Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models?
Understanding methane emissions from the Arctic, a fast-warming carbon reservoir, is important for projecting future changes in the global methane cycle. Here we optimized methane emissions from north of 60° N (pan-Arctic) regions using a nested-grid high-resolution inverse model that assimilates bo...
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ftunicolboulder:oai:scholar.colorado.edu:mcen_facpapers-1012 2023-05-15T14:31:46+02:00 Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? Tan, Zeli Zhuang, Qianlai Henze, Daven K. Frankenberg, Christian Dlugokencky, Ed Sweeney, Colm Turner, Alexander J. Sasakawa, Motoki Machida, Toshinobu 2016-10-12T07:00:00Z application/pdf https://scholar.colorado.edu/mcen_facpapers/9 https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1012&context=mcen_facpapers unknown CU Scholar https://scholar.colorado.edu/mcen_facpapers/9 https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1012&context=mcen_facpapers Mechanical Engineering Faculty Contributions text 2016 ftunicolboulder 2018-10-07T09:09:06Z Understanding methane emissions from the Arctic, a fast-warming carbon reservoir, is important for projecting future changes in the global methane cycle. Here we optimized methane emissions from north of 60° N (pan-Arctic) regions using a nested-grid high-resolution inverse model that assimilates both high-precision surface measurements and column-average SCanning Imaging Absorption spectroMeter for Atmospheric CHartogrphY (SCIAMACHY) satellite retrievals of methane mole fraction. For the first time, methane emissions from lakes were integrated into an atmospheric transport and inversion estimate, together with prior wetland emissions estimated with six biogeochemical models. In our estimates, in 2005, global methane emissions were in the range of 496.4–511.5 Tg yr−1, and pan-Arctic methane emissions were in the range of 11.9–28.5 Tg yr−1. Methane emissions from pan-Arctic wetlands and lakes were 5.5–14.2 and 2.4–14.2 Tg yr−1, respectively. Methane emissions from Siberian wetlands and lakes are the largest and also have the largest uncertainty. Our results indicate that the uncertainty introduced by different wetland models could be much larger than the uncertainty of each inversion. We also show that assimilating satellite retrievals can reduce the uncertainty of the nested-grid inversions. The significance of lake emissions cannot be identified across the pan-Arctic by high-resolution inversions, but it is possible to identify high lake emissions from some specific regions. In contrast to global inversions, high-resolution nested-grid inversions perform better in estimating near-surface methane concentrations. Text arctic methane Arctic University of Colorado, Boulder: CU Scholar Arctic High Lake ENVELOPE(-110.849,-110.849,67.386,67.386) |
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University of Colorado, Boulder: CU Scholar |
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ftunicolboulder |
language |
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description |
Understanding methane emissions from the Arctic, a fast-warming carbon reservoir, is important for projecting future changes in the global methane cycle. Here we optimized methane emissions from north of 60° N (pan-Arctic) regions using a nested-grid high-resolution inverse model that assimilates both high-precision surface measurements and column-average SCanning Imaging Absorption spectroMeter for Atmospheric CHartogrphY (SCIAMACHY) satellite retrievals of methane mole fraction. For the first time, methane emissions from lakes were integrated into an atmospheric transport and inversion estimate, together with prior wetland emissions estimated with six biogeochemical models. In our estimates, in 2005, global methane emissions were in the range of 496.4–511.5 Tg yr−1, and pan-Arctic methane emissions were in the range of 11.9–28.5 Tg yr−1. Methane emissions from pan-Arctic wetlands and lakes were 5.5–14.2 and 2.4–14.2 Tg yr−1, respectively. Methane emissions from Siberian wetlands and lakes are the largest and also have the largest uncertainty. Our results indicate that the uncertainty introduced by different wetland models could be much larger than the uncertainty of each inversion. We also show that assimilating satellite retrievals can reduce the uncertainty of the nested-grid inversions. The significance of lake emissions cannot be identified across the pan-Arctic by high-resolution inversions, but it is possible to identify high lake emissions from some specific regions. In contrast to global inversions, high-resolution nested-grid inversions perform better in estimating near-surface methane concentrations. |
format |
Text |
author |
Tan, Zeli Zhuang, Qianlai Henze, Daven K. Frankenberg, Christian Dlugokencky, Ed Sweeney, Colm Turner, Alexander J. Sasakawa, Motoki Machida, Toshinobu |
spellingShingle |
Tan, Zeli Zhuang, Qianlai Henze, Daven K. Frankenberg, Christian Dlugokencky, Ed Sweeney, Colm Turner, Alexander J. Sasakawa, Motoki Machida, Toshinobu Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? |
author_facet |
Tan, Zeli Zhuang, Qianlai Henze, Daven K. Frankenberg, Christian Dlugokencky, Ed Sweeney, Colm Turner, Alexander J. Sasakawa, Motoki Machida, Toshinobu |
author_sort |
Tan, Zeli |
title |
Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? |
title_short |
Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? |
title_full |
Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? |
title_fullStr |
Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? |
title_full_unstemmed |
Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? |
title_sort |
inverse modeling of pan-arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? |
publisher |
CU Scholar |
publishDate |
2016 |
url |
https://scholar.colorado.edu/mcen_facpapers/9 https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1012&context=mcen_facpapers |
long_lat |
ENVELOPE(-110.849,-110.849,67.386,67.386) |
geographic |
Arctic High Lake |
geographic_facet |
Arctic High Lake |
genre |
arctic methane Arctic |
genre_facet |
arctic methane Arctic |
op_source |
Mechanical Engineering Faculty Contributions |
op_relation |
https://scholar.colorado.edu/mcen_facpapers/9 https://scholar.colorado.edu/cgi/viewcontent.cgi?article=1012&context=mcen_facpapers |
_version_ |
1766305304416354304 |