Investigating permafrost carbon dynamics in Alaska with artificial intelligence
Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and...
Published in: | Environmental Research Letters |
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crioppubl:10.1088/1748-9326/ad0607 2024-06-02T07:54:16+00:00 Investigating permafrost carbon dynamics in Alaska with artificial intelligence Gay, B A Pastick, N J Züfle, A E Armstrong, A H Miner, K R Qu, J J George Mason University Oak Ridge Associated Universities Emory University University of Maryland U.S. Geological Survey National Aeronautics and Space Administration 2023 http://dx.doi.org/10.1088/1748-9326/ad0607 https://iopscience.iop.org/article/10.1088/1748-9326/ad0607 https://iopscience.iop.org/article/10.1088/1748-9326/ad0607/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research Letters volume 18, issue 12, page 125001 ISSN 1748-9326 journal-article 2023 crioppubl https://doi.org/10.1088/1748-9326/ad0607 2024-05-07T14:05:12Z Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697 µ molCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time. Article in Journal/Newspaper Active layer thickness permafrost Tundra Alaska IOP Publishing Environmental Research Letters |
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Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697 µ molCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time. |
author2 |
George Mason University Oak Ridge Associated Universities Emory University University of Maryland U.S. Geological Survey National Aeronautics and Space Administration |
format |
Article in Journal/Newspaper |
author |
Gay, B A Pastick, N J Züfle, A E Armstrong, A H Miner, K R Qu, J J |
spellingShingle |
Gay, B A Pastick, N J Züfle, A E Armstrong, A H Miner, K R Qu, J J Investigating permafrost carbon dynamics in Alaska with artificial intelligence |
author_facet |
Gay, B A Pastick, N J Züfle, A E Armstrong, A H Miner, K R Qu, J J |
author_sort |
Gay, B A |
title |
Investigating permafrost carbon dynamics in Alaska with artificial intelligence |
title_short |
Investigating permafrost carbon dynamics in Alaska with artificial intelligence |
title_full |
Investigating permafrost carbon dynamics in Alaska with artificial intelligence |
title_fullStr |
Investigating permafrost carbon dynamics in Alaska with artificial intelligence |
title_full_unstemmed |
Investigating permafrost carbon dynamics in Alaska with artificial intelligence |
title_sort |
investigating permafrost carbon dynamics in alaska with artificial intelligence |
publisher |
IOP Publishing |
publishDate |
2023 |
url |
http://dx.doi.org/10.1088/1748-9326/ad0607 https://iopscience.iop.org/article/10.1088/1748-9326/ad0607 https://iopscience.iop.org/article/10.1088/1748-9326/ad0607/pdf |
genre |
Active layer thickness permafrost Tundra Alaska |
genre_facet |
Active layer thickness permafrost Tundra Alaska |
op_source |
Environmental Research Letters volume 18, issue 12, page 125001 ISSN 1748-9326 |
op_rights |
http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1748-9326/ad0607 |
container_title |
Environmental Research Letters |
_version_ |
1800750563586998272 |