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Title: Toward “optimal” integration of terrestrial biosphere models

Multimodel ensembles (MME) are commonplace in Earth system modeling. Here we perform MME integration using a 10-member ensemble of terrestrial biosphere models (TBMs) from the Multiscale synthesis and Terrestrial Model Intercomparison Project (MsTMIP). We contrast optimal (skill based for present-day carbon cycling) versus naive (one model-one vote) integration. MsTMIP optimal and naive mean land sink strength estimates (-1.16 versus -1.15 Pg C per annum respectively) are statistically indistinguishable. This holds also for grid cell values and extends to gross uptake, biomass, and net ecosystem productivity. TBM skill is similarly indistinguishable. The added complexity of skill-based integration does not materially change MME values. This suggests that carbon metabolism has predictability limits and/or that all models and references are misspecified. Finally, resolving this issue requires addressing specific uncertainty types (initial conditions, structure, and references) and a change in model development paradigms currently dominant in the TBM community.
 [1] ;  [2] ;  [3] ;  [4] ;  [3] ;  [5] ;  [6] ;  [7] ;  [6] ;  [8] ;  [9] ;  [7] ;  [6] ;  [10] ;  [3] ;  [11] ;  [6] ;  [5] ;  [12] ;  [6] more »;  [13] ;  [6] ;  [5] ;  [11] ;  [14] ;  [6] ;  [11] ;  [15] « less
  1. Northern Arizona Univ., Flagstaff, AZ (United States). Center for Ecosystem Science and Society; Northern Arizona Univ., Flagstaff, AZ (United States). School of Earth Sciences and Environmental Sustainability
  2. Northern Arizona Univ., Flagstaff, AZ (United States). School of Earth Sciences and Environmental Sustainability; Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Civil Engineering, Construction Management, and Environmental Engineering
  3. California Inst. of Technology (CalTech), La Canada Flintridge, CA (United States). Jet Propulsion Lab.
  4. Carnegie Inst. of Science, Stanford, CA (United States). Dept. of Global Ecology
  5. Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette (France)
  6. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division
  7. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Atmospheric Sciences
  8. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Atmospheric Science and Global Change Div. (ASGC)
  9. National Inst. for Environmental Studies, Tsukuba (Japan)
  10. Tsinghua Univ., Beijing (China). Dept. of Hydraulic Engineering
  11. Auburn Univ., AL (United States). International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences
  12. Montana State Univ. Bozeman MT (United States). Dept. of Ecology
  13. National Snow and Ice Data Center, Boulder, CO (United States)
  14. NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States)
  15. Univ. of Maryland, College Park, MD (United States). Dept. of Atmospheric and Oceanic Science
Publication Date:
OSTI Identifier:
Grant/Contract Number:
AC05-00OR22725; AC02-05CH11231; SC0006706; ACI-1238993; OCI-0725070; NNX12AP74G; NSF-AGS-12-43071; NSF-EFRI- 083598; AC05-76RLO1830; NNX10AG01A; NNX11AO08A; NNH10AN681
Accepted Manuscript
Journal Name:
Geophysical Research Letters
Additional Journal Information:
Journal Volume: 42; Journal Issue: 11; Journal ID: ISSN 0094-8276
American Geophysical Union
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Aeronautic and Space Administration (NASA); National Science Foundation (NSF); USDA; LSCE
Country of Publication:
United States
54 ENVIRONMENTAL SCIENCES; modeling; carbon cycle; model integration