Abstract
Phenological transitions determine the timing of changes in land surface properties and the seasonality of exchanges of biosphere-atmosphere energy, water, and carbon. Accurate mechanistic modeling of phenological processes is therefore critical to understand and correctly predict terrestrial ecosystem feedbacks with changing atmospheric and climate conditions. However, the phenological components in the land model of the US Department of Energy's (DOE) Energy Exascale Earth System Model (ELM of E3SM) were previously unable to accurately capture the observed phenological responses to environmental conditions in a well-studied boreal peatland forest. In this research, we introduced new seasonal-deciduous phenology schemes into version 1.0 of ELM and evaluated their performance against the PhenoCam observations at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in northern Minnesota from 2015 to 2018. We found that phenology simulated by the revised ELM (i.e., earlier spring onsets and stronger warming responses of spring onsets and autumn senescence) was closer to observations than simulations from the original algorithms for both the deciduous conifer (Larix laricina) and mixed shrub layers. Moreover, the revised ELM generally produced higher carbon and water fluxes (e.g., photosynthesis and evapotranspiration) during the growing season and stronger flux responses to warming than the default ELM. A parameter sensitivity analysis further indicated the significant contribution of phenology parameters to uncertainty in key carbon and water cycle variables, underscoring the importance of precise phenology parameterization. This phenological modeling effort demonstrates the potential to enhance the E3SM representation of land-climate interactions at broader spatiotemporal scales, especially under anticipated elevated CO2 and warming conditions.
Original language | English |
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Article number | 108556 |
Journal | Agricultural and Forest Meteorology |
Volume | 308-309 |
DOIs | |
State | Published - Oct 15 2021 |
Funding
This manuscript has been authored by UT-Battelle LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This work was supported by the Terrestrial Ecosystem Science Scientific Focus Area project funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the Compute and Data Environment for Science (CADES) at Oak Ridge National Laboratory. L. Meng is also supported by NASA FINESST Program (80NSSC19K1356). ADR acknowledges support from the NSF (DEB 1702697). This work was supported by the Terrestrial Ecosystem Science Scientific Focus Area project funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the Compute and Data Environment for Science (CADES) at Oak Ridge National Laboratory. L. Meng is also supported by NASA FINESST Program (80NSSC19K1356). ADR acknowledges support from the NSF (DEB 1702697). Data and materials availability: PhenoCam datasets and environmental measurements pertaining to this study are in the online project archive at http://mnspruce.ornl.gov and for long-term storage in the US Department of Energy's Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE; http://ess-dive.lbl.gov/).
Funders | Funder number |
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National Science Foundation | DEB 1702697 |
U.S. Department of Energy | |
National Aeronautics and Space Administration | 80NSSC19K1356 |
Office of Science | |
Biological and Environmental Research | DE-AC05-00OR22725 |
Oak Ridge National Laboratory | |
UT-Battelle |
Keywords
- Climate change
- E3SM
- ELM
- Modeling
- PhenoCam
- Phenology