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

Content Provider	Springer Nature Link
Author	Ruddell, Benjamin L. Yu, Rong Kang, Minseok Childers, Daniel L.
Copyright Year	2015
Abstract	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.To model eco-climate macrostate, and its elasticity to seasonal climate forcing (air temperature and precipitation) and ecosystem biophysical state (phenophase).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.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.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.
Starting Page	165
Ending Page	180
Page Count	16
File Format	PDF
ISSN	09212973
Journal	Landscape Ecology
Volume Number	31
Issue Number	1
e-ISSN	15729761
Language	English
Publisher	Springer Netherlands
Publisher Date	2015-08-18
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Dynamical Process Network Phenophase Ecosystem modeling Elasticity Climate Information theory Landscape Ecology Plant Ecology Forestry Management Forestry Ecology Plant Sciences
Content Type	Text
Resource Type	Article
Subject	Geography, Planning and Development Ecology Nature and Landscape Conservation

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