Location: Southwest Watershed Research CenterTitle: Optimizing carbon cycle parameters drastically improves terrestrial biosphere model underestimates of dryland mean net CO2 flux interannual variability
|MAHMUD, K. - Indiana University|
|Scott, Russell - Russ|
|LITVAK, M.E. - University Of New Mexico|
|KOLB, T. - Northern Arizona University|
|MEYERS, T.P. - National Oceanic & Atmospheric Administration (NOAA)|
|KRISHNAN, P. - National Oceanic & Atmospheric Administration (NOAA)|
|BASTRIKOV, V. - Université Paris-Saclay|
|MACBEAN, N. - Indiana University|
Submitted to: Journal of Geophysical Research-Biogeosciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/29/2021
Publication Date: 10/18/2021
Citation: Mahmud, K., Scott, R.L., Biederman, J.A., Litvak, M., Kolb, T., Meyers, T., Krishnan, P., Bastrikov, V., Macbean, N. 2021. Optimizing carbon cycle parameters drastically improves terrestrial biosphere model underestimates of dryland mean net CO2 flux interannual variability. Journal of Geophysical Research-Biogeosciences. 126:10, Article e2021JG006400. https://doi.org/10.1029/2021jg006400.
Interpretive Summary: Dryland agroecosystems occupy 40% of Earth’s land surface and provide critical ecosystem services to more than one billion people. Computer models of Earth’s land surface are among the most powerful tools for predicting agroecosystem functions such as water and food production and storage of atmospheric carbon, yet such models often work poorly in drylands. Here we used 89 site-years of agroecosystem measurements from 12 dryland measurement stations across the US Southwest to train a land surface model to represent cycling of carbon and water. The resulting model had drastically improved ability to represent average carbon and water cycling as well as the naturally high interannual variability induced mainly by the sporadic nature of rainfall in drylands. These results imply that a commonly-used land surface model represents most of the key processed involved but must be trained with dryland measurements.
Technical Abstract: Drylands occupy ~40% of the land surface and are thought to dominate global carbon (C) cycle inter-annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO2 fluxes. Here, we address this gap using a Bayesian data assimilation system and 89 site-years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long-term carbon-climate feedbacks.