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| Titel |
Time of emergence of trends in ocean biogeochemistry |
| VerfasserIn |
Kathrin M. Keller, Fortunat Joos, Christoph C. Raible |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250105236
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| Publikation (Nr.) |
 EGU/EGU2015-4711.pdf |
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| Zusammenfassung |
| The detection of forced trends in biogeochemical cycles and ecosystems is a challenge. A
major issue is the presence of natural variability which has the potential to enhance or mask
trends over decadal timescales. The successful detection of trend signals is thus a
signal-to-noise (S/N) problem, i.e., the signal has to be of a magnitude that durably exceeds
the envelope of background variability. One possible measure to estimate this is the time of
emergence (ToE) of a signal, that is, the point in time at which the ratio S/N exceeds a certain
threshold. We use historical simulations from 17 Earth System Models to investigate
the ToE of trends in surface ocean biogeochemistry. For maximum comparability
with the available observations, we focus on dissolved inorganic carbon (DIC),
pCO2 and pH, and sea-surface temperature (SST). We find that signals in ocean
biogeochemical variables emerge on much shorter timescales than the physical variable SST.
The ToE patterns of pCO2 and pH are spatially very similar to DIC, yet the trends
emerge much faster – after roughly 12 years for the majority of the global ocean area,
compared to between 10-30 years for DIC and 45-90 years for SST. In general, the
background noise is of higher importance in determining ToE than the strength of the
trend signal. In areas with high natural variability, even strong trends both in the
physical climate and carbon cycle system are masked by variability over decadal
timescales. In contrast to the trend, natural variability is affected by the seasonal cycle.
This has important implications for observations, since it implies that intra-annual
variability could question the representativeness of irregularly seasonal sampled
measurements for the entire year and, thus, the interpretation of observed trends. |
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