Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework

Determining the Antarctic contribution to sea‐level rise from observational data is a complex problem. The number of physical processes involved (such as ice dynamics and surface climate) exceeds the number of observables, some of which have very poor spatial definition. This has led, in general, to...

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Published in:Environmetrics
Main Authors: Zammit‐Mangion, Andrew, Rougier, Jonathan, Bamber, Jonathan, Schön, Nana
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2013
Subjects:
Online Access:http://dx.doi.org/10.1002/env.2247
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fenv.2247
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spelling crwiley:10.1002/env.2247 2024-04-28T08:01:26+00:00 Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework Zammit‐Mangion, Andrew Rougier, Jonathan Bamber, Jonathan Schön, Nana 2013 http://dx.doi.org/10.1002/env.2247 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fenv.2247 https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2247 en eng Wiley http://creativecommons.org/licenses/by/3.0/ Environmetrics volume 25, issue 4, page 245-264 ISSN 1180-4009 1099-095X Ecological Modeling Statistics and Probability journal-article 2013 crwiley https://doi.org/10.1002/env.2247 2024-04-08T06:54:38Z Determining the Antarctic contribution to sea‐level rise from observational data is a complex problem. The number of physical processes involved (such as ice dynamics and surface climate) exceeds the number of observables, some of which have very poor spatial definition. This has led, in general, to solutions that utilise strong prior assumptions or physically based deterministic models to simplify the problem. Here, we present a new approach for estimating the Antarctic contribution, which only incorporates descriptive aspects of the physically based models in the analysis and in a statistical manner. By combining physical insights with modern spatial statistical modelling techniques, we are able to provide probability distributions on all processes deemed to play a role in both the observed data and the contribution to sea‐level rise. Specifically, we use stochastic partial differential equations and their relation to geostatistical fields to capture our physical understanding and employ a Gaussian Markov random field approach for efficient computation. The method, an instantiation of Bayesian hierarchical modelling, naturally incorporates uncertainty in order to reveal credible intervals on all estimated quantities. The estimated sea‐level rise contribution using this approach corroborates those found using a statistically independent method. © 2013 The Authors. Environmetrics Published by John Wiley & Sons, Ltd. Article in Journal/Newspaper Antarc* Antarctic Wiley Online Library Environmetrics 25 4 245 264
institution Open Polar
collection Wiley Online Library
op_collection_id crwiley
language English
topic Ecological Modeling
Statistics and Probability
spellingShingle Ecological Modeling
Statistics and Probability
Zammit‐Mangion, Andrew
Rougier, Jonathan
Bamber, Jonathan
Schön, Nana
Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework
topic_facet Ecological Modeling
Statistics and Probability
description Determining the Antarctic contribution to sea‐level rise from observational data is a complex problem. The number of physical processes involved (such as ice dynamics and surface climate) exceeds the number of observables, some of which have very poor spatial definition. This has led, in general, to solutions that utilise strong prior assumptions or physically based deterministic models to simplify the problem. Here, we present a new approach for estimating the Antarctic contribution, which only incorporates descriptive aspects of the physically based models in the analysis and in a statistical manner. By combining physical insights with modern spatial statistical modelling techniques, we are able to provide probability distributions on all processes deemed to play a role in both the observed data and the contribution to sea‐level rise. Specifically, we use stochastic partial differential equations and their relation to geostatistical fields to capture our physical understanding and employ a Gaussian Markov random field approach for efficient computation. The method, an instantiation of Bayesian hierarchical modelling, naturally incorporates uncertainty in order to reveal credible intervals on all estimated quantities. The estimated sea‐level rise contribution using this approach corroborates those found using a statistically independent method. © 2013 The Authors. Environmetrics Published by John Wiley & Sons, Ltd.
format Article in Journal/Newspaper
author Zammit‐Mangion, Andrew
Rougier, Jonathan
Bamber, Jonathan
Schön, Nana
author_facet Zammit‐Mangion, Andrew
Rougier, Jonathan
Bamber, Jonathan
Schön, Nana
author_sort Zammit‐Mangion, Andrew
title Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework
title_short Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework
title_full Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework
title_fullStr Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework
title_full_unstemmed Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework
title_sort resolving the antarctic contribution to sea‐level rise: a hierarchical modelling framework
publisher Wiley
publishDate 2013
url http://dx.doi.org/10.1002/env.2247
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fenv.2247
https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2247
genre Antarc*
Antarctic
genre_facet Antarc*
Antarctic
op_source Environmetrics
volume 25, issue 4, page 245-264
ISSN 1180-4009 1099-095X
op_rights http://creativecommons.org/licenses/by/3.0/
op_doi https://doi.org/10.1002/env.2247
container_title Environmetrics
container_volume 25
container_issue 4
container_start_page 245
op_container_end_page 264
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