Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter

Authors

  • Edward B. Rastetter,

    Corresponding author
    1. The Ecosystems Center, Marine Biological Laboratory, 7 MBL Street, Woods Hole, Massachusetts 02543-1015 USA
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  • Mathew Williams,

    1. School of GeoSciences, University of Edinburgh, Crew Building, The King's Buildings, West Mains Road, Edinburgh EH9 3JN Scotland, United Kingdom
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  • Kevin L. Griffin,

    1. Lamont-Doherty Earth Observatory, Columbia University, 61 Route 9W, Palisades, New York 10964-8000 USA
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  • Bonnie L. Kwiatkowski,

    1. The Ecosystems Center, Marine Biological Laboratory, 7 MBL Street, Woods Hole, Massachusetts 02543-1015 USA
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  • Gabrielle Tomasky,

    1. The Ecosystems Center, Marine Biological Laboratory, 7 MBL Street, Woods Hole, Massachusetts 02543-1015 USA
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  • Mark J. Potosnak,

    1. Environmental Science Program, DePaul University, 203F McGowan South, 1110 W. Belden Avenue, Chicago, Illinois 60614-2245 USA
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  • Paul C. Stoy,

    1. School of GeoSciences, University of Edinburgh, Crew Building, The King's Buildings, West Mains Road, Edinburgh EH9 3JN Scotland, United Kingdom
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    •  Present address: Department of Land Resources and Environmental Sciences, Montana State University, 334 Leon Johnson Hall, Bozeman, Montana 59717 USA.

  • Gaius R. Shaver,

    1. The Ecosystems Center, Marine Biological Laboratory, 7 MBL Street, Woods Hole, Massachusetts 02543-1015 USA
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  • Marc Stieglitz,

    1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Room 101, Daniel Environmental Engineering Laboratory, 200 Bobby Dodd Way, Atlanta, Georgia 30332-0373 USA
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  • John E. Hobbie,

    1. The Ecosystems Center, Marine Biological Laboratory, 7 MBL Street, Woods Hole, Massachusetts 02543-1015 USA
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  • George W. Kling

    1. Department of Ecology and Evolutionary Biology, University of Michigan, 2019 Kraus Natural Science Building, 830 North University, Ann Arbor, Michigan 48109-1048 USA
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  • Corresponding Editor: D. S. Schimel.

Abstract

Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions.

We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data.

We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually.

The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.

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