A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Many scientific and societal questions that draw on ice sheet modelling could be best addressed by sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea-level projecti...
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ftcopernicus:oai:publications.copernicus.org:egusphere110614 2023-06-11T04:11:55+02:00 A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0) Ultee, Lizz Robel, Alexander A. Castruccio, Stefano 2023-04-28 application/pdf https://doi.org/10.5194/egusphere-2023-635 https://egusphere.copernicus.org/preprints/2023/egusphere-2023-635/ eng eng doi:10.5194/egusphere-2023-635 https://egusphere.copernicus.org/preprints/2023/egusphere-2023-635/ eISSN: Text 2023 ftcopernicus https://doi.org/10.5194/egusphere-2023-635 2023-05-01T16:23:11Z Many scientific and societal questions that draw on ice sheet modelling could be best addressed by sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea-level projections with quantified uncertainty. Further, robust attribution of past and future ice sheet change to specific processes or forcings requires a full understanding of the space of possible ice sheet behaviors. The wide sampling required to address such questions is computationally infeasible with sophisticated numerical climate models at the resolution required to accurately force ice sheet models. Stochastic generation of climate forcing of ice sheets offers a complementary alternative. We construct a stochastic generator of Greenland Ice Sheet surface mass balance in time and space. We find that low-order autoregressive models are sufficient to accurately reproduce the interannual variability in process-model simulations of recent Greenland surface mass balance at the glacier-catchment scale. We account for spatial correlations among glacier catchments using sparse covariance techniques, and we apply an elevation-dependent downscaling to recover gridded surface mass balance fields suitable for forcing an ice sheet model while including feedbacks to ice sheet surface elevation. The efficiency gained in the stochastic method supports large ensemble simulations of ice sheet change in a new stochastic ice sheet model. We provide open source Python workflows to support use of our stochastic approach for a broad range of applications. Text glacier Greenland Ice Sheet Copernicus Publications: E-Journals Greenland |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
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English |
description |
Many scientific and societal questions that draw on ice sheet modelling could be best addressed by sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea-level projections with quantified uncertainty. Further, robust attribution of past and future ice sheet change to specific processes or forcings requires a full understanding of the space of possible ice sheet behaviors. The wide sampling required to address such questions is computationally infeasible with sophisticated numerical climate models at the resolution required to accurately force ice sheet models. Stochastic generation of climate forcing of ice sheets offers a complementary alternative. We construct a stochastic generator of Greenland Ice Sheet surface mass balance in time and space. We find that low-order autoregressive models are sufficient to accurately reproduce the interannual variability in process-model simulations of recent Greenland surface mass balance at the glacier-catchment scale. We account for spatial correlations among glacier catchments using sparse covariance techniques, and we apply an elevation-dependent downscaling to recover gridded surface mass balance fields suitable for forcing an ice sheet model while including feedbacks to ice sheet surface elevation. The efficiency gained in the stochastic method supports large ensemble simulations of ice sheet change in a new stochastic ice sheet model. We provide open source Python workflows to support use of our stochastic approach for a broad range of applications. |
format |
Text |
author |
Ultee, Lizz Robel, Alexander A. Castruccio, Stefano |
spellingShingle |
Ultee, Lizz Robel, Alexander A. Castruccio, Stefano A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0) |
author_facet |
Ultee, Lizz Robel, Alexander A. Castruccio, Stefano |
author_sort |
Ultee, Lizz |
title |
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0) |
title_short |
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0) |
title_full |
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0) |
title_fullStr |
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0) |
title_full_unstemmed |
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0) |
title_sort |
stochastic parameterization of ice sheet surface mass balance for the stochastic ice-sheet and sea-level system model (stissm v1.0) |
publishDate |
2023 |
url |
https://doi.org/10.5194/egusphere-2023-635 https://egusphere.copernicus.org/preprints/2023/egusphere-2023-635/ |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
glacier Greenland Ice Sheet |
genre_facet |
glacier Greenland Ice Sheet |
op_source |
eISSN: |
op_relation |
doi:10.5194/egusphere-2023-635 https://egusphere.copernicus.org/preprints/2023/egusphere-2023-635/ |
op_doi |
https://doi.org/10.5194/egusphere-2023-635 |
_version_ |
1768387362212020224 |