Submitted as: model evaluation paper 06 Sep 2021

Submitted as: model evaluation paper | 06 Sep 2021

Review status: this preprint is currently under review for the journal GMD.

Global Evaluation of the Ecosystem Demography Model (ED v3.0)

Lei Ma1, George Hurtt1, Lesley Ott2, Ritvik Sahajpal1, Justin Fisk3, Rachel Lamb1, Hao Tang1,7, Steve Flanagan4, Louise Chini1, Abhishek Chatterjee2,5, and Joseph Sullivan6 Lei Ma et al.
  • 1Department of Geographical Sciences, University of Maryland, College Park, MD 20770, USA
  • 2Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
  • 3Regrow Agriculture Inc., Durham, NH 03824, USA
  • 4Wildland Fire Science, Tall Timbers Research Station and Land Conservancy, Tallahassee, FL 32312, USA
  • 5Universities Space Research Association, Columbia, MD 21046, USA
  • 6Department of Plant Science & Landscape Architecture, University of Maryland, College Park, MD 20770, USA
  • 7Department of Geography, National University of Singapore, 117570, Singapore

Abstract. Terrestrial ecosystems play a critical role in the global carbon cycle but have highly uncertain future dynamics. Ecosystem modelling 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 modelling advances have been made to meet this challenge, including 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 likes its predecessors features the formal scaling of physiological processes of  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 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, new 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) spatial distribution, seasonal cycle, and  interannual trends of global Gross Primary Production (GPP); (iii) 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 modelling framework amendable to data from multiple  traditional and new remote sensing sources, including lidar.

Lei Ma et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-292', Anonymous Referee #1, 02 Nov 2021
  • RC2: 'Comment on gmd-2021-292', Anonymous Referee #2, 22 Nov 2021

Lei Ma et al.

Model code and software

Global Evaluation of the Ecosystem Demography Model (ED v3.0) Lei Ma, George Hurtt, Lesley Ott, Ritvik Sahajpal, Justin Fisk, Rachel Lamb, Hao Tang Steve Flanagan, Louise Chini, Abhishek Chatterjee, and Joseph Sullivan

Lei Ma et al.


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Short summary
We present a global version of the Ecosystem Demography (ED) model which can track vegetation 3-D structure and scale up ecological processes from individual vegetation to ecosystem scale. Model evaluation against multiple benchmarking dataset demonstrate the model capabilities to simulate global vegetation dynamics across a range of temporal and spatial scales. With this version, ED has potential to be linked with remote sensing observations to address key scientific questions.