Impacts of including global vegetation demography and dynamic plant competition in the Energy Exascale Earth System Model

Jennifer A. Holm, Climate and Ecosystems Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, Ryan Knox, Earth Sciences Division, Lawrence Berkeley National Lab, William Riley, Earth and Environmental Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, Charles D. Koven, Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, Rosie Fisher, Climate & Global Dynamics, National Center for Atmospheric Research, Boulder and Chonggang Xu, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM

Background/Question/Methods:
Global terrestrial vegetation plays a vital role in sustaining biogeochemical and water cycles, but is being altered by anthropogenic global change drivers including land-use change, altered disturbance regimes, and climate change. Better representation of vegetation demography and mechanistic competition in Earth System Models (ESMs) is at the forefront of modeling more realistic interactions between biologically driven feedbacks with future climate change. To advance this research we have integrated fine-scale demographic processes into the Energy Exascale Earth System Model’s (E3SM) Land Model (ELM) via the Functionally-Assembled Terrestrial Ecosystem Simulator (FATES). In order to represent ecosystem response from competing vegetation dynamics FATES was used to simulate the growth, reproduction, and mortality of individual trees varying in size- and age-structure, multi-layered canopies, and across many plant functional traits. This enabled ELM to model the fine-scale environmental heterogeneity associated with disturbance and recovery processes.
Results/Conclusions:
We compare ELM-FATES global simulations to investigate dynamic plant functional type (PFT) distributions and turnover processes, now with the absence of ‘bioclimatic envelopes’. Initial global simulations successfully include eight interacting and competing PFTs. Global maps of net primary productivity, leaf area index, and total vegetation biomass by ELM-FATES matched patterns and values compared to several big-leaf models and MODIS estimates. Through model testing and evaluation, we found complex sensitivities due to the many interacting, co-varying plant traits. To improve accuracy and efficiency of model parameterization and calibration against global observations, we used machine learning techniques to generate surrogate models which predicted ELM-FATES responses over many combinations of input parameters, enabling better model benchmarking. Next, we assessed the effects when varying the representation of model vegetation features on carbon storage, net ecosystem exchange, and evapotranspiration. This entailed conducting global simulations with and without FATES, and with and without dynamic plant hydraulics to understand the implications of ecosystem demography on terrestrial carbon storage.