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...
Published in: | Environmetrics |
---|---|
Main Authors: | , , , |
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 https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2247 |
id |
crwiley:10.1002/env.2247 |
---|---|
record_format |
openpolar |
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 |
_version_ |
1797573184151617536 |