Geoscientific Model Development (Mar 2022)
Global evaluation of the Ecosystem Demography model (ED v3.0)
Abstract
Terrestrial ecosystems play a critical role in the global carbon cycle but have highly uncertain future dynamics. Ecosystem modeling that includes the scaling up of underlying mechanistic ecological processes has the potential to improve the accuracy of future projections while retaining key process-level detail. Over the past two decades, multiple modeling advances have been made to meet this challenge, such as the Ecosystem Demography (ED) model and its derivatives, including ED2 and FATES. Here, we present the global evaluation of the Ecosystem Demography model (ED v3.0), which, like its predecessors, features the formal scaling of physiological processes for individual-based vegetation dynamics to ecosystem scales, together with integrated submodules of soil biogeochemistry and soil hydrology, while retaining explicit tracking of vegetation 3-D structure. This new model version builds on previous versions and provides the first global calibration and evaluation, global tracking of the effects of climate and land-use change on vegetation 3-D structure, spin-up process and input datasets, as well as numerous other advances. Model evaluation was performed with respect to a set of important benchmarking datasets, and model estimates were within observational constraints for multiple key variables, including (i) global patterns of dominant plant functional types (broadleaf vs. evergreen), (ii) the spatial distribution, seasonal cycle, and interannual trends for global gross primary production (GPP), (iii) the global interannual variability of net biome production (NBP) and (iv) global patterns of vertical structure, including leaf area and canopy height. With this global model version, it is now possible to simulate vegetation dynamics from local to global scales and from seconds to centuries with a consistent mechanistic modeling framework amendable to data from multiple traditional and new remote sensing sources, including lidar.