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Statistical downscaling of seasonal wave forecasts

Statistical downscaling of seasonal wave forecasts
Statistical downscaling of seasonal wave forecasts
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.
1463-5003
1-12
Camus, P.
66625386-9051-4ea8-a0fa-956751534796
Herrera, S.
15ee87a3-93d7-4ff0-9bba-cf45a3b68756
Gutiérrez, J.m.
a33f8b46-7257-4c04-acb6-25a840d219e8
Losada, I.j.
2ea31ffe-966e-40f9-b742-aefae8363ad3
Camus, P.
66625386-9051-4ea8-a0fa-956751534796
Herrera, S.
15ee87a3-93d7-4ff0-9bba-cf45a3b68756
Gutiérrez, J.m.
a33f8b46-7257-4c04-acb6-25a840d219e8
Losada, I.j.
2ea31ffe-966e-40f9-b742-aefae8363ad3

Camus, P., Herrera, S., Gutiérrez, J.m. and Losada, I.j. (2019) Statistical downscaling of seasonal wave forecasts. Ocean Modelling, 138, 1-12. (doi:10.1016/j.ocemod.2019.04.001).

Record type: Article

Abstract

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.

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OCEMOD_1393_edit_report - Accepted Manuscript
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More information

Accepted/In Press date: 20 April 2019
e-pub ahead of print date: 24 April 2019
Published date: 1 June 2019

Identifiers

Local EPrints ID: 446658
URI: http://eprints.soton.ac.uk/id/eprint/446658
ISSN: 1463-5003
PURE UUID: 62b32af6-a721-4d7a-b5d9-b61405091177

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Date deposited: 17 Feb 2021 17:31
Last modified: 22 Nov 2021 08:18

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Contributors

Author: P. Camus
Author: S. Herrera
Author: J.m. Gutiérrez
Author: I.j. Losada

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