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| Titel |
A novel method for diagnosing seasonal to inter-annual surface ocean carbon dynamics from bottle data using neural networks |
| VerfasserIn |
T. P. Sasse, B. I. McNeil, G. Abramowitz |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 10, no. 6 ; Nr. 10, no. 6 (2013-06-27), S.4319-4340 |
| Datensatznummer |
250018315
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| Publikation (Nr.) |
 copernicus.org/bg-10-4319-2013.pdf |
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| Zusammenfassung |
| The ocean's role in modulating the observed 1–7 Pg C yr−1
inter-annual variability in atmospheric CO2 growth rate is an important,
but poorly constrained process due to current spatio-temporal limitations in
ocean carbon measurements. Here, we investigate and develop a non-linear
empirical approach to predict inorganic CO2 concentrations (total carbon
dioxide (CT) and total alkalinity (AT)) in the global
ocean mixed layer from hydrographic properties (temperature, salinity,
dissolved oxygen and nutrients). The benefit of this approach is that once
the empirical relationship is established, it can be applied to hydrographic
datasets that have better spatio-temporal coverage, and therefore provide an
additional constraint to diagnose ocean carbon dynamics globally. Previous
empirical approaches have employed multiple linear regressions (MLR) and
relied on ad hoc geographic and temporal partitioning of carbon data to
constrain complex global carbon dynamics in the mixed layer. Synthesizing a
new global CT/AT carbon bottle dataset consisting of
~33 000 measurements in the open ocean mixed layer, we develop a
neural network based approach to better constrain the non-linear carbon
system. The approach classifies features in the global biogeochemical dataset
based on their similarity and homogeneity in a self-organizing map (SOM;
Kohonen, 1988). After the initial SOM analysis, which includes geographic
constraints, we apply a local linear optimizer to the neural network, which
considerably enhances the predictive skill of the new approach. We call this
new approach SOMLO, or self-organizing multiple linear output. Using
independent bottle carbon data, we compare a traditional MLR analysis to our
SOMLO approach to capture the spatial CT and AT
distributions. We find the SOMLO approach improves predictive skill globally
by 19% for CT, with a global capacity to predict
CT to within 10.9 μmol kg−1
(9.2 μmol kg−1 for AT). The non-linear SOMLO
approach is particularly powerful in complex but important regions like the
Southern Ocean, North Atlantic and equatorial Pacific, where residual
standard errors were reduced between 25 and 40% over traditional linear
methods. We further test the SOMLO technique using the Bermuda Atlantic
time series (BATS) and Hawaiian ocean time series (HOT) datasets, where
hydrographic data was capable of explaining 90% of the seasonal cycle
and inter-annual variability at those multi-decadal time-series stations. |
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