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Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model

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Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model. / Stauch, Vanessa J.; Jarvis, Andrew J.; Schulz, Karsten.
In: Journal of Geophysical Research Atmospheres, Vol. 113, No. 3, D03101, 16.02.2008.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Stauch, VJ, Jarvis, AJ & Schulz, K 2008, 'Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model', Journal of Geophysical Research Atmospheres, vol. 113, no. 3, D03101. https://doi.org/10.1029/2007JD008603

APA

Stauch, V. J., Jarvis, A. J., & Schulz, K. (2008). Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model. Journal of Geophysical Research Atmospheres, 113(3), Article D03101. https://doi.org/10.1029/2007JD008603

Vancouver

Stauch VJ, Jarvis AJ, Schulz K. Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model. Journal of Geophysical Research Atmospheres. 2008 Feb 16;113(3):D03101. doi: 10.1029/2007JD008603

Author

Stauch, Vanessa J. ; Jarvis, Andrew J. ; Schulz, Karsten. / Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model. In: Journal of Geophysical Research Atmospheres. 2008 ; Vol. 113, No. 3.

Bibtex

@article{b5e47d902c8e4defa3da0c0edb58f78e,
title = "Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model",
abstract = "In this paper we use a stochastic model to estimate annual net carbon exchange (NCE) from eddy covariance data taken from various sites. The stochastic model is comprised of a signal and a noise component. The signal component is characterized using a semiparametric model relating CO2 flux to light, temperature and time fitted to the eddy covariance observations. The noise component is characterized from the resultant model residuals using empirical cumulative probability distributions. The estimates for NCE are then derived from multiple runs of the joint signal-noise model within a Monte Carlo framework. This model-based approach to estimating NCE is evaluated using synthetic data and found to give a reasonable partitioning of the signal and noise in these data. Building on this, we derive estimates of NCE from observed annual eddy covariance data sets for various sites. For the six sites analyzed, the noise to signal ratio for the annual NCE estimates lies between 5 and 17% highlighting the potential value of eddy covariance observations for this application.",
author = "Stauch, {Vanessa J.} and Jarvis, {Andrew J.} and Karsten Schulz",
year = "2008",
month = feb,
day = "16",
doi = "10.1029/2007JD008603",
language = "English",
volume = "113",
journal = "Journal of Geophysical Research Atmospheres",
issn = "0148-0227",
publisher = "American Geophysical Union",
number = "3",

}

RIS

TY - JOUR

T1 - Estimation of net carbon exchange using eddy covariance CO2 flux observations and a stochastic model

AU - Stauch, Vanessa J.

AU - Jarvis, Andrew J.

AU - Schulz, Karsten

PY - 2008/2/16

Y1 - 2008/2/16

N2 - In this paper we use a stochastic model to estimate annual net carbon exchange (NCE) from eddy covariance data taken from various sites. The stochastic model is comprised of a signal and a noise component. The signal component is characterized using a semiparametric model relating CO2 flux to light, temperature and time fitted to the eddy covariance observations. The noise component is characterized from the resultant model residuals using empirical cumulative probability distributions. The estimates for NCE are then derived from multiple runs of the joint signal-noise model within a Monte Carlo framework. This model-based approach to estimating NCE is evaluated using synthetic data and found to give a reasonable partitioning of the signal and noise in these data. Building on this, we derive estimates of NCE from observed annual eddy covariance data sets for various sites. For the six sites analyzed, the noise to signal ratio for the annual NCE estimates lies between 5 and 17% highlighting the potential value of eddy covariance observations for this application.

AB - In this paper we use a stochastic model to estimate annual net carbon exchange (NCE) from eddy covariance data taken from various sites. The stochastic model is comprised of a signal and a noise component. The signal component is characterized using a semiparametric model relating CO2 flux to light, temperature and time fitted to the eddy covariance observations. The noise component is characterized from the resultant model residuals using empirical cumulative probability distributions. The estimates for NCE are then derived from multiple runs of the joint signal-noise model within a Monte Carlo framework. This model-based approach to estimating NCE is evaluated using synthetic data and found to give a reasonable partitioning of the signal and noise in these data. Building on this, we derive estimates of NCE from observed annual eddy covariance data sets for various sites. For the six sites analyzed, the noise to signal ratio for the annual NCE estimates lies between 5 and 17% highlighting the potential value of eddy covariance observations for this application.

U2 - 10.1029/2007JD008603

DO - 10.1029/2007JD008603

M3 - Journal article

AN - SCOPUS:42649097645

VL - 113

JO - Journal of Geophysical Research Atmospheres

JF - Journal of Geophysical Research Atmospheres

SN - 0148-0227

IS - 3

M1 - D03101

ER -