Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial Data. Issue 513 (2nd January 2016)
- Record Type:
- Journal Article
- Title:
- Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial Data. Issue 513 (2nd January 2016)
- Main Title:
- Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial Data
- Authors:
- Chang, Won
Haran, Murali
Applegate, Patrick
Pollard, David - Abstract:
- Abstract : Rapid retreat of ice in the Amundsen Sea sector of West Antarctica may cause drastic sea level rise, posing significant risks to populations in low-lying coastal regions. Calibration of computer models representing the behavior of the West Antarctic Ice Sheet is key for informative projections of future sea level rise. However, both the relevant observations and the model output are high-dimensional binary spatial data; existing computer model calibration methods are unable to handle such data. Here we present a novel calibration method for computer models whose output is in the form of binary spatial data. To mitigate the computational and inferential challenges posed by our approach, we apply a generalized principal component based dimension reduction method. To demonstrate the utility of our method, we calibrate the PSU3D-ICE model by comparing the output from a 499-member perturbed-parameter ensemble with observations from the Amundsen Sea sector of the ice sheet. Our methods help rigorously characterize the parameter uncertainty even in the presence of systematic data-model discrepancies and dependence in the errors. Our method also helps inform environmental risk analyses by contributing to improved projections of sea level rise from the ice sheets. Supplementary materials for this article are available online.
- Is Part Of:
- Journal of the American Statistical Association. Volume 111:Issue 513(2016)
- Journal:
- Journal of the American Statistical Association
- Issue:
- Volume 111:Issue 513(2016)
- Issue Display:
- Volume 111, Issue 513 (2016)
- Year:
- 2016
- Volume:
- 111
- Issue:
- 513
- Issue Sort Value:
- 2016-0111-0513-0000
- Page Start:
- 57
- Page End:
- 72
- Publication Date:
- 2016-01-02
- Subjects:
- Climate change -- Computer experiments -- Gaussian processes -- Principal components -- Spatial generalized linear mixed models.
Statistics -- Periodicals
Statistics -- Periodicals
Statistiques -- Périodiques
États-Unis -- Statistiques -- Périodiques
519.5 - Journal URLs:
- http://www.jstor.org/journals/01621459.html ↗
http://www.ingentaconnect.com/content/asa/jasa ↗
http://www.tandfonline.com/loi/uasa20 ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/01621459.2015.1108199 ↗
- Languages:
- English
- ISSNs:
- 0162-1459
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4694.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 1838.xml