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| Titel |
Projection of extreme marine climate in coastal areas using statistical downscaling |
| VerfasserIn |
Cristina Izaguirre, Paula Camus, Melisa Menendez, Fernando J. Mendez, Iñigo J. Losada |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250057784
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| Zusammenfassung |
| The estimation of future extreme marine climate in coastal areas is addressed obtaining ocean climate projections (associated to different socio-economic scenarios) based on a statistical downscaling approach, which relate the large-scale atmosphere circulation weather types (sea level pressure fields) with extreme local met-ocean parameters (wind, waves and surge).
It is well known nowadays that the seasonal-to-interannual variability of ocean climate (wind, waves and storm surge) is linked to the anomalies of the atmosphere circulation. In this work, we propose an extreme value model for a local met-ocean parameter (wave height, storm surge, …) (predictand) conditioned to the synoptic-scale weather type (predictor). We combine different state-of-the-art extreme value models based on the Generalized Extreme Value (GEV) for block maxima and the Poisson-Pareto model for exceedances over a threshold and clustering techniques (self-organizing maps, K-means) applied to n-days-averaged sea level pressure field (SLP) anomalies to describe the weather types.
We fit the statistical model using as predictor the n-days-averaged SLP fields calculated by NCEP atmospheric reanalysis (1948-2010) and as predictand the distribution of maxima every n-days in a specific shallow water location of the wave (DOW1.0) and storm surge (GOS1.1) reanalysis of IH Cantabria. The spatial and temporal domain of the predictor is chosen by means of a sensitivity analysis and based on physical criteria. We analyze the suitability of this methodology to be used for long-term projection of extreme ocean climate to different climate change scenarios, considering different IPCC-AR4 GCM models. |
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