Optimizing the Temperature Sensitivity of the Isoprene Emission Model MEGAN in Different Ecosystems Using a Metropolis‐Hastings Markov Chain Monte Carlo Method.
Saved in:
| Title: | Optimizing the Temperature Sensitivity of the Isoprene Emission Model MEGAN in Different Ecosystems Using a Metropolis‐Hastings Markov Chain Monte Carlo Method. |
|---|---|
| Authors: | DiMaria, C. A.1 (AUTHOR) christian.dimaria@mail.utoronto.ca, Jones, D. B. A.1 (AUTHOR), Ferracci, V.2,3 (AUTHOR), Bloom, A. A.4 (AUTHOR), Worden, H. M.5 (AUTHOR), Seco, R.6 (AUTHOR), Vettikkat, L.7 (AUTHOR), Yáñez‐Serrano, A. M.6,8,9 (AUTHOR), Guenther, A. B.10 (AUTHOR), Araujo, A.11 (AUTHOR), Goldstein, A. H.12,13 (AUTHOR), Langford, B.14 (AUTHOR), Cash, J.14 (AUTHOR), Harris, N. R. P.2 (AUTHOR), Brown, L.15 (AUTHOR), Rinnan, R.16 (AUTHOR), Schobesberger, S.7 (AUTHOR), Holst, T.17 (AUTHOR), Mak, J. E.18 (AUTHOR) |
| Source: | Journal of Geophysical Research. Biogeosciences. May2025, Vol. 130 Issue 5, p1-26. 26p. |
| Subject Terms: | *Global warming, *Atmospheric chemistry, *Trace gases, Markov chain Monte Carlo, Data assimilation |
| Abstract: | Isoprene is a reactive hydrocarbon emitted to the atmosphere in large quantities by terrestrial vegetation. Annual total isoprene emissions exceed 300 Tg a−1, but emission rates vary widely among plant species and are sensitive to meteorological and environmental conditions including temperature, sunlight, and soil moisture. Due to its high reactivity, isoprene has a large impact on air quality and climate pollutants such as ozone and aerosols. It is also an important sink for the hydroxyl radical which impacts the lifetime of the important greenhouse gas methane along with many other trace gas species. Modeling the impacts of isoprene emissions on atmospheric chemistry and climate requires accurate isoprene emission estimates. These can be obtained using the empirical Model of Emissions of Gases and Aerosols from Nature (MEGAN), but the parameterization of this model is uncertain due in part to limited field observations. In this study, we use ground‐based measurements of isoprene concentrations and fluxes from 11 field sites to assess the variability of the isoprene emission temperature response across ecosystems. We then use these observations in a Metropolis‐Hastings Markov Chain Monte Carlo (MHMCMC) data assimilation framework to optimize the MEGAN temperature response function. We find that the performance of MEGAN can be significantly improved at several high‐latitude field sites by increasing the modeled sensitivity of isoprene emissions to past temperatures. At some sites, the optimized model was nearly four times more sensitive to temperature than the unoptimized model. This has implications for air quality modeling in a warming climate. Plain Language Summary: Many species of plants emit a reactive gas called isoprene in response to environmental stressors. Isoprene is emitted in large quantities globally and readily reacts with other gases in the atmosphere to produce pollutants like ground‐level ozone and aerosols, which impact both air quality and climate. Emission rates are highly variable among plant species and are very sensitive to temperature, with rates increasing exponentially during hot weather. Current models of isoprene emissions appear to underestimate the temperature sensitivity of emissions for some plant species, espcially in high‐latitude regions like the Arctic. In this study, we used measurements from a diverse range of ecosystems around to the world to quantify the temperature sensitivity of isoprene emissions and compare with the predictions of a widely used isoprene emission model. We found that model performed well in many ecosystems but underestimated the temperature sensitivity in several locations, including temperate forests and high‐latitude tundra where the measured temperature sensitivity was up to four times greater than model predictions. We used the observations to optimize parameters in the model, and found that this greatly improved model predictions during high‐temperature periods. This has important implications for air quality and climate modeling in a warming world. Key Points: Measurements show that the sensitivity of biogenic isoprene emission rates to temperature varies widely across different ecosystemsEmission rates at some high‐latitude sites were up to 4 times more sensitive to temperature than predicted by the widely used MEGAN modelOptimizing the empirical parameters in MEGAN with observations yields improved isoprene emission estimates during high‐temperature periods [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Geophysical Research. Biogeosciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: 8gh DbLabel: GreenFILE An: 185489722 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Optimizing the Temperature Sensitivity of the Isoprene Emission Model MEGAN in Different Ecosystems Using a Metropolis‐Hastings Markov Chain Monte Carlo Method. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22DiMaria%2C+C%2E+A%2E%22">DiMaria, C. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> christian.dimaria@mail.utoronto.ca</i><br /><searchLink fieldCode="AR" term="%22Jones%2C+D%2E+B%2E+A%2E%22">Jones, D. B. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ferracci%2C+V%2E%22">Ferracci, V.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bloom%2C+A%2E+A%2E%22">Bloom, A. A.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Worden%2C+H%2E+M%2E%22">Worden, H. M.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Seco%2C+R%2E%22">Seco, R.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vettikkat%2C+L%2E%22">Vettikkat, L.</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yáñez‐Serrano%2C+A%2E+M%2E%22">Yáñez‐Serrano, A. M.</searchLink><relatesTo>6,8,9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guenther%2C+A%2E+B%2E%22">Guenther, A. B.</searchLink><relatesTo>10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Araujo%2C+A%2E%22">Araujo, A.</searchLink><relatesTo>11</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Goldstein%2C+A%2E+H%2E%22">Goldstein, A. H.</searchLink><relatesTo>12,13</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Langford%2C+B%2E%22">Langford, B.</searchLink><relatesTo>14</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cash%2C+J%2E%22">Cash, J.</searchLink><relatesTo>14</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Harris%2C+N%2E+R%2E+P%2E%22">Harris, N. R. P.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brown%2C+L%2E%22">Brown, L.</searchLink><relatesTo>15</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rinnan%2C+R%2E%22">Rinnan, R.</searchLink><relatesTo>16</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schobesberger%2C+S%2E%22">Schobesberger, S.</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Holst%2C+T%2E%22">Holst, T.</searchLink><relatesTo>17</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mak%2C+J%2E+E%2E%22">Mak, J. E.</searchLink><relatesTo>18</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Biogeosciences%22">Journal of Geophysical Research. Biogeosciences</searchLink>. May2025, Vol. 130 Issue 5, p1-26. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Global+warming%22">Global warming</searchLink><br />*<searchLink fieldCode="DE" term="%22Atmospheric+chemistry%22">Atmospheric chemistry</searchLink><br />*<searchLink fieldCode="DE" term="%22Trace+gases%22">Trace gases</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink><br /><searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Isoprene is a reactive hydrocarbon emitted to the atmosphere in large quantities by terrestrial vegetation. Annual total isoprene emissions exceed 300 Tg a−1, but emission rates vary widely among plant species and are sensitive to meteorological and environmental conditions including temperature, sunlight, and soil moisture. Due to its high reactivity, isoprene has a large impact on air quality and climate pollutants such as ozone and aerosols. It is also an important sink for the hydroxyl radical which impacts the lifetime of the important greenhouse gas methane along with many other trace gas species. Modeling the impacts of isoprene emissions on atmospheric chemistry and climate requires accurate isoprene emission estimates. These can be obtained using the empirical Model of Emissions of Gases and Aerosols from Nature (MEGAN), but the parameterization of this model is uncertain due in part to limited field observations. In this study, we use ground‐based measurements of isoprene concentrations and fluxes from 11 field sites to assess the variability of the isoprene emission temperature response across ecosystems. We then use these observations in a Metropolis‐Hastings Markov Chain Monte Carlo (MHMCMC) data assimilation framework to optimize the MEGAN temperature response function. We find that the performance of MEGAN can be significantly improved at several high‐latitude field sites by increasing the modeled sensitivity of isoprene emissions to past temperatures. At some sites, the optimized model was nearly four times more sensitive to temperature than the unoptimized model. This has implications for air quality modeling in a warming climate. Plain Language Summary: Many species of plants emit a reactive gas called isoprene in response to environmental stressors. Isoprene is emitted in large quantities globally and readily reacts with other gases in the atmosphere to produce pollutants like ground‐level ozone and aerosols, which impact both air quality and climate. Emission rates are highly variable among plant species and are very sensitive to temperature, with rates increasing exponentially during hot weather. Current models of isoprene emissions appear to underestimate the temperature sensitivity of emissions for some plant species, espcially in high‐latitude regions like the Arctic. In this study, we used measurements from a diverse range of ecosystems around to the world to quantify the temperature sensitivity of isoprene emissions and compare with the predictions of a widely used isoprene emission model. We found that model performed well in many ecosystems but underestimated the temperature sensitivity in several locations, including temperate forests and high‐latitude tundra where the measured temperature sensitivity was up to four times greater than model predictions. We used the observations to optimize parameters in the model, and found that this greatly improved model predictions during high‐temperature periods. This has important implications for air quality and climate modeling in a warming world. Key Points: Measurements show that the sensitivity of biogenic isoprene emission rates to temperature varies widely across different ecosystemsEmission rates at some high‐latitude sites were up to 4 times more sensitive to temperature than predicted by the widely used MEGAN modelOptimizing the empirical parameters in MEGAN with observations yields improved isoprene emission estimates during high‐temperature periods [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Geophysical Research. Biogeosciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=185489722 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2025JG008806 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Global warming Type: general – SubjectFull: Atmospheric chemistry Type: general – SubjectFull: Trace gases Type: general – SubjectFull: Markov chain Monte Carlo Type: general – SubjectFull: Data assimilation Type: general Titles: – TitleFull: Optimizing the Temperature Sensitivity of the Isoprene Emission Model MEGAN in Different Ecosystems Using a Metropolis‐Hastings Markov Chain Monte Carlo Method. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: DiMaria, C. A. – PersonEntity: Name: NameFull: Jones, D. B. A. – PersonEntity: Name: NameFull: Ferracci, V. – PersonEntity: Name: NameFull: Bloom, A. A. – PersonEntity: Name: NameFull: Worden, H. M. – PersonEntity: Name: NameFull: Seco, R. – PersonEntity: Name: NameFull: Vettikkat, L. – PersonEntity: Name: NameFull: Yáñez‐Serrano, A. M. – PersonEntity: Name: NameFull: Guenther, A. B. – PersonEntity: Name: NameFull: Araujo, A. – PersonEntity: Name: NameFull: Goldstein, A. H. – PersonEntity: Name: NameFull: Langford, B. – PersonEntity: Name: NameFull: Cash, J. – PersonEntity: Name: NameFull: Harris, N. R. P. – PersonEntity: Name: NameFull: Brown, L. – PersonEntity: Name: NameFull: Rinnan, R. – PersonEntity: Name: NameFull: Schobesberger, S. – PersonEntity: Name: NameFull: Holst, T. – PersonEntity: Name: NameFull: Mak, J. E. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 21698953 Numbering: – Type: volume Value: 130 – Type: issue Value: 5 Titles: – TitleFull: Journal of Geophysical Research. Biogeosciences Type: main |
| ResultId | 1 |
