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| Titel |
Added value and signal-to-noise in an eight-member ensemble of the KNMI regional climate model RACMO2 at 12 km resolution. |
| VerfasserIn |
Geert Lenderink, Erik van Meijgaard |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250078165
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| Zusammenfassung |
| Projections of future climate derived from multi-model ensembles with regional
climate models, like those in CORDEX, often show large changes at regional (10-500
km) scales, in particular for precipitation. However, the inter-model differences in
such ensembles are often of the same size. It is therefore not clear which part of
the regional/local information from these regional climate model integrations can
be trusted, and for users of climate information this is an undesirable situation.
Thus, it is important to determine the cause of the inter-model differences within
these multi-model ensembles. In general, three main causes can be distinguished:
i) differences in future emissions (uncertainty in the forcing), ii) differences in
modeling the response to this forcing (uncertainty in the climate models), and iii)
differences due to natural variations not related to the forcing (natural variability). In
multi-model ensembles, such as those in CORDEX, where different regional models
are driven by different global climate models with different emission scenarios it
is difficult to unravel the cause of differences in the projected changes. Here, we
therefore investigated an eight-member ensemble with the regional climate model
RACMO2 driven by one global climate model (EC-EARTH) using one emission
scenario (RCP8.5). In this ensemble inter-model differences are solely attributed to
natural variations. We determined the size of these natural variations compared to the
forced climate change signal (defined as the average response over all ensemble
members). In particular, we investigated whether the forced climate change signal
contains persistent small scale features that would not be captured in the GCMs output
("added value"). Within a perfect model approach we also investigated whether these
small scale structures can be reliably estimated from a limited number of model
simulations. |
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