Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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)Â global patterns of dominant plant functional types (broadleaf vs. evergreen), (ii)Â the spatial distribution, seasonal cycle, and interannual trends for global gross primary production (GPP), (iii)Â the global interannual variability of net biome production (NBP) and (iv)Â 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.

Highlights

  • Terrestrial ecosystems and the associated carbon cycle are of critical importance in providing ecosystem services traditional and new remote sensing sources, including lidar.and regulating global climate

  • Subsequent developments have improved the representation of vegetation demographic processes within Earth System Models (ESMs), including the Ecosystem Demography model (ED) (Hurtt et al, 1998; Moorcroft et al, 2001), ED2 (Medvigy 2006; Medvigy et al, 2009; Longo et al, 2019a), CLM(ED) (Fisher et al, 2015; Lawrence et al, 2019; Massoud et al, 2019), SEIB-DFVM

  • ED PFTs of early-successional broadleaf trees (EaSBT), middle-successional broadleaf trees (MiSBT) and late-successional broadleaf trees (LaSBT) were merged as broadleaf PFTs, northern and southern pines (NSP) and late-successional conifers (LaSC) were merged as needleleaf PFTs, and C3 shrubs and grasses (C3ShG) and C4 shrubs and grasses (C4ShG) were merged as grass and shrub PFTs

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Summary

Introduction

Terrestrial ecosystems and the associated carbon cycle are of critical importance in providing ecosystem services traditional and new remote sensing sources, including lidar. Quantification, attribution and future projections of the terrestrial carbon sink requires in-depth understanding of underlying ecological processes and their sophisticated responses and feedbacks to climate change, elevated CO 2, and land use and land cover change (LULCC) across multiple biomes and spatial and temporal scales (Canadell et al, 2007; Erb et al., 2013; Keenan and Williams, 2018) This demand for information has driven the emergence and development of dynamic global ecosystem models (DGVMs), which simplify the structure and functioning of global vegetation into several plant functional types and simulate vegetation distribution and associated biogeochemical and hydrological cycles with ecophysiological principles (Prentice et al, 2007; Prentice and Cowling, 2013). Multiple key variables are considered in the evaluation, including benchmark datasets on vegetation distribution, vegetation structure, and carbon and water fluxes

Methods
Additional modifications
Model initialization
Forcing data
Vegetation distribution
Carbon fluxes
Evaluation of PFT distribution
Evaluation of GPP, NBP and ET
Evaluation of canopy height and LAI vertical profile
Discussion and Conclusions
Figures and Tables
Full Text
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