Bibcode
Menendez, M.; Perez, J.; Mendez, F. J.; Losada, I. J.
Referencia bibliográfica
EGU General Assembly 2012, held 22-27 April, 2012 in Vienna, Austria., p.14212
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4
2012
Número de citas
0
Número de citas referidas
0
Descripción
The characterization of local wave climate in a particular location is
of paramount importance for the estimation of coastal flooding.
Downscaling is the method to obtain wave climate information at high
spatial resolution from relatively coarse resolution. Dynamic
downscaling, based on the use of numerical wave generation and
propagation models, is perhaps the most widely used methodology. An
alternative approach is statistical downscaling that can be conducted by
means of regression methods or weather pattern-based approaches. The
main advantages of the statistical against the dynamical approach are
the ease of implementation and the low computational requirements.
Moreover, the statistical downscaling allows the reconstruction of local
wave climates from multiple runs of several Climate Models. Therefore,
the estimation of a multi-model local wave climate for a probabilistic
climate change projection is possible. We propose a statistical
downscaling method, Y=f(x), based on the local wave characteristics
(predictand ) which are conditioned to a particular synoptic-scale
weather type (predictor ). The selected predictor is the n-days-averaged
sea level pressure anomaly (SLP). The downscaling relies on the
correspondence between local sea-state parameters and weather types
(Menendez et al., 2011). The method has been validated by using a
high-resolution near-shore wave reanalysis in the Spanish Coast. The
near-shore reanalysis is achieved by means of a hybrid approach based on
statistical (calibration procedures, selection algorithms and
multidimensional interpolation schemes) and dynamic downscaling (SWAN
propagations), following Camus et al (2011) methodology. Finally,
multivariate wave climate parameters (significant wave height, mean
period, mean direction and energy flux) for a specific location under
several scenarios have been projected by using an ensemble approach.