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    Original Research Papers

    Propagating data uncertainty through smoothing spline fits

    Authors:

    I . G. Enting,

    MASCOS, The University of Melbourne, AU
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    C. M. Trudinger ,

    CSIRO Marine and Atmospheric Research, AU
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    D. M. Etheridge

    CSIRO Marine and Atmospheric Research, AU
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    Abstract

    Smoothing splines have been used extensively in the analysis of gas concentration data from bubbles in ice cores. Since the fit is a linear projection of the data, propagation of data uncertainty through the fitting process is formally straightforward and, as we demonstrate, readily achievable from pre-existing spline-fitting procedures. The uncertainty propagation can be extended to determining both uncertainties in derivatives and uncertainties in quantities that reflect rates of input to the atmosphere. As an example, we apply the technique to 1000 yr of methane data from a Law Dome ice core.

    How to Cite: Enting, I.G., Trudinger, C.M. and Etheridge, D.M., 2006. Propagating data uncertainty through smoothing spline fits. Tellus B: Chemical and Physical Meteorology, 58(4), pp.305–309. DOI: http://doi.org/10.1111/j.1600-0889.2006.00193.x
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      Published on 01 Jan 2006
    Peer Reviewed
     CC BY 4.0
     Accepted on 7 Jun 2006            Submitted on 28 Mar 2006

    References

    1. Cox , D. D . 1983 . Asymptotics for M-type smoothing splines . Annals of Statistics , 22 , 530 – 551 .  

    2. Craven , P. and Wahba , G . 1979 . Smoothing noisy data with spline func-tions . Numer. Math ., 31 , 377 – 403 .  

    3. de Boor , C . 1978 . A Practical Guide to Splines . Springer-Verlag , New York .  

    4. Enting , I. G . 1986 . Potential problems with the use of least squares spline fits to filter CO2 data . J. Geophys. Res ., 91D , 6668 – 6670 .  

    5. Enting , I. G . 1987 . On the use of smoothing splines to filter CO2 data. .1. Geophys. Res ., 92D , 10977 – 10984 .  

    6. Enting , I. G . 2000. Characterising the temporal variability of the global carbon cycle. CSIRO Atmospheric Research Technical Paper no. 40. CSIRO, Australia. http://www.cmar.csiro.au/e-print/open/enting_ 2000a.pdf  

    7. Enting , I. G . 2002 . Inverse Problems in Atmospheric Constituent Trans-port . CUP, Cambridge , UK .  

    8. Etheridge , D. M. , Steele , L. P. , Francey , R. J. , and Langenfelds , R. L . 1998 . Atmospheric methane between 1000 A.D. and present: Evi-dence of anthropogenic emissions and climatic variability . J. Geophys. Res ., 103D , 15979 – 15993 .  

    9. Granek , H . 1995 . Generalized smoothing splines in CO2 analysis . J. Geophys. Res ., 100D , 16857 – 16865 .  

    10. Joos , E , Meyer , R. , Bruno , M. , and L,euenberger, M. 1999 . The variability in the carbon sinks as reconstructed for the last 1000 years . Geophys. Res. Lett ., 26 ( 10 ), 1437 – 1440 .  

    11. Prather , M. J . 1994 . Lifetimes and eigenstates in atmospheric chemistry . Geophys. Res. Lett ., 21 , 801 – 804 .  

    12. Silverman , B. W . 1984 . Spline smoothing: The equivalent variable kernel method . Annals of Statistics , 12 , 989 – 916 .  

    13. Silverman , B. W . 1985 . Some aspects of the spline: Smoothing approach to non-parametric regression curve fitting. (with discussion) . J. R. Statist. Soc ., 47 , 1 – 52 .  

    14. Trudinger , C. M. , Enting , I. G. , Francey , R. J. , Etheridge , D. M. , and Rayner , P. J . 1999 . Long-term variability in the global carbon cycle inferred from a high precision CO2 and 31-3C ice core record . Tellus , 51B , 233 – 248 .  

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