Improving ice sheet model calibration using paleoclimate and modern data
Human-induced climate change may cause significant ice volume loss from the West Antarctic Ice Sheet (WAIS). Projections of ice volume change from ice sheet models and corresponding future sea-level rise have large uncertainties due to poorly constrained input parameters. In most future applications...
Published in: | The Annals of Applied Statistics |
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The Institute of Mathematical Statistics
2016
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ftculeuclid:oai:CULeuclid:euclid.aoas/1483606860 2023-05-15T13:31:59+02:00 Improving ice sheet model calibration using paleoclimate and modern data Chang, Won Haran, Murali Applegate, Patrick Pollard, David 2016-12 application/pdf http://projecteuclid.org/euclid.aoas/1483606860 https://doi.org/10.1214/16-AOAS979 en eng The Institute of Mathematical Statistics 1932-6157 1941-7330 Copyright 2016 Institute of Mathematical Statistics Paleoclimate West Antarctic Ice Sheet computer model calibration Gaussian process dimension reduction Text 2016 ftculeuclid https://doi.org/10.1214/16-AOAS979 2018-10-06T12:58:09Z Human-induced climate change may cause significant ice volume loss from the West Antarctic Ice Sheet (WAIS). Projections of ice volume change from ice sheet models and corresponding future sea-level rise have large uncertainties due to poorly constrained input parameters. In most future applications to date, model calibration has utilized only modern or recent (decadal) observations, leaving input parameters that control the long-term behavior of WAIS largely unconstrained. Many paleo-observations are in the form of localized time series, while modern observations are non-Gaussian spatial data; combining information across these types poses nontrivial statistical challenges. Here we introduce a computationally efficient calibration approach that utilizes both modern and paleo-observations to generate better constrained ice volume projections. Using fast emulators built upon principal component analysis and a reduced dimension calibration model, we can efficiently handle high-dimensional and non-Gaussian data. We apply our calibration approach to the PSU3D-ICE model which can realistically simulate long-term behavior of WAIS. Our results show that using paleo-observations in calibration significantly reduces parametric uncertainty, resulting in sharper projections about the future state of WAIS. One benefit of using paleo-observations is found to be that unrealistic simulations with overshoots in past ice retreat and projected future regrowth are eliminated. Text Antarc* Antarctic Ice Sheet Project Euclid (Cornell University Library) Antarctic West Antarctic Ice Sheet The Annals of Applied Statistics 10 4 |
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Open Polar |
collection |
Project Euclid (Cornell University Library) |
op_collection_id |
ftculeuclid |
language |
English |
topic |
Paleoclimate West Antarctic Ice Sheet computer model calibration Gaussian process dimension reduction |
spellingShingle |
Paleoclimate West Antarctic Ice Sheet computer model calibration Gaussian process dimension reduction Chang, Won Haran, Murali Applegate, Patrick Pollard, David Improving ice sheet model calibration using paleoclimate and modern data |
topic_facet |
Paleoclimate West Antarctic Ice Sheet computer model calibration Gaussian process dimension reduction |
description |
Human-induced climate change may cause significant ice volume loss from the West Antarctic Ice Sheet (WAIS). Projections of ice volume change from ice sheet models and corresponding future sea-level rise have large uncertainties due to poorly constrained input parameters. In most future applications to date, model calibration has utilized only modern or recent (decadal) observations, leaving input parameters that control the long-term behavior of WAIS largely unconstrained. Many paleo-observations are in the form of localized time series, while modern observations are non-Gaussian spatial data; combining information across these types poses nontrivial statistical challenges. Here we introduce a computationally efficient calibration approach that utilizes both modern and paleo-observations to generate better constrained ice volume projections. Using fast emulators built upon principal component analysis and a reduced dimension calibration model, we can efficiently handle high-dimensional and non-Gaussian data. We apply our calibration approach to the PSU3D-ICE model which can realistically simulate long-term behavior of WAIS. Our results show that using paleo-observations in calibration significantly reduces parametric uncertainty, resulting in sharper projections about the future state of WAIS. One benefit of using paleo-observations is found to be that unrealistic simulations with overshoots in past ice retreat and projected future regrowth are eliminated. |
format |
Text |
author |
Chang, Won Haran, Murali Applegate, Patrick Pollard, David |
author_facet |
Chang, Won Haran, Murali Applegate, Patrick Pollard, David |
author_sort |
Chang, Won |
title |
Improving ice sheet model calibration using paleoclimate and modern data |
title_short |
Improving ice sheet model calibration using paleoclimate and modern data |
title_full |
Improving ice sheet model calibration using paleoclimate and modern data |
title_fullStr |
Improving ice sheet model calibration using paleoclimate and modern data |
title_full_unstemmed |
Improving ice sheet model calibration using paleoclimate and modern data |
title_sort |
improving ice sheet model calibration using paleoclimate and modern data |
publisher |
The Institute of Mathematical Statistics |
publishDate |
2016 |
url |
http://projecteuclid.org/euclid.aoas/1483606860 https://doi.org/10.1214/16-AOAS979 |
geographic |
Antarctic West Antarctic Ice Sheet |
geographic_facet |
Antarctic West Antarctic Ice Sheet |
genre |
Antarc* Antarctic Ice Sheet |
genre_facet |
Antarc* Antarctic Ice Sheet |
op_relation |
1932-6157 1941-7330 |
op_rights |
Copyright 2016 Institute of Mathematical Statistics |
op_doi |
https://doi.org/10.1214/16-AOAS979 |
container_title |
The Annals of Applied Statistics |
container_volume |
10 |
container_issue |
4 |
_version_ |
1766023329447149568 |