 |
| Titel |
Role of regression model selection and station distribution on the estimation of oceanic anthropogenic carbon change by eMLR |
| VerfasserIn |
Y. Plancherel, K. B. Rodgers, R. M. Key, A. R. Jacobson, J. L. Sarmiento |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 10, no. 7 ; Nr. 10, no. 7 (2013-07-16), S.4801-4831 |
| Datensatznummer |
250018345
|
| Publikation (Nr.) |
 copernicus.org/bg-10-4801-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| Quantifying oceanic anthropogenic carbon uptake by monitoring interior
dissolved inorganic carbon (DIC) concentrations is complicated by the
influence of natural variability. The "eMLR method" aims to address this
issue by using empirical regression fits of the data instead of the data
themselves, inferring the change in anthropogenic carbon in time by
difference between predictions generated by the regressions at each time. The
advantages of the method are that it provides in principle a means to filter
out natural variability, which theoretically becomes the regression
residuals, and a way to deal with sparsely and unevenly distributed data. The
degree to which these advantages are realized in practice is unclear,
however. The ability of the eMLR method to recover the anthropogenic carbon
signal is tested here using a global circulation and biogeochemistry model in
which the true signal is known. Results show that regression model selection
is particularly important when the observational network changes in time.
When the observational network is fixed, the likelihood that co-located
systematic misfits between the empirical model and the underlying, yet
unknown, true model cancel is greater, improving eMLR results. Changing the
observational network modifies how the spatio-temporal variance pattern is
captured by the respective datasets, resulting in empirical models that are
dynamically or regionally inconsistent, leading to systematic errors. In
consequence, the use of regression formulae that change in time to represent
systematically best-fit models at all times does not guarantee the best
estimates of anthropogenic carbon change if the spatial distributions of the
stations emphasize hydrographic features differently in time. Other factors,
such as a balanced and representative station coverage, vertical continuity
of the regression formulae consistent with the hydrographic context and
resiliency of the spatial distribution of the residual field can be used to
help guide model selection. The characteristic spatial scales of the modes of
inter-annual to decadal variability in relation to the size of the North
Atlantic, in concert with the station coverage available, place practical
limits on the ability of eMLR to fully account for natural variability. Due
to its statistical nature, eMLR only efficiently removes the natural
variability whose spatial scales are smaller than the system analyzed. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|