Articles | Volume 19, issue 1
https://doi.org/10.5194/bg-19-29-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-29-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California 91125, USA
Christian Frankenberg
CORRESPONDING AUTHOR
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California 91125, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA
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Stomata are pores on leaves that regulate gas exchange between plants and the atmosphere. Existing land models unrealistically assume stomata can jump between steady states when the environment changes. We implemented dynamic modeling to predict gradual stomatal responses at different scales. Results suggested that considering this effect on plant behavior patterns in diurnal cycles was important. Our framework also simplified simulations and can contribute to further efficiency improvements.
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The AVIRIS-NG hyperspectral imager has been used successfully to identify and quantify anthropogenic methane sources utilizing different retrieval and inversion methods. Here, we examine the adaption and application of the WFM-DOAS algorithm to AVIRIS-NG measurements to retrieve local methane column enhancements, compare the results with other retrievals, and quantify the uncertainties resulting from the retrieval method. Additionally, we estimate emissions from five detected methane plumes.
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Short summary
Modeling vegetation canopy is important in predicting whether the land remains a carbon sink to mitigate climate change in the near future. Vegetation canopy model complexity, however, impacts the model-predicted carbon and water fluxes as well as canopy fluorescence, even if the same suite of model inputs is used. Given the biases caused by canopy model complexity, we recommend not misusing parameters inverted using different models or assumptions.
Modeling vegetation canopy is important in predicting whether the land remains a carbon sink to...
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