Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 10, issue 11
Biogeosciences, 10, 6893-6909, 2013
https://doi.org/10.5194/bg-10-6893-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 10, 6893-6909, 2013
https://doi.org/10.5194/bg-10-6893-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Nov 2013

Research article | 04 Nov 2013

Evaluating the agreement between measurements and models of net ecosystem exchange at different times and timescales using wavelet coherence: an example using data from the North American Carbon Program Site-Level Interim Synthesis

P. C. Stoy1, M. C. Dietze2, A. D. Richardson3, R. Vargas4, A. G. Barr5, R. S. Anderson6, M. A. Arain7, I. T. Baker8, T. A. Black9, J. M. Chen10, R. B. Cook11, C. M. Gough12, R. F. Grant13, D. Y. Hollinger14, R. C. Izaurralde15, C. J. Kucharik16, P. Lafleur17, B. E. Law18, S. Liu19, E. Lokupitiya20, Y. Luo21, J. W. Munger22, C. Peng23, B. Poulter24, D. T. Price25, D. M. Ricciuto11, W. J. Riley26, A. K. Sahoo27, K. Schaefer28, C. R. Schwalm29, H. Tian30, H. Verbeeck31, and E. Weng32 P. C. Stoy et al.
  • 1Department of Land Resources and Environmental Sciences, Bozeman, MT 59717, USA
  • 2Department of Earth and Environment, Boston University, Boston, MA 02215, USA
  • 3Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
  • 4Department of Plant and Soil Sciences, Delaware Environmental Institute, University of Delaware, Newark, DE 19717, USA
  • 5Climate Research Division, Atmospheric Science and Technology Directorate, Saskatoon, SK S7N 3H5, Canada
  • 6Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA
  • 7School of Geography and Earth Sciences and McMaster Centre for Climate Change, McMaster University, Hamilton, ON L8S 4K1, Canada
  • 8Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
  • 9Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
  • 10Department of Geography and Program in Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
  • 11Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
  • 12Department of Biology, Virginia Commonwealth University, Richmond, VA 23284, USA
  • 13Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2E3, Canada
  • 14Northern Research Station, USDA Forest Service, Durham, NH 03824, USA
  • 15Pacific Northwest National Laboratory and University of Maryland, College Park, MD 20740, USA
  • 16Department of Agronomy & Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin – Madison, Madison, WI 53706, USA
  • 17Department of Geography, Trent University, Peterborough, ON K9J 7B8, Canada
  • 18Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA
  • 19US Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
  • 20Department of Zoology, POB 1490, University of Colombo, Colombo 03, Sri Lanka
  • 21Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019, USA
  • 22School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA
  • 23Department of Biology Sciences, University of Quebec at Montreal, Montreal, QC H3C 3P8, Canada
  • 24Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
  • 25Northern Forestry Centre, Canadian Forest Service, Edmonton, AB T6H 3S5, Canada
  • 26Climate and Carbon Sciences, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
  • 27Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
  • 28National Snow and Ice Data Center (NSIDC), University of Colorado, Boulder, CO 80309, USA
  • 29School of Earth Science and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ 86001, USA
  • 30School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA
  • 31Laboratory of Plant Ecology, Ghent University, 9000 Ghent, Belgium
  • 32Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA

Abstract. Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales.

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