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

Landscape Ecology volume 31, pages 165–180 (2016)Cite this article

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.

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Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant Nos. EF-1241960, and BCS-1026865, Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER). Dr. Minseok Kang’s contribution to this research was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2014-3030, and Weather Information Service Engine (WISE) Project, 153-3100-3133-302-350. The findings are those of the authors, and not necessarily the supporting agencies. We thank the Ameriflux and FLUXNET projects, Lawrence Berkeley National Laboratory, and specifically the teams of lead investigators Dr. Dennis Baldocchi, Dr. Peter Blanken, Dr. Gil pBohrer, Dr. Lianghong Gu, Dr. Rich Philips, Dr. Margaret Torn, and Dr. Sonia Wharton for their priceless observational work and for generously publishing their data for use in synthesis studies. Funding for the AmeriFlux Management Project was provided by the U.S. Department of Energy’s Office of Science under Contract No. DE-AC02-05CH11231. The Office of Science from the U.S. Department of Energy funded the MMS site during 2009–2011 through the Terrestrial Ecosystem Science program. The U.S. Department of Energy funds the UMBS site (DE-SC0006708). We thank Dr. Joon Kim, Dr. Michael Toomey, Dr. Koen Hufkens, Dr. Andrew Richardson, and Dr. Praveen Kumar, and the staff of the National Phenology Network and National Ecological Observatory Network for collaborative discussion.

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    1. Fulton Schools of Engineering, Arizona State University, 7171 E. Sonoran Arroyo Mall, Mesa, AZ, 85212, USA

      Benjamin L. Ruddell & Rong Yu

    2. National Center for Agro-Meteorology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-744, South Korea

      Minseok Kang

    3. School of Sustainability, Arizona State University, 800 South Cady Mall, Tempe, AZ, 85287, USA

      Daniel L. Childers

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    Special issue: Macrosystems ecology: Novel methods and new understanding of multi-scale patterns and processes.

    Guest Editors: S. Fei, Q. Guo, and K. Potter.

    Benjamin L. Ruddell and Rong Yu have contributed equally to the work.

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    Ruddell, B.L., Yu, R., Kang, M. et al. Seasonally varied controls of climate and phenophase on terrestrial carbon dynamics: modeling eco-climate system state using Dynamical Process Networks. Landscape Ecol 31, 165–180 (2016). https://doi.org/10.1007/s10980-015-0253-x

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    Keywords

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