LinPejamChanEtAl2011

Reference

Lin, J.C., Pejam, M.R., Chan, E., Wofsy, S.C., Gottlieb, E.W., Margolis, H.A. and McCaughey, J.H. (2011) Attributing uncertainties in simulated biospheric carbon fluxes to different error sources. Global Biogeochemical Cycles, 25(2):GB2018. (Scopus )

Abstract

Estimating the current sources and sinks of carbon and projecting future levels of CO2 and climate require biospheric carbon models that cover the landscape. Such models inevitably suffer from deficiencies and uncertainties. This paper addresses how to quantify errors in modeled carbon fluxes and then trace them to specific input variables. To date, few studies have examined uncertainties in biospheric models in a quantitative fashion that are relevant to landscape-scale simulations. In this paper, we introduce a general framework to quantify errors in biospheric carbon models that "unmix" the contributions to the total uncertainty in simulated carbon fluxes and attribute the error to different variables. To illustrate this framework we apply and use a simple biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM), in boreal forests of central Canada, using eddy covariance flux measurement data from two main sites of the Canadian Carbon Program (CCP). We explicitly distinguish between systematic errors ("biases") and random errors and focus on the impact of errors present in biospheric parameters as well as driver data sets (satellite indices, temperature, solar radiation, and land cover). Biases in downward shortwave radiation accumulated to the most significant amount out of the driver data sets and accounted for a significant percentage of the annually summed carbon uptake. However, the largest cumulative errors were shown to stem from biospheric parameters controlling the light-use efficiency and respiration-temperature relationships. This work represents a step toward a carbon model-data fusion system because in such systems the outcome is determined as much by uncertainties as by the measurements themselves. © 2011 by the American Geophysical Union.

EndNote Format

You can import this reference in EndNote.

BibTeX-CSV Format

You can import this reference in BibTeX-CSV format.

BibTeX Format

You can copy the BibTeX entry of this reference below, orimport it directly in a software like JabRef .

@ARTICLE { LinPejamChanEtAl2011,
    AUTHOR = { Lin, J.C. and Pejam, M.R. and Chan, E. and Wofsy, S.C. and Gottlieb, E.W. and Margolis, H.A. and McCaughey, J.H. },
    TITLE = { Attributing uncertainties in simulated biospheric carbon fluxes to different error sources },
    JOURNAL = { Global Biogeochemical Cycles },
    YEAR = { 2011 },
    VOLUME = { 25 },
    PAGES = { GB2018 },
    NUMBER = { 2 },
    ABSTRACT = { Estimating the current sources and sinks of carbon and projecting future levels of CO2 and climate require biospheric carbon models that cover the landscape. Such models inevitably suffer from deficiencies and uncertainties. This paper addresses how to quantify errors in modeled carbon fluxes and then trace them to specific input variables. To date, few studies have examined uncertainties in biospheric models in a quantitative fashion that are relevant to landscape-scale simulations. In this paper, we introduce a general framework to quantify errors in biospheric carbon models that "unmix" the contributions to the total uncertainty in simulated carbon fluxes and attribute the error to different variables. To illustrate this framework we apply and use a simple biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM), in boreal forests of central Canada, using eddy covariance flux measurement data from two main sites of the Canadian Carbon Program (CCP). We explicitly distinguish between systematic errors ("biases") and random errors and focus on the impact of errors present in biospheric parameters as well as driver data sets (satellite indices, temperature, solar radiation, and land cover). Biases in downward shortwave radiation accumulated to the most significant amount out of the driver data sets and accounted for a significant percentage of the annually summed carbon uptake. However, the largest cumulative errors were shown to stem from biospheric parameters controlling the light-use efficiency and respiration-temperature relationships. This work represents a step toward a carbon model-data fusion system because in such systems the outcome is determined as much by uncertainties as by the measurements themselves. © 2011 by the American Geophysical Union. },
    COMMENT = { Export Date: 20 June 2011 Source: Scopus Art. No.: GB2018 doi: 10.1029/2010GB003884 },
    ISSN = { 08866236 (ISSN) },
    OWNER = { Luc },
    TIMESTAMP = { 2011.06.20 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-79957931412&partnerID=40&md5=4359dac5d05452178e584ca13e870e1d },
}

********************************************************** ***************** Facebook Twitter *********************** **********************************************************

Abonnez-vous à
l'Infolettre du CEF!

********************************************************** ************* Écoles d'été et formation **************************** **********************************************************

Écoles d'été et formations

********************************************************** ***************** Pub - Symphonies_Boreales ****************** **********************************************************

********************************************************** ***************** Boîte à trucs *************** **********************************************************

CEF-Référence
La référence vedette !

Jérémie Alluard (2016) Les statistiques au moments de la rédaction 

  • Ce document a pour but de guider les étudiants à intégrer de manière appropriée une analyse statistique dans leur rapport de recherche.

Voir les autres...