Simulating Complex CO2 Background Conditions for Indianapolis, IN, with a Simple Ecosystem CO2 Flux Model Article Swipe
S. Murphy
,
K. J. Davis
,
N. L. Miles
,
Zachary Barkley
,
Aijun Deng
,
Jason Horne
,
Scott J. Richardson
,
Sharon Gourdji
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22541/essoar.172969472.24181830/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.22541/essoar.172969472.24181830/v1
We evaluated the ability of a simple ecosystem carbon dioxide (CO2) flux model, the Vegetation Photosynthesis and Respiration Model (VPRM), to capture complex CO2 background conditions observed in Indianapolis, IN. Using simulated biogenic CO2 fluxes and mole fraction tower influence functions, we estimated biogenic CO2 mole fractions at three background towers in the Indianapolis Flux Experiment (INFLUX) network from April 2017 to March 2020. The model captures afternoon average CO2 enhancements, the difference between the background towers and a common reference tower, at a monthly time scale with no significant bias, with monthly mean residuals rarely differing significantly from zero. Although not central to our application, the model could not capture day-to-day variations of observed afternoon average CO2 enhancements. Random errors, when averaged over monthly to yearly time scales, were an order of magnitude smaller than typical urban enhancements. VPRM captured site-to-site differences in the average observed daily cycle of CO2 fluxes at agricultural eddy covariance flux sites well. For 13 of 14 site-months, the modeled peak afternoon NEE was within 30% of that observed despite the observed peaks ranging from about -7 to -70 µmol m-2s-1. VPRM can be effectively used in CO2 inversions to represent complex seasonal variations in background conditions observed in Indianapolis. Indianapolis, a modest-size city surrounded by strong ecosystem fluxes, represents a rigorous test for the VPRM system. Further, this study presents an evaluation system that can be applied to assess the performance of other ecosystem CO2 flux models in cities with similar monitoring networks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.22541/essoar.172969472.24181830/v1
- https://essopenarchive.org/doi/pdf/10.22541/essoar.172969472.24181830/v1
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403682258Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22541/essoar.172969472.24181830/v1Digital Object Identifier
- Title
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Simulating Complex CO2 Background Conditions for Indianapolis, IN, with a Simple Ecosystem CO2 Flux ModelWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-23Full publication date if available
- Authors
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S. Murphy, K. J. Davis, N. L. Miles, Zachary Barkley, Aijun Deng, Jason Horne, Scott J. Richardson, Sharon GourdjiList of authors in order
- Landing page
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https://doi.org/10.22541/essoar.172969472.24181830/v1Publisher landing page
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https://essopenarchive.org/doi/pdf/10.22541/essoar.172969472.24181830/v1Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://essopenarchive.org/doi/pdf/10.22541/essoar.172969472.24181830/v1Direct OA link when available
- Concepts
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Eddy covariance, Flux (metallurgy), Ecosystem, Environmental science, Ecosystem respiration, Atmospheric sciences, Diurnal cycle, Magnitude (astronomy), Climatology, Ecology, Chemistry, Physics, Geology, Biology, Organic chemistry, AstronomyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.id | doi:10.22541/essoar.172969472.24181830/v1 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | https://essopenarchive.org/doi/pdf/10.22541/essoar.172969472.24181830/v1 |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
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| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.22541/essoar.172969472.24181830/v1 |
| publication_date | 2024-10-23 |
| publication_year | 2024 |
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