Biases in Forest Distributions in CMIP5 Models Lead to Large Uncertainties in Extratropical Forest Biomass
Researchers at the Oak Ridge National Laboratory and the University of Tennessee, with collaborators from the UK Hadley Centre and Stockholm University, used forest biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes to assess the forest distribution, forest fraction, and mass of component carbon pools in Earth system model (ESM) simulations from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5).
Our analysis indicated ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest biomass (biases ranged from −20 Pg C to 135 Pg C). We found that forest total biomass was primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations.
Oversimplified representations of processes that influence forest biomass in ESMs contribute to large uncertainties in projections. We evaluated forest biomass from eight CMIP5 ESMs in comparison with biomass data synthesized from radar remote sensing and ground-based measurements across northern extratropical latitudes. These ESMs exhibited large biases in the distribution of forests, the fraction of forest coverage, and the carbon mass contained in vegetation component pools (i.e., leaves, wood, and roots) that contributed to uncertainty in forest total biomass. Comparisons of forest biomass between pre-industrial and contemporary periods suggested parametric or structural model differences were 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) accurate reproduction of pre-industrial vegetation conditions in order to better constrain projections of forest biomass in the next generation of ESMs for CMIP6.