Model and Inventory Perspectives on the Role of Forests in the Global Carbon Cycle: Results from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP)
Daniel J Hayes1, Guangsheng Chen2, Jiafu Mao3, Richard Birdsey4, Yude Pan5, Deborah N Huntzinger6, Christopher Schwalm7, Anna M Michalak8, Yaxing Wei2, Robert B Cook2, Kevin M Schaefer9, Andrew R Jacobson10, Muhammad Altaf Arain11, Philippe Ciais12, Joshua Fisher13, Maoyi Huang14, Suo Huang11, Atul K Jain15, Huimin Lei16, Chaoqun Lu17, Fabienne Maignan18, Nick Parazoo19, Changhui Peng20, Shushi Peng18, Benjamin Poulter21, Daniel M Ricciuto3, Xiaoying Shi1, Hanqin Tian22, Ning Zeng23 and Fang Zhao23, (1)Oak Ridge National Laboratory, Oak Ridge, United States, (2)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (3)Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States, (4)Woods Hole Research Center, Falmouth, MA, United States, (5)USDA Forest Service, Durham, NH, United States, (6)Northern Arizona University, School of Earth Sciences and Environmental Sustainability, Flagstaff, AZ, United States, (7)Northern Arizona University, Flagstaff, AZ, United States, (8)Stanford University, Stanford, United States, (9)National Snow and Ice Data Center, Cooperative Institute for Research in the Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, U.S.A, Boulder, United States, (10)University of Colorado at Boulder, CIRES, Boulder, United States, (11)McMaster University, Hamilton, ON, Canada, (12)LSCE Laboratoire des Sciences du Climat et de l'Environnement, Gif-Sur-Yvette, France, (13)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (14)National Weather Service, National Oceanic and Atmospheric Administration, Office of Science and Technology Integration, Silver Spring, MD, United States, (15)University of Illinois at Urbana Champaign, Urbana, United States, (16)Pacific NW Nat'l Lab-Atmos Sci, Richland, WA, United States, (17)Auburn University at Montgomery, Montgomery, AL, United States, (18)CEA Saclay DSM / LSCE, Gif sur Yvette, France, (19)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (20)University of Quebec at Montreal UQAM, Department of Biology Sciences, Montreal, QC, Canada, (21)Spark Climate Solutions, Greenbelt, United States, (22)Auburn University, Auburn, United States, (23)University of Maryland, College Park, MD, United States
Abstract:
Forests play a significant role in the global climate system in large part through their uptake and storage of atmospheric carbon. Biomass inventories provide valuable constraints on annual to decadal changes in forest carbon stocks through periodic, repeated measurements. However, this information offers limited utility for attribution of the major drivers and process-level mechanisms that determine forest carbon dynamics. A better understanding of these mechanisms is needed to project future forest carbon cycle dynamics, which requires the use of process-based modeling for attribution of and sensitivity to the major drivers.
Here we compare and analyze recent estimates of the carbon budget of the world's forests based on national-level resource inventories against a suite of process-based model simulations run with a common protocol for formal inter-comparison. Our analysis of the model simulation experiments from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) suggests a large uncertainty in carbon cycle responses to these drivers, traceable primarily to the wide range in model structure relative to whether and how they represent the impacts of forest growth, management, disturbance and land use change. Although our study illustrates the benefits in retaining independence among the inventory and modeling estimates for comparison and benchmarking, progress toward a better understanding of the role of forests in the current and future global carbon cycle can also be made by more formally integrating these alternative but complementary scaling approaches.
