Processing Arctic Eddy-Flux Data Using a Simple Carbon-Exchange Model Embedded in the Ensemble Kalman Filter

Abstract

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.

Towards an integrated global framework to assess the impacts of land use and management change on soil carbon: current capability and future vision

“…At the same time, account must be taken of the simplifications associated to the application of EnKF (e.g. Rastetter et al, 2010). The current development of these inverse modelling techniques will allow the setup of an integrated measurementmodel platform which can identify key regions in the world for which targeted research plans should be developed to improve the reliability of model predictions.…”

“…Conforming to the standards would allow for new data and models to be easily incorporated into the estimation system. There is also the possibility to use model-data fusion techniques to optimize model performance, and to assimilate new data from diverse sources as they become available Rastetter et al, 2010;Smith et al, 2011).…”

“…1d). The main aim of FLUX-NET is to provide information for validating remote sensing products for net primary productivity (NPP), evaporation, and energy absorption, but the data have been used extensively in model calibration (Rastetter et al, 2010) and model testing (Morales et al, 2005;Wattenbach et al, 2010). The FLUXNET website (FLUX-NET, 2011) provides information about tower location, site characteristics, data availability, and where to obtain the data, and also provides links to the many publications utilizing FLUXNET data, which have been used in many global level analyses of terrestrial C fluxes (e.g.…”

Incident radiation and the allocation of nitrogen within Arctic plant canopies: implications for predicting gross primary productivity

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Predicting ecosystem carbon balance in a warming Arctic: the importance of long-term thermal acclimation potential and inhibitory effects of light on respiration

“…Given the system's large carbon (C) reservoirs (as much as 1/3 of the global soil C pool (Callaghan et al, 2004)) and low vegetative productivity (Williams & Rastetter, 1999), climate change in the Arctic could dramatically and disproportionately alter the global C cycle. Thus, a better understanding of the physiological processes that control productivity and C exchange is needed (Rastetter et al, 2010).…”