Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4). (18th December 2019)

• Record Type: Journal Article
• Authors: Haynes, K. D.; Baker, I. T.; Denning, A. S.; Stöckli, R.; Schaefer, K.; Lokupitiya, E. Y.; Haynes, J. M.
• Is Part Of: Journal of advances in modeling earth systems. Volume 11:Number 12(2019)
• Page Start: 4423
• Page End: 4439
• Publication Date: 2019-12-18
• Subjects: Prognostic Phenology -- Terrestrial Carbon Cycle -- Land Surface Model; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2018MS001540
• Languages: English
• ISSNs: 1942-2466

Abstract

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. Plain Language Summary: Every year, grasslands grow in a sequence of seasonal growth stages that have evolved in nature. For grasslands, climate and weather patterns can be used to establish a strategy for predicting these stages. Using five different possible stages, the current stage can be used to determine which part of the plant grows the fastest. This approach uses a single set of equations to represent grassland ecology, removing the dependence on satellite‐based vegetation information. Rather than being specified, a variety of properties emerge, such as different growth patterns in regions that receive more rainfall and different growth rates per year. This approach links plant processes and provides a way to model plant growth and its interaction with the atmosphere. This paper describes this new method as it is implemented in the Simple Biosphere Model Version 4 and provides an example at a grassland site. Key Points: Grassland phenology can be predicted using a strategy based on growth stages that respond to climate and weather Dynamic prognostic phenology links biophysical and biogeochemical processes to predict the terrestrial carbon cycle