Skip to main content

Resolving the Antartic contribution to sea-level rise: a hierarchial modelling framework

Journal Article


Abstract


  • 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.

Authors


  •   Zammit-Mangion, Andrew
  •   Rougier, Jonathon (external author)
  •   Bamber, Jonathan (external author)
  •   Schoen, Nana (external author)

Publication Date


  • 2013

Citation


  • Zammit-Mangion, A., Rougier, J., Bamber, J. & Schoen, N. (2013). Resolving the Antartic contribution to sea-level rise: a hierarchial modelling framework. Environmetrics, 25 (4), 245-264.

Scopus Eid


  • 2-s2.0-84901471132

Has Global Citation Frequency


Number Of Pages


  • 19

Start Page


  • 245

End Page


  • 264

Volume


  • 25

Issue


  • 4

Place Of Publication


  • United Kingdom

Abstract


  • 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.

Authors


  •   Zammit-Mangion, Andrew
  •   Rougier, Jonathon (external author)
  •   Bamber, Jonathan (external author)
  •   Schoen, Nana (external author)

Publication Date


  • 2013

Citation


  • Zammit-Mangion, A., Rougier, J., Bamber, J. & Schoen, N. (2013). Resolving the Antartic contribution to sea-level rise: a hierarchial modelling framework. Environmetrics, 25 (4), 245-264.

Scopus Eid


  • 2-s2.0-84901471132

Has Global Citation Frequency


Number Of Pages


  • 19

Start Page


  • 245

End Page


  • 264

Volume


  • 25

Issue


  • 4

Place Of Publication


  • United Kingdom