Inversion of Atmospheric CO 2 Concentrations
315
P. P. Tans, J. A. Berry, and R. F. Keeling. Oceanic C 13 /C 12 observations: A new window on
oceanic CO 2 uptake. Glob. Biogeochem. Cycles, 7:353–368, 1993.
A. Tarantola. Inverse Problem Theory: Methods for Data Fitting and Model Parameter
Estimation. Elsevier, Amsterdam, the Netherlands, 1987.
M. L. Thompson, I. G. Enting, G. I. Pearman, and P. Hyson. Interannual variation of atmospheric CO 2 concentrations. J. Atmos. Chem., 4:125–155, 1986.
A. N. Tikhonov. On the solution of incorrectly posed problems. Sov. Math. Dokl., 4:1035–
1042, 1963.
J. Trampert and R. Sneider. Model estimation biased by truncated expansions: Possible artifacts in seismic tomography. Science, 271:1257–1260, 1996.
Y.-P. Wang and D. J. Barrett. Estimating regional terrestrial carbon fl uxes for the Australian
continent using a multiple-constraint approach: I. Using remotely sensed data and ecological observations of net primary production. Tellus, 55B:270–289, 2003.
Y.-P. Wang, C.M. Trudinger, and I. G. Enting. Applications of model-data fusion to studies of terrestrial carbon fl uxes at different scales. Agric. Forest Meteorol. (in press)
2009.
C. Wunsch and J.-F. Minster. Methods for box models and ocean circulation tracers:
Mathematical programming and non-linear inverse theory. J. Geophys. Res., 87C:5647–
5662, 1982.
APPENDIX A: NOTATION
c
Vector of calculated trace gas concentrations, c, especially for CO 2
G
Green’s function describing source-to-concentration relation
n
Latitudinal wave number
p
Generic parameter vector, elements denoted p α
S j (t)
CO 2 fl ux for region j
T j (c)
Transport operator, giving contribution to rate of change of c j due to transport, given a concentration distribution, c
t
Time
x
Generic parameter vector in linear model
X
Generic data covariance matrix for observations, z
Y
Generic data covariance matrix for priors, x prior
z
Generic data vector
Θ
Objective function, minimized in inversion process. Often derived from a
log-likelihood expression
APPENDIX B: ACRONYMS AND ABBREVIATIONS
AGAGE
Advanced Global Atmospheric Gases Experiment (Prinn et al.,
2000). A successor to GAGE and (ALE Atmospheric Lifetime
Experiment)
AMIP
Atmospheric Model Intercomparison Project (Boer, 2000)
CCDAS
Carbon Cycle Data Assimilation System (Rayner et al., 2005)
CSIRO
Commonwealth Scientifi c and Industrial Research Organisation
(Australia)
C4MIP
Coupled-Carbon-Cycle-Climate Intercomparison Program
(Rayner, 2001)
© 2010 by Taylor and Francis Group, LLC
315
P. P. Tans, J. A. Berry, and R. F. Keeling. Oceanic C 13 /C 12 observations: A new window on
oceanic CO 2 uptake. Glob. Biogeochem. Cycles, 7:353–368, 1993.
A. Tarantola. Inverse Problem Theory: Methods for Data Fitting and Model Parameter
Estimation. Elsevier, Amsterdam, the Netherlands, 1987.
M. L. Thompson, I. G. Enting, G. I. Pearman, and P. Hyson. Interannual variation of atmospheric CO 2 concentrations. J. Atmos. Chem., 4:125–155, 1986.
A. N. Tikhonov. On the solution of incorrectly posed problems. Sov. Math. Dokl., 4:1035–
1042, 1963.
J. Trampert and R. Sneider. Model estimation biased by truncated expansions: Possible artifacts in seismic tomography. Science, 271:1257–1260, 1996.
Y.-P. Wang and D. J. Barrett. Estimating regional terrestrial carbon fl uxes for the Australian
continent using a multiple-constraint approach: I. Using remotely sensed data and ecological observations of net primary production. Tellus, 55B:270–289, 2003.
Y.-P. Wang, C.M. Trudinger, and I. G. Enting. Applications of model-data fusion to studies of terrestrial carbon fl uxes at different scales. Agric. Forest Meteorol. (in press)
2009.
C. Wunsch and J.-F. Minster. Methods for box models and ocean circulation tracers:
Mathematical programming and non-linear inverse theory. J. Geophys. Res., 87C:5647–
5662, 1982.
APPENDIX A: NOTATION
c
Vector of calculated trace gas concentrations, c, especially for CO 2
G
Green’s function describing source-to-concentration relation
n
Latitudinal wave number
p
Generic parameter vector, elements denoted p α
S j (t)
CO 2 fl ux for region j
T j (c)
Transport operator, giving contribution to rate of change of c j due to transport, given a concentration distribution, c
t
Time
x
Generic parameter vector in linear model
X
Generic data covariance matrix for observations, z
Y
Generic data covariance matrix for priors, x prior
z
Generic data vector
Θ
Objective function, minimized in inversion process. Often derived from a
log-likelihood expression
APPENDIX B: ACRONYMS AND ABBREVIATIONS
AGAGE
Advanced Global Atmospheric Gases Experiment (Prinn et al.,
2000). A successor to GAGE and (ALE Atmospheric Lifetime
Experiment)
AMIP
Atmospheric Model Intercomparison Project (Boer, 2000)
CCDAS
Carbon Cycle Data Assimilation System (Rayner et al., 2005)
CSIRO
Commonwealth Scientifi c and Industrial Research Organisation
(Australia)
C4MIP
Coupled-Carbon-Cycle-Climate Intercomparison Program
(Rayner, 2001)
© 2010 by Taylor and Francis Group, LLC