Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling
Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost...
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ftosti:oai:osti.gov:1889332 2023-07-30T04:00:17+02:00 Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling Frederick, Jennifer M. Conley, Ethan W. Nole, Michael A. Marchitto, Thomas Wagman, Benjamin M. 2023-05-15 application/pdf http://www.osti.gov/servlets/purl/1889332 https://www.osti.gov/biblio/1889332 https://doi.org/10.2172/1889332 unknown http://www.osti.gov/servlets/purl/1889332 https://www.osti.gov/biblio/1889332 https://doi.org/10.2172/1889332 doi:10.2172/1889332 54 ENVIRONMENTAL SCIENCES 2023 ftosti https://doi.org/10.2172/1889332 2023-07-11T10:15:07Z Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national ... Other/Unknown Material arctic methane Arctic permafrost SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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ftosti |
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54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Frederick, Jennifer M. Conley, Ethan W. Nole, Michael A. Marchitto, Thomas Wagman, Benjamin M. Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national ... |
author |
Frederick, Jennifer M. Conley, Ethan W. Nole, Michael A. Marchitto, Thomas Wagman, Benjamin M. |
author_facet |
Frederick, Jennifer M. Conley, Ethan W. Nole, Michael A. Marchitto, Thomas Wagman, Benjamin M. |
author_sort |
Frederick, Jennifer M. |
title |
Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling |
title_short |
Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling |
title_full |
Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling |
title_fullStr |
Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling |
title_full_unstemmed |
Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling |
title_sort |
quantifying the known unknown: including marine sources of greenhouse gases in climate modeling |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1889332 https://www.osti.gov/biblio/1889332 https://doi.org/10.2172/1889332 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
arctic methane Arctic permafrost |
genre_facet |
arctic methane Arctic permafrost |
op_relation |
http://www.osti.gov/servlets/purl/1889332 https://www.osti.gov/biblio/1889332 https://doi.org/10.2172/1889332 doi:10.2172/1889332 |
op_doi |
https://doi.org/10.2172/1889332 |
_version_ |
1772810798093041664 |