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A Model-Data Integration Framework (MoDIF) for ABoVE Phase I research: simulation, scaling and benchmarking for key indicators of Arctic-boreal ecosystem dynamics

Fisher, Joshua: UCLA (Project Lead)
Hayes, Daniel: University of Maine (Co-Investigator)
Huntzinger, Deborah: Northern Arizona University (Co-Investigator)
Schwalm, Christopher: Woodwell Climate Research Center (Co-Investigator)
Arain, M. Altaf: McMaster University (Collaborator)
Bachelet, Dominique: Oregon State University (Collaborator)
Baker, Ian: Colorado State University (Collaborator)
Genet, Helene: Institute of Arctic Biology (Collaborator)
Harper, Anna: University of Exeter (Collaborator)
Haynes, Katherine: Colorado State University (Collaborator)
Ito, Akihiko: National Institute for Environmental Studies, Japan (Collaborator)
Jain, Atul: University of Illinois (Collaborator)
Koven, Charles: Lawrence Berkeley National Laboratory (Collaborator)
Luo, Yiqi: Northern Arizona University (Collaborator)
Miller, Paul: Lund University, Sweden (Collaborator)
Pavlick, Ryan: Jet Propulsion Laboratory (Collaborator)
Poulter, Benjamin (Ben): NASA GSFC (Collaborator)
Schaefer, Kevin: National Snow and Ice Data Center (Collaborator)
Scheiter, Simon: Senckenberg Gesellschaft fuer Naturforschung (Collaborator)
Tian, Hanqin: Auburn University (Collaborator)
Wang, Weile: CSUMB&NASA/ARC (Collaborator)
Xue, Yongkang: UCLA (Collaborator)
Yang, Zong-Liang: The University of Texas at Austin (Collaborator)
Zeng, Ning: University of Maryland (Collaborator)
Gagne-Landmann, Anna: Université Laval (Participant)
Stofferahn, Eric: Jet Propulsion Laboratory / Caltech (Post-Doc)
Gerlich, Gina: NASA JPL (Student-Graduate)
Hantson, Wouter: University of Maine (Student-Graduate)

Project Funding: 2015 - 2022

NRA: 2014 NASA: Terrestrial Ecology   

Funded by NASA

Abstract:
The Arctic-Boreal region (ABR) contributes among the largest uncertainties to terrestrial biosphere model (TBM) simulations for the entire planet. TBMs are orders of magnitude different from one another in ABR soil carbon, exhibit nearly every possible spatial configuration of net carbon fluxes across models, and, in general, are poorly parameterized with respect to cold/frozen environment sensitivities. This large spread among models defines the large uncertainty for the region, and there are few data with which to benchmark models to guide improvements and, ultimately, reductions in uncertainty. This presents a formidable challenge towards addressing the Overarching Science Question for ABoVE: how vulnerable or resilient are ecosystems and society to environmental change in the Arctic and boreal region of western North America? Addressing the Ecosystem Dynamics Objectives, the focus of ABoVE Phase 1 research, requires an interdisciplinary but coordinated suite of modeling and scaling capabilities for studying the key indicators, namely: 1) disturbance, 2) flora / fauna and related ecosystem structure and function, 3) carbon pools and biogeochemistry, 4) permafrost properties, and 5) hydrology. To reduce this uncertainty, build integrated modeling capacity, and increase confidence in addressing the ABoVE Tier 2 Science Objectives, numerous coordinated activities must come together in Phase 1 research to evaluate and improve model performance in representing, simulating and scaling the key indicators of Arctic-boreal ecosystem dynamics. Given that IPCC-type uncertainties are commonly defined as multi-model variance, it is crucial that multiple TBMs both inform data collection and are improved from the data collected. Here, we propose to coalesce a suite of modeling teams to provide a meta-synthesis of TBM requirements, parameter and structural uncertainties, and the associated data type, range, and co-variables necessary to improve ABR-specific simulations with respect to the Tier 2 science questions. The goals of this “team-of-teams” will be to: 1) exercise and inter compare a suite of TBMs to identify critical data gaps for informing and prioritizing ABoVE remote sensing and field data collection, 2) develop and employ a flexible but consistent data integration, simulation and evaluation framework for ABoVE modeling research, and 3) build the foundational capacity of investigators, data sets, modeling tools and benchmarking targets for addressing the Ecosystem Services Objectives and other scaling research needed for Phase 2 research activities.

Publications:

Fisher J B, Hayes D J, Schwalm C R, Huntzinger D N, Stofferahn E, Schaefer K, Luo Y, Wullschleger S D, Goetz S, Miller C E, Griffith P, Chadburn S, Chatterjee A, Ciais P, Douglas T A, Genet H, Ito A, Neigh C S R, Poulter B, Rogers B M, Sonnentag O, Tian H, Wang W, Xue Y, Yang Z, Zeng N, Zhang Z. 2018 Missing pieces to modeling the Arctic-Boreal puzzle. Environmental Research Letters. 13(2), 020202. DOI: 10.1088/1748-9326/aa9d9a

Fisher J B, Sikka M, Oechel W C, Huntzinger D N, Melton J R, Koven C D, Ahlstrom A, Arain M A, Baker I, Chen J M, Ciais P, Davidson C, Dietze M, El-Masri B, Hayes D, Huntingford C, Jain A K, Levy P E, Lomas M R, Poulter B, Price D, Sahoo A K, Schaefer K, Tian H, Tomelleri E, Verbeeck H, Viovy N, Wania R, Zeng N, Miller C E. 2014 Carbon cycle uncertainty in the Alaskan Arctic. Biogeosciences. 11(15), 4271-4288. DOI: 10.5194/bg-11-4271-2014

Huntzinger D N, Schaefer K, Schwalm C, Fisher J B, Hayes D, Stofferahn E, Carey J, Michalak A M, Wei Y, Jain A K, Kolus H, Mao J, Poulter B, Shi X, Tang J, Tian H. 2020 Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems. Environmental Research Letters. 15(2), 025005. DOI: 10.1088/1748-9326/ab6784

Schwalm, C. R., Huntinzger, D. N., Michalak, A. M., Schaefer, K., Fisher, J. B., Fang, Y., Wei, Y. 2020. Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century. Scientific Reports. 10(1). DOI: 10.1038/s41598-020-66103-9

Stofferahn E, Fisher J B, Hayes D J, Schwalm C R, Huntzinger D N, Hantson W, Poulter B, Zhang Z. 2019 The Arctic-Boreal vulnerability experiment model benchmarking system. Environmental Research Letters. 14(5), 055002. DOI: 10.1088/1748-9326/ab10fa


More details may be found in the following project profile(s):