Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere

Cheng En Yang, Jiafu Mao, Forrest M. Hoffman, Daniel M. Ricciuto, Joshua S. Fu, Chris D. Jones, Martin Thurner

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from −20 Pg C to 135 Pg C). Forest total biomass is primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations. Relatively small differences in forest biomass between the pre-industrial period and the contemporary period indicate uncertainties in forest biomass were introduced in the pre-industrial model equilibration (spin-up), suggesting parametric or structural model differences are a larger source of uncertainty than differences in transient responses. Our findings emphasize the importance of improved (1) models of carbon allocation to biomass compartments, (2) distribution of vegetation types in models, and (3) reproduction of pre-industrial vegetation conditions, in order to reduce the uncertainty in forest biomass simulated by ESMs.

Original languageEnglish
Article number10962
JournalScientific Reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018

Bibliographical note

Publisher Copyright:
© 2018, The Author(s).

Funding

This work was supported through the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA), which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory (ORNL), which is managed by UT-Battelle, LLC for the U.S. Department of Energy under Contract No. DE-AC05– 00OR22725 and was performed at ORNL. C.D.J was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101) and M.T. was supported by the Vetenskapsrådet grant 621-2014-4266 of the Swedish Research Council. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Supplementary Table S1) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We acknowledge the kind assistance of Prof. Shilong Piao from the Department of Ecology at Peking University for sharing the plotting scripts for global spatial correlation maps.

FundersFunder number
Joint UK BEIS/Defra Met Office Hadley Centre Climate ProgrammeGA01101
Office of Biological and Environmental Research
U.S. Department of Energy Office of Science
U.S. Department of EnergyDE-AC05– 00OR22725
Biological and Environmental Research
Oak Ridge National Laboratory
Vetenskapsrådet621-2014-4266

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