@Article{Yang_SciRep_20180719,
 author		= {Cheng-En Yang and Jiafu Mao and Forrest M. Hoffman and Daniel M. Ricciuto and Joshua S. Fu and Chris D. Jones and Martin Thurner},
 title		= {Uncertainty Quantification of Extratropical Forest Biomass in {CMIP5} Models over the {N}orthern {H}emisphere},
 journal	= SciRep,
 volume		= 8,
 number		= 1,
 pages		= 10962,
 doi		= {10.1038/s41598-018-29227-7},
 day		= 19,
 month		= jul,
 year		= 2018,
 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.}
}