First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt
Future Greenland ice sheet (GrIS) melt projections are limited by the lack of explicit melt calculations within most global climate models and the high computational cost of dynamical downscaling with regional climate models (RCMs). Here, we train artificial neural networks (ANNs) to obtain relation...
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ftpubmed:oai:pubmedcentral.nih.gov:9285918 2023-05-15T16:27:26+02:00 First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt Sellevold, Raymond Vizcaino, Miren 2021-08-19 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285918/ http://www.ncbi.nlm.nih.gov/pubmed/35866045 https://doi.org/10.1029/2021GL092449 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285918/ http://www.ncbi.nlm.nih.gov/pubmed/35866045 http://dx.doi.org/10.1029/2021GL092449 © 2021. The Authors. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Geophys Res Lett Research Letter Text 2021 ftpubmed https://doi.org/10.1029/2021GL092449 2022-07-31T01:42:22Z Future Greenland ice sheet (GrIS) melt projections are limited by the lack of explicit melt calculations within most global climate models and the high computational cost of dynamical downscaling with regional climate models (RCMs). Here, we train artificial neural networks (ANNs) to obtain relationships between quantities consistently available from global climate model simulations and annually integrated GrIS surface melt. To this end, we train the ANNs with model output from the Community Earth System Model 2.1 (CESM2), which features an interactive surface melt calculation based on a downscaled surface energy balance. We find that ANNs compare well with an independent CESM2 simulation and RCM simulations forced by a CMIP6 subset. The ANNs estimate a melt increase for 2,081–2,100 ranging from 414 [Formula: see text] 275 Gt [Formula: see text] (SSP1‐2.6) to 1,378 [Formula: see text] 555 Gt [Formula: see text] (SSP5‐8.5) for the full CMIP6 suite. The primary source of uncertainty throughout the 21st century is the spread of climate model sensitivity. Text Greenland Ice Sheet PubMed Central (PMC) Greenland Geophysical Research Letters 48 16 |
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Research Letter |
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Research Letter Sellevold, Raymond Vizcaino, Miren First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt |
topic_facet |
Research Letter |
description |
Future Greenland ice sheet (GrIS) melt projections are limited by the lack of explicit melt calculations within most global climate models and the high computational cost of dynamical downscaling with regional climate models (RCMs). Here, we train artificial neural networks (ANNs) to obtain relationships between quantities consistently available from global climate model simulations and annually integrated GrIS surface melt. To this end, we train the ANNs with model output from the Community Earth System Model 2.1 (CESM2), which features an interactive surface melt calculation based on a downscaled surface energy balance. We find that ANNs compare well with an independent CESM2 simulation and RCM simulations forced by a CMIP6 subset. The ANNs estimate a melt increase for 2,081–2,100 ranging from 414 [Formula: see text] 275 Gt [Formula: see text] (SSP1‐2.6) to 1,378 [Formula: see text] 555 Gt [Formula: see text] (SSP5‐8.5) for the full CMIP6 suite. The primary source of uncertainty throughout the 21st century is the spread of climate model sensitivity. |
format |
Text |
author |
Sellevold, Raymond Vizcaino, Miren |
author_facet |
Sellevold, Raymond Vizcaino, Miren |
author_sort |
Sellevold, Raymond |
title |
First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt |
title_short |
First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt |
title_full |
First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt |
title_fullStr |
First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt |
title_full_unstemmed |
First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt |
title_sort |
first application of artificial neural networks to estimate 21st century greenland ice sheet surface melt |
publisher |
John Wiley and Sons Inc. |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285918/ http://www.ncbi.nlm.nih.gov/pubmed/35866045 https://doi.org/10.1029/2021GL092449 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Geophys Res Lett |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285918/ http://www.ncbi.nlm.nih.gov/pubmed/35866045 http://dx.doi.org/10.1029/2021GL092449 |
op_rights |
© 2021. The Authors. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
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CC-BY |
op_doi |
https://doi.org/10.1029/2021GL092449 |
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Geophysical Research Letters |
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48 |
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16 |
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1766016618960257024 |