Seasonally varied controls of climate and phenophase on terrestrial carbon dynamics: modeling eco-climate system state using Dynamical Process Networks

Benjamin L. Ruddell, Rong Yu, Minseok Kang, Daniel L. Childers

    Research output: Contribution to journalArticlepeer-review

    14 Scopus citations

    Abstract

    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.

    Original languageEnglish (US)
    Pages (from-to)165-180
    Number of pages16
    JournalLandscape Ecology
    Volume31
    Issue number1
    DOIs
    StatePublished - Jan 1 2016

    Keywords

    • Climate
    • Dynamical Process Network
    • Ecosystem modeling
    • Elasticity
    • Information theory
    • Phenophase

    ASJC Scopus subject areas

    • Geography, Planning and Development
    • Ecology
    • Nature and Landscape Conservation

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