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Title: Statistical modeling of global mean wave height considering principal component analysis of sea level pressures and its application to future wave height projection
Authors: Kishimoto, Risako
Shimura, Tomoya  kyouindb  KAKEN_id
Mori, Nobuhito  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-9082-3235 (unconfirmed)
Mase, Hajime  kyouindb  KAKEN_id
Author's alias: 岸本, 理紗子
志村, 智也
森, 信人
間瀬, 肇
Issue Date: 2017
Publisher: Japan Society of Hydrology and Water Resources
Journal title: Hydrological Research Letters
Volume: 11
Issue: 1
Start page: 51
End page: 57
Abstract: Future wave climate projection is important for climate impact assessment of the coastal hazards and environment. In this study, monthly averaged wave heights are estimated by a linear multi-regression model using atmospheric data as explanatory variables. The present statistical model considers local atmospheric information (wind speed at 10 m height, sea level pressure) and large scale atmospheric information obtained from principal component analysis (PCA) of the global sea level pressure field. The representation of swell in the lower latitude is greatly improved by introducing the large scale atmospheric information from the PCA. The present statistical model was applied to the results of the Japan Meteorological Research Institute’s Atmospheric General/Global Circulation Model (MRI-AGCM) climate change projection. The future change of wave heights shows an increase in the northern North Pacific Ocean and a decrease in the North Atlantic Ocean, middle latitude and tropics of the Pacific Ocean.
Rights: © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
URI: http://hdl.handle.net/2433/219508
DOI(Published Version): 10.3178/hrl.11.51
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