Statistical downscaling of seasonal wave forecasts

• Published in: Ocean Modelling
• Main Authors: Camus, Paula, Herrera, S., Gutiérrez, José M., Losada, I. J.
• Other Authors: Ministerio de Economía y Competitividad (España), European Commission, Gobierno de Cantabria
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Elsevier 2019
• Subjects: Seasonal forecast; Statistical downscaling; Significant wave height; Western Pacific; Atlantic Ocean; Pacific; North Atlantic
• Online Access: http://hdl.handle.net/10261/213761; https://doi.org/10.1016/j.ocemod.2019.04.001; https://doi.org/10.13039/501100003329; https://doi.org/10.13039/501100000780

Despite the potential applicability of seasonal forecasting for decision making in construction, maintenance and operations of coastal and offshore infrastructures, tailored climate services have yet to be developed in the marine sector. In this work, we explore the potential of a state-of-the-art seasonal forecast system to predict wave conditions, particularly significant wave height. Since this information is not directly provided by models, a statistical downscaling method is applied to infer significant wave height based on model outputs such as sea level pressure, which drive waves over large wave generation areas beyond the target location over time. This method may be beneficial for seasonal forecasting since skill from wide generation areas can be propagated to wave conditions in (distant and smaller) target regions. We consider seasonal predictions with a one-month lead time of the CFSv2 hindcast in two regions: the Western Pacific around Indonesia during the June–July–August (JJA) season and the North Atlantic Ocean during the January–February–March (JFM) season. In the former case, skillful predictions are found, which are higher during decay years after ENSO warm phases when a negative anomaly of the significant wave height is expected. In contrast, statistical downscaling in the North Atlantic Ocean cannot add value to the signal given by the predictor, which is also very weak. P.C. acknowledges the support of the Spanish Ministerio de Economía y Competitividad (MINECO) and European Regional Development Fund (FEDER) under Grant BIA2015-70644-R (MINECO/FEDER, UE). The authors acknowledge funding from the ERANET ERA4CS (ECLISEA project) and the government of Cantabria and FEDER under the project CLISMO.