Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4)

Haynes, K. D.; Baker, I. T.; Denning, A. S.; Stöckli, R.; Schaefer, K.; Lokupitiya, E. Y.; Haynes, J. M.

doi:10.1029/2018MS001540

Title: Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4)

Journal Article · 17 December 2019 · Journal of Advances in Modeling Earth Systems

DOI: https://doi.org/10.1029/2018MS001540 · OSTI ID:1579991

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Department of Atmospheric Science Colorado State University Fort Collins CO USA
Climate Division Federal Office of Meteorology and Climatology, MeteoSwiss Zurich Switzerland
National Snow and Ice Data Center University of Colorado Boulder Boulder CO USA
Department of Zoology and Environment Sciences University of Colombo Colombo Sri Lanka
Cooperative Institute for Research in the Atmosphere Colorado State University Fort Collins CO USA

Abstract Grasslands grow in a sequence of seasonal growth stages that respond to both climate and weather, and these relationships can be used to establish a strategy for predicting plant phenology. Current plant states (phenophase) can be represented as one of established growth stages that dictate carbon allocation and leaf photosynthetic capacity. Calculating daily phenophases from climate and environmental relationships allows for sequential growth stages (i.e., well‐defined seasonal cycles with a single growth period) or dynamic growth stages (i.e., multiple growth periods during a growing season). Senescence results from biomass mortality in response to environmental conditions. This approach uses a single mechanistic framework to represent grassland ecology, removing the dependence on satellite‐based vegetation indices and individual site tuning of parameters. Rather than being specified, a variety of properties emerge, from allometric relationships such as root‐shoot ratios, to behavior across moisture gradients, to interannual variability in growing season lengths, carbon stores, and land surface fluxes. Using dynamic phenology stages to link biophysical and biogeochemical processes provides a mechanism to predict self‐consistent land‐atmosphere exchanges of carbon, water, energy, radiation, and momentum, as well as carbon storage in cascading pools of biomass; and describing these processes in a mathematically determinate model makes them clear, testable, and usable for predictions. This paper describes this new phenology method as it is implemented in the Simple Biosphere Model Version 4 (SiB4), and a companion paper evaluates this method at grassland sites worldwide.

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Sponsoring Organization:: USDOE

OSTI ID:: 1579991

Journal Information:: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 12 Vol. 11; ISSN 1942-2466

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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