |
Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach |
title_fullStr |
Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach |
title_full_unstemmed |
Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach |
title_sort |
estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach |
publisher |
Oxford University Press |
publishDate |
2016 |
url |
https://mural.maynoothuniversity.ie/12286/ https://mural.maynoothuniversity.ie/12286/1/Duesterhus_Estimating_2016.pdf |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_relation |
https://mural.maynoothuniversity.ie/12286/1/Duesterhus_Estimating_2016.pdf Düsterhus, André and Tamisiea, Mark E. and Jevrejeva, Svetlana (2016) Estimating the sea level highstand during the last interglacial: a probabilistic massive ensemble approach. Geophysical Journal International, 206 (2). pp. 900-920. ISSN 1365-246X |
_version_ |
1766031569614536704 |