A Modified Vegetation Photosynthesis and Respiration Model (VPRM) for the Eastern USA and Canada, Evaluated With Comparison to Atmospheric Observations and Other Biospheric Models. Issue 1 (24th January 2022)

• Record Type: Journal Article
• Authors: Gourdji, Sharon M.; Karion, Anna; Lopez‐Coto, Israel; Ghosh, Subhomoy; Mueller, Kimberly L.; Zhou, Yu; Williams, Christopher A.; Baker, Ian T.; Haynes, Katharine D.; Whetstone, James R.
• Is Part Of: Journal of geophysical research. Volume 127:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-24
• Subjects: terrestrial biosphere models -- CO2 flux -- atmospheric CO2 data -- regional‐scale fluxes -- VPRM -- terrestrial carbon cycle; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2021JG006290
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Atmospheric CO2 measurements from a dense surface network can help to evaluate terrestrial biosphere model (TBM) simulations of Net Ecosystem Exchange (NEE) with two key benefits. First, gridded CO2 flux estimates can be evaluated over regional scales, not possible using flux tower observations at discrete locations for model evaluation. Second, TBM ability to explain atmospheric CO2 fluctuations due to the biosphere can be directly tested, an important objective for anthropogenic emissions monitoring using atmospheric observations. Here, we customize the Vegetation Photosynthesis and Respiration Model (VPRM) for an eastern North American domain with strong biological activity upwind of urban areas. Parameters are optimized using flux tower observations from a historical database with sites in (and near) the domain. In addition, the respiration model (originally a linear function of temperature) is modified to account for impacts of changing foliage, non‐linear temperature, and water stress. Flux estimates from VPRM, the Carnegie‐Ames‐Stanford Approach (CASA) model and the Simple Biosphere Model v4 (SiB4), are convolved with footprints from atmospheric transport models for evaluation with CO2 observations at 21 towers in the domain, with roughly half of the towers used here for the first time. Results show that the new respiration model in VPRM helps to correct a growing season sink bias in the atmosphere associated with underestimated summertime respiration using the original model with annual parameters. The new VPRM also better explains fine‐scale atmospheric CO2 variability compared to other TBMs, due to higher resolution diagnostic phenology, the new respiration model, domain‐specific parameters, and high‐quality input data sets. Plain Language Summary: Photosynthesis and respiration from vegetation and soils contribute to large CO2 fluctuations in the atmosphere, which mix with CO2 sources from fossil fuel combustion. Terrestrial biosphere models simulate biological carbon exchange with the atmosphere, which can then be evaluated with atmospheric CO2 measurements. In this study, we customize a high resolution, data‐driven biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM), for eastern North America, a region with strong biological activity from crops and forests as well as large emission sources. The model equation describing sources to the atmosphere from respiration (i.e., "breathing" from plants and decaying organic matter) is modified to account for increases in foliage and crop biomass during the growing season. Comparisons with other process‐based biospheric models and atmospheric CO2 observations show that the new VPRM model is relatively unbiased and better explains small‐scale biospheric CO2 fluctuations in the atmosphere in this domain compared to other more complex models. Key Points: VPRM is customized for eastern North America with a new respiration model including EVI, non‐linear temperature, and water stress factors Continuous atmospheric CO2 observations from 21 towers are used to evaluate gridded CO2 flux estimates The new VPRM is relatively unbiased and better explains fine‐scale atmospheric CO2 variability in this domain compared to other models