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.

Isoprene emissions from a tundra ecosystem

“…This is much higher than the value typically measured for moist acidic tundra (0.66, Williams et al, 2006) and estimated from a whole-system CO 2 exchange model and measured CO 2 fluxes from the same tower (0.3-0.6, see Fig. 6 in Rastetter et al, 2010). For the second version of the MEGAN model ("localized"; right-hand panels in Fig.…”

“…The eddy covariance technique requires that the landscape is homogeneous (Baldocchi, 2003), and the long-term record of CO 2 fluxes, including turbulence data, is useful for testing this assumption. CO 2 fluxes obtained at this site were well described by an ecosystem model using an ensemble Kalman filter (Rastetter et al, 2010). Ecosystem fluxes were calculated with standard micrometeorological procedures closely related to basic protocols used for analysing eddy covariance data developed by the flux community for the EUROFLUX (now known as CARBOEUROPE) (Aubinet et al, 2000) and AmeriFlux networks (Baldocchi et al, 2001).…”

“…A description of the eddy covariance equipment and data analysis procedures for the CO 2 and H 2 O fluxes is given in Rastetter et al (2010) and is briefly summarized here. Eddy covariance data were collected with an open-path CO 2 /H 2 O gas analyser (LI-7500, LI-COR) and sonic anemometer (CSAT3, Campbell Scientific, Logan, Utah, USA) located approximately 2 m above the land surface.…”

Bulk partitioning the growing season net ecosystem exchange of CO<sub>2</sub> in Siberian tundra reveals the seasonality of its carbon sequestration strength

“…Surface temperature is used as the regressor variable here, based on favourable comparisons to air temperature and based on previous experience at this site (Kutzbach et al, 2007), though air temperature is also commonly used to model tundra ecosystem respiration (Loranty et al, 2010;Rastetter et al, 2009). The P gross portion of the flux is then estimated from the difference between measured NEE and modelled R eco,1 , and is modelled as a function of PAR, using the rectangular hyperbola function…”

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

“…Data assimilation for formally fusing observations and models has gained increased attention in the biogeosciences (Dietze et al, 2013;Hill et al, 2011;Rastetter et al, 2010;Raupach et al, 2005;Williams et al, 2005). LoTEC applied a data assimilation procedure in the NACP modeling exercise, and output in many instances represented a striking improvement against the aggregate output of other models (Fig.…”