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| Titel |
Factors challenging our ability to detect long-term trends in ocean chlorophyll |
| VerfasserIn |
C. Beaulieu, S. A. Henson, Jorge L. Sarmiento, J. P. Dunne, S. C. Doney, R. R. Rykaczewski, L. Bopp |
| 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. 4 ; Nr. 10, no. 4 (2013-04-23), S.2711-2724 |
| Datensatznummer |
250018215
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| Publikation (Nr.) |
 copernicus.org/bg-10-2711-2013.pdf |
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| Zusammenfassung |
| Global climate change is expected to affect the ocean's biological
productivity. The most comprehensive information available about the global
distribution of contemporary ocean primary productivity is derived from
satellite data. Large spatial patchiness and interannual to multidecadal
variability in chlorophyll a concentration challenges efforts to distinguish
a global, secular trend given satellite records which are limited in
duration and continuity. The longest ocean color satellite record comes from
the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in
December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS)
ocean color sensors are beyond their originally planned operational
lifetime. Successful retrieval of a quality signal from the current Visible
Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of
the Ocean and Land Colour Instrument (OLCI) expected in 2014 will hopefully
extend the ocean color time series and increase the potential for detecting
trends in ocean productivity in the future. Alternatively, a potential
discontinuity in the time series of ocean chlorophyll a, introduced by a
change of instrument without overlap and opportunity for cross-calibration,
would make trend detection even more challenging. In this paper, we
demonstrate that there are a few regions with statistically significant
trends over the ten years of SeaWiFS data, but at a global scale the trend
is not large enough to be distinguished from noise. We quantify the degree
to which red noise (autocorrelation) especially challenges trend detection
in these observational time series. We further demonstrate how
discontinuities in the time series at various points would affect our
ability to detect trends in ocean chlorophyll a. We highlight the importance
of maintaining continuous, climate-quality satellite data records for
climate-change detection and attribution studies. |
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