Context: Prediction of climate impacts on terrestrial ecosystems is limited by the complexity of the couplings between biosphere and atmosphere—what we define here as eco-climate. Critical transitions in ecosystem function and structure must be conceptualized, modeled, and ultimately predicted. Eco-climate system macrostate is a pattern of physical couplings between subsystems; each macrostate must be modeled differently because different physical processes are important. Critical transitions are less likely where the elasticity of macrostate is weak or absent. This motivates a fundamentally new complex systems approach. Objective: To model eco-climate macrostate, and its elasticity to seasonal climate forcing (air temperature and precipitation) and ecosystem biophysical state (phenophase). Methods: This Dynamical Process Network approach uses information flow to model an eco-climate system structure using timeseries observations from seven eddy-covariance tower sites in the United States. An aggregate power-law model estimates the elasticity of each location’s macrostate to seasonal climate and phenophase. Results: Macrostate varies by both season and ecosystem type. Evergreen forests are highly elastic to air temperature and are more likely than agricultural or deciduous systems to experience state changes as the climate warms. Precipitation and phenophase elasticity is stronger in some agricultural, grassland, and deciduous forest systems. Conclusions: Different empirical model structures are needed based on season and location, to simulate ecosystem carbon dynamics and critical state transitions. Phenophase directly controls macrostate in some ecosystems. Flux data co-located with in situ ecological monitoring are essential for eco-climate model development and prediction using complex systems approaches.
- Dynamical Process Network
- Ecosystem modeling
- Information theory
ASJC Scopus subject areas
- Geography, Planning and Development
- Nature and Landscape Conservation